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

ABSTRACT

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.

1. Introduction

Dyslipidemia, traditionally defined as isolated alterations in lipid parameters, such as increased low‐density lipoprotein cholesterol (LDL‐C) and triglycerides (TG), or reduced high‐density lipoprotein cholesterol (HDL‐C), has been identified as a major risk factor for both atherosclerotic cardiovascular disease (ASCVD) and metabolic syndrome [1]. While these metrics are widely used for cardiovascular risk assessment, they may be unable to completely capture the biological complexity or clinical diversity of lipid imbalances [2].

Recent studies indicate that lipid dysregulation beyond hyperlipidemia, such as low levels of HDL‐C and LDL‐C, may also be associated with specific clinical outcomes. A retrospective cohort study linked low LDL‐C to a higher risk of hemorrhagic stroke in some patients with a history of ischemic stroke [3]. Similarly, although high HDL‐C is typically considered protective, HDL‐C levels above 90 mg/dL have been associated with adverse health outcomes, as suggested by cohort studies [4, 5, 6]. This observation, often known as the “HDL paradox”, challenges the conventional view of HDL‐C as always beneficial [7]. These findings suggest that lipid abnormalities may occur in directions opposite to traditional hyperlipidemia, and that dyslipidemia encompasses a broader spectrum of lipid disturbances. Moreover, various ratios have been shown in prospective cohort studies to be linked to elevated risk of adverse clinical outcomes [8].

Despite this emerging evidence, current clinical guidelines and routine practice still focus more on single‐parameter measurements and numerical target achievement (e.g., LDL‐C < 70 mg/dL). This narrow view may overlook important aspects of lipid metabolism, including functional interactions, lipid ratios, and dynamic systemic balance, which could offer additional prognostic information for cardiovascular, metabolic, renal, and oncologic outcomes [2, 9, 10, 11]. To address this gap, this review aims to reconceptualize dyslipidemia as a multidimensional disorder, focusing on integrated lipid patterns, subfractions, and ratios, and encourages further research towards a refined and clinically meaningful definition of dyslipidemia. Instead of proposing a new classification, this study provides a framework to guide both clinical assessment and potential therapeutic strategies, suggesting how a multidimensional approach may improve risk stratification and patient management.

2. Methods

A comprehensive search was performed in PubMed, Google Scholar, Scopus, and Web of Science using keywords including “dyslipidemia,” “hyperlipidemia,” “lipid ratio,” “mixed dyslipidemia,” “small dense LDL,” “oxidized LDL,” “lipoprotein(a),” “apolipoprotein B,” “triglyceride‐glucose index (TyG index),” “atherogenic index of plasma (AIP),” “TG/HDL‐C ratio,” “LDL‐C/HDL‐C ratio,” and “non‐HDL‐C/HDL‐C ratio”. The search was limited to human studies published in English up to October 2025. Studies reporting associations of lipid parameters, ratios, or subfractions with clinical outcomes were included. Studies without clear clinical relevance were excluded. As this study is a narrative review with no original data collection or analysis, no statistical tests were applied. In synthesizing the literature, the authors concentrated on mechanistic pathways relevant to lipid metabolism, practical considerations for clinical settings, and gaps in current knowledge. References were selected based on relevance to the conceptual framework, clinical applicability, and strength of available evidence.

2.1. Definition

Fredrickson, Levy, and Lees qualitatively described clinical phenotypes of dyslipidemia using electrophoresis and ultracentrifugation [12]. They categorized conditions related to high TG and/or cholesterol into five distinct phenotypes (types I–V), with each type representing a specific abnormality [13]. In 2007, Allan D. Sniderman and colleagues modified the Fredrickson classification system by including apolipoprotein B (ApoB) as a surrogate measure, alongside TC and TG, to assess lipoprotein concentrations better [14, 15].

FLL phenotypes are still important for prognosis and treatment, and global standards for current lipid diagnostics are mainly focused on detecting increases in LDL‐C [16, 17, 18]. The latest American Heart Association (AHA) guidelines on lipid management primarily rely on the standard lipid panel (TC, TG, and HDL‐C) for ASCVD primary prevention [19]. Current US guidelines do not recommend ApoB as a primary screening test, even though it can be measured accurately and enhances ASCVD risk assessment [20]. Sampson et al. introduced a new classification, suggesting that the definition of dyslipidemia should be expanded to include a broader range of lipid abnormalities, beyond the traditional cutoffs for LDL‐C, HDL‐C, and TG [21]. A comparison of the key features, limitations, and clinical applicability of these classification systems is provided in Table 1.

Table 1.

Comparison of historical and modern dyslipidemia classification systems.

