Genetic Polymorphisms Associated with Obesity in Southeast Asian Populations: A Systematic Review without Meta-Analysis
Bibliographic record
Abstract
Obesity is a growing global public health challenge, with genetic factors playing a crucial role in its development. This review synthesises findings from Southeast Asian studies to investigate the association between gene polymorphisms and obesity risk across various ethnic populations. A comprehensive search of three databases, PubMed, Scopus, and Web of Science, initially retrieved 2,021 articles, from which 25 studies were meticulously selected based on stringent inclusion and exclusion criteria. The quality of the studies was assessed through the Newcastle-Ottawa Scale (NOS), a risk bias tool. These studies encompass 8,312 participants and examined 33 single nucleotide polymorphisms (SNPs). UCP polymorphism demonstrated a significant association with overall adiposity (OR = 2.02, P = 0.01) in Malaysian women, while the rs659366 UCP2 was linked to weight gain in an Indonesian cohort. LEP variants were not significantly associated with obesity in Malaysians, and FTO variants showed mixed results, with rs9939609 (OR = 3.72, P = 0.009) and rs1421085 (OR = 3.22, P < 0.001) variants being associated with obesity and higher body mass index (BMI) in Indonesians, but no significant findings in Malaysians. These results emphasise the genetic diversity within Southeast Asia and the challenges in replicating genetic associations across populations. To address these inconsistencies and improve our understanding of obesity in Southeast Asia, there is a pressing need for more extensive and diverse cohort studies, complemented by comprehensive genome-wide association studies (GWAS), to identify robust obesity biomarkers in Southeast Asia.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".