Predictive Power of a Body Shape Index (ABSI) for Diabetes Mellitus and Arterial Hypertension in Peru: Demographic and Health Survey Analysis - 2020
Bibliographic record
Abstract
Introduction: Given the relationship between obesity and type 2 diabetes mellitus (T2DM) and hypertension, an indicator of body fat, A Body Shape Index (ABSI), has been considered to have apparent predictive power for these diseases.
 Objective: To determine the predictive power of the ABSI for DMT2 and hypertension in Peru through the analysis of the Demographic and Health Survey-2020 (ENDES-by its acronym in Spanish-2020).
 Methods: Cross-sectional analytical study of the ENDES-2020. The variables evaluated were ABSI, body mass index, high abdominal waist, waist-to-height ratio, body roundness index (BRI) and conicity index (COI). Areas under the curves (AUC) together with their 95% confidence interval (95%CI) were used to present each index.
 Results: A total of 19 984 subjects were studied. Regarding hypertension, the highest AUC was presented by the COI: AUC=0.707 (95%CI 0.694-0.719). While the ABSI obtained the penultimate place: AUC=0.702 (95% CI 0.689-0.715). In case of DM2, the highest ABC was presented by BRI: AUC=0.716 (95%CI 0.689-0.743); while ABSI obtained the second place: AUC=0.687 (95%CI 0.658-0.717).
 Conclusions: The results demonstrate that ABSI is not a good predictor for hypertension and DMT2 in the Peruvian population. If these findings are confirmed by other studies, its use would not be recommended for these diseases, and other anthropometric indicators that could perform better should be further explored.
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 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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".