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Confidential: For Review O
nly
Moderate alcohol consumption as a risk factor for adverse
brain outcomes and cognitive decline
Journal: BMJ
Manuscript ID BMJ.2016.034088.R2
Article Type: Research
BMJ Journal: BMJ
Date Submitted by the Author: 05-Dec-2016
Complete List of Authors: Topiwala, Anya; University of Oxford, Psychiatry Allan, Charlotte; University of Oxford, Psychiatry Valkanova, Vyara; University of Oxford, Psychiatry Zsoldos, Eniko; University of Oxford, Psychiatry Filippini, Nicola; University of Oxford, Psychiatry
Sexton, Claire; University of Oxford, Psychiatry Mahmood, Abda; London School of Hygiene and Tropical Medicine, Epidemiology and Population Health Fooks, Peggy; University of Oxford, Psychiatry Singh-Manoux, Archana; INSERM, Centre for Research in Epidemiology and Population Health; University College London, Department of Epidemiology and Public Health Mackay, Clare; University of Oxford, Psychiatry Kivimaki, Mika; University College London, Department of Epidemiology and Public Health Ebmeier, Klaus; University of Oxford, Psychiatry
Keywords: Alcohol, MRI, Brain structure, Hippocampal atrophy, Cognition
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Confidential: For Review O
nlyWhat this paper adds box
1. What is know on this subject
In December 2015 we searched PubMed, MEDLINE and Google Scholar for relevant
reports with the terms: “alcohol” “moderate drinking” “moderate drinking” “cognitive
impairment” “dementia” “brain” “grey matter” “white matter” “diffusion tensor imaging”
and “magnetic resonance imaging” with no language or date restrictions. Reference lists
of relevant papers were also searched.
One previous systematic review (without meta-analysis) has been published on the
alcohol dependence and neuroimaging. Studies have consistently shown heavy drinking
to be associated with Korsakoff’s syndrome, dementia and widespread brain atrophy.
Whilst smaller amounts of alcohol have been linked to protection against cognitive
impairment, there is a paucity of studies examining the effects of moderate alcohol on
the brain. Those that have been done have methodological limitations especially
regarding the lack of prospective alcohol data, been conflicting and failed to provide a
convincing neural correlate.
2. What this adds
We have shown that moderate alcohol is associated with increased risk of adverse brain
outcomes and steeper cognitive decline in the domain of lexical fluency. The area
particularly vulnerable is the hippocampus, which is the most robust imaging marker of
Alzheimer’s disease, and has not been previously linked negatively with moderate
alcohol use. No protective effect was found for small amounts of alcohol over abstinence.
Previous reports claiming a protective effect of light drinking may have been subject to
confounding by associations between increased alcohol and higher social class or IQ.
These findings support the recent reduction in the UK “safe” drinking cut-offs and
question current US guidelines. Moderate drinking may represent a modifiable risk
factor for later cognitive impairment.
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Moderate alcohol consumption as a risk factor for
adverse brain outcomes and cognitive decline:
longitudinal cohort study
Anya Topiwala* – Clinical Lecturer in Old Age Psychiatry, Department of Psychiatry,
University of Oxford, Warneford Hospital, Oxford, UK, OX3 7JX
[email protected], Tel: +44 (0)1865 226469 Fax: + 44 (0)1865 793101
Charlotte L. Allan – Academic Clinical Lecturer in Old Age Psychiatry, Neurobiology of
Aging Group, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK, OX3 7JX
Vyara Valkanova – Specialist registrar in Old Age Psychiatry, Neurobiology of Aging
Group, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK,
OX3 7JX
Enikő Zsoldos – DPhil student, Neurobiology of Aging Group, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK, OX3 7JX
Nicola Filippini – Postdoctoral scientist, Neurobiology of Aging Group, Department of
Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK, OX3 7JX
Claire Sexton – Postdoctoral scientist, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford.
Abda Mahmood – Research assistant, Neurobiology of Aging Group, Department of
Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK, OX3 7JX
Peggy Fooks – Medical student, Neurobiology of Aging Group, University of Oxford,
Warneford Hospital, Oxford, UK, OX3 7JX
Archana Singh-Manoux – Professor of Epidemiology and Public Health, Department of Epidemiology and Public Health, University College London
Clare E. Mackay – Associate Professor, Translational Neuroimaging Group, Department
of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK, OX3 7JX
Mika Kivimäki – Professor, Department of Epidemiology and Public Health, University
College London
Klaus P. Ebmeier – Professor of Old Age Psychiatry, Neurobiology of Aging Group,
University of Oxford, Warneford Hospital, Oxford, UK, OX3 7JX
* Corresponding author
Article word count: 4932
Abstract word count: 281 Number of references: 78
Number of tables: 5 Number of Figures: 5
Supplementary materials: methods and results
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Abstract
Objectives
To investigate whether moderate alcohol consumption has favourable, adverse or no
association with brain structure and function.
Design
Observational cohort study with weekly alcohol intake and cognitive performance
measured repeatedly during a 30-year period (1985 to 2015). Multimodal Magnetic
Resonance Imaging (MRI) was performed at study endpoint (2012-5).
Setting
Community-dwelling adults enrolled in the Whitehall II cohort based in the United
Kingdom (the Whitehall II imaging sub-study).
Participants
We included 550 men and women aged 43.0 years (±5.4) at study baseline, none scoring
“alcohol dependent” in the CAGE screening questionnaire, and safe for brain MRI at
follow-up. Exclusions (n=23) were due to incomplete or poor quality imaging data or
gross structural abnormality (e.g. a brain cyst), incomplete alcohol use,
sociodemographic, health or cognitive data.
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Main outcome measures
Structural brain measures included hippocampal atrophy, grey matter density and
white matter microstructure. Functional measures included cognitive decline over the
study.
Results
More alcohol consumption over the 30-year follow-up was associated with increased
odds of hippocampal atrophy in a dose dependent fashion. Whilst those consuming over
30 units weekly were at the highest risk (odds ratio 5.8 95% CI: 1.8-18.6 p=<0.001),
even those drinking moderately (14-21 units per week) had three times the odds of
right-sided hippocampal atrophy compared with abstainers (odds ratio 3.4 95% CI: 1.4-
8.1 p=0.007). We found no protective effect of light drinking (1- <7 units weekly) over
abstainers. Higher alcohol use was also associated with differences in corpus callosum
microstructure and faster decline in lexical fluency.
Conclusions
Alcohol consumption, even at moderate levels, is associated with adverse brain
outcomes including hippocampal atrophy. These results support the recent reduction in
UK alcohol guidance and question the current USA safe limits.
Funding
Study funding: “Lifelong Health and Wellbeing” Program Grant: “Predicting MRI abnormalities with longitudinal data of the Whitehall II Substudy” (UK Medical Research
Council: G1001354; PI: KPE), the Gordon Edward Small's Charitable Trust (SC008962;
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PI: KPE), and the HDH Wills 1965 Charitable Trust (Charity Number: 1117747; PI: KPE). MK was supported by the Medical Research Council (K013351) and NordForsk.
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Introduction
Alcohol use is widespread and increasing across the developed world.1-3 It has
historically been viewed as harmless in moderation,4 defined variably from 9-18 units
(72-144g) per week.5,6 Recent evidence of associations with cancer risk 7 has prompted
revision of UK government alcohol guidance, although US Federal Dietary guidelines
(2015-2020) allow up to 24.5 units weekly for men.8 Even light drinking (midpoint
<12.5g daily/8 units per week) has been associated with increased risk of
oropharnygeal, oesophageal and breast cancer.7,9 Whilst chronic dependent drinking is
associated with Korsakoff syndrome and alcoholic dementia,10 the long-term effects of
non-dependent alcohol consumption on the brain are poorly understood. Robust
evidence of adverse associations would have vital public health implications.
