Most diseases are not caused by large-effect single factors but by the cumulative impact of small, context-dependent perturbations arising from genetic variants, personal behavior, or environmental exposures, a phenomenon we term the "long tail" of biology. Early disease signals often differ from late-stage biomarkers and evolve across demographic, lifestyle, and environmental contexts. Shifting medicine from reactive treatment to proactive health requires detecting and interpreting these signals. This requires longitudinal, multimodal data collection; non-invasive, scalable biosensing platforms; new technologies for interrogating biological complexity; and AI models capable of contextual, mechanistic reasoning. We propose an "N-of-1 analyzer" framework to track divergence from personal baselines across analytes, relationships, networks, and trajectories, interpreted through digital-twin simulations and knowledge-grounded foundational models. This framework enables early, individualized insights into disease risk and system decline, offering a path toward scalable precision prevention. Regulatory innovations will have to evolve, embracing complexity instead of reducing it to the mean.

Pregnancy complications, including hypertensive disorders of pregnancy (HDP), fetal growth restriction (FGR), and spontaneous preterm birth (sPTB), are leading causes of maternal and neonatal morbidity and mortality. Current clinical diagnosis commonly occurs after opportunities for early intervention have passed. A systems biology approach integrating longitudinal, multimodal data is needed to detect the earliest deviations from healthy pregnancy trajectories for timely personalized interventions. We assembled comprehensive deep-phenotyping datasets tracking 347 women from first prenatal appointment through delivery: 105 (30.3%) developed pregnancy complications. Approximately 20% were high-risk (chronic hypertension, gestational diabetes, or prior pregnancy complications). All study data are linked to electronic health records, providing extensive clinical features and medical history. Vital signs and weight were recorded at each visit. Questionnaires evaluated stress, depression, nausea, and diet each trimester. Environmental exposures were captured via home air pollution sensors, and Fitbits tracked activity, heart rate, and sleep. Blood and urine samples were collected 8-10 weeks and placenta upon delivery for multiomics (lipidomics, metabolomics, proteomics, and transcriptomics). Fecal and vaginal samples were collected every four weeks for 16S microbiome sequencing. We performed multiomic profiling on 133 participants with the most complete temporally paired samples (40 HDP, 20 FGR, 4 FGR+HDP, 13 sPTB, 56 controls. Of those with complications, only 32 (41.6%) were classified as high-risk due to preconception risk factors. This unique dataset enables data-driven methods to model normal system-level pregnancy dynamics, identify complication-associated deviations, and pinpoint the earliest individualized signs of pathology. We are constructing interomic networks to characterize temporal and cross-sectional changes between healthy and complicated pregnancies. Using late-fusion multimodal machine learning, we will identify the earliest points of deviation from healthy trajectories for individual patients. This work lays the foundation for early, multimodal diagnostic and prognostic tools in obstetrics, and the approach is broadly applicable to other complex diseases.

The placenta is essential for pregnancy, and its dysfunction can harm both mother and fetus. To better understand placental physiology and its disruption in disease, we employ a multiomics approach (transcriptomics, metabolomics, and proteomics) combined with clinical data and histopathology from 321 placentas across conditions: severe fetal growth restriction (FGR), FGR with hypertension (FGR + HDP), severe preeclampsia (PE), and spontaneous preterm delivery (PTD). Cellular deconvolution reveals FGR + HDP placentas have more extravillous trophoblasts than controls (p < 0.0001). After adjusting for fetal sex and gestational age, we build condition-specific interomics networks and detect communities (a.k.a. subnetworks). In a control community, miR-365a-3p is the most connected node, whereas in FGR + HDP placentas, it is hypoxia-induced miR-210-3p. From this community, we identify a signature containing mRNAs implicated in placental dysfunction (e.g. FLT1, FSTL3, HTRA4, LEP, and PHYHIP), which distinguishes FGR + HDP placentas from those with other conditions, illustrating the power of interomics in understanding obstetric syndromes.

