Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi‐omics datasets, AI can provide real‐time, patient‐specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI‐driven discoveries to ensure their efficacy, relevance, and cost‐effectiveness in patient care.

(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.

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.

Aim: The present study aimed to characterize the histopathological findings and the phenotype of inflammatory cells in the myocardial tissue of patients with end-stage heart failure (ESHF) secondary to CCC in comparison with ESHF secondary to non-Chagas cardiomyopathies (NCC). Methods: A total of 32 explanted hearts were collected from transplanted patients between 2014 and 2017. Of these, 21 were classified as CCC and 11 as other NCC. A macroscopic analysis followed by a microscopic analysis were performed. Finally, the phenotypes of the inflammatory infiltrates were characterized using flow cytometry. Results: Microscopic analysis revealed more extensive fibrotic involvement in patients with CCC, with more frequent foci of fibrosis, collagen deposits, and degeneration of myocardial fibers, in addition to identifying foci of inflammatory infiltrate of greater magnitude. Finally, cell phenotyping identified more memory T cells, mainly CD8+CD45RO+ T cells, and fewer transitioning T cells (CD45RA+/CD45RO+) in patients with CCC compared with the NCC group. Conclusions: CCC represents a unique form of myocardial involvement characterized by abundant inflammatory infiltrates, severe interstitial fibrosis, extensive collagen deposits, and marked cardiomyocyte degeneration. The structural myocardial changes observed in late-stage Chagas cardiomyopathy appear to be closely related to the presence of cardiac fibrosis and the colocalization of collagen fibers and inflammatory cells, a finding that serves as a basis for the generation of new hypotheses aimed at better understanding the role of inflammation and fibrogenesis in the progression of CCC. Finally, the predominance of memory T cells in CCC compared with NCC hearts highlights the critical role of the parasite-specific lymphocytic response in the course of the infection.

Saliva samples may be an easier, faster, safer, and cost-saving alternative to NPS samples, and can be self-collected by the patient. Whether SARS-CoV-2 RT-qPCR in saliva is more accurate than in nasopharyngeal swaps (NPS) is uncertain. We evaluated the accuracy of the RT-qPCR in both types of samples, assuming both approaches were imperfect. We assessed the limit of detection (LoD) of RT-qPCR in each type of sample. We collected paired NPS and saliva samples and tested them using the Berlin Protocol to detect SARS-CoV-2 envelope protein (E). We used a Bayesian latent class analysis (BLCA) to estimate the sensitivity and specificity of each test, while accounting for their conditional dependence. The LoD were 10 copies/mL in saliva and 100 copies/mL in NPS. Paired samples of saliva and NPS were collected in 412 participants. Out of 68 infected cases, 14 were positive only in saliva. RT-qPCR sensitivity ranged from 82.7 % (95 % CrI: 54.8, 94.8) in NPS to 84.5 % (50.9, 96.5) in saliva. Corresponding specificities were 99.1 % (95 % CrI: 95.3, 99.8) and 98.4 %(95 % CrI: 92.8, 99.7). SARS-CoV-2 RT-qPCR test in saliva specimens has a similar or better accuracy than RT-qPCR test in NPS. Saliva specimens may be ideal for surveillance in general population, particularly in children, and in healthcare or other personnel in need of serial testing.

Fibrosis is characterised by inappropriate wound healing and the buildup of excessive fibrous connective tissue, in particular within the extracellular matrix (ECM). This can occur in multiple organs, ultimately leading to organ failure. Despite the high burden of fibrosis, treatment options only delay disease progression. Therefore, leveraging publicly available proteomics data, we investigated whether common fibrotic proteins and pathways in different organs could be found, to define potential core changes related to fibrosis. We identified 124 significantly differentially expressed proteins in heart fibrosis, four in early‐versus‐mild liver fibrosis, 135 in mild‐versus‐severe liver fibrosis and 160 in early‐versus‐severe liver fibrosis. Functional annotation of each of these groups of proteins demonstrated a consistent upregulation of ECM proteins and a consistent downregulation of proteins associated with mitochondrial activity. Using these data for drug repurposing, 26 compounds were proposed for further investigation, with 20 of them having demonstrated a promising anti‐fibrotic effect. A core set of 18 proteins were shared between heart and liver fibrosis, and are associated with increased ECM deposition and fibroblast activation. This approach can be generalised for other pathologies, improving the knowledge on the affected molecular pathways, and based on this, identifying potential drug candidates/compounds.