Robust Trabecular Microstructure in Type 2 Diabetes Revealed by Individual Trabecula Segmentation Analysis of HR-pQCT Images
Bibliographic record
Abstract
Type 2 diabetes (T2D) patients have an increased fracture risk, which may be partly explained by compromised bone microarchitecture within the cortical bone compartment. Data on trabecular bone parameters in T2D are contradictory. By high-resolution peripheral quantitative computed tomography (HR-pQCT), trabecular microarchitecture is preserved, yet larger trabecular holes are detected in T2D by MRI and DXA-based trabecular bone scores are abnormal. To determine if there are differences in trabecular microstructure, connectivity, and alignment in postmenopausal women with T2D as compared with controls, we performed an individual trabecula segmentation (ITS) analysis on HR-pQCT scans of the distal radius and tibia in 92 women with (n = 42) and without (n = 50) T2D. Unadjusted analyses showed that T2D subjects had greater total trabecular bone volume, trabecular plate volume fraction, plate number density, plate junction density, and axial alignment at the radius and tibia, and increased plate tissue fraction, but decreased rod tissue fraction and rod length at the radius (p < 0.05 for all). After adjustments for clinical covariates, plate number density and plate junction density remained higher at the radius and tibia, whereas total trabecular bone volume was increased and trabecular rod length was decreased at the radius. These differences remained significant after adjustment for hip BMD and trabecular volumetric bone density. Notably, the increased plate-like ITS qualities were seen in those with T2D duration of <10 years, whereas ITS parameters in subjects with T2D duration ≥10 years did not differ from those of control subjects. In conclusion, postmenopausal women with early T2D had a greater plate-like and less rod-like trabecular network. This early advantage in trabecular plate quality does not explain the well-established increased fracture risk in these patients and does not persist in the later stage of T2D. © 2018 American Society for Bone and Mineral Research.
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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".