Body Mass Index and Risk of Hypertension: 8-Year Prospective Findings From a Nationwide Thai Cohort Study
Bibliographic record
Abstract
OBJECTIVE: As Thailand modernizes an ensuing health risk transition associates with rising chronic non-communicable diseases, especially hypertension. This is a driving force for emerging vascular disease, especially stroke and hypertension. Studies in other countries have shown hypertension is associated with obesity. Longitudinal information is needed forthailand and here we present our cohort data collected over 8 years And recording incidence of hypertension and exposure to elevated abnormal BMI. DESIGN & METHODS: BMI effects on incident hypertension were investigated prospectively in a nationwide Thai Cohort Study (TCS) from 2005 to 2013. Data were derived from 42 785 off-campus Sukhothai Thammathirat Open University students returning mail-based questionnaire surveys in both 2005 and 2013. Participants analysed were normotensive at the start (40 548). Multivariable regression estimated adjusted relative risks estimate linking obesity (measured by BMI) and hypertension (self-reported) among Thai men and women. RESULTS: In Thailand from 2005 to 2013 the TCS 8-Year Incidence of hypertension was 5.1% (men 7.1%, women 3.6%), which meant 1958 participants developed hypertension. BMI was directly associated with an increased risk of hypertension. Compared to participants with a normal BMI (18.5-22.9 Kg/M2), The relative risks (95% confidence interval) of developing hypertension with a BMI of ≤ 18.5.23.0–24.9, 25-29.9 and >30 Kg/m2 were 0.54 (0.3-0.97), 1.8(1.49-2.18), 3.27 (2.73-3.91) and 6.73 (5.1-8.97) for men and 0.65 (0.45-0.95), 2.28 (1.81-2.88), 3.71 (2.96-4.64) and 9.72 (7.09-13.32) for women respectively (p-trend <0.0001). CONCLUSION: Our data confirmed the adverse effects of long-term high bmi on an increased risk of hypertension in Thai people. Therefore, Ministry of Public Health should develop a national program to encourage people to remain healthy with a normal BMI. There are many health gains from such a program and the information presented here shows clearly that decreased hypertension would be one of the expected benefits for the Thai population.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".