Methods of treatment using biomarker-guided plasmapheresis

Description

METHODS OF TREATMENT USING BIOMARKER-GUIDED PLASMAPHERESISCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 645,101, which was filed on May 9, 2024; and U.S. Provisional Patent Application No. 63 / 669,188, which was filed on July 9, 2024; and U.S. Provisional Patent Application No. 63 / 705,500, which was filed on October 9, 2024; each of which are incorporated herein in their entirety by reference.BACKGROUND OF THE DISCLOSURE

[0002] During the past century, the earth’s population has more than doubled. It is estimated that more than 20% of the world’s population is aged 65 years or older. The United Nations estimates that, by 2050, this population will have increased beyond 14 billion. Aged humans usually suffer from one or more disorders associated with chronic aging.These can include Alzheimer’s disease, infections, Type II Diabetes, atherosclerotic cardiovascular disease, obesity, osteoporosis, and sarcopenia. The cumulative effect is an enormous financial burden to any medical system.SUMMARY OF THE DISCLOSURE

[0003] Disclosed herein is a method of lowering a biological age of an individual and / or slowing a rate of aging of the individual, the method comprising performing plasmapheresis on the individual, thereby lowering the biological age and / or slowing the rate of aging of the individual.

[0004] In some embodiments, the method further comprises measuring a biomarker, a biological clock, an omic panel, or a combination thereof in the individual to determine the biological age of the individual and / or the rate of aging of the individual. In some embodiments, the method further comprises repeating the plasmapheresis and the measuring until the biological age of the individual and / or the rate of aging of the individual is below a threshold value.

[0005] Further disclosed herein is a method of treating or preventing a condition that is associated with aging in an individual or lowering a health risk associated with aging in the individual, the method comprising: (a) measuring a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in the individual todetermine a stage, severity, or risk of developing the condition that is associated with aging in the individual or the health risk associated with aging in the individual; (b) performing plasmapheresis on the individual; and (c) repeating steps (a) and (b) until the stage, severity, or risk of developing the condition that is associated with aging in the individual or the health risk associated with aging in the individual is below a threshold value; thereby treating or preventing the condition that is associated with aging in the individual or lowering the health risk associated with aging in the individual.

[0006] In some embodiments, a method of treating a condition that is associated with aging in an individual, the method comprising: measuring, before administering the plasmapheresis, the levels of a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in a blood sample of the individual; administering the plasmapheresis to the individual; measuring, following step (b), the levels of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or a combination thereof in a blood sample of the individual; monitoring for a change in the condition; and repeating steps (b), (c), and (d) until the condition is improved, thereby treating the condition in the individual.

[0007] In some embodiments, the method further comprises performing plasmapheresis on the individual a plurality of times. In some embodiments, the method further comprises performing plasmapheresis on the individual until the stage, severity, or risk of developing the condition that is associated with aging in the individual or the health risk associated with aging in the individual are below a threshold level.

[0008] In some embodiments, the method further comprises measuring the biological age of the individual and / or measuring the rate of aging of the individual. In some embodiments, the measuring the biological age of the individual and / or the rate of aging of the individual comprises measuring a biomarker, a biological clock, an omic panel, or a combination thereof.

[0009] In some embodiments, further comprising lowering a health risk associated with the condition in the individual.

[0010] In some embodiments, further comprising repeating steps (a) through (c) a plurality of times. In some embodiments, wherein the measuring is performed before withdrawing the volume of whole blood to yield a pretreatment level of the biomarker.

[0011] In some embodiments, wherein the measuring is performed after returning a cellular fraction and an exchange fluid to the circulatory system of the individual to yield a post-treatment level of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or the combination thereof in the blood sample of the individual.

[0012] In some embodiments, wherein the measuring is performed in real-time during one or more of steps (a) through (c). In some embodiments, comprising repeating steps (a) through (d) until a level of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or a combination thereof in the blood sample of the individual reaches a target level. In some embodiments, wherein steps (a)-(d) are performed during a plurality of treatment sessions. In some embodiments, wherein two treatment sessions of the plurality of treatment sessions are within 72 hours of each other, wherein steps (a) through (d) are performed at least two times. In some embodiments, wherein each of the at least two times comprises removing at least one plasma volume from the individual.

[0013] In some embodiments, wherein a volume of exchange fluid that is returned to the individual is equal in volume to the at least one plasma volume that is withdrawn. In some embodiments, wherein a volume of exchange fluid that is returned to the individual is greater in volume than the at least one plasma volume that is withdrawn. In some embodiments, wherein at least one of the at least two times that plasmapheresis is performed comprises removing at least one- and one-half plasma volumes from the individual. In some embodiments, wherein a volume of exchange fluid that is returned to the individual is equal to the at least one- and one-half plasma volumes that is withdrawn. In some embodiments, wherein a volume of exchange fluid that is returned to the individual is greater than the at least one- and one-half plasma volume that is withdrawn.

[0014] In some embodiments, further comprising infusing an exchange fluid into a vascular system of the individual, wherein the exchange fluid comprises at least one of: saline, lactated Ringer’s, albumin, or a therapeutic agent.

[0015] In some embodiments, wherein the therapeutic agent comprises at least one of: an anti-inflammatory or an immune modulator. In some embodiments, wherein the immune modulator comprises intravenous immunoglobulin.

[0016] In some embodiments, comprising administering the therapeutic agent to the individual following at least one of the at least two times that steps (a) through (3) are performed. In some embodiments, wherein the therapeutic agent comprises at least one of an anti-inflammatory or an immune modulator. In some embodiments, wherein the immune modulator comprises intravenous immunoglobulin.

[0017] In some embodiments, further comprising comparing a post-treatment level of the biomarker with a pre-treatment level of the biomarker, and the comparing the post-treatmentlevel of the biomarker with the pre-treatment level of the biomarker results in a determination of a quantitative difference between the post-treatment level of the biomarker and the pretreatment level of the biomarker. In some embodiments, wherein the biomarker is measured using flow cytometry, near-infrared spectroscopy (NIR), double shot pyrolysis - gas chromatography / mass spectrometry, Fourier transform infrared (FT-IR) spectrometry, visual inspection with an optical microscope Raman spectroscopy, or surface-enhanced Raman scattering, dynamic light scattering (DLS), or surface plasmon resonance.

[0018] In some embodiments, wherein the biomarker is an intracellular protein. In some embodiments, herein the biomarker is at least one of titin (TTN), carboxypeptidase N subunit 2 (CPN2), T-complex protein 1 subunit beta (CCT2), tubulin alpha-lB chain (TUBA1B), transketolase (TKT), alpha-enolase (ENO1), profdin- 1 (PFN1), or actin cytoplasmic 1 (ACTB). In some embodiments, wherein the biomarker is a secreted protein. In some embodiments, wherein the biomarker is at least one of folate receptor gamma (FOLR3). mannose-binding protein C (MBL2), leukocyte immunoglobulin-like receptor subfamily A member 3 (LILRA3), leucine-rich alpha-2 -glycoprotein (LRG1), immunoglobulin heavy constant alpha 1 (IGHA1), gamma-glutamyl hydrolase (GGH), immunoglobulin kappa light chain (IGKC), histidine-rich glycoprotein (HRG), BPI fold-containing family B member 1 (BPIFB1), apolipoprotein C-I (APOCI), or extracellular matrix protein 1 (ECM1). In some embodiments, wherein the biomarker is a cell surface or transmembrane protein. In some embodiments, wherein the biomarker is vasorin (VASN).

[0019] In some embodiments, further comprising upregulating or downregulating a physiological pathway. In some embodiments, wherein the physiological pathway is selected from the group consisting of an immune system pathway, inflammatory response, lipid metabolism, lipid transport, nervous system development, nervous system function, cell communication, cell adhesion, protein transport, vesicle transport, lipid metabolic process, blood coagulation regulation, cardiovascular regulation, drug transport, cell differentiation, hormone secretion, hormone transport, catalytic activity regulation, or hemostasis regulation.

[0020] In some embodiments, wherein the physiological pathway is ameboidal-type cell migration, axon guidance, B cell activation, bundle of His cell to Purkinje myocyte communication, cell-cell adhesion via plasma-membrane adhesion molecules, chylomicron remnant clearance, complement activation, alternative pathway, drug transport, establishment of protein localization to organelle, extrinsic apoptotic signaling pathway, Fc-gamma receptor signaling pathway involved in phagocytosis, foam cell differentiation, gas transport, granulocyte activation, high-density lipoprotein particle assembly, high-density lipoproteinparticle remodeling, hormone secretion, hormone transport, immune response-regulating cell surface receptor signaling pathway, import into cell, inflammatory response, innate immune response-activating signal transduction, isoprenoid metabolic process, lipid catabolic process, lipid modification, lipoprotein metabolic process, low-density lipoprotein particle remodeling, membrane invagination, myeloid cell activation involved in immune response, negative regulation of catalytic activity, negative regulation of hemostasis, negative regulation of lipid localization, nervous system development, neurogenesis, neuron development, neuron differentiation, neutral lipid metabolic process, neutrophil degranulation, organelle fusion, organic substance transport, organophosphate catabolic process, plasma membrane bounded cell projection organization, positive regulation of lipid localization, protein stabilization, protein-containing complex assembly, protein-containing complex remodeling, protein-lipid complex subunit organization, regulated exocytosis, regulation of blood coagulation, regulation of cell projection assembly, regulation of coagulation, regulation of heart rate by cardiac conduction, regulation of hormone secretion, regulation of lipid localization, regulation of protein localization, regulation of tube size, regulation of vesicle-mediated transport, vascular process in circulatory system, vesicle- mediated transport, or a combination thereof.

[0021] In some embodiments, further comprising reducing or increasing a cell population, wherein the cell population is at least one of an a[3 T cell, a B cell, a plasma cell, a B cell, a classical monocyte, a y5 T cell, an intermediate monocyte, a lineage negative (LN) cell, a live leukocyte, a myeloid scatter fraction, a natural killer (NK) cell, a nonclassical monocyte, not a T cell, a myeloid cell, a plasmablast, an activated cell, an activated memory cell, a central memory (CM) cell, a double negative T cell (DN), a double positive T cell (DP), an effector memory T cell (EM), a naive cell, a non-B or T cell, a memory cell, or a combination thereof.

[0022] In some embodiments, wherein the cell is a CCr7 positive, CD 16 positive, cd 163 hi, cd25 positive, cd27 negative, CD27 positive, CD38 hi, CD38 positive, CD4, CD57 positive, CD64 hi, cd64 lo, CD8, CD80 positive, HLADR positive, igMHi-IgDHi, IgMHi-IgDLo, IgMNeg-IgDNegative, KIR positive, KLRG1 positive, myeloid scatter fraction, NK-56-lo- 16-hi, NK-56-lo-16-lo, NK-56hi-16-lo, NK1-1 positive, NKg2a positive, RA negative, RA positive, SCM, SCM and TEMRA, TEMRA SA-bGal hi, SAbGal positive, or TiGit positive cell.

[0023] In some embodiments, wherein the condition comprises cancer, Alzheimer’s disease, neurodegenerative disorders, immune system dysregulation, metabolic disorders,chronic inflammatory disease, autoimmune disease, infectious disease, lipid storage disease, coagulation disorder, hormonal disorders, or a cardiovascular disease.

[0024] In some embodiments, wherein the condition is selected from the group consisting of breast cancer, lung cancer, colorectal cancer, prostate cancer, leukemia, lymphoma, melanoma, pancreatic cancer, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis (ALS), Huntington's disease, multiple sclerosis (MS), frontotemporal dementia, atherosclerosis, coronary artery disease, hypertension, heart failure, myocardial infarction (heart attack), stroke, peripheral artery disease, type 2 diabetes, obesity, metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), hyperlipidemia, gout, rheumatoid arthritis, systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD), Crohn's disease, ulcerative colitis, psoriasis, ankylosing spondylitis, bacterial infections, tuberculosis, staphylococcal infections, viral infections, HIV / AIDS, hepatitis B and C, influenza, parasitic infections, malaria, leishmaniasis, epilepsy, migraine, autism spectrum disorders, attention deficit hyperactivity disorder (ADHD), schizophrenia, bipolar disorder, Gaucher disease, Niemann-Pick disease, Fabry disease, Tay-Sachs disease, Wolman disease, hypothyroidism, hyperthyroidism, Cushing's syndrome, Addison's disease, polycystic ovary syndrome (PCOS), growth hormone deficiency, hemophilia, von Willebrand disease, deep vein thrombosis (DVT), pulmonary embolism, disseminated intravascular coagulation (DIC), phenylketonuria (PKU), maple syrup urine disease (MSUD), homocystinuria, galactosemia, and mitochondrial disorders.

[0025] In some embodiments, the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises an immune cell population, a percentage of live immune cells, or a ratio of immune cell populations.

[0026] In some embodiments, measuring the biological age of the individual, measuring the rate of aging of the individual, or the combination thereof comprises measuring a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof.

[0027] In some embodiment, a method of treating a condition that is associated with aging in an individual, the method comprising:measuring, before administering the plasmapheresis, the levels of a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in a blood sample of the individual; administering the plasmapheresis to the individual; measuring, following step (b), the levels of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or a combination thereof in a blood sample of the individual; monitoring for a change in the condition; andrepeating steps (b), (c), and (d) until the condition is improved, thereby treating the condition in the individual.

[0028] In some embodiments, further comprising determining a stage, severity, or risk of developing the condition that is associated with aging in the individual based on the levels of the levels of a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in a blood sample of the individual. In some embodiments, further comprising lowering a health risk associated with aging in the individual. In some embodiments, further comprising performing plasmapheresis on the individual a plurality of times.

[0029] In some embodiments, further comprising performing plasmapheresis on the individual until the stage, severity, or risk of developing the condition that is associated with aging in the individual or the health risk associated with aging in the individual are below a threshold level. In some embodiments, further comprising measuring the biological age of the individual and / or measuring the rate of aging of the individual.

[0030] In some embodiments, wherein the measuring the biological age of the individual and / or the rate of aging of the individual comprises measuring a biomarker, a biological clock, an omic panel, or a combination thereof.

[0031] In some embodiments, wherein the biomarkers, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises an immune cell population, a percentage of live immune cells, or a ratio of immune cell populations.

[0032] In some embodiments, wherein measuring the biological age of the individual, measuring the rate of aging of the individual, or the combination thereof comprises measuring a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof.

[0033] In some embodiments, i) the plurality of biomarkers comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 150, 200, 250, 300, 400, 500, 750, 1000, 1250, 1500, or 2000 biomarkers; ii) the plurality of biological clocks comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biological clocks; iii) the plurality of omic panels comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 omic panels; or iv) a combination thereof. In some embodiments, the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or the combination thereof comprise at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 types of biological molecules.

[0034] In some embodiments, the method further comprises improving a parameter associated with aging described in in TABLES VII, VIII, and / or IX. In some embodiments,the parameter is selected from: hand grip, balance, strength, up and go, and / or SF12 mental score.

[0035] In some embodiments, the biomarker, the biological clock, the omic panel, the plurality thereof, of the combination thereof comprises a biomarker, biological clock, omic panel, or combination thereof as set forth in in TABLES VII, VIII, and / or IX.

[0036] In some embodiments, the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises adaptation, causal and damage clocks, epigenetic clocks, fitness age, PC clocks, or systems ages. In some aspects, the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises blood, metabolic, brain, heart, SystemsAge, hormone, inflammation, lung, liver, musculoskeletal, kidney, immune, AdaptAge, CausAge, DamAge, Stochastic Horvath, Stochastic PhenoAge, Stochastic Zhang, Retroclock, cAge, Hannum, IntrinClock, PhenoAge, Horvath, OMICmAge, PCHorvath2, PCDNAmTL, PCGrimAge, PCHorvathl, PCHannum, PCPhenoAge, DNAmFitAge, DNAmGait, DNAmV02max, or DNAmGrip. In various aspects, the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises humoral immune response, leukocyte mediated immunity, lymphocyte mediated immunity, leukocyte cell -cell adhesion, activation of immune response, regulation of leukocyte cell-cell adhesion, regulation of cell -cell adhesion, negative regulation of immune system processes, regulation of leukocyte mediated cytotoxicity, regulation of T cell activation, antigen receptor-mediated signaling pathways, regulation of immune effector processes, regulation of cell killing, regulation of peptidyl-tyrosine phosphorylation, regulation of T cell proliferation, complement activation classical pathway, regulation of body fluid levels, antibacterial humoral response, heterotypic cell -cell adhesion, or antimicrobial humoral response. In other aspects, the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises abT CD4 CM CD27, MC INT, FA2BG2S1, FA2(3)G1S1, PC(38:3), SM(42: 1), Ser, AC(8: 1), BPIFA1, HBG1, IGFBP4, CTSD, CD44, TPM2, IGF2, MXRA5, THY1, LAMP1, C7, ICAM1, TXN, LGALS3BP, PRNP, SERPING1, PCOLCE, TWSG1, HBG1, or ITIH3.

[0037] In some embodiments, the plasmapheresis is performed weekly, biweekly, or monthly. In some aspects, the plasmapheresis is performed for at least 1 months, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, or longer. In some aspects, the plasmapheresis is performed for a period of about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, or about 12 months.

[0038] In some embodiments, the individual is less healthy than an individual of comparable age as determined by biomarker analysis. In some aspects, a health level of an individual is determined through measurement of biomarkers comprising at least one of glucose levels, bilirubin levels, globulin levels, circulating albumin, and potassium levels.

[0039] In some embodiments, the individual experiences a persistent decrease in biological age after plasmapheresis.

[0040] In some embodiments, the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises a property selected from abT CD4 CM CD27, MC INT, FA2BG2S1, FA2(3)G1S1, PC(38:3), SM(42: 1), Ser, AC(8: 1), BPIFA1, HBG1, IGFBP4, CTSD, CD44, TPM2, IGF2, MXRA5, THY1, LAMP1, C7, ICAM1, TXN, LGALS3BP, PRNP, SERPING1, PCOLCE, TWSG1, HBG1, or ITIH3.

[0041] In some embodiments, the plasmapheresis is performed weekly, biweekly, or monthly. In some embodiments, the plasmapheresis is performed for at least 1 months, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, or longer. In some embodiments, the plasmapheresis is performed for a period of about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, or about 12 months.

[0042] In some embodiments, the individual has an underlying health condition. In some embodiments, the underlying health condition is determined through measurement of biomarkers comprising at least one of glucose levels, bilirubin levels, globulin levels, circulating albumin, or potassium levels.

[0043] In some embodiments, method of determining a type of plasmapheresis treatment for a condition of an individual comprises of: measuring a biomarker; comparing the biomarker to a reference or baseline biomarker; determining that the individual has a health status; treating the condition with a type of plasmapheresis.

[0044] In some embodiments, the type of plasmapheresis treatment is biweekly, weekly, or plasmapheresis + IVIG.

[0045] In some embodiments, the individual has not received plasmapheresis treatment for at least 12 months, at least 11 months, at least 10 months, at least 9 months, at least 8 months, at least 7 months, at least 6 months, at least 5 months, at least 4 months, at least 3 months, at least 2 months, at least 1 month. In some embodiments, the individual has received plasmapheresis treatment within the past 1 week to 52 weeks.

[0046] In some embodiments, the biomarker comprises a biological clock, an omic panel, or combination thereof. In some embodiments, the omic panel comprises at least one of gly comics, metabolomics, methylomics, lipidomics, proteomics, or cytomics. In some embodiments, the plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in the individual are selected from any combination of features set forth in TABLES VII, VIII, IX, X, XI, XII, XIV, XV, XVI, XVII, XVIII, XIX, XX, XXI, XXII, XXIII, XXIV, and / or XXVI.BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein) of which:

[0048] FIG. 1A is a schematic diagram showing TPE treatment.

[0049] FIG. IB is a schematic diagram showing TPE treatment regimes.

[0050] FIG. 1C is a schematic diagram showing analysis methods including epigenomics, proteomics, metabolomics, lipidomics, cytomics, and glycomics, which together show rates of age acceleration.

[0051] FIG. ID is a heat chart showing biological age estimations using 35 epigenetic clocks.

[0052] FIG. IE is a graph showing average age acceleration difference across epigenetic clocks. Error bars indicate the 95% confidence intervals of the mean. P-values at the top and bottom indicate the significance of the difference between the treatments and sham using a Wilcoxon test.

[0053] FIG. IF is a graph showing average age acceleration difference for different groups of epigenetic clocks, comparing time points 2 vs 1. Asterisks indicate age acceleration differences significantly different from zero.

[0054] FIG. 1G is a graph showing average age acceleration difference for different groups of epigenetic clocks, comparing time points 3 vs 1. Asterisks indicate age acceleration differences significantly different from zero.

[0055] FIG. 1H is a set of graphs showing a comparison of average age acceleration difference between time point 2 vs 1 and time point 3 vs 2, for TPE+IVIG treatment (B, left) and for TPE treatment alone (B, right). Correlations were calculated using Pearson’s method.

[0056] FIG. II is a set of graphs showing a comparison of average age acceleration difference between time point 2 vs 1 and time point 3 vs 2 for TPE treatment alone (M, left), and for sham treatment (right). Correlations were calculated using Pearson’s method.

[0057] FIG. 2A is a graph illustrating correlations between the rejuvenation effects and changes in levels of omics features. Features significantly different from zero and sham in the TPE + IVIG group are displayed. Lines between the features at the center indicate interfeature correlations between cell composition changes and other omics.

[0058] FIG. 2B is a graph illustrating the percentage of features significantly changing in each omics and group. The dotted line indicates the average across omics.

[0059] FIG. 2C is a graph illustrating gene ontology enrichment analysis of proteins with levels correlated with the responses to TPE + IVIG (top) and a schematic diagram of enrichment of aging gene sets among proteins significantly associated with the TPE+IVIG response (bottom).

[0060] FIG. 2D is a graph illustrating the change in abundance in senescent cells of proteins relevant for the TPE + IVIG response.

[0061] FIG. 2E is a set of graphs illustrating features with the strongest positive and negative correlation for each omics with the rejuvenation effects. Features with negative correlation include alpha-beta t cells CD4 CM CD27 (R=-0 / 96), the glycan FA2BG2 S 1 (R=- 0.57), the phosphatidylcholine PC(38:3) (R=-0.92), serine (R=-0.77), and the protein BPI fold-containing family A member 1 (BPIFA1) (R=-0.85). Features with positive correlation include mast cell intermediate (MC INT) (R=0.95), the glycan FA2[3]G1S1 (R=0.63), the sphingomyelin SM(42: 1) (R=0.43), the acetylcamitine AC(8: 1) (F=0.89), and hemoglobin subunit gamma 1 (HBG1) (F=0.85).

[0062] FIG. 2F and FIG. 2G are sets of graphs illustrating features with robust positive and negative correlation with the rejuvenation effects in TPE + IVIG (ranked by FDR against zero). Features include abT CD4 SCM and MC NC (cytomics), A2(6)G1 and FA2 (glycomics), PC(36:2), SM(42: 1), glycine and creatinine (metabolomics), and IL13RA1 and ITIH3 (proteomics).

[0063] FIG. 3A is a circular graph illustrating omics’ features and clinical baseline levels correlated with the biological age response to TPE + IVIG. The lines connecting the features at the center indicate inter-feature correlations between clinical markers and other omics.

[0064] FIGs. 3B-3T are graphs illustrating clinical markers significantly correlated with the rejuvenation effects of TPE + IVIG. FIG. 3B is a graph illustrating change in magnesium, FIG. 3C is a graph illustrating change in bilirubin in the TPE+IVIG treatment, FIG. 3D is agraph illustrating change in HDL, FIG. 3E is a graph illustrating change in globulin, FIG. 3F is a graph illustrating change in glucose, FIG. 3G is a graph illustrating change in ALT, FIG. 3H is a graph illustrating change in platelets FIG. 31 is a graph illustrating change in RBC, FIG. 3J is a graph illustrating change in eosinophils %, FIG. 3K is a graph illustrating change in Trig, FIG. 3L is a graph illustrating change in Chol / HDL, FIG. 3M is a graph illustrating change in albumin, FIG. 3N is a graph illustrating change in absolute eosinophil for the TPE+IVIG treatment, FIG. 30 is a graph illustrating change in albumin / globulin in the TPE(B) treatment, FIG. 3P is a graph illustrating change in potassium, FIG. 3Q is a graph illustrating change in TSH, FIG. 3R is a graph illustrating change in MCH for the TPE(B) treatment, FIG. 3S is a graph illustrating change in creatinine for the TPE(M) treatment, and FIG. 3T is a graph illustrating change in alkaline phosphatase for the TPE(M) treatment.

[0065] FIG. 3U is a graph illustrating the ROC curve for the classification of responders to TPE interventions based on clinical markers.

[0066] FIG. 3V is a network diagram illustrating the coefficients selected for the classification of responders in the TPE interventions

[0067] FIG. 3W (left) is a graph illustrating change in bilirubin and absolute eosinophil in TPE + IVIG (B). FIG. W (right) is a graph illustrating change in MCH and Albumin / Globulin in TPE (B) treatment.

[0068] FIG. 3X is a graph illustrating change in creatine in TPE (M) treatment.

[0069] FIG. 3Y is a graph illustrating change in alkaline phosphatase.

[0070] FIG. 4A is a volcano chart showing changes in levels of proteins found within blood of individuals administered sham treatments once per month for six months, comparing the 4thvisit to the 1stvisit.

[0071] FIG. 4B is a volcano chart showing changes in levels of proteins, comparing the 6thvisit to the 1stvisit.

[0072] FIG. 4C is a volcano chart showing changes in levels of proteins, comparing the 6thvisit to the 4th visit.

[0073] FIG. 4D is a timeline of blood sample collection for groups C and D.

[0074] FIG. 5A is a series of plots of proteomic data showing changes in levels of proteins found within blood of individuals who received IVIG and plasmapheresis, as well as a timeline showing when blood samples were collected.

[0075] FIG. 5B is a timeline of blood sample collection for groups A and B.

[0076] FIG. 6A is a graph that shows pathways of proteins that exhibited significant changes in regulation within blood of individuals who received IVIG and plasmapheresis.

[0077] FIG. 6B is a volcano chart that shows change in pathways of proteins that exhibited significant changes in regulation within blood of individuals who received IVIG and plasmapheresis, comparing the 6thvisit to the 1stvisit baseline.

[0078] FIG. 7 is a series of plots of proteomic data showing changes in levels of proteins found within blood of individuals who received two plasmapheresis treatments within one week, once a month, for three months.

[0079] FIG. 8A is a graph that shows pathways of proteins that exhibited significant changes in regulation within blood of individuals treated in Group B.

[0080] FIG. 8B is a volcano chart showing pathways of proteins that exhibited significant changes in regulation within blood of individuals who received two plasmapheresis treatments within one week, once a month, for three months.

[0081] FIGs. 9A-9C are plots of proteomic data showing changes in levels of proteins found within blood of individuals who received plasmapheresis once a month for six months, including comparing the baseline to the first visit (FIG. 9A), the 4thvisit to baseline (FIG. 9B), and the 6thvisit to the 1stvisit baseline (FIG. 9C).

[0082] FIG. 10 is a plot of proteomic data showing changes in levels of proteins found within blood of individuals who received plasmapheresis twice within one week, once a month, for three months as compared to individuals who received plasmapheresis and IVIG twice within one week, once a month, for three months.

[0083] FIG. 11A is a graph that shows pathways of proteins that exhibited significant changes in regulation within blood of individuals treated in groups B4 compared to A4.

[0084] FIG. 11B is a plot of proteomic data that show pathways of proteins that were upregulated or downregulated within the blood of individuals who received plasmapheresis twice within one week, once a month, for three months as compared to individuals who received plasmapheresis and IVIG twice within one week, once a month, for three months.

[0085] FIG. 12 is a plot of proteomic data showing changes in levels of proteins found within blood of individuals who received plasmapheresis once per month for six months as compared to individuals who received plasmapheresis twice within one week, once a month, for three months.

[0086] FIGs. 13A-13C are plots of proteomic data comparing proteins upregulated and downregulated in treatment groups A and B compared to sham treatment. A comparison of Group B3 to Group C3 is shown in FIG. 13A, a comparison of Group A3 to Group C3 isshown in FIG. 13B, and FIG. 13C is a plot showing significantly upregulated proteins for Group A3 compared to Group C3.

[0087] FIG. 14 is a plot of cell cytometry data standards.

[0088] FIG. 15 is a plot of cell cytometry data including standards and treatment data forGroup B.

[0089] FIG. 16 is a series of volcano plots of cell cytometry data.

[0090] FIG. 17 is a series of volcano plots of cell cytometry data.

[0091] FIG. 18 is a schematic diagram of treatment of disease or disorders through the described methods.

[0092] FIG. 19 is a heat map of senescence-associated secretory phenotype (SASP) markers.

[0093] FIG. 20A is a chart showing change in acylcamitine 14:2 before Group Al and after Group Al treatment (Al refers to the first treatment of the A treatment group).

[0094] FIG. 20B is a chart showing change in sphingomyelin 42:2 before Group Al and after Group Al treatment.

[0095] FIG. 20C is a chart showing change in phosphatidylcholine 36:4 before Group Al and after Group Al treatment.

[0096] FIG. 21 is a graph illustrating the top 25 enriched metabolite sets.

[0097] FIG. 22A is a graph illustrating change in CD4 a[3 T cells in treatment groups including 2x / wk+IVIG, 2x / wk no IVIG, sham TPE, and Ix / mo.

[0098] FIG. 22B is a graph illustrating change in CD8 a[3 T cells in different treatment groups including 2x / wk+IVIG, 2x / wk no IVIG, sham TPE, and Ix / mo.

[0099] FIG. 22C is a graph illustrating change in CD4 / CD8 ratio in different treatment groups including 2x / wk+IVIG, 2x / wk no IVIG, sham TPE, and Ix / mo.

[0100] FIG. 23 is a graph illustrating the change in PCGrimAge due to treatment.

[0101] FIG. 24 is a graph illustrating the change in PCHorvath2 due to treatment.

[0102] FIG. 25 is a graph illustrating the change in PCPhenoAge due to treatment.

[0103] FIG. 26 is a graph illustrating the change in the Hormone systems clock due to treatment.

[0104] FIG. 27 is a graph illustrating the change in the Lung systems clock due to treatment for Groups A, B, C and D.

[0105] FIG. 28 is a set of graphs illustrating the change in grip strength due to treatment.

[0106] FIG. 29 is a set of graphs illustrating the change in Up and Go values due to treatment for Groups A, B, C and D.

[0107] FIG. 30 is a set of graphs illustrating the change in the Balance Test due to treatment for Groups A, B, C and D.

[0108] FIG. 31 is a set of graphs illustrating the change in the physical score due to treatment for Groups A, B, C and D.

[0109] FIG. 32 is a set of graphs illustrating change in the SF-12 mental score due to treatment for Groups A, B, C and D.

[0110] FIG. 33A is a heatmap of average changes in different clocks among the different treatment groups.

[0111] FIG. 33B is a graph illustrating average changes in age acceleration due to treatment for Groups A, B, C and D.

[0112] FIG. 34 is a set of charts illustrating changes in glycomics, metabolomics, methylomics, lipidomics, proteomics, and cytomics due to treatment.

[0113] FIG. 35 is a correlation chart illustrating response to TPE as positive and negative correlations for glycomics, metabolomics, methylomics, lipidomics, proteomics, and cytomics due to treatment.

[0114] FIG. 36 is a heatmap illustrating correlations in SASP markers for structural and immune response proteins, and heat shock proteins and glycolytic enzymes due to treatment.

[0115] FIG. 37 is a set of graphs illustrating correlations of age acceleration with lipidomics, proteomics, methylomics, cytomics, and clinical metrics.

[0116] FIG. 38 is a correlation chart illustrating the response to TPE as a correlation of metrics with the response score, including age acceleration, clinical, glycomics, metabolomics, methylomics, cytomics, proteomics, and lipidomics.

[0117] FIG. 39 is a schematic diagram illustrating the use of baseline networks to guide treatment decisions.DETAILED DESCRIPTION

[0118] The rapid increase in the proportion of older adults worldwide poses a huge challenge for healthcare systems. Currently, age-related chronic diseases account for over 90% of annual healthcare expenditures (more than $4.1 trillion) only in the US. Aging coincides with progressions in diseases and disorders, which affect recognizable and often deleterious changes in comfort, fitness, appearance, and cognition. While some of these progressions manifest as readily identifiable changes in appearance (e.g., in humans, looser skin, increased mouth and nose width, and eye droop), the underlying biochemistry - which is believed to involve, among other things, complex and multifaceted changes in molecularand signaling pathways over time - is not yet fully understood. A great deal of research is currently being conducted to better understand the science of aging and changes (e.g. genetic, physiologic) associated with aging at both the micro and macro level of different organisms including humans. Generally, changes and conditions that are associated with aging are considered negative and there is a great deal of benefit in treatments described herein that can improve a health status of an individual by addressing changes and conditions associated with aging. Thus, new therapies to improve healthy lifespan and to reduce the burden of chronic disease are needed.Overview of Therapeutic Plasma Exchange (TPE)

[0119] This disclosure presents different therapeutic plasma exchange (TPE) modalities and their effect on the biological age of ambulatory individuals. Individuals were profiled longitudinally to measure changes in the epigenome, proteome, metabolome, glycome, and shifts in immune cell composition (cytomics). The present disclosure is based on the finding that administering TPE supplemented with intravenous immunoglobulin (IVIG) (TPE-IVIG) in a biweekly regime (two sessions in the first week, followed by a three-week break) is a robust therapy for biological age rejuvenation. This intervention induced coordinated cellular and molecular ‘omics’ responses, reversed age-related immune decline, and modulated cellular senescence-associated proteins. Integrative analysis revealed baseline biomarkers associated with successful responses, which indicate that TPE-IVIG treatment benefitted those with baseline poorer health status. In summary, this is the first multi -omics study for the effectiveness of various modalities of TPE, which demonstrates unexpected biological age rejuvenation and baseline features associated with TPE and IVIG.

[0120] The present disclosure presents methods for identifying successful treatment modalities, as TPE biweekly and IVIG treatment decreases age acceleration. Highly sensitive measures for detecting a decrease in age acceleration include systems of age clocks. Multiple omics features also help guide treatment decisions and detect improvements in different response types. Testing at baseline allows stratification of patients for treatment type according.BIOLOGICAL AGE AND AGINGChronological Age vs Biological Age

[0121] As used herein, the term “chronological age” refers to the number of years that an individual has existed which is a duration of time which can be expressed as “age” or “years old”. Chronological age is purely a chronological measurement of time having a start point(typically at birth) and an end point at death and is not determined based on any property of the individual either physical or biological. For example, an individual’s chronological age does not change based on how old their physical appearance makes them appear nor does it change based on a family history or genetic feature that would suggest a specific lifespan for the individual.

[0122] The term “biological age,” as used herein, on the other hand, is a measure of the aging process and takes into account physical, biological, genetic, and biochemical features of an individual, including but not limited to biological progressions, genetic and epigenetic features, homeostasis measurements, disease-risk, and various molecular changes associated with an individual.

[0123] Biological age may be expressed as a duration of years similar to how chronological age is expressed. Biological age may refer to an individual as a whole or other aspects such as, for example, an organ, an organ system, or other functional system of the individual. For example, an individual as a whole may have the biological age of 35 and an immune system age of 27 (i.e. where an immune system age is a subset or type of biological age).

[0124] It is notable that biological age and chronological age may be decoupled, leading to appearances, energy levels, and / or biological profdes of chronologically older or younger individuals. For example, an individual may be 55 years old (in terms of chronological age) but have the biological age of 42 years old. Likewise, an individual who is 35 years old may have a biological age of 51 years old.

