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The relation of dietary choline to cognitive performance and
white-matter hyperintensity in the Framingham Offspring Cohort
1–4
Coreyann Poly, Joseph M Massaro, Sudha Seshadri, Philip A Wolf, Eunyoung Cho, Elizabeth Krall, Paul F Jacques, and
Rhoda Au0
ABSTRACT
Background: Choline is the precursor to the neurotransmitter ace-
tylcholine. Loss of cholinergic neurons is associated with impaired
cognitive function, particularly memory loss and Alzheimer disease
(AD). Brain atrophy and white-matter hyperintensity (WMH) are
also associated with impaired cognitive function and AD.
Objective: The objective was to determine whether a relation exists
between dietary choline intake, cognitive function, and brain mor-
phology in a large, nondemented community-based cohort.
Design: A dementia-free cohort of 1391 subjects (744 women, 647
men; age range: 36–83 y; mean 6SD age: 60.9 69.29 y) from the
Framingham Offspring population completed a food-frequency
questionnaire administered from 1991 to 1995 (exam 5; remote
intake) and from 1998 to 2001 (exam 7; concurrent intake). Partic-
ipants underwent neuropsychological evaluation and brain MRI at
exam 7. Four neuropsychological factors were constructed: verbal
memory (VM), visual memory (VsM), verbal learning, and execu-
tive function. MRI measures included WMH volume (WMHV).
Results: Performance on the VM and VsM factors was better with
higher concurrent choline intake in multivariable-adjusted models
for VM (average change in neuropsychological factor per 1-unit
change in choline = 0.60; 95% CI: 0.29, 0.91; P,0.01) and
VsM (0.66; 95% CI: 0.19, 1.13; P,0.01). Remote choline intake
was inversely related to log-transformed WMHV (average change
in log WMHV per 1-unit change in choline = 20.05; 95% CI:
20.10, 20.01; P= 0.02). Furthermore, an inverse association was
observed between remote higher choline intake and presence of
large WMVH (OR: 0.56; 95% CI: 0.34, 0.92; P= 0.01).
Conclusion: In this community-based population of nondemented
individuals, higher concurrent choline intake was related to better cog-
nitive performance, whereas higher remote choline intake was associ-
ated with little to no WMHV. Am J Clin Nutr 2011;94:1584–91.
INTRODUCTION
The global prevalence of AD
5
is predicted to quadruple by
2050 to .100 million, at which time 1 in 85 persons worldwide
will be living with the disease (1, 2). More than 40% of those
cases will be in late-stage AD, which causes a significant burden
to caregivers because activities of daily living decline in this
demented population (2). Previous epidemiologic studies have
shown strong associations between generalized brain atrophy
and increased WMH to cognitive impairment and AD (3–8).
Cognitive impairments that precede the onset of AD have been
related to alterations in brain neurotransmission systems, mainly
cholinergic deficits (9, 10). The cause of these morphologic
changes, which lead to cognitive deterioration and are associated
with this disease, remains unknown.
Choline is an essential nutrient necessary for several biological
functions in the human body. In addition to being the precursor to
acetylcholine, choline also serves as the precursor to sphingo-
myelin and phosphatidylcholine—structural components of cell
membranes. When oxidized, choline forms the methyl donor
betaine for the conversion of homocysteine to methionine (11–
13). High homocysteine concentrations have been shown to be
related to both cognitive impairments in nondemented samples
and an increased risk of AD (14, 15). The US Institute of
Medicine has estimated an AI of choline of 550 mg/d for men and
425 mg/d for women (11).
The neurotransmitter acetylcholine is intricately connected
with the cholinergic neural networks associated with memory
1
From the Department of Neurology, Boston University School of Med-
icine, Boston, MA (CP, SS, and RA); the Department of Biostatistics, Boston
University School of Public Health, Boston, MA (JMM); the Department of
Mathematics & Statistics, Boston University, Boston, MA (JMM); the Chan-
ning Laboratory, Department of Medicine, Brigham and Women’s Hospital
and Harvard Medical School, Boston, MA (PAWand EC); the Department of
Health Policy & Health Services Research, Boston University Henry M
Goldman School of Dental Medicine, Boston, MA (EK); and the USDA
Human Nutrition Research Center on Aging, Tufts University, Boston,
MA (PFJ).
2
PFJ and RA are senior authors.
