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WO2025221811A1 - Méthodes d'évaluation de la maladie d'alzheimer au stade préclinique - Google Patents

Méthodes d'évaluation de la maladie d'alzheimer au stade préclinique

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Publication number
WO2025221811A1
WO2025221811A1 PCT/US2025/024800 US2025024800W WO2025221811A1 WO 2025221811 A1 WO2025221811 A1 WO 2025221811A1 US 2025024800 W US2025024800 W US 2025024800W WO 2025221811 A1 WO2025221811 A1 WO 2025221811A1
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WIPO (PCT)
Prior art keywords
subject
odor
test
computing device
dementia
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PCT/US2025/024800
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English (en)
Inventor
Mark W. Albers
Colin MAGDAMO
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General Hospital Corp
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General Hospital Corp
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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • 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).
  • dementia e.g., dementia associated with Alzheimer’s disease, dementia associated with a subset of Alzheimer’s disease patients with TDP- 43 pathology.
  • AD Alzheimer’s disease
  • pTDP-43 phosphorylated TAR DNA-binding protein 43
  • the present disclosure provides a digital remote AROMHA Brain Health Test (ABHT), an at-home odor identification, discrimination, memory, and intensity assessment.
  • ABHT digital remote AROMHA Brain Health Test
  • 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.
  • 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) 26 was updated herein to include cryptic exon (CE) expression, which is a proxy for TDP-43 pathology 27 32 .
  • CE cryptic exon
  • deucravacitinib - a selective TYK2 inhibitor recently approved for moderate-to-severe plaque psoriasis 33 - 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.
  • SNP single nucleotide polymorphism
  • 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.
  • 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.
  • 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.
  • a smell test for assessment of risk of dementia in a subject further includes an odor episodic memory test.
  • 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
  • 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.
  • 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.
  • 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.”
  • the user interface is a website or a web-based application.
  • the computing device is a computer, a smart phone, or a mobile device.
  • a method of administering a smell test for assessment of risk of dementia in a subject is self-administered by the subject.
  • 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.
  • 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.
  • the subject is identified as high risk for dementia when the calculated confidence metric for the smell test is lower than the predetermined threshold.
  • 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.
  • CXCL10 C-X-C motif chemokine ligand 10
  • CCL2 C-C motif chemokine ligand 2
  • IL-6 interleukin 6
  • 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.
  • 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.
  • a biomarker for dementia e.g., CXCL10, CCL2, IL- 6
  • 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.
  • the smell test administered according to any of the methods disclosed herein quantifying a level of at least one biomarker for dementia (e.g., CXCL10, CCL2, IL-6) in a biological sample collected from the subject.
  • 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.
  • a method of treating a subject for dementia comprises administering to the subject at least one therapeutic agent targeting TYK2.
  • the at least one therapeutic agent targeting TYK2 is a TYK2 inhibitor, optionally wherein the TYK2 inhibitor is deucravacitinib.
  • 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.
  • 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.
  • a score for the smell test is calculated as a total number of odors correctly identified by the subject.
  • 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, and grape; and/or, (ii) from among a plurality of eighteen odors, optionally wherein the plurality of
  • the user interface is a website or a webbased application.
  • the computing device is a computer, a smart phone, or a mobile device.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the TYK2 inhibitor is deucravacitinib.
  • the subject has, is suspected of having, or is at risk of having a neurodegenerative disease.
  • the subject has, is suspected of having, or is at risk of having a neurodegenerative disease associated with TDP-43 pathology.
  • the subject has, is suspected of having, or is at risk of having Alzheimer’s disease.
  • the subject at risk of having Alzheimer’s disease has a loss-of-function SNP in TYK2 (rs34536443).
  • the neurodegenerative disease associated with TDP-43 pathology comprises a subset of Alzheimer’s disease.
  • 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.
  • FIGS. 1A and IB show a schematic of the AROMHA Brain Health Test.
  • 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.
  • FIG. IB is the workflow for these tests as directed by the testyourbrainhealth.com software to generate the 0PID9, OPID9noguess, and average intensity scores.
  • subject participants were instructed to work through Cards B and C using the workflow depicted in the middle box (FIG.
  • 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.
  • 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.
  • 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. 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.
