Longitudinal Examination of Obesity and Cognitive Function: Results from the Baltimore Longitudinal Study of Aging

Abstract

Introduction

Obesity is a leading preventable cause of death in the United States, accounting for an estimated 400,000 deaths each year [1]. Some researchers predict obesity will cause the first decline in life expectancy in 100 years [2,3]. This risk is largely attributable to the many health consequences of obesity, including cardiovascular disease, type 2 diabetes, sleep apnea, and cancer [4]. For example, obese adults are five times more likely to have high blood pressure and forty times more likely to have type 2 diabetes than their normal weight peers [5,6,7,8].

Recent research indicates that obesity is also associated with poor neurocognitive outcome. Elevated BMI has been linked with increased risk of Alzheimer's disease and structural brain changes, including excess age-related atrophy and white matter disease [9,10,11,12,13,14]. Consistent with these findings, excess weight is also associated with reduced cognitive function in a growing number of cross-sectional studies [15,16,17,18,19,20].

Little is known about the prospective relation between obesity and cognitive decline in non-demented individuals. For example, longitudinal data from the Framingham Heart Study have shown that obesity is associated with accelerated cognitive decline in aging men [21,22]. However, most prospective studies have relied upon BMI as their sole index of obesity. BMI can be influenced by a number of factors and indices, such as waist circumference and waist-to-hip ratio (WHR), which are more closely linked to some adverse health outcomes than BMI [23,24]. As a result, it is possible that obesity indices are differentially related to changes in cognitive function over time.

To test this hypothesis, we examined the prospective relation between three obesity indices and neuropsychological test performance in non-demented participants from the Baltimore Longitudinal Study of Aging. Based on findings for other adverse health outcomes, we hypothesized that measures of central obesity (e.g. waist circumference, WHR) would be more highly related to cognitive decline than BMI.

Subjects and Methods

All methods were approved by the local human subject's protection boards prior to data collection and analysis of data.

Results

Global Cognitive Function

Cross sectional analyses showed that multiple obesity indices were associated with poorer cognitive test performance. Among the global cognitive function tests, obesity indices were associated significantly with lower performance on the Mini-Mental State Examination and Blessed IMC test (tables ​(tables3,3, ​,4,4, ​,5).5). Higher BMI was associated with poorer performance on both mental status tests. Higher waist circumference and WHR were associated with the Blessed IMC (fig. 1a–c). Longitudinal analyses showed that BMI did not interact with age on either test, but larger waist circumference and WHR interacted with age and were associated with poorer performance on the Blessed IMC over time (fig. 1a–c).

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Fig. 1

Longitudinal rate of change in cognitive performance as a function of age for number of errors on the Blessed IMC Test by BMI (a), waist circumferences (b), and waist-hip ratio (c), and Trail Making Test A time to completion by BMI (d), waist circumferences (e), and waist-hip ratio (f).

Table 3

Coefficients from longitudinal mixed-effects regression models for the adjusted relation between BMI and cognitive function in 1,703 adults in the Baltimore Longitudinal Study of Aging