3
Supported by the National Heart, Lung, and Blood Institute’s Framing-
ham Heart Study, the NIH (NIH/NHLBI contract N01-HC-25195), the Na-
tional Institute on Aging (NIA 5R01-AG 16495 and NIA 5R01-AG08122),
National Institute of Neurological Disorders and Stroke (NINDS 5R01-
NS17950), and the USDA, Agricultural Research Service (agreement no.
58-1950-7-707).
4
Address reprint requests and correspondence to R Au, Department of
Neurology (Neurological Epidemiology and Genetics Division), Boston Uni-
versity School of Medicine, 715 Albany Street, B-608, Boston, MA 02118-
2526. E-mail: rhodaau@bu.edu.
5
Abbreviations used: AD, Alzheimer disease; AI, adequate intake; apo E,
apolipoprotein E; EF, executive function; FFQ, food-frequency question-
naire; FSRP, Framingham Stroke Risk Profile; TBV, total brain volume;
TCBV, total cranial brain volume; TCV, total cranial volume; TE, echo time;
VL, verbal learning; VM, verbal memory; VsM, visual memory; WMH,
white-matter hyperintensity; WMHV, white-matter hyperintensity volume.
Received November 26, 2010. Accepted for publication September 2, 2011.
First published online November 9, 2011; doi: 10.3945/ajcn.110.008938.
1584 Am J Clin Nutr 2011;94:1584–91. Printed in USA. Ó2011 American Society for Nutrition
(16) and is synthesized from choline and acetyl-CoA through the
action of choline acetyltransferase. Neurons obtain choline from
2 sources: uptake from serum choline mainly derived from di-
etary intake and de novo synthesis (17). The loss of cholinergic
neurons and choline acetyltransferase activity is consistent with
abnormalities in AD and is thought to contribute to the learning
and memory deficits associated with AD (16–18). Adequate
concentrations of choline in the brain are believed to protect
against age-related cognitive decline and certain types of
dementia, including AD (19, 20) because adequate concen-
trations potentially preserve neurons, brain volume, and neuronal
transmissions.
In animal models, prenatal choline supplementation has led to
an improvement in memory function in rats. The behavioral
effects of prenatal choline-supplemented rats were long-lasting
and persisted beyond the age of 2 y—an age at which a rat is
developmentally old. Thus, prenatal supplementation with cho-
line seems protective against normally observed memory decline
due to old age (21, 22). Other studies examined the effects of
choline supplementation on the cognitive impairment of aged rats
and also found that supplementation attenuates age-related
cognitive deficits (23, 24).
Most human evidence comes from pharmacologic research
that has shown cognitive improvement in mild-to-moderate AD
after choline treatment. The loss of cholinergic function in the
hippocampus and neocortex is evident in AD. It was therefore
hypothesized that choline precursor loading may offer thera-
peutic benefit to those who suffer with this progressive neurologic
degenerative disorder. The pharmacologic therapies tested in-
clude intervention with acetylcholine precursors, stimulation of
acetylcholine release, and use of muscarinic or nicotinic receptor
agonists and acetylcholinesterase. Treatment goals included
improvement in cognitive function, control of behavioral dis-
turbances, and slowing down the progression of the disease.
However, only temporary improvement has been found with
these treatments (20, 25, 26). The relation between dietary
choline intake and cognition in a relatively young, well-nourished
population, where potential cases of dementia are still in the
preclinical stage, are unknown. Human studies are needed to
determine the effects of dietary choline on cognition and brain
morphology.
Given the role choline plays in brain function and the pro-
tective effects of choline against age-related cognitive deficits
shown in animal models, we postulate that lower dietary choline
intake may be related to deficit performance on neuro-
psychological tests and to brain structures independent of other
dietary factors. The relation between dietary choline intake,
cognition, and brain morphology in a nondemented population
has not been examined to our knowledge.
SUBJECTS AND METHODS
Subjects
The Framingham Heart Study Offspring Study cohort,
recruited in 1971, has undergone periodic examinations for .30
y to identify risk factors for cardiovascular disease and stroke
(27, 28). From 1999 to 2001, surviving members of this cohort
were invited to participate in a call-back study on cognition and
brain imaging. Of the 2187 participants who agreed to enroll in
the study, 1889 were administered a neuropsychological test
battery and had a brain MRI scan. In addition, for inclusion in
the analysis, participants were required to have completed the
Harvard FFQ administered from 1991 to 1995 (exam 5) and
again from 1998 to 2001 (exam 7). Exclusion criteria included
prevalent dementia, clinical stroke, multiple sclerosis, or certain
other neurologic conditions (n= 69), those who did not have
complete and reliable FFQs at exam 7 (n= 429), and those who
did not have both a neuropsychological test battery and brain
MRI scan (n= 298). After all exclusions, our sample of par-
ticipants with completed the FFQ, the neuropsychological test
battery, and the brain MRI totaled 1391 (744 women, 647 men;
age range: 36–83 y; mean 6SD age: 60.9 69.29 y.)
