Association between leptin and NAFLD: A Two-Sample Mendelian randomization study

Abstract

Background Nonalcoholic fatty liver disease (NAFLD) etiology involves a complex interaction of genetic and environmental factors. Previous observational studies have suggested that increased leptin levels may be associated with a low risk of developing NAFLD, but the causal relationship remains unclear. Due to advances in genome-wide association studies (GWAS) and the discovery of Mendelian randomization (MR), we aimed to investigate the causal effect of leptin and NAFLD using MR analysis. Methods We performed a two-sample Mendelian randomization analysis (TSMR) using summary GWAS data from leptin (up to 50,321 individuals) and NAFLD (894 cases and 217,898 controls) in a European population. Genetic instrumental variables (InstrumentalVariables, IVs) that satisfied the three core assumptions of Mendelian randomization were selected. TSMR analysis was performed using the Inverse Variance Weighted (IVW) method, MR-Egger regression method, and Weighted Median (WM) method. To ensure the accuracy and stability of the study results, heterogeneity tests, multiple validity tests, and sensitivity analyses were conducted. Results The results of TSMR correlation analysis between NAFLD and leptin were IVW (OR: 0.3032; 95% CI 0.1181–0.7783; P = 0.0131), WM method (OR: 0.2816; 95% CI 0.0931–0.8678; P = 0.0273), MR-Egger regression method (P = 0.6955), and Among them, the IVW method and WM method showed P > 0.05, and the results were statistically significant. In addition, TSMR correlation analysis between NAFLD and circulating leptin levels adjusted for Body Mass Index (BMI) resulted in IVW (OR: 0.4416; 95% CI 0.2807–0.6948; P = 0.0004), WM method (OR: 0.4194; 95% CI 0.2279–0.7720; P = 0.0052), MR-Egger regression method (OR: 0.2023; 95% CI 0.0541–0.7562; P = 0.0389), P > 0.05, and the results were statistically significant. It is further demonstrated that increased leptin is causally associated with reduced risk of NAFLD, and leptin may serve as a protective factor for NAFLD. Conclusions In this study, we explored the causal association between leptin and NAFLD from a genetic perspective based on the GWAS database using TSMR analysis. Further studies are needed to explain the underlying mechanisms.

Mendelian randomization identifies 1400 metabolites that may be pathogenic candidates for non-alcoholic fatty liver disease

“…It was also found that lithocholic acid in NAFLD rats was much higher than that in normal rats through the regulation of intestinal ora and related lipid/amino acid metabolism, while diosgenin signi cantly reduced lithocholic acid, thus elucidating the mechanism by which diosgenin ameliorates NAFLD[60]. In addition, our ndings showed a bidirectional causal association between bilirubin levels and NAFLD, with an increased risk of NAFLD progression as bilirubin levels increased, which is consistent with our previous ndings [24]. However, there are fewer studies on the direct correlation between bilirubin and NAFLD.…”

“…In particular, metabolomics, a component of systems biology, can provide insight into the biological mechanisms of disease by identifying modi ed metabolites or metabolic pathways.Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systemslevel effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases [23].Previously, our team conducted a two-sample MR study [24]. The study involved 7824 participants and this genome-wide association study (GWAS) provided data on 486 human blood metabolites [25].…”

Causal relationships between delirium and Parkinson’s Disease: a bidirectional two-sample Mendelian randomization study

“…Further research should explore these potential variations when strati ed GWAS pooled data become accessible. Third, despite the comprehensive sensitivity analyses conducted to test MR study hypotheses, complete elimination of the possibility of horizontal pleiotropy among instrumental variables remains challenging 41 . Moreover, differences in gene annotation analysis platforms across GWAS cohort studies may have contributed to the heterogeneity of this study 42 .…”