  • FIG. 7D 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.
  • 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.
  • MCI mild cognitive impairment
  • ImpNoMCI asymptomatic, preclinical cognitive impairment
  • 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.
  • 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 ROSMAP 37 and MSBB 38 databases 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.
  • 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-validation 90 was used to evaluate model performance (areas under the curve; AUC).
  • 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 ROSMAP 37 patient 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.
  • 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 library 50 to 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. 10A depicts a schematic of the CRISPR screen conducted in ReN VM cells using the Brunello library 50 to 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. 10A depicts a schematic of the CRISPR screen conducted in ReN VM cells using the Brunello library 50 to identify potential therapeutic targets for rescuing the toxic immune response triggered by cdsRNA. Replicates were harvested pre-
  • FIG. 10E shows a representative image (top) and quantification (bottom) of Western blot of pSTATl Y701 in 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).
  • 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) 53 and Icelandic DeCODE database (SomaScan proteomics platform) 54 using genome-wide association tests.
  • CXCL10 FIG. 11B
  • CCL2 FIG. 11C
  • IL6 FIGS. 11A-11F
  • 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.
  • FIG. HF depicts a schematic overview of hypothesized pathomechanisms underlying cdsRNA in neurodegenerative diseases. The error bars represent the standard error of the mean.
  • FIGS. 12A-12C depict p TDP-43 -severity and dsRNA presence in human postmortem brain sections.
  • 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.
  • 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. 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
  • FIG. 14 shows a positive control for cdsRNA staining in iPSC.
  • 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).
  • FIG. 15 shows upregulated ISGs within brain regions that were relevant in
  • AD Heat map of RNA-sequencing data derived from the ROSMAP 37 and MSBB 38 databases 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.
  • 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 protocol 26 .
  • 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.
  • 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.
  • TPM transcripts per million
  • FIG. 17 depicts a comparison of CE transcript abundance between ROSMAP 37 and MSBB 38 datasets. 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.
  • 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.
  • FIGS. 19A-19J show the testing of common stressors on differentiated ReN VM cells.
  • 10 pM etoposide which causes DNA damage
  • 10 pM menadione which induces reactive oxygen species
  • FIGS. 20A and 20B depict a schematic overview comparing a previously employed drug screening assay workflow 4 and 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.
  • 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.
  • 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.
  • 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 symptoms 2 5 .
  • neuropsychological testing is often normal - a stage termed subjective cognitive decline or subjective cognitive complaints (SCC) 6 .
  • SCC subjective cognitive decline
  • MCI mild cognitive impairment
  • a stage preceding dementia where activities of daily living are not impaired yet by cognitive deficits.
  • the measurement of early olfactory impairment is a prime candidate as a component of an early detection assessment 13 .
  • Many brain regions process olfactory input from primary olfactory neurons 14 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 pathology 17 .
  • the amygdala and piriform cortices are also early sites of tau pathology 17,18 .
  • 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.
  • olfactory epithelium shows evidence of amyloid and tau deposition 19 .
  • 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 adults 20 23 .
  • primary olfactory cortex i.e., piriform cortex, amygdala, and entorhinal cortex
  • 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’s 14,52 and Traumatic Brain Injury 53 .
  • odor memory and olfactory discrimination tasks have been associated with earlier preclinical stages of the disease 43,48,54 , and selective odor memory deficits, after correction for odor identification and odor discrimination performance, have been associated with AD biomarkers 43 .
  • 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.
  • POEM percept of odor episodic memory
  • OD odor discrimination
  • 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 dementia 66,67 .
  • SCC subjective cognitive concerns
  • MCI Mild Cognitive Impairment
  • Odor percept identification OPID9 and OPID18noguess and OPID18 scores
  • POEM odor memory
  • ODIO odor discrimination
  • 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.
  • olfactory identification tasks are associated with declarative memory 38,73 and are predictive of cognitive decline in CN older adults 44 49 and of the conversion to MCI 42,50,51 .
  • olfactory discrimination 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 MCI 54 and predictive value of olfactory discrimination in further cognitive decline 48 .
  • both identification and discrimination of odors require high-level cognitive functions such as working memory and decision making, which could explain our results 54 .
