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Article

Genetic Variations in Bitter Taste Receptors and COVID-19 in the Canadian Longitudinal Study on Aging †

by
Marziyeh Shafizadeh
1,2,
Mohd Wasif Khan
2,3,
Britt Drögemöller
2,3,4,5,
Chrysi Stavropoulou
6,
Philip St. John
7,
Rajinder P. Bhullar
1,3,
Prashen Chelikani
1,2,3 and
Carol A. Hitchon
7,*
1
Department of Oral Biology and Manitoba Chemosensory Biology Research Group, Dr. Gerald Niznick College of Dentistry, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0W2, Canada
2
Children’s Hospital Research Institute of Manitoba, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
3
Department of Biochemistry and Medical Genetics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
4
Paul Albrechtsen Research Institute CancerCare Manitoba, Winnipeg, MB R3E 0V9, Canada
5
Centre on Aging, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
6
Department of Dental Diagnostic and Surgical Sciences, Dr. Gerald Niznick College of Dentistry, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0W2, Canada
7
Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada
*
Author to whom correspondence should be addressed.
†
This article is a revised and expanded version of an abstract entitled “Association Between Taste Genetics and COVID-19 in Canadian Adults” which was presented as a poster at the International Association of Dental Research (IADR/PER) General Session & Exhibition, Barcelona, Spain, 25–28 June 2025.
Biomedicines 2025, 13(10), 2473; https://doi.org/10.3390/biomedicines13102473 (registering DOI)
Submission received: 29 July 2025 / Revised: 6 October 2025 / Accepted: 6 October 2025 / Published: 11 October 2025
(This article belongs to the Section Molecular Genetics and Genetic Diseases)
Browse Figure
Versions Notes

Abstract

Background/Objectives: Bitter Taste Receptors (encoded by TAS2R genes) are expressed in mucosal and bronchial epithelia, as well as in immune cells, contributing to defense against airborne pathogens such as SARS-CoV-2. Data on single-nucleotide polymorphisms (SNPs) in TAS2R genes or pseudogenes in COVID-19 are limited. This study examined the association between TAS2R SNPs and COVID-19 infection and seroconversion in European individuals participating in the Canadian Longitudinal Study on Aging. Methods: Data from the Genome-wide Genetic Data, Comprehensive Baseline (version 7.0), Follow-up 2 (version 1.1), COVID-19 Questionnaire Study (4-2020 to 12-2020), and COVID-19 Seroprevalence (Antibody) Study (11-2020 to 7-2021) datasets were accessed. Associations of TAS2R SNPS with COVID-19 infection or seroconversion were determined using logistic regression adjusted for sociodemographics, genetic principal components, smoking, vaccine doses, and chronic medical conditions (diabetes, immune-mediated inflammatory diseases (IMIDs), respiratory disease, and cardiovascular disease). Results: In the COVID-19 Questionnaire Study (N = 14,073), the rs117458236 (C) variant in TAS2R20 showed a trend toward an association with COVID-19 infection (OR = 1.95; 95% Confidence Interval (CI): 0.98, 3.51). In the COVID-19 Antibody Study (N = 8313), the rs2234235(G) variant in TAS2R1 was associated with anti-nucleocapsid (OR = 1.55; CI: 1.06, 2.20) and anti-spike response (OR = 0.74; CI: 0.57, 0.98); the rs2234010(A) variant in TAS2R5 was associated with anti-nucleocapsid (OR = 1.56; CI: 1.08, 2.19); and the rs34039200(A) variant in TAS2R62P was associated with anti-spike (OR = 0.86; CI: 0.77, 0.97). In a subgroup analysis, the rs2234235(G) variant in TAS2R1 was associated with a decreased anti-spike response to infection or vaccination in individuals with IMIDs or respiratory disease and an increased risk of SARS-CoV-2 infection. Conclusions: TAS2R variants are associated with COVID-19 infection and vaccine response. These data may inform personalized management and vaccination strategies.

1. Introduction

Coronavirus disease 2019 (COVID-19) has affected over 777 million individuals and caused more than 7 million deaths worldwide [1]. Despite the availability of effective vaccines and the subsequent lifting of public health restrictions, COVID-19 remains a significant public health concern. Globally, over 65,000 new cases and 3500 deaths are reported each month, posing particular risks to older adults and individuals with compromised immune systems [1,2]. This underscores the importance of understanding the factors that influence susceptibility to, and the immune response against, COVID-19.
The innate immune response in the nasopharyngeal and oral mucosa plays a crucial role in the early defense against airborne pathogens, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): the virus that causes COVID-19. The receptors required for SARS-CoV-2 binding and cell entry are expressed in the nasal and salivary epithelia [3]. Additionally, nasopharyngeal tissues and salivary glands may serve as potential reservoirs for the virus [4,5,6,7].
Bitter Taste Receptors (T2Rs) are G-protein-coupled receptors encoded by TAS2R genes, as designated by the HUGO Gene Nomenclature Committee [8]. T2Rs are expressed in taste buds and in various tissues throughout the body, including mucosal epithelia and immune cells [9,10,11]. They contribute to airway reflex protection, increased ciliary movement, and bronchodilation [12,13,14,15,16,17]. These effects are mediated, in part, by stimulating mucosal cells to release nitric oxide and calcium-mediated antimicrobial peptides [18,19,20]. Nitric oxide inhibits SARS-CoV-2 replication by impairing the interaction between the viral spike protein and host receptors and reducing viral RNA production [21]. Taste loss, a common symptom in COVID-19 patients, with 39.2% reporting taste dysfunction [22], may be partially mediated through T2Rs. Antimicrobial peptides inhibit viral activity through mechanisms such as destabilizing the viral envelope [23]. Finally, T2Rs in lung macrophages inhibit the release of pro-inflammatory cytokines, potentially limiting the damage caused by an excessive immune response to the virus [24].
Given these roles, variations in TAS2R genes may impact susceptibility to COVID-19 and the immune response to infection or vaccination. Identifying TAS2R genetic variants associated with COVID-19 outcomes could provide insights into more effective preventive protocols and help identify individuals who may require early interventions, including those with chronic medical conditions that increase their risk [2,25,26,27,28]. Moreover, understanding the relationship between TAS2R variants and serological response to SARS-CoV-2 could contribute to the development of personalized vaccination strategies.
Humans have 25 TAS2R genes and 12 non-functional TAS2R pseudogenes. Pseudogenes resemble functional genes but cannot encode functional proteins due to structural disruptions; however, they can be transcribed into RNA and impact gene expression regulation [29,30]. A variety of single-nucleotide polymorphisms (SNPs; single-nucleotide variations in the DNA sequence) in TAS2R genes can affect receptor function [31]. Among the 37 human TAS2R genes, TAS2R38 is the most widely studied. It contains three SNPs in linkage disequilibrium (genetic variants that are inherited together; rs1726866, rs713598, and rs10246939), resulting in two common haplotypes. The functional haplotype encodes proline-49, alanine-262, and valine-296 (PAV), while the non-functional haplotype encodes alanine-49, valine-262, and isoleucine-296 (AVI). The “supertaster” phenotype (PAV/PAV) is associated with the higher expression of nitric oxide and antimicrobial peptides, as well as a lower risk of chronic rhinosinusitis compared to the non-taster phenotype (AVI/AVI) [32,33]. Few studies have assessed the association between TAS2R genetic variants and the risk or severity of COVID-19 [34,35,36,37]. Studies examining these associations have largely focused on TAS2R38 but have not accounted for high-risk groups, such as those with chronic medical conditions, and have reported inconsistent findings. Moreover, TAS2R38 has relatively low expression in extra-oral tissues, suggesting that other TAS2R genes with higher expression in extra-oral tissues may have a stronger association with COVID-19 outcomes [9]. To date, no research has examined the association between COVID-19 outcomes and other TAS2R genes or pseudogenes. Additionally, no published data exist on the association between TAS2R variants and SARS-CoV-2 seroconversion, defined as the development of detectable antibodies in the blood following infection or vaccination.
This study aimed to evaluate the association between TAS2R SNPs and COVID-19 outcomes, including seroconversion, and to determine whether this association differs in high-risk groups within the Canadian Longitudinal Study on Aging (CLSA). Our primary objective was to evaluate the association between TAS2R SNPs and COVID-19 infection. The second objective was to assess the relationship between TAS2R SNPs and post-infection and post-vaccination antibody response. Finally, we aimed to determine whether these associations with COVID-19 outcomes and seroconversion differ among individuals with or without chronic medical conditions known to increase the risk of severe COVID-19 infection or reduce seroconversion, including diabetes, cardiovascular disease, respiratory disease, and immune-mediated inflammatory disorders (IMIDs).

2. Materials and Methods

2.1. Study Population and Data Preprocessing

The CLSA is a longitudinal national cohort of 50,000 individuals designed to evaluate the influence of various factors on health and disease [38]. We obtained access to the CLSA Genome-wide Genetic Data Release (version 3), Comprehensive Baseline Dataset (version 7.0), Comprehensive Follow-up 2 Dataset (version 1.1), COVID-19 Questionnaire Study (data collected from April 2020 to December 2020), and COVID-19 Seroprevalence (Antibody) Study (data collected from November 2020 to July 2021), under CLSA Application ID: 23CA016. This study was approved by the University of Manitoba’s Health Research Ethics Board (HREB HS26021-H2023:173).
The CLSA methods, including eligibility criteria, sampling, data collection, and processing, are explained in detail in the Researchers section of the website (https://www.clsa-elcv.ca/). In summary, individuals aged 45 to 85 were enrolled from various locations in Canada, and using standard protocols, information was collected, including demographic, lifestyle, and behavioral data. In the COVID-19 Questionnaire and Seroprevalence (Antibody) studies, additional data was collected on COVID-19 infection, vaccination, and serology (nucleocapsid and spike antibodies). COVID-19 Questionnaire data was collected weekly/biweekly and monthly using web-based and telephone-based interviews and included information on prior COVID-19 infection. The COVID-19 Seroprevalence dataset included data on the number, date and results of prior COVID-19 tests. All serology tests were performed over 14 days following a reported COVID-19 test, with the shortest interval being 20 days. First vaccination ranged from 115 days prior to serology collection to 117 days post serology collection. Only 310 (4%) of participants had a serology sample collected within 2 weeks of first vaccination.
A subset of 26,622 participants provided consent to collect their blood samples for genotyping. The genetic dataset comprises genotypes for 794,409 variants, obtained using the Affymetrix Axiom array, in addition to 308 million imputed variants from the TOPMed reference panel [39]. SNPs within TAS2R genes were extracted using PLINK (v1.9) [40] based on their GRCh38 coordinates retrieved from the National Center for Biotechnology Information (NCBI) Gene database [41]. Annotations were retrieved using the Ensembl Variant Effect Predictor (VEP) [42].