Some authors have suggested an inverted U-shaped relationship between alcohol use
and brain outcomes, similar to cardiovascular disease. Light-to-moderate drinking has
been associated with lower dementia risk,11,12 a reduced incidence of myocardial
infarction13 and stroke.14 However, brain-imaging studies have thus far failed to provide
a convincing neural correlate that could underpin any protective effect. Research into
the effects of moderate alcohol on the brain are inconsistent.15 Moderate alcohol
consumption in older subjects has been associated with reduced total brain volume,16
increased ventricle size,17 grey matter atrophy11 and reduced frontal and parietal grey
matter density,18,19 but others have not found such relationships,20 or only at higher
consumption levels.21 Associations between moderate alcohol consumption and white
matter findings are also inconsistent. De Bruin22 reported increased white matter
volume in moderate drinkers compared with abstainers, whereas Anstey23 found the
inverse relationship. Similarly, whereas increased white matter hyperintensities have
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been described in moderate drinkers compared with abstainers24 others found no
association.17,23,25
Unresolved questions persist because of design limits to existing studies of non-
dependent drinking and brain imaging. Alcohol consumption cannot be randomised.
Most studies to date have been cross-sectional or with very limited prospectively
gathered alcohol data. People typically underestimate their alcohol intake,26 a problem
likely to be worse in a retrospective study. Studies have also included elderly
participants in whom sub-threshold pre-symptomatic cognitive impairment may
already have an impact on drinking patterns.
In this study we use data on alcohol consumption gathered prospectively over 30 years
to investigate associations with brain structural and functional outcomes in 550 non-
alcohol dependent subjects. Our hypotheses were two-fold:
1. Light drinking (<7 units weekly) is protective against adverse brain outcomes
and cognitive decline.
2. Heavier drinking (above recommended guidelines) is associated with adverse
brain and cognitive outcomes.
Images ©Alcohol Concern 2016
Box 1
Methods
Study design and participants
Five hundred and fifty subjects were randomly selected for the current Whitehall II
imaging sub-study (2012-2015) from the Whitehall II cohort study.27 The Whitehall II
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study was established in 1985 at University College London, with the aim of
investigating the relationship between socioeconomic status, stress and cardiovascular
health. It recruited 10,308 non-industrial civil servants across a range of employment
grades.27 Sociodemographic, health and lifestyle variables (including alcohol use) were
measured over a follow-up period of approximately thirty years, at approximately five-
year intervals (Phase 1: 1985-8, Phase 3: 1991-3, Phase 5: 1997-9, Phase 7: 2003-4,
Phase 9: 2007-9, Phase 11: 2011-12). In order to make the sample as representative as
possible of the cohort at baseline, a random list of 1380 participants was drawn from
those who participated in the Whitehall II Phase 11 clinical examination or Phase 10
pilot examination and had consented. Subjects were sampled from high, intermediate
and low socioeconomic groups.
Alcohol variables collected in each phase included: units drunk per week, frequency of
drinking per week over the previous year, as well as the CAGE screening
questionnaire.28 Weekly consumption was used in this analysis, as there is less
likelihood of a ceiling effect in comparison to drinking frequency. Average alcohol use
across the study was calculated as mean consumption per week averaged across all
study phases. Subjects were deemed ‘abstinent’ if they consumed less than 1 unit of
alcohol per week. ‘Light’ drinking was defined as between 1 and <7 units per week and
‘moderate’ drinking as 7 to <14 units per week for women and 7- <21 units for men,
based on use in the existing literature and government guidelines (see box 1). “Unsafe
drinking” was defined according to pre-2016 (21 units (168 g) per week for men and 14
units (112 g) for women) and newly revised UK Department of Health guidelines (>14
units (112 g) for men and women), and further categorised (14-20, 21-30, >30 units
weekly) for the purposes of the logistic regression analysis (see statistical analysis).29
Non-dependent drinkers were defined as those scoring less than 2 on the CAGE
questionnaire.
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Age, sex, education, smoking, social activity e.g. attendance at clubs and visits with
family/friends, physical activity, voluntary work, component measures of the
Framingham Stroke Risk Score e.g. blood pressure, smoking, history of cardiovascular
events, cardiovascular medications, were assessed by self-report questionnaire. Social
class was determined according to occupation at Phase 3 (highest class=1, lowest=4).
Medications (number of psychotropics reported to be taking), and lifetime history of
Major Depressive Disorder (assessed by Structured Clinical Interview for DSM IV) were
assessed at the time of the scan. Information about personality traits was determined by
questionnaire at Phase 1 and included trait impulsivity (question: “Are you hot-
headed?”).
Cognitive function was assessed at Phases 3, 5, 7, 9, and again at the time of the MRI
using short-term recall (20 words), lexical (how many words beginning with a letter can
be generated in one minute) and semantic (how many words in a category can be
named in one minute) fluency tests. Full-scale intelligence quotient (IQ) was estimated
using the Test of Premorbid Functioning - UK Version (TOPF UK, assessed at the time of
the scan) with sex and education adjustment.
Participants were included in the imaging sub-study if they were safe to have MRI and
able to give informed consent. Exclusions were due to incomplete or poor quality
imaging data or gross structural abnormality (e.g. a brain cyst), incomplete alcohol use
data, sociodemographic, health or cognitive data (figure 1).
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MRI analysis
All MRI scans were acquired at the University of Oxford Functional Magnetic Resonance
Imaging of the Brain (FMRIB) Centre, using a 3 Tesla Siemens Verio scanner (2012-15).
T1-weighted and diffusion tensor (DTI) 3T MRI sequences were used for these
analyses.30
For full technical details, see supplementary materials. In brief, relationships between
alcohol use and grey matter were examined initially using voxel-based morphometry, an
objective method to compare grey matter density between individuals in each voxel
(smallest distinguishable image volume) of the structural image. Hippocampal volumes
(adjusted for total intracranial volume) were additionally extracted, using an automated
segmentation/registration tool, for each subject for subsequent analyses. Automated
segmentation of the amygdalae was less reliable in this sample so extracted volumes
were not used in this analysis. Hippocampal atrophy was defined independently
according to visual rating (Scheltens score)31 by three clinicians, who reached a
consensus.
Diffusion tensor images indicate the directional preference of water diffusion in neural
tissue and allow inferences about the structural integrity of white matter tracts. In
healthy myelinated fibres diffusion is restricted perpendicular to the longitudinal axis of
the fibre, i.e. it is anisotropic. Voxelwise statistical analysis of diffusion tensor data
(fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean
diffusivity (MD)) was carried out using Tract-Based Spatial Statistics (TBSS).32 A
Generalised Linear Model (GLM) was applied to the VBM and TBSS analyses using
permutation-based non-parametric testing, correcting for multiple comparisons across
space (threshold-free cluster enhancement, tfce).
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Outcomes
Primary outcomes in the VBM analysis were continuous measures of grey matter
density, and in the TBSS analysis white matter integrity (fractional anisotropy, mean,
radial and axial diffusivity).
Visual ratings of hippocampal atrophy were dichotomised into atrophy vs. no atrophy
based on 0/1 on the (4 point) Scheltens scale to reflect clinical usage (“abnormal” vs.
“normal”).31
Hippocampal volume (%ICV) was used as a continuous variable in a multiple linear
regression analysis.