  • the hippocampal network is also associated with olfactory discrimination 74 and, without wishing to be bound to any particular theory, early damages to this structure in AD could explain these findings.
  • 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.
  • 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 tasks 83 85 .
  • MCI and AD predominantly affect olfactory identification 52,70
  • 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 dementia 87 , frontotemporal dementia 88 , dementia associated with TDP- 43 pathology, and exposure to head impacts and traumatic brain injury 89 91 can also cause olfactory impairments.
  • asymptomatic or newly symptomatic individuals who would benefit from more definitive subsequent tests 92,93 , 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 decline 66 , depression 94 , and genetic risks factors such as the APOE-4 allele 95 .
  • CSF cerebrospinal fluid
  • 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 settings 96 , save time and transportation costs, reduce healthcare system expenses, and increase patient satisfaction 97,98 .
  • 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.
  • TYK2 tyrosine kinase 2
  • a smell test disclosed herein can comprise utilization of at least one odor card and/or a smell test guide (“test guide”).
  • 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.
  • the user interface can include graphical, textual, scanned, and/or auditory information.
  • 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.
  • the smell test disclosed herein can include a computing device.
  • 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.
  • the odor cards disclosed herein can be a printed (e.g., on paper, cardboard) smell test card.
  • an odor card disclosed herein can be suitable for delivery to individuals by mail (e.g., shipped in envelopes).
  • an odor card disclosed herein can be disposable after completion of the smell test.
  • 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.
  • 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.
  • a smell test as described herein can include at least five odor cards.
  • a smell test as described herein can include at least five odor cards wherein at least five odor cards differ from each other.
  • An odor card disclosed herein can be configured to deliver an odor from among a plurality odors.
  • 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.
  • an odor card disclosed herein can be configured to deliver an odor from among a plurality of nine odors.
  • “about” means plus or minus 10%.
  • an odor card disclosed herein can be configured to deliver an odor from among a plurality of eighteen odors.
  • 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.
  • 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.
  • 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.
  • Odor cards disclosed herein can be configured to utilize a wide variety of odors for the administration of the smell test.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • odor cards disclosed herein are configured to encapsulate each individual odor independently in order to prevent crosscontamination of the scents.
  • an individual odor on the odor card can be encapsulated by a removable cover.
  • 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 ’).
  • 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.
  • 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.
  • the odors on the odor card can be individually labeled or stratified into section/rows.
  • 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.
  • the odors can be stratified into sections/rows that may correlate to specific smell tests.
  • 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.
  • An odor card disclosed herein can have an identification mark printed on the card.
  • 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.
  • the identification mark can identify the subject and/or correlate the subject with the unique identification mark.
  • 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.
  • data and results from the smell tests can be associated with demographics and medical records for the subject.
  • 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.
  • a smell test disclosed herein can comprise a smell test guide (“test guide”).
  • 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.
  • 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).
  • 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.
  • 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 computing device e.g., a personal computing device
  • FIGS. 4A-4D 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.
  • a test guide realized in software can include at least one computer program, or use of the same.
  • a test guide realized in software can include at least one database, or use of the same.
  • 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.
  • 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).
  • a computer program comprises one sequence of instructions.
  • a computer program comprises a plurality of sequences of instructions.
  • a computer program is provided from one location. In other embodiments, a computer program is provided from a plurality of locations.
  • 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.
  • software modules disclosed herein can be implemented in a multitude of ways.
  • a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof.
  • 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.
  • 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.
  • 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.
  • 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.
  • a computer program for use in the present disclosure can include a web application.
  • a web application in various embodiments, utilizes one or more software frameworks and one or more database systems.
  • 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.
  • a web application for use herein is written in one or more versions of one or more languages.
  • a computer program disclosed herein can include a mobile application provided to a mobile digital processing device.
  • the mobile application is provided to a mobile digital processing device at the time it is manufactured.
  • the mobile application is provided to a mobile digital processing device via the computer network described herein.
  • 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.
  • a database is internet-based.
  • a database is webbased.
  • a database is cloud computing-based.
  • a database is based on one or more local storage devices.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • biomarker data e.g., concentrations of CXCL10, CCL2, and/or IL-6
  • 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).
  • 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.
  • the level of least one biomarker e.g., CXCL10, CCL2, and/or IL-6
  • 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.