[0125] Biological age, at least in some respects, can be thought of as a measurable performance metric, wherein it is favorable for an individual to have a biological age - as a whole or with respect to a particular feature of the individual - that is less than the chronological age of the individual. For example, an individual who has a chronological age of 70 years, assuming that they have their own liver and not a transplanted liver, has a liver which has a chronological age of 70 years as well. The same individual of the example may have - based on, for example, one or more measures discussed above - a biological age of 68 years as an individual. And, the same individual of the example, may have - based on, for example, one or more measures discussed above - a liver with a biological age of 65 years. That is, in this example an individual may have an overall biological age of 68 years whereas an organ of the same individual (in this example, their liver) has a biological age of 65 years old. In this way biological age can be considered a holistic measure or a measure of individual systems and / or a measure of a processes within the body of an individual.Factors Influencing Biological Aging

[0126] In addition to population-level variation, biological aging can progress at multiple rates within an individual. Owing to genetics, lifestyle, health, and environmental factors, separate organs, tissue-types, or cells within the individual may exhibit disparate biological ages. For example, among a multitude of cumulative and deleterious effects, obesity, diabetes, and renal diseases often accelerate aging in kidneys, such that apparent ages of such a person’s kidneys can be much higher than those of their other organs and omic profiles (e.g., plasma proteomics). Even in healthy individuals, marked biological age variation can occur.

[0127] While biological aging rates appear to be responsive to ranges of genetic and environmental factors, many organisms appear to follow innate and encoded aging timelines. Although biological aging exhibits some intraspecies variation, upper and lower bounds for aging rates appear to be primarily determined by species type. For example, while there are no known cases of humans living past 125 years of age, bowhead whales routinely reach 200 years of age, and certain species of clams consistently live beyond 500 years. Exemplifying the other extreme, African killifish typically only live for between 4 and 6 months, and exhibit signs of advanced aging as early as 2 months. Supporting a genetic underpinning for aging, a number of human diseases modify biological aging rates, with Hutchinson-Gilford Syndrome, Werner Syndrome, and Down Syndrome increasing biological aging by about 100-800%.

[0128] Biological age may be computed using one or more markers or factors that correlate with or indicate the biological age of an individual. Such markers or factors may be measured and / or detected through testing of a biological sample such as, for example, blood, urine, sputum, and sweat.Mechanisms of Aging

[0129] The mechanisms associated with aging are not at this time fully understood, however, a number of observations provide at least empirical insight into factors associated with aging. Observations have, for example, shown that aging is likely influenced by a number of health and lifestyle factors (i.e. ostensibly non-genetic factors), including stress, diet, sleep hygiene, and sun exposure. To say that age is influenced by these factors is to say that observations seem to indicate that affecting a change in one or more of these factors can influence a rate and / or severity of aging. For example, affecting diet through moderate caloricrestriction is a well-established and reliable means for slowing aging which strongly suggests that diet is an important component of the aging process.

[0130] It is also strongly believed, and supported by certain research, that there is a genetic component to aging as well, wherein expression of certain genes is believed to drive aging related processes and either over or under expression of certain genes can lead to slowing or acceleration of the aging process. Gene expression, in the context of aging, can result in the production of peptides capable of acting on or otherwise affecting targets located relatively remotely, within the body, from the cell that contains the genes that are expressed. In this way, genes expressed in one cell type, or one tissue type can have effects on cells or tissues that are remote from where the gene was originally expressed.

[0131] Peptides produced by expression of genes associated with aging may be modified within the body by other chemical processes which then may affect the function of the peptide. Such chemical processes that modify post-translational peptides include but are not limited to methylation, glycation, and glycosylation. The type of chemical modification as well as the degree of chemical modification of certain post-translational peptides may be both a cause of age-related changes and also a marker of aging as well. For example, a degree of glycation of a peptide found in blood such as, for example, albumin may directly corelated with aging generally or a specific aging process. A degree of chemical modification of a post- translational peptide can refer to the percent of peptides found to have undergone the particular modification and / or a degree of modification seen within one or more of the peptides.

[0132] Aging is also associated with an increase in the levels of pro-inflammatory markers in blood and tissues, which is a strong risk factor for multiple diseases that are highly prevalent and frequent causes of disability in elderly individuals. This phenomenon is referred to as “inflammaging.” Reducing or even reversing inflammaging in aging patients is a pathway to treating conditions associated with aging and even treating or reversing aging itself.

[0133] Age is a significant risk factor for chronic disease, and by the year 2030, almost a sixth of the world’s population will be aged 60 or older. These population dynamics pose a huge challenge for healthcare systems. A person’s age is more than the chronological number of years they have lived. Factors like stress, diet, sleep, genetics, and infections influence the rate at which bodies age, and biologically older individuals are more likely to develop diseases and experience premature mortality. In recent years, an increasing number of studies focused on estimating a person’s biological age (BA) at the cellular level based on changes toepigenomics, proteomics, metabolomics, and other ‘omics’ measurements. Some methods utilize changes in the DNA methylation (DNAm) patterns that occur with age, and methods also use machine learning techniques to construct biological age predictors. These BA ‘epigenetic clocks’ are often predictive of diseases and mortality, and lifestyle interventions affect the biological age. For instance, moderate weight loss (<5%) decreases epigenetic age by 1.1 years (Horvath clock), and individuals with more than 5% in weight loss show an increase of 7.2 BA months in an 18-months period. Nutrition has an impact on methylation clocks as well. For example, methylation-supportive diets combined with lifestyle changes in some cases reduces biological age by 4.6 years in females and 1.96 years in males. Healthy dietary habits maintained for up to 2 years in some cases reduce age-adjusted biological age by 0.66 years (GrimAge). The effects of diet and nutritional supplementation are often dependent upon the study population. For instance, in overweight African Americans, vitamin D3 supplementation decreases epigenetic age by approximately 1.90 years in some instances, but in vitamin D-deficient individuals, in some studies, it decreases epigenetic age by only 1.3 years. Interestingly, a 60-day relaxation practice scheme also leads to lowered epigenetic age with a rejuvenation of 4.67 years in healthy individuals. Pharmacological interventions have highlighted the potential of small molecules to reduce biological age. For example, in patients with diabetes mellitus undergoing metformin treatment a reduction in epigenetic age acceleration by 2.77 years (Horvath clock) and 3.43 years (Hannum clock) has been observed. Other pharmacological agents, such as dasatinib and quercetin (NCT04946383), and rapamycin (NCT04608448), are currently undergoing clinical trials for reducing biological age.Effects of Aging

[0134] Many age-related developments are considered unwelcome and deleterious to quality of life, such as, for example, hearing and vision loss, arthritis, and loss of cognitive function. This phenomenon is nearly ubiquitous across species, wherein past a certain point, aging coincides with diminished capabilities and biological function. In addition, certain diseases are highly associated with and possibly interrelated with aging, because, for example, aging corresponds with: degenerative processes, diminished recovery or healing capacity, increased propensity for acute stress and immune response; all of which create an environment where certain disease processes can occur. As an example, rheumatoid arthritis is a disease highly associated with aging, the pathophysiology of which is associated withtissue degeneration, diminished recovery or healing and increased propensity for acute stress and immune response.

[0135] Moreover, other diseases or conditions associated with and possibly interrelated with aging include cancer, Alzheimer’s disease, neurodegenerative disorders, immune system dysregulation, metabolic disorders, chronic inflammatory disease, autoimmune disease, infectious disease, lipid storage disease, coagulation disorder, hormonal disorders, or a cardiovascular disease.

[0136] Additionally, other diseases or conditions associated with and possibly interrelated with aging are breast cancer, lung cancer, colorectal cancer, prostate cancer, leukemia, lymphoma, melanoma, pancreatic cancer, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis (ALS), Huntington's disease, multiple sclerosis (MS), frontotemporal dementia, atherosclerosis, coronary artery disease, hypertension, heart failure, myocardial infarction (heart attack), stroke, peripheral artery disease, type 2 diabetes, obesity, metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), hyperlipidemia, gout, rheumatoid arthritis, systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD), Crohn's disease, ulcerative colitis, psoriasis, ankylosing spondylitis, bacterial infections, tuberculosis, staphylococcal infections, viral infections, HIV / AIDS, hepatitis B and C, influenza, parasitic infections, malaria, leishmaniasis, epilepsy, migraine, autism spectrum disorders, attention deficit hyperactivity disorder (ADHD), schizophrenia, bipolar disorder, Gaucher disease, Niemann-Pick disease, Fabry disease, Tay-Sachs disease, Wolman disease, hypothyroidism, hyperthyroidism, Cushing's syndrome, Addison's disease, polycystic ovary syndrome (PCOS), growth hormone deficiency, hemophilia, von Willebrand disease, deep vein thrombosis (DVT), pulmonary embolism, disseminated intravascular coagulation (DIC), phenylketonuria (PKU), maple syrup urine disease (MSUD), homocystinuria, galactosemia, and mitochondrial disorders

[0137] Physiological effects of aging (i.e. conditions associated with aging) include decreased strength, decreased mobility (and specifically decreased ability to walk), decreased balance, and subjective changes in mental wellbeing.Effect of Aging on Lifespan and Longevity

[0138] The term “lifespan” as used herein is the duration of the life of the individual. Whereas when measured in a population of individuals lifespan can be any cumulative measure across the entire population or a portion of the population (i.e. a sub-population), such as, for example, an average lifespan of the population or sub -population, a medianlife span of the population or sub-population, a variance in life span of the population or subpopulation, and so on.

[0139] The term “longevity” as used herein means that an individual or population of individuals has a lifespan or expected lifespan that lasts longer than a reference lifespan. For example, historical data can provide expected lifespans for a population which can serve as a reference lifespan. An “expected lifespan” as used herein may describe any measure of a lifespan of an individual or lifespans within a population that can be reasonably used as a predictor or marker for a lifespan of another individual or individuals within a population. For example, an expected lifespan of an individual having a particular physiology can be obtained by calculating an average lifespan for a population of individuals having the same particular physiology so that the average lifespan can serve as a reference lifespan. It should be understood that there are a multitude of ways to determine a reference lifespan including using statistical techniques for data of a relevant population such as, for example, mean, median, mode, standard deviation, and variance.

[0140] The aging process counters or limits longevity in the sense that aging has a shortening effect on lifespan. It is well understood that the aging process is not only associated with adverse physiologic change and increased likelihood of development of life threatening disease, but is also either a direct or indirect cause of death, which of course shortens lifespan. It is also well understood that mitigating, preventing, halting, and / or reversing the effects of aging will promote increased lifespan and therefore promote longevity. Generally speaking, if you can significantly counter aging, you promote the ability to avoid death and therefore live longer, thereby, increasing lifespan and promoting longevity. This is true for individuals as well as a population of individuals. Therefore, aside from aging being a good target for therapy in its own right, therapies that address aging are expected to promote longevity as well.

[0141] Therapies that promote longevity can, therefore, be defined as those that promote a relative increase in lifespan in an individual or a population and / or therapies that treat, mitigate, and / or prevent the effects of aging. For example, an expected lifespan for a male human having a particular physiologic feature or features, such as, for example, dark hair and green eye color, may correspond to 89.4 years, where 89.4 years is the average lifespan of a population of males with dark hair and green eyes. It can then be said that a male human with these same physiologic features (i.e. dark hair and green eyes) experiences longevity when he outlives the expected lifespan by, for example, living until the age of 91 years old. Similarly, a population of males from a particular geographic region, such as, for example, Greece, with 1these same physiologic features (i.e. a subset of the larger population of males with dark hair and green eyes but from the specific geographic region of Greece) can be said to have longevity if they all individually have a lifespan that is longer than 89.4 years. Therefore, a therapy associated with or that results in the lifespan of the individual or the lifespans of individuals in a population being longer than an expected lifespan can be said to be a therapy that promotes longevity.

[0142] In addition, therapies that promote longevity can also be defined as those therapies that cause an increase in an expected lifespan of an individual relative to an existing expected lifespan of a reference individual or reference population. For example, a person who is a smoker and has initial expected lifespan, then undergoes a therapy that causes him to quit smoking and the quitting of smoking results in a longer expected lifespan.MULTI-OMICS ANALYSES

[0143] The present disclosure utilizes a multi-omics approach to predict and analyze the effects of plasmapheresis treatments. This comprehensive analysis includes epigenomics, proteomics, metabolomics, lipidomics, cytomics, and glycomics. Each of these omics techniques provides insights into different aspects of cellular and molecular changes occurring in response to the treatments. By integrating data from multiple omics platforms, the disclosure provides a more complete understanding of the biological processes and pathways affected by plasmapheresis, revealing novel biomarkers and mechanisms related to aging and therapeutic responses.Biomarkers

[0144] Aging coincides with diverse and complex progressions at molecular, cellular, and tissue levels. As disclosed herein, select aspects of these progressions can be monitored to determine the biological age of an individual. As many markers for aging can also be responsive to health, lifestyle, and environment, methods for determining biological age can utilize multiple biological markers and may further use non-age-responsive biomarkers as calibrants.

[0145] Exemplary biomolecules (or biological molecules), genetic and epigenetic markers, expression patterns, and associated measurement methods that can be useful for diagnosing chronological and biological age are outlined below. While the biomarkers outlined in this section are of particular utility, they are intended to serve as examples of age-diagnostic species, and are not intended to be limiting.Blood-Based Biomarkers

[0146] Many of the molecular and biological changes associated with aging manifest in altered blood composition. At a population level, aging correlates with consistent, if nonetheless complex, changes in blood phenotypes. While some of these changes can be mapped to straightforward increases or decreases of single biomarkers, such as progressive increases in inflammatory peptide biomarker (e.g., interleukin (IL)-6), C-reactive protein, and tumor necrosis factor-a (TNF- a)) levels with age, aging can also correlate with changes in biomarker processing (e.g., immunoglobulin glycosylation patterns) and ratios among groups of species.(i) Albumin

[0147] For many individuals, albumin, a high abundance serum protein, can serve as a robust biomarker for aging. Albumin is a family of globular transport proteins essential for lipid, hormone, and metabolite clearance and homeostasis. Following typical peak concentrations of 40 to 50 mg / mL during late adolescence, serum albumin concentrations often decrease by hundreds of pg / mL annually, and exhibiting accelerated rates of diminution at advanced ages. While atypical serum albumin level is about 45 and 42 mg / mL for 30- year-old males and females, respectively, by age 60, mean levels decrease to about 42 and 40 mg / mL for males and females, respectively.

[0148] Furthermore, albumin often exhibits age-dependent structural changes which may be useful for aging diagnostics. In humans, the proportion of glycated albumin increases with age, and typically leads to diminished function. As albumin activity is essential for multiple forms of homeostasis, the combined impact of diminished albumin levels and activity can contribute to adverse symptoms of aging (e.g., diminished energy), and may even augment biological aging rates. Albumin glycation can also evidence other age-related developments, including diminished concentrations and functions of regulatory proteins such as insulin. Accordingly, serum albumin concentration, isoform ratios, and post-translational modifications (e.g., glycation patterns) can not only serve as diagnostic markers for age, but can evidence the severity of age-related symptoms.(ii) Ceruloplasmin

[0149] For many individuals, changes in ceruloplasmin levels and morphology can be used to quantitate biological age. Ceruloplasmins are a class of copper proteins which participate in iron oxidation and trafficking. Accordingly, ceruloplasmins perform central roles in iron trafficking and reactive oxygen species prevention. Ceruloplasmins exhibit progressive changes in post-translational modification and isoform populations with aging, which canaffect activity, localization (e.g., intravascular versus extravascular distribution), and clearance rate.

[0150] While ceruloplasmin consortia typically contain complex arrays of isoforms and post-translational modification patterns, age-related progressions often manifest as detectable changes in ceruloplasmin copper centers. Such changes can be detected with paramagnetically sensitive spectroscopies such as electron paramagnetic resonance and magnetic circular dichroism, and can evidence broader changes in structure, isoform ratio, and post-translational modification patterns. Ceruloplasmin also often exhibits age-dependent carbonylation and net charge, with greater than 3-fold more carbonylation (e.g., as measured by mass spectrometry) and 0.1 higher isoelectric points (e.g., as measured by 2-dimensional gel electrophoresis) in 65 -year-old than in 15-year-old subjects. Accordingly, ceruloplasmin structure, isoform ratios, post translational modification patterns, and combinations thereof can be used to assess biological age.(Hi) Immunoglobulins

[0151] As immunoglobulins are present within blood as complex consortia spanning varied structural forms, targets, immune activities (e.g., effector functions and complement binding affinities), and glycosylation patterns, variations in immunoglobulin populations can serve as strong markers for biological aging. Humans express five immunoglobulin isotypes (IgG, IgA, IgM, IgD, and IgE) spanning multiple subclasses (e.g., IgGl, IgG2, IgAl, etc.) and differing in structure, concentration, biodistribution, and immunomodulatory activity. While IgG, IgA, and IgM are the high abundant proteins in serum, each with mg / mL resting levels, IgD and IgE are typically present in serum in pg / mL and ng / mL quantities, respectively.

[0152] Total immunoglobulin concentrations tend to peak during early adulthood, and then decrease steadily with age. Nonetheless, only some immunoglobulin isotypes and subclasses exhibit age-dependent changes in serum levels. Increases in IgA levels and decreases in IgM levels occur with age, as well as age-invariance for total IgG concentrations. However, for certain subjects, only IgGl and IgG3 levels are invariant with age, while IgG2 and potentially IgG4 can exhibit age dependent concentration declines. Contrasting IgA, IgG, and IgM concentration trends, IgD may peak during the first year of life, but remain relatively stable thereafter. For certain subjects, ratios between immunoglobulin isotype and subclass concentrations can provide a strong diagnostic marker for age. For example, the ratio between IgA and IgM, IgA and IgG2, IgA and IgG4, IgM and IgG2, IgM and IgG4, and / or IgG2 and IgG4 serum levels can evidence age.

[0153] Immunoglobulin consortia can also exhibit age-dependent changes in glycosylation. All five human isotypes exhibit diverse glycan modifications which affect immunomodulatory and biodistribution behavior. Within each isotype, glycosylation patterns (gly comes) exhibit high degrees of heterogeneity, as well as health and population variance. For example, IgG antibody populations typically exhibit greater than 30 types of glycans at asparagine 297, in addition to variable Fab and hinge region glycosylation, some of which vary with disease status. Nonetheless, age dependent changes in glycosylation patterns have been observed for all five human isotypes. Within IgG antibody populations, increases in agalactosylation and GlcNAc bisection and decreases in digalactosylation, sialylation, and afucosylation are typically observed with aging. Furthermore, there is some evidence that IgG glycosylation is not only responsive to age, but is a determinant for the rate of biological aging.(iv) Glutathione

[0154] Glutathione is a versatile biomolecule which participates in oxidative homeostasis, nitric oxide signaling, aldehyde catabolism, and multiple forms of anabolism. Glutathione is present in micromolar (pM) concentrations in blood as a mixture of reduced monomers and oxidized disulfide dimers. The ratio of these two forms is not only responsive to blood conditions, such as reactive oxygen species levels, but shift with age. Augmenting this effect, systemic glutathione levels steadily diminish with age. As glutathione mitigates oxidative stress, diminished glutathione levels may be partially responsible for increased oxidative stress and the progression of stress-related conditions (e.g., Parkinson’s disease) among elderly individuals. Accordingly, systemic glutathione levels and monomer-dimer ratios can serve as a strong diagnostic marker for biological age. In men and women, serum glutathione concentrations can steadily diminish from about 1 pM at the age of 20 to about 0.5 pM at the age of 60.Physical Function and Appearance

[0155] In spite of extensive variation in physical fitness and appearance among individuals, diminishing physical abilities and changes in appearance are universal attributes of aging in humans, including diminished strength, diminished ambulation (or walking), and diminished balance.Biological Ages of Tissues

[0156] While some biological age measurements can identify a biological age of an individual, others identify biological ages of individual cells, tissues, organs, or systems (e.g.,immune or endocrine systems). In many individuals, biological aging progresses in cell-, tissue-, organ-, and / or system-specific manners, reflecting distinct environments, stresses, and genetic and regulatory architectures. In the absence of trauma or aberrant health, the range of biological ages of an individual’s tissues and cells is often small, for example less than age measurement experimental error. However, many individuals exhibit multiple, disparate ages. For example, relative to their chronological age, an individual may have a young brain age and advanced immune age. For such an individual, biological age may reflect a sort of median of cell, tissue, organ, and / or system-specific biological ages. Alternatively, the biological age of the individual may be expressed as a set of distinct, organ or system specific biological ages.

[0157] A biological age measurement can utilize one biological age marker or a plurality of biological age markers. In many cases, the accuracy of a biological age determination method increases as more age markers are utilized for analysis. However, for many individuals, use of a single age marker or a small set of age markers are sufficient for accurately determining biological age and / or biological aging rate, for example with a standard error of less than 7 years, less than 5 years, or less than 3 years. Examples of methods for assessing biological age are provided in TABLE I.TABLE I describes different age measurement assays.

[0158] A method for biological age determination can utilize a single assay or a plurality of assays from TABLE I, a cytokine inflammatory marker panel, a metabolomic assay, a glycomics assay, a lipidomic assay, a proteomic assay, a cytomics assay, a methylation clock,an adaptation clock, a causal clock, a damage clock, a stochastic clock, a PC clock, an epigenetic clock, an immune cell population (e.g., percent living immune cells (e.g., percentage of live leukocytes), CD4 cell population, CD8 cell population, CD4 / CD8 cell ratio, etc.), an assay to measure a senescence associated phenotype marker, peripheral blood mononuclear cell (PBMC) analysis, genomic methylation analysis (e.g., a methylomics assay), , inflammatory marker analysis, or a combination thereof. The assay or plurality of assays can assess holistic biological age, organ and / or system-specific biological age, or a combination thereof. In some aspects, the method for biological age determination comprises measuring a plurality of biomarkers. In some aspects, the method for biological age determination utilizes a plurality of assays. In some aspects, the method for biological age determination utilizes a plurality of assays that measure multiple types of biomarkers (e.g., nucleic acids, proteins, metabolites, and lipids). Additional examples of biomarkers, assays, biological clocks, and omics are detailed in Exhibit A. The biological age(s) determined for an individual can be used to calibrate a treatment, such as a treatment for aging disclosed herein.

[0159] The assay or plurality of assays can be performed at regular intervals, for example once per month, once every three months, once every six months, or once per year. In this way (as well as with aging rate-diagnostic methods), biological age(s) determined from the assay or plurality of assays can also be used to determine biological aging rate in the individual, for example to determine whether an aging treatment is slowing a rate of aging in an individual, to calibrate a treatment method to achieve a target age or aging rate in an individual, to decouple disease markers from aging-related symptoms (e.g., to determine whether raised HbAlc levels stem from disease or aging), or a combination thereof.Antibody Assays

[0160] A method for determining biological age can include an assessment of antibody concentration, type, and structure. While antibodies are present as complex consortia spanning multiple isotypes (in humans, IgA, IgD, IgE, IgG, and IgM), paratope structures, and processing (e.g., glycanation), changes among these consortia can be diagnostic of biological aging. For example, as further detailed herein, ratios of antibody isotypes typically shift with aging. Furthermore, individual antibody types, such as anti-nuclear and anti-thyroid peroxidase antibodies, change in concentration with age, and can thereby serve as markers for aging. A number of illustrative antibody assays are outlined below. It is contemplated herein that additional antibody assays may be used with methods of the present disclosure.(i) Anti-nuclear Antibody Screen

[0161] An anti-nuclear antibody (ANA) screen measures cell nucleus-binding antibody concentrations in blood, plasma, or serum. While a range of ANA subtypes are present in humans, ANA screens measure total ANA antibody concentration, with indirect immunofluorescence and enzyme-linked immunosorbent (ELISA) detection following cell binding (e.g., to HEp-2 cells). ANAs are associated with a range of disorders, many of which are associated with aging. However, even among healthy individuals, ANA blood concentrations tend to increase with age, with elderly individuals often exhibiting 3- or greater-fold ANA levels relative to younger individuals. Accordingly, a method consistent with the present disclosure can utilize a blood ANA concentration measurement to determine biological age or biological aging rate.(ii) Rheumatoid Factor Assay

[0162] An age-diagnostic method can assess blood (e.g., whole blood, serum, plasma) levels of rheumatoid factor (RF) factor, an autoantibody against the Fc portion of IgG and implicated in a number of age-related conditions, including rheumatoid arthritis and diminished bone density. While rheumatoid factor can present as a combination of immunoglobulin isotypes (e.g., IgA, IgD, IgE, IgG, and IgM) with ranges of Fc epitopes and binding affinities, total rheumatoid factor levels can be identified with a number of binding assays, including indirect immunofluorescence and enzyme-linked immunosorbent (ELISA). In many people, rheumatoid factor typically appears between the age of 30 and 70 and progressively increases in concentration with age. Accordingly, a method for measuring biological age consistent with the present disclosure can include measuring blood RF concentration.(Hi) Thyroid Peroxidase Antibody Assay

[0163] A thyroid peroxidase antibody assay measures concentration of autoantibodies which target thyroid peroxidase (TPO), an enzyme essential for thyroid hormone production. As thyroid peroxidase is a prevalent thyroid autoantigen, thyroid peroxidase antibody levels can be reflective of total anti-thyroid antibody levels. While thyroid antibodies (including thyroid peroxidase antibodies) are implicated in a number of diseases, thyroid antibody levels also tend to increase spontaneously with age, thereby allowing them to serve as markers for biological aging. Accordingly, a method of the present disclosure can utilize blood thyroid antibody and / or blood thyroid peroxidase antibody levels to determine biological age. Furthermore, in some subjects, the targets of thyroid peroxidase antibodies shift with age, with anti -domain A antibodies typically increasing in prevalence.(iv) Quantitative Immunoglobulin Assay

[0164] A quantitative immunoglobulin assay can assess total antibody levels in a subject sample. Typically, quantitative immunoglobulin assays measure total antibody levels in blood. However, some assays assess concentration by antibody isotype or subtype. For example, a quantitative immunoglobulin assay may measure total IgG and IgA concentrations, or may separately determine concentrations of individual antibody subtypes (e.g., IgGl, IgG2, IgAl, IgA2, etc.). As in many subjects, total antibody, antibody isotype, and antibody subtype concentrations change with age, quantitative immunoglobulin assays can be used to determine biological age.(v) Glycanation Assays

[0165] Biological aging can be evidenced by immunoglobulin glycosylation profdes. All five human antibody isotypes exhibit glycosylation patterns. While the positions of glycosylation are partially isotype dependent, the types of oligosaccharides, or glycans, which couple at these positions can affect antibody localization, subcellular partitioning, aggregation, and Fc and complement receptor affinities. Furthermore, these glycation patterns can be reflective of age, environment, and health status. For example, IgG galactosylation often decreases with age, while fucosylation, sialylation, and bisection can respond to age in a gender-specific manner. Accordingly, a method can utilize an antibody glycanation profile to assess biological age.

[0166] In addition, a glycanation assay for use in assessing biological age is currently marketed as the Glycanage test.Proteomic Assays

[0167] Humans are estimated to have between 20 thousand and 5 million proteins, depending in part on structural variant classifications (e.g., whether splicing variants constitute distinct proteins), such that proteomic shifts with aging and changes in health status are often complex. Nonetheless, as proteins participate in and regulate biological processes, changes in physiology, including those associated with aging, are often reflected in protein expression and activity. To this point, changes in the human proteome with age are thought to include both drivers of (e.g., diminished superoxide dismutase and catalase activity) and responses to (e.g., increased fibrinogen concentration) biological aging. Changes in the human proteome with age include up- and downregulation of individual proteins, as well as proteome-wide changes in abundances, ratios, and activity levels. Despite the complexity of the human proteome, a number of proteins are known to change with aging in detectablemanners. Accordingly, a method for determining biological age can include measuring the abundance, state, distribution, modification, or activity of an individual protein or collection of proteins. In many such cases, the method includes measuring the concentrations of a small number of blood proteins. Proteomic analysis may be performed on plasma samples obtained from individuals before and after plasmapheresis treatments. The methods described herein allow for comprehensive profiling of the plasma proteome to identify changes in protein abundance and modifications associated with the treatments.

[0168] However, a method for determining biological age can also take a broad, proteomic view to assess biological age. As human blood contains over 5000 types of proteins, aggregate analysis of tens, hundreds, or thousands of proteins can often correlate small proteomic shifts to biological age, even in the absence of statistically significant individual protein biomarkers. Advances in high-throughput proteomic analysis, such as in liquid chromatography-mass spectrometry, protein sequencing, and multiplexed immunoassays, can enable rapid quantification of hundreds or thousands of proteins from individual samples.(i) Fibrinogen Assays

[0169] Fibrinogen activity and blood level often changes with age. Fibrinogen is bloodbased glycoprotein complex which polymerizes to fibrin to facilitate blood clotting. While fibrinogen levels can be responsive to health and inflammation, baseline fibrinogen levels typically increase with age, rising by about 250 pg / mL per decade, or from about 2.2 mg / mL to about 3.2 mg / mL from the age of 25 to the age of 65 in many individuals. As elevated fibrinogen levels are associated with a number of age-related conditions, including increased cardiovascular disease susceptibilities and diminished kidney and liver function, fibrinogen levels often correlate with many recognizable, qualitative aspects of aging.

[0170] Fibrinogen assays typically assess at least one of fibrinogen activity and concentration. Fibrinogen activity is typically measured indirectly with blood clotting tests, such as thrombin and prothrombin time tests, thromboelastometry, and qualitative clotting assays. However, these assays can have limited ability to distinguish between low fibrinogen levels and diminished fibrinogen activity. Alternatively, or in addition to activity analyses, some assays directly measure blood fibrinogen concentration, with a wide range of immunoassays commercially available for such measurements.(ii) Creatinine Kinase Assays

[0171] Changes in the creatine system, which furbishes muscle cells with phosphocreatine for rapid energy production, can reflect aging, with creatine and associated metabolite levels typically decreasing with aging. Nonetheless, creatinine, the primary breakdown product ofphosphocreatine catabolism, typically increases in blood concentration with age. This discrepancy is likely due to diminished capacity for creatinine recycling and clearance. Creatinine kinase, an intramuscular enzyme which converts creatinine back into phosphocreatine for further use, diminishes in concentration with age, leading to higher levels in creatinine in blood, muscles, and some extravascular spaces. Accordingly, blood and muscle creatinine kinase levels can be effective for determining biological age.(Hi) Hemoglobin Ale Assays

[0172] During circulation, hemoglobin can promiscuously couple to blood glucose in a process referred to as glycation. While the baseline rate for glycation is typically low, hyperglycemia, hormonal insensitivities, and aging can enhance glycation rate, leading to higher concentrations of glycated hemoglobin. One of the prevalent forms of the resultant glycated hemoglobin is HbAlc (also referred to as hemoglobin Ale), which contains glucose attached at one or both [3-peptide N-terminal valines. While high levels of HbAlc are commonly ascribed to hyperglycemia in diabetes, changes in glucose tolerance and glycemic regulation with aging tend to increase HbAlc baselines irrespective of health status, leading to age-related shifts in HbAlc populations which can be small (for example 5.4% to 5.6% from the age of 25 to greater than 65), but nonetheless significant. When corrected for health and environmental factors, HbAlc levels can be diagnostic for biological age. HbAlc can be measured with a variety of techniques, including quantitative chromatography (e.g., by high- performance liquid chromatography), immunoassays, and capillary electrophoresis.(iv) Cytokine and Inflammatory Marker Panels

[0173] A method for assessing biological age can include analysis of one or more cytokines. In many cases, the biological age assessment includes determination of one or more blood cytokine concentrations. Often referred to as inflammaging, aging often coincides with increased blood cytokine levels through increased cytokine production and diminished anti-inflammatory responses. Central to this process, a number of pro-inflammatory markers, such as C-reactive protein and serum amyloid A, increase by multiple-fold levels through middle and advanced age, often leading to an imbalance between pro- and anti-inflammatory cytokines. As these changes in cytokine levels often foster chronic inflammation, weakened immune states, and diminished energy metabolism, cytokine imbalance is likely both a cause and effect of aging. Accordingly, blood cytokine concentrations can be correlated with progressive age-related changes in cytokine levels to assess biological age.

[0174] A method for determining biological age can include measuring blood concentrations of one or more cytokines. While a blood concentration of a single cytokinecan be sufficient for determining biological age, in many cases the method includes measuring blood concentrations of multiple cytokines. As non-limiting examples, a method for determining biological age can assess levels of one or more of C-reactive protein, soluble tumor necrosis factor receptor 1 (sTNFl), soluble tumor necrosis factor receptor 2 (sTNF2), tumor necrosis factor a (TNF-a), interleukin- 1 a (IL- la), interleukin- 1 P (IL-i ), interleukin- 6 (IL-6), and interleukin- 10 (IL- 10), all of which typically increase in concentration with age. Among these cytokines, C-reative protein, TNF-a, IL-la, IL-ip, IL-6, and IL-10 are implicated in increased proinflammatory responses with aging, and may play roles in atherosclerosis, and insulin insensitivity, among other conditions, while sTNFl and sTNF2 likely aggravate arthritis. Accordingly, blood concentrations of one or more cytokines can evidence aging and age-related symptoms.

[0175] In addition, a test for inflammatory changes in the context of biological age (i.e. an inflammatory age test) is currently marketed as the iAge test.

[0176] Expression of certain cell surface proteins may be associated with inflammatory processes as well including CD16, CD25, CD27, CD38, CD57, CD80, HLADR, IgM, KIR, KLRG1, NK1, NKg2a, and TIGIT. These cell surface proteins may be measured and quantified using flow cytometry.

[0177] Sample preparation may involve perchloric acid precipitation to deplete high- abundance proteins. Briefly, plasma samples may be diluted and mixed with perchloric acid, incubated at low temperature, and centrifuged. The supernatant may then be processed using solid-phase extraction columns to remove residual acid. Proteins may be denatured, reduced, alkylated, and digested with trypsin using filter-aided sample preparation methods.

[0178] Peptides may be analyzed by liquid chromatography-tandem mass spectrometry (LC-MS / MS) using a nanoflow HPLC system coupled to a high-resolution mass spectrometer. Data-independent acquisition (DIA) methods may be employed to enable comprehensive, unbiased profiling. Chromatographic separation may utilize Cl 8 reversephase columns with gradient elution over 2-3 hours. Mass spectrometry parameters may include MS 1 scans followed by MS / MS fragmentation of precursor ions using variable width isolation windows.

[0179] Data analysis may be performed using specialized proteomics software for peptide / protein identification and quantification. A human proteome database may be used for searching spectra. Protein quantification may be based on extracted ion chromatograms of fragment ions. Statistical analysis may involve paired t-tests with multiple testing correction to identify significantly altered proteins between time points.

[0180] The proteomic analysis may quantify hundreds to thousands of plasma proteins. Proteins that may be analyzed include albumin and other abundant plasma proteins, coagulation factors, complement proteins, acute phase proteins, immunoglobulins, cytokines and growth factors, hormones enzymes carrier and transport proteins, and extracellular matrix proteins

[0181] Proteins of interest may include markers of inflammation, oxidative stress, cellular senescence, and tissue remodeling. Changes in post-translational modifications such as glycosylation may also be examined.

