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. 2024 Nov 27;60(1):agae080. doi: 10.1093/alcalc/agae080

Impact of alcohol use disorder on cognition in correlation with aging: a community-based retrospective cohort study

Hesham Essa 1, Hossam M Ali 2, Paul H Min 3,4, Dina N Ali 5, Val Lowe 6, Ronald C Petersen 7, David S Knopman 8, Emily S Lundt 9, Carly T Mester 10, Nicholas L Bormann 11, Doo-Sup Choi 12,13,14,✉
PMCID: PMC11601986  PMID: 39602567

Abstract

Aims

Excessive alcohol use is associated with an increased risk of cognitive impairment. Since increased amyloid plaque burden exacerbates cognitive decline, we sought to assess the potential impact of alcohol use disorder (AUD) on cognition, memory, and amyloid burden corresponding with age.

Methods

We conducted the retrospective analysis with 6036 subjects, including 269 AUD+ subjects. A four-item CAGE (C—Cutting Down, A—Annoyance by Criticism, G—Guilty Feeling, E—Eye-openers) alcohol questionnaire was given during the recruitment to determine AUD in each participant. We assessed cognitive function, focusing on memory using neuropsychological testing. For 1038 participants, including 57 AUD+ subjects, we measured amyloid burden using the 11C Pittsburgh Compound B tracer-based positron emission tomography imaging.

Results

AUD+ was significantly associated with lower scores of cognition and memory function relative to AUD− individuals. No significant association was found with AUD and elevated brain amyloid under the age of 65. However, further analysis showed that those aged ≥65 showed greater odds for abnormal amyloid in AUD+ compared to AUD− participants.

Conclusions

Our results underscore AUD as a risk factor for cognitive decline and diminished memory, particularly in aging populations. The role of AUD in brain amyloid accumulation requires further study.

Keywords: alcohol, cognition, amyloid, aging, memory, dementia

This study investigated the impact of alcohol use disorder (AUD) on cognition, memory, and amyloid burden in a cohort of 6036 subjects. AUD was significantly associated with lower cognitive and memory scores, particularly in those >65. However, the association between AUD and elevated brain amyloid burden requires further investigation.

Introduction

Excessive alcohol consumption is detrimental to nearly all human organ systems. The widespread consumption and socially accepted nature of alcohol contribute to its public health concerns globally (Calder et al. 2009). Chronic alcohol consumption is a leading risk factor for liver cirrhosis, alcohol-related hepatitis, cardiovascular diseases, and neurological disorders (Rehm et al. 2010). The brain is particularly susceptible to alcohol (Alfonso-Loeches and Guerri 2011). A recent retrospective cohort study reported a significant hazard ratio >3 between alcohol use disorder (AUD) and dementia for both sexes (Schwarzinger et al. 2018). Conversely, several large-scale epidemiological studies have shown a J-shaped or U-shaped association between alcohol intake and dementia risk, with light-to-moderate drinkers having a reduced risk or protective effect, as compared to non-drinkers and heavy drinkers (Ruitenberg et al. 2002; Di Castelnuovo et al. 2006; Tian et al. 2023). Light-to-moderate consumption has been associated with a lower burden of brain amyloid (Anstey et al. 2009; Xu et al. 2015; Koch et al. 2020), but claims of protection based on current evidence are premature. Interestingly, heavy alcohol consumption has not been significantly associated with increased amyloid deposits, suggesting that the relationship between alcohol intake and the pathology of Alzheimer’s disease in human populations is complex and multifaceted (Chan et al. 2010). However, acute alcohol intoxication, such as binge drinking, is an established risk factor for traumatic brain injuries (TBIs) (Weil et al. 2018; Koch et al. 2019). TBIs resulting from alcohol can lead to cognitive impairments, such as deficits in memory, attention, and information processing speed, which can persist long after the initial injury (Schulte et al. 2012).

