Skip to main content
NCBI home page
Alcohol and Alcoholism (Oxford, Oxfordshire) logoLink to Alcohol and Alcoholism (Oxford, Oxfordshire)
. 2022 Oct 7;58(1):84–92. doi: 10.1093/alcalc/agac047

Chemosensory Alterations and Impact on Quality of Life in Persistent Alcohol Drinkers

Khushbu Agarwal 1,2, Jeremy W Luk 3, Peter Manza 4, Christian McDuffie 5,6, Leann To 7, Rosario B Jaime-Lara 8,9, Bethany L Stangl 10, Melanie L Schwandt 11, Reza Momenan 12, David Goldman 13,14, Nancy Diazgranados 15, Vijay A Ramchandani 16,✉, Paule V Joseph 17,18,✉
PMCID: PMC9619625  PMID: 36208183

Abstract

Background

Heavy alcohol consumption-associated chemosensory dysfunction is understudied, and early detection can help predict disease-associated comorbidities, especially those related to four quality of life (QOL) domains (physical, psychological, social and environment). We examined self-reports of chemosensory ability of individuals with different alcohol drinking behaviors and their association with changes in QOL domains.

Methods

Participants (n = 466) were recruited between June 2020 and September 2021 into the NIAAA COVID-19 Pandemic Impact on Alcohol study. Group-based trajectory modeling was used to categorize participants without any known COVID-19 infection into three groups (non-drinkers, moderate drinkers and heavy drinkers) based on their Alcohol Use Disorders Identification Test consumption scores at four different time points (at enrollment, week 4, week 8 and week 12). Linear mixed models were used to examine chemosensory differences between these groups. The associations between chemosensory abilities and QOL were determined in each group.

Results

We observed significant impairment in self-reported smell ability of heavy drinking individuals compared to non-drinkers. In contrast, taste ability showed marginal impairment between these groups. There were no significant differences in smell and taste abilities between the moderate and non-drinking groups. Heavy drinkers’ impairment in smell and taste abilities was significantly associated with deterioration in their physical, psychological, social and environmental QOL.

Conclusion

Persistent heavy drinking was associated with lower chemosensory ability. Heavy drinkers’ reduced smell and taste function and association with poorer QOL indicate that early assessment of chemosensory changes may be crucial in identifying poorer well-being outcomes in heavy drinkers at risk for alcohol use disorder.

Short Summary: Persistent heavy alcohol drinking leads to smell dysfunction as evident from our longitudinal four time point study. Group-Based Trajectory Modeling on the AUDIT consumption scores of four hundred and forty participants identified heavy-, moderate-, and non-drinking groups. A significant self-reported smell impairment was seen in heavy drinkers (vs. non-drinkers), which associated positively with the deterioration in their overall quality of life.

INTRODUCTION

Alcohol use disorder (AUD), the most prevalent substance use disorder, leads to 3.3 million global deaths each year (WHO, 2018). To date, heavy alcohol consumption is linked with malnutrition (Santolaria et al., 2003) and mental health conditions like depression and anxiety (Almeida-Filho et al., 2007). Heavy drinkers exhibit alterations in their chemosensory percepts, particularly disturbances in taste (gustation) and smell (olfaction) (Maurage et al., 2014; Silva et al., 2016). Moreover, individuals with AUD (vs. healthy controls) were shown to have taste deficits and impaired olfactory discrimination but preserved threshold detection (Brion et al., 2015). Chemosensory alteration is dependent on alcohol consumption patterns, such that heavy drinkers exhibit increased smell and taste dysfunction, whereas light-to-moderate drinkers show a lower prevalence of smell dysfunction (Liu et al., 2016). Researchers have examined the relationship between chemosensation and alcohol consumption (Peeples, 1962; Hayes et al., 2011; Beckett et al., 2017; Rawal et al., 2021); however, it is unclear if variation in chemosensory function is a determinant or a consequence of alcohol consumption, or possibly a causal bidirectional relationship.

Clinical and preclinical findings reveal that females exhibit enhanced sensitivity to odors (Kobal et al., 2001; Baum and Keverne, 2002) as well as better discrimination and identification abilities (Doty et al., 1985) than males. A recent NHANES 2011–2014 study found that young women with self-reported odor deficit tend to feel the burning effects of alcohol more and lose pleasure in consuming alcohol compared to men who report odor impairment (Rawal et al., 2021). The first reports on sex differences in odor perception appeared at the end of the 19th century (Toulouse and Vaschide, 1899), in which women (vs. men) reportedly showed lower detection thresholds in their trigeminal functioning tested using camphor. In general, women have outperformed men in their smell (Sorokowski et al., 2019) and taste (Wang et al., 2020) abilities. It is important to understand the difference in chemosensory functioning between men and women alcohol drinkers.

Both heavy alcohol consumption (Castillo-Carniglia et al., 2019) and chemosensory dysfunction (Chen et al., 2021) are known contributors to psychological disturbances (e.g. cognitive dysfunction, anxiety and depression). Persistent olfactory or gustatory dysfunction is associated with a significant reduction in a person’s QOL (Croy et al., 2014; Erskine and Philpott, 2020), including increased depressive symptoms (Hur et al., 2018), anxiety (Nordin et al., 2011) and nutritional issues (Toussaint et al., 2015). However, it is unclear if chemosensory dysfunction in heavy drinkers is associated with changes in their QOL.

To lend insight into these questions, we used data from an ongoing longitudinal study to investigate the association between self-reported chemosensory dysfunction and drinking behavior and related outcomes of participants across four time points [at enrollment, weeks 4 (week04), 8 (week08) and 12 (week12)]. The associations between self-reported chemosensory dysfunction and QOL in heavy drinking individuals were also examined. We hypothesized that heavier drinking patterns across the study period would be associated with greater chemosensory dysfunction and worse QOL. The taste and smell data were collected as part of a survey study initiated during the COVID-19 pandemic and were self-reported measures. As discussed recently by various groups using the Global Consortium on Chemosensory Research (GCCR) survey in COVID-19 patients, self-report is a useful tool for clinicians and scientists to determine the ongoing chemosensory dysfunction (Parma et al., 2020; Cecchetto et al., 2021). The specificity of smell loss in predicting COVID-19 positive status reached 75% using the GCCR survey, which demonstrates the potential of this self-report-based instrument in capturing smell dysfunction in the absence of in-person interaction with the subjects (Gerkin et al., 2021). The results obtained in this study address a research gap in alcohol and chemosensory dysfunction and shed light on the importance of early assessment of chemosensory health in chronic alcohol drinking individuals.

