Identification of a Common Variant for Coronary Heart Disease at <i>PDE1A</i> Contributes to Individualized Treatment Goals and Risk Stratification of Cardiovascular Complications in Chinese Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: In this study we aim to unravel genetic determinants of coronary heart disease (CHD) in type 2 diabetes (T2D) and explore their applications. RESEARCH DESIGN AND METHODS: We performed a two-stage genome-wide association study for CHD in Chinese patients with T2D (3,596 case and 8,898 control subjects), followed by replications in European patients with T2D (764 case and 4,276 control subjects) and general populations (n = 51,442-547,261). Each identified variant was examined for its association with a wide range of phenotypes and its interactions with glycemic, blood pressure (BP), and lipid controls in incident cardiovascular diseases. RESULTS: We identified a novel variant (rs10171703) for CHD (odds ratio 1.21 [95% CI 1.13-1.30]; P = 2.4 × 10-8) and BP (β ± SE 0.130 ± 0.017; P = 4.1 × 10-14) at PDE1A in Chinese T2D patients but found only a modest association with CHD in general populations. This variant modulated the effects of BP goal attainment (130/80 mmHg) on CHD (Pinteraction = 0.0155) and myocardial infarction (MI) (Pinteraction = 5.1 × 10-4). Patients with CC genotype of rs10171703 had >40% reduction in either cardiovascular events in response to BP control (2.9 × 10-8 < P < 3.6 × 10-5), those with CT genotype had no difference (0.0726 < P < 0.2614), and those with TT genotype had a threefold increase in MI risk (P = 6.7 × 10-3). CONCLUSIONS: We discovered a novel CHD- and BP-related variant at PDE1A that interacted with BP goal attainment with divergent effects on CHD risk in Chinese patients with T2D. Incorporating this information may facilitate individualized treatment strategies for precision care in diabetes, only when our findings are validated.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".