Mild cognitive impairment (MCI) is characterized as an intermediary stage between normal cognitive aging and dementia, characterized by cognitive decline that exceeds typical age-related changes but that does not significantly impair daily functioning (Petersen 2004). Previous investigations utilizing the Mini Mental State Examination (MMSE) have shown that individuals with AUD performed worse on total and composite MMSE scores, particularly in language comprehension, attention/memory, and motor functions. Educational level was a moderator, with lower education correlating with more significant cognitive deficits (Schuch et al. 2023). A separate study showed that chronic alcohol consumption leads to cognitive deficits in up to 80% of alcohol-dependent individuals (Bernardin et al. 2014). The present study aimed to examine to better understand the correlation between AUD status, cognitive function, and age in a retrospective community-based clinical sample concerning AUD status, amyloid burden, cognitive function, and age correlation. Participants were screened for AUD, assessed for cognitive function, completed Pittsburgh Compound B tracer-based positron emission tomography (PiB-PET) imaging, and evaluated for APOE ε4 genotype status, distinguishing between carriers and non-carriers, to understand better the potential genetic underpinnings of the relationship between AUD, cognitive decline, and amyloid burden.

Methods

Informed consent and ethics

All participants provided informed consent for the use of their data in clinical research, and the Mayo Clinic and Olmsted Medical Center Institutional Review Boards approved this study.

Participants and study design

Participant recruitment for the Mayo Clinic Study of Aging (MCSA) (Roberts et al. 2008; Petersen et al. 2010). Briefly, participants were identified using the medical records–linkage system of the Rochester Epidemiology Project in MCSA. The recruitment process involved a letter of invitation to the participant, a telephone call for recruitment, followed by in-person assessment of all participants. This retrospective cohort study comprised 6036 individuals from Olmsted County in Minnesota, USA, aged 30–92 years from 2004 to 2020, after the exclusion of 242 individuals without cognitive data. A cardiovascular and metabolic conditions (CMC) risk score for each participant was computed as the sum of seven conditions proposed by the U.S. Department of Health and Human Services as indicators of vascular health. Using the Rochester Epidemiology Project database, International Classification of Diseases, Ninth and Tenth Revision codes were used to identify these seven conditions: hypertension, hyperlipidemia, cardiac arrhythmias, coronary artery disease, congestive heart failure, diabetes mellitus, and stroke (Rocca et al. 2014). Participants with a pre-existing diagnosis of dementia were identified by screening their medical records, and the clinical data were reviewed in detail by a neurologist. Participants confirmed to have dementia were not invited to participate in the study (n = 402). A subset of 1038 participants having PiB-PET imaging data and APOE ε4 genotyping were assessed for amyloid burden differences. All data utilized in this analysis were retrieved from the MCSA database (Fig. 1).

Figure 1.

Figure 1

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CONSORT flow diagram. Flow chart describing the number of subjects included in the study and relative demographic information and their alcohol use status along with the number of subjects having genotype status of APOE ε4. Also included is the demographic and gene status of subjects included in the PiB-PET imaging analysis.

Screening for alcohol use disorder

All participants completed the CAGE alcohol questionnaire, a validated tool for evaluating AUD (Ewing 1984). The CAGE questionnaire is a four-question screening tool to identify AUD (Malet et al. 2005). CAGE is an acronym for the focus of the questions: C—Cutting Down, A—Annoyance by Criticism, G—Guilty Feeling, and E—Eye-openers. Each of the four questions of the CAGE questionnaire can be answered with a simple yes or no response. Participants were subsequently categorized as either having alcohol use disorder, AUD+, or not, AUD−, by a CAGE cut-off score of 2.