[0182] Proteins that may be analyzed also include actin, cytoplasmic 1, ADP / ATP translocase 3, a-enolase, a-enolase, angiotensinogen, apolipoprotein A, apolipoprotein C-I, apolipoprotein C-III, apolipoprotein D, apolipoprotein E, apolipoprotein M, attractin, BPI fold-containing family B member 1, BPI fold-containing family B member 1, carboxypeptidase N subunit 2, carboxypeptidase N subunit 2, carboxypeptidase N subunit 2, cholinesterase, elongation factor 1-al, extracellular matrix protein 1, extracellular matrix protein 1, folate receptor gamma, gamma-glutamyl hydrolase, heat shock 70 kDa protein 1A, heat shock protein HSP 90-a, heat shock cognate 71 kDa protein, heterogeneous nuclear ribonucleoprotein K, histidine-rich glycoprotein, immunoglobulin heavy constant alpha 1, immunoglobulin heavy variable 5-51, immunoglobulin kappa light chain, leucine-rich a-2- glycoprotein, leucine-rich alpha-2-glycoprotein, leukocyte immunoglobulin-like receptor subfamily A member 3, lumican, mannose-binding protein C, myosin- 1, phospholipid transfer protein, platelet basic protein, platelet basic protein, profdin- 1, profdin- 1, properdin, serum amyloid A-4 protein, T-complex protein 1 subunit beta, titin, transketolase, tubulin a- 1[3 chain, tubulin alpha-lB chain, and / or vasorin. Any protein listed in TABLES VII, VIII, IX, X, XI, XII, XIV, XV, XVI, XVII, XVIII, XIX, XX, XXI, XXII, XXIII, XXIV, and / or XXVI may be measured in blood samples from individuals receiving plasmapheresis treatment. In some aspects, changes in levels of these proteins and other biomolecules (or biological molecules) may be analyzed to assess the effects of plasmapheresis on biological age and health status. The concentrations and ratios of various plasma proteins, metabolites, lipids, and other factors may provide information about the physiological impacts of plasmapheresis treatments administered at different frequencies and durations.

[0183] In some aspects, proteins analyzed include at least one of A1BG, A2M, A2ML1, ABI3BP, ACAA2, ACTA1, ACTB, ACTN2, ACTN4, ADAMDEC1, ADGRF5, ADGRG6, ADGRL4, AFM, AGT, AHSG, AK1, AKAP13, ALB, ALCAM, ALDOA, ALDOB, AMBP, AMY1B, ANG, ANK2, ANXA1, ANXA2, APCS, APOA1, APOA2, APOA4, APOB,APOCI, APOC2, APOC3, APOC4, APOD, APOE, APOH, APOL1, APOM, ARG1, ART4, ASAHI, ATF6, ATF6B, ATP2A1, ATP5F1A, ATP5F1B, ATRN, AXL, AZGP1, BASP1, BCHE, BLMH, BOC, BPIFA1, BPIFB1, BPIFB4, BST1, C1QB, C1QC, C1R, C1RL, CIS, C2, C3, C4A, C4B, C4BPA, C4BPB, C5, C6, C7, C8A, C8B, C8G, C9, CAI, CACNA2D1, CADM1, CALM1, CALML3, CALML5, CAMP, CAPN1, CARD9, CASP14, CAST, CAT, CBLN1, CBLN4, CCL14, CCL15, CCL16, CCL18, CCL24, CCT2, CCT3, CCT7, CD109, CD200R1, CD300A, CD34, CD44, CD46, CD55, CD58, CD59, CD5L, CDH13, CDH5, CDHR2, CDHR5, CDSN, CEACAM1, CEACAM6, CFB, CFD, CFH, CFHR1, CFHR2, CFHR3, CFI, CFL1, CFP, CHD8, CHGA, CHGB, CHL1, CLEC3B, CLPS, CLU, COL14A1, COL1A1, COL3A1, COL6A3, COLECIO, CP, CPA4, CPN2, CRNN, CSF1R, CST4, CST6, CSTA, CSTB, CTRB2, CTSD, CTSH, CXCL3, DCD, DEFA1, DEFBI, DLK1, DMBT1, DMKN, DNER, DPEP2, DSC1, DSC3, DSG1, DSP, ECM1, EEF1A1, EEF1D, EEF2, EFEMP1, EGFR, EIF4A1, ENG, ENO1, ENSA, EPPK1, ESAM, F12, F13B, F2, F5, FABP1, FABP5, FAS, FAT2, FCGR2A, FCGR3A, FCGR3B, FETUB, FGA, FGB, FGFR1, FGG, FLG, FLG2, FLNC, FLT4, FN1, FOLR3, FSHB, FSTL1, GAPDH, GC, GGCT, GGH, GM2A, G0LM1, G0LM2, GP1BA, GPNMB, GRN, GSDMA, GSN, GSTP1, Hl -4, H2AC4, H2BC12, H3C1, H4C1, HABP2, HAL, HBA1, HBB, HBD, HBG1, HEG1, HEPACAM2, HNRNPK, HP, HPR, HPX, HRG, HRNR, HSP90AA1, HSPA1A, HSPA5, HSPA8, HSPB1, HSPD1, ICAM1, ICAM2, ICAM3, ICOSLG, IDE, IGF1, IGF2, IGFBP3, IGFBP4, IGFBP5, IGFBP6, IGFBP7, IGHA1, IGHG2, IGHG3, IGHG4, IGHM, IGHV2-70D, IGHV3-15, IGHV3-30, IGHV3-49, IGHV3-7, IGHV3-9, IGHV4-34, IGHV5-51, IGKC, IGKV1-39, IGKV1D-13, IGKV2D-24, IGKV3-20, IGKV3D-11, IGKV3D-15, IGKV4-1, IGLC2, IGLL5, IGLV1-47, IGLV1-51, IGLV2-18, IGLV3-10, IGLV3-9, IGLV6-57, IGLV8-61, IL13RA1, IL17F, IL18BP, IL1R1, IL1RAP, IL6ST, INHBC, ITGB1, ITIH1, ITIH2, ITIH3, ITIH4, JCHAIN, JUP, KDR, KIT, KLKB1, KNG1, KPRP, LI CAM, LAMP1, LAMP2, LCN1, LCN15, LCN2, LDHA, LDHB, LEAP2, LEPR, LGALS3BP, LGALS7, LILRA3, LMNA, LOX, LPA, LRG1, LSAMP, LTBP1, LTBP2, LTF, LUM, LY6D, LY6G6C, LYNX1, LYPD3, LYVE1, LYZ, MANF, MARCKS, MBL2, MCAM, MDK, MEGF9, MENT, MEPE, MERTK, MMRN1, MMRN2, MSMB, MT2A, MUC5AC, MUC5B, MXRA5, MYH1, MYH2, MYH3, MYH4, MYH7, MYH8, MYH9, MYL11, NACA, NCAM1, NECTIN1, NECTIN3, NEGRI, NME1, NOTCH1, NPC2, NPM1, NTM, NTRK2, NTRK3, OLFM1, 0RM1, 0RM2, OSCAR, OSMR, PCOLCE, PDAP1, PDCD1LG2, PDGFRB, PECAM1, PF4, PF4V1, PFN1, PGAM1, PGK1, PGLYRP2, PI16, PI3, PIAS4, PIGR, PIP, PKM, PKP1, PLA2G1B, PLA2G2A, PLEC, PLG, PLTP, PLXDC2, POF1B,P0N1, PPBP, PPIA, PRAP1, PRB4, PRDX1, PRDX2, PREXI, PRG4, PRH1, PRNP, PROCR, PROS1, PRSS1, PSMA3, PSMA6, PSMB6, PTGDS, PTMA, PTPRB, PTPRC, PTPRG, PTPRJ, PTPRM, PTPRZ1, PVR, PZP, RACK1, RBP4, REGIA, REGIB, RNASE1, RNASE2, RNASE6, RNASE7, RPS3A, S100A11, S100A7, S100A8, S100A9, SAA1, SAA4, SBSN, SELENOP, SELL, SELP, SEMG1, SEMG2, SERPINA1, SERPINA10, SERPINA3, SERPINA4, SERPINA5, SERPINA6, SERPINA7, SERPINB12, SERPINB3, SERPINB4, SERPINB5, SERPINC1, SERPIND1, SERPINF1, SERPINF2, SERPING1, SFN, SH3BGRL, SH3BGRL2, SH3BGRL3, SIPA1L1, SIRPA, SLC25A6, SLC38A10, SLPI, SLURP 1, SNCA, SNRPD3, SOD3, SPARCL1, SPINK5, SPON1, SPP1, SPRR2E, SPRR3, SRGN, TCN1, TF, TGM1, TGM3, TGM5, TGOLN2, THBS1, THY1, TKT, TMEM25, TMSB4X, TNFRSF1A, TNFRSF1B, TNFRSF21, TNNT3, TPI1, TPM2, TTN, TTR, TUBA1B, TUBA1C, TUBA4A, TUBB, TWSG1, TXN, TYRO3, UBA1, UBB, UMOD, VASN, VCAM1, VDAC1, VDAC2, VEGFD, VIT, VNN1, VTN, WFDC2, YWHAQ, ZG16B, and ZNF451.

[0184] In some aspects, proteins analyzed to guide future course of treatment include at least one of HBG1, TWSG1, CFHR3, ITH3, SAA4, CST6, SERPIND1, PCOLCE, SPON1, PLA2G1B, REGIA, SELENOP, APOH, IGLV1-51, IGHV3-30, APOE, A2M, ALB, PRNP, TMEM25, IGF2, DLK1, F13B, FCGR2A, MEGF9, THY1, BST1, FLG2, IGLV3-10, ALDOB, SERPINA7, IGKV3-20, IGHV2-70D, NPC2, CFHR1, IGHV3-49, ADGRL4, CAPN1, IL6ST, PRAP1, AZGP1, SERPING1, GC, IGHM, GSN, KDR, C8A, FN1, SLC38A10, TXN, CASP14, PSMA3, CAST, NEGRI, ICAM1, IGHV3-7, LYVE1, CEACAM6, ENG, LAMP1, IL13RA1, C7, VEGFD, PTPRC, TPM2, BIPIFA1, IGFBP4, IGKV1D-13, PTPRJ, IGLL5, LY60, APOC4, COL14A1, and MDK (FIG. 2A).

[0185] In some aspects, proteins analyzed to determine type of treatment include at least one of MEGF9, IGLV6-57, BST1, CEACAM1, GSDMA, BASP1, FETUB, PROS1, A1BG, NME1, ITIH3, CCL14, LGALS3BP, MUC5AC, KNG1, TWSG1, HBG1, EEF1D, C4BPB, LY6D, SPARCL1, PTPRG, IGLV3-10, IGLV1-47, LY6G6C, COL14A1, CALM1, MDK, FSTL1, PLXDC2, FLNC, DNER, TMEM25, COL6A3, ESAM, CD55, IGHV2-70D, DEFBI, PI3, MEPE, MARCKS, SH3BGRL2, ADAMDEC1, CCL15, BPIFB4, NACA, MBL2, MENT, MYH1, NPM1, EIF4A1, BLMH, APOB, PGLYRP2, SPRR2E, BPIFA1, FCGR2A, CCT7, CASP14, ITIH1, C4A, CIS, ITIH2, FGFR1, SERPINB5, and APOC4 (FIG. 3A)

[0186] The proteomic data may be integrated with other omics datasets to provide a systems-level view of the molecular changes induced by plasmapheresis. Pathway andnetwork analysis may be performed to identify biological processes affected by the treatments. The proteomics results may reveal potential biomarkers of treatment response and provide mechanistic insights into the effects of plasma exchange on aging -related processes.Metabolomic Assays

[0187] As metabolism encompasses the biochemistry of growth, energy production and consumption, and certain forms of signaling, age-related changes are often reflected by metabolomic shifts. As with proteomic analysis, metabolomic profiling can query individual biomolecules which are responsive to aging, can broadly profile portions of an individual’s metabolome (e.g., by profiling tens, hundreds, or thousands of metabolomic biomarkers), or can include a combination thereof. Owing to a higher number of available metabolites and lower variance than cell and tissue profiling, metabolomic analysis often focuses on blood metabolites. As over 18000 metabolites have been identified in human blood, a biological age measurement could, in theory, accurately determine biological age with a broad, non-targeted profiling approach, even in the absence of a strong diagnostic aging marker. However, a number of age diagnostic studies have identified robust metabolite biomarkers, which, alone or in combination with other measurements, can accurately measure biological age.(1) Total Cholesterol Assays

[0188] Cholesterol is complexed in a variety of lipid-protein macromolecular structures, commonly referred to as lipoproteins, for transport through the blood. Although lipoproteins differ in terms of a number of properties, including size, composition, and receptor affinities, in humans, lipoproteins are divided into five major classes based primarily by on densities. High-density lipoproteins (HDL) tend to have low volumes and high protein and phospholipid contents among the five classes of lipoproteins, with diameters typically ranging from 5-15 nm, densities of greater than 1.063 g / mL, and protein accounting for approximately 1 / 3 of their mass. Low-density lipoproteins (LDL) tend to have slightly lower masses of between about 1.019 and 1.063 g / mL, slightly larger diameters of about 18-28 nm, and higher cholesterol content approaching nearly 50% (by mass). Intermediate-density lipoproteins (IDL), tend to have densities of between 1.006 and 1.019 g / mL, similar cholesterol content as high-density lipoproteins, and diameters ranging from 25 to 50 nm. Very low-density lipoproteins (VLDL) are usually characterized as having densities of between 0.95 and 1.006 g / mL, relatively low protein content (typically about 10% by mass), and diameters of between about 30 and 80 nm. Finally, chylomicrons, large lipoproteins,typically have densities below 0.95 g / mL, protein content below 2% (by mass), and diameters ranging from 75 to 1200 nm.

[0189] Cholesterol analysis often distinguishes between at least some lipoprotein types when assessing cholesterol levels. For example, lipid panels typically distinguish between HDL and LDL-bound cholesterol, and sometimes further distinguish VLDL, IDL, and chylomicron cholesterol content. However, for many conditions, total blood cholesterol content is sufficient for accurate diagnosis.

[0190] In particular, for some individuals, total cholesterol content can be indicative of biological age. In men, blood cholesterol levels tend to steadily increase from about 1.6 mg / mL at the age of 18 to about 2 mg / mL at the age of 50, after which time blood cholesterol levels tend to drop by about 0.05 mg / mL every decade. Slightly more punctuated trends tend to be observed in women, with blood cholesterol levels typically increasing from about 1.7 to 2.1 mg / mL between the ages of 18 and 60, and then plateauing or declining at a slower rate than in men through advanced aging. Accordingly, a method disclosed herein can use blood cholesterol level to determine biological age.

[0191] A number of enzymatic, chemical, electrochemical, and spectroscopic methods can be used to determine cholesterol levels. Commonly, cholesterol, either pooled or collected from a lipoprotein fraction, is coupled to a chromophore or fluorophore through its C3- hydroxyl, and quantitated spectrophotometrically.(ii) Cholesterol, Direct LDL Assays

[0192] In many individuals, low-density lipoprotein (LDL), one of the primary carriers for cholesterol in blood, increases in concentration through middle age, and then remains stable or decrease with advanced aging. In men, LDL tends to plateau between 50 and 60 years of age, whereas in women, this trend is prolonged, with high concentrations typically occurring between 60 and 70 years of age. Although often requiring corrections for certain factors, such as estrogen levels in women, LDL levels can be a powerful metric for biological age. Accordingly, a method for determining biological age can include determining an LDL level of a subject.

[0193] Often, LDL is simultaneously measured along with other lipids and lipoproteins as part of lipid profiles or panels. In some panels, LDL is indirectly identified through total cholesterol measurements. An LDL measurement can identify LDL, LDL-cholesterol (LDL- C, the amount of cholesterol contained within low-density lipoproteins), or both. As cholesterol is primarily carried within LDL, high-density lipoproteins (HDL), and very low- density lipoproteins (VLDL), LDL levels are often determined without measuring LDL, butrather by subtracting non-LDL abundances from a measured level of cholesterol. For example, lipid panels in the United States commonly estimate LDL levels by subtracting measured HDL and triglyceride levels from measured total blood cholesterol.

[0194] LDL can also be measured directly. In many cases, these methods involve separation of LDL from other lipoproteins and lipids, including HDL, VLDL, and lipoprotein a (Lp(a)). Common methods for achieving these separations include centrifugation (e.g., ultracentrifugation) which can separate lipoproteins and lipids by density; electrophoresis, which can separate lipoproteins and lipids based on size and charge; precipitation, in which lipoproteins and / or lipids are selectively drawn from solution; by homogeneous methods, which utilize combinations of conditions, binding molecules, and polymers to separate lipoprotein fractions; and by combinations thereof. The separated lipoproteins and lipids can then be quantified with a range of enzymatic, electrochemical, chemical, and spectroscopic methods.(Hi) Cholesterol, Direct HDL Assays

[0195] Similar to LDL, HDL levels tend to decrease in concentration with advanced age. Complicating the relationship between HDL and aging, low HDL may correlate with mortality, potentially obfuscating otherwise reliable trends in lowered HDL abundance with age. Nonetheless, a number of studies have defined clear decreases in HDL as a function of age, especially among males. Accordingly, HDL levels, alone or in combination with other cholesterol data (e.g., LDL, total cholesterol, etc.), can be used to assess biological age.

[0196] As with LDL-cholesterol, HDL-chole sterol is typically measured through separation followed by cholesterol quantification. However, a number of methods (in particular some homogeneous methods) allow HDL-cholesterol to be measured simultaneously with other forms of cholesterol.(iv) Blood Glucose Assays

[0197] In many individuals, aging coincides with increased resting and post-meal glucose levels. As blood glucose levels are responsive to multiple age-sensitive regulatory mechanisms, including insulin and incretin sensitivities, and can contribute to age-related developments, such as increase HbAlc and albumin glycation, blood glucose levels can capture a broad spectrum of aging-related progressions, and can serve as reliable diagnostic markers for aging. In men and women, resting and post-meal glucose levels tend to increase at rates of about 7 to 11 pg / mL per decade, while 2-hour-post-meal levels tend to increase by a more pronounced rate of 56-66 pg / mL per decade. Following from these trends, bloodglucose levels (either resting, post-meal, or a combination thereof) can be used to determine biological age.

[0198] A number of cheap methods are available for blood glucose measurement. Blood glucose is detected through enzymatic (e.g., through hydrogen peroxide generation by glucose oxidase) or chemical oxidation.General Metabolomic Analyses

[0199] Metabolomic analysis may be performed on plasma samples obtained from individuals before and after plasmapheresis treatments. The methods described herein allow for comprehensive profiling of small molecule metabolites to identify changes associated with the treatments and guide treatment decisions.

[0200] Sample preparation may involve protein precipitation using organic solvents followed by centrifugation. The supernatant containing metabolites may then be dried down and reconstituted for analysis. Targeted and untargeted metabolomics approaches may be utilized.

[0201] For targeted analysis, samples may be analyzed using liquid chromatographytandem mass spectrometry (LC-MS / MS) with multiple reaction monitoring (MRM) for quantification of predefined metabolites. Chromatographic separation may utilize reversed- phase and / or HILIC columns. Isotope -labeled internal standards may be used for accurate quantification.

[0202] For untargeted analysis, high-resolution mass spectrometry may be employed to detect and measure thousands of metabolite features. Data-dependent MS / MS acquisition may be used to aid in metabolite identification. Both positive and negative ionization modes may be utilized to expand metabolome coverage.

[0203] Data processing may involve peak picking, alignment, and normalization.Multivariate statistical analysis such as principal component analysis and partial least squares discriminant analysis may be used to identify metabolite patterns associated with treatment. Pathway analysis may be performed to gain biological insights.

[0204] The metabolomic analysis may quantify hundreds of metabolites across various chemical classes. Metabolites that may be analyzed include amino acids and derivatives, organic acids, nucleotides and nucleosides, biogenic amines, carnitines, bile acids, steroids and steroid hormones, and vitamins and co-factors may be analyzed as part of metabolomic profiling. In some aspects, these metabolites may provide information about biological processes related to aging. The levels of these metabolites in blood or other biological samples may change with age or in response to interventions like plasmapheresis. Analyzing multipleclasses of metabolites may allow for a more comprehensive assessment of metabolic changes associated with aging or therapeutic interventions.

[0205] In some aspects, the metabolites analyzed include at least one of AC(0:0), AC(10:0), AC(10: l), AC(10:2), AC(10:3), AC(l l:0), AC(12:0), AC(12: 1), AC(13:0), AC(14:0), AC(14: 1), AC(14: 1-DC), AC(14: 1-OH), AC(14:2), AC(16:0), AC(16:2-OH), AC(18:0), AC(18: 1), AC(18: 1-OH), AC(18:2), AC(2:0), AC(3:0), AC(4:0), AC(4:0-DC), AC(4:0-OH), AC(5:0), AC(5:0-DC), AC(5:0-OH), AC(5: 1), AC(6:0), AC(6: 1), AC(7:0), AC(8:0), AC(8: 1), AC(8: 1-OH), ADMA, Ala, Arg, Asn, Asp, Cit, Creatinine (M), Gin, Glu, Gly, His, He, Kynurenine, Lys, Met, Met-SO, Nitro-Tyr, Om, PEA, Phe, Pro, Putrescine, SDMA, Sarcosine, Ser, Taurine, Thr, Trp, Tyr, Vai, alpha-AAA, t4-OH-Pro, and xLeu.

[0206] In some aspects, the metabolites analyzed to guide decisions regarding future treatment after treatment has begun include at least one of citruline, serine, leucine or isoleucine (xleu), AC(5:0), glycine, symmetric dimethylarginine (SDMA), AC(18:0), putrescine, AC(8: 1), AC(10:3), and creatinine (FIG. 2A).

[0207] In some aspects, the metabolites analyzed to guide decisions regarding treatment type to be administered, before treatment has begun, include at least one of AC(8: 1-OH), nitrotyrosine, AC(18:0), AC(3:0), putrescine, aspartate, AC(8: 1), AC(10:3), arginine, lysine, and ornithine (FIG. 3A).

[0208] Specific metabolites of interest may include markers of energy metabolism, oxidative stress, and inflammation. Changes in metabolite ratios and pathway intermediates may also be examined. The metabolomics data may be integrated with other omics datasets to provide a systems-level view of the molecular changes induced by plasmapheresis.Urine Analysis

[0209] While water, urea, and sodium chloride account for greater than 96% of its mass, urine contains a complex mixture of chemicals which can acutely reflect age, health and environment. Over 450 species of microbiota and 3000 molecules have been identified in urine, of which 480 have not been detected in blood. Many of the molecules in urine are waste materials, including biproducts of metabolism and damaged and solubilized biomolecules. Accordingly, age-related changes within an individual, in particular their kidney, liver, and bladder ages, are often reflected in urine composition.Lipidomics

[0210] Lipidomic analysis may be performed on plasma samples obtained from individuals before and after plasmapheresis treatments. The methods described herein allow forcomprehensive profiling of lipid species to identify changes associated with the treatments and guide treatment decisions.

[0211] Sample preparation may involve liquid-liquid extraction of lipids from plasma using organic solvents. The lipid extract may then be dried down and reconstituted for analysis. Both targeted and untargeted lipidomics approaches may be utilized.

[0212] For targeted analysis, samples may be analyzed using liquid chromatography-tandem mass spectrometry (LC-MS / MS) with multiple reaction monitoring (MRM) for quantification of predefined lipid species. Chromatographic separation may utilize reversed-phase columns. Isotope-labeled internal standards may be used for accurate quantification.

[0213] For untargeted analysis, high-resolution mass spectrometry may be employed to detect and measure thousands of lipid features. Data-dependent MS / MS acquisition may be used to aid in lipid identification. Both positive and negative ionization modes may be utilized to expand lipidome coverage.

[0214] Data processing may involve peak picking, alignment, and normalization. Multivariate statistical analysis such as principal component analysis and partial least squares discriminant analysis may be used to identify lipid patterns associated with treatment. Pathway analysis may be performed to gain biological insights.

[0215] The lipidomic analysis may quantify hundreds of lipid species across various classes. Lipids that may be analyzed include: glycerophospholipids, sphingolipids, glycerolipids, sterol lipids, and / or fatty acyls. These can include sphingomyelin 42:2 (SM 42:2), phosphatidylcholine 36:4 (PC 36:4), acylcamitine 14:2 (AC 14:2), phosphatidylcholine 38:3 (PC 38:3), sphingomyelin 42: 1 (SM 42: 1), and / or phosphatidylcholine 36:2 (PC 36:2). Lipid species of interest may include markers of membrane composition, lipid signaling, and oxidative stress. Changes in lipid ratios and pathway intermediates may also be examined.

[0216] In some aspects, lipids analyzed include at least one of CE(18:2), CE(20:5), CE(22:5), CE(22:6), Cer(42: l), DG(32: 1), DG(32:2), DG(34: 1), DG(34:3), DG(36:2), DG(36:3), DG(36:4), DG(39:0), DG(41: 1), DG-O(34: 1), DG-O(36:4), Hl, LPC(14:0), LPC(15:0), LPC(16:0), LPC(16: 1), LPC(17:0), LPC(18:0), LPC(18: 1), LPC(18:2), LPC(20: 1), LPC(20:3), LPC(20:4), LPC(22:6), LPC-O(18: 1), LPC-O(18:2), PC(30:0), PC(31:0), PC(32:0), PC(32: 1), PC(32:2), PC(32:3), PC(32:6), PC(33: 1), PC(33:2), PC(34: 1), PC(34:2), PC(34:3), PC(34:4), PC(35: 1), PC(35:2), PC(35:3), PC(35:5), PC(36:2), PC(36:3), PC(36:4), PC(36:5), PC(36:6), PC(37:4), PC(37:5), PC(37:6), PC(38:2), PC(38:3), PC(38:4), PC(38:5), PC(38:6), PC(38:7), PC(39:6), PC(40:4), PC(40:6), PC(40:7), PC(40:8), PC(41:2), PC(42: 10), PC(43:2), PC- 0(32:0), PC-O(32: 1), PC-O(34: 1), PC-O(34:2), PC-O(34:3), PC-O(36:2), PC-O(36:3), PC-0(36:4), PC-O(36:5), PC-O(36:6), PC-O(38:4), PC-O(38:5), PC-O(38:6), PC-O(40:5), PC- 0(40:6), PC-O(40:7), PC-O(40:8), PC-O(42:5), PC-O(42:6), SM(32: 1), SM(32:2), SM(33:1), SM(33:2), SM(34: 1), SM(34:2), SM(35:1), SM(36:2), SM(37: 1), SM(38:1), SM(38:2), SM(39:1), SM(39:2), SM(40: l), SM(40:2), SM(41: 1), SM(41:2), SM(42: 1), SM(42:2), SM(42:3), SM(43:2), TG(44:4), TG(50:3), TG(52:2), TG(54:3), TG(56:8), CE(18:2), CE(20:5), CE(22:5), CE(22:6), Cer(42:l), DG(32: 1), DG(32:2), DG(34: 1), DG(34:3), DG(36:2), DG(36:3), DG(36:4), DG(39:0), DG(41: 1), DG-O(34: 1), DG-O(36:4), Hl,LPC(15:0), LPC(16:0), LPC(16: 1), LPC(17:0), LPC(18:0), LPC(18:1), LPC(18:2), LPC(20: 1), LPC(20:3), LPC(20:4), LPC(22:6), LPC-O(18:1), LPC-O(18:2), PC(30:0), PC(31:0), PC(32:0), PC(32: 1), PC(32:2), PC(32:3), PC(32:6), PC(33:1), PC(33:2), PC(34: 1), PC(34:2), PC(34:3), PC(34:4), PC(35: 1), PC(35:2), PC(35:3), PC(35:5), PC(36:2), PC(36:3), PC(36:4), PC(36:5), PC(36:6), PC(37:4), PC(37:5), PC(37:6), PC(38:2), PC(38:3), PC(38:4), PC(38:5), PC(38:6), PC(38:7), PC(39:6), PC(40:4), PC(40:6), PC(40:7), PC(40:8), PC(41:2), PC(42: 10), PC-O(32: 1), PC-O(34: 1), PC-O(34:2), PC-O(34:3), PC-O(36:2), PC-O(36:3), PC-O(36:4), PC-O(36:5), PC-O(36:6), PC-O(38:4), PC-O(38:5), PC-O(38:6), PC-O(40:5), PC-O(40:6), PC-O(40:7), PC-O(40:8), PC-O(42:5), PC-O(42:6), SM(32:1), SM(32:2), SM(33: 1), SM(33:2), SM(34: 1), SM(34:2), SM(35:1), SM(36:2), SM(37: 1), SM(38: 1), SM(38:2), SM(39: 1), SM(39:2), SM(40:2), SM(41:1), SM(41:2), SM(42: 1), SM(42:2), SM(42:3), SM(43:2), TG(44:4), TG(50:3), TG(52:2), TG(54:3), TG(56:8), CE(18:2), CE(20:5), CE(22:5), CE(22:6), Cer(42: l), DG(32:2), DG(34:1), DG(34:3), DG(36:2), DG(36:3), DG(36:4), DG(39:0), DG-O(34: 1), DG-O(36:4), Hl, LPC(14:0), LPC(15:0), LPC(16:0),LPC(16:1), LPC(17:0), LPC(18:0), LPC(18: 1), LPC(18:2), LPC(20:3), LPC(20:4), LPC(22:6), LPC-O(16:1), LPC-O(18: 1), PC(26:0), PC(30:0), PC(31:0), PC(32:0), PC(32: 1), PC(32:2), PC(32:3), PC(32:6), PC(33: 1), PC(33:2), PC(34: 1), PC(34:2), PC(34:3), PC(34:4), PC(35: 1), PC(35:2), PC(35:3), PC(35:5), PC(36:2), PC(36:3), PC(36:4), PC(36:5), PC(36:6), PC(37:4), PC(37:5), PC(37:6), PC(38:2), PC(38:3), PC(38:4), PC(38:5), PC(38:6), PC(38:7), PC(39:6), PC(40:4), PC(40:5), PC(40:6), PC(40:7), PC(40:8), PC(42:10), PC(43:2), PC-O(32:0), PC- 0(32:1), PC-O(33:6), PC-O(34: 1), PC-O(34:2), PC-O(34:3), PC-O(36:2), PC-O(36:3), PC- 0(36:4), PC-O(36:5), PC-O(36:6), PC-O(38:4), PC-O(38:5), PC-O(38:6), PC-O(40:5), PC- 0(40:6), PC-O(40:7), PC-O(40:8), PC-O(42:5), PC-O(42:6), PC-O(44:3), SM(32: 1), SM(32:2), SM(33: 1), SM(34: 1), SM(34:2), SM(35:1), SM(36: 1), SM(36:2), SM(37: 1), SM(38: 1), SM(38:2), SM(39: 1), SM(39:2), SM(40: l), SM(40:2), SM(41: 1), SM(41:2),SM(42:1), SM(42:2), SM(42:3), SM(43:2), TG(44:4), TG(50:3), TG(52:2), and TG(56:8).

[0217] In some aspects, the lipids analyzed to determine future course of treatment once treatment has begun include at least one of PC(38:7), DG-O(34: 1), TG(50:3), CE(22:6), TG(56:8), SM(35: 1), SM(39: 1), SM(38: 1), PC(38:6), PC(34:2), PC(36:2), and SM(42: 1) (FIG. 2A).

[0218] In some aspects, the lipids analyzed to determine type of treatment used, or to predict treatment success, include at least one of LPC(16:0), LPC(14:0), LPC-O(18:2), PC(38:2), PC(38:7), SM(40: 1), PC(38:3), and DG(34:3) (FIG. 3A).

[0219] The lipidomics data may be integrated with other omics datasets to provide a systems- level view of the molecular changes induced by plasmapheresis. Lipid set enrichment analysis may be performed to identify altered lipid pathways. These lipid species may show significant changes in response to plasmapheresis treatment and may provide insights into alterations in lipid metabolism and cellular membranes associated with the intervention.Cytomics

[0220] Cytomics analyses may be performed on peripheral blood mononuclear cells (PBMCs) obtained from individuals before and after plasmapheresis treatments. The methods described herein allow for comprehensive profiling of immune cell populations and cell surface markers to identify changes associated with the treatments and guide subsequent treatment decisions.

[0221] The disclosed cytomics analyses can include peripheral blood mononuclear cell (PBMC) analysis. PBMCs include blood cells with round nuclei, such as T lymphocytes, B lymphocytes, natural killer cells, and monocytes (and contrasting non-nucleated cells, such as erythrocytes, and cells with monolobed nuclei such as granulocytes). A number of PBMC phenotypic distributions have been shown to shift with age. In certain individuals, T cell populations shift from CD28+ towards CD95+ with age, suggesting reduced proliferation and increase apoptosis. Furthermore, aging often coincides with increased monocyte and regulatory T cell and decreased naive T cell populations. Accordingly, PBMC populations and subpopulation polarization can be used for biological age analysis. Such analysis may include PBMC immunophenotyping, for example to identify T lymphocytes, B lymphocytes, and natural killer cells, as well as CD4, CD8, CD28, naive, effector, and central memory cell subpopulations. As an illustrative example, PBMCs can be isolated from whole blood, and characterized with flow cytometry.

[0222] Flow cytometry may also be combined with fluorescent conjugated antibodies so that in addition to identifying certain cell types, cell surface proteins may be identified and / orquantified as well. Types of cell surface markers (i.e. proteins located on a cell surface) found on cells in blood that may be identified and quantified using flow cytometry include at least one of CD16, CD25, CD27, CD38, CD57, CD80, HLADR, IgM, KIR, KLRG1, NK1, NKg2a, and TIGIT. Types of cells that may have these markers include at least one of white cells and specifically lymphocytes, neutrophils, monocytes, basophils, and eosinophils. For example, T lymphocytes may include one or more of cell surface markers that may be identified and / or quantified using flow cytometry. In some aspects, shared core lineage markers include at least one of CD3, CD4, CD8, CD14, CD16, CD19, CD25, CD27, CD28, CD45, CD45RA, CD56, CD95, CD127, CD138, CCR7, HLA-DR, and TCRyS. These can be analyzed as surface markers CD64, CD38, CD57, CD87, CD159a, CD161, CD163, CD80, IgD, IgM, CD138, CXCR5, CXCR6, SA-Pgal, KLRG1 and TIGIT. They also can be analyzed as intracellular markers BCL6, BLIMP1, CDl lc, CD38, EOMES, FOXP3, GATA3, IgD, IgM, KI-67, Pax5, P16, P21, RORy, PD-1, T-BET and TOX. Sample preparation may involve isolation of PBMCs from whole blood using density gradient centrifugation. The isolated PBMCs may then be cryopreserved until analysis. Prior to staining, cells may be thawed and allowed to recover. Cells may be treated with agents such as Bafilomycin Al and SPiDERGal to assess cellular senescence. Surface and intracellular staining may be performed using fluorescently- conjugated antibodies against various cell surface and intracellular markers.

[0223] Flow cytometry analysis may be conducted using a spectral flow cytometer such as the Cytek Aurora. Data acquisition may involve collection of data for multiple fluorescent parameters. Spectral unmixing may be performed to resolve overlapping fluorescence spectra. Data analysis may include manual gating to identify cell populations as well as automated clustering approaches.

[0224] The cytomic analysis may examine various immune cell populations and subsets. Cell types that may be analyzed include T cells (including up T cell and y5 T cell), B cells, a plasma cell, natural killer cells, monocytes (including a classical monocyte and an intermediate monocyte), a lineage negative (LN) cell, a live leukocyte, a myeloid scatter fraction, a natural killer (NK) cell, a nonclassical monocyte, not a T cell, a myeloid cell, a plasmablast, an activated cell, an activated memory cell, a central memory (CM) cell, a double negative T cell (DN), a double positive T cell (DP), an effector memory T cell (EM), a naive cell, not B or T cell, or a memory cell, and dendritic cells. Within the T cell compartment, CD4+ and CD8+ T cells as well as their naive, memory, and effector subsets may be examined. Markers of T cell differentiation, activation, exhaustion, and senescence may be assessed.

[0225] Cytomics analyses can include populations of cells including at least one of B ACT, B CLSW, B Cells, B FOLL, B MEM, B MZ, B NV, B PB, B PC, MC, MC CL, MC INT, MC NC, NK 56hi 161o, NK 561o 16Hi, NK 561o 161o, NK Cells, abT CD4, abT CD4 CM, abT CD4 CM CD27+, abT CD4 CM CD27-, abT CD4 EM, abT CD4 EMRA, abT CD4 NV, abT CD4 SCM, abT CD8, abT CD8 CM, abT CD8 CM CD27+, abT CD8 CM CD27-, abT CD8 EM, abT CD8 EMRA, abT CD8 NV, abT CD8 SCM, abT DN, abT DP, and gdT cells.

