Methods of assessment of pre-clinical alzheimer's disease

Abstract

Described herein are methods of assessing the risk of dementia in a subject using an odor-based test, or "smell test". These methods can be self-administered by the subject under the instruction of a computing device via a user interface. Methods of administering a smell test as described herein can optionally be combined with measuring and quantifying the level of at least one biomarker for dementia (e.g., CXCL10, CCL2, IL-6) in a biological sample collected from the subject. Also provided are methods to treat dementia (e.g., dementia associated with Alzheimer's disease, dementia associated with a subset of Alzheimer's disease patients with TDP-43 pathology).

Description

[0001] METHODS OF ASSESSMENT OF PRE-CLINICAL ALZHEIMER’S DISEASE

[0002] CLAIM OF PRIORITY

[0003] This application claims the benefit of U.S. Provisional Application Serial Nos. 63 / 634,040, filed on April 15, 2024, and 63 / 776,882, filed on March 24, 2025. The entire contents of the foregoing are incorporated herein by reference.

[0004] FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0005] This invention was made with Government support under Grant Nos. AG058063, AG062130, AG078297, and DC019579 awarded by the National Institutes of Health. The Government has certain rights in the invention.

[0006] TECHNICAL FIELD

[0007] Described herein are methods of assessing the risk of dementia in a subject using an odor-based test, or “smell test”. These methods can be self-administered by the subject under the instruction of a computing device via a user interface. Methods of administering a smell test as described herein can optionally be combined with measuring and quantifying the level of at least one biomarker for dementia (e.g., CXCL10, CCL2, IL-6) in a biological sample collected from the subject. Also provided are methods to treat dementia (e.g., dementia associated with Alzheimer’s disease, dementia associated with a subset of Alzheimer’s disease patients with TDP- 43 pathology).

[0008] BACKGROUND

[0009] Alzheimer’s disease (AD) affects over 6.9 million Americans, and this number is expected to grow to 13.9 million by 2060 with devastating economic consequences for society (>$335B / year in the US) and families (>$330B in unpaid care provided predominantly by family members)1. The dementia syndrome of AD is now considered an advanced stage of the disease and treatment during preclinical stages is likely to be more efficacious7 9. One of the pathologic features that stratify dementia patients in AD includes cytoplasmic inclusions of phosphorylated TAR DNA-binding protein 43 (pTDP-43), indicating the presence of TDP-43 pathology. SUMMARY

[0010] The present disclosure provides a digital remote AROMHA Brain Health Test (ABHT), an at-home odor identification, discrimination, memory, and intensity assessment. The ABHT is a novel remote olfactory battery that exhibited similar performance across observed and unobserved self-administration among cognitively normal participants as well as among English and Spanish-speaking cognitively normal participants, while anosmic patients performed at chance level. Odor percept identification, discrimination, and memory subtests were sensitive to the aging effect on the olfactory system. Each olfactory identification subtest, including the short 9- item version, and the olfactory discrimination subtest showed lower performance in the mild cognitive impairment group, mirroring results in the literature. These results provided herein suggest that the ABHT could be used in clinical research settings in different languages to explore the utility of olfactory biomarkers to predict the presence of blood-based, image-based, or cerebrospinal fluid (CSF)-based biomarkers of neurodegenerative disease and longitudinal development of clinical symptoms.

[0011] The present disclosure further provides the ABHT for use in identifying specific subsets of dementia and, as such, personalized treatment for those identified with specific subsets of dementia. Data herein show that immunogenic cdsRNA and cytoplasmic pTDP-43 inclusions were spatially coincident in brains with coexisting AD pathology as well as in a cell based-model of TDP-43 pathology and demonstrated robust interferon-signaling in AD patients’ brains. A machine learning pipeline for drug repurposing in AD (DRIAD-SP)26was updated herein to include cryptic exon (CE) expression, which is a proxy for TDP-43 pathology27 32. Data herein demonstrated a protective signal for baricitinib and ruxolitinib, both FDA- approved JAK kinase inhibitors that block the interferon response, in only a subset of AD patients with elevated CE expression. To evaluate the mechanism of action of neuroprotective JAK inhibitors, a genome-wide CRISPR screen revealed that knockout of TYK2, a JAK kinase family member, robustly rescued cdsRNA-induced toxicity. In addition to baricitinib and ruxolitinib, deucravacitinib - a selective TYK2 inhibitor recently approved for moderate-to-severe plaque psoriasis33- exhibited a neuroprotective effect in three different neural cell models with more potency compared to the other JAK inhibitors. Potential inflammatory biomarkers for dsRNA-mediated neuropathology were assessed using cell-based assays as well as conducting a human observational study of plasma levels in individuals with a TYK2- activity-reducing single nucleotide polymorphism (SNP) and, as demonstrated herein, CXCL10, CCL2, and IL-6 can be biomarkers for validation in the clinic.

[0012] Provided herein are methods of administering a smell test for assessment of risk of dementia in a subject. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject comprises: (a) generating, by a computing device, a user interface to be displayed on the computing device; (b) instructing, by the computing device via the user interface, the subject to utilize an odor card configured to deliver an odor from among a plurality of odors; (c) instructing, by the computing device via the user interface, the subject to identify the delivered odor from a forced choice list of options provided by the computing device via the user interface; (d) asking, by the computing device via the user interface, the subject to evaluate their confidence in their odor identification decision; (e) receiving, by the computing device via the user interface, a subject input for a smell test; (f) calculating, by the computing device, a confidence metric for the smell test; and (g) identifying the subject as high risk for dementia based on the confidence metric. Methods provided herein can also comprise: (a) instructing a subject to utilize an odor card configured to deliver an odor from among a plurality of odors; (b) instructing the subject to identify the delivered odor from a forced choice list of options; (c) asking the subject to evaluate their confidence in their odor identification decision; and (d) calculating, optionally by a computing device, a confidence metric based on the subject’s confidence in their odor identification decision. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject comprises an odor card configured to deliver an odor from among a plurality of nine odors. In some embodiments, the plurality of nine odors comprises at least at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, and grape. In some embodiments, a smell test for assessment of risk of dementia in a subject further includes an odor episodic memory test. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject further comprises administering an odor episodic memory test, wherein the test comprises: (h) instructing, by the computing device via the user interface, the subject to utilize a second odor card configured to deliver an odor from among a plurality of odors; (i) asking, by the computing device via the user interface, the subject if the odor delivered in the second odor card was delivered in the earlier used odor card (i.e., a first odor card); (j) instructing, by the computing device via the user interface, the subject to identify the delivered odor from a forced choice list of options provided by the computing device via the user interface; (k) asking, by the computing device via the user interface, the subject to evaluate their confidence in their odor identification decision; (1) receiving, by the computing device via the user interface, a subject input for a smell test; (m) calculating, by the computing device, a confidence metric for the smell test; and (n) identifying the subject as high risk for dementia based on the confidence metric. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject comprises an odor card configured to deliver an odor from among a plurality of eighteen odors. In some embodiments, the plurality of eighteen odors comprises at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, grape, coffee, peach, chocolate, orange, dirt, banana, lemon, bubble gum, and rose. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject further comprises asking, by the computing device via the user interface, the subject to evaluate their confidence in their odor identification decision by selecting one answer from the following “I Guessed,” “I Narrowed Down to Three,” “I Narrowed Down to Two,” or “I Am Certain.” In some embodiments, the user interface is a website or a web-based application. In some embodiments, the computing device is a computer, a smart phone, or a mobile device. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject is self-administered by the subject. In some embodiments, the confidence metric for the smell test is calculated by the computing device as the total number of odors identified correctly by the subject where the subject did not evaluate their confidence in their odor identification decision by selecting “I Guessed” when prompted by the computing device via the user interface. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject further comprises comparing, by the computing device, the calculated confidence metric for the smell test relative to a predetermined threshold, wherein the predetermined threshold is determined by a machine learning algorithm based on a plurality of previously entered smell test performance data from healthy subjects. In some embodiments, the subject is identified as high risk for dementia when the calculated confidence metric for the smell test is lower than the predetermined threshold.

[0013] Further provided herein are methods comprising: (a) collecting a biological sample from a subject, preferably a biological sample comprising whole blood, serum, or plasma; (b) quantifying a level of at least one biomarker for dementia in the biological sample, wherein the at least one biomarker for dementia is selected from the group consisting of C-X-C motif chemokine ligand 10 (CXCL10), C-C motif chemokine ligand 2 (CCL2), and interleukin 6 (IL-6); and (c) comparing the level of the least one biomarker for dementia to a predetermined threshold, wherein the predetermined threshold is determined from levels of the biomarker assessed from biological samples collected from a plurality of healthy subjects. In some embodiments, methods disclosed herein can further comprise identifying the subject as at high risk for dementia when the level of at least one biomarker for dementia is higher than the predetermined threshold. In some embodiments, methods disclosed herein can further comprise identifying the subject as at high risk for dementia when the level of at least two biomarkers for dementia are higher than the predetermined thresholds. In some embodiments, methods disclosed herein can further comprise identifying the subject as at high risk for dementia when the level of CXCL10, CCL2, IL-6, or any combination thereof is higher than the predetermined threshold.

[0014] Also provided herein are methods of assessing the risk of dementia in a subject comprising administering the smell test according to any of the methods disclosed herein in combination with any of the methods disclosed herein for quantifying a level of at least one biomarker for dementia (e.g., CXCL10, CCL2, IL- 6) in a biological sample collected from the subject.

[0015] Provided herein are also methods of treating a subject for dementia, wherein the subject is at high risk for dementia as determined by (1) the smell test administered according to any of the methods disclosed herein, (2) quantifying a level of at least one biomarker for dementia (e.g., CXCL10, CCL2, IL-6) in a biological sample collected from the subject, or (3) both the smell test administered according to any of the methods disclosed herein and quantifying a level of at least one biomarker for dementia (e.g., CXCL10, CCL2, IL-6) in a biological sample collected from the subject. In some embodiments, a method of treating a subject for dementia comprises administering to the subject at least one treatment for Alzheimer’s disease, optionally wherein the treatment is selected from the group consisting of benzgalantamine, donepezil, galantamine, rivastigmine, memantine, lecanemab, donanemab, suvorexant, and brexpiprazole. In some embodiments, a method of treating a subject for dementia comprises administering to the subject at least one therapeutic agent targeting TYK2. In some embodiments, the at least one therapeutic agent targeting TYK2 is a TYK2 inhibitor, optionally wherein the TYK2 inhibitor is deucravacitinib.

[0016] Further provided herein are methods of administering a smell test for assessment of neuroanatomical volume in at least one brain region in a subject. In some embodiments, a method of administering a smell test for assessment of neuroanatomical volume in at least one brain region in a subject comprises: (a) administering the smell test according to any of the methods disclosed herein; and (b) identifying a decline in neuroanatomical volume in at least one brain region if the calculated confidence metric for the smell test is lower than the predetermined threshold. In some embodiments, a method of administering a smell test for assessment of neuroanatomical volume in at least one brain region in a subject comprises: (a) generating, by a computing device, a user interface to be displayed on the computing device; (b) instructing, by the computing device via the user interface, the subject to utilize an odor card configured to deliver an odor from among a plurality of odors; (c) instructing, by the computing device via the user interface, the subject to identify the delivered odor from a forced choice list of options provided by the computing device via the user interface; (d) asking, by the computing device via the user interface, the subject to evaluate their confidence in their odor identification decision; (e) receiving, by the computing device via the user interface, a subject input for a smell test; (f) calculating, by the computing device, a score for the smell test; and (g) identifying a decline in neuroanatomical volume in at least one brain region in the subject based on the score for the smell test. In some embodiments, a score for the smell test is calculated as a total number of odors correctly identified by the subject. In some embodiments, an odor card for use in a method of administering a smell test for assessment of neuroanatomical volume in at least one brain region in a subject is configured to deliver an odor from: (i) among a plurality of nine odors, optionally wherein the plurality of nine odors comprises at least at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, and grape; and / or, (ii) from among a plurality of eighteen odors, optionally wherein the plurality of eighteen odors comprises at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, grape, coffee, peach, chocolate, orange, dirt, banana, lemon, bubble gum, and rose. In some embodiments, the user interface is a website or a webbased application. In some embodiments, the computing device is a computer, a smart phone, or a mobile device. In some embodiments, a method of administering a smell test for assessment of neuroanatomical volume in at least one brain region in a subject is self-administered by the subject. In some embodiments, a method of administering a smell test for assessment of neuroanatomical volume in at least one brain region in a subject further comprises comparing, by the computing device, the calculated score for the smell test relative to a predetermined threshold, wherein the predetermined threshold is determined from smell test performance data collected from a plurality of healthy subjects. In some embodiments, the at least one brain region in the subject comprises hippocampus, amygdala, and / or both. In some embodiments, the at least one brain region in the subject comprises the left hippocampus, the right hippocampus, the left amygdala, the right amygdala, or any combination thereof. In some embodiments, the identification of decline in neuroanatomical volume in at least one brain region in the subject correlates to cognitive decline in the subject.

[0017] Also provided herein are methods for selecting a subject for treatment with a TYK2 inhibitor. In some embodiments, a method for selecting a subject for treatment with a TYK2 inhibitor comprises: (a) administering the smell test according to any of the methods disclosed herein; and (b) selecting the subject for treatment with a TYK2 inhibitor if the calculated confidence metric for the smell test is lower than the predetermined threshold. In some embodiments, a method for selecting a subject for treatment with a TYK2 inhibitor optionally further comprises quantifying a level of at least one biomarker for dementia (e.g., CXCL10, CCL2, IL-6) in a biological sample collected from the subject according to the methods disclosed herein, and selecting the subject for treatment with a TYK2 inhibitor if the level of at least one biomarker for dementia is higher than the predetermined threshold. In some embodiments, a method for selecting a subject for treatment with a TYK2 inhibitor further comprises: (c) administering the TYK2 inhibitor to the subject if the subject is selected for treatment. In some embodiments, the TYK2 inhibitor is deucravacitinib.

[0018] For any of the methods as provided herein, the subject has, is suspected of having, or is at risk of having a neurodegenerative disease. In some embodiments, the subject has, is suspected of having, or is at risk of having a neurodegenerative disease associated with TDP-43 pathology. In some embodiments, the subject has, is suspected of having, or is at risk of having Alzheimer’s disease. In some embodiments, the subject at risk of having Alzheimer’s disease has a loss-of-function SNP in TYK2 (rs34536443). In some embodiments, the neurodegenerative disease associated with TDP-43 pathology comprises a subset of Alzheimer’s disease.

[0019] Also provided herein are kits for use in any of the methods disclosed herein, wherein the kits can comprise at least one odor card and / or at least one test guide as disclosed herein.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

[0021] Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.

[0022] DESCRIPTION OF DRAWINGS

[0023] FIGS. 1A and IB show a schematic of the AROMHA Brain Health Test. Following online prescreening, online consent, the web-based program instructed the subject through the 5 bilingual (English / Spanish) cards (FIG. 1A). Card A was comprised of a practice odor “P” followed by the 9 odor labels comprising the 0PID9 test. In the top box (FIG. IB) is the workflow for these tests as directed by the testyourbrainhealth.com software to generate the 0PID9, OPID9noguess, and average intensity scores. After a 10-minute break, subject participants were instructed to work through Cards B and C using the workflow depicted in the middle box (FIG. IB) to generate the POEM, OPID18, and OPID18noguess scores. Then subject participants were instructed to move on to Cards D and E using the workflow depicted in the bottom box (FIG. IB) to generate an ODIO odor discrimination score.

[0024] FIGS. 2A-2G show linear regression models between olfactory scores and age across CN participants, wherein “CN” refers to a cohort of cognitively normal healthy adults without cognitive complaints. Greater age was significantly associated with lower (FIG. 2B) OPID9noguess ( = -0.02, SE = .007, / ? = .002, adj. R2= .06), (FIG. 2C) OPID18 (fi = -0.04, SE = 0.001, p < .001, adj. R2= .12), (FIG. 2D) OPID18noguess (fi = -0.06, SE = 0.03, p < .001, adj. R2= .13), (FIG. 2E) ODIO (fi = -0.02, SE = 0.005, p < .001, adj. R2= .16), and (FIG. 2F) POEM (fi = -0.004, SE = 0.001, p < .001, adj. R2= .08) scores, while the association with (FIG. 2A) OPID9 (J3 = -0.01, SE = 0.005 p = .01, adj. R2= .04) and (FIG. 2G) average intensity (fi = - 0.007, SE = 0.007, p = .28, adj. R2= .001) scores did not reach significance after Bonferroni correction for 21 comparisons (p < .002).

[0025] FIGS. 3A-3C depict representative gas chromatograph / mass spectrometry (GC / MS) for the headspace of each odor (FIG. 3A: grape, pineapple, leather, lilac, clove, smoke, strawberry, lemon; FIG. 3B: soap, menthol, peach, chocolate, orange, dirt, bubble gum, rose; FIG. 3C: peach, chocolate, orange, dirt, bubble gum, rose, banana, coffee). All samples were run on the same day. No common peaks were seen between the headspace.

[0026] FIGS. 4A-4D depict schematics of the web-based application to collect responses for the AROMHA Brain Health Test. FIG. 4A shows an example of the intensity rating score collection. FIG. 4B shows an example of the OPID9 score collection. FIG. 4C shows an example of the POEM score collection. FIG. 4D shows an example of the ODIO score collection.

[0027] FIGS. 5A-5C depict data driven machine learning approaches that predicted cognitive impairment using components of the AROMHA Brain Health Test subjected to a logistic regression model. FIGS. 5A and 5B show that the AROMHA Brain Health Test predicted memory symptoms in subjects ranging from 20 to 95 years of age with an AUC = 0.94. FIG. 5C shows that the AROMHA Brain Health Test predicted memory symptoms in subjects ranging from 55 to 95 years of age with an AUC = 0.78. FIG. 6 shows results of a gradient boosting model to quantify the components of the AROMHA Brain Health Test which predicted preclinical Alzheimer’s disease in subjects aged 55 - 95 (AUC = 0.90). No common peaks were present, indicating that the adhesive from the labels was not volatile and did not contaminate the odors.

[0028] FIGS. 7A-7I depict predicted computed neuroanatomical volumes for subjects subjected to the AROMHA smell test where the subjects had either normal cognition, (normal), subjective cognitive concern (ImpNoMCI), or mild cognitive impairment (MCI). FIG. 7A shows the association of the OPID18 No Guess metric (score, x-axis) with the left hippocampus volume (y-axis). FIG. 7B shows the association of the OPID18 No Guess metric (score, x-axis) with the left amygdala volume (y-axis). Linear relationships between the OPID9 variable and the right amygdala volume are shown in FIG. 7C. Linear relationships between the OPID9 variable and the left amygdala volume are shown in FIG. 7D. Linear relationships between the OPID9 variable and the right hippocampal volume are shown in FIG. 7E. Linear relationships between the OPID9 variable and the left hippocampal volume are shown in FIG. 7F. FIG. 7G shows the age distribution of cognitive phenotypes. FIG. 7H shows the results from 500 simulations of the cross-validation procedure. FIG. 71 shows a representative model fit for left amygdala.

[0029] Data also showed a correlation with OPID9 scores and the volume of the left hippocampus (FIG. 7B), right hippocampus (FIG. 7C), left amygdala (FIG. 7D), and right amygdala (FIG. 7E) in normal individuals and individuals with mild cognitive impairment (MCI) or asymptomatic, preclinical cognitive impairment (ImpNoMCI). Histograms along each axis show the density of the data points.

[0030] FIGS. 8A-8D show that cdsRNA induced IFN-I signaling and was spatially coincident with pTDP-43 inclusions in AD. FIG. 8A shows representative immunohistochemistry (IHC) for cdsRNA and pTDP-43 inclusions in human postmortem brain sections of the amygdala. Top panel: healthy control cases, bottom panel: AD patient cases. Zoom -ins with 2x magnification. FIG. 8B shows representative LHE, IHC and cyclic immunofluorescence (CyCIF) of human postmortem brain sections of the amygdala. IHC for cdsRNA, pTDP-43, tau, and AB on different brain sections of the same healthy control (top panel, healthy control case II) / AD patient (bottom panel, AD case I). CyCIF panels show nuclei (Sytox Blue), AB, dsRNA, pTDP-43, and tau as well as a merge of all channels. CyCIF was done on a single brain section of the same healthy control as the IHC images, but of a different AD patient. FIG. 8C shows representative immunofluorescent staining of wildtype (isoTDP-43+ / +) and mutant (isoTDP-43+ / G298S) differentiated iPSC-derived NGN2 cortical-like neurons showing dsRNA (KI antibody), Tuj 1 (neuronal marker) and nuclei (DAPI) (left). Quantification of the normalized dsRNA / Kl intensity (right) with P < 0.05. Each dot is a well (40x magnification, 2 / 3 well analyzed per condition). Each shape is a different differentiation batch (2 / 3 differentiation batches analyzed). FIG. 8D shows a bubble chart of upregulated ISGs within relevant brain regions in AD patients. Each bubble represents a gene. Bubble size is proportional to fold change of upregulation. Chart is based on a differential gene expression analysis of RNA-sequencing data derived from the ROSMAP37and MSBB38databases comparing AD patients and healthy controls. Individual gene names are shown in FIG. 15. DPFC: dorsolateral prefrontal cortex; FP: frontal pole; IFG: inferior frontal gyrus; PHG: parahippocampal gyrus; STG: superior temporal gyrus.

