ArticleJournal of translational medicine2025
In silico prediction of optimal multifactorial intervention in chronic kidney disease.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Urinary Collagen Peptides Predict Mortality.Proteomics · 2026Article
- Urinary Peptidomic Profiling In Post-Acute Sequelae of SARS-CoV-2 Infection: A Case-Control Study.Proteomics · 2026Article
- Circulating and Urinary CCL20 in Human Kidney Disease.International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
backgroundChronic kidney disease (CKD) contributes to global morbidity and mortality. Early, targeted intervention can help mitigate its impact. CK273 is a urinary peptide classifier previously validated in a prospective clinical trial for the early detection of nephropathy. We hypothesized that drug-induced molecular changes in the urinary peptidome could be predicted in silico and guide selecting interventions for individual patients.
methodsThe efficacy of the urinary peptidomic classifier CKD273 in predicting major adverse kidney events (≥ 40% decline in estimated glomerular filtration rate or kidney failure -median follow-up: 1.50 (95%CI 0.35, 5.0) years), was confirmed in a retrospective cohort of 935 participants. In silico prediction of treatment effects from four drug-based interventions (Mineralocorticoid receptor antagonist, Sodium-glucose co-transporter 2 inhibitor, Glucagon-like peptide-1 receptor agonist, and Angiotensin receptor blocker), dietary intervention (olive oil), and exercise was performed based on: a) individual baseline urinary peptide profiles, and b) previously defined fold changes in peptide abundance after treatment in clinical trials. Following recalibration to align with outcomes of these trials, CKD273 scores were calculated for each patient after in silico treatment. For combination treatments, the effects of multiple interventions were combined.
resultsSimulated interventions demonstrated a significant reduction in median CKD273 scores, from 0.57 (IQR: 0.19-0.81) before to 0.039 (IQR: -0.192-0.363) after the most beneficial intervention (paired Wilcoxon test, P < 0.0001). The combination of all available treatments was not the most frequently predicted optimal intervention. Patients with higher baseline CKD273 scores required more complex intervention combinations to achieve the greatest score reduction.
conclusionsThis study supports the feasibility of in silico predicting effects of therapeutic interventions on CKD progression. By identifying the most beneficial treatment combinations for individual patients, this approach paves the way for precision medicine trials in CKD. A prospective study is currently being planned to validate the in silico-guided intervention approach and determine its exact benefits on patient-relevant outcomes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.