Assessment of cognition

All participants underwent a comprehensive neurological evaluation and neuropsychological testing as previously described (Roberts et al. 2008). Testing included the MMSE. Neuropsychometrists administered the Wechsler Adult Intelligence Scale-Revised and additional cognitive assessments targeting four essential cognitive functions: executive function, language, memory, and visuospatial ability. The Trail-Making Test Part B (Reitan and Skills 1958) and the Digit Symbol Substitution Test (Chelune et al. 1990) assessed executive function. The Boston Naming Test (Roth 2011) and the Category Fluency Test (Lucas et al. 1998) assessed language. The Wechsler Memory Scale assessed delayed recall (Chelune et al. 1991; Ivnik et al. 1992). The Picture Completion and Block Design tests assessed visuospatial ability. Cognitive test scores were converted to z-scores based on a reference cohort of cognitively normal subjects from MCSA, aged 50–89, and enrolled between 2004 and 2012, weighted to the 2013 Olmsted County population. Z-Scores for individual tests were averaged to generate domain-specific z-scores, and a global z-score was computed as the average of the four-domain z-scores. The global z-score is emphasized as it reflects memory function, the primary domain of interest. Participants underwent cognitive assessments approximately every 15 months. Subsequently, a group of physicians, study coordinators, and neuropsychometrists reviewed participant’s history, cognitive performance, and functional abilities; reviewed the test results for each participant; and reached a consensus on their cognitive status based on established criteria. Mild cognitive impairment was diagnosed based on the Mayo Clinic criteria for MCI (Petersen 2004), requiring the fulfillment of four conditions: (1) cognitive concerns expressed by a physician, informant, participant, or nurse; (2) noticeable impairment in one of the four cognitive domains (executive functions, memory, language, or visuospatial skills); (3) normal functional activities and no significant impairment in daily life activities; and (4) absence of dementia diagnosis. Participants received diagnoses ranging from cognitively unimpaired, MCI, or dementia.

PiB-PET imaging of amyloid burden

PET imaging utilizing the 11C Pittsburgh Compound B (PiB) tracer was conducted on a subset of 1038 participants, comprising 57 individuals in the AUD+ cohort and 981 individuals in the AUD− cohort. In brief, a dynamic PiB scan was acquired from participants ~40–60 min post-intravenous injection of 292–728 MBq of 11C-PiB. Subsequently, image analysis was performed using an in-house fully automated pipeline, which involved assessing voxel values within automatically delineated regions of interest. Each participant received a global amyloid-PET standardized uptake value ratio (SUVr). A cut-off value of SUVr >1.48 was used to separate abnormal (amyloid positive) and normal (amyloid negative) scans, consistent with previous literature (Jack Jr et al. 2019).

Statistical analyses

Participants’ characteristics were tested univariately using linear model analysis of variance (ANOVA) or Pearson’s Chi-squared test when appropriate. To assess whether AUD status was related to lower cognition, separate linear regression models were fit for each cognition outcome and AUD group. The cognition outcomes of interest included a global z-score, memory z-score, and MMSE. Age, sex, and education were adjusted for as covariates. Cognition tends to have a curvilinear relationship with age, especially when including subjects aged <65 years. To accurately estimate the shape of the age relationship, we included a restricted cubic spline transformation on age with 3 knots at ages 35, 60, and 85. Interactions of age-by-AUD group and age-by-sex were considered; however, neither met criteria for inclusion at the α = .05 level of significance.

To estimate the relationship between amyloid burden and AUD among the subset having amyloid-PET imaging, linear regression model was fit on amyloid SUVr, including AUD group as a covariate and adjusting for age, sex, and APOE ε4 carrier status. A spline transformation on age was used for the same reasons described above. The interaction age-by-AUD group allows those with AUD to modify the shape of the age relationship to amyloid. Interaction terms for age-by-APOE ε4 carrier status and age-by-sex were considered, but neither were significant at the α = .05 level and were not included. Finally, a natural log transformation of amyloid SUVr was used to reduce skewness and allow for interpretation of results on the percent scale. AUD group differences were tested in the odds for abnormal amyloid (SUVr >1.48) by fitting a logistic regression model. To estimate per-group odds ratios (OR), logistic regression models were fit on abnormal amyloid status, including a four-level group term defined by age in years and AUD: AUD+ over 65, AUD− over 65, AUD+ less than 65, and designating the reference group as AUD− less than 65. Models were adjusted for known confounders of sex and APOE ε4.

We did not want to strongly control the rate of false-positive findings at the expense of false negatives (Rothman 1990). While we do not believe that adjustment for multiple comparisons is warranted in pairwise testing of the ORs, the Bonferroni adjustment would consider P < .05/6 = .0083 to be statistically significant. Unadjusted P-values were provided allowing the reader to perform their own correction. Note that the Bonferroni adjustment is known to be conservative.

