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Jaimes Campos MA, Mavrogeorgis E, Latosinska A, Eder S, Buchwinkler L, Mischak H, Siwy J, Rossing P, Mayer G, Jankowski J. Urinary peptide analysis to predict the response to blood pressure medication. Nephrol Dial Transplant 2024; 39:873-883. [PMID: 37930730 PMCID: PMC11181870 DOI: 10.1093/ndt/gfad223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND The risk of diabetic kidney disease (DKD) progression is significant despite treatment with renin-angiotensin system (RAS) blocking agents. Current clinical tools cannot predict whether or not patients will respond to treatment with RAS inhibitors (RASi). We aimed to investigate whether proteome analysis could identify urinary peptides as biomarkers that could predict the response to angiotensin-converting enzyme inhibitor and angiotensin-receptor blockers treatment to avoid DKD progression. Furthermore, we investigated the comparability of the estimated glomerular filtration rate (eGFR), calculated using four different GFR equations, for DKD progression. METHODS We evaluated urine samples from a discovery cohort of 199 diabetic patients treated with RASi. DKD progression was defined based on eGFR percentage slope results between visits (∼1 year) and for the entire period (∼3 years) based on the eGFR values of each GFR equation. Urine samples were analysed using capillary electrophoresis-coupled mass spectrometry. Statistical analysis was performed between the uncontrolled (patients who did not respond to RASi treatment) and controlled kidney function groups (patients who responded to the RASi treatment). Peptides were combined in a support vector machine-based model. The area under the receiver operating characteristic curve was used to evaluate the risk prediction models in two independent validation cohorts treated with RASi. RESULTS The classification of patients into uncontrolled and controlled kidney function varies depending on the GFR equation used, despite the same sample set. We identified 227 peptides showing nominal significant difference and consistent fold changes between uncontrolled and controlled patients in at least three methods of eGFR calculation. These included fragments of collagens, alpha-1-antitrypsin, antithrombin-III, CD99 antigen and uromodulin. A model based on 189 of 227 peptides (DKDp189) showed a significant prediction of non-response to the treatment/DKD progression in two independent cohorts. CONCLUSIONS The DKDp189 model demonstrates potential as a predictive tool for guiding treatment with RASi in diabetic patients.
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Affiliation(s)
- Mayra Alejandra Jaimes Campos
- Mosaiques Diagnostics GmbH, Hannover, Germany
- University Hospital RWTH Aachen, Institute for Molecular Cardiovascular Research, Aachen, Germany
| | - Emmanouil Mavrogeorgis
- Mosaiques Diagnostics GmbH, Hannover, Germany
- University Hospital RWTH Aachen, Institute for Molecular Cardiovascular Research, Aachen, Germany
| | | | - Susanne Eder
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria
| | - Lukas Buchwinkler
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria
| | | | | | - Peter Rossing
- Steno Diabetes Center Copenhagen, Complications Research, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Gert Mayer
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria
| | - Joachim Jankowski
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, Maastricht, The Netherlands
- Aachen-Maastricht Institute for Cardiorenal Disease (AMICARE), University Hospital RWTH Aachen, Aachen, Germany
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Jaimes Campos MA, Andújar I, Keller F, Mayer G, Rossing P, Staessen JA, Delles C, Beige J, Glorieux G, Clark AL, Mullen W, Schanstra JP, Vlahou A, Rossing K, Peter K, Ortiz A, Campbell A, Persson F, Latosinska A, Mischak H, Siwy J, Jankowski J. Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study. Pharmaceuticals (Basel) 2023; 16:1298. [PMID: 37765106 PMCID: PMC10537115 DOI: 10.3390/ph16091298] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
(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.
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Affiliation(s)
- Mayra Alejandra Jaimes Campos
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.A.J.C.); (A.L.); (H.M.); (J.S.)
- Institute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Iván Andújar
- Proteomic Laboratory, Center for Genetic Engineering and Biotechnology, Havana 10600, Cuba
| | - Felix Keller
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, 6020 Innsbruck, Austria; (F.K.); (G.M.)
| | - Gert Mayer
- Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, 6020 Innsbruck, Austria; (F.K.); (G.M.)
| | - Peter Rossing
- Steno Diabetes Center Copenhagen, 2730 Herlev, Denmark; (P.R.); (F.P.)
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark;
| | - Jan A. Staessen
- Non-Profit Research Institute Alliance for the Promotion of Preventive Medicine, 2800 Mechlin, Belgium;
| | - Christian Delles
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK; (C.D.); (W.M.)
| | - Joachim Beige
- Division of Nephrology and KfH Renal Unit, Hospital St Georg, 04129 Leipzig, Germany;
- Medical Clinic 2, Martin-Luther-University Halle/Wittenberg, 06112 Halle, Germany
| | - Griet Glorieux
- Nephrology Section, Department of Internal Medicine, Ghent University Hospital, 9000 Ghent, Belgium;
| | - Andrew L. Clark
- Hull University Teaching Hospitals NHS Trust, Castle Hill Hospital, Cottingham HU16 5JQ, UK;
| | - William Mullen
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK; (C.D.); (W.M.)
