Comparing the power of obesity indices to predict cardiovascular diseases at different ages: An application of conditional time-dependent ROC curve in Healthy Heart Cohort of Yazd, Iran.
Bibliographic record
Abstract
BACKGROUND: This study was conducted to estimate the power of anthropometric markers to predict 10-year CVD across different age groups in the Yazd Healthy Heart cohort. METHODS: A total of 1,623 individuals aged 20 to 74, who were free of CVD, participated in the study. A conditional time-dependent receiver operating characteristic (ROC) curve was used to estimate the predictive power of anthropometric indices, including the Abdominal Volume Index (AVI), Body Adiposity Index (BAI), and Waist-to-Height Ratio (WHtR), adjusted for age and sex. RESULTS: Of the 1,623 participants, 818 were males (50.40%) and 805 were females (49.60%). The Area Under the Curve (AUC) for the BAI ranged from 0.50 to 0.70 for males aged 40 to 70 years. In females, the BAI biomarker demonstrated considerable to excellent predictive power (AUC > 0.8) for individuals aged 20 to approximately 33 years. For males, AVI and WHtR showed fair to considerable predictive power in participants aged 20 to 30 years. In the age group of 30 to approximately 68 years, the predictive power varied from poor to ineffective, except for individuals close to 50 years old. In females, the predictive power of the AVI and WHtR biomarkers ranged from fair to considerable for those aged 20 to around 33 years. CONCLUSION: This study found that AVI and WHtR can fairly predict 10-year CVD risk in young individuals of both sexes, while the BAI was specifically applicable for predicting risk in young women. These markers are valuable and affordable tools for youth CVD screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, 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".