A sensitivity analysis was conducted to assess the impact of other conditions possibly related to cognitive performance. Regression models described above were additionally adjusted for CMC risk score.

Results

AUD is associated with cognitive impairment

Participant demographics are displayed in Table 1. The AUD+ cohort was, on average, 3 years younger, 68 (SD = 12) versus 71 (SD = 13) (P = .002), with a more significant proportion of males (75% vs 50%). Comorbidities and CMC risk scores were reported in two separate tables; Table 2.1 shows comorbidities by groups among subjects with cognition data, and Table 2.2 shows comorbidities by group among subjects having amyloid-PET data. The AUD+ group had a greater frequency of stroke and diabetes mellitus. However, it has been established that alcohol intake is associated with a higher risk of strokes (Smyth et al. 2023) and a higher risk of diabetes in heavy drinkers and binge drinkers (Polsky and Akturk 2017). Greater than 93% of subjects had complete cognitive data while partial cognitive data were available in n = 286 AUD− and n = 18 AUD+ participants. The AUD+ group exhibited lower global z-scores with a mean score of −.42 (range: −4.74 to 2.35; SD 1.26) compared to the AUD− group, which showed a mean cognitive z-score of −.29 (range: −5.52 to 2.96; SD 1.28) (see Table 3).

Table 1.

Participants’ characteristics

Characteristic AUD− AUD+ P-value
Sample size 5767 269
Age
 Mean (SD) 71 (13) 68 (12) .002a
 Range 31–92 33–90
Sex
 Female (%) 2912 (50) 66 (25) <.001b
 Male (%) 2855 (50) 203 (75)
Education
 Mean (SD) 15 (3) 14 (2) .052a
 Range 11–20 11–20
APOE ε4
 N-miss 392 15 .356b
 Carrier (%) 1475 (27) 63 (25)
 Non-carrier (%) 3900 (73) 191 (75)
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aLinear model ANOVA

bPearson’s Chi-squared test. N-miss, number of participants missed; SD, standard deviation

Table 2.1.

Comorbidities by group among subjects having cognition data

AUD− (N = 5765) AUD+ (N = 268) P-value
Cardiometabolic conditions (CMC) score .006a
Mean (SD) 2.1 (1.4) 2.3 (1.4)
.027b
 0 827 (14%) 31 (12%)
 1 1363 (24%) 53 (20%)
 2 1537 (27%) 62 (23%)
 3 1077 (19%) 64 (24%)
 4 604 (10%) 38 (14%)
 5 278 (5%) 19 (7%)
 6 72 (1%) 1 (0%)
 7 7 (0%) 0 (0%)
Hypertension 3685 185 .085b
Hyperlipidemia 4309 (75%) 206 (77%) .434b
Cardiac arrhythmias: atrial fibrillation 724 (13%) 37 (14%) .548b
Cardiac arrhythmias: atrial flutter 261 (5%) 10 (4%) .539b
Coronary artery disease 368 (6%) 15 (6%) .606b
Congestive heart failure 470 (8%) 22 (8%) .974b
Diabetes mellitus 943 (16%) 60 (22%) .010b
Stroke 239 (4%) 20 (7%) .009b
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aLinear model ANOVA

bPearson’s Chi-squared test. N-Miss = 3; 2 AUD−, 1 AUD+ subjects

Table 2.2.

Comorbidities by group among subjects having amyloid-PET data

AUD− (N = 980) AUD+ (N = 57) P-value
Cardiometabolic conditions (CMC) score .187a
Mean (SD) 1.71 (1.30) 1.95 (1.55)
.549b
 0 187 (19%) 11 (19%)
 1 285 (29%) 15 (26%)
 2 263 (27%) 12 (21%)
 3 147 (15%) 9 (16%)
 4 72 (7%) 6 (11%)
 5 21 (2%) 3 (5%)
 6 4 (0%) 1 (2%)
 7 1 (0%) 0 (0%)
Hypertension 511 29 .533b
Hyperlipidemia 724 (74%) 43 (75%) .794b
Cardiac arrhythmias: atrial fibrillation 66 (7%) 6 (11%) .274b
Cardiac arrhythmias: atrial flutter 25 (3%) 1 (2%) .708b
Coronary artery disease 35 (4%) 1 (2%) .466b
Congestive heart failure 33 (3%) 3 (5%) .447b
Diabetes mellitus 135 (14%) 12 (21%) .126b
Stroke 21 (2%) 4 (7%) .020b
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aLinear model ANOVA

bPearson’s Chi-squared test. N-Miss = 1 AUD− subject

Table 3.

