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PubMed2026

Five tenets for advancing evidence-based precision medicine.

Precision medicine for complex diseases uses individual-level characteristics to improve prediction of risk, therapeutic response and prognosis. Many precision medicine studies leverage existing data types and analytic methods to reveal new insights; however, beyond oncology, there has been limited success in translating precision medicine research for complex diseases into clinical practice. Thus, there is a need to identify areas for improvement, particularly in translation-oriented analytical methods and study designs. In this perspective article, we outline five fundamental tenets to enhance the efficient clinical translation of precision medicine research. These tenets focus on addressing (1) heterogeneity in risk, response and prognosis; (2) signal robustness; (3) structured statistical benchmarking against key performance indicators; (4) precision trial designs; and (5) risks and benefits to individuals and society. Our intention is to promote clinically meaningful, reproducible, scalable and equitable health outcomes through precision medicine, beyond those possible through contemporary approaches.

Precision Medicine↗

PubMed2026

Artificial intelligence agents and agentic artificial intelligence applied to precision medicine.

Precision medicine seeks to individualise care by integrating multimodal biomedical data, yet most deployed clinical artificial intelligence (AI) remains assistive, providing predictions without managing workflows or adapting autonomously. Agentic AI, built on large language models (LLMs), has emerged as a paradigm characterised by autonomy, goal-directed reasoning, memory, planning and tool use. This review synthesises evidence on agentic AI and LLMs applied to precision medicine, encompassing drug discovery, genomics, oncology, rare disease diagnostics and clinical pharmacology. This review also examines architectural components, recent validation milestones and emerging challenges, including hallucination, sociodemographic bias and evolving regulatory frameworks across the FDA, the EU AI Act and the WHO.

agentic AI↗

PubMed2026

Genomic Medicine Sweden: Advancing precision medicine at the national level.

High-throughput sequencing has transformed clinical diagnostics of rare diseases (RD), cancer and infectious diseases by enabling the identification of disease-causing genetic alterations and facilitating individualised treatment and care. In response to these advances, Genomic Medicine Sweden (GMS) was established in 2017 as a national collaborative effort to accelerate implementation of genomics-based precision medicine within Sweden's regionally organized, publicly funded healthcare system. GMS brings together the seven university healthcare regions and their associated medical faculties, in collaboration with healthcare regions across Sweden, Science for Life Laboratory, patient organizations, industry and governmental agencies. Activities are coordinated through national disease-specific expert groups, supported by cross-cutting functions in bioinformatics, health economics, ethics, education and patient engagement. At the operational level, seven Genomic Medicine Centres, embedded at university hospitals, develop and deliver harmonised genomic diagnostics nationwide. The National Genomics Platform provides secure infrastructure for large-scale data storage, analysis, and national and international data sharing. Following initial project-based funding, GMS now receives long-term governmental support. This review describes the national implementation of genomic-based precision diagnostics, discusses challenges and lessons learnt, and highlights key milestones across disease areas, including whole-genome sequencing in RD and paediatric cancer, comprehensive genomic profiling of haematological malignancies and solid tumours, pathogen genomics in microbiology, pharmacogenomic testing and emerging applications of polygenic risk scores in complex diseases. Collectively, these efforts have contributed to more than 500,000 genomic tests being performed within Swedish healthcare between 2017 and 2025. Finally, we outline future diagnostic needs and priority areas to ensure sustainable, scalable and equitable access to precision medicine.

Precision Medicine↗

PubMed2026

A cross-sectional study of genomic knowledge comfort, attitudes, ethical and educational perspectives on precision medicine among medical students in Ecuador.

