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Original Contribution |

Total and Regional Adiposity and Cognitive Change in Older Adults:  The Health, Aging and Body Composition (ABC) Study FREE

Alka M. Kanaya, MD; Karla Lindquist, MS; Tamara B. Harris, MD, MS; Lenore Launer, PhD; Caterina Rosano, MD; Suzanne Satterfield, MD, DrPH; Kristine Yaffe, MD; Health ABC Study
[+] Author Affiliations

Author Affiliations: Departments of Medicine (Dr Kanaya), Epidemiology (Drs Kanaya and Yaffe), Geriatrics (Ms Lindquist), and Psychiatry and Neurology (Dr Yaffe), University of California–San Francisco, San Francisco; Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland (Drs Harris and Launer); Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania (Dr Rosano); and Department of Preventive Medicine, University of Tennessee at Memphis, Memphis (Dr Satterfield).


Arch Neurol. 2009;66(3):329-335. doi:10.1001/archneurol.2008.570.
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Published online

Objectives  To investigate whether total and/or regional adiposity measured by anthropometry and radiographic studies influences cognitive decline in older adults and whether this association is explained by hormones and inflammatory factors known to be secreted by adipose tissue.

Design  Prospective cohort study.

Setting  Two clinical centers.

Participants  Three thousand fifty-four elderly individuals enrolled in the Health ABC Study. Adiposity measures included body mass index, waist circumference, sagittal diameter, total fat mass by dual-energy x-ray absorptiometry, and subcutaneous and visceral fat by abdominal computed tomography. We examined the association between baseline body fat measures and change in Modified Mini-Mental State Examination (3MS) score, sequentially adjusting for confounding and mediating variables, including comorbid diseases, adipocytokines, and sex hormones.

Main Outcome Measure  Scores from the 3MS, administered at the first, third, fifth, and eighth annual clinical examinations.

Results  All baseline adiposity measures varied significantly by sex. In mixed-effects models, the association between total and regional adiposity and change in 3MS score varied significantly by sex, with the highest adiposity tertile being associated with greater cognitive declines in men (for each adiposity measure, P < .05) but not in women (for interaction, P < .05). Total fat mass was significantly associated with greater change in 3MS scores among men (lowest tertile, −1.6; middle tertile, −2.2; highest tertile, −2.7; P = .006), even after adjusting for mediators.

Conclusions  Higher levels of all adiposity measures were associated with worsening cognitive function in men after controlling for metabolic disorders, adipocytokines, and sex hormone levels. Conversely, there was no association between adiposity and cognitive change in women.

Figures in this Article

Most nations are facing growing rates of overweight and obesity, with latest global projections from the World Health Organization of approximately 1.6 billion overweight and 400 million obese adults.1 Well-recognized adverse effects of overweight include type 2 diabetes, hypertension, and cardiovascular disease.

Obesity has also been found to be associated with risk of developing dementia after accounting for cardiovascular risk factors like hypertension and diabetes.2,3 Overweight has been associated with cerebral atrophy4 and cerebral white matter lesions.5 Less is known about the effect of adiposity on rates of cognitive change in elderly people without dementia. Moreover, most studies use surrogate measures of adiposity like body mass index (BMI)6,7 or waist circumference,8 which may be less valid in older populations9 than direct measurements of fat mass with whole-body dual-energy x-ray absorptiometry. Abdominal visceral fat measured by computed tomography is closely associated with metabolic disorders like diabetes, but no prior study has examined radiographically measured regional fat deposits and their effect on change in cognitive function. While prior studies have found that inflammatory factors are independently associated with cognitive decline,10 none to date have examined the effect of adipose-derived hormones, or adipocytokines, on cognitive function and whether they explain the effect of adiposity on cognitive change. Therefore, it is unclear whether or not the body weight associations observed are due to total fat mass, specific fat deposits, or fat-derived hormones.

We analyzed data from participants enrolled in the Health, Aging and Body Composition (ABC) Study to examine associations between baseline measures of overall and regional adiposity and change in cognitive function. We examined whether or not adjusting for potentially mediating diseases, adipocytokines, and sex hormones would explain the association between adiposity and risk of cognitive change.

