Genetics of fasting indices of glucose homeostasis using GWIS unravels tight relationships with inflammatory markers
Bibliographic record
Abstract
Abstract Purpose Homeostasis Model Assessment of β-cell function and Insulin Resistance (HOMA-B/-IR) indices are informative about the pathophysiological processes underlying type 2 diabetes (T2D). Data on both fasting glucose and insulin levels are required to calculate HOMA-B/-IR, leading to underpowered Genome-Wide Association studies (GWAS) of these traits. Methods We overcame such power loss issues by implementing Genome-Wide Inferred Statistics (GWIS) approach and subsequent dense genome-wide imputation of HOMA-B/-IR summary statistics with SS-imp to 1000 Genomes project variant density, reaching an analytical sample size of 75,240 European individuals without diabetes. We dissected mechanistic heterogeneity of glycaemic trait/T2D loci effects on HOMA-B/-IR and their relationships with 36 inflammatory and cardiometabolic phenotypes. Results We identified one/three novel HOMA-B ( FOXA2 )/HOMA-IR ( LYPLAL1, PER4, PPP1R3B ) loci. We detected novel strong genetic correlations between HOMA-IR/-B and Plasminogen Activator Inhibitor 1 (PAI-1, r g =0.92/0.78, P=2.13×10 -4 /2.54×10 -3 ). HOMA-IR/-B were also correlated with C-Reactive Protein ( r g =0.33/0.28, P=4.67×10 -3 /3.65×10 -3 ). HOMA-IR was additionally correlated with T2D ( r g =0.56, P=2.31×10 -9 ), glycated haemoglobin ( r g =0.28, P=0.024) and adiponectin ( r g =-0.30, P=0.012). Conclusion Using innovative GWIS approach for composite phenotypes we report novel evidence for genetic relationships between fasting indices of insulin resistance/beta-cell function and inflammatory markers, providing further support for the role of inflammation in T2D pathogenesis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".