Emerging Mechanistic Insights into Metabolic–Cardiovascular Disease Links: Multi-omics, Real-world Monitoring, and AI-driven Approaches

Cardiovascular disease (CVD) remains a leading global cause of death and disability, with chronic metabolic disorders such as type 2 diabetes, hypertension, and dyslipidemia acting as major contributors. While decades of research have identified well-established risk factors and behavioral influences, the majority of studies rely on traditional epidemiological or genetic association approaches, which often provide correlations but lack mechanistic depth. This has limited our ability to translate findings into precision prevention and targeted interventions.
Recent technological and methodological breakthroughs now offer unprecedented opportunities to move beyond associations and uncover the biological and environmental mechanisms that drive metabolic–cardiovascular interactions. Single-cell and spatial multi-omics are redefining how we understand cellular heterogeneity in diseased tissues. Continuous real-world monitoring with wearables and digital health tools provides new windows into lifestyle, circadian, and physiological dynamics that were previously invisible. Meanwhile, AI- and machine learning–driven integration of multi-layered datasets—including genomics, exposomics, proteomics, and clinical trajectories—enables the discovery of novel patterns and pathways that connect metabolic dysfunction to cardiovascular pathology.

Equally important, a new generation of risk factors is emerging, including microbiome-derived metabolites, endocrine-disrupting chemicals, climate-related exposures, and urban environmental stressors. These factors remain underexplored but may represent critical drivers of the growing global burden of CVD. Integrating these dimensions with genetic predisposition and behavioral patterns is essential to achieve a deeper, mechanistic understanding of the metabolic–cardiovascular continuum.

This Research Topic seeks to attract innovative and mechanistically focused research that advances our understanding of the links between chronic metabolic diseases and CVD. We encourage studies that apply cutting-edge technologies, explore novel exposures, or leverage real-world data to move the field beyond correlation and towards causality, mechanisms, and precision strategies. Our aim is to provide a forum for high-quality contributions that reshape how metabolic–cardiovascular interactions are studied and ultimately addressed in clinical practice.
We invite original research, reviews, and perspectives with a strong emphasis on novelty and mechanistic insight, including but not limited to:

• Single-cell, spatial, and multi-omics studies uncovering molecular pathways in metabolic–CVD links.
• Real-world continuous monitoring (wearables, sensors, digital phenotyping) revealing new behavioral or physiological risk patterns.
• AI- and machine learning–driven integration of genetic, environmental, and clinical datasets to generate novel insights.
• Mechanistic exploration of emerging and under-investigated risk factors, such as microbiome metabolites, endocrine disruptors, air pollution, and climate stressors.
• Development of precision prevention and treatment strategies integrating omics, real-world, and AI-driven approaches.

By prioritizing mechanistic depth and methodological innovation, this Research Topic aims to attract cutting-edge contributions that move the field towards actionable discoveries in the metabolic–cardiovascular disease nexus.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review

Keywords: Cardiovascular disease, Chronic metabolic disease, Multi-omics, Real-world monitoring, Artificial intelligence

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.