Participants enrolled in the Health ABC Study were well-functioning men and women aged between 70 and 79 years who were recruited from April 1997 to June 1998 from a clinical center in Pittsburgh, Pennsylvania, and Memphis, Tennessee. To be eligible, participants had to report no difficulty in walking 0.4 km (0.25 miles), climbing 10 steps, or performing activities of daily living. Individuals who required assistive ambulation devices or who had life-threatening cancers were excluded.

We used the baseline medical history, physical examination measurements, laboratory tests, radiographic assessments, and cognitive function data gathered in 1997-1998. Of the 3075 participants enrolled in the Health ABC Study, we excluded 21 because they did not have any measurements of total adiposity and another 7 individuals who were missing all Modified Mini-Mental State Examination (3MS) results at the 4 times, which resulted in 3054 individuals being included in our analytic cohort. The study was approved by the institutional review boards at the University of California–San Francisco, University of Pittsburgh, and University of Tennessee. All of the study participants provided written informed consent.

Weight was measured on a standard balance scale and height was measured with a stadiometer. Body mass index was calculated as weight in kilograms divided by height in meters squared. Total fat mass was measured by whole-body dual x-ray absorptiometry and analyzed by tertile. We evaluated 4 measures of regional adiposity, 2 anthropometric and 2 radiographic. Waist circumference was measured with a flexible tape measure at the participant's largest circumference. Abdominal sagittal diameter was measured with a Holtain-Kahn abdominal caliper while the participant lay supine. The lower blade of the caliper was placed under the small of the back and the upper blade was lowered to a mark midway between the iliac crests. Abdominal visceral and subcutaneous fat areas were measured by computed tomography. Visceral fat and subcutaneous abdominal fat was measured at the L4-L5 level. Fat areas were calculated by multiplying the number of pixels of a given tissue type by the pixel area using Interactive Data Language software (ITT Visualization Solutions, Boulder, Colorado).

COGNITIVE FUNCTION ASSESSMENT

The 3MS was administered to all participants during the baseline visit (year 1) and repeated at the third, fifth, and eighth annual examinations. This test is a brief general cognitive test with components for orientation, concentration, language, praxis, and immediate and delayed memory with a maximum score of 100.11 The 3MS is more sensitive than the 30-point Mini-Mental State Examination, especially for mild cognitive change.11 We examined cognitive decline by using the change in 3MS score from the baseline examination to the eighth follow-up examination.

COVARIATES AND EXPLANATORY FACTORS

Racial group, age, sex, education, and smoking information was obtained. Physical activity was assessed using self-reported walking and exercise, with kilocalories per week to assigned activities. Literacy was assessed during the second annual visit using the Rapid Estimate of Adult Literacy in Medicine; scores lower than 60 indicated limited literacy.12 Depressive symptoms were measured using the Center for Epidemiologic Study Depression Scale.13 Each participant had seated systolic blood pressures measured with a manual sphygmomanometer. Participants were considered to have diabetes if they self-reported a diagnosis, used diabetes drugs, or if their fasting plasma glucose level was 126 mg/dL or greater (to convert to millimoles per liter, multiply by 0.0555) or their 2-hour postchallenge glucose level was 200 mg/dL or greater.

Participants underwent venipuncture after an overnight fast. Serum samples were frozen at −70°C. Fasting lipoproteins and fasting and 2-hour plasma glucose were measured. The allele producing the ε4 type of apolipoprotein E was assessed and coded as present or not.14 Serum creatinine was measured using the Kodak Ektachem 700 Analyzer (Eastman Kodak, Rochester, New York), and estimated glomerular filtration rate was calculated.15

We evaluated 4 adipocytokines (adiponectin, IL-6 [interleukin 6], tumor necrosis factor α, and plasminogen activator inhibitor-1). Adiponectin was measured in duplicate by radioimmunoassay. Interleukin 6 and tumor necrosis factor α were measured in duplicate with enzyme-linked immunosorbent assay. Plasminogen activator inhibitor-1 was measured using a 2-site enzyme-linked immunosorbent assay.

At baseline, total testosterone was measured by a chemiluminescent immunoassay. At the third annual examination, bioavailable estradiol was measured by radioimmunoassay. All samples were measured in duplicate.

STATISTICAL ANALYSIS

We used the χ2 test and analysis of variance to examine whether baseline characteristics were associated with sex-specific total fat mass tertile. We evaluated the distribution of overall adiposity and regional adiposity separately by sex and race.

