Development of chronic kidney disease influenced by serum urate and body mass index based on young-to-middle-aged Japanese men: a propensity score-matched cohort study
Bibliographic record
Abstract
Objective To investigate the association between serum uric acid (SUA) level and body mass index (BMI) on the development of chronic kidney disease (CKD) in working men aged 20–60 years. Design Retrospective cohort study. Setting Data from employees’ annual health check-ups were collected from two companies in 2009 and 2014. Participants A total of 16 708 working men were recruited. We excluded participants with missing essential data (N=7801), who had basal estimated glomerular filtration rate (eGFR) <60.0 mL/min/1.73 m 2 and/or proteinuria (N=698) or with the absence of follow-up data (N=2). Primary outcome eGFR <60 mL/min/1.73 m 2 and/or proteinuria (≥1+) in 2014 (defined as incident CKD). Results The cut-off values of SUA for incident CKD were 6.6 mg/dL in both young (20–39 years old) and middle-aged (40–60 years old) men analysed by receiver operator characteristics. ORs for incident CKD were assessed on propensity score-matched (1:1) cohorts. In young participants (N=1938), after propensity score matching, a coexistence of high-level SUA (≥6.6 mg/dL) and overweight (BMI ≥25 kg/m 2 ) was a significant risk factor of incident CKD (OR=2.18, 95% CI 1.10 to 4.31, p=0.025), but high-level SUA was not an independent risk factor without overweight status (p=0.174). In middle-aged participants (N=2944) after propensity score matching, high-level SUA was a significant risk factor of incident CKD both with or without overweight (OR=1.44, 95% CI 1.02 to 2.04, p=0.037; OR=1.32, 95% CI 1.01 to 1.73, p=0.041, respectively). Conclusion These findings suggest that high-level SUA is strongly associated with incident CKD in overweight young adult men.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".