	Intercept	Age	BMI	Sex	Years of education	Hypertension	Glucose-diabetes	Anti-lipid meds	Age × BMI
Global
MMSE	27.36∗∗	−0.70∗	−0.03∗∗	−0.25∗∗	0.13∗∗	0.04	0.06	−0.00	0.01
Blessed IMC errors	3.23∗∗	0.049	0.03∗∗	−0.17∗∗	−0.14∗∗	0.04	−0.15∗	−0.13	0.00
Attention/executive
Digit span forward	6.85∗∗	−0.34	−0.03∗	0.38∗∗	0.10∗∗	−0.06	0.18	0.03	0.00
Digit span backward	6.52∗∗	−0.38	−0.02	0.43∗∗	0.10∗∗	0.03	0.18	−0.13	0.00
Trails A	55.64∗∗	13.71∗∗	−0.24∗∗	−0.35	−0.71∗∗	0.39	0.18	−1.64∗	−0.31∗∗
Trails B	148.32∗∗	40.54∗∗	0.25	−0.79	−3.78∗∗	−0.27	0.98	−4.76	−0.54
Memory
CVLT trial 1	7.03∗∗	−0.51∗	−0.00	−1.12∗∗	0.05∗	0.02	−0.01	0.08	−0.00
CVLTA	45.83∗∗	−3.37∗∗	−0.02	−6.40∗∗	0.54∗∗	1.47∗∗	0.28	0.58	0.01
CVLTB	5.66∗∗	−0.49∗	−0.02	−0.99∗∗	0.09∗∗	0.32∗∗	0.04	0.23	−0.01
CVLT short free	8.27∗∗	−1.05∗∗	0.00	−1.25∗∗	0.14∗∗	0.48∗∗	−0.02	0.15	0.01
CVLT long free	8.70∗∗	−0.90∗∗	0.01	−1.27∗∗	0.14∗∗	0.41∗∗	−0.02	0.13	0.00
Prospective memory	4.01∗∗	−0.27∗	0.02∗	−0.10	0.04∗∗	0.03	0.00	0.14∗	0.00
BVRT	8.11∗∗	0.45∗∗	0.03	−0.11	−0.18∗∗	0.00	−0.30∗∗	0.32∗	0.02∗∗
Language
Letter Fluency	10.42∗∗	−1.78∗∗	−0.11∗∗	−0.54	0.45∗∗	−0.11	0.43	−0.10	0.03
Category Fluency	13.64∗∗	−2.65∗∗	−0.08∗∗	−1.56∗∗	0.28∗∗	0.26∗∗	0.35	0.13	0.04
Boston Naming	44.59∗∗	−2.84∗∗	−0.00	0.60	0.55∗∗	−0.10	0.03	0.10	0.07
Visuospatial
Card Rotations	44.87∗∗	−13.39∗∗	0.31	16.23∗∗	1.30∗∗	−0.70	−3.57	1.32	0.21

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CVLT = California Verbal Learning Test; BVRT = Benton Visual Retention Test. Age was employed as both a fixed and random factor with the age × BMI interaction term denoting change over time.

^∗p < 0.05

^∗∗p < 0.01.

Table 4

Coefficients from longitudinal mixed-effects regression models for the adjusted relation between waist circumference and cognitive function in 1,703 adults in the Baltimore Longitudinal Study of Aging

	Intercept	Age	Waist circumference	Sex	Years of education	Hypertension	Glucose-diabetes	Anti-lipid meds	Age × waist
Global
MMSE	26.97∗∗	−0.44	−0.00	−0.20∗	0.13∗∗	0.02	0.03	0.01	0.00
Blessed IMC errors	3.39∗∗	−0.12	0.01∗	−0.18∗∗	−0.15∗∗	0.06	−0.13	−0.12	0.00∗
Attention/executive
Digit span forward	6.66∗∗	−0.05	−0.01	0.45∗∗	0.11∗∗	−0.06	0.17	0.02	−0.00
Digit span backward	6.25∗∗	0.01	−0.00	0.48∗∗	0.11∗∗	0.02	0.16	−0.18	−0.00
Trails A	60.61∗∗	11.99∗∗	−0.14∗∗	1.04	−0.69∗∗	0.66	0.27	−1.47	−0.07∗
Trails B	144.69∗∗	16.94	0.14	−1.66	−3.87∗∗	−0.30	0.95	−4.13	0.09
Memory
CVLT trial 1	7.29∗∗	−0.20	−0.00	−1.08∗∗	0.05∗	0.00	−0.03	0.04	−0.00
CVLTA	46.50∗∗	−2.02	−0.02	−6.33∗∗	0.55∗∗	1.41∗∗	0.24	0.43	−0.01
CVLTB	5.54∗∗	−0.58∗	−0.01	−0.92∗∗	0.09∗∗	0.35∗∗	0.02	0.15	−0.00
CVLT short free	8.47∗∗	−0.71∗	−0.00	−1.25∗∗	0.15∗∗	0.48∗∗	−0.02	0.14	−0.00
CVLT long free	8.99∗∗	−0.52	−0.00	−1.30∗∗	0.15∗∗	0.41∗∗	−0.02	0.18	−0.00
Prospective memory	3.95∗∗	−0.25	0.01∗	−0.14∗	0.04∗∗	0.03	−0.01	0.17∗	0.00
BVRT	7.27∗∗	−0.03	0.02∗∗	−0.10	−0.18∗∗	−0.03	−0.25∗	0.29	0.01∗∗
Language
Letter Fluency	9.64∗∗	−0.77	−0.02∗∗	−0.22	0.44∗∗	−0.11	0.41	−0.15	−0.00
Category Fluency	12.45∗∗	−1.32∗	−0.01	−1.43∗∗	0.28∗∗	0.25∗∗	0.31	0.14	−0.00
Boston Naming	43.47∗∗	−0.93	0.01	0.52	0.56∗∗	−0.15	−0.12	0.18	−0.00
Visuospatial
Card Rotations	43.62∗∗	−6.25	0.12∗	15.12∗∗	1.26∗∗	−1.35	−4.19	1.94	−0.03

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CVLT = California Verbal Learning Test; BVRT = Benton Visual Retention Test. Age was employed as both a fixed and random factor with the age × waist circumference interaction term denoting change over time.