Feature	Fredrickson classification (1967) [12]	Sniderman ApoB‐based classification (2007–2010) [14, 15]	Sampson phenotypic classification (2021) [21]
Basis of classification	Electrophoresis + ultracentrifugation	Integration of ApoB with Fredrickson phenotypes	Non‐HDL‐C and TG (standard lipid panel only)
Primary markers	Lipoprotein patterns (CM, VLDL, LDL)	ApoB, TC, TG	Non‐HDL‐C and triglycerides
Number of phenotypes	5 types (I, II, III, IV, V); Type II later subdivided into IIa and IIb	Works within Fredrickson framework	6 types (I, IIa, IIb, IV, V, VI)
Covers low HDL‐C?	No	No	No
Covers elevated Lp(a)?	No	No	No
Clinical applicability	Limited (specialized lab required)	Moderate (requires ApoB assay)	High (uses routine lipid panel)
ASCVD risk association	Diagnostic (qualitative)	Diagnostic + prognostic	Prognostic (Type V highest, followed by IIb/IVb, then IIa/IVa)
Key limitation	Does not include HDL‐C or Lp(a); impractical	ApoB not universally available	Cannot detect isolated ApoB elevation or Type III

Note: Fredrickson originally described 5 phenotypes (I, II, III, IV, V) using electrophoresis and ultracentrifugation. Sniderman and colleagues modified this system by including apolipoprotein B (ApoB) alongside total cholesterol and triglycerides as surrogate measures of lipoprotein concentrations. Sampson et al. introduced a new phenotypic classification based solely on non‐HDL‐C and triglycerides from the standard lipid panel, adding a novel hypolipidemic phenotype (Type VI). None of these three classification systems address low HDL‐C or elevated lipoprotein(a), highlighting the need for the multidimensional approach proposed in this review.

Abbreviations: ApoB, apolipoprotein B; ASCVD, atherosclerotic cardiovascular disease; CM, chylomicrons; HDL‐C, high‐density lipoprotein cholesterol; LDL, low‐density lipoprotein; Lp(a), lipoprotein(a); non‐HDL‐C, non‐high‐density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; VLDL, very low‐density lipoprotein.

Furthermore, lipid ratios reflect the balance between various lipid components of the blood, and changes in any of them are associated with specific medical conditions [22]. Table 2 compares the traditional view of dyslipidemia with the ratio‐based approach, highlighting differences in clinical utility, limitations, and predictive value. A broader clinical perspective may consider dyslipidemia as a disturbance in lipid homeostasis, involving lipid concentrations, functional properties, and interrelationships among lipid components. This multidimensional concept is summarized in Figure 2, which illustrates how quantitative abnormalities, qualitative lipoprotein dysfunction, and lipid relationships collectively contribute to disrupted lipid homeostasis and associated metabolic and cardiovascular outcomes. This perspective is also consistent with emerging concepts related to lipid dysregulation, such as lipid‐based chronic disease (LBCD) models, which consider lipid dysregulation as a continuous process from metabolic imbalance to clinical complications (additional evidence regarding LBCD is provided in Supplementary Appendix).

Table 2.

Comparison of Traditional Versus Ratio‐Based Dyslipidemia.

Aspect	Traditional dyslipidemia	Ratio‐based dyslipidemia
Clinical Utility	Determination of treatment thresholds	Reflecting particle quality (e.g., small dense LDL) and metabolic disturbances (e.g., insulin resistance, oxidative stress).
Limitations	Limited data about particle heterogeneity (e.g., LDL particle size or oxidation status). Possible discordancy with actual risk in some populations (e.g., T2DM, MASLD).	Interpretation may vary between populations and assays; optimal cutoffs are less standardized and may be influenced by non‐lipid factors such as glycemic control.
Predictive Value	Established predictors of atherosclerotic cardiovascular disease when elevated (e.g., high LDL, low HDL).	Ratios such as TC/HDL, LDL/HDL, and TyG Index, ApoB/ApoA‐I have been shown to correlate more closely with CAD severity, insulin resistance, and adverse outcomes (e.g., MACE) than individual markers alone.

Abbreviations: ApoA‑I, apolipoprotein A‑I; ApoB, apolipoprotein B; CAD, coronary artery disease; MACE, major adverse cardiovascular events; MASLD, metabolic dysfunction‑associated steatotic liver disease; T2DM, Type 2 diabetes mellitus; TyG Index, triglyceride‑glucose index.

Figure 2.

Conceptual framework for dyslipidemia as a disrupted lipid homeostasis Quantitative (e.g., elevated LDL‐C), qualitative (dysfunctional HDL), and relational abnormalities reflected by lipid ratios and indices (e.g., TG/HDL‐C, ApoB/ApoA‐I, AIP, and TyG) collectively contribute to disturbed lipid balance. This broader framework links dyslipidemia to both metabolic (including insulin resistance, metabolic syndrome, and MASLD) and cardiovascular consequences (including atherosclerotic cardiovascular disease and major adverse cardiovascular events).

