Association between basal metabolic rate and cardio-metabolic risk factors: Evidence from a Mendelian Randomization study

基础代谢率与心血管代谢风险因素之间的关联:来自孟德尔随机化研究的证据

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Abstract

BACKGROUND: Cardio-metabolic risk factors play a crucial role in the development of cardiovascular and metabolic diseases. Basal metabolic rate (BMR) is a fundamental physiological parameter that affects energy expenditure and might contribute to variations in these risk factors. However, the exact relationship between BMR and cardio-metabolic risk factors has remained unclear. METHODS: We employed Mendelian Randomization (MR) analysis to explore the association between BMR (N: 534,045) and various cardio-metabolic risk factors, including body mass index (BMI, N: 681,275), fasting glucose (N: 200,622), high-density lipoprotein (HDL) cholesterol (N = 403,943), low-density lipoprotein (LDL) cholesterol (N = 431,167), total cholesterol (N: 344,278), and triglycerides (N: 441,016), C-reactive protein (N: 436,939), waist circumference (N: 232,101), systolic blood pressure (N: 810,865), diastolic blood pressure (N: 810,865), glycated haemoglobin (N: 389,889), and N-terminal prohormone brain natriuretic peptide (N: 21,758). We leveraged genetic variants strongly associated with BMR as instrumental variables to investigate potential causal relationships, with the primary analysis using the Inverse Variance Weighted (IVW) method. RESULTS: Our MR analysis revealed compelling evidence of a causal link between BMR and specific cardio-metabolic risk factors. Specifically, genetically determined higher BMR was associated with an increased BMI (β = 0.7538, 95% confidence interval [CI]: 0.6418 to 0.8659, p < 0.001), lower levels of HDL cholesterol (β = -0.3293, 95% CI: 0.4474 to -0.2111, p < 0.001), higher levels of triglycerides (β = 0.1472, 95% CI: 0.0370 to 0.2574, p = 0.0088), waist circumference (β = 0.4416, 95% CI: 0.2949 to 0.5883, p < 0.001), and glycated haemoglobin (β = 0.1037, 95% CI: 0.0080 to 0.1995, p = 0.0377). However, we did not observe any significant association between BMR and fasting glucose, LDL cholesterol, total cholesterol, C-reactive protein, systolic blood pressure, diastolic blood pressure, or N-terminal prohormone brain natriuretic peptide (all p-values>0.05). CONCLUSION: This MR study provides valuable insights into the relationship between BMR and cardio-metabolic risk factors. Understanding the causal links between BMR and these factors could have important implications for the development of targeted interventions and therapies.

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