RESUMO
Magnesium and calcium are elements that have been associated with cardiometabolic risk factors related to metabolic syndrome (MetS). However, there are gaps in the knowledge regarding the impact of the calcium to magnesium (Ca/Mg) ratio in plasma. Thus, we aim to evaluate the associations between magnesium and calcium levels in plasma, and the Ca/Mg ratio in plasma with MetS components and other cardiometabolic risk factors. This cross-sectional study was carried out with 112 adults and older people, distributed into groups with (n = 60) and without MetS (n = 52). We evaluated sociodemographic, anthropometric, and biochemical data. Magnesium and calcium levels in plasma were measured by inductively coupled plasma mass spectrometry technique (ICP-MS). There was a high frequency of MetS, with no significant differences in magnesium and calcium levels and Ca/Mg ratio in plasma observed between groups. There were no associations between magnesium and MetS components or other cardiometabolic risk factors (all p > 0.05). Calcium levels were associated with total cholesterol (ß = - 0.020; p = 0.000) and high-density lipoprotein cholesterol (HDL-c) (ß = - 0.046; p = 0.005). The total cholesterol (ß = - 0.025; p = 0.000) and low-density lipoprotein cholesterol (LDL-c) (ß = 0.017; p = 0.020) were preditors of the Ca/Mg ratio. These results indicate important associations of calcium and the Ca/Mg ratio in plasma with cardiometabolic risk factors related to MetS.
RESUMO
Lipidomics studies have indicated an association between obesity and lipid metabolism dysfunction. This study aimed to evaluate and compare cardiometabolic risk factors, and the lipidomic profile in adults and older people. A cross-sectional study was conducted with 72 individuals, divided into two sex and age-matched groups: obese (body mass index-BMI ≥ 30 kg/m2; n = 36) and non-obese (BMI < 30 kg/m2; n = 36). The lipidomic profiles were evaluated in plasma using 1H nuclear magnetic resonance (1H-NMR) spectroscopy. Obese individuals had higher waist circumference (p < 0.001), visceral adiposity index (p = 0.029), homeostatic model assessment insulin resistance (HOMA-IR) (p = 0.010), and triacylglycerols (TAG) levels (p = 0.018). 1H-NMR analysis identified higher amounts of saturated lipid metabolite fragments, lower levels of unsaturated lipids, and some phosphatidylcholine species in the obese group. Two powerful machine learning (ML) models-k-nearest neighbors (kNN) and XGBoost (XGB) were employed to characterize the lipidomic profile of obese individuals. The results revealed metabolic alterations associated with obesity in the NMR signals. The models achieved high accuracy of 86% and 81%, respectively. The feature importance analysis identified signal at 1.50-1.60 ppm (-CO-CH2-CH2-, Cholesterol and fatty acid in TAG, Phospholipids) to have the highest importance in the two models.