We determined the unadjusted association between sex-specific tertiles of each adiposity measure with change in 3MS score between baseline and the eighth annual clinical examination and tested for sex and race interaction. Because 3MS scores were negatively skewed, we used the Box-Cox method to find an appropriate transformation.16 Because we found an interaction by sex with all adiposity measures, we stratified subsequent models by sex.

We used mixed-effects models with random participant-specific intercepts and slopes and an unstructured covariance matrix, allowing the use of all available data without imputation of any missing values. We adjusted for the fixed effects of time in years from the baseline cognitive measurement, potential baseline confounders, and their interactions with time. Baseline cognitive scores were included in every model. Cognitive scores were obtained from each model for every time and tertile and back-transformed to 3MS score. Changes in score were calculated by subtracting the score at baseline from the score at the final examination; standard errors were calculated by bootstrapping the resulting change in score with 1000 replications.17 We adjusted for potential confounders in separate stages. First, we adjusted for demographic variables: age, race, education, physical activity, and literacy. We then added chronic risk factors: diabetes and systolic blood pressure. Next, we added adipocytokines to the model to determine whether or not they would explain the relationship between adiposity and cognitive change. In an exploratory analysis, we further adjusted for endogenous sex hormones. Lastly, we performed a sensitivity analysis, excluding participants with involuntary weight loss of 5% or more from baseline weight through the eighth clinical examination. Statistical analyses were performed using Stata, version 9 (Stata Corp, College Station, Texas).

Of the 3054 participants, men had lower total fat mass (24.2 vs 29.2 kg, P < .001), BMI (27.1 vs 27.7, P < .001), and subcutaneous fat area (228 vs 339 cm2, P < .001) values and greater abdominal visceral fat area (155 vs 131 cm2, P < .001) than women (Table 1). The higher total fat mass tertile was associated with higher proportions of black race, lower educational attainment, and low literacy level, primarily in women (Table 2). Diabetes was significantly associated with a higher tertile of fat mass in both men and women. Most types of adipocytokine in men and women were significantly associated with fat mass; adiponectin was inversely associated with increased fat mass. Baseline 3MS score was not significantly associated with fat mass tertile in the overall population. However, the association with baseline 3MS score and fat mass varied significantly by sex (P < .001), with a trend toward a positive association in men (mean 3MS [standard deviation (SD)] score: lowest fat mass tertile, 88.5 [9.4]; middle tertile, 89.6 [8.3]; highest tertile, 90.4 [7.7]) and women having an inverse association between fat mass and baseline 3MS score (lowest tertile, 91.2 [7.8]; middle tertile, 91.2 [7.5]; highest tertile, 89.9 [7.3]).

Table Graphic Jump LocationTable 1. Distribution of Adiposity Measures by Sex in the 3054 Health, Aging and Body Composition Study Participants
Table Graphic Jump LocationTable 2. Characteristics of Men and Women by Total Fat Mass Tertile

In Table 3, we show the predicted change in 3MS score between the baseline and eighth annual examination by each adiposity measure in tertiles for men and women. In unadjusted analyses, higher tertiles of total fat mass, sagittal diameter, and subcutaneous fat were associated with greater change in 3MS scores in men (total fat: lowest tertile, −1.6; middle tertile, −2.3; highest tertile,: −2.7; P = .009). Conversely, among women, higher tertiles of total fat mass and BMI were associated with less change in 3MS score, though higher waist circumference was associated with greater change in score. In analyses adjusted for age, race, education, literacy, physical activity, systolic blood pressure, diabetes, IL-6, tumor necrosis factor α, plasminogen activator inhibitor-1, and adiponectin, there were no significant associations between baseline adiposity measures and change in 3MS score in women. However higher tertiles of total fat mass, BMI, waist circumference, sagittal diameter, and subcutaneous fat were all associated with a significantly greater change in 3MS score in men, even after adjustment for covariates and potential mediators. There was a similar trend toward greater change in 3MS score with increasing tertile of abdominal visceral fat in men. Race did not modify the association between adiposity measures and change in 3MS score.

Table Graphic Jump LocationTable 3. Change in 3MS Scores From Baseline to the Eighth Annual Examination

The Figure shows the adjusted change in 3MS score between the baseline and eighth annual examination for each of the 4 adiposity measures. Sex significantly modified the association between adiposity and change in 3MS score (for total fat mass, BMI, and abdominal subcutaneous fat, P < .001; for visceral fat, P = .01). To determine whether endogenous sex hormones may explain this sex interaction, we further adjusted for total testosterone and bioavailable estradiol. The interaction between sex and each adiposity measure remained robust (for total fat mass and BMI, P = .003; for abdominal subcutaneous fat, P = .005; for visceral fat, P = .05). Finally, the exclusion of 676 individuals who unintentionally lost a significant amount of body weight did not affect our results.

