{"title":"Multi-Disciplinary and Omics-Driven Approaches to Advance Personalized Medicine in Cardiovascular and Chronic Kidney Disease","authors":"Griet Glorieux, Julie Klein, Agnieszka Latosinska","doi":"10.1002/pmic.202500093","DOIUrl":null,"url":null,"abstract":"<p>We are living in an omics era, in which molecular profiling technologies can detect thousands of molecules across multiple biological layers. Yet chronic diseases—such as chronic kidney disease (CKD) and cardiovascular disease (CVD)—are still diagnosed only after overt signs and symptoms appear, relying on biomarkers that indicate established organ damage (e.g., estimated glomerular filtration rate (eGFR), albuminuria, troponin T, natriuretic peptides) [<span>1</span>]. In other words, by the time a chronic disease is recognized, curative treatment is generally no longer possible, as irreversible organ damage has already occurred. These conditions are termed “chronic” because, once they develop, patients live with them for the rest of their lives. Additionally, their life expectancy is shorter with a significant loss of quality of life.</p><p>Preventive measures to reduce the global burden of chronic diseases are therefore of paramount importance. The impact is enormous: in 2021, CKD caused 1.5 million deaths [<span>2</span>], while CVD accounted for more than 20 million deaths [<span>3</span>], with ischemic heart disease the leading and CKD the eleventh leading cause of mortality worldwide. Disability-adjusted life-years (DALYs) totaled 212 million for ischemic heart disease and 44.5 million for CKD [<span>4</span>]. In addition, the economic impact of both CKD and CVD is huge and is estimated to further increase in the coming years [<span>5-7</span>]. However, diagnosed cases represent only the “tip of the iceberg” (Figure 1); most patients remain undiagnosed because these diseases develop silently and progressively over the years.</p><p>CKD and CVD originate at the molecular level (bottom of the iceberg), are tightly interconnected—each increasing the risk of the other—and share common risk factors such as diabetes and hypertension. Additionally, therapies overlap for example, in patients with established CKD, renin–angiotensin system inhibitors, sodium-glucose co-transporter 2 inhibitors, and the non-steroidal mineralocorticoid receptor agonist finerenone reduce not only the risk of kidney disease progression but also cardiovascular events [<span>3</span>]. Considering the continuum of disease development, it is logical to intervene as early as possible, when the disease-associated changes are only at the molecular level. Moreover, early intervention has been demonstrated to be the most effective approach. In fact, intervention before irreversible organ damage should ideally even prevent onset of chronic disease. At the same time, no single biomarker can capture the complexity of these systemic disorders, which involve multiple organs and show marked heterogeneity in progression and treatment response. The societal, healthcare, and economic burden of CKD and CVD underscores the need for personalized, omics-based approaches that accommodate this multifactorial complexity and enable personalized intervention.</p><p>Personalized medicine represents a major shift in healthcare by tailoring prevention, diagnosis, and treatment to each individual's biological profile. This is enabled by advances in omics technologies, data processing, analytical tools, and artificial intelligence (AI). By integrating multilayer molecular data, diseases can be characterized more precisely and detected before clinical symptoms appear, allowing earlier and more targeted interventions. Such an approach is already well established in oncology, where several therapies are matched to tumor molecular profiles [<span>8</span>].</p><p>With this aim, the PerMediK program, a European research initiative dedicated to accelerating the development and clinical implementation of personalized medicine for CKD and CVD, was established. It fosters interdisciplinary collaboration across omics science, computational biology, and clinical research, supporting innovation in biomarker discovery, therapeutic targeting, and patient stratification, with a strong emphasis on data integration, reproducibility, and equity in healthcare.</p><p>This Special Issue, entitled <i>Omics in Personalized Management of Cardiovascular and Kidney Disease</i>, is dedicated to exploring the application of omics and computational methodologies to better understand and manage CKD and CVD in a personalized manner. The contributions span the continuum from basic science to translational and clinical applications, highlighting the transformative potential of personalized medicine in chronic disease management, as summarized below.</p><p>Rroji et al., Beige et al., and Lopes et al. each contribute distinct yet complementary perspectives on how multi-omics and computational approaches can transform chronic disease management [<span>9-11</span>].