To make this reconceptualization clinically more useful, we suggest a four‑tier hierarchical framework for assessing dyslipidemia. Tier 1 includes traditional lipid parameters (LDL‑C, HDL‑C, TG). Tier 2 adds lipid ratios (TG/HDL‑C, TC/HDL‑C, non‑HDL‑C/HDL‑C). Tier 3 incorporates functional and metabolic indices (TyG index, AIP). Tier 4 includes lipoprotein subfractions and advanced markers (sdLDL‑C, oxLDL‑C, ApoB, Lp(a)). A potential clinical implementation of this conceptual framework can be considered in these three steps: Step 1 (Universal, Tiers 1–2): lipid parameters and calculated ratios from standard lipid panels may be assessed routinely. Step 2 (Targeted, Tier 3): TyG index or AIP may be considered when insulin resistance or metabolic syndrome is suspected. Step 3 (Advanced, Tier 4): advanced lipoprotein testing may be considered in high‐risk patients (e.g., recurrent events despite LDL‐C target achievement, strong family history, or selected high‐risk populations). This proposed framework may help address the limitations of traditional classification systems and provide an actionable model for assessing multidimensional dyslipidemia. This framework is aimed at complementing and enhancing, not replacing, the current guideline‐based lipid management and established risk calculators such as ASCVD. Their primary clinical role is in risk reassessment, particularly in individuals with borderline estimated risk or discordant lipid profiles (e.g., normal LDL‐C but elevated TG/HDL‐C), where traditional risk models may underestimate residual cardiovascular risk.

2.2. LDL‐C Beyond Concentration: The Hidden Impact of Small Dense and Oxidized Lipoproteins on Vascular Health

2.2.1. LDL‐C: The Main Suspect

Elevated LDL‐C is generally viewed as an important contributor to atherogenesis, resulting in endothelial dysfunction, cardiovascular events, diabetes, and its vascular complications [23, 24, 25]. Long‐term studies, such as the IMPROVE‐IT and FOURIER‐OLE trials, have shown that lower levels of LDL‐C, even below 20 mg/dL, are linked to a reduced risk of cardiovascular events, with no significant rise in adverse outcomes like cancer, hemorrhagic stroke, or neurocognitive issues [26, 27, 28, 29, 30]. However, despite aggressive LDL‐C lowering, the residual cardiovascular risk persists, which is attributed to other factors such as TG‐rich lipoproteins, lipoproteins, and inflammation, even when LDL‐C levels are low [31].

While lowering LDL‐C to very low levels is generally considered safe, some studies have raised concerns about potential side effects [32]. A 20‑year study that followed 20,954 individuals aged 35 to 64 indicated that LDL‑C levels below 70 mg/dL were associated with an increased risk of hemorrhagic stroke, particularly in individuals with uncontrolled hypertension [33]. The observational associations are more likely explained by confounding factors, including uncontrolled hypertension, frailty, or reverse causality. Consequently, for most patients, the cardiovascular benefits of LDL‐C reduction clearly outweigh potential risks. Nevertheless, caution is advised in patients with uncontrolled hypertension, as increased hemorrhagic stroke risk may occur at LDL‐C levels under 70 mg/dL [33].

Lowering LDL‐C levels does not necessarily reduce the atherogenic subfractions, including sdLDL‐C and oxLDL‐C, which continue to drive atherosclerotic and inflammatory processes [34]. Figure 1 illustrates how LDL, sdLDL‐C, and ox‐LDL are involved in cardiovascular disease (CVD) and cancer progression. Additional evidence regarding LDL‐C variability and LDL‐related markers is provided in Supporting Information Section 1. Notably, no significant correlation between LDL and ox‐LDL levels was observed in patients with type 2 diabetes mellitus (T2DM) [35]. Therefore, the development of atherosclerosis and related cardiovascular complications depends not only on the quantity of LDL‐C but also on the quality and composition of the lipoprotein profile [36]. sdLDL‐C and oxidized LDL‐C are significant contributors to the residual risk despite LDL‐C reduction.

Figure 1.

Mechanisms Linking LDL, sd‐LDL, and ox‐LDL to Cardiovascular Diseases and Cancer Progression Blue boxes indicate lipid abnormalities (elevated LDL, small dense LDL, and oxidized LDL). Yellow boxes show early pathogenic responses (endothelial dysfunction, chronic inflammation, and oxidative stress). Purple boxes represent intermediate pathological circumstances (atherosclerosis and immune dysregulation). Red boxes indicate the clinical outcomes (cardiovascular complications and cancer). Through interconnected pathways, this model illustrates how lipid subfractions contribute to both cardiovascular and oncologic outcomes.

