This is the first study to use two separate cohorts to show that higher body tissue adiposity is correlated with brain atrophy in patients diagnosed with MCI and AD. These correlations are still found within
diagnostic groups, and they persist after adjusting for other factors that influence brain volumes or brain atrophy, such as age, sex, and education. Many of the affected brain regions are responsible for cognitive function, such as the hippocampus and frontal lobes, and are consistent with regions identified in prior work that focused only on healthy subjects (Gazdzinski et al., 2008
, Gazdzinski et al., 2009
, Raji et al., 2009a
). These findings were generated in a pooled sample of MCI and AD subjects () but also held when the MCI () and AD () subjects were analyzed separately. The lack of statistically significant findings in the CHS-CS AD subjects may just reflect the relatively smaller sample size in that group (N=36) compared to the number of ADNI AD subjects (N=188).
Prior work has also suggested that the amount of adipose tissue in the body can modify the metabolism of beta-amyloid, thus increasing risk for AD (Moroz et al., 2008
). Obesity can also raise risk for vascular diseases such as hypertension and type II diabetes mellitus that themselves can induce deficits in brain structure and function (Dai et al., 2008
, Iadecola 2004
, Watson et al., 2003
). Even so, associations between brain structure and BMI persist even after adjusting for the presence of diabetes (DM2) and for fasting plasma insulin levels (Raji et al., 2009). All these studies suggest that obesity can modify risk for AD through at least several mechanisms.
Even though a fairly consistent pattern of brain atrophy associated with obesity was observed in both ADNI and CHS samples, several key brain regions linked with higher BMI in the ADNI subjects but were not detected in the CHS subjects. In MCI and AD subjects scanned as a part of ADNI, higher BMI was correlated with lower brain tissue in more broadly distributed regions including the cerebellum and brainstem (i.e., pons, midbrain, and upper part of the medulla; ,). Further, CSF expansion - an indication of lower brain volume in adjacent brain regions - was also observed in the ADNI group. These differences could be attributed to the sample size difference between the ADNI (N=587) and CHS (N=113) groups. A 5-fold increase in sample size greatly increases the power to detect a more broadly distributed pattern of brain atrophy in the ADNI sample that may go undetected in the smaller CHS sample.
In the MCI subjects, BMI is significantly correlated with volume variation in brain regions that do not completely overlap in the CHS (, top row
) and the ADNI cohorts (, bottom row
). This difference could be attributed to the percentage of amnestic MCI subjects present in the ADNI (100%) versus the CHS (8%, probable amnestic MCI; 6.2% possible amnestic MCI) groups. A previous CHS study analyzing MCI subjects found greater volume deficits in the hippocampus, amygdala, entorhinal cortex, dorsolateral prefrontal cortex, superior temporal, and parietal cortices of amnestic MCI subjects compared to MCI subjects without memory impairments (Lopez et al., 2006
). Thus, these structural brain differences in amnestic versus non-amnestic MCI subtypes may affect the strength and location of correlations between brain structure and BMI. In ADNI, there is more widespread atrophy overall compared to the CHS group. The brainstem does not show detectable atrophy in the CHS group, but is shown to be a brain region whose volume is negatively associated with BMI. Even so, similar brain areas, including frontal, temporal, and occipital lobe regions, were correlated with BMI in both ADNI and CHS populations.
Another possibility is that the differences in the subject demographics of each cohort are contributing to the differences in these ADNI and CHS brain maps (,), even after controlling for effects of age, sex, and education on brain structure. For example, the AD patients are, on average, younger in the ADNI group than the CHS group (ADNI=75.4±7.5 versus CHS=81.9±5.2 years; F1,222=24.5, P-value=1.47 × 10−6). Similarly, MCI patients are also younger in ADNI (ADNI=74.8±7.4 versus CHS=79.8±4.3 years; F1,474=32.2, P-value=2.40 × 10−8). The effect of age on brain structure was corrected for in our analysis; however, the overall age difference between the ADNI and CHS groups may be an indication that the referral-based cohort, ADNI, may include subjects with more severe symptoms of AD at an earlier age.