Place holder to copy figure label and caption
Figure.

Tertiles of each adiposity measure by predicted change in Modified Mini-Mental State Examination (3MS) score adjusted for age, race, education, literacy (in women), physical activity, systolic blood pressure, diabetes, IL-6 (interleukin-6), tumor necrosis factor α, plasminogen activator inhibitor-1, and adiponectin. Fat mass: for trend in men, P = .006; in women, P = .62; sex × tertile interaction, P = .009. Body mass index (BMI) group: for trend in men, P = .04; in women, P = .16; sex × tertile interaction, P < .001. Subcutaneous fat: for trend in men, P < .001; in women, P = .9; sex × tertile interaction, P < .001. Abdominal fat: for trend in men, P = .1; in women, P = .52; sex × tertile interaction, P = .01. Error bars indicate 95% confidence intervals.

Graphic Jump Location

In this cohort of well-functioning older adults, higher tertiles of radiographically measured total fat mass and subcutaneous fat were associated with worsening cognitive function after 7 years in men. This association remained significant even after adjusting for potential explanatory links between adiposity and cognitive function, including metabolic risk factors and adipocytokines. We found a striking paradoxical sex interaction with increasing adiposity measures that showed trends toward less cognitive change in women but greater cognitive change in men. This sex interaction was consistent with all adiposity variables and remained significant after adjustment for metabolic variables and sex hormone levels.

Several studies have examined the effect of overweight on cognitive function.2,6,7 Some longitudinal studies have found that a higher BMI is associated with increased risk of developing dementia,1821 while others have found no association.2224 Fewer studies have evaluated the association between adiposity and cognitive function or decline in adults without dementia. We found 2 prospective studies that evaluated the effect of BMI on cognitive function.6,7 The first observed 1423 individuals in the Framingham Heart Study for 4 to 6 years and found that higher BMI was associated with worse cognitive function scores in men,6,25 with a significant interaction between obesity and sex (P < .02). The second study was performed in 5607 postmenopausal Danish women observed for 7 years; it examined baseline body weight, yearly change in weight, and central fat mass by whole-body dual x-ray absorptiometry.7 The authors found a protective association of body fat mass with cognitive impairment in the elderly women and showed that those who lost the most weight had the worst cognitive performance at follow-up.7 Neither of these studies had baseline measures of cognitive function or more precise measures of regional adiposity. Our findings that men with higher total fat mass have greater cognitive decline is consistent with the Framingham results, and our finding that women show a trend toward inverse associations with total fat mass and cognitive change is consistent with the Danish study. We have extended the literature by observing that total body fat and subcutaneous abdominal fat are the 2 adiposity measures that have the strongest effect on cognitive change in men.

There is less literature that evaluates the effects of regional adiposity on change in cognitive function. Two longitudinal studies have examined the effect of central adiposity on cognition, using either waist to hip ratio or sagittal abdominal diameter. The first, the Framingham Offspring Study, found that the individuals in the uppermost quartile of waist to hip ratio had significantly poorer performance on executive function and visuomotor skills testing after a 12-year follow-up.8 The second study observed 6583 members of Kaiser Permanente for 36 years and found that those in the highest quintile of sagittal abdominal diameter had a 3-fold increased risk of dementia independent of BMI and other cardiovascular risk factors.3 Our study is the first to more closely examine regional adiposity measured radiographically. Surprisingly, our direct measure of visceral fat, which has been most closely tied to poor metabolic outcomes, had only a borderline significant association with cognitive change. The anthropometric measures of visceral fat in our study (waist and sagittal diameter) only showed stronger associations with cognitive change in men. Because the previous studies have only found associations in the highest quartile or quintile of each anthropometric measurement, it is possible that their findings correspond to higher levels of overall obesity rather than to visceral adiposity.