As cognitive outcomes we used decline on short-term memory, semantic and lexical
fluency (see below).
Statistical analysis
All analyses were done with R,33 unless otherwise stated.
Differences between included and excluded subjects, i.e. representativeness of included
participants, were examined using t-tests of means (continuous variables) or chi-square
tests (categorical). Subject characteristics for included subjects were summarised by
mean (with standard deviation) for continuous variables, and as percentage of the
sample for categorical variables split by safe vs. unsafe average alcohol use averaged
over all phases, on the basis of UK contemporary (pre-2016) guidelines. Significant
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differences between safe and unsafe drinkers were tested using t-tests of means for
continuous variables and chi-square tests for categorical variables. Weekly consumption
of alcohol (units and grams) was described using means, standard deviations, medians
and interquartile ranges, when data were not normally distributed.
Alcohol trends over time were examined using mixed effects modelling, with time from
study baseline (Phase 1) as the independent variable and alcohol consumption (units
per week) as the dependent variable. This method accounts for missing data, and
correlation of repeated measures (in this case alcohol use). Intercepts (baseline
consumption) and slopes (trends over study) were calculated for each participant.
Bivariate Person’s correlations were calculated between slopes/intercepts of alcohol
consumption and: age, sex, premorbid IQ, education, social class, Framingham Risk
Score (a composite measure including smoking, cardiovascular disease or diabetes,
cardiovascular medication), current psychotropic medications (number), lifetime
history of Major Depressive Disorder (SCID, binarised yes(2)/no(1)), exercise
frequency, club attendance, voluntary work, visits with friends and family. Correlations
were deemed significant if p<0.05 on two-tailed tests.
Mean alcohol consumption (units per week) across all study phases was included as an
independent variable in voxel-based morphometry (grey matter density as dependent
variable) and TBSS analyses (FA/MD/RD/AD as dependent variable). Voxelwise, a
Generalised Linear Model (GLM) was applied using permutation-based non-parametric
testing (randomise)34, correcting for multiple comparisons across space (threshold-free
cluster enhancement, tfce). Results were judged significant, if adjusted p<0.05.
Two post-hoc tests were used to confirm the associations between alcohol consumption
and hippocampal size following the VBM analysis. First, logistic regression was used to
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calculate odds ratios for left and right hippocampal atrophy versus no atrophy (visual
atrophy ratings based on a cut-off of 0/1 on the Scheltens’ scale),31 given average alcohol
consumption across study phases. The latter was categorised as abstinent (<1 unit,
reference group), 1 to <7 units, 14 to <21 units, 21 to <30 units and >30 units per week.
Second, multiple linear regression with hippocampal volume (extracted from FIRST, ICV-
adjusted) as the dependent variable and alcohol consumption as an independent
variable was performed.
In all analyses, the following potential confounding variables (identified from
knowledge of the literature) were included as independent variables: age, sex,
premorbid IQ, education, social class, Framingham Risk Score (a composite measure
including smoking, cardiovascular disease or diabetes, cardiovascular medication),
current psychotropic medications (number), lifetime history of Major Depressive
Disorder (SCID, binarised as yes (2)/no (1)), exercise frequency, club attendance,
voluntary work, visits with friends and family. In the subset with data on personality
traits (n=179), analyses were additionally adjusted for impulsiveness.
Changes in cognition over time (short-term verbal recall, lexical and semantic fluency)
were modelled using mixed effects models in a similar manner to alcohol, with test
score as the dependent variable and time from baseline (Phase 1) as the independent
variable. Slopes (trends over time) were calculated for each subject for further analyses,
which included binarisation into “cognitive improvers” (positive slope) vs. “cognitive
decliners” (negative slope). Independent t tests were used to identify significant
differences in improvers and decliners with respect to sociodemographic and clinical
variables.
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Structural equation modelling (SEM) (Amos 22 for Windows) was used for hypothesis
testing and to generate fit statistics for models of relationships between alcohol use,
brain measures and cognitive decline. SEM allows simultaneous analysis of multiple
variables in one model, can handle incomplete data and time series with auto-correlated
errors. The hypothesised underlying structure of the model was constructed following
the VBM and TBSS analyses, with average alcohol consumption as an exogenous
variable, hippocampal volume, corpus callosum MD (generally the most sensitive
measure of loss of white matter integrity), and decline in lexical fluency (slope from
mixed effects model) included as endogenous variables (using latent variables to
account for measurement error). Covariance of alcohol with sex and IQ, and between
brain measures was modelled. The model was improved by iteratively eliminating paths
with p>0.1, and monitoring of the successive improvement of the model fits statistics
(chi square, Comparative Fit Index, Root Mean Square Error of Approximation and the
Tucker-Lewis Index) until the most parsimonious model was identified.
Patient involvement
Participants are from the Whitehall II cohort. No patients were involved in setting the
research question or the outcome measures, nor were they involved in the design,
recruitment to, or conduct of the study. No patients were asked to advise on
interpretation or writing up of results. Results were disseminated to the study
participants in abstract format and as presentations at the 30th anniversary day for the
Whitehall II cohort. Participants are thanked in the acknowledgements.
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Figure 1: Flow chart of participants included in analysis.
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Figure 2: Frequency distribution of alcohol consumption on average across the study by sex.
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Safe drinkers
N=428 Mean (SD) or
n (%)
Unsafe drinkers
N=99 Mean (SD) or n (%)
Continuous variables:
mean difference (95%
CI)
Categorical variables:
chi squared (degrees
of freedom, p value)
Age at start (years) 43.0 (5.4) 42.8 (5.1) 0.2 (0.0 to 1.4)
Sex, n male (%) 339 (79.2%) 85 (85.9%) 2.3 (1, 0.1)
Marital status (% married) 308 (72.0%) 81 (81.8%) 5.3 (5, 0.4)
Full-time education (years) 14.5 (3.3) 14.9 (2.9) -0.4 (-1.0 to 0.3)
Full Scale Intelligence Quotient (estimated from TOPF1)*
117.4 (10.6) 120.0 (8.3) -2.6 (-4.5 to -0.7)
Social class 1, 2, 3 & 4 (%)$ 62, 328, 34, 4
(14.5%, 76.6%,
7.9%, 0.9%)
21, 76, 2, 0 (21.2%,
76.8%, 2.0%, 0%) 7.4 (3, 0.6)
Smoker (%) 11 (2.6%) 11 (11.1%) 14.7 (1, <0.001)
Systolic blood pressure 140.3 (17.8) 143.3 (16.7) -3.0 (-6.9 to 0.8)
Diastolic blood pressure 76.3 (10.5) 77.7 (10.9) -1.4 (-3.7 to 0.9)
Framingham Stroke Risk total score (%)*
11.4 (8.3) 12.8 (8.3) -1.4 (-3.2 to 0.4)
History of Major Depressive
disorder (%)*5
79 (18.5%) 16 (16.2%) 0.3 (1, 0.6)
Social visits (weekly)* 4.4 (3.2) 3.9 (3.1) 0.5 (-0.3 to 1.2)
Psychotropic medication (%)* 62 (14.5%) 12 (12.1%) 5.9 (4, 0.2)
MoCA3 total (/30)* 27.1 (2.4) 27.1(2.1) 0.0 (-0.5 to 0.5)
MMSE baseline total (/30)4 28.8 (1.1) 28.9 (1.2) -0.1 (-0.3 to 0.2)
1Test of Premorbid Function, 2Centre for Epidemiological Studies Depression Scale,3 Montreal Cognitive Assessment, 4Phase 7 (n=389), 5Structured Clinical Interview for DSM IV (SCID), $Social class based on occupation at phase 3: 1=professional, 2=managerial, 3=skilled non-manual, 4=skilled manual, *Time of Scan
Table 1: Baseline (Phase 1 unless otherwise indicated) summary characteristics of 527 participants (unless marked) included in analysis by safe (<14 units weekly women, <21 units weekly men) alcohol consumption, defined by contemporaneous (pre-2016) UK Department of Health guidelines, on average over study duration. Bold results significant as defined by p<0.05 on t test of means (continuous variables) and chi-square test (categorical variables).
