Risk factors for adolescent obesity in LMICs: a meta-analysis using multiple adiposity indicators
Bibliographic record
Abstract
INTRODUCTION: Adolescent obesity is an escalating public health challenge in low- and middle-income countries (LMICs). Most evidence has relied on body mass index (BMI), which may underestimate central adiposity. We conducted a systematic review and meta-analysis to synthesize risk factors using multiple anthropometric indicators. CONTENT: A systematic search of PubMed, Scopus, and Web of Science was conducted for studies published between January 2013 and December 2023. Studies were included if they reported adolescent obesity risk factors, were peer-reviewed, and published in English. Studies that did not assess risk factors, review articles, editorials, case reports, and animal studies were excluded. Data were extracted and synthesized both narratively and quantitatively, and the risk of bias of included studies was assessed using the Newcastle Ottawa Scale. From 196,775 records, 21 studies were included (n≈46,000 adolescents). Significant risk factors were genetic predisposition (OR 1.80; 95 % CI 1.35-2.40), socioeconomic status (OR 1.31; 95 % CI 1.13-1.52), unhealthy dietary patterns (OR 2.07; 95 % CI 1.11-3.88), environmental exposures (OR 1.25; 95 % CI 1.09-1.44), low physical activity (OR 1.14; 95 % CI 1.03-1.27), and psychosocial stress (OR 1.29; 95 % CI 1.08-1.54). Subgroup analyses revealed that the waist-to-height ratio was the most consistent predictor of obesity risk, whereas BMI exhibited more heterogeneous associations. Regional disparities were evident, with stronger associations in East Asia and Latin America. SUMMARY: Adolescent obesity in LMICs arises from intersecting biological, behavioral, and social determinants. The waist-to-height ratio may provide a more accurate measure of adiposity than the BMI. Effective prevention requires multisectoral policies addressing unhealthy diets, limited physical activity, psychosocial stress, and obesogenic environments. OUTLOOK: Further research is expected to involve interventions to address obesity in LMICs by considering measurements using the waist-to-height ratio to measure adiposity rather than using BMI.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.067 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".