Potential of facial biomarkers for Alzheimer's disease and obstructive sleep apnea in Down syndrome and general population
Bibliographic record
Abstract
Down syndrome (DS), caused by trisomy 21, is associated with an increased risk of Alzheimer's disease (AD) and obstructive sleep apnea (OSA). Traditional diagnostic methods for AD and OSA, like cerebrospinal fluid analysis and polysomnography, are invasive and challenging for people with DS. In this study, we assessed whether facial morphology could be used as a potential noninvasive biomarker for these conditions in both DS and the general population. We performed a comprehensive 3D analysis of facial shape variation by registering the 3D coordinates of 21 landmarks on facial models extracted from magnetic resonance images of 131 individuals with DS and 216 euploid (EU) adult controls, including AD and OSA cases. Procrustes ANOVA and MANOVA quantified shape variation by sex, age, and facial size, while geometric morphometrics assessed diagnostic group differences. Significant facial shape differences were observed between the DS and EU groups, indicating sex-dependent differences and altered age-related changes in DS, particularly in females. Facial shape correlated with the amyloid beta ratio (Aβ1-42/Aβ1-40), a key AD biomarker. In DS, facial shape differences by AD diagnosis were not significant after adjusting for age and facial size, but significant shape differences were detected in the EU population. For OSA, facial shape correlated with the apnea-hypopnea index (AHI), and DS individuals with severe OSA showed distinct facial morphology compared with those without OSA, suggesting an association between facial shape and sleep respiratory disturbances. These results highlight the potential of facial morphology as a noninvasive biomarker for AD and OSA detection and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".