METHODS

Participants

The COVID-19 Pandemic Impact (C19-PIA) study was initiated in the NIAAA Intramural Clinical Research Program in June 2020. This study was approved by the NIH Intramural Institutional Review Board (IRB) and is registered in clinicaltrials.gov (NCT04391816). The overall goal of this study was to examine the impact of the COVID-19 pandemic on alcohol use and consequences in individuals across the spectrum of alcohol use. The details on the study design and the primary results are reported elsewhere (Luk Jeremy et al., 2022; Revision et al., 2022). While the C-19 PIA study was initiated due to the pandemic, the current analysis was designed to examine, as a secondary outcome, changes in chemosensation and relationship to alcohol drinking in this sample. Four hundred and sixty-six participants were enrolled between June 2020 and November 2021 into the NIAAA C19-PIA on Alcohol study conducted online and/or by phone. Most participants (83.7%) were residing in the Greater Washington DC area, specifically in Maryland (53.2%), Washington DC (17.6%) and Virginia (12.9%). The present study used repeated measurements [at enrollment, weeks 4 (week04), 8 (week08) and 12 (week12)] from this ongoing longitudinal study with a focus on drinking patterns, chemosensory functioning and QOL. We excluded participants with COVID-19 positive status (n = 17) as taste and smell impairments are vital symptoms of COVID-19 (Parma et al., 2020) and could be a confounding factor. In addition, since chemosensory functioning diminishes with age (Boyce and Shone, 2006), participants who were older than 70 years of age (n = 9) were also excluded, yielding a final analysis sample of n = 440. The NIH Intramural IRB approved the present ongoing study and is registered in clinicaltrials.gov (NCT04391816).

Alcohol consumption

The Alcohol Use Disorders Identification Test-consumption (AUDIT_C) subscale, a three-item screening questionnaire on alcohol consumption, can be used as a screener for heavy drinking or alcohol use disorder (Synofzik et al., 1993). We used AUDIT-C as a measure of alcohol consumption rather than the total AUDIT score or other AUDIT subscales (AUDIT-harmful or AUDIT-dependence), which include the assessment of the negative consequences of drinking. The participants were administered the AUDIT at all four study time points [at enrollment, week04, week08 and week12] in the C19 PIA study.

Smoking status

Smoking history was assessed using the Fagerström Test for Nicotine Dependence to characterize the participant’s lifetime smoking status. The smoking status obtained was utilized as a control variable in analyses of taste and smell to account for impairment that is not linked to alcohol consumption (Schneller et al., 2018).

Primary outcome measure

Taste and smell

The smell and taste questionnaires were modified survey questions initially designed by GCCR (Parma et al., 2020). Participants were asked to complete an online questionnaire to ‘Rate your ability to smell CURRENTLY’ and ‘Rate your ability to taste CURRENTLY’ at all four time points. The smell and taste abilities were scored based on self-rating on a visual analog scale (0–100), with higher scores indicating a better sense of taste/smell.

Secondary outcome measures

Flu-like, cough and cold symptoms

Participants were asked if they were experiencing any of the following symptoms: fever/feeling feverish, cough, sore throat, runny or stuffy nose, fatigue, difficulty breathing, diarrhea or stomach upset. The participants were to respond ‘yes’ or ‘no’ for the presence or absence of each symptom at each time point.

Quality of life

The World Health Organization Quality of Life—Brief scale was administered at all time points to evaluate several aspects of QOL, including physical health, psychological, social relationships and environment (WHO, 1998). These outcomes provide information related to individuals’ energy level for everyday activities, ability to focus and feel lively, feeling of satisfaction in relationships and peer support, as well as satisfaction level regarding their current living situations and access to health services. QOL measures can provide vital information about the overall health outcomes due to chemosensory changes in individuals associated with alcohol use. Responses for the 24 items utilized to calculate the four domain subscales were rated on a scale from 1 to 5, coded as: 1 = very poor, 2 = poor, 3 = neither poor nor good, 4 = good and 5 = very good. Raw scores for each domain are calculated by summing the items within each domain, and these scores are then transformed to a 0–100 scale. Higher scores indicated a better QOL.

Statistical analyses

Group-based trajectory modeling (GBTM) is a finite mixed modeling method employed in developmental and clinical research to capture behavior heterogeneity over time (Nagin and Odgers, 2010). Using the traj command in STATA, we utilized GBTM to identify alcohol drinking groups using the heterogeneous alcohol consumption data of the study participants derived from longitudinal data across four study time points. This is an appropriate method for our research question as it effectively captures group differences in the longitudinal pattern of alcohol consumption over time; it also allows for using a zero-inflated Poisson model to account for the positive skewness and zero-inflation in the alcohol consumption data (Nagin and Odgers, 2010). Missing data were handled using Full Information Maximum Likelihood (Supplementary Table S1). GBTM analysis on alcohol consumption scores at the four study time points categorized the participants into three drinking groups (Table 1). We chose the three-group model (Fig. 1) as the optimal model due to significant decreases in the absolute values of the log-likelihood and information criteria from 2 to 3 groups, a reasonably high entropy and interpretable trajectory with reasonable proportions of alcohol drinking groups (non-drinkers, moderate drinkers and heavy drinkers). These trajectories showed substantial stability in drinking patterns over the study period, with heavy drinkers exhibiting persistently high levels of alcohol consumption and non-drinkers exhibiting fairly low levels of alcohol consumption over time. The group of moderate drinkers showed a slight reduction in alcohol consumption over time (estimate of the linear slope = −0.16; P < 0.001) (Supplementary Table S1).

Table 1.