</p><p>Building the case for personalized medicine, Rroji et al. provide an overview of how integrated multi-omics and machine learning models can overcome the limitations of conventional markers such as eGFR and albuminuria, enabling early risk prediction and individualized therapeutic strategies in CKD [<span>9</span>]. Beige et al. contribute a clinically grounded viewpoint advocating for the incorporation of urinary proteomics into routine care, especially to detect early-stage disease and guide non-invasive management of both CKD and cardiovascular comorbidities [<span>10</span>]. A roadmap for integrating omics datasets with machine learning approaches to support early diagnosis, risk prediction, and cost-effective personalized treatment in CKD is presented by Lopes et al. [<span>11</span>]. They emphasize not only the technical advances in supervised and unsupervised machine learning models but also the critical importance of preclinical validation, health economic assessment, and interdisciplinary collaboration in translating these tools into practice.</p><p>These contributions collectively underline the importance of aligning technological innovation with clinical needs and underscore the potential of omics to improve care trajectories in complex chronic diseases. At the same time, the implementation of the findings should move forward to ultimately benefit the community.</p><p>Urinary peptidomics, a cornerstone of this issue, is by now a well-established powerful non-invasive method to assess kidney and cardiovascular health. Several studies in this collection exemplify how urinary peptide profiling can be leveraged to explore both population-level risk and mechanistic insights into disease.</p><p>In a large multi-ethnic study, An et al. illustrate how urinary peptide profiling can provide population-level insights, identifying eight proteins that may underlie differential susceptibility to salt sensitivity and hypertension-related complications between Black and White populations [<span>12</span>]. This work underscores the importance of inclusive omics research in understanding population-specific disease risks.</p><p>Fibrosis, a hallmark of CKD, is another area where urinary peptidomics is proving to be highly informative. Martin et al. demonstrated that urinary peptides derived from collagen type III degradation, incorporating the sequence targeted by the antibody-based C3M assay, are associated with kidney function decline and fibrosis [<span>13</span>]. Mina et al. extend this investigation to collagen type I degradation, proposing a model of stepwise breakdown process and the potential role of impaired degradation in disease progression [<span>14</span>]. Together, these studies underscore the diagnostic and mechanistic value of urinary peptidomics to assess diseases through the lens of protein degradation, a particularly relevant yet understudied component in fibrosis.</p><p>While urine offers a direct window into kidney-specific processes and to some extent, systemic changes due to its origin from glomerular filtration of the blood, plasma provides a broader perspective on systemic changes—particularly those relevant to the cardiovascular system. To capture these systemic signatures, Fernandez et al. have developed and validated a robust CE-MS pipeline for plasma peptidomics [<span>15</span>]. This approach complements urinary profiling and may offer more direct and detailed insight into inflammation, coagulation, and other vascular processes critical in both CKD and CVD. The study by Fernandez et al. also exemplifies the importance of robust, standardized workflows, particularly as omics technologies continue to evolve and generate increasingly complex datasets. Establishing such methodological consistency is key to ensure reproducibility and support subsequent clinical implementation.</p><p>Moving beyond traditional models, Bourdakou et al. present a compelling systems biology study that utilizes gene expression data from human induced pluripotent stem cell-derived cardiomyocytes exposed to spaceflight conditions [<span>16</span>]. Their findings implicate oxidative stress and nuclear factor erythroid 2-related factor 2 (NRF2) signaling in cardiovascular dysfunction, and their integrative pipeline identifies potential repurposable drugs. This unique approach underscores how omics-driven translational tools can inform disease mechanisms and therapeutic opportunities, even in unconventional settings.</p><p>García-Sáez et al. further extend the translational potential of omics by comparing molecular signatures across species [<span>17</span>]. Their work aims to improve the reliability of preclinical models, thereby enhancing the relevance and predictability of findings when applied to human disease.