A microRNA Approach to Discriminate Cortical Low Bone Turnover in Renal Osteodystrophy
Bibliographic record
Abstract
ABSTRACT A main obstacle to diagnose and manage renal osteodystrophy (ROD) is the identification of intracortical bone turnover type (low, normal, high). The gold standard, tetracycline‐labeled transiliac crest bone biopsy, is impractical to obtain in most patients. The Kidney Disease Improving Global Outcomes Guidelines recommend PTH and bone‐specific alkaline phosphatase (BSAP) for the diagnosis of turnover type. However, PTH and BSAP have insufficient diagnostic accuracy to differentiate low from non‐low turnover and were validated for trabecular turnover. We hypothesized that four circulating microRNAs (miRNAs) that regulate osteoblast (miRNA‐30b, 30c, 125b) and osteoclast development (miRNA‐155) would provide superior discrimination of low from non‐low turnover than biomarkers in clinical use. In 23 patients with CKD 3‐5D, we obtained tetracycline‐labeled transiliac crest bone biopsy and measured circulating levels of intact PTH, BSAP, and miRNA‐30b, 30c, 125b, and 155. Spearman correlations assessed relationships between miRNAs and histomorphometry and PTH and BSAP. Diagnostic test characteristics for discriminating low from non‐low intracortical turnover were determined by receiver operator curve analysis; areas under the curve (AUC) were compared by χ2test. In CKD rat models of low and high turnover ROD, we performed histomorphometry and determined the expression of bone tissue miRNAs. Circulating miRNAs moderately correlated with bone formation rate and adjusted apposition rate at the endo‐ and intracortical envelopes (ρ = 0.43 to 0.51;p< 0.05). Discrimination of low versus non‐low turnover was 0.866, 0.813, 0.813, and 0.723 for miRNA‐30b, 30c, 125b, and 155, respectively, and 0.509 and 0.589 for PTH and BSAP, respectively. For all four miRNAs combined, the AUC was 0.929, which was superior to that of PTH and BSAP alone and together (p< 0.05). In CKD rats, bone tissue levels of the four miRNAs reflected the findings in human serum. These data suggest that a panel of circulating miRNAs provide accurate noninvasive identification of bone turnover in ROD. © 2020 The Authors.JBMR Pluspublished by Wiley Periodicals, Inc. on behalf of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".