The Association of Body Mass Index and Adiposity-Estimating Equations with Measures of Obstructive Sleep Apnea Severity: A Cross-Sectional Study
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Bibliographic record
Abstract
Background and Purpose: Obesity, a risk factor for obstructive sleep apnea (OSA), is usually estimated by body mass index (BMI). However, other adiposity-estimating equations may better capture variations in fat distribution. This study assessed the relationship between OSA severity and 15 adiposity-estimating equations, compared to BMI, with subgroup analyses by sex and age (<50 vs ≥50). Patients and Methods: We conducted a cross-sectional cohort study using data from 5021 consecutive adults who underwent a Level 1 polysomnography (2015-2017) in a large academic sleep center in Ottawa, Canada. We assessed correlations between adiposity measures and the apnea-hypopnea index (AHI) and examined discriminative ability for moderate-to-severe (AHI ≥15/h) and severe OSA (AHI >30/h) using univariate logistic regressions. Results: and 12.7% had severe OSA. All adiposity equations showed negligible (Pearson r 0.0 to ±0.3) to low (Pearson r ± 0.30 to 0.50) statistically significant correlations with AHI, with many of the equations having a marginally stronger correlation coefficient than BMI, in total and subgroup analysis. Discriminative ability for severe OSA was generally low, with c-indices ranging from 0.52 to 0.67 in the overall sample. However, in females under 50, several equations (eg, Gallagher 2000, Deurenberg 1991 and 1998, ECORE BF) reached excellent discriminative ability (c-indices 0.81), including BMI (c-index 0.80). This pattern was not observed in other subgroups. Conclusion: In this clinical cohort, BMI was associated poorly with AHI; however, the other equations did not outperform BMI. Moreover, BMI demonstrated poor discriminative ability for moderate/severe and severe OSA, with none of the other equations performing better in this context. Notable subgroup differences-particularly among younger females-suggest that tailoring screening strategies by age and sex may improve risk stratification and support refining obesity-based screening approaches.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it