Kidney Volume and Risk of Incident CKD
Bibliographic record
Abstract
Background: Lowtotal kidney volume (TKV) is a risk factor for CKD. However, evaluations of causal inference and prognostic utility beyond traditional biomarkers are lacking. Methods: TKV of 34,595 White British ancestry participants were derived from the UK Biobank. Association with incident CKD were assessed with Cox proportional hazard models. Prognostic thresholds for CKD risk stratification were identified using a modified Mazumdar method with bootstrap resampling. Overall improvement in model performance was assessed using likelihood ratio test. Risk reclassification was evaluated with 5-fold cross-validation. Bidirectional associations of genetically predicted TKV with kidney traits were assessed using two-sample Mendelian randomization (MR). Results: Adjusted for eGFR and albuminuria, a lower TKV of 10 mL was associated with a 6% higher risk of incident CKD (HR 1.06, 95% CI 1.03 to 1.08, P = 5.8 x 10-6). Individuals below the prognostic threshold of body surface area adjusted TKV (BSA-TKV) at 119 mL/m2 (10th percentile) exhibited 2.8-fold (95% CI 2.03 to 3.85, P = 2.9 x 10-10) higher risk of incident CKD after accounting for eGFR, albuminuria, and traditional risk factors. Addition of BSA-TKV improved model performance of the CKD Prognosis Consortium Incident CKD Risk Score (likelihood ratio P = 4.8 x 10-14) and improved reclassification of high-risk prognostication at a rate of 1 per 3.3 cases (95% CI 3.1 to 3.6). In MR, a lower genetically predicted TKV by 10 mL was associated with 10% higher CKD risk (OR 1.10, 95% CI 1.06 to 1.14, P = 1.3 x 10-7). Reciprocally, an elevated risk of genetically predicted CKD by 2-fold was associated with a lower TKV by 7.88 mL (95% CI -9.81 to -5.95, P = 1.2 x 10-15). Conclusion: Kidney volume was associated with incident CKD independent of traditional risk factors including baseline eGFR and albuminuria. Mendelian randomization demonstrated a bidirectional relationship between kidney volume and CKD. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".