We evaluated the effect of many potential mediators that may lie in the causal pathway between adiposity and change in cognition. We adjusted for diabetes and blood pressure and novel fat–secreted hormones and inflammatory factors.26 The observed sex difference appeared strong and consistent for all fat measures and was only slightly attenuated with additional adjustment for adipocytokines and metabolic variables, such as high-density lipoprotein, insulin, and triglycerides. Endogenous sex hormones did not mediate this sex interaction. Other unmeasured metabolic and disease differences, such as the severity of hepatic or peripheral insulin resistance, intramyocellular steatosis, or newer adipocyte hormones, may provide additional mechanistic links to explain this sex interaction.

There are several possible biologic mechanisms that link adipose tissue to cognitive impairment. Some have proposed that adiposity in fetal development may influence cerebrovascular function and dementia risk.27,28 Second, adipose tissue hormones that cross the blood-brain barrier may influence brain function and health by affecting energy balance mechanisms and memory.29,30 Another possible mechanism is intrinsic differences in brain structure and function that can influence adiposity through energy homeostasis, reward, and other behavioral pathways.28

While our study stands apart from others, with radiographically measured adiposity, adipocytokines, and repeated measures of cognitive function, we cannot determine if there would be different effects with tests of other cognitive domains. We cannot determine whether the cognitive change that occurred was due to underlying Alzheimer disease or vascular dementia processes. Although we did not exclude participants with clinical dementia from the Health ABC Study, our cohort may represent healthy survivors, as they had no evidence of significant physical disability during the baseline examination, and it is possible that effect of adiposity on cognition differs in other, more frail individuals.

In conclusion, increasing levels of total fat mass, BMI, waist circumference, sagittal diameter, and subcutaneous abdominal fat are strongly associated with worsening cognitive function in men after controlling for metabolic disorders and adipocytokines. A more direct measure of visceral fat was not significantly associated with cognitive change. Women show trends toward inverse associations, with higher levels of adiposity being associated with less cognitive change. Traditional metabolic factors, adipocytokines, and sex hormones do not explain this sex difference. Future studies should confirm these longitudinal associations with adiposity and cognitive change and investigate why adiposity has inverse associations in men and women.

Correspondence: Alka M. Kanaya, MD, University of California–San Francisco, Box 1793, 1635 Divisadero St, Ste 600, San Francisco, CA 94115 (alka.kanaya@ucsf.edu).

Accepted for Publication: October 23, 2008.

Author Contributions: Dr Kanaya had full access to all of the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. Study concept and design: Kanaya and Yaffe. Acquisition of data: Harris and Satterfield. Analysis and interpretation of data: Kanaya, Lindquist, Launer, Rosano, and Yaffe. Drafting of the manuscript: Kanaya. Critical revision of the manuscript for important intellectual content: Lindquist, Harris, Launer, Rosano, Satterfield, and Yaffe. Statistical analysis: Lindquist. Obtained funding: Harris and Yaffe. Administrative, technical, and material support: Satterfield and Yaffe. Study supervision: Kanaya and Yaffe.

Health ABC Study Investigators: Anne B. Newman, MD, MPH, and Piera Kost, BA, University of Pittsburgh, Pittsburgh, Pennsylvania; Suzanne Satterfield, MD, DrPH, and Susan Thomas, BSN, University of Tennessee, Memphis; Stephen B. Kritchevsky, PhD, Wake Forest University, Winston-Salem, North Carolina; Michael C. Nevitt, PhD, and Susan M. Rubin, MPH, University of California–San Francisco, San Francisco; and Melissa E. Garcia, MPH, National Institute on Aging, Bethesda, Maryland.

Financial Disclosure: None reported.

Funding/Support: Dr Kanaya was funded by grants K23-HL080026 and R21-DK068608 from the National Institutes of Health. Dr Yaffe was supported in part by grant R01-AG021918 from the National Institutes of Health. The Health ABC Study was funded through contracts with the National Institute on Aging (N01-AG-6-2101, N01-AG-6-2103, and N01-AG-6-2106). This research was supported in part by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, and included substantial involvement of National Institute on Aging staff in data collection, analysis, interpretation, review, and approval of the manuscript.

