Predictive biomarkers for diabetes

Abstract

Provided herein are methods and compositions for detecting protein biomarkers indicative of type 1 or type 2 diabetes.

Description

PREDICTIVE BIOMARKERS FOR DIABETESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Application No. 63 / 598.480 filed on November 13, 2023, the content of which is incorporated by reference in its entirety.BACKGROUND

[0002] Type 1 Diabetes (T1D) is a metabolic disease that results from the gradual autoimmune assault of insulin-producing beta cells, ultimately culminating in the complete loss of insulin secretion. A complex interplay of genetic susceptibilities, epigenetic, and environmental factors contribute to its etiology7, thrusting patients into a lifetime reliance on exogenous insulin and an increased risk of numerous chronic health complications. While biomarkers, such as susceptibility7genes and islet autoantibodies are commonly used in T1D risk determination, a number of limitations such as, lack of specificity, sensitivity, and temporal relevance in the disease course, remain major drawbacks. For example, islet autoantibodies [such as insulin autoantibody (IAA), islet cell cytoplasmic autoantibody (ICA) ty rosine phosphatase-related islet antigen 2 autoantibody (IA-2A), glutamic acid decarboxylase 65 autoantibody, the cation efflux transporter zinc transporter 8 (ZnT8) autoantibody] present relatively late in the development of T1D, truncating their efficacy in early disease prediction. Other limitations include false positives wherein individuals never develop the disease, lack of definitive disease onset associated with the presence of autoantibodies, the inability to track disease progression or monitor therapeutic outcomes due to the persistence of GAD antibodies, decrease in IA-2 antibodies after diagnosis, and / or increased prevalence of insulin autoantibodies in young children. Similarly, susceptibility gene biomarkers for T1D [such as the human leukocyte antigen (HLA) genes, specifically7HLA- DR and HLA-DQ, insulin gene, protein ty rosine phosphatase non-receptor type 22 gene (PTPN22), cytotoxic T-lymphocyte-associated protein 4 gene (CTLA4), interleukin-2 receptor alpha (IL2RA) gene, interferon-induced helicase 1 (IFIH1) gene, C-type lectin domain family 16, member A (CLEC16A), Erb-B2 Receptor Tyrosine Kinase 3 (ERBB3) gene] form only part of the puzzle, since environmental factors, such as viral infections, diet, and other unknown triggers, also contribute to T1D risk. It is this complexity that makes it challenging to rely solely on genetic markers for predicting T ID. Other limitations also exist, such as their low predictive value, limited diagnostic value, ethnic and geographic variation, lack of specific interventions, and potentialethical and psychological concerns. Currently, there are no specific interv entions or preventive measures that can be recommended based solely on the presence of T1D susceptibility genes. Given the above-mentioned limitations, a compelling need for accurate and reliable biomarkers for T1D prediction and diagnosis exists, that will allow for earlier intervention, personalized treatment strategies, and a better understanding of the disease's mechanisms and could eventually lead to improved outcomes and a reduced burden on individuals living with T1D and their families.SUMMARY

[0003] In an aspect, provided herein is a method comprising: contacting a biological sample from a subject suspected of having type 1 diabetes with a panel of capture agents, wherein the panel comprises at least one primary capture agent that is capable of binding to a primary analyte selected from a primary group of proteins comprising: hydroxyacylglutathione hydrolase (GLO2); INPP5B; CCT5; ARHGAP6; SRC. l; ARL8A; SRC; ATP5PO; PYGB; DARS2; RAB32; TTC1; SHMT2; ATP5PF; PRTFDC1; ACADVL; LDLRAP1; PNPT1; ARL6IP5; ACOT13; YARS1; CCT8; ITGA2B.ITGB3; PKM; PLEKHF2; GAP43; RAN; FN3K; RAB8B; STK24; PPID; UBE2D1.UBB; UBASH3B; CCT7; IVD; PSG2; BTK; GPD1.1; TFAM; DAB2; LANCL2; ITGA2B.ITGB3.1; MAPRE3; BTK.1; ILK; GSK3B; ACOX1; APRT; CIAO1; PRKCA; EIF1B; AKR7A2; RAB3C; PPP ICC; ACTN1; ARRB1; UNC45A.1; CEP41; RAB21; ME2; PDHA2; RHOC; PMM2; SGTA; ECHS1; HSPD1; SPHK1; MGMT: MBNL2; NRGN; ENO1; NME7; SAR1 A; PPIF; MARS1 ; MBNL1 ; PPIF. l ; UFD1 ; PCBP2; VCP; INPP5A; FHOD1 ; COX5A; NAP1L1; GMPR; CLIC4.1; MRPL1; DBT; ZADH2; MAP4K5; HSD17B10; NAP1L4; HSPB1; NCKIPSD: VILE DDX6; CLIC4; MAPRE2; SNTA1; NME4; PDIA5; HAGH; CLIC1; PRPSAP2; PIP4K2A; NACA; PDE5A: KIFBP; PIP4K2B; DECR1; GSK3A; RAB4B; CAP1; ADH6; MTIF3; ACADM; DNM1; COPB2; SYTL4.1; DTD1; PTRHD1; PRDX3; PDE5A.1; EIF2B1; CTPS1; RTCA; RBPMS2; DDI2; RAB6B; PGD; RILPL2; HADH; RAB1B; TARSI; MAPK10; RHOT1; AGFG2; PIH1D1; ALAD; CDC42; DAPP1; ABHD10; EIF4A2.1; SERPINH1; LYN: and PEARL 1; and detecting whether the at least one primary capture agent binds to at least one primary analyte in the sample. In embodiments, the panel comprises at least three primary capture agents; and the method comprises detecting whether each of the at least three primary' capture agents bind to a first analyte in the sample. In embodiments, the panel comprises primary capture agents that are capable of binding to each of: hydroxyacylglutathione hydrolase (GLO2); INPP5B; CCT5; ARHGAP6; SRC. l; ARL8A; SRC; ATP5PO; PYGB; DARS2; RAB32; TTC1; SHMT2; ATP5PF; PRTFDC 1; ACADVL; LDLRAP1; PNPT1;ARL6IP5; AC0T13; YARS1; CCT8; ITGA2B.ITGB3; PKM; PLEKHF2; GAP43; RAN; FN3K; RAB8B; STK24; PPID; UBE2D1.UBB; UBASH3B; CCT7; IVD; PSG2; BTK; GPDE 1; TFAM; DAB2; LANCL2; ITGA2B.ITGB3.1; MAPRE3; BTK.1; ILK; GSK3B; AC0X1; APRT; CIAO1; PRKCA; EIF1B; AKR7A2; RAB3C; PPP1CC; ACTN1; ARRB1; UNC45A.1; CEP41; RAB21; ME2; PDHA2; RHOC; PMM2; SGT A; ECHS1; HSPD1; SPHK1; MGMT; MBNL2; NRGN; ENO1; NME7; SARI A; PPIF; MARS1; MBNL1; PPIF. l; UFD1; PCBP2; VCP; INPP5A; FHOD1; COX5A; NAP1L1; GMPR; CLIC4.1; MRPL1; DBT; ZADH2; MAP4K5; HSD17B10; NAP1L4; HSPB1; NCKIPSD; VIL1; DDX6; CLIC4; MAPRE2; SNTA1; NME4; PDIA5; HAGH; CLIC1; PRPSAP2; PIP4K2A; NACA; PDE5A; KIFBP; PIP4K2B; DECR1; GSK3A; RAB4B; CAP1; ADH6; MTIF3; ACADM; DNM1; COPB2; SYTL4.1; DTD1; PTRHD1; PRDX3; PDE5A.1; EIF2B1; CTPS1; RTCA; RBPMS2; DDI2; RAB6B; PGD; RILPL2; HADH; RAB1B; TARSI; MAPK10; RHOT1; AGFG2; PIH1D1; ALAD; CDC42; DAPP1; ABHD10; EIF4A2.1; SERPINH1; LYN; and PEARL 1.

[0004] In embodiments, the panel further comprises at least one secondary' capture agent that is capable of binding to a secondary analyte selected from a secondary’ group of proteins selected from: Gamma-interferon-inducible protein 16:Isoform 2, Hematopoietic expression, interferon- inducible nature, and nuclear localization 1 (IF16:HIN 1); Neutrophil cytosol factor 1 (NCF-1); High mobility group protein B2 (HMG-2); Histone H1.2; Protein S100-A8 / A9 heterodimer (S100A8 / S100A9); Activated RNA polymerase II transcriptional coactivator pl5 (TCP4); Calgranulin A; Myeloid cell nuclear differentiation antigen (MNDA); Phosphoglycerate mutase 1 ; Ubiquinone biosynthesis protein COQ9, mitochondrial (COQ9); and Pulmonary surfactant- associated protein D (SP-D); XAGE2; SPI1; HK3; HNRNPA1.1; S100A9; SUB1; HMGB2; PGAM2; LSP1; SFTPD. l; FNBP1; RBP7; CAPG; MAP3K3.1; PSTPIP1; ZBP1; S 100A12; ADSS2; S100A8; SNRPA; SMNDC1; S100A8.S100A9; HNRNPA2B1; NADK; NCF1; IL21R; and IL18; and wherein the method further comprises detecting whether the at least one secondary primary capture agent binds to at least one secondary analyte in the sample. In embodiments, the panel comprises at least three secondary capture agents; and wherein the method comprises detecting whether each of the at least three secondary capture agents bind to a secondary’ analyte in the sample. In embodiments the panel comprises primary capture agents that are capable of binding to each of: Gamma-interferon-inducible protein 16:Isoform 2, Hematopoietic expression, interferon-inducible nature, and nuclear localization 1 (IF16:HIN 1); Neutrophil cytosol factor 1 (NCF-1); High mobility' group protein B2 (HMG-2); Histone Hl.2; Protein S100-A8 / A9 heterodimer (S100A8 / S100A9); Activated RNA polymerase II transcriptional coactivator p!5 (TCP4); Calgranulin A; Myeloid cell nuclear differentiation antigen (MNDA); Phosphoglyceratemutase 1; Ubiquinone biosynthesis protein COQ9, mitochondrial (COQ9); and Pulmonary surfactant-associated protein D (SP-D); XAGE2; SPI1; HK3; HNRNPA1.1; S100A9; SUB1; HMGB2; PGAM2; LSP1; SFTPD. l ; FNBP1; RBP7; CAPG; MAP3K3.1; PSTPIP1; ZBP1; S100A12; ADSS2; S100A8; SNRPA; SMNDC1; S100A8.S100A9; HNRNPA2B1; NADK; NCF1; IL21R; and IL18.

[0005] The method may further comprise measuring the amount of the at least one primary’ analyte bound to the at least one primary capture agent and the amount of the at least one secondary analyte bound to the at least one secondary capture agent. The method may further comprise measuring the amount of the at least one primary' analyte and the at least one secondary' analyte in a control sample; wherein a higher amount of the primary' analyte in the biological sample compared to the control sample is indicative of type 1 diabetes in the subject; and wherein a lower amount of the secondary analyte in the biological sample compared to the control sample is indicative of type 1 diabetes in the subject. If ty pe 1 diabetes is identified in the subject, the method further comprises administering to the subject an effective amount of a type 1 diabetes therapeutic. The diabetes therapeutic may comprise an IgM antibody.

[0006] The method may further comprise obtaining the biological sample from the subject before contacting the sample with the panel of capture agents.

[0007] In another aspect, provided herein is a method comprising contacting a biological sample from a subject suspected of having type 2 diabetes with a panel of capture agents, wherein the panel comprises at least one primary capture agent that is capable of binding to a primary’ analyte selected from a primary group of proteins comprising: Ribonuclease A family member 1 ; Platelet- Derived Grow th Factor Subunit B; C-X-C motif chemokine ligand 11.1; Integrin-Linked Kinase; Tropomyosin 4; Hepcidin Antimicrobial Peptide; and Synaptotagmin Like 4; Platelet-Derived Growth Factor Subunit B (PDGFB); and Integrin-Linked Kinase; and detecting whether the at least one primary capture agent binds to at least one primary analyte in the sample.

[0008] In embodiments, the panel comprises at least three primary capture agents; and the method comprises detecting whether each of the at least three primary capture agents bind to a first analyte in the sample. In embodiments, the panel comprises primary capture agents that are capable of binding to each of: Ribonuclease A family member 1; Platelet-Derived Growth Factor Subunit B; C-X-C motif chemokine ligand 11.1; Integrin-Linked Kinase; Tropomyosin 4; Hepcidin Antimicrobial Peptide; and Synaptotagmin Like 4; Platelet-Derived Grow th Factor Subunit B (PDGFB); and Integrin-Linked Kinase.

[0009] The panel may further comprise at least one secondary’ capture agent that is capable of binding to a secondary analyte selected from a secondary group of proteins selected from:Phosphoglycerate Mutase 1; Zymogen Granule Protein 16; and PGAM1; and wherein the method further comprises detecting whether the at least one secondary primary capture agent binds to at least one secondary analyte in the sample. The panel may further comprise primary capture agents that are capable of binding to each of: Phosphoglycerate Mutase 1; Zymogen Granule Protein 16; and PGAM1.

[0010] The method may further comprise measuring the amount of the at least one primary’ analyte bound to the at least one primary capture agent and the amount of the at least one secondary analyte bound to the at least one secondary capture agent. The method may further comprise measuring the amount of the at least one primary' analyte and the at least one secondary' analyte in a control sample; wherein a higher amount of the primary' analyte in the biological sample compared to the control sample is indicative of type 2 diabetes in the subject; and wherein a lower amount of the secondary analyte in the biological sample compared to the control sample is indicative of type 2 diabetes in the subject. If ty pe 2 diabetes is identified in the subject, the method further comprises administering to the subject an effective amount of a type 2 diabetes therapeutic. The method may further comprise obtaining the biological sample from the subject before contacting the sample with the panel of capture agents.

[0011] In another aspect, provided herein is a detection reagent comprising one or more primary capture agents, wherein each primary' capture agent binds to a primary' analyte selected from a primary group of proteins comprising: hydroxyacylglutathione hydrolase (GLO2); INPP5B; CCT5; ARHGAP6; SRC. l; ARL8A; SRC; ATP5PO; PYGB; DARS2; RAB32; TTC1; SHMT2; ATP5PF; PRTFDC1 ; ACADVL; LDLRAP1 ; PNPT1 ; ARL6IP5; ACOT13; YARS1 ; CCT8; ITGA2B.ITGB3; PKM; PLEKHF2; GAP43; RAN; FN3K; RAB8B; STK24; PPID; UBE2D1.UBB; UBASH3B; CCT7; IVD; PSG2; BTK; GPD1.1; TFAM; DAB2; LANCL2; ITGA2B.ITGB3.1: MAPRE3; BTK.1: ILK; GSK3B; ACOX1; APRT; CIAO1; PRKCA; EIF1B; AKR7A2; RAB3C; PPP1CC; ACTN1; ARRB1; UNC45A.1; CEP41; RAB21; ME2; PDHA2; RHOC; PMM2; SGTA; ECHS1; HSPD1; SPHK1; MGMT; MBNL2; NRGN; ENO1; NME7; SAR1A; PPIF; MARS1; MBNL1; PPIF. l; UFD1; PCBP2; VCP; INPP5A; FHOD1; COX5A; NAP1L1; GMPR; CLIC4.1; MRPL1; DBT; ZADH2; MAP4K5; HSD17B10; NAP1L4; HSPB1; NCKIPSD: VIL1; DDX6; CLIC4; MAPRE2; SNTA1; NME4; PDIA5; HAGH; CLIC1; PRPSAP2; PIP4K2A; NACA; PDE5A; KIFBP; PIP4K2B; DECR1; GSK3A; RAB4B; CAP1; ADH6; MTIF3; ACADM; DNM1; COPB2; SYTL4.1; DTD1; PTRHD1; PRDX3; PDE5A.1; EIF2B1; CTPS1; RTCA; RBPMS2; DDI2; RAB6B; PGD; RILPL2; HADH; RAB1B; TARSI; MAPK10; RHOT1; AGFG2; PIH1D1; ALAD; CDC42; DAPP1; ABHD10; EIF4A2.1; SERPINH1; LYN; and PEARL 1; wherein the one or more primary capture agents is linked to asolid support. The detection reagent may further comprise one or more secondary capture agents, wherein each secondary capture agent binds to a secondary analyte selected from a secondary group of proteins comprising: Gamma-interferon-inducible protein 16:Isoform 2, Hematopoietic expression, interferon-inducible nature, and nuclear localization 1 (IF16:HIN 1); Neutrophil cytosol factor 1 (NCF-1); High mobility7group protein B2 (HMG-2); Histone Hl.2; Protein S100- A8 / A9 heterodimer (S100A8 / S100A9); Activated RNA polymerase II transcriptional coactivator pl 5 (TCP4); Calgranulin A; Myeloid cell nuclear differentiation antigen (MNDA); Phosphoglycerate mutase 1; Ubiquinone biosynthesis protein COQ9, mitochondrial (COQ9); and Pulmonary7surfactant-associated protein D (SP-D); XAGE2; SPI1; HK3; HNRNPA1.1; S100A9; SUB1; HMGB2; PGAM2; LSP1; SFTPD. l; FNBP1: RBP7; CAPG; MAP3K3.1; PSTPIP1; ZBP1; S100A12; ADSS2; S100A8; SNRPA; SMNDC1; SI00A8.SI00A9; HNRNPA2B1; NADK; NCF1; IL21R; and IL 18.

[0012] In another aspect, provided herein is a method comprising contacting the detection reagent described herein with a biological sample from a subject. The subject may be suspected of having type 1 diabetes.

[0013] In another aspect, provided herein is a detection reagent comprising one or more primary capture agents, wherein each primary^ capture agent binds to a primary analyte selected from a primary7group of proteins comprising: Ribonuclease A family member 1 ; Platelet-Derived Growth Factor Subunit B; C-X-C motif chemokine ligand 11.1; Integrin-Linked Kinase; Tropomyosin 4; Hepcidin Antimicrobial Peptide; and Synaptotagmin Like 4; Platelet-Derived Growth Factor Subunit B (PDGFB); and Integrin-Linked Kinase; wherein the one or more primary capture agents is linked to a solid support.

[0014] The detection reagent may further comprise one or more secondary capture agents, wherein each secondary capture agent binds to a secondary analyte selected from a secondary7group of proteins comprising: Phosphoglycerate Mutase 1; Zymogen Granule Protein 16; and PGAM1.

[0015] In another aspect, provided herein is a method comprising contacting the detection reagent described herein with a biological sample from a subject. The subject may be suspected of having type I diabetes.BRIEF DESCRIPTION OF THE DRAWINGS

[0016] FIG. 1: Principal component analysis (PCA) of Significant Proteins. 199 DE proteins (FDR<=0.05, FC>=2) were used for PCA. Strong and consistent clustering of Diabetic and Healthy Controls was observed.

[0017] FIG. 2. Clustering of pathways detected by gprofder using differentially expressed proteins with fdr<=0.05 (n= 2,168) demonstrated 28 pathways were enriched at FDR <=0.25. Several enriched pathways that were associated with differentially expressed proteins were identified in type 1 diabetes. Many of these pathways fall into clusters involved with peptide metabolism, intracellular transport and localization, receptor signaling and RNA processing. Cytoscape and Enrichment Map were used for clustering and visualization of the enrichment results. Nodes represent enriched gene sets, which are then clustered with related gene sets according to their gene content. Enrichment results were mapped as a network of gene sets (nodes). Node size is proportional to the total number of genes within each gene set. Proportion of shared genes between gene sets is represented as the thickness of the edge connecting nodes.

[0018] FIG. 3. Classification Model Performance. Receiver Operating Characteristic (ROC) plot shows Cross validation of models using Leave One Out Cross Validation (LOOCV) of an 80% validation set. AUC ranged between 0.97 and 0.996. A 20% hold out test set showed 100% accuracy for all models.DETAILED DESCRIPTION

[0019] Proteomics, through its utilization of high-throughput technologies and analytical methods, provides expansive examinations across genetic polymorphisms as well as global gene and protein expression profiling leading to the discovery of novel biomarkers for early diagnosis, risk assessment, and monitoring disease progression. By examining protein expression variances, it provides insights into the molecular mechanisms underlying disease development, thereby helping researchers understand how autoimmunity, beta cell destruction, and inflammation interrelate at the protein level. Common pitfalls of proteomic studies with techniques like mass spectroscopy include the inability to detect low abundant proteins16and a large sample volume requirement, while immunoaffinity depletion techniques result in loss of low-abundance proteins17. Protein isoforms further complicate analysis and quantification of specific proteins18. The novel SomaScan v4.1 assay (SomaLogic Inc) overcomes the above-mentioned pitfalls. It measures >7,000 unique human protein analytes in 50uls of plasma with high specificity, sensitivity, and reproducibility19. The assay utilizes protein-capture reagents called SOM Amers®— short single-stranded DNA sequences that incorporate hydrophobic modifications— bind with very high specificity to 3D epitopes on human protein targets.

[0020] Polyclonal Immunoglobulin M (IgM) therapy is a benign, transient, and highly effective therapy that prevents the onset of T1D, halts its progression, and reverses new onset T1D9 11. It does so by normalizing B lymphocyte selection, thereby eliminating autoreactive B cells and auto-antibody production, and by inducing regulatory T cells and enhancing islet beta cell recovery. In conjunction with minimal dose islet transplantation, IgM therapy also reverses established autoimmune diabetes by establishing durable beta cell specific hypo-responsiveness. Proteomics can also be used to determine the efficacy of novel treatments such as IgM therapy.

[0021] The inventors demonstrate herein proteomics as a tool to predict the development of Type 1 Diabetes (T1D), its recurrence of T1D post-transplantation, and the efficacy of IgM treatment.

