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 hypothesize...
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2025-08-01
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| Online Access: | https://doi.org/10.1186/s12967-025-06977-3 |
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| author | Agnieszka Latosinska Ioanna K. Mina Thi Minh Nghia Nguyen Igor Golovko Felix Keller Gert Mayer Peter Rossing Jan A. Staessen Christian Delles Joachim Beige Griet Glorieux Andrew L. Clark Joost P. Schanstra Antonia Vlahou Karlheinz Peter Ivan Rychlík Alberto Ortiz Archie Campbell Harald Rupprecht Frederik Persson Harald Mischak Justyna Siwy |
| author_facet | Agnieszka Latosinska Ioanna K. Mina Thi Minh Nghia Nguyen Igor Golovko Felix Keller Gert Mayer Peter Rossing Jan A. Staessen Christian Delles Joachim Beige Griet Glorieux Andrew L. Clark Joost P. Schanstra Antonia Vlahou Karlheinz Peter Ivan Rychlík Alberto Ortiz Archie Campbell Harald Rupprecht Frederik Persson Harald Mischak Justyna Siwy |
| author_sort | Agnieszka Latosinska |
| collection | DOAJ |
| description | 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 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. Conclusions This 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. |
| format | Article |
| id | doaj-art-1a6940316b3f4968b18d2edb45bdeb1f |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
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| series | Journal of Translational Medicine |
| spelling | doaj-art-1a6940316b3f4968b18d2edb45bdeb1f2025-08-24T11:48:04ZengBMCJournal of Translational Medicine1479-58762025-08-0123111010.1186/s12967-025-06977-3In silico prediction of optimal multifactorial intervention in chronic kidney diseaseAgnieszka Latosinska0Ioanna K. Mina1Thi Minh Nghia Nguyen2Igor Golovko3Felix Keller4Gert Mayer5Peter Rossing6Jan A. Staessen7Christian Delles8Joachim Beige9Griet Glorieux10Andrew L. Clark11Joost P. Schanstra12Antonia Vlahou13Karlheinz Peter14Ivan Rychlík15Alberto Ortiz16Archie Campbell17Harald Rupprecht18Frederik Persson19Harald Mischak20Justyna Siwy21Mosaiques Diagnostics GmbHMosaiques Diagnostics GmbHMosaiques Diagnostics GmbHMosaiques Diagnostics GmbHDepartment of Internal Medicine IV (Nephrology and Hypertension), Medical University InnsbruckDepartment of Internal Medicine IV (Nephrology and Hypertension), Medical University InnsbruckSteno Diabetes Center CopenhagenNon-Profit Research Institute Alliance for the Promotion of Preventive MedicineSchool of Cardiovascular and Metabolic Health, University of GlasgowDivision of Nephrology and KfH Renal Unit, Hospital St GeorgDepartment of Internal Medicine and Paediatrics, Nephrology Section, Ghent University HospitalHull and East Yorkshire NHS Hospitals Trust, Castle Hill HospitalInstitute of Cardiovascular and Metabolic Disease, Institut National de La Santé Et de La Recherche Médicale (INSERM)Centre of Systems Biology, Biomedical Research Foundation of the Academy of AthensAtherothrombosis and Vascular Biology Program, Baker Heart and Diabetes InstituteDepartment of Internal Medicine, Third Faculty of Medicine, Charles University, and University Hospital Královské VinohradyInstituto de Investigación Sanitaria de La Fundación Jiménez Díaz (IIS-FJD) UAMCentre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of EdinburghDepartment of Nephrology, Angiology and Rheumatology, Klinikum Bayreuth GmbHDepartment of Clinical Medicine, University of CopenhagenMosaiques Diagnostics GmbHMosaiques Diagnostics GmbHAbstract 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 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. Conclusions This 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.https://doi.org/10.1186/s12967-025-06977-3Chronic kidney diseaseClinical proteomicsDrug response predictionOptimizing interventionUrine peptides |
| spellingShingle | Agnieszka Latosinska Ioanna K. Mina Thi Minh Nghia Nguyen Igor Golovko Felix Keller Gert Mayer Peter Rossing Jan A. Staessen Christian Delles Joachim Beige Griet Glorieux Andrew L. Clark Joost P. Schanstra Antonia Vlahou Karlheinz Peter Ivan Rychlík Alberto Ortiz Archie Campbell Harald Rupprecht Frederik Persson Harald Mischak Justyna Siwy In silico prediction of optimal multifactorial intervention in chronic kidney disease Journal of Translational Medicine Chronic kidney disease Clinical proteomics Drug response prediction Optimizing intervention Urine peptides |
| title | In silico prediction of optimal multifactorial intervention in chronic kidney disease |
| title_full | In silico prediction of optimal multifactorial intervention in chronic kidney disease |
| title_fullStr | In silico prediction of optimal multifactorial intervention in chronic kidney disease |
| title_full_unstemmed | In silico prediction of optimal multifactorial intervention in chronic kidney disease |
| title_short | In silico prediction of optimal multifactorial intervention in chronic kidney disease |
| title_sort | in silico prediction of optimal multifactorial intervention in chronic kidney disease |
| topic | Chronic kidney disease Clinical proteomics Drug response prediction Optimizing intervention Urine peptides |
| url | https://doi.org/10.1186/s12967-025-06977-3 |
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