[0031] FIGS. 9A-9E depict prediction of drug efficacy in patients stratified by TDP- 43 pathology. FIG. 9A shows a schematic representation of the DRIAD-SP drug efficacy prediction pipeline. From left to right (i) gene sets were assembled from differential gene expression analysis of cell lines treated with the drugs of interest against DMSO controls. Random gene sets of the matching sizes were generated for comparison, (ii) RNA-sequencing data from AD brain samples was subset to only contain genes that were present in the gene set that is being tested, (iii) The RNA- sequencing subset was used to fit ordinal ridge regression models predicting Braak disease stage of the patient cohorts. Leave pair out cross-validation90was used to evaluate model performance (areas under the curve; AUC). (iv) Drug efficacy was predicted by comparing the performance of the model using the drug gene sets to the size-matched random gene sets. FIG. 9B shows a schematic representation of TDP-43 pathology associated splice variants that included CEs in STMN2 and UNCI 3 A. FIG. 9C depicts expression of TDP-43 associated splice variants in the PCC brain tissues of the ROSMAP37patient cohort. Striped regions are below, and shaded regions are above the chosen threshold for expression to be considered positive. FIG. 9D depicts per-patient-quantification of the number of CE transcripts that were expressed above the chosen thresholds. Patients were predicted to be TDP-43 pathology negative if they expressed one or fewer of the CE transcripts, and positive if they expressed two or more. FIG. 9E depicts performance of DRIAD-SP models for three selected drugs. Drug efficacy was assessed separately in patient populations according to their predicted TDP-43 pathology. Dashed lines indicate the DRIAD-SP model performance trained on the drug gene sets, whereas the gray shaded regions correspond to the distribution of model performances based on random gene sets.

[0032] FIGS. 10A-10F show that a CRISPR screen and subsequent validation identified TYK2 as a therapeutic target. FIG. 10A depicts a schematic of the CRISPR screen conducted in ReN VM cells using the Brunello library50to identify potential therapeutic targets for rescuing the toxic immune response triggered by cdsRNA. Replicates were harvested pre- and post-differentiation as well as after treatment with poly(I:C) or lipofectamine. FIG. 10B depicts a volcano plot showing the LFC of genes targeted in the CRISPR screen against their -log(P-value) and identifying IFNAR2 (average LFC = 2.23825, P = 0.0001), IRF9 (average LFC = 2.0625, P = 0.0002) and TYK2 (average LFC = 1.95, P = 0.0004) as the most prominent and PLCG1 (average LFC = -1.9875, P = 0.0002), HGS (average LFC = -1.775, P = 0.0004) and SQSTM1 (average LFC = -1.7475, P = 0.0005) as the least abundant knockouts. Gray dots represent negative control sgRNAs. FIG. 10C shows quantification of cell survival of ReN VM cell-derived neurons treated with different concentrations (no antibody P < 0.0001, n=4; 1 :50 P = 0.0001, n = 3; 1 :25 P = 0.0002, n = 3; 1 : 10 P = 0.0011, n = 3) of anti-IFNAR2 antibody after transfection with poly(I:C) or lipofectamine as vehicle control (ctrl, n = 4). FIG. 10D shows quantification of cell survival of ReN VM cell-derived neurons pre-treated with deucravacitinib, a selective TYK2 inhibitor, at different concentrations (0 pM P < 0.0001, n = 18; 0.01 pM = 0.1273, n = 9; 0.1 pM = 0.1920, n = 9; 1 pM = 0.8455, n = 9; 10 pM = 0.3562, n = 9) and afterwards transfected with poly(I:C) or lipofectamine as a vehicle control (ctrl, n = 18). FIG. 10E shows a representative image (top) and quantification (bottom) of Western blot of pSTATlY701in ReN VM cell-derived neurons 24 hours (h) after treatment with 10 pM deucravacitinib and transfection with poly(I:C) normalized to the housekeeping protein beta actin (ACTH). Top: n = 3 per condition. Bottom: n = 9 per condition (all P < 0.0001). FIG. 10F shows a two-way hierarchical clustering of relative protein abundance of IFN-L related proteins acquired through TMT multiplex mass spectrometry. Control = lipofectamine (vehicle control). Conditions with drug: n = 2, conditions without drug: n = 3. All replicates in FIGS. 10A-10F were biological replicates. The error bars represent the standard deviation.

[0033] FIGS. 11A-11F show candidate biomarkers for cdsRNA-positive AD. FIG. 11A shows that an established partial loss-of-function SNP in TYK2 (rs34536443) affected plasma levels of CXCL10 based on data from the UK Biobank (Olink proteomics platform)53and Icelandic DeCODE database (SomaScan proteomics platform)54using genome-wide association tests. CXCL10 (FIG. 11B), CCL2 (FIG. 11C), and IL6 (FIG. 11D) concentration was measured in pg / ml in the media of ReN VM cell-derived neurons treated with 10 pM baricitinib, ruxolitinib, or deucravacitinib and transfected with poly(I:C) (n = 3 per condition; P < 0.0001 in all conditions). FIG. HE shows CXCL10 concentration in the media of ReN VM cell- derived neurons treated with 1 pM MG- 132 for the translocation of TDP-43 from the nucleus to the cytoplasm without any additional drug (ctrl), or with additional 10 pM baricitinib, ruxolitinib, or deucravacitinib treatment. ROUT (Q = 5%) and Grubbs (a = 0.05) outlier tests were carried out confirming no outliers (n = 9 per condition; P < 0.005 in all significant conditions). FIG. HF depicts a schematic overview of hypothesized pathomechanisms underlying cdsRNA in neurodegenerative diseases. The error bars represent the standard error of the mean.

[0034] FIGS. 12A-12C depict p TDP-43 -severity and dsRNA presence in human postmortem brain sections. FIG. 12A shows a histogram of assessed FFPE sections (n = 10 with AD, n = 9 healthy controls) that show normal, mild, and severe pTDP-43 severity compared to healthy controls as well as the coincidence with dsRNA in percent of cases (control + AD cases). FIG. 12B shows histological examples for the classification of pTDP-43 severity into normal, mild, and severe. FIG. 12C shows the proportion of cells that stained for cdsRNA, pTDP-43, or both.

[0035] FIGS. 13A and 13B depict an IFN-I response to cdsRNA. FIG. 13A shows a pathway schematic of innate immune response to cdsRNA in AD leading to the expression of ISGs. FIG. 13B shows representative immunofluorescence staining of human postmortem brain sections of the amygdala comparing the phosphorylation of PKR in cdsRNA-positive AD (left and middle image) to a cdsRNA-negative control (right image) as proof of the immunogenicity of cdsRNA. Sytox Blue was used to stain nuclei. Neuropathology diagnosis was based on IHC staining and read by a neuropathologist. FIG. 14 shows a positive control for cdsRNA staining in iPSC. Immunofluorescent staining of wildtype (isoTDP-43+ / +) differentiated iPSC-derived NGN2 cortical-like neurons transfected with poly(I:C) as a positive control showing dsRNA (KI antibody), Tuj l (neuronal marker) and nuclei (DAPI) (left). Quantification of the normalized dsRNA / Kl intensity (right) with P < 0.05. Each dot is a field of view (40x magnification, one well analyzed per condition).

[0036] FIG. 15 shows upregulated ISGs within brain regions that were relevant in

[0037] AD. Heat map of RNA-sequencing data derived from the ROSMAP37and MSBB38databases comparing AD patients and healthy controls. Differential expression is shown in log2 fold change (logFC) for pre-selected ISGs. DFPC: dorsolateral prefrontal cortex; FP: frontal pole; IFG: inferior frontal gyrus; PHG: parahippocampal gyrus; STG: superior temporal gyrus.

[0038] FIGS. 16A and 16B depict a DRIAD-SP showing proof of concept for using CE expression as a proxy for TDP-43 pathology in extended version. FIG. 16A shows a prediction of drug efficacy of three selected compounds in AD using DRIAD-SP. Drug efficacy was assessed according to the previously published protocol26. Vertical dashed lines indicate the DRIAD-SP model performance of the drug gene sets, whereas the gray shaded regions correspond to the distribution of model performances based on size-matched random gene sets. FIG. 16B shows the expression of TDP-43 -associated transcripts in single neuronal nuclei as proof of concept for using CE expression as a proxy for TDP-43 pathology. Neuronal nuclei from ALS patients’ neocortical tissues were FACS-sorted by the presence of nuclear TDP-43 and NeuN43. Here, TDP-43 -positive nuclei (circled by a dotted line), indicating normal levels of nuclear TDP-43, indicated cases without TDP-43 pathology, while TDP-43 -negative nuclei (circled by a solid line) corresponded to cases with TDP-43 pathology. Each point corresponds to the abundance (transcripts per million; TPM) of TDP-43 -associated CE transcripts or their canonical counterparts in one of the samples. (STMN2 short P < 0.0001; UNC13A-CE1 P =

[0039] O.0002; UNC13 A-CE2 P = 0.0006; STMN2 P = 0.0017; UNC13 A P = 0.11). Triangles indicate zero counts.

[0040] FIG. 17 depicts a comparison of CE transcript abundance between ROSMAP37and MSBB38datasets. The proportion of samples with zero (solid outline) and above zero (dashed outline) abundance (transcripts per million; TPM) of the given transcripts is shown. MSBB samples had an appreciable lower abundance of UNC13A CE transcripts compared to ROSMAP samples. This difference can most likely be attributed to MSBB using single-end sequencing compared to the paired-end sequencing employed by ROSMAP, making detection of CEs and therefore determination of TDP-43 pathology in MSBB difficult.

[0041] FIGS. 18A and 18B show Braak staging of TDP-43 cases and additional replicates for the prediction of drug efficacy through DRIAD-SP. FIG. 18A shows a comparison of Braak stage distribution of ROSMAP patient data (posterior cingulate cortex) relative to their predicted TDP-43 pathology. FIG. 18B shows performance of baricitinib, ruxolitinib and tofacitinib in DRIAD-SP as shown in FIG. 9E, here including additional replicates of ruxolitinib and tofacitinib.

[0042] FIGS. 19A-19J show the testing of common stressors on differentiated ReN VM cells. To test whether general toxins may also damage differentiated ReN VM cells in a similar way as poly(I:C), 10 pM etoposide, which causes DNA damage, and 10 pM menadione, which induces reactive oxygen species, were added to the differentiated ReN VM cells that express GFP. Control ReN VM cells treated with DMSO vehicle for 0 hours (FIG. 19A) and 33 hours (FIG. 19B) as well as ReN VM cells treated with toxins for 0 hours (FIG. 19C) and 33 hours (FIG. 19D) showed no significant morphological difference. Total cell surface areas were not altered by DMSO (FIG. 19E) or the two toxins (FIG. 19F) over 33 hours. All areas at various time points were normalized to time 0 (n = 4). Average fluorescence intensity stayed constant over 33 hours for both DMSO-treated (FIG. 19G) and toxin-treated (FIG. 19H) ReN VM cell groups. Fluorescence intensity at each time point was normalized to time 0 (n = 4). Total area fold change and average intensity fold change (T = 33 hours / T = 0 hours) were not significantly different between the DMSO control (FIG. 191) and toxin-treated groups (FIG. 19J).

[0043] FIGS. 20A and 20B depict a schematic overview comparing a previously employed drug screening assay workflow4and a new, refined workflow. FIG. 20A shows a graphical summary of main steps in the previous drug screening assay (“One Pot” Differentiation). FIG. 20A depicts a new workflow including an additional step that comprises the differentiation of cells in a separate dish before being seeded into the final assay plate (“Separate Pot” Differentiation). This ensured less variability in the cell count per well and higher reproducibility with lower variance in the results. FIGS. 21A-21I show further validation of baricitinib, ruxolitinib, and deucravacitinib as well as validation of IFNAR2 as a drug target. Quantification of cell survival of ReN VM cell-derived neurons pre-treated with baricitinib (FIG. 21 A) or ruxolitinib (FIG. 21C) at different concentrations (baricitinib: 0 pM P < 0.0001, n = 18; 0.01 pM < 0.0001, n = 9; 0.1 pM < 0.0001, n = 9; 1 pM = 0.0872, n = 9; 10 pM < 0.0001, n = 9; ruxolitinib: 0 pM < 0.0001, n = 18; 0.01 pM < 0.0001, n = 9; 0.1 pM < 0.0001, n = 9; 1 pM = 0.0016, n = 9; 10 pM < 0.0001, n = 9) and afterwards transfected with poly(I:C) or lipofectamine as a vehicle control (ctrl, n = 18). Baricitinib: EC50 = 199.6 nM; ruxolitinib: EC50 = 360.7 nM. Shown are representative images (top) and quantification (bottom) of Western blot of pSTATlY701in ReN VM cell-derived neurons 24 h after treatment with 10 pM baricitinib (FIG. 21B) or ruxolitinib (FIG. 21D) and transfection with poly(I:C) normalized to the housekeeping protein beta actin (ACTB). Top: n = 3 per condition. Bottom: n = 9 per condition (all P < 0.0001). FIG. 21E shows quantification of cell survival of ReN VM cell -derived neurons treated with different doses of interferon-a (n = 3 per condition). Quantification of cell survival of ReN CX cell-derived neurons (FIG. 21F) and SH-SY5Y cells (FIG. 21H) pre-treated with deucravacitinib at different concentrations (CX cells: 0 pM P < 0.0001, n = 18; 0.01 pM P = 0.0408, n = 9; 0.1 pM P < 0.0001, n = 9; 1 pM P = 0.9868, n = 9; 10 pM P < 0.0001, n = 9; SH- SY5Y: 0 pMP < 0.0001, n = 24; 0.01 pMP < 0.0001, n = 12; 0.1 pM P < 0.0001, n = 12; 1 pMP = 0.4446, n = 12; 10 pMP < 0.0001, n = 12) and afterwards transfected with poly(I:C) or lipofectamine as a vehicle control (ctrl, CX cells n = 18, SH-SY5Y n = 12). CX cells: EC50 = 11 nM; ruxolitinib: EC50 = 203.8 nM. Shown are representative images (top) and quantification (bottom) of Western blot of pSTATlY701in ReN CX cell-derived neurons (FIG. 21G) and SH-SY5Y cells (FIG. 211) 24 h after treatment with 1 pM deucravacitinib and transfection with poly(I:C) normalized to the housekeeping protein beta actin (ACTB). Top: n = 3 per condition. Bottom: n = 9 per condition (all P < 0.0001). The error bars represent the standard deviation.

[0044] FIG. 22 depicts translocation of TDP-43 after treatment with MG-132. Shown is a representative image of immunofluorescence staining of fixed ReN VM-derived neural cells comparing the localization of TDP-43 in cells that were left untreated (left) or treated with 1 pM MG- 132 for 48 hours (right). Nuclei were stained with Hoechst. Zoom-ins with 3x magnification.

[0045] FIG. 23 depicts a scatterplot showing the relationship between 0PID18 no guess (x axis) and ptau217 levels (y axis) for subjects subjected to the AROMHA smell test where the subjects had either normal cognition, (normal), subjective cognitive concern (ImpNoMCI), or mild cognitive impairment (MCI). The marginal density estimates showed greater spread among the OPID18 axis relative to the ptau217 axis.

[0046] DETAILED DESCRIPTION

[0047] The dementia syndrome of AD is now considered an advanced stage of the disease since radiological and pathological evidence demonstrate that pathology begins to accumulate 15-20 years before the onset of memory symptoms2 5. At the onset of self-reported memory symptoms, neuropsychological testing is often normal - a stage termed subjective cognitive decline or subjective cognitive complaints (SCC)6. As the disease progresses to amnestic memory deficits revealed by psychometric testing, this stage becomes mild cognitive impairment (MCI), a stage preceding dementia where activities of daily living are not impaired yet by cognitive deficits. Cost-effective, noninvasive screening methods for generally identifying preclinical AD, as well as for specific subsets of dementia (e.g., those with TDP-43 pathology) remain an unmet need.

[0048] The measurement of early olfactory impairment is a prime candidate as a component of an early detection assessment13. Many brain regions process olfactory input from primary olfactory neurons14 l 6, and these regions are damaged early in the disease - with both the olfactory bulb and entorhinal cortex among the first sites of tau pathology17. The amygdala and piriform cortices are also early sites of tau pathology17,18. TDP-43 pathology in AD begins with the amygdala and the presence of TDP-43 pathology was associated with significantly greater amygdala, hippocampal, and anterior temporal atrophy (see, e.g., Kawakami et al., Acta Neuropathol. 2019 Nov; 138(5):751-770). In addition, the olfactory epithelium shows evidence of amyloid and tau deposition19. MRI studies demonstrated reduced olfactory bulb volume in AD patients and a smaller primary olfactory cortex (i.e., piriform cortex, amygdala, and entorhinal cortex) in MCI compared to cognitively unimpaired older adults20 23.

[0049] The hypothesis that cognitive processing of odor input may be compromised at early stages of the disease has been tested predominantly with smell identification performance, usually assessed by forced-choice measures like the University of Pennsylvania Smell Identification Test (UPSIT)24or Sniffin Sticks25where the four odor name choices are viewed prior to or in parallel with sniffing the odor. Smell identification performance has been associated with AD biomarkers, elevated levels of CSF and PET tau18,26 30and worse performance is associated with smaller hippocampal volume in older adults31,32and in patients with cognitive impairment on the AD clinical continuum22,33 37Smell identification score is related to declarative memory in older adults38and survives in models to predict the conversion from MCI to dem entia14, 39 43. Furthermore, smell identification scores have been shown to help predict cognitive decline in cognitively unimpaired older adults44 49and the conversion to MCI42,50,51.

[0050] However, additional olfactory cognitive assessment tasks that probe other neural circuits vulnerable to aging and neurodegeneration could add sensitivity and specificity for olfactory screens of early damage in aging and a variety of neurodegenerative diseases, including AD, Parkinson’s14,52and Traumatic Brain Injury53. For instance, odor memory and olfactory discrimination tasks have been associated with earlier preclinical stages of the disease43,48,54, and selective odor memory deficits, after correction for odor identification and odor discrimination performance, have been associated with AD biomarkers43. The incorporation of selfconfidence within olfactory testing could additionally improve the sensitivity and specificity of olfactory testing since metacognition and self-awareness were found to be predictive of cognitive decline and biomarkers in patients with AD or MCI55 59.

[0051] While olfaction has been suggested as a potential screening tool for AD, logistical challenges and questions of specificity have hindered its widespread adoption43,60,61. The present disclosure addressed these limitations in its development of a battery of olfactory tests. This battery disclosed herein includes an odor percept identification (OP ID) test, where participants smell an odor, answer a question, and then choose from four provided odor names. The battery also includes a percept of odor episodic memory (POEM) test, where participants distinguish between new odors and those presented earlier; and an odor discrimination (OD) test, where participants identify pairs of smells as either the same or different. This battery was administered using an olfactometer to deliver odors in earlier work (see, e.g., U.S. Pat. No. 10,610,147), and demonstrated selective odor memory loss in participants at risk of developing MCI43. The present disclosure describes the modification of one-time use labels with embedded odors62(see, e.g., U.S. Pat. App. Pub. No. 2023-0248297 Al), originally designed as an early at-home screen for SARS-CoV2 infection, for use as described herein. Subsequent testing using one-time odor labels in both English and Spanish in 30 states and Puerto Rico63, as well as in Argentina, provided valuable pilot data that allowed for adaptation of the olfactory battery into a bilingual at-home self-administered brain health test to screen for both nasal and cognitive deficits in processing olfactory deficits.

[0052] Provided in the present disclosure is a tested and validated self-administered olfactory test battery that can be performed on cognitively healthy subjects, e.g., English and Spanish-speaking subjects, in the home setting, under observed and unobserved self-administered conditions. In the Examples provided herein, testing began with observed self-administered testing of participants who were cognitively normal, had expressed subjective cognitive concerns (SCC), and had a diagnosis of Mild Cognitive Impairment (MCI). Both participants with SCC and MCI are at risk of developing Alzheimer’s disease dementia66,67. The majority of this observed testing was completed with the convenience of a remote in-home setting, using Zoom to share the testing screen and a video of the participant with a research assistant observing the workflow, noting challenges, and being available if questions or concerns arose. Some of the participants chose to self-administer the test without the Zoom interface and with a research assistant in the room for questions because they preferred this or found it more convenient. After about three months of observed and largely remote self-administered testing across the spectrum of cognitive impairment (from CN to MCI), testing was shifted to a completely unobserved remote testing option for any cognitively normal participants who felt comfortable engaging with the test entirely on their own. When given the option to self-administer the test remotely, independent of a research assistant, the majority of cognitively healthy participants, representing a wide age spectrum ranging from 20 to 92 years old, chose unobserved self-administration.

[0053] Equivalent olfactory performance was observed when comparing observed and unobserved self-administration of the ABHT among CN participants in unadjusted analyses, other than shorter test duration and a younger population on average in the observed setting. The tests disclosed herein were also validated on an anosmic subsample, demonstrating that anosmic patients performed as expected at chance level for each olfactory test. When comparing the olfactory battery scores between languages, no differences were found regarding olfactory subtest scores between CN English and Spanish-speaking populations in unadjusted analyses. Odor percept identification (OPID9 and OPID18noguess and OPID18 scores), odor memory (POEM) and odor discrimination (ODIO) scores were negatively associated with age. By comparing olfactory scores between participants aged 55+ with CN, SCC, and MCI, it was found that the means of all odor percept identification scores (OPID9, OPID9noguess, OPID18, OPID18noguess) and the odor discrimination score were associated with cognitive decline, i.e., lower scores in the MCI subgroup relative to the older CN subgroup.

[0054] Data provided herein also revealed a relationship between lower olfactory identification scores adjusted for metacognitive confidence (OPID9noguess and OPID18noguess) and increasing age and cognitive impairment. Previous studies suggested that a tendency for olfactory overconfidence — particularly when odors elicit an emotional reaction — is associated with decreased identification accuracy68,69. The present disclosure expands upon this existing body of literature, positing that metacognitive processes may influence olfactory performance in patients with an MCI.

[0055] The brain health test for remote at home self-administration as provided herein can reach target populations of interest. This remote testing method includes protocols for online screening for eligibility, online consenting, and an optimized remote user interface to guide participants through various olfactory tasks. Odor delivery for the tests disclosed herein can be administered remotely using odor labels on mailable cards. The ABHT test of the present disclosure is particularly targeted for participants aged 55+, a population generally less familiar with fully online test administration. Participants as old as 88 in the CN and SCC groups and 95 in the MCI group were able to successfully self-administer the test disclosed herein and enter responses on the web-based platform. The remote administration paradigm afforded participation from 21 different US states and Puerto Rico through online recruitment via the Massachusetts General Hospital and clinicaltrials.gov websites. The robust engagement of older participants in entirely independent, unobserved testing — yielding olfactory performance outcomes statistically indistinguishable from those obtained in observed settings — constitutes a significant finding. This challenges the prevailing assumption that older individuals are either unable or unwilling to effectively engage in self-administered remote screening methods such as ours. This screen was designed to address the unmet need for early detection of biomarkers in participants who are CN and those who have SCC but may not meet testing thresholds for MCI.