Characteristics of participants

Variables Alcohol consumption groups Test statistics
T-test or, Mann–Whitney for continuous/chi-square for categorical variables
Non-drinkers
(n = 115)		 Moderate drinkers
(n = 201)		 Heavy drinkers
(n = 124)		 Non-drinkers vs. heavy drinkers Non-drinkers vs. moderate drinkers
Age (years)
Mean ± SEM		 45.7 ± 13.6 42.5 ± 13.2 46.4 ± 13.5 P = 0.05 P = 0.69
Sex
 Men, n (%) 57 (49.6%) 92 (45.8%) 83 (66.9%)* χ2 = 7.4; df = 1; P = 0.01 χ2 = 0.42; df = 1; P = 0.52
 Women, n (%) 58 (50.4%) 109 (54.2%) 41 (33.1%)
Years of education (n; Mean ± SD) 106; 15.3 ± 3.4 190; 15.5 ± 3.2 121; 14.7 ± 2.9 P = 0.05 P = 0.13
Household income (n; Mean ± SD) 108; 5.0 ± 2.7 191; 6.3 ± 2.5 121; 5.0 ± 2.7 P < 0.001 P = 0.99
Smoking
 Yes, n (%) 25 (21.7%) 40 (19.9%) 52 (41.9%)* χ2 = 12.4; df = 2; P = 0.002 χ2 = 0.27; df = 2; P = 0.87
 No, n (%) 84 (73.0%) 152 (75.6%) 70 (56.5%)
 NA, n (%) 6 (5.2%) 9 (4.5%) 2 (1.6%)
Ethnicity, n (%)
 Non-Hispanic or Latino 101 (87.8%) 175 (87.1%) 112 (90.3%) χ2 = 0.04; df = 1; P = 0.84 χ2 = 0.00; df = 1; P = 1.00
 Hispanic or Latino 9 (7.8%) 17 (8.5%) 10 (8.1%)
 Unknown/not reported 5 (4.3%) 9 (4.5%) 2 (1.6%)
Race, n (%)
 White 49 (42.6%) 99 (49.3%) 76 (61.3%) * χ2 = 4.9; df = 1; P = 0.03 χ2 = 1.06; df = 1; P = 0.30
 Black/African American 44 (38.3%) 68 (33.8%) 36 (29.0%)
 Asian 9 (7.8%) 18 (9.0%) 3 (2.4%)
 American Indian or Alaska Native - - 2 (1.6%)
 Multiracial 4 (3.5%) 8 (4.0%) 4 (3.2%)
 Unknown race 9 (7.8%) 8 (4.0%) 3 (2.4%)
Alcohol Consumption/AUDIT_C
 At enrollment (n; Mean ± SD) 115; 0.32 ± 0.63 201; 3.8 ± 2.9* 124; 9.0 ± 2.8* P < 0.001 P < 0.001
 Week04 (n; Mean ± SD) 69; 0.04 ± 0.21 146; 2.20 ± 1.57* 72; 8.0 ± 2.6* P < 0.001 P < 0.001
 Week08 (n; Mean ± SD) 59; 0.03 ± 0.18 117; 2.09 ± 1.46* 66; 8.0 ± 2.5* P < 0.001 P < 0.001
 Week12 (n; Mean ± SD) 65; 0.12 ± 0.41 116; 2.49 ± 1.84* 66; 8.0 ± 2.7* P < 0.001 P < 0.001
Open in a new tab

Note: NA, not available; Household Income: 1 = < $5000; 2 = $5000–$9999; 3 = $10,000–$19,999; 4 = $20,000–$29,999; 5 = $30,000–$39,999; 6 = $40,000–$49,999; 7 = $50,000–$74,999; 8 = $75,000–$100,000; 9 = > $100,000. Here, asterisk represents statistical significance with P value <0.05 on comparing moderate with non-drinkers and heavy with non-drinkers ; only white and black racial populations were included for chi-square analysis for racial differences.

Fig. 1.

Fig. 1

Open in a new tab

The lines on the GBTM plot represent three different classes/groups of individuals (non-drinkers,25.4%; moderate drinkers,46.2%;heavy drinkers,28.4%) based on their alcohol consumption behaviors at four study time points (at enrollment (0), Week04, Week08 and Week12).

Descriptive analyses captured participants’ baseline clinical and demographic characteristics at the time of enrollment. Differences in characteristics between the three GBTM groups, non-drinkers, moderate drinkers and heavy drinkers, were examined using two-tailed independent t-tests for continuous variables and Pearson’s chi-squared test with Yates’ continuity correction analysis for categorical variables. The percentage of participants with reports on the presence of any cold/flu symptoms was evaluated at all time points to understand if the observed chemosensory alteration was influenced by the presence of known confounders (cold symptoms or upper respiratory tract infections) of chemosensory perception.

Linear mixed models (LMMs) with repeated measures of self-reported smell and taste were built to determine the difference in smell and taste abilities of both heavy and moderate drinkers compared to the non-drinkers and to compare smell and taste abilities of men and women participants within the heavy drinking group. We included the following factors as fixed effects: group (non-drinker vs. moderate drinker; non-drinker vs. heavy drinker) and time (at enrollment vs. week04 vs. week08 vs. week12). Participant ID was added as a random effect to account for the non-independence between observations within the same individuals. We used a stepwise regression approach considering variables like age, sex, smoking status and AUD history, which are known confounders of taste and smell function. Similar models were built to determine the difference in QOL domains between the groups.

The association between the smell and taste abilities and QOL domains was investigated in each alcohol group using mixed models. For all analyses, the significance level was set at P < 0.05. The output from the mixed model analysis is reported as ‘F-ratio (D.F.); P-value’ to assess the overall model fit. Next, we provided the ‘beta estimates (β); 95% CI; P-value’ to report between group differences.

Sensitivity analyses

We did three sets of sensitivity analyses. First, we evaluated the impact of missing data on the chemosensory measures and compared the demographic characteristics (age, gender, smoking and AUD history) of participants with follow-up missing and non-missing datasets. Second, we examined LMM for our outcome measures (smell and taste) in dataset with no missing data values. Third, to further investigate the possibility that age or any self-report of existing cold/flu symptoms was a substantial confounder, two separate LMMs were tested between the alcohol drinking groups (heavy drinkers vs. non-drinkers) excluding participants’ data: (a) above 60 years of age and (b) with any self-report on existing fever, runny nose, stuffed nose, diarrhea, cold or flu symptoms. STATA 16 (StataCorp, 2019, College Station, TX) and SPSS 28.0 (IBM , New York, USA) were used for all statistical analyses.

RESULTS

Demographic and clinical variables of the alcohol drinking groups

Characteristics of our study sample are summarized in Table 1. Significant sex differences were observed between the non-drinking and heavy drinking groups. Heavy drinkers comprised a higher percentage of men (66.9%) than women (33.1%). The percentage of men was significantly lower in non-drinking (49.6%) than in the heavy drinking group (66.9%, P = 0.01). Notably, the heavy drinking group comprised a higher percentage of smokers (41.9%) than non-drinkers (21.7%, P = 0.002). The heavy drinking group consisted of a higher percentage of participants (78%) with a previous history of AUD diagnosis than moderate (29%) and non-drinkers (31%). This finding supports the alcohol group categorization in our study. The percentage of the white population was higher in the heavy drinking group (61.3%) than in the non-drinking group (42.6%, P = 0.03). The AUDIT_C scores of heavy drinkers were significantly different from the non-drinkers across study time points (Table 1).

