In silico prediction of optimal multifactorial intervention in chronic kidney disease
Abstract
BACKGROUND: Chronic 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. METHODS: The 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. RESULTS: Simulated interventions demonstrated a significant reduction in median CKD273…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 935 participants or records.
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