  • 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.
  • 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.
  • 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.
  • OIT odor intensity test
  • OID at least one odor percept identification test
  • POEM percepts of odor episodic memory test
  • OD odor discrimination test
  • 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.
  • a subject can self-administer a smell test.
  • a subject can self-administer a smell test under the instruction provided by the user interface generated by a computing device as disclosed 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.
  • 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.
  • 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.
  • 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.
  • the subject 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • a smell test for use herein can comprise more than one odor identification tests.
  • a smell test disclosed herein can comprise two odor identification tests.
  • a smell test disclosed herein can comprise two odor identification tests wherein the odor identification tests are performed subsequently.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • subject response can be used to calculate a POEM index.
  • a POEM index can be calculated as the difference between the proportion of correct and incorrect recognitions, with scores ranging from -1 to 1.
  • 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.
  • the subject is instructed to smell an odor on the odor card for a fixed period, using techniques analogous to those previously disclosed above.
  • 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.
  • 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.
  • kits for use in performing any of the methods for administering a smell test as disclosed herein.
  • a kit can comprise at least one odor card and at least one test guide.
  • a kit can further include an instructional card and/or a card with information to direct a subject to a website comprising instructions.
  • a kit can further include one or more materials needed for collecting, transporting, and preserving a biological sample.
  • 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.
  • a neurodegenerative disease e.g., Alzheimer’s disease.
  • Subject and “patient” refer to either a human or non -human, such as mammals, e.g., vertebrates, e.g., primates.
  • the subject is a human or a non-human veterinary subject such as a non-human primate, cat, dog, horse, cow, goat, or rabbit.
  • the subject is an adult human.
  • the subject is an adult human that is at least about or about 55 years of age.
  • the subject is an adult human that is at least about or about 55-100, 55-95, or 55-90 years of age.
  • 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.
  • 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.
  • a subject in need thereof has a loss-of-function SNP in TYK2 (rs34536443).
  • a subject in need thereof has as subset of Alzheimer’s disease associated with a TDP-43.
  • 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.
  • methods provided herein also comprise administering a smell test for assessment of risk of AD in a subject.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a subject is instructed to repeat the method above for each of the odors provided on the odor card.
  • a subject performs the method above for each of nine odors provided on the odor card.
  • a subject performs the method above for each of eighteen odors provided on the odor card.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the calculated confidence metric for the smell test can be compared to a predetermined threshold.
  • the calculated confidence metric for the smell test can be compared to a predetermined threshold by a computing device.
  • 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).
  • a subject 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.
  • 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 (cm 3 ) 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.
  • SCD subjective cognitive decline
  • MCI mild cognitive impairment
  • AD dementia in a subject.
  • a smell test can be administered to identify a decline in neuroanatomical volume in at least one brain region in a subject.
  • 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.
  • 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.
  • the predetermined threshold is determined from smell test performance data collected from a plurality of healthy subjects (i.e., cognitively normal subjects).
  • the healthy subjects i.e., cognitively normal subjects
  • 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.
  • 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.
  • the at least one brain region in the subject can be the hippocampus, the amygdala, and/or both.
  • 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.
  • the identification of a decline in neuroanatomical volume in at least one brain region in the subject correlates to cognitive decline in the subject.
  • the identification of a decline in neuroanatomical volume in at least one brain region in the subject correlates to dementia in the subject.
  • the identification of a decline in neuroanatomical volume in at least one brain region in the subject correlates to AD in the subject.
  • 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).
  • 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).
  • MCI mild cognitive impairment
  • 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).
  • 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).
  • ADAS-cog e.g., ADAS-Cog 3, ADAS-Cog 5, the original ADAS-Cog 11, and ADAS-Cog 13
  • MMSE Mini Mental State Examination
  • 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).
  • 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).
  • a biological sample for use herein can comprise whole blood, serum, plasma, and/or cerebrospinal fluid (CSF).
  • CSF cerebrospinal fluid
  • 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.
  • a biological sample e.g., whole blood, serum, or plasma
  • 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).
  • immunoassay e.g., sandwich immunoassay (V-PLEX), ELISA, flow cytometry.
  • 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.
  • 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.
  • the level of least one biomarker for dementia e.g., CXCL10, CCL2, and/or IL-6
  • 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.
