Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

Abstract

There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropomorphic features fail to describe regional differences in body fat distribution and composition, and to determine visceral adiposity, which is related to a range of metabolic conditions. In this mixed cross-sectional and longitudinal design (interval mean = 19.7, standard deviation = 0.5 months), including 790 healthy individuals (mean (range) age = 46.7 (18-94) years, 53% women), we investigated cross-sectional body magnetic resonance imaging (MRI, n = 286) measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women

“…The associations between abdominal adipose tissue and brain characteristics are consistent with previous studies linking elevated adipose tissue to older brain age (Beck et al, 2022;Subramaniapillai et al, 2022), lower brain volume (Cho et al, 2021;Debette & Markus, 2010;Gurholt et al, 2021;Isaac et al, 2011;Veit et al, 2014), and higher WMH load (Arnoldussen et al, 2019;Lampe et al, 2019;Park et al, 2018;Vuorinen et al, 2014). The relationships were strongest for WMH volume, suggesting that brain white matter may be particularly sensitive to elevated abdominal adipose tissue in post-menopausal females.…”

“…The relationships between oestrogen exposure, body composition, and brain health in females are complex and largely unexplored. The menopause transition is linked to an accelerated increase of central fat accumulation (Lizcano & Guzmán, 2014), and abdominal adipose tissue has been associated with higher grey matter (GM) and white matter (WM) brain age (Beck et al, 2022(Beck et al, , 2021bSubramaniapillai et al, 2022), WM hyperintensities (WMH) (Arnoldussen et al, 2019;Han et al, 2021;Lampe et al, 2019;Pasha et al, 2017;Park et al, 2018;Vuorinen et al, 2014), and dementia risk (Kiliaan et al, 2014;Tang et al, 2021;Razay et al, 2006;Whitmer et al, 2008). However, in females, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause (Steiner & Berry, 2022;Bhardwaj et al, 2019;Kershaw & Flier, 2004;Siiteri, 1987;Simpson, 2003).…”

Sex‐ and age‐specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

“…While the current study focused on WM measures given their susceptibility to CMRs, future studies may aim to include several brain modalities to directly compare sex‐ and age‐specific effects. More detailed measures of fat distribution obtained with body MRI (Beck, de Lange, Alnaes, et al, 2021 ; Gurholt et al, 2021 ; Leinhard et al, 2008 ; Linge et al, 2018 ) may also clarify the divergent associations observed in females, and provide a more complete understanding of adipose tissue distribution in relation to cardiometabolic disease (Linge et al, 2018 ; Linge et al, 2019 ), AD risk (Diehl‐Wiesenecker et al, 2015 ), and endocrine aging processes (El Khoudary et al, 2015 ). Due to sample size restrictions in relation to our study goals of determining sex‐ and age‐specific effects, we could not currently probe body MRI in our subgroups, but ongoing data collection of this measure from UK Biobank participants will render future analyses of these measures feasible.…”

“…Positive BAG values have also been associated with negative outcomes in population‐based studies, including cardiovascular risk, cognitive impairments, and dementia risk (Biondo et al, 2021 ; de Lange, Anatürk, et al, 2020 ; Egorova et al, 2019 ; Franke & Gaser, 2012 ; Gaser et al, 2013 ; Kolbeinsson et al, 2020 ; Löwe et al, 2016 ; Wang et al, 2019 ). Previous studies have shown accurate age prediction based on diffusion‐weighted imaging measures (Beck, de Lange, Maximov, et al, 2021 ; Cole, 2020 ; Richard et al, 2018 ; Voldsbekk et al, 2021 ), as well as associations between WM BAG and CMRs (Beck, de Lange, Alnaes, et al, 2021 ; Beck, de Lange, Pedersen, et al, 2021 ). However, these previous studies did not assess sex‐specific effects, or whether CMRs interact with APOE genotype to influence WM BAG during certain life phases.…”

The oxytocin signalling gene pathway contributes to the association between loneliness and cardiometabolic health

“…ectopic fat tissue) most associated with metabolic disturbances (Bergman et al, 2006;Després and Lemieux, 2006). As BMI is not particularly sensitive to the distribution of body fat, alternative techniques, such as body magnetic resonance imaging (MRI) segmentation, are required to measure the individual contributions of different fat depots (Beck et al, 2021;Gurholt et al, 2021;Linge et al, 2019Linge et al, , 2018. In the current study, we use an oxytocin pathway polygenic score (PGS oxt ) (Winterton et al, 2021) to evaluate the genetic contribution of SNPs belonging to 154 genes attributed to the oxytocin signalling pathway on cardiometabolic risk factors, including body fat distribution and infiltration indexed via body MRI, haematological markers, blood pressure, and bone mineral density in a large sample of adults from the UK Biobank.…”