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CROSS-SECTIONAL MEASURES
Cognitive test Mean/ median*
(SD)
Unstandardised coefficient
Change in test score with each
10 unit increase in
weekly alcohol consumption
P value 95% CI
MoCA
(adjusted for
education)
28.0*
(2.4)
-0.04 -0.02 0.7 -0.2 to
0.01
Semantic
fluency
22.0
(5.7)
0.1 0.1 0.8 -0.04 to
0.5
Lexical fluency 15.7
(4.5)
-0.1 -0.2 0.7 -0.5 to 0.3
TMT A
(sec)
29.0*
(11.4)
-0.2 -0.2 0.7 -1.0 to 0.9
TMT B
(sec)
59.0*
(36.8)
0.2 0.5 0.3 -1.0 to 5.0
Rey copy 32.0*
(4.0)
-0.04 -0.1 0.8 -0.4 to 0.3
Rey immediate 15.5
(6.5)
-0.3 -0.4 0.3 -1.0 to
0.03
Rey delay 15.2
(6.3)
-0.4 -0.7 0.1 -1.0 to 0.1
HVLT total
recall
27.5
(4.8)
0.4 0.8 0.06 -0.02 to
0.7
HVLT delayed
recall
10.0*
(2.8)
0.08 0.3 0.5 -0.2 to 0.3
BNT 59.0
(4.7)
0.4 0.9 0.02 0.1 to 0.7
Digit span total 30.1
(5.8)
0.2 0.3 0.5 -0.3 to 0.6
Digit
substitution
test
62.1
(13.7)
-0.6 -0.5 0.3 -2.0 to 0.5
LONGITUDINAL MEASURES
Short-term
memory
-0.02
(0.03)
-0.0004 -0.2 0.7 -0.003.to
0.002
Lexical fluency -0.1
(0.05)
-0.004 -1.0 0.04 -0.008 to -
1.4 Semantic
fluency
-0.05
(0.05)
-0.002 -0.4 0.4 -0.007 to
0.003 *Median was used to describe the central tendency of cognitive test scores where the distribution
was not normal.
Table 2: Effects of alcohol consumption on cognitive function and ~ trajectory. Results of regression analyses, with cross-sectional cognitive test performance or longitudinal cognitive test slope as the dependent variable, and average alcohol across the study as an independent variable. Adjusted for: age, sex, education, FSIQ; MoCA=Montreal Cognitive Assessment, TMT=Trail making test, Rey=complex figure task, HVLT=Hopkins Verbal Learning Test, BNT=Boston naming test (associated with IQ, hence positive association).
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Men Women All
R Hippocampal atrophy
(vs. none)
L Hippocampal atrophy
(vs. none)
R Hippocampal atrophy
(vs. none)
L Hippocampal atrophy (vs.
none
R Hippocampal atrophy (vs.
none) L Hippocampal atrophy (vs.
none)
Alcohol
(units
weekly)
N
(Total) OR
(95%
CI)
P N
(Total) OR (95%
CI) P N
(Total) OR (95%
CI) P N
(Total) OR (95%
CI) P N
(Total) OR
(95% CI)
P N
(Total) OR (95%
CI) P
0-<1 9 (22) 12 (22) 4 (15) 7 (15) 13 (37) 19
(37)
1 to <7 55 (99) 1.6
(0.6-
4.3)
0.4 69 (99) 1.7 (0.6-4.8) 0.3 12 (41) 1.1 (0.2-
5.6) 0.9 20 (41) 0.8 (0.2-3.6) 0.8 67
(140) 1.5 (0.7-
3.4) 0.3 89
(140) 1.3 (0.6-3.0) 0.5
7 to
<14 68
(132) 1.7
(0.6-
4.5)
0.3 85
(132) 1.5 (0.6-4.2) 0.4 19 (33) 3.1 (0.6-
16.6) 0.2 22 (33) 2.0 (0.4-
10.2) 0.4 87
(165) 2.0 (0.9-
4.4) 0.1 107
(165) 1.4 (0.6-3.2) 0.4
14 to
<21 57 (86) 3.2
(1.1-
9.3)
0.02 59 (86) 2.0 (0.7-5.7) 0.2 6 (11) 4.2 (0.6-
28.8) 0.1 9 (11) 6.2 (0.7-
55.2) 0.1 63 (97) 3.4 (1.4-
8.1) 0.007 68
(97) 1.9 (0.8-4.6) 0.1
21 to
<30 38 (54) 3.9
(1.3-
12.0)
0.02 39 (54) 2.2 (0.7-6.8) 0.2 1 (3) 1.1 (0.04-
26.9) 0.97 2 (3) 0.4 (0.4-
10.2) 0.4 39 (57) 3.6 (1.4-
9.6) 0.009 41
(57) 1.9 (0.7-4.9) 0.2
≥30 24 (31) 5.2
(1.4-
19.0)
0.01 27 (31) 6.3 (1.5-
27.0) 0.01 24 (31) 5.8 (1.8-
18.6) <0.001 27
(31) 5.7 (1.5-21.6) 0.01
Table 3: Adjusted ORs for left and right sided hippocampal atrophy on Scheltens visual rating score (reference based on abstainers), with average alcohol consumption (weekly units) 1- <7, 7- <14, 14- <21 and 21- <30, ≥30 units (abstinence <1 units as reference category). Significant (p<0.05) results in bold. N=527. Analyses adjusted for age, sex, premorbid IQ, education, social class, Framingham Risk Score, history of Major Depressive Disorder (SCID), exercise frequency, club attendance, social visits, current psychotropic medication. Numbers with hippocampal atrophy (N) and total numbers in drinking category (Total N) are also given.
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Figure 3: Voxel-based morphometry results (threshold-free cluster enhancement (tfce) corrected): significant (p<0·05, red/yellow) negative correlation between weekly alcohol units (average of all phases across study) and grey matter density. N=527. Adjusted for age, sex, education, premorbid FSIQ, social class, physical exercise, club attendance, social activity, Framingham Stroke Risk Score, psychotropic medication, and history of Major Depressive Disorder.
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Figure 4: Tract-based spatial statistics results (threshold-free cluster enhancement, tfce, corrected) showing negative correlation between average alcohol across study (all phases) and fractional anisotropy (top), radial diffusivity, mean diffusivity and axial diffusivity (bottom). N=511. Significant results (p<0·05) in red/yellow Adjusted for age, sex, education, premorbid FSIQ, social class, physical exercise, club attendance, social activity, Framingham Stroke Risk Score, psychotropic medication, and history of Major Depressive Disorder
Unstandardised B
(95% CI)*
Change in
hippocampal volume
(% intracranial
volume)*
P value
Unadjusted alcohol -0.06 (-0.08 to -0.03) -2.0 <0.001
Adjusted alcohol1 -0.04 (-0.06 to -0.02) -1.5 0.001
* For every 10 unit increase in alcohol weekly 1 Analyses adjusted for age, sex, premorbid IQ, education, social class, Framingham Risk Score, history of Major Depressive Disorder (SCID), exercise frequency, club attendance, social visits, current psychotropic medication.