2.2. Eligibility Criteria

We used PLINK (v.1.9) for sample and marker quality control based on previously established protocols [39,43]. The exclusion criteria for genetic markers were as follows: inconsistencies in genotype frequency between control replicates and batches; deviation from the Hardy–Weinberg equilibrium (p < 3.15 × 10−10); genotype missingness exceeding 5%; minor allele frequency (MAF; the prevalence of the less common allele of a SNP within the study population) ≤ 0.01; and insertions/deletions (indels). Samples were excluded based on the following: heterozygosity outliers; familial relatedness of at least third-degree (assessed by identity-by-descent analysis); mismatches between chromosomal and self-reported sex; and more than 5% missing genotypes. Additionally, imputed variants with a quality score >0.8 were included [44]. To account for population stratification, only individuals with European ancestry were included, who were previously identified by the CLSA based on genetic principal component analysis (PCA) [39].

2.3. COVID-19 Infection

Data from the CLSA COVID-19 Questionnaire Study were used to assess the association between TAS2R variants and COVID-19 infection. Data were collected at baseline, weekly, biweekly, monthly, and at the time of exit. Two outcomes were evaluated: A. Confirmed infection: Participants who reported a positive nucleic acid amplification test for COVID-19. B. Probable/confirmed infection: Participants who either reported a positive nucleic acid amplification test or were told by a healthcare professional that they had COVID-19 without a confirmed test. The probable cases were included, as many individuals were not tested in the early pandemic [45]. Individuals were coded as positive for infection if they reported at least one positive response at any time point during the questionnaire; otherwise, they were coded as negative. Those with missing data at all time points were excluded from the analysis.

2.4. COVID-19 Antibodies

The CLSA COVID-19 Seroprevalence (Antibody) Study was used to evaluate the association between TAS2R variants and the presence of nucleocapsid antibody (a marker of serologically confirmed COVID-19 infection) and spike antibody (anti-receptor binding domain, a marker of COVID-19 infection or vaccine seroconversion). To assess the relationship between TAS2R SNPs and the serological response to COVID-19 vaccination, we focused on participants who had received at least one vaccine dose and showed no evidence of prior infection (i.e., no detectable nucleocapsid antibody). Within this subgroup, we investigated the association between TAS2R variants and the presence of spike antibody, defining cases as individuals with a positive spike antibody status and controls as those with a negative spike antibody status.

2.5. Case Definition for Chronic Medical Conditions

Diabetes, respiratory disease, and cardiovascular conditions were included in logistic regression models as covariates and in subgroup analyses since they are associated with increased risk of severe COVID-19 infection and are among the most common chronic conditions in Canada [46,47]. Immunocompromised individuals, such as those with immune-mediated inflammatory diseases (IMIDs), are also at a higher risk of severe COVID-19 infection or poorer vaccine-induced immunogenicity, potentially due to immune dysregulation or the use of immunomodulatory medications [27,28,48,49]. Among IMIDs, we focused on rheumatoid arthritis and inflammatory bowel disease, due to their high prevalence in Canada, overlapping use of immunomodulatory agents, and evidence of association with COVID-19 severity [50,51,52,53,54,55]. We generated case definitions for these conditions using self-reported data and medication use, based on algorithms developed through an extensive literature review (Figure S1 and Table S1) [56,57,58,59,60,61,62,63,64,65,66,67,68,69]. To confirm the relative accuracy of the definitions, we compared the prevalences for each condition with those reported by the Canadian Chronic Disease Surveillance System (CCDSS) and other CLSA publications [59,65,68,70,71].

2.6. Statistical Analysis

Study population characteristics were summarized using the gtsummary package in R. TAS2R variants were initially tested in univariable models with chi-squared tests using PLINK (v.1.9) to identify variants associated with the outcomes of interest (COVID-19 infection; COVID-19 seroconversion). Independent SNPs (variants with very low correlation, r2 < 0.1 within a 1000 kb genomic window, N = 35) [39] were then selected for inclusion in multivariable logistic regression under an additive genetic model. The list of covariates that were initially included is provided in Table S2. Assessment of multicollinearity using Pearson correlation analysis revealed no covariate pairs exceeding 0.6; therefore, all covariates were retained for further analysis (Figure S2). Stepwise variable selection was then applied to identify the best-fitting models, and the selected variables were included in the final analysis. The final models included the following variables: age, sex, top 10 principal components for the European ancestry subset released by the CLSA (ePC1-10), smoking status, dwelling area (available only in the Questionnaire Study), dwelling ownership, number of household residents, number of vaccine doses (available only in the Antibody Study), post-secondary education, diabetes, respiratory disease, cardiovascular disease, and IMID. In the COVID-19 Questionnaire analyses, dwelling ownership and number of household residents were excluded due to a high rate of missing data. Individuals with missing data were excluded from the logistic regression analyses.
The analyses were conducted separately using data from the COVID-19 Questionnaire and Seroprevalence (Antibody) studies, as these datasets include overlapping but nonidentical populations. Subgroup analyses were performed for each outcome, stratifying individuals based on the presence or absence of chronic medical conditions. Statistical significance was set at p <0.05, and Bonferroni correction was applied to adjust for multiple comparisons, considering the number of independent outcomes as 1 due to the high correlation between outcomes (Phi coefficient > 0.1) [72]. Odds Ratios (ORs) with 95% Confidence Interval (CI) are reported. To explore the potential functional mechanisms of the associated variants, we queried RegulomeDB v2.2 and HaploReg v4.2, and used the RNAsnp web server to predict their effects on mRNA secondary structures [73,74,75].

3. Results

After applying the eligibility criteria, 14,073 individuals from the COVID-19 Questionnaire Study and 8313 from the COVID-19 Antibody Study were included. Population characteristics are summarized in Table 1.
Figure 1 provides an overview of the sample selection, data analysis, and results.
Chi-squared tests identified 16 SNPs with differing allele distributions between cases and controls (Table S3). Among these, eight SNPs were independent (r2 < 0.1) and were included in logistic regression analyses.

3.1. CLSA COVID-19 Questionnaire Study

In the Questionnaire Study, 2299 (16.33%) participants were tested for COVID-19, with 45 (1.96% of those tested) having a positive result (confirmed cases). The total number of confirmed/probable cases was 153 (1.09% of the cohort). Confirmed/probable COVID-19 infection was significantly associated with age (OR: 0.97; 95% Confidence Interval (CI): 0.95, 0.99; p < 0.001), female sex (OR: 1.80; CI: 1.28, 2.58; p < 0.001), rental dwelling status (OR: 1.66; CI: 1.08, 2.47; p = 0.017), and respiratory disease (OR: 1.78; CI: 1.24, 2.52; p = 0.001) (Table 2). The rs117458236 variant, located in the 3′ untranslated region (UTR) of TAS2R20, was associated with a trend toward increased odds of confirmed/probable infection (OR: 1.95, CI: 0.98, 3.51; p = 0.039). The RNAsnp predicted minimal changes in mRNA secondary structure and base-pairing probabilities (Figure S3). HaploReg v4.2 indicated that this variant alters binding sites for immune-related transcription factors (Ik-2, NF-AT, and NF-AT1), with higher predicted affinity for the T allele, suggesting a potential regulatory effect. No significant association was found between confirmed infection and other TAS2R variants (Table 2).

3.2. CLSA COVID-19 Seroprevalence (Antibody) Study

Vaccination history and antibody response data are summarized in Table 3. A total of 370 (4.45%) participants had detectable nucleocapsid antibody, indicating serologically confirmed infection, while 3773 had detectable spike antibody, which could result from infection or vaccination. Of the 310 samples collected within 2 weeks of first vaccination, 222 (76%) or 3% total cohort were negative for spike antibody.
Logistic regression results are summarized in Table 4. Spike antibody was positively associated with female sex (OR: 1.25; CI: 1.08, 1.43; p = 0.002), number of vaccine doses (OR: 90.2; CI: 72.6, 114; p < 0.001), and negatively associated with IMID diagnosis (OR: 0.65; CI: 0.49, 9.85; p = 0.003). Three TAS2R variants were associated with antibody response: rs2234235, a synonymous variant in TAS2R1, with higher odds of nucleocapsid antibody (OR: 1.55; CI: 1.06, 2.20; p = 0.018) and lower odds of spike antibody (OR: 0.74; CI: 0.57, 0.98; p = 0.033); rs2234010, located in the 5′ UTR in TAS2R5, with higher odds of nucleocapsid antibody (OR: 1.56; CI: 1.08, 2.19; p = 0.014); and rs34039200 in TAS2R62P (pseudogene) with lower odds of spike antibody (OR: 0.86; CI: 0.77, 0.97; p = 0.013). The associations of rs2234010 with higher odds of nucleocapsid antibody and rs34039200 with lower odds of spike antibody remained significant after applying a more conservative p value estimate (p < 0.017) adjusted for the 3 statistically important comparisons.
RNA secondary structure prediction using RNAsnp revealed subtle changes in mRNA stability between alleles of rs2234235 in TAS2R1 (Figure S3) [75]. RegulomeDB indicated that this variant maps to quiescent or heterochromatic regions in immune cells, including thymus and T/B lymphocytes, suggesting low basal regulatory activity [73]. RNAsnp predicted significant changes in mRNA secondary structure for the rs2234010 variant in TAS2R5 (Figure S3). Ensembl VEP annotated rs34039200 as a non-coding exon variant in the TAS2R62P pseudogene, overlapping several lncRNAs such as EPHA1-AS1 [42]. RegulomeDB placed this variant in enhancer-like regions across immune cells and embryonic stem cells, indicating potential regulatory activity.
Among the 8313 participants, 4267 had received at least one vaccine dose, with Pfizer-BioNTech being the most commonly administered (N = 3901). Logistic regression in vaccinated individuals without detectable nucleocapsid antibody (Table 5) confirmed the association of rs2234235 in TAS2R1 and rs34039200 in TAS2R62P with lower odds of spike antibody in vaccinated individuals (OR: 0.71; CI: 0.54, 0.95; p = 0.021; OR: 0.86; CI: 0.76, 0.97; p = 0.017, respectively).

3.3. Subgroup Analysis

In the Questionnaire Study, subgroup analysis was performed for confirmed/probable infection due to the larger sample size (Table S4). The rs117458236 variant in TAS2R20 remained associated with infection in individuals with diabetes (OR: 3.27; CI: 0.93, 8.91; p = 0.035) or cardiovascular disease (OR: 2.69; CI: 1.10, 5.65; p = 0.016). Furthermore, this variant was associated with an increased risk of infection in individuals without IMID (OR: 2.15; CI: 1.08, 3.88; p = 0.018), but not in those with IMID. The rs2234235 in TAS2R1 was linked to confirmed/probable infection in individuals with respiratory disease (OR: 2.65; CI: 1.08, 5.61; p = 0.018) or IMID (OR: 7.39; 95% CI: 1.36, 33.8; p = 0.012), as well as in those without diabetes (OR: 2.08; CI: 1.11, 3.57; p = 0.013). rs1726866, a missense variant (Val 262 Ala) in TAS2R38, was associated with infection in individuals with respiratory disease (OR: 1.86; CI: 1.22, 2.88; p = 0.004).
In the Antibody Study, a subgroup analysis confirmed the association of rs2234235 in TAS2R1 and rs2234010 in TAS2R5 with nucleocapsid antibody (OR > 1; p < 0.05) in most healthy subgroups (Table S5). Additionally, rs77837442 in TAS2R19 was associated with reduced anti-nucleocapsid antibody levels in individuals without diabetes (OR: 0.23; CI: 0.04, 0.71; p = 0.036). No significant association was found with other variants. The association of rs34039200 in TAS2R62P with spike antibody remained significant in individuals with diabetes (OR: 0.72; CI: 0.56, 0.92; p < 0.01), as well as in those without respiratory disease or IMID (Table S6). Although rs2234235 in TAS2R1 was associated with the spike antibody in the entire cohort (OR: 0.74; CI: 0.57, 0.98; p = 0.033), a subgroup analysis revealed a significant association only in patients with respiratory disease (OR: 0.48; CI: 0.27, 0.85; p = 0.012) and with a trend toward significance in patients with IMID (OR: 0.37; CI: 0.13, 1.04; p = 0.058). The two SNPs in the 5′ UTR of TAS2R5, which were not associated with spike antibody in the overall cohort, showed a significant association in patients with diabetes (OR: 2.19; CI: 1.17, 4.24; OR: 2.15; CI: 1.13, 4.16).