[0226] Specific cell surface markers that may be analyzed include at least one of CD3, CD4, CD8, CD14, CD16, CD19, CD25, CD27, CD28, CD38, CD45, CD45RA, CD56, CD57, CD64, CD80, CD95, CD127, CD138, CCR7, igMHi-IgDHi, IgMHi-IgDLo, IgMNeg-IgDNegative, KIR positive, and HLA-DR. Additional markers such as PD-1, TIGIT, and KLRG1, myeloid scatter fraction, NK-564o-16-hi, NK-56-lo-16-lo, NK-56hi-164o, NK1-1 positive, NKg2a positive, RA negative, RA positive, SCM, SCM and TEMRA, TEMRA SA-bGal hi, SAbGal positive, or TiGit positive cell may be examined to assess T cell exhaustion, cell differentiation state, and senescence. Intracellular markers like FOXP3, Ki-67, and pl6 may provide information on regulatory T cells, proliferation, and cellular senescence respectively.

[0227] The cytomic data may be integrated with other omics datasets to provide a systems- level view of the molecular and cellular changes induced by plasmapheresis. Changes in immune cell populations and phenotypes may be correlated with alterations in plasma proteins, metabolites, and lipids. This integrated analysis may reveal potential mechanisms by which plasmapheresis affects the immune system and overall health status.

[0228] Specific cell populations and markers of interest discussed in the example’s sections include changes in CD4+ and CD 8+ T cell subsets, particularly naive and memory populations. The CD4 / CD8 ratio may be examined as a marker of immune health. Natural killer cell and monocyte populations may also be assessed. Markers of cellular senescence like SA-[3-gal may be quantified to evaluate the impact of plasmapheresis on cellular aging processes.

[0229] In some aspects, cell populations used for determining treatment success or ongoing treatment decisions include at least one of B ACT, B CLSW, B PB, B PC, MC NC, NK 56hi 161o, NK 561o 161o, NK Cells, abT CD4, abT CD4 CM, abT CD4 CM CD27+, abT CD4 CM CD27-, abT CD4 NV, abT CD4 SCM, abT CD8, abT CD8 CM, , abT CD8 CM CD27+, abT CD8 CM CD27-, abT CD8 EM, abT CD8 NV, abT CD8 SCM, abT DN, abT DP, and gdT Cells (FIG. 2A).

[0230] In some aspects, cell populations that are used to determine type of treatment prior to starting treatment, or cell populations used to predict response to any given treatment, include at least one of MC, MC CL, MC INT, NK 56hi 161o, NK 561o 161o, abT CD4, abT CD4 CM,abT CD4 CM CD27+, abT CD4 CM CD27-, abT CD4 EM, abT CD8 CM, abT CD8 CM CD27- , abT CD8 EM, abT CD8 NV, abT CD8 SCM, abT DN, and gdT Cells (FIG. 3A).

[0231] The cytomic analysis may provide insights into the effects of plasmapheresis on immune system composition and function. Changes in immune cell populations and phenotypes may reflect alterations in inflammation, immune senescence, and overall immune health status. These changes may contribute to the potential rejuvenation effects of plasmapheresis treatment.Glycomics

[0232] Glycomic analysis may be performed on plasma samples obtained from individuals before and after plasmapheresis treatments. The methods described herein allow for comprehensive profiling of glycan structures to identify changes associated with the treatments.

[0233] Sample preparation may involve isolation of glycoproteins from plasma using affinity chromatography or lectin-based methods. Glycans may then be released from proteins using enzymatic or chemical methods. Released glycans may be labeled with fluorescent tags to enhance detection sensitivity.

[0234] For analysis, samples may be analyzed using liquid chromatography coupled to mass spectrometry (LC-MS) or capillary electrophoresis with laser-induced fluorescence detection (CE-LIF). High-resolution mass spectrometry may be employed for detailed structural characterization of glycans. Multiple reaction monitoring (MRM) may be used for targeted quantification of specific glycan structures.

[0235] Data processing may involve peak identification, integration, and normalization. Statistical analysis may be performed to identify glycan patterns associated with treatment. Glycan structural databases may be used to assist with annotation of detected species.

[0236] The glycomic analysis may quantify hundreds of glycan structures across various classes. Glycans that may be analyzed include N-linked glycans, O-linked glycans, glycosaminoglycans, and glycolipids. Specific glycan features of interest may include branching, fucosylation, sialylation, and galactosylation patterns.

[0237] Glycomic markers that may be analyzed include at least one of A2B, A2BG2, A2BG2S1, A2BG2S2, A2B[3]G1, A2B[6]G1, A2G2S2, A2G2[3]S1, A2G2[6]S1, A2[3]G1, A2[3]G1S1, A2[6]G1, A2[6]G1S1, FA2, FA2B, FA2BG2, FA2BG2S1, FA2BG2S2, FA2B[3]G1, FA2B[6]G1, A2G2, FA2G2, FA2G2S1, M5, A2, FA2G2S2, FA2[3]G1,FA2[3]G1S1, FA2[6]G1, and FA2[6]G1S1. These glycan structures may show changes in abundance or relative proportions in response to plasmapheresis treatment.

[0238] In some aspects, the glycomic markers analyzed to determine future course of treatment after treatment has started include at least one of A2BG2; A2BG2S1; A2BG2S2; A2B[3]G1; A2B[6]G1; A2G2[3]S1; A2[3]G1; A2[3]G1S1; A2[6]G1; FA2; FA2B; FA2BG2; FA2BG2S1; FA2B[6]G1, A2G2; FA2G2; FA2G2S1, M5, A2; FA2G2S2; FA2[3]G1; FA2[3]G1S1; and FA2[6]G1 (FIG. 2A).

[0239] In some aspects, the glycomic markers analyzed to guide decisions regarding type of treatment, before treatment has started, include at least one of A2BG2; A2BG2S1; A2BG2S2; A2B[6]G1; A2G2[3]S1; A2[3]G1; A2[3]G1S1; A2[6]G1; A2[6]G1S1; FA2; FA2BG2S1; FA2B[6]G1, A2G2; FA2G2; FA2[3]G1; FA2[3]G1S1; an FA2[6]G1S1 (FIG. 3A).

[0240] The glycomics data may be integrated with other omics datasets to provide a systems-level view of the molecular changes induced by plasmapheresis. Glycan set enrichment analysis may be performed to identify altered glycosylation pathways. Changes in glycosylation patterns may provide insights into alterations in protein function, cellular communication, and immune regulation associated with the intervention.Genomic Methylation Assays

[0241] DNA methylation is a prevalent epigenetic modification which can strongly affect expression, and correspondingly phenotype, in an individual. Recent studies have demonstrated that aging coincides with genome-wide DNA methylation and demethylation, with a greater prevalence of demethylation than methylation during human lifespans. While total genomic methylation can correlate with age, a number of recent studies have identified specific sites which can serve as aging markers based on methylation or hydroxy methylation status. Accordingly, a method of the present disclosure may determine methylation status at a site or plurality of sites in genomic DNA to ascertain biological age.

[0242] In some aspects, genomic methylation assays may utilize bisulfite conversion followed by sequencing to detect methylation patterns. Bisulfite treatment converts unmethylated cytosines to uracils while leaving methylated cytosines unchanged, allowing methylation status to be determined by sequencing.

[0243] The methylation detection methods may include whole genome bisulfite sequencing (WGBS) to provide a comprehensive view of methylation across the entire genome. Reduced representation bisulfite sequencing (RRBS) may also be used to focus on CpG-rich regions ofthe genome. In some cases, targeted bisulfite sequencing approaches may be employed to analyze specific genomic regions of interest.

[0244] Array-based methods such as the Illumina Infinium MethylationEPIC BeadChip may be utilized to assess methylation at over 850,000 CpG sites across the genome. Such arrays include broad coverage of potentially relevant methylation sites, such as CpG sites.

[0245] In some implementations, methylation-sensitive restriction enzyme digestion followed by sequencing or PCR may be used to detect methylation status. Enzymes such as Hpall and MspI, which have different sensitivities to CpG methylation, can be employed to distinguish methylated from unmethylated sites.

[0246] Methylation-specific PCR techniques may also be applied in some cases, using primers designed to amplify either methylated or unmethylated versions of a target sequence after bisulfite conversion. Quantitative PCR approaches may allow relative quantification of methylation levels.

[0247] In certain aspects, enrichment-based methods like methylated DNA immunoprecipitation (MeDIP) followed by sequencing or array analysis may be used to profile genome-wide methylation patterns. This approach uses antibodies to isolate methylated DNA fragments for downstream analysis.

[0248] The methylation data obtained through these various methods may be analyzed using bioinformatics tools to identify differentially methylated regions associated with aging.Clinical Markers

[0249] An aging treatment disclosed herein can be combined with a safety or health assessment. In addition, or alternatively to monitoring biological age in an individual receiving treatment for aging, one or more markers for health can be monitored to determine type of treatment , to provide a calibrant for age measurement, or a combination thereof. Some aging treatments can cause adverse effects, such as diminished red blood cell (RBC) count or diminished liver function in certain individuals. Monitoring health prior to or concurrently with an aging treatment can allow the treatment to be tailored to the individual, can determine which treatment would work best, and can ensure that the treatment is modified or discontinued following an adverse response.

[0250] Furthermore, in some cases, markers for overall health can be important for determining biological age, response to TPE, and treatment options. As many age markers fluctuate in response to health status, calibration to an individual’s health can be useful for accurate biological age assessments. For example, certain conditions can increaseautoantibody concentrations by greater degrees than aging, rendering such autoantibody analyses effective for only certain subjects. Nonlimiting examples of clinical health assessments include measurements of at least one of magnesium, alanine aminotransferase (ALT), aspartate aminotransferase (AST), absolute eosinophil, absolute lymphocytes, absolute monocyte, absolute neutrophils, albumin, albumin / globulin, alkaline phosphatase, blood urea nitrogen (BUN), BUN / creatinine, basophils %, bilirubin, calcium, chloride, cholesterol / high-density lipoprotein ratio (chol / HDL), cholesterol, creatinine, eosinophils %, free testosterone, globulin, glucose, HDL, high-sensitivity c-reactive protein (HSCRP), hematocrit, hemoglobin, low-density lipoprotein (LDL), lymphocytes %, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), monocytes %, neutrophils %, platelets, potassium, protein, red blood cells (RBC), red cell distribution width (ROW), sodium, thyroid-stimulating hormone (TSH), triglycerides, vitamin D, white blood cells (WBC), and estimated glomerular filtration rate (eGFR).

[0251] In some aspects, clinical assessments to be analyzed to guide decisions of which treatment is appropriate before treatment has started include at least one of magnesium, albumin, globulin, albumin, potassium, absolutely eosinophil count, triglycerides, thyroid- stimulating hormone (TSH), eosinophil %, cholesterol, high-density lipoprotein (HDL), platelet count, creatinine, glucose, bilirubin, albumin / globulin ratio, absolutely neutrophil, white blood cell count (WBC), chloride, mean corpuscular hemoglobin (MCH), absolute lymphocytes, hemoglobin (Hgb), calcium, alkaline phosphatase, cholesterol, low-density lipoprotein (LDL), and estimated glomerular filtration rate (eGFR) (see FIG. 3A).(1) Blood-Based Assays

[0252] An individual undergoing an aging treatment method can be assessed with a complete blood count (CBC) assay before, during, and / or following the treatment. Complete blood count assays typically quantitatively measure multiple components of blood, such as red blood cells, white blood cells, hemoglobin, and platelets, as well as health and aging factors, such as red blood cell to plasma ratios. In addition to screening for overall health, complete blood count assays can identify adverse responses to some aging treatments, such as blood diffusion-based anemia. Adverse reactions to plasmapheresis are lower when albumin is used (3%) than when fresh frozen plasma is used as an exchange fluid (57%). Albumin is a powerful antioxidant, is anti-inflammatory, and immunomodulatory, and is a beneficial additive to an exchange fluid in the disclosed methods.

[0253] Analogously, an individual undergoing an aging treatment can be assessed with a total protein test to determine protein levels in a biofluid (e.g., blood). While some total protein tests quantify specific proteins, such as albumin and globulin concentrations and / or ratios (e.g., with immunoassays), others determine total protein concentration in the biofluid (e.g., based on the 280 nm protein band in a spectrophotometric absorbance assay). Total protein tests can identify some potential side effects of aging treatments, including fatigue, edema, and nutritional deficits.

[0254] Liver function assays can test for a number of substances indicative of proper liver function and health. Many liver function assays determine blood levels for liver-based or secreted enzymes, including alanine transaminase, aspartate transaminase, alkaline phosphatase, albumin, and gamma-glutamyltranserase. A liver function assay can also assess levels of metabolites regulated (e.g., cleared) by the liver, such as bilirubin, a heme degradation product. A liver function assay can also determine a quality of blood, such as pH or prothrombin time (how quickly blood clots). A specific set of assessments included in a liver function assay can depend on the health of the individual, as well as the type of aging treatments and diagnostics to which they are subjected.

[0255] A blood urea nitrogen (BUN) assay can assess kidney and metabolic function in an individual receiving an aging treatment. Blood urea nitrogen assays measure urea levels in blood. Blood-based ureas, which primarily derive from protein degradation, are maintained at low levels by kidney filtration. In addition to improper kidney function, high blood urea levels can indicate dehydration (a risk associated with some blood dilution methods), internal bleeding, and shock.

[0256] Creatinine assays provide an additional form of assessment for kidney function. Creatinine assays measure concentrations of creatinine, a catabolic waste product, in blood. While kidneys typically filter and thus maintain low levels of creatinine in blood, this function is hindered by a number of conditions that are identifiable with creatinine assays. In certain individuals, use of a creatinine assay can be important for monitoring kidney health prior to, during, or following an aging treatment.

[0257] C-reactive protein (CRP) assays can serve as a measure for inflammation and infections in an individual receiving an aging treatment. As some aging treatments increase susceptibility to infection, C-reactive protein assays can be important measures for determining treatment type and assessing improvements in individual health.PLASMAPHERESIS METHODS

[0258] Methods to manipulate blood composition decrease biological age as measured using biological age clocks and also improve health status. For instance, intramuscular injection of human umbilical cord plasma concentrate into elderly human individuals has shown that youth factors substantially improve clinical biomarkers and reduce biological age by 0.82 years (using GrimAge). Therapeutic plasma exchange (TPE), first studied in animals in 1914, has been shown to provide a beneficial effect in improving the outcomes of multiple medical problems. TPE, first used to treat macroglobulinemia in 1963, has been recently given Food and Drug Administration Emergency Use Authorization for the treatment of COVID- 19. Strikingly, up to 65% of patients with long COVID have shown improved peripheral neuropathy, fatigue, stamina, and brain fog after TPE treatment. Interestingly, in cases of yellow phosphorus poisoning, TPE showed strong beneficial effects such as removal of the poison and improved liver function with associated changes in secreted circulating proteins and metabolites.

[0259] Previous studies have shown that in the context of aging, there is remodeling of the immune system in the blood of older individuals and decreasing the proteins associated with aging after over five repeated TPE treatments (Kim D, et al., 2022).

[0260] The present disclosure describes a multi -omics systems biology approach to longitudinally profile 30 individuals who underwent three therapeutic plasma exchange (TPE) modalities (10 individuals each): monthly TPE (TPE(M)), biweekly TPE (TPE(B)), and biweekly TPE combined with intravenous immunoglobulin (IVIG).

[0261] For the present invention, a BA deceleration was utilized as the primary endpoint by measuring 35 independent epigenetic clocks and the BA rejuvenation effects caused by each intervention were also estimated. Integrative analysis encompassing lipidomics, proteomics, metabolomics, cytomics, and glycomics, identified biomarkers in each ‘omics’ that are correlated with the responses to TPE and baseline clinical and ‘omics’ features that are predictive of the rejuvenation response to TPE treatment.

[0262] The present disclosure describes methods to manipulate biological age and treat conditions by decreasing inflammatory response, decreasing DNA damage, decreasing biological noise, and decreasing the risk of diseases through repetitive TPE (5). These methods regulate pathways and gene expression to treat individuals with conditions such as chronic inflammation (FIG. 18).

[0263] Markers modified by the present methods include keratins, metabolic enzymes, and antioxidants. These compounds influence ECM maintenance, metabolism, redox signaling,and regulate the complement system. Extracellular matrix and growth factor regulators are also modified, influencing cell adhesion, IGF signaling, and cholesterol trafficking. Structural and immune response proteins such as the complement system, cellular stress response proteins, protein folding markers, and detoxification proteins are regulated with the described methods. Diverse cellular functions and pathways, including the unfolded protein response, IGF signaling, and TGF-J3 signaling, are also influenced. In addition to the above, heat shock proteins and glycolytic enzymes are regulated with the described methods, influencing proteostasis, cellular stress response, glycolysis, and chaperone-mediated autophagy (FIG. 19).

[0264] The described methods can decrease metabolites such as AC 14:2, SM42:2, and PC36:4 after the first plasmapheresis in Group A (FIG. 20A-20C). Set of metabolites influenced include phospholipid biosynthesis metabolites, sphingolipid metabolism, mitochondrial beta-oxidation of long chain saturated fatty acids, arachidonic acid metabolism, oxidation of branched-chain fatty acids, lysine degradation, and beta-oxidation of very long chain fatty acids (for example, FIG. 21).

[0265] Pathways that are downregulated by the described methods in Group B include foam cell differentiation, regulation of tube size, low-density lipoprotein particle remodeling, chylomicron remnant clearance, lipoprotein metabolic processes, vascular processes in the circulatory system, protein-containing complex remodeling, regulation of hormone secretion, hormone secretion, complement activation and alternative pathway regulation, protein-lipid complex subunit organization, positive regulation of lipid localization, regulation of lipid localization, hormone transport, isoprenoid metabolic process, negative regulation of hemostasis, extrinsic apoptotic signaling pathway, innate immune response-activating signal transduction, regulation of blood coagulation, regulation of vesicle-mediated transport, and regulation of coagulation (for example, FIG. 8A and 8B).Overview of Plasmapheresis ProceduresTreating and Preventing the Effects of Aging Using Plasmapheresis

[0266] A plasmapheresis method can include one or a plurality of plasmapheresis therapies. A plasmapheresis method can begin with the step of identifying an individual in need of a plasmapheresis treatment. The treatment method can further include withdrawing whole blood from a blood vessel of the individual receiving plasmapheresis using an apheresis device or other technique for withdrawing blood. The plasmapheresis method can further include separating the whole blood withdrawn into a cellular fraction and a plasma fractionusing the apheresis device or other technique for separating blood components. The plasmaphersis method can further include in fusing back to the individual receiving plasmapheresis an exchange fluid and the cellular fraction while removing the plasma fraction from the individual. An amount of exchange fluid returned to the individual may be approximately equal to an amount of plasma that is removed. For example, if one plasma volume is removed from an individual receiving plasmapheresis, in certain methods described herein, an amount of an exchange fluid returned to the individual will also be approximately equal to one plasma volume. Alternatively, an amount of exchange fluid returned may also exceed the amount of plasma volume removed. For example, if one plasma volume is removed, more than one plasma volume may be infused back to the individual receiving the plasmapheresis.

[0267] The term “plasmapheresis” as used herein is interchangeable with the term “therapeutic plasma exchange,” and plasmapheresis is a form of apheresis wherein some amount of a plasma of an individual is withdrawn from the body of the individual and removed. Plasmapheresis methods are described herein for treating and / or preventing a symptom or a condition associated with aging. In treating and / or preventing a symptom or a condition associated with aging, the plasmapheresis methods described herein may also increase lifespan and promote longevity.

[0268] A plasmapheresis treatment typically comprises - and begins with - the withdrawing of whole blood from an individual receiving the plasmapheresis treatment. Whole blood that is withdrawn is separated into a cellular fraction and a plasma fraction. The term “cellular fraction” as used herein can refer to or comprise red blood cells, white blood cells, and platelets. The terms “plasma fraction” or “plasma” as used herein can refer to or comprise a liquid portion of whole blood which contains, among other things, proteins, electrolytes, vitamins, and hormones. Typically, in a plasmapheresis treatment, the plasma fraction that is separated is removed while the cellular fraction is returned to the individual receiving the plasmapheresis treatment. As used herein in the context of plasmapheresis administration (or any other type of apheresis procedure), the terms “withdraw,” “withdrawal,” “withdrawn,” and “withdrawing” (or any other conjugation of “withdraw”) means to draw blood out (actively or passively) from the vascular system of an individual receiving plasmapheresis (or other type of apheresis procedure) which may be achieved using any suitable vascular access, which includes but is not limited to peripheral intravenous lines and central lines. As used herein in the context of plasmapheresis administration (or any other type of apheresis procedure), the terms “return” or “returning” (or any other conjugation of“return”) or “infuse,” or “infusing” (or any other conjugation of “infuse”) means to return blood back (actively or passively) to the vascular system of an individual receiving plasmapheresis (or other type of apheresis procedure) which may be achieved using any suitable vascular access. As used herein in the context of plasmapheresis administration (or any other type of apheresis procedure), the terms “remove,” “removal,” “removed,” and “removing” (or any other conjugations of “remove”) means to remove at least a portion of whole blood withdrawn from an individual receiving plasmapheresis (or any other apheresis procedure) and not returning the at least portion of the whole blood to the individual receiving plasmapheresis so that it is removed from their body. As used herein, in the context of plasmapheresis (or any other apheresis procedure), the terms “separate,” “separated,” or “separating” (or any other conjugation of “separate”) means to separate apart components of blood from one another. For example, in plasmapheresis, whole blood is withdrawn, and plasma is separated from the cellular fraction of the withdrawn whole blood. As used herein, the term “plasmapheresis” may be combined with other terms such as “therapy” (i.e. plasmapheresis therapy) or “treatment” (i.e. plasmapheresis treatment) or “procedure” (i.e. plasmapheresis procedure) and, unless otherwise indicated, no specific meaning should be attributed to the use of one of these terms or the other in the context in which they appear. Of note, the term “plasmapheresis” is often used interchangeably with therapeutic plasma exchange and at other times it is used to denote a form of therapeutic plasma exchange where less plasma is removed than that removed in therapeutic plasma exchange. To avoid confusion with respect to terminology, the term plasmapheresis is used throughout, and as stated, as used herein the term therapeutic plasma exchange is interchangeable with the term plasmapheresis. However, in no way should the term plasmapheresis be deemed to be limiting on the scope of the disclosure found herein which may be relevant to different types of apheresis based on context.

[0269] A plasmapheresis therapy session may begin with the initial step of withdrawing whole blood from a blood vessel of a patient using an apheresis device. Apheresis devices are well known and are machines configured to carry out procedures including plasmapheresis. Apheresis devices may be configured to withdraw whole blood from an individual through an intravenous line, separate the whole blood into components, and return an infusion to the individual through an intravenous line. The infusion returned to the individual may include separate components which may include blood that has had the plasma component removed from it. In addition, an infusion given to the individual may include an exchange fluid as well. An apheresis device can be an ex vivo apheresis system or machine comprising one ormore centrifugal chambers. An ex vivo apheresis system or machine can also comprise a return flow controller and one or more sensors for monitoring plasma or blood density. An apheresis device can also be configured to deliver an anticoagulant to the patient during the procedure. In some embodiments, the anticoagulant can be citrate dextrose. It should, however, be understood that any method or device for carrying out plasmapheresis is suitable for use with the methods and formulations described herein and the description provided should not be deemed to limit the inventive methods or formulations described herein which are suitable for use with any device or method for carrying out plasmapheresis.Exchange Fluid Composition and Use

[0270] An exchange fluid is typically administered with plasmapheresis wherein the exchange fluid is administered to the individual receiving plasmapheresis intravascularly (i.e. through intravenous access; e.g. peripheral or central line) during the plasmapheresis treatment. An exchange fluid may comprise any fluid that is suitable for use in intravenous fluid administration. For example, non-limiting examples of fluids suitable for intravascular administration with the administration of plasmapheresis as described herein are commonly referred to as isotonic fluids and include Normal Saline (i.e. a 0.9% saline solution) and Lactated Ringer’s. An exchange fluid solution suitable for use in plasmapheresis as described herein may further include albumin such as human albumin. For example, an exchange fluid may comprise a normal saline solution that includes 5% albumin by weight. Typically, a source of albumin is human derived albumin also referred to as human serum albumin (HSA). As an example, an exchange fluid suitable for use with plasmapheresis may comprise a sterile liquid preparation comprising an amount of human-derived protein in the amount of 50 g per 1000 ml of the sterile liquid preparation wherein at least 96% of the human-derived protein is human serum albumin protein. Besides the human-derived serum albumin protein, the remainder of the HSA preparation can comprise a saline solution and small amounts of potassium, N-acetyl-DL-tryptophan, caprylic acid, or a combination thereof. A 5% HSA preparation of an exchange fluid can be an FDA-approved 5% HSA preparation. More specifically, the 5% HSA preparation can be manufactured by an FDA-approved procedure such as the Cohn-Oncley cold ethanol fractionation procedure followed by ultra-filtration and pasteurization. Replacing plasma withdrawn from an individual receiving plasmapheresis with 5% HSA is useful for regulating and stabilizing the volume of circulatory blood within the individual.

[0271] An exchange fluid may be mixed or combined in any suitable way with the cellular fraction of the whole blood that is withdrawn during plasmapheresis, which remains after separation of plasma, and which is returned to the individual receiving plasmapheresis during the procedure. An exchange fluid suitable for use with plasmapheresis may also include one or more therapeutics. For example, a therapeutic that reduces inflammation may be provided with plasmapheresis by, for example, mixing the therapeutic with an exchange fluid. An exchange fluid for plasmapheresis may also comprise or be mixed together one or more blood products (i.e. not including the cellular fraction that is returned) including but not limited to fresh frozen plasma and platelets. It should be understood, that when mixed with an exchange fluid, therapeutics and blood products will to at least some degree be withdrawn back from the individual receiving the plasmapheresis and as such it may be beneficial to administer or infuse a therapeutic and / or blood product after completing the blood withdrawal from the individual so that the therapeutic and / or blood product will not be withdrawn from the individual during the plasmapheresis therapy.

[0272] As stated, in a typical plasmapheresis procedure, a volume of whole blood is withdrawn from an individual and a portion of it is returned to the individual along with an exchange fluid. Typically, the exchange fluid is returned to the individual simultaneously with the withdrawal of the whole blood which makes calculations related to the plasmapheresis process not simple and straightforward.

[0273] Typically, as stated above, exchange fluid is infused to the individual receiving plasmapheresis simultaneously with withdrawal of the whole blood and removal of the plasma so that the exchange fluid is essentially reconstituting some of the plasma volume during the procedure, and it is not, therefore, possible to remove the entire plasma volume during a typical plasmapheresis procedure because, to some degree, plasma is being continuously replenished as it is being removed. As used herein, “plasma volume” refers to the entire volume of plasma within the whole blood of an individual, which can be calculated using numerous methods that are well known for calculating plasma volume. In a typical plasmapheresis procedure, a volume of plasma that is equal to about 1 plasma volume is removed from the individual receiving plasmapheresis or 1 plasma volume up to 1.5 plasma volumes may be removed in a typical plasmapheresis procedure. Typically, during a plasmapheresis administration as described herein, an amount of exchange fluid is returned to the patient that is essentially equal to the amount of plasma volume that is withdrawn from the patient so that the removed plasma volume (that is not returned to the patient) is “exchanged” with the exchange fluid that is returned to the patient and replaces the volume ofplasma that is removed. While the exchange fluid in some embodiments may be more or less than the plasma volume removed, again, typically it is essentially equal in volume. For example, where one plasma volume is removed from an individual in a method described herein, a volume of exchange fluid equal (or essentially equal) to one plasma volume is returned to the individual.

[0274] As explained, because exchange fluid is simultaneously infused to an individual during a plasmapheresis procedure, removing anywhere from 1-1.5 plasma volumes during a plasmapheresis procedure does not typically mean that the plasma (and its contents) are completely removed even though a volume equal to 1-1.5 plasma volumes is removed, because the removed volume includes exchange fluid that had been infused to the individual and then removed as part of the total volume removed. In a typical plasmapheresis treatment where 1-1.5 of plasma volume exchanged, approximately 60%-70% of substances present in the plasma at the start of plasmapheresis will be removed which means that approximately 30-40% of substances present in the plasma at the start of plasmapheresis will remain in the body of the individual receiving plasmapheresis following a typical single plasmapheresis treatment.

[0275] Depending on the weight of the individual receiving plasmapheresis, the volume of plasma removed can be between approximately 2 L to 4 L. When 2 L to 4 L of plasma is removed during plasmapheresis, the volume of whole blood that is withdrawn from the patient is greater than 2.0 L to 4.0 L, which is to say that the withdrawn whole blood volume contains the volume of plasma to be removed and therefore the whole blood volume withdrawn is larger than the volume of plasma that is removed. It should be understood that plasma volume in an individual is dependent on a number of factors including weight and gender and so 2 L to 4 L is used here only as a non-limiting example of a range of plasma that might be removed in a plasmapheresis procedure, plasmapheresis, in some instances may involve removal of less than 2 L of plasma or more than 4 L of plasma.Vascular Access Sites

[0276] Blood can be withdrawn from a blood vessel of the patient including a peripheral blood vessel, a central blood vessel, or a combination thereof. Since the blood flow from the patient into the apheresis device has to be steady and preferably faster than 50 mL / min, the site of vascular access is typically a blood vessel capable of withstanding high negative pressure without collapsing.

[0277] Moreover, a site of vascular access for receiving an exchange fluid or a mixture that includes an exchange fluid and one or more other components is typically another blood vessel (or another peripheral or central access point) capable of tolerating relatively high positive pressure. In some embodiments of the methods described herein, whole blood can be withdrawn using a large-bore needle or cannula from a patient’s peripheral vein such as the antecubital fossa, the basilica vein, or the cephalic vein. Additionally, if determined by a plasmapheresis provider to be a good vascular access point, whole blood can also be withdrawn by cannulation of a radial artery of an individual receiving plasmapheresis. If determined by a plasmapheresis provider to be a good vascular access point, whole blood can be withdrawn using an intravascular or implantable device such as a central venous catheter (CVC), an arteriovenous (AV) shunt, an AV fistulae, or a port-CVC. For example, whole blood can be withdrawn from an internal jugular vein, a subclavian vein, or a femoral vein or artery of the individual receiving plasmapheresis. Blood from an individual receiving plasmapheresis can be withdrawn at a rate of approximately 90 ml / min. However, it is also suitable, in the methods described herein, that blood from an individual receiving plasmapheresis be withdrawn at a rate of between approximately 90 ml / min and 135 ml / min. It should be understood that, under certain conditions, withdrawal at a rate of less than 90 ml / min or more than 135 ml / min may be therapeutic for the individual receiving plasmapheresis. As previously discussed, typically, a site of vascular access for receiving a fluid to be returned to an individual receiving plasmapheresis, which may comprise, for example, an exchange fluid, a cellular fraction, a therapeutic, or a blood product and mixtures thereof, is different from a site of vascular access for initial blood withdrawal. For example, a cannula or catheter extending from or otherwise coupled to an apheresis device can be used to deliver a fluid to be returned to an individual receiving plasmapheresis comprising a cellular fraction and an exchange fluid to a blood vessel in an arm, hand, neck, or chest of an individual receiving plasmapheresis.Treatment Session Regimens and Protocols

[0278] In some embodiments of the methods described herein, each plasmapheresis treatment session can last approximately 90 minutes to 2 hours. However, it should be understood that plasmapheresis session length can be varied based on the objective of the plasmapheresis treatment and sessions shorter than 90 minutes or longer than 2 hours are suitable with the methods and formulations described herein. In addition, a plasmapheresis treatment session duration can vary depending on certain factors associated with theindividual receiving the plasmapheresis including but not limited to the weight of the individual receiving the plasmapheresis or the overall health of the individual.

[0279] Factors involved in aging are located within the plasma of a patient, and therefore, using the innovative plasmapheresis methods described herein, the factors involved with aging are removed from the body of an individual receiving plasmapheresis with removal of plasma from the body of the individual.

[0280] As explained, aging is likely a multifactorial process that currently has not been fully elucidated, which has generally precluded the development of effective therapies to treat and prevent the effects of aging. However, while the entire pathophysiology of aging may not yet be completely clear, there are clear choke-points in the pathophysiology of aging where the innovative methods described herein are effective. For example, it is understood that factors that affect aging including cytokines and peptides associated with aging can all be found in plasma. As used herein “plasma content” describes all of the separate components of plasma, and at least some of the plasma content has components in it that affect aging. The presence of these aging factor or factors within the plasma, makes the plasma a potential choke point of the aging process because these targeted aging-related factor or factors travel through the vascular system within plasma in order to get to the cells and / or tissue upon which they act, and, therefore, these factor or factors can be effectively collected and removed from the vascular system choke point using the innovative methods described herein. Even if it is not known which factor or factors found in blood are to be targeted by an age-related therapy, removing all of the plasma content or as much as is possible using the innovative methods and formulations described herein also removes the factor or factors that affect aging from the body and therefore diminishes or prevents the aging activity that removed factor or factors are associated with.

[0281] As already explained, a plasmapheresis session typically removes about 60-70% of plasma content which means that about 30-40% of the plasma content in the body of the individual receiving a typical single plasmapheresis treatment, including those factors associated with aging, remain within the blood of the individual receiving plasmapheresis after a single plasmapheresis treatment.

[0282] In certain methods described herein, a plasmapheresis protocol removes more plasma than is removed in a typical single plasmapheresis treatment so that more than 60- 70% of the plasma content of an individual receiving plasmapheresis is removed from the body of the individual. For example, in certain methods described herein, a first plasmapheresis treatment may remove 60-70% of plasma content of an individual and theindividual then undergoes an additional plasmapheresis treatment within a window of time relative to the first plasmapheresis treatment so that the first plasmapheresis treatment together with the additional plasmapheresis treatment achieves a cumulative removal of plasma greater than 60-70%. In these embodiments, the additional plasmapheresis treatment is administered before the plasma that is removed from the individual who received the plasmapheresis is fully replenished in the blood of the individual who received the plasmapheresis. In these embodiments, a plasmapheresis therapy method as described herein may be carried out and then a subsequent plasmapheresis therapy method as described herein may be carried out a time that is within 72 hours of the initial plasmapheresis therapy. Similarly, a plasmapheresis therapy method as described herein may be carried out and then an additional plasmapheresis therapy method as described herein may be carried out a time that is within 48 hours of the initial plasmapheresis therapy. For example, a plasmapheresis therapy method as described herein may be carried out and then an additional plasmapheresis therapy method as described herein may be carried out a time that is within 24 hours of the initial plasmapheresis therapy. Similarly, in these methods, an additional plasmapheresis treatment may also be carried out within a window of time where an additional plasmapheresis treatment in an individual is within four days, within five days, within six days, within seven days, within 8 days, within 9 days, within 10 days, within 11 days, within 12 days, within 13 days, or within 14 days of an initial plasmapheresis treatment in the individual.

[0283] Other approaches to achieving as much removal of plasma content as is possible include withdrawing more than 1.5 times of the plasma volume in a single plasmapheresis session. For example, a method for performing plasmapheresis as described herein includes withdrawing 1.5 times the plasma volume of an individual receiving plasmapheresis in a single plasmapheresis treatment. For example, a method for performing plasmapheresis as described herein includes withdrawing 1.6 times the plasma volume of an individual receiving plasmapheresis in a single plasmapheresis treatment. For example, a method for performing plasmapheresis as described herein includes withdrawing 1.7 times the plasma volume of an individual receiving plasmapheresis in a single plasmapheresis treatment. For example, a method for performing plasmapheresis as described herein includes withdrawing 1.8 times the plasma volume of an individual receiving plasmapheresis in a single plasmapheresis treatment. For example, a method for performing plasmapheresis as described herein includes withdrawing 1.9 times the plasma volume of an individual receiving plasmapheresis in a single plasmapheresis treatment. For example, a method for performingplasmapheresis as described herein includes withdrawing 2 times the plasma volume of an individual receiving plasmapheresis in a single plasmapheresis treatment. Likewise, a method for performing plasmapheresis as described herein may include withdrawing 2 or more times the plasma volume of an individual receiving plasmapheresis in a single plasmapheresis treatment.

