PIII-29Genotype frequencies for ten polymorphisms of the renin-angiotensin-aldosterone system between healthy and heart failure patients in the French-Canadian population
Bibliographic record
Abstract
BACKGROUND/AIMS Certain studies suggest a difference in genetic polymorphism frequencies between healthy and heart failure (HF) patients. The aim of this study was to verify if such a difference exists in ten polymorphisms of RAAS in our population. METHODS This is a case-control study performed on 200 healthy volunteers and 58 HF patients. The healthy control group consisted of males aged between 18 and 25 years. The HF group were between 50 and 65 years old, in NYHA class II-IV HF and were recruited in a tertiary care hospital. Both groups were from French-Canadian origin. The analysed polymorphisms were: ACE I/D, ATR1 A1166C, AGT M235T, AGT T174M, eNOS T-786C, eNOS G298A, Beta-2 adrenergic receptor Q27E, Bradykinin β2R +9/−9, CYP11B2 T-344C and α-adducin G460W. The Gen Elute Blood Genomic DNA kit (Sigma PC# NA2020) was used to extract DNA. The polymorphisms were then analysed by the standard PCR/enzymatic digestion/electrophoresis method. RESULTS All the polymorphisms tested were in Hardy-Weinberg equilibrium, except for ATR1 in our HF group. We obtained a statistically significant difference in the genotype frequency of the AGT235 gene, where the study group contained more T/T (mutant allele) and less M/M homozygotes. The difference in genotype frequency reached borderline significance with respect to the AGT174 and the bradykinin β2R polymorphisms. CONCLUSION This study demonstrates than the AGT235 polymorphism may be associated with HF in a French-Canadian population. Clinical Pharmacology & Therapeutics (2005) 79, P66–P66; doi: 10.1016/j.clpt.2005.12.237
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".