</p><p>The search for new therapeutic strategies is another highly relevant focus of this special issue. Drug repurposing is a cost-effective, time-efficient approach to finding new therapeutic uses for existing drugs. Perco et al. provide a comprehensive overview of computational drug repositioning in cardiorenal disease [<span>18</span>]. Their viewpoint outlines how omics-derived signatures, protein networks, and machine learning can reveal novel drug-disease relationships. Bourdakou et al. complement this perspective by demonstrating another practical application of these methods to identify cardiovascular drug candidates following spaceflight-induced stress [<span>16</span>]. Together, these studies highlight the efficiency and promise of leveraging existing pharmacological agents for new indications using omics-driven insights, an approach of great importance in the treatment of complex diseases.</p><p>No personalized medicine effort is complete without attention to ethical, legal, and social implications. Azéma et al. explore this dimension through their work in the KidneySign project, calling for the active engagement of patients, ethicists, and multidisciplinary stakeholders early in the research process to ensure that personalized medicine evolves responsibly [<span>19</span>]. As mentioned above, the study by An et al., explicitly considering racial diversity, emphasizes the imperative for representative and equitable representation in omics research [<span>12</span>].</p><p>Taken together, the contributions to this special issue demonstrate the clinical relevance and growing maturity of omics and computational approaches in personalized medicine for chronic diseases, with existing platforms ready for implementation. They underscore the versatility of these technologies across the entire spectrum of biomedical research and clinical care.</p><p>From insights into collagen turnover and hypertension risk to AI-driven biomarker discovery, drug repositioning, and cost-effective patient stratification, this collection showcases the rapid progress being made. Importantly, these efforts also illustrate the feasibility of moving toward real-world applications (and in some cases implementation), supported by robust technologies and validated workflows.</p><p>Yet, key challenges remain. These include the integration of omics into routine clinical practice, standardization of data pipelines, validation ensuring reproducibility, and the need to address ethical, legal, and social implications. This body of work lays a solid foundation for addressing these challenges. It reflects a growing consensus that the promise of personalized medicine for complex chronic diseases can only be realized through multidisciplinary collaboration and inclusive research.</p><p>We hope this special issue not only informs but also inspires innovation, continued collaboration, and concrete action toward accelerating the adoption of personalized medicine in cardiorenal care.</p><p>A.L. is employed by Mosaiques Diagnostics. All other authors declare no conflict of interest.</p><p>Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the granting authorities. Neither the European Union nor the granting authority can be held responsible for them.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 11-12","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202500093","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proteomics","FirstCategoryId":"99","ListUrlMain":"https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202500093","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
We are living in an omics era, in which molecular profiling technologies can detect thousands of molecules across multiple biological layers. Yet chronic diseases—such as chronic kidney disease (CKD) and cardiovascular disease (CVD)—are still diagnosed only after overt signs and symptoms appear, relying on biomarkers that indicate established organ damage (e.g., estimated glomerular filtration rate (eGFR), albuminuria, troponin T, natriuretic peptides) [1]. In other words, by the time a chronic disease is recognized, curative treatment is generally no longer possible, as irreversible organ damage has already occurred. These conditions are termed “chronic” because, once they develop, patients live with them for the rest of their lives. Additionally, their life expectancy is shorter with a significant loss of quality of life.
Preventive measures to reduce the global burden of chronic diseases are therefore of paramount importance. The impact is enormous: in 2021, CKD caused 1.5 million deaths [2], while CVD accounted for more than 20 million deaths [3], with ischemic heart disease the leading and CKD the eleventh leading cause of mortality worldwide. Disability-adjusted life-years (DALYs) totaled 212 million for ischemic heart disease and 44.5 million for CKD [4]. In addition, the economic impact of both CKD and CVD is huge and is estimated to further increase in the coming years [5-7]. However, diagnosed cases represent only the “tip of the iceberg” (Figure 1); most patients remain undiagnosed because these diseases develop silently and progressively over the years.