BACKGROUND: Precision medicine is increasingly transforming clinical practice, yet its effective implementation depends on adequately trained healthcare professionals. OBJECTIVE: This study assessed genomic knowledge comfort, attitudes, ethical perceptions, and educational perspectives regarding precision medicine among medical students in Samborondón, Greater Guayaquil, Ecuador. METHODS: A cross-sectional survey was conducted between August and November 2025 using a structured questionnaire. A total of 340 students participated. Descriptive and inferential statistical analyses, including non-parametric tests and Spearman correlation, were performed. RESULTS: Participants demonstrated relatively high but uneven genomic knowledge comfort (median 81.3%) and positive attitudes toward precision medicine (65.6%), alongside moderate ethical (62.5%) and educational perception scores (68.8%). While students strongly recognized the importance of precision medicine, perceived preparedness remained limited. Lower confidence was observed in advanced topics such as pharmacogenomics and next-generation sequencing. Ethical concerns were more pronounced at the societal level, particularly regarding health inequities, rather than individual risks. Correlation analyses revealed generally weak associations across domains, with only a moderate relationship between ethical and educational perceptions. CONCLUSION: These outcomes highlight a gap between acceptance and readiness, suggesting fragmented competency development. Strengthening curricula through integrated, applied, and context-specific training is essential to support effective implementation of precision medicine in low- and middle-income settings.

Attitudes↗

PubMed2026

Identifying General Practitioners, Nurses, and Pharmacists Training Needs in Precision Medicine: A Survey Study.

INTRODUCTION: The clinical advances of precision medicine, elevating clinical decision-making through use of genetic and genomic data, lifestyle, and environment factors, is improving patient outcomes. Health care professionals report they are not competent or confident to deliver precision medicine to patients. To design continuing education for advanced practice nurses, general practitioners, and pharmacists, this research investigates perceived knowledge, importance, and self-efficacy in precision medicine. METHODS: Items were informed from results of a literature review and focus group study. A one-sample t test was used to compare differences for each item toward the theoretical neutral value (indifferent in our case, to number 3). To analyze differences between professions, parametric analyses of variance (ANOVA) was used. RESULTS: A lack of time, further complicated by a lack of knowledge about precision medicine services, and how to safely share patient data, with a perceived limited network of professionals in precision medicine were reported. Respondents demonstrated low confidence in precision medicine topics while indicating a willingness to pursue training aimed at improving their genetic and genomic competencies. Participants rated topics as only slightly important to their current professional roles. No significant differences were found across professional groups. DISCUSSION: Flexible formats for continuing education aligned to needs are required to target the knowledge and skills gap in precision medicine. Improved knowledge on topics including ethics, legal frameworks, and precision services is needed. Effective educational interventions aimed at enhancing the confidence and competence of health care professionals are essential to making precision care a reality for patients.

CPD↗

PubMed2026

Current knowledge in pharmacogenomics and precision medicine: perspectives of the PGRN global PGx committee on improving drug therapies in underrepresented ethnic populations.

A precision medicine strategy is likely to be more impactful, when pharmacogenomics (PGx) guided selection of drugs and dosage wherever applicable is implemented across the globe. In regions where resources are disproportionately distributed, PGx implementation in routine clinical care can play a critical role in ensuring the optimal use of limited healthcare infrastructure. At present, PGx data from the majority of the distinct ethnic populations across Asia, Africa, and South America is limited. While international consortia, working groups, and scientific bodies have made significant contributions toward evaluating the evidence for PGx implementation, the majority of existing guidelines and recommendations are derived primarily from studies conducted in a limited number of ethnic groups. Precision Medicine Initiatives in countries like Korea, Taiwan, and Malaysia and PGx organizations like the African Institute of Biomedical Science and Technology (AiBST), Consortium for Genomics & Therapeutics in Africa (CGTA), implementation of pharmacogenetic testing for the effective care and treatment in Africa, Greater Middle East (GME) whole exome sequencing program, Ibero-American Network of Pharmacogenetics and Pharmacogenomics (RIBEF), Latin American Society of Pharmacogenomics and Personalized Medicine (SOLFAGEM), Latin American Network for Validation and Implementation of Pharmacogenomic Clinical Guidelines (RELIVAF), IndiGen initiative, Southeast Asian Pharmacogenomics Research Network (SEAPharm), are working toward consolidating the PGx presence in these regions.

Precision Medicine↗

PubMed2026

Precision medicine in combating antimicrobial resistance: A comprehensive review.