Both samples in this study were predominantly Caucasian, so care must be exercised in generalizing the findings to other ethnic groups. There are known ethnic differences in the prevalence of known risk genes for obesity, including a commonly carried variant in the FTO (fat mass and obesity-associated) gene. For example, 51% of Yorubans (West African natives), 46% of Western and Central Europeans, and only 16% of Chinese carry the “adverse” FTO allele that is associated with a higher BMI (Frazer et al., 2007
). Further, the prevalence of AD and other dementias is about two times higher in African-Americans than in elderly whites of the same age group, according to the Aging, Demographics, and Memory Study (ADAMS) (Plassman et al., 2007
). Another study showed that Hispanics were two and half times more likely than whites of the same age and sex to have AD and other dementias (Gurland et al., 1999
). Other conditions including high lood pressure, heart disease, diabetes, and stroke have been shown to increase the risk for AD (Luchsinger et al., 2005
), and these tend to be more common in African-Americans and Hispanics (Langa et al., 2006
). The 2006 Health and Retirement Study showed that high blood pressure and diabetes tended to be more common in African-Americans and Hispanics than in whites, but heart disease was more common in whites than in African-American and Hispanic groups (Langa et al., 2006
). Thus, consideration of ethnic and racial differences is critical to a complete understanding of how obesity influences brain structure and risk for cognitive impairment and AD.
Prior studies report mixed findings regarding how the BMI-brain structure association may be influenced by sex differences. One study found a correlation between BMI and cerebral volume atrophy in Japanese men but not women (Taki et al., 2008
), but another study of Swedish women showed substantial temporal lobe atrophy (Gustafson et al., 2004
). Another CHS study did not detect a sex difference in BMI-related brain atrophy in 94 elderly cognitively healthy subjects (Raji et al., 2009a
). These past studies focus on non-demented populations, so further study is needed on the effect of sex differences in MCI and AD subjects. In our study, we controlled for the effects of sex differences in brain structure by including sex as a covariate in our model; however, BMI by sex interactions may be detectable in future studies.
Our referral-based and community-based cohorts differ in several ways, including the percentage of subjects carrying the ApoE4 risk gene for Alzheimer’s disease. The % of patients carrying the ApoE4 genetic variant is much higher in the ADNI study compared to CHS for both the AD (ADNI=67.0% versus CHS=23.3%; X21
= 18.8, P-value=1.5 × 10−5
) and MCI groups (ADNI=54.6% versus CHS=27.5%; X21
= 16.0, P-value= 6.5 × 10−5
). Again, the higher % of subjects carrying the ApoE4 variant in the referral-based group supports the notion that ADNI subjects may experience more severe symptoms compared to their CHS counterparts. Prior ADNI studies found greater hippocampal (Schuff et al., 2009
) and temporal lobe atrophy in ApoE4 carriers versus non-carriers (Hua et al., 2008b
). Even after accounting for the presence of the ApoE4 allele, in addition to the effects of age, sex, and education, BMI was still correlated with lower regional brain volumes (ADNI: critical uncorrected P-value=0.025; CHS: critical uncorrected P-value= 0.004).
Some prior studies have reported low late-life BMI and rapid weight loss in patients with dementia (Fitzpatrick et al., 2009
), yet our results provide evidence that higher BMI is associated with lower brain volumes, even in MCI () and AD (). Intriguingly, with higher BMI, our AD group showed not only lower brain volumes, but also ventricular expansion – a further indicator of brain matter loss. One interpretation of these cross-sectional findings is that more rapid weight loss, and thus a lower BMI, tends to occur in the later stages of dementia, but the present results show brain structure differences resulting from a lifetime of high BMI. As a result, one might expect the relationship between brain volumes and BMI would change in degree if not in kind from early AD to late AD, depending on whether weight loss due to the disease becomes dominant. Another possibility, and a limitation to using BMI as a measure of obesity, is that conventional adiposity measures may not capture changes in lean body mass and adipose tissue related to the aging process (Stevens et al., 1999
). Waist circumference and waist to hip ratio have been argued to be better measurements of adiposity, but these were not available for both the ADNI and CHS datasets.
