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Yaffe K, Kanaya A, Lindquist K, Simonsick EM, Harris T, Shorr RI, Tylavsky FA, Newman AB. The Metabolic Syndrome, Inflammation, and Risk of Cognitive Decline. JAMA. 2004;292(18):2237–2242. doi:10.1001/jama.292.18.2237
Context Several studies have reported an association between the metabolic syndrome
and cardiovascular disease. Despite an increasing awareness that cardiovascular
risk factors increase risk of cognitive decline and dementia, there are few
data on the metabolic syndrome and cognition.
Objective To determine if the metabolic syndrome is a risk factor for cognitive
decline and if this association is modified by inflammation.
Design and Setting A 5-year prospective observational study conducted from 1997 to 2002
at community clinics at 2 sites.
Participants A total of 2632 black and white elders (mean age, 74 years).
Main Outcome Measures Association of the metabolic syndrome (measured using National Cholesterol
Education Program guidelines) and high inflammation (defined as above median
serum level of interleukin 6 and C-reactive protein) with change in cognition
(Modified Mini-Mental State Examination [3MS]) at 3 and 5 years. Cognitive
impairment was defined as at least a 5-point decline.
Results Compared with those without the metabolic syndrome (n = 1616),
elders with the metabolic syndrome (n = 1016) were more likely to
have cognitive impairment (26% vs 21%, multivariate adjusted relative risk
[RR], 1.20; 95% confidence interval [CI], 1.02-1.41). There was a statistically
significant interaction with inflammation and the metabolic syndrome (P = .03) on cognitive impairment. After stratifying
for inflammation, those with the metabolic syndrome and high inflammation
(n = 348) had an increased likelihood of cognitive impairment compared
with those without the metabolic syndrome (multivariate adjusted RR, 1.66;
95% CI, 1.19-2.32). Those with the metabolic syndrome and low inflammation
(n = 668) did not exhibit an increased likelihood of impairment
(multivariate adjusted RR, 1.08; 95% CI, 0.89-1.30). Stratified multivariate
random-effects models demonstrated that participants with the metabolic syndrome
and high inflammation had greater 4-year decline on 3MS (P = .04) compared with those without the metabolic syndrome,
whereas those with the metabolic syndrome and low inflammation did not (P = .44).
Conclusion These findings support the hypothesis that the metabolic syndrome contributes
to cognitive impairment in elders, but primarily in those with high level
Cardiovascular and metabolic risk factors such as hypertension and diabetes
have been hypothesized to play a role in the pathogenesis of Alzheimer disease
(AD) as well as in development of vascular dementia.1-4 The
metabolic syndrome,5 a clustering of several
commonly occurring disorders that include (1) abdominal obesity, (2) hypertriglyceridemia,
(3) low high-density lipoprotein (HDL) level, (4) hypertension, and (5) hyperglycemia,
has not been specifically investigated as a risk factor for cognitive decline
in elderly individuals. The metabolic syndrome may be a risk factor for cognitive
decline because it summarizes the joint effects of these risk factors. As
obesity and sedentary lifestyle rise in the United States, identification
and explication of the role of these modifiable behaviors in increasing risk
for developing deleterious outcomes such as cognitive impairment is critical.
If the metabolic syndrome is associated with increased risk of developing
cognitive impairment, then early identification and treatment of these individuals
might offer avenues for disease course modification.
High levels of inflammation increase the risk of the development of
diabetes and atherosclerosis and are thought to be a possible mechanism for
the adverse consequences of the metabolic syndrome.6,7 Indeed,
level of inflammation in the setting of the metabolic syndrome may help identify
those at especially high risk for adverse outcomes. Furthermore, subclinical
inflammation might be an underlying factor for an association between the
metabolic syndrome and cognitive decline since inflammatory mechanisms are
also hypothesized to be involved in the pathogenesis of cognitive impairment.8-10 Thus, we conducted
this study to investigate whether the metabolic syndrome is associated with
cognitive decline and modified by level of inflammation. Our hypothesis was
that presence of the metabolic syndrome would be associated with more cognitive
decline and greater risk of developing cognitive impairment and that this
association would be modified by inflammation.
Participants were part of the Health, Aging and Body Composition (ABC)
study, a prospective cohort study conducted from 1997 to 2002 of 3075 community-dwelling
elders aged 70 to 79 years living in Memphis, Tenn, and Pittsburgh, Pa. Elders
were recruited from a random sample of white and all black Medicare-eligible
adults living in designated ZIP codes. Race was defined by self-report and
was assessed because rates of cognitive impairment have been shown to differ
by race. Sampled participants were mailed a brochure describing the study
and then contacted by telephone to establish functional status and to recruit
eligible residents to join the study. Community-based activities were also
used to enhance the recruitment of black participants. Well-functioning was
determined by self-report and was defined as having no difficulty in walking
a quarter of a mile or going up 10 steps without resting reported during 2
separate interviews prior to enrollment into the study.