[0284] In each case where a high percentage of plasma content is removed, clotting factors and / or platelets may be infused back to the individual receiving the plasmapheresis to replenish the clotting factors and platelets removed with the plasma content to address any elevated bleeding risk post the plasmapheresis treatment due to removal of the clotting factors and platelets.

[0285] A “plasmapheresis regimen” as used herein includes within its scope any application of plasmapheresis as described herein for the treatment or prevention of aging and / or promotion of longevity. A plasmapheresis regimen may include one or more treatments carried out over a particular time period and may in certain implementations include therapeutics provided together with plasmapheresis.

[0286] In the innovative methods described herein, plasmapheresis may be used in a plasmapheresis regimen in order to remove a relatively large amount of plasma content from an individual receiving plasmapheresis. In certain plasmapheresis regimens, plasmapheresis may be administered on a twice a week schedule to achieve a cumulative effect with respect to plasma content removal as described herein with the time period between a first and a second plasmapheresis treatment being delivered to an individual within 24 hours of each other (including within the same day), within 36 hours of each other, within 48 hours of each other, within 60 hours of each other, within 72 hours of each other, within 84 hours of each other, within 96 hours of each other, within 108 hours of each other, within 120 hours of each other, within 134 hours of each other, within 156 hours of each other, or within 168 hours of each other. An additional plasmapheresis treatment may also be carried out within a window of time where an additional plasmapheresis treatment in an individual is within four days, within five days, within six days, within seven days, within eight days, within nine days, within ten days, within eleven days, within twelve days, within thirteen days, or within fourteen days of an initial plasmapheresis treatment in the individual.

[0287] A plasmapheresis regimen as described that is carried out twice a month may be performed every week in a month for a total of eight plasmapheresis treatments per month, every other week for a total of four plasmapheresis treatments per month, or once a month for a total of two treatments in a month. A plasmapheresis regimen as described herein withtwice weekly treatments may be carried out for 6 months in total of twice weekly treatments. Within that 6-month period the twice weekly treatments may all be carried out with the same amount of time between each of the two weekly treatments or the amount of time may be varied between the two weekly treatments.

[0288] A plasmapheresis regimen as described herein may comprise performance of plasmapheresis once per week. For example, a plasmapheresis regimen as described that is carried out once a month may be performed once a week every week in a month for a total of four plasmapheresis treatments per month, every other week for a total of two plasmapheresis treatments per month, or once a month for a total of one treatment in a month. Any plasmapheresis regimen as described herein with once weekly treatments may be carried out for 6 months in total.

[0289] A plasmapheresis regimen as described herein may comprise performance of plasmapheresis for at least 1 months, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, or longer.

[0290] A plasmapheresis regimen as described herein may comprise performance of plasmapheresis for a period of about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, or about 12 months.

[0291] In certain plasmapheresis treatments described herein, any subsequent plasmapheresis treatment session can be undertaken only after 24 days have passed since the last plasmapheresis treatment session. In these and other embodiments, the entire treatment method can be discontinued after 125 days have passed since the first plasmapheresis treatment session. The treatment method can also comprise continuing the treatment method by repeating the method when at least 24 days have passed since the last treatment session. In these embodiments, no plasmapheresis treatment sessions should occur during this intervening waiting period. For example, the various plasmapheresis treatment sessions can be separated by 24 days, 25 days, 26 days, 27 days, 28 days, 29 days, or any combination thereof. The various plasmapheresis treatment sessions can be separated by the same number of days or be separated by a differing number of days. As a more specific example, the first plasmapheresis treatment session can be separated by the second plasmapheresis treatment session by 24 days and the second plasmapheresis treatment session can be separated by the third plasmapheresis treatment session by 25 days or 26 days. In other example methods, the first plasmapheresis treatment session can be separated by the second plasmapheresis treatment session by 26 days and the second plasmapheresis treatment session can beseparated by the third plasmapheresis treatment session by 25 days or 24 days. In some embodiments, the treatment method can be discontinued when 125 days have passed since the first treatment session. In other embodiments, the treatment method can be discontinued when at least six plasmapheresis treatment sessions have been undertaken, regardless of the number of days passed since the first plasmapheresis treatment session. In additional embodiments, the treatment method can be discontinued when at least seven or at least eight plasmapheresis treatment sessions have been undertaken, regardless of the number of days passed since the first plasmapheresis treatment session. In another treatment method for performing plasmapheresis, the treatment method can comprise six (6) plasmapheresis treatment sessions in total. The first plasmapheresis treatment session can occur on the first day (day 1) of the treatment, the second plasmapheresis treatment session can occur on the twenty-fifth day (day 25) of the treatment method, the third plasmapheresis treatment session can occur on the fiftieth day (day 50) of the treatment method, the fourth plasmapheresis treatment session can occur on the seventy-fifth day (day 75) of the treatment method, the fifth plasmapheresis treatment session can occur on the one-hundredth day (day 100) of the treatment method, and the sixth plasmapheresis treatment session can occur on the one- hundred and twenty-fifth day (day 125) of the treatment method. In some embodiments, no plasmapheresis treatment sessions occur during the intervening periods between the aforementioned treatment sessions. In other embodiments, a plasmapheresis treatment regimen may extend as long as needed and include as many separate sessions / treatments as needed to achieve one or more measurable effects.Anti-Inflammatory and Immune -Modulating Therapeutics

[0292] With respect to the use of plasmapheresis in the treatment of aging and aging related conditions, an anti-inflammatory or other immune -modulating therapeutic may be used together with plasmapheresis treatment in a synergistic way. More specifically, an antiinflammatory or immune-modulating therapeutic may synergistically modulate, reduce, or eliminate the effect of inflammatory and immune cells and factors (e.g. cytokines) that remain in the blood following plasmapheresis treatment and thereby further reducing the effect of these cells and factors. In certain of the methods described herein, a therapeutic is administered to target an aspect of aging found within blood of an individual receiving the plasmapheresis treatment. For example, inflammation is a known part of the aging process and is sometimes referred to as “inflammaging” wherein effects of aging correlate with an elevated level of inflammatory agents within the blood. As such, an anti-inflammatorydelivered together with plasmapheresis can act to reduce inflammatory activity and as such disrupt the effect of inflammation in age related conditions. With plasmapheresis in particular, there’s a strong synergy in that using the methods described herein many of the non-cellular inflammatory factors found within the plasma content are removed from the body of the individual receiving the plasmapheresis treatment and any remaining inflammatory cells may be further affected with therapeutics. As such, in the example provided, with a single typical plasmapheresis about 30-40% of the inflammatory factors within the plasma content will remain after the single typical plasmapheresis treatment and if an anti-inflammatory or other immune-modulator is administered with or soon after plasmapheresis is performed, the anti-inflammatory or other immune-modulator can act synergistically with the removal of the inflammatory factors to further attenuate the effect of the inflammatory cells and factors remaining after the plasmapheresis treatment. Applying a therapy in this targeted way creates a cumulative inhibition effect that increases the benefit to the individual receiving the plasmapheresis.

[0293] A plasmapheresis treatment as described herein can further comprise delivering a therapeutically effective dosage of intravenous immunoglobulin (IVIG) to a blood vessel of the individual receiving the plasmapheresis treatment. In certain plasmapheresis treatments as described herein, IVIG is delivered to an individual receiving plasmapheresis after returning the cellular fraction and the exchange fluid to the individual receiving the plasmapheresis. This is to say that a therapeutically effective dosage of IVIG may be provided to an individual separately from infusion of the cellular fraction and the exchange fluid to the individual receiving the plasmapheresis. For example, IVIG may be infused in the individual receiving plasmapheresis after a plasmapheresis treatment is completed so that any infused IVIG will not be removed in the plasmapheresis process. It is also possible to administer IVIG concurrently with the administration of plasmapheresis.

[0294] The therapeutically effective dosage of IVIG can be approximately 1 g / kg to 5 g / kg, 2 g / kg to 4 g / kg, or 2.0 g / kg of IVIG (g IVIG / kg of bodyweight of the individual receiving plasmapheresis). The IVIG delivered can contain certain antibodies and cytokines which can have a positive effect on the immune system of the individual receiving plasmapheresis and may contribute to establishing a therapeutic systemic environment for cell growth. As stated above, it is suitable for administration of a therapeutic such as IVIG mixed together with an exchange fluid and / or a cellular fraction during a portion of the plasmapheresis procedure when blood is being withdrawn or it is also suitable to administer IVIG to the individual as part of a method for administering plasmapheresis as described herein when blood is nolonger being withdrawn from the individual receiving plasmapheresis. The therapeutically effective dosage of IVIG can be any FDA-approved IVIG or immune globulin intravenous (IGIV) infusion or preparation. For example, the IVIG can comprise primarily of gamma globulins.

[0295] Each of the plasmapheresis treatment sessions can comprise the steps of withdrawing whole blood from a blood vessel of a patient using an apheresis device, separating the whole blood withdrawn into a cellular fraction and a plasma fraction using the apheresis device, admixing the cellular fraction with an exchange fluid comprising albumin derived from human plasma, returning the mixture comprising the cellular fraction and the exchange fluid to the blood vessel of the patient using an apheresis device, and delivering a therapeutically-effective dosage of intravenous immunoglobulin (IVIG) to the blood vessel of the individual receiving plasmapheresis after returning the mixture comprising the cellular fraction and the exchange fluid to a blood vessel of the individual.Measurement of Biological Age During Treatment

[0296] A biological age or any other measurable feature or marker of the efficacy of plasmapheresis on an individual may be measured using the numerous markers and assays described herein. In particular, a biological age, physiological measurement (e.g. strength, walking, or balance), mental assessment (e.g. an emotional wellness survey), or marker in the blood of the individual (e.g. cell surface marker) that is measured using any of the analysis techniques, markers, and assays described herein may be used in conjunction with a plasmapheresis regimen as described herein wherein a biological age, physiological measurement, mental assessment, or marker that is identified, quantified, or determined to be present is used to affect a modification in a plasmapheresis regimen.

[0297] For example, a biological age, physiological measurement, mental assessment, or marker in the blood of an individual may be measured, using any of the markers and / or assays described herein, before and after a plasmapheresis regimen as described herein (including one or more plasmapheresis treatments over a period of time) and the plasmapheresis regimen may be extended if a decrease (or increase if an increase and not a decrease is beneficial with respect to the item that is measured) in the biological age, physiological measurement, mental assessment, or marker in the blood of the individual (as measured before the regimen is administered) is not achieved. The plasmapheresis regimen can then be continued until the decrease (or increase if an increase and not a decrease isbeneficial with respect to the item that is measured) in the biological age, physiological measurement, mental assessment, or marker that is sought is achieved.

[0298] Similarly, biological age, a physiological measurement, a mental assessment, or a marker found in the blood of an individual can shape the plasmapheresis regimen itself, wherein, for example, a biological age, a physiological measurement, a mental assessment, or a marker in the blood of an individual is measured before and after a single plasmapheresis therapy and subsequent plasmapheresis therapies are administered until the biological age is decreased below a level that is sought.Example of Plasmapheresis Treatment Method

[0299] In an exemplary method, a plasmapheresis method can begin with the step of identifying an individual in need of a plasmapheresis therapy. A blood sample is taken from the individual, either before initiation of the plasmapheresis, or from the whole blood that is withdrawn and used to determine a biological age of the individual using the markers and / or assays described herein. In some embodiments, physiological and mental assessment is carried to assess one or more of the individual’s strength, walking, balance, or mental status (wherein exemplary techniques for obtaining these measurements are described herein such as in the section below describing the pilot study and results). The plasmapheresis method can further comprise withdrawing whole blood from a blood vessel of the individual receiving plasmapheresis using an apheresis device or other technique for withdrawing blood. The plasmapheresis method can further comprise separating the whole blood withdrawn into a cellular fraction and a plasma fraction using the apheresis device or other technique for separating blood components. The treatment method can further comprise infusing back to the individual receiving plasmapheresis an exchange fluid and the cellular fraction while removing the plasma fraction from the individual. An amount of exchange fluid returned to the individual may be approximately equal to an amount of plasma that is removed. For example, if one plasma volume is removed from an individual receiving plasmapheresis, in certain methods described herein, an amount of an exchange fluid returned to the individual will also be approximately equal to one plasma volume. Alternatively, an amount of exchange fluid returned may also exceed the amount of plasma volume removed. For example, if one plasma volume is removed, more than one plasma volume may be infused back to the individual receiving the plasmapheresis. Once the plasmapheresis therapy is completed, a second sample is obtained, and a second biological age is determined using the markers and / or assays described herein. Should one or more of the biological age,physiological measurement, mental assessment, or marker found in the blood of the individual be determined after the plasmapheresis therapy not to have been lowered or raised sufficiently (as compared to the biological age, physiological measurement, mental assessment, or the marker determined before the start of the plasmapheresis therapy), additional plasmapheresis therapy is administered with repeated sampling and measuring of biological age, physiological measurement, mental assessment, or the marker with the plasmapheresis continuing until the biological age, physiological measurement, mental assessment, or the marker is decreased by an amount that is sought.Blood Dilution Calculations

[0300] Also described herein is a method for treating aging by carrying out a blood dilution. As described herein, plasma content can be removed and exchange fluid added. The removal of plasma content (even with partial replacement) together with the addition of exchange fluid typically lowers the concentrations of one or more constituents of plasma in an individual receiving plasmapheresis treatment. Essentially, solute (the plasma content) is decreased while the solvent (the liquid portion of plasma replaced by the exchange fluid) remains the same. In this way, in addition to removing plasma content components, the methods described herein dilute one or more components of plasma content that remain in the blood of the individual receiving plasmapheresis following a plasmapheresis treatment.

[0301] Plasma content dilution can achieve synergistic affects to removal of plasma content alone in that, at least, dilution causes the plasma composition of an individual receiving plasmapheresis to resemble a plasma composition of a biologically younger individual. This is to say that factors found in plasma that are associated with aging are found empirically to have a lower concentration in younger individuals than older individuals. As in a younger individual where the aging-associated factors found in plasma are less active, the decrease in concentration of the plasma content in individuals receiving plasmapheresis as described herein lowers the relative activity further of the factors associated with aging in those individuals receiving the plasmapheresis therapy as described herein.(i) Modeling Blood Dilution

[0302] A number of models may be used to calculate an amount of dilution that is achieved with a plasmapheresis treatment as described herein. For example, Reverberi and Reverberi (Blood Transfus, 2007; 5(3): 164-174) provide formulas for modeling residual plasma analyte concentrations from which Formulas (I), (la), and (lb) are derived:Residual Solute Concentration = 100% * evb (I)wherein vPis plasma volume, n is number of plasma volume units exchanged, and vb is total blood volume (i.e. whole blood volume).

[0303] As an illustrative example of Formula (I), using a plasma volume of 3L, 1 plasma volume to be exchanged, and a total volume of 5 L of whole blood would be expected to result in a residual plasma content concentration of approximately 54% of the original concentration following a single plasmapheresis treatment. For the same plasma volume and total volume of blood but a plasma exchange of 1.5 plasma volumes, the residual solute concentration would be approximately 40%.

[0304] Typically, plasma analytes exhibit an instantaneous concentration drops following blood exchange, followed by reconcentration along a logarithmic-like trends to pre-blood dilution levels. Formula (I) can be modified to Formula (la) to estimate plasma analyte levels na days following plasma exchange, assuming a half-time ti / 2 for the analytes to return to preblood dilution levels:(la).Noting that not all plasma analytes reestablish homeostasis at equal rates, as typical plasma analytes return to pre-blood dilution levels after about 10 days, ti / 2 of 3 to 4 days is often a suitable estimate. Once again using 3L, 1 plasma volume, and 5L of whole blood with a halflife of 3 days and measured 3 days after the initial plasmapheresis treatment, the residual solute concentration increases from approximately 54% following the plasmapheresis treatment to approximately 72% three days (i.e. 72 hours) later.

[0305] Winters (cited above) provides the following simplified formula: Y / Yo = e~' where Y is the final concentration of a substance, Yo is the initial concentration, and X is the number of times the patient’s plasma volume is exchanged. Continuing with the approximately 72% residual concentration calculated above using Formula (la), if plasmapheresis is administered once again 72 hours after the initial plasmapheresis, Yo = 72%, x = 1, and Y = 26.5% residual plasma concentration following the second plasmapheresis treatment.

[0306] Formula (la) can further be used to determine the degree of plasma analyte clearance following multiple rounds of plasma exchanges by setting n as above. For such an application, Formula (la) can be estimated as a sum, expressed as Formula (lb), where eachplasma exchange event, i, is attenuated by analyte regeneration over nai days following the plasma exchange event:This equation does not account for diminished plasma exchange efficacy over multiple cycles, reflecting exchange of partially diluted plasma. However, in cases where ti / 2 is approximately equal to or less than the time between plasma exchange events, and for therapies with limited numbers of plasma exchanges, discrepancies from this estimation are typically small.

[0307] Formulas (I), (la), (lb), and the formula all provide excellent and useful approximations of dilution levels which are suitable for use with the methods described herein. It is also noted that if higher accuracy in calculating plasma content dilution should be achieved, the following factors and issues should also be considered: First, many plasma separation methods only partially separate plasma from cellular components. While multiple rounds of separation can increase efficiencies, single iterations of centrifuge -based plasma separation and membrane-based plasma separation typically achieve 80% and 30% plasma separation efficiencies. For a 500 mb blood draw, which will typically contain about 55% (275 mb) plasma these efficiencies translate to 220 mb plasma removed by centrifugation and 82.5 mb plasma removed by filtration. The effect of incomplete plasma separation from cellular components is a concomitant decrease in plasma exchange efficiency. If plasma is centrifugally separated from blood with 80% efficiency, then plasma exchange will typically be 80% efficient given a volume of blood separated and exchanged. Second, many plasma components actively exchange into spaces outside of the vasculature. While a typical adult male human has about 5 liters of blood, a high proportion of the fluids, and analogously, analytes, are contained in interstitial and intracellular spaces, which account for about 10.5 and 28 liters of fluid, respectively. As used herein, intracellular spaces can include all volumes contained within cell membranes of an organism, including all fluids within these spaces, while interstitial spaces can denote spaces surrounding tissues. Many plasma analytes actively equilibrate between the blood and these spaces, such that fractions of their total populations are contained within the blood at any given time. Removal of such species by plasma exchange can be attenuated by their partitioning outside of the blood. For example, only about 60% to 70% of IgGl immunoglobulins are present in the blood at any given time, such that plasma exchange can only target 60% to 70% of the IgGl population. Furthermore, disruption of homeostasis, for example through blood dilution, can affect osmotic gradientswhich draw species into blood from extravascular spaces, thereby hastening return to pretreatment blood analyte levels. Third, plasma analytes regenerate at a range of rates. Following a blood composition altering event, such as blood dilution, blood analyte concentrations tend to return to their original, resting levels. Although blood dilution can alter resting levels of individual blood analytes, diluted species tend to increase in concentration while concentrated species (e.g., albumin provided from a high concentration exchange fluid) tend to decrease in concentration to reestablish pre-dilution levels. While some species (in particular many cytokines) exhibit complex re-equilibration patterns, many follow simple exponential growth or decay curves. However, the rates of these processes can vary significantly between species. For example, IgG immunoglobulins often return to pre-blood dilution levels in about 4 days, while low-density lipoproteins can take more than 2 weeks to return to resting levels.

[0308] A model is presented below which accounts for extravascular compartmentalization and species regeneration. This model, adapted as Formula (II) below, captures rates of plasma analyte loss through plasma exchange and clearance:wherein At denotes a plasma analyte concentration at time t, Ao denotes the initial concentration of the analyte, ki denotes a rate of clearance of the analyte (e.g., through degradation, biliary clearance, etc.), and k2 denotes a rate at which the analyte is released from extracellular tissues. The above model does not account for regeneration of the analyte, and further assumes plasma analyte distribution according to a two-compartment system (intravascular and extravascular). Accordingly, the model may not be appropriate for analytes dynamically exchanged across multiple compartments (e.g., antibodies with appreciable endosomal and interstitial concentrations), or analytes which are rapidly produced following dilution. However, this model can be corrected for inefficient plasma removal during exchange, as outlined above, through correction to clearance rate (ki).TREATMENT EFFECTS OF TPE

[0309] The therapeutic plasma exchange (TPE) treatments described herein may induce significant changes across multiple biological systems, as evidenced by the comprehensive multi-omics analyses conducted. These changes may include alterations in inflammatory markers, metabolic profiles, lipid composition, glycosylation patterns, and immune cell populations.

[0310] In some aspects, TPE treatment may lead to a reduction in inflammatory markers. For example, levels of pro-inflammatory cytokines such as IL-6, TNF-a, and C-reactive protein may decrease following treatment. This reduction in inflammatory markers may contribute to the potential anti-aging effects of TPE by mitigating chronic low-grade inflammation associated with aging.

[0311] Metabolomic analyses may reveal substantial changes in metabolite profiles following TPE treatment. In some cases, levels of metabolites associated with oxidative stress and cellular senescence may decrease. For instance, the concentration of certain acylcamitines, such as acylcamitine 14:2 (AC 14:2), may be reduced after treatment. Additionally, amino acid metabolism may be altered, with changes observed in the levels of glycine, serine, and other amino acids.

[0312] Lipidomic profiling may indicate significant shifts in lipid composition following TPE. In some aspects, levels of certain sphingomyelins and phosphatidylcholines may be modulated. For example, sphingomyelin 42:2 (SM 42:2) and phosphatidylcholine 36:4 (PC 36:4) may show altered concentrations post-treatment. These changes in lipid profiles may reflect alterations in membrane composition and cellular signaling pathways.

[0313] Glycomic analyses may reveal changes in protein glycosylation patterns following TPE treatment. In some cases, alterations in the abundance of specific N-linked glycan structures may be observed. For instance, levels of glycans such as FA2BG2 SI and FA2[3]G1S1 may be modulated. These changes in glycosylation may impact protein function and cellular communication processes.

[0314] Cytomic profiling may demonstrate significant alterations in immune cell populations and phenotypes following TPE treatment. In some aspects, the treatment may lead to an increase in the proportion of naive T cells and a decrease in senescent T cells. The CD4 / CD8 T cell ratio may also be modulated. Additionally, changes in the expression of cell surface markers such as CD27, CD57, and KLRG1 may be observed, potentially indicating alterations in T cell differentiation and senescence states.

[0315] The multi-omics analyses may reveal coordinated changes across different biological systems. For instance, alterations in lipid metabolism may correlate with changes in inflammatory markers and immune cell populations. These interconnected changes may provide insights into the mechanisms by which TPE treatment may influence overall health status and biological age.

[0316] In some aspects, the effects of TPE treatment may vary depending on the treatment regimen. For example, more frequent treatments or the addition of intravenousimmunoglobulin (IVIG) to the treatment protocol may lead to more pronounced or sustained changes in certain biological markers.

[0317] The comprehensive molecular and cellular changes induced by TPE treatment may contribute to improvements in various health parameters. These may include enhanced physical performance, improved cognitive function, and better overall health status, as measured by standardized assessments and quality of life questionnaires.Quantitative Clocks

[0318] Epigenetic clocks are molecular biomarkers that have been developed to estimate biological age based on DNA methylation patterns. These clocks may provide a quantitative measure of aging processes and health status. In the context of plasmapheresis treatments, epigenetic clock analyses may reveal significant changes in biological age estimates.

[0319] PCHorvathl: is a principal component-based version of the original Horvath clock. It was developed to address challenges in longitudinal studies and clinical applications where technical variability can impact the consistency of age estimations. Utilizes PCA to transform DNA methylation data, capturing the most significant variance in methylation patterns across samples. Utilizes approximately 5,000 CpG sites has been found to provide reliable age predictions.

[0320] PCHorvath : updated iteration of PCHorvathl, incorporating enhancements to further improve the model's performance and applicability. Incorporates a broader range of principal components to capture more subtle variations in DNA methylation patterns and it utilizes a larger number of CpG sites, approximately 78,464, to enhance the model's sensitivity and specificity.

[0321] PCHannum: is a principal component-based adaptation of the original Hannum epigenetic clock, which was developed by Gregory Hannum in 2013. The original Hannum clock utilized DNA methylation data from 71 CpG sites in whole blood samples to estimate chronological age. PCHannum, on the other hand, employs principal component analysis (PCA) to transform DNA methylation data, capturing the most significant variance in methylation patterns across samples.

[0322] PCPhenoAge: is an advanced adaptation of the original Pheno Age clock, which estimates biological age based on DNA methylation data. While PhenoAge was trained using DNA methylation data from 513 CpG sites, PCPhenoAge employs principal component analysis (PCA) to reduce dimensionality and enhance the reliability of age predictions.1

[0323] PCGrimAge: is a principal component-based adaptation of the original GrimAge clock. While GrimAge was trained using DNA methylation data from 1,030 CpG sites associated with plasma proteins and smoking pack-years, PCGrimAge employs principal component analysis (PCA) to reduce dimensionality and enhance the reliability of age predictions. This adaptation aims to mitigate technical noise and improve the robustness of the clock across different datasets and tissues.

[0324] PCDNAmTL: an epigenetic clock that integrates DNA methylation data with telomere length information to estimate biological age.

[0325] Horvath: one of the most influential and widely used epigenetic clocks in the field of aging research. It estimates biological age — the age of tissues and cells based on molecular markers — using DNA methylation patterns.

[0326] Hannum: is one of the earliest and most influential DNA methylation-based (epigenetic) clocks. It estimates an individual's chronological age from DNA methylation data, with particular application to blood samples.

[0327] PhenoAge: is a second-generation epigenetic clock designed to estimate biological age more accurately than earlier models by incorporating both DNA methylation data and clinical biomarkers of aging. It aims to capture not just chronological aging, but also physiological decline and disease risk.

[0328] OMICmAge: is a cutting-edge, multi -omics epigenetic aging biomarker developed to provide a comprehensive assessment of biological age by integrating DNA methylation data with clinical, proteomic, and metabolomic information. This approach aims to offer a more accurate and holistic understanding of the aging process compared to traditional single- omics clocks.

[0329] DNAmFitAge: is an advanced epigenetic tool developed to assess biological age by integrating DNA methylation data with physical fitness parameters. Unlike traditional clocks that primarily focus on chronological age, DNAmFitAge offers a more nuanced understanding of how physical fitness influences the aging process at the molecular level.

[0330] DNAmGait: is a specialized epigenetic biomarker developed to assess biological age through DNA methylation patterns associated with gait speed — a key indicator of physical function and mobility. This clock is part of a broader effort to integrate physical fitness parameters into epigenetic aging models, providing a more comprehensive understanding of how lifestyle factors influence aging at the molecular level.

[0331] DNAmGrip: is an epigenetic biomarker developed to assess biological age by incorporating maximum handgrip strength — a well-established indicator of muscular functionand overall physical fitness — into DNA methylation-based aging models. This clock is part of the DNAmFitAge framework, which integrates multiple physical fitness parameters to provide a comprehensive measure of biological age.

[0332] DNAmVO2max: is an epigenetic biomarker developed to assess biological age by incorporating maximal oxygen uptake (VCfmax) — a key indicator of cardiovascular fitness — into DNA methylation-based aging models. This clock is part of the DNAmFitAge framework, which integrates multiple physical fitness parameters to provide a comprehensive measure of biological age.

[0333] AdaptAge: is an epigenetic biomarker developed to assess biological age by focusing on DNA methylation changes associated with protective adaptations against aging. Unlike traditional clocks that primarily reflect cumulative damage, AdaptAge aims to capture beneficial molecular adjustments that may contribute to healthy longevity.

[0334] CausAge: is an advanced epigenetic tool developed to assess biological age by distinguishing between DNA methylation changes that causally influence aging and those that are merely correlated with it. Unlike traditional clocks that aggregate all age-related methylation patterns, CausAge focuses on identifying and quantifying causal methylation sites, providing a more precise measure of biological aging.

[0335] DamAge: is an epigenetic biomarker developed to assess biological age by focusing on DNA methylation changes that are causally linked to aging -related damage. Unlike traditional epigenetic clocks that aggregate all age-related methylation patterns, DamAge specifically identifies and quantifies harmful methylation alterations, providing a more precise measure of biological aging.

[0336] IntrinClock: is an epigenetic clock developed to measure biological age by focusing on DNA methylation changes that are intrinsic to cells, independent of variations in immune cell composition. Traditional epigenetic clocks, such as Horvath's and Hannum's clocks, can be confounded by changes in immune cell proportions that occur with aging. IntrinClock addresses this limitation by isolating cell-intrinsic aging signals, providing a more accurate assessment of biological age.

[0337] cAge: is a clock that estimates chronological age based on DNA methylation patterns, serving as a baseline for comparison with other clocks.

[0338] Stochastic Zhang: is an epigenetic model developed to quantify the contribution of stochastic (random) DNA methylation changes to the predictive accuracy of Zhang’s epigenetic clock.

[0339] Stochastic Horvath: is a variant of the original Horvath epigenetic clock, which measures biological age based on DNA methylation patterns. The "stochastic" version incorporates a component that accounts for the random, non-deterministic changes in DNA methylation that occur over time.

[0340] Stochastic PhenoAge: is an epigenetic model developed to quantify the contribution of stochastic (random) DNA methylation changes to the predictive accuracy of the PhenoAge clock.

[0341] Retroclock: is an epigenetic aging model that leverages DNA methylation patterns in retroelements — specifically human endogenous retroviruses (HERVs) and long interspersed nuclear elements (LINEs) — to estimate biological age.

[0342] Age Acceleration Timepoint: data shows age acceleration at a specific baseline at different timepoints. Age acceleration difference at time point 2 = EAA (time point 2) - EAA (time point 1). Age acceleration difference at time point 3 = EAA (time point 3) - EAA (time point 1). Time point 1 refers to time point before the first treatment. Time point 2 refers to time point before the second treatment. Time point 3 refers to time point before the third treatment.

[0343] Age Acceleration Baseline: data shows age acceleration at a specific baseline at different timepoints.

[0344] Age Acceleration Difference: using Epigenetic Age Acceleration (EAA), the age acceleration difference was calculated as: Age acceleration difference at time point x = EAA (time point x) - EAA (time point 1).

[0345] In some aspects, multiple epigenetic clocks may be utilized to provide a comprehensive assessment of biological age changes following plasmapheresis treatment. These may include first-generation clocks such as the Horvath clock and Hannum clock, as well as second-generation clocks like PhenoAge, GrimAge, and DunedinPACE. Each clock may capture different aspects of biological aging, allowing for a multi-faceted evaluation of treatment effects.

[0346] Following plasmapheresis treatment, some individuals may exhibit a decrease in epigenetic age acceleration across multiple clock measures. For example, the GrimAge clock, which is associated with morbidity and mortality risk, may show a reduction in age acceleration after treatment. In some cases, this decrease may be in the range of 1-3 years of biological age.

[0347] The effects of plasmapheresis on epigenetic age may vary depending on the specific treatment regimen. In some aspects, more frequent treatments or the addition of intravenousimmunoglobulin (IVIG) to the protocol may lead to more pronounced reductions in epigenetic age estimates. For instance, a biweekly plasmapheresis regimen combined with IVIG may result in greater decreases in epigenetic age compared to monthly plasmapheresis alone.

[0348] Changes in epigenetic age following plasmapheresis may correlate with alterations in other biological markers. For example, reductions in epigenetic age acceleration may be associated with improvements in inflammatory markers, metabolic profiles, or immune cell populations. These correlations may provide insights into the potential mechanisms by which plasmapheresis influences biological aging processes.

[0349] In some cases, the effects of plasmapheresis on epigenetic age may be more pronounced in certain epigenetic clock measures compared to others. For instance, clocks that capture aspects of immune system aging or inflammatory processes may show larger changes following treatment compared to clocks that primarily reflect chronological age.

[0350] The duration of epigenetic age reductions following plasmapheresis may vary. In some individuals, the effects may persist for several months after treatment, while in others, epigenetic age estimates may gradually return to baseline levels. This variability may reflect individual differences in response to treatment or the need for ongoing maintenance treatments to sustain the effects.

[0351] Epigenetic clock analyses may also reveal differences in treatment response among individuals. Some participants may show more substantial reductions in epigenetic age following plasmapheresis, while others may exhibit more modest changes. These differences in response may be related to factors such as baseline health status, genetic background, or lifestyle factors.Development of TPE Response Scoring System

[0352] Individuals with high or low levels of -omic measurements can be treated with TPE(M), TPE(B), TPE+IVIG(B), sham, or another treatment.

[0353] Measurements used to select treatment types for individuals before treatment begins include bilirubin levels, magnesium, HDL, globulin, glucose, sodium, ALT, platelets, RBC, eosinophils %, trig, cholesterol / HDL, albumin, absolutely eosinophil, albumin / globulin ratio, potassium, TSH, MCH, creatinine, and alkaline phosphatase. In some aspects, elevated levels of mean corpuscular hemoglobin (MCH) and creatinine at baseline are used to choose individuals for whom treatments should include TPE biweekly and monthly, respectively.

[0354] Measurements used to guide treatment decisions after treatment has begun can include any of the above markers. In some aspects, the measurements include interleukin 13 (IL13RA1), glycine (Gly), alpha-beta t cells CD4 CM CD27, the glycan FA2BG2 SI, the phosphatidylcholine PC(38:3), serine, and the protein BPI fold-containing family A member 1 (BPIFA1), mast cell intermediate (MC INT), the glycan FA2[3]G1S1, the sphingomyelin SM(42: 1), the acetylcamitine AC(8: 1), and hemoglobin subunit gamma 1 (HBG1), abT CD4 SCM and MC NC, A2(6)G1 and FA2, glycine and creatinine (metabolomics), and IL13RA1 and ITIH3 (proteomics), PC(36:2) and SM(42: 1). In some aspects, these -omics measurements guide treatment decisions; an individual with low changes in related -omics measurements after one, two, three or more treatment sessions can have treatment stopped or changed from one type of treatment to another, such as from TPE+IVIG to TPE(B) or TPE (M).

[0355] In some aspects, individuals with high or low measurements of ferritin, platelets, monocyte %, HDL, potassium, absolute eosinophil, and carbon dioxide are treated with TPE+IVIG (B). In some aspects, individuals with high or low measurements of ALT, calcium, eGFR, alkaline phosphatase, chol / HDL, BUN / creatinine, albumin, and creatinine are treated with TPE (M). In some aspects, individuals with high or low measurements of Hgb, hematocrit, chloride, absolute lymphocytes, protein, and BUN are treated with TPE (B) . In some aspects, individuals with high or low measurements of bilirubin are treated with either TPE(B) or TPE(M). In some aspects, individuals with high or low measurements of globulin are treated with TPE+IVIG (B). In some aspects, individuals with high or low measurements of sodium, glucose, or a combination thereof are treated with TPE+IVIG (B) or TPE (M).

