Nutrient biomarker patterns, cognitive
function, and MRI measures of brain aging
L.C. Silbert, MD, MCR
D. Howieson, PhD
H.H. Dodge, PhD
M.G. Traber, PhD
B. Frei, PhD
J.A. Kaye, MD
J. Shannon, PhD, MPH
J.F. Quinn, MD
Objective: To examine the cross-sectional relationship between nutrient status and psychometric
and imaging indices of brain health in dementia-free elders.
Methods: Thirty plasma biomarkers of diet were assayed in the Oregon Brain Aging Study cohort
(n ? 104). Principal component analysis constructed nutrient biomarker patterns (NBPs) and re-
gression models assessed the relationship of these with cognitive and MRI outcomes.
Results: Mean age was 87 ? 10 years and 62% of subjects were female. Two NBPs associated
with more favorable cognitive and MRI measures: one high in plasma vitamins B (B1, B2, B6,
folate, and B12), C, D, and E, and another high in plasma marine ?-3 fatty acids. A third pattern
characterized by high trans fat was associated with less favorable cognitive function and less
total cerebral brain volume. Depression attenuated the relationship between the marine ?-3 pat-
tern and white matter hyperintensity volume.
Conclusion: Distinct nutrient biomarker patterns detected in plasma are interpretable and ac-
count for a significant degree of variance in both cognitive function and brain volume. Objective
and multivariate approaches to the study of nutrition in brain health warrant further study. These
findings should be confirmed in a separate population. Neurology®2012;78:241–249
AD ? Alzheimer disease; CDR ? Clinical Dementia Rating; EDTA ? ethylenediaminetetraacetic acid; FFQ ? food frequency
questionnaire; HDL ? high-density lipoprotein; HPLC ? high-performance liquid chromatography; ICC ? intraclass correla-
tion coefficient; MMSE ? Mini-Mental State Examination; NBP ? nutrient biomarker pattern; OBAS ? Oregon Brain Aging
Study; PCA ? principal component analysis; TCBV ? total cerebral brain volume; TIV ? total intracranial volume; WMH ?
white matter hyperintensity.
The epidemiology of Alzheimer disease (AD) suggests a role for nutrition.1-7Despite studies in
favor of a single or a few nutrients in the prevention of AD, the translation to formal clinical
trials testing vitamin E, B vitamins, or docosahexaenoic acid have been disappointing.8-12Given
the interactive nature of nutrient action and metabolism, it is not surprising that a single or few
nutrient approaches for neurodegenerative disease are tenuous.13-15These results impart the
rationale for novel methodologic approaches that appreciate the interactive features of nutri-
ents and model their collective influence in the promotion of brain health.
Food frequency questionnaires (FFQ) have traditionally been used to construct dietary
patterns.16FFQ is relatively inexpensive and fairly comprehensive, but this method is subject to
faulty recall of dietary intake and does not account for variability in nutrient absorption, both
of which are issues in the elderly.17,18We have recently reported a reliable blood test that
assesses nutritional status in people at risk for dementia.19In the current study, we examine the
relationship of nutrient biomarkers with cognitive function and MRI.
To capture the effect of nutrients in combination, we construct nutrient biomarker patterns
using principal component analysis (PCA). Cluster analysis,20index scores,21and reduced rank
From the Departments of Neurology (G.L.B., L.C.S., D.H., H.H.D., J.A.K., J.F.Q.) and Public Health and Preventive Medicine (G.L.B., J.S.), and
Center for Research in Occupational and Environmental Toxicology (J.S.), Oregon Health & Science University, Portland; Portland VA Medical
Center (J.A.K., J.S., J.F.Q.); and the Linus Pauling Institute (M.G.T., B.F.), Oregon State University, Corvallis.
References e1–e13 are available on the Neurology®Website at www.neurology.org
Study funding: Supported by NIH/NCCAM AT004777 (G.L.B.), NIH/NIA P30 AG008017 (J.A.K.), NIH/NCRR UL1 RR024140 Oregon Clinical
and Translational Research Institute, and Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development.
Disclosure: Author disclosures are provided at the end of the article.
Correspondence & reprint
requests to Dr. Bowman:
Copyright © 2011 by AAN Enterprises, Inc.
Published Ahead of Print on December 28, 2011 as 10.1212/WNL.0b013e3182436598
regression22have each been applied to FFQ
data to assemble dietary patterns, but none
have applied PCA to biological markers of
diet. One goal is to define dietary patterns
that promote cognitive health in the same
manner that dietary approaches for hyperten-
sion have been derived and applied.23
METHODS Population. The Oregon Brain Aging Study
(OBAS) was initiated in 1989 and recruited 293 community-
dwelling men and women aged 65 years and older who were
cognitive decline (i.e., vascular disease, hypertension, diabetes) to
permit a purer study of aging effects on brain parameters.e1,e2The
current cohort density is 76 and assessments are performed until
death. The MMSE, neuropsychological assessments, and the
Clinical Dementia Rating (CDR) reflect cognitive status. Partic-
ipants attend annual study visits with a collateral historian for an
evaluation by a staff neurologist, neuropsychologist, and research
study member for clinical and cognitive evaluation, MRI, and
blood collection. This study utilized circa 2006–2007 banked
specimens and participants with a CDR ?0.5 were excluded.