[0056] Patients with MCI were recruited to validate the efficacy of this screen to detect differences in the disclosed olfactory tests (i.e., “smell test”) under conditions of more apparent cognitive impairment, as previously demonstrated40,43’60’70. Each olfactory identification score from the ABHT, including the 9-item subscores (i.e., 0PID9, 0PID9 no guess), was lower in the MCI group compared to CN aged +55 groups. This result indicates that the ABHT could be used as a marker of cognitive decline in older adults and replicates the findings of two meta-analyses that demonstrated a specific pattern of olfactory impairment targeting more severely olfactory identification in patients with Alzheimer’s disease dementia52and MCI70. This early decline in odor identification likely reflects damage in specific limbic and medial temporal lobe olfactory areas involved in olfactory identification in the earliest stages of AD (i.e., piriform cortex, amygdala, entorhinal cortex, hippocampus)20'21 3137,71,72the development of proteinopathies (e.g., a TDP-43 proteinopathy) in said brain regions. At the cognitive level, olfactory identification tasks are associated with declarative memory38,73and are predictive of cognitive decline in CN older adults44 49and of the conversion to MCI42,50,51.

[0057] Using the smell test as disclosed herein, olfactory discrimination (ODIO) was also lower in the MCI group compared to CN aged 55+ group. This result replicates and is aligned with the results of previous studies showing a lower olfactory discrimination performance in MCI54and predictive value of olfactory discrimination in further cognitive decline48. Unlike the basic detection of odors, both identification and discrimination of odors require high-level cognitive functions such as working memory and decision making, which could explain our results54. The hippocampal network is also associated with olfactory discrimination74and, without wishing to be bound to any particular theory, early damages to this structure in AD could explain these findings.

[0058] Using the smell test as disclosed herein, objective measurements of olfactory identification, discrimination, and memory were negatively associated with age in the asymptomatic CN sample. These results replicate literature on the effect of normal aging on olfactory function. According to a meta-analysis including 175,073 participants (18-101 years), olfactory impairment would be prevalent at 34.5% in studies with a mean age above 55 years, while the prevalence would be at 7.5% in studies with a mean age below 55 years75. The decline starts in the fifth decade of life76and is general across different olfactory capacities such as olfactory detection, discrimination, identification77, and memory78,79. The data provided in the present disclosure indicate that perceived olfactory average intensity was not related to age. While some studies using magnitude estimation procedures to assess the perceived intensity of odors across different concentrations found no significant age-related differences80,81, another study found that older individuals perceived increases in menthol concentration as less intense than younger individuals82. These mixed results suggest that while perceived odor intensity may not always show a clear age-related decline, other aspects of olfactory function, such as identification, discrimination, and memory, are consistently negatively impacted by aging.

[0059] Having validated remote unobserved self-administration in CN people and demonstrated that remote observed self-administration of the ABHT is feasible in an MCI population (95% of MCI participants completed testing in this mode), and by finding expected differences in olfactory identification scores as shown in previous studies, the smell test of the present disclosure can be used in deeply phenotyped populations to quantify the predictive value of ABHT outcomes on biomarkers of neurodegenerative disease, including Alzheimer’s, Lewy Body disease, and concussive and non-concussive head trauma. These studies can incorporate both English and Spanish-speaking participants, as there are no differences in olfactory scores among the CN English and Spanish-speaking participants. Different olfactory- behavioral profiles are hypothesized to emerge depending on disease neuropathology, as different brain areas and networks of the central olfactory system are associated with different olfactory tasks83 85. While previous studies showed that MCI and AD predominantly affect olfactory identification52,70, the olfactory bulb is affected at the earliest stage of Parkinson’s disease resulting in a general olfactory impairment with reduced olfactory detection performance and leads to a decrease in various olfactory tasks (e.g., discrimination of odors, identification of odors)52,86. Other conditions such as Lewy body dementia87, frontotemporal dementia88, dementia associated with TDP- 43 pathology, and exposure to head impacts and traumatic brain injury89 91can also cause olfactory impairments.

[0060] When combined with other digital biomarkers, use of the smell test disclosed herein can accelerate screening for neurodegenerative diseases in asymptomatic or newly symptomatic individuals who would benefit from more definitive subsequent tests92,93, such as blood-based, image-based, or cerebrospinal fluid (CSF)-based diagnostics, especially in individuals presenting additional risk factors for dementia such as subjective cognitive decline66, depression94, and genetic risks factors such as the APOE-4 allele95. Although home-based tests have logistical costs such as material distribution and production, these are likely offset by the clinical benefits of increased accessibility and early screening potential. Home-based tests have the potential to enhance the involvement of underrepresented groups in research settings96, save time and transportation costs, reduce healthcare system expenses, and increase patient satisfaction97,98.

[0061] The present disclosure provides a smell test and methods of use thereof for assessment of risk of dementia in a subject. The present disclosure also provides for methods of identifying the neurodegenerative disease associated with a TDP-43 pathology in a subject, and optionally treating the subject with a tyrosine kinase 2 (TYK2) inhibitor.

[0062] Smell Tests

[0063] The present disclosure provides systems and methods for the selfadministration of an odor-based test, or “smell test”. In some embodiments, a smell test disclosed herein can comprise utilization of at least one odor card and / or a smell test guide (“test guide”). In some embodiments, a test guide for use herein can be pre- printed or can be made available as a user interface. The user interface can allow the user / subject / patient to interact with the smell tests and display the results of the smell tests to the subject. As detailed herein, the user interface can include graphical, textual, scanned, and / or auditory information. For example, a computer program or application can present said information to the subject, and monitor / store the control sequences such as keystrokes, movements of the computer mouse, selections with a touch screen, scanned information, and the like, which are used to control the application. In some embodiments, the smell test disclosed herein can include a computing device. In some embodiments, the computing device can be a personal computer, a smart phone, or another mobile device having the capacity to generate the test guide and conduct the smell test, e.g., via a user interface.

[0064] Odor Cards

[0065] The odor cards disclosed herein can be a printed (e.g., on paper, cardboard) smell test card. In some embodiments, an odor card disclosed herein can be suitable for delivery to individuals by mail (e.g., shipped in envelopes). In some embodiments, an odor card disclosed herein can be disposable after completion of the smell test. In some embodiments, an odor card disclosed herein can be configured to be easy to use such that a smell test can be self-administered by the subject using the odor cards.

[0066] A smell test as described herein can include at least one, two, three, four, five, six, seven, eight, nine, or ten odor cards. A non-limiting example of a smell test comprising multiple odor cards is illustrated in FIG. 1A. In some embodiments, a smell test as described herein can include at least five odor cards. In some embodiments, a smell test as described herein can include at least five odor cards wherein at least five odor cards differ from each other.

[0067] An odor card disclosed herein can be configured to deliver an odor from among a plurality odors. In some embodiments, an odor card disclosed herein can be configured to deliver an odor from among a plurality of at least about or about one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more than twenty odors. In some embodiments, an odor card disclosed herein can be configured to deliver an odor from among a plurality of nine odors. As used herein, “about” means plus or minus 10%. In some embodiments, an odor card disclosed herein can be configured to deliver an odor from among a plurality of eighteen odors. In some embodiments, an odor card disclosed herein can be configured to deliver a practice odor and deliver a plurality of odors used in the smell test. The practice odor can be used to increase subject familiarly with the odor card and / or calibrate with the computing device / user interface. In some embodiments, an odor card disclosed herein can be configured to deliver a practice odor and deliver an odor from among a plurality of at least about or about one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more than twenty odors for use in the smell test. In some embodiments, an odor card disclosed herein can be configured to deliver a practice odor and deliver an odor from among a plurality of nine odors for use in the smell test. In some embodiments, an odor card disclosed herein can be configured to deliver a practice odor and deliver an odor from among a plurality of eighteen odors for use in the smell test.

[0068] Odor cards disclosed herein can be configured to utilize a wide variety of odors for the administration of the smell test. For example, odor cards can provide odors selected to be specific to a specific region, culture, gender, age, etc., of the subject. Non-limiting examples of odors include menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, grape, menthol, clove, leather, strawberry, lilac, pineapple, soap, grape, coffee, peach, chocolate, orange, dirt, banana, lemon, bubble gum, and rose. Odor cards can be configured to utilize a wide variety of odors or scents for the administration of the smell tests. In some embodiments, an odor card disclosed herein can be configured to deliver an odor from among a plurality of nine odors wherein the plurality of nine odors comprises at least at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, and grape. In some embodiments, an odor card disclosed herein can be configured to deliver an odor from among a plurality of eighteen odors, wherein the plurality of eighteen odors comprises at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, grape, coffee, peach, chocolate, orange, dirt, banana, lemon, bubble gum, and rose.

[0069] In some embodiments, an odor card disclosed herein can be configured to deliver at least one odor at a plurality of concentrations. The intensity of an odorant is related to its concentration (see, e.g., Chastrette et al., Chem Senses (1998) 23: 181- 196). As such, an odor card disclosed herein that is configured to deliver at least one odor at a plurality of concentrations is understood to deliver at least one odor at a plurality of intensities.

[0070] In some of the smell tests disclosed herein, the subject can be asked to distinguish between scents. As such, odor cards disclosed herein are configured to encapsulate each individual odor independently in order to prevent crosscontamination of the scents. In some embodiments, an individual odor on the odor card can be encapsulated by a removable cover. In some embodiments, a removable cover can be a coating embedded microcapsules comprising the odor to be tested, such that when the coating is removed (i.e., scratched), the odor is released (“scratch- and-sniff ’). In some embodiments, a removable cover can be a "peel-and-stick" cover that can be peeled back by the subject prior to smelling the odor and replaced by the subject after smelling the odor. In some embodiments, the peel-and-stick cover is preferred in that it allows the subject to only sample a single odor, which can prevent contamination when smelling any adjacent odors or conducting subsequent smell tests.

[0071] As illustrated in the non-limiting example (FIG. 1A), the odors on the odor card can be individually labeled or stratified into section / rows. In some embodiments, the odors can be labeled (e.g., “odorl”, “odor2”, “Al”, “A2”, “1”, “2” etc.) such that, when a subject is using the test guide disclosed herein, the subject can be instructed on which specific odors to smell. In some embodiments, the odors can be stratified into sections / rows that may correlate to specific smell tests. For example, odor cards as shown in in FIG. 1A, can be labeled “Card A”, “Card B”, “Card C”, “Card D”, “Card E” for use in a specific smell test, as instructed by the test guide disclosed herein.

[0072] An odor card disclosed herein can have an identification mark printed on the card. In some embodiments, the identification mark can be an identification number, a barcode, and / or a QR code. The identification mark can be unique to each odor card such that, when utilizing the test guide disclosed herein, the user can be able to pull the predetermined “correct” responses from storage (e.g., a database). In this way, the computing device will be able to store or report correct / incorrect responses from the subject and / or identify the specific scent for each odor provided on said odor card, and the location thereof, printed on the test card. In some embodiments, the identification mark can identify the subject and / or correlate the subject with the unique identification mark. In some embodiments, a subject's data can be correlated to the odor card and the smell test may be administered by comparing the subject's answers / responses to the correct odors on the odor card. In some embodiments, data and results from the smell tests can be associated with demographics and medical records for the subject. In some embodiments, using the identification mark, the software modules running on server(s) and / or client(s) can generate a display for the subject, including the options presented to the subject, using data stored in database that is correlated to the identification mark. The software modules may also be configured to store these results in database and link them to the identification mark.

[0073] Test Guide

[0074] A smell test disclosed herein can comprise a smell test guide (“test guide”). In some embodiments, a test guide for use herein can be printed (e.g., paper, cardboard) and included with at least one odor card disclosed herein to perform one or more smell tests. In some embodiments, a test guide for use herein can be provided via a computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked computing device (e.g., a personal computing device). Non-limiting examples of a computer readable storage medium includes CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like. In some embodiments, a test guide for use herein can be realized in software that is readily available via a computing device (e.g., a personal computing device) and configured to generate and display a user interface thereon. A non-limiting example of an odor card and a test guide as provided by a computing device via the user interface for conducting a smell test are provided in FIGS. 4A-4D. In some embodiments, a test guide realized in software can include at least one computer program, or use of the same. In some embodiments, a test guide realized in software can include at least one database, or use of the same.

[0075] A computer program for use in the present disclosure can include a sequence of instructions (e.g., instructions for conducting a smell test), executable in the digital processing device's CPU, written to perform a specified task. Those of skill in the art will recognize that a computer program may be written in various versions of various languages, any of which are suitable for use herein (see., e.g., Gabbrielli & Martini, (2023). PROGRAMMING L NGUAGES: PRINCIPLES AND PARADIGMS (Second edition). Springer International Publishing, doi.org / 10.1007 / 978-3-031-34144-1). In some embodiments, a computer program comprises one sequence of instructions. In some embodiments, a computer program comprises a plurality of sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from a plurality of locations.

[0076] In some embodiments, a computer program includes one or more software modules. Software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. In some embodiments, software modules disclosed herein can be implemented in a multitude of ways. In some embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof. In some embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, or combinations thereof. In some embodiments, the one or more software modules disclosed herein can comprise, by way of non-limiting examples, a web application, a mobile application, and a standalone application. In some embodiments, software modules can be in one computer program or application. In other embodiments, software modules can be more than one computer program or application. In some embodiments, software modules can be hosted on one machine. In other embodiments, software modules can be hosted on more than one machine. In some embodiments, software modules can be hosted on cloud computing platforms. In some embodiments, software modules can be hosted on one or more machines in one location. In some embodiments, software modules can be hosted on one or more machines in more than one location.

[0077] In some embodiments, a computer program disclosed herein can include, in part or in whole, one or more web applications (i.e., web-based applications), one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof. In some embodiments, a computer program for use in the present disclosure can include a web application. Those of skill in the art will appreciate that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, a web application for use herein can utilize one or more database systems including, by way of non-limiting examples, relational, nonrelational, object oriented, associative, and XML database systems. In some embodiments, a web application for use herein is written in one or more versions of one or more languages.

[0078] In some embodiments, a computer program disclosed herein can include a mobile application provided to a mobile digital processing device. In some embodiments, the mobile application is provided to a mobile digital processing device at the time it is manufactured. In other embodiments, the mobile application is provided to a mobile digital processing device via the computer network described herein.

[0079] A database for use in the present disclosure can be suitable for storage and retrieval of data from a smell test as performed according to the methods disclosed herein, data entered by a data or system administrator, data entered by a subject, etc. Suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entityrelationship model databases, associative databases, and XML databases. In some embodiments, a database is internet-based. In some embodiments, a database is webbased. In some embodiments, a database is cloud computing-based. In some embodiments, a database is based on one or more local storage devices.

[0080] Software modules as disclosed herein can be configured to query the database for data including previously-entered performance data (e.g., studies, test results) relevant to odor memory / identification test results (e.g., from previous studies and / or entered by a data or system administrator). Using this previously-entered performance data returned from the database query, the software modules disclosed herein can calculate a predicted odor memory score (a prediction of an expected memory score) based on the subject's odor memory / identification score and the previously -entered performance data in the database. The predicted odor memory score based on the linear relationship between the subject's odor memory / identification score and the previously-entered performance data in the database may include or be used to calculate a confidence interval threshold for the predicted odor memory score, based on a variance / scattering of the dataset. In some embodiments, software modules disclosed herein can calculate (by the computing device) a confidence metric based on the data entered by the user during the smell test. In some embodiments, software modules disclosed herein can compare (by the computing device) the calculated confidence metric for the smell test relative to a predetermined threshold (which is entered in the database), wherein the predetermined threshold is determined by a machine learning algorithm based on a plurality of previously entered smell test performance data from healthy subjects (i.e., subjects that are cognitively normal). In some embodiments, software modules disclosed herein can identify a subject who has completed at least one smell test as high risk for dementia when the calculated confidence metric for the smell test is lower than the predetermined threshold. In some embodiments, software modules disclosed herein can select a subject who has completed at least one smell test for treatment with a TYK2 inhibitor when the calculated confidence metric for the smell test is lower than the predetermined threshold. In some embodiments, software modules disclosed herein can retrieve biomarker data (e.g., concentrations of CXCL10, CCL2, and / or IL-6) collected from a biological sample (e.g., whole blood, serum, or plasma) from a subject as entered by the user from the database and quantify the level of the biomarker. In some embodiments, software modules disclosed herein can compare the level of the biomarker to a predetermined threshold, wherein the predetermined threshold is determined from data entered in the database that is comprised of the levels of the biomarker assessed from biological samples collected from a plurality of healthy subjects (i.e., subjects that are cognitively normal). In some embodiments, software modules disclosed herein can identify a subject as high risk for dementia when the level of least one biomarker (e.g., CXCL10, CCL2, and / or IL-6) is higher than the predetermined threshold. In some embodiments, software modules disclosed herein can select a subject for treatment with a TYK2 inhibitor when the level of least one biomarker (e.g., CXCL10, CCL2, and / or IL-6) is higher than the predetermined threshold. In some embodiments, software modules disclosed herein can identify a subject who has completed at least one smell test as high risk for dementia when the calculated confidence metric for the smell test is lower than the predetermined threshold and when the level of least one biomarker (e.g., CXCL10, CCL2, and / or IL- 6) is higher than the predetermined threshold for the subject. In some embodiments, software modules disclosed herein can select a subject who has completed at least one smell test for treatment with a TYK2 inhibitor when the calculated confidence metric for the smell test is lower than the predetermined threshold and when the level of least one biomarker (e.g., CXCL10, CCL2, and / or IL-6) is higher than the predetermined threshold for the subject.

[0081] Methods of Administering Smell Test

[0082] The present disclosure provides methods for administration of a smell test. Methods of administering the smell test can utilize at least one odor card as disclosed herein, at least one test guide as disclosed herein, and / or a computing device as disclosed herein. A smell test as understood herein can encompass at least one odor sub-test. In some embodiments, an odor sub-test comprises at least one odor intensity test(s) (OIT), at least one odor percept identification test (OPID, or odor identification test), at least one percepts of odor episodic memory test (POEM, or odor episodic memory test), at least one odor discrimination test (OD), and / or any combination thereof. Combinations can include variables such as a subject incorrectly responding to one or more (e.g., two or more, etc.) of the odor identification / discrimination tests. Other combinations can include engineered variables such as a subject incorrectly responding to one or more (e.g., two or more, etc.) of the odor identification / discrimination tests and providing a low score (e.g., relative to a predetermined threshold) to one or more odor intensity tests. It is to be understood by one of ordinary skill in the art that various other combinations can also be included to develop engineered variables, including combinations of one or more responses from the symptom questionnaire. In some embodiments, a subject can self-administer a smell test. In some embodiments, a subject can self-administer a smell test under the instruction provided by the user interface generated by a computing device as disclosed herein. In some embodiments, when conducting the one or more smell tests according to the steps described herein, the smell test responses or results from the subject are compiled in a database for storage, further processing, or calculations according to the software modules as disclosed herein.

[0083] Odor Identification Test

[0084] An odor identification test as disclosed herein can include the tests illustrated in the top and middle panels of FIG. IB and can utilize odor cards A, B and / or C as shown in FIG. 1A. The odor identification test employs a plurality of odors (e.g., nine and / or eighteen odors) found to be predictive for conversion from Mild Cognitive Impairment to a neurodegenerative disease, such as Alzheimer's disease. This test is referred to as an odor percept identification test (e.g., 0PID9 or 0PID18) because it requires the subject to identify an odor based on the subject's odor percept, or what the subject remembers, and by so doing, builds the subject's working memory into that test.

[0085] In an odor identification test disclosed herein, the subject can be instructed (e.g., by a computing device via a user interface) to prepare to sniff at least one odor from the odor card. In some embodiments, after the odor presentation, a subject is instructed to rate the intensity of the odor on a Likert scale from 0 to 10. An example of such instruction is demonstrated in FIG. 4A. In an odor identification test disclosed herein, after rating the intensity of the odor, the subject can be presented (e.g., by a computing device via a user interface) with one or more odor names and instructed and to choose the label that best represented the odor they smelled. In some embodiments, a subject is then instructed to rate their confidence in their identification of the odor from the one or more odor names provided by the user interface. For example, a subject is instructed to rate their confidence in their identification of the odor using the following scale: “I Guessed,” “I Narrowed Down to Three,” “I Narrowed Down to Two,” or “I Am Certain.” An example of such instructions is demonstrated in FIG. 4B. Responses provided by the subject can be analyzed and scored, forwarded to the software modules / algorithms disclosed herein for calculating and displaying the results of the test, and / or may be stored within the database. In some embodiments, responses provided by the subject can be used to calculate an average intensity score, wherein the average intensity score can be derived from the mean intensity ratings of the plurality of odors on the Likert scale. In some embodiments, responses provided by the subject can be used to calculate a score for the odor identification test, wherein the score can be calculated as the total number of correctly identified odors. In some embodiments, responses provided by the subject can be used to generate a confidence metric for the smell test. A confidence metric for the smell test is calculated by the computing device as the total number of odors identified correctly by the subject where the subject did not evaluate their confidence in their odor identification decision by selecting “I Guessed” when prompted by the computing device via the user interface. Once calculated, a confidence metric for the smell test can be compared to a predetermined threshold. A predetermined threshold as used herein can be determined by a machine learning algorithm (e.g., by a computing device) based on a plurality of previously entered smell test performance data from healthy subjects (i.e., subjects with no cognitive impairment).

[0086] A smell test for use herein can comprise more than one odor identification tests. In some embodiments, a smell test disclosed herein can comprise two odor identification tests. In some embodiments, a smell test disclosed herein can comprise two odor identification tests wherein the odor identification tests are performed subsequently. In some embodiments, a smell test disclosed herein can comprise two odor identification tests wherein the odor identification tests are performed subsequently with at least a 10-minute break between administering each test. In some embodiments, a smell test disclosed herein can comprise two odor identification tests wherein a first odor identification test is performed, followed by a different odor sub-test (e.g., an odor episodic memory test), and then a second odor identification test.