Table 4: Multiple linear regression results, with hippocampal volume (% of ICV) as the dependent variable and average alcohol consumption as an independent variable.
Figure 5: Final parsimonious structural equation model (excluding covariance arrows for simplicity) illustrating relationships among alcohol consumption (average across study phases, units per week), hippocampal volume (average, %ICV), corpus callosum mean diffusivity (MD), decline in lexical fluency (slopes), and age. Values on arrows are standardized regression weights. Red arrows indicate inverse relationships and green positive relationships. Model explained 21% corpus callosum MD, 14% of hippocampal variance and 1% of lexical fluency decline variance (R2). Model fit: Chi-square=14.2, degrees of freedom=9, p=0.12, Root Mean Square Error of Approximation=0.03, Comparative Fit Index=0.98, Tucker-Lewis Index=0.95.
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Path Standardised regression
coefficient P value
Average alcohol to
hippocampal volume -0.20 <0.001
Average alcohol to corpus
callosum MD 0.11 0.005
Average alcohol to lexical
fluency decline -0.08 0.008
Corpus callosum MD to
lexical fluency decline -0.07 0.1
Age to hippocampal volume -0.31 <0.001
Age to corpus callosum MD 0.44 <0.001
Table 5: Regression coefficients for paths in the final structural equation model (figure 5) with their standard errors and p values.
Results
Participants/descriptive data
Sociodemographic, health and lifestyle data are reported for the 527 included
participants, separated into alcohol consumption groups (table 1). Twenty-three
subjects were excluded from the VBM and visual ratings analyses on the basis of
structural brain abnormalities, poor quality images or missing confounder data (figure
1). A further sixteen subjects were excluded from the TBSS analysis due to missing or
poor quality diffusion tensor images. Excluded subjects did not significantly differ from
included subjects on any of the reported characteristics (data available from author on
request). Subjects were slightly less educated, with higher blood pressure and lower
depressive symptoms compared to the larger Whitehall II cohort (see supplementary
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materials, table 1). Mean age was 43.0 years (SD=5·4) at the study start (supplementary
materials, table 2). Unsafe drinkers differed from safe drinkers only by having a higher
premorbid IQ, a higher percentage of men and more smokers (see table 1).
Median alcohol consumption across study phases (supplementary materials, table 3)
was 10.2 units (81.3 g) per week (SD=10.2). Baseline consumption (intercept in mixed
effects model) was higher in men (r=0.24, p<0.001). Weekly alcohol intake did not
significantly increase over the phases of the study for the group as a whole, (β=-0.006,
SE=0.02 p<0.7), but trends over time correlated with baseline intake (intercepts and
slopes correlated negatively (r= -0.4, 95%CI: 0.5 to 0.3), i.e. those drinking more at
baseline tended to lower their consumption more over the course of the study. Other
sociodemographic and clinical factors were not related to longitudinal trends in
consumption. Average alcohol use over the study was over “safe limits” in 13.6% women
and 20.0% men, as judged by pre-2016 UK guidelines (>21 units (168g)/week for men,
>14 units (112g)/week for women), and 40.3 % as judged by the 2016 revised UK
guidelines (>14 units (112g) per week for men and women) (see supplementary
materials for consumption data for single phases). Scores on the CAGE questionnaire
were below the sensitive screening cut-off of 228 for all participants at all Whitehall II
phases (see supplementary materials, table 4).
Alcohol and brain structure
Higher alcohol use was associated with reduced grey matter density, hippocampal
atrophy, and reduced white matter microstructural integrity.
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Grey matter
Average alcohol consumption over the study (units per week) was negatively correlated
with grey matter density in the VBM analyses, especially in hippocampi (figure 3), even
after adjustment for multiple potential confounders. Associations also extended
anteriorly into the amygdalae. Frontal regions were unaffected.
Increased alcohol consumption was also associated with increased odds of abnormally
rated hippocampal atrophy (defined as score >0 on Scheltens visual rating scale; table
3). This was a dose-dependent effect. The highest odds were in those drinking in excess
of 30 units per week (OR 5.8 95% CI: 1.8 to 18.6 p=<0.001), but odds of atrophy were
higher compared with abstinence even in those drinking at moderate levels of 7 to <14
units per week (OR 3.4 95% CI: 1.4 to 8.1 p=0.007). There was no protective effect (i.e.
reduced odds of atrophy) of light drinking (1- <7 units per week). Findings were similar
in sub-analyses of men alone, but not in the smaller subgroup of women. Associations
were stronger and beginning at lower alcohol consumptions for right-sided atrophy. No
significant association was found with trend in alcohol use over time (slopes from mixed
effects models).
Mean hippocampal volumes (raw and ICV-adjusted) were within the range cited in the
literature (see supplementary materials, table 5),35-37 and correlated with visual ratings
of hippocampal atrophy (Pearson’s r= -0.4 p<0.001). Consistent with VBM and visual
ratings findings, alcohol consumption independently predicted FIRST-extracted
hippocampal volume (%ICV) (see supplementary materials). Excluding the three highest
drinkers (>60 units weekly) did not substantially change the results (supplementary
materials, table 6). On the subset, for whom personality trait data were available from
Phase 1 (n=179), additionally adjusting the analysis for trait impulsivity did not alter the
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findings.
White matter
Participants, who on average across the study drank more alcohol, had abnormalities in
white matter microstructure (figure 4), reflected by reduced corpus callosum fractional
anisotropy, and increased radial, axial and mean diffusivity. These associations were
focused on the anterior corpus callosum (genu and anterior body, figure 4).
Alcohol and cognitive function
Scores on all three cognitive tests significantly declined over the study for the group as a
whole (short-term recall: B= -0.02 95% CI= -0.03 to -0.006; lexical fluency: B= -0.10
95% CI= -0.12 to -0.08; semantic fluency: B= -0.05 95% CI= -0.07 to -0.04). “Cognitive
improvers” on the short-term recall test had a significantly higher FSIQ than “cognitive
decliners” (see supplementary materials, table 8). Average alcohol consumption across
phases predicted decline in lexical fluency (slopes), independently of age, sex, education
and FSIQ (supplementary materials). Additional adjustment for corpus callosum mean
diffusivity rendered the association insignificant, although the effect size changed only
marginally. No other sociodemographic or clinical variable was associated with lexical
decline.
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Modelling alcohol consumption and brain structure and function
In order to see how alcohol consumption and the associated brain regions interacted
with cognitive decline, structural equation modelling was used. Hippocampal volume
and corpus callosum MD were included as exogenous variables. Age, sex and premorbid
FSIQ were also incorporated.
Removing regression arrows from age, sex, FSIQ and hippocampal volume to lexical
fluency decline improved the model fit. Alcohol consumption independently predicted
decline in lexical fluency. The final parsimonious model explained 21% of corpus
callosum MD, 14% of right hippocampal volume, and 1% of lexical fluency decline
variance (see figure 5 & table 5), with good model fit (Chi square=14.2, degrees of
freedom=9, p=0.12, Root Mean Square Error of Approximation=0.03, Comparative Fit
Index=0.98, Tucker-Lewis Index=0.95). Alcohol consumption (in addition to age)
predicted smaller hippocampal volume, and greater corpus callosum MD. Through its
relationship with corpus callosum MD, and through a direct path, increased alcohol
consumption predicted faster decline of lexical fluency.
