Insulin Response to Oral Glucose and Cardiometabolic Disease: A Mendelian Randomization Study to Assess Potential Causality
Bibliographic record
Abstract
Mendelian randomization (MR) suggests that postprandial hyperinsulinemia (unadjusted for plasma glucose) increases BMI, but its impact on cardiometabolic disease, a leading cause for mortality and morbidity in people with obesity, is not established. Fat distribution i.e., increased centripetal and/or reduced femoro-gluteal adiposity, is causally associated with and better predicts cardiometabolic disease than BMI. We therefore undertook bidirectional MR to assess the effect of corrected insulin response (CIR) (insulin 30 min after a glucose challenge adjusted for plasma glucose) on BMI, waist-to-hip ratio (WHR), leg fat, type 2 diabetes (T2D), triglyceride (TG), HDL, liver fat, hypertension (HTN), and coronary artery disease (CAD) in people of European descent. Inverse variance-weighted MR suggests a potential causal association between increased CIR and increased BMI (b = 0.048 ± 0.02, P = 0.03), increased leg fat (b = 0.029 ± 0.012, P = 0.01), reduced T2D (b = -0.73 ± 0.15, P = 6 × 10-7, odds ratio [OR] 0.48 [95% CI 0.36-0.64]), reduced TG (b = -0.07 ± 0.02, P = 0.003), and increased HDL (b = 0.04 ± 0.01, P = 0.006) with some evidence of horizontal pleiotropy. CIR had neutral effects on WHR (b = 0.009 ± 0.02, P = 0.69), liver fat (b = -0.08 ± 0.04, P = 0.06), HTN (b = -0.001 ± 0.004, P = 0.7, OR 1.00 [95% CI 0.99-1.01]), and CAD (b = -0.002 ± 0.002, P = 0.48, OR 0.99 [95% CI 0.81-1.21]). T2D decreased CIR (b -0.22 ± 0.04, P = 1.3 × 10-7), with no evidence that BMI, TG, HDL, liver fat, HTN, and CAD modulate CIR. In conclusion, we did not find evidence that increased CIR increases cardiometabolic disease. It might increase BMI with favorable fat distribution, reduce T2D, and improve lipids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".