Implication of Leptin and Leptin Receptor Gene Variations in Type 2 Diabetes Mellitus: A Case-Control Study
Bibliographic record
Abstract
Background: Global increase in the prevalence of type 2 diabetes mellitus (T2DM) has affected about 6% of the population and is one of the major healthcare challenges worldwide. Apart from other factors, genetics play a pivotal role in the development of diabetes. Recent studies have drawn attention to the role of leptin ( LEP ) and leptin receptor ( LEPR ) gene polymorphisms in the pathogenesis of T2DM, that being the reason for the uptake of this study. Methods: A total of 390 T2DM cases and 408 controls matched with respect to age and gender were taken for the study. Biochemical analysis was performed on all study subjects. Polymerase chain reaction (PCR) amplification of the genomic regions encompassing the single nucleotide polymorphisms (SNPs) under study was followed by digestion using specific restriction enzymes to analyze the SNP genotype through restriction fragment length polymorphism (RFLP). Results: Serum leptin levels were elevated in 57.9% (226 of 390) of cases as compared to 11.8% (48 of 408) of controls (P < 0.0001). Cases had significant homeostatic model assessment-insulin resistance (HOMA-IR) as compared to controls (5.3 ± 5.9 vs. 1.4 ± 0.4; P < 0.0001). In case of LEP G2548A SNP, the frequency of a variant genotype (GA + AA) was found to be higher for cases than controls (69.7% vs. 29.4%; P < 0.0001). For LEPR Q223R SNP, the frequency of a variant genotype (AG + GG) was found to be higher for cases than controls (69.2% vs. 23.6%; P < 0.0001). Conclusion: We observed a significant association between the LEP/LEPR polymorphisms and T2DM in the ethnic population of Kashmir indicating that genetic susceptibility may play an important role in the pathogenesis of T2DM. J Endocrinol Metab. 2022;12(1):19-31 doi: https://doi.org/10.14740/jem785
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".