Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk
Bibliographic record
Abstract
Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a "one-size-fits-all" approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).
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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 | 0.002 |
| 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".