4. Discussion

This study examined the relationship between TAS2R genetic variants and COVID-19 outcomes in individuals of European ancestry participating in the CLSA cohort. The prevalence of confirmed/probable COVID-19 infection (1.09%) and serologically confirmed infection (4.45%) was consistent with findings from other studies on the CLSA dataset [37,60,76,77]. One allele in TAS2R20 was associated with a trend to an increased risk of confirmed or probable infection. Additionally, two alleles in TAS2R1 and TAS2R5 were associated with the presence of nucleocapsid antibody, and two alleles in TAS2R1 and TAS2R62P were linked to lower odds of spike antibody seroconversion following COVID-19 vaccination or infection.
This study builds upon prior studies assessing TAS2R variants and COVID-19 outcomes, with a specific focus on TAS2R38 [34,35,36,37]. These prior studies primarily focused on three variants (rs1726866, rs713598, and rs10246939) that give rise to the two common haplotypes. Research has focused on these variants because the three TAS2R38 phenotypes—supertaster (PAV/PAV), taster (PAV/AVI), and non-taster (AVI/AVI)—can be clinically assessed using taste strip testing. A recent study using CLSA data found no significant association between TAS2R38 haplotypes and COVID-19 infection or symptoms, aligning with the findings of this study on TAS2R38 [37,72]. Similarly, Risso et al. (2022) found no significant association between the TAS2R38 phenotype and either the presence or severity of SARS-CoV-2 infection [36]. In contrast, Barham et al. reported an association between the nontaster phenotype and more severe COVID-19 symptoms in a cohort of 100 patients [78]. Similarly, Parsa et al. in a 2021 systematic review and meta-analysis, found the PAV haplotype (C allele in rs10246939) to be associated with lower COVID-19 mortality [35]. Another study by Barham et al. on a cohort of 1935 individuals found that non-tasters were more likely to test positive for COVID-19, need hospitalization after infection, and experience prolonged symptom duration [34].
The SNP associated with COVID-19 infection identified in this study was located in TAS2R20. Additionally, rs3851584 in TAS2R14 showed a borderline significant association with the infection (p = 0.058). TAS2Rs are expressed in the bronchial epithelium and airway smooth muscles, where they play a role in airway clearance [12,13,14,15,16,17]. Additionally, they are expressed in immune cells, such as macrophages, monocytes, and neutrophils, and contribute to the innate immune response against viral infections through nitric oxide production, calcium-mediated antimicrobial peptides, and enhanced phagocytosis [18,19,20,21,79]. TAS2R20 and TAS2R14 exhibit the highest expression levels in the bronchial epithelium [9], which may explain the association with SARS-CoV-2 infection. The rs117458236 variant associated with infection is located in the 3′ untranslated region (UTR) of TAS2R20. Genetic variants in UTRs can influence post-transcriptional gene regulation by affecting mRNA secondary structure, stability, translation efficiency, and localization [80,81]. Although none of the variants associated with seroconversion alter T2R protein sequences, functional annotations from RegulomeDB and RNAsnp suggest regulatory potential. Notably, rs2234010 in TAS2R5 is predicted to modify mRNA stability and secondary structure, potentially affecting post-transcriptional gene expression (Figure S3). In addition, rs34039200, despite being located in the TAS2R62P pseudogene, overlaps enhancer chromatin and may exert regulatory effects via lncRNA-mediated mechanisms. Collectively, the associated variants may influence COVID-19 infection and antibody seroconversion through non-coding regulatory mechanisms.
TAS2R polymorphisms have been associated with various clinical conditions, although the underlying biological mechanisms are yet to be elucidated [82]. TAS2R38 genotypes have been associated with the abundance of buccal bacterial species linked to rheumatoid arthritis [83]. Variants in TAS2R3, TAS2R4, TAS2R5, and TAS2R60 have also been associated with dental plaque microbial abundance in severe early childhood caries [84]. Machine learning analyses suggest that Streptococcus mutans, a cariogenic bacterium, may act as a mediator between TAS2R variants and early childhood caries [85]. Quorum-sensing molecules released by S. mutans are detected by the T2R family [86], and infection of gingival epithelial cells with S. mutans induces T2R14-dependent IL-8 secretion [87]. T2Rs further modulate innate immune responses, including autophagy, in response to S. mutans [88,89]. Beyond the oral cavity, studies on bronchial epithelial cells from cystic fibrosis revealed that T2R14 contributes to calcium-mediated immune responses, promoting nitric oxide, interleukin, and human β-defensin 2 production, thereby enhancing airway epithelial defense against pathogens [89]. Consistently, specific TAS2R genotypes have been associated with higher expression of antimicrobial peptides and nitric oxide, and a lower risk of chronic rhinosinusitis [32]. Collectively, these findings, together with evidence of T2R roles in airway clearance [12,13,14,15,16,17], suggest that TAS2R variants may influence disease susceptibility by modulating airway defense mechanisms.
We identified three SNPs in TAS2R1, TAS2R5, and TAS2R62P associated with the SARS-CoV-2 antibody response. Growing evidence suggests that, in addition to their role in innate immunity, T2Rs regulate the adaptive immune response [79]. TAS2Rs are expressed in lymphocytes, with higher levels observed in younger adults compared to older adults [11,79]. Tran et al. reported significantly higher levels of T2R38 in activated lymphocytes compared to controls, as well as in central and effector memory cells compared to naïve cells [11]. Grassin-Delyle et al. demonstrated that T2R5 activation in human macrophages suppresses the secretion of pro-inflammatory cytokines [24]. Since these cytokines are crucial for B cell activation and proliferation, T2Rs may indirectly impact the antibody response to SARS-CoV-2 [11,24,90]. The SNPs identified to be associated with antibody seroconversion in this study include synonymous, 5′ UTR, and pseudogene variants. Although pseudogenes do not produce functional proteins, they are transcribed into RNA and contribute to gene expression regulation [29,30]. Additionally, synonymous variants may regulate post-transcriptional gene expression by affecting mRNA stability and translation speed [91,92].
The logistic regression results of this study demonstrated a significant association between respiratory diseases and SARS-CoV-2 confirmed or probable infection. This may be because probable cases, defined as those diagnosed by a physician, were more likely to present symptoms. Thus, respiratory disease appears to be associated with symptomatic or more severe COVID-19, which is consistent with previous research [93,94]. Interestingly, results from the UK Biobank Imputed Version 3 dataset, generated by the Neale Lab [95], support a connection between the same TAS2R variants reported here and non-COVID-19-related respiratory diseases: rs3851584 (G) in TAS2R14 and respiratory disease symptoms (p < 0.001; effect size reported for T allele:−0.054); rs117458236 (T) in TAS2R20 and pulmonary inflammation or edema (p = 0.02; effect size: 1.5); rs2234010 (A) in TAS2R5 and dependence on respirator (p = 0.001; effect size: 0.72). On the other hand, rs2234235 (G) in TAS2R1, identified in this study as a risk allele for nucleocapsid antibody response, was linked to a decreased risk of chronic airway obstruction in the UK Biobank data (p = 0.01; effect size: −0.097) [95].
In addition, consistent with previous research, we found a significant association between IMID and lower odds of spike antibody response (OR [95% CI]: 0.65 [0.49, 0.86]; p = 0.003), which is likely due to medication use and immune system dysregulation in IMID patients [28,48,96,97,98]. Patients with autoimmune diseases, such as rheumatoid arthritis, have approximately twice the risk of COVID-19, primarily due to glucocorticoid medication [49]. Our previous study on 21,991 CLSA participants showed an association between TAS2R genetic variants and temporomandibular symptoms, which can occur in rheumatoid arthritis involving the temporomandibular joint [43,99,100,101,102,103]. Nine of the fifteen variants linked to temporomandibular joint syndromes were located in TAS2R20 [43]. A different SNP in the same gene was found to be associated with COVID-19 infection in the present study. Furthermore, the SNP rs3851584 in TAS2R14, which showed a borderline association with COVID-19 infection in this study, was also linked to temporomandibular joint symptoms in the previous study [43].
Subgroup analyses may clarify the interplay between TAS2R variants, COVID-19 outcomes, IMID, and respiratory disease. The rs2234235 (G) variant in TAS2R1 was associated with higher odds of an anti-nucleocapsid response and lower odds of an anti-spike response (Table 4), despite showing no association with confirmed/probable infection in the overall cohort (Table 2). However, it was significantly associated with higher odds of confirmed/probable infection in individuals with respiratory disease or IMID. Additionally, although this variant was linked to reduced odds of anti-spike in the overall cohort, a subgroup analysis revealed a stronger effect in patients with respiratory disease or IMID. These findings suggest that the G allele of rs2234235 may impair antibody responses, potentially increasing susceptibility to SARS-CoV-2 infection in individuals at risk. This finding is further supported by GWAS results from the UK Biobank COVID-19 dataset, available through the GRASP portal [104], which revealed a significant association between the G allele of rs2234235 and severe COVID-19 outcomes in the European population (p = 0.001).
Self-reported diagnosis can be challenging to validate. A systematic review of systemic conditions in the CLSA found that certain conditions, such as respiratory disease, cardiovascular disease, and diabetes, could be accurately diagnosed using algorithms based on questionnaire data [56]. However, validating IMID diagnosis, including inflammatory bowel disease and musculoskeletal conditions such as rheumatoid arthritis, poses challenges, as necessary clinical variables are not included in the CLSA [56]. To improve the accuracy of IMID diagnosis, we incorporated specific medications used for these conditions into our algorithms (Table S1). Another study on the CLSA cohort found that incorporating medication data into chronic disease assessment can resolve over 93% of inconsistent responses [57]. Additionally, Oremus et al. demonstrated that an ischemic heart disease algorithm could achieve optimal sensitivity and specificity using self-reported data alone, without ECG confirmation [58]. The prevalence estimates for systemic conditions in this study were comparable to those reported by the Canadian Chronic Disease Surveillance System (CCDSS) for individuals aged 45 to 84 years and other CLSA studies [59,65,68,70,71].
This study has some limitations. Only individuals of European ancestry were included, which limits the extension of the findings to other populations. Furthermore, the CLSA cohort consists of individuals who volunteered to participate. This may result in selection bias and the underrepresentation of populations at higher risk for COVID-19, including those with lower socioeconomic status, limited access to healthcare, minorities, and individuals with comorbidities. Further studies in more diverse populations are needed to confirm the generalizability of these results. Additionally, only 16% of the Questionnaire cohort reported a nucleic acid amplification test for COVID-19, resulting in a small sample size for the confirmed infection outcome. Although a larger population was included in the confirmed/probable category, some individuals may not have been examined by a physician or may have been asymptomatic, resulting in data that remained skewed towards individuals without COVID-19 infection. Although we controlled for multiple potential confounders, environmental factors and genetic variants in other genes were not taken into account. Furthermore, as this study focused on COVID-19, it remains unclear whether the identified variants are also associated with other viral or bacterial infections. The potential impact of medications known to affect serological responses, particularly B-cell-targeted therapies often used in IMIDs, could not be accounted for in this study due to the small numbers of individuals on these medications. All serological samples were submitted over 2 weeks following any reported COVID-19 test; thus, the risk of false-negative nucleocapsid antibody testing is low. However, 4% of serological samples were submitted within 2 weeks of the first vaccine dose, and of these 76% (representing 3% of the total cohort) tested negative for spike antibody and thus were possible false negatives. Additionally, infection and vaccine-mediated immunogenicity are known to decrease over time, which may have impacted the serological data, resulting in low rates of serologically defined infection or vaccine response. Further research is needed to investigate the role of T2Rs in B lymphocyte activation and antibody production, as well as to explore the functional impact of the associated variants on T2Rs and their potential causal relationship with SARS-CoV-2 and other airborne pathogens. Importantly, the identified associations of TAS2R variants with infection or serology are preliminary and require validation in separate, larger cohorts with higher infection prevalence.