  • 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) 1,2 and 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 death 4 .
  • IFN-I type-I interferon
  • 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.
  • DRIAD-SP Drug Repurposing In Alzheimer’s Disease with Systems Pharmacology
  • 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.
  • SNP single nucleotide polymorphism
  • 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).
  • 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.
  • 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.
  • the method comprises administering a smell test according to the methods disclosed herein.
  • 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.
  • 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.
  • 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.
  • a subject can be selected for treatment with a TYK2 inhibitor when the level of CXCL10 is higher than the predetermined threshold for CXCL10.
  • 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.
  • 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.
  • Drug repurposing accelerates testing therapeutic hypotheses expeditiously in clinical trials, and FDA approved drugs can serve as chemical biology probes to elucidate novel targets.
  • 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.
  • TYK2 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 events 77 , which have not been reported in people with TYK2 polymorphisms 78,79 or those treated with a potent TYK2 inhibitor 80 .
  • 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.
  • 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.
  • a subject selected for treatment with a TYK2 inhibitor can be administered a therapeutically effective amount of at least one TYK2 inhibitor.
  • a subject selected for treatment with a TYK2 inhibitor can be administered a therapeutically effective amount of a selective TYK2 inhibitor.
  • a subject selected for treatment with a TYK2 inhibitor can be administered a therapeutically effective amount of baricitinib, ruxolitinib, deucravacitinib, or any combination thereof.
  • 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):
  • 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.
  • 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.
  • a biomarker for dementia e.g., CXCL10, CCL2, and/or IL-6
  • 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.
  • the method of treatment comprises administering to the subject at least one treatment for Alzheimer’s disease.
  • the method of treatment comprises administering to the subject benzgalantamine, donepezil, galantamine, rivastigmine, memantine, lecanemab, donanemab, suvorexant, brexpiprazole, or any combination thereof.
  • 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.
  • the method of treatment comprises administering to the subject a medical device.
  • 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.
  • 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.
  • 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.
  • the target disease e.g., dementia/dementia associated with Alzheimer’s disease
  • 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.
  • 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.
  • 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.
  • 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).
  • 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 Alzheimer’s Disease Assessment Scale-Cognitive Subscale
  • 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.
  • the ADAS-cog score decreases in a subject treated in accordance with a method provided herein compared to baseline, indicating an improvement of symptoms.
  • the ADAS-cog score remains unchanged in a subject treated in accordance with a method provided herein compared to baseline.
  • 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.
  • 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).
  • a method of treating a subject for dementia as disclosed herein can prevent progression of a TDP-43 pathology.
  • 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.
  • 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 [43], 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.
  • Odor labels were manufactured by MFR Samplings using Living LibraryTM odors purchased from International Flavors and Fragrances (IFF) [62], 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).
  • 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.
  • the web-based 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.
  • 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.
  • 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) [39], After each odor presentation, participants rated the intensity on a Likert scale from 0 to 10.
  • AD Alzheimer’s disease
  • MCI mild cognitive impairment
  • Part 2 Percepts of Odor Episodic Memory (POEM) / OPED 18.
  • POEM Odor Episodic Memory
  • OPED 18 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.
  • Part 3 Odor Discrimination (ODIO).
  • ODIO Odor Discrimination
  • 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.
  • CN age-matched control subjects
  • SCC Subjective Cognitive Concerns
  • MCI Mild Cognitive Impairment
  • NACC National Alzheimer’s Coordinating Center
  • CDR Clinical Dementia Rating
  • NACC National Alzheimer’s Coordinating Center
  • UDS Ultra Data Set
  • 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 MCI participants were aged 55+ and reported a clinical diagnosis of MCI by a certified physician.
  • NIA-AA National Institute on Aging- Alzheimer’s Association
  • Table 1 Clinical values and olfactory function across cognitive status in verified older adult participants.
  • 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.
  • the alpha value was set at 0.05 and Bonferroni correction was used for multiple comparisons.
  • Differential Gene Expression was conducted based on a previously established protocol 91 .
  • clinical diagnosis was used to define AD versus control conditions within the bulk RNA-sequencing data taken from the ROSMAP 37 and MSBB 38 databases (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.