[0087] In some embodiments, a smell test disclosed herein can comprise two odor identification tests wherein a first odor identification test is comprised of odors that are not presented in the second odor identification test. In some embodiments, a smell test disclosed herein can comprise two odor identification tests wherein a first odor identification test is comprised of a different plurality of odors that are more / less than the number of odors provided in the second odor identification test. In some embodiments, an odor identification test is comprised of at least nine odors (0PID9). In some embodiments, an odor identification test is comprised of nine odors (0PID9) consisting of menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, and grape. In some embodiments, an odor identification test is comprised of at least eighteen odors (0PID18). In some embodiments, an odor identification test is comprised of eighteen odors (0PID18) consisting of menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, grape, coffee, peach, chocolate, orange, dirt, banana, lemon, bubble gum, and rose. In some embodiments, an 0PID9 test is performed before an 0PID18 test.

[0088] Odor Episodic Memory Test

[0089] An odor episodic memory test (e.g., POEM) as disclosed herein can include the tests illustrated in the top and middle panels of FIG. IB and can utilize odor cards A, B and / or C as shown in FIG. 1A. An odor episodic memory test disclosed herein, can comprise the subject being instructed (e.g., by a computing device via a user interface) to prepare to sniff at least one odor from the odor card. The method of administering an odor episodic memory test is similar to the original odor identification test described above except for asking the subject the question (e.g., provided by the use interface) following the presentation of the odor: “Did you smell this odor in the previous test?” An example of such instructions is demonstrated in FIG. 4C. An odor episodic memory test disclosed herein measures a subject's ability to remember previously presented odors after a delay of a fixed period (e.g., 10 minutes) with no clues, and further measures how accurate the subject was in identifying the odor. Put another way, subjects can be asked if the odor presented was new or had been presented in the earlier odor identification test. In some configurations, the instruction phase of the test may include an explicit indication that the new / old designation refers to the current testing session and not to a broader lifetime exposure. In some embodiments, following this measure of episodic or autobiographical odor memory, a subject subjects can be presented with a plurality of odor names, and asked to choose which name associated with their memory of the odor percept they experienced at the start of the smell test (e.g., from an odor identification test administered before the odor episodic memory test). The odor memory / identification response (e.g., yes or no) can be received from the subject, optionally or in addition to, the odor name associated with their memory / identification of the odor percept experienced. These responses may be analyzed and scored, forwarded to the software modules / algorithms disclosed herein for calculating and displaying the results of the test, and / or can be stored within a database as disclosed herein. In some embodiments, subject response can be used to calculate a POEM index. In some embodiments, a POEM index can be calculated as the difference between the proportion of correct and incorrect recognitions, with scores ranging from -1 to 1.

[0090] Odor Discrimination Test

[0091] An odor discrimination test (e.g., OD) as disclosed herein can include the tests illustrated in the bottom panel of FIG. IB and can utilize odor cards D and / or E as shown in FIG. 1A. An odor discrimination test disclosed herein, can comprise the subject being instructed (e.g., by a computing device via a user interface) to prepare to sniff at two odors from the odor card consecutively. In some embodiments, the subject is instructed to smell an odor on the odor card for a fixed period, using techniques analogous to those previously disclosed above. At the end of the specified time period (e.g., two seconds), the subject is instructed to smell an odor on the odor card for the same fixed period, one after the other, using techniques analogous to those previously disclosed. In some embodiments, a subject can then be asked if the two odors presented were the same or different (yes / no). An example of such instructions is demonstrated in FIG. 4D. These responses may be analyzed and scored, forwarded to the software modules / algorithms disclosed herein for calculating and displaying the results of the test, and / or can be stored within a database as disclosed herein. In some embodiments, subject response can be used to calculate an odor discrimination (OD) score. In some embodiments, an OD score can be calculated as the total number of correctly discriminated odor pairs.

[0092] Kits

[0093] The present disclosure provides kits for use in performing any of the methods for administering a smell test as disclosed herein. In some embodiments, a kit can comprise at least one odor card and at least one test guide. In some embodiments, a kit can further include an instructional card and / or a card with information to direct a subject to a website comprising instructions. In some embodiments, a kit can further include one or more materials needed for collecting, transporting, and preserving a biological sample.

[0094] Methods of Use

[0095] Provided herein are methods of assessing risk of dementia in a subject. Also provided herein are methods of assessing a subject for their potential responsiveness to a therapy that comprises administration of a TYK2 inhibitor. In some embodiments, a subject identified as at risk of dementia according to the methods disclosed herein can be administered at least one therapy for treating, preventing (reducing further risk of), and / or ameliorating a neurodegenerative disease, e.g., Alzheimer’s disease.

[0096] Subject Populations

[0097] Methods of assessing risk of dementia and / or responsiveness to a TYK2 inhibitor, as provided in the present disclosure, are suitable for use in a subject in need thereof. “Subject” and “patient” refer to either a human or non -human, such as mammals, e.g., vertebrates, e.g., primates. In some embodiments, the subject is a human or a non-human veterinary subject such as a non-human primate, cat, dog, horse, cow, goat, or rabbit. In some embodiments, the subject is an adult human. In some embodiments, the subject is an adult human that is at least about or about 55 years of age. In some embodiments, the subject is an adult human that is at least about or about 55-100, 55-95, or 55-90 years of age.

[0098] In some embodiments, a subject in need thereof is a subject suspected of having or has been diagnosed as having cognitive impairment. In some embodiments, a subject in need thereof has, is suspected of having, or is at risk of having a neurodegenerative disease associated with a TDP-43 pathology. Methods of diagnosing TDP-43 pathology in a subject are known in the art (see, e.g., Lopez - Carbonero et al., Transl Neurodegener. 2024 Jun 3 ; 13(1):29; Ducharme et al., Am J Geriatr Psychiatry. 2024 Jan;32(l):98-113) and are suitable for use herein. In some embodiments, a subject in need thereof has, is suspected of having, or is at risk of having Alzheimer’s disease. Methods of diagnosing AD in a subject are known in the art (see, e.g., Podhorna et al., Alzheimers Res Ther. 2016 Feb 12;8:8; van Oostveen et al., IntJMol Sci (2021) Feb 20;22(4):2110; Dinis-Oliveira et al., Forensic Sci Res (2017) Jan 16; 1 (l):42-51 ; Teunissen et al., Alzheimers Dement (2025) Jan;21(l):el4397) and are suitable for use herein. In some embodiments, a subject in need thereof has a loss-of-function SNP in TYK2 (rs34536443). In some embodiments, a subject in need thereof has as subset of Alzheimer’s disease associated with a TDP-43.

[0099] Assessment of Risk of Dementia

[0100] Methods provided herein comprise administering a smell test for assessment of risk of dementia in a subject. Dementia can result from neurodegeneration with Alzheimer's Disease (AD) being the most common cause of dementia. As such, methods provided herein also comprise administering a smell test for assessment of risk of AD in a subject.

[0101] In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject can comprise administering at least one odor identification test, at least one odor episodic memory test, and / or at least one odor discrimination test. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject can comprise administering at least one odor identification test. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject can comprise administering at least two odor identification tests. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject can comprise administering at least two odor identification tests wherein at least one odor episodic memory test is administered between the administration of the at least two odor identification tests.

[0102] A method of administering a smell test for assessment of risk of dementia (e.g., of risk of dementia associated with AD) in a subject can comprise administering at least one odor identification test. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject can comprise instructing a subject to utilize an odor card disclosed herein, instructing a subject to identify an odor delivered by the odor card, and asking the subject to evaluate their confidence in their odor identification decision. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject can comprise instructing a subject to utilize an odor card disclosed herein, instructing a subject to identify an odor delivered by the odor card, and asking the subject to evaluate their confidence in their odor identification decision, wherein the administering is by the computing device via the user interface. In some embodiments, a subject is instructed to repeat the method above for each of the odors provided on the odor card. In some embodiments, a subject performs the method above for each of nine odors provided on the odor card. In some embodiments, a subject performs the method above for each of eighteen odors provided on the odor card.

[0103] A method of administering a smell test for assessment of risk of dementia (e.g., of risk of dementia associated with AD) in a subject can further comprise administering at least one odor episodic memory test. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject can comprise instructing a subject to utilize an odor card disclosed herein, asking the subject if the odor delivered in the odor card was delivered in the earlier used odor card / earlier odor identification test, instructing a subject to identify an odor delivered by the odor card, and asking the subject to evaluate their confidence in their odor identification decision. In some embodiments, a method of administering a smell test for assessment of risk of dementia in a subject can comprise instructing a subject to utilize an odor card disclosed herein, asking the subject if the odor delivered in the odor card was delivered in the earlier used odor card / earlier odor identification test, instructing a subject to identify an odor delivered by the odor card, and asking the subject to evaluate their confidence in their odor identification decision, wherein the administering is by the computing device via the user interface.

[0104] A subject’s confidence in their odor identification decision can be used to calculate, by the computing device, a confidence metric for the smell test. Using any of the software modules / algorithms as disclosed herein, a confidence metric for the smell test can be calculated by the computing device as the total number of odors identified correctly by the subject where the subject did not evaluate their confidence in their odor identification decision by selecting “I Guessed” when prompted by the computing device via the user interface. In some embodiments, the calculated confidence metric for the smell test can be compared to a predetermined threshold. In some embodiments, the calculated confidence metric for the smell test can be compared to a predetermined threshold by a computing device. In some embodiments, a predetermined threshold is determined by a machine learning algorithm based on a plurality of previously entered smell test performance data from healthy subjects (i.e., cognitively normal subjects).

[0105] After administering a smell test according to the methods disclosed herein, a subject is identified as high risk for dementia when the calculated confidence metric for the smell test is lower than the predetermined threshold. In some embodiments, after administering a smell test according to the methods disclosed herein, a subject is identified as high risk for AD and / or AD-associated dementia when the calculated confidence metric for the smell test is lower than the predetermined threshold.

[0106] Methods of administering a smell test as disclosed herein can assess neuroanatomical volume in at least one brain region in a subject. Neuroanatomical volume refers to the spatial measurement of different brain structures and regions, typically in cubic centimeters (cm3) or milliliters (mL). Neuroanatomical volume calculations from MRI scans, as described herein, can involve software-based automated methods, manual tracing, or a combination of both (see, e.g., Giorgio & Stefano, Journal of Magnetic Resonance Imaging. 2013;37: 1-14). Determination of neuroanatomical volumes can be used as a quantitative measure of AD severity and to diagnose subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD dementia in a subject. In some embodiments, a smell test can be administered to identify a decline in neuroanatomical volume in at least one brain region in a subject. In some embodiments, after administering a smell test according to the methods disclosed herein, a subject is identified as having a decline in neuroanatomical volume in at least one brain region when the calculated confidence metric for the smell test is lower than the predetermined threshold. In some embodiments, after administering a smell test according to the methods disclosed herein, a subject is identified as having a decline in neuroanatomical volume in at least one brain region when the calculated score for the smell test is lower than the predetermined threshold. In some embodiments, the predetermined threshold is determined from smell test performance data collected from a plurality of healthy subjects (i.e., cognitively normal subjects). In some embodiments, the healthy subjects (i.e., cognitively normal subjects) have normal computed brain volumes as measured from MRI scans. In some embodiments, an OPID9 smell test as disclosed herein can be administered to identify a decline in neuroanatomical volume in at least one brain region in a subject. In some embodiments, an OPID18 smell test as disclosed herein can be administered to identify a decline in neuroanatomical volume in at least one brain region in a subject. In some embodiments, the at least one brain region in the subject can be the hippocampus, the amygdala, and / or both. In some embodiments, the at least one brain region in the subject can be the left hippocampus, the right hippocampus, the left amygdala, the right amygdala, or any combination thereof. In some embodiments, the identification of a decline in neuroanatomical volume in at least one brain region in the subject correlates to cognitive decline in the subject. In some embodiments, the identification of a decline in neuroanatomical volume in at least one brain region in the subject correlates to dementia in the subject. In some embodiments, the identification of a decline in neuroanatomical volume in at least one brain region in the subject correlates to AD in the subject. In some embodiments, a subject is diagnosed with subjective cognitive decline (SCD) following administration of the smell test, wherein the smell test identified a decline in neuroanatomical volume in at least one brain region (e.g., the hippocampus, the amygdala). In some embodiments, a subject is diagnosed with mild cognitive impairment (MCI) following administration of the smell test, wherein the smell test identified a decline in neuroanatomical volume in at least one brain region (e.g., the hippocampus, the amygdala). In some embodiments, a subject is diagnosed with dementia / AD dementia following administration of the smell test, wherein the smell test identified a decline in neuroanatomical volume in at least one brain region (e.g., the hippocampus, the amygdala).

[0107] Methods of administering a smell test as disclosed herein can be combined with one or more other methods for assessing risk of dementia / risk of AD. In some embodiments, a smell test can be administered in addition to an assessment of a subject’s cognitive impairment as measured by ADAS-cog or a variant thereof (e.g., ADAS-Cog 3, ADAS-Cog 5, the original ADAS-Cog 11, and ADAS-Cog 13), Mini Mental State Examination (MMSE), or a combination thereof (see, e.g., Podhorna et al., Alzheimers Res Ther (2016) Feb 12;8:8). In some embodiments, a smell test can be administered in addition to one or more suitable imaging techniques (e.g., computed topography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)) (see, e.g., van Oostveen et al., IntJMol Set (2021) Feb 20;22(4):2110). In some embodiments, a smell test can be administered in addition to measurement of one or more biomarkers. Biomarkers can include biomolecules, for example but not limited to, carbohydrates, proteins, lipids to genes, DNA, RNA, platelets, enzymes, hormones, and the like. A biomarker for use herein can be measured in a biological sample collected from a subject according to methods known in the art (see, e.g., Dinis-Oliveira et al., Forensic Sci Res (2017) Jan 16; 1 (l):42-51). In some embodiments, a biological sample for use herein can comprise whole blood, serum, plasma, and / or cerebrospinal fluid (CSF).

[0108] In some embodiments, a smell test can be administered in addition to measurement of one or more biomarkers as identified in the present disclosure. In some embodiments, the one or more biomarkers as identified in the present disclosure can comprise C-X-C motif chemokine ligand 10 (CXCL10), C-C motif chemokine ligand 2 (CCL2), and interleukin 6 (IL-6). Methods of assessing these biological samples can comprise (1) collecting a biological sample (e.g., whole blood, serum, or plasma) from a subject, (2) quantifying the level of CXCL10, CCL2, and / or IL-6 in the biological sample; and (3) comparing the level of CXCL10, CCL2, and / or IL-6 to a predetermined threshold, wherein the predetermined threshold is determined from levels of the corresponding biomarker (e.g., of CXCL10, CCL2, and / or IL-6) as assessed from biological samples collected from a plurality of healthy subjects. In some embodiments, the level of CXCL10, CCL2, and / or IL-6 can be measured by subjected the biological sample to and immunoassay (e.g., sandwich immunoassay (V-PLEX), ELISA, flow cytometry). In some embodiments, comparing the level of CXCL10, CCL2, and / or IL-6 to a predetermined threshold can be performed by a computing device as disclosed herein, wherein a user inputs the measured levels of CXCL10, CCL2, and / or IL-6 into the database. In some embodiments, a subject can be identified as at high risk for dementia when the level of least one biomarker for dementia (e.g., CXCL10, CCL2, and / or IL-6) is higher than the predetermined threshold. In some embodiments, a subject can be identified as at high risk for dementia when the level of CXCL10 is higher than the predetermined threshold for CXCL10. In some embodiments, a subject can be identified as at high risk for dementia when the level of CCL2 is higher than the predetermined threshold for CCL2. In some embodiments, a subject can be identified as at high risk for dementia when the level of IL-6 is higher than the predetermined threshold for IL-6. In some embodiments, a subject can be identified as at high risk for dementia when the levels of CXCL10 and CCL2 are higher than the predetermined thresholds for CXCL10 and CCL2, respectively. In some embodiments, a subject can be identified as at high risk for dementia when the levels of CXCL10 and IL-6 are higher than the predetermined thresholds for CXCL10 and IL-6, respectively. In some embodiments, a subject can be identified as at high risk for dementia when the levels of CCL2 and IL-6 are higher than the predetermined thresholds for CCL2 and IL-6, respectively. In some embodiments, a subject can be identified as at high risk for dementia when the levels of CXCL10, CCL2, and IL-6 are higher than the predetermined thresholds for CXCL10, CCL2 and IL-6, respectively.

[0109] Assessment of Responsiveness to TYK2 Inhibitors

[0110] Methods provided herein comprise administering a smell test for assessing if a subject in need thereof will be responsive to treatment with at least one TYK2 inhibitor. Neuroinflammation is a pathological feature of many neurodegenerative diseases, including Alzheimer’s disease (AD)1,2and amyotrophic lateral sclerosis (ALS)3, raising the possibility of common therapeutic targets. Cytoplasmic doublestranded RNA (cdsRNA) is spatially coincident with cytoplasmic pTDP-43 inclusions in neurons of patients with C9ORF72-mediated ALS4. CdsRNA triggers a type-I interferon (IFN-I)-based innate immune response in human neural cells, resulting in their death4. As provided in the present disclosure, cdsRNA is present and spatially coincident with pTDP-43 cytoplasmic inclusions in brain cells of patients with AD pathology, and that IFN-I response genes are significantly upregulated in brain regions affected by AD. cdsRNA also accumulates in two human cellular models of TDP-43 pathology. Cryptic exon (CE) detection as a proxy of pTDP-43 inclusions was used herein and demonstrated, using a machine-learning pipeline DRIAD-SP (Drug Repurposing In Alzheimer’s Disease with Systems Pharmacology), that the FDA-approved JAK inhibitors baricitinib and ruxolitinib that block interferon signaling show a protective signal only in a subset of brains with elevated CE expression. Furthermore, a CRISPR screen of cdsRNA-mediated death in differentiated human neural cells revealed the JAK family member TYK2 as a top hit. The selective TYK2 inhibitor deucravacitinib, an FDA-approved drug for psoriasis, rescued toxicity elicited by cdsRNA. Converging evidence from cell-based assays and from human observational studies of a TYK2-activity-reducing single nucleotide polymorphism (SNP) support CXCL10 as a candidate predictive biomarker for cdsRNA-related neurodegenerative diseases. Together, the present disclsosure provides parallel neuroinflammatory mechanisms between dementia patients with TDP-43 -associated AD pathology and ALS that depend on TYK2, a possible diseasemodifying target of these thus far incurable neurodegenerative diseases. The present disclosure provides (1) inflammatory biomarkers for identification of neurodegenerative diseases with cdsRNA-pathology and (2) a correlation of the smell test with dementia and TDP-43 pathology - both of which can aid in identifying patients that could benefit from therapies mediating TYK2 activity (e.g., TYK2 inhibitor).

[0111] As shown herein, cdsRNA-induced innate immune responses contribute to disease progression in the subset of AD patients with co-existing TDP-43 pathology and also in other neurodegenerative diseases, including ALS and FTD.

[0112] Methods of detection of the activation of cdsRNA-mediated neurodegeneration in the patients affords the possibility of precision medicine in clinical trials for AD and other neurodegenerative diseases. Provided herein are methods for selecting a subject for treatment with a TYK2 inhibitor. In some embodiments, the method comprises administering a smell test according to the methods disclosed herein. In some embodiments, the method comprises selecting the subject for treatment with a TYK2 inhibitor if the calculated confidence metric for the smell test is lower than the predetermined threshold.

[0113] In some embodiments, a method for selecting a subject for treatment with a TYK2 inhibitor method comprises quantifying a level of at least one biomarker for dementia in a biological sample collected from the subject according to the methods disclosed herein. In some embodiments, a subject can be selected for treatment with a TYK2 inhibitor when the level of least one biomarker for dementia (e.g., CXCL10, CCL2, and / or IL-6) is higher than the predetermined threshold. In some embodiments, a subject can be selected for treatment with a TYK2 inhibitor when the level of CXCL10 is higher than the predetermined threshold for CXCL10. In some embodiments, a subject can be selected for treatment with a TYK2 inhibitor when the level of CCL2 is higher than the predetermined threshold for CCL2. In some embodiments, a subject can be selected for treatment with a TYK2 inhibitor when the level of IL-6 is higher than the predetermined threshold for IL-6. In some embodiments, a subject can be selected for treatment with a TYK2 inhibitor when the levels of CXCL10 and CCL2 are higher than the predetermined thresholds for CXCL10 and CCL2, respectively. In some embodiments, a subject can be selected for treatment with a TYK2 inhibitor when the levels of CXCL10 and IL-6 are higher than the predetermined thresholds for CXCL10 and IL-6, respectively. In some embodiments, a subject can be selected for treatment with a TYK2 inhibitor when the levels of CCL2 and IL-6 are higher than the predetermined thresholds for CCL2 and IL-6, respectively. In some embodiments, a subject can be selected for treatment with a TYK2 inhibitor when the levels of CXCL10, CCL2, and IL-6 are higher than the predetermined thresholds for CXCL10, CCL2 and IL-6, respectively.

[0114] TYK2 Inhibitors

[0115] Drug repurposing accelerates testing therapeutic hypotheses expeditiously in clinical trials, and FDA approved drugs can serve as chemical biology probes to elucidate novel targets. By elucidating the mechanism of action of baricitinib and ruxolitinib, which target the JAK kinase family which lies downstream of TBK1 signaling, in human neural cells, TYK2 was identified in the present disclosure as a target to rescue cdsRNA-induced toxicity. Deucravacitinib reverses dsRNA-induced toxicity at lower doses than either ruxolitinib or baricitinib. Its high selectivity for TYK2 could achieve improved safety profiles relative to baricitinib and ruxolitinib. The inhibition of JAK1-3 is associated with adverse infectious, embolic, and thrombotic, neoplastic, and gastrointestinal perforation events77, which have not been reported in people with TYK2 polymorphisms78,79or those treated with a potent TYK2 inhibitor80. Therapeutic interventions that target TYK2 to inhibit cdsRNA- induced neuroinflammation may have potential not only in AD but also in other neurodegenerative diseases with TDP-43 pathology.