[0022] Accordingly, in a first aspect, provided herein is a method comprising contacting a biological sample from a subject suspected of having type 1 diabetes with a panel of capture agents, wherein the panel comprises at least one primary capture agent that is capable of binding to a primary analyte selected from a primary' group of proteins comprising: hydroxyacylglutathione hydrolase (GLO2); INPP5B; CCT5; ARHGAP6; SRC.l; ARL8A; SRC; ATP5PO; PYGB; DARS2; RAB32; TTC1; SHMT2; ATP5PF; PRTFDC 1 ; AC ADVL; LDLRAP1; PNPT1; ARL6IP5; ACOT13; YARS1; CCT8; ITGA2B.ITGB3; PKM; PLEKHF2; GAP43; RAN; FN3K; RAB8B; STK24; PPID; UBE2D1.UBB; UBASH3B; CCT7; IVD; PSG2; BTK; GPD1.1; TFAM; DAB2; LANCL2; ITGA2B.ITGB3. E MAPRE3; BTK. L ILK; GSK3B; ACOX1; APRT; CIAO1; PRKCA; EIF1B; AKR7A2; RAB3C; PPP1CC; ACINI; ARRB1; UNC45A.1; CEP41; RAB21; ME2; PDHA2; RHOC; PMM2; SGTA; ECHS1; HSPD1; SPHK1; MGMT; MBNL2; NRGN; ENO1; NME7; SAR1A; PPIF; MARS1; MBNL1; PPIF. l; UFD1; PCBP2; VCP; INPP5A; FHOD1; COX5A; NAP1L1; GMPR; CLIC4.1; MRPL1; DBT; ZADH2; MAP4K5; HSD17B10; NAP1L4; HSPB1; NCKIPSD: VIL1; DDX6; CLIC4; MAPRE2; SNTA1; NME4; PDIA5; HAGH; CLICL PRPSAP2; PIP4K2A; NACA; PDE5A; KIFBP; PIP4K2B; DECR1 ; GSK3A; RAB4B; CAP1; ADH6; MTIF3; ACADM; DNM1; COPB2; SYTL4.1; DTD1; PTRHD1; PRDX3; PDE5A.1; EIF2B1; CTPS1; RTCA; RBPMS2; DDI2; RAB6B; PGD; RILPL2; HADH; RAB1B; TARSI; MAPK10; RHOT1; AGFG2; PIH1D1; ALAD; CDC42; DAPP1; ABHD10; EIF4A2.1; SERPINH1; LYN; and PEARL 1; and detecting whether the at least one primary capture agent binds to at least one primary analyte in the sample. The panel may comprise additional primary capture agents that are capable of binding to any combination of the primary’ analytes listed in Tables 1, 3, 4, and 8.

[0023] The panel may comprise two or more of the primary capture agents. The panel may comprise between two and twenty of the primary capture agents, and any amount or range in between. The panel may comprise two, three, four, five, six, seven, ten, fifteen, twenty7, thirty7, forty, fifty, sixty, etc. of the primary capture agents. The panel may comprise all of the primary' capture agents. The method may comprise detecting whether two or more primary capture agents bind a primary analyte in the sample. The method may comprise detecting binding of betweentwo and twenty of the primary capture agents, or any amount or range in between. The method may comprise detecting binding of two, three, four, five, six, seven, ten. fifteen, twenty, etc. of the primary capture agents. The method may comprise detecting binding of all of the primary capture agents. “Primary capture agents” are proteins that are upregulated in diabetes patients in comparison healthy (non-diabetic) subjects.

[0024] Type 1 diabetes is an autoimmune disorder in which the pancreas makes little or no insulin, which leads to elevated blood glucose concentrations. The progression of type 1 diabetes can be characterized in three stages. At stage 1, the subject has tested positive for at least one diabetes- related antibody, but has normal blood glucose concentrations and is experiencing no symptoms. At stage 2, the subject’s blood glucose concentrations have become abnormal due to increasing loss of beta cells, but the subject is still experiencing no symptoms. At stage 3, there is significant beta cell loss and symptoms have arisen.

[0025] As used herein, the term “biological sample” refers to a sample taken from a subject. Suitable samples include fluid samples (e.g., saliva, blood, serum, plasma, urine, stool, cerebrospinal fluid) and tissue samples. In preferred embodiments, the sample comprises plasma or blood.

[0026] The term “subject” may be used interchangeably with the terms “individual” and “patient” and includes human and non-human mammalian subjects. In preferred embodiments, the subject is a human. As used herein, “a subject in need thereof’ refers to a subject having or at risk of developing type 1 or type 2 diabetes.

[0027] The term “capture agent” refers to a biological or chemical molecule that specifically binds to an analyte. The capture agent may be a protein or a peptide. The capture agent may be an antibody, an aptamer, an affibody, a scaffold protein, a phage display protein, yeast display protein, a uniquely shaped chemical, a single-stranded DNA or RNA sequence, etc. The term “analyte” refers to a protein.

[0028] As used herein, the terms “protein” or “polypeptide” or “peptide” may be used interchangeable to refer to a polymer of amino acids. Typically, a “polypeptide” or “protein” is defined as a longer polymer of amino acids, of a length typically of greater than 50, 60, 70, 80, 90. or 100 amino acids. A “peptide” is defined as a short polymer of amino acids, of a length typically of 50, 40, 30, 20 or less amino acids. A protein typically comprises a polymer of naturally or non-naturally occurring amino acids (e.g., alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine).

[0029] As used herein, the term "panel" refers to a collection of at least one, and preferably twoor more biomolecules (e.g. capture agents) wherein the two or more biomolecules are organized such that they can be differentiated from each other in a detecting step. This organization can be a spatial arrangement (e.g. biomolecule 1 is in section 1 of the panel, biomolecule 2 is in section 2 of the panel, etc.). The organization can be a difference in a detectable aspect, e.g. each biomolecule can be associated with a different detectable agent or label (e.g. biomolecule 1 can be detected with color 1, biomolecule 2 can be detected with color 2, etc.).

[0030] Terms such as primary, secondary, etc. as used in the description and claims refer to separate groupings of proteins are and not meant to be construed to indicate any particular order of steps or hierarchy or importance of such groupings.

[0031] As used herein, the terms "measure" and "measuring" refer to identifying a quantitative level of the material be measured. As used herein, the term measuring with regard to an analyte level refers to both detecting and determining a relative and / or absolute amount of the analyte in a sample. Standard detection and measuring methods for include, for example, radioisotope immunoassay, an enzyme-linked immunosorbent assay (ELISA), SISCAPA (Stable Isotope Standards and Capture by Anti-Peptide Antibodies, mass spectrometry, immunofluorescence assays. Western blot, affinity chromatography (e.g. affinity ligand bound to a solid phase), fluorescent antibody assays, immunochromatography, and in situ detection with labeled antibodies. Although any appropriate method can be selected, taking various factors into consideration, ELISA methods are particularly sensitive. In some embodiments, the detecting step and the measuring step comprise an ELISA or Western Blot assay.

[0032] The panel may comprise at least one secondary capture agent capable of binding to a secondary analyte selected from a secondary7group of proteins comprising Gamma-interferon- inducible protein 16: Isoform 2, Hematopoietic expression, interferon-inducible nature, and nuclear localization 1 (IF16:HIN 1); Neutrophil cytosol factor 1 (NCF-1); High mobility group protein B2 (HMG-2); Histone H1.2; Protein S100-A8 / A9 heterodimer (S100A8 / S 100A9); Activated RNA polymerase II transcriptional coactivator pl 5 (TCP4); Calgranulin A; Myeloid cell nuclear differentiation antigen (MNDA); Phosphoglycerate mutase 1; Ubiquinone biosynthesis protein COQ9, mitochondrial (COQ9); and Pulmonary surfactant-associated protein D (SP-D); XAGE2; SPH; HK3; HNRNPA1.I: S100A9; SUB1; HMGB2; PGAM2; LSP1; SFTPD.l; FNBP1; RBP7; CAPG; MAP3K3. L PSTPIP1; ZBP1; S100A12; ADSS2; S100A8; SNRPA; SMNDC1; S100A8.S100A9; HNRNPA2B1; NADK; NCF1; IL21R; and IL18. The panel may comprise additional secondary capture agents that are capable of binding to any combination of the secondary analytes of Tables 1. 3, 4, and 8. "‘Secondary capture agents” are proteins that are downregulated in diabetes patients in comparison healthy subjects. The methodmay further comprise detecting whether the at least one secondary capture agent binds to at least one secondary analyte in the sample.

[0033] The panel may comprise two or more of the secondary capture agents. The panel may comprise betw een two and tw enty of the secondary capture agents, and any amount or range in between. The panel may comprise two, three, four, five, six, seven, ten, fifteen, twenty, thirty, forty, fifty, sixty, etc. of the secondary capture agents. The panel may comprise all of the secondary’ capture agents. The method may comprise detecting whether two or more secondary capture agents bind a secondary analyte in the sample. The method may comprise detecting binding of betw een tw o and twenty of the secondary’ capture agents, or any amount or range in betw een. The method may comprise detecting binding of tw o, three, four, five, six, seven, ten, fifteen, twenty, etc. of the secondary’ capture agents. The method may comprise detecting binding of all of the secondary capture agents.

[0034] The method may further comprise measuring the amount of at least one primary analyte bound to at least one primary' capture agent and the amount of the at least one secondary' analyte bound to the at least one secondary capture agent. The method may further comprise measuring the amount of the at least one primary analyte and the at least one secondary analyte in a control sample; wherein a higher amount of the primary analyte in the biological sample compared to the control sample is indicative of type 1 diabetes in the subj ect; and w herein a low er amount of the secondary analyte in the biological sample compared to the control sample is indicative of type 1 diabetes in the subject. In preferred embodiments, the control sample is a sample from a healthy subject. The terms "quantity," "amount," and "level" are synonymous and generally’ well- understood in the art. The terms as used herein may particularly refer to an absolute quantification of a molecule or an analyte in a sample, or to a relative quantification of a molecule or analyte in a sample, i.e., relative to another value such as relative to a reference value as taught herein, or to a range of values indicating a base-line expression of the molecule or analyte in a sample obtained from a healthy subject.

[0035] If type 1 diabetes is detected in a sample derived from a subject, the method may further comprise administering to the subject an effective amount of a type 1 diabetes therapeutic.

[0036] As used herein, the term "administering", refers to dispensing, delivering, or applying a therapeutic, to a subject by any suitable route for delivery’ of the inhibitor to the desired location in the subject, including delivery’ by either the parenteral or oral route, intramuscular injection, subcutaneous / intradermal injection, intravenous injection, intraperitoneal injection, intrathecal administration, buccal administration, transdermal delivery, topical administration, and administration by the intranasal or respiratory tract route.

[0037] As used herein, the terms “therapeutic” and “therapeutic agent” refer to an agent used to treat or prevent a disease, e.g. diabetes. The terms "effective amount" or “therapeutically effective amount” refer to an amount sufficient to effect beneficial or desirable biological and / or clinical results. The amount of the agent or pharmaceutical composition that is therapeutically effective may vary depending on the particular condition of the subject. Appropriate dosages may be determined, for example, by extrapolation from cell culture assays, animal studies, or human clinical trials taking into account body weight of the patient, absorption rate, half-life, disease severity and the like. The dosage lies preferably within a range of circulating concentrations that include the ED50 with little or no toxicity. The dosage may vary’ within this range depending upon the dosage form employed and the route of administration utilized. A dose can be formulated in animal models to achieve a circulating plasma concentration range that includes the IC50 (i.e., the concentration of the test compound which achieves a half-maximal inhibition of symptoms) as determined in cell culture. Such information can be used to more accurately determine useful doses in humans. Levels in plasma may be measured, for example, by high performance liquid chromatography.

[0038] The terms "treating," "treat" and "treatment" include (i) preventing a disease, pathologic or medical condition from occurring (e.g., prophylaxis); (ii) inhibiting the disease, pathologic or medical condition or arresting its development; (iii) relieving the disease, pathologic or medical condition; and / or (iv) diminishing symptoms associated with the disease, pathologic or medical condition. Thus, the terms "treat", "treatment", and "treating" can extend to prophylaxis and can include prevent, prevention, preventing, lowering, stopping or reversing the progression or severity of the condition or symptoms being treated. As such, the term "treatment" can include medical, therapeutic, and / or prophylactic administration, as appropriate.

[0039] In embodiments, the type 1 diabetes therapeutic is an IgM antibody. The term “antibody” as used herein refers to immunoglobulin molecules or other molecules which comprise an antigen binding domain. The term “antibody” is thus intended to include whole antibodies, monoclonal antibodies, chimeric antibodies, humanized antibodies, and antibody fragments, including single chain variable fragments (ScFv), single domain antibodies (nanobodies), and antigen-binding fragments, genetically engineered antibodies, among others, as long as the characteristic properties (e.g., ability to bind CD30) are retained. The term "antibody fragment" as used herein is intended to include any appropriate antibody fragment that displays antigen binding function, for example, Fab, Fab', F(ab')2, scFv, Fv, dsFv, ds-scFv, Fd, mini bodies, monobodies, and multimers thereof and bispecific antibody fragments. The term “IgM” can be used, depending on the context, to include natural, synthetic, or recombinant IgM as well as IgM from various sources.including polyclonal serum IgM and purified naturally occurring IgM.

[0040] The methods may further comprise obtaining the biological sample from the subject before contacting the sample with the panel of capture agents. Methods for obtaining and preparing blood and serum samples are well known in the art.

[0041] In embodiments, the method comprises only detecting the secondary7analytes.

[0042] In a second aspect, provided herein is a method comprising contacting a biological sample from a subject suspected of having type 2 diabetes with a panel of capture agents, wherein the panel comprises at least one primary capture agent that is capable of binding to a primary analyte selected from a primary group of proteins comprising Ribonuclease A family member 1; Platelet- Derived Growth Factor Subunit B; C-X-C motif chemokine ligand 11.1; Integrin-Linked Kinase; Tropomyosin 4; Hepcidin Antimicrobial Peptide; and Synaptotagmin Like 4; Platelet-Derived Growth Factor Subunit B (PDGFB); and Integrin-Linked Kinase; and detecting whether the at least one primary capture agent binds to at least one primary analyte in the sample. The panel may comprise additional primary7capture agents that are capable of binding to any combination of the primary analytes of Tables 5 and 6.

[0043] Type 2 diabetes is a condition in which the body (e.g. muscles, liver, and fat) stop responding to insulin. This is called insulin resistance.

[0044] The panel may comprise two or more of the primary capture agents. The panel may comprise between two and tw enty of the primary capture agents, and any amount or range in between. The panel may comprise two, three, four, five, six, seven, ten, fifteen, twenty, thirty, forty, fifty, sixty, etc. of the primary7capture agents. The panel may comprise all of the primary capture agents. The method may comprise detecting whether two or more primary capture agents bind a primary analy te in the sample. The method may comprise detecting binding of between two and twenty7of the primary7capture agents, or any amount or range in between. The method may comprise detecting binding of two, three, four, five, six, seven, ten, fifteen, twenty, etc. of the primary capture agents. The method may comprise detecting binding of all of the primary7capture agents.

[0045] The panel may comprise at least one secondary capture agent capable of binding to a secondary analyte selected from a secondary group of proteins comprising Phosphoglycerate Mutase 1; Zymogen Granule Protein 16; and PGAM1. The panel may comprise additional secondary capture agents that are capable of binding to any combination of the secondary7analytes of Tables 5 and 6. The method may further comprise detecting whether the at least one secondary7capture agent binds to at least one secondary analyte in the sample.

[0046] The panel may comprise tw o or more of the secondary capture agents. The panel maycomprise between two and twenty of the secondary capture agents, and any amount or range in between. The panel may comprise two, three, four, five, six, seven, ten, fifteen, twenty, thirty, forty, fifty, sixty, etc. of the secondary capture agents. The panel may comprise all of the secondary capture agents. The method may comprise detecting whether two or more secondary7capture agents bind a secondary analyte in the sample. The method may comprise detecting binding of between two and twenty of the secondary capture agents, or any amount or range in between. The method may comprise detecting binding of two, three, four, five, six, seven, ten, fifteen, twenty, etc. of the secondary capture agents. The method may comprise detecting binding of all of the secondary capture agents.

[0047] The method may further comprise measuring the amount of the at least one primary7analyte bound to the at least one primary capture agent and the amount of the at least one secondary analyte bound to the at least one secondary capture agent. The method may further comprise measuring the amount of the at least one primary analyte and the at least one secondary7analyte in a control sample; wherein a higher amount of the primary7analyte in the biological sample compared to the control sample is indicative of type 2 diabetes in the subject; and wherein a lower amount of the secondary analyte in the biological sample compared to the control sample is indicative of ty pe 2 diabetes in the subject. If the subject has ty pe 2 diabetes, the method further comprises administering to the subject an effective amount of a ty pe 2 diabetes therapeutic.

[0048] In embodiments, the method comprises only detecting the secondary analytes.

[0049] In a third aspect, provided herein is a detection reagent that may be used for diagnosing ty pe 1 diabetes, comprising one or more primary capture agents, wherein each primary7capture agent binds to a primary analyte selected from a primary7group of proteins comprising hydroxyacylglutathione hydrolase (GLO2); INPP5B; CCT5; ARHGAP6; SRC.l; ARL8A; SRC; ATP5PO; PYGB; DARS2; RAB32; TTC1; SHMT2; ATP5PF; PRTFDC 1 ; ACADVL; LDLRAP1; PNPT1; ARL6IP5; ACOT13; YARS1; CCT8; ITGA2B.ITGB3; PKM; PLEKHF2; GAP43; RAN; FN3K; RAB8B; STK24; PPID; UBE2D1.UBB; UBASH3B; CCT7; IVD; PSG2; BTK; GPD1.1; TFAM; DAB2; LANCL2; ITGA2B.ITGB3.1; MAPRE3; BTK.1; ILK; GSK3B; ACOX1; APRT; CIAO1; PRKCA; EIF1B; AKR7A2; RAB3C; PPP1CC; ACTN1; ARRB1; UNC45A.1; CEP41; RAB21; ME2; PDHA2; RHOC; PMM2; SGTA; ECHS1; HSPD1; SPHK1; MGMT; MBNL2; NRGN; ENO1; NME7; SAR1A; PPIF; MARS1; MBNL1; PPIF. l; UFD1; PCBP2; VCP; INPP5A; FHOD1; COX5A; NAP1L1; GMPR; CLIC4.1; MRPL1; DBT; ZADH2; MAP4K5; HSD17B10; NAP1L4; HSPB1; NCKIPSD; VIL1; DDX6; CLIC4; MAPRE2; SNTA1; NME4; PDIA5; HAGH; CLIC1; PRPSAP2; PIP4K2A; NACA; PDE5A; KIFBP; PIP4K2B; DECR1; GSK3A; RAB4B; CAP1; ADH6; MTIF3; ACADM; DNM1; COPB2; SYTL4.1; DTD1;PTRHD1; PRDX3; PDE5A.1; EIF2B1; CTPS1; RTCA; RBPMS2; DDI2; RAB6B; PGD; RILPL2; HADH; RAB1B; TARSI; MAPK10; RHOT1; AGFG2; PIH1D1; ALAD; CDC42; DAPP1; ABHD10; EIF4A2.1; SERPINH1; LYN; and PEARL 1; wherein the one or more primary capture agents is linked to a solid support. The primary group of proteins may further comprise any combination of the primary' analytes of Tables 1, 3, 4, and 8. The detection reagent may comprise one or more secondary’ capture agents, wherein the secondary capture agent binds to a secondary analyte selected from a secondary group of proteins comprising Gamma-interferon- inducible protein 16:Isoform 2, Hematopoietic expression, interferon-inducible nature, and nuclear localization 1 (IF16:HIN 1); Neutrophil cytosol factor 1 (NCF-1); High mobility7group protein B2 (HMG-2): Histone H1.2; Protein S100-A8 / A9 heterodimer (S100A8 / S100A9); Activated RNA polymerase II transcriptional coactivator pl5 (TCP4); Calgranulin A; Myeloid cell nuclear differentiation antigen (MNDA); Phosphoglycerate mutase 1 ; Ubiquinone biosynthesis protein COQ9, mitochondrial (COQ9); and Pulmonary surfactant-associated protein D (SP-D); XAGE2; SPH; HK3; HNRNPA1.1; S100A9; SUB1; HMGB2; PGAM2; LSP1; SFTPD.l; FNBP1; RBP7; CAPG; MAP3K3.1; PSTPIP1; ZBP1; SI00A12; ADSS2; S100A8; SNRPA; SMNDC1; SI00A8.S100A9; HNRNPA2B1; NADK; NCF1; IL21R; and IL18. The secondary group of proteins may further comprise any combination of the secondary' analytes of Tables 1, 3, 4, and 8. In embodiments, the detection reagent comprises secondary7capture agents and no primary capture agents.

[0050] In a fourth aspect, provided herein is a detection reagent that may be used for diagnosing ty pe 2 diabetes, comprising one or more primary capture agents, wherein each primary7capture agent binds to a primary7analyte selected from a primary7group of proteins comprising Ribonuclease A family member 1; Platelet-Derived Growth Factor Subunit B; C-X-C motif chemokine ligand 11.1; Integrin-Linked Kinase; Tropomyosin 4; Hepcidin Antimicrobial Peptide; and Synaptotagmin Like 4; Platelet-Derived Growth Factor Subunit B (PDGFB); and Integrin- Linked Kinase; wherein the one or more primary capture agents is linked to a solid support. The primary7group of proteins may further comprise any combination of the primary analytes of Tables 5 and 6. The detection reagent may comprise one or more secondary capture agents, wherein the secondary capture agent binds to a secondary analyte selected from a secondary group of proteins comprising Phosphoglycerate Mutase 1; Zymogen Granule Protein 16; and PGAM1. The secondary7group of proteins may further comprise any combination of the secondary7analytes of Tables 5 and 6.

[0051] In embodiments, the detection reagent comprises secondary capture agents and no primary capture agents.

[0052] As used herein, the term "solid support" refers to any substrate or support matrix to which a molecule (such as a lipid, antibody, or antibody-lipid complex) can be bound, either reversibly or irreversibly. Non-limiting examples of solid supports include, without limitation, a bead, plate, slide, flow chamber, chip, cartridge, flow cell, etc. The solid support may be formed from glass, ceramic, polymers (e.g., plastic, latex, polystyrene, polyacrylamide, polyvinylchloride, polypropylene, polyethylene, polylactic acid), cellulose (e.g., paper). In some embodiments, a lipid is immobilized on a solid support. In some embodiments, the lipid is a phospholipid. The lipid may be phosphatidic acid (PA), phosphatidyl choline (PC), and / or phosphatidylserine (PS). In some embodiments, the immobilized lipid is bound to the solid support, either reversibly or irreversibly, to form a platform for an antibody binding reaction. In some embodiments, there are two or more lipids which are immobilized on the solid support in configuration so as to form a lipid panel.