Discussion
Principle findings
We have found a previously uncharacterized dose-dependent relationship between
alcohol consumption over 30 years of follow-up and hippocampal atrophy, as well as
impaired white matter microstructure. Additionally, higher alcohol consumption
predicted greater cognitive decline. There was no evidence of a protective effect of light
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drinking on brain structure or function. The hippocampal findings were consistent
between the brain-wide voxel-based approach, automatically extracted volumes, and
clinical visual ratings of hippocampal atrophy. The relationship was dose dependent,
and increased odds of hippocampal atrophy were found even in moderate drinkers (14-
<21 units weekly in men). The association between alcohol consumption and white
matter microstructure in non-dependent drinkers is also novel and appeared to be
driven by greater radial relative to axial diffusivity.
Strengths and limitations
Strengths of this study are the 30-year longitudinal alcohol data and the detail of
available confounder data. Additional strengths include the availability of a large
number of MRI data and the advanced imaging analysis methods. Grey matter findings
were replicated using a voxel-based approach, automated hippocampal volumes, and
visual ratings. Visual atrophy ratings are known to correlate closely with automated
methods (own data) and are more applicable to clinical settings.38 In large neuroimaging
studies automatic segmentation is widespread.39,40 The automated approach we use
(FIRST) has been shown to give accurate and robust results.41
When interpreting these results, some caveats are necessary. Whilst the sample is
community dwelling, it may not be representative of the wider UK population. They are
mostly male, educated and middle-class. The hippocampal atrophy associations we
found in the total sample were replicated in men alone, but not in women. This may
reflect a lower power to detect an effect in women, in part due to the sample being
dominated by men (a reflection of the civil service gender disparity in 1980s), and in
part because few of the included women drank heavily. This is an observational study,
as alcohol use cannot be randomised. Observer bias may have influenced participants to
lead healthier lifestyles as they were enrolled in Whitehall II “Stress and Health” study.
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The alcohol use data are self-reported, and participants may have underestimated their
drinking, although the longitudinal rather than cross-sectional approach taken in other
reported studies may minimise this,26 and the percentage of subjects drinking “unsafely”
are comparable to other reported studies.42-44 We used the CAGE screening instrument
to identify alcohol dependence, as it is well validated.28,45 Some subjects reported
drinking high levels of alcohol whilst screening negative on the CAGE, indicating a
possible inclusion of subjects with an alcohol use disorder in the sample. However,
increased odds of hippocampal atrophy were found even in those drinking moderate
amounts. Although the alcohol and cognitive data was longitudinal, the analyses with
MRI measures were cross-sectional, raising the possibility that the associations between
brain structure and alcohol were the result of a confounding variable. Longitudinal
imaging over more than a couple of years adds further confounders as the physical
scanner and imaging sequences are unlikely to be the same due to developments in MRI
science, making results difficult to interpret. Whilst efforts have been made to control
for multiple potential sources of confounding, we cannot exclude the possibility of
residual confounding from unmeasured sources. However to produce the adjusted
associations we found, any uncontrolled confounders would need to be associated with
both alcohol consumption and risk of brain abnormalities and unrelated to the multiple
factors we controlled for. Finally, we cannot exclude the possibility, of unlikely face
validity, that those with hippocampal atrophy at study baseline were more likely to
drink more.
Comparison with other studies
On average, 20% of men and 14% of women were drinking above contemporary
guidelines (>21 units/ >14 units weekly, respectively). Other studies vary in reported
rates of heavy drinking, but our rates are comparable.42,43 Alcohol consumption may
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vary according to country, as highlighted by a study utilizing the WHO Global Alcohol
Database.44
Hippocampal atrophy is a sensitive and relatively specific marker of Alzheimer’s
disease,46 although it has been reported in chronic alcoholics.18,47 The brain regions most
vulnerable to alcohol abuse are said to be the frontal lobes.21 Higher but non-dependent
alcohol use was not associated with subsequent frontal brain atrophy or impaired
cognition in our sample. Only one study has reported hippocampal findings in non-
dependent drinkers. This used a manual tracing rather than voxel-based or visual rating
approach to estimate hippocampal size. They reported a protective effect of moderate
alcohol in comparison with abstinence, which conflicts with our results.18 However,
alcohol consumption was determined cross-sectionally, making it difficult to exclude
reverse causation. In contrast, due to the longitudinal cognitive component of our study
we were able to demonstrate an association between higher alcohol consumption and
cognitive decline. Additionally, a number of known hippocampal size confounders, such
as depression, were not controlled for in Den Heijer et al.48 Other studies in non-
dependent drinkers have reported either no effect 26,49,50 or a negative correlation with
global grey matter, but not hippocampal atrophy.17,51 In contrast with our first
hypothesis and some other studies,11,12,18,52 we observed no evidence of a protective
effect of light drinking compared with abstinence on brain structure or cognitive
function. Previous studies did not control for (premorbid) IQ,11,12 and only a few for
socioeconomic class.53-55 The observed protective effect may be due to confounding as
we and others found a positive association between alcohol intake and IQ.56 These
factors separately predict better performance on cognitive tests. Supporting our second
hypothesis, we found heavier alcohol consumption to be associated with adverse brain
outcomes. The biological mechanism for this is unclear. Ethanol and acetaldehyde (a
metabolite) are neurotoxic57 and cause reduced numbers58,59 and morphological
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changes in hippocampal neurones in animal models.60 Associated thiamine and folate
deficiency,61 repeated head trauma, cerebrovascular events, liver damage, and repeated
intoxication and withdrawal have also been implicated in more severe drinkers. The risk
of hippocampal atrophy appeared to be stronger and at lower levels of alcohol
consumption for the right side. More significant hippocampal atrophy on the right has
been described in those at higher risk of Alzheimer’s disease (asymptomatic ApoE4
homozygotes),62 as well as in those with mild cognitive impairment or Alzheimer’s
disease.63 We also found no structural laterality in associations with cognitive function.
The literature on this is scarce and conflicting. Stronger associations between right
hippocampal volume and visuo-spatial memory have been reported.64
The VBM analysis also showed associations between increased alcohol consumption and
reduced grey matter density in the amygdalae. This result could not be confirmed using
other methods, as automated segmentation of these regions was unreliable, and we are
unaware of any reliable visual atrophy rating scales. Amygdala atrophy has been
described in Alzheimer’s disease,65 and is implicated in preclinical models of alcohol
misuse,66 alcohol abuse relapse67 and in abstinent alcoholics,68 although others have
found no association with lower levels of consumption.50
In animals, radial diffusivity reflects differences in myelination.69,70 Prior studies have
highlighted the corpus callosum as an area affected in foetal alcohol syndrome,71 and in
chronic alcoholism in Marchiafava-Bignami disease.72,73 One study reported increased
mean diffusivity in the amygdala in a post-hoc analysis of female non-dependent
drinkers.25 We are not aware of any studies investigating white matter microstructural
changes in moderate drinkers using a data driven skeletonised tract approach to
diffusion tensor images, such as TBSS. Alternative voxel-wise methods may compromise
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optimal analysis of multiple subjects, as there are alignment problems causing potential
difficulties with interpretation of voxel-wise statistics.74
Increased alcohol consumption was associated with a small but significant increase in
decline of lexical fluency over the study. Lexical fluency involves selecting and retrieving
information based on spelling (orthography), and has characteristically been associated
with frontal executive function,75 in contrast to semantic fluency which may depend
more on temporal lobe integrity.76 However, the distinction may not be as clear cut as
functional networks overlap.77 The inverse relationship between alcohol consumption
and lexical decline was perhaps unsurprising given the frontal predominance of the
negative associations with white matter integrity. We suggest two possibilities for the
lack of more widespread associations with cognition, particularly with semantic fluency
and short-term memory decline, given the structural brain findings (hippocampal
atrophy). First, there appears to be a practice effect over the study, i.e. at least some
subjects improve their performance after repeated testing. Therefore the trend in test
score may reflect learning ability of a subject, which obscures detection of cognitive
decline. Whilst only 2% of subjects were “cognitive improvers” (had a positive slope
indicated improving cognition over the study) for lexical fluency, 8% were for semantic
fluency, and 25% were for short-term memory. This suggests that certain tests were
more susceptible to practice effects. Furthermore, variables predicting the ability to
learn may be different from those protecting against cognitive impairment due to a
neurodegenerative process. “Cognitive improvers” on the short-term memory test had
significantly higher FSIQ than “cognitive decliners”. The correlation between heavier
alcohol consumption and a larger learning effect may be explained through confounding
for instance by IQ, which was associated with drinking. Adjustment for FSIQ alone may
be insufficient to remove the confounding effect if a third variable, for example diet,
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mediates the relationship between IQ and learning but is not in the model. Second, the
brain changes may reflect an intermediate phenotype and cognitive change is not yet
evident. It is now well documented that hippocampal atrophy precedes symptoms in
Alzheimer’s by several years,78 so a similar phenomenon in alcohol-related changes is
plausible.
