55. Incidence and Sex-Specific Predictors of Progression from Prehypertension to Hypertension in Asian Population: A Systematic Review of Cohort Study
Bibliographic record
Abstract
Background: Prehypertension was more likely to develop hypertension compared to normotension. However, the predictors of this progression remain poorly understood. Objective: This study aimed to determine the incidence and predictor for progression from prehypertension to hypertension in Asian population according to sex. Methods: Systematic review was conducted adhering to PRISMA 2020 guidelines using databases PubMed, ScienceDirect, Proquest, and Epistemonikos. Studies included were cohort studies published in 2013-2023 from Asian region with minimum follow-up duration of 2 years. We used Newcastle-Ottawa Scale to measure the quality of included studies. Result: Nine cohort studies were included. The progression rate from prehypertension to hypertension ranged from 18.04-42.37%, with an average of 29.45%. Seven studies agreed that increasing age and BMI were independent predictive factors for progression from prehypertension to hypertension. Alcohol consumption, smoking, and hypertriglyceridemia were specific risk factors in men. Among women, progression to hypertension was associated with married status, nap time ≥30 minutes, income status, hemoglobin ≥15.5 g/dL, and family history of premature CVD. Irrespective of sex, hyperuricemia, history of chronic disease, total cholesterol ≥200 mg/dL, pulse pressure >60 mmHg, ALT ≥36 U/l, family history of hypertension, low HDL, baseline SBP and DBP, and waist-circumference gain significantly increased the risk of progression for both sexes. Conclusion: High incidence of progression to hypertension from prehypertension should be warning for every health worker. We emphasize the need for an easy assessment system (e.g., prediction score model) to categorize the risk in order to prevent unnecessary treatment in low-risk group while providing the best prevention to high-risk group.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".