5. Conclusions

This study highlights TAS2R variants as potential contributors to COVID-19 infection and immune response. Several TAS2R variants were found to increase the risk of infection while reducing antibody seroconversion following vaccination. Identifying specific variants linked to disease risk could help in evaluating prognosis, guiding treatment planning, and prioritizing vaccination for individuals carrying high-risk genotypes. Given that COVID-19 continues to generate significant healthcare burdens, particularly for older adults and individuals with chronic diseases, these data may inform vaccination strategies for COVID-19 and provide insights into early innate immune responses against emerging and endemic airborne pathogens.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biomedicines13102473/s1. Figure S1: Algorithms for defining chronic conditions in CLSA. Follow-up 2 data was used to determine medication use, based on responses to the question: “Do you use any medications for …?” For rheumatoid arthritis and inflammatory bowel disease, medication data (listed in Table S1) was obtained from the Baseline dataset, as this information was not available in the Follow-up 2 data; Table S1: List of medications for rheumatoid arthritis and inflammatory bowel disease, excluding steroids. Drug identification numbers (DINs) were retrieved from the Health Canada Drug Product Database; Table S2: Variables initially extracted for logistic regression; Figure S2: Correlation plots of covariates. (A) COVID-19 cohort (n = 14,073). (B) COVID-19 Antibody cohort (n = 8313). IMID: immune-mediated inflammatory disease; Table S3: TAS2R variants with differing allele frequencies by COVID-19-related phenotypes. Only variants with P < 0.05 are presented (chi-squared test); Table S4: Association between TAS2R variants and confirmed/probable COVID-19 infection in patients with chronic medical conditions and in corresponding control groups, defined as individuals without the specific condition under analysis; Table S5: Association between TAS2R variants and SARS-CoV-2 nucleocapsid antibody in patients with chronic medical conditions and in corresponding control groups, defined as individuals without the specific condition under analysis; Table S6: Association between TAS2R variants and SARS-CoV-2 spike antibody in patients with chronic medical conditions and in corresponding control groups, defined as individuals without the specific condition under analysis; Figure S3: Predicted mRNA secondary structures and base pair probabilities calculated by RNAsnp. In each matrix, the upper triangle represents base pair probabilities for the wild-type sequence, and the lower triangle represents probabilities for the mutant sequence. Red and green dots indicate base pairs whose probabilities differ between the two alleles. The results reveal structural changes in mRNA for the variants, with the rs2234010 in TAS2R5 predicted to have the most notable impact.

Author Contributions

Conceptualization: C.A.H. and P.C.; Methodology: M.S., M.W.K., B.D., P.S.J. and C.S.; Investigation: M.S.; Writing—original draft preparation: M.S.; Writing—review and editing: M.S., M.W.K., R.P.B., P.C., B.D., C.S., P.S.J. and C.A.H.; Visualization: M.S.; Supervision: C.A.H., P.C. and R.P.B. All authors have read and agreed to the published version of the manuscript.

Funding

Funding for support of the CLSA COVID-19 questionnaire-based study is provided by the Juravinski Research Institute, Faculty of Health Sciences, McMaster University, the Provost Fund from McMaster University, the McMaster Institute for Research on Aging, the Public Health Agency of Canada/CIHR grant reference CMO 174125 and the government of Nova Scotia. The Seroprevalence project was supported by funding from the Government of Canada through the COVID-19 Immunity Task Force. This study was funded by a Canadian Institutes of Health Research (CIHR) Catalyst Grant (ACD 187255) and an NSERC (Natural Sciences and Engineering Research Council of Canada) Discovery Grant (RGPIN-2020-05670).

Institutional Review Board Statement

We obtained access to the CLSA Genome-wide Genetic Data Release (version 3), Comprehensive Baseline Dataset (version 7.0), Comprehensive Follow-up 2 Dataset (version 1.1), COVID-19 Questionnaire Study (data collected from April 2020 to December 2020), and COVID-19 Seroprevalence (Antibody) Study (data collected from November 2020 to July 2021) under CLSA Application ID: 23CA016. This study was approved by the University of Manitoba’s Health Research Ethics Board (HREB HS26021-H2023:173), 20 May 2024.

Informed Consent Statement

Informed consent was obtained from all participants involved in the CLSA study.

Data Availability Statement

This study has been conducted using the CLSA Genome-wide Genetic Data Release (version 3), Comprehensive Baseline Dataset (version v7.0), Comprehensive Follow-up 2 Dataset (v1.1), COVID-19 Questionnaire Study, and COVID-19 Seroprevalence (Antibody) Study. Data are available from the Canadian Longitudinal Study on Aging (https://www.clsa-elcv.ca/) for researchers who meet the criteria for access to de-identified CLSA data. The source code of this article has been deposited at https://github.com/mshafizadeh/TAS2R_COVID19 (accessed on 7 March 2025) and is publicly available as of the date of publication.

Acknowledgments

This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces: Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. This research has been conducted using the CLSA Genome-wide Genetic Data Release (version 3), Comprehensive Baseline Dataset (v7.0), Comprehensive Follow-up 2 Dataset (v1.1), COVID-19 Questionnaire Study, and COVID-19 Seroprevalence (Antibody) Study, under Application ID 23CA016. The CLSA is led by Parminder Raina, Christina Wolfson and Susan Kirkland. No GenAI tools were used in the conduct or reporting of the study. This article is a revised and expanded version of a paper [105], which was presented at International Association of Dental Research (IADR/PER) General Session & Exhibition, Barcelona, Spain, 25–28 June 2025.

Conflicts of Interest

The authors declare that they have no competing interests related to this work. CAH has the following disclosures unrelated to this work: research funding from Pfizer Canada; advisory boards for Astra-Zeneca, Fresenius-Kabi and Accord Healthcare; and speaker fees for Sanofi. The funders had no role in the study design; collection analysis or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
CLSACanadian Longitudinal Study on Aging
COVID-19Coronavirus disease 2019
CI(95% confidence interval)
IMIDImmune-mediated inflammatory disease
ORodds ratio
PCAprincipal component analysis
SARS-CoV-2severe acute respiratory syndrome coronavirus 2
SNPssingle nucleotide polymorphisms
T2Rbitter taste receptor
TAS2Rbitter taste receptor gene