Antimicrobial resistance (AMR) represents one of the most pressing threats to global public health, undermining the effectiveness of modern antimicrobial therapy and challenging decades of medical progress. This comprehensive review examines the transition from broad-spectrum empirical therapy toward precision medicine as an integrated framework for improving antimicrobial use and combating AMR. Precision medicine seeks to tailor treatment decisions by combining pathogen-specific genomic and resistance data with relevant host characteristics to optimize therapy while limiting unnecessary antimicrobial exposure and the selective pressures that drive resistance. The review synthesizes advances reported from 2020, highlighting established and emerging approaches including rapid molecular diagnostics, next-generation sequencing, CRISPR-based detection, machine learning (ML)-assisted decision support, precision dosing, and targeted therapeutics such as bacteriophage therapy, antimicrobial peptides, and bacterial proteolysis-targeting chimeras. Rather than functioning as isolated technologies, these approaches achieve their greatest clinical value when integrated within antimicrobial stewardship programs and a One Health framework that recognizes the interconnected human, animal, and environmental drivers of resistance. Despite considerable progress, important challenges remain, including equitable access to advanced technologies, interpretation of increasingly complex datasets, workforce and infrastructure limitations, and evolving regulatory pathways for novel diagnostics and therapeutics. This review concludes that while precision medicine is not a standalone solution, its successful implementation will depend on coordinated integration of diagnostics, host factors, computational tools, pharmacological optimization, and stewardship strategies to improve patient outcomes while preserving the long-term effectiveness of existing antimicrobials.

Antimicrobial resistance↗

PubMed2026

Population proteomics for equitable precision medicine.

Population proteomics is emerging as a new framework for equitable precision medicine. By studying protein variation across populations, this field bridges population genomics and conventional proteomics to capture the functional molecular states through which genetic ancestry, environmental exposures and other contextual factors shape human health. Here, we discuss how recent advances are moving population proteomics beyond biomarker discovery toward equitable clinical translation through cross-population validation, mechanistic multiomics and global research infrastructures. Its ultimate promise is not to classify populations as fixed biological categories, but to make human diversity measurable, interpretable and clinically actionable for equitable precision medicine.

Journal Article↗

PubMed2026

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease↗

PubMed2026

Precision medicine and parental experience: a longitudinal study of psychosocial responses to germline genomic results in pediatric oncology.

INTRODUCTION: Precision medicine has become central to pediatric oncology, with germline genomic sequencing commonly integrated into routine care. Families must interpret complex genomic findings during emotionally vulnerable periods, generating mixed reactions ranging from clarity and relief to anxiety and uncertainty. Palliative care clinicians, genetic counselors, psychologists, social workers, and oncology providers may each contribute to supporting families as they interpret and integrate these findings over the course of a child's cancer care and beyond. Little is known about the trajectory of parental emotional and cognitive responses after receiving germline sequencing results, limiting clinicians' ability to anticipate support needs across the cancer care continuum. This study quantitatively examines parental emotional and cognitive responses across time following disclosure of germline sequencing results in a pediatric oncology setting. METHODS: Parents (n = 218) self-reported sequencing-related distress, positive feelings, intrusive thoughts, certainty, and self-efficacy using validated measures at two longitudinal follow-up points after disclosure of their child's germline test results. Outcomes were compared across germline test result types (pathogenic/likely pathogenic [P/LP], n = 31 [14%]; variants of uncertain significance [VUS], n = 86 [39%]; and negative, n = 101[46%]). RESULTS: Parents of children receiving P/LP or P/LP+VUS results reported significantly higher distress yet greater positive feelings than parents receiving negative results. Notably, certainty and self-efficacy increased from Timepoint 1 (median 254 days following return of results) to Timepoint 2 (median 537 days). Intrusive thoughts did not significantly differ by genetic result type or change over time; however, the factors contributing to intrusive thoughts could not be determined from the current study. DISCUSSION: These findings provide insight into how families adapt to germline genomic information following a pediatric cancer diagnosis. As precision medicine becomes increasingly embedded in pediatric oncology, structured follow-up and communication that address families' evolving informational and psychosocial needs are essential to ensure care that is scientifically precise, emotionally attuned, and centered on the family experience.

family-centered care↗

PubMed2026

Precision Medicine in Transfusion-Dependent and Non-Transfusion-Dependent β-Thalassemia: Toward Personalized Diagnosis and Therapy.

