Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Feb 15.
Published in final edited form as: J Aging Health. 2021 Aug-Sep;33(7-8 Suppl):51S–59S. doi: 10.1177/08982643211013696

Associations of binge drinking with vascular brain injury and atrophy in older American Indians: The Strong Heart Study

Jordan P Lewis a, Astrid M Suchy-Dicey b,c, Carolyn Noonan b,c, Valarie Blue Bird Jernigan d, Jason G Umans e, Kimiko Domoto-Reilly f, Dedra S Buchwald b,c, Spero Manson g
PMCID: PMC8845484  NIHMSID: NIHMS1777006  PMID: 34167344

Abstract

American Indians (AIs) generally consume less alcohol than the U.S. general population, although the prevalence of alcohol use disorder is higher. Binge drinking (the consumption of at least 5 standard alcohol units within a two-hour period) may confer heightened risk of cerebrovascular disease, resulting in vascular brain injury (VBI) or atrophy. We examined the contribution of binge drinking to risk of VBI and cerebral atrophy in older AIs. The Strong Heart Study and its ancillary study, Cerebrovascular Disease and Its Consequences in American Indians (CDCAI), comprises 25 years of longitudinal data collection on self-reported binge drinking behaviors; the CDCAI study also included brain MRIs. The sample size for this study was 817 participants. Binge drinking was independently associated with increased prevalence of abnormal sulcal or ventricle dilatation. These observed associations for cerebral atrophy are consistent with previous findings among children, among patients with alcohol use disorders and dependence, and among mental health patients. No known studies have reported these associations among general population adults. The mechanism for these effects may include neurotoxicity. The observed associations, especially those for hippocampal volume, are of unclear temporality. This is the first large cohort study to examine binge drinking as a risk factor for vascular brain injury and cerebral atrophy, independent of smoking, obesity, diabetes, hypertension, and hyperlipidemia, among older AIs.

INTRODUCTION

American Indian (AI) adults generally consume alcohol in similar or lower quantities, compared to the majority U.S. population, although use patterns vary by tribe, age, and sex Beals et al., 2003; Greene et al., 2014; Whitesell et al., 2012). However. prevalence of substance use disorder or binge drinking is higher among AIs, compared with non-AIs (Gillespie & Hurvitz, 2013; Mozaffarian et al., 2016; CDC, 2012; Whitesell et al., 2012), with the most extreme differences observed among young adults aged 26 to 49 (Akins et al., 2013; Dickerson et al., 2012; Robin et al., 1998). Binge drinking, defined as consumption of at least 5 standard alcohol units within a two-hour period, has been linked to increased risk of cerebrovascular disease (CBVD; Knopman et al., 2001; Marchant et al., 2012). Several mechanisms may underlie these effects, including impairment of endothelial cell function and nitric oxide production, resulting in disruption of arterial-vascular function, hormonal imbalance in fluid and blood pressure regulation, and development of plaque in atherosclerosis (Piano, 2017). CBVD can result in vascular brain injury (VBI), including ischemic or hemorrhagic lesions, and white matter small vessel disease, or in cerebral atrophy, such as sulcal widening, ventricle enlargement, or loss of brain volume in regions such as the hippocampus. Together, these neuropathological changes may contribute to functional losses including dementia. VBI may be clinically recognized, as with transient ischemic attack (TIA) and stroke, or it may be covert, only detected with imaging such as magnetic resonance imaging (MRI). High prevalence of covert VBI has been reported for the general population, even in people without a history of CBVD (Akins et al., 2013; Dickerson et al., 2012), and it has also been associated with increased risk of subsequent stroke and death (Go et al., 2013; Ong et al., 2007; Robin et al., 1998). Older AIs suffer substantial disparities in CBVD comorbidities such as hypertension (Howard, 1996a; Howard, Lee, Yeh, et al., 1996; W. Wang et al., 2006; Zhang et al., 2008), diabetes (Howard, 1996b; Howard, Lee, Fabsitz, et al., 1996; Lee et al., 1995; W. Wang et al., 2017), and obesity (Gray et al., 2000; Welty et al., 1995) as well as high prevalence of MRI-defined VBI and cerebral atrophy (Suchy-Dicey et al., 2017). Yet, no study has examined the relative contribution of binge drinking or heavy alcohol use to neurological changes in this population. Identifying how alcohol use patterns may contribute to VBI and cerebral atrophy in older AIs may suggest potentially modifiable health behaviors and offer novel opportunities for neurological risk reduction and, ultimately, disease prevention in an underserved population.