Measurement of dietary choline
The Harvard FFQ is a well-validated instrument (29, 30). FFQs
provide a relatively simple, cost-effective method for assessing
habitual dietary intake over a specified period of time. In the
current study, participants were asked to report how often they
had eaten each particular food from a standard list of foods for the
prior 12-mo period.
Studies have shown that choline intake was associated with
plasma choline concentrations (31–33). Cho et al (34) showed
that choline intake from the 131 food item Harvard FFQ was
predictive of fasting homocysteine concentrations under con-
ditions of low folate intake, providing validation of dietary
choline intake derived from this FFQ.
A nutrient database was derived from the responses on the
FFQs and included the composition of choline-contributing
compounds (free choline, glycerophosphocholine, phosphocho-
line, phosphatidylcholine, and sphingomyelin). Total choline
intake was calculated by summing these compounds.
Participants were excluded from further analysis if
reported energy intakes were ,2.51 MJ/d (600 kcal/d) or .16.74
MJ/d (4000 kcal/d) for women and .17.57 MJ/d (4200 kcal/d)
for men or if 12 food items were left blank on the questionnaire.
Participants who met the energy intake criteria and had ,12
blank items were included in analyses and were considered to be
nonconsumers of the blank items. Line items that contributed
largely to total choline intake included eggs, meat, bread, and
dairy products. We used dietary choline intake at exam 7 to ex-
amine the association between concurrent choline intake, cogni-
tive function, and brain morphology and choline intake at exam 5
to examine the association of remote choline intake on cognitive
function and brain morphology.
Neuropsychological tests
Neuropsychological tests were administered to participants
according to standard protocols. Because the protocol consists of
numerous tests, a factor analysis was conducted by using criteria
such as eigenvalues to identify domain-specific factors. Within
a sex, each of the neuropsychological measures (log transformed
as necessary) was regressed onto age and education (group), and
the residuals were standardized to have a mean of 0 and an SD of
1. Similar results were obtained if the factor analysis was re-
peated restricting to those participants aged 65 y. Within each
factor, loadings on each of the variables were similar, so the
variables were summed to create the factor composite scores. As
DIETARY CHOLINE, COGNITION, AND BRAIN IMAGING 1585
an example, factor 1 is a linear combination of logical memory-
immediate and logical memory-delayed, with nearly identical
coefficients; therefore, we defined factor 1 as the sum of the 2
coefficients. Finally, we applied zscore transformations to the
factors.
After the factor analysis, 4 factors were identified for which,
within each factor, the variable loadings were similar to each
other or near 0 and were reflective of the cognitive domain. Thus,
it was decided to sum the variables with the non-zero loadings to
create the factors. So, for example, factor 1 is the sum of logical
memory-immediate and logical memory-delayed standardized
scores. The 4 domain-specific factors and the individual test
measures that compose each (35–40) are listed in Table 1.
Brain MRI measures
The MRI measures used were previously described (34). In
summary, subjects received brain imaging from a Magnetom 1-T
field strength machine (Siemens). T2-weighted sequences were
performed with double spin-echo coronal imaging, 4-mm con-
tiguous slices from nasion to occiput with a repetition time of
2420 ms, a TE of TE1 20/TE2 90 ms, an echo train length of 8,
a field of view of 22 cm, and an acquisition matrix of 192_256
interpolated to 256_256 with excitation. Images were analyzed
and interpreted blindly to subject data and in random order by
using a custom-designed image analysis package.
TCBV
DeCarli et al (42) described the quantification of TCBV.
Briefly, brain volume was determined by manually outlining the
intracranial vault to determine TCV. Once nonbrain elements
from the image were removed, mathematical modeling was
performed to determine TBV. The ratio of TBV to TCV (TCBV)
was used for this analysis.