Plasma, clinical, and neuropsychological data were available for
104 subjects and 42 had MRI within a month of the blood draw.
Standard protocol approval and patient consent. In-
formed consent was obtained from all patients for participation
in this study, which was approved by the OHSU institutional
review board for human study.
Nutrient biomarker acquisition and analysis. Preferably,
fasting plasma was collected between 07:00 and 12:00 noon Pacific
Time beginning in September 2006 and ending December 2007.
Heparin plasma was deproteinized with 10% metaphosphoric acid
and analyzed for ascorbic acid using high-performance liquid chro-
matography (HPLC).e3Ethylenediaminetetraacetic acid (EDTA)
plasma carotenoids, tocopherol, and retinol were analyzed by
HPLC using diode array detector and fluorescence detection.e4
EDTA plasma thiamin, riboflavin, niacin, and pyridoxal
5-phoshate were analyzed by liquid chromatography–mass spec-
trometry/mass spectrometry.e4EDTA plasma folate and vitamin
B12 was measured with a chemiluminescence-based assay on an
Immulite analyzer (Siemens Corporation, Washington, DC).
Radioimmunoassay measured EDTA plasma 25-OH vitamin D
(Immunodiagnostics Systems Inc., Scottsdale, AZ). Gas chroma-
tography equipped with a flame ionization detector quantified
plasma fatty acid concentrations.e4Plasma lipids were measured
with standard enzymatic methods. Reliability statistics for these
assays are readily available.19
Neuropsychological tests. The battery includes the follow-
ing: Trail Making Test, Consortium to Establish a Registry for
Alzheimer’s Disease Word List acquisition and delayed recall,
abbreviated Boston Naming Test, Wechsler Memory Scale–
Revised Logical Memory Story A I and II and Wechsler Adult
Intelligence Scale–Revised Digit Span and Block Design, and
the abbreviated Geriatric Depression Scale.
Volumetric MRI acquisition and analysis. Regional vol-
umes of interest are scaled using a 1.5-T MRI and semiauto-
mated REGION image analysis software. Recursive regression
analysis of bifeature space based on relative tissue intensities was
used to separate tissue types on each coronal image. The sums of
pixel areas for all slices were converted to volumetric measures by
multiplying by the slice thickness for each of the following re-
gions of interest: total white matter hyperintensity volume
(WMH, includes periventricular and subcortical deep signals)
and supratentorial brain volume as total cerebral brain volume
(TCBV, excluding cerebellum and brainstem). Regression for
brain tissue, CSF, and WMH collectively against bone creates a
boundary along the inner table of the skull to determine the total
intracranial volume (TIV). Additional boundaries were manu-
ally traced along the tentorium cerebelli and the superior border
of the superior colliculus, the pons, and the fourth ventricle. The
pituitary, vessels in the sphenoid region, and any sinuses that
may have been included by the automatic regression were ex-
cluded manually. All REGION methods have an intraclass cor-
relation coefficient (ICC) of 0.95 or better except WMH volume
(ICC ? 0.85).
Covariates. PCR assay determined APOE4 carrier status.e5
Age, gender, years of education, body mass index, socioeconomic
status,e6blood pressure, current drinking and smoking, active
diabetes and hypertension, active depression within the past 2
years, Hachinski scale,e7current multivitamin use, duration
of fasting, and plasma creatinine were queried from clinician
Statistical analysis. Descriptive statistics were calculated for
demographic, clinical, and nutritional measures.
Nutrient biomarker pattern construction. Eight distinct
nutrient biomarker patterns (NBPs) were extracted from the
original set of 30 biomarkers via multivariate analysis (PCA).e8,e9
An eigenvalue of 1.0 was set a priori to determine the NBPs to
carry into hypothesis testing. Each participant receives a stan-
dardized NBP score for each pattern that corresponds to a linear
combination of the plasma nutrients that load heavily within
Cognitive z scores and MRI. Cognitive z scores generated
on the study sample (n ? 104) were conceptually combined to
represent specific cognitive domainse10and combined all to-
gether to generate a global cognitive z score (table e-1 on the
Neurology®Web site at www.neurology.org). MRI of the supra-
tentorial brain divided by intracranial volume adjusted for differ-
ences in head size and the sum of the periventricular and
subcortical deep white matter signals divided by the supratento-
rial volume adjusted for brain size prior to hypothesis testing.
Primary models. The outcomes of interest include the fol-
lowing: cognitive z scores (global and domain specific), TCBV
(% of intracranial volume), and WMH volume (% of TCBV).
