Detailed body composition metrics and comprehensive lipid and lipoprotein profiles could better explain cardiometabolic disease (CMD) risk over body mass index (BMI) and traditional clinical lipids. This study aimed to investigate the association of fat and lean mass distribution with an extensive panel of plasma lipids and lipoproteins.
Plasma lipids from Busselton Healthy Ageing Study (n = 1731) participants were acquired using targeted ultrahigh-performance liquid chromatography–mass spectrometry. Plasma lipoprotein subfractions, supramolecular phospholipid composite and glycoprotein parameters were measured using 1H nuclear magnetic resonance spectroscopy. Multiple linear regression models on sex-stratified data identified significant associations of lipids and lipoproteins with dual X-ray absorptiometry (DXA)–derived measures of body composition. P-values were corrected using the Benjamini and Hochberg method and a significance threshold of p<0.05 was employed.
Overall, fat mass exhibited stronger associations with lipids and lipoproteins than lean mass, and arm fat mass produced far fewer significant associations than visceral adipose tissue (VAT) and leg fat mass. VAT showed strong associations with pro-atherogenic lipids and lipoproteins, and displayed lipoprotein size- and lipid sidechain-specific trends. Individuals with high VAT exhibited elevated glycerolipids, cholesteryl esters, ceramides, very-low-density lipoprotein, intermediate-density lipoprotein, small low-density-lipoprotein and small high-density lipoprotein, and decreased large high-density lipoprotein and lactosylceramides. Leg fat mass displayed inverse lipid and lipoprotein associations to VAT, indicating a potential protective effect against CMD risk. However, this effect was only significant in females, partially explaining the sex gap in CMD prevalence.
Body composition describes the plasma lipidome more accurately than whole-body measures, such as BMI. Increased VAT is associated with pro-atherogenic lipids and lipoproteins that likely contribute to increased CMD risk. Such insights aid the understanding of metabolic changes underpinning CMD risk, with potential to improve risk prediction and identify potential interventions to improve clinical outcomes.