Association of functionally significant Melanocortin-4 but not Melanocortin-3 receptor mutations with severe adult obesity in a large North American case-control study
Bibliographic record
Abstract
Functionally significant heterozygous mutations in the Melanocortin-4 receptor (MC4R) have been implicated in 2.5% of early onset obesity cases in European cohorts. The role of mutations in this gene in severely obese adults, particularly in smaller North American patient cohorts, has been less convincing. More recently, it has been proposed that mutations in a phylogenetically and physiologically related receptor, the Melanocortin-3 receptor (MC3R), could also be a cause of severe human obesity. The objectives of this study were to determine if mutations impairing the function of MC4R or MC3R were associated with severe obesity in North American adults. We studied MC4R and MC3R mutations detected in a total of 1821 adults (889 severely obese and 932 lean controls) from two cohorts. We systematically and comparatively evaluated the functional consequences of all mutations found in both MC4R and MC3R. The total prevalence of rare MC4R variants in severely obese North American adults was 2.25% (CI(95%): 1.44-3.47) compared with 0.64% (CI(95%): 0.26-1.43) in lean controls (P < 0.005). After classification of functional consequence, the prevalence of MC4R mutations with functional alterations was significantly greater when compared with controls (P < 0.005). In contrast, the prevalence of rare MC3R variants was not significantly increased in severely obese adults [0.67% (CI(95%): 0.27-1.50) versus 0.32% (CI(95%): 0.06-0.99)] (P = 0.332). Our results confirm that mutations in MC4R are a significant cause of severe obesity, extending this finding to North American adults. However, our data suggest that MC3R mutations are not associated with severe obesity in this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".