Reconceptualizing Dyslipidemia: Diversity of Lipid Imbalances and Their Clinical Implications; A Narrative Review

Background and Aims The traditional definition of dyslipidemia focuses on isolated lipid changes (LDL‐C, HDL‐C, triglycerides) and may not fully capture the biological complexity or clinical variability of lipid imbalances. Recent evidence challenges this narrow view, suggesting that dyslipidemia involves broader disruptions in lipid homeostasis influenced not only by metabolic disorders but also by pharmacological treatments. This review aims to reconceptualize dyslipidemia as a multidimensional disorder, emphasizing lipid ratios, subfractions (e.g., oxidized LDL‐C, small dense LDL‐C), and integrated lipid patterns rather than single‐parameter deviations. Methods A narrative review was conducted focusing on patterns of dyslipidemia, lipid ratios, lipoprotein subfractions, and emerging cardiometabolic biomarkers. Relevant studies were identified through PubMed, Google Scholar, Scopus, and Web of Science up to April 2026 using predefined keywords including “dyslipidemia,” “lipid ratio,” “small dense LDL,” “oxidized LDL,” “TyG index,” “AIP,” and related terms. Articles were screened for clinical relevance and selected through author consensus. As a narrative review, no statistical tests were applied. Results Mixed dyslipidemia, lipid ratios, and subfractions have been suggested to be associated with clinically relevant risks even when traditional lipid values appear normal. Observational evidence suggests that these markers may offer additive or higher predictive value for cardiovascular disease, metabolic dysfunction, renal outcomes, and cancer than isolated lipid measurements. Although supportive evidence exists, current guidelines have not fully adopted lipid ratios or novel markers in clinical guidelines. Conclusion Understanding dyslipidemia as a complex imbalance of lipid metabolism highlights the importance of lipid interactions, functional quality, and systemic balance. This reconceptualization may enhance patient risk stratification and guide diagnostic and therapeutic strategies beyond conventional numerical targets.