- Top of page
Although a link between body mass index (BMI) and brain volume has been established in several cross-sectional studies, evidence of the association between change in BMI over time and changes in brain structure is limited. Using data from a cohort of 347 former lead workers and community controls with two magnetic resonance imaging scans over a period of ∼5 years, we estimated cross-sectional and longitudinal associations of BMI and brain volume using both region of interest (ROI) and voxel-based morphometric (VBM) methods. We found that associations of BMI and brain volume were not significantly different in former lead workers when compared with community controls. In the cross-sectional analysis, higher BMIs were associated with smaller brain volumes in gray matter (GM) using both ROI and VBM approaches. No associations with white matter (WM) were observed. In the longitudinal analysis, higher baseline BMI was associated with greater decline in temporal and occipital GM ROI volumes. Change in BMI over the 5-year period was only associated with change in hippocampal volume and was not associated with change in any of the GM ROIs. Overall, higher BMI was associated with lower GM volume in several ROIs and with declines in volume in temporal and occipital GM over time. These results suggest that sustained high body mass may contribute to progressive temporal and occipital atrophy. Hum Brain Mapp 35:75–88, 2014. © 2012 Wiley Periodicals, Inc.
- Top of page
Obesity has rapidly increased in prevalence across the United States in the last three decades [Baskin et al., 2005; Flegal et al., 2010]. The health consequences of overweight and obesity, which include type 2 diabetes, coronary heart disease, osteoarthritis, and certain cancers, among others, have been well established [Must et al., 1999]. Additionally, there is a growing body of evidence linking obesity with greater cognitive decline and higher risk of neurodegenerative diseases, such as Alzheimer's disease [Beydoun et al., 2008; Cournot et al., 2006; Whitmer et al., 2005]. However, the mechanisms by which obesity may impact cognitive function are not clearly understood. A better understanding of the associations between obesity and brain volume may elucidate the role of obesity as a risk factor for neurodegenerative diseases.
One measure of obesity, body mass index (BMI), has been correlated with atrophy in several areas of the brain. Although prior studies of BMI and brain volumes have been published, these generally had small sample sizes, were cross-sectional in design, used either region of interest (ROI) or voxel-wise approaches, but not both, and most did not control for intracranial volume (ICV) in the analyses [Driscoll et al., 2011; Gustafson et al., 2004; Ho et al., 2010a, 2010b; Pannacciulli et al., 2006; Raji et al., 2010; Taki et al., 2008; Walther et al., 2010]. In addition, we found just two studies looking at the longitudinal association of BMI and brain volume, although neither of these had both BMI and brain volume measured at multiple time points, and neither found any longitudinal association [Driscoll et al., 2011; Gustafson et al., 2004].
In this study, we leverage rich longitudinal data on BMI and brain volume while avoiding limitations of prior work. First, we consider analysis of brain volume on two scales: an ROI approach that targets large-scale diffuse, nonlocalized effects in the brain as well as highly localized effects in smaller substructures, and voxel-based morphometry (VBM) that investigates localized brain volumetrics without a priori identification of regions. Second, in our multivariate regression models, we adjust for a measure of ICV. Although clearly ICV is an important predictor of regional and voxel-level brain volume, it has not consistently been controlled for in studies of the BMI-brain volume association. Third, we consider separate gray matter (GM) and white matter (WM) ROIs and conduct voxel-wise analyses separately for GM and WM partitions, which enables the detection of differences in the BMI–brain volume association in the two types of tissue. Fourth, by studying longitudinally BMI measures and magnetic resonance imaging (MRI) scans from two time points, we can assess several features of the relation between BMI and brain volume, including temporality, persistence, and reversibility. Finally, we conduct our study on a cohort of 347 middle-aged men having MRI scans at two visits, the largest study to thoroughly investigate the BMI–brain volume association in healthy American subjects.
- Top of page
In this manuscript we have pursued a large-scale investigation of the potential relationship between obesity, as measured by BMI, and brain structure, as measured by processed MRI. The study is notable in its scale, with a large number of subjects measured longitudinally, and scope, having processed separately for GM and WM both regional and voxel-based volumetric summaries. To our knowledge this is the first study to investigate the longitudinal association of BMI with brain volume using repeated measures of both BMI and imaging data. Moreover, we applied computationally difficult, yet robust statistical methodology for voxel-level analyses by using permutation testing.
