Association of Bone Mineral Density With Fractures Across the Spectrum of Chronic Kidney Disease: The Regina CKD-MBD Study
Bibliographic record
Abstract
Background: Recent studies have demonstrated that measurement of areal bone mineral density by dual-energy x-ray absorptiometry (DXA) predicts fractures in patients with chronic kidney disease (CKD). However, whether fracture risk prediction through bone mineral density (BMD) is enhanced due to the assessment of biochemical markers of chronic kidney disease and mineral and bone disease (CKD-MBD) or clinical risk factors is not clear. We hypothesized that in a select cohort of patients managed in a CKD clinic, that combining T-Scores with biochemical markers would optimize fracture discrimination than using DXA alone. Objective: To examine the relationships among BMD, biochemical markers of CKD-MBD, and fracture risk across Kidney Disease Improving Global Outcomes (KDIGO) glomerular filtration rate (GFR) categories G3a to G5. Design: Retrospective study. Setting: Patients were recruited from the multidisciplinary CKD clinic, Regina General Hospital, Canada. Patients: A total of 374 patients who received a DXA scan upon initial referral to Regina Multidisciplinary CKD Program from January 31, 2001 to January 31, 2010, were included in this study. The patients were followed for a total of 5 years. Methods: We conducted a retrospective review of 374 consecutive patients who underwent DXA imaging at the point of entry into our multidisciplinary CKD program. Areal BMD, T- and Z-Scores were obtained at the lumbar spine, total hip, mean of left and right femoral neck, and the one-third radius. We collected data on demographic, cross-sectional biochemical markers of mineral metabolism and fractures (identified through self-reported questionnaires, hospital electronic medical records, and physician billing records). We were able to gather data on 8/11 variables of Fracture Risk Assessment (FRAX) tool. Results: In our cohort, 14.3% of GFR categories G3a and G3b, 15.7% of GFR category G4, and 19.7% of GFR category G5 experienced a clinical fracture during the study period. On multivariate analysis, each decline of 1.0 SD in total hip BMD T-Score was associated with a significant increase in the risk of fracture (OR = 1.46, 95% confidence interval [CI], 1.12-1.89). Adding CKD-MBD markers and clinical risk factors did not further contribute to the model. Low BMD was the only independent risk factor for fracture in patients with CKD. Limitations: Self-reporting by patients and administrative records were used to identify fractures. We did not perform spine imaging to ascertain morphometric vertebral fractures. We were unable to gather all 11 variables of FRAX score and information on ethnicity. We were unable to capture site of fracture (hips, spine, etc) from billing records. Albumin excretion rates were not collected at baseline. Treatment of the underlying bone disease with pharmacotherapeutic agents may have attenuated patients’ fracture risk and thus underestimated the association between BMD and future fracture. Conclusions: Our findings confirm that BMD predicts fracture. The addition of cross-sectional CKD-MBD parameters and clinical risk factors to BMD did not add to fracture prediction. Prospective studies should investigate the utility of longitudinal biochemical markers on improving fracture risk 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 | 0.003 |
| 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".