β-thalassemia comprises a clinically heterogeneous group of disorders in which anemia severity, transfusion exposure, iron loading, and organ complications vary widely among individuals. This structured narrative review summarizes practical applications of precision medicine in transfusion-dependent thalassemia (TDT) and non-transfusion-dependent thalassemia (NTDT), with explicit attention to which strategies apply to each clinical category. Literature indexed in PubMed and Scopus from 2000 to 2025 was reviewed using terms related to thalassemia, precision medicine, magnetic resonance imaging (MRI), chelation tailoring, next-generation sequencing (NGS), fetal hemoglobin (HbF) modifiers, luspatercept, mitapivat, hepcidin, gene therapy, gene editing, and artificial intelligence (AI). Evidence was synthesized descriptively because interventions, outcomes, and populations were heterogeneous, and no pooled meta-analysis was performed. In TDT, precision care is centered on individualized transfusion planning, extended red-cell antigen matching, MRI-guided cardiac and hepatic iron monitoring, organ-directed chelation intensification, and selection of disease-modifying or curative approaches. In NTDT, precision care emphasizes accurate phenotype classification, MRI liver iron concentration, because serum ferritin may underestimate iron burden, selective chelation, surveillance for NTDT-specific complications, and individualized use of agents that improve anemia. Personalized chelation should include deferiprone, either alone or in combination, when cardiac iron is increased. Comprehensive molecular diagnosis should include HBB together with HBA1 and HBA2 assessment, while secondary and tertiary modifiers help explain phenotypic variability and complication risk. Hepcidin and growth differentiation factor 15 (GDF-15) are discussed as investigational biomarkers; transferrin saturation is not recommended for routine iron-overload assessment in thalassemia. AI currently has its strongest role in screening and diagnosis, whereas risk-stratification models remain exploratory. Equitable implementation requires standardized TDT/NTDT pathways, regional MRI and genomics access, longitudinal registries, and multidisciplinary interpretation.

Humans↗

PubMed2026

Financing and health system capacity for precision medicine in Asia: a six country landscape analysis.

BACKGROUND: Precision medicine (PM) adoption is accelerating across Asia, but implementation remains uneven due to differences in financing, infrastructure, governance, and health-system readiness. OBJECTIVES: To examine how six Asian countries (Singapore, South Korea, China, Malaysia, Thailand, and Indonesia) adopt, finance, and integrate PM technologies, and identify common implementation patterns and challenges. METHODS: A landscape review of peer-reviewed literature, government publications, and HTA reports (2010-2026) was conducted, supplemented by stakeholder consultations. PM applications were grouped into public health screening (hereditary breast and ovarian cancer [HBOC] and familial hypercholesterolemia [FH] cascade testing), next-generation sequencing (NGS) applications (rare diseases, oncology, pharmacogenomics), and AI-enabled PM. Evidence was synthesized across access, awareness, reimbursement, and implementation. RESULTS: Public health genomic screening demonstrated the highest implementation readiness, followed by precision oncology, while rare disease diagnostics remained infrastructure-dependent and pharmacogenomics and AI-enabled PM platforms were at earlier stages. Four readiness profiles emerged: highly aligned systems; reimbursement-constrained systems with strong governance and infrastructure; systems strengthening governance, public financing and infrastructure; and strategy-led systems expanding implementation through pilot programs and referral centers. CONCLUSIONS: PM implementation across Asia remains heterogeneous. The identified readiness profiles highlight governance, financing, and infrastructure priorities for sustainable and equitable PM diffusion.

Asia↗

PubMed2025

Exploring precision medicine by utilizing individual genetic information for the management of Alzheimer's disease.

Alzheimer's Disease (AD) represents a formidable challenge in neurology, characterized by progressive neurodegeneration and cognitive decline. Traditional therapeutic approaches have failed to deliver significant outcomes, underscoring the need for innovative paradigms such as precision medicine. The review explores integrating genomic, biomarker-driven, and individualized therapeutic strategies to tackle AD. It examines the role of key genetic factors, including APOE and MTHFR polymorphisms, in influencing disease susceptibility and treatment responses. Advances in biomarker technologies, such as blood-based and imaging biomarkers, are highlighted for their potential in early diagnosis and patient stratification. Additionally, the review underscores the importance of tailoring interventions across different stages of AD, incorporating lifestyle modifications and emerging tools like artificial intelligence & recent patented technologies. Precision medicine offers a transformative pathway, aiming to deliver personalized, effective care that addresses the complex and multifactorial nature of AD. The paradigm shift promises improved clinical outcomes and enhanced patient quality of life.