CKD and CVD originate at the molecular level (bottom of the iceberg), are tightly interconnected—each increasing the risk of the other—and share common risk factors such as diabetes and hypertension. Additionally, therapies overlap for example, in patients with established CKD, renin–angiotensin system inhibitors, sodium-glucose co-transporter 2 inhibitors, and the non-steroidal mineralocorticoid receptor agonist finerenone reduce not only the risk of kidney disease progression but also cardiovascular events [3]. Considering the continuum of disease development, it is logical to intervene as early as possible, when the disease-associated changes are only at the molecular level. Moreover, early intervention has been demonstrated to be the most effective approach. In fact, intervention before irreversible organ damage should ideally even prevent onset of chronic disease. At the same time, no single biomarker can capture the complexity of these systemic disorders, which involve multiple organs and show marked heterogeneity in progression and treatment response. The societal, healthcare, and economic burden of CKD and CVD underscores the need for personalized, omics-based approaches that accommodate this multifactorial complexity and enable personalized intervention.
Personalized medicine represents a major shift in healthcare by tailoring prevention, diagnosis, and treatment to each individual's biological profile. This is enabled by advances in omics technologies, data processing, analytical tools, and artificial intelligence (AI). By integrating multilayer molecular data, diseases can be characterized more precisely and detected before clinical symptoms appear, allowing earlier and more targeted interventions. Such an approach is already well established in oncology, where several therapies are matched to tumor molecular profiles [8].
With this aim, the PerMediK program, a European research initiative dedicated to accelerating the development and clinical implementation of personalized medicine for CKD and CVD, was established. It fosters interdisciplinary collaboration across omics science, computational biology, and clinical research, supporting innovation in biomarker discovery, therapeutic targeting, and patient stratification, with a strong emphasis on data integration, reproducibility, and equity in healthcare.
This Special Issue, entitled Omics in Personalized Management of Cardiovascular and Kidney Disease, is dedicated to exploring the application of omics and computational methodologies to better understand and manage CKD and CVD in a personalized manner. The contributions span the continuum from basic science to translational and clinical applications, highlighting the transformative potential of personalized medicine in chronic disease management, as summarized below.
Rroji et al., Beige et al., and Lopes et al. each contribute distinct yet complementary perspectives on how multi-omics and computational approaches can transform chronic disease management [9-11].
Building the case for personalized medicine, Rroji et al. provide an overview of how integrated multi-omics and machine learning models can overcome the limitations of conventional markers such as eGFR and albuminuria, enabling early risk prediction and individualized therapeutic strategies in CKD [9]. Beige et al. contribute a clinically grounded viewpoint advocating for the incorporation of urinary proteomics into routine care, especially to detect early-stage disease and guide non-invasive management of both CKD and cardiovascular comorbidities [10]. A roadmap for integrating omics datasets with machine learning approaches to support early diagnosis, risk prediction, and cost-effective personalized treatment in CKD is presented by Lopes et al. [11]. They emphasize not only the technical advances in supervised and unsupervised machine learning models but also the critical importance of preclinical validation, health economic assessment, and interdisciplinary collaboration in translating these tools into practice.
These contributions collectively underline the importance of aligning technological innovation with clinical needs and underscore the potential of omics to improve care trajectories in complex chronic diseases. At the same time, the implementation of the findings should move forward to ultimately benefit the community.
Urinary peptidomics, a cornerstone of this issue, is by now a well-established powerful non-invasive method to assess kidney and cardiovascular health. Several studies in this collection exemplify how urinary peptide profiling can be leveraged to explore both population-level risk and mechanistic insights into disease.
In a large multi-ethnic study, An et al. illustrate how urinary peptide profiling can provide population-level insights, identifying eight proteins that may underlie differential susceptibility to salt sensitivity and hypertension-related complications between Black and White populations [12]. This work underscores the importance of inclusive omics research in understanding population-specific disease risks.
Fibrosis, a hallmark of CKD, is another area where urinary peptidomics is proving to be highly informative. Martin et al. demonstrated that urinary peptides derived from collagen type III degradation, incorporating the sequence targeted by the antibody-based C3M assay, are associated with kidney function decline and fibrosis [13]. Mina et al. extend this investigation to collagen type I degradation, proposing a model of stepwise breakdown process and the potential role of impaired degradation in disease progression [14]. Together, these studies underscore the diagnostic and mechanistic value of urinary peptidomics to assess diseases through the lens of protein degradation, a particularly relevant yet understudied component in fibrosis.