[0116] Methods provided herein comprise treating a subject with at least one TYK2 inhibitor after the subject has been subjected to a smell test according to the methods of administering said test disclosed herein. In some embodiments, a subject selected for treatment with a TYK2 inhibitor can be administered a therapeutically effective amount of at least one TYK2 inhibitor. In some embodiments, a subject selected for treatment with a TYK2 inhibitor can be administered a therapeutically effective amount of a selective TYK2 inhibitor. In some embodiments, a subject selected for treatment with a TYK2 inhibitor can be administered a therapeutically effective amount of baricitinib, ruxolitinib, deucravacitinib, or any combination thereof. In some embodiments, a subject selected for treatment with a TYK2 inhibitor can be administered a therapeutically effective amount of deucravacitinib. Deucravacitinib is also known as 6-(cyclopropanecarboxamido)-4-((2-methoxy-3-(l-methyl-lH-l,2,4- triazol-3-yl)phenyl) amino)-N-(methyl-d3)pyridazine-3-carboxamide, having the structure of Formula (I):

[0117] Other TYK2 inhibitors suitable for use herein include, for example, TYK2 inhibitors as described in WO 2012 / 000970, WO 2012 / 035039, WO 2013 / 174895, WO 2015 / 091584, WO 2015 / 032423, WO 2017 / 040757, WO 2018 / 071794, WO 2018 / 075937, WO 2019 / 023468, US 2015 / 0045349, US 2015 / 0094296, and US 2016 / 0159773, the contents of each of which are hereby incorporated by reference in their entirety herein.

[0118] Methods of Treatment and Treatment Outcomes

[0119] The present disclosure provides methods of treating a subject for dementia. In some embodiments, a method of treating a subject for dementia comprises assessing that the subject is at high risk for dementia as determined by the smell test according to the methods disclosed herein. In some embodiments, a method of treating a subject for dementia comprises assessing that the subject is at high risk for dementia as determined by measuring the level of least one biomarker for dementia (e.g., CXCL10, CCL2, and / or IL-6) according to the methods disclosed herein.

[0120] In some embodiments, a subject that is identified as being at high risk for dementia according to the methods disclosed herein is administered at least one pharmacologic therapy for treating dementia / dementia associated with Alzheimer’s disease. In some embodiments, the method of treatment comprises administering to the subject at least one treatment for Alzheimer’s disease. In some embodiments, the method of treatment comprises administering to the subject benzgalantamine, donepezil, galantamine, rivastigmine, memantine, lecanemab, donanemab, suvorexant, brexpiprazole, or any combination thereof.

[0121] In some embodiments, a subject that is identified as being at high risk for dementia according to the methods disclosed herein is administered at least one non- pharmacologic therapy for treating dementia / dementia associated with Alzheimer’s disease. In some embodiments, the method of treatment comprises administering to the subject a medical device. Non-limiting examples of medical devices suitable for use herein include deep brain stimulation (DBS), transcranial stimulation (tCS), continuous positive airway Pressure (CPAP), electroconvulsive therapy (ECT), low- energy infrared / laser LED light (IRL), photobiomodulation (PBM), transcutaneous vagal nerve stimluation (TVNS), hyperbaric oxygen chamber, low intensity pulsed ultrasound (LIPU), hearing aid placement, or any combination thereof. In some embodiments, the method of treatment comprises administering to the subject cognitive retraining. In some embodiments, the method of treatment comprises modification of a subject’s overall diet, administering specific foods, administering at least one vitamin, administering at least one vitamin minerals, or any combination thereof. In some embodiments, the method of treatment comprises administering to the subject an exercise regimen. Non -pharmacologic therapies for treating dementia / dementia associated with Alzheimer’s disease are known in the art (e.g., Olazaran et al., Dementia and Geriatric Cognitive Disorders 30.2 (2010): 161-178; and Xiao et al., Sci Rep. 2024 Apr 15; 14(1):8693) and are suitable for use herein.

[0122] Methods of treating a subject for dementia as disclosed herein can result in an improvement in at least one symptom of the target disease (e.g., dementia / dementia associated with Alzheimer’s disease) following administration. Non-limiting symptoms of dementia / dementia associated with Alzheimer’s disease can include: memory loss that disrupts daily life; challenges in planning or solving problems; difficulty completing familiar tasks at home, at work, or at leisure; confusion with time or place; trouble understanding visual images and spatial relations; problems with words in speaking or writing; misplacing things and not being able to retrace steps; decreased or poor judgment; and withdrawal from work or social activities.

[0123] Methods of treating a subject for dementia as disclosed herein can prevent progression of the target disease (e.g., dementia / dementia associated with Alzheimer’s disease) following administration. In some embodiments, a method of treating a subject for dementia as disclosed herein results in a change in the mini-mental state examination (MMSE) score compared to baseline. In the context of an assessment of the effect of a method of treatment, the term “baseline” refers to a measurement pretreatment. The MMSE score measures overall Alzheimer’s disease symptoms (see, e.g., Podhoma et al., Alzheimers Res Ther (2016) Feb 12;8:8). In some embodiments, the MMSE score increases in a subject treated in accordance with a method provided herein compared to baseline, indicating an improvement of symptoms. In other embodiments, the MMSE score remains unchanged in a subject treated in accordance with a method provided herein compared to baseline. In some embodiments, a method of treating a subject for dementia as disclosed herein results in a change in the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) score (e.g., ADAS-Cog 3, ADAS-Cog 5, the original ADAS-Cog 11, and / or ADAS-Cog 13 scores) compared to baseline. ADAS-cog tests cognitive performance and has an upper limit is 85 (poor performance) and lower limit is zero (best performance) (see, e.g., Podhoma et al., Alzheimers Res Ther (2016) Feb 12;8:8; Cohen et al., J Prev Alzheimers Dis. 2022;9(3):507-522. In some embodiments, the ADAS-cog score decreases in a subject treated in accordance with a method provided herein compared to baseline, indicating an improvement of symptoms. In other embodiments, the ADAS-cog score remains unchanged in a subject treated in accordance with a method provided herein compared to baseline.

[0124] Methods of treating a subject for dementia as disclosed herein can of prevent progression of a pathology associated with a neurodegenerative disease (e.g., dementia / dementia associated with Alzheimer’s disease) following administration. In some embodiments, a method of treating a subject for dementia as disclosed herein can prevent further accumulation of amyloid-beta (PA), tau proteins, intraneuronal neurofibrillary tangles (NFTs), or a combination thereof (see, e.g., Mehta et al., Clin Geriatr Med. 2023 Feb;39(l):91 -107). In some embodiments, a method of treating a subject for dementia as disclosed herein can prevent progression of a TDP-43 pathology. In some embodiments, a method of treating a subject for dementia as disclosed herein can prevent progression of cerebral atrophy. In some embodiments, a method of treating a subject for dementia as disclosed herein can prevent progression of atrophy of the hippocampus. In some embodiments, a method of treating a subject for dementia as disclosed herein can prevent progression of atrophy of the amygdala.

[0125] EXAMPLES

[0126] The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

[0127] Materials and Methods

[0128] The following materials and methods were used in the Examples below.

[0129] Development and quality control of the AROMHA brain health test

[0130] The ABHT was updated for remote at-home self-administration of previously developed Odor Percept Identification (OPID), Percepts of Odor Episodic Memory (POEM), and Odor Discrimination (OD) subtests

[0043] , All pre-screening, informed consent, and test administration occurred online through a web-based interface (testyourbrainhealth.com). This updated version of the test consisted of five different 8.5 inch x 11 inch single-use cards packaged in one envelope and mailed to the participant’s home. We expanded the manufacturing of odor labels from the three odors utilized in the COVID smell test

[0062] to include additional 15 odors. Odor labels were manufactured by MFR Samplings using Living Library™ odors purchased from International Flavors and Fragrances (IFF)

[0062] , Odors were presented to participants in a peel-and-sniff manner and contained proprietary naturalistic odors from the Living Library developed by IFF (iff.com). Gas chromatography / mass spectrometry (GC / M) were conducted at the Mass Spectrometry core at the Bauer Laboratory in the Harvard Chemistry Department. Briefly, each odor label was completely opened in a stoppered 15 milliliter (ml) conical tube and allowed to reach equilibrium for 1 minute (min) at room temperature. Then a Hamilton Syringe was used to inject a representative sample of the headspace into the GC / MS instrument. The peaks were normalized to 2-methyl-3-heptanone equivalents, and analyzed for common set of peaks that might represent a common contaminant from the adhesive. All samples were run the same day to eliminate batch effects (FIGS. 3A-3C).

[0131] Participant responses to all components of the ABHT were collected on a webbased application at testyourbrainhealth.com designed for independent selfadministration of the survey questions and the olfactory battery (FIGS. 4A-4D). The data was stored on a HIPPA-compliant AWS server. The prescreening module and the informed consent module were developed on a RedCAP platform at the Massachusetts General Hospital. All protected health information was kept on RedCAP platform. Participants had the option to call a research assistant for live help in English or Spanish at any time during remote testing. The web-based AHBT application directed participants to a RedCap secure e-consent project to collect identifiers. Once consented, participants were sent back to the AROMHA, Inc. webbased application to walk through all three parts of the battery and collect olfactory information associated with their card ID. The web-based application was designed to lead participants through every stage of testing, including directions on how to peel odor labels and sample odors as well as how to respond to questions regarding odor intensity, odor identification & naming confidence, odor memory, and odor discrimination. The web-based application had the ability to run in an English or Spanish language mode, based on participant preference. During testing, the webbased application collected participant responses for all aspects of the olfactory battery in addition to the timing of those inputs. The web-based application generated summary and item-specific data on these metrics that was downloaded by researchers for analysis and joined offline to demographic information collected in RedCap following the e-consent process. These results were not shared with participants.

[0132] Part 1: Odor Percept Identification Test (0PID9). Participants first completed the 0PID9, which involved identifying nine distinct odors: menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, and grape. These odors were selected for their predictive value in identifying the conversion to Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI)

[0039] , After each odor presentation, participants rated the intensity on a Likert scale from 0 to 10. Subsequently, participants were presented with four odor names, asked to choose the label that best represented the odor they sampled, and asked to rate their confidence in their identification choice using the following scale: “I Guessed,” “I Narrowed Down to Three,” “I Narrowed Down to Two,” or “I Am Certain.” (FIG. 4B). This process was repeated for all nine odors. Following the completion of the OPID9 test, there was a 10-minute delay during which participants answered demographic, medical, nasal, and memory-related questions.

[0133] Part 2: Percepts of Odor Episodic Memory (POEM) / OPED 18. After the 10-minute break, participants completed the POEM / OPID18 tests. These tests included the nine odors from Part 1 and nine additional odors: coffee, peach, chocolate, orange, dirt, banana, lemon, bubble gum, and rose. The odors were presented in a stereotyped random order that was held consistent across all participants. For each odor, participants first indicated whether the odor sampled was presented in Part 1 (yes / no), OPID9. As in the earlier odor identification test, participants then selected the odor name most representative of the odor from four choices and rated their confidence in their selection.

[0134] Part 3: Odor Discrimination (ODIO). In Part 3, participants were presented with 10 pairs of odors, all of which were previously presented in Parts 1 and 2. Participants were asked to determine whether the paired odors were the same or different (yes / no).

[0135] The POEM index was calculated as the difference between the proportion of correct and incorrect recognitions, with scores ranging from -1 to 1. OPID9 and OPID18 scores were calculated as the total number of correctly identified odors, with maximum scores of 9 and 18, respectively. The ODIO score was the total number of correctly discriminated odor pairs, with a maximum score of 10. The average intensity score was derived from the mean intensity ratings of the nine odors from Part 1 on the Likert scale. OPID9noguess and OPID18noguess scores were calculated as the total number of odors identified correctly where the participant did not select “I Guessed” for the confidence question immediately following identification.

[0136] After verifying participants’ comfort with using the web-based application in conjunction with the AROMHA Brain Health Test’s smell cards through the use of research assistant observation, cognitively healthy participants were given the option to complete the test with or without the observation of a research assistant. Verified cognitively impaired participants were not given this option and were only able to self-administer the smell test remotely or in person under the observation of a research assistant.

[0137] Participants

[0138] In the examples, 127 age-matched control subjects (CN), 34 subjects with Subjective Cognitive Concerns (SCC), and 19 subjects with Mild Cognitive Impairment (MCI) participated in the study. The cognitive status classification was verified for subjects recruited (n=59) using the National Alzheimer’s Coordinating Center (NACC) uniform dataset procedure

[0099] , Cognitive status classifications (CN, SCC, MCI) were made clinically, either by a consensus committee of expert clinicians (e.g., neurologists, neuropsychologists, geriatricians) or by one or more clinicians. The decision was based on (1) the presence of concern about cognitive change raised by the subject, a co-parti cipant, or a clinician, (2) the presence / absence of impairment in one or more cognitive domains, and (3) the independence in functional abilities. Demented participants were excluded from the study. The severity of cognitive and functional impairment was measured with the Clinical Dementia Rating (CDR) scale; cognition was assessed using the neuropsychological battery from the National Alzheimer’s Coordinating Center (NACC) uniformed data set (UDS). This battery included a cognitive screening (Montreal Cognitive Assessment - MoCA), and assessments of memory (Immediate and Delayed Recall from Logical Memory or Craft Story), attention / working memory (Forward and Backward Digit Span or Number Span), processing speed / executive functioning (Trail Making Test, Parts A and B), language (Category Fluency, Boston Naming Test or Multilingual Naming Test), and visuospatial skills (Benson Complex Figure Copy). Unverified participants recruited were classified cognitively based on the presence or absence of self-reported cognitive complaints (SCC) or medical diagnoses affecting cognitive function (MCI) (n=l 18). All participants underwent informed consent before participation. The research protocol was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Partners Health.

[0139] Unverified MCI participants (n=4) were aged 55+ and reported a clinical diagnosis of MCI by a certified physician. Verified MCI participants (n=15) were aged 55+, met the National Institute on Aging- Alzheimer’s Association (NIA-AA) diagnostic criteria for MCI

[0100] , with a performance below the demographic / education-adjusted norm on neuropsychological assessment indicating MCI, had a CDR score from 0.5 to 1 and had preserved IADL (determined by a clinician), and were not demented. In total, 19 participants with MCI were recruited.

[0140] Unverified Subjective Cognitive Concern (SCC) participants (n=13) were aged 55+ and reported SCC based on the following 3 questions: (1) Have you experienced a change in your memory in the last 1-3 years?; (2) Has this been a persistent change over the last 6 months?; and (3) Are you concerned about this change? Responses were on a Likert scale: “Not at all,” “Slightly,” “Moderately,” “Considerably,” and “Extremely.” If participants endorsed “Slightly” or worse to all 3 questions, they were categorized as SCC. Verified SCC participants (n=18) reported significant SCC based on the above three questions while not meeting MCI criteria. Three participants under 55 years old reported SCC and were removed from the analyses. In total, 31 participants with SCC were included.

[0141] Unverified CN (n=99) were recruited from the internet and were aged 18 years old and over and did not report any cognitive complaints or medical diagnoses affecting cognitive function. From the unverified CN participants, subgroup of unverified CN aged 55+ (n=44) was created. Verified CN aged 55+ recruited (n=29), had a normal cognition (CDR=0 and / or neuropsychological testing within normal range), did not report subjective memory concerns based on the three questions presented above, and did not meet MCI criteria. It was ensured that demographic variables were similar across verified and unverified CN older adults (control group). T-test and chi-square analysis revealed no statistical difference across modalities for age (p=0.47), sex (p=0.99), and education (p=0.27).

[0142] Wilcoxon rank sum / Mann Whitney tests were performed to compare the median performance on each olfactory measure between verified and unverified participants in each subgroup (CN, SCC, and MCI). Since, after adjustment for multiple comparisons, no difference was significant at a threshold of 0.05, the verified and unverified participants were combined within a subgroup for the purposes of these analyses. Clinical data comparing the verified participants in each group are presented and compared in Table 1.

[0143] Table 1: Clinical values and olfactory function across cognitive status in verified older adult participants.

[0144] Statistical analysis (AROMHA brain health test) All analyses were conducted in R using Base R, effectsize, emmeans, and rstatix packages. Student T-tests were used to compare demographics and olfactory variables between observed and unobserved conditions and between English and Spanish-speaking groups, while the Wilcoxon signed-rank test was used to compare anosmic patients with CN participants. To assess the effect of age on olfaction, linear regression models were conducted with age and olfactory scores. This analysis was performed for each olfactory subtests (0PID9, OPID9noguess, 0PID18, OPID18noguess, ODIO, POEM, and average intensity). To assess the effect of cognitive status on olfaction, ANCOVAs were performed to compare olfactory functioning (0PID9, OPID9noguess, 0PID18, OPID18noguess, ODIO, POEM, and average intensity) among older adults without cognitive impairment, participants with SCC, and with MCI, including age, sex, and education as covariates. For all the analyses, the alpha value was set at 0.05 and Bonferroni correction was used for multiple comparisons.

[0145] Histological Staining and Imaging

[0146] All human tissues were acquired from the MGH MADRC brain bank in accordance with protocols approved by the MGB Institutional Review Board. Similar to what was previously reported4, ~5 pm thick formalin-fixed paraffin-embedded (FFPE) sections of the amygdala were dewaxed followed by antigen retrieval in a Leica Bond Fully Automated Slide Stainer. After deparaffination and rehydration by sequential incubation in Leica dewing solution (3 x 5 min), 100% ethanol (2 x 5 min), 90% ethanol (2 x 5 min), 70% ethanol (2 x 5 min), and distilled water (3 x 5 min), antigen retrieval was achieved with citrate-based antigen retrieval solution (ER2) and steam cooking for 1 hour. Afterwards, sections were cooled to room temperature and rinsed in water and l x PBS. For permeabilization, 0.03% hydrogen peroxide solution was applied for 8 min. After blocking in tris-buffered saline (TBS)-based Odyssey blocking buffer (cat. # 927-60001, LLCOR, NE, USA) for 1 hour, for immunohistochemistry, primary antibodies (J2 for dsRNA (cat. # 76651, Cell Signaling Technology, MA, USA); pTDP-43 S409 / 410 (cat. # CAC-TIP-PTD-P02, Cosmo Bio, CA, USA); tau (cat. # NBP2-25162, Novus Biologicals, CO, USA); AB (cat. # CST-D54D2, Cell Signaling Technology)) diluted in blocking buffer were applied overnight at 4°C in a humidified chamber. Following a wash with PBS (3 x 5 min), secondary antibodies were applied using the VECTASTAIN ABC Staining Kit (cat. # PK-6200, Vector Laboratories, CA, USA) and DAB Staining Kit (cat. # SK- 4100, Vector Laboratories). For luxol fast blue-hematoxylin and eosin (LHE) staining, the protocol of the Luxol Fast Blue Stain Kit (cat. # ab 150675, abeam, MA, USA) and the Sakura Prisma H&E Stain Kit (cat. # 76318-76, VWR, PA, USA) were followed. For tissue-based cyclic immunofluorescence (CyCIF)34, unspecific binding sites were blocked, slides were incubated in 3% H2O2 and 20 mM HC1 in PBS at room temperature with light-emitting diode illumination for 1 hour to reduce autofluorescence, and the first primary antibody set (J2 and pTDP-43 S409 / 410) was applied. After washing, the secondary antibodies (anti-rabbit Alexa Flour 555 (cat. # A-31572, Thermo Fisher, MA, USA); anti-mouse Alexa Flour 647 (cat. # A-21237, Thermo Fisher)) 1 : 1000 diluted in blocking buffer were applied for 1 hour in a humidified chamber at room temperature. After a wash, Sytox Blue Nucleic Acid Stain (cat. # SI 1348, Thermo Fisher) was applied 1 : 15000 in blocking buffer for 15 min at room temperature. Following a final washing step, slides were imaged. After this first cycle, the fluorophores were inactivated by 4.5% H2O2 and 24 mM NaOH in PBS at room temperature under light exposure for 1 hour. After rinsing, the second primary antibody set (tau and AB) was applied and staining was continued in the same manner as described above.

[0147] Differential Gene Expression Analysis of Different Brain Regions

[0148] Differential Gene Expression was conducted based on a previously established protocol91. In brief, clinical diagnosis was used to define AD versus control conditions within the bulk RNA-sequencing data taken from the ROSMAP37and MSBB38databases (data obtained from the AMP-AD Knowledge Portal (doi: 10.7303 / syn2580853)). The analysis focused on a list of 354 pre-selected interferon-related genes. Of these genes, 309 could be found in the analyzed brain regions (from ROSMAP: dorsolateral prefrontal cortex (DFPC), from MSBB: frontal pole (FP), inferior frontal gyrus (IFG), parahippocampal gyrus (PHG), superior temporal gyrus (STG)). 139 genes were significantly upregulated (adjusted P value < 0.05) while 51 were downregulated.

[0149] Induced Pluripotent Stem Cell (iPSC) differentiation and treatment

[0150] An isogenic pair of a recently established iPSC line that harbors a pathogenic TDP-43 mutation (TDP-43+ / G298S) in the FA0000011 genetic background35,92was differentiated into NGN2 cortical-like neurons using a recently published protocol36,93in a 384-well plate format (10,000 cells per well). Where indicated, wildtype FA0000011 cells were transfected with poly(I:C) HMW (cat. # tlrl-pic, Invitrogen, CA, USA) by lipofection with lipofectamine 2000 (cat. # 11668027, Thermo Fisher) (final dsRNA concentration: 4-8 pg / ml, depending on poly(I:C) batch effects).

[0151] ReNcell VM and CX cell culturing and differentiation

[0152] ReN VM (cat. # SCC008, Millipore, MA, USA) and CX cells (cat. # SCC007, Millipore) were cultured in ReNcell Maintenance media (cat. # SCM005, Millipore) containing 1 : 100 of Penicillin-Streptomycin (cat. #30-002-CI, Corning, NY, USA), 20 ng / ml of epidermal growth factor (cat. # GF001, Millipore) and 20 ng / ml of basic fibroblast growth factor (cat. # 03-002, Stemgent, MD, USA) and differentiated into neural cells for one week by removing the growth factors as previously described4,94, with the difference that the differentiation took place in a separate dish (FIGS. 20A- 20B) to minimize variability downstream.

