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Review Article
This review summarizes protocols used to assess behavioral risk factors and metabolic profiles in clinical cohorts. It highlights biomarkers, mechanistic pathways, and translational applications for early detection, risk stratification, and targeted lifestyle interventions for metabolic dysfunction.
Metabolic abnormalities have been identified in relation to behavioral risk factors in clinical cohorts. Individual risk factors, such as smoking, alcohol intake, sedentary lifestyle, and poor dietary quality, remain predominant contributors to disease, yet their combined effects on metabolic physiology require a clear characterization. This review summarizes current evidence linking behavioral risk factors with altered metabolic profiles in clinical cohorts, with a focus on mechanistic pathways, biomarker changes, and interaction effects. Across multiple cohorts, poor health behaviors were associated with impaired insulin sensitivity, dyslipidemia, increased visceral adiposity, hypertension, and chronic low-grade inflammation. Behaviors have been shown to exert additive and, in some cases, synergistic metabolic effects. Psychological stress, sleep disruption, and genetic predisposition acted as modifiers, further influencing metabolic patterns. Behavioral risk factors significantly affect metabolic health, with cumulative effects exceeding the impact of individual behaviors. Integrating structured behavioral assessment into routine care may improve early detection and guide targeted intervention.
Behavioral risk factors such as poor sleep, physical inactivity, unhealthy diet, smoking, and psychosocial stress are associated with adverse metabolic profiles. These profiles are characterized by elevated glucose, increased triglycerides, raised LDL cholesterol with reduced HDL cholesterol, hypertension, and central adiposity. These behavioral risk factors, associated metabolic alterations, clinical outcomes, and key supporting references are summarized in Table 1. Collectively, these abnormalities contribute to an increased risk of cardiovascular disease, type 2 diabetes, mood disorders, and other chronic conditions1,
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Conceptual framework
To address the multidimensional nature of metabolic dysfunction in clinical populations, this review adopts an integrative conceptual framework that connects behavioral risk factors, metabolic alterations, psychiatric comorbidity, and biological modifiers within a unified systems-based model. Rather than treating these domains as independent contributors, the framework conceptualizes them as interdependent components of a dynamic biopsychosocial network that collectively shapes metabolic health outcomes1,2,3,
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Future research must adopt multidimensional, integrative approaches that account for the reciprocal interplay between behavioral, metabolic, and psychiatric factors. Behavioral risk factors—including diet, physical activity, sleep, and substance use—are closely associated with altered metabolic profiles in clinical populations, shaping the risk and progression of multiple chronic diseases. Longitudinal, mechanistic, and intervention-based studies are essential to generate actionable evid.......
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The authors have no conflicts of interest to declare.
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