How well do obesity indices predict undiagnosed hypertension in the Persian cohort (Shahedieh) adults community population of all ages?
Bibliographic record
Abstract
Abstract Background and Aims Hypertension is the leading preventable risk factor for cardiovascular disease, chronic kidney disease and cognitive impairment, and mortality and disability worldwide. Since prevention, early detection, and treatment of blood pressure improve public health, the aim of present study was to determine the best obesity indices and estimate the optimal cut‐off point for each one to predict the risk of elevated/stage 1 and undiagnosed hypertension in the population of center of Iran based on American ACC/AHA 2020 guidelines. Methods This cross‐sectional study was performed on 9715 people who enrolled in 2018 in Persian Adult Cohort in Shahedieh area of Yazd, Iran in 2018. The anthropometric indices including body mass index (BMI) and waist circumference (WC), wrist circumference, hip circumference, waist‐to‐hip ratio, and waist‐to height ratio of individuals, were extracted. The receiver operating characteristic curve was utilized to determine the optimum cut‐off point of each anthropometric index to predict hypertension stages and compare their predictive power by age‐sex categories. Statistical analysis was done using SPSS version 23.0. Results The results showed that BMI has the best predictive power to recognize the risk of elevated/stage 1 hypertension for female (area under the curve [AUC] = 0.72 and optimal cut‐off = 30.10 kg/m 2 ) and WC for male (AUC = 0.66 and optimal cut‐off = 93.5 cm) in 35−45 age group. BMI had the best predictive power for the risk of undiagnosed hypertension for 35−45 years old male (AUC = 0.73 and optimal cut‐off = 28.90 kg/m 2 ) and female (AUC = 0.75 and optimal cut‐off = 5.10 kg/m 2 ), and hip circumference revealed similar predictive power for female as well (AUC = 0.75 and optimal cut‐off = 112 cm). Conclusion Based on our findings, BMI and WC, which are simple, inexpensive, and noninvasive means, are the best markers to predict the risk of elevated/stage 1 and undiagnosed hypertension in young Iranians. It shows that the approach of reducing hypertension prevalence through primary prevention, early detection, and enhancing its treatment is achievable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".