Contribution of rare coding mutations in CD36 to type 2 diabetes and cardio-metabolic complications
Bibliographic record
Abstract
We sequenced coding regions of the cluster of differentiation 36 (CD36) gene in 184 French individuals of European ancestry presenting simultaneously with type 2 diabetes (T2D), arterial hypertension, dyslipidemia, and coronary heart disease. We identified rare missense mutations (p.Pro191Leu/rs143150225 and p.Ala252Val/rs147624636) in two heterozygous cases. The two CD36 mutation carriers had no family history of T2D and no clustering of cardio-metabolic complications. While the p.Pro191Leu mutation was found in 84 heterozygous carriers from five ethnic groups from the genome aggregation database (global frequency: 0.0297%, N = 141,321), only one European carrier of the p.Ala252Val mutation was identified (global frequency: 0.00040%, N = 125,523). The Pro191 and Ala252 amino acids were not conserved (74.8% and 68.9% across 131 animal species, respectively). In vitro experiments showed that the two CD36 mutant proteins are expressed and trafficked to the plasma membrane where they bind modified low-density-lipoprotein (LDL) cholesterol as normal. However, molecular modelling of the recent CD36 crystal structure showed that Pro191 was located at the exit/entrance gate of the lipid binding chamber and Ala252 was in line with the chamber. Overall, our data do not support a major contribution of CD36 rare coding mutations to T2D and its cardio-metabolic complications in the French 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".