[0356] In some aspects, initial levels of -omics biomarkers lead to improved treatment response. These biomarkers can include P22 (glycomics), levels of O- hydroxyhexadecanoylcamitine (metabolomics), levels of ACSM2B, BUD31, CAMPC2N2, DNAJB2, FAM222A-AS1, KLHL34, NOUFA6-DT, OXER1, PGBP, PVR1G, RB1-DT, SMG9, TMEM38B, ZNF841 (methylomics), levels of lysophosphatidylcholine 16:0, N- pentacosanoylsphingosine, phosphatidylcholine 0-32:3, sphingomyelin 42:3, triacylglycerol 44:4 (lipidomics), IGHV5.51, MT2A.MT1G.MT1X, PTPRG (proteomics),EXEMPLARY ASPECTS

[0357] The invention is further illustrated by the following non-limiting examples.EXAMPLE 1TPE INDUCES BIOLOGICAL AGE REJUVENATION, AND IVIG SUPPLEMENTATION ENHANCES THE EFFECTExperimental Interventions

[0358] Individuals underwent therapeutic plasma exchange in two temporal regimes. Ten individuals were subjected to two sessions during the first week, followed by three weeks with no sessions for 3 months (biweekly regime, Group A and Group B), and ten individuals received one session per month 6 times (monthly regime, Group C and Group D) (FIG. 1A, FIG. IB). Blood samples were taken in both regimes before sessions 1 (baseline), 4 and 6. In TPE, blood cells and plasma were separated. Plasma was then filtered and replaced with IVIG or fluids, and together with blood cells were returned to the patient’s circulation. TPE was performed using a centrifugal blood separator (Spectra Optia, Lakewood, CO). During each procedure, one plasma volume plasma was removed and replaced with 5% albumin. The patients in the TPE-IVIG group (Group A) received 2 gm of IVIG immediately after the TPE procedure. Two temporal regimes were tested, with three blood samples taken per session in each case before the same session number. Samples were used to perform multi-omics profiling. Epigenomics data was used to calculate the differences in biological age induced by the treatments. Correlation analyses were performed, comparing baseline and changing levels of the omics features and biological age differences. In addition to TPE, the effects of biweekly intravenous injections of immunoglobulin (IVIG) supplementation were evaluated in ten individuals, which had been shown to enhance the immune system’s ability to fight infections. The control group received sham plasma exchange, which mimicked TPE but without fluid replacement.

[0359] FIGs. 4A-13C show proteomic data from mass spectrometry assays on blood samples taken from participants in the trial. These assays measure changes in types of proteins found within the blood of the participants. For each participant, a blood sample was taken before their first treatment, before their fourth treatment, and before their sixth treatment. Participants had blood taken after completion of the entire regimen of six treatments as well. In these figures, a label of “A” indicates that the data was for the group of study participants who received IVIG together with their plasmapheresis treatment (i.e. IVIG was administered after the plasmapheresis was completed) and (as described previously) participants in this group received two treatments within one week, once a month, for three months (total of six treatments), a label of “B” indicates (as described previously) that participants in this group received two treatments within one week, once a month, for threemonths (total of six treatments), a label of “C” indicates (as described previously) that participants in this group received six sham treatments administered once a month for six months, and a label of “D” indicates (as described previously) that participants in this group received one treatment administered once a month for six months (total of six treatments). A label of “1” indicates that the blood sample was the first blood sample, drawn before the first administration of plasmapheresis. For example, “Al” refers to the blood samples for all group A participants that were drawn before their first plasmapheresis administration. A label of “2” indicates that the blood sample was the second blood sample, drawn before the fourth administration of plasmapheresis. For example, “ T refers to the blood samples for all group A participants that were drawn before their fourth plasmapheresis administration (i.e. the second time blood was drawn in the protocol). A label of “3” indicates that the blood sample was the third blood sample, drawn before the sixth administration of plasmapheresis. For example, “A3” refers to the blood samples for all group A participants that were drawn before their sixth plasmapheresis administration (i.e. the third time blood was drawn in the protocol). A label of “4” indicates that the blood sample was the fourth blood sample, drawn after the plasmapheresis protocol was completed. For example, “A4” refers to the blood samples for all group A participants that were drawn after their sixth plasmapheresis administration (i.e. the fourth time blood was drawn in the protocol). As can be seen in the figures, there was both significant upregulation and downregulation of proteins in the treated groups “A”, “B”, and “D” as compared to the sham group “C.” As shown on FIGS. 5 and 7 for A4 and B4, which were assays of blood samples taken following completion of the six treatment cycles, there was upregulation of a number of proteins following completion of the treatment cycle as compared to Al and Bl respectively - 154 for group A (i.e. A4 compared to Al) and 88 for group B (i.e. A4 compared to Al).

[0360] FIGS. 14-17 describe cell cytometry data from assays on blood samples taken from participants in the trial. These assays measure cellular changes and / or changes in cellular populations found in blood samples. In these figures, a label of “A”, for Group A, indicates that the data was for the group of study participants who received IVIG together with their plasmapheresis treatment (i.e. IVIG was administered after the plasmapheresis was completed) and (as described previously) participants in this group received two treatments within one week, once a month, for three months (total of six treatments), a label of “B”, for Group B, indicates (as described previously) that participants in this group received two treatments within one week, once a month, for three months (total of six treatments), a label of “C” indicates (for Group C, as described previously) that participants in this groupreceived six sham treatments administered once a month for six months, and a label of “D” indicates (for Group D as described previously) that participants in this group received one treatment administered once a month for six months (total of six treatments). As shown, the treated groups A, B, and D groups all have more significant changes than the sham C group. The A and B groups have similar population signatures, while the D group may have an inverse correlation with both A and B. Abundance of T cells vs. monocytes and expression of NK markers are frequently modified populations across all treated groups A, B, and D.

[0361] TABLES II- VI summarize hand grip, balance test, ‘Up & Go’ test, SF-12 physical scores, and SF-12 mental scores for each of the participants. In these tables, 'Group A’ indicates that the data was for the group of study participants who received IVIG together with their plasmapheresis treatment (i.e. IVIG was administered after the plasmapheresis was completed) and (as described previously) participants in this group received two treatments within one week, once a month, for three months (total of six treatments), ‘Group B’ indicates (as described previously) that participants in this group received two treatments within one week, once a month, for three months (total of six treatments), ‘Group C’ indicates (as described previously) that participants in this group received six sham treatments administered once a month for six months, and ‘Group D’ indicates (as described previously) that participants in this group received one treatment administered once a month for six months (total of six treatments). TABLE II summarizes the six hand grip results for the participants, as well as average scores for each group of participants. TABLE III summarizes the six balance test results for each of the participants, as well as average scores for each group of participants. TABLE IV summarizes the six ‘Up & Go’ test results for each of the participants. TABLE V summarizes the six SF-12 physical scores for each of the participants, as well as average scores for each group of participants. TABLE VI summarizes the six SF-12 mental scores for each of the participants, as well as average scores for each group of participants.

[0362] TABLE II describes data for hand grip strength for treated individuals.

[0363] TABLE III describes data for the balance test for treated individuals.

[0364] TABLE IV describes Up & Go data for treated individuals.

[0365] TABLE V describes data for the SF-12 physical score for treated individuals.| AVERAGE | 57,74 | 58,53 | 59,06 | 57,35 | 57,97 | 57,67 |

[0366] TABLE VI describes data for the SF-12 mental score for treated individuals.

[0367] The results from the study, as detailed in TABLES II- VI, indicate significant improvements in different health metrics for participants undergoing plasmapheresis treatments. Hand grip strength increased across all treatment groups, with Group A (TPE + IVIG) showing an average increase from 38.75 to 41.40, Group B (TPE biweekly) from 33.55 to 36.55, and Group D (TPE monthly) from 33.72 to 39.80. Balance test results also improved, with Group A increasing from 66.10 seconds to 88.80 seconds, Group B from 86.08 seconds to 106.30 seconds, and Group D from 78.50 seconds to 91.80 seconds. The 'Up & Go' test times decreased, indicating better mobility, with Group A improving from 8.30 seconds to 6.82 seconds, Group B from 8.55 seconds to 6.68 seconds, and Group D from 8.67 seconds to 6.30 seconds. SF-12 physical health scores showed positive changes, with Group A increasing from 54.59 to 57.00, Group B from 55.39 to 56.92, and Group D from 55.49 to 58.24. Mental health scores also saw improvements, with Group A increasing from 47.31 to 55.50, Group B from 48.69 to 54.44, and Group D from 49.80 to 57.85. In contrast, the control group showed minimal or no improvements across these metrics.

[0368] Results of the clinical metrics are also shown in FIG. 28 (Hand Grip data), FIG. 29 (Up and Go data), FIG. 30 (Balance Test), FIG. 31 (SF-12 physical score), and FIG. 32 SF- 12 mental score. Significant differences were seen for Group D in Hand Grip FIG. 28), for all treatment groups (not the sham group) for Up and Go (FIG. 29), marginally significant results were seen for the Balance Test (FIG. 30), an almost significant difference was seen in the physical score for Group D (FIG. 31), and significant differences for all treatment groups were seen in the mental score for all treatment groups (FIG. 32, not the sham group).EXAMPLE 2MOLECULAR CHANGES RESULTING FROM TPE AND CORRESPONDING HEALTH STATUS IMPROVEMENTSExperimental Interventions

[0369] Epigenetic data were collected from the treated individuals in Example 1 and DNA methylation age was calculated using 35 different epigenetic clocks. Biological ages were adjusted for each clock by the chronological age, resulting in a metric of age acceleration. This metric represented the deviation of biological age from individuals of the same age. To estimate the effects on biological age induced by the interventions, the difference between theage acceleration at time points 2 or 3 were compared to baseline (time point 1). FIG. ID shows that in all groups, the difference between the age-adjusted biological age at time points 2 and 3 was compared to time point 1 (age acceleration difference). This age acceleration difference was negative if the interventions reduced biological age and positive if the interventions increased biological age, independently of age (TABLE VII and FIG. ID).

[0370] At time point 2, all interventions induced a negative age acceleration difference compared to sham (FIG. IE). TPE + IVIG treatment displayed large reduction in biological age, with an average decrease of 2.61 years (p = l.le-05). In the case ofTPE, the monthly regime showed a greater decrease in biological age than the biweekly regime, with an average biological age rejuvenation of 1.32 years (p = 5.7e-03), suggesting that more frequent sessions do not always lead to a greater biological age rejuvenation effect. Surprisingly, no significant biological age differences were observed at time point 3 compared to sham in any group, suggesting potential compensatory mechanisms that mitigate the anti-aging effects after multiple sessions. For Example, GrimAge is an epigenetic morbidity and mortality predictor, and reduced values are linked to improved long-term health outcomes and increased survival. In the sham-pheresis control group, values tend toward an increase, but there is no statistically significant effect. In the treatment groups, GrimAge is significantly decreased.

[0371] A variety of epigenetic clocks are available for characterizing the separate aspects of aging. The age acceleration difference attributed to each biological age clock was estimated. Clocks were grouped into 6 different types, depending on the method and outcome regressed. At time point 2, Systems age clocks showed large decreases in age-adjusted biological age in the TPE + IVIG group (4.85 years, FDR = 2.52e-04) and the monthly TPE intervention (2.55 years, FDR = 9.90e-03) but not in the TPE biweekly group (FDR = 0.91) (FIG. ID). More traditional epigenetic clocks also showed significant age acceleration differences in the TPE + IVIG (FDR = 5. 12e-03) and TPE (FDR = 1 ,75e-03) interventions under a biweekly regime. Consistent with the average calculations across epigenetic clocks, many of the clock groups showed a reduced biological age rejuvenation at time point 3. Given the attenuated effect at time point 3, the attenuation may be dependent on the effects observed at the previous session (time point 2), which would indicate a potential compensatory mechanism. For all interventions, a strong negative correlation was observed between the age acceleration differences at time points 2 from baseline and those between time points 3 and 2 (FIG. 1H and FIG. II). The sham group showed no such correlation. Overall, these results indicate thatindividuals with rejuvenation effects during the first three sessions tend to show a greater biological age increase in subsequent sessions.Omics Changes Linked to Rejuvenation in TPE+IVIG

[0372] To provide a better understanding of the molecular basis of the biological age rejuvenation observed, multi-omics profiling was performed on the same samples, including cytomics, glycomics, lipidomics, metabolomics, and proteomics. To identify omics markers associated with the rejuvenation effects observed in time point 2, the correlations between the age acceleration differences induced by each intervention and the change in levels of each feature for each omics was calculated. Features whose correlation was significantly different from zero and the sham intervention with an FDR<0.01 (TABLE VIII) were used (FIG. 2A).

[0373] TPE + IVIG induced high changes in omics profiles covering 86 of the 143 features affected by all interventions (FIG. 2A), with nearly 72% of the cell types measured in the cytomics changed in proportion in coordination with the biological age differences after TPE + IVIG treatment (FIG. 2B). However, minor or no changes in cell types in TPE interventions were observed, indicating that IVIG induced large changes in cell type composition, some of which could potentially contribute to the rejuvenation effects of biological aging. For example, the biological age rejuvenation effects were associated with a higher proportion of CD8 and CD4 naive T cells, a hallmark of immune aging, and lower levels of NK cells and monocytes. These results are consistent with previous observations that CD8 and CD4 naive T cells significantly drop with age, while NK cells and monocytes tend to increase with age. Across interventions, glycomics showed large proportion of changes, with nearly a third (33.3%) of the glycans measured changing. In proteomics, 43 proteins, representing 8.3% of the proteome measured in this study, displayed significant correlations with the biological age changes in TPE + IVIG, compared to around 3% for the TPE group.

[0374] To provide a better understanding of the function of these proteins, gene ontology enrichment analysis was performed. Proteins with levels correlated with rejuvenation effects in TPE + IVIG were involved in activation of the immune response, T cell proliferation, and cell-cell adhesion (FIG. 2C, top). The association of these proteins with the hallmarks of aging was also analyzed. To do so, these proteins were compared against precalculated sets of genes linked with the hallmarks of aging. A significant enrichment of proteins involved in altered intercellular communication (FDR=2.96e-04), chronic inflammation (FDR=1.0e-03) and cellular senescence (FDR=0.01) (FIG. 2C bottom) was observed. Proteins significantlycorrelated with the biological age rejuvenation effects were also correlated with cellular senescence. The set of proteins changing with TPE were compared against those in the SASP Atlas. The SASP Atlas included proteins changing during senescence induced in fibroblasts by X-ray irradiation (genotoxic stress-induced), RAS overexpression (oncogene-induced) and atazanivir treatment (treatment-induced). A significant up-regulation occurred of proteins correlated with rejuvenation effects in senescent fibroblasts, suggesting that the modulation of senescence may partially drive these rejuvenation effects (FIG. 2D).

[0375] Inter-omics correlations were observed as well, as shown in FIG. 2A (lines). Several strong omics changes were highly correlated (|r| > 0.9) with cell type changes due to TPE+IVIG intervention, as shown in FIGs. 2E-2G. For instance, 30% (8 / 26) of the cell type changes linked with rejuvenation in TPE+IVIG showed a significant correlation with the level of the soluble receptor for interleukin 13 (IL13RA1), a Th2 cytokine with a large effect in fibrosis. Similarly, changes in the levels of glycine (Gly) were correlated with the changes in 6 cell types. Surprisingly, glycine has been shown to act on a variety of inflammatory cells like macrophages to reduce the formation of free radicals and inflammatory cytokines throughout the modulation of the expression of nuclear factor kappa B (NF-KB). Features with negative correlation included alpha-beta t cells CD4 CM CD27 (R=-0 / 96), the glycan FA2BG2 SI (R=-0.57), the phosphatidylcholine PC(38:3) (R=-0.92), serine (R=-0.77), and the protein BPI fold-containing family A member 1 (BPIFA1) (R=-0.85). Features with positive correlation include mast cell intermediate (MC INT) (R=0.95), the glycan FA2[3]G1S1 (R=0.63), the sphingomyelin SM(42: 1) (R=0.43), the acetylcamitine AC(8: 1) (F=0.89), and hemoglobin subunit gamma 1 (HBG1) (F=0.85) (FIG. 2E). Useful -omics measurements also include abT CD4 SCM and MC NC (cytomics), A2(6)G1 and FA2 (glycomics), glycine and creatinine (metabolomics), and aPC(36:2) and SM(42: 1) for lipidomics (FIG. 2F). Also included are IL13RA1 and ITIH3 (proteomics) (FIG. 2G). These markers are associated with neurological function, cell signaling, aging, and the immune system. Overall, these results show that the modulation of cell type composition and proteomic changes associated with immunosenescence largely drive biological age effects induced by TPE + IVIG intervention. In some aspects, these -omics measurements guide treatment decisions; an individual with low changes in related -omics measurements after one, two, three or more treatment sessions can have treatment stopped or changed from one type of treatment to another, such as from TPE+IVIG to TPE(B) or TPE (M).Biological Age Effects from TPE+IVIG are Determined by Health Status

[0376] One important aspect of interventional studies is the ability to determine a priori which individuals will respond to treatment. This corresponds to individuals who displayed a decreased age acceleration after the TPE interventions. To answer this, omics data from individuals at baseline (before treatment) was used to investigate whether the magnitude of the rejuvenation effects correlated with the levels of any clinical or omics markers prior to treatment. The correlation between the age-adjusted age acceleration difference at time point 2 and the levels of clinical and omics markers at baseline were analyzed (TABLE IX, FIG. 3A). In addition to the omics markers, 57 clinical markers were analyzed, which are more accessible and practical to measure in a clinical setting. A significant correlation was found between 25 clinical markers and the age acceleration differences in at least one experimental group (FDR < 0.01). Surprisingly, bilirubin levels show robust correlation with age acceleration difference in TPE + IVIG (FIGs. 3B-3T), with higher levels being associated with large rejuvenation effects. Other levels associated with rejuvenation effects include those of magnesium, HDL, globulin, glucose, ALT, platelets, RBC, eosinophils %, trig, chol / HDL, albumin, absolutely eosinophil, albumin / globulin ratio, potassium, TSH, MCH, creatinine, and alkaline phosphatase (FIGs. 3B-3T). While individuals in this study were nominally healthy, high levels of circulating bilirubin can lead to mitochondrial dysfunction and immune system disruption which is consistent with the notion that individuals in poorer health benefit from this treatment modality. Similarly, elevated levels of mean corpuscular hemoglobin (MCH) and creatinine at baseline were observed in individuals with large rejuvenation effects in TPE biweekly and monthly, respectively. Features that were predictive of the response to the interventions were also compared in classification models (TABLE X, FIG. 3U). Baseline levels of 8 (TPE biweekly) to 11 (TPE monthly) clinical markers were useful to classify responders (age acceleration difference < 0) from non-responders with bilirubin, globulin, glucose and sodium contributing to the prediction of response to at least two interventions (FIG. 3V). Also, classification of responders to TPE + IVIG was higher (AUC = 0.73) than TPE biweekly (AUC = 0.7) or monthly (AUC 0.63). Overall, this analysis shows that individuals with poorer health experience significant improvements and that baseline levels are useful in predicting the biological age response to TPE.

[0377] As shown in FIG. 3V, the TPE+IVIG (B) group shows post-treatment differences in ferritin, platelets, monocyte %, HDL, potassium, absolute eosinophil, and carbon dioxide. The TPE (M) group shows differences in ALT, calcium, eGFR, alkaline phosphatase, chol / HDL, BUN / creatinine, albumin, and creatinine. The TPE (B) group shows differences in Hgb, hematocrit, chloride, absolute lymphocytes, protein, and BUN. Both the TPE (B) andthe TPE (M) groups show differences in bilirubin, while both the TPE+IVIG (B) and the TPE (B) groups show differences in globulin. The TPE+IVIG (B) and TPE (M) groups both show differences in sodium and glucose.

[0378] TABLE X describes biomarker changes after treatment compared amongTPE+IVIG(B), TPE(B), and TPE(M) groups.MethodsParticipants and settings

[0379] Eligible patients were men and women over 50 years of age, with the exception of one woman aged 43, who had no known chronic clinical conditions. A subset of patients had clinical blood tests completed at Quest Labs (Bay Area, CA) or Life Extension Foundation Labs (online). Exclusion criteria included poor peripheral vascular access, diagnosis of active malignancy or active infection, late-stage Alzheimer’s disease, symptomatic coronary artery disease, congestive heart failure, restrictive pulmonary disease, asthma, taking growth hormones, stem cells, stem cell products or any other anti-aging medications (such as metformin or rapamycin), presence of an active infection and having a psychiatric disorder.Study Design

[0380] Enrolled patients were randomly allocated to four groups (in a 1 : 1 : 1 : 1 scheme): three TPE treatments and one control group that underwent a simulated TPE treatment through a noninvasive procedure (sham) that mimicked PE but without any actual fluid replacement with the patient receiving approximately 250 cc of normal saline. The three TPE groups included a twice a week treatment once per month, a twice a week treatment once per month with IVIG added and a once a week once per month treatment. Samples were taken before the first, fourth and sixth TPE treatment. Samples were sent to each respective core facility or third-party company for processing and analysis. Patients, caregivers, and raters were blinded.

[0381] Subjects in the Sham group had a peripheral vein IV inserted in each arm, similar to the actual treatment subjects, but with only normal saline IV fluid. Dark curtains were used to hide the apheresis device and the pumps from the participants’ field of view. The apheresis device was turned on and ran using a container filled with water, with both the access and the return lines submerged in the container to simulate blood flow through the machine. The device was programmed using the patient’s information (height, weight, etc.) and a rinse back procedure was done to maintain the perception that the actual TPE procedure was finished.Epigenomics

[0382] DNA methylation was evaluated using TruAge (developed by TruDiagnostic Inc., Lexington, KY). Peripheral whole blood samples were obtained and then mixed with a lysis buffer to preserve the cells. DNA extraction was performed, and 500 ng of DNA was subjected to bisulfite conversion using the EZ DNA Methylation Kit from Zymo Research, following the manufacturer’s protocol. The bisulfite-converted DNA samples were randomly allocated to designated Illumina Infinium EPIC850k Beadchip wells. The samples were amplified, hybridized onto the array, and subsequently stained. After washing steps, the variety was imaged using the Illumina iScan SQ instrument to capture raw image intensities, enabling further analysis. Raw ID AT files were processed using the minfi pipeline. Low- quality samples were detected using ENMix by examining the variance of internal controls and flagging those with values more than 3 standard deviations from the mean control probe value. However, no outlier samples were found, so all samples were included in the analysis. Single-sample Noob (ssNoob) normalization was used in order to consistently normalize the samples across the multiple array types. The algorithms analyzed by TruAge include first- (Horvath and Hannum) and second- (pheno Age, systemsAge, OMICAge, and GrimAge).Other algorithms, and / or biological age indicators, and / or biological age biomarkers used herein to calculate biological age include: AdaptAge, CausAge, DamAge, Stochastic Horvath, Stochastic PhenoAge , Stochastic Zhang, Retroclock, cAge, Hannum, IntrinClock, Pheno Age, Horvath, OMICmAge , PCHorvath2, PCDNAmTL , PCGrimAge, PCHorvathl , PCHannum, PCPhenoAge, DNAmFitAge, DNAmGait, DNAmV02max , or DNAmGrip. Epigenetic age acceleration (EAA) of the first- and second-generation clocks was calculated as the residual of each clock regressed upon chronological age. To account for any perceived batch effects, the first 3 principal components calculated from the technical probes were calculated and used as adjustment factors when calculating the EAA.

[0383] In some embodiments herein, the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises a property selected from a humoral immune response, leukocyte mediated immunity, lymphocyte mediated immunity, leukocyte cell -cell adhesion, activation of immune response, regulation of leukocyte cell-cell adhesion, regulation of cell-cell adhesion, negative regulation of immune system processes, regulation of leukocyte mediated cytotoxicity, regulation of T cell activation, antigen receptor-mediated signaling pathways, regulation of immune effector processes, regulation of cell killing, regulation of peptidyl -tyrosine phosphorylation, regulation of T cell proliferation, complement activation classical pathway, regulation of body fluid levels, antibacterial humoral response, heterotypic cell-cell adhesion, or antimicrobial humoral response.Glycomics

[0384] Glycan data was derived according to various techniques. For the analytical precision analysis, AMC and long-term variability, the IgG isolation, IgG N-glycan release and labeling were performed using a Genos-Glycanage IgG glycome profiling kit (Genos, Osijek, Croatia) and subsequent capillary gel electrophoresis with laser-induced fluorescence (CGE-LIF) analysis was adapted from previously published protocols. The process of extracting IgG involved diluting 25 pL of individual plasma samples and three distinct plasma standards in quadruplicate, serving as technical replicates of a known, previously analyzed, glycome. This dilution was carried out using a 1:7 ratio with a 1 x PBS buffer, which was prepared in-house. Additionally, blank samples containing ultrapure water, without any analyte, were included to monitor and control for potential cross-contamination. The diluted samples were resuspended and filtered through a wwPTFE filter plate with 0.45- pm pore size (Pall corporation, New York, NY, USA) using a vacuum manifold and pump (Pall corporation, New York, NY, USA). The filtered samples were transferred to a CIM® r-Protein G LLD 0.05 mL monolithic 96-well plate (Sartorius BIA Separations, Ajdovscina, Slovenia), where they underwent binding and subsequent washing steps with phosphate- buffered saline (1 x PBS) buffer (0.25 M NaCl, increased ionic strength, prepared in-house). Elution of the bound IgG was achieved by employing 0.1 M formic acid neutralized with ammonium bicarbonate buffer (Sigma- Aldrich, St. Louis, MO, USA). The eluted IgG fraction (20 pL) was dried and prepared for the subsequent steps in the protocol.

[0385] The dried IgG samples were consecutively treated with 1.66 x PBS, 0.5% sodium dodecyl sulfate (SDS) and 2% Igepal (Sigma- Aldrich, St. Louis, MO, USA / Invitrogen Thermo Lisher Scientific, Carlsbad, CA, USA), to denature the IgG, followed by incubation with 1.2U of the enzyme PNGase F (Promega, Madison, WI, USA) at 37°C for 3 h to release its N-glycans. The released glycans were then labeled by mixing APTS (8-aminopyrene- 1,3,6-trisulfonic acid) (Synchem, Felsberg, Germany) fluorescent dye with the reducing agent 2-picoline borane (Sigma- Aldrich, St. Louis, MO, USA) and subjected to a 16-h incubation at 37 °C.

[0386] After incubation, the labeling reaction was halted by the addition of 80% acetonitrile (ACN, Carlo Erba, Milan, Italy). The clean-up of the released fluorescently labeled IgG N- glycans was conducted using solid-phase extraction utilizing Bio-Gel P-10 as a hydrophilic stationary phase. The entire sample volume was transferred to the fdter plate containing the Bio-Gel P-10. The excess label and reducing agent were removed by five washes with 80% ACN / 100 mM triethylamine (Sigma- Aldrich, St. Louis, MO, USA), followed by three washes with 80% ACN. Finally, APTS labeled IgG N-glycans were eluted in ultra-pure water.

[0387] For CGE-LIF analysis, 3 pL of purified IgG N-glycans combined with 7 pL of Hi- Di Formamide were analyzed using an ABI3500 Genetic Analyzer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a 50-cm long 8-capillary array filled with POP-7 polymer as a separation matrix. Run parameters were set as follows: run time 1000 s, injection time 12 s, injection voltage 15 kV, run voltage 15 kV, and oven temperature 60 °C. The resulting electropherograms were manually integrated into 27 glycan peaks using the Empower 3 software (Waters, Milford, MA, USA). The amount of glycan structures in a peak was expressed as a percentage of the total integrated area (total area normalization). In addition, six derived glycan traits were calculated for glycans with shared structural features.Metabolomics and Lipidomics

[0388] Targeted metabolomics was performed on plasma samples utilizing the Biocrates AbsolutelDQ p400 HR kit. Prior to analysis, a system suitability test and instrument calibration were performed with the QExactive mass spectrometer (Thermo Scientific). The experimental procedure involved processing plasma samples, blanks, calibration standards (7-point), and quality controls according to the manufacturer’s recommendations.

[0389] Specifically, 10 pL of plasma was added to a pre-loaded filter plate containing internal standards and dried using ultra-pure nitrogen. Subsequent derivatization with 5% phenylisothiocyanate in pyridine, ethanol, and water (in a 1 : 1 : 1 ratio), followed by extraction with 5 mM ammonium acetate in methanol, yielded extracts that were collected into a 96- deep well plate via centrifugation. Mass spectrometric analysis was conducted using the Thermo QExactive mass spectrometer in positive ionization mode. Chromatographic separation utilized a proprietary Biocrates column, employing 0.2% formic acid in water as buffer A and 0.2% formic acid in acetonitrile as buffer B.

[0390] For the analysis of lipids, acylcamitines, and hexoses, flow injection analysis (FIA) was employed without a column, utilizing the Biocrates FIA additive mobile phase. Data processing and lipid quantification were performed using Thermo Excalibur, QuanBrowser, and MetIDQ software. Normalization of peak areas corresponding to metabolites was conducted relative to their respective internal standards. Target metabolite concentrations were estimated linearly based on observed concentrations in quality control samples, and a seven-point quadratic calibration approach was implemented where applicable. All chemicals and solvents used were LCMS grade.

[0391] For proteomics analyses methods and data, see Example 3, and for cytomics analysis methods and data, see Example 4.Bioinformatic Analysis

[0392] Using the Epigenetic age acceleration (EAA) calculated (see Methods - Epigenomics) age acceleration difference was calculated as: age acceleration difference at time point 2 = EAA (time point 2) - EAA (time point 1); age acceleration difference at time point 3 = EAA (time point 3) - EAA (time point 1). The mean age acceleration difference across individuals was calculated for each of the 35 different epigenetic clocks in each group. The average age acceleration difference for the epigenetic clocks was compared between treatment groups and sham using the Wilcoxon test.

[0393] Changes in feature levels in each omics were calculated for time points 2 or 3 versus 1 and correlated against the age acceleration difference at time points 2 and 3. For down-stream analysis, only features measured in at least 3 samples were considered. Correlations were transformed into z-scores using Fisher transformation and compared against zero and sham using a t-test. P-values were adjusted for multiple tests in each omics using Benjamini- Hochberg method. Features with FDR < 0.05 against zero and sham were considered statistically significant. In the case of the comparison against baseline levels of clinical and omics markers, the correlations were calculated between the baseline levels and age acceleration difference and the same method was used to calculate the statistical significance. Circular and rectangular heatmaps were generated using the package circlize and ComplexHeatmap .

[0394] Gene ontology enrichment analysis was performed using the function enrichGO implemented in the package clusterProfiler. Only GO biological process terms with a set size between 50 and 500 were analyzed. Enrichment results were plotted using the package CellPlot. To identify genes associated with each of the 12 hallmarks of aging, a corpus of 36 million abstracts was taken from PubMed (https: / / huggingface.co / datasets / ncbi / pubmed). First, 71,129 abstracts were identified which included the word “aging” or “ageing” in the title or abstract. Then, large language models (GPT-4o mini) were used to analyze each abstract using the following query: “Your task is to identify genes associated with the hallmarks of aging from the following scientific abstract. For each gene mentioned in the abstract, annotate it with the corresponding hallmark of aging (genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, disabled autophagy, chronic inflammation, dysbiosis)”. To perform enrichment analysis using this gene sets, genes in the query signature were ranked by - loglO(p-value) and then a one-tailed gene set enrichment analysis was performed using the package fgsea70. To build the classification models only samples that measured at least 50% of the clinical markers were considered, and markers that were measured in at least 50% of the samples. Missing values were imputed using the R package impute. Each individual was classified into responder or non-responder categories for each epigenetic clock based on the age acceleration difference (e.g responder: age acceleration difference < 0, non-responder: age acceleration difference > 0). Elastic net logistic regression was performed on the clinical data using 10-fold cross validation and shrinkage parameter (lambda) of classification models was analyzed using area under the curve.Determine TPE + 1 VIC Response with Clinical Markers

[0395] One important aspect of interventional studies is the ability to determine a priori which individuals will respond to treatment. Individuals who displayed a decreased age acceleration after the treatments were responders. Omics data were obtained from individuals at baseline (before any treatment), and correlations were evaluated between the age-adjusted age acceleration difference at time point 2 and the levels of clinical and omics markers at baseline (FIG. 3A). In addition to the omic markers, 57 clinical markers were evaluated, which are more accessible and practical to measure in a clinical setting. There was a significant correlation between 16 (28%) of these markers and the age acceleration differences (FIGs. 3B-3Q). High baseline levels of circulating bilirubin, glucose and alanine aminotransferase (ALT), which can signal liver dysfunction and metabolic disturbances, were linked with large rejuvenation effects, suggesting that individuals with poorer health may experience significant improvements.

[0396] In this study, the efficacy of TPE + / - IVIG in reducing BA was assessed, and multi - omics profiling was used to identify markers associated with the BA changes. Findings demonstrate that three sessions of TPE treatment were sufficient to reduce biological age by 1.32 years, and that IVIG supplementation doubles the effect to 2.61 years. The present disclosure presents data showing a surprising decrease in biological age by TPE using well- validated epigenetic metrics of biological age. Despite the initial rejuvenation effects, subsequent therapy sessions restored positive effects in biological age induced by the first 3 sessions, with the restoration being linearly dependent on the initial BA effects. This may be due to either cell-intrinsic programming, which means that cells have mechanisms to compensate and adapt to changes induced by the therapy, or exposome effects, in which lifestyle and environmental factors restore the body back to its baseline state, reducing the response to treatment over time.

[0397] Different types of epigenetic clocks were evaluated, and systems age clocks displayed sensitive measures to the treatments, with biological age of the inflammatory and immune systems decreasing 7. 1 and 9.7 years, respectively, after TPE + IVIG treatment. However, TPE biweekly and monthly treatments showed a smaller effect, ranging from 2.5 to 5.3 years. This result was consistent with the observation that TPE + IVIG but not TPE alone induces dramatic changes in immune cell composition and that many of the changes reversed are characteristic of immunosenescence. For example, CD4 and CD8 naive T cells decrease significantly with age, contributing to a reduced ability of the immune system and increased susceptibility to infections, increasing upon TPE + IVIG treatment. In particular, the strongest cell type change accompanying biological age rejuvenation was an increase inpercentage of CD4 and CD8 stem cell memory cells (SMC), a rare subset of T cells with selfrenewing and pluripotent properties (stem cell-like) that also retain immunologic memory. Interestingly, these cells display a strong age-related decrease44 and are negatively correlated with disease severity in infections, suggesting that the increase upon TPE + IVIG therapy might induce immunological rejuvenation. Given that these cell type changes are not observed in the TPE treatment, it is plausible that these effects are solely due to IVIG supplementation. However, it is possible that the supportive effects of IVIG in immunosenescence synergize or enhance the rejuvenation effects of TPE. Thus, further experiments measuring BA and cell type composition changes associated with IVIG therapy are needed to disentangle these effects. At the proteomics level, we found that the increase in CD4 and CD8 sub-cell types associated with BA rejuvenation was strongly correlated with an increase in the abundance of proteins involved in host defense through complement activation (C7), immune signaling (IL13RA1), airway defense (BPIFA1), and antibody generation (IGKV1D-13). The present disclosure shows that TPE + IVIG therapy reverses not only features of immunosenescence but also promotes a more resilient immune response by enhancing host defense mechanisms.

[0398] Examples of changes in biological age clocks in the present data include large reductions in PCGrimAge as shown in FIG. 23, PCHorvath2 as shown in FIG. 24, and PCPhenoAge as shown in FIG. 25. Systems clock analyses show additional changes, including Hormone as shown in FIG. 26, and Lung as shown in FIG. 27.

[0399] When the average changes of the clock measurements are analyzed, age acceleration differences are shown in significant amounts for TPE+IVIG (biweekly) for PC clocks, and for TPE (monthly) for PC clocks, fitness age, and systems age clocks (FIG. 33A). Age acceleration was, on average, low in the TPE+IVIG group (FIG. 33B), and high in the sham treatment group, showing successful reduction of age acceleration using the described methods. Baseline clinical testing stratifies patients for determining type of treatment. Here, whereas all individuals in this study were generally healthy and had laboratory values within the normal range, individuals with levels indicative of poorer health status, such as high blood glucose, elevated bilirubin and globulin, and lower circulating albumin and potassium, benefitted from TPE+IVIG treatment. This finding suggests that pre-treatment clinical assessment may help in identifying personalized treatments more likely to induce biological age rejuvenation and that TPE + IVIG therapy might be particularly beneficial in individuals with health challenges.EXAMPLE 3 EVALUATION OF PROTEIN UPREGULATION AND DOWNREGULATION RESULTING FROM TPE

[0400] All water, methanol, acetonitrile, formic acid, and trifluoracetic acid used were LC / MS grade. Perchloric acid (70%) was purchased from Sigma Aldrich (311421-50ML). Sequencing grade modified trypsin was obtained from Promega (V51 IB). Indexed retention time standards (iRT peptides) were purchased from Biognosys. HLB columns (lee 10 mg) were purchased from Waters (186000383).