There are, however, several caveats to consider in this work. Our study relates body mass index to brain atrophy in persons who are relatively early
in the course of their cognitive decline. These results would not apply to persons in very advanced states in dementia, for whom lack of adequate food intake is often a major predictor of mortality (Franzoni et al., 1996
). Rather, our work is best applied from the standpoint of understanding how control of body weight and other lifestyle factors may promote healthy brain aging and reduce the partly preventable risks for cognitive impairment and dementia.
One notable aspect of the topography of brain matter loss in – is that the atrophy associated with BMI appears to lie in the white matter rather than the cortical surface. This is mainly because (1) the registration fields in TBM are spatially smooth and partial volume averaging effects diminish the signal somewhat at tissue boundaries, such as the cortex/CSF interface, and (2) the registration accuracy of TBM is poorer at the cortical surface, at least relative to some approaches designed to explicitly model the cortical surface, and align cortical regions (Lepore et al., 2010
, Thompson et al., 2004
As noted in prior work (Hua et al., 2008b
, Leow et al., 2009
), to better sensitize the TBM approach for detecting cortical gray matter loss, several approaches have been considered: (1) use of voxel-based morphometry (VBM) (Ashburner and Friston 2000
) or a related approach termed RAVENS (Davatzikos et al., 2001
), (2) adaptively smooth deformation-based compression signals at each point based on the amount of gray matter lying under the filter kernel (Studholme et al., 2003
), or (3) run deformation maps at a very high spatial resolution and with less spatial regularization or with a regularization term that enforces continuity but not smoothness (Leow et al., 2005
). In general, however, cortical differences in AD are better studied using cortical modeling methods that explicitly match cortical landmarks, although studies are more labor-intensive to perform (Thompson et al., 2003
, Thompson et al., 2001
There remain, however, several key implications from this work. First, body tissue adiposity is independently linked to brain atrophy in cognitively impaired persons with MCI or AD. Second, this is evident in both a community cohort study and a referral clinic population – highlighting that the relationship is reproducible. Third, the strength of the BMI-brain atrophy relationship throughout the spectrum of normal cognition, MCI, and AD underscores the need to consider being overweight and obese as modifying the risk for cognitive impairment because of the link to compromised brain structure. An extended clinical possibility arising from this line of work is that controlling body fat content even in late life may reduce risk for dementia – though this will ultimately only be tested in randomized clinical trials, or interventional studies with a longitudinal component.
Methodologically, this study pools data across cohorts to improve the reproducibility and reliability of the findings. Future studies could benefit from this data-sharing approach in much the same way genomic studies pool data across different cohorts (Thompson and Martin 2010
The present data have a number of interesting applications. One is derived from the field of cognitive epidemiology - a relatively new field of study that examines intelligence-health associations (Deary 2009
). In our current study, , show that there is a difference in educational level for MCI and AD in both the ADNI (χ21
) and CHS (χ21
=0.05) cohorts, with the AD group having a smaller percentage of subjects with more than 12 years of education. This could suggest a hypothetical causal chain whereby a lower educational level is associated with poorer skills for choosing healthy behaviors leading to higher BMI values and lower brain volumes. Clearly, whether or not an individual chooses healthy behaviors depends on many factors – including access to exercise, education, and other cultural factors. General intelligence may also be a contributing factor.
Specifically, general intelligence has been found to contribute to overall educational achievement (Deary et al., 2007
). It is unfortunate that a measure of intelligence was not consistently collected in both the datasets analyzed here, as intelligence is the best single predictor of achieved educational level (Deary et al., 2007
). This hypothetical causal chain could show that poorer intellectual function leads to a lower level of educational attainment, which may be associated with poorer skills for choosing healthy behaviors, higher BMI values, and thus lower brain volumes. Interestingly, intelligence is itself positively correlated with brain volume (Luders et al., 2009b
, McDaniel 2005
If this line of reasoning is correct, cognitive epidemiology may provide some insight for public policy (Lubinski 2009
). We have shown that obesity may modify the risk for cognitive impairment because of the link to compromised brain structure. Thus, when proposing behaviors for controlling body fat content, the population distribution of intelligence should also be considered as the relationship between general intelligence and healthy outcomes has been established (Arden et al., 2009
, Lubinski 2009
). Preventing obesity requires healthy behaviors that may already be more evident in better educated people, so healthcare systems may have greater success by developing targeting messages to populations with poorer access to education, or poorer educational attainment.