While urine offers a direct window into kidney-specific processes and to some extent, systemic changes due to its origin from glomerular filtration of the blood, plasma provides a broader perspective on systemic changes—particularly those relevant to the cardiovascular system. To capture these systemic signatures, Fernandez et al. have developed and validated a robust CE-MS pipeline for plasma peptidomics [15]. This approach complements urinary profiling and may offer more direct and detailed insight into inflammation, coagulation, and other vascular processes critical in both CKD and CVD. The study by Fernandez et al. also exemplifies the importance of robust, standardized workflows, particularly as omics technologies continue to evolve and generate increasingly complex datasets. Establishing such methodological consistency is key to ensure reproducibility and support subsequent clinical implementation.
Moving beyond traditional models, Bourdakou et al. present a compelling systems biology study that utilizes gene expression data from human induced pluripotent stem cell-derived cardiomyocytes exposed to spaceflight conditions [16]. Their findings implicate oxidative stress and nuclear factor erythroid 2-related factor 2 (NRF2) signaling in cardiovascular dysfunction, and their integrative pipeline identifies potential repurposable drugs. This unique approach underscores how omics-driven translational tools can inform disease mechanisms and therapeutic opportunities, even in unconventional settings.
García-Sáez et al. further extend the translational potential of omics by comparing molecular signatures across species [17]. Their work aims to improve the reliability of preclinical models, thereby enhancing the relevance and predictability of findings when applied to human disease.
The search for new therapeutic strategies is another highly relevant focus of this special issue. Drug repurposing is a cost-effective, time-efficient approach to finding new therapeutic uses for existing drugs. Perco et al. provide a comprehensive overview of computational drug repositioning in cardiorenal disease [18]. Their viewpoint outlines how omics-derived signatures, protein networks, and machine learning can reveal novel drug-disease relationships. Bourdakou et al. complement this perspective by demonstrating another practical application of these methods to identify cardiovascular drug candidates following spaceflight-induced stress [16]. Together, these studies highlight the efficiency and promise of leveraging existing pharmacological agents for new indications using omics-driven insights, an approach of great importance in the treatment of complex diseases.
No personalized medicine effort is complete without attention to ethical, legal, and social implications. Azéma et al. explore this dimension through their work in the KidneySign project, calling for the active engagement of patients, ethicists, and multidisciplinary stakeholders early in the research process to ensure that personalized medicine evolves responsibly [19]. As mentioned above, the study by An et al., explicitly considering racial diversity, emphasizes the imperative for representative and equitable representation in omics research [12].
Taken together, the contributions to this special issue demonstrate the clinical relevance and growing maturity of omics and computational approaches in personalized medicine for chronic diseases, with existing platforms ready for implementation. They underscore the versatility of these technologies across the entire spectrum of biomedical research and clinical care.
From insights into collagen turnover and hypertension risk to AI-driven biomarker discovery, drug repositioning, and cost-effective patient stratification, this collection showcases the rapid progress being made. Importantly, these efforts also illustrate the feasibility of moving toward real-world applications (and in some cases implementation), supported by robust technologies and validated workflows.
Yet, key challenges remain. These include the integration of omics into routine clinical practice, standardization of data pipelines, validation ensuring reproducibility, and the need to address ethical, legal, and social implications. This body of work lays a solid foundation for addressing these challenges. It reflects a growing consensus that the promise of personalized medicine for complex chronic diseases can only be realized through multidisciplinary collaboration and inclusive research.
We hope this special issue not only informs but also inspires innovation, continued collaboration, and concrete action toward accelerating the adoption of personalized medicine in cardiorenal care.
A.L. is employed by Mosaiques Diagnostics. All other authors declare no conflict of interest.
Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the granting authorities. Neither the European Union nor the granting authority can be held responsible for them.
期刊介绍:
PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.