Potential confounders considered for inclusion were based on a
previously recognized relationship with the outcomes and an as-
sociation identified with the NBPs in this study. Two linear
regression models were fit for each cognitive and MRI outcome:
model 1 includes all 8 NBPs entered simultaneously plus age,
gender, education, and APOE4, and model 2 is further adjusted
for hypertension and depression simultaneously.
Exploratory analysis. To appreciate the proportion of vari-
ance explained in the brain outcomes by the NBPs, we examined
the R2generated by the clinical-demographic variables initially
and again with all the NBPs included in the model simultane-
ously. To explore the hypothesis that WMH and brain atrophy
mediate the association between NBPs and cognitive function,
we examined changes in significance and coefficient magnitude
with and without MRI measures in the model. All analyses were
performed with IBM SPSS statistical software, version 19 for
Macintosh. All p values reported are 2-sided.
Neurology 78January 24, 2012
RESULTS Demographic, clinical, and nutritional
characteristics. As expected, comorbidities and vascu-
lar risk factors were low, with the exception of hyper-
tension (44%). The study cohort is 62% female and
10% carry the APOE4 allele. The mean MMSE was
27 and no participants had a CDR ?0.5 (table 1).
The overall nutritional status in the sample popula-
tion was mostly replete with prevalence of vitamin
B12 deficiency (?200 pg/mL) at 7% and vitamin D
deficiency (?20 ng/mL) at 25% (table e-2). Partici-
pants with MRI (n ? 42) were 85 years and older
(mean age ? 92.6, SD 3.8, range 85–101). Mean age
difference between those with and without MRI was
9.4 years (p ? 0.0001). The other demographic and
clinical characteristics were not different (data not
Nutrient biomarker pattern construction and interpre-
tation. Table 2 displays the composition of 8 NBPs.
After the eighth NBP extraction with PCA, 72.8% of
the total variance was accounted for in the original
set of nutrient biomarkers (table 2, cumulative %
variance after NBP8 extraction). For the sake of ref-
erence in the following results, NBP1 is described as
the BCDE pattern (all loading coefficients ? 0.50).
NBP2 is described as the saturated fat pattern, NBP3
as the carotenoid pattern, NBP4 as the cholesterol
pattern, NBP5 as the marine ?-3 fatty acid pattern,
NBP6 as the ?-6 ? retinol pattern, NBP7 as the
lutein ? high-density lipoprotein (HDL) cholesterol
pattern, and NBP8 as the trans fat pattern mostly
represented by trans linolelaidic acid (18:2?-6t).
Nutrient biomarker patterns and demographic–clini-
cal characteristics. The NBP1-BCDE and NBP5-
marine ?-3 patterns were not associated with any
demographic–clinical characteristics. The mean
NBP8–trans fat score was 0.713 SD units higher
in subjects with hypertension. These findings and
the remaining for the other NBPs are presented in
Nutrient biomarker patterns and cognitive function.
The NBP1-BCDE and NBP8–trans fat patterns
were the most significant to cognitive function (ta-
ble 3). Subjects with higher BCDE scores had better
global cognitive function, particularly in domains of
executive, attention, and visuospatial function. Par-
ticipants with higher plasma trans fat scores had
worse cognitive function overall (memory, attention,
language, processing speed, and global). Subjects
with higher NBP5-marine ?-3 scores had better ex-
ecutive function. Memory was better in those with
higher NBP7-lutein ? HDL cholesterol scores.
Memory and language were worse in those with
higher NBP6-?-6 ? retinol scores. Adjustment for
Table 1Demographic and clinical characteristicsa
n ? 104
Age, y, mean (SD)
Female, n (%)
Education, y, mean (SD)
APOE4 carrier, n/total (%)
Body mass index, mean (SD), kg/m2
Socioeconomic status, mean (SD)
Depression, n/total (%)
Hypertension, n/total (%)
Blood pressure, mean (SD), mm Hg
Diabetes, n/total (%)
Smoking, n/total (%)
Drinking, n/total (%)
Hachinski ischemic scale, mean (SD)
Creatinine, plasma, mg/dL, mean (SD)
Multivitamin use, n/total (%)
Fasting duration, h, mean (SD)
Neuropsychological tests, mean (SD)
Mini-Mental State Examination
Clinical Dementia Rating
Digit Span forward
Digit Span backward
Trail Making Test B
Boston Naming Test
Category fluency: animals
Category fluency: vegetables
Logical Memory IA
Logical Memory IIA
Trail Making Test A
Volumetric MRI, cm3, mean (SD)n ? 42
Total intracranial volume (TIV)
Total cerebral brain volume (TCBV)b
White matter hyperintensity volume (WMH)
TCBV (% of TIV)
WMH (% of TCBV)
aComorbidities require active treatment to qualify.
bTotal cerebral brain volume excludes brainstem and cerebellum.
Neurology 78 January 24, 2012
age, gender, education, APOE4, hypertension, and
depression did not attenuate these relationships.
