Age-related physiological and molecular biomarkers associated with kidney function: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence of chronic kidney disease (CKD) is rising rapidly due to population ageing, with significant consequences for morbidity and mortality. The use of effective, predictive biomarkers would enable early introduction of targeted, proactive management of kidney disease. AIM: The aim of this review is to summarize all available studies investigating the association of neurocardiovascular, inflammatory and epigenetic biomarkers with kidney function and their ability to predict CKD incidence or progression. DESIGN: Systematic review and meta-analysis. METHODS: Systematic searches were conducted in Scopus, Embase, MEDLINE and CINAHL covering available literature until 29 November 2023. Studies assessing the relationship between named biomarkers and kidney outcomes in adults were included. Title, abstract and full text screening involved two independent reviewers using Covidence software. Data extraction and quality assessment, using the Newcastle-Ottawa scale (NOS), were completed by two reviewers. Systematic narrative analysis was performed for all biomarkers, and meta-analysis was conducted for studies reporting odds or hazard ratios. RESULTS: Sixty-eight observational studies were included. Several biomarkers showed significant association with kidney function but significant independent associations with CKD incidence and progression were limited. Results from the meta-analysis: heart rate variability and CKD progression: pooled hazard ratio 1.75 (1.25-2.45), arterial stiffness and kidney function and CKD incidence: pooled odds ratios 1.08 (1.03-1.13) and 1.14 (1.01-1.29). CONCLUSIONS: Further longitudinal research focussing on the outcomes of CKD incidence and progression is required. The use of physiological and molecular biomarkers has the potential to improve the management and prognostication of CKD.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".