Gain‐of‐function variants in the melanocortin 4 receptor gene confer susceptibility to binge eating disorder in subjects with obesity: a systematic review and meta‐analysis
Bibliographic record
Abstract
Summary The association between coding variants in the melanocortin 4 receptor gene (MC4R) and binge eating disorder (BED) in patients with obesity is controversial. Two independent reviewers systematically searched MEDLINE, Embase, PsycINFO, BIOSIS Previews, Web of Science Core Collection and Google Scholar up to February 2018, using terms describing the MC4R gene and BED. Six of 103 identified references were included. Studies examined associations between at least one coding variant/mutation in MC4R and BED and screened for BED as per the Diagnostic and Statistical Manual of Mental Disorders. Risk of bias was assessed using a modified version of the Q‐Genie tool, and overall quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation guidance. Meta‐analysis was conducted via logistic regression models. A positive association between gain‐of‐function (GOF) variants in the MC4R and BED was observed (odds ratio [OR] = 3.05; 95% confidence interval [CI]: 1.82, 5.04; p = 1.7 × 10−5), while no association was detected between loss‐of‐function (LOF) mutations and BED (OR = 1.50; 95% CI: 0.73, 2.96; p = 0.25). Similar results were found after accounting for study quality (GOF variants: OR = 3.15; 95% CI: 1.76, 5.66; p = 1.1 × 10−4; LOF mutations: OR = 1.50; 95% CI: 0.73, 2.97; p = 0.25). Our systematic review and meta‐analysis provides evidence that GOF variants as opposed to LOF mutations in MC4R are associated with BED in subjects with obesity.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".