Humans↗

PubMed2026

Osteoarthritis phenotypes: advancing precision medicine through clinical, structural, and molecular stratification.

PURPOSE: Osteoarthritis (OA) is now understood as a heterogeneous syndrome driven by diverse biological, biomechanical, metabolic, genetic, and molecular mechanisms. This variability explains differences in disease progression and treatment response, challenging the traditional "one-size-fits-all" approach. This review highlights OA phenotyping as a key step toward precision medicine, focusing on clinical, structural, and molecular classifications that inform individualized care. METHODS: A narrative review was conducted using a non-systematic search of major databases and Osteoarthritis Research Society International sources (2010-2026). Evidence was thematically synthesized across clinical, imaging, and molecular domains to characterize OA phenotypes and their potential relevance to precision medicine. RESULTS: Multiple OA phenotypes were identified: inflammatory, metabolic, biomechanical, cartilage-subchondral, pain-sensitization, and aging/senescence. These exhibit distinct clinical features, risk factors, and therapeutic responses. Imaging-based phenotypes (e.g., inflammatory, meniscus-cartilage, subchondral bone, atrophic, hypertrophic) and molecular endotypes (low turnover, structural damage, systemic inflammation) further refine stratification. Pain-structure discordance is notable in sensitization phenotypes and may predict poorer surgical outcomes. Joint-specific variations and emerging genomic and epigenetic insights underscore disease complexity. Advances in imaging, biomarkers, and machine learning may enable earlier detection and patient clustering, though clinical application remains limited. CONCLUSION: Phenotype- and endotype-based classification represents a critical advancement toward precision OA management. Tailored interventions based on stratification hold promise for improving outcomes; however, clinical translation remains limited by overlapping phenotypes, lack of validated biomarkers, and inconsistent results from phenotype-driven trials. Wider clinical adoption requires standardized definitions, validation across joints, and integration of multimodal diagnostic tools into routine practice.

Humans↗

PubMed2026

Precision medicine in mental health: applications, challenges, and recommendations.

Mental disorders represent a major and growing public health challenge in Europe and worldwide, characterised by marked clinical, biological, and functional heterogeneity, that limits the effectiveness of current diagnostic and therapeutic approaches. In recent years, advances in precision medicine have initiated a paradigm shift in psychiatry, offering new opportunities to improve prevention, prediction, diagnosis, treatment selection, and long-term management by integrating biological, psychological, social, and environmental information.This EPA Guidance Paper provides an overview of the current state of precision medicine in mental health and outlines its potential clinical, scientific, and policy implications. We review key advances in genomics, epigenetics, neuroimaging, transcriptomics, digital technologies, and artificial intelligence, highlighting their relevance across the full clinical pathway, from risk prediction and early detection to treatment personalisation and monitoring. We also examine major barriers to implementation, including limited biomarker validation, insufficient representativeness of research populations, ethical and regulatory challenges, data protection concerns, and inequalities in access across healthcare systems.Based on the available evidence, we propose strategic recommendations to support the responsible and equitable integration of precision approaches into mental health care in Europe. These include strengthening translational research, promoting multidisciplinary collaboration, updating regulatory and ethical frameworks, enhancing professional training, and prioritising mental health within national and European research and health agendas. By addressing these challenges, precision psychiatry has the potential to contribute to more effective, person-centred, and sustainable mental health care, while supporting innovation, reducing stigma, and improving outcomes for patients and society.

Humans↗

PubMed2025

AI-HOPE: an AI-driven conversational agent for enhanced clinical and genomic data integration in precision medicine research.