Each 1-SD increase in BCDE score associated with a
0.28-SD increase in global cognitive score. Each
1-SD increase in the trans fat score associated with a
0.30-SD decrease in global cognitive score.
Nutrient biomarker patterns and MRI. Subjects with
higher plasma BCDE scores had more TCBV and
those with higher trans fat scores had less TCBV.
Subjects with higher marine ?-3 scores had less
WMH volume, but after adjustment for depres-
sion and hypertension the association was attenu-
ated (table 4, WMH model 2). Significance of the
?-3 to WMH was lost after adding depression to
model 1 (p ? 0.030 to 0.097). Adding hyperten-
sion had no effect. After stratifying by depression
Table 2 Nutrient biomarker pattern construction: Pattern structure and variance explaineda
Nutrient biomarker patternsb
Plasma nutrient biomarkers12345678
Pyridoxal 5-phosphate (B6)
Ascorbic acid (vitamin C)
?-Tocopherol (vitamin E)
?0.209 0.343 0.2530.215
?-Linolenic acid (18:3?-3)
Palmitic acid (16:0)
Heptadecanoic acid (17:0)
Linoleic acid (18:2?-6)
Trans-elaidic acid (18:1?-9t)
Eicosapentaenoic acid (20:5?-3)
Docosahexaenoic acid (22:6?-3)
Arachidonic acid (20:4?-6)
?-Linolenic acid (18:3?-6)
?0.239 0.1920.256 0.3020.669c
Lutein ? zeaxanthin
Trans-linolelaidic acid (18:2?-6t)
% Variance explained by each NBP
21151175.44.5 4.5 4.1
Cumulative % of variance explained
with each extraction
2136 47 54 5064 6873
Abbreviations: HDL ? high-density lipoprotein; LDL ? low-density lipoprotein; NBP ? nutrient biomarker pattern.
aExtraction method: principal component analysis. Rotation method: varimax with kaiser normalization.
bNBP interpretation is based on the strongest loading coefficients within each pattern. For example, a high NBP1 score is interpreted as high plasma
vitamins B, C, D, and E. Each standardized summary score is a linear combination of the plasma nutrients that mostly represent the respective pattern.
Coefficients ?0.15 were excluded to simplify the table and emphasize dominant nutrients within each pattern.
cConsidered the dominant nutrients in the pattern.
Neurology 78January 24, 2012
in model 1 for WMH it was apparent that ?-3s
were significant only in those without depression
(? ? ?0.845, p ? 0.021). The unadjusted pro-
portions of variance explained in brain volumes by
each significant NBP are provided in the figure.
Exploratory analysis. Age, gender, education years,
APOE4 carrier status, depression, and hyperten-
sion together explained 46% of the variation in the
global cognitive z score. Adding the NBPs ex-
plained an additional 17% (global cognitive z
0.63). In regards to the MRI-TCBV, the covari-
ates explained 40% of the total variation and the
NBPs explained an additional 37% (TCBV:
Covariates? 0.46; R2
Covariates ? NBPs?
Covariates? 0.396; R2
Covariates ? NBPs? 0.766).
The covariates explained 52% of the WMH varia-
tion and the NBPs explained an additional 9%
measures to the model. In the reduced subset with MRI
available (n ? 42), the association between NBP8–trans
fat scores and global cognitive function was undetectable
(p ? 0.054), leaving us unable to pursue this hypothesis
for trans fat. However, the relationship between NBP1-
BCDE scores and global function was maintained in this
subset, and adding MRI measures did not attenuate this
covariates? 0.512, R2
covariates ? NBPs?
Table 3Nutrient biomarker patterns associated with cognitive function (n ? 104)
ExecutiveMemoryAttention Visual spatial Language ProcessingGlobala
0.100.10 0.310.31 0.080.08 0.11 0.110.07 0.070.09 0.09 0.090.09
0.090.08 0.13 0.16
0.10 0.10 0.31 0.310.08 0.080.11 0.11 0.08 0.08 0.09 0.090.09 0.09
0.10 0.100.32 0.32 0.080.08 0.110.110.08 0.080.090.09 0.09 0.09
?0.130.13 0.10 0.04 0.040.06 0.06 0.020.01
?0.06 0.04 0.04
0.100.10 0.300.30 0.080.08 0.100.10 0.080.07 0.090.09 0.090.09
0.260.240.02 0.02 0.030.01
0.10 0.100.33 0.330.080.08 0.110.11 0.080.080.09 0.090.09 0.09
0.11 0.11 0.320.330.08 0.080.11 0.110.08 0.080.09 0.090.090.09
0.090.100.290.300.07 0.080.100.100.07 0.070.08 0.080.08 0.08
0.100.11 0.310.340.08 0.080.110.120.070.08 0.09 0.100.090.10
Abbreviations: NBP ? nutrient biomarker pattern; NBP1 ? BCDE; NBP2 ? saturated fat; NBP3 ? carotenoid; NBP4 ? cholesterol; NBP5 ? marine ?-3;
NBP6 ? ?-6 ? retinol; NBP7 ? lutein ? high-density lipoprotein cholesterol; NBP8 ? trans fat.
aGlobal z score includes the sum of the domain z scores divided by 6. Reverse coding for executive and processing speed created a uniform direction for
the coefficient (positive coefficient ? superior performance).
bModel 1: cognitive measure ? 8 NBPs ? age ? gender ? education ? APOE4.
cModel 2: cognitive measure ? model 1 ? hypertension ? depression.
dp ? 0.01.
ep ? 0.05.
