Obesity is a well-established risk factor for various diseases, but the mechanisms through which it influences disease development remain unclear. Using Mendelian randomization (MR) analysis, we examined the causal relationship between BMI, 249 metabolic traits, and cholelithiasis. BMI data were obtained from four sources, and cholelithiasis data were from two distinct datasets. We analyzed the direct effect of BMI on cholelithiasis and identified key metabolic mediators. BMI was found to be positively associated with the risk of cholelithiasis across all datasets analyzed. A total of 176 metabolites were identified to be significantly associated with BMI, including amino acids, cholesterol esters, free cholesterol, triglycerides, and phospholipids. Among these, 49 metabolites were identified as mediators in the BMI-cholelithiasis relationship. Specifically, fatty acid levels, cholesteryl esters, phospholipids, triglycerides, and free cholesterol were key mediators in this relationship, with mediation proportions ranging from - 2.38-7.14%. This study provides robust evidence that BMI significantly impacts metabolic biomarkers, which in turn affect the risk of cholelithiasis. These findings highlight the importance of managing BMI to mitigate metabolic dysfunction and reduce the risk of gallstone formation. Future research should explore the specific metabolic pathways involved to identify potential therapeutic targets.
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http://dx.doi.org/10.1038/s41598-024-83217-6 | DOI Listing |
Food Chem
December 2024
Department of Food Science and Technology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China. Electronic address:
Atemoya fruit deteriorates rapidly during post-harvest storage. A complete understanding of the metabolic mechanisms underlying this process is crucial for developing effective preservation strategies. Metabolomic approaches combined with machine learning offer new opportunities to identify quality-related biomarkers.
View Article and Find Full Text PDFJ Biochem Mol Toxicol
January 2025
Department of Biochemistry and Molecular Biology, Kunming Medical University, Kunming, China.
This study investigates the metabolic disruptions caused by nicotine (NIC) exposure, with a particular focus on amino acid and lipid metabolism, and evaluates resveratrol (RSV) as a potential protective agent. Mice were divided into four groups: control (CON), NIC-exposed, NIC + RSV-treated, and RSV-only. NIC exposure resulted in significant weight loss, elevated glucose levels, altered lipid profiles, and organ damage, particularly in the liver and kidneys.
View Article and Find Full Text PDFActa Neuropathol Commun
January 2025
Department of Biological Sciences, Purdue University, 915 Mitch Daniels Blvd, West Lafayette, IN, USA.
Dementia refers to an umbrella phenotype of many different underlying pathologies with Alzheimer's disease (AD) being the most common type. Neuropathological examination remains the gold standard for accurate AD diagnosis, however, most that we know about AD genetics is based on Genome-Wide Association Studies (GWAS) of clinically defined AD. Such studies have identified multiple AD susceptibility variants with a significant portion of the heritability unexplained and highlighting the phenotypic and genetic heterogeneity of the clinically defined entity.
View Article and Find Full Text PDFEnviron Pollut
January 2025
School of Medicine, Taizhou University, Taizhou 318000, China.
Allergic asthma is a significant international concern in respiratory health, which can be exacerbated by the increasing levels of non-allergenic pollutants. This rise in airborne pollutants is a primary driver behind the growing prevalence of asthma, posing a health emergency. Additionally, climatic risk factors can contribute to the onset and progression of asthma.
View Article and Find Full Text PDFLung Cancer
January 2025
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China. Electronic address:
Neuron-specific enolase (NSE) is one of the most common biomarkers of small cell lung cancer (SCLC) and is widely used in lung cancer screening. But its specificity is affected by many factors. Using residual correction and machine learning, corrected NSE and its reference range were constructed based on metabolic factors and smoking history affecting NSE in the training set of 48,009 healthy individuals recruited from the First Affiliated Hospital of Nanjing Medical University.
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