Abstract

Introduction:

TAS2R38 is a taste receptor gene located on human chromosome 7 that influences sensitivity to bitter tastes and has been implicated in innate immunity, glucose level, and human longevity. However, its potential association with Alzheimer’s Disease (AD) has not been explored. Identifying such a genetic connection could support developing new drugs or repurposing existing ones for AD treatment.

Methods:

In this work, we examined the relationship between allele counts of TAS2R38 taster variants and AD risk using linear mixed-effects models, utilizing genetic, clinical, and biomarker data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We investigated the potential molecular mechanisms of the association by identifying expression quantitative trait loci (eQTLs) using RNA-seq data from postmortem tissues across brain regions from the Religious Orders Study/Memory and Aging Project (ROSMAP). We evaluated whether FDA-approved drugs targeting the identified e-gene could reduce dementia risk using 1:1 propensity score-matched groups from longitudinal data in the National Alzheimer’s Coordinating Center (NACC) study, by comparing clinical dementia progression trends between the drug-taking and non-taking groups with linear mixed-effects models.

Results:

Our results show that TAS2R38 supertasters were connected to a reduced AD risk with advancing age due to its association with various AD biomarkers (p < 0.001). eQTL analysis linked the nontaster allele to increased expression of the gene MGAM in AD-affected brain regions (p < 0.001). Furthermore, elevated MGAM expression correlated with more severe Tau burden (p < 0.05) and implicated in mitochondrial dysfunction in AD subjects. Notably, MGAM is a known drug target for diabetes mellitus. In NACC data, individuals taking MGAM-inhibiting drugs (acarbose and miglitol) showed slower clinical dementia rating progression (p < 0.01) in comparison with the non-taking group.

Discussion:

This study is the first to report a genetic association between TAS2R38 and AD biomarkers. Our findings, validated in multiple cohorts/matching groups, suggest MGAM as a novel AD drug target with existing FDA-approved inhibitors and demonstrate the potential of TAS2R38 haplotypes to inform precision drug repurposing strategies for AD, which warrants further in-depth preclinical and clinical studies.

1 Introduction

TAS2R38 is a gene on human chromosome 7 that encodes a G-protein–coupled receptor involved in the perception of bitter tastes. It was first discovered when chemist Arthur Fox found varying individual responses to the taste of the chemicals phenylthiocarbamide (PTC) and 6-n-propylthiouracil (PROP), in which some individuals identified the taste of the chemical as bitter while others not. Studies identified three single nucleotide polymorphism (SNP) locations on the coding region of the gene that are responsible for this sensitivity of bitter taste compounds: rs10246939, rs1726866, and rs713598 (Behrens et al., 2013). They result in three amino acid substitutions (A49P, A262V, I296V), with their haplotype combinations shown with the gene model in Figure 1A. The three sites are in strong linkage disequilibrium (R2 = 0.80-0.93, D’ = 0.997-0.998) with the PAV and AVI haplotypes predominant (> 90% frequency in a global population study) and more diversity in the African populations (Risso et al., 2016). The three most frequent genotypes are commonly referred to in the literature as supertaster (PAV/PAV), heterozygous taster (PAV/AVI), and non-taster (AVI/AVI) (Figure 1A).

TAS2R38 taster variants investigated for AD association, the mechanism and associated therapeutical potential. (A) Gene model of TAS2R38 with the 3 relevant SNPs investigated in this work. The SNP IDs were simplified as g1-g3 throughout this manuscript. (B) Project workflow to identify genetic association of TAS2R38 taster variants to AD and its linked MGAM gene expression as a novel drug repurposing target. Demographics for each cohort were reported in Tables 1, 2, as well as Supplementary Tables 1–3. (C) Flowchart for the procedures in selecting a propensity score-matched cohort of A10BF (acarbose or miglitol) ever users and never users in NACC data.