^∗p < 0.05

^∗∗p < 0.01.

Table 5

Coefficients from longitudinal mixed-effects regression models for the adjusted relation between WHR and cognitive function in 1,703 adults in the Baltimore Longitudinal Study of Aging

	Intercept	Age	WHR	Sex	Years of education	Hypertension	Glucose-diabetes	Anti-lipid meds	Age × WHR
Global
MMSE	26.74∗∗	−0.19	−0.11	−0.24∗	0.13∗∗	0.01	0.01	−0.01	−0.25
Blessed IMC errors	3.23∗∗	−0.20	0.88∗	−0.19∗	−0.15∗∗	0.06	−0.13	−0.13	0.39∗
Attention/executive
Digit span forward	6.44∗∗	−0.07	−0.50	0.42∗∗	0.11∗∗	−0.05	0.16	0.01	−0.26
Digit span back	5.74∗∗	−0.01	0.11	0.40∗∗	0.11∗∗	0.03	0.15	−0.19	−0.37
Trails A	63.03∗∗	12.39∗∗	−16.35∗∗	1.46	−0.70∗∗	0.66	0.01	−1.59	−7.09
Trails B	148.55∗∗	−12.51	7.23	0.93	−3.89∗∗	−0.06	1.86	−3.65	42.01∗∗
Memory
CVLT Trial 1	7.67∗∗	−0.14	−0.83	−1.04∗∗	0.05∗	0.01	−0.02	0.04	−0.41
CVLTA	46.80∗∗	−2.87∗	−1.98	−6.14∗∗	0.55∗∗	1.44∗∗	0.27	0.42	−0.32
CVLTB	5.87∗∗	−0.88∗∗	−1.00	−0.84∗∗	0.09∗∗	0.36∗∗	0.03	0.15	0.29
CVLT short free	8.40∗∗	−0.87∗	−0.22	−1.23∗∗	0.15∗∗	0.48∗∗	−0.01	0.13	−0.02
CVLT long free	8.83∗∗	−0.71	−0.09	−1.29∗∗	0.15∗∗	0.42∗∗	−0.01	0.17	−0.17
Prospective memory	4.33∗∗	−0.22	0.16	−0.10	0.03∗∗	0.04	0.01	0.18∗∗	0.01
BVRT	6.73∗∗	−0.14	2.37∗∗	−0.15	−0.18∗∗	−0.05	−0.27∗∗	0.31∗	1.23∗∗
Language
Letter Fluency	8.69∗∗	−0.40	−1.22	−0.34	0.44∗∗	−0.12	0.34	−0.17	−0.49
Category Fluency	11.80∗∗	−0.76	−0.37	−1.51∗∗	0.28∗∗	0.24∗∗	0.27	0.12	−0.98
Boston Naming	43.35∗∗	−0.59	1.17	0.51	0.56∗∗	−0.15	−0.10	0.18	−0.48
Visuospatial
Card Rotations	45.63∗∗	0.98	10.41	14.39∗∗	1.28∗∗	−1.50	−4.08	1.94	−10.98∗

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CVLT = California Verbal Learning Test; BVRT = Benton Visual Retention Test. Age was employed as both a fixed and random factor with the age × WHR interaction term denoting change over time.

^∗p < 0.05

^∗∗p < 0.01.

Discussion

Results from the present study provide further evidence for an independent association between obesity and cognitive test performance. Cross-sectional analyses showed that larger body composition was associated with poorer performance on measures of global cognitive function, memory, and language. This pattern is consistent with the growing number of studies demonstrating that obesity is an independent risk factor for poor neurocognitive outcome [9,10,11,12,13,14]. However, in contrast to hypotheses and other medical outcomes [23,24], no clear pattern of cognitive differences emerged between measures of central obesity and BMI. The exact reason for this finding is unclear, as the mechanisms for obesity-related cognitive impairment (discussed later) remain poorly understood.

