Adipose tissue morphology, imaging and metabolomics predicting cardiometabolic risk and family history of type 2 diabetes in non-obese men.
Aidin RawshaniBjörn EliassonAraz RawshaniJosefin HenningerAdil MardingluÅsa CarlssonMaja SohlinMaria LjungbergAnn HammarstedtAnnika RosengrenUlf SmithPublished in: Scientific reports (2020)
We evaluated the importance of body composition, amount of subcutaneous and visceral fat, liver and heart ectopic fat, adipose tissue distribution and cell size as predictors of cardio-metabolic risk in 53 non-obese male individuals. Known family history of type 2 diabetes was identified in 25 individuals. The participants also underwent extensive phenotyping together with measuring different biomarkers and non-targeted serum metabolomics. We used ensemble learning and other machine learning approaches to identify predictors with considerable relative importance and their intricate interactions. Visceral fat and age were strong individual predictors of ectopic fat accumulation in liver and heart along with markers of lipid oxidation and reduced glucose tolerance. Subcutaneous adipose cell size was the strongest individual predictor of whole-body insulin sensitivity and also a marker of visceral and ectopic fat accumulation. The metabolite 3-MOB along with related branched-chain amino acids demonstrated strong predictability for family history of type 2 diabetes.
Keyphrases
- adipose tissue
- insulin resistance
- type diabetes
- body composition
- high fat diet
- machine learning
- single cell
- heart failure
- glycemic control
- mass spectrometry
- fatty acid
- metabolic syndrome
- cardiovascular disease
- bone mineral density
- resistance training
- skeletal muscle
- cancer therapy
- big data
- drug delivery
- amino acid
- high throughput
- convolutional neural network
- deep learning