[0153] SH-SY5Y culturing

[0154] SH-SY5Y Neuroblastoma cells (cat. # CRL-2266, ATCC, VA, USA) were cultured in 1 : 1 Eagle’s Minimum Essential Medium (EMEM) (cat. # 10-009-CV, Corning) and F12 medium (cat. # 11765-047, Thermo Fisher) containing 1 : 100 of Penicillin-Streptomycin (cat. #30-002-CI, Coming) and 10% fetal bovine serum (FBS; cat. # 10438-026, Thermo Fisher). Prior to any treatments, cell media was changed to media containing 1% FBS instead of 10% to slow down cell division.

[0155] ReN VM, CX and SH-SY5Y cell treatments

[0156] SH-SY5Y cells as well as differentiated ReN VM and CX cells were seeded into a 96-well plates (VM / CX: 20,000 cells per well with Matrigel -coating (cat. # 354320, Corning), SH-SY5Y cells: 10,000 cells per well without coating) and where applicable, respective drugs dissolved in dimethyl sulfoxide (DMSO) (baricitinib: HMS LINCS ID # 10354, ruxolitinib: HMS LINCS ID # 10138, deucravacitinib: cat. # HY-117287, MedChem Express, NJ, USA, MG-132: cat. # HY-13259, MedChem Express, etoposide: cat. # HY-13629, MedChem Express, menadione: cat. # M9429- 25G, Sigma-Aldrich, MO, USA) were added using a D300e Digital Dispenser (Hewlett-Packard, CA, USA). After 1 hour of incubation at 37 °C, cells were transfected with poly(I:C) HMW where applicable (cat. # tlrl-pic, Invitrogen) by lipofection with lipofectamine 2000 (cat. # 11668027, Thermo Fisher) (final dsRNA concentration: 4-8 pg / ml, depending on poly(I:C) batch effects). To test the relevance of IFNAR2, a neutralizing anti-IFNAR2 antibody (cat. # 21385-1, PBL Assay Science, NJ, USA) was added in the respective amount to each well after lipofection and without prior drug treatment. To test whether interferon a and poly(I:C) have comparable effects on neurons, interferon a (cat. # 407294-5MU, Millipore) was diluted in media and added to the cells without prior drug treatment. Western blot samples and cell media for biomarker testing were collected 24 h (48 h for MG- 132 treatment) post-treatment. Cell viability was assessed 72 hours (VM, SH-SY5Y) and 7 days (CX) post-treatment using the CellTiter-Glo Assay (cat. # G7572, Promega, WI, USA) following the manufacturer’s instructions. All conditions were normalized to 0.1% DMSO content. Where applicable, lipofectamine 2000 (VM, CX) or lipofectamine 2000 + 10 pM drug (SH-SY5Y) was used as the respective control. For mass spectrometry, two million ReN VM cells were expended in a 15-cm-dish (2 dishes per replicate) for 4 days and subsequently differentiated for 1 week, then treated with lOpM of drug, transfected with poly(I:C) after 1 hour of incubation at 37 °C, and 24 hours later, washed with ice-cold lx PBS and collected by scraping followed by centrifugation at 500 g for 5 min. For the testing of common stressors, time-lapse images were captured every 3 hours for 33 hours using GE In-Cell 6000 Analyzer (GE Healthcare, IL, USA) under the confocal mode. Each image's total cell surface area and average fluorescence intensity were quantified using Fiji95and plotted using GraphPad Prism. Four biological repeats were taken for each group.

[0157] Cell Staining and Imaging

[0158] Differentiated ReN VM cells treated with 1 pM MG- 132 were fixed 48 h posttreatment and iPSC-derived MGM2 cortical-like neurons were fixed after ten days of differentiation using 4% paraformaldehyde (cat. # 28908, Thermo Fisher) at room temperature and protected from light. Additionally, after three washes with 1 x PBS, ReN VM cells were permeabilized using ice-cold methanol for 10 min. Cells were washed gently with 1 x PBS three times and then incubated with blocking solution (cat. # 927-60001, LLCOR) for 1 hour at room temperature. All blocking solution was removed, and cells were incubated with fresh blocking solution and primary antibodies overnight on a shaker at 4 °C. Primary antibodies used were anti-TUJl (1:250, cat. # TUJ, Aves Lab, CA, USA), KI anti-dsRNA (1 :250, cat. # 10020200, Scicons, Susteren, The Netherlands), and anti-TDP-43 (1 : 100, cat. # 3448, Cell Signaling Technology). For poly(I:C) antibody incubation controls, poly(I:C) was incubated with the dsRNA primary antibodies at a ratio of 5x poly(I:C) : lx dsRNA primary antibody for 1 hour before being added to cells. Three PBS washes were done, and cells were incubated with secondary antibodies based on species at a 1 :500 (iPSCs) and 1 : 1000 (ReN VM) dilution in the blocking solution for 1 hour at room temperature on a shaker. Hoechst 33342 (cat. # H3570, Thermo Fisher) was added at a 1 : 1000 dilution to the well with iPSCs for the final ten minutes. For ReN VM cells, cells were first washed three times with 1 x PBS and then Hoechst was added to the wells in a 1 :5000 dilution in PBS for 30 min at room temperature. Secondaries used were donkey anti-chicken Alexa Fluor 488 (cat. # 703-545-155, Jackson ImmunoResearch, PA, USA), donkey anti -rabbit Alexa Fluor 568 (cat. # Al 0042, Invitrogen), donkey anti-mouse Alexa Fluor 647 (cat. # A31571, Invitrogen), and donkey anti-rabbit Alexa Fluor 488 (cat. # A21206, Invitrogen). After three final washes with 1 x PBS, Confocal images were acquired with an Image X-Press Micro Confocal (Molecular Devices, CA, USA). Images were taken with a 20X (ReN VM) or 40X (iPSC) objective with four z-stacks (1.5 pM step size) per field for between 25 and 55 fields per well. All groups were imaged with the same acquisition settings within each experiment. Automatic quantifications were performed using a custom Fiji / ImageJ-based plugin (National Institutes of Health, version 1.53c)95that measures integrated intensity of KI dsRNA antibody within TUJ1 -positive cells.

[0159] RNA-sequencing data processing and prediction of TDP-43 status

[0160] RNA-sequencing reads of brain specimens from Alzheimer’s disease patients in the ROSMAP study, along with their corresponding clinical annotations, were downloaded from the AMP -AD Synapse portal at adknowledgeportal.synapse.org. RNA-sequencing reads from ALS patient neurons that were sorted for presence or absence of TDP-43 were downloaded from Sequence Read Archive (SRA) accession GSE12654243. Transcripts were quantified using Salmon vl.9.096against release 107 of the hg38 human transcriptome from Ensembl. The transcriptome was amended with three transcripts that have not yet been annotated in Ensembl, but that are known to be associated with loss of nuclear TDP-4318. Specifically, we included two splice variants of UNCI 3 A, each including an additional CE between the canonical exons 20 and 21 (hg38; chrl9: 17,642,414-17,642,541 (CE1); chrl9: 17,642,414- 17,642,591 (CE2)), and one splice variant of STMN2, which included an alternative exon 2 termed exon 2a (hg38; chr8:79, 616, 822-79, 617, 048).

[0161] Given our quantification of TDP-43 pathology associated transcripts, we classified patient samples as TDP-43 -positive or -negative based on the number of expressed CEs. We dichotomized transcript abundance using thresholds set above the lowest non-zero peak in the abundance histograms (TPM>1.3 STMN2short; TPM>0.09 for UNC13A-CE1; TPM>0.08 UNC13-CE2). This threshold determined whether each individual CE was present or absent. Next, patient samples expressing one or none of the three CEs (STMN short, UNC13A-CE1 or UNC13A-CE2) were considered TDP-43 -negative and patient samples expressing two or all three of the CEs were considered TDP-43 -positive. Using this classification, the proportion of TDP-43 pathology positive samples was approximately 37%. Our classification was a conservative prediction, given that the clinical incidence of TDP-43 inclusions in AD patients was up to 57%14.

[0162] Prediction of disease stage and assessing gene set significance

[0163] For assessing how well any given gene set predicts Alzheimer’s disease progression we used an updated variant of the published DRIAD-SP methodology26. For predicting disease stage, we collapsed transcript abundances to gene-level abundances using tximport97The entire transcriptional feature space was filtered down to the about ~20 k protein-coding genes in the human genome to ensure that only genes with gene products that can conceivably be targeted using conventional small molecule inhibitors are included. Every AMP-AD specimen was assigned a label of disease severity based on the following mapping to the Braak annotations: A — early (Braak 1-2), B — intermediate (Braak 3-4), and C — late (Braak 5-6). We set up an ordinal regression task classifying patients according to their disease stage using the ordinalRidge R package (github.com / labsyspharm / ordinalRidge). We used the gene expression measurements from AMP-AD as predictors, filtered to only contain genes in the current gene set under investigation. This way, the performance (AUC) of a model utilizing a given gene set could be viewed as a measure of how strongly the given genes are predictive of AD progression. To address overfitting, ordinalRidge utilized a ridge regularization term that penalized the L2-norm of feature weights. No LASSO regularization was used, as it induced sparsity and excluded features that were specifically preselected to be included in the model.

[0164] Model performance was evaluated through leave-pair-out cross-validation. For a given regression task, each example in the dataset was associated with an example from the other classes that was the closest match in age. If there were multiple candidates for the age match, the pairing was selected uniformly at random. The resulting set of age-matched pairs was evaluated in a standard cross validation setting by asking whether the later-stage example in each withheld pair was correctly assigned a higher score by the corresponding predictor. The fraction of correctly ranked pairs constituted an estimate of the area under the ROC curve98.

[0165] Comparing model performances across gene sets directly was not possible, as their set sizes can differ. Therefore, we normalized AUC values by relating them to performances observed on size-matched random gene sets. For each gene set of interest, 1000 random gene sets of matching sizes were sampled from a uniform distribution over the protein-coding space. After evaluating all lists through cross- validation, an empirical P value was computed as the fraction of background sets that yield higher predictor performance than the gene list of interest.

[0166] CRISPR screen

[0167] Low passage (P2) ReNcell VM cells stably expressing Cas9 were expanded and transduced with lentiviral particles containing the Brunello library50. Lentiviral transduction efficiency was tested by plating cells into two 6-well plates and varying volumes of stock preparation of Brunello library lentiviral vectors with 8 pg / ml of polybrene. One plate was treated with puromycin, the other plate remained untreated, and the proportions of cells measured for each condition using CellTiter-Glo were used to calculate the efficiency which was found to be at 84% averaged from triplicates. Transduced cells were expanded in the presence of puromycin until the required number of cells was achieved to obtain triplicates of 1000 cells per sgRNA. Then, cells were differentiated as described above. After seven days, 3 pg / ml dsRNA in complex with lipof ectamine (concentration according to manufacturer’s protocol) or lipofectamine alone as a control were added to each flask. After 3 days of treatment, genomic DNA was isolated from each group of cells. PCR and sequencing were performed as previously described99,100. For analysis, the read counts were normalized to reads per million and then log2 transformed and the LFC of treatment and control were compared to the lipofectamine controls.

[0168] Mass spectrometry

[0169] First, cells were lysed in lysis buffer (2% SDS, 150 mM NaCl, 50 mM Tris pH 8.5) supplemented with protease and phosphatase inhibitor (cat. # 11873580001 and 4906845001, Millipore) and homogenized with a QIAshredder column (cat. # 79656, Qiagen, Hilden, Germany). Lysates were reduced with freshly prepared dithiothreitol (DTT) (final concentration: 5 mM) and heated at 37 °C for 1 hour. Samples were then alkylated with iodoacetamide (final concentration^!) mM) for 25 min with 50 mM ammonium bicarbonate in the dark whereupon the reaction was stopped by adding DTT (final concentration: 50 mM). After methanol / chloroform precipitation, the purified protein was solubilized in freshly prepared 8 M urea in 200 mM EPPS, at pH 8.5 for 30 mins at 37 °C. For digestion, the urea concentration was diluted with 200 mM EPPS to 4 M final concentration and 2% acetonitrile (v / v) was added. Digestion was performed using lysyl endopeptidase® Lys-C (2 mg / ml; cat. # 121-05063; Wako, Osaka, Japan), (enzyme-to-substrate ratio: 1 :50) for 3 hours at 37 °C. Following dilution of the sample with 200 mM EPPS to a final urea concentration of 1.6 M, further digestion was performed by the addition of trypsin (cat. # V5111, Promega), (enzyme-to-substrate ratio: 1 : 100) for 8 hours at 37 °C. A small aliquot was analyzed for its missed cleavage rate by mass spectrometry. Equal amounts of digested protein (60 pg) were removed from each sample and labeled using TMTpro 16plex Mass Tag Labelling Kit (TMT; cat. # A44520, Thermo Fisher) following the manufacturer’s instructions. Labeling efficiency of > 95% was determined along with ratio checks by mass spectrometry MS3, while the labeling reactions were stored at -80 °C. To quench the reaction, hydroxylamine was added to a final concentration of 0.5% (v / v) for 10 mins. After acidification using formic acid, equal amounts of labelled peptide were pooled from each sample (as judged from ratio check data). Then, solvent was evaporated. The labeled peptide multiplex was desalted by solid-phase extraction (SPE; SepPak tC18 Vac RC Cartridge). The peptides were fractionated by HPLC alkaline reverse phase chromatography (Agilent 1200 Series) into 96 fractions and combined into 24 samples. Lastly, peptides were desalted over Stage Tips101prior to MS analysis.

[0170] A MultiNotch SPS-MS3TMT method102was used on an Orbitrap Lumos mass spectrometer (Thermo Fisher) coupled to a Proxeon EASY-nLC 1200 liquid chromatography (LC) system (Thermo Fisher). Samples were injected onto a 40 cm, 100 pm (internal diameter) column packed with 2.6 pm Accucore C18resin (flow rate of 450 nl / min). Over the course of 4 hours, the peptide fractions were separated through acidic acetonitrile gradients by the LC before being injected into the mass spectrometer. First, an MS1spectrum (Orbitrap analysis; resolution 120,00; mass range 400-1400 Th) was taken. Then, an MS2spectrum was collected after collision- induced dissociation (CID, CE = 35) with a maximum ion injection time of 150 ms and an isolation window of 0.7 Da. For tandem mass tag (TMT) quantification of peptides, MS3precursors were fragmented by high-energy collision-induced dissociation (HCD, CE = 40%) and analyzed in the Orbitrap at a resolution of 50,000 at 200 Th. See, e.g., Paulo JA et al., J Proteomics 148, 85-93 (2016).

[0171] A Sequest-based in-house software was used to search peptides against a human database with a target decoy database strategy and a false discovery rate of 1%. Oxidized methionine residues (+15.9949 Da) were dynamically searched, along with static modifications for alkylated cysteines (+57.0215 Da) and the TMTpro reagents (+304.207145 Da) on lysines and the N-termini of peptides. Relative protein quantification required a summed MS3TMT signal / noise > 200 over all TMT channels per peptide and an isolation specificity > 70% for any given peptide. Quant tables were generated and exported to Excel for further procession. More details on the TMT intensity quantification and certain parameters, see, e.g., Paulo JA et al., J Am SocMass Spectrom 27, 1620-1625 (2016).

[0172] Western blotting

[0173] First, 15 pl of x Laemmli Sample Buffer (cat. # 1610737; Bio-Rad Laboratories, CA, USA) with 1 :20 2-mercaptoethanol was added to each well after aspiration of the media. Subsequently, the lysates were boiled at 100 °C for 5 min. Then, the samples were loaded onto a precast gel (cat. # 4569036; Bio-Rad Laboratories, CA, USA) which was run in lx running buffer diluted from a lOx Tris / Glycine / SDS stock (cat. # 1610772; Bio-Rad Laboratories) at 160 V for 40 min. The transfer was performed using a PVDF membrane (cat. # IPFL00010; Millipore) and a lx transfer buffer diluted from a lOx stock (cat. # PI35040; Thermo Fisher, MA, USA) mixed with 20% methanol. For 90 min, the gel was blotted at 4 °C at 90 V. Finally, unspecific binding sites were blocked by phosphate-buffered saline (PBS)- based Odyssey® blocking buffer (cat. # 927-40150; LI-COR) for 1 hour at room temperature. Then, primary antibody (pSTATl Y701 (cat. # 9167S; Cell Signaling Technology); beta actin (ACTB, cat. # 3700; Cell Signaling Technology) diluted 1 : 1000 in blocking buffer was applied overnight at 4 °C. Then, the blot was washed with tris-buffered saline (cat. # sc-262305; Santa Cruz, TX, USA) containing 0.05% Tween 20 (cat. # BP337-500; Thermo Fisher) (3 x 5min). Afterwards, secondary antibody (IRDye 800CW (cat. # 926-32211; LLCOR); IRDye 680RD (cat. # 926- 68070; LI-COR)) diluted 1 :2500 in blocking buffer was applied for 1 hour at room temperature. After three final washes, the blot was imaged with the Odyssey® DLx Imager (LI-COR). Images were analyzed using ImageStudioLite (LI-COR).

[0174] Genome-wide association tests using TYK2 polymorphism (rs34536443)

[0175] We used an established partial loss-of-function in TYK2 (rs34536443) to estimate the effect of pharmacological TYK2 inhibition on the circulating CXCL10 concentration55 57. Summary genetic association data was obtained from a genomewide association study (GWAS) using the Olink proteomics platform in 54,219 participants of British ancestry in the UK Biobank53. Genome-wide association tests were performed using REGENIE v.2.2.1 in a two-step procedure to account for population structure in a population of European ancestry (n = 34,557). Protein levels were inverse-rank normalized. Association models included the following covariates: age, age2, sex, age x sex, age2x sex, batch, UK Biobank center, UK Biobank genetic array, time between blood sampling and measurement, and the first 20 genetic principal components of ancestry. Summary genetic association data on these measures were also obtained from a GWAS of these measures using the SomaScan platform in 35,559 individuals of Icelandic ancestry in DeCODE54. Rank-inverse normal transformed proteins were adjusted for age, sex, and sample age. Residuals were re-standardized using rank-inverse normal transformation and standardized values were used in genome-wide association testing using the linear mixed model implemented in BOLT-LMM105.

[0176] Meso Scale Diagnostics (MSD) electrochemiluminescence (ECL) biomarker assay

[0177] To measure CXCL10 (IP-10) biomarker levels in collected cell media, we used the commercially available V-PLEX assay from MSD, LLC (cat. # K151NND, MSD, Cambridge, MA, USA). We used the proposed procedures with the exception that a 1 : 100 dilution of cell media was required for the sample values to fall within the 0.37-500 pg / ml dynamic range of the assay. All plates were analyzed under the MESO QuickPlex SQ 120 (Model # 1300) plate reader and concentrations were quantified via the Meso Scale Discovery WorkBench. Samples with concentrations reported as “NaN” were quantified as zero, as these concentrations fell below the dynamic ranges reported above.

[0178] Statistical analysis

[0179] Statistical analyses were conducted on GraphPad Prism for unpaired t tests with two tails and ordinary one-way analysis of variance (ANOVA) with post hoc tests. Correction for multiple comparison was done using Dunnett’s statistical hypothesis testing. For image quantification, data were plotted and statistically analyzed on R-Studio software (Version 4.3.3). Boxplots and bars represent the mean and standard error of the mean, respectively. Differences were considered significant if P < 0.05. P value style: P > 0.05 and P = 0.05 (no asterisks), P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Where necessary, an outlier test (ROUT with Q = 5% and Grubbs with a = 0.05) were carried out. No outliers were eliminated from the dataset.

[0180] Example 1: Design and implementation of the AROMHA brain health test.

[0181] The workflow of the self-administered ABHT included tests of odor percept identification (OPID), percepts of odor episodic memory (POEM), and odor discrimination (FIGS. 1A and IB), which paralleled previous researcher- administered tests, where odors were delivered through an olfactometer

[0043] or through hand-held, repeat use devices (Whispis)

[0064] , The ABHT leveraged the remote administration aspects of the CO VID Smell Test

[0062] by delivering the odor stimuli using odor labels arrayed on mailable cards, by including an odor intensity measure, and by enabling self-administration by developing a web-based platform

[0065] to collect responses. Additionally, the ABHT added a meta-cognition measure embedded in the odor percept identification tasks. Participants were instructed by the web-based application to sample the odor, and then choose an odor name from a forced choice list of 4 options. They were then asked to evaluate their confidence in each odor identification decision with a scale that included the following options: “I Guessed,” “I Narrowed Down to Three,” “I Narrowed Down to Two,” or “I Am Certain .”. This confidence metric was quantified for the OPID9 and OPID18 odor identification tests as the number answered correct among items paired with the “I am Certain”, “I Narrowed Down to Three”, and “I Narrowed Down to Two” responses (OPID9noguess, OPID18noguess scores).

[0182] Three sets of bilingual (English / Spanish) cards, which were arrayed with odor labels, were designed to administer the OPID9 odor percept identification and odor intensity test (Part 1, Card A (FIG. IB, top panel)), the OPID 18 odor percept identification and POEM memory test (Part 2, Cards B and C (FIG. IB, middle panel)), and the ODIO odor discrimination test (Part 3, Cards D and E (FIG. IB, bottom panel)). Numerous concentrations of each odor were packaged in different labels, and perceptions of odor intensity using a 10-point Likert scale that ranges from 0 (no odor) to 10 (strongest odor imaginable) were obtained from healthy collegeaged participants in pilot studies. The final label-embedded odorant concentrations were selected with a mean perceived intensity of 7-7.5. To ensure that the headspace for each label was not contaminated by one or more components of the adhesive to hold the labels together, gas chromatography / mass spectrometry for each odor label was performed (FIGS. 3A-3B). A common component in the headspace of all labels, which could confound olfactory performance, was not found.

[0183] The foil choices were orthogonal to the target odor and evocative rather than generalized. This minimized cognitive load by offering distinct alternatives and prevented real-world contextual biases that could lead to associative errors. For example, foils describing fruit odors were not used when the target odor was a fruit. Evocative foil names like “coconut” or “fresh bread” were also incorporated. Additionally, foils with contextual associations often encountered with the target odor in real life (e.g., soap and vanilla, lavender and chamomile) were specifically avoided to further reduce potential bias. Finally, the set of odor names was expanded so that each odor name was only presented once within the OPID9 or OPID18 odor identification tests, either as a target odor or a foil (Tables 2 and 3). When the 9 odors, presented in the first OPID9 odor identification and intensity test, were presented again (after a 10-minute break) in the OPID18 odor identification and POEM memory test, new sets of foils were presented with the correct name for each target odor to vary the identification experience and reduce learning carryover from OPID9 (Tables 2 and 3).