METHODS

Setting

The Strong Heart Study investigated cardiovascular disease and related risk factors among 4,549 AIs aged 45–74 years, from 13 tribes across 3 regions of the United States (Lee et al., 1990). Participants completed health assessments including extensive behavior and alcohol use questionnaires at multiple visits, including phase 1 (1989–91), phase 2 (1993–95), and phase 3 (1998–99) of the original cohort. The Cerebrovascular Disease and its Consequences in American Indians (CDCAI) study comprised an ancillary examination of 1,033 surviving participants from the original cohort (2010–2013), and included many of the same behavior and alcohol use assessments, in addition to 1.5T structural MRI of the brain (Suchy-Dicey et al., 2016). After data collections were completed, one community withdrew permission to use their data, and were excluded from further analysis. The current analyses included participants who completed one or more of the phase 1, 2, and 3 visits of the original Strong Heart Study as well as the MRI visit of the CDCAI study (N = 817).

Data Collection

Primary Exposure:

At all 4 analytic time-points (1989–91; 1993–95; 1998–99; 2010–13), field site staff administered questionnaires to collect detailed health behaviors including quantity and frequency of alcohol use. Binge drinking was assessed with the question “How many times during the past year did you have 5 or more drinks on an occasion?” To capture cumulative binge drinking behavior over the 25-year time period spanning participants’ middle- and late-life, the number of reported binge drinking episodes was averaged over the four time-points. To examine differences between recent (2010–2013) and more distant past (1989–91, 1993–95, 1998–99) binge drinking behaviors, as of the time of the CDCAI (MRI) examinations, participants were also categorized as having no binge drinking behavior at any time-point; having distant past but not recent binge drinking behavior; or having both distant past and recent binge drinking behavior. A fourth category, having recent but not distant past binge drinking, did not include enough participants for meaningful analysis (N = 12) and were excluded from the categorical analyses.

Outcomes:

At the CDCAI (MRI) examination, 1.5T scanners were used to obtain six image sequences of the brain in contiguous slices, as previously described in detail (Suchy-Dicey et al., 2016; Suchy-Dicey et al., 2017). Study neuroradiologists, blinded to all participant characteristics, scored the scans for the presence of brain infarcts and hemorrhages, and for extent (graded on scale 0–9) of white matter (small vessel) disease, sulcal widening, and ventricle enlargement. Intracranial volumes and white matter hyperintensities were assessed by software processing.

Covariates:

Demographic characteristics were assessed at the CDCAI (MRI) examination, and included age, sex, self-reported years of education, self-reported annual household income, and study center (geographic region). Body mass index (BMI) was calculated as weight (in kg) divided by height (in m)2 and categorized as normal weight (< 25 kg/m2), overweight (25–29.9 kg/m2), and obese (≥ 30 kg/m2). Smoking behavior was self-reported as never / not having smoked more than 100 cigarettes over the lifetime, former smoker, or current smoker. Diabetes was defined as fasting glucose ≥ 126 mg/dL or anti-diabetic medication use. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or anti-hypertensive medication use. Hyperlipidemia was defined as low-density lipoprotein (LDL) ≥ 100 mg/dL or antilipidemic drug use (e.g., statins).

Statistical Analysis

The associations between binge drinking behaviors, modeled both continuously and categorically, with MRI findings of VBI or of isolated structural atrophy were evaluated using regression modeling. Linear regression was used for continuous dependent variables, including white matter hyperintensity (WMH), hippocampus, and brain volumes and graded measures of white matter (small vessel) disease, sulcal widening, and ventricular enlargement (scale 0–9). Poisson regression was used for binary dependent variables, including presence of lacunar and non-lacunar infarcts. Nested models adjusted first only for demographic factors. A second model additionally included adjustment for BMI, smoking behavior, diabetes, hypertension, and high LDL measured at the CDCAI (MRI) examination, because these factors could be mediators in causal pathways between alcohol use patterns and VBI. Models for volumetric outcomes (WMH, hippocampus, brain) were additionally adjusted for intracranial volume to account for interindividual variation in head size. Inferential results were calculated with 95% confidence intervals based on two-tailed tests of significance.