Cognition scores and tests for group differences

AUD− (n = 5767) AUD+ (n = 269) P-value
N-miss Mean (SD) or count (%) a Range a N-miss Mean (SD) or count (%) a Range a
Global cognition, z 286 −0.29 (1.28) −5.52 to 2.96 18 −0.42 (1.26) −4.74 to 2.35 <.001b
Memory function, z −0.31 (1.19) −3.26 to 2.83 −0.49 (1.16) −3.18 to 2.28 <.0001b
MMSE score 26 28 (2) 5–30 1 28 (2) 17–30 .02b
Cognitive impairment
 Cognitively unimpaired 5038 (87) 223 (83)
 MCI 648 (11) 41 (15) .1023
 Dementia 81 (1) 5 (2)
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aUnadjusted for covariates

bLinear model adjusted for age, sex, and education, 3Pearson’s Chi-squared test. N-miss, number of participants missed; SD, standard deviation; MMSE, Mini Mental State Examination; MCI, minimal cognitive impairment

A scatter plot with unadjusted loess curve demonstrated lower cognitive z-scores for the AUD+ group (age-by-cognition) (Fig. 2A), with covariate-adjusted linear regression analysis confirming this observation (see Fig. 2B). Within our sample, 17% of participants with AUD were diagnosed with cognitive impairment, compared to only 13% of the AUD− group (P = .033). Notably, the relationship with age exhibited a downward inflection around age 55, shifting across all ages toward lower z-scores for the AUD+ group. Given significant age and sex disparities between the two groups, a linear regression model was employed to adjust for these confounding variables. Although no interaction between age and AUD was observed (P = .6), after accounting for age, sex, and education, the AUD+ group demonstrated lower global cognition z-scores on average across all ages (estimate = −.245, 95% confidence interval (CI) −.359 to −.13; P < .001). Similarly, the AUD+ group exhibited an average .2 z lower memory across all ages (estimate = −.203, 95% CI −.319 to −.088; P < .001). Furthermore, following adjustment for age, sex, and education, the AUD+ group displayed lower MMSE scores on average across all ages (estimate = −.247, 95% CI −.458 to −.035; P = .02). These findings support the hypothesis that AUD is associated with impaired cognitive and memory function.

Figure 2.

Figure 2

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AUD is associated with global cognitive impairment. (A) The first panel is a scatterplot between global cognition and age with loess curves for each group. Global cognition is the average of four z-scored cognition domains memory, attention, language, and visuospatial. The relationship with age appears nonlinear, and there is a shift toward lower z-scores for AUD+. (B) The second panel shows the predicted z-score by year of age from a linear regression model on a global z-score adjusted for age, sex, education, and AUD group. Predictions for groups AUD− (blue) and AUD+ (red) with 95% confidence band (shaded gray band) with remaining covariate set to the median value. The overall message is that AUD+ have lower global z-scores on average across all ages by around a quarter of a z-score.

