Abstract P253: Adiposity Indicators and Blood Pressure in South Asian Children and Adolescents
Bibliographic record
Abstract
Background: The prevalence of childhood obesity is increasing globally. This has severe implications for cardiometabolic risk including hypertension. South Asian children are particularly vulnerable given their unique phenotype which results in higher body fat at similar body mass index (BMI) levels compared to Europeans. As a result, we sought to explore the association between adiposity indicators and blood pressure, and to compare the strength of these indicators in determining hypertension in South Asian children and adolescents. Methods: Children ages 7-8 and 14-15 with South Asian origin were recruited from two Canadian cities. They were then assessed for height, weight and waist circumference (WC). Waist to height ratio (WHtR), and BMI were calculated. Body fat percentage was determined by bioelectrical impedance analysis. Blood pressure (BP) was assessed three times using an automated device. All variables (except body fat percentage) were transformed to z-scores using published standards. Unadjusted and adjusted (age, sex, household income, exposure to bullying or violence) logistic regression was used to explore associations between adiposity and hypertension. Subsequently, area under the curve (AUC) analysis was used to explore the strength of the adiposity metrics in determining hypertension. Results: This study consisted of 306 boys and 402 girls (n=762) with mean age 9.5 (S.D 3.2) years. Significant association were detected in unadjusted models between the adiposity indicators with systolic and diastolic hypertension (p<0.0001 for all). In the fully adjusted model, the association between adiposity with systolic and diastolic hypertension remained after adjusting for covariates (p<0.01 for all). BMI was the strongest predictor of systolic hypertension (AUC= 0.791), while body fat percentage was the strongest for diastolic hypertension (AUC= 0.712). The other indicators were within close ranges in their ability to predict both systolic and diastolic hypertension. Conclusion: Significant associations were observed between adiposity indicators and hypertension. Moreover, the adiposity indicators were observed to be strong predictors of hypertension in AUC analysis. This highlights the relationship between adiposity and hypertension, and suggests that BMI, WC, WHtR and body fat percentage are good indicators of hypertension in South Asian children.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".