Neurology 78 January 24, 2012
DISCUSSION This cross-sectional study describes
the nutrient biomarker patterns identified in plasma
from a sample of elders at risk for dementia. This
objective and multivariate approach yielded 3 dis-
tinct NBPs significant to both cognitive function
and MRI measures of brain aging. To our knowl-
edge, this is the first study to apply principal compo-
nents analysis to biological markers of diet.
Dietary patterns associated with cognitive decline
or Alzheimer incidence have historically derived the
patterns from FFQ data. Dietary intake can be in-
dexed as “healthy” or “unhealthy” based on existing
knowledge and examined in relation to disease
risk.21,24Data-driven cluster analysis places subjects
into exclusive dietary patterns a posteriori20and re-
duced rank regression combines existing knowledge
and the data at hand to derive dietary patterns.22
These studies using FFQ have identified an intake
higher in dark and green leafy vegetables, cruciferous
vegetables,22fish,25and fruit21,22and lower in organ
meats, red meat, high-fat dairy, butter,22and trans
fat26as favorable for cognitive health. In thinking
about the plasma signature of this diet, we propose
that the favorable BCDE pattern and ?-3 pattern
would be sensitive to the frequent consumption of
dark and green leafy and cruciferous vegetables, fruit,
and fish. In addition, a NBP high in trans fat and
retinol would be expected in people frequently con-
suming bakery and fried foods, margarine spreads,27
red meat,27and offal.28These consistencies are en-
couraging and provide impetus for further develop-
ment of biological markers of diet.
The neuroimaging results suggest that the mecha-
nisms through which the 2 favorable patterns
(NBP1-BCDE and NBP5-marine ?-3) affect cogni-
tive function are distinct. Cognitive benefit gained
by a plasma profile high in antioxidants C and E, B
vitamins, and vitamin D may partially operate on the
neurobiology that governs rate of total brain atrophy
(e.g., Alzheimer type pathology), whereas the effects
of the marine ?-3s may be mediated through more
vascular mechanisms.29,30The favorable relationship
between the BCDE pattern and global cognitive
function was maintained after adding TCBV to the
model in our study. This suggests that the effects of
this combination on cognition are not entirely medi-
ated through structural changes. Other mechanisms
through which this pattern may offer cognitive bene-
fit include the promotion of hippocampal neurogen-
esis,31reduction of ?-secretase activity,32oxidative
stress,33,34and hyperhomocysteinemia-induced neu-
rotoxicity,35and perhaps by maintaining blood–
brain barrier integrity.36
The high trans fat pattern was consistently associ-
ated with worse cognitive performance and less
TCBV. Linolelaidic acid is predominantly found in
bakery foods such as cookies, doughnuts, cakes, pas-
tries, and pies.27These foods are often prepared with
hydrogenated vegetable oils to allow for a long shelf
life. Higher trans fatty acid intake increases cardio-
vascular risk, systemic inflammation, and endothelial
dysfunction, all of which may explain an association
with cognition.37,38Unfortunately, very few studies
have assessed trans fat and risk for cognitive de-
cline.26Trans fat may aggravate cognitive function
independently and jointly through interaction with
other dietary factors.e11Trans fat may displace DHA
in neuronal membranes, but apparently does not im-
pact the neuropathologic Alzheimer hallmarks in
mice.39The consistency of the association of plasma
Table 4Nutrient biomarker patterns and volumetric MRI (n ? 42)
1.29 0.710.08 1.56 0.600.018c
NBP2 saturated fat
0.10 0.900.91 0.61 0.730.42 0.24 0.24 0.330.23 0.24 0.35
0.42 0.720.570.77 0.60 0.21
?0.030.19 0.86 0.030.20 0.90
0.350.700.62 0.190.56 0.74
?0.10 0.18 0.60
NBP5 marine ?-3
?0.75 0.63 0.25
NBP6 ?-6 ? retinol
0.41 0.810.62 0.120.65 0.86 0.010.21 0.950.020.21 0.91
NBP7 lutein ? HDL
0.530.810.52 0.730.73 0.330.06 0.21 0.78
?0.11 0.24 0.65
NBP8 trans fat
0.08 0.15 0.60
Abbreviations: HDL ? high-density lipoprotein; TCBV ? total cerebral brain volume as a % of total intracranial volume;
WMH ? white matter hyperintensity volume as % of TCBV.
aModel 1: MRI ? 8 NBP ? age ? gender ? education ? APOE4.
bModel 2: MRI ? model 1 ? hypertension ? depression.
cHighlights statistical significance.