World Health Organization, Obesity and overweight [WHO fact sheet No. 311] http://www.who.int/mediacentre/factsheets/fs311/en/index.html. Accessed December 18, 2007
Gorospe  ECDave  JK The risk of dementia with increased body mass index. Age Ageing 2007;36 (1) 23- 29
PubMed Link to Article
Whitmer  RAGustafson  DRBarrett-Connor  EHaan  MNGunderson  EPYaffe  K Central obesity and increased risk of dementia more than three decades later [published online ahead of print March 26, 2008]. Neurology 2008;71 (14) 1057- 1064
PubMed Link to Article
Gustafson  DLissner  LBengtsson  CBjorkelund  CSkoog  I A 24-year follow-up of body mass index and cerebral atrophy. Neurology 2004;63 (10) 1876- 1881
PubMed Link to Article
Gustafson  DRSteen  BSkoog  I Body mass index and white matter lesions in elderly women: an 18-year longitudinal study. Int Psychogeriatr 2004;16 (3) 327- 336
PubMed Link to Article
Elias  MFElias  PKSullivan  LMWolf  PAD’Agostino  RB Lower cognitive function in the presence of obesity and hypertension: the Framingham Heart Study. Int J Obes Relat Metab Disord 2003;27 (2) 260- 268
PubMed Link to Article
Bagger  YZTanko  LBAlexandersen  PQin  GChristiansen  C The implications of body fat mass and fat distribution for cognitive function in elderly women. Obes Res 2004;12 (9) 1519- 1526
PubMed Link to Article
Wolf  PABeiser  AElias  MFAu  RVasan  RSSeshadri  S Relation of obesity to cognitive function: importance of central obesity and synergistic influence of concomitant hypertension. The Framingham Heart Study. Curr Alzheimer Res 2007;4 (2) 111- 116
PubMed Link to Article
Harris  TBVisser  MEverhart  J  et al.  Waist circumference and sagittal diameter reflect total body fat better than visceral fat in older men and women: The Health, Aging and Body Composition Study. Ann N Y Acad Sci 2000;904462- 473
PubMed Link to Article
Yaffe  KBlackwell  TKanaya  AMDavidowitz  NBarrett-Connor  EKrueger  K Diabetes, impaired fasting glucose, and development of cognitive impairment in older women. Neurology 2004;63 (4) 658- 663
PubMed Link to Article
Teng  ELChui  HC The Modified Mini-Mental State (3MS) examination. J Clin Psychiatry 1987;48 (8) 314- 318
PubMed
Davis  TCLong  SWJackson  RH  et al.  Rapid estimate of adult literacy in medicine: a shortened screening instrument. Fam Med 1993;25 (6) 391- 395
PubMed
Radloff  L The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1977;1 (3) 385- 401
Link to Article
Hixson  JEVernier  DT Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J Lipid Res 1990;31 (3) 545- 548
PubMed
National Kidney Foundation, K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002;39 (2) ((suppl 1)) S1- S266
PubMed Link to Article
Box  GEPCox  DR An analysis of transformations. J Royal Statist Soc 1964;26 (2, series B) 211- 243
Efron  B Bootstrap methods: another look at the jackknife. Ann Stat 1979;7 (1) 1- 26
Link to Article
Whitmer  RAGunderson  EPBarrett-Connor  EQuesenberry  CP  JrYaffe  K Obesity in middle age and future risk of dementia: a 27 year longitudinal population based study. BMJ 2005;330 (7504) 1360
PubMed Link to Article
Rosengren  ASkoog  IGustafson  DWilhelmsen  L Body mass index, other cardiovascular risk factors, and hospitalization for dementia. Arch Intern Med 2005;165 (3) 321- 326
PubMed Link to Article
Gustafson  DRothenberg  EBlennow  KSteen  BSkoog  I An 18-year follow-up of overweight and risk of Alzheimer disease. Arch Intern Med 2003;163 (13) 1524- 1528
PubMed Link to Article
Kalmijn  SFoley  DWhite  L  et al.  Metabolic cardiovascular syndrome and risk of dementia in Japanese-American elderly men: The Honolulu-Asia aging study. Arterioscler Thromb Vasc Biol 2000;20 (10) 2255- 2260
PubMed Link to Article
Kivipelto  MNgandu  TFratiglioni  L  et al.  Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Arch Neurol 2005;62 (10) 1556- 1560
PubMed Link to Article
Nourhashémi  FDeschamps  VLarrieu  SLetenneur  LDartigues  JFBarberger-Gateau  PPAQUID study; Personnes Agées Quid, Body mass index and incidence of dementia: the PAQUID study. Neurology 2003;60 (1) 117- 119
PubMed Link to Article
Yoshitake  TKiyohara  YKato  I  et al.  Incidence and risk factors of vascular dementia and Alzheimer's disease in a defined elderly Japanese population: the Hisayama Study. Neurology 1995;45 (6) 1161- 1168
PubMed Link to Article
Elias  MFElias  PKSullivan  LMWolf  PAD'Agostino  RB Obesity, diabetes and cognitive deficit: The Framingham Heart Study. Neurobiol Aging 2005;26 ((suppl 1)) 11- 16
PubMed Link to Article
Matsuzawa  YFunahashi  TNakamura  T Molecular mechanism of metabolic syndrome X: contribution of adipocytokines adipocyte-derived bioactive substances. Ann N Y Acad Sci 1999;892146- 154
PubMed Link to Article
Finch  CE Developmental origins of aging in brain and blood vessels: an overview. Neurobiol Aging 2005;26 (3) 281- 291
PubMed Link to Article
Gustafson  D A life course of adiposity and dementia. Eur J Pharmacol 2008;585 (1) 163- 175
PubMed Link to Article
Goossens  GHBlaak  EEvan Baak  MA Possible involvement of the adipose tissue renin-angiotensin system in the pathophysiology of obesity and obesity-related disorders. Obes Rev 2003;4 (1) 43- 55
PubMed Link to Article
Davidson  TLKanoski  SEWalls  EKJarrard  LE Memory inhibition and energy regulation. Physiol Behav 2005;86 (5) 731- 746
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure.

