Contribution of the low-frequency, loss-of-function p.R270H mutation in <i>FFAR4</i> (<i>GPR120</i>) to increased fasting plasma glucose levels
Bibliographic record
Abstract
BACKGROUND: We previously reported that the low-frequency, loss-of-function variant p.R270H in FFAR4 encoding the lipid sensor GPR120 was associated with obesity. Gpr120-deficient mice develop obesity and both impaired fasting glucose and glucose intolerance under a high-fat diet. We aimed to assess the contribution of p.R270H to type 2 diabetes (T2D) risk and the variation of glucose-related traits. METHODS: We genotyped p.R270H in 8996 non-diabetic individuals (among whom 4523 had an oral glucose tolerance test (OGTT)) and in a T2D case-control study including 4725 cases and 4339 controls. The regression models were adjusted for age, sex and body mass index (BMI). RESULTS: We found a significant association between p.R270H and increased fasting glucose levels (β=0.092±0.05 mmol/L; p=4.13×10(-4)). Furthermore, p.R270H nominally contributed to decreased homeostasis model of pancreatic β-cell function (HOMA-B; β=-0.090±0.06; p=6.01×10(-3)). Despite a high statistical power, we did not find any significant association between p.R270H and T2D risk or the variation of fasting insulin levels, the homeostasis model of insulin resistance or OGTT-derived indices. CONCLUSIONS: These results suggest that the low-frequency p.R270H variant which inhibits GPR120 activity might influence fasting glucose levels in a normal physiological range. This study does not exclude that other coding mutations in FFAR4 with stronger functional effect than p.R270H may be associated with T2D.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".