Mitochondrial DNA Copy Number Is Associated with Incident AKI, CKD, and Inflammatory Biomarkers
Bibliographic record
Abstract
Background: Mitochondrial DNA copy number (mtDNA-CN) is an estimate of the number of mitochondria per leukocyte and a surrogate measure of net mitochondrial function. Reduced mtDNA-CN has been reported associated with diabetes, cardiovascular disease, and CKD. We sought to evaluate the association of blood mtDNA-CN with incident acute kidney injury (AKI), CKD, and inflammatory biomarkers. Methods: mtDNA-CN was estimated from whole-genome sequencing data in the UK Biobank cohort, consisting of 38,440 samples from the general population. We estimated mtDNA-CN using a quantitative polymerase chain reaction (qPCR) in the Canadian study of prediction of death, dialysis, and interim cardiovascular events (CanPREDDICT) cohort, consisting of 1,435 patients with advanced CKD. Linear and Cox proportional hazard regressions were adjusted for blood cell counts, age, sex, and comorbidities. We also used two-sample Mendelian randomization to test if genetically predicted mtDNA-CN was associated with eGFR. Reverse Mendelian randomization tested the opposite direction: if genetically predicted kidney function was associated with mtDNA-CN. Results: In the UK Biobank, we observed a 12% higher risk of incident AKI (hazard ratio (HR)=0.88, 95% CI = 0.86–0.91, P=2.4x10-6) and 8% increased odds of incident CKD (odds ratio=0.92, 95% CI = 0.89-0.94, P=0.0008) per 1 standard deviation (SD) decrease in mtDNA-CN while adjusting for baseline eGFR. Meta-analyzing across CanPREDDICT and UK Biobank revealed a 16% higher risk of incident kidney failure (HR=0.84, 95% CI = 0.76-0.93; P=0.0006) and a 3% increase in uACR (95% CI = 1%-5%, P=0.002) per 1 SD decrease in mtDNA-CN. In CanPREDDICT, mtDNA-CN was also associated with transforming growth factor-ß1 (TGFß1, ß=-9.5% per 1 SD decrease, P=0.0005) and C reactive protein (CRP, ß=8.9% per 1 SD decrease, P=0.005). Bidirectional Mendelian randomization analysis did not support either mtDNA-CN or eGFR as causally impacting the other (P>0.05). Conclusion: mtDNA-CN was associated with incident AKI, CKD, and kidney failure, as well as pro-inflammatory markers TGFß1 and CRP. However, Mendelian randomization analysis did not support a causal relationship between kidney function and mtDNA-CN, suggesting a separate causal pathway mediates the association. Funding: Government Support – Non-U.S.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".