  • iPSC Induced Pluripotent Stem Cell
  • 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 described 4,94 , with the difference that the differentiation took place in a separate dish (FIGS. 20A- 20B) to minimize variability downstream.
  • 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.
  • EMEM Eagle’s Minimum Essential Medium
  • F12 medium cat. # 11765-047, Thermo Fisher
  • FBS fetal bovine serum
  • 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.
  • DMSO dimethyl sulfoxide
  • 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.
  • lipofectamine 2000 (VM, CX) or lipofectamine 2000 + 10 pM drug (SH-SY5Y) was used as the respective control.
  • VM, CX lipofectamine 2000
  • SH-SY5Y lipofectamine 2000 + 10 pM drug
  • 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.
  • 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 Fiji 95 and plotted using GraphPad Prism. Four biological repeats were taken for each group.
  • 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.
  • blocking solution cat. # 927-60001, LLCOR
  • Transcripts were quantified using Salmon vl.9.0 96 against 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-43 18 .
  • 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).
  • TDP-43 pathology associated transcripts 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 .
  • 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 curve 98 .
  • ReNcell VM cells stably expressing Cas9 were expanded and transduced with lentiviral particles containing the Brunello library 50 .
  • 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.
  • dsRNA in complex with lipof ectamine (concentration according to manufacturer’s protocol) or lipofectamine alone as a control were added to each flask.
  • genomic DNA was isolated from each group of cells. PCR and sequencing were performed as previously described 99,100 . 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.
  • lysis buffer 2% SDS, 150 mM NaCl, 50 mM Tris pH 8.5
  • protease and phosphatase inhibitor cat. # 11873580001 and 4906845001, Millipore
  • 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.
  • DTT dithiothreitol
  • a MultiNotch SPS-MS 3 TMT method 102 was 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 C 18 resin (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 MS 1 spectrum (Orbitrap analysis; resolution 120,00; mass range 400-1400 Th) was taken.
  • 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 MS 3 TMT 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).
  • 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).
  • 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.
  • the blot was imaged with the Odyssey® DLx Imager (LI-COR). Images were analyzed using ImageStudioLite (LI-COR).
  • Association models included the following covariates: age, age 2 , sex, age x sex, age 2 x 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 DeCODE 54 . 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-LMM 105 .
  • MSD Meso Scale Diagnostics
  • ECL electrochemiluminescence
  • Example 1 Design and implementation of the AROMHA brain health test.
  • 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 [43] or through hand-held, repeat use devices (Whispis) [64],
  • the ABHT leveraged the remote administration aspects of the CO VID Smell Test [62] 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 [65] to collect responses.
  • 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).
  • 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.
  • 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.
  • 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).
  • Example 2 Validation of unobserved remote testing of the AROMHA brain health test in cognitively normal individuals.
  • Table 4 Distribution of Participants by Administration Modality.
  • 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.
  • Example 3 Validation of the AROMHA brain health test in anosmic patients.
  • Table 6 displays the demographics and olfactory scores of 7 patients with anosmia recruited from a smell loss clinic and CN participants.
  • 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.
  • Table 6 Distribution of olfactory scores of anosmic patients and CN participants on the AROMHA brain health test.
  • 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.
  • Example 4 Equivalence of the AROMHA brain health test in English vs. Spanish-speaking cognitively normal participants.
  • Table 7 Demographic information and olfactory function in cognitively normal English-speaking and Spanish-speaking participants.
  • Example 5 Diminished olfactory measured in the AROMHA brain health test with increasing age.
  • 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).
  • CN cognitively normal
  • SCC subjective cognitive concerns
  • MCI mild cognitive impairment
  • Table 8 Olfactory function across cognitive status among participants aged 55+.
  • ANCOVAs 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.
  • Example 7 Data driven machine learning approaches predicted cognitive impairment using many components of the AROMHA Brain Health Test.
  • 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.
  • AUC 0.90
  • 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.
  • Example 8 AROMHA Brain Health Assessment (ABHA) predicted computed brain volumes.
  • TDP-43 pathology is frequently found in cases with Alzheimer’s disease (AD).
  • AD Alzheimer’s disease
  • 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.
  • 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.
  • 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).
