Bone size and sex are key determinants of the longitudinal bone phenotype trajectory in aging males and females
Notice bibliographique
Résumé
Introduction: A previously developed phenotyping model groups common combinations of bone characteristics into three primary phenotypes: healthy , low volume , and low density from high resolution peripheral quantitative computed tomography (HR-pQCT). While these phenotypes have been characterized in cross-sectional cohorts, their longitudinal progression and transitions with aging remains unknown. Therefore, the aim of this study was to investigate how bone microarchitecture phenotypes evolves over time, identify common transitional patterns, and examine their association with fracture risk. Methods: Our cohort included 606 adult male and female participants from a longitudinal population study. HR-pQCT scans of the distal radius and tibia were acquired at two visits. Bone phenotypes, defined as healthy , low volume , and low density , were determined at baseline and follow-up. Transitional patterns in phenotypes were examined by age group and sex, and further analyzed in relation to average bone size based on cross-sectional area. The relative contributions of sex and bone size to phenotype transitions were evaluated using nested linear regression models. Results: The average age of participants was 60.4 ± 16.2 years, with a mean follow-up duration of 6.76 ± 1.78 years. Phenotype membership remained relatively stable over the follow-up time but exhibited an average transitional pattern from the healthy phenotype in younger adults (18–40 years), to low volume (40–60 years), and eventually to low density (60+ years), particularly among females. When stratified by bone size, individuals with larger bones tended to follow an average trajectory from healthy to low density , whereas those with smaller bones typically transitioned from healthy to low volume to low density . Although sex and bone size were strongly correlated (R =0.43), and showed similar transitional patterns, sex remained a significant predictor of phenotype membership even after adjusting for bone size (p 0.001). Conclusion: These findings demonstrate that the bone phenotype model provides a dynamic, interpretable framework for monitoring skeletal health over time, capturing age-, sex-, and size-related trajectories, and offering potential for individualized bone health assessment. Lay Summary: We investigated the longitudinal progression of bone phenotypes, categorized as healthy , low volume , and low density , in adult males and females. Phenotype transitions were analyzed by age, sex, and average bone size. Most individuals followed an average trajectory from healthy to low volume to low density , with fracture risk increasing most notably from low volume to low density . Larger bones tended to skip the low volume stage, transitioning directly to low density . Findings demonstrate the potential of bone phenotypes for individualized bone health assessment.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».