[0184] Table 2: Odors and four choices presented to participants for the OPID9 test.

[0185] Table 3: Odors and four choices presented to participants for the OPID18 test.

[0186] Example 2: Validation of unobserved remote testing of the AROMHA brain health test in cognitively normal individuals.

[0187] In the first phase of self-administered testing, all participants completed the ABHT in an observed setting (remote via video conference (e.g., Zoom™) or in person) during a scheduled appointment with the research assistant (n=70).

[0188] Participants shared their testing screen and video with the research assistant via video conference during remote observed testing or completed the testing in person with their testing screen visible to the research assistant. In both these conditions, the research assistant observed and noted participant interactions with the software and the cards and remained on standby if there were questions or confusion. Once confidence in the self-administered testing workflow was established, an unobserved (vs. observed) self-administration option was offered to interested, cognitively normal participants in order to validate the feasibility of this administration mode and the ability to scale future data collection. When cognitively healthy participants, enrolled in the study, were given the option to self-administer remotely, independent of a research assistant, 70% chose to test on their own with the option of live help over the telephone, if needed. The overall distribution of participants by each administration modality is described in Table 4.

[0189] Table 4: Distribution of Participants by Administration Modality. When comparing olfactory scores between CN observed and unobserved groups, there were no significant differences in the olfactory outcomes after Bonferroni correction. In the unobserved modality group, age ( / ?<0.001) was significantly greater and Spanish language ( / ?<0.001) was significantly less common, due to operational factors such as the availability of the unobserved option during phases of recruitment, scheduling, and participant preference. Test duration ( / ?<0.001) was significantly greater in the unobserved group compared to the observed group. Sex and education were not different between the observed and unobserved groups (Table 5).

[0190] Table 5: Demographic information and olfactory function in cognitively normal participants who underwent observed and unobserved self-administration conditions of the AROMHA brain health test.

[0191] As mean age significantly differed across observed and unobserved groups, olfactory scores were compared across groups, including age as a covariate. An ANCOVA revealed no significant interaction effects between age and groups for 0PID18 (F [l, 123]=0.86, / ?=0.36; partial r|2=0.01), OPID18noguess (F [1, 123]=2.16, =0.14; partial r| =0.02), OPID9 (F [1, 123]=0.41,p=0.52; partial r|2<0.001),

[0192] OPID9noguess (F [1, 123]=2.64,p=0.11; partial r|2=0.02), ODIO (F [l, 123]=1.92, =0.17; partial r|2= 0.02), POEM (F [1, 123]= 1.73, p=0.19; partial r|2=0.01), or Intensity (F [1, 123]=1.33, / ?=0.25; partial r|2=0.01). These results suggested that the effect of group (observed vs. unobserved) on olfactory scores was not moderated by age.

[0193] Example 3: Validation of the AROMHA brain health test in anosmic patients.

[0194] Table 6 displays the demographics and olfactory scores of 7 patients with anosmia recruited from a smell loss clinic and CN participants. For context, the first column of Table 6 lists the score following random selection of the answers for each test, e.g., the participant had no olfactory information to guide selection of the answers.

[0195] Table 6: Distribution of olfactory scores of anosmic patients and CN participants on the AROMHA brain health test.

[0196] The anosmic group performed significantly worse on every olfactory metric ( / ?<0.001), as compared to the CN group although they did not take significantly longer to complete the battery when grouped across various modalities (Table 4). The anosmic group did not perform statistically differently from chance performance on every olfactory measure, which was not the case for the CN control group.

[0197] Example 4: Equivalence of the AROMHA brain health test in English vs. Spanish-speaking cognitively normal participants.

[0198] When assessing the potential effect of language, no significant differences were found across olfactory scores between cognitively normal English-speaking and Spanish-speaking participants. Demographically, only age was significantly different as the English-speaking group was significantly older than the Spanish-speaking group ( / ?=0.001) (Table 7).

[0199] Table 7: Demographic information and olfactory function in cognitively normal English-speaking and Spanish-speaking participants.

[0200] As mean age significantly differed across language groups, olfactory scores were compared across groups, including age as a covariate. An ANCOVA revealed no significant interaction effects between age and language for 0PID18 (F [1, 123]=1.54,p=0.22; partial r|2= 0.01), OPID18noguess (F [1, 123]=0.63, / ?=0.43; partial r]2= 0.01), OPID9 (F [1, 123]=0.04, / ?=0.85; partial r]2<0.001), OPID9noguess (F [1, 123]=1.25,p=0.27; partial r|2= 0.01), OD10 (F [1, 123]=0.73, / ?=0.39; partial r|2= 0.01), POEM (F [1, 123]=3.42, / ?=007; partial r]2= 0.03), or Intensity (F [1, 123]=1.21, / ?=0.27; partial r2= 0.01). These results suggested that the effect of language on olfactory scores was not moderated by age.

[0201] Example 5: Diminished olfactory measured in the AROMHA brain health test with increasing age.

[0202] When assessing the effect of age on olfaction, linear regression models of olfactory scores as a function of age in CN participants showed that greater age was significantly associated with lower OPID9noguess (fi = -0.02, / ?=0.002), OPID18 (fi = -0.04, p<0.001), lower OPID18noguess (fi = -0.06, p<0.001), lower OD10 (fi = -0.02, / ?<0.001), and lower POEM (fi = -0.004, p<0.001) scores, while the association with OPID9 (fi = -0.01, / ?=0.01) and average intensity (fi = -0.007, p=0.28) scores did not reach significance after Bonferroni correction (FIGS. 2A-2G).

[0203] Example 6: Performance of AROMHA brain health test distinguished participants Aged +55 who were cognitively normal (CN), with subjective cognitive concerns (SCC) or with mild cognitive impairment (MCI).

[0204] The sex and education of participants across the CN, SCC, and MCI groups were not significantly different after Bonferroni correction. Participants in the MCI group were older than those in the CN group (p=0.01). Comparisons of each olfactory score of the ABHT for participants who were CN, had SCC, or had MCI revealed significant olfactory differences between subgroups, other than in the ODIO, POEM odor memory score, and evaluations of average odor intensity (Table 8). Comparisons across cognitive groups in this +55 sample revealed no demographic differences in sex and education, or in the test duration time.

[0205] Table 8: Olfactory function across cognitive status among participants aged 55+.

[0206] When assessing the effect of cognitive status group on olfactory test components, ANCOVAs revealed a significant effect on OPID9, OPID9noguess, OPID18, OPID18noguess, and ODIO scores (Table 9). No effects of cognitive status group were found for the POEM or average intensity scores. After Bonferroni correction for 21 comparisons ( / ?<0.002), post-hoc pairwise comparisons revealed significantly lower scores in the MCI group compared to CN older adults for the OPID9, OPID18, OPID18noguess, ODIO and to the SCC group for the OPID18 and OPID9 scores.

[0207] Interaction effects between group and age were found for the OPID9noguess, OPID18 no guess scores, and ODIO; however, they did not remain significant after Bonferroni correction for 7 comparisons (p<0.007). No other significant interaction effects between group and age were found regarding other olfactory components (p>0.05). No other interaction effects with sex or education were found (p>0.05) (Table 9) Table 9: ANCOVA models comparing different olfactory scores across groups.

[0208] Example 7: Data driven machine learning approaches predicted cognitive impairment using many components of the AROMHA Brain Health Test.

[0209] To control for age, sex, education, and comorbidities, we used both least absolute shrinkage and selection operator (LASSO) and Gradient Boosting Machine (GBM) approaches to fit models on a training set (n = 78) and then predicted cognitive impairment in a held-out test set (n = 19). FIGS. 5A-5C show that, using a regularized logistic regression model, the AROMHA Brain Health Test predicted memory symptoms in subjects aged 20 - 95 (AUC = 0.94; FIGS. 5A and 5B) and in subjects aged 55 - 95 (AUC = 0.78; FIG. 5C).

[0210] The GBM model iteratively constructed an ensemble of decision trees by fitting the error of the previous tree in the ensemble. The training process could be halted via an early stopping criterion to prevent overfitting to the training data. Hyperparameters of the GBM such as the total number of trees and the depth of each tree were tuned via exhaustive grid search. GBMs, which can fit arbitrarily complex functional forms to accommodate non-linear relationships and interactions, predicted preclinical Alzheimer’s disease in subjects aged 55 - 95 (AUC = 0.90). We then used a variable importance analysis to quantify the contribution of each input feature on the prediction of each test set example (FIG. 6). After age, 0PID18 no guess provided the strongest signal in the variable importance analysis.

[0211] These results indicated that the AROMHA Brain Health Test can predict cognitive impairment in preclinical Alzheimer’s disease (AD) and uses metrics not available by the University of Pennsylvania Smell Identification Test (UPSIT) or other conventional smell tests.

[0212] Example 8: AROMHA Brain Health Assessment (ABHA) predicted computed brain volumes.

[0213] Transactive Response DNA Binding Protein 43 kDa (TDP-43) pathology is frequently found in cases with Alzheimer’s disease (AD). TDP-43 pathology is associated with hippocampal and amygdala atrophy and greater AD severity. See, e.g., Meneses et al., Mol Neurodegener. 2021 Dec 20;16(l):84 and Huie et al., J Alzheimers Dis. 2023;91(4): 1291-1301. As the amygdala volume is the first place of TDP-43 pathology in Alzheimer’s patients, use of the smell test to predict loss of amygdala volume can provide an early marker of TDP-43 pathology and, as such, a predictor of patient responsiveness to an AD treatment specific for the subset of AD patients with TDP-43 pathology.

[0214] Of the participants that took the AROMHA Brain Health Test as described herein, 40 individuals (1) received an expertly ascertained clinical cognitive phenotype and (2) had MRI brain volume data available. In the present example, we present data on the n = 31 subjects that had a MRI scan date within + / - 3 years of the olfactory test.

[0215] FIG. 7A shows the association of the OPID18 No Guess metric (score, x-axis) with the left hippocampus volume (y-axis). The left hippocampus discriminated the cognitive phenotype closest to the scan. This corroborated the findings that hippocampal volume was a powerful biomarker to detect early cognitive decline. The data suggest that because the AROMHA brain health test (smell test) could accurately predict hippocampal volume, then the smell test could also play a role in detecting early cognitive decline. FIG. 7B shows the association of the OPID18 No Guess metric (score, x-axis) with the left amygdala volume (y-axis). The data show that the AROMHA brain health test (smell test) could also predict amygdala volume according to cognitive phenotype. In FIG. 7B, we noted a cleaner gradation in density estimations for the cognitive phenotypes where normal (healthy) cognition had the highest mean and coverage of the top of the left amygdala volume range, subjective cognitive concern (SCC, or “impaired, not MCI (ImpNoMCI)” as shown on the graph) had the second highest mean, and mild cognitive impairment (MCI) had the lowest.

[0216] The AROMHA OPID9 derived measure (the number out of 9 identification questions answered correctly when presented with forced choice options) was also found to be predictive for amygdala volume. Linear relationships between the 0PID9 variable and the right amygdala volume are shown in FIG. 7C and linear relationships between the 0PID9 variable and the left amygdala volume are shown in FIG. 7D. We noted stronger linear relationships between the 0PID9 variable and the right amygdala volume relative to the 0PID18 no guess variable. We also noted a uniformity of the slopes of the estimated linear fits for each of the cognitive phenotypes for both the right and left amygdala (FIGS. 7C-7D). Linear relationships between the 0PID9 variable and the right hippocampal volume are shown in FIG. 7E and linear relationships between the 0PID9 variable and the left hippocampal volume are shown in FIG. 7F.

[0217] Age was a confounder as it was associated with poorer olfactory function and cognitive decline. The age distribution by cognitive phenotype closest to the scan date (FIG. 7G) showed clear distributional differences by cognitive label. Therefore, any prediction model leveraging the AROMHA Brain Health Test battery would need to demonstrate the olfactory metrics of the test have predictive utility via a pathway other than correlation with age. The strategy was thus to fit regularized linear regressions with a brain volume as the outcome to be predicted as a function of olfactory metrics derived from the AROMHA brain health test. If an olfactory metric was selected in a model fitting procedure, it was defined to have predictive utility beyond a correlation with age alone.

[0218] The candidate predictor variables were 0PID9 no guess, 0PID9, ODIO, 0PID18.no. guess, OPID18.ID.score, total elapsed test time in minutes, OPID 18. recall, preexisting condition index, age, average confidence, and sex. The volumes to be predicted were left hippocampus volume, right hippocampus volume, left amygdala volume, right amygdala volume, left thalamus volume, right thalamus volume, left entorhinal thickness, right enthorhinal thickness, right medialorbitofrontal thickness, and right medialorbitofrontal thickness. Each volume model was a 3 -fold cross validated regularized linear regression fit using the glmnet library in R. (see, e.g., Friedman et al., 2010 Journal of Statistical Software, Articles 33 (1): 1-22; Simon et al., 2011 Journal of Statistical Software, Articles 39 (5): 1-13; and Tibshirani et al., 2012. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74 (2): 245-66). Three (3)-fold cross validation divided the available data into 3 mutually exclusive equally sized subsets. Two (2) of the 3 subsets were used to train a linear model and the data from the third subset was used to test the model performance. Each fold played the role of the test set once. Performance was then averaged across the three folds. The cross validation procedure was used to select the hyperparameter lambda, the amount of penalization when fitting coefficients in the linear model. Heavy penalization implied a sparse solution where coefficients shrink to 0 and variables drop out of the model. No penalization amounted to a standard linear regression with no shrinkage in coefficients. The optimal lambda was the value that minimized the mean squared error average across the 3 folds. This value of lambda then implied a regularized linear model that could be used for future predictions. If an olfactory variable had a nonzero coefficient after the cross validation procedure, it improved the ability to predict the volumes on average. Cross validation could be sensitive to the random allocation of data across the folds. Therefore, the process could be repeated via sampling without replacement to quantify the stability of the selection process. We simulated this 500 times and plotted the fraction of times out of the 500 that each candidate variable was selected (had a nonzero coefficient at the optimal lambda) for each volume outcome. FIG. 7H shows the results from 500 simulations of cross validation procedure demonstrating that age, left amygdala, and OPID9 were selected almost always and thus carried the largest signal. These data demonstrated that for the left amygdala, age and OPID9 were almost always selected in the regularization process (FIG. 7H).

[0219] An example of one specific model fit for left amygdala is shown in FIG. 71. In this particular regularized model as shown, only OPID9, age, and the model intercept were selected. Age had a negative coefficient, implying older individuals were predicted to have smaller left amygdala volumes. OPID9 had a positive coefficient, implying those that performed better on this AROMHA olfactory metric were predicted to have larger left amygdala volumes. For the left hippocampus, age and 0PID9 were also almost always selected. The OPID18 recall and preexisting index were also selected in roughly half of the simulations. The right amygdala process selected age and 0PID9 in most models. It also selected OPIDIO, OPID18 ID score, OPID18 recall, OPID9 No guess, sex, preexisting condition index, and total elapsed time in roughly half of all models.

[0220] Collectively, these models implied that the AROMHA brain health test had improved predictive performance as measured via cross validation averaged mean squared error relative to models using age and sex alone. For scenarios where it would be meaningful to screen based on neuroanatomical volumes, the AROMHA brain health test could generate accurate predictions of volumes via a cost effective and accessible option.

[0221] Example 9: Presence and immunogenicity of cdsRNA in AD with and without TDP-43 pathology.

[0222] To assess the presence of cdsRNA in neurons of patients with AD pathology, we stained formalin-fixed paraffin-embedded (FFPE) amygdala sections of human postmortem brains of healthy individuals (n = 9) and of patients who met the pathologic diagnosis of AD (n = 10) (FIG. 8A). We stratified the patients and controls based on the severity of pTDP-43 pathology and detected cdsRNA in all individuals with severe pTDP-43 pathology by immunostaining. In contrast, whenever pTDP-43 inclusions were absent, our immunostainings were negative for cdsRNA as well (FIGS. 12A-12B). In individuals with mild pTDP-43 pathology, we detected cdsRNA in 33% of cases. Next, we evaluated the spatial coincidence of cdsRNA and pTDP-43 by performing cyclic immunofluorescence (CyCIF)34which enabled multiplexed staining of pTDP-43 inclusions, cdsRNA, NFTs, A0 plaques, and nuclei on the same tissue section (FIG. 8B, lower panel of healthy control and AD, respectively). We compared the acquired images to luxol fast blue-hematoxylin and eosin (LHE) staining as well as immunohistochemical staining of cdsRNA, pTDP-43, tau, and A0 (FIG. 8B, upper panel of healthy control and AD, respectively). While NFTs and A0 plaques were detected in the tissue, neither pathology overlapped spatially with cytoplasmic pTDP-43 inclusions nor cdsRNA (FIG. 8B) However, pTDP-43 and cdsRNA were spatially coincident (enriched twofold in AD cases; FIG. 12C), paralleling our previous observations in ALS brains4. If this cdsRNA acted as a damage-associated molecular pattern (DAMP) in neurons, then it would trigger an IFN-I response through the activation of the PRRs RIG-I and MDA5 (FIG. 13A). To test whether cdsRNA was immunogenic in brains with AD pathology, we immunostained amygdala sections for phosphorylated protein kinase R (PKR), another PRR that binds to cdsRNA and activates autophosphorylation34(FIG. 13B). We observed phosphorylated PKR only in cdsRNA-positive regions of AD brains, indicating a potential direct link between cdsRNA and the activation of PRRs.

[0223] Furthermore, we wanted to see if we could replicate the observation of increased cdsRNA levels in a more controlled setting of TDP-43 pathology. For this purpose, we used the recently established human induced pluripotent stem cell (iPSC) line that harbored a pathogenic TDP-43 mutation (TDP-43+ / G298S)35and also showed increased interferon signaling36. An isogenic pair of these iPSCs was differentiated into NGN2 cortical-like neurons36and stained for cdsRNA (FIG. 8C). We observed a significant increase in dsRNA in cells with the familial TDP-43 mutation compared to control cells. As a positive control, we transfected the same control iPSC-derived neurons with poly(I:C), a dsRNA mimetic consisting of polyinosinic-polycytidylic acid, and saw a comparable increase in cdsRNA (FIG. 14). These observations identified shared characteristics of human AD-brain cells and two smaller, better controlled model systems for TDP-43 pathology.

[0224] Next, we conducted a differential gene expression analysis of RNA- sequencing data from five relevant brain regions available from the Religious Order Study / Memory and Aging Project (ROSMAP)37and Mount Sinai Brain Bank (MSBB)38AMP -AD databases to test for elevated interferon signaling. We found 139 unique interferon-stimulated genes (ISGs) were significantly upregulated and 51 ISGs were significantly downregulated (adjusted -value < 0.05) in AD patients compared to healthy controls. The parahippocampal gyrus (PHG), a brain region that shows atrophy early on in disease progression39,40, had the greatest number of upregulated ISGs, followed by the dorsolateral prefrontal cortex (DPFC) and the inferior frontal gyrus (IFG). In addition, we observed upregulation of ISGs, to a lesser extent, in the superior temporal gyrus (STG) and frontal pole (FP; FIGS. 8D and 15). Data demonstrated the presence of immunogenic cdsRNA-mediated IFN-I induction in AD-associated brain regions, although STING-based IFN-I induction may also be contributing to this signal41. Example 10: DRIAD-SP predicted efficacy of blocking interferon signaling.

[0225] To examine whether interferon signaling in brains with AD pathology was associated with pTDP-43 pathology, we extended our DRIAD-SP framework along two axes. A machine learning framework for Drug Repurposing In Alzheimer’s Disease with Systems Pharmacology (DRIAD-SP)26linked the prediction of disease stage (early- vs. late-stage AD based on the Braak AD staging system42) to drug- induced molecular signatures in human neural cells. Through this pipeline, we identified baricitinib, ruxolitinib, and tofacitinib, which were FDA-approved JAK inhibitors that inhibit interferon signaling, as drug candidates for repurposing in AD26(FIG. 16A)

[0226] We improved DRIAD-SP’ s predictive power by training and evaluating predictors of Braak disease stage in a linear manner using ordinal ridge regression (FIG. 9A), instead of the binary classification, as was done previously. This approach allowed for training on the full scale of stage I to stage VI along the Braak spectrum42, without the need to stratify patients into “early” and “late” categories. Second, we used the expression of CEs as a proxy for the presence of pTDP-43 inclusions (FIG. 9B). Previous work demonstrated that the pre-mRNAs of stathmin-2 (STMN2)20 22and UNCI 3 A18,19have binding sites for TDP-43 and that TDP-43 represses CE inclusion in the canonical mRNA of both STMN2 and UNC13A during splicing. pTDP-43 pathology was associated with nuclear hypofunction of TDP-43 which led to CEs being included in the processed mRNA - resulting in a short variant of STMN2 mRNA20 22(STMN2 short) due to a premature stop codon, and two versions of CE-inclusive UNC13 A mRNA, which we referred to as UNC13A-CE1 and UNC13A-CE218,19. Studies demonstrated the expression of CE-containing mRNA and de novo proteins also in AD27 32. Based on these findings, we posited the presence of these CEs in AD brains as a proxy for pTDP-43 pathology. We tested our computational approach by checking for the presence of CEs in RNA-sequencing data43from FACS-sorted single neuronal nuclei from the neocortex of deceased ALS patients as about 97% of ALS patients exhibit pTDP-43 inclusions44. We confirmed that neuronal nuclei with loss of nuclear TDP-43 into cytoplasmic aggregates have higher reads of the CEs STMN2 short (log2 fold change (LFC) = 3.55; P < 0.0001), UNC13A-CE1 (LFC = 4.20; P = 0.0002) and UNC13A-CE2 (LFC = 4.05; P = 0.0006) relative to control neuronal nuclei that contain TDP-43 (FIG. 16B). By contrast, the number of reads of canonical STMN2 were reduced to a smaller degree (LFC = -1.76; P = 0.0017) and not reduced for UNC13A (LFC = 0.278; P = 0.11) mRNAs in TDP-43 -deficient neuronal nuclei.