TAS2R38 has been implicated in innate immunity; it was found to be associated with susceptibility to diseases such as the early detection of pathogens in airways by using these receptors to distinguish helpful and harmful bacteria (Carey and Lee, 2019). It is also associated with longevity, with longer-lived groups having a higher supertaster ratio compared to groups with lower longevity (Melis et al., 2019). The non-tasting variant (AVI) of the gene has also been shown to be a possible genetic risk for Parkinson’s Disease (PD) (Vascellari et al., 2020), and high postprandial glycemia (Gervis et al., 2025). However, its association with other neurodegenerative diseases such as AD has yet to be investigated. With the aging of global populations, AD has become the most devastating disease for the elderly with no effective treatment. Current treatment strategy primarily relies on symptomatic therapies like cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists, which offer modest cognitive benefits without altering disease progression. Recently, monoclonal antibodies targeting amyloid-β, such as lecanemab and donanemab, have been approved as disease-modifying therapies for early stage AD; although the drug conveys some benefits, it also comes with risks such as Amyloid-Related Imaging Abnormalities (ARIA) (Zimmer et al., 2025). Ongoing research focuses on tau pathology, neuroinflammation, and synaptic repair, with an increasing shift toward early intervention and biomarker-guided precision treatment (Zhang et al., 2024). Despite progress, AD remains incurable, and most therapies only modestly slow cognitive decline, warranting broader strategic approaches given the enormous complexity of its pathophysiology and associated comorbidities.

AD susceptibility has been linked to the composition of gut microbiota (Seo and Holtzman, 2024), innate immunity (Chen and Holtzman, 2022), or high glucose (Rawlings et al., 2017). Previous research has shown that TAS2R38 has mainly cytoplasmic and membranous expression in glandular tissues in the peripheral organs such as the tongue, respiratory tract, gut, and lungs (Douglas et al., 2019; Kobayashi et al., 2022), yet their variants could play a role in neurogenerative diseases via regulatory effects (Tong et al., 2025). By investigating the relationship between TAS2R38 haplotypes and AD, the underlying molecular mechanism could potentially lead to the discovery and implementation of new methods for precision treatment of AD, such as repurposed drugs that significantly reduce development time and cost. In this work, we investigated this TAS2R38 variants/AD relationship, beginning with the genetic, longitudinal clinical and biomarker data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We reported a genetic association of the TAS2R38 taster variants with AD, specifically the lower risk of AD for the supertaster variants (PAV) with the advancement of aging and vice versa. From the genetic variants, we identified the linked eQTL gene MGAM, whose expression is significantly elevated in AD affected brain regions correlated with Braak staging. From causal inference test, the elevated MGAM expression was found to be implicated in mitochondrial dysfunction in AD. We additionally investigated the potential of inhibiting MGAM as a novel AD drug target since it is a target for type II diabetes (T2D) with FDA approved drugs. In propensity score matched groups of T2D patients, the group taking MGAM inhibiting drugs showed a significantly slow cognitive decline. The whole flowchart for this work is shown in Figure 1B. In each part of the work, the findings were validated in additional cohorts or by using different matching ratios in the propensity score matching process. These findings, including TAS2R38 genetic association to AD and the molecular mechanism underlying it, may enable new opportunities for precision drug repurposing in AD treatment.

2 Materials and methods2.1 Genetic association of TAS2R38 taster variants to AD

Data were obtained from the ADNI database1 (Okonkwo et al., 2025). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The original goal of ADNI was to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. The current goals include validating biomarkers for clinical trials, improving the generalizability of ADNI data by increasing diversity in the participant cohort, and to provide data concerning the diagnosis and progression of AD to the scientific community.