References

  1. World Health Organization (WHO). COVID-19 Dashboard. February 2025. Available online: https://data.who.int/dashboards/covid19/ (accessed on 15 February 2025).
  2. Gallo, A.; Pero, E.; Pellegrino, S.; Macerola, N.; Murace, C.A.; Ibba, F.; Agnitelli, M.C.; Landi, F.; Montalto, M. How can biology of aging explain the severity of COVID-19 in older adults. Clin. Geriatr. Med. 2022, 38, 461–472. [Google Scholar] [CrossRef]
  3. Hamming, I.; Timens, W.; Bulthuis, M.; Lely, A.; Navis, G.V.; van Goor, H. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J. Pathol. J. Pathol. Soc. Great Br. Irel. 2004, 203, 631–637. [Google Scholar] [CrossRef]
  4. Huang, N.; Pérez, P.; Kato, T.; Mikami, Y.; Okuda, K.; Gilmore, R.C.; Conde, C.D.; Gasmi, B.; Stein, S.; Beach, M.; et al. SARS-CoV-2 infection of the oral cavity and saliva. Nat. Med. 2021, 27, 892–903. [Google Scholar] [CrossRef] [PubMed]
  5. Marais, G.; Hsiao, N.-y.; Iranzadeh, A.; Doolabh, D.; Enoch, A.; Chu, C.-y.; Williamson, C.; Brink, A.; Hardie, D. Saliva swabs are the preferred sample for Omicron detection. medRxiv 2021. [Google Scholar] [CrossRef]
  6. To, K.K.; Tsang, O.T.; Leung, W.S.; Tam, A.R.; Wu, T.C.; Lung, D.C.; Yip, C.C.; Cai, J.P.; Chan, J.M.; Chik, T.S.; et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: An observational cohort study. Lancet Infect. Dis. 2020, 20, 565–574. [Google Scholar] [CrossRef] [PubMed]
  7. To, K.K.; Tsang, O.T.; Yip, C.C.; Chan, K.H.; Wu, T.C.; Chan, J.M.; Leung, W.S.; Chik, T.S.; Choi, C.Y.; Kandamby, D.H.; et al. Consistent Detection of 2019 Novel Coronavirus in Saliva. Clin. Infect. Dis. 2020, 71, 841–843. [Google Scholar] [CrossRef]
  8. Braschi, B.; Denny, P.; Gray, K.; Jones, T.; Seal, R.; Tweedie, S.; Yates, B.; Bruford, E. Genenames.org: The HGNC and VGNC resources in 2019. Nucleic Acids Res. 2019, 47, D786–D792. [Google Scholar] [CrossRef]
  9. Jaggupilli, A.; Singh, N.; Upadhyaya, J.; Sikarwar, A.S.; Arakawa, M.; Dakshinamurti, S.; Bhullar, R.P.; Duan, K.; Chelikani, P. Analysis of the expression of human bitter taste receptors in extraoral tissues. Mol. Cell. Biochem. 2017, 426, 137–147. [Google Scholar] [CrossRef]
  10. Maurer, S.; Wabnitz, G.H.; Kahle, N.A.; Stegmaier, S.; Prior, B.; Giese, T.; Gaida, M.M.; Samstag, Y.; Hänsch, G.M. Tasting Pseudomonas aeruginosa Biofilms: Human Neutrophils Express the Bitter Receptor T2R38 as Sensor for the Quorum Sensing Molecule N-(3-Oxododecanoyl)-l-Homoserine Lactone. Front. Immunol. 2015, 6, 369. [Google Scholar] [CrossRef]
  11. Tran, H.T.T.; Herz, C.; Ruf, P.; Stetter, R.; Lamy, E. Human T2R38 Bitter Taste Receptor Expression in Resting and Activated Lymphocytes. Front. Immunol. 2018, 9, 2949. [Google Scholar] [CrossRef]
  12. Shah, A.S.; Ben-Shahar, Y.; Moninger, T.O.; Kline, J.N.; Welsh, M.J. Motile cilia of human airway epithelia are chemosensory. Science 2009, 325, 1131–1134. [Google Scholar] [CrossRef]
  13. Krasteva, G.; Canning, B.J.; Hartmann, P.; Veres, T.Z.; Papadakis, T.; Mühlfeld, C.; Schliecker, K.; Tallini, Y.N.; Braun, A.; Hackstein, H.; et al. Cholinergic chemosensory cells in the trachea regulate breathing. Proc. Natl. Acad. Sci. USA 2011, 108, 9478–9483. [Google Scholar] [CrossRef]
  14. Deshpande, D.A.; Robinett, K.S.; Wang, W.C.H.; Sham, J.S.K.; An, S.S.; Liggett, S.B. Bronchodilator activity of bitter tastants in human tissue. Nat. Med. 2011, 17, 776–778. [Google Scholar] [CrossRef]
  15. Deshpande, D.A.; Wang, W.C.; McIlmoyle, E.L.; Robinett, K.S.; Schillinger, R.M.; An, S.S.; Sham, J.S.; Liggett, S.B. Bitter taste receptors on airway smooth muscle bronchodilate by localized calcium signaling and reverse obstruction. Nat. Med. 2010, 16, 1299–1304. [Google Scholar] [CrossRef] [PubMed]
  16. Orsmark-Pietras, C.; James, A.; Konradsen, J.R.; Nordlund, B.; Söderhäll, C.; Pulkkinen, V.; Pedroletti, C.; Daham, K.; Kupczyk, M.; Dahlén, B. Transcriptome analysis reveals upregulation of bitter taste receptors in severe asthmatics. Eur. Respir. J. 2013, 42, 65–78. [Google Scholar] [CrossRef] [PubMed]
  17. Finger, T.E.; Böttger, B.; Hansen, A.; Anderson, K.T.; Alimohammadi, H.; Silver, W.L. Solitary chemoreceptor cells in the nasal cavity serve as sentinels of respiration. Proc. Natl. Acad. Sci. USA 2003, 100, 8981–8986. [Google Scholar] [CrossRef] [PubMed]
  18. Jaggupilli, A.; Singh, N.; De Jesus, V.C.; Gounni, M.S.; Dhanaraj, P.; Chelikani, P. Chemosensory bitter taste receptors (T2Rs) are activated by multiple antibiotics. FASEB J. 2019, 33, 501–517. [Google Scholar] [CrossRef]
  19. Jaggupilli, A.; Howard, R.; Aluko, R.E.; Chelikani, P. Advanced glycation end-products can activate or block bitter taste receptors. Nutrients 2019, 11, 1317. [Google Scholar] [CrossRef]
  20. Jaggupilli, A.; Singh, N.; Jesus, V.C.D.; Duan, K.; Chelikani, P. Characterization of the binding sites for bacterial acyl homoserine lactones (AHLs) on human bitter taste receptors (T2Rs). ACS Infect. Dis. 2018, 4, 1146–1156. [Google Scholar] [CrossRef]
  21. Åkerström, S.; Gunalan, V.; Keng, C.T.; Tan, Y.-J.; Mirazimi, A. Dual effect of nitric oxide on SARS-CoV replication: Viral RNA production and palmitoylation of the S protein are affected. Virology 2009, 395, 1–9. [Google Scholar] [CrossRef]
  22. Hannum, M.E.; Koch, R.J.; Ramirez, V.A.; Marks, S.S.; Toskala, A.K.; Herriman, R.D.; Lin, C.; Joseph, P.V.; Reed, D.R. Taste loss as a distinct symptom of COVID-19: A systematic review and meta-analysis. medRxiv 2021. [Google Scholar] [CrossRef] [PubMed]
  23. Currie, S.M.; Findlay, E.G.; McHugh, B.J.; Mackellar, A.; Man, T.; Macmillan, D.; Wang, H.; Fitch, P.M.; Schwarze, J.; Davidson, D.J. The human cathelicidin LL-37 has antiviral activity against respiratory syncytial virus. PLoS ONE 2013, 8, e73659. [Google Scholar] [CrossRef] [PubMed]
  24. Grassin-Delyle, S.; Salvator, H.; Mantov, N.; Abrial, C.; Brollo, M.; Faisy, C.; Naline, E.; Couderc, L.J.; Devillier, P. Bitter Taste Receptors (TAS2Rs) in Human Lung Macrophages: Receptor Expression and Inhibitory Effects of TAS2R Agonists. Front. Physiol. 2019, 10, 1267. [Google Scholar] [CrossRef] [PubMed]
  25. CDC COVID-19 Response Team. Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019—United States, February 12–March 28, 2020. Morb. Mortal. Wkly. Rep. 2020, 69, 382. [Google Scholar] [CrossRef]
  26. Marfella, R.; Sardu, C.; D’Onofrio, N.; Prattichizzo, F.; Scisciola, L.; Messina, V.; La Grotta, R.; Balestrieri, M.L.; Maggi, P.; Napoli, C. Glycaemic control is associated with SARS-CoV-2 breakthrough infections in vaccinated patients with type 2 diabetes. Nat. Commun. 2022, 13, 2318. [Google Scholar] [CrossRef]
  27. Grainger, R.; Kim, A.H.; Conway, R.; Yazdany, J.; Robinson, P.C. COVID-19 in people with rheumatic diseases: Risks, outcomes, treatment considerations. Nat. Rev. Rheumatol. 2022, 18, 191–204. [Google Scholar] [CrossRef]
  28. Simpson-Yap, S.; Pirmani, A.; Kalincik, T.; De Brouwer, E.; Geys, L.; Parciak, T.; Helme, A.; Rijke, N.; Hillert, J.A.; Moreau, Y. Updated results of the COVID-19 in MS global data sharing initiative: Anti-CD20 and other risk factors associated with COVID-19 severity. Neurol.-Neuroimmunol. Neuroinflamm. 2022, 9, e200021. [Google Scholar] [CrossRef]
  29. Tam, O.H.; Aravin, A.A.; Stein, P.; Girard, A.; Murchison, E.P.; Cheloufi, S.; Hodges, E.; Anger, M.; Sachidanandam, R.; Schultz, R.M.; et al. Pseudogene-derived small interfering RNAs regulate gene expression in mouse oocytes. Nature 2008, 453, 534–538. [Google Scholar] [CrossRef]
  30. An, Y.; Furber, K.L.; Ji, S. Pseudogenes regulate parental gene expression via ceRNA network. J. Cell. Mol. Med. 2017, 21, 185–192. [Google Scholar] [CrossRef]
  31. Kim, U.; Wooding, S.; Ricci, D.; Jorde, L.B.; Drayna, D. Worldwide haplotype diversity and coding sequence variation at human bitter taste receptor loci. Hum. Mutat. 2005, 26, 199–204. [Google Scholar] [CrossRef]
  32. Lee, R.J.; Xiong, G.; Kofonow, J.M.; Chen, B.; Lysenko, A.; Jiang, P.; Abraham, V.; Doghramji, L.; Adappa, N.D.; Palmer, J.N. T2R38 taste receptor polymorphisms underlie susceptibility to upper respiratory infection. J. Clin. Investig. 2012, 122, 4145–4159. [Google Scholar] [CrossRef] [PubMed]
  33. Zheng, X.; Tizzano, M.; Redding, K.; He, J.; Peng, X.; Jiang, P.; Xu, X.; Zhou, X.; Margolskee, R.F. Gingival solitary chemosensory cells are immune sentinels for periodontitis. Nat. Commun. 2019, 10, 4496. [Google Scholar] [CrossRef] [PubMed]
  34. Barham, H.P.; Taha, M.A.; Broyles, S.T.; Stevenson, M.M.; Zito, B.A.; Hall, C.A. Association Between Bitter Taste Receptor Phenotype and Clinical Outcomes Among Patients With COVID-19. JAMA Netw. Open 2021, 4, e2111410. [Google Scholar] [CrossRef]
  35. Parsa, S.; Mogharab, V.; Ebrahimi, M.; Ahmadi, S.R.; Shahi, B.; Mehramiz, N.J.; Foroughian, M.; Zarenezhad, M.; Kalani, N.; Abdi, M.H. COVID-19 as a worldwide selective event and bitter taste receptor polymorphisms: An ecological correlational study. Int. J. Biol. Macromol. 2021, 177, 204–210. [Google Scholar] [CrossRef]
  36. Risso, D.; Carmagnola, D.; Morini, G.; Pellegrini, G.; Canciani, E.; Antinucci, M.; Henin, D.; Dellavia, C. Distribution of TAS2R38 bitter taste receptor phenotype and haplotypes among COVID-19 patients. Sci. Rep. 2022, 12, 7381. [Google Scholar] [CrossRef]
  37. Meng, T.; Nielsen, D.E. TAS2R38 haplotypes, COVID-19 infection, and symptomatology: A cross-sectional analysis of data from the Canadian Longitudinal Study on Aging. Sci. Rep. 2024, 14, 4673. [Google Scholar] [CrossRef]
  38. Raina, P.; Wolfson, C.; Kirkland, S.; Griffith, L.E.; Balion, C.; Cossette, B.; Dionne, I.; Hofer, S.; Hogan, D.; van den Heuvel, E.R.; et al. Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA). Int. J. Epidemiol. 2019, 48, 1752–1753j. [Google Scholar] [CrossRef]
  39. Forgetta, V.; Li, R.; Darmond-Zwaig, C.; Belisle, A.; Balion, C.; Roshandel, D.; Wolfson, C.; Lettre, G.; Pare, G.; Paterson, A.D.; et al. Cohort profile: Genomic data for 26 622 individuals from the Canadian Longitudinal Study on Aging (CLSA). BMJ Open 2022, 12, e059021. [Google Scholar] [CrossRef]
  40. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef]
  41. Sayers, E.W.; Bolton, E.E.; Brister, J.R.; Canese, K.; Chan, J.; Comeau, D.C.; Connor, R.; Funk, K.; Kelly, C.; Kim, S.; et al. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2022, 50, D20–D26. [Google Scholar] [CrossRef] [PubMed]
  42. McLaren, W.; Gil, L.; Hunt, S.E.; Riat, H.S.; Ritchie, G.R.S.; Thormann, A.; Flicek, P.; Cunningham, F. The Ensembl Variant Effect Predictor. Genome Biol. 2016, 17, 122. [Google Scholar] [CrossRef]
  43. Shafizadeh, M.; Bhatia, V.; Ahmed, S.; Drögemöller, B.; Stavropoulou, C.; John, P.S.; Bhullar, R.P.; Chelikani, P.; Hitchon, C.A. Bitter taste genetics and oral health in Canadian Longitudinal Study on Aging. bioRxiv 2024. [Google Scholar] [CrossRef]
  44. Han, X.; Steven, K.; Qassim, A.; Marshall, H.N.; Bean, C.; Tremeer, M.; An, J.; Siggs, O.M.; Gharahkhani, P.; Craig, J.E.; et al. Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA. Am. J. Hum. Genet. 2021, 108, 1204–1216. [Google Scholar] [CrossRef] [PubMed]
  45. Li, R.; Pei, S.; Chen, B.; Song, Y.; Zhang, T.; Yang, W.; Shaman, J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 2020, 368, 489–493. [Google Scholar] [CrossRef] [PubMed]
  46. Public Health Agency of Canada. Prevalence of Chronic Diseases Among Canadian Adults. 2019. Available online: https://www.canada.ca/en/public-health/services/chronic-diseases/prevalence-canadian-adults-infographic-2019.html (accessed on 1 October 2024).