Exclusion criteria included (1) any difficulty with activities of daily
living, (2) clinical dementia (based on Diagnostic and Statistical
Manual of Mental Disorders, 4th edition criteria), (3) inability to
communicate with the interviewer, (4) intention of moving out of the vicinity
in the next year, (5) active treatment for cancer in the previous 3 years,
and (6) participation in a trial involving a lifestyle intervention. Data
on the metabolic syndrome were missing for 40 participants, 70 had missing
inflammatory marker data, and 16 had missing baseline cognitive data, leaving
2949 participants. Our analytic cohort includes the 2632 participants who
had at least 1 cognitive follow-up assessment. Of the remaining 317 participants,
164 died, 69 were lost to follow-up, and 84 did not have repeat cognitive
testing. Those with and without the metabolic syndrome had similar rates of
follow-up (90.2% vs 89.2%, P = .40). All
participants signed an informed written consent, approved by the institutional
review boards of the clinical sites. This study was approved by the University
of California, San Francisco Committee of Human Research.
Cognitive Test. The Teng Modified Mini-Mental
State Examination (3MS) was administered to all participants during the baseline
visit and repeated at the year 3 and 5 follow-up visits. It is a brief, general
cognitive battery with components for orientation, concentration, language,
praxis, and immediate and delayed memory with a maximum (best) score of 100.11 The 3MS is more sensitive than the traditional 30-point
Mini-Mental State Examination, especially for mild cognitive change.11 Cognitive impairment was defined as a 3MS change
of 5 or more points at either follow-up visit as has been previously recommended.12
The Metabolic Syndrome. Presence of the metabolic
syndrome at baseline was calculated by sex as defined by the National Cholesterol
Education Program Third Adult Treatment Panel guidelines of at least 3 of
the following: (1) waist measurement (greater than 88 cm for women and greater
than 102 cm for men); (2) hypertriglyceridemia (150 mg/dL or higher [≥1.69
mmol/L]), (3) low HDL cholesterol (less than 40 mg/dL [<1.03 mmol/L] in
men and less than 50 mg/dL [1.29 mmol/L] in women), (4) high blood pressure
(systolic, ≥130 mm Hg; diastolic, ≥85 mm Hg using the average of 2 seated
measurements or currently using an antihypertensive medication), and (5) high
fasting glucose (110 mg/dL or higher [≥6.10 mmol/L] or currently using
antidiabetic [insulin or oral agents] medication).5 Lipid
levels were measured after fasting.
To focus on those without clinically evident disease, we constructed
an alternate definition for the metabolic syndrome, excluding those participants
with overt diabetes (either by self-report, using antidiabetic medication,
or fasting glucose ≥126 mg/dL [6.99 mmol/L]), frank hypertension (blood
pressure ≥140/90 mm Hg), or clinically significant hyperlipidemia (triglycerides
≥200 mg/dL [2.26 mmol/L]).
Inflammatory Markers. Measurements for interleukin
6 (IL-6) and for C-reactive protein (CRP) were obtained from frozen stored
plasma or serum taken at baseline. Blood samples were obtained in the morning
and, after processing, the specimens were aliquoted, frozen at −70°C,
and shipped to the Health ABC Core Laboratory at the University of Vermont.
Plasma IL-6 levels were measured in duplicate by enzyme-linked immunosorbent
assay (ELISA) kits (R&D Systems, Minneapolis, Minn). The detectable limit
for IL-6 (HS600 Quantikine kit, R & D Systems) was 0.10 pg/mL. Serum levels
of CRP were also measured in duplicate by ELISA based on purified protein
and polyclonal anti-CRP antibodies (Calbiochem, EMD Biosciences Inc, Darmstadt,
Germany). The CRP assay was standardized according to the World Health Organization
First International Reference Standard with a sensitivity of 0.08 μg/mL.
Assays of blind duplicates collected for 150 participants yielded an average
interassay coefficient of variation of 10.3% for IL-6 and 8.0% for CRP. We
defined “high” inflammation as present if a participant had higher
than the median values for both CRP (≥2.0 mg/L) and IL-6 (≥2.0
pg/mL). The correlation between CRP and IL-6 was 0.40.
