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Untargeted metabolomics reveals plasma metabolites predictive of ectopic fat in pancreas and liver as assessed by magnetic resonance imaging: the TOFI_Asia study.

Food Nutrition & Health, Food and Bio-based Products, AgResearch Limited, Palmerston North, New Zealand. School of Health Sciences, Massey University, Palmerston North, New Zealand. High-Value Nutrition National Science Challenge, Auckland, New Zealand. Food Nutrition & Health, Food and Bio-based Products, AgResearch Limited, Palmerston North, New Zealand. karl.fraser@agresearch.co.nz. High-Value Nutrition National Science Challenge, Auckland, New Zealand. karl.fraser@agresearch.co.nz. Riddet Institute, Massey University, Palmerston North, New Zealand. karl.fraser@agresearch.co.nz. Riddet Institute, Massey University, Palmerston North, New Zealand. Human Nutrition Unit, School of Biological Sciences, University of Auckland, Auckland, New Zealand. Department of Surgery, University of Auckland, Auckland, New Zealand. Department of Medicine, University of Auckland, Auckland, New Zealand. School of Biological Sciences University of Auckland, Auckland, New Zealand. Centre for Advanced Discovery and Experimental Therapeutics, School of Medical Sciences, University of Manchester, Manchester, UK. Aix-Marseille University, INSERM, INRAe, C2VN, BioMeT, Marseille, France. Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.

International journal of obesity (2005). 2021;(8):1844-1854
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Abstract

BACKGROUND Excess visceral obesity and ectopic organ fat is associated with increased risk of cardiometabolic disease. However, circulating markers for early detection of ectopic fat, particularly pancreas and liver, are lacking. METHODS Lipid storage in pancreas, liver, abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) from 68 healthy or pre-diabetic Caucasian and Chinese women enroled in the TOFI_Asia study was assessed by magnetic resonance imaging/spectroscopy (MRI/S). Plasma metabolites were measured with untargeted liquid chromatography-mass spectroscopy (LC-MS). Multivariate partial least squares (PLS) regression identified metabolites predictive of VAT/SAT and ectopic fat; univariate linear regression adjusting for potential covariates identified individual metabolites associated with VAT/SAT and ectopic fat; linear regression adjusted for ethnicity identified clinical and anthropometric correlates for each fat depot. RESULTS PLS identified 56, 64 and 31 metabolites which jointly predicted pancreatic fat (R2Y = 0.81, Q2 = 0.69), liver fat (RY2 = 0.8, Q2 = 0.66) and VAT/SAT ((R2Y = 0.7, Q2 = 0.62)) respectively. Among the PLS-identified metabolites, none of them remained significantly associated with pancreatic fat after adjusting for all covariates. Dihydrosphingomyelin (dhSM(d36:0)), 3 phosphatidylethanolamines, 5 diacylglycerols (DG) and 40 triacylglycerols (TG) were associated with liver fat independent of covariates. Three DGs and 12 TGs were associated with VAT/SAT independent of covariates. Notably, comparison with clinical correlates showed better predictivity of ectopic fat by these PLS-identified plasma metabolite markers. CONCLUSIONS Untargeted metabolomics identified candidate markers of visceral and ectopic fat that improved fat level prediction over clinical markers. Several plasma metabolites were associated with level of liver fat and VAT/SAT ratio independent of age, total and visceral adiposity, whereas pancreatic fat deposition was only associated with increased sulfolithocholic acid independent of adiposity-related parameters, but not age.