| | - Joost P. Schanstra
- Institut National de la Santé et de la Recherche Médicale, Institute of Cardiovascular and Metabolic Disease, UMRS 1297, 31432 Toulouse, France;
- Renal Fibrosis, Université Toulouse III Paul-Sabatier, Route de Narbonne, 31062 Toulouse, France
| | - Antonia Vlahou
- Centre of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), 115 27 Athens, Greece;
| | - Kasper Rossing
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark;
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Karlheinz Peter
- Atherothrombosis and Vascular Biology Program, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia;
- Department of Physiology, Anatomy, Microbiology, La Trobe University, Melbourne, VIC 3083, Australia
- Department of Medicine and Immunology, Monash University, Melbourne, VIC 3800, Australia
- Department of Cardiometabolic Health, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alberto Ortiz
- Instituto de Investigación Sanitaria de la Fundación Jiménez Díaz UAM, 28040 Madrid, Spain;
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH16 4SB, UK;
| | - Frederik Persson
- Steno Diabetes Center Copenhagen, 2730 Herlev, Denmark; (P.R.); (F.P.)
| | - Agnieszka Latosinska
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.A.J.C.); (A.L.); (H.M.); (J.S.)
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.A.J.C.); (A.L.); (H.M.); (J.S.)
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, UK; (C.D.); (W.M.)
| | - Justyna Siwy
- Mosaiques Diagnostics GmbH, 30659 Hannover, Germany; (M.A.J.C.); (A.L.); (H.M.); (J.S.)
| | - Joachim Jankowski
- Institute for Molecular Cardiovascular Research, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, 6211 Maastricht, The Netherlands
- Aachen-Maastricht Institute for Cardiorenal Disease (AMICARE), University Hospital RWTH Aachen, 52074 Aachen, Germany
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Rossing P, Frimodt-Møller M, Persson F. Precision Medicine and/or Biomarker Based Therapy in T2DM: Ready for Prime Time? Semin Nephrol 2023; 43:151430. [PMID: 37862744 DOI: 10.1016/j.semnephrol.2023.151430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
Approximately 30-40% of people with type 2 diabetes mellitus develop chronic kidney disease. This is characterised by elevated blood pressure, declining kidney function and enhanced cardiovascular morbidity and mortality. Increased albuminuria and decreasing estimated glomerular function has to be evaluated regularly to diagsnose kidney disease. New biomarkers may facilitate early diagnosis and provide infomation on undlying pathology thereby supporting early precision intervention for the optimal benefit. A number of biomarkers have been suggested but are not yet implemented in clinical practice. iI the future such bimarkers may pave the way for personalized treatment.
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Affiliation(s)
- Peter Rossing
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | | | - Frederik Persson
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
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Wang DD, Zhang C, Hu K, He SM, Zhu P, Chen X. Therapeutic effect and rebound evaluation of dapagliflozin on glycated hemoglobin (HbA1c) in type 1 diabetes mellitus patients. Front Pharmacol 2023; 13:972878. [PMID: 36686651 PMCID: PMC9845776 DOI: 10.3389/fphar.2022.972878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/13/2022] [Indexed: 01/05/2023] Open
Abstract
Dapagliflozin has been used to treat patients with type 1 diabetes mellitus; however, the actual drug efficacy of dapagliflozin on glycated hemoglobin (HbA1c) and whether there is a rebound from dapagliflozin efficacy on HbA1c remain unknown. The present study aimed to explore the actual therapeutic effect and rebound situation of dapagliflozin on HbA1c in type 1 diabetes mellitus patients. A total of 1,594 type 1 diabetes mellitus patients were enrolled for analysis using a non-linear mixed effect model from randomized controlled trials from published literature works including two 5 mg/day dapagliflozin dosage groups and three 10 mg/day dapagliflozin dosage groups. The change rate of HbA1c from a baseline value was chosen as a dapagliflozin pharmacodynamic evaluation index. After deducting control group effects, the therapeutic effect of 5 and 10 mg/day dapagliflozin on HbA1c in type 1 diabetes mellitus patients had no significant difference. In addition, the actual maximal efficacy (AEmax) of dapagliflozin on HbA1c was -6.24% at week 9. When it reached the AEmax, the dapagliflozin pharmacodynamic rebound on HbA1c occurred, and when the treatment was continued for 0.5 and 1 year, the actual efficacies were -4.70% (75% AEmax) and -3.27% (52% AEmax), respectively. This was the first time to clarify the actual therapeutic effect and rebound situation of dapagliflozin on HbA1c in type 1 diabetes mellitus patients, providing a reference value for clinical practices.
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Affiliation(s)
- Dong-Dong Wang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy and School of Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Cun Zhang
- Department of Pharmacy, Xuzhou Oriental Hospital Affiliated to Xuzhou Medical University, Xuzhou, China
| | - Ke Hu
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy and School of Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Su-Mei He
- Department of Pharmacy, Suzhou Science and Technology Town Hospital, Suzhou, China,*Correspondence: Su-Mei He, ; Ping Zhu, ; Xiao Chen,
| | - Ping Zhu
- Department of Endocrinology, Huaian Hospital of Huaian City, Huaian, China,*Correspondence: Su-Mei He, ; Ping Zhu, ; Xiao Chen,
| | - Xiao Chen
- School of Nursing, Xuzhou Medical University, Xuzhou, China,*Correspondence: Su-Mei He, ; Ping Zhu, ; Xiao Chen,
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