Association between plasma magnesium levels and glycolipid metabolism in a southern Chinese population: a cross-sectional study in Shenzhen

Background: Numerous studies have delved into the relationship between magnesium (Mg) and metabolic diseases, however, the impact of Mg on novel glycolipid metabolic indicators remains largely unexplored. This study aims to conduct a comprehensive evaluation of the relationship between plasma Mg levels and glycolipid metabolism among a general population in Shenzhen, China. Methods: A cross-sectional study was performed in 1,429 adults who underwent medical check-ups at a hospital in Shenzhen, China. Plasma Mg levels were measured using inductively coupled plasma mass spectrometry (ICP-MS). To investigate the association between plasma Mg levels and glycolipid metabolism indicators, the multivariate linear and logistic regression models, along with the restricted cubic spline (RCS) model were employed. Results: Regarding to the glucose indicators, plasma Mg showed a negative linear association with the triglyceride-glucose index adjusted for Body Mass Index (TyG-BMI) and a positive linear association with the single point insulin sensitivity estimator (SPISE), while demonstrating a negative non-linear association with fasting blood glucose (FBG) and the metabolic score for insulin resistance (METS-IR). For lipid indicators, Mg exhibited a negative linear association with the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol ratio (HDL-c), while the LDL-c/HDL-c ratio showed a V-shaped non-linear relationship with Mg. Furthermore, the level of Mg exhibited a negative linear association with diabetes and a V-shaped non-linear association with hyperlipidemia. Conclusions: Mg plays a critical role in the glucose and lipid metabolism, particularly highlighting its association with the novel indicators for glycolipid metabolism. The results of our study need to be confirmed in large-scale prospective research in the future.