  47. Haybar, H.; Kazemnia, K.; Rahim, F. Underlying Chronic Disease and COVID-19 Infection: A State-of-the-Art Review. Jundishapur J. Chronic Dis. Care 2020, 9, e103452. [Google Scholar] [CrossRef]
  48. Maddur, M.S.; Vani, J.; Lacroix-Desmazes, S.; Kaveri, S.; Bayry, J. Autoimmunity as a predisposition for infectious diseases. PLoS Pathog. 2010, 6, e1001077. [Google Scholar] [CrossRef]
  49. Akiyama, S.; Hamdeh, S.; Micic, D.; Sakuraba, A. Prevalence and clinical outcomes of COVID-19 in patients with autoimmune diseases: A systematic review and meta-analysis. Ann. Rheum. Dis. 2021, 80, 384–391. [Google Scholar] [CrossRef]
  50. Xiang, Y.; Zhang, M.; Jiang, D.; Su, Q.; Shi, J. The role of inflammation in autoimmune disease: A therapeutic target. Front. Immunol. 2023, 14, 1267091. [Google Scholar] [CrossRef]
  51. Cooper, G.S.; Stroehla, B.C. The epidemiology of autoimmune diseases. Autoimmun. Rev. 2003, 2, 119–125. [Google Scholar] [CrossRef]
  52. Hassen, N.; Lacaille, D.; Xu, A.; Alandejani, A.; Sidi, S.; Mansourian, M.; Butt, Z.A.; Cahill, L.E.; Iyamu, I.O.; Lang, J.J.; et al. National burden of rheumatoid arthritis in Canada, 1990–2019: Findings from the Global Burden of Disease Study 2019—A GBD collaborator-led study. RMD Open 2024, 10, e003533. [Google Scholar] [CrossRef]
  53. Coward, S.; Benchimol, E.I.; Kuenzig, M.E.; Windsor, J.W.; Bernstein, C.N.; Bitton, A.; Jones, J.L.; Lee, K.; Murthy, S.K.; Targownik, L.E.; et al. The 2023 Impact of Inflammatory Bowel Disease in Canada: Epidemiology of IBD. J. Can. Assoc. Gastroenterol. 2023, 6, S9–S15. [Google Scholar] [CrossRef] [PubMed]
  54. Macaluso, F.S.; Giuliano, A.; Fries, W.; Viola, A.; Abbruzzese, A.; Cappello, M.; Giuffrida, E.; Carrozza, L.; Privitera, A.C.; Magnano, A.; et al. Severe Activity of Inflammatory Bowel Disease is a Risk Factor for Severe COVID-19. Inflamm. Bowel Dis. 2023, 29, 217–221. [Google Scholar] [CrossRef] [PubMed]
  55. Wang, Y.; D’Silva, K.M.; Jorge, A.M.; Li, X.; Lyv, H.; Wei, J.; Zeng, C.; Lei, G.; Zhang, Y. Increased Risk of COVID-19 in Patients With Rheumatoid Arthritis: A General Population-Based Cohort Study. Arthritis Care Res. 2022, 74, 741–747. [Google Scholar] [CrossRef] [PubMed]
  56. Raina, P.S.; Wolfson, C.; Kirkland, S.A.; Keshavarz, H.; Griffith, L.E.; Patterson, C.; Uniat, J.; Strople, G.; Pelletier, A.; Angus, C.L. Ascertainment of chronic diseases in the Canadian longitudinal study on aging (CLSA), systematic review. Can. J. Aging/Rev. Can. Vieil. 2009, 28, 275–285. [Google Scholar] [CrossRef]
  57. Andreacchi, A.T.; Brini, A.; Van den Heuvel, E.; Muniz-Terrera, G.; Mayhew, A.; St John, P.; Stirland, L.E.; Griffith, L.E. An Exploration of Methods to Resolve Inconsistent Self-Reporting of Chronic Conditions and Impact on Multimorbidity in the Canadian Longitudinal Study on Aging. J. Aging Health 2023, 37, 8982643231215476. [Google Scholar] [CrossRef]
  58. Oremus, M.; Postuma, R.; Griffith, L.; Balion, C.; Wolfson, C.; Kirkland, S.; Patterson, C.; Shannon, H.S.; Raina, P. Validating Chronic Disease Ascertainment Algorithms for Use in the Canadian Longitudinal Study on Aging. Can. J. Aging/Rev. Can. Vieil. 2013, 32, 232–239. [Google Scholar] [CrossRef]
  59. Sakib, M.N.; Ramezan, R.; Hall, P.A. Diabetes status and cognitive function in middle-aged and older adults in the Canadian longitudinal study on aging. Front. Endocrinol. 2023, 14, 1293988. [Google Scholar] [CrossRef]
  60. MacNeil, A.; Cottagiri, S.A.; Villeneuve, P.J.; Jiang, Y.; de Groh, M.; Fuller-Thomson, E. Incident Functional Limitations Among Older Adults With Diabetes During the COVID-19 Pandemic: An Analysis of Prospective Data From the Canadian Longitudinal Study on Aging. Can. J. Diabetes 2024, 48, 290–298.e292. [Google Scholar] [CrossRef]
  61. Fishbook, B.N.; Brinton, C.D.; Siever, J.; Klassen, T.D.; Sakakibara, B.M. Cardiometabolic multimorbidity and activity limitation: A cross-sectional study of adults using the Canadian Longitudinal Study on Aging data. Fam. Pract. 2022, 39, 455–463. [Google Scholar] [CrossRef]
  62. Sekhon, H.; Allali, G.; Beauchet, O. Motoric cognitive risk syndrome and cardiovascular diseases and risk factors in the Canadian population: Results from the baseline assessment of the Canadian longitudinal study on aging. Arch. Gerontol. Geriatr. 2019, 85, 103932. [Google Scholar] [CrossRef]
  63. Liu, J.; Son, S.; McIntyre, J.; Narushima, M. Depression and cardiovascular diseases among Canadian older adults: A cross-sectional analysis of baseline data from the CLSA Comprehensive Cohort. J. Geriatr. Cardiol. JGC 2019, 16, 847–854. [Google Scholar] [CrossRef]
  64. Husein, N.; Josephson, C.B.; Keezer, M.R. Understanding cardiovascular disease in older adults with epilepsy. Epilepsia 2021, 62, 2060–2071. [Google Scholar] [CrossRef] [PubMed]
  65. Duong, M.; Usman, A.; Ma, J.; Xie, Y.; Huang, J.; Zaman, M.; Dragoman, A.; Jiatong Chen, S.; Farooqi, M.; Raina, P. Associations between lung function and physical and cognitive health in the Canadian Longitudinal Study on Aging (CLSA): A cross-sectional study from a multicenter national cohort. PLoS Med. 2022, 19, e1003909. [Google Scholar] [CrossRef] [PubMed]
  66. Odimba, U.; Senthilselvan, A.; Farrell, J.; Gao, Z. Identification of Sex-Specific Genetic Polymorphisms Associated with Asthma in Middle-Aged and Older Canadian Adults: An Analysis of CLSA Data. J. Asthma Allergy 2023, 16, 553–566. [Google Scholar] [CrossRef] [PubMed]
  67. Taunque, A.; Li, G.; MacNeil, A.; Gulati, I.; Jiang, Y.; de Groh, M.; Fuller-Thomson, E. Breathless and Blue in the Canadian Longitudinal Study on Aging: Incident and Recurrent Depression Among Older Adults with COPD During the COVID-19 Pandemic. Int. J. Chronic Obstr. Pulm. Dis. 2023, 18, 1975–1993. [Google Scholar] [CrossRef]
  68. O’Mahony, J.; Bernstein, C.N.; Marrie, R.A. Adverse childhood experiences and psychiatric comorbidity in multiple sclerosis, inflammatory bowel disease, and rheumatoid arthritis in the Canadian longitudinal study on aging. J. Psychosom. Res. 2024, 187, 111893. [Google Scholar] [CrossRef]
  69. Dal Bello-Haas, V.P.M.; O’Connell, M.E.; Ursenbach, J. Comparison across age groups of causes, circumstances, and consequences of falls among individuals living in Canada: A cross-sectional analysis of participants aged 45 to 85 years from the Canadian Longitudinal Study on Aging. PLoS ONE 2024, 19, e0300026. [Google Scholar] [CrossRef]
  70. Public Health Agency of Canada. Canadian Chronic Disease Surveillance System (CCDSS). 2025. Available online: https://health-infobase.canada.ca/ccdss/Index (accessed on 15 February 2025).
  71. Freeman, E.E.; Bastasic, J.; Grant, A.; Leung, G.; Li, G.; Buhrmann, R.; Roy-Gagnon, M.H. Inverse Association of APOE ε4 and Glaucoma Modified by Systemic Hypertension: The Canadian Longitudinal Study on Aging. Investig. Ophthalmol. Vis. Sci. 2022, 63, 9. [Google Scholar] [CrossRef]
  72. Akoglu, H. User’s guide to correlation coefficients. Turk. J. Emerg. Med. 2018, 18, 91–93. [Google Scholar] [CrossRef]
  73. Boyle, A.P.; Hong, E.L.; Hariharan, M.; Cheng, Y.; Schaub, M.A.; Kasowski, M.; Karczewski, K.J.; Park, J.; Hitz, B.C.; Weng, S.; et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012, 22, 1790–1797. [Google Scholar] [CrossRef]
  74. Ward, L.D.; Kellis, M. HaploReg v4: Systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016, 44, D877–D881. [Google Scholar] [CrossRef] [PubMed]
  75. RTH Center for Non-Coding in Technology and Health. RNAsnp: Predicting RNA Secondary Structure Changes upon SNPs. Available online: https://rth.dk/resources/rnasnp/ (accessed on 29 September 2025).
  76. Griffith, L.E.; Beauchamp, M.; McMillan, J.; Borhan, S.; Oz, U.E.; Wolfson, C.; Kirkland, S.; Basta, N.E.; Thompson, M.; Raina, P. Persistent COVID-19 symptoms in community-living older adults from the Canadian Longitudinal Study on Aging (CLSA). Commun. Med. 2023, 3, 36. [Google Scholar] [CrossRef] [PubMed]
  77. Oremus, M.; Tyas, S.L.; Zeng, L.; Newall, N.; Maxwell, C.J. The association between memory, COVID-19 testing, and COVID-19 incidence in middle-aged and older adults: A prospective analysis of the CLSA. Neuropsychol. Dev. Cogn. Sect. B Aging Neuropsychol. Cogn. 2024, 32, 1–18. [Google Scholar] [CrossRef] [PubMed]
  78. Barham, H.P.; Taha, M.A.; Hall, C.A. Does phenotypic expression of bitter taste receptor T2R38 show association with COVID-19 severity? Int. Forum Allergy Rhinol. 2020, 10, 1255–1257. [Google Scholar] [CrossRef]
  79. Tuzim, K.; Korolczuk, A. An update on extra-oral bitter taste receptors. J. Transl. Med. 2021, 19, 440. [Google Scholar] [CrossRef]
  80. Schuster, S.L.; Hsieh, A.C. The Untranslated Regions of mRNAs in Cancer. Trends Cancer 2019, 5, 245–262. [Google Scholar] [CrossRef]
  81. Steri, M.; Idda, M.L.; Whalen, M.B.; Orrù, V. Genetic variants in mRNA untranslated regions. Wiley Interdiscip. Rev. RNA 2018, 9, e1474. [Google Scholar] [CrossRef]
  82. Mao, Z.; Cheng, W.; Li, Z.; Yao, M.; Sun, K. Clinical Associations of Bitter Taste Perception and Bitter Taste Receptor Variants and the Potential for Personalized Healthcare. Pharmacogenom. Pers. Med. 2023, 16, 121–132. [Google Scholar] [CrossRef]
  83. De Jesus, V.C.; Singh, M.; Schroth, R.J.; Chelikani, P.; Hitchon, C.A. Association of Bitter Taste Receptor T2R38 Polymorphisms, Oral Microbiota, and Rheumatoid Arthritis. Curr. Issues Mol. Biol. 2021, 43, 1460–1472. [Google Scholar] [CrossRef]
  84. De Jesus, V.C.; Mittermuller, B.-A.; Hu, P.; Schroth, R.J.; Chelikani, P. Genetic variants in taste genes play a role in oral microbial composition and severe early childhood caries. iscience 2022, 25, 105489. [Google Scholar] [CrossRef]
  85. Khan, M.W.; Cruz de Jesus, V.; Mittermuller, B.A.; Schroth, R.J.; Hu, P.; Chelikani, P. Integrative analysis of taste genetics and the dental plaque microbiome in early childhood caries. Cell Rep. 2025, 44, 116245. [Google Scholar] [CrossRef] [PubMed]
  86. Medapati, M.R.; Singh, N.; Bhagirath, A.Y.; Duan, K.; Triggs-Raine, B.; Batista, E.L., Jr.; Chelikani, P. Bitter taste receptor T2R14 detects quorum sensing molecules from cariogenic Streptococcus mutans and mediates innate immune responses in gingival epithelial cells. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 2021, 35, e21375. [Google Scholar] [CrossRef] [PubMed]
  87. Medapati, M.R.; Bhagirath, A.Y.; Singh, N.; Schroth, R.J.; Bhullar, R.P.; Duan, K.; Chelikani, P. Bitter Taste Receptor T2R14 Modulates Gram-Positive Bacterial Internalization and Survival in Gingival Epithelial Cells. Int. J. Mol. Sci. 2021, 22, 9920. [Google Scholar] [CrossRef] [PubMed]
  88. Singh, N.; Ulmer, B.; Medapati, M.R.; Zhang, C.; Schroth, R.J.; Ghavami, S.; Chelikani, P. Bitter Taste Receptor T2R14 and Autophagy Flux in Gingival Epithelial Cells. Cells 2024, 13, 531. [Google Scholar] [CrossRef]
  89. Singh, N.; Cunnington, R.H.; Bhagirath, A.; Vaishampayan, A.; Khan, M.W.; Gupte, T.; Duan, K.; Gounni, A.S.; Dakshisnamurti, S.; Hanrahan, J.W.; et al. Bitter taste receptor T2R14-Gαi coupling mediates innate immune responses to microbial quorum sensing molecules in cystic fibrosis. iScience 2024, 27, 111286. [Google Scholar] [CrossRef]
  90. Vazquez, M.I.; Catalan-Dibene, J.; Zlotnik, A. B cells responses and cytokine production are regulated by their immune microenvironment. Cytokine 2015, 74, 318–326. [Google Scholar] [CrossRef]
  91. Hunt, R.C.; Simhadri, V.L.; Iandoli, M.; Sauna, Z.E.; Kimchi-Sarfaty, C. Exposing synonymous mutations. Trends Genet. 2014, 30, 308–321. [Google Scholar] [CrossRef]
  92. Spencer, P.S.; Siller, E.; Anderson, J.F.; Barral, J.M. Silent substitutions predictably alter translation elongation rates and protein folding efficiencies. J. Mol. Biol. 2012, 422, 328–335. [Google Scholar] [CrossRef]