Conclusions and policy implications
Prospective studies of the effects of alcohol use on the brain are few, and replication of
these findings will be important. Alcohol consumption for individuals was remarkably
stable across the study phases. This sample was therefore underpowered to detect
differences in those significantly changing their intake from others who drink
consistently. Larger investigations are needed to clarify whether there are graded risks
between a short versus long periods of higher alcohol consumption.
The finding that alcohol consumption in moderate quantities is associated with multiple
markers of abnormal brain structure and cognitive function has important potential
public health implications for a large sector of the population. By example, in our sample
nearly half of the men and a quarter of women were currently drinking in this range.
Additionally, drinking habits were remarkably stable over a 30-year period, suggesting
that risky drinking habits may be embarked upon in midlife. Recommended guidelines
for drinking have remained unchanged in the UK from 1987 until 2016. Our findings
support the recent reduction in UK safe limits and call into question the current US
guidelines which suggest that up to 24.5 units weekly is safe for men, as we found
increased odds of hippocampal atrophy at just 14-21 units weekly, and we found no
support for a protective effect of light consumption on brain structure. Alcohol may
represent a modifiable risk factor for cognitive impairment, and primary prevention
interventions targeted to later life may be too late.
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Acknowledgements
We would like to thank the Whitehall II cohort participants for their time.
Contributions
Author Planning of Study
Data acquisition
Data analysis
Contributed to paper
Anya Topiwala X X X X
Charlotte L. Allan X X X X
Vyara Valkanova X X
Enikő Zsoldos X X
Nicola Filippini X X X
Claire Sexton X X X
Abda Mahmood X X
Peggy Fooks X X
Archana Singh-Manoux
X X
Clare E. Mackay X X
Mika Kivimäki X X
Klaus P. Ebmeier X X X
Competing interests All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: grant support for the submitted work is
detailed above; no financial relationships with any organisations that might have an
interest in the submitted work in the previous three years; no other relationships or
activities that could appear to have influenced the submitted work.
Ethical approval
Predicting MRI abnormalities with longitudinal data of the Whitehall II sub- study”
(MSD/IDREC/C1/2011/71). IDREC,Research Services,University of
Oxford,Wellington Square, Oxford, OX1 2JD
Data sharing
Data sharing policy referenced on: https://www.psych.ox.ac.uk/research/neurobiology-
of-ageing/research-projects-1/whitehall-oxford - Reference to Whitehall II Data sharing policy here: http://www.ucl.ac.uk/whitehallII/data-sharing.
Transparency
Dr Anya Topiwala, Profs Klaus P Ebmeier and Mika Kivimäki (the manuscript's
guarantors) affirm that the manuscript is an honest, accurate, and transparent account
of the study being reported; that no important aspects of the study have been omitted;
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and that any discrepancies from the study as planned (and, if relevant, registered) have
been explained.
A CC BY licence is required.
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Averagealcohol
Hippocampalvolume
Age
Lexicalfluencydecline-0.20
-0.31
0.44
-0.07
CorpuscallosumMD
0.11
-0.08
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nlySupplementary methods
MRI analysis
Tissue segmentation
T1-weighted images were processed using FSL tools (FMRIB Software Library,
www.fmrib.ox.ac.uk/fsl) and ‘fsl_anat (Beta version)’
(http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/fsl_anat). This reorients images to standard (MNI) space,
corrects for bias field, registers the images to standard space (using linear FLIRT25,26 and non-
linear FNIRT27 registration), and extracts whole brain volumes (‘BET’)
28. FMRIB's
Automated Segmentation Tool (FAST) allowed extraction of measures of total grey matter
(GM), white matter (WM) and cerebrospinal fluid (CSF). GM and WM volumes were
adjusted for total intracranial volume.
Voxel-based morphometry (VBM)
Relationships between alcohol use and grey matter were examined initially using voxel-
based morphometry, an objective method to compare grey matter density between
individuals in each voxel (smallest distinguishable image volume) of the structural
image. Structural data were analysed with FSL-VBM 29
http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM), an optimised VBM protocol 30 carried out with
FSL tools 31. First, structural images were brain-extracted and grey matter-segmented before
being registered to the MNI 152 standard space using non-linear registration 27. The resulting
images were averaged and flipped along the x-axis to create a left-right symmetric, study-
specific grey matter template. Second, all native grey matter images were non-linearly
registered to this study-specific template and "modulated" to correct for local expansion (or
contraction) due to the non-linear component of the spatial transformation. The modulated
grey matter images were then smoothed with an isotropic Gaussian kernel with a sigma of 3
mm. Finally, voxelwise, a Generalised Linear Model (GLM) was applied using permutation-
based non-parametric testing, correcting for multiple comparisons across space (threshold-
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nlyfree cluster enhancement, tfce).
Visual rating of hippocampal atrophy
Structural T1 scans were assessed independently, by three researchers for hippocampal
atrophy (HA) using the Scheltens scale according to the width of the choroid fissure, width of
the temporal horn, and height of the hippocampus (0-4).32 Raters remained blind to all other
participant data. Intra- (on a random 10% of 208 scans) and inter-rater reliability (n=208) for
visual rating scores was high (intra-class correlation coefficients: 0.8 to 0.9 and 0.7 to 0.9,
respectively). Left and right hippocampal atrophy was defined independently according
to visual rating (Scheltens score33 >0) by three clinicians, who reached a consensus.
Hippocampal volumes
Hippocampal volumes were calculated using FIRST (http://fsl.fmrib.ox.ac.
uk/fsl/fslwiki/FIRST)34 an automated model-based segmentation/registration tool in a two-
stage process – first using all subcortical masks, and second a hippocampal mask only. Both
were visually inspected to check optimal segmentation. Extracted hippocampal volumes were
corrected for intracranial volume and averaged across left and right sides.
Diffusion tensor imaging
Diffusion tensor imaging (DTI) measures the directional preference of water diffusion in
neural tissue and allows inferences about the structural integrity of white matter tracts.
