Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report

Artificial intelligence and the impact of multiomics on the reporting of case reports

The integration of artificial intelligence (AI) and multiomics has transformed clinical and life sciences, enabling precision medicine and redefining disease understanding. Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022, with AI research tripling during this period. Multiomics fields, including genomics and proteomics, also advanced, exemplified by the Human Proteome Project achieving a 90% complete blueprint by 2021. This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting. A review of studies and case reports was conducted to evaluate AI and multiomics integration. Key areas analyzed included diagnostic accuracy, predictive modeling, and personalized treatment approaches driven by AI tools. Case examples were studied to assess impacts on clinical decision-making. AI and multiomics enhanced data integration, predictive insights, and treatment personalization. Fields like radiomics, genomics, and proteomics improved diagnostics and guided therapy. For instance, the "AI radiomics, genomics, oncopathomics, and surgomics project" combined radiomics and genomics for surgical decision-making, enabling preoperative, intraoperative, and postoperative interventions. AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data. AI and multiomics enable standardized data analysis, dynamic updates, and predictive modeling in case reports. Traditional reports often lack objectivity, but AI enhances reproducibility and decision-making by processing large datasets. Challenges include data standardization, biases, and ethical concerns. Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine. AI and multiomics integration is revolutionizing clinical research and practice. Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential. Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.

Fluid biomarkers in atypical Parkinsonism: current state and future perspectives

Diagnosing Atypical Parkinsonian Syndromes (APS) may be challenging due to overlapping clinical features of Parkinson's disease (PD), and the lack of pathognomonic diagnostic tests. Fluid biomarkers can be useful tools that make it easier to identify and track different APS. Objectives: this narrative review aim to update the current state of fluid biomarker research in APS and their potential implications in clinical practice. A comprehensive literature search was conducted in PubMed and Scopus using the following terms: "Aβ42 amyloid beta with 42 amino acids'', " alpha-synuclein'', "Atypical Parkinsonian Syndromes'', "corticobasaldegeneration'', "C reactive protein'', "cerebrospinal fluid'', "dementia with Lewy bodies'', "multiple system atrophy'', "neurofilament light, oligomericαsyn, phosphorylated α -syn'', "tau phosphorylated at threonine 181'', "progressive supranuclear palsy'', "Seeding Amplification Assay'', "t-tau; total tau". The lack of high-affinity α-syn antibodies and ligands may contribute to α-syn's low efficacy as a diagnostic biomarker of APS. Cerebrospinal fluid (CSF) biomarkers reflecting Alzheimer pathology, axonal damage (neurofilament light chain) add valuable diagnostic and prognostic information in the neurochemical characterization of APS. Inflammatoryand microRNAs markers need to be further validated before their clinical use. Seeding Amplification Assays (SAA), despite their high sensitivity and specificity, are at this point used only as a research tool, and they are not quantitative or reflective of disease severity. Biomarker research for early identification and prognosis of APS patients requires multicenter collaboration, validation, and AI-based diagnostics, despite immature biological classification systems.

Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine

Despite advances in cardiovascular care and improved outcomes, fragmented healthcare systems, nonequitable access to health care, and nonuniform and unbiased collection and access to healthcare data have exacerbated disparities in healthcare provision and further delayed the technological-enabled implementation of precision medicine. Precision medicine relies on a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic health records. Big data approaches in noncardiovascular healthcare domains have helped improve efficiency and expedite the development of novel therapeutics; therefore, applying such an approach to cardiovascular precision medicine is an opportunity to further advance the field. Several endeavors, including the American Heart Association Precision Medicine platform and public-private partnerships (such as BigData@Heart in Europe), as well as cloud-based platforms, such as Terra used for the National Institutes of Health All of Us, are attempting to temporally and ontologically harmonize data. This state-of-the-art review summarizes best practices used in cardiovascular genomic and precision medicine and provides recommendations for systems' requirements that could enhance and accelerate the integration of these platforms.

