Circulating levels of DDIT4 and mTOR, and contributions of BMI, inflammation and insulin sensitivity in hyperlipidemia.

Evidence shows a high incidence of insulin resistance, inflammation and excess body mass index (BMI) in adults with hyperlipidemia. The present study aimed to determine the circulating levels of DNA damage inducible transcript 4 (DDIT4) and mTOR and assess the contributions of lipids, inflammatory markers, insulin sensitivity and BMI in hyperlipidemia. The study subjects were divided into a hyperlipidemia group and a normal control group (n=55 per group). Sex, age, blood pressure, waist circumference (WC), height, weight and BMI were recorded. Fasting venous blood samples were collected and an automatic biochemical analyzer was used to detect fasting blood glucose (FBG), fasting insulin (FINS), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C). Quantitative ELISA kits were used to determine the levels of DDIT4, mTOR and inflammatory markers and calculate the homeostatic model assessment of insulin resistance (HOMA-IR). Compared with the normal control group, the hyperlipidemia group had significantly increased blood pressure, WC, weight, BMI, FBG, FINS, HOMA-IR, mTOR and inflammatory markers, but significantly reduced DDIT4. A concurrent correlation analysis showed that insulin resistance was positively correlated with blood pressure, BMI, lipid profiles (TG, TC, LDL-C), mTOR and inflammatory markers, but negatively correlated with HDL-C and DDIT4. Lipid profiles were positively correlated with BMI, mTOR and inflammatory markers, but negatively correlated with DDIT4. A factor analysis identified four domains in hyperlipidemia (inflammation-lipid 1 domain, 44.429%; overweight domain, 21.695%; insulin sensitivity domain, 11.782%; lipid 2 domain, 6.723%). In conclusion, people with hyperlipidemia have elevated mTOR and reduced DDIT4 and are accompanied by abnormal indicators such as insulin sensitivity, BMI and inflammatory factors. The identified domains may be applied to predict the outcomes of cardiovascular diseases and metabolic diseases in the future.