[0053] As used herein, the term "bead" refers to a microbead or relatively small bead having a diameter less than about 500 pm, typically with a diameter of less than about 100 pm, 50 pm, or 10 pm. While a bead may vary in shape, typically a bead is substantially spherical, e g. a microsphere. Microbeads are known in the art and can comprise various materials such as, e.g., latex, polystyrene, polyacrylamide, polyvinylchloride, polypropylene, polyethylene, polylactic acid, ceramic, glass, and magnetic compositions.

[0054] As used herein, the term "plate" encompasses a solid support comprising a variety of types, including plastic or glass plates. In some embodiments, the plate is a 96-well, 384-well, or 1536- well plastic plate.

[0055] The method may comprise the step of washing away unbound antibodies after contacting the sample with a reagent.

[0056] The method may comprise immobilizing antibody-antigen complexes to a solid support. The solid support may comprise a bead, plate, or flow cell.

[0057] The method may comprise flowing the sample through a flow-chamber or plurality of microfluidic chambers.

[0058] The detecting or measuring step may comprise using a detectable binding agent to determine the presence / absence and / or level of antigen specific antibodies. The detectable binding agent may comprise one or more anti-human IgG and / or anti-human IgA antibodies comprising a detectable label or conjugated to a detectable agent.

[0059] As used herein, a "detectable binding agent" refers to a molecule that binds, either directly or indirectly, to a target molecule, and can be detected either directly, or with further chemical or enzymatic reaction. By way of example, but not by way of limitations, a detectable binding agentmay comprise an antibody linked to an enzyme, such as horseradish peroxidase (HRP). Addition of an HRP substrate, under enzymatic reaction conditions, will allow detection of the HRP-bound molecule and the target. Other examples of detectable binding agents and include antibodies linked to detectable labels, such as fluorescent labels. Such constructs are well known in the art and are not intended to limit the scope of the present methods and compositions.

[0060] As used herein, a "detectable agent" or "detectable label" refers to an agent or label that can be detected either directly, or with further chemical or enzymatic reaction. By way of example, but not by way of limitations, a detectable agent or label may comprise a fluorescent moiety or an enzyme, such as HRP. Other examples of detectable agents and labels include radioisotope. Such agents and labels are well known in the art and are not intended to limit the scope of the present methods and compositions.

[0061] Miscellaneous

[0062] The following definitions are included to provide a clear and consistent understanding of the specification and claims. As used herein, the recited terms have the following meanings. All other terms and phrases used in this specification have their ordinary meanings as one of skill in the art would understand. Such ordinary meanings may be obtained by reference to technical dictionaries, such as Hawley's Condensed Chemical Dictionary 14th Edition, by R.J. Lewis, John Wiley & Sons, New York, N.Y., 2001.

[0063] References in the specification to "one embodiment." "an embodiment," etc., indicate that the embodiment described may include a particular aspect, feature, structure, moiety, or characteristic, but not every embodiment necessarily includes that aspect, feature, structure, moiety, or characteristic. Moreover, such phrases may, but do not necessarily, refer to the same embodiment referred to in other portions of the specification. Further, when a particular aspect, feature, structure, moiety, or characteristic is described in connection with an embodiment, it is within the knowledge of one skilled in the art to affect or connect such aspect, feature, structure, moiety, or characteristic with other embodiments, whether or not explicitly described.

[0064] The singular forms "a," "an," and "the" include plural reference unless the context clearly dictates otherwise. Thus, for example, a reference to "a compound" includes a plurality of such compounds, so that a compound X includes a plurality of compounds X. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to sen e as antecedent basis for the use of exclusive terminology, such as "solely," "only," and the like, in connection with any element described herein, and / or the recitation of claim elements or use of "negative" limitations.

[0065] The term "and / or" means any one of the items, any combination of the items, or all of theitems with which this term is associated. The phrase "one or more" is readily understood by one of skill in the art. particularly when read in context of its usage. For example, one or more substituents on a phenyl ring refers to one to five, or one to four, for example if the phenyl ring is di-substituted.

[0066] As used herein, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating a listing of items, “and / or” or “or” shall be interpreted as being inclusive, e.g.. the inclusion of at least one. but also including more than one of a number of items, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e., “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.”

[0067] As used herein, the terms “including,” “includes,” “having,” “has,” “with,” or variants thereof, are intended to be inclusive similar to the term “comprising.”

[0068] The term "about" can refer to a variation of ± 5%, ± 10%, ± 20%, or ± 25% of the value specified. For example, "about 50" percent can in some embodiments carry a variation from 45 to 55 percent. For integer ranges, the term "about" can include one or two integers greater than and / or less than a recited integer at each end of the range. Unless indicated otherwise herein, the term "about" is intended to include values, e.g.. weight percentages, proximate to the recited range that are equivalent in terms of the functionality of the individual ingredient, the composition, or the embodiment. The term about can also modify the endpoints of a recited range as discuss above in this paragraph.

[0069] The term "substantial identity" or "substantial similarity" of polynucleotide or peptide sequences means that a polynucleotide or peptide comprises a sequence that has at least 75% sequence identify. Alternatively, percent identify can be any integer from 75% to 100%. More preferred embodiments include at least: 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%. 96%. 97%. 98% or 99% compared to a reference sequence using the programs described herein: preferably BLAST using standard parameters, as described. These values can be appropriately adjusted to determine corresponding identify of proteins encoded by two nucleotide sequences by taking into account codon degeneracy, amino acid similarity, reading frame positioning and the like.

[0070] The determination of percent identity between two nucleotide or amino acid sequences can be accomplished using a mathematical algorithm. For example, a mathematical algorithm usefulfor comparing two sequences is the algorithm of Karlin and Altschul (1990, Proc. Natl. Acad. Sci. USA 87:2264-2268), modified as in Karlin and Altschul (1993, Proc. Natl. Acad. Sci. USA 90:5873-5877). This algorithm is incorporated into the NBLAST and XBLAST programs of Altschul, et al. (1990, J. Mol. Biol. 215:403-410), and can be accessed, for example at the National Center for Biotechnology' Information (NCBI) world wide web site having the universal resource locator using the BLAST tool at the NCBI website. BLAST nucleotide searches can be performed with the NBLAST program (designated “blastn” at the NCBI web site), using the following parameters: gap penalty = 5; gap extension penalty = 2; mismatch penalty = 3; match reward = 1; expectation value 10.0; and word size = 11 to obtain nucleotide sequences homologous to a nucleic acid described herein. BLAST protein searches can be performed with the XBLAST program (designated "blastn" at the NCBI web site) or the NCBI "blastp" program, using the following parameters: expectation value 10.0, BLOSUM62 scoring matrix to obtain amino acid sequences homologous to a protein molecule described herein. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al. (1997, Nucleic Acids Res. 25:3389-3402). Alternatively, PSI-Blast or PHI-Blast can be used to perform an iterated search which detects distant relationships between molecules (Id.) and relationships between molecules which share a common pattern. When utilizing BLAST, Gapped BLAST, PSI-Blast, and PHI-Blast programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used.

[0071] The percent identity between two sequences can be determined using techniques similar to those described above, with or without allowing gaps. In calculating percent identity, typically exact matches are counted.

[0072] As will be understood by the skilled artisan, all numbers, including those expressing quantities of ingredients, properties such as molecular weight, reaction conditions, and so forth, are approximations and are understood as being optionally modified in all instances by the term "about." These values can vary depending upon the desired properties sought to be obtained by those skilled in the art utilizing the teachings of the descriptions herein. It is also understood that such values inherently contain variability necessarily resulting from the standard deviations found in their respective testing measurements.

[0073] The term “standard,” refers to something used for comparison. For example, it can be a known standard agent or compound which is administered and used for comparing results when administering a test compound, or it can be a standard parameter or function which is measured to obtain a control value when measuring an effect of an agent or compound on a parameter or function. Standard can also refer to an “internal standard”, such as an agent or compound whichis added at known amounts to a sample and is useful in determining such things as purification or recovery rates when a sample is processed or subjected to purification or extraction procedures before a marker of interest is measured. Internal standards are often a purified marker of interest which has been labeled, such as with a radioactive isotope, allowing it to be distinguished from an endogenous marker.

[0074] As will be understood by one skilled in the art. for any and all purposes, particularly in terms of providing a written description, all ranges recited herein also encompass any and all possible sub-ranges and combinations of sub-ranges thereof, as well as the individual values making up the range, particularly integer values. A recited range (e.g., weight percentages or carbon groups) includes each specific value, integer, decimal, or identity within the range. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, or tenths. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art, all language such as "up to," "at least," "greater than," "less than," "more than," "or more." and the like, include the number recited and such terms refer to ranges that can be subsequently broken down into sub-ranges as discussed above. In the same manner, all ratios recited herein also include all sub-ratios falling within the broader ratio. Accordingly, specific values recited for radicals, substituents, and ranges, are for illustration only; they do not exclude other defined values or other values within defined ranges for radicals and substituents.

[0075] One skilled in the art will also readily recognize that where members are grouped together in a common manner, such as in a Markush group, the invention encompasses not only the entire group listed as a whole, but each member of the group individually and all possible subgroups of the main group.

[0076] Additionally, for all purposes, the invention encompasses not only the main group, but also the main group absent one or more of the group members. The invention therefore envisages the explicit exclusion of any one or more of members of a recited group. Accordingly, provisos may apply to any of the disclosed categories or embodiments whereby any one or more of the recited elements, species, or embodiments, may be excluded from such categories or embodiments, for example, for use in an explicit negative limitation.

[0077] Methods involving conventional molecular biology techniques are described herein. Such techniques are generally known in the art and are described in detail in methodology treatises, such as Molecular Cloning: A Laboratory’ Manual. 2nd ed., vol. 1-3, ed. Sambrook et al., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 1989; and Current Protocols inMolecular Biology, ed. Ausubel et al., Greene Publishing and Wiley-Interscience, New York, 1992 (with periodic updates). Methods for chemical synthesis of nucleic acids are discussed, for example, in Beaucage and Carruthers, Tetra. Letts. 22: 1859-1862, 1981, and Matteucci et al., J. Am. Chem. Soc. 103:3185, 1981.EXAMPLES

[0078] Example 1 - Human Type 1 Diabetes (Boulder Bioconsulting)

[0079] Methods:

[0080] Human Samples:

[0081] Methods: 76 K2-EDTA plasma samples from type 1 diabetic patients (n=38) and from age and sex matched control healthy humans (n=38) were obtained from Precision for Medicine (Norton, MA 02766, US; www. precision biospecimens.com). Precision for Medicine is FDA registered, College of American Pathologists (CAP) accredited and Clinical Laboratory Improvement Amendments (CLIA) certified. Biospecimens were collected under Institutional Review Board (IRB) approval with proper consent via Precision for Medicine’s network of collection sites in accordance with federal guidelines. Information regarding Lot Number, Inventory Barcode, Matrix, date of draw as well as patient characteristics such as Age, Sex, Ethnicity7, Race, Concurrent Medical Conditions and Medications were provided. One test sample was discarded as it was HIV positive. All remaining samples were negative for HIV, COVID-19 and other infectious diseases.

[0082] SomaScan Assay: Profiling of the plasma proteome was carried out using the SomaScan® Assay, which is a highly multiplexed aptamer-based proteomic technology7capable of making over 7,000 protein measurements. These chemically modified aptamers, called SOMAmer® Reagents, form complex three-dimensional shapes which bind to epitopes on their target protein with high affinity and specificity. The amount of the available protein epitope is read out by hybridizing the SOMAmer reagents in the SomaScan Assay eluate to complementary sequences on a DNA microarray7and the fluorescence measured as relative fluorescence units. The assay results provided by SomaLogic in a proprietary7text-based format called AD AT, contained the reagent intensities, sample and sequence annotation and experimental metadata. This technology has a dynamic range of more than 10 logs, allowing quantitation of both low and high abundant proteins which might otherwise be missed.

[0083] Data analyses.

[0084] Signal Pre-processing and Quality Control of Proteomics data: Signal from .adat files was normalized using cyclic loess normalization (1) and log2 transformed prior to downstreamanalysis. An outlier detection approach based on sample correlation was performed. The median and mad (median absolute deviation) was calculated for all sample pairwise Pearson correlations (estimated across all SomaMers (proteins)). If the median pairw ise correlation for a given sample was less than 3 MAD units from the global median, the sample was considered an outlier. No outliers were detected and / or removed based on this method. Principal Components Analysis w as used during the QC process to look for global differences between diabetic and healthy control sample treatment, as well as look for potential outliers. Based on these results and a cursory analysis of protein level signal differences within and between treatment groups a significant batch effect was observed. Ballman KV, Grill DE, Oberg AL, Themeau TM. Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics. 2004 Nov l;20(16):2778-86. doi: 10.1093 / bioinformatics / bth327. Epub 2004 May 27. PMID: 15166021.

[0085] Surrogate Variable Analysis (SV A). Based on the QC analysis. Surrogate Variable Analysis (SV A) was performed to remove potential noise (latent or hidden batch effects) using the SVA library in R (2). Signal data was used for downstream plotting, PC A, clustering and development of classification models. Differential Expression (DE) analysis of proteins was performed using the limma package in R (3). Differential expression analysis was performed with a linear model containing the discrete treatment effect of interest (diabetic vs non-diabetic) and the SV was treated as a covariate in the model. DE proteins were selected based on a false discovery rate (FDR) q value <=0.05 and >=2 absolute fold-change criteria unless otherwise stated.

[0086] Pathway Analysis

[0087] Analysis of enriched pathways was performed for DE proteins detected by the limma regression model (FDR q<0.05, n=2,168 proteins), using gprofiler2 (4) in R. Annotated gene sets representing Gene Ontology biological processes, Hallmark pathways, C6 Oncogenic signature gene sets and Canonical Pathways (incl. Biocarta, KEGG, PID, Reactome and Wikipathways) were downloaded from the Molecular Signatures Database v7.5.1 for use as target pathways (5). Due to the semi-targeted nature of the SomaScan protein quantification method a unique list of all proteins interrogated on the platform (n=6,365) were used as a statistical null background. An FDR threshold of q<0.25 w as used to select enriched gene sets for further analysis. The Cytoscape Enrichment Map plugin v3.3.4 (6) w as used for clustering and visualization of enriched pathways. Default clustering parameters were applied.

[0088] Predictive model Analysis: Four different modeling methods: elastic net, random forests, KNN, and SVM were used to assess the abi 1 i ty to classify the diabetic vs control specimens based on proteomic profiles using the caret framework in R (7). For all models constructed, a 20% holdout test set was used for final model evaluation (n=14 samples, control=7, diabetic=7). The 80% of samples (n=61) remaining were used for Leave One Out Cross Validation (LOOCV) and final model parameter estimation. SV A adjusted signal from the 199 DE proteins found with FDR<0.05 and >2 fold-change was used as input for the four classifiers. All protein signal data was centered and scaled prior to modeling. For all models, 10 distinct values of model hyperparameters were optimized during LOOCV (i.e. tuneLength=10). ROC AUC was used for LOOCV parameter optimization and model performance evaluation. Final test accuracy was assessed for the 20% hold out set.

[0089] RESULTS:

[0090] Human samples: Information regarding Lot Number, Inventory Barcode, Matrix, date of draw as well as patient characteristics such as Age, Sex, Ethnicity. Race, Concurrent Medical Conditions and Medications were provided (not shown).

[0091] Quality control: Raw signal boxplots showed significant variability between samples. Both anmlSMP and Cyclic loess normalization reduced intersample variance to acceptable levels. Cyclic loess was chosen for subsequent analysis due to improved intersample correlation. Limit of detection analysis showed good performance with the exception of sample Cl which had slightly lower sensitivity than other samples (4.6% of SomaMers fell below the eLOD). Although 3 samples were flagged by anmlSMP method for QC (Cl, C35, T19), correlation based detection of outlier samples did not show any samples falling below the threshold. Therefore, no samples were removed for subsequent downstream analysis.

[0092] Principal component analysis of Significant Proteins (shown in FIG. 1) confirm strong clustering of healthy vs. sick samples. This is also confirmed in a heat map with Hierarchical Clustering (Euclidean distance / Average Linkage) of Significant Proteins (data not shown) which confirms the strong trends seen in the PCA plot.

[0093] Differential expression analysis. Table 1 shows 199 SomaMers that were detected based on an FDR q value <=0.05 and at least a Log2 Fold Change of >1.0 or <-1.0.

[0094] Table 1. The 199 differentially expressed (DE) proteins. 45 of the differentially expressed proteins had a Log2 FC>1 5 and Log2 FC<-1.5 (bolded). Positive Log2FC indicates protein was upregulated in the diabetic sample. Negative Log2FC indicates protein was downregulated in the diabetic sample.

[0095] Pathway Analysis of DE SomaMers

[0096] Pathway analysis was performed to identify significantly enriched gene ontology (GO) biological processes and pathways. Pathway analysis using differentially expressed proteins with FDR<=0.05 (n=2,168) was performed. Differentially expressed proteins were used as input for the g: profiler pathway enrichment method using gprofiler2.v0.2.1 inR. Several enriched pathways that were associated with differentially expressed proteins were identified in type 1 diabetes. Clustering of pathways detected by gprofiler using differentially expressed somamers genes withfdr<=0.05 (n=2,168) demonstrated 43 pathways were enriched. 28 enriched pathways were detected at FDR Clustering of enriched pathways showed that many were involved with peptide metabolism, intracellular transport and localization, receptor signaling and RNA processing. See FIG. 2 and Table 2.

[0097] Table 2. Enriched Pathways

[0098] Predictive Model Analysis: Elastic net, random forests, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) were used to assess the ability to differentiate the diabetic vs control specimens using the 199 DE proteins detected above. Classifiers were developed to determine if proteomic data can be used to predict type 1 diabetes. SVM had the highest classification performance (AUC=0.996), followed by Elastic Net (AUC=0.994), KNN (0.9875) and RF (AUC=0.98) (see FIG. 3). The testing accuracy was 100% with all 4 models (meaning the models correctly identified all specimens in the 20% holdout set).

[0099] Example 2 - Human Type 1 Diabetes (Bioinformatics Core, University of Virginia)

[0100] The samples of Example 1 were re-analyzed with Bioinformatics core. UVA. Of 7323 proteins, 98 differentially expressed proteins were identified. Table 3 shows 98 differentially expressed proteins detected based on at least a Log2 Fold Change of >1.0 or <-1.0.

[0101] Table 3. The 98 differentially expressed (DE) proteins. 12 of the differentially expressed proteins had a Log2 FC>1.5 and Log2 FC<-1.5 (bolded).

[0102] Example 3 - Human Type 1 Diabetes (Bioinformatics Core, University of Virginia)

[0103] A second set of samples were analyzed by the Bioinformatics Core. The samples included28 non-diabetic healthy controls and 28 diabetic patients. Table 4 shows 591 differentially expressed proteins (Log2 FC> 1.0 and Log2 F C<- 1.0).

[0104] Table 4. The 591 differentially expressed (DE) proteins. 304 of the differentially expressed proteins had a Log2 FO1.5 and Log2 FC<-1.5 (bolded).

[0105] The DE proteins identified in the Example 2 and Example 3 samples were compared. There are about 136 overlapping proteins between these two data sets, with a criteria of fold change |1| and a p value of < 0.05. Common up-regulated proteins are MYL12A and MYL9. Common down- regulated proteins are PARP1. MUCL ST6GALNAC3, HTATIP2, OLFM3, INSIGL HSPA1 A. 1, ADAM7, BTNL3, ASPRV1, RUNX3, EQTN, APOL1, APOL1.1, OTUB2, SETD2 HECW1, GJA8, FBXL5, NEDD9, STAU2, RELL1, DIXDC1, SFB4, ACBD7, GTF2I, GZMB, SERPINA4, BLMH, ID2, VIM, BMP4, MAGOHB, SEMA4A, PSIP1, STOML1, AFM, SVIP, C4A.C4B, HDGFL3, PDE4A, UBLCP1, RPRD1B, RPL12, NDRG1, PEF1, LRRTM1, LAMA3, BASP1, EIF4EBP2, CREB1, TCEA2.1, BUD31, FAM50A, PIK3IP1, MMACHC, DUSP18, POLR2J2, MAGOH, ITPK1, ,CHRNAS, MEP1A.1, RPS20, MGAT5 1, IL17A.IL17F, WNT16. UPB1, ETV4, TEX30, RNF141, TCEA3, APOL2, PPP1R27, KIAA11143, KLHL2, VSIG10L. OCLN, CEACAM16, TRAPPC6A. PYCARD, PLCD1, TIAM2, HCE000104, SOX9, EEF1A1.2, HMOX2, IL13RA1. GDF1 1.MSTN. 1GF1, ABL2. 1L17B 1. MAP2K2. KLK3.2, DAPK2, LCK.l, COTL1, PLA2G7, PRDX5, CD83, MMP16, CCNB1, OMD, RPS3A, BPIFA2. 1 , CLPS,CD70, GDNF, PCSK2.1, DEFB107A, GALNT3, FUT9, CHSTS, None .10, CKAP4, ITGB6, SH3BP2. CLEC2B, PRDX4, MSRB3. MRAP, MSMP, SOSTDC1. ETNK1, OXT. l. RNASEH1, CILP2, HNRNPAB. l, GNPDA1, NXPH2, GZMK, TXNDC11.1, NDE1, NXT1.

[0106] Example 4 - Human Type 2 Diabetes (Bioinformatics Core UVA)

[0107] Methods: 76 age- and sex-matched, healthy and type 2 diabetic human K2-EDTA plasma samples were obtained from Precision for Medicine, and sent to Somalogic for proteomics analyses. 7,288 SomaMers (proteins) were evaluated. Proteomic data from Somalogic was received as .AD AT files containing raw and / or anmlSMP normalized microarray signals. The data was further analyzed using R software. Pairwise contrasts were extracted and used for dow nstream analysis.