We downloaded the demographic, various longitudinal clinical assessment and biomarker data from ADNI data portal. Genetic data were obtained from the Alzheimer’s Disease Sequencing Project (Leung et al., 2025) (ADSP).2 All genetic data were first converted into plink format using PLINK (Chang et al., 2015) for principal component analysis (PCA) of population structure. The plink genetic file on all the autosome chromosomes was used for PCA. We removed variant sites with missing data > 5%, minor allele frequency <5%, or significantly deviating from Hardy-Weinberg Equilibrium (HWE) (plink –geno 0.05 –maf 0.05 –hwe 1e-6). We further pruned the variants to exclude sites exhibiting high levels of linkage disequilibrium (LD), using the command “plink –indep-pairwise 50 5 0.2.” In addition, variants located in regions known to be under recent selection (Anderson et al., 2010) were removed from the dataset. PCA was performed in PLINK for the first 20 components, and based on the Scree plot, first five PCs were included in the subsequent association analysis.

We considered the alternative allele counts (dosages) of all three SNP sites to account for the diversity of the haplotypes and each variant’s contribution to the targets. The genotypes were extracted from vcf format using bcftools (Danecek et al., 2021) and converted into csv format. Cognitive tests and imaging biomarker data included clinical dementia rating (CDR, all six domains), PET-based amyloid loads (Centiloids), composite PET Tau scores (Braak staging converted from threshold-based tau-positivity in brain regions), and structural T1 MRI measurements from the ADSP Phenotype Harmonization Consortium (ADSP-PHC) (Hohman, 2023) harmonized datasets (release 3). The final dataset consists of 2,168 subjects with the demographic information reported in Table 1. All the data were read into R, where linear mixed effects (LME) models were built using the package “lmerTest”(Kuznetsova et al., 2017), comparing effects of the alternative allele counts (dosages) of the three SNPs (collectively referred to g1, g2, and g3 as shown in Figure 1A) to the longitudinal changes of the target variable with an interaction term for age, while controlling for several covariates such as sex, years of education, APOE4 allele count (the largest known genetic risk for AD), and the first five PCs, with the following Equation 1 in R programming language:

Taster groupSuperHeteroNonOther less common genotypesAllLongitudinal subjects4369226161942,168Sex (M/F)232/204481/441329/287118/761,160/1,008Age at first visit (years)73.77 ± 7.4273.11 ± 7.2772.97 ± 6.8872.81 ± 7.0873.17 ± 7.18Education (years)16.18 ± 2.6216.02 ± 2.8216.16 ± 2.6216.02 ± 2.9316.09 ± 2.74APOE4 allele count (0/1/2)252/145/39518/324/80316/238/6292/77/251,178/784/206

Demographics for the subjects in this study from ADNI cohort (Okonkwo et al., 2025).

where RID is the unique subject ID.

We validated the results of this analysis in NACC (National Alzheimer’s Coordination Center)3 (Beekly et al., 2007), with longitudinal data from over 42 current and former Alzheimer’s Disease research centers (ADRCs) across the US. Demographics, APOE4 allele count, and longitudinal clinical cognitive assessments (CDR) were obtained from Uniform Data Set (UDS) V3. Genetic data were obtained from the ADRC GWAS Datasets (ADC1-15) profiled by genotyping SNP array and harmonized by Alzheimer’s Disease Genetics Consortium (ADGC) (Naj et al., 2011). Samples closer than second-degree were removed in PLINK (–king-cutoff 0.0884). We only kept subjects with > 60 years of age at the first visit. Variant sites were pruned similarly as ADNI data for PCA. Based on the PCA, first 10 PCs were used in the LME models, where the targets were CDRs in six domains and the global and sum of boxes scores. The final dataset consists of 21,950 subjects with the demographic information reported in Supplementary Table 1.