Sixty-two percent of participants had complete data for all outcome and exposure variables and covariates. Binge drinking behavior was missing for 2.7%, 15.5%, 8.3%, and 0.2% of observations at Strong Heart Study phase 1, 2, 3, and the CDCAI study, respectively. The amount of missing data for the outcome variables ranged from 3.9% (infarcts) to 17.3% (brain volume) with most missing < 7%. Most covariates had no missing data; body mass index was missing for 0.6% of participants and hyperlipidemia was missing for 1.4% of participants. Multiple imputation by chained equations was used to impute missing data across all time points (White et al., 2011). Imputation models included all variables in the regression models; auxiliary variables included binge drinking in the past month and quantity and frequency of alcohol use at each time point. Predictive mean matching was used to impute continuous and ordinal variables to eliminate out-of-sample predictions and because most variables had a skewed distribution. Logistic regression was used to impute binary variables. Fifty imputations were performed, and Rubin’s rule was used to compute combined point estimates and standard errors that account for variability due to the imputation process (Toutenburg & Rubin, 1990). A complete case analysis requiring non-missing binge drinking behavior variables for all 4 time points was also performed as a sensitivity analysis. All analyses, including multiple imputation, were conducted using StataCorp Statistical Software (Version 15).

RESULTS

Participants were predominantly female (68%) and older (mean age 73 years). Most were at least high school graduates (80%) and most had annual household incomes below $20,000 (60%). The majority were either never (34%) or former (45%) smokers. At the MRI examination, most participants were obese (55%), diabetic (49%), and hypertensive (81%), and 67% had high LDL. Participants who endorsed recent binge drinking behavior (Table 1) tended to be younger and more often male and current smokers compared to those who never endorsed binge drinking behavior or only endorsed it during exams prior to the MRI.

Table 1:

Selected Participant Characteristics, According to Past and Recent Binge Drinking Behavior Categories.

Characteristic No Binge Ever % Past, Not Recent % Past and Recent %
Age, years
 60–69.9 31 36 53
 70–74.9 30 34 26
 75–79.9 21 19 15
 80+ 18 11 6
 Male sex 24 39 65
Education
 < High school 19 22 20
 High school graduate 26 25 27
 Some college 39 41 36
 ≥ College degree 16 13 17
Annual household income
 < $10,000 30 31 35
 $10,000–$19,999 27 34 23
 $20,000–$34,999 22 22 16
 ≥ $35,000 21 12 26
Body mass index (BMI)
 Normal weight (BMI < 25) 17 12 14
 Overweight (BMI 25–29) 29 31 35
 Obese (BMI 30+) 54 57 50
 Tobacco smoking
 Never 39 30 8
 Former 47 46 34
 Current 14 24 57
 Diabetes 49 54 40
 Hypertension 80 81 80
 High LDL 68 66 66
Open in a new tab

Note. LDL = low-density lipoprotein.

Sulcal widening was positively associated with binge drinking, with an estimated 0.007 increase in grade (95% CI: [0.002, 0.013]) for each additional episode of binge drinking in the last year (Table 2). This finding was not substantively different with additional adjustment for cardiovascular comorbidities or smoking behavior (beta: 0.006, 95% CI: [0.001, 0.012]). There was little evidence to suggest an association between average binge drinking over 20+ years of middle- and late-life with brain volume, hippocampus volume, white matter disease (volume or grade), or ventricular enlargement (dilatation); nor with presence of infarcts (prevalence ratio: 1.003, 95% CI: [0.995–1.010]).

Table 2.

Association Between Average Binge Drinking Over 25 Years and Findings From Brain MRI.

Model 1a Model 2a
Betab 95% CI Betab 95% CI
Brain volume, mL −.200 −.483, .084 −1.88 −.473, .098
Hippocampus volume, mL −.002 −.007, .003 −.001 −.006, .004
WMH volume, mL .007 −.025, .038 .003 −.029, .035
White matter grade −.001 −.007, .004 −.002 −.008, .004
Sulcal widening grade .007 .002, .013 .006 .001, .012
Ventricle enlargement grade −.001 −.007, .006 −.002 −.008, .005
Open in a new tab
a

Model 1 adjusted for demographics: age, sex, education, income, study center; Model 2 additionally adjusted for body mass index, smoking behavior, diabetes, hypertension, high LDL.

b

Beta coefficient is interpretable as the change in the outcome per one episode increase in average binge drinking exposure.