[0108] Results: 639 proteins were differentially expressed when a cut-off of log2FC>1.0 and adj pvalue<0.001 was applied. A principal component analysis (PCA) plot (not shown) showed perfect clustering of the type 2 diabetic and healthy plasma samples, indicating the data was good to use for further downstream analysis. The log-transformed normalized gene protein expression of the most variable genes was used to perform PCA before performing differential gene expression. A cutoff of log2FC>1.5 demonstrated a number of differentially-expressed proteins in type 2 diabetics. The top 20 novel proteins are shown in Table 5. While all these proteins are significant, RNASE1, PDGFB, CXCL11.1, ILK, TPM4, HAMP, SYTL4.1, PGAM1 and ZG16 are potential biomarkers for Type 2 diabetes prediction and diagnosis.

[0109] Table 5. Top 20 novel, differentially-expressed genes based on highest Log2FoldChange

[0110] Using a cut-off of log2FC>1.5 and Padj value< 2.30E-05 to further narrow down the most relevant, novel, differentially-expressed proteins, 277 significant proteins were obtained. Of these, the role of some of these proteins in diabetes are listed below (Table 6). All of these proteins are very highly significant since they are differentially-expressed with a log2FC>1.5, an Padj value<2.30E-05 which is extremely stringent, and play a role in diabetes. They are all potential biomarkers of T2D, especially PDGFB, ILK, and PGAM1.[OHl] Table 6. Relevant Proteins expressed in type 2 diabetic plasma samples obtained fromPrecision for Medicine and their role in Diabetes

[0112] In the samples obtained from Precision for Medicine, there were 86 differentially expressed proteins in the plasma of type 2 diabetic patients that overlapped with significant and differentially expressed proteins in type 1 diabetics.

[0113] These are: DTD1 NAPA CXCL3 RAC1 MBNL2 ME2 DAB2 SERPINH1 CDYL2LDLRAP1 PLEK ACOT13 TPM4 SMC3 PADI4 HNRNPF CXCL2 TRA2B FHOD1 PIP4K2A PF4 MBNL1 HSPD1 UBA7 DNM1 ACOX1 KIFBP DDX6 PPIF PIP4K2B MAPRE1 CS PCDHGA1 DAPP1 SFTPB LRRC59 MNDA FTL BTK TMPO PPP ICC LANCL2 MANF CCL5 LRRFIP2 PGAM1 MYL9 PDIA6 MAPRE3 ARHGAP6 PDGFB RGS10 CXCL6 ACADVL SPHK1 ARHGAP25 SH3BGRL2 CCT5 CAP1 NAP1L4 INPP5B SNTA1 NCK2 CALD1 DDX39B SH3GL2 HAMP EIF4G2 S100A12 ABHD14A ACTN1 PGAM2 MDH2 BPI RBPMS2 MAP4K5 PRKCA TRIP 10 1NPP5A MMP1 SHMT2 ATP5PF FN3K NAP1L1 PD1A5 DARS2

[0114] Of these 86 common proteins, 43 representative proteins and their roles in diabetes are listed in Table 7.

[0115] Table 7. Representative proteins common in type 1 and type 2 diabetes in humans. 43 out of 86 are shown below.

[0116] Pathway Analysis: The differentially expressed genes proteins were ranked based on the log2fold change and adjusted p- values. The ranked file was used to perform pathway analysis using GSEA software (Subramanian et al. 2005). The enriched pathways were selected based on enrichment scores as well as normalized enrichment scores. Majority of the 639 differentially expressed proteins were implicated in diabetes, immunity, endosomal recycling, endocytosis, and vesicular trafficking, cellular and insulin signaling, metabolic functions including glucose and lipid metabolism, protein synthesis, cell survival and stress response, and actin / tubulin / cytoskeletal organization.

[0117] Table 8. All significantly differentially expressed proteins from ty pe 1 diabetes and healthy samples. The top 30 are bolded.

[0118] Example 5

[0119] Purpose: To demonstrate proteomics as a tool to predict the development of Type 1 Diabetes (T1D), its recurrence of T1D post-transplantation, and the efficacy of IgM treatment.

[0120] Methods: Heparin plasma was obtained from 6-weeks old NOD / ShiLtJ littermates divided into Control (BG<120mg / dL); Prehyperglycemic (BG=175-250mg / dL), Diabetic (BG>250mg / dL); and IgM-treated (IP injection, 50-75 pg once in 5 days until 18-weeks of age) groups. IgM was administered intraperitoneal (50-75pg once in 5 days) until 18-weeks old. Samples (n=5 / group) were also collected at 10 time points (5, 7. 9. 11. 13. 15, 17, 19, 21, 23 weeks of age). Human EDTA plasma samples from T1D and T2D patients and age and sex matched healthy controls were obtained from Precision for Medicine. First T1D batch 75 samples total (37 diabetic and 38 healthy). Second T1D batch 56 samples (28 normal and 28 diabetic) Total 131 human samples for the study 39 T2Diabetic and 37 healthy controls.

[0121] Samples were analyzed using SomaScan proteomic platform. 7288 somamers were analyzed. Signal was normalized using Cyclic Loess normalized Log2 signal; linear regression models fit and ANOVA performed. Pairwise contrasts coefficients and predictions were extracted and used for downstream analysis using R. FDR correction was performed for p values and differentially expressed (DE) protein selection was based on pval<0.05 and / or >log2FC. GSEA v4.2.2 and Cytoscape v3.9.1 EnrichmentMap pipeline vl.1.0 was used for Pathway analysis.

[0122] The first goal was to identify novel protein biomarkers as predictors of Type 1 Diabetes development in non-obese diabetic (NOD) mice. Boulder Bioconsulting was used. Individual results were provided for 3 experiments. We used a cut-off of (FDR) <0.05 and fold change>2. The following differentially expressed proteins were considered highly significant as they had extremely low FDR and very high fold change. Proteomic analysis of NOD mouse samples. This experiment was performed three times. Heparin plasma samples was obtained from controls (healthy, non-diabetic; n=30), and Prehyperglycemic (Blood glucose BG=175-250mg / dL; n=25) NOD mice, as well as from Diabetic (BG>250mg / d;n=25) and age- and sex-matched IgM treated mice n=15. Pancreatic tissues, lymph nodes, thymus and spleen were also collected. Plasma samples were sent to Somalogic for proteomics. 7,288 somamers were evaluated. Data was sent to Boulder Bioconsulting for further proteomic analysis. In order to identify as many significant differentially-expressed proteins as possible while still maintaining a low false positive rate of discovery, we utilized the False Discovery Rate (FDR) statistical feature and its analog, the q- value. Basically, the lower the FDR, the more highly significant the expression of the differentially-expressed proteins. We used a cut-off of (FDR) <0.05 and fold change>2. The following differentially-expressed proteins were considered highly significant as they had extremely low FDR and very high fold change. Results are shown in Table 9. Highly significantdifferentially-expressed proteins (common across all three independently conducted experiments) in Prehyperglycemic stage are: Matrilin-3, Hyaluronan and proteoglycan link protein 1, Sia-alpha- 2,3-Gal-beta-l,4-GlcNAc-R: alpha 2,8-sialyltransferase, Carboxypeptidase M, Tumor necrosis factor ligand superfamily member 10, Glycolipid transfer protein, Mimitin (mitochondrial), and Chondrocalcin. These differentially expressed proteins can be used as predictive biomarkers for T1D development and progression.

[0123] Table 9: Differentially-expressed predictive biomarker proteins detected in prehyperglycemic mice

[0124] Differentially expressed proteins in diabetic mice vs control mice are shown in Table 10. Highly significant DE proteins (common across all three independently conducted experiments) in the diabetic stage are: Matrilin-3. Hyaluronan and proteoglycan link protein 1, Insulin-like growth factor-binding protein 2. Semaphorin-6D. Microfibril-associated glycoprotein 4, Sia- alpha-2,3-Gal-beta-l ,4-GlcNAc-R: alpha 2,8-sialyltransferase, Carboxypeptidase M, Tumor necrosis factor ligand superfamily member 10. Gamma enolase and Ferritin (mitochondrial) are also important.

[0125] Table 10. Differentially expressed diagnostic biomarker proteins detected in diabetic mice

[0126] RESULT 2:

[0127] SECTION 1 - Consolidation of Three Experiments

[0128] BIOINFORMATICS CORE UVA

[0129] Results: Below are the consolidated results of 3 experiments.

[0130] Differentially expressed proteins in prehyperglycemic stage of diabetes. 1250 proteins were significantly and differentially expressed using a log2FC>l cut-off , and 22 proteins when a log2FC>l .5 was used (Table 11). The top significantly and differentially expressed proteins in the prehyperglycemic stage of diabetes.

[0131] Table 11 : Differentially expressed proteins in the prehyperglycemic stage of diabetes with log2FC>1.5 as cut-off

[0132] PDILT (Protein Disulfide Isomerase Like Testis Expressed): there is no clear connection between PDILT and diabetes.

[0133] TNFSF10 (Tumor Necrosis Factor Superfamily, Member 10): Also known as TRAIL, TNFSF10 is part of the TNF superfamily, which is involved in apoptosis and immune cell regulation. Dysregulated apoptosis and inflammation are linked to both T1D and T2D. However, the specific role of TNFSF10 in diabetes is not completely understood. It is involved in immune surveillance and plays a role in immune response. Dysregulation of apoptosis is a key feature of certain autoimmune diseases, though a specific link to TNFSF10 is not well established.

[0134] CTH (Cystathionine Gamma-Lyase): This enzyme is involved in the transsulfuration pathway to produce cysteine.

[0135] GLTP (Glycolipid Transfer Protein): The specific role of GLTP in diabetes is not clear.

[0136] STAU1 (Staufen Double-Stranded RNA Binding Protein 1): involved in mRNA transport and localization, there is no clear connection between STAU1 and diabetes.

[0137] NAPEPLD (N-Acyl Phosphatidylethanolamine Phospholipase D): This enzyme plays a role in the biosynthesis of endocannabinoids, and its expression has been found to be dysregulated in the adipose tissue o f individuals with obesity and T2D. How ever, its specific role in diabetes remains to be fully elucidated.

[0138] IL3 (Interleukin 3): This cytokine is involved in immune cell proliferation and regulation. While immune dysregulation is involved in T1D, a specific role for IL3 is not well established. This cytokine is involved in the growth and differentiation of many hematopoietic cells and is part of the immune response. Dysregulated cytokine production is implicated in various autoimmune diseases.

[0139] SCMH1 (Sex Comb On Midleg Homolog 1): there is no clear connection between SCMH1and diabetes.

[0140] CPM (Carboxypeptidase M): This enzyme has not been directly linked to diabetes, though it does play roles in protein and peptide processing which are key in many biological processes, including potentially those dysregulated in diabetes.

[0141] CASP8 (Caspase 8): This enzyme is involved in apoptosis, which can play a role in the death of insulin-producing beta cells in T1D. However, the specific role of CASP8 in diabetes is not well established. This protein is an enzyme involved in apoptosis, a process that plays a critical role in immune response and autoimmunity.

[0142] TBC1D22B (TBC1 Domain Family Member 22B): there is no clear connection between TBC1D22B and diabetes.

[0143] NDUFAF2 (NADH:Ubiquinone Oxidoreductase Complex Assembly Factor 2): This protein is involved in the assembly of mitochondrial complex I. The specific role of this mitochondrial protein in diabetes is not clear.

[0144] COL1A1 (Collagen Type I Alpha 1 Chain): This is a major collagen protein, and while collagen and extracellular matrix remodeling are implicated in the complications of diabetes, a specific role for COL1A1 is not well established. Collagen proteins such as COL1A1 can contribute to the structure of various tissues, including those involved in immune function.

[0145] THOP1 (Thimet Oligopeptidase 1): there is no clear connection between THOP1 and diabetes. THOP1 is involved in the degradation of small peptides, including some involved in immune responses. However, the specific role of THOP1 in immunity or autoimmunity is not well established.

[0146] TSSK1B (Testis-Specific Serine Kinase IB): there is no clear connection between TSSK1B and diabetes.

[0147] RTRAF (RNA Transcription, Translation And Repair Factor): there is no clear connection between RTRAF and diabetes.

[0148] COL2A1 (Collagen Type II Alpha 1 Chain): This collagen protein is primarily associated with cartilage and eye health. It's not directly linked with diabetes, but systemic complications of diabetes can affect various tissues.

[0149] MATN3 (Matrilin 3): This extracellular matrix protein is primarily associated with skeletal disorders, it has not been directly linked with diabetes.

[0150] HAPLN1 (Hyaluronan And Proteoglycan Link Protein 1): This protein is involved in the organization of the extracellular matrix, which can be affected by the systemic complications of diabetes. However, the direct role of HAPLN1 in diabetes is not clear. HAPLN1 helps in the organization of the extracellular matrix, which plays a role in tissue health and may have indirecteffects on immune function.

[0151] KLK3 (Kallikrein Related Peptidase 3): there is no clear connection between KLK3 and diabetes.

[0152] TBX3 (T-Box 3): TBX3 is a transcription factor important for development, it has not been directly linked to diabetes.

[0153] PMP2 (Peripheral Myelin Protein 2): This protein is related to myelin structure in peripheral nerves. Diabetic neuropathy can result from long-term high blood glucose levels in diabetes, which can damage nerves throughout the body. However, the direct role of PMP2 in this process is not clear. While immune responses can target myelin in certain autoimmune conditions like multiple sclerosis, the specific role of PMP2 in these processes is not well established.

[0154] B. Differentially expressed proteins in diabetes. 118 proteins were significantly and differentially expressed using a log2FC>l cut-off and 29 if a log2FC>1.5 was used (Table 12).

[0155] Table 12: Differentially expressed proteins in diabetes with log2FC>1.5

[0156] The top significantly and differentially expressed proteins with highest log2FC in diabetes are shown below in Table 13.

[0157] Table 13: Top significantly and Differentially expressed proteins in diabetes

[0158] 1. RGMB (Repulsive Guidance Molecule BMP Co-Receptor B): The primary known function of RGMB is related to the development and function of the nervous system, and it's not directly linked to diabetes. However, given the importance of cellular signaling in both neural function and diabetes, there could potentially be an indirect role.

[0159] 2. APP (Amyloid Precursor Protein): This protein gives rise to the amyloid-beta peptides that form plaques in Alzheimer's disease. This disease and diabetes have many overlapping risk factors and potential pathophysiological connections, and some research suggests that insulin dysregulation can affect APP processing. The specifics of how APP might contribute to diabetes are still being explored. While APP isn't traditionally associated with immune function, the plaques formed in Alzheimer's disease can incite an immune response in the brain, data demonstrate the presence of APP within the pancreas, including pancreatic islets in both mouse and human samples. Additionally, we report that the APP / PS 1 mouse model of AD overexpresses APP within pancreatic islets, although this did not result in detectable levels of A(3. Additionally, both mouse and human islets processed APP to release sAPP into cell culture media. Moreover, sAPP stimulated insulin but not glucagon secretion from islet cultures. We conclude that APP and its metabolites are capable of influencing the basic physiology of the pancreas, possibly through the release of sAPP acting in an autocrine or paracrine manner.

[0160] 3. LPO (Lactoperoxidase): This enzyme plays an essential role in the body's defense system against bacteria and viruses. LPO is part of the body's first line of defense against microbes. It's an enzyme found in saliva and milk that helps to kill bacteria and fungi. It does this by catalyzing the oxidation of halides and pseudohalides in the presence of hydrogen peroxide to produce antimicrobial substances.

[0161] 4. KCTD4 (Potassium Channel Tetramerization Domain Containing 4): KCTD4 is involved in protein-protein interactions, and its dysfunction could potentially disrupt various biological processes, possibly including insulin signaling or beta-cell function. KCTD4 is known to play a role in protein-protein interactions, so it might potentially be involved in immune processes at some level

[0162] 5. SHISAL1 (Shisa Like 1): SHISAL1 is known to be involved in protein processing within the cell, which could potentially have implications for a variety of cellular functions, including those related to immunity.

[0163] 6. IGFBP2 (Insulin-Like Grow th Factor Binding Protein 2): IGFBP2 is one of several proteins that bind insulin-like growth factors and regulate their activity. Given this function, it could play a role in metabolic processes relevant to diabetes. Some research suggests that levels of IGFBP2 might be altered in diabetes, and it might have a protective role in metabolic health. IGFs can influence the function of immune cells and might play a role in inflammation and autoimmune disease. For instance, some research suggests that IGFBP2 might be involved in the immune response in the brain, and altered levels of this protein have been reported in certain autoimmune conditions.

[0164] 7. PMP2 (Peripheral Myelin Protein 2): This protein is found in peripheral nerves and is thought to be involved in the process of myelination of nerves. Since it is associated with nerve function, and damage to the nerves (neuropathy) is a common complication of diabetes, that may be the role it plays. In some autoimmune diseases like Guillain-Barre syndrome or chronic inflammatory demyelinating polyneuropathy, the immune system can attack peripheral nerves, potentially implicating proteins like PMP2. Immune-mediated attacks on myelin are a characteristic of certain autoimmune disorders such as multiple sclerosis. However, the exact role of PMP2 in these diseases is not clear.

[0165] 8. TNFSF10 (Tumor Necrosis Factor (Ligand) Superfamily, Member 10): Also known as TRAIL, it plays a role in regulating immune responses and cell death. It can induce apoptosis (programmed cell death) in cancer cells. TNF proteins have been associated with insulin resistance and inflammation, which are key aspects of type 2 diabetes. TNF proteins, including TNFSF10, are associated with the regulation of immune responses and inflammation, both ofwhich are key factors in autoimmune diseases. TNFSF10 can induce apoptosis, which could be relevant in situations where abnormal apoptosis is contributing to autoimmune pathology’.

[0166] 9. HAPLN1 (Hyaluronan and Proteoglycan Link Protein 1): This protein is involved in stabilizing the interaction between hyaluronan and proteoglycans, which are components of the extracellular matrix in connective tissue. It could play a role in tissue remodeling, which could be related to diabetic complications. HAPLN1 is involved in stabilizing extracellular matrix interactions. Extracellular matrix plays a role in many biological processes, including those relevant to immunity7. Alterations in extracellular matrix components can influence inflammation and other immune responses.

[0167] 10. MATN3 (Matrilin 3): It is an extracellular matrix protein, and mutations in the gene encoding this protein have been associated with certain types of skeletal disorders like Multiple Epiphyseal Dysplasia. This extracellular matrix protein is primarily associated with cartilage structure and function, and it has not been directly linked to immune function.

[0168] 11. TBX3 (T-Box 3): This protein is a transcription factor, meaning it regulates the expression of other genes. It plays a critical role in development, and mutations in the gene encoding TBX3 can lead to Ulnar-Mammary Syndrome. Some studies have suggested that TBX3 may be involved in the development and function of the pancreas, which could potentially7link it to diabetes. Altered expression of TBX3 could theoretically affect the development or function of immune cells.

[0169] C. Overlapping proteins between diabetic stage and prehyperglycemic stage of diabetes. Ten proteins overlap between the diabetic stage and the prehyperglycemic stage of diabetes. These are MATN3 - Matrilin-3, HAPLN1 - Hyaluronan and proteoglycan link protein 1 (also known as Cartilage Link Protein CRTL1), GLTP - Glycolipid transfer protein, CPM - Carboxypeptidase M, TNFSF10 - Tumor necrosis factor ligand superfamily member 10 (also known as TRAIL. Apo2L, or CD253), PMP2 - Peripheral myelin protein 2, THOP1 - Thimet oligopeptidase 1, TSSK1B - Testis-specific , serine / threonine-protein kinase IB, TBX3 - T-box transcription factor TBX3, and RTRAF - RING-ty pe E3 ubiquitin transferase RTRAF

[0170] MATN3 (Matrilin 3): It is an extracellular matrix protein, and mutations in the gene encoding this protein have been associated with certain types of skeletal disorders like Multiple Epiphyseal Dysplasia. This extracellular matrix protein is primarily associated with cartilage structure and function, and it has not been directly linked to immune function.

[0171] HAPLN1 (Hyaluronan and Proteoglycan Link Protein 1): This protein is involved in stabilizing the interaction between hyaluronan and proteoglycans, which are components of the extracellular matrix in connective tissue. It could play a role in tissue remodeling, which could berelated to diabetic complications. HAPLN1 is involved in stabilizing extracellular matrix interactions. Extracellular matrix plays a role in many biological processes, including those relevant to immunity. Alterations in extracellular matrix components can influence inflammation and other immune responses.

[0172] GLTP (Glycolipid Transfer Protein): This protein functions in the process of transferring glycolipids from one cellular membrane to another. Glycolipids are part of cell membranes and involved in many cellular processes, including signaling related to insulin, so GLTP may have an indirect role in diabetes, but more research is needed to understand this. Glycolipids are part of cell membranes and are involved in cellular processes including immune cell signaling. Glycolipids can act as antigens and be recognized by the immune system, so GLTP may indirectly contribute to immune responses in this way.

[0173] CPM (Carboxypeptidase M): This is an enzyme that catalyzes the hydrolytic cleavage of C -terminal amino acids from proteins and peptides. It is involved in regulating various peptides' biological activity. It may have a role in processes such as insulin release or glucose regulation. Since CPM is involved in the metabolism of peptides, including bioactive peptides, which can modulate various physiological processes including immune responses.

[0174] TNFSF10 (Tumor Necrosis Factor (Ligand) Superfamily, Member 10): Also known as TRAIL, it plays a role in regulating immune responses and cell death. It can induce apoptosis (programmed cell death) in cancer cells. TNF proteins have been associated with insulin resistance and inflammation, which are key aspects of type 2 diabetes. TNF proteins, including TNFSF10, are associated with the regulation of immune responses and inflammation, both of which are key factors in autoimmune diseases. TNFSF10 can induce apoptosis, which could be relevant in situations where abnormal apoptosis is contributing to autoimmune pathology.

[0175] PMP2 (Peripheral Myelin Protein 2): This protein is found in peripheral nerves and is thought to be involved in the process of myelination of nerves. Since it is associated with nerve function, and damage to the nerves (neuropathy) is a common complication of diabetes, that may be the role it plays. In some autoimmune diseases like Guillain-Barre syndrome or chronic inflammatory’ demyelinating polyneuropathy, the immune system can attack peripheral nerves, potentially implicating proteins like PMP2. Immune-mediated attacks on myehn are a characteristic of certain autoimmune disorders such as multiple sclerosis. However, the exact role of PMP2 in these diseases is not clear.