We found intriguing relationships between brain structure and obesity. Primarily in GM, higher baseline BMI was associated cross-sectionally with lower brain volume at baseline and longitudinally with further decline in volume subsequent to the first MRI. Cross-sectional ROI results were robust to model specification and suggested a diffuse impact across the cortex. Cross-sectional VBM demonstrated the possibility for some localized association, identifying two large clusters in the frontal GM in both hemispheres. WM voxel-based analyses did not yield any significant associations. The two longitudinal models we considered quantified different features of the potential temporal association. For the model regressing change in ROI volume against baseline BMI adjusted for baseline volume, associations with two of the ROIs associated in the cross-sectional model were found, suggesting that BMI may lead to progressive atrophy in the temporal and occipital GM regions. These regions were not significantly associated in the model regressing change in ROI volume versus change in BMI, adjusted for baseline BMI, providing no evidence that the association of BMI with change in volumes over time was reversible.
An interesting secondary finding in the manuscript was the importance of a good measure of ICV. In adults, ICV clearly represents a form of intrinsic volumetric capacity for calibration. Using height as a surrogate was insufficient and drastically changed results.
Table 5 summarizes previous studies of the association between BMI and brain volume [Driscoll et al., 2011; Gustafson et al., 2004; Ho et al., 2010a, 2010b; Pannacciulli et al., 2006; Raji et al., 2010; Taki et al., 2008; Walther et al., 2010]. Two of these studies had sample sizes larger than the present analysis, but they did not include longitudinal measurements of both BMI and imaging data. Additionally, only two of the studies adjusted for ICV in their ROI or voxel-wise regression models [Driscoll et al., 2011; Walther et al., 2010]. A third study used as outcome measure the percentage of ICV that was GM in a global volumetric analysis, though their voxel-wise analysis did not similarly adjust for ICV [Taki et al., 2008]. Our cross-sectional findings are consistent with results from previous studies identifying negative associations between BMI and total GM, as well as GM more specifically in the frontal, parietal, temporal, and occipital regions, and in smaller structures (Table 5). Across studies, higher BMI was generally associated with lower brain volumes, though there were some exceptions, including increased brain volumes in specific regions in both the gray [Pannacciulli et al., 2006; Taki et al., 2008] and WM [Walther et al., 2010]. In addition, for some studies only the potential for atrophy was assessed and so any possible positive associations could not be identified [Gustafson et al., 2004; Raji et al., 2010].
Table 5. Overview of previous voxel-based morphometric and longitudinal studies of the BMI–brain volume association
|References||Design||Cohort||BMI Variable||Methods||Main findings||Covariate Adjustment|
|Pannacciulli et al. (2006)||Cross-sectional||60 nondiabetic Caucasians (24 obese and 36 lean)||Binary: obese (BMI ≥ 30) versus lean (BMI < 25)||VBM (separately for GM and WM segmentations)||GM density reduced in obese versus lean in R. cerebellum, L. post-central gyrus, R. frontal operculum, R. and L. putamina, R. and L. middle frontal gyri; GM density elevated in obese versus lean in L. calcarine cortex, L. middle occipital gyrus, L. inferior frontal gyrus, and R. cuneus; negative association between GM density of L. post-cenral gyrus in obese but not lean; WM density elevated in obese versus lean in striatal region||Sex, age, handedness, global tissue density|
|Taki et al. (2008)||Cross-sectional||1,428 Japanese including 27 obese and 273 overweight||Categorical: 0 (BMI < 20), 1 (BMI 20–24.9), 2 (BMI 25–29.9), 3 (BMI ≥ 30)||Assessed association of BMI with “GM ratio” (whole brain); VBM (GM segmentation)||Negative association between BMI and GM ratio (% GM volume in intracranial volume) in men but not women; for men, negative correlation between GM volume and BMI category in bilateral medial temporal lobes, anterior lobe of the cerebellum, occipital lobe, frontal lobe, precuneus, and midbrain; positive correlations in bilateral inferior frontal gyri, posterior lobe of the cerebellum, frontal lobes, temporal lobes, thalami, and caudate heads||age, lifetime alcohol intake, history of hypertension, and diabetes mellitus|
|Raji et al. (2010)||Cross-sectional||94 healthy elderly subjects (14 obese, 51 overweight, and 29 normal BMI)||Categorical: normal, overweight, and obese||TBM||BMI associated with atrophy in frontal, temporal, and subcortical brain regions in unadjusted model. In adjusted models, those with BMI > 30 showed atrophy in frontal lobes, anterior cingulate gyrus, hippocampus, and thalamus compared to those with BMI 18.5–25; Atrophy in basal ganglia and corona radiata of WM among overweight (BMI 25–30). Overall brain volume did not differ between overweight and obese subjects||age, gender, race, type 2 diabetes mellitus|
