This report describes major pathomechanisms of disease in which the dysregulation of host inflammatory processes is a major factor, with cardiovascular disease (CVD) as a primary model, and reviews strategies for countermeasures based on synergistic interaction between various agents, including drugs and generally regarded as safe (GRAS) natural medical material (NMM), such as Ginkgo biloba, spice phytochemicals, and fruit seed flavonoids. The 15 well-defined CVD classes are explored with particular emphasis on the extent to which oxidative stressors and associated ischemia-reperfusion tissue injury contribute to major symptoms. The four major categories of pharmaceutical agents used for the prevention of and therapy for CVD: statins, beta blockers (β-blockers), blood thinners (anticoagulants), and aspirin, are presented along with their adverse effects. Analyses of major cellular and molecular features of drug- and NMM-mediated cardioprotective processes are provided in the context of their development for human clinical application. Future directions of the evolving research described here will be particularly focused on the characterization and manipulation of calcium- and calcineurin-mediated cascades of signaling from cell surface receptors on cardiovascular and immune cells to the nucleus, with the emergence of both protective and pathological epigenetic features that may be modulated by synergistically-acting combinations of drugs and phytochemicals in which phytochemicals interact with cells to promote signaling that reduces the effective dosage and thus (often) toxicity of drugs.

This review explores the transformative potentials of artificial intelligence (AI) in promoting healthspan and longevity. Healthspan focuses on enhancing quality of life free from chronic conditions, while longevity defines current lifespan limits within a particular species and encompasses biological aging at multiple levels. AI methodologies—including machine learning, deep learning, natural language processing, robotics, and data analytics—offer unprecedented tools to analyze complex biological data, accelerate biomarker discovery, optimize therapeutic interventions, and personalize medicine. Notably, AI has facilitated breakthroughs in identifying accurate biomarkers of biological age, developing precision medicine approaches, accelerating drug discovery, and enhancing genomic editing technologies such as CRISPR. Further, AI-based analysis of endogenous cytoprotection, especially the activity of molecules such as heme oxygenase, with particular application to hemolytic diseases. AI-driven robotics and automated monitoring systems significantly improve elderly care, lifestyle interventions, and clinical trials, demonstrating considerable potential to extend both healthspan and lifespan. However, the integration of AI into longevity research poses ethical and societal challenges, including concerns over privacy, equitable access, and broader implications of extended human lifespans. Strategic interdisciplinary collaboration, transparent AI methodologies, standardized data frameworks, and equitable policy approaches are essential to responsibly harness AI’s full potential in transforming longevity science and improving human health.

Aging is a multidimensional biological process that is not adequately captured by chronological age alone. Geroscience—the study of biological mechanisms that drive age-related functional decline and disease susceptibility—therefore requires biomarker frameworks that can identify biological aging trajectories and assess whether lifestyle-associated interventions meaningfully influence aging-related pathways. In this review, we propose a practical, mechanism-based framework for studying lifestyle-associated effects on cellular and whole-organism aging in humans. We examine major biomarker domains, including DNA-methylation-based epigenetic aging clocks, cellular senescence-associated measures, senescence-associated secretory phenotype factors, inflammaging mediators, oxidative stress indicators, mitochondrial function, metabolic and nutrient-sensing pathways, proteomic and metabolomic signatures, microbiome-associated markers, and functional measures of frailty, mobility, cognition, and resilience. We also discuss criteria for biomarker selection in exploratory and multicenter trials, emphasizing biological relevance, analytical validity, responsiveness to intervention, feasibility, reproducibility, and clinical interpretability. Finally, we outline a practical study-design roadmap integrating biospecimen collection, repeated assessments, core and exploratory biomarker panels, and functional endpoints. A biomarker-guided geroscience strategy may provide a practical route for connecting mechanistic aging biology with clinically relevant human studies designed to preserve healthspan and functional independence.