Covariates. Covariates included characteristics
previously shown in the literature to be associated with cognitive function
or with the metabolic syndrome. At baseline, we obtained information on participants’
age, race, sex, years of education, current smoking, and alcohol use during
the past year (percentage with >1 drink per day). At each clinic examination,
we measured weight and height; body mass index was defined as weight in kilograms
divided by the square of height in meters. In addition, standardized algorithms
were used to assess prevalent myocardial infarction (by self-report) and stroke
(by self-report). Participants were also asked to rate their overall health
compared with others as excellent, good, fair, poor, or very poor. Depressive
symptoms were assessed with the Center for Epidemiologic Studies-Depression
(CES-D) Scale,13 with higher scores indicating
greater number of symptoms. An inventory of prescription and over-the-counter
medications was obtained by checking the participants’ medication container(s).
We classified current use of medications as those regularly taken in the past
2 weeks and coded them according to the Iowa Drug Information System (IDIS)
code.14 Using this drug inventory, we documented
daily current use of anti-inflammatory drugs (IDIS code 2808) and statins
(IDIS code 2406).
χ2 Analyses or t tests were
conducted to assess baseline characteristics by presence of the metabolic
syndrome. We conducted unadjusted and multivariate adjusted logistic regression
analyses to determine if presence of the metabolic syndrome was associated
with odds of cognitive impairment. We then corrected for possible overestimation
of odds ratios by adjusting to approximate risk ratios according to the method
of Zhang and Yu.15 All models contained baseline
cognitive score as a covariate. The multivariate adjusted logistic regression
model included covariates that were associated with cognitive impairment (P<.10) and were entered into the model using backward
elimination. We added an interaction term to these models to assess whether
inflammation modified the association of the metabolic syndrome with cognitive
outcomes. Since this term was statistically significant, we conducted stratified
analyses by inflammation level.
We used random-effects models to analyze the association between the
metabolic syndrome and 4-year change on 3MS score. Random-effects models account
for between-subject variation and within-subject correlations between repeated
cognitive measurements.16 The Bayesian Information
Criterion was used to determine which random effects to include.17 Candidates
for the random-effect terms included both the intercept and the slope of cognitive
scores over time. Fixed effects were chosen by backward elimination until
all were associated with cognitive scores at P<.05.
The candidates for fixed-effect terms included all baseline covariates plus
their interactions with time and with a missing pattern indicator. Time was
considered as a continuous covariate, measured in days from baseline to follow-up
test. By including the missing pattern indicator and its interaction with
other covariates, we performed a simplified pattern-mixture model to help
account for possible nonrandom dropout. Pattern-mixture models jointly model
observed responses and dropout times, thus reducing the effect of biases that
would result if dropout was assumed to be independent of unobserved responses.18 All analyses were conducted with SAS (version 8.2,
SAS Institute, Cary, NC).
The mean (SD) age of the participants at baseline was 73.6 (2.9) years;
52% were women, 40% were black, and 25% had high markers of inflammation.
Compared with participants without the metabolic syndrome (n = 1616),
those with the metabolic syndrome (n = 1016) were more likely to
be women, and white, and to smoke; to have higher depression scores, higher
BMI, and a history of a myocardial infarction; to use statins and nonsteroidal
anti-inflammatory drugs; and to have high markers of inflammation. In addition,
several baseline characteristics were statistically significantly different
when comparing participants by inflammatory status and presence of the metabolic
syndrome (Table 1). Among those with
the metabolic syndrome, 56% met 3, 33% met 4, and 11% met 5 of the National
Cholesterol Education Program criteria. The most common criterion met was
hypertension (92%), followed by large waist circumference (86%), hypertriglyceridemia
(65%), low HDL cholesterol (62%), and high fasting glucose/antidiabetic medication
Overall, the mean (SD) baseline 3MS score was 90.5 (8.0), and cognitive
impairment (3MS decline ≥5 points) occurred in 598 (22.7%) of the 2632
participants. Baseline 3MS scores did not differ significantly for those with
and without the metabolic syndrome (90.6 [ 7.6] vs 90.4 [ 8.3],
respectively; P = .46). However, compared
with those without the metabolic syndrome, elders with the metabolic syndrome
were somewhat more likely to have cognitive impairment (260 [26%] vs 338 (21%),
multivariate adjusted relative risk [RR], 1.20; 95% confidence interval [CI
We assessed for presence of an interaction between high inflammation
and the metabolic syndrome on risk of cognitive impairment. The P for interaction was.05 in the unadjusted models and.03 in the adjusted
models (adjustment for age, education, race, baseline cognitive score, depression
score, alcohol use, stroke, and statin use). Given this significant interaction,
we stratified the remainder of our analyses by inflammation. Those participants
with the metabolic syndrome and high inflammation were significantly more
likely than those without the syndrome to develop cognitive impairment (105
[30%] vs 67 [21%]; multivariate-adjusted RR, 1.66; 95% CI, 1.19-2.32) (Table 2). However, those elders with the metabolic
syndrome and low inflammation were not more likely to develop impairment (155
[23%] vs 271 [21%]; adjusted RR, 1.08; 95% CI, 0.89-1.30) (Table 2). We repeated these analyses after excluding patients with
baseline stroke (n = 55) and found almost identical results. Those participants
without the metabolic syndrome but with high inflammation (n = 317) did not
have an elevated risk of developing cognitive impairment (adjusted RR, 0.81;
95% CI, 0.60-1.08) compared with those without the metabolic syndrome and
When further stratified by race, the association between the metabolic
syndrome and cognitive impairment remained elevated for blacks and whites
with high inflammation but not for those with low inflammation (for blacks
with high inflammation, adjusted RR, 1.67; 95% CI, 1.11-2.53; with low inflammation,
adjusted RR, 1.04; 95% CI, 0.80-1.36; and for whites, with high inflammation
adjusted RR, 1.75; 95% CI, 0.99-3.08; with low inflammation, adjusted RR,
1.17; 95% CI, 0.89-1.54).