2.2 eQTL identification and causal inference test

We performed the eQTL analysis based on the Accelerating Medicines Project for Alzheimer’s Disease (AMP-AD) (Hodes and Buckholtz, 2016) for the subjects within the Religious Orders Study/Memory and Aging Project (ROSMAP) (Bennett et al., 2018). All the demographic, clinical and pathological data of the ROSMAP cohort were obtained from the Rush Alzheimer’s Disease Center Research Resource Sharing Hub,4 upon approval of the data-usage agreement. Genetic data were from the variants called on whole genome sequencing (WGS) data from the AMP-AD data portal.5 Additional genetic data were obtained from the AMP-AD diverse cohorts study.6 The genetic data were merged and filtered similarly to what was done for the ADNI subjects, and PCA was performed. For gene expression data, we downloaded post-mortem gene expression data (RNA-seq) from three brain regions including dorsolateral prefrontal cortex (DLPFC), posterior cingulate cortex (PCC), and head of caudate nucleus (HCN) for the ROSMAP cohort from the AMP-AD data portal7 (Bennett et al., 2018). The gene expression data used was the filtered, normalized, and residualized counts to limit the effects of technical artifacts. After combining gene expression and genetic data, the final dataset consists of 947 unique individuals reported in Table 2.

Taster groupSuperHeteroNonOther less common genotypesAlleQTL subjects (all regions)*13442728997947Sex (M/F)91/43277/150190/9967/30625/322Age death (years)88.92 ± 6.7589.50 ± 6.7288.81 ± 6.5788.66 ± 6.9289.12 ± 6.70Education (years)16.60 ± 3.4116.19 ± 3.5716.45 ± 3.6715.88 ± 3.4716.29 ± 3.57APOE4 allele count (0/1/2)109/22/3317/104/6215/68/668/29/0709/223/15Braak staging02371131112621664210432210853341117319237440138944231453610268182246144110

Demographics for the subjects from ROSMAP cohort (Bennett et al., 2018) for eQTL study (all regions).

• The brain regions for each eQTL study included: DLPFC (n = 915), PCC (n = 562), and HCN (n = 650).

We identified cis-eQTLs by linear regression of the expression of all the genes on chromosome 7 within 1 Mb of the three SNPs (chr7: 140,792,804–142,973,545, 65 genes) with the alternative allele count of the three SNPs for each brain region using the following Equation 2:

Covariates include technical covariates [RNA integrity number (RIN), post-mortem interval (PMI), sequencing batch (batch)], various biological covariates (age, sex, years of education, APOE4 allele count), and the first five PCs based on the PCA of the genetic data. Significant genes (e-genes) with FDR-corrected p< 0.05 were then identified as eQTL of the SNP. The three SNPs were assessed individually for their corresponding e-genes. Semi-quantitative measurements of neuropathologies (Braak staging or CERAD score, Lewy body, TDP-43, and other vascular related neuropathologies) were added as covariates when they were additionally considered.

We carried out additional validations for the identified eQTL in two external cohorts from the AMP-AD study: the Mayo RNA-seq (MAYO) cohort (Allen et al., 2016) and the Mount Sinai Brain Bank (MSBB) cohort (Wang et al., 2018). The data were obtained from the same synapse entry as ROSMAP. The subject demographics are reported in Supplementary Tables 2, 3, respectively. In MSBB, expression data were available for four brain regions: frontal pole (FP), inferior frontal gyrus (IFG), parahippocampus (PHG), and superior frontal gyrus (STG). For MAYO, two regions were profiled: temporal cortex (TCX) and cerebellum (CBE). Each region was assessed, respectively for the eQTL. Neuropathology data for these two cohorts were downloaded from synapse entry syn27000096.

We performed causal inference test (CIT) to identify genes significantly affected by the strongest eQTL rs10246939 (g1) -MGAM using the RNAseq data of 502 AD subjects (defined as niareagansc < 3, i.e., NIA-Reagan diagnosis of AD (Neurobiology Aging, 1997) high or intermediate) from DLPFC of ROSMAP. CIT has been well-described previously (Millstein et al., 2009). In short, it offers a hypothesis test for whether a molecule (in this case MGAM) is potentially mediating a causal association between a DNA locus (g1, i.e., rs10246939), and some other quantitative trait (such as the expression of genes correlated with MGAM and rs10246939). Causal relationships can be inferred from a chain of mathematic conditions, requiring that for a given trio of loci (L i.e., rs10246939), a potential causal mediator (G, i.e., MGAM) and a quantitative trait (T, i.e., some other genes), the following conditions must be satisfied to establish that G is a causal mediator of the association between L and T:

(a) 

  L and G are associated

(b) 

  L and T are associated

(c) 

  L is associated with G, given T

(d) 

  L is independent of T, given G

We used the R software package “cit” (Millstein et al., 2016) to perform the causal inference test, calculating a false discovery rate using 1,000 test permutations. Trios with a Q-value (FDR) < 0.05 were classified as significant, and the associated T genes were considered downstream of MGAM. We investigated the significant gene hits for functional annotation and enrichment in knowledge databases such as KEGG (Kanehisa et al., 2023) pathways and Gene Ontology (Gene Ontology Consortium et al., 2023) terms through Metascape (Zhou et al., 2019).

2.3 Impact of MGAM inhibitor use on dementia risk and cognitive decline in diabetic subjects

To examine the effects of MGAM inhibiting drugs (acarbose or miglitol, Anatomical Therapeutic Chemical (ATC) code A10BF) on AD risk, we downloaded the comprehensive medical records including medication data from longitudinal assessments at NACC (National Alzheimer’s Coordination Center) (see text footnote 3; Beekly et al., 2007), with data from over 42 current and former Alzheimer’s Disease research centers (ADRCs) across the US. Demographics, APOE4 allele count, longitudinal clinical cognitive assessments, comorbidities, and medication data were obtained from Uniform Data Set (UDS) V3.

The procedures used to create a cohort of 1:1 matched pair of ever users and never users of A10BF are shown in Figure 1C. We chose subjects with type II diabetes mellitus (T2D) based on diabetes diagnosis or use of antidiabetic medications at the first visit. Subjects with any dementia or PD diagnosis at the first visit were excluded. We matched the subjects who took MGAM inhibitors at any time in the follow-up period (ever-users) with another member of the never-user group with propensity score matching, using the R package “matchit”(Ho et al., 2011) by the following command (Equation 3):

We considered the following factors in the matching: age at first visit, sex, race, years of education, APOE4 allele count, follow-up duration, total number of visits, and whether any other antidiabetic drugs taken in the duration of follow-ups, classified by their corresponding ATC subcategories A10A (insulin and analogs), A10BA (biguanides), A10BB (sulfonylureas), A10BG (thiazolidinediones), A10BH [dipeptidyl peptidase 4 (DPP-4) inhibitors], A10BJ [glucagon-like peptide-1 (GLP-1) analogs], A10BK [sodium-glucose co-transporter 2 (SGLT2) inhibitors], and A10BX (other blood glucose lowering drugs). Since it has been reported that A10BF could have joint effects with metformin (A10BA) and pioglitazone (A10BG) in reducing dementia incidences (Tseng, 2020), they were set as exact match together with insulin (A10A), sex, and APOE4 allele count. To reduce overfitting in high-dimensional settings, we chose “glmnet” as the distance method to estimate propensity scores using L1/L2-regularized logistic regression (Friedman et al., 2010). Each individual in the user group was thus matched to the closest individual in the non-user group.

The final dataset contained 76 subjects in total, with 38 subjects taking MGAM inhibitors acarbose or miglitol, matched with non-users by a ratio of 1. Matching balance was evaluated with their subject characteristics reported in Table 3. Standard mean difference (SMD) was calculated for the two groups using the R package “tableone” (Yoshida and Bartel, 2022).