Guidance and Position of RINN22 regarding Precision Nutrition and Nutriomics

BACKGROUND

Precision nutrition is based on the integration of individual's phenotypical and biological characteristics including genetic variants, epigenetic marks, gut microbiota profiles, and metabolite fingerprints as well as medical history, lifestyle practices, and environmental and cultural factors. Thus, nutriomics areas including nutrigenomics, nutrigenetics, nutriepigenetics, nutrimetabolomics, and nutrimetagenomics have emerged to comprehensively understand the complex interactions between nutrients, diet, and the human body's molecular processes through precision nutrition.

SUMMARY

This document from the Ibero-American Network of Nutriomics and Precision Nutrition (RINN22; https://rinn22.com/) provides a comprehensive overview of the concepts of precision nutrition approaches to guide their application in clinical and public health as well as establish the position of RINN22 regarding the current and future state of precision nutrition.

KEY MESSAGES

The progress and participation of nutriomics to precision nutrition is an essential pillar for addressing diet-related diseases and developing innovative managing strategies, which will be promoted by advances in bioinformatics, machine learning, and integrative software, as well as the description of specific novel biomarkers. In this context, synthesizing and critically evaluating the latest developments, potential applications, and future needs in the field of nutrition is necessary with a holistic perspective, incorporating progress in omics technologies aimed at precision nutrition interventions. This approach must address and confront healthy, social, food security, physically active lifestyle, sanitation, and sustainability challenges with preventive, participatory, and predictive strategies of personalized, population, and planetary nutrition for a precision tailored health.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research

The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.

Metabolomic and proteomic applications to exercise biomedicine

Objectives

'OMICs encapsulates study of scaled data acquisition, at the levels of DNA, RNA, protein, and metabolite species. The broad objectives of OMICs in biomedical exercise research are multifarious, but commonly relate to biomarker development and understanding features of exercise adaptation in health, ageing and metabolic diseases.

Methods

This field is one of exponential technical (i.e., depth of feature coverage) and scientific (i.e., in health, metabolic conditions and ageing, multi-OMICs) progress adopting targeted and untargeted approaches.

Results

Key findings in exercise biomedicine have led to the identification of OMIC features linking to heritability or adaptive responses to exercise e.g., the forging of GWAS/proteome/metabolome links to cardiovascular fitness and metabolic health adaptations. The recent addition of stable isotope tracing to proteomics ('dynamic proteomics') and metabolomics ('fluxomics') represents the next phase of state-of-the-art in 'OMICS.

Conclusions

These methods overcome limitations associated with point-in-time 'OMICs and can be achieved using substrate-specific tracers or deuterium oxide (D2O), depending on the question; these methods could help identify how individual protein turnover and metabolite flux may explain exercise responses. We contend application of these methods will shed new light in translational exercise biomedicine.

Multiomics tools for improved atherosclerotic cardiovascular disease management

Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple 'omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care.

Clinical Blood Metabogram: Application to Overweight and Obese Patients

Recently, the concept of a mass spectrometric blood metabogram was introduced, which allows the analysis of the blood metabolome in terms of the time, cost, and reproducibility of clinical laboratory tests. It was demonstrated that the components of the metabogram are related groups of the blood metabolites associated with humoral regulation; the metabolism of lipids, carbohydrates, and amines; lipid intake into the organism; and liver function, thereby providing clinically relevant information. The purpose of this work was to evaluate the relevance of using the metabogram in a disease. To do this, the metabogram was used to analyze patients with various degrees of metabolic alterations associated with obesity. The study involved 20 healthy individuals, 20 overweight individuals, and 60 individuals with class 1, 2, or 3 obesity. The results showed that the metabogram revealed obesity-associated metabolic alterations, including changes in the blood levels of steroids, amino acids, fatty acids, and phospholipids, which are consistent with the available scientific data to date. Therefore, the metabogram allows testing of metabolically unhealthy overweight or obese patients, providing both a general overview of their metabolic alterations and detailing their individual characteristics. It was concluded that the metabogram is an accurate and clinically applicable test for assessing an individual's metabolic status in disease.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.