Associations of AUD with brain PET amyloid

A linear regression analysis on amyloid burden adjusted for age, sex, APOE ε4, and AUD status showed that individuals carrying the APOE ε4 allele displayed an elevated amyloid burden regardless of AUD status in both males and females. Interestingly, the estimated sex effect was negligible, with an estimated coefficient of nearly 0 (−.018, .018) and a P-value of .99, implying that the sexes are almost identical (Fig. 3B). Notably, while amyloid burden appeared similar in individuals aged <65, those aged >65 within the AUD+ group displayed significantly elevated amyloid burden (P < .001). Post hoc, groups were then stratified into ‘AUD+ over 65’, ‘AUD+ under 65’, and ‘AUD− over 65’, with ‘AUD− under 65’ serving as the reference group. Logistic regression for abnormal amyloid burden showed that the odds of abnormal amyloid did not significantly differ between the AUD+ under 65 group and the reference group (OR = 1.0, 95% CI = .9 to 1.2; P = .87), as expected. However, among individuals aged >65, both AUD− and AUD+ groups demonstrated elevated odds of abnormal amyloid burden compared to their respective younger counterparts. The AUD− over 65 group exhibited 30% greater odds (OR = 1.3, 95% CI = 1.3 to 1.4; P < .001), while the AUD+ over 65 group displayed 60% greater odds (OR = 1.6, 95% CI 1.3 to 1.8; P < .001). The AUD+ over 65 groups demonstrated a 23% increase in the odds of an abnormal amyloid burden compared to their non-AUD counterparts (23% = (1.6 − 1.3)/1.3 × 100; OR = 1.2, 95% CI = 1.02 to 1.3; P = .03).

Figure 3.

Figure 3

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AUD is associated with a greater amyloid burden in the aged >65 AUD+ group. (A) Without adjusting for covariates, the scatterplot between age and PiB indicates that the relationship between age and PIB is modified by having a history of alcohol use disorder. The relationship with age is nonlinear. The red curve for AUD+ appears to have a more significant load of amyloid plaques starting around age ≥65. (B) The predicted amyloid scores on a log scale at each year of age were separately calculated for AUD− and AUD+ in each combination of APOE ε4 and sex. Predictions are from a model on log-transformed amyloid and adjusted for age, sex, APOE ε4 genotype, AUD group, and age-by-AUD group interaction. No interactions with APOE ε4 or sex met the inclusion criteria; hence, AUD− and AUD+ are equidistant from each other in the panels.

Sensitivity analysis adjusting for cardiovascular and metabolic conditions

Adjustment for conditions on the causal pathway may lead to biased estimates; therefore, models were adjusted for CMC risk score. The cognition model estimates were comparable even after adjusting for CMC having similar estimated effect sizes versus primary models (global z estimate = −.232, 95% CI −.346 to −.117, P < .001; memory z estimate = −.201, 95% CI −.316 to −.085, P < .001; MMSE estimate = −.227, 95% CI −.438 to −.016, P = .04). The amyloid-PET model estimates were similar such that conclusions were unchanged even after adjusting for comorbidities. AUD+ had 20% greater odds of abnormal amyloid versus AUD− among subjects aged >65 (OR = 1.2, 95% CI 1.02 to 1.3, P = .03).

Discussion

Our study contributes to the existing literature by examining associations of AUD with cognitive function and amyloid burden. In contrast to previous studies, we analyzed a relatively large community-based sample that included longitudinal neuropsychological testing and neuroimaging data. The CAGE questionnaire offers simplicity, high sensitivity, cross-cultural applicability, and established validity, making it preferable over the Alcohol Use Disorders Identification Test (AUDIT) or the Alcohol Timeline Followback (TLFB) in certain situations, particularly when quick and efficient alcohol screening is required (Smith et al. 2009). The large sample size of the current study (981 AUD− and 57 AUD+) is larger than prior studies (Flanigan et al. 2021) and allowed us to stratify our cohort by age. Although we did not find an association with AUD status and brain amyloid-PET levels in the group as a whole, a post hoc analysis suggested that AUD+ persons aged >65 years had higher brain amyloid. This result needs to be viewed as tentative, but it should prompt explorations of the link between alcoholism and brain amyloid accumulation in other cohorts. A recent review concludes that while alcohol use exacerbates brain aging and cognitive impairments, it does not necessarily increase the risk of Alzheimer’s disease (Zahr 2024). The complex relationship between alcohol use and dementia requires further investigation to understand the underlying mechanisms and improve diagnostic criteria and neuropsychological tests.

