Prognostic Utility of Total Kidney Volume for Chronic Kidney Disease Risk Prediction: An Observational and Mendelian Randomization Study
Bibliographic record
Abstract
Abstract Importance Low total kidney volume (TKV) is a risk factor for chronic kidney disease (CKD). However, evaluations of causal inference and prognostic utility beyond traditional biomarkers are lacking. Objective To investigate the observational and Mendelian randomization (MR) association of TKV with kidney and cardiovascular traits and assess improvement in CKD risk prediction when TKV is added to estimated glomerular filtration rate (eGFR) and urine albumin creatinine ratio (uACR). Design Case-control study based on UK Biobank and two-sample MR analysis. Setting 22 assessment centers, United Kingdom. Participants Individuals of European ancestry with kidney volume assessment derived from magnetic resonance imaging. Exclusion criteria include records of kidney transplantation, excision, congenital malformation, and cystic kidney disease. Exposures TKV, height adjusted (htTKV), and body surface area adjusted TKV (BSA-TKV). Main Outcomes Observational and bidirectional MR association estimates of TKV with CKD risk. Incident CKD stage G3 or worse prediction performance using likelihood ratio, C-statistic, calibration, and category-free net absolute reclassification index (NARI). Results Observational analysis included 34,595 individuals [median (IQR) age 64 (12) years, 17,835 (51.6%) females]. Adjusted for confounders and risk factors including eGFR and uACR, a 10 mL decrease in TKV was associated with 7% increase in the risk of incident CKD stage G3 or worse (HR 1.07, 95% CI 1.04 to 1.10, P < 0.001). Addition of prognostically significant BSA-TKV thresholds of 119 and 145 mL/m 2 led to the greatest improvement in prediction performance beyond eGFR and uACR across likelihood ratio, discrimination (C-statistic 0.87, 95% CI 0.85 to 0.89, P = 0.017), calibration, and reclassification (NARI 228 per 1,000, P < 0.001). In MR, a 10 mL decrease in genetically predicted TKV was associated with 10% increase in CKD risk (OR 1.10, 95% CI 1.05 to 1.15, P < 0.001). Reciprocally, an increased risk of genetically predicted CKD by 2-fold was associated with an 8.75 mL reduction in TKV (95% CI -10.8 to -6.66, P < 0.001). There were no significant observational or MR associations of TKV with cardiovascular complications. Conclusions A bidirectional relationship exists between TKV and CKD. Addition of TKV thresholds to eGFR and uACR can improve CKD risk stratification.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".