Smell and taste differences between the alcohol consumption groups

The adjusted model for age and smoking status revealed significant group (F1,217 = 5.1; P = 0.03) and time (F3,156 = 8.5; P < 0.001) effects on smell ability, and a trend-level group (F1,200 = 3.5; P = 0.06) and significant time effect (F3,169 = 8.5; P < 0.001; Table 2) on taste of non-drinkers versus heavy drinkers. Specifically, heavy drinkers exhibited significantly lower smell (β = −4.4 [95% CI 8.21 to −0.55]; P = 0.03) ability than non-drinkers. No differences in smell and taste abilities were seen between moderate and non-drinkers (Fig. 2A and B; Table 2; Supplementary Table S2).

Table 2.

Effects of alcohol drinking groups, sex (heavy drinkers), and time on smell, and taste ability, adjusted for age and smoking status

Smell Taste
Sample size Group effect Time effect Sample size Group effect Time effect
Non-drinkers vs.
moderate drinkers
Non-drinkers vs. heavy drinkers		 837
604		 F 1,289 = 2.7; P = 0.11
F1,217 = 5.1; P = 0.03		 F 3,246 = 6.6; P < 0.001
F3,156 = 8.5; P < 0.001		 836
603		 F 1,291 = 2.3; P = 0.13
F1,200 = 3.5; P = 0.06		 F 3,294 = 9.3; P < 0.001
F3,169 = 11.9; P < 0.001
Male vs. female heavy drinkers 316 F 1,112 = 4.3; P = 0.04 F 3,80 = 3.5; P = 0.02 315 F 1,101 = 1.9; P = 0.16 F 3,86 = 6.1; P < 0.001
Open in a new tab

Note: 114/114 participants at enrollment responded to smell/taste questions, 67/67 at week04, 58/58 at week08 and 63/64 at week12 in the non-drinking group; 200/200 at enrollment, 143/146 at week04, 115/115 at week08 and 112/113 at week12 in moderate drinking group, while 122/123 at enrollment, 71/71 at week04, 64/65 at week08 and 62/63 at week12 in heavy drinking group.

Fig. 2.

Fig. 2

Open in a new tab

Bar plots illustrating smell (A) and taste (B) reports across study time of 12 weeks in alcohol consumption groups: non-drinkers, moderate drinkers and heavy drinker.

Further, the adjusted model for age and smoking status revealed both significant sex (F1,112 = 4.3; P = 0.04) and time (F3,80 = 3.5; P = 0.02) effects on smell ability of men versus women heavy drinkers. Men reported greater smell impairment than women (β = −6.7 [95% CI −13.14 to −0.32]; P = 0.04). No sex difference in the taste ability of heavy drinking participants was seen (Table 2; Supplementary Table S2).

QOL differences between the alcohol consumption groups

The adjusted model for age and smoking status revealed significant group and time effects on all QOL domains, except for social relationships compared between non-drinkers versus heavy drinkers. A significant difference in the social relationships QOL domain was only seen between non-drinkers and moderate drinkers (Supplementary Table S4).

Flu-like, cough and cold symptoms

The presence of flu-like, cough and cold symptoms was reported by less than 5% of participants at enrollment, with a similar percentage at week04, and a reduction to less than 3% of the participants at weeks 8 and 12. The prevalence of these symptoms remained similar across drinking groups (Supplementary Table S3).

Association of QOL domains with chemosensory alterations by alcohol consumption groups

Moderate and heavy drinkers revealed significant positive associations between their smell and taste abilities and all QOL domains. In non-drinkers, a significant positive association between smell ability and physical health was seen, while no other significant associations between smell and taste ability and other QOL domains were observed (Table 3; Supplementary Table S5).

Table 3.

Association between chemosensory ability and QOL domains by alcohol drinking group

Physical QOL Psychological QOL Social QOL Environment QOL
Non-drinkers (n = 115)
Smell: Sample size (n) 270 268 269 271
Smell effect F 1,219 = 4.2; P = 0.04 F 1,224 = 2.0; P = 0.16 F 1,211 = 0.06; P = 0.80 F 1,215 = 0.09; P = 0.76
Time effect F 3,184 = 7.3; P < 0.001 F 3,186 = 7.6; P < 0.001 F 3,185 = 0.51; P = 0.67 F 3,187 = 5.0; P = 0.002
Taste: Sample size (n) 269 267 268 270
Taste effect F 1,228 = 2.1; P = 0.15 F 1,242 = 3.9; P = 0.05 F 1,226 = 0.03; P = 0.86 F 1,224 = 0.53; P = 0.47
Time effect F 3,182 = 6.7; P < 0.001 F 3,186 = 7.8; P < 0.001 F 3,184 = 0.56; P = 0.64 F 3,186 = 5.2; P = 0.002
Moderate drinkers (n = 201)
Smell: Sample size (n) 518 518 520 520
Smell effect F 1,496 = 4.1; P = 0.04 F 1,508 = 9.4; P = 0.002 F 1,506 = 4.9; P = 0.03 F 1,503 = 12.3; P < 0.001
Time effect F 3,358 = 29.4; P < 0.001 F 3,360 = 22.5; P < 0.001 F 3,357 = 4.3; P = 0.01 F 3,358 = 22.3; P < 0.001
Taste: Sample size (n) 516 516 518 518
Taste effect F 1,485 = 6.2; P = 0.01 F 1,491 = 5.1; P = 0.03 F 1,491 = 7.2; P = 0.01 F 1,487 = 3.6; P = 0.06
Time effect F 3,356 = 28.9; P < 0.001 F 3,356 = 20.8; P < 0.001 F 3,355 = 4.2; P = 0.01 F 3,355 = 20.4; P < 0.001
Heavy drinkers (n = 124)
Smell: Sample size (n) 279 276 279 278
Smell effect F 1,237 = 12.9; P < 0.001 F 1,229 = 16.2; P < 0.001 F 1,239 = 15.8; P < 0.001 F 1,239 = 21.5; P < 0.001
Time effect F 3,188 = 4.3; P = 0.01 F 3,187 = 6.3; P < 0.001 F 3,190 = 1.5; P = 0.21 F 3,189 = 3.4; P = 0.02
Taste: Sample size (n) 279 276 279 278
Taste effect F 1,216 = 11.5; P < 0.001 F 1,210 = 11.6; P < 0.001 F 1,218 = 28.2; P < 0.001 F 1,216 = 24.8; P < 0.001
Time effect F 3,187 = 4.5; P = 0.01 F 3,186 = 6.2; P < 0.001 F 3,189 = 1.6; P = 0.19 F 3,187 = 3.7; P = 0.01
Open in a new tab

Our sensitivity analyses revealed no significant difference in demographic characteristics (age, gender, smoking and AUD history) of the two datasets (follow-up missing and the non-missing datasets). Furthermore, the findings were unchanged when excluding participants with age >60 years (n = 63) as well as analyzing the datasets with no missing data values at any of the study time points. Repeating analyses with exclusion of participants with cold/flu symptoms revealed marginal significance for outcome measures; however, we noted similar effect sizes to those seen in our primary analyses (Supplementary Table S6).

