Identification of Susceptibility Variants in ADIPOR1 Gene Associated with Type 2 Diabetes, Coronary Artery Disease and the Comorbidity of Type 2 Diabetes and Coronary Artery Disease
Bibliographic record
Abstract
OBJECTIVE: Adiponectin receptor 1 (encoded by ADIPOR1) is one of the major adiponectin receptors, and plays an important role in glucose and lipid metabolism. However, few studies have reported simultaneous associations between ADIPOR1 variants and type 2 diabetes (T2D), coronary artery disease (CAD) and T2D with CAD. Based on the "common soil" hypothesis, we investigated whether ADIPOR1 polymorphisms contributed to the etiology of T2D, CAD, or T2D with CAD in a Northern Han Chinese population. METHODS: Our multi-disease comparison study enrolled 657 subjects, including 165 with T2D, 173 with CAD, 174 with both T2D and CAD (T2D+CAD), and 145 local healthy controls. Six ADIPOR1 single nucleotide polymorphisms (SNPs) were genotyped and their association with disease risk was analyzed. RESULTS: Multi-case-control comparison identified two ADIPOR1 variants: rs3737884-G, which was simultaneously associated with an increased risk of T2D, CAD, and T2D+CAD (P-value range, 9.80×10(-5)-6.30×10(-4); odds ratio (OR) range: 1.96-2.42) and 16850797-C, which was separately associated with T2D and T2D+CAD (P-value range: 0.007-0.014; OR range: 1.71-1.77). The risk genotypes of both rs3737884 and 16850797 were consistently associated with common metabolic phenotypes in all three diseases (P-value range: 4.81×10(-42)-0.001). We observed an increase in the genetic dose-dependent cumulative risk with increasing risk allele numbers in T2D, CAD and T2D+CAD (P trend from 1.35×10(-5)-0.002). CONCLUSIONS: Our results suggest that ADIPOR1 risk polymorphisms are a strong candidate for the "common soil" hypothesis and could partially contribute to disease susceptibility to T2D, CAD, and T2D with CAD in the Northern Han Chinese 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".