Generating longitudinal and multi-layered big biological data is crucial for effectively implementing artificial intelligence (AI) and systems biology approaches in characterising whole-body biological functions in health and complex disease states. Big biological data consists of multi-omics, clinical, wearable device, and imaging data, and information on diet, drugs, toxins, and other environmental factors. Given the significant advancements in omics technologies, human metabologenomics, and computational capabilities, several multi-omics studies are underway. Here, we first review the recent application of AI and systems biology in integrating and interpreting multi-omics data, highlighting their contributions to the creation of digital twins and the discovery of novel biomarkers and drug targets. Next, we review the multi-omics datasets generated worldwide to reveal interactions across multiple biological layers of information over time, which enhance precision health and medicine. Finally, we address the need to incorporate big biological data into clinical practice, supporting the development of a clinical decision support system essential for AI-driven hospitals and creating the foundation for an AI and systems biology-based healthcare model.

The cancer problem is expanding, particularly in low‐ and middle‐income countries (LMICs). Preventive measures can reduce the incidence by 40–50%, and cure rates have increased during the past decades in a number of cancers. However, optimizing prevention programmes and increasing cure rates of cancer remain significant research challenges. The main focus of the conference was on P4 Cancer Medicine (Predictive, Preventive, Personalized and Participatory), a comprehensive strategy encompassing Health‐Related Quality of Life (HRQoL) research, aiming to enhance the well‐being of patients and individuals at risk. Addressing the cancer problem requires two key elements: translational cancer research and the development of relevant infrastructures. A Comprehensive Cancer Centre (CCC) acts as an innovation hub by integrating high‐quality, multidisciplinary therapy and care, with healthcare‐dependent prevention, research, and education. The United States has been at the forefront, providing quality‐assured CCCs and the Cancer Moonshot for strategic cancer research. The EU has followed with the European Research Council for basic research, the European Innovation Council to boost disruptive innovation, and two EU initiatives on cancer, Europe's Beating Cancer Plan (EBCP) and the Mission on Cancer. The increasing complexity of cancer biology and technologies presents both a research challenge and a healthcare demand. For most patients, a CCC is not available. A critical discussion focused on quality assurance of healthcare outside the catchment area of a CCC and involving patients in clinical research. The strategic deployment of resources to support collective healthcare efforts and research aimed at reducing the cancer problem was discussed with representatives from the United States, EU, Africa, China, India and Taiwan. Analyses of translational cancer research have revealed important gaps in implementing innovations, assessment of clinical effectiveness, HRQoL, outcome and health economics research. The increased release of new anticancer agents over the last 25 years, accompanied by insufficient information on clinical benefits, presents both an economic and ethical problem. Direct healthcare costs have increased due to expenses for anticancer agents for the treatment of patients with incurable diseases. Evidence‐based treatment based on HRQoL research is an unmet need. Basic/preclinical research aimed at increasing the cure rate should identify new, broader targets for therapy and develop extended diagnostic technologies for stratifying patients, to inform innovative clinical trials. Present research strategies convert cancer to a chronic disease, a growing burden for the healthcare systems. The increasing complexity of cancer biology and technology, the growing need for translational cancer research, and the demand for supporting infrastructures underscore the importance of international collaborations between CCCs. However, funding for cancer research is not currently aligned to reduce the cancer problem. While public funding for cancer research doubled between 2005 and 2024, the pharmaceutical industry's spending on cancer research increased tenfold. Increasing funding by public and non‐profit funding organizations is mandatory. Education is another significant need, but it is currently fragmented and underfunded. The last session of the conference summarized the strategies in a Statement with a strong emphasis on global collaboration addressing the growing cancer burden and pronounced inequalities. Expanding partnerships and fostering innovative, multidisciplinary approaches to cancer prevention, therapeutics/care, as well as research, are not just urgent but essential steps towards reducing incidence, increasing cure rates and enhancing the well‐being of cancer patients. Data‐driven cancer medicine is currently under development, and modern communication technologies for diagnostics may facilitate interactions across geographical distances. A global cancer research agenda can become a model of solidarity, sustainability, and ethical responsibility.