Despite the well-documented health risks associated with alcohol consumption, its societal acceptance and accessibility continue to foster its widespread use. Moreover, alcohol consumption has long been linked to cognitive decline and impairment, affecting various cognitive domains such as memory, attention, executive function, and information processing speed (Sullivan and Pfefferbaum 2005). Chronic alcohol use can lead to structural and functional changes in the brain, including neuronal loss, alterations in neurotransmitter systems, and disruption of white matter integrity (Oscar-Berman and Marinkovic 2003). Furthermore, acute alcohol intoxication can result in temporary cognitive impairments, including impaired judgment, slowed reaction times, and decreased inhibitory control (Field et al. 2010). Addressing this public health challenge necessitates comprehensive strategies encompassing prevention, intervention, and policy measures aimed at reducing alcohol-related harm and promoting healthier lifestyles.

Our study contains several limitations. Given the retrospective community-based recruitment approach, 269 AUD+ participants are a considerably smaller sample size than that of 5767 AUD− participants. Future prospective studies investigating the temporal changes of amyloid burden in AUD+ and AUD− cohorts would provide more impactful correlation. Another limitation as the analyses do not include control for other risk factors that could potentially influence amyloid burden such as sleep disorders, chronic stress, and cardiovascular conditions like hypertension, obesity, diabetes, and hypercholesterolemia. This is a limitation of the data available. Additionally, although the CAGE questionnaire has demonstrated reliability and validity in identifying individuals with AUD (Fiellin et al. 2000), clinical interviews using the AUDIT, or TLFB interview, may provide a more accurate diagnosis of AUD. Therefore, further studies with comprehensive AUD assessments will solidify our findings.

Conclusion

This study reveals associations between AUD and cognitive impairment within a large community-based sample. Our findings emphasize the significant correlation between AUD and cognitive decline, memory, and amyloid deposition. Consistent with prior research, we show that individuals with AUD exhibit lower cognitive performance across various cognitive domains.

Acknowledgements

We thank the faculty in MCSA (Mayo Clinic Study of Aging) and Choi laboratory members for their helpful and insightful discussion.

Contributor Information

Hesham Essa, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Hossam M Ali, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Paul H Min, Department of Radiology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States; Department of Neurosurgery, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Dina N Ali, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Val Lowe, Department of Radiology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Ronald C Petersen, Department of Neurology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

David S Knopman, Department of Neurology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Emily S Lundt, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Carly T Mester, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Nicholas L Bormann, Department of Psychiatry and Psychology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Doo-Sup Choi, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States; Department of Psychiatry and Psychology, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States; Neuroscience Program, Mayo Clinic College of Medicine and Science, 200 First Street SW, Rochester, MN 55905, United States.

Author contributions

Hesham Essa (Methodology [equal], Visualization [equal], Writing—original draft [equal]), Hossam Ali (Conceptualization [equal], Data curation [equal], Writing—review & editing [equal]), Paul Min (Investigation [equal], Methodology [equal], Visualization [equal], Writing—original draft [equal]), Dina Ali (Methodology [equal]), Val Lowe (Conceptualization [equal], Methodology [equal], Visualization [equal]), Ronald Petersen (Funding acquisition [equal], Supervision [equal], Writing—review & editing [equal]), David Knopman (Conceptualization [equal], Writing—review & editing [equal]), Emily Lundt (Conceptualization [equal], Data curation [equal], Formal analysis [equal], Software [equal], Writing—original draft [equal], Writing—review & editing [equal]), Carly Mester (Data curation [equal], Formal analysis [equal], Software [equal]), Nicholas Bormann (Writing—review & editing [equal]), and Doo-Sup Choi (Conceptualization [equal], Funding acquisition [equal], Investigation [equal], Project administration [equal], Supervision [equal], Writing—original draft [equal], Writing—review & editing [equal]).

Conflict of interest. None declared.

Funding

This work was supported by the Samuel C. Johnson Genomics of Addiction Program at Mayo Clinic, the Ulm Foundation, the National Institute on Alcohol Abuse and Alcoholism (NIAAA; R21 AA028968, R01 AA029258), and National Institute on Aging (NIA; R01 AG072898, U01 AG006786).

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

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