|Ho et al. (2010, 2010a)||Cross-sectional||226 healthy elderly subjects||Continuous||TBM||BMI associated with atrophy in frontal, temporal, parietal, and occipital lobe regions, as well as in brain stem and cerebellar region||age, sex, education level, and physical activity|
|Ho et al. (2010, 2010b)||Cross-sectional||700 patients with MCI or AD||Continuous||TBM||Negative association between BMI and brain volume in frontal, temporal, parietal, and occipital lobes||age, sex, and years of education|
|Walther et al. (2010)||Cross-sectional||95 community- dwelling females, ages 52–92||Continuous||VBM (separately for GM and WM segmentations)||Higher BMI associated with lower GM volume in L. orbitofrontal, R. inferior frontal, R. precentral gyri, posterior region containing parahippocampal, fusiform, and lingual gyri, and R. cerebellar regions; higher WM volume in frontal, temporal, and parietal lobes||age and ICV; sensitivity analysis included hypertension indicator|
|Gustafson et al. (2004)||Longitudinal BMI; single CT scan||290 middle-aged Swedish women||Continuous||ROI (Four regions: temporal, frontal, occipital, and parietal lobes); atrophy visually rated||Found atrophy in temporal lobe but not frontal, occipital, or parietal lobes; in multivariate analysis only age and BMI significant predictors; Increased risk of temporal atrophy of 13-16% per 1.0kg/m2 of BMI; Did not find relation between change in BMI and atrophy; waist-to-hip ratio not associated with atrophy||age, diastolic blood pressure, serum triglycerides, education, smoking, socioeconomic status, presence of psychiatric disorder;|
|Driscoll et al. (2011)||Longitudinal MRI; single BMI||152 community- dwelling participants, ages 56–86||Continuous and Binary (BMI ≥ 30 versus BMI < 25)||ROI (16 regions, including both GM and WM)||No associations found between BMI at age 50 and subsequent changes in brain volume among those who did not become impaired at follow-up visits; BMI ≥ 30 associated with greater decline in GM volume (total, frontal, temporal), precuneous, cingulate, and orbitofrontal gyri, and higher continuous BMI with greater decline only in cingulate when individuals who later became impaired were included||Backward step-wise selection on ICV, sex, race/ethnicity, education, and smoking status|
To our knowledge only one previous study had imaging data available from multiple visits, from which they estimated the association between BMI at age 50 and subsequent longitudinal trajectories of brain volume ROIs [Driscoll et al., 2011]. When considering the entire cohort, which included 17 individuals who later became cognitively impaired, that study found that obese individuals (BMI ≥ 30) had greater decline in temporal GM, which was one of the regions negatively associated with baseline BMI in both our cross-sectional and longitudinal analyses, as well as decreased volume in frontal and total GM, which were associated in our cross-sectional analysis, and in a few smaller structures (Table 5). However, these associations were only identified when BMI was modeled as a categorical variable; when BMI was modeled as continuous just the cingulate had P < 0.05. In addition, no positive associations with WM ROIs were identified, which differs from our results. Another study considered longitudinal BMI measures, though imaging data was only available from a single time point and atrophy was visually rated in four regions rather than using processed volumetrics on a finer regional scale [Gustafson et al., 2004]. While the methodology used was different, that study also found that obesity was associated with atrophy of the temporal lobe [Gustafson et al., 2004].
In addition to strengthening the evidence of the BMI-brain volume association, longitudinal study designs can provide complementary insights. Since Driscoll et al. (2011) considered the interaction of the longitudinal brain volume trajectory with a single BMI time point prior to the MRI scans, their study allows the investigation into whether age-related decline in brain volume is modified by mid-life BMI. Other questions of interest that are not directly addressed by this design include: at which age(s) in life is BMI most associated with changing brain structure? Are changes in BMI associated with changes in volume? On what time scale might obesity play a role in changing brain volume? Finding, for example, that high BMI in one's 30s was most predictive of future decline in brain volume or that weight loss might reverse decline would have important clinical and public health implications. Using data from two time points, we explored the association of baseline BMI with baseline brain volume, baseline BMI with the subsequent change in volume, and change in BMI with change in brain volume, so as to assess persistence, progression, and reversibility [Bandeen-Roche et al., 2009] of the BMI-brain volume association. Our findings suggest that the association of higher BMI with lower brain volume is persistent and may be progressive in some lobar GM. Further large-scale studies investigating the association between BMI measured at multiple time points and longitudinal image acquisitions would further elucidate the complex associations among obesity, aging, and brain volume.