WMHV
WMH measures for the Framingham Heart Study were pre-
viously published (3, 5, 43). WMHV was expressed as a pro-
portion of TCV to correct for head size (WMHV = WMH/TCV),
and this value was designated as WMHV. The distribution of
WMHV was markedly skewed; hence, natural log transformation
was applied for all regression analyses. We used log-transformed
WMHV as a continuous variable in our multilinear regression
models assessing the relation of WMHV to choline intake. We
further dichotomized WMHV into nonlarge and large WMHV (3,
43). Participants were categorized by age group and determined
as having large WMHV when the residual from a regression of
natural log-transformed WMHV compared with age was .1SD
of the mean residual for the participant’s age group. In our
multivariate logistic regression models relating WMHV to
choline intake, we used large WMH (yes or no) as a di-
chotomous outcome.
Statistical analyses
The analyses focused on assessing the relation between
neuropsychological factors, WMHV, and TCBV measured at
exam 7 compared with choline intake measured at each of exams
5 and 7. Variables are presented as means 6SDs (continuous) or
number and percentage (categorical). In multivariate linear re-
gression models, we used the natural log-transformed values for
choline intake, which provided the best-fitting model for the
analyses in which they were treated as continuous variables.
Age and sex-adjusted linear regression models were first used
to test the relation between each neuropsychological factor
(primary dependent variable) and log-transformed dietary cho-
line intake as measured by the FFQ (primary independent var-
iable), followed by multivariable-adjusted linear regression.
Candidates for entry as covariates into the multivariable models
were determined based on current literature for those variables
that affect cognitive function, brain morphology, and/or the
metabolic functions of choline in the body. Variables that were
candidates for entry into the model were age, sex (age and sex
were forced into the model), education, BMI, homocysteine
concentration, apo E, the FSRP (a composite score of cardio-
vascular disease risk factors that predict the 10-y probability of
a stroke), and total energy, saturated fat, vitamin B-12, and vi-
tamin B-6 intakes (15, 44–54). A level of entry and stay of 0.05
was used to develop the stepwise model.
A similar regression approach was used to test the multivariable-
adjustedlineartrendof each ofTCBVandlog-transformedWMHV
TABLE 1
Neuropsychological test battery
Cognitive factors Cognitive domains assessed Performance measures Unstandardized range
Factor 1: verbal memory
WMS
1
logical memory Verbal memory Immediate recall 0–24
Delayed recall 0–24
Factor 2: visual memory
WMS visual reproductions Visual memory Immediate recall 0–14
Delayed recall 0–14
Factor 3: verbal learning
WMS paired associates Verbal learning Total score at immediate recall 0–21
Total score at delayed recall 0–21
Factor 4: executive function
Trail Making Test A
2
Attention Time to completion in seconds 0–300
Trail Making Test B
2
Executive function Time to completion in seconds 0–300
1
WMS, Wechsler Memory Scale.
2
Halstead Reitan Trail Making Test.
1586 POLY ET AL
(dependent variables) across quartiles of log-transformed dietary
choline intake. As secondary analyses, we used similar multi-
variate-adjusted linear regressions to analyze a linear trend in
individual neuropsychological factors across quartiles of choline
intake. In separate multivariate logistic regression models, we
assessed the trend in WMHVas a dichotomous variable (presence
of large WMHV) across quartiles of choline intake.
All statistical analyses were performed by using Statistical
Analyses System software 9.1 (SAS Institute). Two-sided P
values 0.05 were considered statistically significant. Given the
exploratory nature of the study, no multiple comparison ad-
justment was made.
RESULTS
Demographics, average choline intake, and other covariate
used in the analyses are summarized in Table 2. In age- and sex-
adjusted models, dietary choline intake at exam 7 (concurrent
intake) was positively associated with the cognitive factors VM
(adjusted average change in neuropsychological factor per 1-
unit change in choline = 0.55; 95% CI: 0.25, 0.85; P,0.01),
VsM (0.35; 95% CI: 0.05, 0.64; P= 0.02), and VL (0.51; 95%
CI: 0.21, 0.80; P,0.01), but not with EF (P= 0.23). In mul-
tivariate-adjusted models, choline intake at exam 7 remained
significantly related to VM ( 0.60; 95% CI: 0.29, 0.91; P,
0.01) and VsM (0.66; 95% CI: 0.19, 1.13; P,0.01), but not to
VL (P= 0.48) or EF (P= 0.27; Table 3). No significant asso-
ciation was observed between remote choline intake (exam 5)
and neuropsychological factors (data not shown).