Note. 95% CI = 95% Confidence Interval; LDL = low-density lipoprotein; MRI = magnetic resonance imaging; WMH = white matter hyperintensity.

When binge drinking behavior was categorized to examine the impact of past versus recent usage patterns, positive associations were detected for sulcal widening grade and ventricular enlargement grade (Table 3). Sulcal widening was associated with more extreme abnormality identified for participants with past but not recent binge drinking (beta: 0.244, 95% CI: [0.066, 0.422]) and past and recent binge drinking (beta: 0.360, 95% CI: [0.079, 0.641]), even after adjustment for vascular comorbidities; ventricle enlargement grade was most abnormal among those with past and recent binge drinking (beta: 0.514, 95% CI: [0.165, 0.863]). There was little evidence to suggest any associations for total brain volume, WMH volume or white matter grade, hippocampal volume, or for infarcts (prevalence ratio: 0.938, 95% CI: [0.570–1.543]) for past and recent binge drinking.

Table 3.

Association Between Past and Recent Binge Drinking Categories and Findings From Cranial MRI.

Continuous MRI measures
Model 1a Model 2a
Outcome and Binge Category Betab 95% CI Betab 95% CI
Total brain volume, mL
 No binge at any time point Referent Referent
 Past but not recent binge −3.089 −12.45, 6.268 −2.459 −11.82, 6.899
 Past and recent binge −6.999 −21.32, 7.317 −7.949 −22.86, 6.962
WMH volume, mL
 No binge at any time point Referent Referent
 Past but not recent binge .426 −.635, 1.486 .383 −.687, 1.454
 Past and recent binge 1.645 −.024, 3.314 1.529 −.212, 3.271
Hippocampus volume, mL
 No binge at any time point Referent Referent
 Past but not recent binge −.028 −.183, .128 −.011 −.167, .146
 Past and recent binge −.231 −.489, .027 −.176 −.445, .093
WMH grade
 No binge at any time point Referent Referent
 Past but not recent binge .009 −.181, .199 .009 −.183, .200
 Past and recent binge .266 −.032, .563 .259 −.051, .569
Sulcal widening grade
 No binge at any time point Referent Referent
 Past but not recent binge .244 .066, .422 .227 .049, .405
 Past and recent binge .360 .079, .641 .335 .045, .624
Ventricle enlargement grade
 No binge at any time point Referent Referent
 Past but not recent binge .074 −.142, .290 .058 −.157, .272
 Past and recent binge .512 .174, .850 .514 .165, .863
Open in a new tab
a

Model 1 adjusted for demographics: age, sex, education, income, study center; Model 2 additionally adjusted for body mass index, smoking behavior, diabetes, hypertension, high LDL.

b

Beta coefficient is interpretable as the change in outcome comparing participants in the indicated binge drinking category with those in the no binge drinking category.

Note. 95% CI = 95% Confidence Interval; LDL = low-density lipoprotein; MRI = magnetic resonance imaging; WMH = white matter hyperintensity.

Results from complete case sensitivity analyses showed some differences from our primary results. The magnitude increased for the association between brain volume with average binge drinking and categorical binge drinking behavior; there was an estimated 0.337 mL decrease (95% CI: [−0.634, −0.039]) for each additional episode of average binge drinking in the last year (Supplement Table 1). The magnitude of associations between other outcome variables with average binge drinking (Supplement Table 1) and categorical binge drinking behavior (Supplement Table 2) remained similar to the primary results; however, confidence intervals were wider in the complete case analysis, likely due to the reduced sample size.

DISCUSSION

We used 25 years of prospectively-obtained, population-based cohort data, including self-reported binge drinking or alcohol use and clinically-assessed CBVD disease risk factors, along with brain MRI findings in AI elders, to examine the associations of VBI or cerebral atrophy with past and current binge-drinking in elderly AIs with high burden of diabetes and CBVD risk. We expected binge drinking to be associated with MRI-assessed evidence of VBI and cerebral atrophy. Binge drinking behavior, modeled continuously as number of typical binge episodes per year, was not associated with infarcts, white matter (small vessel) disease, or total brain volume, but was associated with abnormal sulcal widening. When categorized by recency, binge drinking was also associated with ventricle dilatation.