While progressive striatal gene expression changes and epigenetic alterations are a prominent feature of Huntington's disease (HD), the mechanistic basis remains poorly understood. Using chromatin immunoprecipitation and sequencing (ChIP-seq), we show that the huntingtin protein (HTT) reproducibly occupies specific locations in the mouse genome. Striatal HTT ChIP-seq peaks were enriched in coding regions of spiny projection neuron identity genes that were found to have reduced expression in HD patients and mouse models, and had reduced occupancy in expanded polyglutamine HTT knock-in mice (HttQ111/Q111). By contrast, HTT occupancy was depleted near genes that are upregulated in HD. ChIP-seq of striatal histone modifications revealed genotype-specific colocalization of HTT with active chromatin marks and enhancer of zeste homolog 2 (EZH2), a key enzymatic component of the PRC2 complex. In the vicinity of genes that are differentially regulated in HD, greater HTT occupancy in HttQ111/Q111 vs wild-type mice was associated with increased EZH2 occupancy, increased H3K4me3 levels and decreased H3K27me3 levels. Our study suggests that HTT-chromatin interactions may play a role in organizing chromatin and promoting cell type-specific gene expression, with HTT occupancy predicting transcriptional dysregulation in HD.

The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.

Precision medicine demands a shift from static, single-analyte diagnostics toward dynamic, systems-level understanding of health and disease. This review explores how the convergence of systems biology, multiomics, and artificial intelligence (AI) redefines biomarker discovery to drive early disease detection and personalized intervention. We highlight pioneering efforts that use longitudinal, multimodal data to map individual health trajectories and uncover early disease signals. Advances in AI, including machine learning and contextualization using knowledge graphs and digital twins, are accelerating clinical translation by enabling predictive, context-aware analyses. Real-world applications, including omics-informed diagnostics and digital health monitoring, demonstrate the potential of this approach to transform health care from reactive treatment to proactive wellness. These technologies also inform the development of targeted therapeutics that intervene earlier, personalize treatment, and potentially halt or reverse disease progression. We outline challenges, emerging solutions, and future directions that position AI-driven systems biology at the center of next-generation precision health.

Age and APOE genotype are the strongest known risk factors for late-onset Alzheimer's disease (AD), but the mechanisms linking them to neuronal loss remain incompletely defined. Using multiomic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we propose a unified hypothesis in which two interdependent failure modes-saturation of microglial lipid flux capacity and disruption of the astrocyte-neuron lactate shuttle (ANLS) due to excess astrocytic membrane cholesterol-drive disease progression upstream of amyloid and tau pathology. Stratifying participants by cognitive score quartiles, we find consistent associations linking impaired lipid clearance, metabolic stress, and genetic variants regulating cholesterol handling. These processes appear to reinforce each other, resulting in accelerating neurodegeneration. Our hypothesis reframes AD as a systems-level collapse in metabolic coordination, rather than a purely linear pathological cascade. These insights emerged during the development of digital twin models for personalized interventions, highlighting the power of systems approaches to reveal hidden drivers of neurodegeneration.

Alzheimer’s disease (AD) leading to cognitive decline and dementia results from the interplay of multiple interacting dysfunctional biological systems. These systems can be categorized by domain, such as inflammation, cardiovascular health, proteostasis, or metabolism. Specific causes of AD differ between individuals, but each individual is likely to have causes stemming from multiple domains. Personalized multidomain therapy has been proposed as a standard of care for AD. We sought to enumerate and describe prospective randomized controlled trials (RCTs) for multidomain interventions for AD, and to extract their inclusion criteria, trial design parameters (length, number of participants), and outcome measures. We sought to clarify gaps and opportunities in research and clinical translation. We conducted a scoping review using the standardized PRISMA-ScR methodological framework. We include all cohort studies and RCTs for multidomain (also known as multimodal, multicomponent, multidimensional, or multisystem) therapy of any stage of AD, published for all dates through July 28, 2025. There have been 23 studies (completed or reported as ongoing) of multidomain interventions for AD, including 19 RCTs. Of the 15 completed RCTs, 12 demonstrate benefit from their intervention in at least one arm. Although these RCTs differ widely in their parameters, the majority support the use of multidomain therapy, and show effect sizes greater than reported for unimodal therapies, including pharmaceuticals. Multidomain therapy should be the standard of care for AD. Multidomain interventions (also known as treatments) should be employed widely, early, and first-line. Treatment or prevention is likely to be most effective at early, presymptomatic stages, but is worthwhile at all stages of disease. In order to influence multiple domains, multiple modes of therapy are likely necessary in all patients. Some individual modes, such as particular lifestyle interventions, may target multiple domains. Nevertheless, most patients will benefit from multiple modes of intervention (multimodal intervention) that together target multiple domains. Standard-of-care guidelines should explicitly include multidomain interventions. Future clinical trials must be designed to iteratively improve multidomain therapies. Payors should embrace reimbursement for effective multidomain intervention, including personalized coaching.