Neurology 78 January 24, 2012
trans fat with poorer cognitive function and more
brain atrophy suggests neurologic consequences in
humans, but these findings need to be confirmed.
PCA of fatty acids expressed as weight percentages
of total in serum and in erythrocyte membranes have
been studied.e12,e13The patterns, including eicosa-
pentaenoic and docosahexaenoic acid loading to-
gether, were similar to our findings using fatty acids
expressed as absolute concentrations in plasma. The
interactive metabolism of EPA and DHA, in addi-
tion to the similar dietary sources, may explain why
these 2 fatty acids load together. PCA constructs the
patterns on a basis of collinearity, and this “related-
ness” may be partially attributed to interactive me-
tabolism when applied to biological markers of
diet. Our observation that the carotenoids
(NBP3), total and low-density lipoprotein choles-
terol (NBP4), saturated fats (NBP2), and the ?-6
fatty acids (NBP6) load together adds further sup-
port to the notion that interactive metabolism is a
contributor to NBP construction.
There are limitations of this study. PCA may re-
quire investigator decisions with the data in hand.
For example, using an eigenvalue of ?1.0 as inclu-
sion criteria for the number of patterns extracted to
carry forward into hypothesis testing may require
more field-specific criteria. Our nutrient biomarkers
were selected a priori capitalizing on existing knowl-
edge of an association with neurodegeneration, but
this may not reflect the ideal set. Observational stud-
ies are susceptible to residual confounding, and our
cross-sectional design is not suited for inferring any
causal association since the temporal relationship is
unattainable. Our sample population was restricted
to a relatively healthy and well-educated cohort of
white, non-Hispanic elders with minimal genetic risk
for AD. These attributes may limit the generalizabil-
ity of the results.
Future studies should consider validating the ex-
ternal consistency of these findings. The ability of
NBPs to predict cognitive and brain volume changes
would offer more compelling data. Gene–nutrient
interactions underlying a relationship between nutri-
tion and cognition may be important to consider
since APOE4 carriers may benefit less from nutri-
tional interventions.6,10,40The significance of these
NBPs at different stages of cognitive status are un-
known. These studies will decipher the key nutrient
combinations and the population best suited for in-
Dr. Bowman conceptualized the study, led the study procedures, the anal-
ysis, interpretation, and drafting of the manuscript. Dr. Silbert contrib-
uted to the interpretation of the neuroimaging studies and made
substantive contribution to revising the manuscript for intellectual con-
tent. Dr. Howieson consulted in the interpretation of neuropsychological
evaluations and made a substantive contribution in revising the manu-
script for intellectual content. Dr. Dodge assisted in the data analysis for
this manuscript and made a substantive contribution in revising the man-
uscript for intellectual content. Dr. Traber assisted in the interpretation of
the data and made a substantive contribution in revising the manuscript
for intellectual content. Dr. Frei assisted in the interpretation of the data
and made a substantive contribution in revising the manuscript for intel-
Figure Nutrient biomarker patterns and volumetric MRI (n ? 42)
Total cerebral brain volume (A, B) expressed as a % of total intracranial volume; white mat-
ter hyperintensity volume (C) includes periventricular and subcortical deep signals ex-
pressed as a % of total cerebral brain volume; x-axis represents the standardized score for
NBP1-BCDE, NBP8–trans fat, and NBP5-marine ?-3 patterns.
Neurology 78 January 24, 2012
lectual content. Dr. Kaye made a substantive contribution in revising the
manuscript for intellectual content. Dr. Shannon assisted with the con-
ceptualization and interpretation of the results. She also made a substan-
tive contribution in revising the manuscript for intellectual content. Dr.
Quinn assisted with the conceptualization and interpretation of the re-
sults. He also made a substantive contribution in revising the manuscript
for intellectual content.
