Multi-Disciplinary and Omics-Driven Approaches to Advance Personalized Medicine in Cardiovascular and Chronic Kidney Disease

IF 3.9 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Proteomics Pub Date : 2025-06-27 DOI:10.1002/pmic.202500093
Griet Glorieux, Julie Klein, Agnieszka Latosinska
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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. 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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.

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多学科和组学驱动的方法推进心血管和慢性肾脏疾病的个性化医疗
我们生活在一个组学时代,在这个时代,分子分析技术可以在多个生物层中检测到数千个分子。然而,慢性疾病,如慢性肾病(CKD)和心血管疾病(CVD),仍然只能在出现明显的体征和症状后才能诊断,依赖于指示已确定的器官损伤的生物标志物(例如,估计的肾小球滤过率(eGFR)、蛋白尿、肌钙蛋白T、利钠肽)[1]。换句话说,当一种慢性疾病被确诊时,由于已经发生了不可逆转的器官损伤,治愈性治疗通常已经不可能了。这些疾病被称为“慢性”,因为一旦发病,患者将伴随其一生。此外,他们的预期寿命更短,生活质量显著下降。因此,减少全球慢性病负担的预防措施至关重要。影响是巨大的:2021年,CKD造成150万人死亡,而CVD造成2000多万人死亡,其中缺血性心脏病是全球第一大死亡原因,CKD是第11大死亡原因。缺血性心脏病的残疾调整生命年(DALYs)为2.12亿,CKD为4450万。此外,CKD和CVD的经济影响都是巨大的,预计未来几年将进一步增加[5-7]。然而,确诊病例仅代表“冰山一角”(图1);大多数患者仍未得到诊断,因为这些疾病是悄无声息地、多年来逐渐发展的。CKD和CVD起源于分子水平(冰山底部),它们紧密相连——每一种都增加了另一种的风险——并且有共同的风险因素,如糖尿病和高血压。此外,治疗重叠,例如,在已确定的CKD患者中,肾素-血管紧张素系统抑制剂、钠-葡萄糖共转运蛋白2抑制剂和非甾体矿物皮质激素受体激动剂芬纳酮不仅可以降低肾脏疾病进展的风险,还可以降低心血管事件的风险。考虑到疾病发展的连续性,当疾病相关的变化仅在分子水平上时,尽早干预是合乎逻辑的。此外,早期干预已被证明是最有效的方法。事实上,在不可逆器官损伤之前进行干预,理想情况下甚至可以预防慢性疾病的发生。同时,没有单一的生物标志物可以捕捉到这些系统性疾病的复杂性,这些疾病涉及多个器官,在进展和治疗反应方面表现出明显的异质性。CKD和CVD的社会、医疗和经济负担强调了个性化、基于组学的方法的需求,以适应这种多因素复杂性并实现个性化干预。个性化医疗通过根据每个人的生物特征定制预防、诊断和治疗,代表了医疗保健领域的重大转变。这得益于组学技术、数据处理、分析工具和人工智能(AI)的进步。通过整合多层分子数据,可以更精确地表征疾病,并在临床症状出现之前发现疾病,从而实现更早、更有针对性的干预。这种方法已经在肿瘤学中得到了很好的应用,其中有几种治疗方法与肿瘤分子谱相匹配。为此,PerMediK项目成立,这是一项欧洲研究计划,致力于加速CKD和CVD个性化医疗的开发和临床实施。它促进了组学科学、计算生物学和临床研究之间的跨学科合作,支持生物标志物发现、治疗靶向和患者分层方面的创新,并强调了医疗保健领域的数据集成、可再现性和公平性。这期特刊题为《组学在心血管和肾脏疾病的个性化管理》,致力于探索组学和计算方法的应用,以更好地以个性化的方式了解和管理CKD和CVD。这些贡献跨越了从基础科学到转化和临床应用的连续体,突出了个性化医学在慢性疾病管理中的变革潜力,总结如下。Rroji等人、Beige等人和Lopes等人各自对多组学和计算方法如何改变慢性疾病管理贡献了不同但互补的观点[9-11]。Rroji等人建立了个体化医疗的案例,概述了集成多组学和机器学习模型如何克服传统标志物(如eGFR和蛋白尿)的局限性,从而实现CKD bb0的早期风险预测和个性化治疗策略。米色等人。 提出基于临床的观点,提倡将尿蛋白质组学纳入常规护理,特别是在早期疾病检测和指导CKD和心血管合并症的无创管理方面。Lopes等人提出了将组学数据集与机器学习方法相结合的路线图,以支持CKD的早期诊断、风险预测和具有成本效益的个性化治疗。