913 COMMON GENETIC BACKGROUND FOR HYPERTENSION, METABOLIC SYNDROME TRAITS AND RESTING HEART RATE IN MICROALBUMINURIC SUBJECTS
Bibliographic record
Abstract
Objectives: 1. To identify the impact of metabolic syndrome (MetS) components on risk of microalbuminuria (MAU). 2. To examine the association between validated hypertension and type-2 diabetes (T2D) susceptibility genes and MetS traits with resting heart rate (HR) in microalbuminuric individuals. Methods: 1. In the Czech Post-Monica study, we examined a representative population sample (n=3612) for MAU and MetS. 2. We conducted genome-wide association case-control study using Affymetrix SNP Array 6.0. Cases (n= 63) with MAU and MetS were age- and sex-matched with controls (n=63) without MAU and MetS, while cases (n=31) from the upper extreme of the ACR distribution curve were matched to their controls (n=31) irrespective of MetS. Only individuals without hypertension, T2D and cardiovascular disease (CVD) were eligible as controls. Results: 1. MetS was independently associated with MAU (p<0.0001). Hypertension was independent predictor of MAU (p=0.022) after adjusting for MetS components and confounders. 2. We report an association between 3SNPs in hypertension CACNB2 gene and MetSy (p=0.0005), hypertension (p=0.0006) and TG (p=0.00008). We found an association between SNPs in T2D gene GLIS3 and MetS, CVD and hypertension; TCF7L2 and MetS; GCKR and hypertension; CAMK1D, IGF2BP2 and resting HR. In multivariate regression, rs1017568 in GLIS3 (p=0.007) and rs4918790 in TCF7L2 genes (p=0.0002) were independent predictors of MAU. When summing up risk alleles of SNPs in T2D genes, the OR for MAU per genetic risk score unit was 1.085 ([CI 1.04-1.13], p=0.0003). Conclusion: Association between MAU and the MetS is based on common genetic background conferring higher cardiovascular risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 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".