To further investigate the association between exam 7 neu-
ropsychological factors and exam 7 choline intake, multivariate-
adjusted linear regressions were performed to assess the linear
trend in individual neuropsychological items across choline
quartiles. These models showed that a higher choline intake was
significantly and positively related to VM in both the immediate
(adjusted average change across choline quartiles = 0.28, 95% CI:
0.05, 0.51; P= 0.02) and delayed (0.30; 95% CI: 0.13, 0.46; P,
0.01) recall. Higher choline intake was also significantly posi-
tively related to VsM in both the immediate (0.25; 95% CI: 0.05,
0.45; P= 0.01) and delayed (0.26; 95% CI: 0.05, 0.47; P= 0.01)
recall. We also noted an inverse association between the EF Trail
Making Test A and higher choline intake (20.01; 95% CI: 20.03,
0.00; P= 0.05). No significant relation was found between cho-
line and the EF Trail Making Test B (P=0.32;Tab le 4).
No significant association between TCBV and choline intake
was found at exam 5 (P= 0.82) or exam 7 (P= 0.32). Log-
transformed WMHV was significantly and inversely related to
choline intake at exam 5 in multivariate-adjusted models (ad-
justed average change in log-transformed WMHV across choline
quartiles = 20.05; 95% CI: 20.10, 20.01; P= 0.02. However,
no such significant relation between WMHV and choline was
seen for concurrent exam 7 choline intake (P= 0.29; Table 5).
A significant inverse relation was observed between large
WMHV and higher choline intake at exam 5. For example, the
multivariable adjusted OR for large WMHV for choline quartile 4
compared with quartile1 is 0.56 (95% CI: 0.34, 0.92; P= 0.01 for
linear trend across all 4 quartiles; Table 6). No other significant
relations were observed for brain measures and choline intake at
exam 5 or exam 7. The results were unaffected by age or sex
(interaction age-by-choline intake and sex-by-choline intake P
values 0.2).
DISCUSSION
In this study, the principal findings show that better memory
performance is related to a higher concurrent choline intake
(exam 7), whereas remote choline intake (exam 5) is associated
with a significant inverse relation to larger WMH in a large,
nondemented, community-based population.
VM and VsM were found to be strongly associated with
choline intake in both age- and sex-adjusted models as well as
final models. Dietary choline intake was significantly associated
with verbal learning in an age- and sex-adjusted model, but lost
its significance when saturated fat was added into the model.
EF was not related to choline intake in covariate-adjusted
models. Further investigation of the individual cognitive tests for
each factor confirmed a significant positive association between
choline intake and VM and VsM. Memory impairment is a
hallmark sign of AD (2, 4, 8, 13, 18, 55). Preservation of the
neurologic pathways associated with memory may be key in pre-
venting adverse morphologic changes in the brain that lead to AD.
WMHs are patchy areas with increased signal on T2-weighted
and fluid-attenuated inversion recovery MRI sequences of the
brain and seen in up to 90% of persons with vascular dementia
and AD. Researchers have found that subjects with large WMHV
had significantly poorer cognitive function and brain atrophy (7,
43, 54). Large amounts of WMH are, therefore, pathologic in
nature and prevention is important. Whereas WMH is thought to
be a measure of subclinical vascular disease and thus a potential
biomarker of vascular dementia (55), researchers have also
shown that changes in white matter are also present in up to 70%
of persons with AD (3, 47, 56).
Our findings show that early higher choline intake is signifi-
cantly related to smaller WMHV, which suggests that choline
TABLE 2
Characteristics and risk factors of the study sample (n= 1391) and average
choline intake
Characteristic Value
Female (%) 53.4
Age at neuropsychological exam (y) 60.8 69.3
1
Education (%)
At most some high school 0.2
High school graduate 2.5
Some college or vocational 31.6
College graduate/postgraduate 65.6
BMI (kg/m
2
) 27.8 65.1
Total energy (kcal/d) 1857.9 6595.9
Saturated fat (g/d) 22.4 610.1
Folate intake (lg/d) 605.3 6300.6
Vitamin B-6 intake (mg/d) 8.4 624.0
Vitamin B-12 intake (lg/d) 13.1 624.8
Average choline intake, exam 5 (mg/d) 322.7 6106.2
Average choline intake, exam 5 (log) 5.7 60.3
Average choline intake, exam 7 (mg/d) 321.1 6105.2
Average choline intake, exam 7 (log) 5.7 60.3
Homocysteine (lmol/L) 8.3 63.1
Framingham Stroke Risk Profile
2
0.1 60.1
APOE gene 0.2 60.4
1
Mean 6SD (all such values).