他们不仅强调了监督和无监督机器学习模型的技术进步,还强调了临床前验证、健康经济评估和跨学科合作在将这些工具转化为实践中的关键重要性。这些贡献共同强调了将技术创新与临床需求结合起来的重要性,并强调了组学在改善复杂慢性疾病护理轨迹方面的潜力。与此同时,研究结果的实施应向前推进,最终使社会受益。尿肽组学是这一问题的基石,目前是一种公认的强大的非侵入性方法来评估肾脏和心血管健康。本系列中的几项研究举例说明了如何利用尿肽谱分析来探索人群水平的风险和疾病的机制见解。在一项大型的多种族研究中,An等人阐述了尿肽谱分析如何能够提供人群水平的见解,确定了8种蛋白质,这些蛋白质可能是黑人和白人人群对盐敏感性和高血压相关并发症易感性差异的基础[10]。这项工作强调了包容性组学研究在了解人群特异性疾病风险方面的重要性。纤维化,CKD的标志,是尿肽组学被证明具有高度信息性的另一个领域。Martin等人证明了III型胶原降解产生的尿肽,包括基于抗体的C3M测定的靶向序列,与肾功能下降和纤维化[13]相关。Mina等人将这项研究扩展到I型胶原降解,提出了一个逐步分解过程的模型,以及降解受损在疾病进展中的潜在作用[14]。总之,这些研究强调了尿肽组学通过蛋白质降解来评估疾病的诊断和机制价值,蛋白质降解是纤维化中一个特别相关但尚未得到充分研究的成分。尿液为肾脏特异性过程提供了一个直接的窗口,在某种程度上,由于其起源于血液的肾小球滤过,血浆为全身变化提供了一个更广阔的视角,特别是与心血管系统相关的变化。为了捕获这些系统特征,Fernandez等人开发并验证了血浆肽组学[15]的强大CE-MS管道。这种方法补充了尿谱分析,可以更直接、更详细地了解炎症、凝血和其他CKD和CVD中关键的血管过程。Fernandez等人的研究还举例说明了健壮、标准化工作流程的重要性,特别是在组学技术不断发展并产生越来越复杂的数据集的情况下。建立这种方法的一致性是确保可重复性和支持后续临床实施的关键。超越传统模型,Bourdakou等人提出了一项引人注目的系统生物学研究,该研究利用暴露于航天条件下的人类诱导多能干细胞衍生的心肌细胞的基因表达数据。他们的发现暗示了氧化应激和核因子-红细胞2相关因子2 (NRF2)信号在心血管功能障碍中的作用,并且他们的整合管道确定了潜在的可重复利用的药物。这种独特的方法强调了组学驱动的翻译工具如何为疾病机制和治疗机会提供信息,即使在非常规环境中也是如此。García-Sáez等人通过比较不同物种[17]的分子特征进一步拓展了组学的翻译潜力。他们的工作旨在提高临床前模型的可靠性,从而提高研究结果在应用于人类疾病时的相关性和可预测性。寻找新的治疗策略是本期特刊的另一个高度相关的焦点。药物再利用是为现有药物寻找新的治疗用途的一种成本效益高、时间效率高的方法。Perco等人提供了计算药物重新定位在心肾疾病[18]的全面概述。他们的观点概述了组学衍生的特征、蛋白质网络和机器学习如何揭示新的药物-疾病关系。Bourdakou等人通过展示这些方法的另一种实际应用来补充这一观点,以确定航天飞行引起的应激bb0后的心血管候选药物。 总之,这些研究突出了利用组学驱动的见解利用现有药物治疗新适应症的效率和前景,这是治疗复杂疾病的一种非常重要的方法。没有对伦理、法律和社会影响的关注,任何个性化医疗努力都是不完整的。azsamma等人通过他们在肾脏设计项目中的工作探索了这一维度,呼吁患者、伦理学家和多学科利益相关者在研究过程的早期积极参与,以确保个性化医疗负责任地发展。如上所述,An等人的研究明确考虑了种族多样性,强调了组学研究中代表性和公平代表性的必要性[10]。总而言之,对本期特刊的贡献表明,组学和计算方法在慢性病个性化医疗中的临床相关性和日益成熟,现有平台已准备好实施。它们强调了这些技术在整个生物医学研究和临床护理领域的多功能性。从对胶原蛋白转化和高血压风险的洞察,到人工智能驱动的生物标志物发现、药物重新定位和具有成本效益的患者分层,这些收集展示了正在取得的快速进展。重要的是,这些努力还说明了在健壮的技术和经过验证的工作流的支持下,向现实世界应用程序(在某些情况下实现)移动的可行性。然而,关键挑战依然存在。这些挑战包括将组学整合到常规临床实践、数据管道的标准化、确保可重复性的验证,以及解决伦理、法律和社会影响的需要。这项工作为应对这些挑战奠定了坚实的基础。它反映了一种日益增长的共识,即只有通过多学科合作和包容性研究才能实现复杂慢性疾病个性化医疗的承诺。我们希望这期特刊不仅能提供信息,还能激发创新、持续合作和具体行动,以加速在心肾护理中采用个性化医疗。受雇于Mosaiques Diagnostics公司。所有其他作者声明无利益冲突。然而,所表达的观点和意见仅代表作者的观点和意见,并不一定反映欧洲联盟或授予当局的观点和意见。欧盟和授权机构都不能对此负责。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
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.
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