Ventricle enlargement and cerebral atrophy have been reported in association with heavy alcohol use, both independently and in combination with brain changes observed in mental illness (Lange et al., 2017; Sullivan et al., 2000). The mechanism for these effects has recently been hypothesized as occurring through neurotoxic effect on ventricle-lining glial cells (Omran et al., 2017), in addition to neurovascular effects (Klatsky, 2015). One study found that alcohol-dependent patients with use disorders did recover some cerebral volume after abstinence intervention, suggesting some possibility of reversibility (Wang et al., 2016). One or both of the estimates in this study may be biased and further investigation is needed.

The relationship between alcohol consumption and brain volume have been explored in previous studies but are limited in scope because alcohol consumption was assessed during only midlife or late life and was not examined or included in their analyses (Downer et al., 2015). Conversely, previous studies have not taken into account the brain volume when examining the relationship between alcohol consumption and cognition among older adults. The Framingham Offspring Cohort study examined the relationship between midlife and late life alcohol consumption and regional brain volumes and is one of the few studies exploring this relationship (Paul et al., 2008).

Smaller hippocampus volume has recently been implicated in compulsive behavior patterns, including alcohol use disorders (Yoon et al., 2017), but we did not see an association with hippocampal volume. However, abnormal cortical change and the resulting cognitive impairment may also result from the neurotoxic effects of heavy alcohol use, suggesting a possible iterative or circular effect (Liput et al., 2017; Ozsoy et al., 2013).

This study population was comprised only of older AIs; therefore, comparisons with other populations are not possible. Although the exposure data are longitudinal, the outcome data are cross-sectional, so temporal sequence cannot be directly observed and findings on the MRI may be the cause and not the consequences for the alcohol use behaviors. Survival from the original cohort recruitment in 1989–1991 to the CDCAI MRI examination in 2010–2013 may influence participant selection if likelihood of participation is influenced by either exposure or outcome; however, such biases, if present, would be likely to selectively remove the most extreme cases, increasing the possibility of Type II error and, therefore, limiting the opportunity to detect associations if they do exist. Limited information was available regarding binge drinking behaviors in the population from which this cohort is selected. The binge drinking behaviors that were measured were coarsely defined, so there may be some degree of measurement error. However, the results were consistent for two different approaches to the definition of exposure. Because of limited statistical power, this study did not stratify by sex or age. Previous studies have demonstrated that women may be more sensitive to the harmful effects of alcohol (Wardzala et al., 2018; Wilhelm et al., 2015), especially after chronic intoxication (Alfonso-Loeches et al., 2013; Wilhelm et al., 2015), increasing their vulnerability to alcohol-induced neurological damage; future research should examine effect modification by sex and other factors. In summary, this is the first large cohort study of older AIs examining binge drinking as a risk factor for VBI and atrophy.

Supplementary Material

Supplementary Material

Acknowledgements

We wish to thank all study participants, field sites, and staff.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by funding from the National Heart Lung and Blood Institute [U01HL41642, U01HL41652, U01HL41654, U01HL65520, U01HL65521, R01HL109315, R01HL109301, R01HL109284, R01HL109282, R01HL109319, R01HL093086, P60MD000507, P30AG15297] and the University of Washington Alzheimer’s Disease Research Center [P50AG005136]. The opinions expressed in this paper are those of the author(s) and do not necessarily reflect the views of the National Institutes of Health (NIH).

Footnotes

Declaration of Conflicting Interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