DISCUSSION

In this study, we observed a notable impairment in self-reported smell and taste abilities of heavy alcohol drinkers compared to non-drinkers, indicating negative impact of continued chronic alcohol consumption on chemosensation. These results held after controlling for confounders like age and smoking status, which are known to influence taste and smell perceptions (Boyce and Shone, 2006; Vennemann et al., 2008) significantly. Further, less than 5% of these participants reported cold or flu symptoms (Supplementary Table S3), making those effects unlikely to have a substantial contribution to these results.

Heavy drinking could affect measures of olfactory function either via true sensory changes (for threshold, intensity, etc.) or via altered cognition/memory function (for odor identification). The impact of heavy alcohol consumption on olfactory and taste dysfunction has been demonstrated using both subjective and objective measures (Brion et al., 2015; Hoffman et al., 2016; Liu et al., 2016). According to National Health and Nutrition Examination Survey 2013–2014 data, age, gender, ethnicity, educational attainment, family income, light-to-moderate alcohol consumption, cardiovascular disease (CVD) and history of asthma or cancer were reported as potential risk factors for smell dysfunction, while ethnicity, heavy alcohol consumption and CVD were associated with a higher prevalence of taste dysfunction (Liu et al., 2016). Olfactory dysfunction [tested both objectively (UPSIT) and subjectively] was also reported in heavy alcohol drinking individuals (Hoffman et al., 2016). The quality and intensity of olfactory perception depend on the functional state of the nasal epithelium and the central and peripheral nervous systems. The damage caused to the olfactory epithelium presumably pertains to the disrupted immune system caused by chronic alcohol consumption (Pasala et al., 2015). It has long been posited that heavy alcohol consumption leads to impairment of the cognitive component of olfactory function as tested by olfactory identification (Maurage et al., 2011b). The connection of the olfactory system with emotional (amygdala) and cognitive (orbitofrontal cortex, OFC) brain regions is well known (Price, 1987), and olfactory judgements knowingly rely mainly on OFC (a crucial area involved in emotional, executive and olfactory processing) (Rolls, 2008). In many neurodegenerative disorders (schizophrenia, autism, depression and anorexia nervosa), olfactory testing is used to understand cognitive impairments (Roessner et al., 2005; Wiggins et al., 2009; Clepce et al., 2010; Velayudhan et al., 2013) and is even stated to possibly constitute as a cognitive marker in psychiatry (Atanasova et al., 2008). Specifically, AUD was associated with both impaired odor identification and source memory (on confabulation task), with a strong association between olfaction and source memory performances in the same individuals, which indicates the role of OFC in olfaction functioning in AUD individuals (Schnider et al., 1996). Notably, Maurage et al. used electrophysiological recordings to reveal that olfactory impairments in individuals with AUD are solely a consequence of cognitive impairment (abnormal latency and amplitudes of N1 and P2 waveforms) and not a consequence of general impairment that also impacts trigeminal functioning (Maurage et al., 2011a).

The present study was not focused on understanding the underlying mechanism behind the decreased smell functioning in heavy drinking individuals. It has been known that olfactory receptor neurons (OSNs) continually regenerate, approximately every 30–90 days, in the olfactory epithelium in response to normal turnover (Graziadei and Graziadei, 1979). The regeneration often accelerates following loss/damage due to drug (alcohol/tobacco), pathogen, or toxicant exposure through both inhalatory and non-inhalatory routes (Graziadei and Graziadei, 1979; Bergman et al., 2002). However, continuous alcohol consumption might inhibit the regeneration of OSNs in heavy drinkers, potentially similar to what is seen in other disease processes like chronic rhinosinusitis (Turner et al., 2010). Although we could hypothesize the potential involvement of the above noted mechanisms in smell dysfunction seen in heavy drinking individuals, this investigation needs to be extended in future studies to understand the causal effect of heavy drinking on the olfactory system. A significant decrease in smell perception of continuous heavy drinkers over the study period of 12 weeks was seen, even after adjusting for the demographic characteristics of patients. Given the stability in the drinking trajectories, it is possible that worsening of smell perception was not only driven by alcohol consumption observed during the 12-week study period but also reflected a more substantial drinking history prior to the study. Since our primary results remained unchanged after controlling for all possible confounders (including age, smoking status) and even our sensitivity analysis excluding individuals age >60 years and with any cold/flu symptoms did not change our primary results, we demonstrated that the smell deficits reported by heavy drinkers were uniquely explained by their chronic alcohol consumption. Furthermore, in our study, we noticed significant impairment in the smell ability of heavy drinking men compared to heavy drinking women. This finding may pertain to overall higher alcohol consumption in men than women (Bratberg et al., 2016). We note in our study that the percentage of men heavy drinkers was significantly higher than women heavy drinkers. However, a significant sex difference in taste reports was not evident. We also noticed a marginal self-reported taste dysfunction in heavy drinking group (vs. non-drinkers); some possible mechanisms implicated in this alteration of taste function in chronic drinkers include genetic variations (Bachmanov et al., 2002), changes in gene expression, epigenetic modifications of taste receptors (Xiao et al., 2021) and morphological changes in the salivary gland ranging from extremely dilated ducts with desquamated cells and stasis of content to epithelial atrophy (Ferraris et al., 2000). Due to taste deficiencies, negative food choices and intake ultimately disrupt nutritional health and impair immune function (Mattes and Cowart, 1994).

Finally, the smell and taste dysfunction seen with increased alcohol consumption was associated with deterioration in the overall life quality of both moderate and heavy drinkers, within all four domains, namely, physical health, psychological, social relationships, and environment. However, the effect was more significant in heavy drinkers. Smell and taste deterioration, in general, are associated with a reduced appetite, which can lead to malnourishment (Malaty and Malaty, 2013). Similarly, chemosensory dysfunction can impair one’s ability to maintain personal hygiene, which can reduce social life, affect mental stability, and deplete the overall life quality (Hummel et al., 2011). The observed impairment in chemosensory ability with increased alcohol consumption and association of smell and taste dysfunction with reduced life quality highlight the importance of early detection of any change in these sensory variables as a crucial step towards reducing the likelihood for the occurrence of comorbid health conditions, including malnutrition, obesity, anxiety, or depression in individuals at risk for AUD.