[0176] THOP1 (Thimet Oligopeptidase 1): This protein plays a role in the metabolism of neuropeptides under 30 amino acids long, including many molecules involved in physiological functions such as pain, learning, and memory. It might influence biological processes that couldindirectly be related to diabetes.

[0177] TSSK1B (Testis-Specific Serine Kinase IB): This protein is found in the testis and is involved in spermatogenesis, the process by which sperm cells are produced.

[0178] TBX3 (T-Box 3): This protein is a transcription factor, meaning it regulates the expression of other genes. It plays a critical role in development, and mutations in the gene encoding TBX3 can lead to Ulnar-Mammary Syndrome. Some studies have suggested that TBX3 may be involved in the development and function of the pancreas, which could potentially link it to diabetes. Altered expression of TBX3 could theoretically affect the development or function of immune cells.

[0179] RTRAF (RNA Transcription, Translation And Repair Factor): This protein was not well- characterized.

[0180] D. Table 14. Comparison of the expression levels of the overlapping highly significant proteins in the prehyperglycemic stage and diabetic stageRTRAF, MATN3, HAPLN1, TBX3, and PMP2 can be used as a predictive biomarker signature profde for prediction of Type 1 diabetes development. Further narrowing these down, TNFSF10, GLTP, CPM, MATN3, HAPLN1, TBX3 and PMP2 can be used as the top predictive biomarkers for the development of diabetes, based on the NOD mouse model.

[0182] E. An extra comparison to identify and confirm novel significant differentially expressed protein biomarkers in the prehyperglycemic + diabetic stage of T1D in non-obese diabetic (NOD) mice.

[0183] Methods: Heparin plasma was obtained from 6-weeks old NOD / ShiLtJ littermates divided into Control (BG<120mg / dL); Prehyperglycemic (BG=175-250mg / dL), Diabetic (BG>250mg / dL); and IgM treated groups. IgM was administered intraperitoneal (50-75pg once in 5 days) until 18-weeks old. Plasma samples were analyzed using SomaScan proteomic platform. 7288 somamers were evaluated. The experiment was performed thrice for statistical significance. (Detailed Methods follow at the end of this document).

[0184] Results: Below are the consolidated results of 3 experiments.

[0185] Table 15. Differentially expressed proteins in merged prehyperglycemic + diabetic. 56 proteins are differentially expressed with a log2FC>l cutoff.

[0186] CONFIRMATION: Of these, the ones with the most fold change are the following 18 proteins

[0187] Table 16: Differentially expressed proteins in prehyperglycemic and Diabetic stage of autoimmune diabetes in the NOD mouse

[0188] SECTION 2 - IgM Treatment

[0189] Determination of a signature profile for IgM therapy and determining which proteins that are dysregulated by diabetes are maintained at / restored to normal levels by IgM therapy in the NOD mouse model.

[0190] Differentially expressed proteins with IgM therapy that have a LogFC2>l.

[0191] 175 proteins with logFC2>l are significantly and differentially expressed with IgM therapy. Of these, 39 proteins have a logFC2>1.5. Interestingly, some of the proteins that are dysregulated in diabetes are maintained at normal levels by IgM treatment.

[0192] Proteins that are dysregulated by diabetes but are maintained at normal levels with IgM therapy.

[0193] 12 proteins differentially upregulated by diabetes are maintained at / restored to normal levels by IgM therapy that are statistically significant are TNSF10, GLTP, HOXA5, CPM, LPO, NDUFAF2, ADGRF2, FTHL17, TMUB1, APP, LDHB, AND PLSCR3.

[0194] 20 proteins differentially downregulated by diabetes are maintained at / restored to normal by IgM therapy that were statistically significant are IL12A, IL12B, ENO3, COL10A1, CDC25A, PEA15, IL11RA.1, CCDC80, RBM41, ARL5A, CD3OOE, MUC16, PTGR2, TCEAL4, FAM171A1, ETHE1, ANOS1, FTMT, PAPOLG, MDH. l, and LST1.

[0195] Roles of the proteins that are differentially expressed in diabetes but maintained / restoredat normal levels are:

[0196] PLSCR3 (Phospholipid Scramblase 3): This protein is involved in the scrambling of phospholipids in cellular membranes. It is thought to play a role in apoptotic cell death.

[0197] GLTP (Glycolipid Transfer Protein): As its name suggests, this protein is involved in the transfer of glycolipids between various membranes in the cell.

[0198] NDUFAF2 (NADH:Ubiquinone Oxidoreductase Complex Assembly Factor 2): This protein helps in the assembly of complex I of the mitochondrial electron transport chain, which plays a critical role in cellular energy production.

[0199] ADGRF2 (Adhesion G Protein-Coupled Receptor F2): As a G protein-coupled receptor, this protein is likely involved in signal transduction.

[0200] HOXA5 (Homeobox A5): Homeobox proteins are transcription factors involved in regulating development and body plan organization in embryogenesis.

[0201] FTHL17 (Ferritin Heavy Chain Like 17): The exact function of this protein is not clear, but it is similar to the ferritin heavy chain, which is involved in iron storage.

[0202] TMUB1 (Transmembrane And Ubiquitin Like Domain Containing 1): This protein plays a role in cellular processes related to ubiquitination, a key process involved in protein degradation.

[0203] APP (Amyloid Precursor Protein): This protein is notorious for its role in Alzheimer's disease. When abnormally processed, it leads to the formation of amyloid-beta plaques in the brain. There is some evidence that amyloid deposition may play a role in type 2 diabetes, particularly in the context of islet amyloid polypeptide (IAPP or amylin). However, APP's involvement in this is less well established.

[0204] LDHB (Lactate Dehydrogenase B): LDHB is an enzyme involved in the conversion of lactate to pyruvate, part of the process of cellular respiration. LDHB is involved in glucose metabolism, and alterations in glucose metabolism are central to diabetes. However, a direct role of LDHB in diabetes has not been well-established.

[0205] LPO (Lactoperoxidase): An enzyme with antimicrobial properties, it catalyzes the oxidation of thiocyanate by hydrogen peroxide to produce hypothiocyanate, which has antimicrobial properties.

[0206] CPM (Carboxypeptidase M): An enzyme that catalyzes the hydrolysis of C-terminal amino acids of proteins.

[0207] TNFSF10 (Tumor Necrosis Factor Superfamily Member 10): Also known as TRAIL, it can induce apoptosis in tumor cells. It can be involved in the regulation of immune responses. Disruptions in its function or signaling pathways could potentially contribute to autoimmune conditions, though this isn't a primary protein studied in most autoimmune diseases. This proteincan trigger apoptosis (programmed cell death) in cells, an important aspect of immune system regulation.

[0208] IL12A and IL12B (Interleukin 12A and 12B): They form a cytokine, IL-12, which is involved in the immune response, particularly in linking innate and adaptive immunity. It is involved in the differentiation of T cells, helping to drive the adaptive immune response. Alterations in immune response and inflammation are associated with type 1 diabetes (an autoimmune condition) and type 2 diabetes. IL- 12 can be a part of these processes, but it would not be considered a primary player in diabetes pathophysiology. IL-12 also plays a key role in the immune response and has been implicated in several autoimmune diseases, including psoriasis and inflammatory bowel disease.

[0209] CD300E (CD300e Molecule): A member of the CD300 family of proteins that likely plays a role in immune responses. This is an immune receptor that could be involved in the regulation of immune responses, potentially contributing to autoimmune conditions.

[0210] MUC16 (Mucin 16): Mucins like MUC16 are glycoproteins that form a protective layer over epithelial tissues, the body's first line of defense against pathogens. MUC16 is also known as CA-125, a marker for ovarian cancer. Abnormalities in mucins and other protective barriers can sometimes contribute to autoimmune conditions, though MUC16's direct involvement isn't well-established.

[0211] CDC25A (Cell Division Cycle 25 A): This is a phosphatase, which regulates the cell cycle by dephosphorylating and thus activating cyclin-dependent kinases.

[0212] COL10A1 (Collagen Type X Alpha 1 Chain): An important structural protein in the extracellular matrix, it is mostly found in hypertrophic cartilage.

[0213] ENO3 (Enolase 3): An enzyme involved in the last step of glycolysis, converting 2- phosphoglycerate (2-PG) to phosphoenolpyruvate (PEP). ENO3 plays a role in glycolysis, a key metabolic pathway for glucose. While it's not directly implicated in diabetes, metabolic dysfunctions are central to the pathogenesis of diabetes.

[0214] ARL5A (ADP Ribosylation Factor Like GTPase 5A): As a member of the ARF-like protein family, it may be involved in intracellular transport or other GTPase-dependent processes.

[0215] PEA15 (Phosphoprotein Enriched In Astrocytes 15): This protein is involved in multiple cell processes including apoptosis and cell proliferation, predominantly by modulating the ERK / MAPK signaling pathway. PEA15 has been implicated in insulin resistance, a key component of type 2 diabetes. It appears to regulate insulin receptor signaling in some cell types.

[0216] PTGR2 (Prostaglandin Reductase 2): This enzyme participates in the metabolism of prostaglandins and leukotrienes, important signaling molecules.

[0217] TCEAL4 (Transcription Elongation Factor A Like 4): Likely plays a role in the regulation of gene expression, though the specific function is not well-characterized.

[0218] RBM41 (RNA Binding Motif Protein 41): Likely involved in post-transcriptional regulation of mRNA, though specific roles are not well known.

[0219] CCDC80 (Coiled-Coil Domain Containing 80): Likely plays a role in the organization of the cytoskeleton and cellular structure.

[0220] IL1 1RA.1 (Interleukin 11 Receptor Subunit Alpha 1): This protein is part of the receptor for interleukin 11, a cytokine involved in several processes including inflammation and hematopoiesis. As part of the receptor for interleukin 11, it's involved in several processes related to immunity and inflammation.

[0221] FAM171A1 (Family With Sequence Similarity 171 Member Al): The function of this protein is not well-known.

[0222] ETHE1 (Ethylmalonic Encephalopathy 1): This protein is involved in the metabolism of sulfur-containing amino acids. Mutations in this gene are associated with a rare metabolic disorder called ethylmalonic encephalopathy.

[0223] ANOS1 (Anosmin 1): This protein is involved in neural cell adhesion and migration. Mutations are associated with Kallmann syndrome, a disorder characterized by hypogonadotropic hypogonadism and anosmia.

[0224] FTMT (Ferritin Mitochondrial): This protein is involved in iron storage within the mitochondria.

[0225] PAPOLG (Poly(A) Polymerase Gamma): This enzyme adds adenine residues to the 3' end of mRNA molecules, a process called polyadenylation, which is a key step in mRNA maturation.

[0226] MDH1 (Malate Dehydrogenase 1): This enzyme is involved in the citric acid cycle, facilitating the conversion of malate into oxaloacetate. Like ENO3, MDH1 is involved in cellular metabolism, specifically the citric acid cycle. While metabolic dysfunction is central to diabetes, a direct role for MDH1 has not been clearly established.

[0227] LST1 (Leukocyte Specific Transcript 1): This gene plays a role in the immune response, specifically involved in the regulation of immune cell processes like proliferation and differentiation. Altered immune cell function is a hallmark of autoimmune diseases, so it's plausible that LST1 could play a role, though the specific link isn't well-established.

[0228] PATHWAY ANALYSIS

[0229] Signaling Pathways associated with IgM

[0230] 476 pathways were found associated with IgM (For complete list, see Table 17). Narrowing this list further, we found the following listed in Table 17 were most important. It isinteresting to note that apart from the expected complement and coagulation pathways and immune pathways; transcription factors in the beta cell development pathways are involved along with glycosylation pathways. Regulation of IGF transport and uptake by IGF binding pathway is also affected by IgM. Myeloid cell development pathways are also affected. We have observed a massive increase in MDSCs. Pathw ays involved in COVID are also associated IgM therapy which when linked with the results of the coagulation pathway explains the beneficial effects that were observed in our COVID studies. Also pathways associated with the microbiome were affected by IgM therapy. Pathways associated with extracellular matrix and cytoskeleton were also affected by IgM therapy. Pathways associated with cargo transport within the golgi and ER are also affected.

[0231] Table 17: Signaling pathways associated with IgM therapy in the NOD mouse.

[0232] SECTION 3 - To determine longitudinal proteomic changes in NOD mice.

[0233] Method: 50 mice (NOD Shilt / J 001976) five weeks of age were divided into 10 groups (5 mice per group). Heparin plasma samples samples (n=5 / group) were collected at 10 time points, namely, when mice were 5, 7, 9, 11, 13, 15, 17, 19, 21, 23 and 25 weeks of age. Additionally, pancreatic tissue, mesenteric lymph nodes (LNs), Gastric (pancreatic) LNs. lumbar LNs, thymus, and spleen were also collected before euthanizing the mice. Samples were sent to Somalogic for proteomic analysis. The count table from SomaScan Assay “.adat files’’ were imported to R to perform differential protein / gene expression analysis using the DESeq2 package. The log- transformed normalized gene / protein expression of 500 most variable genes were used to perform principal component analysis and differential gene expression, and the latter ranked based on the log2fold change and false discovery rate (FDR) corrected p-values.

[0234] Results:

[0235] High Risk Prediabetic Stage

[0236] In the high risk prediabetic stage between ages 7 to 13 weeks of age, proteins that are differentially expressed in non-diabetic NOD mice and highly significant that can be used as biomarkers to predict the development of diabetes are shown below in Table 18. Of these, GLTP, ST8SIA3, CPM, ZAP70, CD38, COL1A1, COL2A1.1, HAPLN1, MATN3, and APLP2.1 havethe highest fold change.

[0237] Table 18. Differentially expressed proteins as predictors of type 1 diabetes development

[0238] Functions of these biomarker proteins: Proteins can contribute to autoimmunity in various ways - they could be the target of autoimmune responses, mediate or perpetuate autoimmune processes, or be part of a signaling pathway that contributes to immune dysregulation. While some of the functions of the proteins in Type 1 diabetes listed in Table 18 are known, the role of the remaining in type 1 diabetes remain unknown.

[0239] ZAP70 is a kinase enzyme primarily found in T cells and natural killer cells. It plays a crucial role in signal transduction during T cell activation by interacting with the T cell receptor and initiating downstream signaling pathways. Dysregulation of ZAP70 signaling has been implicated in the pathogenesis of autoimmune diseases due to its role in T-cell receptor signaling, for example, rheumatoid arthritis and multiple sclerosis. Mutations can lead to a form of severe combined immunodeficiency (SCID).

[0240] CD38 is a cell surface glycoprotein that functions as an enzyme, catalyzing the synthesis and hydrolysis of cyclic ADP-ribose. It is involved in calcium signaling, cell adhesion, immune responses, and cellular communication. CD38 is also associated with regulatory T-cell function and immune tolerance, and has been linked to autoimmune disorders such as systemic lupus erythematosus (SLE) and type 1 diabetes. It is a marker of insulin resistance in muscle tissue and has been studied in type 2 diabetes in this context.

[0241] SFRP4 is associated with insulin resistance and has been linked to diabetes. Increased levels have been found in patients with type 2 diabetes. It may also play a role in inflammation, which can contribute to autoimmune responses. Dysregulated Wnt signaling can also potentially influence immune cell differentiation and function, which could contribute to autoimmunity.

[0242] TMUB1 has been associated with type I diabetes and may play a role in immune response regulation.

[0243] TNFSF11, also known as RANKL, is involved in bone metabolism and immune cell regulation. It plays a role in conditions like rheumatoid arthritis and osteoporosis, which can have autoimmune components.

[0244] WIFI is involved in regulating Wnt signaling, which has implications for autoimmunediseases like systemic sclerosis and type 1 diabetes due to its role in cell differentiation and inflammation.

[0245] CPM is an enzyme that cleaves amino acids from the C-terminus of peptides. It is involved in the regulation of peptide hormone activity, neurotransmitter processing, and other physiological processes. Peptides and peptide modifications can be targets for autoimmune recognition.

[0246] GLTP is involved in lipid metabolism, specifically in the transfer of glycolipids between membranes, and maintains the balance of hpids within cellular membranes. Alterations in glycolipid metabolism and signaling can influence immune cell functions.

[0247] ST8SIA3 is an enzyme that catalyzes the transfer of sialic acid molecules to glycoproteins and glycolipids. Sialic acid modification is important for cell adhesion, immune response, and signaling. Abnormal glycosylation can influence cell-to-cell interactions and immune recognition.

[0248] APLP2 is a protein that shares similarities with and is likely a variant of amyloid precursor protein (APP). It is involved in neuronal development and synaptic plasticity, and has been implicated in immune-related processes. It can be involved in cell adhesion and signaling and disruptions in cell adhesion can influence tissue integrity and inflammation.

[0249] GFRA1 (GDNF Family Receptor Alpha 1): Functions as a receptor for glial cell line- derived neurotrophic factor (GDNF).

[0250] COL1A1 is a component of collagen type I, which is a major structural protein in connective tissues such as skin, bone, and tendons. It provides strength and support to these tissues. COL2A1 is a component of collagen type II, which is a major structural protein in cartilage. It gives cartilage its tensile strength and helps maintain its integrity. Autoantibodies against COL1A1 have been seen in conditions like systemic sclerosis.

[0251] COL2A1 Mutations in COL2A1 have been associated with certain autoimmune disorders involving joint and connective tissue, such as rheumatoid arthritis. Autoantibodies against COL2A1 are seen in rheumatoid arthritis.

[0252] HAPLN1 is involved in the binding of proteoglycans to hyaluronan, an important component of the extracellular matrix. It helps maintain the structure and hydration of tissues, and helps stabilize the interaction between hyaluronic acid and proteoglycans, key components of the extracellular matrix.

[0253] MATN3 is a protein found in cartilage that plays a role in its development and maintenance. It is involved in mediating interactions between cells and the extracellular matrix in cartilage tissues, and the formation of filamentous networks in extracellular matrices of various tissues.

[0254] New Onset Diabetes:

[0255] In New-Onset Diabetes, in addition to expression of above-mentioned predictive biomarkers like COL2A1, SPARC. 1, PTN, COL1A1, COL2A1.1, HAPLN1, APLP2.1, MATN3, GALNT16, FMOD, WIF1.1, and WIFI, there are several other novel and differentially expressed proteins that are significant. These are, amongst others, CEL, SERPINB13, ERLEC1, CNTFR, IGFBP2.2, IGFBP2.3, ATL3, CTLA4, SOX9, PIP4K2B, EIF5, PLEKHF2, PSME2, VPS25,AMH, SLITRK1, COL3A1, CCDC80, COL11A2 and LACRT.

[0256] Function of prehyperglycemic biomarker proteins observed in New Onset Diabetics:

[0257] The functions of proteins that are common with predictive biomarkers for high risk of T1D development are given in the section above.

[0258] CTLA4 (Cytotoxic T-Lymphocyte Associated Protein 4) is an immune checkpoint protein. Polymorphisms in the CTLA4 gene have been linked to an increased risk for type 1 diabetes. CTLA4 has a critical role in regulating T-cell activity. Dysregulation can lead to autoimmune diseases, and variants of the CTLA4 gene are associated with various autoimmune conditions.

[0259] LACRT (Lacritin): Our studies indicate that Lacritin plays a role in type 1 diabetes. It increases insulin secretion, enhances beta cell proliferation and promotes immune tolerance.

[0260] CEL (Carboxyl Ester Lipase): has been linked with diabetes, especially monogenic forms of diabetes. Variants in the CEL gene can result in reduced enzyme activity that may contribute to pancreatic beta-cell dysfunction. It is a pancreatic enzyme involved in the metabolism of dietary lipids, synthesized and secreted by the pancreatic acinar cells and plays a role in the hydrolysis of dietary triglycerides and cholesteryl esters in the small intestine. Variants of the CEL gene (specifically a variable number of tandem repeats, or VNTRs, in this gene) have been associated with an increased risk of developing diabetes, especially a subtype of diabetes known as maturityonset diabetes of the young (MODY). MODY is a form of monogenic diabetes that typically presents in adolescence or early adulthood. There's evidence to suggest that mutations in the CEL gene can affect the enzyme's functionality and, consequently, lipid metabolism, which might contribute to the development or progression of diabetes in some people.

[0261] ERLEC1 (Endoplasmic Reticulum Lectin 1) is involved in protein quality control in the endoplasmic reticulum, which can be relevant for beta-cell function in the pancreas. Chronic ER stress can lead to beta-cell apoptosis, a factor in diabetes progression.

[0262] IGFBP2 (Insulin-like growth factor-binding protein 2) IGFBP2 is involved in insulin signaling and metabolism. Variations or post-translational modifications could potentially impact its functions.

[0263] PIP4K2B (Phosphatidylinositol-5-Phosphate 4-Kinase Type 2 Beta). Its role in phosphoinositide metabolism might influence pathways related to metabolism, but a direct link to diabetes is not well-established.

[0264] Diabetes:

[0265] Using a cut-off of log2FC >1.5, 86 proteins were significantly and differentially expressed. Many of these were present in the new onset stage of diabetes.

[0266] Of these, the following were upregulated. ERP27, GBA, SIRT3, CEL, GDF11, CPM. 1, HNMT, IGFBP2.3, MY0M2, TNNI2, IGFBP1.1, CLYBL, CXCL9.1,, IGFBP2.2, ZNF382, SNCA. l, IGFBP2. BRICD5, GPT2, INSR. FTHL17, FH, ACAT1, ACBD6, ALDOB. SNCA, ACBD6.1, PIP4K2A, PIP4K2B, PKLR, GFRAL PLEKHF2, RPIA, ALDOC, PNP, DLD. l, DNAJB1, OXSR1, DNAJB8, C2CD2L, MDGA2, CAT, GMFG, SEMA6D, VPS25, SCLY, PNP.l, SEMA6B, GLCE, SEMA6A, and SEMA6A.1.

[0267] And the following were downregulated. MATN3, APLP2. 1, HAPLN1. COL11A2, C3.2, IGFBP5. SERPINA3. PTN, INHBA. l, COL1A1, AMY2B.1, KLK7. AMY2A, COL2A1.1, INHBA.2, C3.1 , SPARC. 1, SERPINA1 1, SMOC2, AMY2B, C4A.C4B.2, ZAP70, COL2A1 , FMOD, GALNT16, HPX, KNG1.2, EFNA1, CCDC80, MATN2, AMBP, HDAC4, MFAP2.1, and MSTN

[0268] The top 15 upregulated and down regulated proteins are:

[0269] Functions of proteins that are differentially expressed in diabetes.