The Coaching for Cognition in Alzheimer’s (COCOA) Trial was a prospective RCT testing a remotely coached multimodal lifestyle intervention for participants early on the Alzheimer’s disease spectrum. Intervention focused on diet, exercise, cognitive training, sleep, stress, and social engagement. Enrollment criteria targeted individuals with cognitive decline who were able to engage remotely with a professional coach. COCOA demonstrated cognitive and functional benefits. Dense omics data were collected on 53 individuals (≥ 58 years). We sought to identify blood analytes that mediated the effects of specific elements of the multimodal intervention on specific outcomes. Outcomes were assessed with the MCI Screen (MCIS) and the Functional Assessment Staging Tool (FAST). We combined these and other measures with proteomics and metabolomics data. We analyzed the resulting dataset of over 300,000 distinct molecular data points—reflecting over 1400 measures— assayed over a period of two years. We used MEGENA to hierarchically multiscale cluster analytes based on correlated responses and identified individual metabolites and functional clusters associated with each intervention and outcome. We analyzed individual time courses of key analyte mediators to illustrate personalized effects of interventions and individualized functional and cognitive outcomes. Distinct sets of correlated serum analytes (“communities”) convey effects to functional (FAST) outcome and to cognitive (MCIS) outcome. Distinct communities respond to different modalities of intervention. Participants followed different aspects of the multimodal recommendations to different extents, and the analytes in their blood also responded idiosyncratically; analyte trajectories in different individuals show distinct dynamics. We made personalized predictions of future inflections in outcome based on observed changes in key serum mediators. We validated results with data from the Precision Recommendations for Environmental Variables, Exercise, Nutrition and Training Interventions to Optimize Neurocognition (PREVENTION) Trial. Lifestyle interventions have profound effects on blood metabolites ( Figure 1 ). These in turn convey subtler specific effects to cognition and broad-based effects to function. Pathways that ameliorate the impact of AD via lifestyle interventions in some individuals include nitrogen subsystems, kidney function, and mitochondrial metabolism. These highlight the importance of clinical attention to overall health spanning multiple organ systems in individuals across the Alzheimer’s disease spectrum.

Apolipoprotein E (APOE) modifies human aging; specifically, the ε2 and ε4 alleles are among the strongest genetic predictors of longevity and Alzheimer's disease (AD) risk, respectively. However, detailed mechanisms for their influence on aging remain unclear. In the present study, we analyzed multi-omic association patterns across APOE genotypes, sex, and biological age (BA) axes in 2,229 community dwelling individuals. Our analysis, supported by validation in an independent cohort, identified diacylglycerols as the top APOE-associated plasma metabolites. However, despite the known opposing aging effects of the allele variants, both ε2- and ε4-carriers showed higher diacylglycerols compared to ε3-homozygotes. 'Omics association patterns of ε2-carriers and increased biological age were also counter-intuitively similar, displaying significantly increased associations between insulin resistance markers and energy-generating pathway metabolites. These results demonstrate the context-dependence of the influence of APOE, with ε2 potentially strengthening insulin resistance-like pathways in the decades prior to imparting its longevity benefits. Additionally, they provide an atlas of APOE-related 'omic associations and support the involvement of bioenergetic pathways in mediating the impact of APOE on aging.

