Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study
(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Online Access: | https://www.mdpi.com/1424-8247/16/9/1298 |
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| author | Mayra Alejandra Jaimes Campos Iván Andújar Felix Keller Gert Mayer Peter Rossing Jan A. Staessen Christian Delles Joachim Beige Griet Glorieux Andrew L. Clark William Mullen Joost P. Schanstra Antonia Vlahou Kasper Rossing Karlheinz Peter Alberto Ortiz Archie Campbell Frederik Persson Agnieszka Latosinska Harald Mischak Justyna Siwy Joachim Jankowski |
| author_facet | Mayra Alejandra Jaimes Campos Iván Andújar Felix Keller Gert Mayer Peter Rossing Jan A. Staessen Christian Delles Joachim Beige Griet Glorieux Andrew L. Clark William Mullen Joost P. Schanstra Antonia Vlahou Kasper Rossing Karlheinz Peter Alberto Ortiz Archie Campbell Frederik Persson Agnieszka Latosinska Harald Mischak Justyna Siwy Joachim Jankowski |
| author_sort | Mayra Alejandra Jaimes Campos |
| collection | DOAJ |
| description | (1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient’s urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted. |
| first_indexed | 2024-03-10T22:16:50Z |
| format | Article |
| id | doaj.art-e3789a7feddf4e3a80ab26fa30895ea4 |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-10T22:16:50Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-e3789a7feddf4e3a80ab26fa30895ea42023-11-19T12:25:24ZengMDPI AGPharmaceuticals1424-82472023-09-01169129810.3390/ph16091298Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept StudyMayra Alejandra Jaimes Campos0Iván Andújar1Felix Keller2Gert Mayer3Peter Rossing4Jan A. Staessen5Christian Delles6Joachim Beige7Griet Glorieux8Andrew L. Clark9William Mullen10Joost P. Schanstra11Antonia Vlahou12Kasper Rossing13Karlheinz Peter14Alberto Ortiz15Archie Campbell16Frederik Persson17Agnieszka Latosinska18Harald Mischak19Justyna Siwy20Joachim Jankowski21Mosaiques Diagnostics GmbH, 30659 Hannover, GermanyProteomic Laboratory, Center for Genetic Engineering and Biotechnology, Havana 10600, CubaDepartment of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, 6020 Innsbruck, AustriaDepartment of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, 6020 Innsbruck, AustriaSteno Diabetes Center Copenhagen, 2730 Herlev, DenmarkNon-Profit Research Institute Alliance for the Promotion of Preventive Medicine, 2800 Mechlin, BelgiumSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UKDivision of Nephrology and KfH Renal Unit, Hospital St Georg, 04129 Leipzig, GermanyNephrology Section, Department of Internal Medicine, Ghent University Hospital, 9000 Ghent, BelgiumHull University Teaching Hospitals NHS Trust, Castle Hill Hospital, Cottingham HU16 5JQ, UKSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UKInstitut National de la Santé et de la Recherche Médicale, Institute of Cardiovascular and Metabolic Disease, UMRS 1297, 31432 Toulouse, FranceCentre of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), 115 27 Athens, GreeceDepartment of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, DenmarkAtherothrombosis and Vascular Biology Program, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, AustraliaInstituto de Investigación Sanitaria de la Fundación Jiménez Díaz UAM, 28040 Madrid, SpainCentre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH16 4SB, UKSteno Diabetes Center Copenhagen, 2730 Herlev, DenmarkMosaiques Diagnostics GmbH, 30659 Hannover, GermanyMosaiques Diagnostics GmbH, 30659 Hannover, GermanyMosaiques Diagnostics GmbH, 30659 Hannover, GermanyInstitute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, 52074 Aachen, Germany(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient’s urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted.https://www.mdpi.com/1424-8247/16/9/1298cardiovascular eventscoronary artery diseaseheart failurechronic kidney diseasepersonalized medicineurinary biomarkers |
| spellingShingle | Mayra Alejandra Jaimes Campos Iván Andújar Felix Keller Gert Mayer Peter Rossing Jan A. Staessen Christian Delles Joachim Beige Griet Glorieux Andrew L. Clark William Mullen Joost P. Schanstra Antonia Vlahou Kasper Rossing Karlheinz Peter Alberto Ortiz Archie Campbell Frederik Persson Agnieszka Latosinska Harald Mischak Justyna Siwy Joachim Jankowski Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study Pharmaceuticals cardiovascular events coronary artery disease heart failure chronic kidney disease personalized medicine urinary biomarkers |
| title | Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study |
| title_full | Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study |
| title_fullStr | Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study |
| title_full_unstemmed | Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study |
| title_short | Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study |
| title_sort | prognosis and personalized in silico prediction of treatment efficacy in cardiovascular and chronic kidney disease a proof of concept study |
| topic | cardiovascular events coronary artery disease heart failure chronic kidney disease personalized medicine urinary biomarkers |
| url | https://www.mdpi.com/1424-8247/16/9/1298 |
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