  93. Aveyard, P.; Gao, M.; Lindson, N.; Hartmann-Boyce, J.; Watkinson, P.; Young, D.; Coupland, C.A.C.; Tan, P.S.; Clift, A.K.; Harrison, D.; et al. Association between pre-existing respiratory disease and its treatment, and severe COVID-19: A population cohort study. Lancet. Respir. Med. 2021, 9, 909–923. [Google Scholar] [CrossRef]
  94. Sanchez-Ramirez, D.C.; Mackey, D. Underlying respiratory diseases, specifically COPD, and smoking are associated with severe COVID-19 outcomes: A systematic review and meta-analysis. Respir. Med. 2020, 171, 106096. [Google Scholar] [CrossRef]
  95. Bycroft, C.; Freeman, C.; Petkova, D.; Band, G.; Elliott, L.T.; Sharp, K.; Motyer, A.; Vukcevic, D.; Delaneau, O.; O’Connell, J.; et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018, 562, 203–209. [Google Scholar] [CrossRef] [PubMed]
  96. Simon, D.; Tascilar, K.; Fagni, F.; Kleyer, A.; Krönke, G.; Meder, C.; Dietrich, P.; Orlemann, T.; Mößner, J.; Taubmann, J.; et al. Intensity and longevity of SARS-CoV-2 vaccination response in patients with immune-mediated inflammatory disease: A prospective cohort study. Lancet Rheumatol. 2022, 4, e614–e625. [Google Scholar] [CrossRef] [PubMed]
  97. Hitchon, C.A.; Mesa, C.; Bernstein, C.N.; Marrie, R.A.; Card, C.; O’Brien, S.F.; Kim, J. Immunogenicity and safety of mixed COVID-19 vaccine regimens in patients with immune-mediated inflammatory diseases: A single-centre prospective cohort study. BMJ Open 2023, 13, e071397. [Google Scholar] [CrossRef] [PubMed]
  98. Hitchon, C.A.; Bowdish, D.M.E.; Boire, G.; Fortin, P.R.; Flamand, L.; Chandran, V.; Dayam, R.M.; Gingras, A.C.; Card, C.M.; Colmegna, I.; et al. Methotrexate and Tumor Necrosis Factor Inhibitors Independently Decrease Neutralizing Antibodies after SARS-CoV-2 Vaccination: Updated Results from the SUCCEED Study. Vaccines 2024, 12, 1061. [Google Scholar] [CrossRef]
  99. Scott, D.L.; Wolfe, F.; Huizinga, T.W. Rheumatoid arthritis. Lancet 2010, 376, 1094–1108. [Google Scholar] [CrossRef]
  100. Kroese, J.M.; Volgenant, C.M.C.; Crielaard, W.; Loos, B.; van Schaardenburg, D.; Visscher, C.M.; Lobbezoo, F. Temporomandibular disorders in patients with early rheumatoid arthritis and at-risk individuals in the Dutch population: A cross-sectional study. RMD Open 2021, 7, 90–101. [Google Scholar] [CrossRef]
  101. Gabriel, S.E. The epidemiology of rheumatoid arthritis. Rheum. Dis. Clin. N. Am. 2001, 27, 269–281. [Google Scholar] [CrossRef]
  102. Barut, K.; Adrovic, A.; Şahin, S.; Kasapçopur, Ö. Juvenile Idiopathic Arthritis. Balk. Med. J. 2017, 34, 90–101. [Google Scholar] [CrossRef]
  103. Larheim, T.A.; Haanaes, H.R. Micrognathia, temporomandibular joint changes and dental occlusion in juvenile rheumatoid arthritis of adolescents and adults. Scand. J. Dent. Res. 1981, 89, 329–338. [Google Scholar] [CrossRef]
  104. Thibord, F.; Chan, M.V.; Chen, M.-H.; Johnson, A.D. A year of COVID-19 GWAS results from the GRASP portal reveals potential genetic risk factors. Hum. Genet. Genom. Adv. 2022, 3, 100095. [Google Scholar] [CrossRef]
  105. Shafizadeh, M.W.; Drögemöller, B.; John, P.S.; Bhullar, R.P.; Chelikani, P.; Hitchon, C.A. Association Between Taste Genetics and COVID-19 in Canadian Adults. In Proceedings of the International Association of Dental Research (IADR/PER) General Session & Exhibition, Barcelona, Spain, 25–28 June 2025. [Google Scholar]
Biomedicines 13 02473 g001
Figure 1. Overview of sample selection, data analysis, and results.
Figure 1. Overview of sample selection, data analysis, and results.
Biomedicines 13 02473 g001
Table 1. Population characteristics of the study cohorts.
Table 1. Population characteristics of the study cohorts.
CharacteristicCOVID-19 Questionnaire
(N = 14,073) 1		COVID-19 Antibody
(N = 8313) 1
Sex
  Male 6831 (49%)4133 (50%)
  Female7242 (51%)4180 (50%)
Age67 (61, 75)67 (60, 75)
BMI26.9 (24.1, 30.5)27.1 (24.3, 30.5)
Marital partner status
  Single/never married1113 (7.9%)617 (7.4%)
  Married/in a common-law relationship9728 (69%)5816 (70%)
  Widowed/divorced/separated3201 (23.2%)1857 (22.3%)
Dwelling ownership
  Own11,824 (85%)7075 (86%)
  Rent2064 (15%)1125 (14%)
Smoking857 (6.2)401 (4.9%)
Alcohol consumption
  Never1843 (13%)1039 (13%)
  Less than 4 times per month3725 (27%)2182 (26%)
  1–3 times per week4405 (31%)2635 (32%)
  More than 3 times per week4080 (29%)2445 (29%)
Number of household residents2 (2, 2)2 (2, 2)
Post-secondary education12,307 (88%)7321 (88%)
Diabetes 23099 (22%)1828 (22%)
Cardiovascular disease 37229 (51%)4204 (51%)
Respiratory disease 42890 (21%)1723 (21%)
Immune-mediated inflammatory disease 5829 (5.9%)491 (5.9%)
Tested for COVID-19 62299 (16%)2510 (31%)
Self-reported positive test for COVID-1945 78
1 n (%); median (IQR); percentages are calculated based on respondents, excluding missing data. 2 Diabetes, borderline diabetes, or high blood sugar. 3 Hypertension, peripheral arterial disease or poor circulation in limbs, heart disease, heart attack or myocardial infarction, angina, or history of stroke or CVA. 4 Asthma, emphysema, chronic bronchitis, chronic obstructive pulmonary disease (COPD), or chronic pulmonary changes due to smoking. 5 Rheumatoid arthritis or inflammatory bowel disease.6 Self-reported nucleic acid amplification test.
Table 2. Association between TAS2R variants and COVID-19 infection in the CLSA COVID-19 Questionnaire cohort with available genetic data.
Table 2. Association between TAS2R variants and COVID-19 infection in the CLSA COVID-19 Questionnaire cohort with available genetic data.
CharacteristicConfirmed/Probable COVID-19 (N = 13,536) 1Confirmed COVID-19
(N = 2204) 2
Controls
(N = 13,389)		Cases
(N = 147)		Controls
(N = 2161)		Cases
(N = 43)
OR 395% CI 3p-ValueOR95% CIp-Value
Age0.970.95, 0.99<0.0011.020.99, 1.060.24
Sex (female)1.801.28, 2.58<0.0011.450.76, 2.870.27
Smoking 0.750.33, 1.450.44NA 6NANA
Dwelling area (urban core)1.480.91, 2.560.141.370.57, 4.090.52
Dwelling ownership (rent)1.661.08, 2.490.0170.960.42, 2.010.91
Post-secondary education1.440.83, 2.710.221.250.53, 3.490.64
Diabetes1.400.94, 2.030.0881.050.47, 2.140.91
Respiratory disease1.781.24, 2.520.0011.150.55, 2.250.69
Cardiovascular disease0.840.59, 1.200.350.890.46, 1.730.73
IMID 41.330.69, 2.340.352.620.95, 6.180.041
rs2234235, TAS2R1 (A/G)1.480.80, 2.520.181.010.24, 2.780.99
rs2234009, TAS2R5 (C/T)1.090.49, 2.070.811.390.33, 4.010.60
rs2234010, TAS2R5 (G/A)0.890.40, 1.700.740.810.13, 2.810.78
rs1726866, TAS2R38 (A/G)1.090.86, 1.380.471.060.68, 1.660.79
rs34039200, TAS2R62P (G/A)1.170.89, 1.520.261.410.86, 2.280.16
rs3851584, TAS2R14 5 (T/G)1.260.99, 1.600.0581.180.75, 1.850.47
rs77837442, TAS2R19 (C/T)0.750.18, 2.020.63NA 6NANA
rs117458236, TAS2R20 (C/T)1.950.98, 3.510.0392.400.55, 7.350.17
1 Defined as a “yes” response to either of the following questions: “Have you had a positive test result?” or “Have you been told by a healthcare provider that you have COVID-19, but you did NOT have a test to confirm this?”. 2 Defined as a “yes” response to the question, “Have you had a positive test result?”. 3 Logistic regression; OR = Odds Ratio; CI = Confidence Interval. 4 IMID = Immune-mediated inflammatory disease. 5 In linkage disequilibrium with rs10772397 in TAS2R50 (r2 = 0.83) and rs1015443 and rs1015442 in TAS2R13 (r2 > 0.99). 6 Not included in logistic regression.
Table 3. COVID-19 vaccination history and antibody response in the COVID-19 Antibody Study cohort with available genetic data.
Table 3. COVID-19 vaccination history and antibody response in the COVID-19 Antibody Study cohort with available genetic data.
CharacteristicNumber (N = 8313)
Number of doses of COVID-19 vaccine 1,2
  01438
  14267
  2 or more869
  Missing 1739
Vaccine type 3
  Pfizer BioNTech3901
  Moderna600
  AstraZeneca695
  Other type28
  Missing 55
Nucleocapsid antibody response370
Spike antibody response3773
Antibody result interpretation
  Prior SARS-CoV-2 infection 4151
  Prior SARS-CoV-2 infection and/or vaccination 53554
  Prior SARS-CoV-2 infection OR infection and vaccination 6219
  No antibodies detected3757
  Missing632
1 “Have you received at least one dose of a COVID-19 vaccine?”. 2 “How many doses of COVID-19 vaccine have you received so far?”. 3 “Which vaccine did you receive?”. 4 Positive nucleocapsid antibody and negative spike antibody. 5 Negative nucleocapsid antibody and positive spike antibody. 6 Positive nucleocapsid antibody and positive spike antibody.
Table 4. Association between TAS2R variants and SARS-CoV-2 seroconversion in the CLSA COVID-19 Antibody cohort with available genetic data.
Table 4. Association between TAS2R variants and SARS-CoV-2 seroconversion in the CLSA COVID-19 Antibody cohort with available genetic data.
CharacteristicNucleocapsid Antibody (N = 7475)Spike Antibody (N = 7475)
Controls
(N = 7115)		Cases
(N = 360)		Controls
(N = 3818)		Cases
(N = 3657)
OR 195% CI 1p-ValueOR95% CIp-Value
Age0.990.98, 1.010.301.000.99, 1.010.81
Sex (female)0.870.70, 1.080.221.251.08, 1.430.002
Smoking0.840.47, 1.390.540.940.68, 1.330.74
Dwelling ownership (rent)0.940.67, 1.300.730.930.76, 1.150.50
Number of household residents1.050.95, 1.120.271.050.98, 1.120.21
Number of vaccine doses 0.980.82, 1.160.7990.272.6, 114<0.001
Post-secondary education0.840.61, 1.170.291.160.93, 1.430.19
Diabetes0.940.71, 1.230.670.890.75, 1.060.19
Respiratory disease1.080.82, 1.400.570.930.79, 1.100.40
Cardiovascular disease0.960.76, 1.210.720.900.78, 1.040.16
IMID 21.160.73, 1.760.500.650.49, 0.860.003
rs2234235, TAS2R1 (A/G)1.551.06, 2.200.0180.740.57, 0.980.033
rs2234009, TAS2R5 (C/T)1.010.62, 1.560.961.330.99, 1.800.064
rs2234010, TAS2R5 (G/A)1.561.08, 2.190.0141.260.95, 1.690.11
rs1726866, TAS2R38 (A/G)0.890.76, 1.030.131.040.94, 1.140.46
rs34039200, TAS2R62P (G/A)0.990.82, 1.190.930.860.77, 0.970.013
rs3851584, TAS2R14 (T/G)0.960.82, 1.120.631.020.93, 1.130.65
rs77837442, TAS2R19 (C/T)0.450.16, 0.990.0800.910.63, 1.340.63
rs117458236, TAS2R20 (C/T)0.990.53, 1.680.960.940.67, 1.330.73
1 Logistic regression; OR = Odds Ratio; CI = Confidence Interval. 2 IMID = immune-mediated inflammatory disease.
Table 5. Association between TAS2R variants and SARS-CoV-2 spike antibody in individuals who received at least one dose of vaccine and did not have detectable nucleocapsid antibody (infection) (N = 4404).
Table 5. Association between TAS2R variants and SARS-CoV-2 spike antibody in individuals who received at least one dose of vaccine and did not have detectable nucleocapsid antibody (infection) (N = 4404).
CharacteristicSpike Antibody
OR 195% CI 1p-Value
Age1.000.99, 1.010.72
Sex1.311.13, 1.53<0.001
Smoking0.920.65, 1.340.66
Dwelling ownership (rent)0.900.73, 1.130.37
Number of household residents1.060.98, 1.160.17
Number doses8.265.71, 12.5<0.001
Vaccine type
  Moderna2.331.34, 4.050.003
  Pfizer BioNTech1.520.91, 2.510.11
  AstraZeneca1.250.73, 2.140.42
Post-secondary education1.140.90, 1.430.28
Diabetes0.850.71, 1.020.078
Respiratory disease0.870.72, 1.040.12
Cardiovascular disease0.870.74, 1.010.074
IMID 20.620.46, 0.830.001
rs2234235, TAS2R1 (A/G)0.710.54, 0.950.021
rs2234009, TAS2R5 (C/T)1.260.92, 1.750.17
rs2234010, TAS2R5 (G/A)1.170.86, 1.620.34
rs1726866, TAS2R38 (A/G)1.020.92, 1.140.69
rs34039200, TAS2R62P (G/A)0.860.76, 0.970.017
rs3851584, TAS2R14 (T/G)1.050.95, 1.170.34
rs77837442, TAS2R19 (C/T)0.950.64, 1.430.79
rs117458236, TAS2R20 (C/T)0.880.62, 1.270.47
1 Logistic regression; OR = Odds Ratio; CI = Confidence Interval. 2 IMID = immune-mediated inflammatory disease.
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MDPI and ACS Style