REFERENCES

  1. Akins S, Lanfear C, Cline S, & Mosher C (2013). Patterns and correlates of adult American Indian substance use. Journal of Drug Issues, 43(4), 497–516. 10.1177/0022042613491100 [DOI] [Google Scholar]
  2. Alfonso-Loeches S, Pascual M, & Guerri C (2013). Gender differences in alcohol-induced neurotoxicity and brain damage. Toxicology, 311(1–2), 27–34. 10.1016/j.tox.2013.03.001 [DOI] [PubMed] [Google Scholar]
  3. Beals J, Spicer P, Mitchell CM, Novins DK, Manson SM, Big Crow CK, Buchwald D, Chambers B, Christensen ML, Dillard DA, DuBray K, Espinoza PA, Fleming CM, Frederick AW, Gurley D, Jervis LL, Jim SM, Kaufman CE, Keane EM, Klein SA, … Yazzie LL (2003). Racial disparities in alcohol use: comparison of 2 American Indian reservation populations with national data. American Journal of Public Health, 93(10), 1683–1685. 10.2105/ajph.93.10.1683 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Centers for Disease Control & Prevention (CDC). (2012). Vital signs: Binge drinking prevalence, frequency, and intensity among adults - United States, 2010. MMWR Morbidity and Mortality Weekly Report, 61(1), 14–19. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6101a4.htm [PubMed] [Google Scholar]
  5. Dickerson DL, Fisher DG, Reynolds GL, Baig S, Napper LE, & Anglin MD (2012). Substance use patterns among high-risk American Indians/Alaska Natives in Los Angeles County. American Journal on Addictions, 21(5), 445–452. 10.1111/j.1521-0391.2012.00258.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Downer B, Jiang Y, Zanjani F, & Fardo D (2015). Effects of alcohol consumption on cognition and regional brain volumes among older adults. American Journal of Alzheimer’s Disease and Other Dementias, 30(4), 364–374. 10.1177/1533317514549411 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Gillespie CD, & Hurvitz KA (2013). Prevalence of hypertension and controlled hypertension - United States, 2007–2010. MMWR Suppl, 62(3), 144–148. https://www.cdc.gov/Mmwr/preview/mmwrhtml/su6203a24.htm [PubMed] [Google Scholar]
  8. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Kissela BM, Kittner SJ, Lackland DT, … Turner MB (2013). Executive summary: Heart disease and stroke statistics--2013 update: A report from the American Heart Association. Circulation, 127(1), 143–152. 10.1161/CIR.0b013e318282ab8f [DOI] [PubMed] [Google Scholar]
  9. Gray RS, Fabsitz RR, Cowan LD, Lee ET, Welty TK, Jablonski KA, & Howard BV (2000). Relation of generalized and central obesity to cardiovascular risk factors and prevalent coronary heart disease in a sample of American Indians: The Strong Heart Study. International Journal of Obesity and Related Metabolic Disorders, 24(7), 849–860. 10.1038/sj.ijo.0801243 [DOI] [PubMed] [Google Scholar]
  10. Greene KM, Eitle TM, & Eitle D (2014). Adult social roles and alcohol use among American Indians. Addictive Behaviors, 39(9), 1357–1360. 10.1016/j.addbeh.2014.04.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Howard BV (1996a). Blood pressure in 13 American Indian communities: The Strong Heart Study. Public Health Reports, 111(Suppl 2), 47–48. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1381664/ [PMC free article] [PubMed] [Google Scholar]
  12. Howard BV (1996b). Risk factors for cardiovascular disease in individuals with diabetes. The Strong Heart Study. Acta Diabetologica, 33(3), 180–184. https://link.springer.com/article/10.1007/BF02048540 [DOI] [PubMed] [Google Scholar]
  13. Howard BV, Lee ET, Fabsitz RR, Robbins DC, Yeh JL, Cowan LD, & Welty TK (1996). Diabetes and coronary heart disease in American Indians: The Strong Heart Study. Diabetes, 45(Suppl 3), S6–13. 10.2337/diab.45.3.s6 [DOI] [PubMed] [Google Scholar]
  14. Howard BV, Lee ET, Yeh JL, Go O, Fabsitz RR, Devereux RB, & Welty TK (1996). Hypertension in adult American Indians. The Strong Heart Study. Hypertension, 28(2), 256–264. 10.1161/01.HYP.28.2.256 [DOI] [PubMed] [Google Scholar]
  15. Klatsky AL (2015). Alcohol and cardiovascular diseases: where do we stand today? Journal of Internal Medicine, 278(3), 238–250. 10.1111/joim.12390 [DOI] [PubMed] [Google Scholar]