[0401] Plasma samples were thawed on ice, and two 125 pL aliquots per sample were transferred to 1.5 mL tubes. Samples were diluted 10-fold with water and mixed with 70% perchloric acid to a final concentration of 3.5%. Samples were incubated at -20 °C for 15 min and subsequently centrifuged at 3,200 xg for 60 min at 4 °C. The supernatant was transferred to new tubes and acidified with 1% trifluoracetic acid. Perchloric acid was removed using HLB columns (10 mg, Waters). Columns were conditioned with 800 pL methanol and washed with 1,600 pL of 0.1% trifluoracetic acid (TFA). The acidified supernatant was loaded onto the column, washed with 2,400 pL of 0.1% TFA, and proteins were eluted with 800 pL of 0.1% TFA in 90% acetonitrile. Eluates were dried using a speed-vac and resuspended in 50 pL of 0.5% sodium dodecyl sulfate in 100 mM triethylammonium bicarbonate (TEAB).

[0402] Proteins were denatured with 10% SDS at 90°C for 10 min, cooled to room temperature, and pH adjusted to ~7 using IM TEAB. Proteins were reduced with 250 mM dithiothreitol at 56 °C for 10 min, alkylated with 250 mM iodoacetamide in the dark for 30 min, and acidified with 12% phosphoric acid. Proteins were trapped and digested on S-Trap mini spin columns according to the manufacturer’s instructions. Briefly, reduced and alkylated proteins were diluted with S-Trap buffer, loaded onto the column, and digested with 4 pg trypsin in 50 mM TEAB in two stages - 1 h at 47 °C followed by additional 4 pg trypsin and overnight incubation at 37 °C. Peptides were eluted sequentially with 50 mM TEAB, 0.5% formic acid, and 0.5% formic acid in 50% acetonitrile. Eluates were pooled, dried using a speed-vac, and resuspended in 1% formic acid. Peptides were desalted using HLB columns (10 mg, Waters). Columns were conditioned with 0.2% formic acid in 50% acetonitrile and washed with 0.2% formic acid. Samples were loaded, washed with 0.2% formic acid, and eluted with 0.2% formic acid in 50% acetonitrile. Eluates were dried using a speed-vac and resuspended in 100 pL 0.2% formic acid. Samples were stored at -20 °C until analysis. Priorto LC-MS / MS analysis, peptides were diluted 1: 1 with 0.2% formic acid and spiked with 0.5 pL of iRT peptides (Biognosys).

[0403] Reverse-phase HPLC-MS / MS data was acquired using a Waters M-Class HPLC (Waters, Massachusetts, MA) connected to a ZenoTOF 7600 (SCIEX, Redwood City, CA) with an OptiFlow Turbo V Ion Source (SCIEX) equipped with a microelectrode54. The chromatographic solvent system consisted of 0.1% formic acid in water (solvent A) and 99.9% acetonitrile, 0.1% formic acid in water (solvent B). Digested peptides (4 pL) were loaded onto a Luna Micro C18 trap column (20 x 0.30 mm, 5 pm particle size; Phenomenex, Torrance, CA) over a period of 2 minutes at a flow rate of 10 pL / min using 100% solvent A. Peptides were eluted onto a Kinetex XB-C18 analytical column (150 x 0.30 mm, 2.6 pm particle size; Phenomenex) at a flow rate of 5 pL / min using a 120 minute microflow gradient (as described below), with each gradient ranging from 5 to 32% solvent B. Briefly, 4 pL of digested peptides were loaded at 5% B and separated using a 120 minute linear gradient from 5 to 32% B, followed by an increase to 80% B for 1 minute, a hold at 80% B for 2 minutes, a decrease to 5% B for 1 minute, and a hold at 5% B for 6 minutes. The total HPLC acquisition time was 130 minutes. The following MS parameters were used for all acquisitions: ion source gas 1 at 10 psi, ion source gas 2 at 25 psi, curtain gas at 30 psi, CAD gas at 7 psi, source temperature at 200°C, column temperature at 30°C, polarity set to positive, and spray voltage at 5000V.

[0404] All human samples were acquired in data-independent acquisition mode (DIA) analysis with two technical replicates for each biological sample replicate. Briefly, the DIAMS method on the ZenoTOF 7600 system is comprised of a survey MSI scan (mass range: 395-1005 m / z), with an accumulation time of 100 ms, a declustering potential of 80 V, and a collision energy of 10 V. MS2 scans were acquired using 80 variable width windows across the precursor ion mass range (399.5-1000.5 m / z) , with an MS2 accumulation time of 25 ms, dynamic collision energy enabled, charge state 2 selected, and Zeno pulsing enabled (total cycle time 2.5 seconds).

[0405] All data files were processed with Spectronaut vl6 (version 16.0.220524.5300; Biognosys) performing a directDIA search using the UniProt Homo sapiens reference proteome with 20,423 entries, accessed on 06 / 30 / 2023. Dynamic data extraction parameters and precision iRT calibration with local non-linear regression were used. Trypsin / P was specified as the digestion enzyme, allowing for specific cleavages and up to two missed cleavages. Methionine oxidation and protein N-terminus acetylation were set as dynamic modifications, while carbamidomethylation of cysteine was set as a static modification.Protein group identification (grouping for protein isomers) used at least 2 unique peptides and was performed using a 1% q-value cutoff for both the precursor ion and protein level. Protein quantification was based on the peak areas of extracted ion chromatograms (XICs) of 3 - 6 MS2 fragment ions, specifically b- and y-ions, with automatic normalization and 1% q-value data filtering applied. Relative protein abundance changes were compared using the Storey method with paired t-tests and p-values corrected for multiple testing using group wise testing corrections.

[0406] In the proteomic analysis, 564 protein groups were quantified, and significantly altered protein groups were identified when lob2(FC) > 0.58 with a q-value of < 0.01. The upregulated proteins include folate receptor gamma (FOLR3) with an average log2 ratio of 2.92 and mannose-binding protein C (MBL2) with an average log2 ratio of 2.18, among others (see TABLE XI). Conversely, the downregulated proteins include T-complex protein 1 subunit beta (CCT2) with an average log2 ratio of -4.60 and tubulin alpha-lB chain (TUBA1B) with an average log2 ratio of -2.22 (TABLE XII).

[0407] TABLE XI lists 10 significantly upregulated proteins comparing Groups B4 andA4, including their gene IDs, protein descriptions, average log2 ratios, and Q-values.

[0408] TABLE XII lists the top 10 significantly downregulated proteins comparing Groups B4 and A4, including their gene IDs, protein descriptions, average log2 ratios, and Q-values.

[0409] In treatment Group C, proteins were upregulated and downregulated when the 4thvisit is compared to the 1stvisit (FIG. 4A), when the 6thvisit is compared to the 1stvisit (FIG. 4B), and when the 6thvisit is compared to the 4thvisit (FIG. 4C). Sampling time DI is the 1stvisit baseline and the 1stvisit post-TPE measurement, while sampling time D2 is at 8 weeks (FIG. 4D) For treatment group A, proteins were often downregulated when the 1stvisit post TPE was compared to the 1stvisit baseline, when the 4thvisit was compared to the 1stvisit baseline, and when the 6thvisit was compared to the 1stvisit baseline (FIG. 5A). However, many proteins were upregulated when comparing the follow-up visit to the 1stvisit baseline (FIG. 5A). The 1stvisit baseline for Groups A and B is Al and Bl, at 0 weeks. The 4thvisit measurements are labeled A2 and B2, the 6thvisit measurements are referred to as A3 and B3, and the follow-up measurement is at 24 months (FIG. 5B).

[0410] In the Group A treatment, when the 6thvisit is compared to the 1stvisit baseline, pathways of significantly regulated proteins for Group A included bundle of his cell to Purkinje myocyte communication, organelle fusion, regulation of heart rate by cardiac conduction, FC-gamma receptor signaling pathway involved in phagocytosis, establishment of protein localization to organelle, protein stabilization, membrane invagination, B cell activation, regulation of protein localization, immune response-activating cell surface receptor signaling pathway, import into cell, immune response-regulating cell surface receptor signaling pathway, and vesicle-mediated transport (FIG. 6A). Many pathways were downregulated (FIG. 6B).

[0411] In the Group B treatment, comparisons of the 1stvisit post-TPE to the 1stvisit baseline show over 100 proteins downregulated, while the 4thvisit to the 1stvisit had 80 downregulated proteins. Comparison of the 1stvisit to the 6thvisit showed fewer downregulated proteins (55), and interestingly, at the follow-up visit, 88 proteins were upregulated compared to baseline (FIG. 7). Several pathways were significantly regulated when comparing the 6thvisit for Group B to the baseline visit, including foam celldifferentiation, regulation of tube size, low-density lipoprotein (LDL) particle remodeling, chylomicron remnant clearance, lipoprotein metabolic process, vascular process in circulatory system, protein-containing complex remodeling, regulation of hormone secretion, hormone secretion, complement activation, alternative pathway, protein-lipid complex subunit organization, positive regulation of lipid localization, regulation of lipid localization, hormone transport, isoprenoid metabolic process, negative regulation of hemostasis, extrinsic apoptotic signaling pathway, innate immune response-activating signal transduction, regulation of blood coagulation, regulation of vesicle-mediated transport, and regulation of coagulation (FIG. 8A). Overall, proteins tended to be downregulated at this comparison time for this treatment group (FIG. 8B).

[0412] Group D measurements showed downregulation of proteins when baseline measurements were compared to 1stvisit post-TPE (FIG. 9A), 4thvisit (FIG. 9B), and 6thvisit (FIG. 9C).

[0413] When Groups B4 and A4 are compared, the proteins regulated are similar but have significant changes, as seen in FIG. 10, with 23 proteins upregulated and 16 proteins downregulated. These changes include upregulated proteins involved in pathways such as gas transport, drug transport, neutrophil degranulation, granulocyte activation, myeloid cell activation involved in immune response, regulated exocytosis, and vesicle-mediated transport (FIG. 11A). Significantly downregulated protein pathways included regulation of cell projection assembly, negative regulation of lipid localization, high-density lipoprotein particle assembly, chylomicron remnant clearance, lipid modification, high-density lipoprotein particle remodeling, lipoprotein metabolic process, protein stabilization, neutral lipid metabolic process, organophosphate catabolic process, protein-containing complex remodeling, protein-lipid complex subunit organization, regulation of lipid localization, lipid catabolic process, negative regulation of catalytic activity, organic substance transport, and protein-containing complex assembly (FIG. 11B).

[0414] Once a month treatment (Group D) compared to twice a week treatment (Group B) yielded many changes, even when the 6thvisit was compared to the follow-up visit (FIG. 12). While 2 proteins were upregulated, 125 proteins were downregulated.

[0415] Comparison of treatment Groups A and B to sham treatment also shows differences, with many proteins upregulated when B3 is compared to C3 (FIG. 13A), or when A3 is compared to C3 (FIG. 13B). Pathways involved included axon guidance, high-density lipoprotein particle remodeling, cell-cell adhesion via plasma-membrane adhesion molecules, leukocyte cell-cell adhesion, neurogenesis, neuron development, ameboidal-type cellmigration, nervous system development, neuron differentiation, plasma membrane bounded cell projection organization, and inflammatory response (FIG. 13C).EXAMPLE 4EVALUATION OF CELL POPULATION UPREGULATION AND DOWNREGULATION RESULTING FROM TPECytomics

[0416] Blood was collected into lavender-top vacutainer tubes (K-EDTA, BD) and kept on ice until processing the same day. Peripheral blood mononuclear cells (PBMC) were prepared from the whole blood by density gradient centrifugation using Ficoll-Paque PLUS (Cytivia) according to the package directions, followed by lysis of residual erythrocytes with ACK buffer (Gibco). After washing twice in dPBS, PBMC were cryopreserved in 90% fetal bovine serum (FBS) and 10% DMSO and stored in liquid nitrogen until analysis.

[0417] Vials of frozen PBMC (~2 million) were thawed rapidly by swirling in a 37°C water bath and immediately transferred to 15mL conical tubes with 10 mL of pre-warmed RPMI medium with 10% FBS, then pelleted for 5 minutes at 300g in a benchtop TC centrifuge. After removal of the supernatant, the cells were resuspended in 4 mL of warmed RPMI / 10% FBS and allowed to recover in a TC incubator for three hours prior to staining. After recovery, the cells were divided equally for analysis with the SPiDERGal and intracellular reagent panels. For the SPiDERGal workflow, the cells were left in medium and treated with 1 pM Bafilomycin Al (Abeam) for 1 hour, followed by 667 nM of SPiDERGal (ThermoFisher) for one additional hour, while the cells for the intracellular panel were pelleted and kept on ice for staining. For both panels, the cells were resuspended in 200 pL of dPBS in V-bottom plates and incubated for 20 minutes on ice with IX Live / Dead Blue (ThermoFisher) plus 5 pg / mL of human IgG (Sigma). Surface marker-specific antibodies plus FBS to 2% v / v were then added, and the cells were incubated on ice in the dark for 1 hour, then washed once in PBS / 2% FBS. For the SPiDER panel, the cells were then taken up in 150pL of PBS / 2% FBS and acquired. For the intracellular panel, the cells were taken up in 150pL of FOXP3 Fix / perm buffer (ThermoFisher) and incubated on ice for 20 minutes, pelleted, washed once with FOXP3 Perm-wash buffer (ThermoFisher), and then stained with the IC marker-specific antibodies in Perm-wash buffer. They were then pelleted, washed once in Perm-wash buffer, and resuspended in PBS / 2% FBS for acquisition.

[0418] The stained PBMC were acquired on a 5 -laser Cytek Aurora spectral flow cytometer (Cytek), and the raw data spectrally unmixed using the SpectroFlo software package (Cytek).Subsequent correction of spectral compensation and manual gating to identify populations were performed using FlowJo (software (BD), and population frequencies (as % of Parent pop and percent of live leukocytes) and numbers were tabulated for subsequent bioinformatic analysis.

[0419] All TPE groups for the surface and intracellular panels show more significant changes in cell population levels than the sham TPE group (see FIG. 17). The CD4 / CD8 ratio is reduced in short-course treatments, but not in long course treatments or sham treatments, as seen in FIGs. 23A-23C.

[0420] The effects of treatment on cell populations are also shown in FIG. 14 and FIG. 15, where FIG. 14 shows the log fold change compared to the parent for standards, and FIG. 15 adds treatment Group B % parent measurements. Clearly, treatment has a large effect on cell populations. These differences are seen among groups as summarized in FIG. 16, where large differences are seen in Group A compared to Group D, but even larger differences are seen in FIG. 17

[0421] TABLE XIII lists cell populations upregulated and downregulated in different treatment groups for the surface panel, calculated as the percent of the parent population.

[0422] TABLE XIV lists cell populations upregulated and downregulated in both A and B treatment groups, from the surface panel, identified as the percent of the parent population.

[0423] TABLE XV lists cell populations that are upregulated and downregulated in treatment group A, and non-overlapping between treatment groups (surface panel, % of parent).

[0424] TABLE XVI lists cell populations upregulated only in treatment Group B, and not overlapping with other treatment groups (surface panel, % of parent).

[0425] TABLE XVII lists cell populations upregulated and downregulated only in Group C (surface panel, % of parent).

[0426] TABLE XVIII lists cell populations upregulated and downregulated only in GroupD (surface panel, % of parent).

[0427] When HLA-DR populations were analyzed in the surface panel (% of live), 23 cell populations were upregulated and 110 populations were downregulated in group A only. For group B treatment, 55 cell populations were upregulated only in group B, and 6 populations were downregulated. When looking at cell populations that were upregulated or downregulated in both A and B treatment groups, 11 cell populations were upregulated in both groups, and 22 were downregulated. In the control / sham group, 19 cell populations were upregulated that were not upregulated in groups A or B, and none were downregulated. Interestingly, one cell population was upregulated both in the sham and in Group A treatment groups.

[0428] For the treatment group C, two cell populations were upregulated only in group C, and 40 were downregulated after treatment only in group C. In group D, 82 cell populations were upregulated only in Group D, and 8 were downregulated, only in group D. Individuals undergoing sham treatment had 18 cell populations upregulated that were not shared with group C or D, but 2 cell populations were upregulated both in the sham treatment and in group D. No populations were upregulated or downregulated in both group C or D, in both group C and the sham treatment, or in group C, D, and the sham treatment.

[0429] TABLE XIX lists cell populations upregulated and downregulated in both groups A and B (surface panel, % of live).I l l

[0430] TABLE XX lists cell populations upregulated in group A or upregulated in group B but not upregulated in both treatment groups (surface panel, % of live).

[0431] TABLE XXI lists cell populations downregulated only in Group A (surface panel,% of live).

[0432] TABLE XXII lists cell populations downregulated in Group B only (surface panel, % of live).

[0433] TABLE XXIII lists cell populations upregulated only in Group D (surface panel, % of live).

[0434] TABLE XXIV lists cell populations downregulated only in Group C (surface panel,% of live).

[0435] One cell population from the intracellular panel (% of parent) was downregulated in groups A, B and D: ab T cells / CD4 / RA neg / EM / CD38 pos. No cell populations were upregulated in groups A, B and D (intracellular panel, % of parent). No analyzed cell populations were upregulated or downregulated in both A / B and D in the intracellular panel % of live cells analysis.

[0436] For the intracellular panel (% of parent) analysis for groups A / B vs group D treatments, 76 cell types were upregulated in group A or B but not group D, while 3 cell populations were downregulated in group D but not upregulated in group A or B. 3 cellpopulations were upregulated in group A or B and downregulated in group D. 122 cell populations were downregulated in group A or B but not upregulated in group D, and 8 cell populations were both downregulated in group A or B and upregulated in Group D. 8 populations were upregulated in group D and not downregulated in group A or B.

[0437] In the surface panel (% of live) analysis, 89 cell populations were upregulated in group A or B but not group D, and no populations were downregulated in group D while not upregulated in group A or B. 8 cell populations were upregulated in group A or B and downregulated in group D. There were 90 populations that were downregulated in group A or B but not upregulated in Group D. In contrast, there were 42 cell populations upregulated in group D but not downregulated in group A or B. 40 cell populations were downregulated in group A or B and also upregulated in group D.

[0438] Multiple shared proteins in Group A increase with treatment, as shown in the list of the top 15 significantly-altered proteins shared between Al post / Al vs. A3 / A1.

[0439] TABLE XXV is a list of proteins increased after treatment in Group A.

[0440] Multiple shared proteins in Group B decrease with treatment, as shown in the following table which describes altered proteins shared between Bl Post / Bl vs. B3 / B1.TABLE XXVI is a list of proteins decreased after treatment in Group B.

[0441] The study on therapeutic plasma exchange (TPE) and intravenous immunoglobulin (IVIG) treatments revealed significant changes in cell populations across different treatment groups. The results indicated that TPE, both with and without IVIG, led to notable alterations in various immune cell populations.EXAMPLE 5 USE OF OMICS PARAMETERS FOR EVALUATION OF TPE RESPONSE SCORING

[0442] This example shows that a combination of various omics scores can direct treatment decisions regarding plasmapheresis timings and methods. The described comprehensive analysis of omics responses includes glycomics, metabolomics, proteomics, lipidomics, and cytomics, in relation to the described TPE treatments. The strength and direction of the correlation can be used to guide patient treatment decisions. A subject who is predicted to have a low response to TPE can be treated with a low frequency treatment regime as a maintenance treatment, while a subject who is predicted to have a high response to TPE can be treated with a high frequency treatment regime. A subject in greater need of immune modulation may be treated with IVIG in addition to higher frequency treatment. Other variations in treatment modalities are also possible depending on the results of the omics data.

[0443] A combination of the omics parameters analyzed correlates with the age acceleration and other health data from the treatment modalities, as shown in FIG. 34 and FIG. 35. Subjects with large decreases in age acceleration, such as some of the subjects in the TPE- IVIG biweekly treatment group in FIG. 33B, had changes in omic scores as well, shown in FIG. 35. Glycomics, metabolomics, methylomics, cytomics, proteomics, and lipidomics, all corresponded with the response to TPE, either positively or negatively, either one of which is useful (FIG. 35). High differences were seen in the 2x / 2k TPE+IVIG group in the structural and response protein analysis group and in the heat shock proteins and glycolytic enzymes protein analysis group, shown in FIG. 36, where the structural and response protein group is upregulated, and the heat shock group of proteins is downregulated after treatment. Surprisingly, the baseline “omics” score is predictive of the TPE response score, as seen in FIG. 37 and FIG. 38. Highly correlated measurements for each group of measurements include: cytomics immune age (age acceleration); cholesteryl octadecatrienoate, phosphatidylcholine 44: 1, diacylglycerol 41: 1, and phosphatidylcholine 41:2 (lipidomics),CACNA2D1, IGLV3.9IGLV3.21, KDR, L1CAM, LYNX1, MERTK, MT2A.MT1G.MT1X, PECAM1, PRSS1, TUBA1B, and VDAC1 (proteomics), CACNG8, GAS8, MGAT4B, SERPINB9P1, SLC7A6, SMO, SUMF2, ZBTB18, ZFP92-CNTF, and ZNF941 (methylomics), several cell types analyzed in cytomics; and absolute basophil, absolute monocyte, albumin / globlin ratio, basophil, eosinophil, globulin, lymphocyte, monocyte, and potassium (clinical) (FIG. 37). FIG. 38 shows that many lipidomics measurements have a positive correlation with the response score, and metabolomics measurements are mixed with regard to positive or negative correlation with response scores. Glycomics measurements are also correlated, as are metabolomics measurements cytomics measurements. Some proteomics measurements, age acceleration measurements, and clinical measurements were also correlated, with each analysis group contributing at least two measurements that correlated with the response score. One example of a treatment decision tree is shown in FIG. 39, where an individual presents with high baseline levels of glutamine. When this is correlated with low levels of glycomic features, a composite response feature, and low methylation levels of TUBA IB, the individual is likely to be a responder to TPE. The individual is then treated with TPE. When the TPE score is similar to the TPE score of individuals who have responded to TPE(B), the individual is treated with TPE biweekly. When the TPE score is similar to the score of individuals who have responded to TPE(M), the patient is treated with TPE monthly. When the TPE score is similar to the score of individuals who have responded to TPE+IVIG, the individual is treated with TPE+IVIG.

[0444] In summary, the integration of multi-omics analysis, including glycomics, metabolomics, proteomics, lipidomics, and cytomics, provides a robust framework for predicting patient response to therapeutic plasma exchange (TPE) treatment. By leveraging the detailed correlations between these omics features and clinical markers, we can accurately assess biological age, predict treatment outcomes, and tailor individualized treatment schedules. This comprehensive approach not only enhances the precision of TPE interventions but also improves patient care by ensuring that treatments are both effective and personalized. The findings underscore the potential of multi-omics analysis as a transformative tool in the field of age-related therapies and chronic disease management, paving the way for more targeted and efficacious medical interventions.

[0445] TABLE VII, TABLE VIII, and TABLE IX as presented below are discussed in further detail in the preceding examples.

[0446] TABLE VII provides an evaluation of differences observed between the age- adjusted biological age at time points 2 and 3 compared to time point 1 (age accelerationdifference). Epigenetic data were collected from the treated individuals: TPE (Therapeutic Plasma Exchange), IVIG: intravenous immunoglobulin, PC: phosphatidylcholine and sham (control group that received sham plasma exchange, which mimicked TPE but without fluid replacement). DNA methylation age was calculated using 35 different epigenetic clocks. Biological ages were adjusted for each clock by the chronological age, resulting in a metric of age acceleration. This metric represented the deviation of biological age from individuals of the same age. To estimate the effects on biological age induced by the interventions, the difference between the age acceleration at time-points 2 or 3 were compared to baseline (time point 1). Data shows that in all groups, the difference between the age-adjusted biological age at time points 2 and 3 was compared to time point 1 (age acceleration difference). This ageacceleration difference was negative if the interventions reduced biological age and positive if the interventions increased biological age, independently of age. At time point 2, all interventions induced a negative age acceleration difference compared to the sham treated group. TPE + IVIG treatment displayed large reduction in biological age, with an average decrease of 2.61 years (p = l. le-05). In the case ofTPE, the monthly regime showed a greater decrease in biological age than the biweekly regime, with an average biological age rejuvenation of 1.32 years (p = 5.7e-03), suggesting that more frequent sessions do not always lead to a greater biological age rejuvenation effect. Surprisingly, no significant biological age differences were observed at time point 3 compared to sham in any group, suggesting potential compensatory mechanisms that mitigate the anti-aging effects after multiple sessions. A variety of epigenetic clocks are available for characterizing the separate aspects of aging. The age acceleration difference attributed to each biological age clock was estimated. Clocks were grouped into 6 different types, depending on the method and outcome regressed. At time point 2, Systems age clocks showed large decreases in age-adjusted biological age in the TPE + IVIG group (4.85 years, FDR = 2.52e-04) and the monthly TPE intervention (2.55 years, FDR = 9.90e-03), but not in the TPE biweekly group (FDR = 0.91) (FIG. ID). More traditional epigenetic clocks also showed significant age acceleration differences in the TPE + IVIG (FDR = 5.12e-03) and TPE (FDR = 1 ,75e-03) interventions under a biweekly regime. Consistent with the average calculations across epigenetic clocks, many of the clock groups showed a reduced biological age rejuvenation at time point 3. Given the attenuated effect at time-point 3, the attenuation may be dependent on the effects observed at the previous session (time point 2), which would indicate a potential compensatory mechanism. For all interventions, a strong negative correlation was observed between the age acceleration differences at time points 2 from baseline and those betweentime points 3 and 2. The sham group showed no such correlation. Overall, the results indicate that individuals with rejuvenation effects during the first three sessions tend to show a greater biological age increase in subsequent sessions.

[0447] TABLE VIII shows the results of multi -omics profiling, including cytomics, glycomics, lipidomics, metabolomics, and proteomics, which provides a better understanding of the molecular basis of the biological age rejuvenation observed. To identify omics markers associated with the rejuvenation effects observed in time-point 2, the correlations between the age acceleration differences induced by each intervention and the change in levels of each feature for each omics was calculated. Features whose correlation was significantly different from zero and the sham intervention with an FDR<0.01. TPE + IVIG induced high changes in omics profiles covering 86 of the 143 features affected by all interventions, with nearly 72% of the cell types measured in the cytomics changed in proportion in coordination with the biological age differences after TPE + IVIG treatment. However, minor or no changes in cell types in TPE interventions were observed, indicating that IVIG induced large changes in cell type composition, some of which could potentially contribute to the rejuvenation effects of biological aging. Across interventions, glycomics showed large proportion of changes, with nearly a third (33.3%) of the glycans measured changing. In proteomics, 43 proteins, representing 8.3% of the proteome measured in this study, displayed significant correlations with the biological age changes in TPE + IVIG, compared to around 3% for the TPE group. To provide a better understanding of the function of these proteins, gene ontology enrichment analysis was performed. Proteins with levels correlated with rejuvenation effects in TPE + IVIG were involved in activation of the immune response, T cell proliferation, and cell-cell adhesion. The association of these proteins with the hallmarks of aging was also analyzed. To do so, these proteins were compared against precalculated sets of genes linked with the hallmarks of aging. A significant enrichment of proteins involved in altered intercellular communication (FDR=2.96e-04), chronic inflammation (FDR=1.0e-03) and cellular senescence (FDR=0.01) was observed. Proteins significantly correlated with the biological age rejuvenation effects were also correlated with cellular senescence. Inter-omics correlations were observed as well. Several strong omics changes were highly correlated (|r| > 0.9) with cell type changes due to TPE+IVIG intervention. For instance, 30% (8 / 26) of the cell type changes linked with rejuvenation in TPE+IVIG showed a significant correlation with the level of the soluble receptor for interleukin 13 (IL13RA1), a Th2 cytokine with a large effect in fibrosis. Similarly, changes in the levels of glycine (Gly) were correlated with the changes in 6 cell types. Surprisingly, glycine has been shown to act on a variety of inflammatory cells like macrophages to reduce the formation of free radicals and inflammatory cytokines throughout the modulation of the expression of nuclear factor kappa264B (NF-KB). Features with negative correlation included alpha-beta t cells CD4 CM CD27 (R=-0 / 96), the glycan FA2BG2 SI (R=-0.57), the phosphatidylcholine PC(38:3) (R=-0.92), serine (R=-0.77), and the protein BPI fold-containing family A member 1 (BPIFA1) (R=- 0.85). Features with positive correlation include mast cell intermediate (MC INT) (R=0.95), the glycan FA2[3]G1S1 (R=0.63), the sphingomyelin SM(42: 1) (R=0.43), the acetylcamitine AC(8: 1) (F=0.89), and hemoglobin subunit gamma 1 (HBG1) (F=0.85) (FIG. 2E). Useful - omics measurements also include abT CD4 SCM and MC NC (cytomics), A2(6)G1 and FA2 (glycomics), glycine and creatinine (metabolomics), and aPC(36:2) and SM(42: 1) for lipidomics (FIG. 2F). Also included are IL13RA1 and ITIH3 (proteomics). Overall, these results show that the modulation of cell type composition and proteomic changes associated with immunosenescence largely drive biological age effects induced by TPE + IVIG intervention.

[0448] Cytomics analyses were used to measure shifts in immune cell composition. Cytomics feature analyses include populations of cells including at least one of B ACT, B CLSW, B Cells, B FOLL, B MEM, B MZ, B NV, B PB, B PC, MC, MC CL, MC INT, MC NC, NK 56hi 161o, NK 561o 16Hi, NK 561o 161o, NK Cells, abT CD4, abT CD4 CM, abT CD4 CM CD27+, abT CD4 CM CD27-, abT CD4 EM, abT CD4 EMRA, abT CD4 NV, abT CD4 SCM, abT CD8, abT CD8 CM, abT CD8 CM CD27+, abT CD8 CM CD27-, abT CD8 EM, abT CD8 EMRA, abT CD8 NV, abT CD8 SCM, abT DN, abT DP, and gdT cells.

[0449] Glycomics analyses were used to measure comprehensive profiling of glycan structures to identify changes associated with the treatments. Gly comic feature analysis includes: A2B, A2BG2, A2BG2S1, A2BG2S2, A2B[3]G1, A2B[6]G1, A2G2S2, A2G2[3]S1, A2G2[6]S1, A2[3]G1, A2[3]G1S1, A2[6]G1, A2[6]G1S1, FA2, FA2B, FA2BG2, FA2BG2S1, FA2BG2S2, FA2B[3]G1, FA2B[6]G1, A2G2, FA2G2, FA2G2S1, M5, A2, FA2G2S2, FA2[3]G1, FA2[3]G1S1, FA2[6]G1, and FA2[6]G1S1. These glycan structures may show changes in abundance or relative proportions in response to plasmapheresis treatment.