The study of the human phenome is essential for understanding the complexities of wellness and disease and their transitions, with molecular imaging being a vital tool in this exploration. Molecular imaging embodies the 4 principles of human phenomics: precise measurement, accurate calculation or analysis, well-controlled manipulation or intervention, and innovative invention or creation. Its application has significantly enhanced the precision, individualization, and effectiveness of medical interventions. This article provides an overview of molecular imaging's technologic advancements and presents the potential use of molecular imaging in human phenomics and precision medicine. The integration of molecular imaging with multiomics data and artificial intelligence has the potential to transform health care, promoting proactive and preventive strategies. This evolving approach promises to deepen our understanding of the human phenome, lead to preclinical diagnostics and treatments, and establish quantitative frameworks for precision health management.

ObjectiveThere is uncertainty around the safety of SSRIs for treating depression during pregnancy. Nevertheless, the use of SSRIs has been gradually increasing, especially during the COVID-19 pandemic period. We aimed to (1) characterize maternal depression rate and use of SSRIs in a recent 10-year period, (2) address confounding by indication, as well as socioeconomic and environmental factors, and (3) evaluate associations of the timing of SSRI exposure in pregnancy with risk for preterm birth (PTB), low birthweight (LBW), and small for gestational age (SGA) infants among women with depression before pregnancy.MethodsWe conducted propensity score-adjusted regression to calculate odds ratios (ORs) of PTB, LBW, and SGA. We accounted for maternal/pregnancy characteristics, comorbidity, depression severity, time of delivery, social vulnerability, and rural residence.ResultsThere were 50.3% and 40.3% increases in the prevalence rate of prenatal depression and prenatal SSRI prescription rate during the pandemic. We identified women with depression <= 180 days before pregnancy (n = 8406). Women with no SSRI order during pregnancy (n = 3760) constituted the unexposed group. The late SSRI exposure group consisted of women with an SSRI order after the first trimester (n = 3759). The early-only SSRI exposure group consisted of women with SSRI orders only in the first trimester (n = 887). The late SSRI exposure group had an increased risk of PTB of OR = 1.5 ([1.2,1.8]) and LBW of OR = 1.5 ([1.2,2.0]), relative to the unexposed group. Associations between late SSRI exposure and risk of PTB/LBW were similar among a subsample of patients who delivered during the pandemic.ConclusionsThese findings suggest an association between PTB/LBW and SSRI exposure is dependent on exposure timing during pregnancy. Small for gestational age is not associated with SSRI exposure.

Background Immune -mediated inflammatory diseases (IMIDs) are likely to complicate maternal health. However, literature on patients with IMIDs undergoing pregnancy is scarce and often overlooks the presence of comorbidities. We aimed to evaluate the impact of IMIDs on adverse pregnancy outcomes after assessing and addressing any discrepancies in the distribution of covariates associated with adverse pregnancy outcomes between patients with and without IMIDs. Methods We conducted a retrospective cohort study using data from an integrated U.S. community healthcare system that provides care across Alaska, California, Montana, Oregon, New Mexico, Texas, and Washington. We used a database containing all structured data from electronic health record (EHRs) and analyzed the cohort of pregnant people who had live births from January 1, 2013, through December 31, 2022. We investigated 12 selected IMIDs: psoriasis, inflammatory bowel disease, rheumatoid arthritis, spondyloarthritis, multiple sclerosis, systemic lupus erythematosus, psoriatic arthritis, antiphospholipid syndrome, Sjogren's syndrome, vasculitides, sarcoidosis, and systemic sclerosis. We characterized patients with IMIDs prior to pregnancy (IMIDs group) based on pregnancy/ maternal characteristics, comorbidities, and pre-pregnancy/prenatal immunomodulatory medications (IMMs) prescription patterns. We 1:1 propensity score matched the IMIDs cohort with people who had no IMID diagnoses prior to pregnancy (non-IMIDs cohort). Outcome measures were preterm birth (PTB), low birth weight (LBW), small for gestational age (SGA), and caesarean section. Findings Our analytic cohort had 365,075 people, of which 5784 were in the IMIDs group and 359,291 were in the non-IMIDs group. The prevalence rate of pregnancy of at least 20 weeks duration in people with a previous IMID diagnosis has doubled in the past ten years. 17% of the IMIDs group had at least one prenatal IMM prescription. Depending on the type of IMM, 48%-70% of the patients taking IMMs before pregnancy continued them throughout pregnancy. Overall, patients with one or more of these 12 IMIDs had increased risk of PTB (Relative risk (RR) = 1.1 [1.0, 1.3]; p = 0.08), LBW (RR = 1.2 [1.0, 1.4]; p = 0.02), SGA (RR = 1.1 [1.0, 1.2]; p = 0.03), and caesarean section (RR = 1.1 [1.1, 1.2], p < 0.0001) compared to a matched cohort of people without IMIDs. When adjusted for comorbidities, patients with rheumatoid arthritis (PTB RR = 1.2, p = 0.5; LBW RR = 1.1, p = 0.6) and/or inflammatory bowel disease (PTB RR = 1.2, p = 0.3; LBW RR = 1.0, p = 0.8) did not have significantly increased risk for PTB and LBW. Interpretation For patients who have been pregnant for 20 weeks or greater, the association between IMIDs and adverse pregnancy outcomes depends on both the nature of the IMID and the presence of comorbidities. Because this study was limited to pregnancies resulting in live births, results must be interpreted together with other studies on early pregnancy loss and stillbirth in patient with IMIDs. Funding National Institutes of Health.