CharacteristicsUser (n = 38)Non-user (n = 38)P-valueStandard mean differenceDemographic factorsAge at first visit72.37 ± 7.9071.92 ± 8.390.810.06Sex (M/F)20/1820/181.000.00Education level (years)13.76 ± 3.8813.68 ± 4.270.930.02Race (white/non-white)23/1521/170.270.11Hispanic/latino ethnicity1190.790.12Family history of dementia1340.030.59APOE ε4 (0/1/2/NA)18/4/2/1418/4/2/141.000.00Health behaviorsEver smoker19181.000.05Alcohol abuse221.000.00Drug abuse101.000.23Obesity (BMI)30.43 ± 8.5129.25 ± 9.090.540.15ComorbiditiesCardiovascular disease651.000.11Neurological diseases9100.87−0.06Neuropsychiatric disorders16171.00−0.05Medication useLipid−lowering drugs27281.00−0.06Anti−hypertensive drugs31340.52−0.22Non−steroidal anti−inflammatory medication16171.00−0.05Antidepressants850.540.21Antipsychotic drugs011.00−0.23Anti−Parkinson’s drugs011.00−0.23Anxiolytic, sedative, or hypnotic agents490.22−0.35Other diabetes drugsSGLT2 inhibitors (A10BK)030.24−0.41DPP−4 inhibitors (A10BH)1050.250.34GLP−1RAs (A10BJ)360.48−0.25Sulfonylureas (A10BB)21170.490.21Thiazolidinediones (A10BG)661.000.00Insulin (A10A)13131.000.00Metformin (A10BA)22221.000.00Other (A10BX)101.000.23

Characteristics for matched group of A10BF drug users in NACC data (Beekly et al., 2007).

Using the time elapsed at first dementia diagnosis (DEMENT = 1), we performed survival analysis using the R package “survminer” (Kassambara et al., 2024) to examine the group difference in dementia susceptibility between ever-users and never users, and p-value was computed from the χ2 statistic in a log-rank test. Kaplan-Meier plots were created to present time to dementia diagnosis. To compare the drug’s effect on cognitive performances between the two groups, we built LME models to assess the group difference in multiple domains of CDR, by the following Equation 4:

For robustness, we repeated the process with matching ratio set to 2 and 3, respectively.

2.4 Statistical analysis

Descriptive statistics for each study were reported for all subject characteristics using means and standard deviations for continuous variables and counts for categorical variables. Continuous variables were compared using a paired t-test between matched groups to check match balance. Categorical variables were compared using the Fisher’s exact test. P-values from other models were obtained from each respective package. All the analyses were performed in R (v4.2.2) statistical language.

3 Results3.1 TAS2R38 supertasters are associated with lower risk of AD with the advancement of age

From LME modeling of the longitudinal change in cognitive assessments and biomarkers in ADNI (n = 2,168), highly significant interactions (P < 0.001) were found between age and the alternative allele counts of each of the SNPs. Their contributions to the target variables are also significant (P < 0.001) yet in different directions (directed logP values and standardized coefficients reported in Figure 2, and raw effect size reported in Supplementary Table 4). This trend could be seen across different types of target variables, including multiple clinical and imaging biomarkers. The alternative alleles of g1 and g2 are found to have a negative effect on cognitive function in their interaction terms with age, while g3 has a positive effect; thus, with their comparable effect sizes, supertasters who have two copies of the alternative alleles at g1 and g3 (PAV haplotype, allele count combination 202) will have a lower negative effect on the cognitive performance thus slower cognitive decline when compared to non-tasters with only two copies of the g2 alternative allele (AVI haplotype, allele count combination 020). Similar results were also found in imaging biomarkers from structural MRI and PET imaging data, especially for left lateral ventricle, a prominent biomarker for AD (Apostolova et al., 2012; Figure 2A). The results were consistent although less significant in PET imaging for amyloid or tau burden (Supplementary Figure 1 and Supplementary Table 5) due to smaller sample size (n = 1,767 for amyloid, n = 870 for Tau) with limited number of visits (mean = 2.2 for amyloid, 1.7 for Tau).

TAS2R38 supertasters are associated with a lower risk of AD with the advancement of age. (A) Longitudinal changes in clinical assessment (CDRSUM) and imaging biomarker (left lateral ventricle volume), with fitted lines stratified by different taster groups. (B) Heatmap of signed adjusted P-values showing correlations between predictor variables and target variables in the ADNI LME models. CDMEMORY, CDR memory score; CDORIENT, CDR orientation score; CDJUDGE, CDR judgment score; CDCOMMUN,