Regular articleHigher body mass index in older adults is associated with lower gray matter volume: implications for memory performance
Introduction
Numerous epidemiological studies found obesity in midlife to be a risk factor for cognitive impairments later in life, including Alzheimer's Disease (AD; e.g., Anstey et al., 2011, Beydoun et al., 2008, Fitzpatrick et al., 2009, Gustafson, 2006, Kivipelto et al., 2005, Whitmer et al., 2005, Whitmer et al., 2008; for review, see Emmerzaal et al., 2015; but, see Qizilbash et al., 2015 for recent discussions). However, reports of cognitive performance in nondemented obese compared to lean individuals are equivocal, showing either lower performance in most domains including executive functions and memory (e.g., Benito-León et al., 2013, Walther et al., 2010), or no reliable effect, partly dependent on sex and age range studied (e.g., Elias et al., 2005; for a review, see Smith et al., 2011). A recent systematic review pointed out that due to methodological limitations, such as incomplete confounder adjustment, the evidence is still not sufficient to draw definite conclusions (Prickett et al., 2015). Potential negative effects on cognition could be a consequence of obesity-associated changes in structural brain properties, due to physiological alterations such as insulin resistance and low-grade inflammation, but also secondary cardiovascular diseases (Biessels et al., 2008, Shefer et al., 2013). For example, obesity has been linked to reduced gray matter volume (GMV) and thickness in frontal, temporal, and subcortical areas, yet the regional patterns often varied between studies (Pannacciulli et al., 2006, Raji et al., 2010, Walther et al., 2010; for a meta-analysis, see Willette and Kapogiannis, 2014). In addition, obesity-related cardiovascular risk factors such as hypertension and type 2 diabetes mellitus have been associated with lower GMV in some, but not all studies, showing again varying regions to be affected (reviewed in Friedman et al., 2014). Interestingly, even in the absence of manifest obesity, better metabolic profiles have been shown to exert protective effects on AD and related temporal brain areas (Crane et al., 2013, Kerti et al., 2013, Villeneuve et al., 2014), suggesting significant effects of body composition even in the normal-to-overweight range. However, other studies, partly including younger subjects, reported besides negative effects of obesity also positive associations, for example in orbitofrontal and inferior frontal, occipital, temporal, and cerebellar areas, sometimes restricted to males or females (Horstmann et al., 2011, Pannacciulli et al., 2006, Taki et al., 2008, Willette and Kapogiannis, 2014).
In sum, although negative effects of overweight and obesity on the brain even in older ages seem biologically plausible, previous studies could not establish a consistent pattern of regional changes in gray matter structure, and if this would affect cognitive performance. This might be partly due to different sample characteristics such as age, sex, and obesity measures studied (Willette and Kapogiannis, 2014). Moreover, important confounders, such as age- and obesity-associated conditions and related medication intake, for example, hypertension, diabetes, hyperlipidemia, or estrogen supplementation, have not consistently been considered in previous studies (e.g., Brooks et al., 2013, Ho et al., 2011), rendering potential independent effects of higher body mass index (BMI) in older age still debatable (Friedman et al., 2014, Prickett et al., 2015).
To overcome these limitations, the aim of the present study was to comprehensively assess the effect of BMI on regional GMV independent of potential confounders in a well-characterized population-based cohort of otherwise healthy older individuals, using whole-brain GMV analyses and a large sample size. Further, we examined potential interactions with regard to age, sex, and severity of obesity, and aimed to control for manifesting comorbidities including hypertension, diabetes and intake of antihyperlipidemic medication (Biessels et al., 2008, Elias et al., 2005) in both adjusted and sensitivity statistical analyses, as for example, related medical treatment could have confounded the effects of BMI in older age (Beeri et al., 2008, Jennings and Zanstra, 2009, Nadkarni et al., 2015, Patrone et al., 2014). In addition, we sought to additionally control for various factors known to affect brain structure and cognition, including white matter hyperintensities (Wen et al., 2006), depression (Kirwan et al., 2008), smoking and education status (Garibotto et al., 2008, Karama et al., 2015), intake of estrogen supplements (Lord et al., 2008), as well as apolipoprotein E (APOE) e4 genotype (Wishart et al., 2006). We additionally evaluated if BMI indirectly affected cognitive performance through changes in regional GMV using simple mediation analyses, and if this effect was moderated by age. We hypothesized that in our cohort of cognitively healthy older individuals, a higher BMI would on average be associated with lower regional GMV in several brain areas, including frontal, temporal, and parietal areas, independent of confounders. In addition, we speculate that this would correlate with lower performance in higher-order cognitive domains including memory and executive functions, as these processes are known to correlate with regional GMV, for example, in the prefrontal and temporal lobe (Buckner, 2004, Mander et al., 2013, Steffener et al., 2013, Yuan and Raz, 2014). Eventually, this might help to further determine if overweight and obesity have an independent negative impact on the brain in aging populations.
Section snippets
Participants
All participants were enrolled in the “Health Study of the Leipzig Research Centre for Civilization Diseases” (LIFE). Adult Leipzig inhabitants were randomly invited via the population registry (n = 10,000) and a proportion underwent neuropsychological testing, medical examinations, and magnetic resonance imaging (MRI) of the head at 3T (n = ∼2600). Medical history and medication intake was assessed by means of a structured interview by trained staff. Brain pathologies due to stroke,
Results
In total, 617 healthy older participants (258 women) were included in main analyses, see Table 1 for demographic characteristics and Table 2 for distribution of raw cognitive test scores. Owing to the nature of our exclusion criteria, participants excluded from main analyses (n = 368) were on average slightly older, more frequently women, exhibited a higher BMI and were less educated compared to those included (all p < 0.05, Supplementary Table 1). The subgroup with available APOE genotype
Discussion
Using a large population-based cohort of 617 healthy older adults, we found that a higher BMI is associated with lower GMV in multiple cortical and subcortical areas and in the cerebellum, even after adjusting for age, sex, obesity status, and other confounders such as arterial hypertension, diabetes, and APOE genotype. In addition, mediation analyses indicated that a higher BMI affected memory through reduced regional GMV in frontal and thalamic brain areas. Further, mediation analyses
Disclosure statement
The authors declare no competing interest.
Acknowledgements
This work is supported by the European Union, the European Regional Development Fund, and the Free State of Saxony within the framework of the excellence initiative, and LIFE–Leipzig Research Center for Civilization Diseases, University of Leipzig (project numbers 713-241202, 713-241202, 14505/2470, 14575/2470), and by the German Research Foundation (CRC1052 Obesity mechanisms Project A01 A. Villringer/M. Stumvoll). The authors would like to thank all participants and the staff at the LIFE
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2022, NeuroImage: ClinicalCitation Excerpt :The final dataset comprised a total of 7650 participants, of which 497 could be classified as normal-weight volunteers, and 315 were classified obese patients. The remaining 6838 subjects are taken from correlational analyses between grey matter density and BMI and specifications were not given on whether they fell into the obesity class or normal weight class (as an example, see: Hayakawa et al., 2018 Janowitz et al., 2015; Kharabian Masouleh et al., 2016; Taki et al., 2008). The mean age of the participants classified as obese was 36.65 years (age range: 14.9–77.2), while the mean age of the normal-weight participants was 36.49 years (age range: 16.4–77.5).