Sex-specific body fat distribution predicts cardiovascular ageing

Abstract

Background and aims: Cardiovascular ageing is a progressive loss of physiological reserve, modified by environmental and genetic risk factors, that contributes to multi-morbidity due to accumulated damage across diverse cell types, tissues, and organs. Obesity is implicated in premature ageing, but the effect of body fat distribution in humans is unknown. This study determined the influence of sex-dependent fat phenotypes on human cardiovascular ageing.

Methods: Data from 21 241 participants in the UK Biobank were analysed. Machine learning was used to predict cardiovascular age from 126 image-derived traits of vascular function, cardiac motion, and myocardial fibrosis. An age-delta was calculated as the difference between predicted age and chronological age. The volume and distribution of body fat was assessed from whole-body imaging. The association between fat phenotypes and cardiovascular age-delta was assessed using multivariable linear regression with age and sex as co-covariates, reporting β coefficients with 95% confidence intervals (CI). Two-sample Mendelian randomization was used to assess causal associations.

Results: Visceral adipose tissue volume [β = 0.656, (95% CI, .537-.775), P < .0001], muscle adipose tissue infiltration [β = 0.183, (95% CI, .122-.244), P = .0003], and liver fat fraction [β = 1.066, (95% CI .835-1.298), P < .0001] were the strongest predictors of increased cardiovascular age-delta for both sexes. Abdominal subcutaneous adipose tissue volume [β = 0.432, (95% CI, .269-.596), P < .0001] and android fat mass [β = 0.983, (95% CI, .64-1.326), P < .0001] were each associated with increased age-delta only in males. Genetically predicted gynoid fat showed an association with decreased age-delta.

Conclusions: Shared and sex-specific patterns of body fat are associated with both protective and harmful changes in cardiovascular ageing, highlighting adipose tissue distribution and function as a key target for interventions to extend healthy lifespan.

Keywords: Ageing; Body composition; MRI; Sex differences.

Structured Graphical Abstract

The association of body fat phenotypes and cardiovascular ageing was assessed in 21 241 participants. This showed how shared and sex-specific patterns of body fat are associated with protective and harmful changes in cardiovascular ageing. *Protective effects of oestradiol in pre-menopausal women.

Figure 1

Analysis of fat phenotypes and cardiovascular ageing. A) Flowchart of analyses performed in UK Biobank participants. B) Fat phenotyping was performed by segmentation of whole-body MRI into visceral, subcutaneous, and muscle compartments (credit: AMRA Medical). C) Phenotypes derived from cardiac MRI were used for age prediction. These included automated time-resolved segmentations of the aorta and cardiac chambers, as well as strain rate analysis and T1 mapping. D) Integration of MRI and DXA-enabled regional body composition analysis (credit: Rhydian Windsor). MRI, magnetic resonance imaging; DXA, dual X-ray absorptiometry; Asc Ao, ascending aorta; Dsc Ao, descending aorta.

Figure 2

Distribution of fat phenotypes. Ridge plots summarizing the distribution densities of adiposity phenotypes. Unadjusted and normalized values shown. Body mass index, BMI; Magnetic resonance imaging, MRI; Adiposity phenotypes n = 21 241; BMI and MRI assessed adiposity n = 21 241; Android and gynoid adipose tissue deposition n = 5168

Figure 3

Adiposity associations with chronological age. A selection of representative phenotypes grouped by MRI-derived adipose features (n = 21 241), android and gynoid fat mass (n = 5168) are shown with their relationship to chronological age at the time of imaging (ages jittered, density contours, point colours represent coefficient of determination (R²). VAT, visceral adipose tissue; ASAT, abdominal subcutaneous adipose tissue; TTFM, total trunk fat mass; WBFM, whole-body fat mass; MATI, muscle adipose tissue infiltration; TAAT, total abdominal adipose tissue; and PDFF, proton density fat fraction (of the liver)

Figure 4

Adiposity phenotype, cardiometabolic and endocrine associations with cardiovascular age-delta. A) Linear regression analysis of quantitative adipose tissue traits (n = 21,241, of which 5168 had android and gynoid fat mass values) with cardiovascular age-delta as the dependent variable. P values, standardized beta-coefficient point estimates, and 95% confidence intervals shown stratified by sex. Linear regression analysis of B) circulating lipids (n = 19 856) and C) sex hormones (n = 3588) with cardiovascular age-delta as the dependent variable. P values, standardized beta-coefficient point estimates, and 95% confidence intervals shown stratified by sex. LDL, low-density lipoprotein; HDL, high-density lipoprotein; SHBG, sex hormone–binding globulin

Figure 5

Associations between cardiovascular age-delta and NMR metabolites. Linear regression analysis of 10 significant metabolites with the largest effect sizes (seven positive and three negative) after Benjamini–Hochberg correction (P < 1 × 10⁻⁴) with cardiovascular age-delta (n = 22 534). Error bars indicate the beta coefficients' point estimates per standard deviation 95% confidence intervals, adjusted for age, age², and statin users, and stratified by sex

Figure 6

Reclassification of body mass index groups by whole-body fat mass. Series of alluvial plots that show the redistribution of participants in each body mass index (BMI) group to equivalent centile ranges of whole-body fat mass. A) Overall population (n = 21 241), B) females (n = 10 558), and C) males (n = 10 683)