While no WM ROIs were associated in the cross-sectional analysis, it is possible that the positive longitudinal associations seen in WM are more interesting than artifactual. A recent cross-sectional study of 95 females reported positive associations in extensive WM regions in a voxel-wise analysis [Walther et al., 2010], though most studies have either found little association in WM regions or did not conduct separate gray and WM analyses. Changes in brain structure, as well as lesions that primarily occur in WM, can dramatically impact segmentation and registration. Thus the positive longitudinal structural changes associated with BMI and WM may be real associations from structural changes that are highlighted, perhaps in the wrong direction, through image processing. Of course, one cannot discount the possibility that the associations are potentially due to chance or confounding.
Though the majority of study participants were former lead workers, sensitivity analyses demonstrated that these individuals were not appreciably different than community-based controls in the BMI-brain volume association. In particular, including a BMI-by-control status interaction term made no difference in model fit except in a single ROI. Additionally, restricting the model to just include the former lead workers and adjusting for PTL did not materially impact our findings (Table 2).
It is worth noting the complexities inherent in studying longitudinal brain volumetrics. First, it is important to avoid any causal interpretations of associations as BMI is correlated with many factors (many unaccounted for) that are presumably associated with brain structure, such as physical activity and cardiovascular health. Second, voxel-based analyses have lower power, by virtue of the number of tests performed, and presume spatial localization of associations. Nonlocalized associations, or localized associations that vary in location across subjects would not be detectable by VBM, with the latter case being difficult to detect by any method. In our ROI-based analyses, we presented all results from a range of models rather than formally adjust for multiple comparisons. A Bonferroni correction in this setting would likely be highly conservative as the ROIs are overlapping regions, with some completely contained within others. The robust significant cross-sectional ROI associations across a broad range of GM areas, including the whole GM, suggest a diffuse, nonlocalized potential impact of BMI on brain volumetrics.
The relation of obesity with cognitive decline [Whitmer et al., 2005] and dementia [Beydoun et al., 2008] may be explained in part by preceding changes in brain structure. For example, parallel to our findings on brain volume, one study found a cross-sectional association of higher BMI with lower cognitive scores, and that higher baseline BMI was associated with an additional decline in scores at a follow up visit, but that change in BMI was not significantly correlated with change in cognitive function [Cournot et al., 2006]. Other studies looked at the relation of measures of obesity with specific cognitive tasks and generally found higher BMI associated with poorer performance on memory, executive function, language, and visuomotor tasks [Smith et al., 2011].
It is worth hypothesizing the potential mechanistic pathways for an impact of BMI on brain structure. Obese individuals may be resistant to the effects of leptin [Enriori et al., 2006], a hormone involved in appetite and the regulation of food intake, despite having higher leptin levels than the normal-weight [Considine et al., 1996]. One VBM study of 32 young adults found fasting plasma leptin concentrations were associated with changes in GM regional volumes [Pannacciulli et al., 2007], and results from another study of three adults suggested that leptin replacement may lead to subsequent increases in GM volume among the leptin deficient [Matochik et al., 2005]. It has been established that enlarged adipocytes among the obese may induce a low-grade state of systemic inflammation [Greenberg and Obin, 2006; Gregor and Hotamisligil, 2011]. Higher levels of inflammatory markers have been associated with lower total brain volume [Jefferson et al., 2007], and females taking anti-inflammatory drugs were found to experience a smaller decline in brain volume associated with normal aging than those not taking the drugs [Walther et al., 2011]. Insulin resistance and diabetes, which are highly correlated with obesity, could also play a role in brain volume deficits [Korf et al., 2007; Tan et al., 2011], though one VBM study found that when BMI, fasting plasma insulin levels, and type II diabetes variables were included together in the model, only BMI remained independently associated with lower brain volumes [Raji et al., 2010].
The combination of increasing obesity prevalence and a large aging population in the United States have exacerbated the public health burden in recent years, and these trends are expected to continue in the coming decades [Wang et al., 2011]. The implications of this and previous studies that higher BMI may also be a risk factor for changes in brain structure and for neurodegenerative disorders could further strain public health resources if obesity-related cognitive decline compounds the normal effects of aging. Consequently, obesity prevention programs and interventions targeting obesity as a risk factor have the potential not just for reductions in type 2 diabetes and improved cardiovascular health but also for more robust brain health in mid- and late-life.