[0227] With this validation, we stratified patients’ RNA-sequencing data derived from ROSMAP and MSBB AMP -AD databases based on the presence of CEs. First, we set the threshold for the presence or absence of each individual CE above the lowest non-zero peak (essentially > 1 CE transcript detected) in the abundance histograms (transcripts per million TPM > 1.3 STMN2 short; TPM > 0.09 for UNC13A-CE1; TPM > 0.08 UNC13-CE2; FIG. 9C). We classified patients with two or more CE transcripts as TDP-43 positive (FIG. 9D). In the MSBB data, the counts for UNC13A-CE1 and UNC13A-CE2 were much lower relative to the ROSMAP data. We reasoned that the single-end sequencing protocol to generate the MSBB RNA-sequencing data was less sensitive for CE detection, relative to the paired-end reads in the ROSMAP data (FIG. 17). Consequently, we restricted our analysis to ROSMAP sequencing data from the posterior cingulate cortex (PCC), a brain region impaired early on in disease progression and therefore highly relevant for our study45. Of 546 total patients, no CEs were detectable in 85 patients, and one type of CE was detected in 257 patients; 183 patients had two types of CEs, and 21 patients had all three CEs present (FIG. 9D). Hence, 37% of cases were classified “TDP-43- positive”-AD patients, which is a conservative prediction considering that the clinical incidence of TDP-43 -pathology in AD patients can reach 57%14. To determine whether predicted TDP-43 positivity was associated with later Braak stages, we used an ordinal regression model and found no significant difference in Braak staging (P = 0.27) between “positive” and “negative” cases (FIG. 18A), indicating that CE production was orthogonal to the stage of tau progression.

[0228] We assessed the predicted efficacy of baricitinib, ruxolitinib and tofacitinib in AD patients with 2 or more CEs (as a proxy of TDP-43 dysfunction) relative to AD patients with one or no CEs (FIG. 9E, further replicates in FIG. 18B). Both baricitinib and ruxolitinib, but not tofacitinib, were significant hits in the predictor trained on RNA-sequencing data from AD patients with 2 or more CEs. On the other hand, none of the three drugs (baricitinib P = 0.76, ruxolitinib P = 0.34, tofacitinib P = 0.16) were significant hits in the predictor trained on no / low CE-expressing AD patients. These findings support that cdsRNA associated with TDP-43 dysfunction contributed to IFN-I-mediated neuroinflammatory processes occurring in AD patients’ brains.

[0229] Example 11: CRISPR screen and validation of TYK2.

[0230] To replicate cdsRNA-mediated inflammation and further determine that cdsRNA was not only immunogenic but also triggered neural cell death, we transfected differentiated ReN VM cells with poly(I:C). These differentiated human neural cells contained markers for neurons, astrocytes, and oligodendrocytes, but not microglia46and were not sensitive to other common stressors such as DNA-damaging agents and reactive oxygen species (FIGS. 19A-19J), perhaps because this cell line does not express STING46. In contrast to our previous work (“One Pot” Differentiation, FIG. 20A)4, we found that differentiating neural cells in a separate dish and then transferring differentiated cells into the assay plate reduced variability between technical replicates (“Separate Pot” Differentiation; FIG. 20B). Both baricitinib and ruxolitinib rescued human neural cell death in this new workflow with greater reproducibility and lower variance with nearly complete rescue at 1 pM and > 100% at 10 pM - presumably due to preventing toxicity from the transfection reagent lipofectamine 2000 present in the control. Both drugs significantly inhibited the phosphorylation of STAT1 and the downstream transcription of ISGs (FIGS. 21 A- 21D)

[0231] Since the ECso values of baricitinib (ECso = 199.6 nM) and ruxolitinib (ECso = 360.7 nM) were much greater than their affinities to their target proteins (baricitinib: IC5O(JAK1) = 5.9 nM, IC5o(JAK2) = 5.7 nM47; ruxolitinib: IC5O(JAK1) = 3.3 nM, ICso(JAK2) = 2.8 nM48), the inhibition of other kinases may be responsible for their neuroprotective effect. To test this, we conducted a genome-wide CRISPR screen to rescue cdsRNA-induced toxicity in differentiated human neural cells4,49. We used the Brunello library which consisted of four single guide RNAs (sgRNAs) per human gene and 1,000 control sgRNAs50(FIG. 10A) expressed in barcoded lentiviral particles to transduce ReN VM cells in their neural progenitor state. After a weeklong differentiation, one replicate was harvested to identify genes that were essential for ReN cell differentiation and were not present at the initiation of either poly(I:C) or lipofectamine. Cells carrying sgRNA targeting eight different genes were significantly depleted post-differentiation (DGAT2, SIGLECU, GSTA2, ABH, FBXL8, AHCY, C7orf55-LUC7L2, ARHGEF 12), indicating that these genes may be essential for neural cell survival and differentiation. These post-differentiation cells were treated for 48 hours with poly(I:C) or mock lipofectamine transfection, were harvested, genomic DNA was extracted, and the sgRNA sequences within the gDNA were PCR- amplified and sequenced to identify which sgRNA barcodes are enriched. Enriched sgRNAs in the post-poly(I:C) treatment arm versus the mock transfection arm correspond to candidate target genes whose loss-of-function enhances survival.

[0232] We found interferon receptor alpha and beta subunit 2 (IFNAR2,- average LFC = 2.23825, P = 0.0001), interferon regulatory factor 9 (IRF9,' average LFC = 2.0625, P = 0.0002), and TYK2 (average LFC = 1.95, P = 0.0004) were the best candidates for neural cell rescue upon knockout, whereas knockout of phospholipase C gamma 1 (PLCGP, average LFC = -1.9875, P = 0.0002), hepatocyte growth factor-regulated tyrosine kinase substrate (HGS; average LFC = -1.775, P = 0.0004), and SQSTM1 (p62, average LFC = -1.7475, P = 0.0005) markedly increased the toxicity induced by poly(I:C) (FIG. 10B).

[0233] We independently validated IFNAR2 and TYK2 using complementary chemical probes. To evaluate IFNAR2, we treated ReN VM cell-derived neural cells with an anti-IFNAR2 antibody before introducing poly(I:C) into the cells (FIG. 10C). The blocking antibody significantly rescued dsRNA-mediated neuronal death, but the response was not dose-dependent and plateaued at about 50% rescue (no antibody P < 0.0001; antibody 1 :50 P = 0.0001; 1 :25 P = 0.0002; 1 : 10 P = 0.0011). This was consistent with our observation that activating IFNAR2 with interferon-alpha was not sufficient to trigger human neural cell death. We treated cells with interferon-a at a range of doses. After 48 hours, there was no decrease in cell viability at any dose tested (FIG. 21E).

[0234] To evaluate TYK2, we used deucravacitinib, a potent small molecule inhibitor33that was highly specific for TYK2 over other JAK kinase family members (IC50 = 0.2 nM compared to IC5O(JAK1) = 1 nM and IC5o(JAK2) > 200 nM)51’52. We observed that exposure to deucravacitinib rescued dsRNA-toxicity up to 100% at all tested concentrations (FIG. 10D) with an EC50 < 10 nM. Deucravacitinib was also found to inhibit STAT1 phosphorylation (downstream protein of TYK2) completely. (FIG. 10E, all P < 0.0001). We validated these results in differentiated ReN CX cells, a cell line that showed the same characteristics as ReN VM cells but was derived from the cortex instead of the ventral mesencephalon and generated glutamatergic neurons rather than dopaminergic neurons, and in SH-SY5Y cells, a neuroblastoma cell line (FIGS. 21F-21I) In both cell types, full rescue of cdsRNA-mediated toxicity was achieved with a 1 pM dose of deucravacitinib. Higher concentrations of deucravacitinib seemed to be toxic to these cells.

[0235] To further evaluate TYK2 as a key signaling kinase for IFN-I signaling, we performed a deep proteomics analysis from ReN VM cell-derived neural cells that were treated with baricitinib and deucravacitinib at high doses (10 pM) by quantitative multiplex tandem mass tag (TMT) mass spectrometry (FIG. 10F). Here, we saw that all quantified ISGs were upregulated by poly(I:C), and this ISG induction was reversed by both baricitinib and deucravacitinib. Taken together, our results indicated that the toxic innate immune reaction to cdsRNA required the activation of IFNAR and was dependent on TYK2. Furthermore, the action of TYK2 appeared to be more complex than just mediating the IFN-I pathway alone. In sum, the CRISPR screen analysis provided herein implicated the selective inhibition of TYK2 as a target of rescuing cdsRNA-induced toxicity in cdsRNA / TDP-43 -positive neurodegenerative diseases (e.g., AD patients with TDP-43 pathology).

[0236] Example 12: Biomarkers for dsRNA pathology.

[0237] Specific biomarkers for neuroinflammatory-based neurodegenerative disease subtypes associated with dsRNA-induced pathology would help identify patients who could benefit from treatments modulating TYK2. We screened potential biomarkers in a genome-wide association study (GWAS) using proteomics data from the UK Biobank53and Icelandic DeCODE database54. Even though processed on different proteomics platforms (UK Biobank: Olink, DeCODE: SomaScan), we consistently found that individuals with an established partial loss-of-function polymorphism in TYK2 (rs34536443) in both cohorts have significantly lower plasma CXCL10 levels (UK Biobank: Effect: -0.12, SE = 0.02, P = 2.72 x 10’11; DeCODE: Effect: -0.10, SE = 0.02, P = 5.04 x 10'11) (FIG. HA). This minor allele coding mutation (Pl 104A) led to a near-complete (-80%) loss of TYK2 function in homozygotes, while heterozygotes have <40% reduction of function55 57.

[0238] We also examined potential biomarkers in cell-based assays by conducting Meso Scale Discovery (MSD) immunoassays on a selected array of ISG products. We quantified CXCL10, CCL2, and IL-6 in media taken from ReN VM cells transfected with poly(I:C) and treated with baricitinib, ruxolitinib, deucravacitinib, or vehicle (FIGS. 11B-11D) The dsRNAmi increased the level of these candidate biomarkers, and the drugs diminished their expression (P < 0.0001 in all conditions) to levels comparable to the lipofectamine-only control condition.

[0239] Next, to evaluate if this candidate biomarker can be seen in association with TDP-43 pathology, we modified our cell-based assay by treating differentiated ReN VM cells with 1 pM MG- 132, a proteasome inhibitor known to induce cytoplasmic aggregation of TDP-4361,62. We confirmed translocation of TDP-43 from the nucleus to the cytoplasm by immunofluorescent staining (FIG. 22) and quantified CXCL10 levels in the media by ECL immunoassay. CXCL10 levels were significantly elevated after MG-132 treatment (P < 0.005) and were reduced to control levels by 10 pM deucravacitinib (P < 0.005) but not by baricitinib and ruxolitinib treatment, indicating that selective TYK2 inhibition was most effective in reducing IFN-I mediated CXCL10 levels among the JAK kinases (FIG. HE).

[0240] Taken together, these results nominated CXCL10, CCL2, and IL-6 as candidate biomarkers for the assessment of dsRNA-mediated neuroinflammation using the CSF of patients with AD and could help determine whether these patients would benefit from therapeutic approaches reducing TYK2 activity (FIG. HF).

[0241] Example 13: The AROMHA olfactory battery was a strong discriminator of clinical cognitive phenotype relative to the biomarker, ptau217.

[0242] Of the participants that have taken the AROMHA brain health test as described herein, 51 individuals had received (1) a clinical cognitive phenotype ascertained by expert clinical investigators and affirmed in a consensus diagnostic conference at the Massachusetts Alzheimer’s Disease Research Center and (2) plasma biomarker quantification for phospho-tau217 (ptau217), a biomarker of interest for early detection of Alzheimer’s Disease pathology. Presented herein are data on the n = 27 individuals that had a biomarker draw within + / -3 years of the olfactory test date. This time interval was chosen as it was hypothesized that the rate of change of biomarkers would not vary substantially relative to the timing of the olfactory testing.

[0243] FIG. 23 demonstrates the association of the derived ‘OPID18 No Guess’ variable (defined as the number of olfactory identification questions answered correctly out of 18 possible questions without attestation of guessing amongst the forced choice options) with ptau217. FIG. 23 also displays the marginal density estimation of each respective variable along the axes colored by the expertly ascertained cognitive label closest in time to the biomarker draw. 24 out of 27 individuals had coincident biomarker draws with cognitive label ascertainment. The remaining 3 had a cognitive ascertainment within 0.6 years on either side of the biomarker draw. From these data, it was noted that the OPID18 No Guess variable was able to discriminate between the 3 cognitive phenotype classes better than ptau217.

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[0443] OTHER EMBODIMENTS

[0444] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A method of administering a smell test for assessment of risk of dementia in a subject comprising:(a) generating, by a computing device, a user interface to be displayed on the computing device;(b) instructing, by the computing device via the user interface, the subject to utilize an odor card configured to deliver an odor from among a plurality of odors;(c) instructing, by the computing device via the user interface, the subject to identify the delivered odor from a forced choice list of options provided by the computing device via the user interface;(d) asking, by the computing device via the user interface, the subject to evaluate their confidence in their odor identification decision;(e) receiving, by the computing device via the user interface, a subject input for a smell test;(f) calculating, by the computing device, a confidence metric for the smell test; and(g) identifying the subject as high risk for dementia based on the confidence metric.

2. A method comprising:(a) instructing a subject to utilize an odor card configured to deliver an odor from among a plurality of odors;(b) instructing the subject to identify the delivered odor from a forced choice list of options;(c) asking the subject to evaluate their confidence in their odor identification decision; and(d) calculating, optionally by a computing device, a confidence metric based on the subject’s confidence in their odor identification decision.

3. The method of claim 1 or 2, wherein the odor card is configured to deliver an odor from among a plurality of nine odors.

4. The method of claim 3, wherein the plurality of nine odors comprises at least at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, and grape.

5. The method of any one of claims 1-4, wherein the smell test further includes an odor episodic memory test.

6. The method of claim 1, further comprising administering an odor episodic memory test, wherein the test comprises:(h) instructing, by the computing device via the user interface, the subject to utilize a second odor card configured to deliver an odor from among a plurality of odors;(i) asking, by the computing device via the user interface, the subject if the odor delivered in the second odor card was delivered in the earlier used odor card (i.e., a first odor card);(j) instructing, by the computing device via the user interface, the subject to identify the delivered odor from a forced choice list of options provided by the computing device via the user interface;(k) asking, by the computing device via the user interface, the subject to evaluate their confidence in their odor identification decision;(l) receiving, by the computing device via the user interface, a subject input for a smell test;(m) calculating, by the computing device, a confidence metric for the smell test; and(n) identifying the subject as high risk for dementia based on the confidence metric.

7. The method of claim 6, wherein the odor card is configured to deliver an odor from among a plurality of eighteen odors.

8. The method of claim 7, wherein the plurality of eighteen odors comprises at least two, three, four, five or more of, or consists of all of, menthol, clove, leather,strawberry, lilac, pineapple, smoke, soap, grape, coffee, peach, chocolate, orange, dirt, banana, lemon, bubble gum, and rose.

9. The method of any one of claims 1-8, wherein the asking, by the computing device via the user interface, prompts the subject to evaluate their confidence in their odor identification decision by selecting one answer from the following “I Guessed,” “I Narrowed Down to Three,” “I Narrowed Down to Two,” or “I Am Certain.”10. The method of any one of claims 1-9, wherein the user interface is a website or a web-based application.

11. The method of any one of claims 1-10, wherein the computing device is a computer, a smart phone, or a mobile device.

12. The method of any one of claims 1-11, wherein the method is selfadministered by the subject.

13. The method of any one of claims 1-12, wherein the confidence metric for the smell test is calculated by the computing device as the total number of odors identified correctly by the subject where the subject did not evaluate their confidence in their odor identification decision by selecting “I Guessed” when prompted by the computing device via the user interface.

14. The method of any one of claims 1-13, further comprising comparing, by the computing device, the calculated confidence metric for the smell test relative to a predetermined threshold, wherein the predetermined threshold is determined by a machine learning algorithm based on a plurality of previously entered smell test performance data from healthy subjects.

15. The method of any one of claims 1-14, wherein the subject is identified as high risk for dementia when the calculated confidence metric for the smell test is lower than the predetermined threshold.

16. A method comprising:(a) collecting a biological sample from a subject, preferably a biological sample comprising whole blood, serum, or plasma;(b) quantifying a level of at least one biomarker for dementia in the biological sample, wherein the at least one biomarker for dementia is selected from the group consisting of C-X-C motif chemokine ligand 10 (CXCL10), C-C motif chemokine ligand 2 (CCL2), and interleukin 6 (IL-6); and(c) comparing the level of the least one biomarker for dementia to a predetermined threshold, wherein the predetermined threshold is determined from levels of the biomarker assessed from biological samples collected from a plurality of healthy subjects.

17. The method of claim 16, further comprising identifying the subject as at high risk for dementia when the level of at least one biomarker for dementia is higher than the predetermined threshold.

18. The method of claim 16, further comprising identifying the subject as at high risk for dementia when the level of at least two biomarkers for dementia are higher than the predetermined thresholds.

19. The method of claim 16, further comprising identifying the subject as at high risk for dementia when the level of CXCL10, CCL2, IL-6, or any combination thereof is higher than the predetermined threshold.

20. A method of assessing the risk of dementia in a subject comprising administering the smell test according to the method of any one of claims 1-15 in combination with the method of any one of claims 16-19.

21. A method of treating a subject for dementia, wherein the subject is at high risk for dementia as determined by the smell test according to the method of any one of claims 1-15, the method of any one of claims 16-19, or a combination thereof.

22. The method of claim 21, wherein the treatment comprises administering to thesubject at least one treatment for Alzheimer’s disease, optionally wherein the treatment is selected from the group consisting of benzgalantamine, donepezil, galantamine, rivastigmine, memantine, lecanemab, donanemab, suvorexant, and brexpiprazole.

23. The method of claim 21, wherein the treatment comprises administering to the subject at least one therapeutic agent targeting TYK2.

24. The method of claim 23, wherein the at least one therapeutic agent targeting TYK2 is a TYK2 inhibitor, optionally wherein the TYK2 inhibitor is deucravacitinib.

25. A method of administering a smell test for assessment of neuroanatomical volume in at least one brain region in a subject comprising:(a) administering the smell test according to the methods of any one of claims 1-15; and(b) identifying a decline in neuroanatomical volume in at least one brain region if the calculated confidence metric for the smell test is lower than the predetermined threshold.

26. A method of administering a smell test for assessment of neuroanatomical volume in at least one brain region in a subject comprising:(a) generating, by a computing device, a user interface to be displayed on the computing device;(b) instructing, by the computing device via the user interface, the subject to utilize an odor card configured to deliver an odor from among a plurality of odors;(c) instructing, by the computing device via the user interface, the subject to identify the delivered odor from a forced choice list of options provided by the computing device via the user interface;(d) asking, by the computing device via the user interface, the subject to evaluate their confidence in their odor identification decision;(e) receiving, by the computing device via the user interface, a subject input for a smell test;(f) calculating, by the computing device, a score for the smell test; and(g) identifying a decline in neuroanatomical volume in at least one brain region in the subject based on the score for the smell test.

27. The method of claim 26, wherein the score for the smell test is calculated as a total number of odors correctly identified by the subject.

28. The method of claim 26 or 27, wherein the odor card is configured to deliver an odor from:(i) among a plurality of nine odors, optionally wherein the plurality of nine odors comprises at least at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, and grape; and / or,(ii) from among a plurality of eighteen odors, optionally wherein the plurality of eighteen odors comprises at least two, three, four, five or more of, or consists of all of, menthol, clove, leather, strawberry, lilac, pineapple, smoke, soap, grape, coffee, peach, chocolate, orange, dirt, banana, lemon, bubble gum, and rose.

29. The method of any one of claims 26-28, wherein the user interface is a website or a web-based application.

30. The method of any one of claims 26-29, wherein the computing device is a computer, a smart phone, or a mobile device.

31. The method of any one of claims 26-30, wherein the method is selfadministered by the subject.

32. The method of any one of claims 26-31, further comprising comparing, by the computing device, the calculated score for the smell test relative to a predetermined threshold, wherein the predetermined threshold is determined from smell test performance data collected from a plurality of healthy subjects.

33. The method of any one of claims 25-32, wherein the at least one brain region in the subject comprises hippocampus, amygdala, and / or both.

34. The method of claim 33, wherein the at least one brain region in the subject comprises the left hippocampus, the right hippocampus, the left amygdala, the right amygdala, or any combination thereof.

35. The method of any one of claims 25-34, wherein the identification of decline in neuroanatomical volume in at least one brain region in the subject correlates to cognitive decline in the subject.

36. The method of any one of claims 1-35, wherein the subject has, is suspected of having, or is at risk of having a neurodegenerative disease associated with TDP-43 pathology.

37. The method of any one of claims 1-36, wherein the subject has, is suspected of having, or is at risk of having Alzheimer’s disease.

38. The method of claim 37, wherein the subject at risk of having Alzheimer’s disease has a loss-of-function SNP in TYK2 (rs34536443).

39. A method for selecting a subject for treatment with a TYK2 inhibitor, the method comprising:(a) administering the smell test according to the methods of any one of claims 1-15 and 25-39; and(b) selecting the subject for treatment with a TYK2 inhibitor if the calculated confidence metric for the smell test is lower than the predetermined threshold.

40. The method of claim 39, optionally further comprising quantifying a level of at least one biomarker for dementia in a biological sample collected from the subject according to the method of any one of claims 16-19, and selecting the subject for treatment with a TYK2 inhibitor if the level of at least one biomarker for dementia is higher than the predetermined threshold.

41. The method of claim 39 or 40, further comprising (c) administering the TYK2 inhibitor to the subject if the subject is selected for treatment.

42. The method of any one of claims 39-41, wherein the TYK2 inhibitor is deucravacitinib.

43. The method of any one of claims 39-42, wherein the subject has, is suspected of having, or is at risk of having a neurodegenerative disease associated with TDP-43 pathology.

44. The method of claim 43, wherein the neurodegenerative disease associated with TDP-43 pathology comprises a subset of Alzheimer’s disease.

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