  • SCC subjective cognitive concern
  • MCI mild cognitive impairment
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • FIG. 71 An example of one specific model fit for left amygdala is shown in FIG. 71.
  • 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.
  • 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.
  • Example 9 Presence and immunogenicity of cdsRNA in AD with and without TDP-43 pathology.
  • cdsRNA was immunogenic in brains with AD pathology
  • PPKR phosphorylated protein kinase R
  • 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.
  • TDP-43 +/G298S human induced pluripotent stem cell
  • RNA- sequencing data from five relevant brain regions available from the Religious Order Study /Memory and Aging Project (ROSMAP) 37 and Mount Sinai Brain Bank (MSBB) 38 AMP -AD databases to test for elevated interferon signaling.
  • ROSMAP Religious Order Study /Memory and Aging Project
  • MSBB Mount Sinai Brain Bank
  • Example 10 DRIAD-SP predicted efficacy of blocking interferon signaling.
  • DRIAD-SP machine learning framework for Drug Repurposing In Alzheimer’s Disease with Systems Pharmacology
  • RNA-sequencing data derived from ROSMAP and MSBB AMP -AD databases based on the presence of CEs.
  • 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).
  • the counts for UNC13A-CE1 and UNC13A-CE2 were much lower relative to the ROSMAP data.
  • Example 11 CRISPR screen and validation of TYK2.
  • PLCGP phospholipase C gamma 1
  • HGS hepatocyte growth factor-regulated tyrosine kinase substrate
  • 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)
  • a neuroblastoma cell line 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.
  • TYK2 as a key signaling kinase for IFN-I signaling
  • TMT quantitative multiplex tandem mass tag
  • TYK2 appeared to be more complex than just mediating the IFN-I pathway alone.
  • 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).
  • Example 12 Biomarkers for dsRNA pathology.
  • biomarkers for neuroinflammatory-based neurodegenerative disease subtypes associated with dsRNA-induced pathology would help identify patients who could benefit from treatments modulating TYK2.
  • 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).
  • Example 13 The AROMHA olfactory battery was a strong discriminator of clinical cognitive phenotype relative to the biomarker, ptau217.
  • 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.
  • Jobin, B. et al. Olfactory function is predictive of brain volumes and memory of former professional football players in the Harvard Football Players Health Study, in (Reykjavik, Iceland., 2024).
  • Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia. J Clin Invest 130, 6080-6092 (2020).

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Abstract

L'invention concerne des méthodes d'évaluation du risque de démence chez un sujet à l'aide d'un test olfactif, ou « test de l'odeur ». Ces méthodes peuvent être autoadministrées par le sujet sous les instructions d'un dispositif informatique par le biais d'une interface utilisateur. Les méthodes d'administration d'un test de l'odeur telles que décrites ici peuvent éventuellement être combinées avec la mesure et la quantification du niveau d'au moins un biomarqueur de la démence (p. ex. CXCL10, CCL2, IL-6) dans un échantillon biologique prélevé sur le sujet. L'invention concerne également des méthodes de traitement de la démence (p. ex. la démence associée à la maladie d'Alzheimer, la démence associée à un sous-ensemble de patients atteints de la maladie d'Alzheimer présentant une pathologie TDP-43).
PCT/US2025/024800 2024-04-15 2025-04-15 Méthodes d'évaluation de la maladie d'alzheimer au stade préclinique Pending WO2025221811A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030113701A1 (en) * 2001-12-13 2003-06-19 William Gartner Self-scoring method and apparatus for early self-screening of neurological disease
US20170290541A1 (en) * 2014-09-19 2017-10-12 The General Hospital Corporation Neurodegenerative disease screening using an olfactometer
US20230248297A1 (en) * 2020-04-24 2023-08-10 The General Hospital Corporation Systems and methods for administering a smell test for sars coronaviruses and covid-19

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030113701A1 (en) * 2001-12-13 2003-06-19 William Gartner Self-scoring method and apparatus for early self-screening of neurological disease
US20170290541A1 (en) * 2014-09-19 2017-10-12 The General Hospital Corporation Neurodegenerative disease screening using an olfactometer
US20230248297A1 (en) * 2020-04-24 2023-08-10 The General Hospital Corporation Systems and methods for administering a smell test for sars coronaviruses and covid-19

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