Shafizadeh, M.; Khan, M.W.; Drögemöller, B.; Stavropoulou, C.; St. John, P.; Bhullar, R.P.; Chelikani, P.; Hitchon, C.A. Genetic Variations in Bitter Taste Receptors and COVID-19 in the Canadian Longitudinal Study on Aging. Biomedicines 2025, 13, 2473. https://doi.org/10.3390/biomedicines13102473

AMA Style

Shafizadeh M, Khan MW, Drögemöller B, Stavropoulou C, St. John P, Bhullar RP, Chelikani P, Hitchon CA. Genetic Variations in Bitter Taste Receptors and COVID-19 in the Canadian Longitudinal Study on Aging. Biomedicines. 2025; 13(10):2473. https://doi.org/10.3390/biomedicines13102473

Chicago/Turabian Style

Shafizadeh, Marziyeh, Mohd Wasif Khan, Britt Drögemöller, Chrysi Stavropoulou, Philip St. John, Rajinder P. Bhullar, Prashen Chelikani, and Carol A. Hitchon. 2025. "Genetic Variations in Bitter Taste Receptors and COVID-19 in the Canadian Longitudinal Study on Aging" Biomedicines 13, no. 10: 2473. https://doi.org/10.3390/biomedicines13102473

APA Style

Shafizadeh, M., Khan, M. W., Drögemöller, B., Stavropoulou, C., St. John, P., Bhullar, R. P., Chelikani, P., & Hitchon, C. A. (2025). Genetic Variations in Bitter Taste Receptors and COVID-19 in the Canadian Longitudinal Study on Aging. Biomedicines, 13(10), 2473. https://doi.org/10.3390/biomedicines13102473

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