  16. Knopman D, Boland LL, Mosley T, Howard G, Liao D, Szklo M, McGovern P, & Folsom AR (2001). Cardiovascular risk factors and cognitive decline in middle-aged adults. Neurology, 56(1), 42–48. 10.1212/wnl.56.1.42 [DOI] [PubMed] [Google Scholar]
  17. Lange EH, Nerland S, Jorgensen KN, Morch-Johnsen L, Nesvag R, Hartberg CB, Haukvik UK, Osnes K, Melle I, Andreassen OA, & Agartz I (2017). Alcohol use is associated with thinner cerebral cortex and larger ventricles in schizophrenia, bipolar disorder and healthy controls. Psychological Medicine, 47(4), 655–668. 10.1017/s0033291716002920 [DOI] [PubMed] [Google Scholar]
  18. Lee ET, Howard BV, Savage PJ, Cowan LD, Fabsitz RR, Oopik AJ, Yeh J, Go O, Robbins DC, & Welty TK (1995). Diabetes and impaired glucose tolerance in three American Indian populations aged 45–74 years. The Strong Heart Study. Diabetes Care, 18(5), 599–610. 10.2337/diacare.18.5.599 [DOI] [PubMed] [Google Scholar]
  19. Lee ET, Welty TK, Fabsitz R, Cowan LD, Le NA, Oopik AJ, Cucchiara AJ, Savage PJ, & Howard BV (1990). The Strong Heart Study. A study of cardiovascular disease in American Indians: Design and methods. American Journal of Epidemiology, 132(6), 1141–1155. 10.1093/oxfordjournals.aje.a115757 [DOI] [PubMed] [Google Scholar]
  20. Liput DJ, Pauly JR, Stinchcomb AL, & Nixon K (2017). Binge alcohol exposure transiently changes the endocannabinoid system: A potential target to prevent alcohol-induced neurodegeneration. Brain Sciences, 7(12), 1–18. 10.3390/brainsci7120158 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Marchant NL, Reed BR, DeCarli CS, Madison CM, Weiner MW, Chui HC, & Jagust WJ (2012). Cerebrovascular disease, beta-amyloid, and cognition in aging. Neurobiol Aging, 33(5), 1006.e25–e36. 10.1016/j.neurobiolaging.2011.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jimenez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, … Turner MB (2016). Executive summary: Heart disease and stroke statistics--2016 update: A report from the American Heart Association. Circulation, 133(4), 447–454. 10.1161/cir.0000000000000366 [DOI] [PubMed] [Google Scholar]
  23. Omran AJA, Saternos HC, Althobaiti YS, Wisner A, Sari Y, Nauli SM, & AbouAlaiwi WA (2017). Alcohol consumption impairs the ependymal cilia motility in the brain ventricles. Scientific Reports, 7(1), 13652. 10.1038/s41598-017-13947-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ong KL, Cheung BM, Man YB, Lau CP, & Lam KS (2007). Prevalence, awareness, treatment, and control of hypertension among United States adults 1999–2004. Hypertension, 49(1), 69–75. 10.1161/01.hyp.0000252676.46043.18 [DOI] [PubMed] [Google Scholar]
  25. Ozsoy S, Durak AC, & Esel E (2013). Hippocampal volumes and cognitive functions in adult alcoholic patients with adolescent-onset. Alcohol, 47(1), 9–14. 10.1016/j.alcohol.2012.09.002 [DOI] [PubMed] [Google Scholar]
  26. Paul CA, Au R, Fredman L, Massaro JM, Seshadri S, Decarli C, & Wolf PA (2008). Association of alcohol consumption with brain volume in the Framingham study. Archives of Neurology, 65(10), 1363–1367. 10.1001/archneur.65.10.1363 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Piano MR (2017). Alcohol’s Effects on the Cardiovascular System. Alcohol Research, 38(2), 219–241. https://www.ncbi.nlm.nih.gov/pubmed/28988575 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Robin RW, Long JC, Rasmussen JK, Albaugh B, & Goldman D (1998). Relationship of binge drinking to alcohol dependence, other psychiatric disorders, and behavioral problems in an American Indian tribe. Alcoholism Clinical & Experimental Research, 22(2), 518–523. 10.1111/j.1530-0277.1998.tb03682.x [DOI] [PubMed] [Google Scholar]
