Fat distribution measurements by chemical shift‐encoded transition region extraction predict the risk of hyperglycaemia, dyslipidaemia and metabolic syndrome in mice

Abstract

AbstractMetabolically healthy or unhealthy obesity is closely related to metabolic syndrome (MetS). To validate a more accurate diagnostic method for obesity that reflects the risk of metabolic disorders in a pre‐clinical mouse model, C57BL/6J mice were fed high‐sucrose–high‐fat and chow diets for 12 weeks to induce obesity. MRI was performed and analysed by chemical shift‐encoded fat–water separation based on the transition region extraction method. Abdominal fat was divided into upper and lower abdominal regions at the horizontal lower border of the liver. Blood samples were collected, and the glucose level, lipid profile, liver function, HbA1c and insulin were tested. k‐means clustering and stepwise logistic regression were applied to validate the diagnosis of hyperglycaemia, dyslipidaemia and MetS, and to ascertain the predictive effect of MRI‐derived parameters to the metabolic disorders. Pearson or Spearman correlation was used to assess the relationship between MRI‐derived parameters and metabolic traits. The receiver‐operating characteristic curve was used to evaluate the diagnostic effect of each logistic regression model. A two‐sided p value less than 0.05 was considered to indicate statistical significance for all tests. We made the precise diagnosis of obesity, dyslipidaemia, hyperglycaemia and MetS in mice. In all, 14 mice could be diagnosed as having MetS, and the levels of body weight, HbA1c, triglyceride, total cholesterol and low‐density lipoprotein cholesterol were significantly higher than in the normal group. Upper abdominal fat better predicted dyslipidaemia (odds ratio, OR = 2.673; area under the receiver‐operating characteristic curve, AUCROC = 0.9153) and hyperglycaemia (OR = 2.456; AUCROC = 0.9454), and the abdominal visceral adipose tissue (VAT) was better for predicting MetS risk (OR = 1.187; AUCROC = 0.9619). We identified the predictive effect of fat volume and distribution in dyslipidaemia, hyperglycaemia and MetS. The upper abdominal fat played a better predictive role for the risk of dyslipidaemia and hyperglycaemia, and the abdominal VAT played a better predictive role for the risk of MetS.