Tertiles of each adiposity measure by predicted change in Modified Mini-Mental State Examination (3MS) score adjusted for age, race, education, literacy (in women), physical activity, systolic blood pressure, diabetes, IL-6 (interleukin-6), tumor necrosis factor α, plasminogen activator inhibitor-1, and adiponectin. Fat mass: for trend in men, P = .006; in women, P = .62; sex × tertile interaction, P = .009. Body mass index (BMI) group: for trend in men, P = .04; in women, P = .16; sex × tertile interaction, P < .001. Subcutaneous fat: for trend in men, P < .001; in women, P = .9; sex × tertile interaction, P < .001. Abdominal fat: for trend in men, P = .1; in women, P = .52; sex × tertile interaction, P = .01. Error bars indicate 95% confidence intervals.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Distribution of Adiposity Measures by Sex in the 3054 Health, Aging and Body Composition Study Participants
Table Graphic Jump LocationTable 2. Characteristics of Men and Women by Total Fat Mass Tertile
Table Graphic Jump LocationTable 3. Change in 3MS Scores From Baseline to the Eighth Annual Examination

References

World Health Organization, Obesity and overweight [WHO fact sheet No. 311] http://www.who.int/mediacentre/factsheets/fs311/en/index.html. Accessed December 18, 2007
Gorospe  ECDave  JK The risk of dementia with increased body mass index. Age Ageing 2007;36 (1) 23- 29
PubMed Link to Article
Whitmer  RAGustafson  DRBarrett-Connor  EHaan  MNGunderson  EPYaffe  K Central obesity and increased risk of dementia more than three decades later [published online ahead of print March 26, 2008]. Neurology 2008;71 (14) 1057- 1064
PubMed Link to Article
Gustafson  DLissner  LBengtsson  CBjorkelund  CSkoog  I A 24-year follow-up of body mass index and cerebral atrophy. Neurology 2004;63 (10) 1876- 1881
PubMed Link to Article
Gustafson  DRSteen  BSkoog  I Body mass index and white matter lesions in elderly women: an 18-year longitudinal study. Int Psychogeriatr 2004;16 (3) 327- 336
PubMed Link to Article
Elias  MFElias  PKSullivan  LMWolf  PAD’Agostino  RB Lower cognitive function in the presence of obesity and hypertension: the Framingham Heart Study. Int J Obes Relat Metab Disord 2003;27 (2) 260- 268
PubMed Link to Article
Bagger  YZTanko  LBAlexandersen  PQin  GChristiansen  C The implications of body fat mass and fat distribution for cognitive function in elderly women. Obes Res 2004;12 (9) 1519- 1526
PubMed Link to Article
Wolf  PABeiser  AElias  MFAu  RVasan  RSSeshadri  S Relation of obesity to cognitive function: importance of central obesity and synergistic influence of concomitant hypertension. The Framingham Heart Study. Curr Alzheimer Res 2007;4 (2) 111- 116
PubMed Link to Article
Harris  TBVisser  MEverhart  J  et al.  Waist circumference and sagittal diameter reflect total body fat better than visceral fat in older men and women: The Health, Aging and Body Composition Study. Ann N Y Acad Sci 2000;904462- 473
PubMed Link to Article
Yaffe  KBlackwell  TKanaya  AMDavidowitz  NBarrett-Connor  EKrueger  K Diabetes, impaired fasting glucose, and development of cognitive impairment in older women. Neurology 2004;63 (4) 658- 663
PubMed Link to Article
Teng  ELChui  HC The Modified Mini-Mental State (3MS) examination. J Clin Psychiatry 1987;48 (8) 314- 318