2
Provides the estimated stroke risk over the subsequent 10-y period.
DIETARY CHOLINE, COGNITION, AND BRAIN IMAGING 1587
intake at midlife may be neuroprotective. The mechanism by
which choline affects WMH is unknown. Yoshita et al (6) report
that an increase in WMH is positively associated with cognitive
impairment and AD. In another prospective cohort study, WMHV
was determined to be a risk factor for the incidence of mild
cognitive impairment (5). Possible biological mechanisms un-
derlying our findings include choline’s role as the precursor to the
neurotransmitter acetylcholine, which is essential to normal
cognition and brain function (17). Some evidence indicates that
acetylcholine depletion occurs in AD and contributes to the
cognitive decline observed in AD (9, 13, 16, 57).
Animal studies have shown that choline supplementation is
neuroprotective. Researchers have shown that prenatal supple-
mentation of choline improved memory function in rats well
into adulthood (21, 22, 58). Teather and Wurtman (23)
examined the effects of dietary supplementation of cytidine (5#)-
diphosphocholine, a source of choline, on memory impairment
in aged rats and found supplementation to be protective against
age-related memory deficits.
Choline’s metabolites are important for the structural integrity
of cell membranes and for cholinergic transmission and signaling
during the development of neuron cells (57). Several studies
suggest that pharmacologic doses of choline and choline deriva-
tives may have clinical efficacy in elderly patients with cognitive
deficits, inefficient memory, and early-stage AD (59–61). Magil
et al (31) showed that dietary intake of choline in the form of
lecithin elevated blood choline, brain choline, and brain acetyl-
choline concentrations significantly (31).
Neuropathologic studies of brains showed reductions in cor-
tical cholinergic markers correlated significantly with AD (9, 13,
62). The postmortem samples of people who have died of AD
show an accelerated rate in turnover of membrane
TABLE 4
Relation between dietary choline intake and individual adjusted mean (2-sided 95% CI) neuropsychological measures of significant neuropsychological
measure factors at exam 7
1
Domain-specific factors
Choline intake quartile
Average change (95% CI)
across quartiles Pvalue
2
1234
Verbal memory
Immediate recall
3
11.0 (10.6, 11.4) 11.5 (11.2, 11.8) 11.7 (11.4, 12.0) 11.9 (11.4, 12.2) 0.28 (0.05, 0.51) 0.02
Delayed recall
4
10.1 (9.7, 10.4) 10.6 (10.3, 11.0) 10.8 (10.5, 11.2) 11.0 (10.6, 11.4) 0.30 (0.13, 0.46) ,0.01
Visual memory
Immediate recall
5
8.8 (8.4, 9.1) 8.9 (8.6, 9.2) 9.3 (9.0, 9.6) 9.4 (9.1, 9.8) 0.25 (0.05, 0.45) 0.01
Delayed recall
5
7.8 (7.4, 8.2) 8.1 (7.8, 8.4) 8.5 (8.2, 8.8) 8.5 (8.1, 8.9) 0.26 (0.05, 0.47) 0.01
Verbal learning
Immediate recall
6
13.8 (13.5, 14.2) 13.9 (13.6, 14.2) 14.1 (13.8, 14.4) 13.9 (13.6, 14.3) 0.05 (20.12, 0.23) 0.55
Delayed recall
6
8.3 (8.2, 8.5) 8.2 (8.1, 8.4) 8.3 (8.2, 8.5) 8.4 (8.2, 8.6) 0.04 (20.04, 0.11) 0.38
Executive function
Trail Making Test A
7
0.57 (0.54, 0.59) 0.54 (0.52, 0.56) 0.53 (0.51, 0.55) 0.52 (0.50, 0.55) 20.01 (20.03, 0.00) 0.05
Trail making Test B
8
1.37 (1.31, 1.43) 1.34 (1.28, 1.40) 1.31 (1.25, 1.37) 1.33 (1.27, 1.39) 20.01 (20.04, 0.01) 0.32
1
Data derived from the final multivariate linear regression model. Candidates for entry included age, sex, education, BMI, apolipoprotein E, Wide Range
Achievement Test, the Framingham Stroke Risk Profile, and intakes of total energy, saturated fat, vitamin B-12, vitamin B-6, and homocysteine.