The authors thank Dara Wasserman and Robin Guariglia for data entry
and management and the Oregon Brain Aging Study participants for their
Dr. Bowman serves on the editorial board of the Journal of Alzheimer’s
Disease, receives salary and research support from the NIH, and insurance
reimbursement for patient care. Dr. Silbert receives research support from
the NIH; receives reimbursement through Medicare or commercial insur-
ance plans for providing clinical assessment and care for patients and for
intraoperative neurophysiological monitoring; and is salaried to see pa-
tients at the Portland VA Medical Center. Dr. Howieson receives salary
support from the NIH/NIA and insurance reimbursement from Medicare
and other sources for providing patient care. Dr. Dodge receives research
support from the NIH and serves on the Scientific Review Board of the
National Alzheimer’s Coordinating Center. Dr. Traber receives research
support from the NIH and USDA National Institute for Food and Agri-
culture. Dr. Frei currently serves on the Scientific Advisory Board for
Unilever, Englewood Cliffs, NJ; the Almond Board Nutrition & Health
Advisory Council of the Almond Board of California, Modesto, CA; the
Neutrogena Naturals Advisory Board, Los Angeles, CA; and is a consul-
tant for Bayer Consumer Care Ltd., Basel, Switzerland. He receives re-
search funding from NIH grants P01 AT002034 and T32 AT002688,
and USANA Health Sciences, Inc., Salt Lake City, UT. Dr. Kaye receives
research support from the Department of Veterans Affairs (Merit Review
grant) and the NIH; directs a center that receives research support from
the NIH, Elan Corporation, Intel Corporation; receives reimbursement
through Medicare and commercial insurance plans for providing patient
care; is salaried to see patients at the Portland VA Medical Center; serves
as an unpaid Chair for the Work Group on Technology and for the
National Alzheimer’s Association and as an unpaid Commissioner for the
Center for Aging Services and Technologies; receives an annual royalty
from sales of the book, Evidence-based Dementia Practice; and serves on
the editorial advisory board of Alzheimer’s & Dementia. Dr. Shannon re-
ports no disclosures. Dr. Quinn has received honoraria for speaking from
Pfizer Inc, Novartis, and Forest Laboratories, Inc. and for consulting from
Phylogeny, Inc.; is a co-inventor on a patent for the use of DHA for the
treatment of Alzheimer’s disease; receives compensation for conducting
clinical trials from Elan Corporation, Bristol-Myers Squibb, and Baxter
International Inc.; and receives funding from the NIH and Department of
Received April 10, 2011. Accepted in final form July 18, 2011.
1.Morris MC, Evans DA, Bienias JL, et al. Dietary intake of
antioxidant nutrients and the risk of incident Alzheimer
disease in a biracial community study. JAMA 2002;287:
2.Rinaldi P, Polidori MC, Metastasio A, et al. Plasma anti-
oxidants are similarly depleted in mild cognitive impair-
ment and in Alzheimer’s disease. Neurobiol Aging 2003;
3.Seshadri S, Beiser A, Selhub J, et al. Plasma homocysteine
as a risk factor for dementia and Alzheimer’s disease.
N Engl J Med 2002;346:476–483.
4.Morris MC, Evans DA, Tangney CC, Bienias JL, Wilson
RS. Fish consumption and cognitive decline with age in a
large community study. Arch Neurol 2005;62:1849–
Scarmeas N, Stern Y, Mayeux R, Luchsinger JA. Mediter-
ranean diet, Alzheimer disease, and vascular mediation.
Arch Neurol 2006;63:1709–1717.
Barberger-Gateau P, Raffaitin C, Letenneur L, et al. Di-
etary patterns and risk of dementia: the Three-City cohort
study. Neurology 2007;69:1921–1930.
Schaefer EJ, Bongard V, Beiser AS, et al. Plasma phos-
phatidylcholine docosahexaenoic acid content and risk of
dementia and Alzheimer disease: the Framingham Heart
Study. Arch Neurol 2006;63:1545–1550.
Petersen RC, Thomas RG, Grundman M, et al. Vitamin E
and donepezil for the treatment of mild cognitive impair-
ment. N Engl J Med 2005;352:2379–2388.
Aisen PS, Schneider LS, Sano M, et al. High-dose B vita-
min supplementation and cognitive decline in Alzheimer
disease: a randomized controlled trial. JAMA 2008;300:
Quinn JF, Raman R, Thomas RG, et al. Docosahexaenoic
acid supplementation and cognitive decline in Alzheimer
disease: a randomized trial. JAMA 2010;304:1903–1911.
Ford AH, Flicker L, Alfonso H, et al. Vitamins B(12),
B(6), and folic acid for cognition in older men. Neurology
Kang JH, Cook N, Manson J, Buring JE, Grodstein F. A
randomized trial of vitamin E supplementation and cogni-
tive function in women. Arch Intern Med 2006;166:
The effect of vitamin E and beta carotene on the incidence
of lung cancer and other cancers in male smokers: The
Alpha-Tocopherol, Beta Carotene Cancer Prevention
Study Group. N Engl J Med 1994;330:1029–1035.
Greenberg ER, Baron JA, Tosteson TD, et al. A clinical
trial of antioxidant vitamins to prevent colorectal ade-
noma: Polyp Prevention Study Group. N Engl J Med
Hennekens CH, Buring JE, Manson JE, et al. Lack of ef-
fect of long-term supplementation with beta carotene on
the incidence of malignant neoplasms and cardiovascular
disease. N Engl J Med 1996;334:1145–1149.
Hu FB. Dietary pattern analysis: a new direction in nutri-
tional epidemiology. Curr Opin Lipidol 2002;13:3–9.
