National trends in hypertension stratified by central adiposity using waist-to-height ratio in South Korea, 2005 to 2022
Bibliographic record
Abstract
Despite the growing prevalence of hypertension due to lifestyle changes and aging populations, with central obesity as a critical risk factor, there is limited research examining the association between central adiposity and hypertension trends. To address this gap, this study analyzed trends in hypertension prevalence among Korean adults categorized by healthy and increased central adiposity based on waist-to-height ratio (WHtR) and investigated the associated risk factors. We utilized data from 98,396 adults aged 30 years and older from the Korea National Health and Nutrition Examination Survey from 2005 to 2022 in South Korea. Participants were categorized into 2 groups according to central adiposity: low-WHtR group (<0.50) and high-WHtR group (≥0.50). Long-term trends in hypertension and adjusted odds ratios were analyzed, with stratified analyses by sociodemographic factors. Weighted linear regression and binary logistic regression were used to calculate β coefficients, βdiff, and adjusted odds ratios with 95% confidence intervals. From 2005 to 2019, the prevalence of hypertension increased in both low and high-WHtR groups, with a more pronounced increase in the increased central obesity group. Notably, the prevalence was higher in males than in females in the low-WHtR group (20.47% in males vs 12.33% in females), but similar in both sexes in the high-WHtR group (43.44% vs 41.74%). Furthermore, subgroup analyses showed that females aged 30-59 years in the high-WHtR group had higher odds of hypertension than males, whereas males aged 60 years and older had higher odds than females. This study suggests that the association between central obesity and hypertension risk varies significantly across age and sex categories, highlighting the necessity for age- and sex-specific prevention and management strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".