Plasma Biomarkers and Incident CKD Among Individuals Without Diabetes
Bibliographic record
Abstract
Rationale & Objective Biomarkers of kidney disease progression have been identified in individuals with diabetes and underlying chronic kidney disease (CKD). Whether or not these markers are associated with the development of CKD in a general population without diabetes or CKD is not well established. Study Design Prospective observational cohort. Setting & Participants In the Atherosclerosis Risk in Communities) study, 948 participants were studied. Exposures The baseline plasma biomarkers of kidney injury molecule-1 (KIM-1), monocyte chemoattractant protein-1 (MCP-1), soluble urokinase plasminogen activator receptor (suPAR), tumor necrosis factor receptor 1 (TNFR-1), tumor necrosis factor receptor 2 (TNFR-2), and human cartilage glycoprotein-39 (YKL-40) measured in 1996-1998. Outcome Incident CKD after 15 years of follow-up defined as≥40% estimated glomerular filtration rate decline to<60mL/min/1.73m 2 or dialysis dependence through United States Renal Data System linkage. Analytical Approach Logistic regression and C statistics. Results There were 523 cases of incident CKD. Compared with a random sample of 425 controls, there were greater odds of incident CKD per 2-fold higher concentration of KIM-1 (OR, 1.49; 95% CI, 1.25-1.78), suPAR (OR, 2.57; 95% CI, 1.74-3.84), TNFR-1 (OR, 2.20; 95% CI, 1.58-3.09), TNFR-2 (OR, 2.03; 95% CI, 1.37-3.04). After adjustment for all biomarkers, KIM-1 (OR, 1.42; 95% CI, 1.19-1.71), and suPAR (OR, 1.86; 95% CI, 1.18-2.92) remained associated with incident CKD. Compared with traditional risk factors, the addition of all 6 biomarkers improved the C statistic from 0.695-0.731 ( P <0.01) and using the observed risk of 12% for incident CKD, the predicted risk gradient changed from 5%-40% (for the 1st–5th quintile) to 4%-44%. Limitations Biomarkers and creatinine were measured at one time point. Conclusions Higher levels of KIM-1, suPAR, TNFR-1, and TNFR-2 were associated with higher odds of incident CKD among individuals without diabetes. Plain-Language Summary For people with diabetes or kidney disease, several biomarkers have been shown to be associated with worsening kidney disease. Whether these biomarkers have prognostic significance in people without diabetes or kidney disease is less studied. Using the Atherosclerosis Risk in Communities study, we followed individuals without diabetes or kidney disease for an average of 15 years after biomarker measurement to see if these biomarkers were associated with the development of kidney disease. We found that elevated levels of KIM-1, suPAR, TNFR-1, and TNFR-2 were associated with the development of kidney disease. These biomarkers may help identify individuals who would benefit from interventions to prevent the development of kidney disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".