0873 Obstructive Sleep Apnea Impacts Brain Development in Obese Children and Adolescents: An MRI Study
Bibliographic record
Abstract
Abstract Introduction Obstructive sleep apnea (OSA) is a breathing disorder characterized by episodes of nocturnal hypoxia and chronic systemic inflammation, affecting more than 50% of obese youths. Both obesity and OSA independently have a negative impact on brain structure and function, but their combined effect on the developing brain is unknown. The purpose of this study was to assess MRI measurements of cortical thickness (CT) in obese youths with various degrees of OSA severity. We hypothesized that CT is abnormal in obese adolescents with OSA. Methods 55 obese subjects (26 females, 29 males, mean 14.3 ± 2.4 years) were included in the analysis. All subjects were assessed with polysomnography (PSG) to evaluate presence and severity of OSA. T1-weighted MPRAGE images were acquired using a 3T MRI scanner following PSG. CT was extracted using the CIVET 2.1.1 pipeline, and statistical analysis was performed on SurfStat to examine global and regional CT in relation to age using a general linear model. Results Based on PSG outcome, subjects were divided into 3 groups, no OSA (OAHI < 1.5 events/hr., n = 15), mild OSA (OAHI < 5, n = 14), and moderate/severe OSA (OAHI ≥ 5, n = 26). Cortical thickness analysis revealed a negative-trending correlation between global CT and age in no OSA (T = -0.49, P > 0.6), as seen in typical development. This correlation weakened in the presence of mild OSA (T = -0.20, P > 0.8) and became significantly positive in moderate/severe OSA (T = 3.87, P = 0.001), affecting several cortical areas. Conclusion These results indicate that brain development in obese adolescents with moderate/severe OSA significantly deviates from the typical trajectory of cortical thinning. This thickening could be due to exacerbated inflammation from the combined effect of both diseases, or a neurotrophic effect of leptin. More data is needed to validate these findings. Support None
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".