[0450] Lipidomics analyses were used to measure comprehensive profiling of lipid species to identify changes associated with the treatments and guide treatment decisions. Lipidomics features analyzed include: CE(18:2), CE(22:5), CE(22:6), Cer(42: l), DG-O(34: 1), Hl, LPC(15:0), LPC(16:0), LPC(16: 1), LPC(17:0), LPC(18:0), LPC(18: 1), LPC(18:2), LPC(20:3), LPC(20:4), LPC(22:6), LPC-O(18: 1), PC(30:0), PC(32:0), PC(32: 1), PC(32:3), PC(33: 1), PC(33:2), PC(34: 1), PC(34:2), PC(34:3), PC(35: 1), PC(35:2), PC(36:2), PC(36:3), PC(36:4), PC(36:6), PC(37:4), PC(37:5), PC(38:4), PC(38:5), PC(38:6), PC(38:7), PC(40:6),265PC(40:7), PC-O(34:1), PC-O(34:2), PC-O(34:3), PC-O(36:3), PC-O(36:4), PC-O(36:5), PC- 0(36:6), PC-O(38:4), PC-O(38:5), PC-O(38:6), PC-O(40:6), PC-O(40:7), PC-O(40:8), PC- 0(42:6), SM(32: 1), SM(32:2), SM(33: 1), SM(34: 1), SM(34:2), SM(35:1), SM(36:2), SM(38:1), SM(38:2), SM(39:1), SM(40:2), SM(41: 1), SM(41:2), SM(42: 1), SM(42:2), SM(42:3), SM(43:2), TG(50:3), TG(52:2), TG(56:8), CE(18:2), CE(22:5), CE(22:6), Cer(42:l), DG-O(34: 1), Hl, LPC(15:0), LPC(16:0), LPC(16: 1), LPC(17:0), LPC(18:0), LPC(18:1), LPC(18:2), LPC(20:4), LPC(22:6), LPC-O(18: l),TG(50:3)

[0451] Metabolomics analyses were used to measure comprehensive study of metabolites — small molecules like sugars, amino acids, lipids, and other intermediates and products of metabolism. Metabolomics features include: AC(0:0), AC(10:0), AC(10: l), AC(10:2), AC(10:3), AC(11:O), AC(12:0), AC(12:1), AC(13:0), AC(14:0), AC(14: 1), AC(14: 1-DC), AC(16:0), AC(18:0), AC(18:1), AC(18: l-0H), AC(18:2), AC(2:0), AC(3:0), AC(4:0), AC(4:0-OH), AC(5:0), AC(5:0-DC), AC(5:0-OH), AC(5: 1), AC(6:0), AC(6: 1), AC(7:0), AC(8:0), AC(8: 1), AC(8: 1-OH), ADMA, Ala, Arg, Asn, Asp, Cit, Creatinine, Gin, Glu, Gly, His, He, Kynurenine, Lys, Met, Met-SO, Nitro-Tyr, Om, Phe, Pro, Putrescine, SDMA, Sarcosine, Ser, Taurine, Thr, Trp, Tyr, Vai, alpha-AAA, t4-OH-Pro, xLeu

[0452] Proteomics evaluations were used to measure potential biomarkers of treatment response and provide mechanistic insights into the effects of plasma exchange on aging- related processes. Proteomic features include: A1BG, A2M, A2ML1, ABI3BP, ACAA2, ACTA1, ACTB, ACTN2, ACTN4, ADAMDEC1, ADGRF5, ADGRG6, ADGRL4, AFM, AGT, AHSG, AK1, AKAP13, ALB, ALCAM, ALDOA, ALDOB, AMBP, AMY1B, ANG, ANK2, ANXA1, ANXA2, APCS, AP0A1, AP0A2, AP0A4, APOB, APOCI, APOC2, APOC3, APOC4, APOD, APOE, APOH, APOL1, APOM, ARG1, ART4, ASAHI, ATF6, ATF6B, ATP2A1, ATP5F1A, ATP5F1B, ATRN, AXL, AZGP1, BASP1, BCHE, BLMH, BOC, BPIFA1, BPIFB1, BPIFB4, BST1, C1QB, C1QC, C1R, C1RL, CIS, C2, C3, C4A, C4B, C4BPA, C4BPB, C5, C6, C7, C8A, C8B, C8G, C9, CAI, CACNA2D1, CADM1, CALM1, CALML3, CALML5, CAMP, CAPN1, CARD9, CASP14, CAST, CAT, CBLN1, CBLN4, CCL14, CCL15, CCL16, CCL18, CCL24, CCT2, CCT3, CCT7, CD109, CD200R1, CD300A, CD34, CD44, CD46, CD55, CD58, CD59, CD5L, CDH13, CDH5, CDHR2, CDSN, CEACAM1, CEACAM6, CFB, CFD, CFH, CFHR1, CFHR2, CFHR3, CFI, CFL1, CFP, CHD8, CHGA, CHGB, CHL1, CLEC3B, CLPS, CLU, COL14A1, COL1A1, COL3A1, COL6A3, COLECIO, CP, CPA4, CPN2, CRNN, CSF1R, CST4, CST6, CSTA, CSTB, CTRB2, CTSD, CTSH, CXCL3, DCD, DEFA1, DEFBI, DLK1, DMBT1, DMKN, DNER, DPEP2, DSC1, DSC3, DSG1, DSP, ECM1 EEF1A1, EEF1D, EEF2, EFEMP1, EGFR,266EIF4A1, ENG, EN01, ENSA, EPPK1, ESAM, F12, F13B, F2, F5 FABP1, FABP5, FAS, FAT2, FCGR2A, FCGR3A, FCGR3B, FETUB, FGA, FGB, FGFR1, FGG, FLG, FLG2, FLNC, FLT4, FN1, FOLR3, FSHB, FSTL1, GAPDH, GC, GGCT, GGH, GM2A, GOLM1, GOLM2, GP1BA, GPNMB, GRN, GSDMA, GSN, GSTP1, Hl-4, H2AC4, H2BC12, H3C1, H4C1, HABP2, HAL, HBA1, HBB, HBD, HBG1, HEG1, HEPACAM2, HNRNPK, HP, HPR, HPX, HRG, HRNR, HSP90AA1, HSPA1A, HSPA5, HSPA8,HSPB1, HSPD1, ICAM1,, ICAM2, ICAM3, ICOSLG, IDE, IGF1, IGF2, IGFBP3, IGFBP4, IGFBP5, IGFBP6, IGFBP7, IGHA1, IGHG2, IGHG3, IGHG4, IGHM, IGHV2-70D, IGHV3-15, IGHV3-30, IGHV3-49, IGHV3-7, IGHV3-9, IGHV4-34, IGHV5-51, IGKC, IGKV1-39, IGKV1D-13, IGKV2D-24, IGKV3-20, IGKV3D-11, IGKV3D-15, IGKV4-1, IGLC2, IGLL5, IGLV1-47, IGLV1-51, IGLV2-18, IGLV3-10, IGLV3-9, IGLV6-57, IGLV8-61, IL13RA1, IL17F, IL18BP, IL1R1, IL1RAP, IL6ST, INHBC, ITGB1, ITIH1, ITIH2, ITIH3, ITIH4, JCHAIN, JUP, KDR, KIT, KLKB1, KNG1, KPRP, LI CAM, LAMP1, LAMP2, LCN1, LCN2, LDHA, LDHB, LEAP2, LEPR, LGALS3BP, LGALS7, LILRA3, LMNA, LOX, LPA, LRG1, LSAMP, LTBP1, LTBP2, LTF, LUM, LY6D, LY6G6C, LYNX1, LYPD3, LYVE1, LYZ, MANF, MARCKS, MBL2, MCAM, MDK, MEGF9, MENT, MEPE, MERTK, MMRN1, MMRN2, MSMB, MT2A, MUC5AC, MUC5B, MXRA5, MYH1, MYH2, MYH3, MYH4, MYH7, MYH8, MYH9, MYL11, NACA, NCAM1, NECTIN1, NECTIN3, NEGRI, NME1, NOTCH1, NPC2, NPM1, NTM, NTRK2, NTRK3, OLFM1, 0RM1, 0RM2, OSCAR, OSMR, PCOLCE, PDAP1, PDCD1LG2, PDGFRB, PECAM1, PF4, PF4V1, PFN1, PGAM1, PGK1, PGLYRP2, PI 16, PI3, PIAS4, PIGR, PIP, PKM, PKP1, PLA2G1B, PLA2G2A, PLEC, PLG, PLTP, PLXDC2, POF1B, PON1, PPBP, PRAP1, PRB4, PRDX1, PRDX2, PREXI, PRG4, PRH1, PRNP, PROCR, PROS1, PRSS1, PSMA3, PSMA6, PSMB6, PTGDS, PTMA, PTPRB, PTPRC, PTPRG, PTPRJ, PTPRM, PTPRZ1, PVR, RACK1, RBP4, REGIA, REGIB, RNASE1, RNASE2, RNASE6, RNASE7, RPS3A, S100A11, S100A7, S100A8, S100A9, SAA1, SAA4, SBSN, SELENOP, SELL, SELP, SEMG1, SEMG2, SERPINA1, SERPINA10, SERPINA3, SERPINA4, SERPINA5, SERPINA6, SERPINA7, SERPINB12, SERPINB3, SERPINB4, SERPINB5, SERPINC1, SERPIND1, SERPINF1, SERPINF2, SERPING1, SFN, SH3BGRL, SH3BGRL2, SH3BGRL3, SIPA1L1, SIRPA, SLC25A6, SLC38A10, SLPI, SLURP1, SNCA, SNRPD3, SOD3, SPARCL1, SPINK5, SPON1, SPP1, SPRR2E, SPRR3, SRGN, TCN1, TF, TGM1, TGM3, TGM5, TGOLN2, THBS1, THY1, TKT, TMEM25, TMSB4X, TNFRSF1A, TNFRSF1B, TNFRSF21, TNNT3, TPI1, TPM2, TTN, TTR, TUBA1B, TUBA1C, TUBA4A,267TUBB, TWSG1, TXN, TYR03, UBA1, UBB, UMOD, VASN, VCAM1, VDAC1, VDAC2, VEGFD, VIT, VNN1, VTN, WFDC2, YWHAQ,, ZG16B, ZNF451.

[0453] TABLE VIII sets forth the results of various omics markers associated with the rejuvenation effects observed in time point 2, the correlations between the age acceleration differences induced by each intervention and the change in levels of each feature for each omics.268

[0454] TABLE IX provides the results of age acceleration evaluation after the TPE interventions to determine a priori which individuals may be ore likely to respond to treatment. Data shows omics data from individuals at baseline (before treatment) showing whether the magnitude of the rejuvenation effects correlated with the levels of any clinical or omics markers prior to treatment. The correlation between the age-adjusted age acceleration difference at time point 2 and the levels of clinical and omics markers at baseline were analyzed. In addition to the omics markers, 57 clinical markers were analyzed. A significant correlation was found between 25 clinical markers and the age acceleration differences in at least one experimental group (FDR < 0.01). Surprisingly, bilirubin levels show robust correlation with age acceleration difference in TPE + IVIG, with higher levels being associated with large rejuvenation effects. Other levels associated with rejuvenation effects include those of magnesium, HDL, globulin, glucose, ALT, platelets, RBC, eosinophils %, trig, chol / HDL, albumin, absolutely eosinophil, albumin / globulin ratio, potassium, TSH, MCH, creatinine, and alkaline phosphatase. While individuals in this study were nominally healthy, high levels of circulating bilirubin can lead to mitochondrial dysfunction and immune system disruption which is consistent with the notion that individuals in poorer health benefit from this treatment modality. Similarly, elevated levels of mean corpuscular hemoglobin (MCH) and creatinine at baseline were observed in individuals with large rejuvenation effects in TPE biweekly and monthly, respectively.

[0455] Cytomics evaluations measured a shift in immune cell composition. Cytomics feature analyses include populations of cells including at least one of B ACT, B CLSW, B Cells, B FOLL, B MEM, B MZ, B NV, B PB, B PC, MC, MC CL, MC INT, MC NC, NK 56hi 161o, NK 561o 16Hi, NK 561o 161o, NK Cells, abT CD4, abT CD4 CM, abT CD4 CM CD27+, abT CD4 CM CD27-, abT CD4 EM, abT CD4 EMRA, abT CD4 NV, abT CD4 SCM, abT CD8, abT CD8 CM, abT CD8 CM CD27+, abT CD8 CM CD27-, abT CD8 EM, abT CD8 EMRA, abT CD8 NV, abT CD8 SCM, abT DN, abT DP, and gdT cells.

[0456] Glycomics evaluations measured comprehensive profiling of glycan structures to identify changes associated with the treatments. Glycomic feature analysis includes: A2B, A2BG2, A2BG2S1, A2BG2S2, A2B[3]G1, A2B[6]G1, A2G2S2, A2G2[3]S1, A2G2[6]S1, A2[3]G1, A2[3]G1S1, A2[6]G1, A2[6]G1S1, FA2, FA2B, FA2BG2, FA2BG2S1, FA2BG2S2, FA2B[3]G1, FA2B[6]G1, A2G2, FA2G2, FA2G2S1, M5, A2, FA2G2S2, FA2[3]G1, FA2[3]G1S1, FA2[6]G1, and FA2[6]G1S1. These glycan structures may show changes in abundance or relative proportions in response to plasmapheresis treatment.336

[0457] Lipidomics evaluations measured comprehensive profiling of lipid species to identify changes associated with the treatments and guide treatment decisions. Lipidomics feature analysis include: CE(18:2), CE(22:5), CE(22:6), Cer(42: l), DG-O(34:1), Hl, LPC(15:0), LPC(16:0), LPC(16:1), LPC(17:0), LPC(18:0), LPC(18:1), LPC(18:2), LPC(20:3), LPC(20:4), LPC(22:6), LPC-O(18:1), PC(30:0), PC(32:0), PC(32:1), PC(32:3), PC(33:1), PC(33:2), PC(34: 1), PC(34:2), PC(34:3), PC(35: 1), PC(35:2), PC(36:2), PC(36:3), PC(36:4), PC(36:6), PC(37:4), PC(37:5), PC(38:4), PC(38:5), PC(38:6), PC(38:7), PC(40:6), PC(40:7), PC-O(34:1), PC-O(34:2), PC-O(34:3), PC-O(36:3), PC-O(36:4), PC-O(36:5), PC- 0(36:6), PC-O(38:4), PC-O(38:5), PC-O(38:6), PC-O(40:6), PC-O(40:7), PC-O(40:8), PC- 0(42:6), SM(32: 1), SM(32:2), SM(33: 1), SM(34: 1), SM(34:2), SM(35:1), SM(36:2), SM(38: 1), SM(38:2), SM(39:1), SM(40:2), SM(41: 1), SM(41:2), SM(42: 1), SM(42:2), SM(42:3), SM(43:2), TG(50:3), TG(52:2), TG(56:8), CE(18:2), CE(22:5), CE(22:6), Cer(42:l), DG-O(34:1), Hl, LPC(15:0), LPC(16:0), LPC(16: 1), LPC(17:0), LPC(18:0), LPC(18:1), LPC(18:2), LPC(20:4), LPC(22:6), LPC-O(18: l),TG(50:3)

[0458] Metabolomics evaluations were used to assess a comprehensive set of metabolites — small molecules like sugars, amino acids, lipids, and other intermediates and products of metabolism. Metabolomics features include: AC(0:0), AC(10:0), AC(10: l), AC(10:2), AC(10:3), AC(11:O), AC(12:0), AC(12: 1), AC(13:0), AC(14:0), AC(14: 1), AC(14: 1-DC), AC(16:0), AC(18:0), AC(18:1), AC(18: l-0H), AC(18:2), AC(2:0), AC(3:0), AC(4:0), AC(4:0-OH), AC(5:0), AC(5:0-DC), AC(5:0-OH), AC(5: 1), AC(6:0), AC(6: 1), AC(7:0), AC(8:0), AC(8: 1), AC(8: 1-OH), ADMA, Ala, Arg, Asn, Asp, Cit, Creatinine, Gin, Glu, Gly, His, He, Kynurenine, Lys, Met, Met-SO, Nitro-Tyr, Om, Phe, Pro, Putrescine, SDMA, Sarcosine, Ser, Taurine, Thr, Trp, Tyr, Vai, alpha-AAA, t4-OH-Pro, xLeu

[0459] Proteomics evaluations measured potential biomarkers of treatment response and provide mechanistic insights into the effects of plasma exchange on aging-related processes. Proteomic features include: A1BG, A2M, A2ML1, ABI3BP, ACAA2, ACTA1, ACTB, ACTN2, ACTN4, ADAMDEC1, ADGRF5, ADGRG6, ADGRL4, AFM, AGT, AHSG, AK1, AKAP13, ALB, ALCAM, ALDOA, ALDOB, AMBP, AMY IB, ANG, ANK2, ANXA1, ANXA2, APCS, APOA1, APOA2, APOA4, APOB, APOCI, APOC2, APOC3, APOC4, APOD, APOE, APOH, APOL1, APOM, ARG1, ART4, ASAHI, ATF6, ATF6B, ATP2A1, ATP5F1A, ATP5F1B, ATRN, AXL, AZGP1, BASP1, BCHE, BLMH, BOC, BPIFA1, BPIFB1, BPIFB4, BST1, C1QB, C1QC, C1R, C1RL, CIS, C2, C3, C4A, C4B, C4BPA, C4BPB, C5, C6, C7, C8A, C8B, C8G, C9, CAI, CACNA2D1, CADM1, CALM1, CALML3, CALML5, CAMP, CAPN1, CARD9, CASP14, CAST, CAT, CBLN1, CBLN4, CCL14,337CCL15, CCL16, CCL18, CCL24, CCT2, CCT3, CCT7, CD109, CD200R1, CD300A, CD34, CD44, CD46, CD55, CD58, CD59, CD5L, CDH13, CDH5, CDHR2, CDSN, CEACAM1, CEACAM6, CFB, CFD, CFH, CFHR1, CFHR2, CFHR3, CFI, CFL1, CFP, CHD8, CHGA, CHGB, CHL1, CLEC3B, CLPS, CLU, C0L14A1, C0L1A1, COL3A1, COL6A3, COLECIO, CP, CPA4, CPN2, CRNN, CSF1R, CST4, CST6, CSTA, CSTB, CTRB2, CTSD, CTSH, CXCL3, DCD, DEFA1, DEFBI, DLK1, DMBT1, DMKN, DNER, DPEP2, DSC1, DSC3, DSG1, DSP, ECM1 EEF1A1, EEF1D, EEF2, EFEMP1, EGFR, EIF4A1, ENG, ENO1, ENSA, EPPK1, ESAM, F12, F13B, F2, F5 FABP1, FABP5, FAS, FAT2, FCGR2A, FCGR3A, FCGR3B, FETUB, FGA, FGB, FGFR1, FGG, FLG, FLG2, FLNC, FLT4, FN1, FOLR3, FSHB, FSTL1, GAPDH, GC, GGCT, GGH, GM2A, GOLM1, GOLM2, GP1BA, GPNMB, GRN, GSDMA, GSN, GSTP1, Hl-4, H2AC4, H2BC12, H3C1, H4C1, HABP2, HAL, HBA1, HBB, HBD, HBG1, HEG1, HEPACAM2, HNRNPK, HP, HPR, HPX, HRG, HRNR, HSP90AA1, HSPA1A, HSPA5, HSPA8,HSPB1, HSPD1, ICAM1,, ICAM2, ICAM3, ICOSLG, IDE, IGF1, IGF2, IGFBP3, IGFBP4, IGFBP5, IGFBP6, IGFBP7, IGHA1, IGHG2, IGHG3, IGHG4, IGHM, IGHV2-70D, IGHV3-15, IGHV3-30, IGHV3-49, IGHV3-7, IGHV3-9, IGHV4-34, IGHV5-51, IGKC, IGKV1-39, IGKV1D-13, IGKV2D-24, IGKV3-20, IGKV3D-11, IGKV3D-15, IGKV4-1, IGLC2, IGLL5, IGLV1-47, IGLV1-51, IGLV2-18, IGLV3-10, IGLV3-9, IGLV6-57, IGLV8-61, IL13RA1, IL17F, IL18BP, IL1R1, IL1RAP, IL6ST, INHBC, ITGB1, ITIH1, ITIH2, ITIH3, ITIH4, JCHAIN, JUP, KDR, KIT, KLKB1, KNG1, KPRP, L1CAM, LAMP1, LAMP2, LCN1, LCN2, LDHA, LDHB, LEAP2, LEPR, LGALS3BP, LGALS7, LILRA3, LMNA, LOX, LPA, LRG1, LSAMP, LTBP1, LTBP2, LTF, LUM, LY6D, LY6G6C, LYNX1, LYPD3, LYVE1, LYZ, MANF, MARCKS, MBL2, MCAM, MDK, MEGF9, MENT, MEPE, MERTK, MMRN1, MMRN2, MSMB, MT2A, MUC5AC, MUC5B, MXRA5, MYH1, MYH2, MYH3, MYH4, MYH7, MYH8, MYH9, MYL11, NACA, NCAM1, NECTIN1, NECTIN3, NEGRI, NME1, NOTCH 1, NPC2, NPM1, NTM, NTRK2, NTRK3, OLFM1, 0RM1, 0RM2, OSCAR, OSMR, PCOLCE, PDAP1, PDCD1LG2, PDGFRB, PECAM1, PF4, PF4V1, PFN1, PGAM1, PGK1, PGLYRP2, PI 16, PI3, PIAS4, PIGR, PIP, PKM, PKP1, PLA2G1B, PLA2G2A, PLEC, PLG, PLTP, PLXDC2, POF1B, PON1, PPBP, PRAP1, PRB4, PRDX1, PRDX2, PREXI, PRG4, PRH1, PRNP, PROCR, PROS1, PRSS1, PSMA3, PSMA6, PSMB6, PTGDS, PTMA, PTPRB, PTPRC, PTPRG, PTPRJ, PTPRM, PTPRZ1, PVR, RACK1, RBP4, REGIA, REGIB, RNASE1, RNASE2, RNASE6, RNASE7, RPS3A, S100A11, S100A7, S100A8, S100A9, SAA1, SAA4, SBSN, SELENOP, SELL, SELP, SEMG1, SEMG2, SERPINA1, SERPINA10, SERPINA3, SERPINA4, SERPINA5, SERPINA6, SERPINA7, SERPINB12,338SERPINB3, SERPINB4, SERPINB5, SERPINC1, SERPIND1, SERPINF1, SERPINF2, SERPING1, SFN, SH3BGRL, SH3BGRL2, SH3BGRL3, SIPA1L1, SIRPA, SLC25A6, SLC38A10, SLPI, SLURP1, SNCA, SNRPD3, S0D3, SPARCL1, SPINK5, SP0N1, SPP1, SPRR2E, SPRR3, SRGN, TCN1, TF, TGM1, TGM3, TGM5, TG0LN2, THBS1, THY1, TKT, TMEM25, TMSB4X, TNFRSF1A, TNFRSF1B, TNFRSF21, TNNT3, TPI1, TPM2, TTN, TTR, TUBA1B, TUBA1C, TUBA4A, TUBB, TWSG1, TXN, TYR03, UBA1, UBB, UMOD, VASN, VCAM1, VDAC1, VDAC2, VEGFD, VIT, VNN1, VTN, WFDC2, YWHAQ,, ZG16B, ZNF45E

[0460] TABLE IX sets forth the results of various omics e...

Claims

CLAIMSWhat is claimed is:

1. A method for using plasmapheresis to treat a condition in an individual in need thereof, the method comprising the steps of:(a) measuring, before administering the plasmapheresis, levels of a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in the individual to a blood sample of the individual;(b) administering the plasmapheresis to the individual;(c) measuring, following step (b), the levels of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or a combination thereof in a blood sample of the individual;(d) monitoring for a change in the condition; and(e) repeating steps (b), (c), and (d) until the condition is improved, thereby treating the condition in the individual.

2. The method of claim 1, further comprising determining a stage, severity, or risk of developing the condition in the individual based on the levels of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or the combination thereof in the blood sample of the individual.

3. The method of claim 1 or 2, further comprising lowering a health risk associated with the condition in the individual.

4. The method of any one of claims 1-3, further comprising repeating steps (a) through (c) a plurality of times.

5. The method of claim 4, wherein the measuring is performed before withdrawing the volume of whole blood to yield a pretreatment level of the biomarker.

6. The method of any one of claims 1-5, wherein the measuring is performed after returning a cellular fraction and an exchange fluid to the circulatory system of the individual to yield a post-treatment level of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or the combination thereof in the blood sample of the individual.

7. The method of any one of claims 1-6, wherein the measuring is performed in realtime during one or more of steps (a) through (c).

8. The method of any one of claims 1-7, comprising repeating steps (a) through (d) until a level of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or a combination thereof in the blood sample of the individual reaches a target level.

9. The method of any one of claims 1-8, wherein steps (a)-(d) are performed during a plurality of treatment sessions.

10. The method of claim 9, wherein two treatment sessions of the plurality of treatment sessions are within 72 hours of each other.

11. The method of any one of claims 1-10, wherein steps (a) through (d) are performed at least two times.

12. The method of claim 11, wherein each of the at least two times comprises removing at least one plasma volume from the individual.

13. The method of any one of claims 1-12, wherein a volume of exchange fluid that is returned to the individual is equal in volume to the at least one plasma volume that is withdrawn.

14. The method of claim 13, wherein a volume of exchange fluid that is returned to the individual is greater in volume than the at least one plasma volume that is withdrawn.

15. The method of any one of claims 11-14, wherein at least one of the at least two times that plasmapheresis is performed comprises removing at least one- and one-half plasma volumes from the individual.

16. The method of claim 15, wherein a volume of exchange fluid that is returned to the individual is equal to the at least one- and one-half plasma volumes that is withdrawn.

17. The method of claims 15-16, wherein a volume of exchange fluid that is returned to the individual is greater than the at least one- and one-half plasma volume that is withdrawn.

18. The method of any one of claims 1-17, further comprising infusing an exchange fluid into a vascular system of the individual, wherein the exchange fluid comprises at least one of: saline, lactated Ringer’s, albumin, or a therapeutic agent.

19. The method of claim 18, wherein the therapeutic agent comprises at least one of: an anti-inflammatory or an immune modulator.

20. The method of claim 19, wherein the immune modulator comprises intravenous immunoglobulin .

21. The method of any one of claims 18-20, comprising administering the therapeutic agent to the individual following at least one of the at least two times that steps (a) through (3) are performed.

22. The method of any one of claims 18-21, wherein the therapeutic agent comprises at least one of an anti-inflammatory or an immune modulator.

23. The method of any claim 22, wherein the immune modulator comprises intravenous immunoglobulin .

24. The method of any one of claims 1-23, further comprising comparing a post-treatment level of the biomarker with a pre-treatment level of the biomarker, and the comparing the post-treatment level of the biomarker with the pre-treatment level of the biomarker results in a determination of a quantitative difference between the post-treatment level of the biomarker and the pre-treatment level of the biomarker.

25. The method of any one of claims 1-24, wherein the biomarker is measured using flow cytometry, near-infrared spectroscopy (NIR), double shot pyrolysis - gas chromatography / mass spectrometry, Fourier transform infrared (FT-IR) spectrometry, visual inspection with an optical microscope Raman spectroscopy, or surface-enhanced Raman scattering, dynamic light scattering (DLS), or surface plasmon resonance.

26. The method of any one of claims 1-25, wherein the biomarker is an intracellular protein.

27. The method of any one of claims 1-25, wherein the biomarker is at least one of titin (TTN), carboxypeptidase N subunit 2 (CPN2), T-complex protein 1 subunit beta (CCT2), tubulin alpha-lB chain (TUBA1B), transketolase (TKT), alpha-enolase (ENO1), profilin-1 (PFN1), or actin cytoplasmic 1 (ACTB).

28. The method of any one of claims 1-25, wherein the biomarker is a secreted protein.

29. The method of any one of claims 1-28, wherein the biomarker is at least one of folate receptor gamma (FOLR3), mannose-binding protein C (MBL2), leukocyte immunoglobulin- like receptor subfamily A member 3 (LILRA3), leucine-rich alpha-2-glycoprotein (LRG1), immunoglobulin heavy constant alpha 1 (IGHA1), gamma-glutamyl hydrolase (GGH),immunoglobulin kappa light chain (IGKC), histidine-rich glycoprotein (HRG), BPI foldcontaining family B member 1 (BPIFB1), apolipoprotein C-I (APOCI), or extracellular matrix protein 1 (ECM1).

30. The method of any one of claims 1-29, wherein the biomarker is a cell surface or transmembrane protein.

31. The method of any one of claims 1-30, wherein the biomarker is vasorin (VASN).

32. The method of any one of claims 1-31, further comprising upregulating or downregulating a physiological pathway.

33. The method of claim 32, wherein the physiological pathway is selected from the group consisting of an immune system pathway, inflammatory response, lipid metabolism, lipid transport, nervous system development, nervous system function, cell communication, cell adhesion, protein transport, vesicle transport, lipid metabolic process, blood coagulation regulation, cardiovascular regulation, drug transport, cell differentiation, hormone secretion, hormone transport, catalytic activity regulation, or hemostasis regulation.

34. The method of any one of claims claim 32-33, wherein the physiological pathway is ameboidal-type cell migration, axon guidance, B cell activation, bundle of His cell to Purkinje myocyte communication, cell-cell adhesion via plasma-membrane adhesion molecules, chylomicron remnant clearance, complement activation, alternative pathway, drug transport, establishment of protein localization to organelle, extrinsic apoptotic signaling pathway, Fc-gamma receptor signaling pathway involved in phagocytosis, foam cell differentiation, gas transport, granulocyte activation, high-density lipoprotein particle assembly, high-density lipoprotein particle remodeling, hormone secretion, hormone transport, immune response-regulating cell surface receptor signaling pathway, import into cell, inflammatory response, innate immune response-activating signal transduction, isoprenoid metabolic process, lipid catabolic process, lipid modification, lipoprotein metabolic process, low-density lipoprotein particle remodeling, membrane invagination, myeloid cell activation involved in immune response, negative regulation of catalytic activity, negative regulation of hemostasis, negative regulation of lipid localization, nervous system development, neurogenesis, neuron development, neuron differentiation, neutral lipid metabolic process, neutrophil degranulation, organelle fusion, organic substance transport, organophosphate catabolic process, plasma membrane bounded cell projection organization, positive regulation of lipid localization, protein stabilization, protein-containing complexassembly, protein-containing complex remodeling, protein-lipid complex subunit organization, regulated exocytosis, regulation of blood coagulation, regulation of cell projection assembly, regulation of coagulation, regulation of heart rate by cardiac conduction, regulation of hormone secretion, regulation of lipid localization, regulation of protein localization, regulation of tube size, regulation of vesicle-mediated transport, vascular process in circulatory system, vesicle-mediated transport, or a combination thereof.

35. The method of any one of claims 1-34, further comprising reducing or increasing a cell population, wherein the cell population is at least one of an up T cell, a B cell, a plasma cell, a B cell, a classical monocyte, a y5 T cell, an intermediate monocyte, a lineage negative (LN) cell, a live leukocyte, a myeloid scatter fraction, a natural killer (NK) cell, a nonclassical monocyte, not a T cell, a myeloid cell, a plasmablast, an activated cell, an activated memory cell, a central memory (CM) cell, a double negative T cell (DN), a double positive T cell (DP), an effector memory T cell (EM), a naive cell, a non-B or T cell, a memory cell, or a combination thereof.

36. The method of claim 35, wherein the cell is a CCr7 positive, CD16 positive, cdl63 hi, cd25 positive, cd27 negative, CD27 positive, CD38 hi, CD38 positive, CD4, CD57 positive, CD64 hi, cd64 lo, CD8, CD80 positive, HLADR positive, igMHi-IgDHi, IgMHi-IgDLo, IgMNeg-IgDNegative, KIR positive, KLRG1 positive, myeloid scatter fraction, NK-56-lo- 16-hi, NK-56-lo-16-lo, NK-56hi-16-lo, NK1-1 positive, NKg2a positive, RA negative, RA positive, SCM, SCM and TEMRA, TEMRA SA-bGal hi, SAbGal positive, or TiGit positive cell.

37. The method of any of claims 1-36, wherein the condition comprises cancer, Alzheimer’s disease, neurodegenerative disorders, immune system dysregulation, metabolic disorders, chronic inflammatory disease, autoimmune disease, infectious disease, lipid storage disease, coagulation disorder, hormonal disorders, or a cardiovascular disease.

38. The method of any of claims 1-37, wherein the condition is selected from the group consisting of breast cancer, lung cancer, colorectal cancer, prostate cancer, leukemia, lymphoma, melanoma, pancreatic cancer, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis (ALS), Huntington's disease, multiple sclerosis (MS), frontotemporal dementia, atherosclerosis, coronary artery disease, hypertension, heart failure, myocardial infarction (heart attack), stroke, peripheral artery disease, type 2 diabetes, obesity, metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), hyperlipidemia, gout,rheumatoid arthritis, systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD), Crohn's disease, ulcerative colitis, psoriasis, ankylosing spondylitis, bacterial infections, tuberculosis, staphylococcal infections, viral infections, HIV / AIDS, hepatitis B and C, influenza, parasitic infections, malaria, leishmaniasis, epilepsy, migraine, autism spectrum disorders, attention deficit hyperactivity disorder (ADHD), schizophrenia, bipolar disorder, Gaucher disease, Niemann-Pick disease, Fabry disease, Tay-Sachs disease, Wolman disease, hypothyroidism, hyperthyroidism, Cushing's syndrome, Addison's disease, polycystic ovary syndrome (PCOS), growth hormone deficiency, hemophilia, von Willebrand disease, deep vein thrombosis (DVT), pulmonary embolism, disseminated intravascular coagulation (DIC), phenylketonuria (PKU), maple syrup urine disease (MSUD), homocystinuria, galactosemia, and mitochondrial disorders.

39. A method of treating a condition that is associated with aging in an individual, the method comprising:(d) measuring, before administering the plasmapheresis, the levels of a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in a blood sample of the individual;(e) administering the plasmapheresis to the individual;(f) measuring, following step (b), the levels of the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or a combination thereof in a blood sample of the individual;(g) monitoring for a change in the condition; and(h) repeating steps (b), (c), and (d) until the condition is improved, thereby treating the condition in the individual.

40. The method of claim 39, further comprising determining a stage, severity, or risk of developing the condition that is associated with aging in the individual based on the levels of the levels of a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in a blood sample of the individual.

41. The method of claim 39 or 40, further comprising lowering a health risk associated with aging in the individual.

42. The method of any one of claims 39-41, further comprising performing plasmapheresis on the individual a plurality of times.42443. The method of any one of claims 39-42, further comprising performing plasmapheresis on the individual until the stage, severity, or risk of developing the condition that is associated with aging in the individual or the health risk associated with aging in the individual are below a threshold level.

44. The method of any one of claims 39-44, further comprising measuring the biological age of the individual and / or measuring the rate of aging of the individual.

45. The method of claim 44, wherein the measuring the biological age of the individual and / or the rate of aging of the individual comprises measuring a biomarker, a biological clock, an omic panel, or a combination thereof.

46. The method of any one of claims 39-45, wherein the biomarkers, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises an immune cell population, a percentage of live immune cells, or a ratio of immune cell populations.

47. The method of any one of claims 44-46, wherein measuring the biological age of the individual, measuring the rate of aging of the individual, or the combination thereof comprises measuring a plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof.

48. The method of any one of claim 1-47, wherein: v) the plurality of biomarkers comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 150, 200, 250, 300, 400, 500, 750, 1000, 1250, 1500, or 2000 biomarkers; and / or vi) the plurality of biological clocks comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biological clocks; and / or vii)the plurality of omic panels comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 omic panels; and / or viii) a combination thereof.

49. The method of any one of claims 1-48, wherein the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or the combination thereof comprise at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 types of biological molecules.

50. The method of any one of claims 39-49, further comprising improving hand grip, balance, strength, up and go, and / or SF12 mental score.42551. The method of any one of claims 1-50, wherein the plurality of biomarkers, the plurality of biological clocks, the plurality of omic panels, or the combination thereof comprises a biomarker, a biological clock, an omic panel, or a combination thereof.

52. The method of any one of claims 1- 1, wherein the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises adaptation, causal and damage clocks, epigenetic clocks, fitness age, PC clocks, or systems ages.

53. The method of any one of claims 1-52, wherein the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises a component or property of blood, metabolic, brain, heart, SystemsAge, hormone, inflammation, lung, liver, musculoskeletal, kidney, immune, AdaptAge, CausAge, DamAge, Stochastic Horvath, Stochastic PhenoAge, Stochastic Zhang, Retroclock, cAge, Hannum, IntrinClock, PhenoAge, Horvath, OMICmAge, PCHorvath2, PCDNAmTL, PCGrimAge, PCHorvathl, PCHannum, PCPhenoAge, DNAmFitAge, DNAmGait, DNAmV02max, or DNAmGrip.

54. The method of any one of claims 1-53, wherein the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises a property selected from a humoral immune response, leukocyte mediated immunity, lymphocyte mediated immunity, leukocyte cell-cell adhesion, activation of immune response, regulation of leukocyte cell-cell adhesion, regulation of cell-cell adhesion, negative regulation of immune system processes, regulation of leukocyte mediated cytotoxicity, regulation of T cell activation, antigen receptor-mediated signaling pathways, regulation of immune effector processes, regulation of cell killing, regulation of peptidyl-tyrosine phosphorylation, regulation of T cell proliferation, complement activation classical pathway, regulation of body fluid levels, antibacterial humoral response, heterotypic cell-cell adhesion, or antimicrobial humoral response.

55. The method of any one of claims 1-54, wherein the biomarker, the biological clock, the omic panel, the plurality thereof, or the combination thereof comprises a property selected from abT CD4 CM CD27, MC INT, FA2BG2S1, FA2(3)G1S1, PC(38:3), SM(42: 1), Ser, AC(8: 1), BPIFA1, HBG1, IGFBP4, CTSD, CD44, TPM2, IGF2, MXRA5, THY1, LAMP1, C7, ICAM1, TXN, LGALS3BP, PRNP, SERPING1, PCOLCE, TWSG1, HBG1, or ITIH3.

56. The method of any one of claims 1-55, wherein the plasmapheresis is performed weekly, biweekly, or monthly.42657. The method of any one of claims 1-56, wherein the plasmapheresis is performed for at least 1 months, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, or longer.

58. The method of any one of claims 1-57, wherein the plasmapheresis is performed for a period of about 1 month, about 2 months, about 3 months, about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, or about 12 months.

59. The method of any one of claims 1-58, wherein the individual has an underlying health condition.

60. The method of claim 59, wherein the underlying health condition is determined through measurement of biomarkers comprising at least one of glucose levels, bilirubin levels, globulin levels, circulating albumin, or potassium levels.

62. The method of any one of claims 39-60, further comprising persistently decreasing the biological age of the individual after the plasmapheresis.

63. A method of determining a type of plasmapheresis treatment for a condition of an individual, the method comprising: measuring a biomarker; comparing the biomarker to a reference or baseline biomarker; determining that the individual has a health status; treating the condition with a type of plasmapheresis.

64. The method of claim 63, wherein the type of plasmapheresis treatment is biweekly, weekly, or plasmapheresis + IVIG.

65. The method of claim 63 or 64, wherein the individual has not received plasmapheresis treatment for at least 12 months, at least 11 months, at least 10 months, at least 9 months, at least 8 months, at least 7 months, at least 6 months, at least 5 months, at least 4 months, at least 3 months, at least 2 months, at least 1 month.

66. The method of claim 63 or 64, wherein the individual has received plasmapheresis treatment within the past 1 week to 52 weeks.

67. The method of any one of claims 63-66, wherein the biomarker comprises a biological clock, an omic panel, or combination thereof.42768. The method of claim 68, wherein the omic panel comprises at least one of glycomics, metabolomics, methylomics, lipidomics, proteomics, or cytomics.

69. The method of any one of claims 1-68, wherein the plurality of biomarkers, a plurality of biological clocks, a plurality of omic panels, or a combination thereof in the individual are selected from any combination of features set forth in TABLES VII, VIII, IX, X, XI, XII, XIV, XV, XVI, XVII, XVIII, XIX, XX, XXI, XXII, XXIII, XXIV, and / or XXVI428

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