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However DKD patients present high heterogeneity in disease trajectory and response to treatment, making the <jats:italic>one-model-fits-all<\/jats:italic> protocol for estimating prognosis and expected response to therapy as proposed by guidelines obsolete. As a solution, precision or stratified medicine aims to define subgroups of patients with similar pathophysiology and response to the therapy, allowing to select the best drug combinations for each subgroup. We focus on eGFR when aiming to identify eGFR decline trends by clustering patients according to their eGFR trajectory shape-similarity.<\/jats:p><jats:p>The study involved 256 DKD patients observed annually for four years. Using the Fr\u00e9chet distance, we built clusters of patients according to the similarity of their eGFR trajectories to identify distinct clusters. We formalized the trajectory-clustering approach through category theory. Characteristics of patients within different progression clusters were compared at the baseline and over time.<\/jats:p><jats:p>We identified five clusters of eGFR progression over time. We noticed a bifurcation of eGFR mean trajectories and a switch between two other mean trajectories. This particular clustering approach identified different mean eGFR trajectories. Our findings suggest the existence of distinct dynamical behaviors in the disease progression.<\/jats:p>","DOI":"10.1007\/978-3-031-57430-6_21","type":"book-chapter","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T15:01:50Z","timestamp":1711724510000},"page":"271-283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clustering Trajectories to\u00a0Study Diabetic Kidney Disease"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7592-5583","authenticated-orcid":false,"given":"Veronica","family":"Distefano","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3606-3436","authenticated-orcid":false,"given":"Maria","family":"Mannone","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7828-2480","authenticated-orcid":false,"given":"Irene","family":"Poli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4605-1789","authenticated-orcid":false,"given":"Gert","family":"Mayer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,30]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"391","DOI":"10.2337\/dc16-2202","volume":"40","author":"G Mayer","year":"2017","unstructured":"Mayer, G., Heerspink, H.J.L., Aschauer, C., Heinzel, A., Heinze, G., Kainz, A., et al.: Systems biology-derived biomarkers to predict progression of renal function decline in type 2 diabetes. 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Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. <b>Institutional review.<\/b> DC-ren approval number of the Ethics Committee of the Medical University Innsbruck: EK Nr. 1188\/2020, 19.06.2020. <b>Informed consent.<\/b> The DC-ren cohort consists of patients from PROVALID and informed consent was obtained from all patients. A: Ethical approval from the Ethics Committee of the Medical University Innsbruck AN4959 322\/4.5 370\/5.9 (4012a); 29.01.2013 and approval of the Ethics Committee of Upper Austria, Study Nr. I-1-11; 30.12.2010. H: Approval from Semmelweis University, Regional and Institutional Committee Of Science And Research Ethics; No.12656-0\/2011-EKU (421\/PV11.); 17.06.2011. UK: Approval from WoSRES, NHS; Rec. Reference: 12\/WS\/0005 (13.01.2012). 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