In the stratified, unadjusted, and multivariate-adjusted random-effects
models, within the high inflammation group, scores for those with the metabolic
syndrome declined significantly more than for those without the syndrome
(Table 3). We next determined whether the association
remained between the metabolic syndrome and cognitive impairment after excluding
participants with clinically significant diabetes, hypertension, or hyperlipidemia.
After excluding these elders (n = 797), the risk of developing cognitive impairment
remained elevated among participants with the metabolic syndrome and high
inflammation but was no longer statistically significant (adjusted RR, 1.35;
95% CI, 0.83-2.19); the power was reduced with only 118 elders with both the
metabolic syndrome and high inflammation. The metabolic syndrome and cognitive
impairment were not associated with low inflammation.
Finally, we assessed whether the association between the metabolic syndrome
and inflammation and cognitive decline was related to the number of components
of the metabolic syndrome and degree of inflammation. For participants with
high inflammation and the metabolic syndrome, the number of components of
the metabolic syndrome did not affect the risk of cognitive decline (for the
185 participants meeting 3 criteria, RR, 1.64; 95% CI, 1.13-2.38, whereas
for the 163 participants meeting 4 or 5 criteria, RR, 1.49; 95% CI, 1.01-2.21).
However, more inflammation (assessed as tertiles) was associated with greater
cognitive decline. For participants with 1 or both low tertiles of inflammation
(n = 743), the adjusted RR for presence of the metabolic syndrome vs no presence
of the metabolic syndrome was 1.09 (95% CI, 0.84-1.40); for 1 or both medium
tertiles of inflammation (n = 1442), the adjusted RR was 1.26 (95% CI, 1.00-1.59);
and for 1 or both high tertiles of inflammation (n = 447), the adjusted RR
was 1.62 (95% CI, 1.10-2.38).
Among high-functioning elders, those with the metabolic syndrome show
an increased risk of developing cognitive impairment and decline over 4 years.
This association remained after adjustment for possible confounders such as
demographics, lifestyle variables, and chronic health conditions. The increased
rate of cognitive impairment was primarily observed in those elders who had
high levels of serum markers of inflammation, suggesting that at least some
of the increased risk associated with the metabolic syndrome is modified by
inflammation. To our knowledge, this is the first study to document that the
metabolic syndrome is associated with poor cognitive outcomes.
Several components of the metabolic syndrome have been individually
related to cognitive outcomes. Mid- or late-life hypertension,19,20 hyperlipidemia,4,21,22 and diabetes3,23,24 have been reported
to increase risk of developing dementia or cognitive decline, both due to
vascular disease and AD. Our findings are also consistent with results from
a study of middle-aged Japanese American men, in which a composite score of
7 cardiovascular risk factors was associated with increased risk of developing
vascular dementia but not with increased AD25 and
another study of mid- to late-life black and white Americans in which diabetes
and hypertension were associated with increased risk of developing cognitive
decline.20 The finding that the metabolic syndrome
without clinically significant diabetes, hypertension, or hyperlipidemia shows
the same pattern of increased risk of cognitive impairment implies that the
adverse effect of the metabolic syndrome is related, but not solely, due to
the contribution of these conditions.