[0270] ERP27: Endoplasmic reticulum resident protein, involved in protein folding.

[0271] GBA: Glucocerebrosidase. While not directly involved in glucose metabolism, breaks down a specific lipid called glucocerebroside. Dysregulation can indirectly affect glucose-related pathways. There's some evidence that carriers might have an increased risk for chronic pancreatitis. Mutations in the GBA gene are primarily associated with Gaucher disease, a lysosomal storage disease. The immune system is often affected in patients with Gaucher's disease, there is also some evidence linking to an increased risk of Parkinson's disease. There's ongoing research on the link between Gaucher's disease and an increased risk of developing type 2 diabetes.

[0272] SIRT3: one of the seven mammalian sirtuins, which are NAD+-dependent protein deacetylases, it plays roles in metabolism and has been studied in the context of diabetes, although it is not strictly a diabetes protein. It is primarily located in the mitochondria, and plays a crucial role in regulating mitochondrial function and metabolism. Mitochondrial dysfunction is often associated with insulin resistance and the progression of type 2 diabetes. SIRT3 plays a protective role in maintaining mitochondrial integrity . Reduced SIRT3 expression or activity can lead to mitochondrial dysfunction, potentially contributing to insulin resistance. SIRT3 has antioxidant effects, mainly through the deacetylation and activation of superoxide dismutase 2 (SOD2). which reduces oxidative stress. Reduced SIRT3 activity can increase oxidative stress, which is implicated in diabetes complications. SIRT3 is involved in fatty acid oxidation. Dysregulated lipid metabolism is another hallmark of insulin resistance and type 2 diabetes. Proper SIRT3 function can thus help maintain metabolic homeostasis. There is emerging evidence that sirtuins, including SIRT3, can influence immune cell function, inflammation, and potentially autoimmunity.

[0273] GDF11 (Growth Differentiation Factor 11): While primarily recognized for roles in agingand tissue regeneration, members of the TGF-beta superfamily (which GDF11 belongs to) have roles in immune regulation. TGF-beta itself is a major player in inducing regulatory T cells and promoting immune tolerance. GDFl lmight have relevance in pancreatic development, and this could involve the islets of Langerhans. There's some evidence that GDF11 levels might be altered in diabetes, and it might play a role in glucose metabolism, but the exact mechanisms and significance in diabetes pathology aren't entirely clear.

[0274] Carboxypeptidase M (CPM) is a membrane-bound enzyme that is known to cleave C- terminal amino acids from proteins and peptides, especially if the C-terminal amino acid is basic. Some studies have suggested a role for CPM in inflammation due to its capacity to process and modify certain inflammatory mediators. Given that both diabetes and many autoimmune conditions have inflammatory components, it is conceivable that CPM might play some role in these conditions, albeit indirectly. CPM can also process certain peptides and proteins involved in cell signaling, which could theoretically influence various cellular processes including those related to immune response and metabolism. CPM is expressed in endothelial cells. Given the importance of vascular health in diabetes (and complications associated with diabetes), there could be a potential link between CPM function and vascular aspects of diabetes.

[0275] HNMT (Histamine N-Methyltransferase) is primarily responsible for the methylation of histamine, a key process in the inactivation of histamine. Histamine is an important mediator involved in various physiological functions, including the immune response, gastric acid secretion, and neurotransmission. Histamine plays a role in immune reactions, primarily through its effects on vasodilation, increasing vascular permeability, and attracting immune cells to sites of inflammation. Given its role in histamine catabolism, HNMT can indirectly influence immune responses. Dysregulated histamine levels might affect the intensity' and duration of immune reactions. While histamine is recognized as an immune mediator, the specific role of HNMT in the context of autoimmunity is not extensively studied. However, histamine receptors (particularly H4 receptors) have been implicated in certain autoimmune conditions. Changes in HNMT activity or expression could influence histamine levels in specific tissues, potentially impacting localized immune reactions. The direct link between HNMT and diabetes is not well-established. However, since histamine can influence inflammatory responses and vasodilation, it's plausible to consider that dysregulated histamine metabolism might play a role in some diabetes-related complications or in inflammatory' components of the disease. Histamine has been studied more in the context of obesity, where it's suggested that histamine plays a role in appetite regulation. Obesity is a significant risk factor for type 2 diabetes. It's worth noting that many biochemical pathways are interconnected, so changes in histamine metabolism might have indirect effects on variousphysiological processes.

[0276] MY0M2 (Myomesin 2): Structural protein found in the M-band of muscle sarcomeres, but there's potential for expression in various tissues.

[0277] TNNI2 (Troponin I Type 2): Involved in muscle contraction.

[0278] CLYBL: Mitochondrial enzy me with roles in vitamin B12 metabolism.

[0279] ZNF382: zinc finger protein 382. Some members of the zinc finger protein family are involved in regulating immune processes, but the specific role of ZNF382 is less clear. Zinc finger proteins, in general, are involved in DNA binding and can regulate gene expression. While it's theoretically possible that ZNF382 could influence pathways related to immune response or metabolic regulation, such connections would need to be confirmed by research studies.

[0280] INSR: This stands for Insulin Receptor, a key component in the insulin signaling pathway, which regulates glucose uptake and metabolism in various cells. Mutations in this gene or disruptions in its signaling pathway are often associated with insulin resistance, which plays a significant role in type 2 diabetes. Any anomalies or mutations in the INSR can lead to issues with insulin signaling and thus diabetes. Insulin receptor can be a target in some autoimmune forms of diabetes, particularly Type 1 diabetes. Dysregulated INSR function or expression might play roles in islet health or stress. Autoantibodies against the insulin receptor can lead to a rare form of diabetes known as Type B insulin resistance, which is an autoimmune disorder, there are studies suggesting that insulin signaling may influence immune function. This is the insulin receptor, and while it's primarily associated with diabetes, disruptions in pancreatic function can lead to endocrine disorders. Any disruption in the function of pancreatic beta cells (which produce insulin) might have implications in pancreatic health.

[0281] HAPLN1 is involved in the binding of proteoglycans to hyaluronan, an important component of the extracellular matrix. It helps maintain the structure and hydration of tissues, and helps stabilize the interaction between hyaluronic acid and proteoglycans, key components of the extracellular matrix.

[0282] MATN3 is a protein found in cartilage that plays a role in its development and maintenance. It is involved in mediating interactions between cells and the extracellular matrix in cartilage tissues, and the formation of filamentous networks in extracellular matrices of various tissues.

[0283] APLP2 is a protein that shares similarities with and is likely a variant of amyloid precursor protein (APP). It is involved in neuronal development and synaptic plasticity, and has been implicated in immune-related processes. It can be involved in cell adhesion and signaling and disruptions in cell adhesion can influence tissue integrity and inflammation.

[0284] COL11A2 (Collagen Type XI Alpha 2 Chain): Part of the collagen family, providing structural support in connective tissues. COL1A1 : Collagen, type I, alpha 1 is widely expressed and is a major component of the extracellular matrix. It can be found in various tissues, including the pancreas, it might be involved in the islet's extracellular matrix. COL2A1.1 appears to be a variant or subtj pe of COL2A1, but specific details would be needed for precise characterization.

[0285] COL2A1 (Collagen Type II Alpha 1 Chain): Found in cartilaginous tissues. COL2A1.1 is a variant or subtype of COL2A1. COL2A1 refers to the gene that encodes the alpha- 1 chain of type II collagen. Type II collagen is predominantly found in articular cartilage and the vitreous humor of the eye. Mutations in COL2A1 are known to lead to a variety of skeletal disorders, such as achondrogenesis, Stickler syndrome, and others. Since COL2A1 is involved in the makeup of the vitreous humor of the eye, there could be potential implications or associations in diabetic retinopathy. Chronic hyperglycemia and diabetes might affect joint health, potentially leading to osteoarthritis (OA). Since COL2A1 is critical for cartilage structure, understanding its expression and function in diabetic individuals could be of interest, but if s not directly implicated in diabetes per se. Collagens, including type II collagen, can become targets of immune responses in some autoimmune diseases. In rheumatoid arthritis, autoantibodies against certain collagen types can be present. Furthermore, the breakdown of type II collagen is a characteristic of osteoarthritis, even though OA is primarily degenerative and not autoimmune.Collagen-Induced Arthritis (CIA): CIA is an experimental model of rheumatoid arthritis in rodents, where an immune response against injected type II collagen leads to an RA-like disease. This model underscores the potential for type II collagen to be involved in autoimmune reactions.

[0286] C3.1 and C3.2: These refer to components of the complement system, which play roles in inflammation. The complement system is activated in several kidney diseases. C3.1 The complement system is part of the immune response, and dysregulation can contribute to autoimmune conditions. Mutations or malfunctions in C3 have been implicated in conditions like systemic lupus erythematosus (SLE).

[0287] IGFBP1.1, IGFBP2, IGFBP5: IGFBP1.1 is a variant of IGFBP1 (Insulin-like Grow th Factor Binding Protein 1) that binds IGFs and regulates their actions, and is known to play roles in insulin signaling. IGFBP2, IGFBP5: These are part of the insulin-like growth factor-binding protein family. These proteins can modulate insulin-like growth factor actions, which can be relevant to diabetes and metabolic health.IGFBP2 has been implicated in insulin sensitivity and is associated with various metabolic processes processes that can influence diabetes. IGFBP5, like IGFBP2, has been studied in the context of diabetes, as well as inflammation and immune cell functions. Both have relevance to islet biology and islet health as well as renal function.Alterations in their levels might also be indicative of kidney disorders. IGFBP1.1, IGFBP2.3, IGFBP2.2, IGFBP2: Insulin-like growth factor-binding proteins (IGFBPs) are involved in the regulation of insulin-like growth factors (IGFs). Some of these proteins are associated with glucose metabolism and might have implications in diabetes. IGFBP2, for instance, has been studied for its potential roles in insulin sensitivity and other aspects of diabetes.

[0288] SERPINA3: Also known as alpha- 1 -anti chymotrypsin, has been studied in relation to autoimmune conditions, like rheumatoid arthritis due to its role in inflammation, immune responses, and protease inhibition. Its altered expression has been observed in some kidney diseases. SERPINA11 is a member of the serpin family, but its specific function isn't well- documented.

[0289] PTN (Pleiotrophin): Involved in various biological processes including neurite outgrowth, angiogenesis, and cell growth.

[0290] INHBA.l and INHBA.2: Inhibin beta A chain. Inhibins are produced in the gonads but have been detected in other tissues, including the pancreas.

[0291] AMY2A and AMY2B: These represent pancreatic amylase, alpha 2A (pancreatic) and amylase, alpha 2B (pancreatic) respectively. Amylases are enzymes that break down long chain carbohydrates like dietary starch into glucose units, which can then be used by the body. Their levels can be influenced in diabetes, especially with complications involving the pancreas. Elevated levels of amylase in the blood can be an indication of acute pancreatitis.

[0292] KLK7 (Kallikrein-Related Peptidase 7). has roles in skin desquamation and keratinocyte proliferation.

[0293] ZAP70: Zeta-chain (TCR) associated protein kinase 70kDa (ZAP-70) is involved in T-cell receptor signaling. Deficiencies or mutations in ZAP70 have been linked to immunodeficiencies and potentially autoimmunity. ZAP70 Zeta-chain (TCR) associated protein kinase 70kDa (ZAP- 70) is directly involved in T-cell activation and signaling and is linked to autoimmunity, specifically in relation to rheumatoid arthritis and systemic lupus erythematosus. T cells are the primary immune cells implicated in insulitis and T1D. Deficiencies or Mutations in ZAP70 can lead to immune deficiencies and can be involved in autoimmune disorders due to T-cell dysregulation..

[0294] SNCA and SNCA. 1 : Synuclein, Alpha (SNCA) is primarily associated with Parkinson's disease. There's been some interest in the potential connections between Parkinson's disease and diabetes, particularly regarding insulin resistance in the brain, but the direct role of SNCA in diabetes is not well-established.

[0295] GPT2: Glutamic Pyruvate Transaminase 2 has been implicated in some metabolicprocesses and might have some association with metabolic diseases. However, its direct role in diabetes is not well-documented as of my last update.

[0296] CXCL9.1 : CXCL9 (if CXCL9.1 refers to a variant or subtype) is a chemokine with some evidence linking it to autoimmunity, particularly in the context of transplant rejection and some autoimmune diseases. It is involved in immune cell recruitment, especially in inflammatory conditions.

[0297] SPARC. 1 : Secreted Protein Acidic and Rich in Cystein), also known as osteonectin or BM-40, is a matricellular protein involved in the regulation of cell-matrix interactions. While its primary functions pertain to cell adhesion, migration, and tissue remodeling, there have been studies that suggest SPARC's potential role in various diseases, including diabetes and autoimmune conditions. It is expressed in many tissues, including the pancreas. It's often associated with tissue remodeling and has been studied in the context of pancreatic cancer, and has been indicated in various studies related to islet biology, including islet transplantation. One of the most established connections between SPARC and diabetes is its association with diabetic nephropathy, a complication of diabetes that affects the kidneys. Increased expression of SPARC has been observed in the kidneys of diabetic animals and humans, suggesting that it might play a role in the pathological changes observed in diabetic nephropathy. Some studies have suggested that SPARC can influence pancreatic beta-cell function and insulin resistance, but the mechanistic details and implications in human diabetes need further exploration. Impaired wound healing is a complication of diabetes. Given SPARC's role in tissue remodeling and matrix deposition, it might have implications in the wound healing process in diabetic individuals. SPARC can modulate inflammatory responses. Chronic inflammation is a hallmark of many autoimmune diseases. Therefore, any protein influencing inflammation, like SPARC, might have indirect implications in autoimmunity. In some autoimmune diseases, tissue damage and subsequent remodeling are common features. Given SPARC's role in matrix regulation, it might play a role in these processes. For example, SPARC has been studied in the context of rheumatoid arthritis and scleroderma, both of which involve tissue remodeling. SPARC can modulate growth factor activities, influence angiogenesis, and play a role in fibrosis. All of these processes can be relevant in the context of chronic diseases, including diabetes and some autoimmune conditions. SPARC is involved in various cellular processes that could have implications for pancreatic islet function and insulin resistance.

[0298] CCDC80: CCDC80 (Coiled-Coil Domain Containing 80), also known as DRO1 or URB, is a secreted protein that has been implicated in several biological processes. It's known to be involved in adipogenesis, cell proliferation, and differentiation. Functions in adipocytedifferentiation and may play a role in obesity-related insulin resistance. Given that obesity is a significant risk factor for type 2 diabetes, any gene or protein influencing adipose tissue development and function could have indirect implications for diabetes. There's potential for CCDC80's involvement in pathways related to insulin sensitivity, especially given its role in adipose tissues. Adipose tissue dysfunction can lead to systemic insulin resistance, a key factor in type 2 diabetes. One potential angle of exploration could be the cylokme-like role of CCDC80. Cytokines are signaling proteins that can influence inflammation, immune response, and other cellular behaviors. Since both diabetes (especially type 2) and autoimmune conditions have inflammatory components, understanding the signaling roles of proteins like CCDC80 could be of interest.

[0299] GALNT16 (Polypeptide N-acetylgalactosaminyltransferase 16) is part of a family of enzymes that mediate the transfer of N-acetyl galactosamine to serine or threonine residues in proteins, a modification known as O-linked glycosylation. This process can influence protein function, stability, and localization, thus affecting a wide range of biological processes. Glycosylation plays a role in immune cell signaling and recognition. Altered glycosylation can influence immune responses, which can be relevant to autoimmune conditions. It would be necessary to determine if GALNT16 specifically affects immune-related proteins and pathways to establish its role in autoimmunity. Some autoimmune conditions involve the presence of autoantibodies that target self-proteins. If these proteins are aberrantly glycosylated, it could influence autoantibody recognition or formation. Aberrant glycosylation patterns have been observed in several diseases, including diabetes. Changes in glycosylation can influence the function of proteins relevant to glucose metabolism or insulin signaling. While it's plausible for GALNT16 to be involved given its enzymatic function, direct evidence linking it to diabetes would be necessary to establish its role.

[0300] FMOD (Fibromodulin) is a small leucine-rich proteoglycan (SLRP) found in the extracellular matrix of various connective tissues, involved in the regulation of collagen fibrillogenesis with roles in tissue repair and remodeling. FMOD has been studied in the context of several conditions, including osteoarthritis and fibrotic diseases, due to its involvement in collagen organization and tissue structure. Delayed wound healing is a complication of diabetes. Given FMOD's role in tissue repair and fibrosis, it could have implications in the wound healing process in diabetic individuals. While it's not a primary' player in glucose metabolism, its involvement in tissue-related complications of diabetes is plausible. FMOD expression may be associated with certain pathological changes in diabetic retinopathy, a complication of diabetes that affects the eyes. This is due to its role in extracellular matrix remodeling in the retina.

[0301] The extracellular matrix's composition and organization can influence immune cell behavior and inflammation. Since FMOD plays a role in matrix organization, it might indirectly affect immune responses in the context of certain autoimmune diseases, especially those with a prominent fibrotic or tissue remodeling component. While FMOD's role in widespread autoimmune diseases like rheumatoid arthritis or lupus might not be extensively studied, its involvement in diseases with significant tissue remodeling or fibrosis is plausible.

[0302] SEMA6B & SEMA6A: Members of the semaphorin family, involved in axonal guidance during nervous system development. SEMA6A.1 & SEMA6D are variants or family members of semaphorins, involved in axonal guidance. SEMA6A and SEMA6D also have roles in various phy siological and pathological processes, including immune response and angiogenesis, both of which can have implications in diabetes. They have been implicated in the immune system's regulation, and dysregulation can potentially lead to autoimmunity. They play roles in T-cell activation and other immune processes.

[0303] PIP4K2B and PIP4K2A are involved in phosphoinositide signaling. PIP4K2B (Phosphatidylinositol-5-phosphate 4-kinase type-2 beta) and PIP4K2A (Phosphatidylinositol-5- phosphate 4-kinase type-2 alpha) are enzymes involved in the metabolism of phosphoinositides, which play critical roles in cell signaling, membrane trafficking, and cytoskeletal rearrangements. Both PIP4K2B and PIP4K2A contribute to the phosphorylation of phosphatidylinositol 5- phosphate (PI5P) to produce phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2). Both enzymes participate in phosphoinositide metabolism, which indirectly influences various cellular processes including insulin signaling. Dysregulation in phosphoinositide metabolism can influence the responsiveness of cells to insulin and might contribute to insulin resistance. Phosphoinositides play a role in various immune cell functions, including activation, migration, and signaling. Alterations in enzymes like PIP4K2B and PIP4K2A that modulate phosphoinositide levels could influence immune cell behavior, which might be relevant to autoimmune diseases.

[0304] SMOC2: Modulates cellular responses to growth factors. SMOC2 (SPARC-related modular calcium-binding protein 2) is a protein belonging to the SPARC family, which is involved in various biological processes, including tissue remodeling, cell-matrix interactions, and angiogenesis. Given the potential role of SMOC2 in tissue remodeling and angiogenesis, it could be implicated in processes relevant to diabetic complications, such as impaired wound healing or diabetic retinopathy. However, direct evidence linking SMOC2 to these processes would be necessary. SMOC2 has been implicated in the regulation of angiogenesis (formation of new blood vessels). Dysregulated angiogenesis plays a role in diabetic complications, such as retinopathy. Therefore, any protein influencing angiogenesis could have potential relevance to diabetes. Anyprotein that might influence cell-matrix interactions or tissue remodeling in the pancreas could theoretically have implications in diabetes, particularly in terms of P-cell funchon and survival. But again, concrete evidence linking SMOC2 to diabetes or P-cell function isn't prominently reported. SMOC2 might play roles in tissue remodeling processes, which can be relevant to autoimmune conditions, especially those involving tissue damage and subsequent remodeling. Chronic inflammation, a hallmark of many autoimmune diseases, often leads to tissue remodeling. If SMOC2 influences these processes, it could theoretically be implicated in some autoimmune conditions. Since SMOC2 is related to SPARC, which has been shown to influence cell-matrix interactions, there's potential for SMOC2 to be involved in autoimmune conditions where cellmatrix interactions play a role.

[0305] HDAC4: Histone deacetylases (HDACs) have been implicated in various processes including metabolic diseases. HDAC4, in particular, has been studied in the context of diabetes and skeletal muscle glucose metabolism. Histone deacetylases, including HDAC4, have been implicated in the regulation of genes related to autoimmunity and inflammation, and are involved in epigenetic regulation. HD AC inhibitors have been researched for potential roles in autoimmune disease treatments due to their impact on immune cell function and inflammation. HDAC4 can also influence the transcription of genes. Their modulation can impact the function of immune cells.

[0306] C4A.C4B.2: The genes C4A and C4B encode for the complement component 4A and 4B proteins, respectively. These proteins are part of the complement system, a vital component of the innate immune system that helps clear pathogens and facilitates adaptive immune responses. Its dysregulation can be involved in various autoimmune or inflammatory conditions, although not specific to pancreatitis.

[0307] Variants and deficiencies in complement components, including C4A and C4B, have been linked to an increased risk of developing Systemic Lupus Erythematosus (SLE). This is because the complement system plays a role in clearing immune complexes, and deficiencies can result in these complexes accumulating and depositing in tissues, leading to inflammation.Variations in complement genes can affect susceptibility to various autoimmune diseases. For example, C4A deficiency has been reported to be more common in individuals with autoimmune conditions. Overactivation of the complement system can lead to tissue damage, which can contribute to autoimmune pathogenesis. On the other hand, deficiencies in certain components can impair proper immune function and clearance of pathogens or immune complexes. Some studies have suggested associations between complement system components (including C4) and T1D, though these findings might not be as robust as those in diseases like SLE. The complement system hasalso been implicated in low-grade inflammation observed in T2D. However, the direct involvement of specific genes like C4A or C4B in T2D is not as well-established as their roles in certain autoimmune conditions. The complement system has been implicated in various complications of diabetes, including diabetic nephropathy, where complement activation can contribute to kidney damage.

[0308] FH: Fumarate hydratase (or fumarase) is an enzyme involved in the Krebs cycle and is present in many tissues, including the pancreas. FH would be present in most cell types, including islet cells, but it's not specific to them.