Interestingly, cross-sectional analyses found no association between body composition and executive function, and those higher indices were actually associated with better performance on tests of attention/psychomotor speed and visuospatial skills. Several recent studies have also found higher BMI is associated with better cognition, causing some to hypothesize that weight loss in older adults may reflect reduced functional abilities in a prodromal stage of Alzheimer's disease or a common neurological pathway [25], [26]. This possibility appears less likely in the current sample, as the average age (55 years) is younger than the usual onset for Alzheimer's disease and the pattern of impaired test performance does not involve significant amnestic and naming impairments. Further work is much needed to better delineate the association between obesity and cognitive function across the lifespan.

In the current study, longitudinal analyses showed higher body composition was associated with more rapid decline on measures of global functioning, executive function, and memory over time. Such findings are consistent with past studies showing elevated risk of cognitive decline in obese individuals. However, the etiology of the finding that larger body composition indices were associated with better performance on a test of attention/psychomotor speed while also associated with poorer executive function over time is unclear. Attention and executive function are largely mediated by frontal brain regions and highly correlated [27]. Given that the overall sample exhibits a pattern generally consistent with age-associated cognitive decline, the current findings raise the possibility that obesity produces differential effects on attention and executive function. Some evidence for this notion may already exist in the literature, as a cross-sectional study of otherwise healthy individuals showed higher BMI was associated with deficits in executive function but not attention [15]. This speculation requires confirmation in other samples, particularly studies using functional neuroimaging to better understand cognitive function in obese individuals.

Closely related, the observed pattern of findings from the current study also poses interesting questions about the mechanisms by which obesity is associated with cognitive function. Though medical conditions frequently comorbid to obesity (e.g. hypertension, type 2 diabetes) are known contributors to cognitive decline and dementia [28,29,30], many studies adjust for these conditions and suggest an independent effect for obesity [14,15,21]. There are several alternative explanations that might better account for these findings, including vascular pathology (e.g. endothelial dysfunction), reduced cardiovascular fitness, inflammatory processes, and neuroendocrine dysregulation. Each of these conditions is prevalent in obese individuals and associated with poor neurocognitive function [31,32,33,34,35,36,37]. Recent studies also indicate a possible link between obesity and levels of amyloid-β, which may help account for these cognitive findings and the increased risk of Alzheimer's disease in obese individuals [10,29,38,39,40]. In addition, both obesity and cognitive performance are correlated with dopamine pathway dysregulation, which may negatively affect cognition in older individuals [41,42]. Finally, obesity is associated with alterations in levels of brain-derived neurotrophic factor and leptin – biomarkers that have recently been linked to cognitive function in human studies [43,44]. Each of these mechanisms, in isolation or combination, may contribute to the observed cognitive performance.

However, the current results also revealed that lower BMI was associated with better function and reduced decline on Trail Making Test A, and perhaps on another, Card Rotations. This finding initially appears to contradict the proposed neuroprotective qualities of caloric restriction [45,46] and encourage other explanations. As noted above, the average age and cognitive profile argue against low BMI being a marker of prodromal Alzheimer's disease in the current sample. An alternate explanation is the possible cognitive impact of intentional dieting in older adults. The few existing studies have examined the association between dieting and cognitive function in younger adults, with findings ranging from mild decline to mild improvement [47,48,49,50]. Data regarding dieting status were not available for the current study and could not be directly analyzed. Another possibility is that the long-term neurocognitive impact of obesity is at least partly determined by an individual's weight history across the lifespan. Recent work shows inconsistent effects of obesity on cognitive function in children and adolescents [51,52,53] and it is possible that this trajectory continues through adulthood. Studies that incorporate lifetime weight history will provide key insight into this possibility.

The present findings are limited in several ways. First, the current sample is relatively well-educated (average >16 years of education) and this high level of cognitive reserve may limit the observed rate of cognitive decline [54,55]. Similarly, a comprehensive IQ estimate (e.g. WAIS-IV) was not available for analyses and may help account for patterns of decline over time. However, the present sample's relative homogeneity is also a strength because it may minimize the confounding effects of demographic variables such as occupational status, socioeconomic status, and educational attainment. In addition, the present sample was relatively healthy: none of the observed weight changes were attributable to acute or chronic diseases.

In summary, findings from the current study indicate that multiple indices of body composition show cross-sectional and longitudinal associations with cognitive function. Further work is much needed to identify etiological mechanisms for these associations, particularly those that can directly examine underlying physiological processes.

Acknowledgements

References