Loss‐of‐Function Mutations in MC4R Are Very Rare in the Greek Severely Obese Adult Population
Bibliographic record
Abstract
Melanocortin-4 receptor (MC4R) loss-of-function mutations are the commonest genetic cause of human monogenic obesity, so far. The contribution of MC4R coding mutations to severe obesity in the high-obesity prone Greek population has not been investigated to date. We determined the MC4R coding sequence of 510 obese and 469 lean control subjects of Greek origin, and we estimated the prevalence and the penetrance on obesity of MC4R loss-of-function mutations. The functional impact of novel nonsynonymous variants detected was investigated in vitro. We found two novel synonymous mutations (L23L and I102I), four nonsynonymous mutations (T112M, S127L, N274S, and S295L), and two polymorphisms (V103I and I251L) previously described in literature. We also detected a novel mutation (L207V) in a severely obese 69-year-old female patient, although the mutation did not cosegregate with obesity in the corresponding pedigree and had no functional consequences on MC4R protein function. Loss-of-function mutations represented 75% of all nonsynonymous rare mutations identified among lean carriers and only 25% among obese subjects (P = 0.0001). The prevalence of loss-of-function mutations was lower in the obese group than in lean control subjects (0.20 vs. 0.64%) but this difference was not significant. Therefore, the estimated penetrance of deleterious MC4R mutations was very low (6.3%) in heterozygous Greek carriers of MC4R loss-of-function mutations. Our data suggest that MC4R loss-of-function mutations are rare in the Greek population. MC4R genetic deficiency is unlikely to explain the high propensity to develop severe obesity in this specific 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".