Association between the A46G polymorphism (rs1042713) in the β2-adrenergic receptor gene and essential hypertension susceptibility in the Chinese population
Bibliographic record
Abstract
BACKGROUND: Recently, many studies have been conducted to investigate the relationship between the A46G polymorphism in the β2-adrenergic receptor (ADRB2) gene and essential hypertension risk in the Chinese population. However, the results of previous studies were conflicting. OBJECTIVES: The present study aimed to investigate the association between the ADRB2 A46G polymorphism and the risk of essential hypertension in the Chinese population. METHODS: We performed a systematic search of possible relevant studies on PubMed, Embase, Ovid, Web of Science, China National Knowledge Infrastructure, Wanfang, and China Biology Medicine disc databases up to January 3, 2020. Two authors independently extracted information from included articles and assessed the quality of each study by the use of the Newcastle-Ottawa Scale. According to the extent of interstudy heterogeneity, either a random-effect model or a fixed-effect model was used to calculate the combined odds ratio (OR) and 95% confidence interval (CI). RESULTS: Finally, 16 studies containing 3390 cases and 2528 controls were included in our meta-analysis. Significant associations were found between the ADRB2 A46G polymorphism and essential hypertension risk in the Chinese population under four genetic models: allele genetic model (OR: 1.14, 95% CI: 1.06-1.23, P = .001, Pheterogeneity = .09), homozygote genetic model (OR: 1.29, 95% CI: 1.11-1.51, P = .001, Pheterogeneity = .25), dominant genetic model (OR: 1.17, 95% CI: 1.05-1.32, P = .005, Pheterogeneity = .04), and recessive genetic model (OR: 1.21, 95% CI: 1.05-1.38, P = .007, Pheterogeneity = .72). CONCLUSION: The ADRB2 A46G polymorphism may increase the risk of essential hypertension in the 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".