2
Pvalue is based on a linear regression of continuous item score compared with continuous natural log-transformed choline. Significance indicated at
P0.05.
3
Adjusted for age, sex, education, BMI, energy and saturated fat intakes, and Wide Range Achievement Test.
4
Adjusted for age, sex, education, BMI, folate intake, apolipoprotein E, and Wide Range Achievement Test.
5
Adjusted for age, sex, education, energy intake, Framingham Stroke Risk Profile, and Wide Range Achievement Test.
6
Adjusted for age, sex, saturated fat intake, and Wide Range Achievement Test.
7
Adjusted for age, sex, education, energy intake, homocysteine concentration, and Framingham Stroke Risk Profile.
8
Adjusted for age, sex, education, homocysteine concentration, apolipoprotein E, Framingham Stroke Risk Profile, and Wide Range Achievement Test.
TABLE 3
Results of 4 domain-specific cognitive factors in stepwise linear regression of neuropsychological factor compared with continuous log-transformed choline
adjusted for age and sex and significant covariates at exam 7
1
Domain-specific factors
Age- and sex-adjusted:
regression coefficient (95% CI)
2
Pvalue
3
Final models: regression
coefficient (95% CI)
2
Pvalue
3
Verbal memory 0.55 (0.25, 0.85) ,0.01 0.60 (0.29, 0.91) ,0.01
Visual memory 0.35 (0.05, 0.64) 0.02 0.66 (0.19, 1.13) ,0.01
Verbal learning 0.51 (0.21, 0.80) ,0.01 0.14 (20.23, 0.51) 0.48
Executive function 0.16 (20.09, 0.42) 0.23 0.14 (20.11, 0.39) 0.27
1
Candidates for entry included age, sex, education, BMI, homocysteine concentration, apolipoprotein E, Wide Range Achievement Test, the Framing-
ham Stroke Risk Profile, and total energy, saturated fat, vitamin B-12, and vitamin B-6 intakes.
2
Regression coefficient represents the change in the factor (created from summing individual standardized test items) score per 1-unit change in log
choline.
3
Significance indicated at P0.05.
1588 POLY ET AL
phosphatidylcholine and decreased choline availability throughout
the brain. It has been suggested that cholinergic neurons auto-
cannibalize the choline-containing membrane, thus resulting in their
demise (16, 63). Conversely, adequate concentrations of acetyl-
choline in the brain are believed to be protective against certain
types of dementia, including AD.
It is important to note that the reported average dietary choline
intake in this study is significantly lower than the Food and
Nutrition Board’s recommended adequate intake. Therefore,
these results may not fully reflect choline’s potential role re-
garding brain preservation and cognitive function. Consequently,
to better understand the relation between choline intake and the
development of age-related diseases and cognitive impairment, it
is important to either have multiple measures of choline intake
over the appropriate exposure period and/or show that choline
intake is relatively consistent within individuals over time. We
therefore assessed the stability of choline intake in this cohort
using the same choline-validated instrument. Mean choline in-
take between exams was virtually identical. Although a small
proportion of individuals had relatively large changes over time,
the average difference was essentially zero. We also showed that
individuals who consumed the highest or lowest intakes at exam 5
also tended to consume these same amounts at exam 7. The
correlations of choline compounds and kilocalories that we
observed in this study between exams 5 and 7 were comparable
with those reported previously in reproducibility studies for other
nutrients, in which the questionnaires were administered 1 y apart
(29, 30).
Although choline is synthesized in small amounts in the body,
studies indicate that additional choline from the diet is needed for
normal health (12). These results support the hypothesis that
dietary choline intake is neuroprotective over time and promotes
improved cognitive function. We posit that an increase in dietary
choline intake ensures adequate acetylcholine concentrations for
cholinergic neurotransmission and prevents cell breakdown by
preserving phosphatidylcholine within the cell membrane as
a result of more choline available to cross the blood-brain barrier.
This is confirmed by our findings that past choline intake was
significantly associated with changes in WMHV in the brain,
whereas cognitive function was only affected by concurrent
choline intake.