Limitations

The findings are based on subjective (self-report) evaluation of taste and smell functions since the study was conducted during the COVID-19 pandemic. We acknowledge that the results may be influenced by reporting bias. Although in our analysis we excluded any participant with a positive COVID-19 diagnosis, we could not completely rule out the possibility of reporting biases by the participants. Because of limited access to reliable SARS-CoV2 testing options, the number of positive cases was likely underestimated. Furthermore, we could not collect objective chemosensory measures; however, once safety guidelines surrounding the pandemic allow for the safe collection of objective measures in a clinical setting, we plan to incorporate these measures into this ongoing study. Chemesthesis, a third chemosensory modality, which is of high relevance to the burning effects of alcohol, has not been explored in the present study due to the lack of descriptive variables on the type of alcoholic beverage consumed. Fourthly, it is difficult to interpret the participants’ rating of taste and smell, which may often be influenced by the loss of flavor perception through retronasal olfaction. Retronasal olfaction critically determines the appreciation of flavor in both foods and beverages and in the absence of adequate explanation participants fail to distinguish between taste and flavor. Therefore, data from the present study should be taken cautiously. Data on any pre-existing pathological conditions, head trauma or concussion, that could impact chemosensation were not assessed and should be considered as potential confounders for consideration in future studies. Lastly, the findings obtained support our hypothesis suggesting that it is alcohol consumption that has led to chemosensory function changes in heavy drinking individuals but given this is an observational study, we cannot determine the direction of effects, and future experimental studies are needed to address this question.

CONCLUSION

Our study documents the effects of consistent alcohol consumption over the study period of 12 weeks on chemosensory perception. Although studies have reported an association between alcohol consumption and chemosensory alterations, the present study reports the effect of continuous alcohol consumption on chemosensation across the spectrum of alcohol use. Our results help address a critical literature gap and provide evidence supporting the inclusion of chemosensory assessments as indicators of heavy alcohol consumption indices. Understanding symptom severity can help improve the overall physical and psychological health of heavy drinkers. Early investigation in smell and taste changes may help design more effective treatments to delay the progression and complications of olfactory and taste alterations. Improvement in assessment of chemosensory disturbances may help inform and improve patient counseling, particularly regarding safety issues, and for determination of disability and monitoring the overall life quality of these individuals.

ABBREVIATIONS

QOL, quality of life; AUD, alcohol use disorder; GCCR, Global Consortium on Chemosensory Research; AUDIT_C, Alcohol Use Disorders Identification Test-consumption; GBTM, group-based trajectory modeling; LMMs, linear mixed models; OSNs, olfactory sensory neurons

AUTHOR CONTRIBUTIONS

Conceptualization: K.A., P.V.J., N.D. and V.A.R. Writing—original draft: K.A. Data curation: M.L.S., J.W.L. and B.L.S. Data analysis and interpretation: K.A., J.W.L. and P.M. Reviewing and editing: K.A., J.W.L., P.V.J., P.M., V.A.R., N.D., D.G., C.M., L.T., R.B.J-L. and R.M. Supervision: P.V.J. and V.A.R.

DISCLOSURE

The content is solely the authors’ responsibility and does not necessarily represent the official views of the NIH.

Supplementary Material

Supplemental_FigureS1_agac047
Click here for additional data file. (425.8KB, jpeg)
SUPPLEMENTAL_TABLE_S1_agac047
Click here for additional data file. (13.6KB, docx)
SUPPLEMENTAL_TABLE_S2_agac047
Click here for additional data file. (14.6KB, docx)
SUPPLEMENTAL_TABLE_S3_agac047
Click here for additional data file. (15.2KB, docx)
SUPPLEMENTAL_TABLE_S4_agac047
Click here for additional data file. (15.2KB, docx)
SUPPLEMENTAL_TABLE_S5_agac047
Click here for additional data file. (15.5KB, docx)
SUPPLEMENTAL_TABLE_S6_agac047
Click here for additional data file. (16KB, docx)
Supplemental_Figure_S1_legend_agac047
Click here for additional data file. (12.3KB, docx)

ACKNOWLEDGEMENTS

We thank Megan Carraco, Beth Lee, Sheila Walsh, Betsy Davis, Cheryl Jones, Samantha Fede, Alyssa Brooks, Tonette Vinson, Yvonne Horneffer, LaToya Sewell, the ClinDB IT team (Thuy Van, Etienne Lamoreaux, Denise Gates-Nee, Nancy Agarwal, Patty Bates, Jonathan Folkers) and the intrepid Postbaccalaureate Intramural Research Training Award fellows (Jared Axelowitz, James Morris, Hannah Kim, Emma McCabe, Carlos Melendez, Kurren Parida, Rhianna Vergeer, Ugne Ziausyte) for supporting the execution of the C19-PIA Study.

Contributor Information

Khushbu Agarwal, Section of Sensory Science and Metabolism, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA; National Institute of Nursing Research, Bethesda, MD, 20892 USA.

Jeremy W Luk, Office of the Clinical Director, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Peter Manza, Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Christian McDuffie, Section of Sensory Science and Metabolism, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA; Office of the Clinical Director, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Leann To, Section of Sensory Science and Metabolism, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Rosario B Jaime-Lara, Section of Sensory Science and Metabolism, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA; National Institute of Nursing Research, Bethesda, MD, 20892 USA.

Bethany L Stangl, Human Psychopharmacology Laboratory, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Melanie L Schwandt, Office of the Clinical Director, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Reza Momenan, Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

David Goldman, Office of the Clinical Director, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA; Laboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, Rockville, Maryland 20892, USA.

Nancy Diazgranados, Office of the Clinical Director, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Vijay A Ramchandani, Human Psychopharmacology Laboratory, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA.

Paule V Joseph, Section of Sensory Science and Metabolism, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892, USA; National Institute of Nursing Research, Bethesda, MD, 20892 USA.

FUNDING

This study was supported by NIAAA Division of Intramural Clinical and Biological Research (Z1A AA 000130, Z1A AA000213, Z1A AA000466) and an NIAID Intramural Targeted Anti-COVID (ITAC) Award. PVJ is supported by the Division of Intramural Research National Institute on Alcohol Abuse and Alcoholism (Z01AA000135) and Institute of Nursing Research and the Office of Workforce Diversity, National Institutes of Health Distinguished Scholar, and the Rockefeller University Heilbrunn Nurse Scholar Award. K.A. received Intramural Research Training Awards, National Institute of Nursing Research, National Institutes of Health, Department of Health, and Human Service and support from the NIH Center for Compulsive Behaviors Fellowship.

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interests.

