Genetic and Behavioral Predictors of Long-Term Weight Loss Maintenance: A Systematic Review of Evidence From Observational and Genetic Studies
Bibliographic record
Abstract
BACKGROUND: Long-term weight loss maintenance remains a significant challenge in obesity management, despite advances in behavioral, dietary, and medical interventions. The objective of this study is to identify consistent genetic and behavioral predictors associated with sustained weight loss in adults with overweight or obesity. METHODS: We searched PubMed, Embase, PsycINFO, Web of Science, and Google Scholar for peer-reviewed studies published from January 2010 to April 2025. Eligible studies included observational and genetic investigations involving adults who maintained at least 10% weight loss for one year or more. Quality was assessed using the Newcastle-Ottawa Scale (NOS) and genetic validity criteria. A thematic synthesis categorized predictors as behavioral or genetic. RESULTS: 24 studies met the inclusion criteria (15 observational, nine genetic). Consistent behavioral predictors included increased physical activity, dietary restraint, low disinhibition, and improved psychological health. Genetically, FTO risk alleles and higher polygenic risk scores were associated with weight regain; however, structured behavioral interventions mitigated this effect. Specific gene variants (e.g., PPARγ, TIMP4) were linked to enhanced weight loss outcomes in response to multidisciplinary interventions. CONCLUSION: Both genetic and behavioral factors independently and interactively influence long-term weight loss maintenance. Integrating genetic risk profiling with personalized behavioral strategies may improve obesity treatment outcomes.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".