[0309] CAT: Catalase primarily functions as an antioxidant enzyme breaking down hydrogen peroxide, but any imbalance in reactive oxygen species can impact glucose metabolism and insulin signaling. Oxidative stress has been implicated in the pathogenesis of diabetes, and altered catalase activity has been observed in diabetic conditions. Catalase has antioxidant properties and has been implicated in oxidative stress-related conditions, including pancreatitis. It has been implicated in some studies concerning its levels in chronic pancreatitis.

[0310] PKLR: Pyruvate kinase, is crucial for glycolysis, a pathway that's fundamental for glucose metabolism in cells, including those of the pancreas. Pyruvate kinase is involved in the final step of glycolysis, converting phosphoenolpyruvate to pyruvate, yielding ATP. Mutations can lead to hemolytic anemia and affect red blood cell metabolism.

[0311] CEL: Carboxyl ester lipase, also known as bile salt-stimulated lipase, is produced in the pancreas. Carboxyl-ester lipase (CEL) is a carboxyl ester lipase (a bile-salt stimulated lipase) that plays a role in the hydrolysis of ietary lipids, especially cholesterol esters and fat-soluble vitamin esters, in the intestine. The enzyme is primarily secreted by the exocrine pancreas and is found in pancreatic juice. Over the years, there have been a few- studies linking CEL to diabetes and autoimmunity, although it's not one of the major known contributors to these conditions. There's evidence to suggest that mutations in the CEL gene can affect the enzyme's functionality and, consequently, lipid metabolism, which might contribute to the development or progression of diabetes in some people. Variants of the CEL gene (specifically a variable number of tandem repeats, or VNTRs, in this gene) have been associated with an increased risk of developing diabetes, especially a subtype of diabetes known as maturity-onset diabetes of the young (MODY). MODY is a form of monogenic diabetes that typically presents in adolescence or early adulthood. Specific mutations in the CEL gene have been linked to MODY. MODY is a rare subtype of diabetes that is distinct from both type 1 and type 2 diabetes. It typically presents in adolescence or early adulthood and is inherited in an autosomal dominant manner.The exact mechanism by which CEL mutations lead to MODY isn't fully understood, but it's believed thatthey might affect the normal secretion and action of insulin. Certain autoimmune conditions like type 1 diabetes have a complex etiology involving multiple genes, environmental factors, and possibly factors like the gut microbiota. Any disturbances in lipid metabolism or digestion might theoretically play a role in gut health and subsequently in immune system functioning. The extent to which CEL might be implicated in this process is not yet fully known. Mutations in the CEL gene have been linked to chronic pancreatitis. Chronic inflammation of the pancreas can increase the risk of diabetes, as the inflamed pancreas may not produce enough insulin. CEL mutations have also been implicated in other diseases, such as macular degeneration, due to the enzyme's role in lipid metabolism.

[0312] PNP: Purine nucleoside phosphorylase is an enzyme present in many tissues, and it's likely to be found in the pancreas, given its involvement in purine metabolism. This enzyme is likely present in most cells, including those of the islets. Mutations can lead to immunodeficiency disorders.

[0313] VPS25: Vacuolar protein sorting-associated protein 25 might be expressed in various tissues, including the pancreas, given its involvement in endosomal trafficking. It is a part of the ESCRT machinery, which is involved in vesicular transport.

[0314] ALDOB and ALDOC: These enzymes are associated with metabolic processes. While aldolase A is more commonly associated with muscle, the B and C forms could have implications in various disorders. ALDOB, Aldolase B enzyme is involved in the glycolytic pathway and fructose metabolism. It catalyzes the reversible conversion of fructose- 1 -phosphate to glyceraldehyde and dihydroxyacetone phosphate. Hereditary fructose intolerance, a condition linked to ALDOB mutations, is not directly a form of diabetes but involves sugar metabolism. Though primarily known for its function in the liver, it can be relevant to the overall metabolism linked to the pancreas. ALDOC, Aldolase C enzy me, similar to ALDOB, plays a role in glycolysis. Aldolase C is an isozyme of aldolase that participates in the glycolytic pathway, converting fructose-l,6-bisphosphate to glyceraldehyde-3-phosphate and dihydroxy acetone phosphate. Aldolase C enzyme also has metabolic implications, and while its major expression is in the brain, it's part of a metabolic pathway that has significance to the pancreas.

[0315] GPT2: Glutamic pyruvate transaminase 2 is involved in amino acid metabolism and alterations in its function can impact glucose homeostasis. , While it has broader implications, it can be relevant to the metabolic context of the pancreas. Glutamic pyruvate transaminase 2 has a role in amino acid metabolism and the conversion of alanine and alpha-ketoglutarate to pyruvate and glutamate, respectively. This indirectly influences glucose metabolism as pyruvate can be used in gluconeogenesis or be converted to acetyl-CoA for entry into the citric acid cycle.

[0316] DLD.l : Dihydrolipoamide dehydrogenase is a component of several multi-enzyme complexes that regulate the citric acid cycle and the pyruvate dehydrogenase complex, both crucial in glucose oxidation. Dihydrolipoamide dehydrogenase, is a of several enzyme complexes involved in the mitochondrial matrix, including those associated with the citric acid cycle, a central metabolic pathway.

[0317] GLCE: Involved in heparan sulfate biosynthesis, and there is some evidence linking alterations in heparan sulfate to diabetic nephropathy. This enzyme can also impact glucose metabolism indirectly because of its role in the synthesis of glucuronic acid.

[0318] SCLY: Selenocysteine lyase transforms selenocysteine to alanine, releasing selenium. While it isn't directly a part of primary glucose metabolism, alanine, the product of this reaction, can be involved in gluconeogenesis.

[0319] SNCA: Alpha-synuclein (encoded by the SNCA gene) is primarily linked to neurodegenerative diseases like Parkinson's. Still, there's some evidence to suggest that immune responses against alpha-synuclein could be implicated in some autoimmune reactions.

[0320] RPIA: Ribose 5-phosphate isomerase A is involved in the pentose phosphate pathway, a metabolic pathway parallel to glycolysis that generates NADPH and pentoses.

[0321] ACAT1: Acetyl-CoA acetyltransferase 1 is involved in ketone body metabolism and is expressed in many tissues, including the pancreas.

[0322] ACAT1 plays a role in cholesterol metabolism. Some studies have looked at its potential role in autoimmune diseases like multiple sclerosis, but the exact mechanisms remain an area of investigation. While its primary association isn't directly with diabetes, dysregulation in lipid metabolism is often seen in diabetic conditions. There's potential overlap in pathways.

[0323] PLEKHF2: While this protein has been studied in the context of other tissues, it's worth noting potential expression or relevance in the pancreas. The exact role of this protein in autoimmunity is not well-established, but any protein involved in cellular signaling or immune cell regulation could theoretically play a role in autoimmune processes.

[0324] GMFG: Glia maturation factor gamma has roles in immune cell signaling, especially in cells like neutrophils. GMFG is involved in actin cytoskeleton organization.

[0325] EFNA1: Ephrin-Al has been implicated in various immune processes, including T cell function.

[0326] OXSR1 : Also known as oxidative-stress responsive kinase 1, plays a role in cellular responses to oxidative stress, which can impact immune cell function.

[0327] KNG1.2: Depending on its specific context, KNG1 typically refers to kininogen, which has implications in inflammation.

[0328] AMBP: alpha- 1 -mi croglobulin / bikunin precursor, plays roles in the regulation of immune system processes and has been implicated in certain kidney disorders.

[0329] MSTN (Myostatin): Myostatin (MSTN) primarily functions as a negative regulator of muscle growth. There's evidence to suggest that myostatin inhibition might have potential therapeutic benefits in the context of type 2 diabetes by improving muscle mass and potentially insulin sensitivity. Although best known for its role in regulating muscle growth, there are studies suggesting that myostatin might have immunomodulatory effects, potentially influencing immune tolerance.

[0330] DNAJB8: DNAJ (Hsp40) homolog, subfamily B, member 8, is a molecular chaperone associated with heat shock proteins. While it's expressed in various tissues, it's worth noting its potential expression in the pancreas. Members of the DNAJ / HSP40 family are involved in protein homeostasis, and aberrations in these pathways can have implications in autoimmunity. It is also involved in protein homeostasis. While its direct role in diabetes isn't clearly established, proteins involved in endoplasmic reticulum (ER) stress and protein folding can influence beta-cell survival and function, which are critical in diabetes.

[0331] FTHL17 : Ferritin Heavy Chain Like 17. Likely involved in iron storage or transport, given its similarity to ferritin heavy chains.

[0332] HPX (Hemopexin): Binds heme and transports it to the liver for breakdown and iron recovery. Hemopexin has been studied in the context of kidney injury and its potential protective effects against hemoglobin-mediated kidney damage.

[0333] ACBD6: Acyl-CoA binding domain-containing protein, involved in lipid metabolism. ACBD6.1 a variant.

[0334] C2CD2L: Has roles related to lipid droplets in cells.

[0335] MFAP2. 1 : Microfibrillar-associated protein 2, plays a role in elastinogenesis and maintenance of arterial elasticity.

[0336] MDGA2: MAM Domain Containing Glycosylphosphatidylinositol Anchor 2. Functions aren't fully elucidated but likely involved in cell adhesion processes.

[0337] METHODS

[0338] Differential Gene Expression Analysis: The count table (from SomaScan Assay " adat files”) was imported to R to perform differential protein / gene expression analysis using the DESeq2 package (Love, Huber, and Anders 2014). Low expressed genes (genes expressed only in a few- replicates with low counts) was excluded from the analysis before identifying differentially expressed genes. Data normalization, dispersion estimates, and model fitting (negative binomial) was carried out with the DESeq function. The log-transformed normalizedgene expression of 500 most variable genes was used to perform an unsupervised principal component analysis and the outlier samples (samples that do not cluster with their group) was removed before performing differential gene expression.

[0339] Pathway Analysis: The differentially expressed genes was ranked based on the log2fold change and FDR corrected p-values. The ranked file was used to perform pathway analysis using GSEA software (Subramanian et al. 2005). The enriched pathways were selected based on enrichment scores as well as normalized enrichment scores.

[0340] Sample Removal: To address the batch effect and ensure efficient removal, it was necessary' to have samples from each contrast group present across all experiment days. Therefore, any samples from contrast groups that were not consistently present in all experiment days were removed from the count matrix before performing batch effect removal. Following the batch effect removal, an unsupervised principal component analysis (PCA) was conducted. This analysis identified three outlier samples, which were subsequently removed from the analysis to ensure the data's integrity. As a result of these steps, the final dataset consisted of a total of 77 samples, encompassing the nondiabetic, Prehyperglycemic, Diabetic, and IgM contrast groups. This streamlined dataset with the outlier samples removed was used for differential gene expression analysis.

[0341] Identifying and removing the batch effect: To address the batch effect present in the data, the experiment day information from the metadata was utilized to identify sample clustering based on the day the experiments were performed. The initial PCA plot indicated the presence of a batch effect. To remove the batch effect, the SVA (Surrogate Variable Analysis) package was employed (Leek and Storey 2007). SVA is a statistical method commonly used for correcting batch effects by estimating and incorporating surrogate variables into the analysis. These surrogate variables capture the unwanted variation caused by the batch effect, allowing for the separation of biological signals from technical artifacts. By utilizing the SVA package, the batch effect was effectively minimized or eliminated from the data (see PCA plot after batch correction), thereby improving the qualify and accuracy of downstream analyses. Upon closer examination of the batch-corrected count matrix, it was observed that some of the counts had negative values for certain genes in one or more samples. To address this issue and ensure that the count matrix is suitable for processing with the DESeq pipeline, a correction was applied. For genes where negative counts were present in any sample, the correction involved adding the absolute value of the lowest count for that specific gene to all counts across all samples. This adjustment ensures that all count values become non-negative, allowing for proper processing and analysis using the DESeq pipeline. By making this correction, the count matrix is now prepared to undergo further analysis, such as differentialexpression analysis using DESeq, which typically requires non-negative count values. The resulting dataset is now better suited for examining the biological factors of interest without the confounding influence of the batch effect.

[0342] Identifying Unique and overlapping genes between contrasts: To identify and visualize the unique and overlapping genes between different contrasts, the UpSet plot proves to be a more efficient approach compared to traditional methods like the Venn Diagram. Firstly, samplespecific differentially expressed up-regulated genes meeting the criteria of a log2foldchange greater than 1 and a p-value less than 0.05 were identified. These genes were then visualized using an UpSet plot (Conway, Lex, and Gehlenborg 2017), which displays the intersections of these genes across the different contrasts. Similarly, sample-specific differentially expressed do unregulated genes meeting the criteria of a log2foldchange less than -1 and a p-value less than 0.05 were also identified and visualized using an UpSet plot. The UpSet plot represents the intersections of genes as a matrix of bars, where each bar corresponds to a unique combination of gene sets. The height of each bar indicates the number of genes within the intersection, allowing for a clear visualization of the size and distribution of overlaps.

[0343] Heatmap: A heatmap was generated using the pheatmap package in R to visualize the expression patterns of the top 100 differentially expressed genes selected based on adjusted p- values, indicating their significance in the respective contrasts. The batch normalized count data was employed to generate the heatmap. ensuring the data was appropriately processed and normalized. In order to improve the visualization, the samples were annotated and highlighted based on two key factors: their contrast group and the "experiment day." By considering these factors, it becomes possible to identify and analyze expression patterns specific to each contrast group. It's worth noting that the "experiment day" was not found to have any potential influence on gene expression; however, including this information allows for comprehensive annotation and further exploration of the data.

[0344] Identification of genes / proteins stabilized by IgM treatment that was dysregulated in diabetic or Prehyperglycemic group compared to non- diabetic: To identify7genes / proteins that are stabilized back to non-diabetic level by IgM treatment and are dysregulated (up-regulated) in either the diabetic or Prehyperglycemic group compared to the non-diabetic group, the following steps w ere performed:1. Genes with a log2foldchange greater than 0.7 and a padj (adjusted p-value) less than or equal to 0.05 were identified between the "diabetic and non-diabetic" contrast and the "(diabetic + hyperglycemic group) and non-diabetic" contrast.2. The common genes obtained from the previous step w ere then intersected with the genes havinga log2foldchange greater than 0.7 and a padj less than or equal to 0.05 identified between the "IgM and non-diabetic" contrast. This step ensures excluding any genes already present in the "IgM and non-diabetic" contrast.

[0345] Finally, the normalized count data for the identified genes, along with their significance, was plotted using a box plot to visualize their expression differences between the relevant contrast groups. This analysis allows for the identification of genes / proteins that are both dysregulated in the diabetic or Prehyperglycemic group compared to the non-diabetic group and stabilized by IgM treatment. The box plot provides a visual representation of the expression patterns and differences in gene expression levels, highlighting the significance of these changes. Similar methods (by considering those genes that had log2foldchange less than -0.7 and a padj (adjusted p-value) less than or equal to 0.05) were followed to identify genes / proteins that are stabilized back to non- diabetic level by IgM treatment and are down-regulated in either the diabetic or Prehyperglycemic group compared to the non-diabetic group.

[0346] References

[0347] Conway, J. R., A. Lex, and N. Gehlenborg. 2017. 'UpSetR: an R package for the visualization of intersecting sets and their properties'. Bioinformatics, 33: 2938-40.

[0348] Leek, J. T., and J. D. Storey. 2007. 'Capturing heterogeneity in gene expression studies by surrogate variable analysis', PLoS Genet, 3: 1724-35.

[0349] Love. M. L, W. Huber, and S. Anders. 2014. 'Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2', Genome Biol, 15: 550.

[0350] Subramanian, A., P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub, E. S. Lander, and J. P. Mesirov. 2005. 'Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles', Proc Natl Acad Sci USA, 102: 15545-50.

[0351] Comparison between differentially expressed human type 1 diabetic samples and the mouse diabetic samples

[0352] Six Protein samples are common between the differentially expressed human typel diabetic samples and the mouse diabetic samples with log2FC>l. These are ZAP70 IFI16 DBT MTIF3 CCT7 HSPE1.

[0353] Fifty Protein samples are common between the human typel diabetic samples and the mouse prehypergly cemic samples. These are RAC1 PACSIN2 PLEK MYL12A HSPD1 RCSD1 PIP4K2B MAPRE1 DAPP1 MNDA BTK MANF CAPG ARHGAP25 ATP5PO IL21R IL18 NACA MDH2 ENPP2 RBPMS2 MAP4K5 FN3K DTD1 ME2 PRDX3 SERPINH1 CDYL2LDLRAP1 NAMPT TPM4 HNRNPF FH0D1 PIP4K2A PPIF LRRC59 LRRFIP2 MYL9 ARHGAP6 RGS10 CXCL6 CCT5 CALD1 SH3GL2 YARS1 ACTN1 PGAM2 PRKCA INPP5A FNBP1.

[0354] Of the above proteins that are common, proteins connected to Diabetic Signaling Pathways are:

[0355] 1. BTK (Bruton's tyrosine kinase): BTK is involved in B-cell signaling and has been associated with insulin signaling and glucose metabolism in the context of diabetes.

[0356] 2. IL21R (Interleukin-21 receptor): IL21R is involved in immune response regulation and has been linked to autoimmune diseases, which may have implications in diabetes and its associated signaling pathways.

[0357] 3. NAMPT (Nicotinamide phosphoribosyltransferase): NAMPT plays a role in NAD biosynthesis, which is relevant to cellular energy metabolism and signaling pathways.

[0358] 4. PRDX3 (Peroxiredoxin-3): PRDX3 has implications in oxidative stress-related diseases, and redox signaling is important in various cellular processes, including those involved in diabetes.

[0359] 5. IFI16 (Interferon gamma-inducible protein 16): IFI16 has been associated with autoimmune diseases and viral infections and may have implications in diabetes signaling pathways.

[0360] Proteins that are connected to immunity are:

[0361] 1. RAC1: RAC1 is involved in various cellular processes, including immune cell activation and migration.

[0362] 2. BTK (Bruton's tyrosine kinase): BTK is a crucial protein in B-cell signaling and plays a significant role in the development and functioning of B cells, a type of immune cell.

[0363] 3. IL21R (Interleukin-21 receptor): IL21R is involved in immune response regulation and mediates the effects of the cytokine interleukin-21, which plays a role in the immune system.

[0364] 4. IL18 (Interleukin- 18): IL18 is a pro-inflammatory cytokine that plays a role in immune responses and inflammation.

[0365] 5. NAMPT (Nicotinamide phosphoribosyltransferase): NAMPT has been associated with immune regulation and inflammation.

[0366] 6. PRDX3 (Peroxiredoxin-3): PRDX3 is an antioxidant enzyme and is involved in cellular defense mechanisms, including immune response regulation.

[0367] 7. IFI16 (Interferon gamma-inducible protein 16): IFI16 is involved in immune response regulation and is a cytoplasmic DNA sensor that triggers immune responses to viral infections and other stimuli.

[0368] 8. CXCL6 (Chemokine C-X-C motif ligand 6): CXCL6 is a chemokine involved in the recruitment and activation of immune cells.

[0369] 9. ZAP70 (Zeta-chain-associated protein kinase 70): ZAP70 is a key kinase involved in T-cell receptor signaling and T cell activation.

[0370] 10. IFI 16 (Interferon gamma-inducible protein 16): IFI16 is a cytoplasmic DNA sensor that triggers immune responses.

[0371] The following proteins among the listed ones are connected with glucose or are involved in glucose metabolism:

[0372] 1. PLEK (Pleckstrin): PLEK has been associated with glucose metabolism and insulin signaling pathways.

[0373] 2. BTK (Bruton's tyrosine kinase): BTK has been linked to glucose metabolism and insulin signaling, particularly in the context of insulin resistance.

[0374] 3. NAMPT (Nicotinamide phosphoribosyltransferase): NAMPT plays a role in NAD biosynthesis, which is important for glucose metabolism and cellular energy7production.

[0375] 4. PRDX3 (Peroxiredoxin-3): PRDX3 has implications in oxidative stress-related diseases, which can impact glucose metabolism.

[0376] 5. FN3K (Fructosamine-3-kinase): FN3K is involved in glucose metabolism and the regulation of fructosamine levels.

[0377] 6. ME2 (Malic enzyme 2): ME2 is an enzyme involved in glucose metabolism and contributes to the generation of NADPH, an important cellular reducing agent.

[0378] 7. PIP4K2A (Phosphatidylinositol 5-phosphate 4-kinase type-2 alpha): PIP4K2A has been associated with glucose metabolism and insulin signaling.

[0379] 8. LDRLAP1 (Low-density lipoprotein receptor adapter protein 1): LDLRAP1 has been linked to glucose metabolism and insulin sensitivity.

[0380] 9. GLYATL2 (Glycine N-acyltransferase-like protein 2): GLYATL2 is involved in glycine conjugation, which can be related to glucose metabolism.

[0381] The following proteins are associated with hyperglycemia.

[0382] 1. PLEK (Pleckstrin): PLEK has been associated with glucose metabolism and insulin signaling pathways.

[0383] 2. BTK (Bruton's tyrosine kinase): BTK has been linked to glucose metabolism and insulin signaling, particularly in the context of insulin resistance.

[0384] 3. NAMPT (Nicotinamide phosphoribosyltransferase): NAMPT plays a role in NAD biosynthesis, which is important for glucose metabolism and cellular energy production.

[0385] 4. FN3K (Fructosamine-3-kinase): FN3K is involved in glucose metabolism and theregulation of fructosamine levels.

[0386] 5. ME2 (Malic enzyme 2): ME2 is an enzyme involved in glucose metabolism and contributes to the generation of NADPH, an important cellular reducing agent.

[0387] 6. PIP4K2A (Phosphatidylinositol 5-phosphate 4-kinase type-2 alpha): PIP4K2A has been associated with glucose metabolism and insulin signaling.

[0388] 7. LDRLAP1 (Low-density lipoprotein receptor adapter protein 1): LDLRAP1 has been linked to glucose metabolism and insulin sensitivity.

[0389] 8. BTK (Bruton's tyrosine kinase): BTK has been linked to glucose metabolism and insulin signaling, and its dysregulation can contribute to hyperglycemia and insulin resistance.

[0390] 9. NAMPT (Nicotinamide phosphoribosyltransferase): NAMPT plays a role in NAD biosynthesis, and disturbances in NAD metabolism can affect glucose homeostasis and contribute to hyperglycemia.

