ArticleMedicine2025
Obesity genetic association prospective cohort utilizing single center real-world evidence data.
Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Obesity is a complex disease resulting from the interaction of genetic and environmental factors. In this study, 414 single nucleotide polymorphism (SNPs) were analyzed in DNA samples obtained from 48 obese patients and 50 healthy controls of Turkish origin to identify genetic variants associated with obesity. Genotype frequency analysis revealed 18 variants significantly or near-significantly associated with obesity. Among these, rs12199580 (PNPLA1), rs34911341 (GHRL), and rs116843064 (ANGPTL4) emerged as novel candidate variants not previously reported in the context of obesity. Functional annotation analyses confirmed that most of the significant variants were located in exonic or regulatory regions, and the related genes were primarily involved in neuroendocrine control, lipid metabolism, and energy homeostasis. Pathway enrichment analysis indicated significant overrepresentation of pathways such as PPAR-alpha-regulated lipid metabolism, ghrelin synthesis and secretion, and cholesterol transport, which are all closely linked to obesity pathophysiology. Polygenic risk score models constructed from the significant SNPs demonstrated a markedly increased genetic risk burden when rare high-effect variants were included. In regression analyses adjusted for age, sex, and Body Mass Index (BMI), the variant rs17024258 in the GNAT2 gene maintained a statistically significant and independent association with BMI (P < .02), whereas most other variants lost significance after covariate adjustment. Furthermore, certain variants were found to exhibit markedly different allele frequencies in the Turkish cohort compared to global reference populations, highlighting potential population-specific genetic architecture. This study contributes to the identification of both previously known and novel genetic variants associated with obesity and underscores the importance of population-specific genomic data in understanding genetic predisposition to complex diseases such as obesity.
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