In many observational studies and animal models, inflammation is associated
with AD and vascular disease.9,26-28 We
previously have shown that elevated CRP and IL-6 is associated with accelerated
cognitive decline.8 Similarly, those with elevated
CRP and IL-6 are at greater risk for developing diabetes, atherosclerosis,
and other complications.6,7,29 How
may inflammation account for the deleterious effect of the metabolic syndrome
on cognition? Given the range of possible inclusion criteria for the metabolic
syndrome, it may be that level of inflammation may serve as a marker of active
pathologic process. For example, an individual with well-controlled hypertension
and borderline dyslipidemia may have a markedly different risk for adverse
outcomes than an elder with poorly controlled hypertension, obesity, and hyperglycemia.
We find some evidence for this hypothesis, as the association between the
metabolic syndrome and cognitive impairment was lessened somewhat when we
excluded those with clinically significant diseases such as diabetes, hypertension,
and hyperlipidemia. Most likely, the metabolic syndrome contributes to accelerated
atherosclerosis that is associated with an inflammatory response and in turn,
either the atherosclerosis or inflammation or both, contribute to cognitive
decline.30,31 However, the direction
of an association between inflammation and the metabolic syndrome is controversial,
with some advocating that the metabolic syndrome is due to subclinical inflammation.32 Alternatively, genetic predisposition to a heightened
inflammatory response that may predispose to adverse outcomes of the metabolic
syndrome, including cognitive impairment.
The strengths of our study include that we studied high-functioning
elders who did not have dementia at baseline, thereby allowing us to prospectively
investigate whether the metabolic syndrome influenced cognitive impairment
and decline. This high-functioning status most likely explains the lack of
a difference on baseline cognitive scores among those with and without the
metabolic syndrome. The 39% prevalence of the metabolic syndrome in the Health
ABC cohort is similar to that observed in other studies of elderly individuals.33,34 Due to carefully assessed data on
the participants, we were able to adjust statistically for a number of possible
confounding variables. Finally, because our cohort was biracial, we were able
to determine that the link between the metabolic syndrome and cognitive impairment
was similar among blacks and whites.
Several limitations of our study may affect the interpretation of our
results. Those individuals without follow-up cognitive testing were older
and had lower baseline cognitive scores; exclusion of these individuals could
lead to bias. However, we used random-effects models to reduce the bias that
comes from having missing outcome values. While we used a standard measure
of global cognitive function, the 3MS may be insensitive to subclinical cognitive
impairment and thus, there may have been some individuals included in Health
ABC at baseline who had some cognitive dysfunction. In addition, the physiology
underlying the cognitive impairment was not assessed, so we do not know etiology
of cognitive impairment and if the underlying pathophysiology is more consistent
with vascular disease or AD or yet another cause. Finally, our cohort consisted
of elders who were relatively well-functioning at baseline and therefore we
cannot generalize to community-dwelling elders who are initially more functionally
We found that high-functioning elders with the metabolic syndrome had
an increased risk of developing cognitive impairment and that this remained
after accounting for demographics, health habits, and comorbidities. This
was primarily true for those elders with high serum markers of inflammation.
Future studies will need to address whether preventing the metabolic syndrome
or lowering inflammation prevents cognitive impairment in elderly individuals.
Corresponding Author: Kristine Yaffe, MD,
University of California, San Francisco, Box 181, 4150 Clement St, San Francisco,
CA 94121 (firstname.lastname@example.org).
Author Contributions: Dr Yaffe had full access
to all of the data in the study and takes responsibility for the integrity
of the data and the accuracy of the data analysis.
Study concept and design: Yaffe, Tylavsky,
Acquisition of data: Simonsick, Harris, Tylavsky,
Analysis and interpretation of data: Yaffe,
Kanaya, Lindquist, Simonsick, Shorr, Tylavsky.
Drafting of the manuscript: Yaffe, Shorr.
Critical revision of the manuscript for important
intellectual content: Kanaya, Lindquist, Simonsick, Harris, Shorr,
Statistical analysis: Yaffe, Lindquist.
Obtained funding: Harris, Newman.
Administrative, technical, or material support:
Yaffe, Simonsick, Harris, Tylavsky, Newman.
Study supervision: Kanaya.
Funding/Support: This work was supported by
NIH grants NIA NO1-AG-6-2101, N01-AG-2103, and N01-AG-2106. Dr Yaffe is supported
by the Paul Beeson Faculty Scholars Program, The Mt Zion/UCSF Women’s
Health Grant, and NIH grant NIA-R01 AG021918-01.
Role of the Sponsor: In their role as coauthors,
representatives of the NIH participated in the design and conduct of the study,
in the collection, analysis, and interpretation of the data, and in the preparation,
review, and approval of the manuscript.