[0391] 10. FN3K (Fructosamine-3-kinase): FN3K is involved in glucose metabolism and the regulation of fructosamine levels, which are markers of hyperglycemia.

[0392] 11. PRDX3 (Peroxiredoxin-3): PRDX3 has implications in oxidative stress-related diseases, and oxidative stress can contribute to insulin resistance and hyperglycemia.

[0393] 12. IFI16 (Interferon gamma-inducible protein 16): IFI16 is involved in immune response regulation, and chronic inflammation, mediated by proteins like IFI16, can contribute to insulin resistance and hyperglycemia.

[0394] Proteins associated with diabetic nephropathy are:

[0395] 1. PLEK (Pleckstrin): PLEK has been associated with kidney diseases, particularly diabetic nephropathy, which is a kidney complication resulting from diabetes.

[0396] 2. IL18 (Interleukin-18): IL18 is a pro-inflammatory cytokine that has been implicated in kidney diseases, including diabetic nephropathy.

[0397] 3. NAMPT (Nicotinamide phosphoribosyltransferase): NAMPT has been associated with kidney diseases, particularly diabetic nephropathy, which involves kidney damage due to diabetes.

[0398] 4. PRDX3 (Peroxiredoxin-3): PRDX3 has been linked to kidney injury and chronic kidney disease, which may include diabetic nephropathy.

[0399] 5. FN3K (Fructosamine-3-kinase): FN3K has been implicated in diabetic nephropathy, a kidney disease associated with diabetes.

[0400] And protein associated with diabetic neuropathy is IFI16 (Interferon gamma-inducible protein 16): IFI16 has been associated with autoimmune diseases and viral infections, and it has also been studied in the context of diabetic neuropathy, a nen e-related complication of diabetes.

[0401] Proteins connected to Diabetic Retinopathy:

[0402] 1. RAC1: RAC1 is involved in various cellular processes and has been linked to diabetic retinopathy, particularly in the context of retinal endothelial cell dysfunction and neovascularization.

[0403] 2. IL18 (Interleukin-18): IL18 is a pro-inflammatory cytokine and has been implicated in diabetic retinopathy, where inflammation plays a role in the development and progression of the disease.

[0404] 3. NAMPT (Nicotinamide phosphoribosyltransferase): NAMPT has been associated with diabetic retinopathy, and its modulation may impact retinal cell survival and vascularization.

[0405] 4. PRDX3 (Peroxiredoxin-3): PRDX3 has been linked to oxidative stress-related diseases, and oxidative stress is a contributing factor to diabetic retinopathy.

[0406]

[0407] Some of these proteins are connected to disease conditions. Here are some of the diseases or conditions that some of the listed proteins are connected with:

[0408] 1. RAC1 : RAC1 is involved in various cellular processes and has been associated with cancer, cardiovascular diseases, and neurodegenerative disorders.

[0409] 2. PLEK: PLEK (also known as Pleckstrin) is associated with platelet activation and has implications in cardiovascular diseases.

[0410] 3. HSPD1 : HSPD1 (Heat shock protein 60) is a chaperone protein and has been linked to various neurodegenerative diseases and inflammatory conditions.

[0411] 4. BTK: BTK (Bruton's tyrosine kinase) is involved in B-cell signaling and has been associated with primary immunodeficiencies and B-cell malignancies.

[0412] 5. CAPG: CAPG (Capping protein, actin filament, gelsolin-like) is involved in cytoskeleton regulation and has been linked to cancer metastasis and inflammatory diseases.

[0413] 6. IL21R: IL21R (Interleukin-21 receptor) is involved in immune response regulation and has been associated with autoimmune diseases.

[0414] 7. IL 18: IL 18 (Interleukin- 18) is a pro-inflammatory cytokine and has been linked to various inflammatory’ conditions.

[0415] 8. NAMPT: NAMPT (Nicotinamide phosphoribosyltransferase) is involved in nicotinamide adenine dinucleotide (NAD) biosynthesis and has been associated with metabolic disorders and inflammatory’ diseases.

[0416] 9. PRDX3: PRDX3 (Peroxiredoxin-3) is an antioxidant enzyme and has implications in oxidative stress-related diseases.

[0417] 10. IFI16: IFI16 (Interferon gamma-inducible protein 16) is involved in immuneresponse regulation and has been linked to autoimmune diseases and viral infections.

[0418] 11. IL21R (Interleukin-21 receptor): IL21R is involved in immune response regulation and has been associated with autoimmune diseases. Interleukin-21 plays a role in promoting immune responses, and alterations in IL21R signaling have been implicated in certain autoimmune conditions.

[0419] 12. IL18 (Interleukin- 18): IL18 is a pro-inflammatory cytokine and has been linked to various inflammatory conditions, including certain autoimmune diseases. It plays a role in immune system activation and inflammation.

[0420] 13. IFI16 (Interferon gamma-inducible protein 16): IFI16 is involved in immune response regulation and has been linked to autoimmune diseases. It is a cytoplasmic DNA sensor that can trigger immune responses, and its dysregulation has been associated with autoimmune conditions.

[0421] 14. BTK (Bruton's tyrosine kinase): BTK is involved in B-cell signaling, but it also plays a role in insulin signaling and glucose metabolism. Mutations in the BTK gene have been linked to insulin resistance and type 2 diabetes.

[0422] 15. NAMPT (Nicotinamide phosphoribosyltransferase): NAMPT is involved in nicotinamide adenine dinucleotide (NAD) biosynthesis, which plays a critical role in cellular energy metabolism. NAMPT has been associated with insulin resistance and type 2 diabetes.

[0423] 16. PRDX3 (Peroxiredoxin-3): PRDX3 is an antioxidant enzyme and has implications in oxidative stress-related diseases. Oxidative stress can influence insulin sensitivity and contribute to insulin resistance.

[0424] 17. IFI16 (Interferon gamma-inducible protein 16): IFI16 has been linked to insulin resistance and type 2 diabetes. It is a cytoplasmic DNA sensor that can trigger immune responses, and its dysregulation has been associated with insulin resistance.

[0425] All publications, patents, and patent applications, Genbank sequences, websites and other published materials referred to throughout the disclosure herein are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application, Genbank sequences, websites and other published materials was specifically and individually indicated to be incorporated by reference. In the event that the definition of a term incorporated by reference conflicts with a term defined herein, this specification shall control.

Claims

CLAIMSWhat is claimed is:

1. A method comprising: contacting a biological sample from a subject suspected of having type 1 diabetes with a panel of capture agents, wherein the panel comprises at least one primary’ capture agent that is capable of binding to a primary analyte selected from a primary group of proteins comprising: hydroxyacylglutathione hydrolase (GLO2); INPP5B; CCT5; ARHGAP6; SRC.l; ARL8A; SRC; ATP5PO; PYGB; DARS2; RAB32; TTC1; SHMT2; ATP5PF; PRTFDC1; ACADVL; LDLRAP1; PNPT1; ARL6IP5; ACOT13; YARS1; CCT8; ITGA2B.ITGB3; PKM; PLEKHF2; GAP43; RAN; FN3K; RAB8B; STK24; PPID; UBE2D1.UBB; UBASH3B; CCT7; IVD; PSG2; BTK; GPD1.1; TFAM; DAB2; LANCL2; ITGA2B.ITGB3.1; MAPRE3; BTK. 1; ILK; GSK3B; ACOX1; APRT; CIAO1; PRKCA; EIF1B; AKR7A2; RAB3C; PPP1CC; ACTN1; ARRB1; UNC45A.1; CEP41; RAB21; ME2; PDHA2; RHOC; PMM2; SGTA; ECHS1; HSPD1; SPHK1; MGMT; MBNL2; NRGN; ENO1; NME7; SAR1A; PPIF: MARS1; MBNL1; PPIF. l; UFD1; PCBP2; VCP; INPP5A; FHOD1; COX5A; NAP1L1; GMPR; CLIC4.1; MRPL1; DBT; ZADH2; MAP4K5; HSD17B10; NAP1L4; HSPB1; NCKIPSD; VIL1; DDX6; CLIC4; MAPRE2; SNTA1; NME4; PDIA5; HAGH; CLIC1; PRPSAP2; PIP4K2A; NACA; PDE5A; KIFBP; PIP4K2B; DECR1; GSK3A; RAB4B; CAP1; ADH6; MTIF3; ACADM; DNM1; COPB2; SYTL4.1; DTD1; PTRHD1; PRDX3; PDE5A.1; EIF2B1; CTPS1; RTCA; RBPMS2; DDI2; RAB6B; PGD; RILPL2; HADH; RAB1B; TARSI ; MAPK10; RHOT1 ; AGFG2; PIH1 D1 ; ALAD; CDC42; DAPP1; ABHD10; EIF4A2.1; SERPINH1; LYN; and PEAR1.1; and detecting whether the at least one primary capture agent binds to at least one primary analyte in the sample.

2. The method of claim 1, wherein the panel comprises at least three primary' capture agents; and the method comprises detecting whether each of the at least three primary' capture agents bind to a first analyte in the sample.

3. The method of claim 2. wherein the panel comprises primary capture agents that are capable of binding to each of: hydroxyacylglutathione hydrolase (GLO2); INPP5B; CCT5; ARHGAP6; SRC. l; ARL8A; SRC; ATP5PO; PYGB; DARS2; RAB32; TTC1; SHMT2; ATP5PF; PRTFDC1; ACADVL; LDLRAP1; PNPT1; ARL6IP5; ACOT13; YARS1; CCT8; ITGA2B.ITGB3; PKM; PLEKHF2; GAP43; RAN; FN3K; RAB8B; STK24; PPID; UBE2D1.UBB; UBASH3B; CCT7; IVD; PSG2; BTK; GPD1.1; TFAM; DAB2; LANCL2; ITGA2B.ITGB3.1 ; MAPRE3; BTK.l ; ILK; GSK3B; ACOX1; APRT; CIAO1 ; PRKCA; EIF1B;AKR7A2; RAB3C; PPP1CC; ACTN1; ARRB1; UNC45A.1; CEP41; RAB21; ME2; PDHA2; RHOC; PMM2; SGTA; ECHS1; HSPD1; SPHK1; MGMT; MBNL2; NRGN; ENO1; NME7; SAR1A; PPIF; MARS1; MBNL1; PPIF. l; UFD1; PCBP2; VCP; INPP5A; FHOD1; COX5A; NAP1L1; GMPR; CLIC4.1; MRPL1; DBT; ZADH2; MAP4K5; HSD17B10; NAP1L4; HSPB1; NCKIPSD; VIL1; DDX6; CLIC4; MAPRE2; SNTA1; NME4; PDIA5; HAGH; CLIC1; PRPSAP2; PIP4K2A; NACA; PDE5A; KIFBP; PIP4K2B; DECR1; GSK3A; RAB4B; CAP1; ADH6; MTIF3; ACADM; DNM1; COPB2; SYTL4.1; DTD1; PTRHD1; PRDX3; PDE5A.1; EIF2B1; CTPS1; RTCA; RBPMS2; DDI2; RAB6B; PGD; RILPL2; HADH; RAB1B; TARSI; MAPK10; RHOT1; AGFG2; PIH1D1; ALAD; CDC42; DAPP1; ABHD10; EIF4A2.1; SERPINH1; LYN; and PEARL 1.

4. The method of any one of claims 1-3, wherein the panel further comprises at least one secondary capture agent that is capable of binding to a secondary analyte selected from a secondary group of proteins selected from: Gamma-interferon-inducible protein 16: Isoform 2, Hematopoietic expression, interferon-inducible nature, and nuclear localization 1 (IF16:HIN 1); Neutrophil cytosol factor 1 (NCF-1); High mobility group protein B2 (HMG-2); Histone H1.2; Protein S100-A8 / A9 heterodimer (S100A8 / S100A9); Activated RNA polymerase II transcriptional coactivator pl5 (TCP4); Calgranulin A; Myeloid cell nuclear differentiation antigen (MNDA); Phosphoglycerate mutase 1; Ubiquinone biosynthesis protein COQ9, mitochondrial (COQ9); and Pulmonary surfactant-associated protein D (SP-D); XAGE2; SPH; HK3; HNRNPA1.1; S100A9; SUB1; HMGB2; PGAM2; LSP1; SFTPD. l; FNBP1; RBP7; CAPG; MAP3K3.1 ; PSTPIP1 ; ZBP1 ; S100A12; ADSS2; S100A8; SNRPA; SMNDC1 ; S100A8.S100A9; HNRNPA2B1; NADK; NCF1; IL21R; and IL18; and wherein the method further comprises detecting whether the at least one secondary primary capture agent binds to at least one secondary analyte in the sample.

5. The method of claim 4, wherein the panel comprises at least three secondary capture agents; and wherein the method comprises detecting whether each of the at least three secondary capture agents bind to a secondary analyte in the sample.

6. The method of claim 5. wherein the panel comprises primary capture agents that are capable of binding to each of: Gamma-interferon-inducible protein 16:Isoform 2, Hematopoietic expression, interferon-inducible nature, and nuclear localization 1 (IF16:HIN 1); Neutrophil cytosol factor 1 (NCF-1); High mobility group protein B2 (HMG-2); Histone H1.2; Protein S100- A8 / A9 heterodimer (S100A8 / S100A9); Activated RNA polymerase II transcriptional coactivator pl 5 (TCP4); Calgranulin A; Myeloid cell nuclear differentiation antigen (MNDA); Phosphoglycerate mutase 1; Ubiquinone biosynthesis protein COQ9, mitochondrial (COQ9); andPulmonary surfactant-associated protein D (SP-D); XAGE2; SPI1; HK3; HNRNPA1.1; S100A9; SUB1; HMGB2; PGAM2; LSP1; SFTPD. l; FNBP1: RBP7; CAPG; MAP3K3.1; PSTPIP1; ZBP1; S 100A12; ADSS2; S100A8; SNRPA; SMNDC1; S100A8.S100A9; HNRNPA2B1; NADK; NCF1; IL21R; and IL18.

7. The method of any one of claims 1-6, further comprising: measuring the amount of the at least one primary analyte bound to the at least one primary’ capture agent and the amount of the at least one secondary analyte bound to the at least one secondary capture agent.

8. The method of claim 7, further comprising measuring the amount of the at least one primary- analyte and the at least one secondary' analyte in a control sample; yvherein a higher amount of the primary analyte in the biological sample compared to the control sample is indicative of type 1 diabetes in the subject; and wherein a lower amount of the secondary’ analyte in the biological sample compared to the control sample is indicative of type 1 diabetes in the subject.

9. The method of claim 8, wherein if the subject has ty pe 1 diabetes, the method further comprises administering to the subject an effective amount of a type 1 diabetes therapeutic.

10. The method of claim 9, yvherein the diabetes therapeutic comprises an IgM antibody.

11. The method of any one of claims 1-10, further comprising obtaining the biological sample from the subject before contacting the sample with the panel of capture agents.

12. A method comprising: contacting a biological sample from a subject suspected of having type 2 diabetes with a panel of capture agents, wherein the panel comprises at least one primary' capture agent that is capable of binding to a primary’ analyte selected from a primary group of proteins comprising: Ribonuclease A family member 1; Platelet-Derived Growth Factor Subunit B; C-X-C motif chemokine ligand 11.1; Integrin-Linked Kinase; Tropomyosin 4; Hepcidin Antimicrobial Peptide; and Synaptotagmin Like 4; Platelet-Derived Growth Factor Subunit B (PDGFB); and Integrin- Linked Kinase; and detecting whether the at least one primary capture agent binds to at least one primary' analyte in the sample.

13. The method of claim 12, wherein the panel comprises at least three primary capture agents; and the method comprises detecting whether each of the at least three primary' capture agents bind to a first analyte in the sample.

14. The method of claim 13, wherein the panel comprises primary capture agents that are capable of binding to each of: Ribonuclease A family member 1; Platelet-Derived Growth Factor Subunit B; C-X-C motif chemokine ligand 11.1; Integrin-Linked Kinase; Tropomyosin 4;Hepcidin Antimicrobial Peptide; and Synaptotagmin Like 4; Platelet-Derived Growth Factor Subunit B (PDGFB); and Integrin-Linked Kinase.

15. The method of any one of claims 12-14, wherein the panel further comprises at least one secondary capture agent that is capable of binding to a secondary analyte selected from a secondary group of proteins selected from: Phosphoglycerate Mutase 1 ; Zymogen Granule Protein 16; and PGAM1; and wherein the method further comprises detecting whether the at least one secondary primary capture agent binds to at least one secondary analyte in the sample.

16. The method of claim 15, wherein the panel comprises primary capture agents that are capable of binding to each of: Phosphoglycerate Mutase 1; Zymogen Granule Protein 16; and PGAM1.

17. The method of any one of claims 12-16. further comprising: measuring the amount of the at least one primary analyte bound to the at least one primary capture agent and the amount of the at least one secondary analyte bound to the at least one secondary capture agent.

18. The method of claim 17, further comprising measuring the amount of the at least one primary analyte and the at least one secondary’ analyte in a control sample; wherein a higher amount of the primary analyte in the biological sample compared to the control sample is indicative of ty pe 2 diabetes in the subject; and wherein a lower amount of the secondary' analyte in the biological sample compared to the control sample is indicative of ty pe 2 diabetes in the subject.

19. The method of claim 18, wherein if the subject has type 2 diabetes, the method further comprises administering to the subject an effective amount of a type 2 diabetes therapeutic.

20. The method of any one of claims 12-19, further comprising obtaining the biological sample from the subject before contacting the sample with the panel of capture agents.

21. A detection reagent comprising one or more primary capture agents, wherein each primary’ capture agent binds to a primary' analyte selected from a primary' group of proteins comprising: hydroxyacylglutathione hydrolase (GLO2); INPP5B; CCT5; ARHGAP6; SRC. l; ARL8A; SRC; ATP5PO; PYGB; DARS2; RAB32; TTC1; SHMT2; ATP5PF; PRTFDC1; ACADVL; LDLRAP1; PNPT1; ARL6IP5; ACOT13; YARS1; CCT8; ITGA2B.ITGB3; PKM; PLEKHF2; GAP43; RAN; FN3K; RAB8B; STK24; PPID; UBE2D1.UBB; UBASH3B; CCT7; IVD; PSG2; BTK; GPD1.1; TFAM; DAB2; LANCL2; ITGA2B.ITGB3. 1; MAPRE3; BTK. 1; ILK; GSK3B; ACOX1; APRT; CIAO1; PRKCA; EIF1B; AKR7A2; RAB3C; PPP1CC; ACTN1; ARRB1; UNC45A.1; CEP41; RAB21; ME2; PDHA2; RHOC; PMM2; SGTA; ECHS1; HSPD1; SPHK1; MGMT; MBNL2; NRGN; ENO1; NME7; SAR1A; PPIF; MARS1; MBNL1; PPIF. l; UFD1; PCBP2; VCP; INPP5A; FHOD1; COX5A; NAP1L1; GMPR; CLIC4.1; MRPL1; DBT; ZADH2; MAP4K5;HSD17B10; NAP1L4; HSPB1; NCKIPSD; VIL1; DDX6; CLIC4; MAPRE2; SNTA1; NME4; PDIA5; HAGH; CLIC1; PRPSAP2; PIP4K2A; NACA; PDE5A; KIFBP; PIP4K2B; DECR1; GSK3A; RAB4B; CAPE ADH6; MTIF3; ACADM; DNM1; C0PB2; SYTL4.1; DTD1; PTRHD1; PRDX3; PDE5A.1; EIF2B1; CTPS1; RTCA; RBPMS2; DDI2; RAB6B; PGD; RILPL2; HADH; RAB1B; TARSI; MAPK10; RHOT1; AGFG2; PIH1D1; ALAD; CDC42; DAPP1; ABHD10; EIF4A2.1; SERPINH1; LYN; and PEARL 1; wherein the one or more primary capture agents is linked to a solid support.

22. The detection reagent of claim 21, further comprising one or more secondary capture agents, wherein each secondary capture agent binds to a secondary analyte selected from a secondary' group of proteins comprising: Gamma-interferon-inducible protein 16:Isoform 2, Hematopoietic expression, interferon-inducible nature, and nuclear localization 1 (IF16:HIN 1); Neutrophil cytosol factor 1 (NCF-1); High mobility group protein B2 (HMG-2); Histone H1.2; Protein S100-A8 / A9 heterodimer (S100A8 / S100A9); Activated RNA polymerase II transcriptional coactivator pl5 (TCP4); Calgranulin A; Myeloid cell nuclear differentiation antigen (MNDA); Phosphoglycerate mutase 1; Ubiquinone biosynthesis protein COQ9, mitochondrial (COQ9); and Pulmonary surfactant-associated protein D (SP-D); XAGE2; SPH; HK3; HNRNPA1.1; S100A9; SUB1; HMGB2; PGAM2; LSP1; SFTPD. l; FNBP1; RBP7; CAPG; MAP3K3.1; PSTPIP1; ZBP1; S100A12; ADSS2; S100A8; SNRPA; SMNDC1; S 100A8. S 100A9; HNRNP A2B 1 ; NADK; NCF 1 ; IL21 R; and IL 18.

23. A method comprising contacting the detection reagent of claim 21 or 22 with a biological sample from a subject.

24. The method of claim 23, wherein the subject is suspected of having type 1 diabetes.

25. A detection reagent comprising one or more primary capture agents, wherein each primary' capture agent binds to a primary’ analyte selected from a primary’ group of proteins comprising: Ribonuclease A family member 1; Platelet-Derived Growth Factor Subunit B; C-X-C motif chemokine ligand 11.1; Integrin-Linked Kinase; Tropomyosin 4; Hepcidin Antimicrobial Peptide; and Synaptotagmin Like 4; Platelet-Derived Grow th Factor Subunit B (PDGFB); and Integrin- Linked Kinase; wherein the one or more primary capture agents is linked to a solid support.

26. The detection reagent of claim 25, further comprising one or more secondary capture agents, wherein each secondary capture agent binds to a secondary^ analyte selected from a secondary group of proteins comprising: Phosphoglycerate Mutase 1; Zymogen Granule Protein 16; and PGAM1.

27. A method comprising contacting the detection reagent of claim 25 or 26 with a biological sample from a subject.

28. The method of claim 27, wherein the subject is suspected of having type 1 diabetes.

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