Noninvasive Method to Diagnose Renal Osteodystrophy: A Study on 19 Circulating MicroRNAs
Bibliographic record
Abstract
Background: Current approach to manage bone fragility in chronic kidney disease (CKD) is in part based on assessment of bone turnover (BT) and mineralization. Prior work suggests that circulating microRNAs (miRNAs) identify low vs non-low cortical BT in CKD. We aimed to assess whether 19 miRNAs discriminate BT and biopsy proven contraindications (CI) to antiresorptive therapies (ART) in CKD patients . Methods: Single-site cross-sectional study. Patients with stage 4-5 CKD having a high fracture risk underwent iliac crest bone biopsy for assessment of BT and mineralization levels according to ASBMR guidelines. Absolute CI to ART were defined by the presence of adynamic bone disease or osteomalacia. Relative CI were defined by the presence of mineralization defects without osteomalacia and low BT without adynamic bone. At the time of the biopsy, blood test were drawn for assessment of mineral biochemistry as per local standard and for measurement of 19 circulating miRNAs using the osteomiR kit (TAmiRNA, Austria). Each miRNA was compared between low vs high/normal BT, low/normal vs high BT, patients with absolute CI vs relative or no-CI to ART. Diagnostic accuracy was tested using the median and lowest tertile of each miRNA. Results: Fourty four patients were included (women 56.8%, mean age 69.6±9.2, 45.4% dialysis). 19 had low BT, 16 normal, and 9 high BT; 7 patients had absolute CI to ART (2 osteomalacia, 5 adynamic bone). miRNA levels did not differ between low and normal/high BT groups. However, miRNA-31-5p was higher in patients with high BT (p=0,02), a value above the lowest tertile being associated with high BT status (sensitivity 100%, specificity 57,1% (95%CI: 40,8-73,5%), negative predicted value (NPV) 100%). We found 7 miRNAs (let7b5p, miRNA-141-3p, 143-3p, 17-5p, 19b-3p, 29b-3p, 550a-3p) with a very good capability to rule out CI to ART. A value above the median being associated with absolute CI to ART (sensitivity 85,7% (95%CI: 59,8-100%), specificity<60%, NPV 95.5% (95%CI: 86.8-100%)). A value above the lowest tertile was even more discriminant (NPV 100% for 5/7). Conclusion: Circulating miRNAs can help clinical decision in the approach of bone fragility in CKD. Large studies in heterogeneous cohorts are needed to validate these results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".