In healthy myelinated fibres diffusion is restricted perpendicular to the longitudinal axis
of the fibre, i.e. it is anisotropic. Voxelwise statistical analysis of fractional anisotropy
(FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) data was
carried out using Tract-Based Spatial Statistics (TBSS).35 This involves non-linear
registration followed by projection onto an alignment-invariant tract representation
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nly(the “mean FA skeleton”). This avoids alignment problems for multiple subjects and
avoids arbitrariness of spatial smoothing extent, improving the sensitivity, objectivity
and interpretability of analysis of multi-subject diffusion imaging studies.36 Multiple
diffusion indices were analysed to allow a richer investigation of localised connectivity
related changes. AD describes diffusion parallel to, and RD perpendicular to the to the
principal fibre direction. MD is the apparent diffusion coefficient averaged over all
directions. FA reflects AD in relation to RD and is widely used as a marker of tract
integrity.37 38 Diffusion images were corrected for head movement and eddy currents
(eddy_correct) and brain masks generated using BET. Fractional anisotropy, mean
diffusivity, axial diffusivity and radial diffusivity maps were generated using DTIFit
(http://fsl.fmrib.ox.ac.uk/fsl/fdt) that fits a diffusion tensor model at each voxel. Tract-
based spatial statistics (TBSS) were used in a 4-stage process. Pre-processing prepared
images for registration to standard space. Mean fractional anisotropy (FA), diffusivity
(MD), radial diffusivity (RD), axial diffusivity (AD) and skeletonized FA, MD, RD and AD
images were created, and thresholded. Lastly each FA, MD, RD and AD image was
projected onto the relevant skeleton and ‘randomize’ used for statistical analyses. Masks
of specific white matter tracts were created using ICBM-DTI-81 white-matter labels
atlas39 and used to extract mean white matter integrity indices.
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nlySupplementary results
Variable MRI Sample Phase 11 Participants
N=6306
Mean difference (95%
CI)/chi square (degrees of
freedom, p value)
N Mean/ % S.D. N Mean/% S.D.
Age [years] 527 69.6 5.3 6306 69.8 5.9 -0.2 (-0.7 to 0.3)
Sex
Female
Male
527
103
424
19.5%
80.5%
6306
1947
4459
29.3%
70.7%
27.5 (1, <0.001)
Full time education*
[years]
527 14.6 3.2 5101 15.1 4.2 -0.5 (-0.9 to -0.1)
CES-D* 527 5.0 5.8 5855 7.3 7.6 -2.3 (-3.0 to -1.6)
Systolic BP [mmHg]* 527 140.8 17.6 5652 127.8 16.5 13.0 (11.5 to 14.5)
Diastolic BP [mmHg]* 527 76.5 10.6 5652 70.8 9.9 5.7 (4.8 to 6.6)
Table 1: Comparison of imaging sample with Phase 11.
Men Women
Phase Age (mean,
s.d.)
% ‘unsafe’ drinkers
(Pre-2016 guidelines)
% ‘unsafe’ drinkers
(Post-2016 guidelines)
% ‘unsafe’ (Pre- = Post-
2016 guidelines)
1 43.0 (5.4) 17.1 32.1 11.9
3 48.2 (5.4) 17.9 32.1 14.4
5 53.1 (8.8) 28.5 44.6 17.2
7 59.5 (5.3) 22.0 40.0 16.5
9 64.4 (5.3) 18.4 34.0 13.7
11 68.5 (5.4) 14.6 28.5 7.8
Time of
Scan
69.6 (5.3) 25.6 47.8 27.0
Average 20.0 40.3 13.6
Table 2: Percent (male and female separately) drinking over safe weekly limits, as defined
by pre-2016 (>14 units/112g women, >21 units/168g men), and post-2016 guidelines
(>14 units/112g)
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nlyPhase Mean Standard deviation Median Interquartile range
1 11.7 (93.9) 12.4 (98.9) 8.0 (64.0) 13.0 (104.0)
3 11.4 (91.6) 11.4 (91.1) 8.0 (64.0) 13.0 (104.0)
5 15.0 (120.1) 14.8 (118.8) 11.0 (88.0) 16.0 (128.0)
7 13.0 (104.0) 12.0 (96.0) 10.0 (80.0) 14.0 (120.0)
9 11.2 (89.3) 10.3 (82.7) 9.0 (72.0) 14.0 (112.0)
11 10.3 (82.4) 10.0 (80.1) 8.0 (64.0) 12.0 (96.0)
Time of Scan 15.0 (117.7) 10.2 (118.2) 12.0 (91.6) 16.9 (135.2)
Average 12.5 (99.7) 10.2 (81.5) 10.2 (81.3) 12.6 (96.8)
Table 3: Summary of alcohol units (grams) consumed per week at each study phase for
included subjects (n=527).
CAGE score (%)
Phase 0 1 2/3
3 88.6 11.4 0
5 89·2 10.8 0
7 88.7 11.3 0
9 88.5 11.5 0
11 88.8 11.2 0
Table 4: Summary of CAGE (screen for alcohol dependence) scores.
Mean (S.D.)
Right hippocampal volume (unadjusted, mm3) 3474 (433)
Left hippocampal volume (unadjusted, mm3) 3368 (444)
Right hippocampal volume (adjusted as % of ICV) 2.42 (0.30)
Left hippocampal volume (adjusted as % of ICV) 2.35 (0.32)
Table 5: Mean (S.D.) raw and adjusted hippocampal volumes as percentage of intracranial
volume (%ICV) extracted using FIRST for the sample.
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Figure 1: Partial regression plot from multiple linear regression with hippocampal volume
(% of ICV) as the dependent variable and average alcohol consumption as an independent
variable, corrected for age, sex, education, premorbid FSIQ, social class, Framingham Risk
Score, history of Major Depressive Disorder (SCID), exercise frequency, club attendance,
social visits, current psychotropic medication.
Unstandardised B (95% CI) Change in
hippocampal
volume (%
intracranial
volume)*
P value
Alcohol adjusted1 (all
cases)
-0.004 (-0.006 to -0.003) -0.15 0.001
Alcohol adjusted1
(excluding highest 3
drinkers)
-0.004 (-0.006 to -0.001) -0.13 0.003
* For every 10 unit increase in alcohol weekly
1 Analyses adjusted for age, sex, premorbid IQ, education, social class, Framingham Risk Score, smoking, history
of Major Depressive Disorder (SCID), exercise frequency, club attendance, social visits, current psychotropic
medication.
Table 6: Outlier analysis of multiple linear regression results, with hippocampal volume
(% of ICV) as the dependent variable and average alcohol consumption as an independent
variable.
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Average alcohol1 -0.1 (0.03) -0.001 to 0.00
Average alcohol2 -0.08 (0.09) -0.001 to 0.00
1 Adjusted age, sex, education, FSIQ 2 As in 1 and additionally corpus callosum MD
Table 7: Results from multiple linear regression analysis, with decline in lexical fluency
(slopes, Phase 3 to time of scan) as the dependent variable, and average alcohol
consumption (weekly units) as an independent variable.
“Cognitive improvers”
Mean (S.D.)
“Cognitive decliners”
Mean (S.D.)
Mean difference (95%
CI)
FSIQ 120.5 (8.7) 117.0 (10.6) -3.6 (-5.6 to -1.6)
Table 8: Significant results from a t test of means between “cognitive improvers” (defined
as those with positive slopes on short-term memory tests over time) and “cognitive
decliners” (defined as those with negative slopes on short-term memory tests over time).
A. Semantic decline over study
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B. Lexical decline
C. Memory decline over study
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D. Hippocampal volume (%ICV)
Figure 2: Partial regression plots for multiple regression analyses with the following as
dependent variable: A – semantic fluency decline over study (slopes from mixed effects
model), B – lexical fluency decline over study, C – memory decline over study, D –
hippocampal volume (extracted from FIRST and as %ICV).
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