  29. Suchy-Dicey AM, Shibata D, Best LG, Verney SP, Longstreth WT Jr., Lee ET, Okin PM, Devereux R, O’Leary M, Ali T, Jensen PN, Muller C, Nelson LA, Rhoades E, Madhyastha T, Grabowski TJ, Beauchamp N, Umans JG, & Buchwald D (2016). Cranial magnetic resonance imaging in elderly American Indians: Design, methods, and implementation of the Cerebrovascular Disease and its Consequences in American Indians Study. Neuroepidemiology, 47(2), 67–75. 10.1159/000443277 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Suchy-Dicey AM, Shibata DK, Madhyastha TM, Grabowski TJ, Longstreth WT Jr., & Buchwald DS (2017). Findings of vascular brain injury and structural loss from cranial magnetic resonance imaging in elderly American Indians: The Strong Heart Study. Neuroepidemiology, 48(1–2), 39–47. 10.1159/000459624 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Sullivan EV, Deshmukh A, Desmond JE, Mathalon DH, Rosenbloom MJ, Lim KO, & Pfefferbaum A (2000). Contribution of alcohol abuse to cerebellar volume deficits in men with schizophrenia. Archives of General Psychiatry, 57(9), 894–902. 10.1001/archpsyc.57.9.894 [DOI] [PubMed] [Google Scholar]
  32. Toutenburg H, & Rubin DB (1990). Multiple imputation for nonresponse in surveys. Statistical Papers, 31(1), 180–180. [Google Scholar]
  33. Wang GY, Demirakca T, van Eijk J, Frischknecht U, Ruf M, Ucar S, Hermann D, Mann K, Kiefer F, & Ende G (2016). Longitudinal mapping of gyral and sulcal patterns of cortical thickness and brain volume regain during early alcohol abstinence. European Addiction Research, 22(2), 80–89. 10.1159/000438456 [DOI] [PubMed] [Google Scholar]
  34. Wang W, Lee ET, Fabsitz RR, Devereux R, Best L, Welty TK, & Howard BV (2006). A longitudinal study of hypertension risk factors and their relation to cardiovascular disease: The Strong Heart Study. Hypertension, 47(3), 403–409. 10.1161/01.hyp.0000200710.29498.80 [DOI] [PubMed] [Google Scholar]
  35. Wang W, Zhang Y, Lee ET, Howard BV, Devereux RB, Cole SA, Best LG, Welty TK, Rhoades E, Yeh J, Ali T, Kizer JR, Kamel H, Shara N, Wiebers DO, & Stoner JA (2017). Risk factors and prediction of stroke in a population with high prevalence of diabetes: The Strong Heart Study. World Journal of Cardiovascular Diseases, 7(5), 145–162. 10.4236/wjcd.2017.75014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Wardzala C, Murchison C, Loftis JM, Schenning KJ, Mattek N, Woltjer R, Kaye J, Quinn JF, & Wilhelm CJ (2018). Sex differences in the association of alcohol with cognitive decline and brain pathology in a cohort of octogenarians. Psychopharmacology (Berl), 235(3), 761–770. 10.1007/s00213-017-4791-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Welty TK, Lee ET, Yeh J, Cowan LD, Go O, Fabsitz RR, Le NA, Oopik AJ, Robbins DC, & Howard BV (1995). Cardiovascular disease risk factors among American Indians. The Strong Heart Study. American Journal of Epidemiology, 142(3), 269–287. 10.1093/oxfordjournals.aje.a117633 [DOI] [PubMed] [Google Scholar]
  38. Whitesell NR, Beals J, Crow CB, Mitchell CM, & Novins DK (2012). Epidemiology and etiology of substance use among American Indians and Alaska Natives: Risk, protection, and implications for prevention. American Journal of Drug and Alcohol Abuse, 38(5), 376–382. 10.3109/00952990.2012.694527 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Wilhelm CJ, Hashimoto JG, Roberts ML, Bloom SH, Beard DK, & Wiren KM (2015). Females uniquely vulnerable to alcohol-induced neurotoxicity show altered glucocorticoid signaling. Brain Research, 1601, 102–116. 10.1016/j.brainres.2015.01.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Yoon EJ, Choi JS, Kim H, Sohn BK, Jung HY, Lee JY, Kim DJ, Park SW, & Kim YK (2017). Altered hippocampal volume and functional connectivity in males with Internet gaming disorder comparing to those with alcohol use disorder. Scientific Reports, 7(1), 5744. 10.1038/s41598-017-06057-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Zhang Y, Galloway JM, Welty TK, Wiebers DO, Whisnant JP, Devereux RB, Kizer JR, Howard BV, Cowan LD, Yeh J, Howard WJ, Wang W, Best L, & Lee ET (2008). Incidence and risk factors for stroke in American Indians: The Strong Heart Study. Circulation, 118(15), 1577–1584. 10.1161/CIRCULATIONAHA.108.772285 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Material
NIHMS1777006-supplement-Supplementary_Material.pdf (311.3KB, pdf)

RESOURCES