Objective We applied propensity score matching method at scale on patient records to confirm signals of known drug effects on preterm birth and detect previously unidentified potential drug effects. Materials and Methods This was a retrospective study on women who had continuity of care at Providence St. Joseph Health (PSJH) both before and after pregnancy and delivered live births between 2013/01/01 and 2022/12/31 (n=365,075). Our exposures of interest were all outpatient medications prescribed during pregnancy. We limited our analyses to medication that met the minimal sample size (n=600). The primary outcome of interest was preterm birth. Secondary outcomes of interest were small for gestational age and low birth weight. We used propensity score matching at scale to evaluate the risk of these adverse pregnancy outcomes associated with drug exposure after adjusting for demographics, pregnancy characteristics, and comorbidities. Results The total medication prescription rate increased from 58.5% to 75.3% ( P <0.0001) from 2013 to 2022. The prevalence rate of preterm birth was 7.7%. 175 out of 1329 prenatally prescribed outpatient medications met the minimum sample size. We identified 58 medications statistically significantly associated with the risk of preterm birth (P≤0.1; decreased: 12, increased: 46). Discussion We narrowed down from 1329 medications to 58 medications that showed statistically significant association with the risk of preterm birth even after addressing numerous covariates through propensity score matching. Conclusion This data-driven approach demonstrated that multiple testable hypotheses in pregnancy pharmacology can be prioritized at scale, laying the foundation for application in other pregnancy outcomes. ### Competing Interest Statement YH and SNP declare no conflict of interest. JJH has received research funding (paid to the institute) from Pfizer, Novartis, Janssen, Gilead and Bristol Myers Squibb. QW has been funded in part by Pfizer, Novartis, and Janssen (paid to the institute). LH and NDP are scientific advisors for Sera Prognostics, a pregnancy diagnostics company and NDP holds stock options. Sera Prognostics is not associated with this study or any of the findings. ### Funding Statement Support was provided in part by the United States National Institute of Child Health and Human Development under Grant HD091527; Eunice Kennedy Shriver National Institute of Child Health and Human Development. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All procedures were reviewed and approved by the Institutional Review Board at the PSJH through expedited review on 11-04-2020 (study number STUDY2020000196). Consent was waived because disclosure of protected health information for the study involved no more than minimal risk to the privacy of individuals. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All clinical logic has been shared and available online at [https://github.com/Hadlock-Lab/PSM\_Maternity\_at_scale][1]. Results have been aggregated and reported within this paper to the extent possible while maintaining privacy from personal health information (PHI) as required by law. All data is archived within PSJH systems in a HIPAA-secure audited compute environment to facilitate verification of study conclusions. [https://github.com/Hadlock-Lab/PSM\_Maternity\_at_scale][1] [1]: https://github.com/Hadlock-Lab/PSM_Maternity_at_scale

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

IntroductionA digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes.MethodsHere, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic–disease relationships.Results and discussionOur findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.