Unverzagt FW, Gao S, Baiyewu O, et al. Prevalence of
cognitive impairment: data from the Indianapolis Study of
Health and Aging. Neurology 2001;57:1655–1662.
Krasinski SD, Russell RM, Samloff IM, et al. Fundic atro-
phic gastritis in an elderly population: effect on hemoglo-
bin and several serum nutritional indicators. J Am Geriatr
Bowman GL, Shannon J, Ho E, et al. Reliability and valid-
ity of food frequency questionnaire and nutrient biomark-
ers in elders with and without mild cognitive impairment.
Alzheimer Dis Assoc Disord 2011;25:49–57.
Samieri C, Jutand MA, Feart C, Capuron L, Letenneur L,
Barberger-Gateau P. Dietary patterns derived by hybrid
clustering method in older people: association with cogni-
tion, mood, and self-rated health. J Am Diet Assoc 2008;
Scarmeas N, Stern Y, Tang MX, Mayeux R, Luchsinger
JA. Mediterranean diet and risk for Alzheimer’s disease.
Ann Neurol 2006;59:912–921.
Neurology 78 January 24, 2012
22.Gu Y, Nieves JW, Stern Y, Luchsinger JA, Scarmeas N.
Food combination and Alzheimer disease risk: a protective
diet. Arch Neurol 2010;67:699–706.
Appel LJ, Moore TJ, Obarzanek E, et al. A clinical trial of
the effects of dietary patterns on blood pressure: DASH
Collaborative Research Group. N Engl J Med 1997;336:
Tangney CC, Kwasny MJ, Li H, Wilson RS, Evans DA,
Morris MC. Adherence to a Mediterranean-type dietary
pattern and cognitive decline in a community population.
Am J Clin Nutr 2011;93:601–607.
Morris MC, Evans DA, Bienias JL, et al. Consumption of
fish and n-3 fatty acids and risk of incident Alzheimer dis-
ease. Arch Neurol 2003;60:940–946.
Dietary fat intake and 6-year cognitive change in an older bira-
Micha R, King IB, Lemaitre RN, et al. Food sources of indi-
vidual plasma phospholipid trans fatty acid isomers: the Car-
tion with socio-demographic and dietary characteristics of
free-living older persons: the Bordeaux sample of the Three-
City Study. Int J Vitam Nutr Res 2010;80:32–44.
Carmichael O, Schwarz C, Drucker D, et al. Longitudinal
changes in white matter disease and cognition in the first
year of the Alzheimer Disease Neuroimaging Initiative.
Arch Neurol 2010;67:1370–1378.
Silbert LC, Nelson C, Howieson DB, Moore MM, Kaye
JA. Impact of white matter hyperintensity volume progres-
sion on rate of cognitive and motor decline. Neurology
Zhao N, Zhong C, Wang Y, et al. Impaired hippocampal
neurogenesis is involved in cognitive dysfunction induced
by thiamine deficiency at early pre-pathological lesion
stage. Neurobiol Dis 2008;29:176–185.
32. Zhang Q, Yang G, Li W, et al. Thiamine deficiency in-
creases beta-secretase activity and accumulation of beta-
amyloid peptides. Neurobiol Aging 2011;32:42–53.
Karuppagounder SS, Xu H, Shi Q, et al. Thiamine defi-
ciency induces oxidative stress and exacerbates the plaque
pathology in Alzheimer’s mouse model. Neurobiol Aging
Bowman GL, Dodge H, Frei B, et al. Ascorbic acid and
rates of cognitive decline in Alzheimer’s disease. J Alzhei-
mers Dis 2009;16:93–98.
Troen AM, Shea-Budgell M, Shukitt-Hale B, Smith
DE, Selhub J, Rosenberg IH. B-vitamin deficiency
causes hyperhomocysteinemia and vascular cognitive
impairment in mice. Proc Natl Acad Sci USA 2008;105:
Lehmann M, Regland B, Blennow K, Gottfries CG. Vita-
min B12-B6-folate treatment improves blood-brain barrier
function in patients with hyperhomocysteinaemia and
mild cognitive impairment. Dement Geriatr Cogn Disord
Mozaffarian D, Katan MB, Ascherio A, Stampfer MJ, Wil-
lett WC. Trans fatty acids and cardiovascular disease.
N Engl J Med 2006;354:1601–1613.
Lopez-Garcia E, Schulze MB, Meigs JB, et al. Consump-
tion of trans fatty acids is related to plasma biomarkers of
inflammation and endothelial dysfunction. J Nutr 2005;
Phivilay A, Julien C, Tremblay C, et al. High dietary con-
sumption of trans fatty acids decreases brain docosa-
hexaenoic acid but does not alter amyloid-beta and tau
pathologies in the 3xTg-AD model of Alzheimer’s disease.
Huang TL, Zandi PP, Tucker KL, et al. Benefits of fatty
fish on dementia risk are stronger for those without APOE
?4. Neurology 2005;65:1409–1414.
Neurology 78 January 24, 2012