Addressing the Chronic Disease Epidemic: Multiomics as a Powerful Tool  

Chronic Disease Crisis: Uncertainty Meets Urgency

The life sciences industry is currently navigating a period of significant uncertainty. The nomination of Robert F. Kennedy Jr. as Secretary of the Department of Health overseeing critical agencies such as the FDA, CDC, NIH, and CMS (Medicare/Medicaid), alongside the recent implementation of a 15% cap on indirect funding costs, has sparked intense debate about the future direction of health policy and research funding  [1], [2], [3]. 

Amid these policy shifts, there is growing recognition of the need to better understand environmental risks associated with chronic diseases. This need aligns closely with mounting scientific evidence highlighting environmental exposures as major contributors to chronic disease prevalence [4], [5], [6]. Unlike the shifting landscape of health policy, the reality of chronic diseases remains clear and pressing.  Conditions such as cardiovascular disorders, diabetes, cancer, and respiratory illnesses represent the leading causes of death and disability worldwide, accounting for approximately 71% of global mortality [7]. The economic implications are equally staggering; chronic illnesses are projected to cost the global economy an estimated $47 trillion by 2030 [8].  

Current clinical approaches for diagnosing and managing these diseases are limited in scope and often fail to provide a mechanistic understanding of disease progression at a molecular level [9], [10]. The emergence of glucagon-like peptide-1 receptor agonists has marked a groundbreaking moment in chronic disease management, particularly for obesity and metabolic disorders.  Drugs such as Eli Lilly’s Mounjaro (tirzepatide) and Novo Nordisk’s Wegovy (semaglutide) have shown remarkable efficacy in promoting weight loss and improving metabolic health outcomes [11]. However, concerns remain regarding long-term effects such as muscle mass loss, weight regain after treatment cessation, and side effects that include nausea and hair loss [12]. Post-observational large-scale trials incorporating multiomic analyses are needed to fully understand the safety profiles and physiological impacts of these therapies [13].  

Beyond Genetic Predisposition Towards Multimodal Risk Profiles 

While genomics has been the primary technique for epidemiological studies in developing risk profiling scores and offering valuable insights into chronic disease predisposition, genomics alone cannot account for the rising prevalence of chronic conditions we observe today.  Environmental and lifestyle factors are estimated to account for approximately 70 to 90% of human disease burden [6]. Metabolomics, the biochemical representation of the phenotype enhances genetic studies by capturing molecular processes influenced by both genetic and environmental factors. This highlights its essential role in a multiomics approach to understanding disease risk. 

Lind et al. (2023) [14] utilized metabolomics in the EpiHealth cohort and identified 37 metabolites associated with incident cardiovascular events, such as myocardial infarction, stroke, and heart failure.  These findings demonstrated improved cardiovascular risk prediction when metabolite biomarkers were integrated with traditional risk factors. Arage et al. (2025) [15], using data from the SCAPIS, POEM, and EpiHealth cohorts, found that certain metabolites related to lipid metabolism and amino acid pathways were strongly associated with cardiometabolic traits, including insulin resistance and obesity. Similarly, (Piettzner et al., 2021) [16] analyzed plasma metabolomics in 11,966 participants from the EPIC-Norfolk cohort (219,415 person-years follow-up) to assess risk across non-communicable diseases (NCDs). They identified 420 metabolites shared across 27 non-communicable diseases, implicating liver/kidney function, lipid metabolism, inflammation, and gut microbiome pathways. These large-scale studies have produced metabolic signatures that give insights into mechanisms linking lifestyle factors to disease progression and shared underlying pathways across multiple diseases. These clinically relevant insights can guide the identification of subtypes of multimorbidity, offering potential targets for early prevention and management of chronic diseases.  

Multiomics in Population Health Studies 

Addressing chronic disease epidemics requires a holistic approach, as these conditions are influenced by a complex array of factors, including genetics, environment, lifestyle, and the microbiome. A multiomics strategy, which integrates data across various biological levels, is essential for enabling precision medicine.  This approach allows for the creation of personalized risk profiles and dynamic biomarkers, which are crucial for early detection and effective management. By integrating diverse data types, multiomics enhances our understanding of disease mechanisms and offers significant potential for large-scale epidemiological research.  Recent studies have begun to illustrate this potential, demonstrating how multiomics can be applied to improve insights in large-scale studies. 

Louca et al. [17] utilized an array of genetic (single nucleotide polymorphisms), metabolomics, blood chemistry, food frequency questionnaires, and demographic information to better predict systolic and diastolic blood pressure (BP) measurements in two separate populations (i.e., the TwinsUK and Qatari biobank cohorts). To effectively assess each feature and its contribution to predicting systolic and diastolic blood pressure measurements, the authors applied an XGBoost algorithm on the TwinsUK dataset and validated this in the Qatar Biobank.  Comparatively, when providing a more holistic approach, such as through multimodal data, it is possible to delineate which factors (e.g., demographic, metabolic, genetic, etc.) are correlated with hypertension and to what degree each feature explains hypertension. In this study, with the exception of age and BMI, the main feature contributing to BP regulation was the metabolome, representing 35 of the top 50 features, not genetics (Figure 1).  

Surendran et al. [18] analyzed 19,994 individuals from the INTERVAL and EPIC-Norfolk cohorts to map the genetic architecture of the plasma metabolome, profiling 913 metabolites. They identified 2,599 genetic variant–metabolite associations across 330 genomic regions, including rare variants, and defined 423 genetically influenced metabotypes (GIMs) (Figure 2). The study revealed novel causal genes, links to inborn errors of metabolism, and potential drug targets, showcasing the clinical relevance of genetic-metabolite mapping for disease risk prediction and drug safety. These studies highlight the utility of applying multiple, comprehensive biological and/or molecular assessments (multi-omics or multimodal data) to identify the individual contribution at each biological layer and combined relationships to address chronic diseases.  

Shifting Metabolomics to the Forefront of Chronic Disease Research 

While future directions around health policy and research funding remain uncertain, the urgent need to address the escalating global burdens posed by chronic diseases is undeniable. Multi-omics, with its ability to reveal deep insights into complex disease mechanisms beyond individual omics, is crucial for addressing chronic diseases effectively.  As an industry leader, Metabolon has played a pivotal role in numerous large-population health projects, including those highlighted in this blog.  These studies have yielded valuable insights into the underlying physiology of chronic diseases and facilitated risk stratification.  To enhance the accessibility and utility of multi-omics further, Metabolon has developed its Integrated Bioinformatics Platform (IBP), which streamlines the integration of omics data through a more intuitive interface.  This platform empowers scientists to analyze and visualize data more efficiently and extract meaningful interpretations that inform decision-making. Through collaborative partnerships with academic institutions, biotech and pharma, and Population Health, Metabolon remains steadfast in its commitment to advancing chronic diseases research globally.  

Figure 1: SHAP plot of the top 50 features influencing prediction of Systolic Blood Pressure. Features are ranked in descending order based on their influence on the mode. 35 out of the top 50 features are metabolites.  Image adapted from Louca et al, 2022 [17]

Figure 2: Circular plot illustrating the genomic location of regional association with metabolites. Metabolites occupy circular bands, within colored sections for each of the assigned metabolic classes. Metabolite-region associations are indicated by black points.

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