Chronic kidney disease and incident cancer risk: an individual participant data meta-analysis
Bibliographic record
Abstract
BACKGROUND: Studies examining the association of chronic kidney disease (CKD) with cancer risk have demonstrated conflicting results. METHODS: This was an individual participant data meta-analysis including 54 international cohorts contributing to the CKD Prognosis Consortium. Included cohorts had data on albuminuria [urine albumin-to-creatinine ratio (ACR)], estimated glomerular filtration rate (eGFR), overall and site-specific cancer incidence, and established risk factors for cancer. Included participants were aged 18 years or older, without previous cancer or kidney failure. RESULTS: Among 1,319,308 individuals, the incidence rate of overall cancer was 17.3 per 1000 person-years. Higher ACR was positively associated with cancer risk [adjusted hazard ratio 1.08 (95% CI 1.06-1.10) per 8-fold increase in ACR]. No association of eGFR with overall cancer risk was seen. For site-specific cancers, lower eGFR was associated with urological cancer and multiple myeloma, whereas higher ACR was associated with many cancer types (kidney, head/neck, colorectal, liver, pancreas, bile duct, stomach, larynx, lung, hemolymphatic, leukaemia, and multiple myeloma). Results were similar in a 1-year landmark analysis. DISCUSSION: Albuminuria, but not necessarily eGFR, was independently associated with the subsequent risk of cancer. Our results warrant an investigation into mechanisms that explain the link between albuminuria and cancer.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.046 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".