DATA AVAILABILITY

Data described in the manuscript will be made available by contacting the corresponding author (P.V.J.).

References

  1. Almeida-Filho N, Lessa I, Magalhães Let al. (2007) Co-occurrence patterns of anxiety, depression and alcohol use disorders. Eur Arch Psychiatry Clin Neurosci 257:423–31. [DOI] [PubMed] [Google Scholar]
  2. Atanasova B, Graux J, el Hage Wet al. (2008) Olfaction: a potential cognitive marker of psychiatric disorders. Neurosci Biobehav Rev 32:1315–25. [DOI] [PubMed] [Google Scholar]
  3. Bachmanov AA, Reed DR, Li Xet al. (2002) Voluntary ethanol consumption by mice: genome-wide analysis of quantitative trait loci and their interactions in a C57BL/6ByJ x 129P3/J F2 intercross. Genome Res 12:1257–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Baum MJ, Keverne EB. (2002) Sex difference in attraction thresholds for volatile odors from male and estrous female mouse urine. Horm Behav 41:213–9. [DOI] [PubMed] [Google Scholar]
  5. Beckett EL, Duesing K, Boyd Let al. (2017) A potential sex dimorphism in the relationship between bitter taste and alcohol consumption. Food Funct 8:1116–23. [DOI] [PubMed] [Google Scholar]
  6. Bergman U, Ostergren A, Gustafson ALet al. (2002) Differential effects of olfactory toxicants on olfactory regeneration. Arch Toxicol 76:104–12. [DOI] [PubMed] [Google Scholar]
  7. Boyce JM, Shone GR. (2006) Effects of ageing on smell and taste. Postgrad Med J 82:239–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bratberg GH, C Wilsnack S, Wilsnack Ret al. (2016) Gender differences and gender convergence in alcohol use over the past three decades (1984–2008), The HUNT Study, Norway. BMC Public Health 16:723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Brion M, de Timary P, Vander Stappen Cet al. (2015) Chemosensory dysfunction in alcohol-related disorders: a joint exploration of olfaction and taste. Chem Senses 40:605–8. [DOI] [PubMed] [Google Scholar]
  10. Castillo-Carniglia A, Keyes KM, Hasin DSet al. (2019) Psychiatric comorbidities in alcohol use disorder. Lancet Psychiatry 6:1068–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cecchetto C, Di Pizio A, Genovese F, et al. Assessing the extent and timing of chemosensory impairments during COVID-19 pandemic. Sci Rep. 2021 Sep 1;11(1):17504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chen B, Benzien C, Faria Vet al. (2021) Symptoms of depression in patients with chemosensory disorders. ORL J Otorhinolaryngol Relat Spec 83:135–43. [DOI] [PubMed] [Google Scholar]
  13. Clepce M, Gossler A, Reich Ket al. (2010) The relation between depression, anhedonia and olfactory hedonic estimates--a pilot study in major depression. Neurosci Lett 471:139–43. [DOI] [PubMed] [Google Scholar]
  14. Croy I, Nordin S, Hummel T. (2014) Olfactory disorders and quality of life—an updated review. Chem Senses 39:185–94. [DOI] [PubMed] [Google Scholar]
  15. Doty RL, Applebaum S, Zusho Het al. (1985) Sex differences in odor identification ability: a cross-cultural analysis. Neuropsychologia 23:667–72. [DOI] [PubMed] [Google Scholar]
  16. Erskine SE, Philpott CM. (2020) An unmet need: patients with smell and taste disorders. Clin Otolaryngol 45:197–203. [DOI] [PubMed] [Google Scholar]
  17. Ferraris ME, Carranza M, Arriaga A. (2000) A structural and immunocytochemical study of palatine and labial salivary glands from chronic alcoholics. Acta odontologica latinoamericana: AOL 13:113–21. [PubMed] [Google Scholar]
  18. Gerkin RC, Ohla K, Veldhuizen MGet al. (2021) Recent smell loss is the best predictor of COVID-19 among individuals with recent respiratory symptoms. Chem Senses 46:1-12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Graziadei GA, Graziadei PP. (1979) Neurogenesis and neuron regeneration in the olfactory system of mammals. II. Degeneration and reconstitution of the olfactory sensory neurons after axotomy. J Neurocytol 8:197–213. [DOI] [PubMed] [Google Scholar]
  20. Hayes JE, Wallace MR, Knopik VSet al. (2011) Allelic variation in TAS2R bitter receptor genes associates with variation in sensations from and ingestive behaviors toward common bitter beverages in adults. Chem Senses 36:311–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hoffman HJ, Rawal S, Li CMet al. (2016) New chemosensory component in the U.S. National Health and Nutrition Examination Survey (NHANES): first-year results for measured olfactory dysfunction. Rev Endocr Metab Disord 17:221–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hummel T, Landis BN, Hüttenbrink K-B. (2011) Smell and taste disorders. GMS Curr Top Otorhinolaryngol Head Neck Surg 10:Doc04–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hur K, Choi JS, Zheng Met al. (2018) Association of alterations in smell and taste with depression in older adults. Laryngoscope Investig Otolaryngol 3:94–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kobal G, Palisch K, Wolf SRet al. (2001) A threshold-like measure for the assessment of olfactory sensitivity: the “random” procedure. Eur Arch Otorhinolaryngol 258:168–72. [DOI] [PubMed] [Google Scholar]
  25. Liu G, Zong G, Doty RLet al. (2016) Prevalence and risk factors of taste and smell impairment in a nationwide representative sample of the US population: a cross-sectional study. BMJ Open 6:e013246–e46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Luk JW, Stangl BL, Schwandt ML, et al. A person-centered approach to capture health disparities and multidimensional impact of COVID-related stressors. Am Psychol. 2022 Aug 25. doi: 10.1037/amp0001044. Epub ahead of print. PMID: 36006708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Luk, J. W., Stangl, B. L., Gunawan, T., et al (in press). Changes in Alcohol-Related Behaviors and Quality of Life during the COVID-19 Pandemic: Impact of Alcohol Use Disorder Diagnosis and Treatment History. Journal of Clinical Psychiatry. 2022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Malaty J, Malaty IA. (2013) Smell and taste disorders in primary care. Am Fam Physician 88:852–9. [PubMed] [Google Scholar]
  29. Mattes RD, Cowart BJ. (1994) Dietary assessment of patients with chemosensory disorders. J Am Diet Assoc 94:50–6. [DOI] [PubMed] [Google Scholar]
