Bone size and sex are key determinants of the longitudinal bone phenotype trajectory in aging males and females
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".