Leptin Signaling: Decoding of Genetic Pathways using Bioinformatics; Shaping Bariatric Surgery Outcomes
Bibliographic record
Abstract
Background: Leptin, a hormone central to energy homeostasis and appetite regulation, plays a pivotal role in obesity and metabolic health. Single nucleotide polymorphisms (SNPs) in the leptin (LEP) and leptin receptor (LEPR) genes influence leptin signaling and may explain variability in outcomes following bariatric surgery. This bioinformatics-driven study examines the role of LEP and LEPR SNPs in modulating weight loss, metabolic changes, and hormonal responses post-surgery. Methods: A total of 55 leptin SNPs and 216 leptin receptor SNPs were assessed for functional impact using SIFT, PolyPhen-2, and Mutation Assessor. Pathway enrichment analyses using DAVID and g:Profiler identified biological processes and signaling pathways linked to leptin function. Protein-protein interaction (PPI) networks were constructed via STRING and visualized in Cytoscape to explore molecular interactions. Statistical models evaluated associations between SNPs and surgical outcomes, including weight loss and metabolic improvements. Key pathways with false discovery rates (FDR) < 0.01 were highlighted to emphasize significance. Results: Bioinformatics analyses revealed LEP and LEPR as critical variants associated with bariatric surgery outcomes. Specifically, LEP rs7799039 G allele carriers exhibited diminished weight loss (p < 0.05) and metabolic improvements. Functional prediction tools consistently indicated deleterious effects on leptin signaling. Pathway enrichment analyses identified leptin's involvement in critical pathways, including the adipocytokine signaling pathway (hsa04920, 2 of 68 genes, strength = 2.46, FDR = 0.0042)," "AMPK signaling pathway (hsa04152, 2 of 120 genes, strength = 2.22, FDR = 0.0064)," and "non-alcoholic fatty liver disease (NAFLD) pathway (hsa04932, 2 of 146 genes, strength = 2.13, FDR = 0.0064). PPI networks underscored leptin’s interactions with key metabolic and inflammatory regulators, such as TNF-α and IL-6, suggesting a broader impact on energy metabolism and inflammation. Conclusion: This study demonstrates the utility of bioinformatics in elucidating the genetic basis of variable bariatric surgery outcomes. LEP and LEPR SNPs modulate critical pathways influencing weight loss and metabolic responses. Integrating genetic insights with bariatric care could advance precision medicine approaches for obesity management. Future studies with larger cohorts are warranted to confirm these findings and strengthen predictive models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".