  30. Maurage P, Callot C, Philippot Pet al. (2011a) Chemosensory event-related potentials in alcoholism: a specific impairment for olfactory function. Biol Psychol 88:28–36. [DOI] [PubMed] [Google Scholar]
  31. Maurage P, Callot C, Chang Bet al. (2011b) Olfactory impairment is correlated with confabulation in alcoholism: towards a multimodal testing of orbitofrontal cortex. PLoS One 6:e23190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Maurage P, Rombaux P, Timary P. (2014) Olfaction in alcohol-dependence: a neglected yet promising research field. Front Psychol 4:1007–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Nagin DS, Odgers CL. (2010) Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol 6:109–38. [DOI] [PubMed] [Google Scholar]
  34. Nordin S, Hedén Blomqvist E, Olsson Pet al. (2011) Effects of smell loss on daily life and adopted coping strategies in patients with nasal polyposis with asthma. Acta Otolaryngol 131:826–32. [DOI] [PubMed] [Google Scholar]
  35. Parma V, Ohla K, Veldhuizen MGet al. (2020) More than smell-COVID-19 is associated with severe impairment of smell, taste, and chemesthesis. Chem Senses 45:609–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pasala S, Barr T, Messaoudi I. (2015) Impact of alcohol abuse on the adaptive immune system. Alcohol Res 37:185–97. [PMC free article] [PubMed] [Google Scholar]
  37. Peeples E. E. 1962. Taste sensitivity to phenylthiocarbamide in alcoholics. Unpublished Master's thesis, Stetson Univeristy, Deland, FL.
  38. Price J.L. The central olfactory and accessory olfactory systems, T.E. Finger, W.L. Silver (Eds.), Neurobiology of Taste and Smell, John Wiley and Sons, New York (1987), pp. 179-203 [Google Scholar]
  39. Rawal S, Duffy VB, Berube Let al. (2021) Self-reported olfactory dysfunction and diet quality: findings from the 2011-2014 National Health and Nutrition Examination Survey (NHANES). Nutrients 13(12), 4561 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Roessner V, Bleich S, Banaschewski Tet al. (2005) Olfactory deficits in anorexia nervosa. Eur Arch Psychiatry Clin Neurosci 255:6–9. [DOI] [PubMed] [Google Scholar]
  41. Rolls ET. (2008) Functions of the orbitofrontal and pregenual cingulate cortex in taste, olfaction, appetite and emotion. Acta Physiol Hung 95:131–64. [DOI] [PubMed] [Google Scholar]
  42. Santolaria F, Perez-Cejas A, Aleman MRet al. (2003) Low serum leptin levels and malnutrition in chronic alcohol misusers hospitalized by somatic complications. Alcohol Alcohol 38:60–6. [DOI] [PubMed] [Google Scholar]
  43. Schneller LM, et al. (2018) Tobacco use and chemosensory impairments among current adult tobacco users in the US: data from NHANES 2013-2014. Tob Induc Dis 16:43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Schnider A, Däniken C, Gutbrod K. (1996) The mechanisms of spontaneous and provoked confabulations. Brain 119:1365–75. [DOI] [PubMed] [Google Scholar]
  45. Silva CS, Dias VR, Almeida JARet al. (2016) Effect of heavy consumption of alcoholic beverages on the perception of sweet and salty taste. Alcohol Alcohol 51:302–6. [DOI] [PubMed] [Google Scholar]
  46. Sorokowski P, Karwowski M, Misiak Met al. (2019) Sex differences in human olfaction: a meta-analysis. Front Psychol 10:242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Synofzik M, Hufnagel RB, Züchner S. (1993) PNPLA6-related disorders. In Adam MP, et al. (eds). Seattle: University of Washington. [PubMed] [Google Scholar]
  48. Toulouse E, Vaschide N. (1899) Mesure de l’odorat chez l’homme et chez la femme. C R Soc Biol 51:381–3. [Google Scholar]
  49. Toussaint N, de Roon M, van Campen JPCMet al. (2015) Loss of olfactory function and nutritional status in vital older adults and geriatric patients. Chem Senses 40:197–203. [DOI] [PubMed] [Google Scholar]
  50. Turner JH, Liang KL, May Let al. (2010) Tumor necrosis factor alpha inhibits olfactory regeneration in a transgenic model of chronic rhinosinusitis-associated olfactory loss. Am J Rhinol Allergy 24:336–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Velayudhan L, Pritchard M, Powell JFet al. (2013) Smell identification function as a severity and progression marker in Alzheimer's disease. Int Psychogeriatr 25:1157–66. [DOI] [PubMed] [Google Scholar]
  52. Vennemann MM, Hummel T, Berger K. (2008) The association between smoking and smell and taste impairment in the general population. J Neurol 255:1121–6. [DOI] [PubMed] [Google Scholar]
  53. Wang J-J, Liang K-L, Lin W-Jet al. (2020) Influence of age and sex on taste function of healthy subjects. PLoS One 15:e0227014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. WHO . (1998) The World Health Organization Quality of Life Assessment (WHOQOL): development and general psychometric properties. Soc Sci Med 46:1569–85. [DOI] [PubMed] [Google Scholar]
  55. WHO . (2018) Global Status Report on Alcohol and Health 2018. Vladimir Poznyak and Dag Rekve, eds. pp.450 [Google Scholar]
  56. Wiggins LD, Robins DL, Bakeman Ret al. (2009) Brief report: sensory abnormalities as distinguishing symptoms of autism spectrum disorders in young children. J Autism Dev Disord 39:1087–91. [DOI] [PubMed] [Google Scholar]
  57. Xiao Y, Zhou H, Jiang Let al. (2021) Epigenetic regulation of ion channels in the sense of taste. Pharmacol Res 172:105760. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental_FigureS1_agac047
Click here for additional data file. (425.8KB, jpeg)
SUPPLEMENTAL_TABLE_S1_agac047
Click here for additional data file. (13.6KB, docx)
SUPPLEMENTAL_TABLE_S2_agac047
Click here for additional data file. (14.6KB, docx)
SUPPLEMENTAL_TABLE_S3_agac047
Click here for additional data file. (15.2KB, docx)
SUPPLEMENTAL_TABLE_S4_agac047
Click here for additional data file. (15.2KB, docx)
SUPPLEMENTAL_TABLE_S5_agac047
Click here for additional data file. (15.5KB, docx)
SUPPLEMENTAL_TABLE_S6_agac047
Click here for additional data file. (16KB, docx)
Supplemental_Figure_S1_legend_agac047
Click here for additional data file. (12.3KB, docx)

Data Availability Statement

Data described in the manuscript will be made available by contacting the corresponding author (P.V.J.).

Articles from Alcohol and Alcoholism (Oxford, Oxfordshire) are provided here courtesy of Oxford University Press

RESOURCES