MOTIVATION: The growing complexity of clinical cancer research has fueled a surge in demand for automated bioinformatics tools capable of integrating clinical and genomic data to accelerate discovery efforts. RESULTS: We present the Artificial Intelligence Agent for High-Optimization and Precision Medicine (AI-HOPE), an AI-driven system that enables domain experts to conduct integrative data analyses through natural language interactions. Powered by Large Language Models, AI-HOPE interprets user instructions, converts them into executable code, and autonomously analyzes locally stored data. It supports flexible association studies, subset comparisons, clinical prevalence assessments and survival analyses. In addition, AI-HOPE enables global variable scans to identify features significantly associated with a user-defined outcome, making a powerful and intuitive tool for advancing precision medicine research. Importantly, its closed-system design prevents clinical data leakage. To demonstrate its utility, AI-HOPE was applied to The Cancer Genome Atlas data to address two clinical questions. First, it identified significant enrichment of TP53 mutations in late-stage colorectal cancer compared to early-stage cases. Second, it uncovered a strong association between KRAS mutations and poorer progression-free survival in FOLFOX-treated patients. These findings align with established literature and demonstrate AI-HOPE's ability to generate meaningful insights independently, without prior assumptions. By removing programming barriers and simplifying complex analyses, AI-HOPE bridges the gap between data complexity and research needs. With its scalable and adaptable framework, AI-HOPE has the potential to support diverse biomedical research fields, driving innovation and efficiency in translational studies. AVAILABILITY AND IMPLEMENTATION: The AI-HOPE software and demonstration data is available at https://github.com/Velazquez-Villarreal-Lab/AI-HOPE.

Precision Medicine↗

PubMed2025

Innovative strategies for mitochondrial dysfunction in myeloproliferative neoplasms a step toward precision medicine.

Myeloproliferative neoplasms (MPNs) are clonal disorders of hematopoietic stem cells characterized by aberrant proliferation of myeloid lineages, driven primarily by mutations in JAK2, CALR, and myeloproliferative leukemia, leading to constitutive activation of the JAK-STAT pathway. Emerging evidence highlights mitochondrial dysfunction as a key factor in MPN pathogenesis, contributing to increased reactive oxygen species production, mitochondrial DNA mutations, and dysregulated mitochondrial dynamics, which collectively promote clonal expansion and apoptosis resistance. Targeting mitochondrial pathways has gained attention as a therapeutic strategy, with approaches including mitochondria-targeted antioxidants, metabolic inhibitors, and modulation of mitophagy and mitochondrial fission/fusion dynamics. However, challenges such as drug delivery specificity, therapeutic resistance, and off-target effects remain significant. Recent advances in precision medicine, incorporating genomic, transcriptomic, and proteomic profiling, offer a more personalized approach to MPN treatment by tailoring interventions to individual mutation patterns. Additionally, novel therapeutic strategies, including gene editing technologies, RNA-based therapies, and nanoparticle-mediated drug delivery systems, hold promise for overcoming current treatment limitations. The integration of artificial intelligence in drug discovery and biomarker identification further enhances the potential for targeted therapies. Future research should focus on refining these strategies, developing reliable biomarkers for patient stratification, and exploring combination therapies that enhance treatment efficacy while minimizing adverse effects. By addressing mitochondrial dysfunction as an underlying driver of MPNs, these emerging approaches have the potential to improve disease management, extend patient survival, and enhance quality of life. Also, this new approach of precision medicine allows patient stratification and ensures that treatments are formed according to the individual disease biology of each patient, which results in overall better outcomes.

combination drug therapy↗

PubMed2025

Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program.

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. From our comparison of multiple methods, we provided a set of reasonable strategies for outlier and imputation handling to process metabolite data and show that inverse normalization by study and half-minimum imputation provide mostly similar results for pooled or meta-analysis. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole-genome sequencing data from 16,359 individuals passing the quality-control filters and discovered 1,775 independent loci associated with 667 metabolites. Among 160 unreported locus-metabolite pairs, we identified associations with loci locating within previously implicated metabolite-associated genes, as well as associations with loci locating in genes such as GAB3 and VSIG4 (located on the X chromosome) that may play a role in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, which were located in well-known metabolic genes such as FADS2, D2HGDH, SUGP1, and UGT2B17, strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

Humans↗