PubMed
Davis  TCLong  SWJackson  RH  et al.  Rapid estimate of adult literacy in medicine: a shortened screening instrument. Fam Med 1993;25 (6) 391- 395
PubMed
Radloff  L The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1977;1 (3) 385- 401
Link to Article
Hixson  JEVernier  DT Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J Lipid Res 1990;31 (3) 545- 548
PubMed
National Kidney Foundation, K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002;39 (2) ((suppl 1)) S1- S266
PubMed Link to Article
Box  GEPCox  DR An analysis of transformations. J Royal Statist Soc 1964;26 (2, series B) 211- 243
Efron  B Bootstrap methods: another look at the jackknife. Ann Stat 1979;7 (1) 1- 26
Link to Article
Whitmer  RAGunderson  EPBarrett-Connor  EQuesenberry  CP  JrYaffe  K Obesity in middle age and future risk of dementia: a 27 year longitudinal population based study. BMJ 2005;330 (7504) 1360
PubMed Link to Article
Rosengren  ASkoog  IGustafson  DWilhelmsen  L Body mass index, other cardiovascular risk factors, and hospitalization for dementia. Arch Intern Med 2005;165 (3) 321- 326
PubMed Link to Article
Gustafson  DRothenberg  EBlennow  KSteen  BSkoog  I An 18-year follow-up of overweight and risk of Alzheimer disease. Arch Intern Med 2003;163 (13) 1524- 1528
PubMed Link to Article
Kalmijn  SFoley  DWhite  L  et al.  Metabolic cardiovascular syndrome and risk of dementia in Japanese-American elderly men: The Honolulu-Asia aging study. Arterioscler Thromb Vasc Biol 2000;20 (10) 2255- 2260
PubMed Link to Article
Kivipelto  MNgandu  TFratiglioni  L  et al.  Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Arch Neurol 2005;62 (10) 1556- 1560
PubMed Link to Article
Nourhashémi  FDeschamps  VLarrieu  SLetenneur  LDartigues  JFBarberger-Gateau  PPAQUID study; Personnes Agées Quid, Body mass index and incidence of dementia: the PAQUID study. Neurology 2003;60 (1) 117- 119
PubMed Link to Article
Yoshitake  TKiyohara  YKato  I  et al.  Incidence and risk factors of vascular dementia and Alzheimer's disease in a defined elderly Japanese population: the Hisayama Study. Neurology 1995;45 (6) 1161- 1168
PubMed Link to Article
Elias  MFElias  PKSullivan  LMWolf  PAD'Agostino  RB Obesity, diabetes and cognitive deficit: The Framingham Heart Study. Neurobiol Aging 2005;26 ((suppl 1)) 11- 16
PubMed Link to Article
Matsuzawa  YFunahashi  TNakamura  T Molecular mechanism of metabolic syndrome X: contribution of adipocytokines adipocyte-derived bioactive substances. Ann N Y Acad Sci 1999;892146- 154
PubMed Link to Article
Finch  CE Developmental origins of aging in brain and blood vessels: an overview. Neurobiol Aging 2005;26 (3) 281- 291
PubMed Link to Article
Gustafson  D A life course of adiposity and dementia. Eur J Pharmacol 2008;585 (1) 163- 175
PubMed Link to Article
Goossens  GHBlaak  EEvan Baak  MA Possible involvement of the adipose tissue renin-angiotensin system in the pathophysiology of obesity and obesity-related disorders. Obes Rev 2003;4 (1) 43- 55
PubMed Link to Article
Davidson  TLKanoski  SEWalls  EKJarrard  LE Memory inhibition and energy regulation. Physiol Behav 2005;86 (5) 731- 746
PubMed Link to Article

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