A key strength of this study was the application to a large
relatively young and cognitively healthy community-based co-
hort. However, this study had several limitations that must be
considered. Our study focused on TCBV, global brain volume,
and did not include examination of medial temporal regions (64,
65) that have been linked to early stages of AD and mild cognitive
impairment—a prodromal phase of AD (64, 66). This analysis
was based on cross-sectional data and would require further
studies to confirm these findings. Cognitive performance data
were only available at exam 7 for this offspring cohort; therefore,
choline data could not be compared with cognitive measures at
TABLE 6
Adjusted ORs (compared with quartile 1, reference quartile) of large WMHV (dichotomous variable) and choline intake as a continuous variable (n= 1414)
1
Choline quartile
Exam 1 2 3 4 Pvalue
Adjusted for age and sex
Exam 5 1 0.64 (0.40, 1.04) 0.79 (0.50, 1.25) 0.57 (0.34, 0.93) 0.01
Exam 7 1 1.01 (0.63, 1.63) 0.70 (0.42, 1.17) 1.08 (0.68, 1.74) 0.98
Adjusted for age, sex, and multivariates
2
Exam 5 1 0.64 (0.40, 1.04) 0.79 (0.50, 1.25) 0.56 (0.34, 0.92) 0.01
Exam 7 1 1.04 (0.65, 1.66) 0.72 (0.43, 1.20) 1.08 (0.67, 1.74) 0.98
1
Pvalues were based on a logistic regression of dichotomous volume compared with continuous natural log-transformed choline. Significance indicated
at P0.05. WMHV, white-matter hyperintensity volume.
2
Only the stroke risk profile was significantly related to the outcome of large WMHV and hence is the only covariate that was used in final model to
assess the relation of WMHV with choline at exam 5 and exam 7.
TABLE 5
Relation between dietary choline intake and covariate-adjusted mean (2-sided 95% CI) TCBV and WMHV at exams 5 and 7 (n= 1414)
1
Choline quartile
Average change
across quartilesFinal models exam 1 2 3 4 Pvalue
Exam 5
Natural log(WMHV)
2
27.52 (27.62, 27.42) 27.65 (27.75, 27.55) 27.60 (27.69, 27.50) 27.71 (27.81, 27.61) 20.05 (20.10,-0.01) 0.02
TCBV
3
79.5 (79.2, 79.8) 79.5 (79.2, 79.8) 79.4 (79.1, 79.7) 79.6 (79.3, 79.9) 0.02 (20.12, 0.15) 0.82
Exam 7
Natural log(WMHV)
2
27.62 (27.72, 27.52) 27.54 (27.64, 27.44) 27.65 (27.75, 27.55) 27.67 (27.77, 27.57) 20.02 (20.07, 0.02) 0.29
TCBV
4
79.6 (79.3, 79.9) 79.5 (79.2, 79.8) 79.5 (79.2, 79.8) 79.4 (79.2, 79.7) 20.07 (20.21, 0.07) 0.32
1
Pvalues were based on a linear regression of continuous volume compared with continuous natural log-transformed choline. Significance indicated at
P0.05. TCBV, total cranial brain volume; WMHV, white-matter hyperintensity volume.
2
Adjusted for age and sex.
3
Adjusted for age, sex, BMI, vitamin B-12, and the Framingham Stroke Risk Profile.
4
Adjusted for age, sex, BMI, folate, and Framingham Stroke Risk Profile.
DIETARY CHOLINE, COGNITION, AND BRAIN IMAGING 1589
exam 5. Also limited by having brain volume measures from only
one period of time, we were unable to look at choline’s effect on
brain volume over a period of time. Another limitation was the
lack of a biomarker for choline, such as serum choline con-
centrations, that might have eliminated the limitation of potential
bias related to self-reporting on the FFQs. Finally, the Fra-
mingham Offspring cohort consisted primarily of whites, which
did not permit generalization of these results to other populations.
Preserving cognitive function and TBV are related to a de-
creased risk of AD. The goal of this research was to potentially
identify a dietary, and therefore modifiable, risk factor for pre-
venting decline in cognitive function and identifying a potential
mechanism for decreasing the risk of dementia. Further study is
necessary to determine whether an adequate dietary intake of
choline is related to improved cognitive function throughout the
life span and to determine the role it plays regarding the pres-
ervation of brain health.
We thank the study participants for their endless dedication, participation,
and commitment to the Framingham Heart Study.
The authors’ responsibilities were as follows—PAW and RA: involved in
the collection of the outcome measures; PFJ: created the choline measures
based on existing dietary data; CP, RA, and PFJ: responsible for the study
design; and CP, RA, PFJ, and JMM: responsible for the analysis and inter-
pretation of the data. All authors were responsible for writing and critically
revising the manuscript. None of the authors had a personal or financial con-
flict of interest.
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