2.2.2. Small Dense LDL‐C

SdLDL‐C is a subtype of LDL‐C marked by particle size, reduced affinity for LDL‐C receptors, and prolonged circulation time [37, 38]. Because of their size, sdLDL‐C particles are more likely to penetrate the arterial wall, promoting endothelial dysfunction more than LDL‐C. Their prolonged circulation increases oxidative susceptibility, which exacerbates inflammation and foam cell accumulation [39, 40]. A study of 3684 patients with T2DM found that sdLDL‐C levels ≥ 30.4 mg/dL independently predicted coronary heart disease (CHD) risk (odds ratio [OR] = 2.25, 95% CI: 1.792–5.064) over 5 years and were correlated with multi‐vessel coronary disease (OR = 3.28, 95% CI: 1.866–7.285) and higher Gensini scores (a measure of coronary lesion severity) (OR = 2.554, 95% CI: 2.044–5.399) [10]. Although these associations remained significant after adjustment for conventional risk factors, their generalizability may be limited by population characteristics and the observational design. Although current guidelines do not universally recommend sdLDL‐C testing, it is increasingly recognized as a useful marker for the early identification of chronic atherosclerosis in high‐risk populations, including patients with T2DM or metabolic syndrome [40].

2.2.3. Oxidized LDL‐C

The oxidation of LDL‐C particles is an important step in the development of atherosclerosis [41]. OxLDL‐C is more atherogenic than LDL‐C and contributes to the initiation and progression of atherosclerosis [42, 43]. Elevated oxLDL‐C levels are commonly observed in impaired glucose tolerance, insulin resistance, and T2DM [44, 45]. In a cohort of T2DM and CAD patients with a 5‐year follow‐up, ox‑LDL‑C above a threshold of 25.71 ng/mL was independently linked to an elevated risk of MACE, even after adjusting for LDL‑C and other established risk factors [46].

Independent of the impact of maintaining LDL‐C under 100 mg/dL or using statins, other mechanisms, such as the duration of diabetes, contribute to the increase in oxLDL‐C with prolonged diabetes [47]. Although ox‐LDL is strongly linked to inflammation and atherogenesis, its routine clinical use is limited due to the lack of standardized assays and universally accepted reference thresholds [48]. Nevertheless, increasing evidence suggests that ox‐LDL may offer valuable information beyond traditional lipid measurements [49].

2.2.4. HDL‐C and Triglycerides: The Lipid Puzzle of Metabolic Syndrome

2.2.4.1. Triglycerides: The Metabolic Amplifier

Elevated TG levels are associated with a higher incidence of MACE, including myocardial infarction and stroke [50, 51]. TG reduction does not consistently result in reduced cardiovascular events [52, 53]. While pemafibrate effectively reduces TG levels, it does not significantly decrease the cardiovascular event rates in T2DM patients with mild to moderate hypertriglyceridemia [52, 53]. In contrast, it has been found that icosapent ethyl lowers the risk of ischemic outcomes, including cardiovascular death, in statin‑treated patients with high TG levels [54]. A meta‐analysis by Emara et al. (2026) indicated that olezarsen, a novel TG‐lowering agent, reduces TG by up to 54% and decreases acute pancreatitis risk, though a modest LDL‐C elevation was observed [55]. This observation suggests that lipid‐lowering interventions should be assessed within a multidimensional framework rather than focusing on single parameters.

Elevated TG levels in the blood increase the cholesteryl ester transfer protein (CETP) activity. CETP promotes the transfer of TG from TG‐rich lipoproteins, including very low‐density lipoprotein cholesterol (VLDL‐C), to HDL‐C particles in exchange for cholesteryl esters. This process results in more rapid catabolism and a lower circulating concentration of HDL‐C [56]. Overall, the interrelationship between high TG levels and low HDL‐C in metabolic syndrome is an important contributor to the elevated cardiovascular risk seen in these patients. Together, these factors not only serve as diagnostic markers but also point to therapeutic targets for reducing cardiovascular risk [57]. This indicates that, rather than viewing low HDL‑C and high triglycerides as isolated dyslipidemias, they might be better assessed as a combined metabolic pattern [58].

2.3. HDL‐C: Beyond the “Good Cholesterol”

Low HDL‐C has been considered a major risk factor for ASCVD, especially in T2DM patients. In two large Danish population‑based cohort studies with up to 25 years of follow‑up, low HDL‑C (< 39 mg/dL) was correlated with an increased risk of autoimmune disease (multivariable‑adjusted HR = 1.84; 95% CI: 1.52–2.22) and also with increased risk of several malignancies, particularly hematological, nervous system, and breast cancer (HR for any cancer = 1.29; 95% CI: 1.12–1.48) [59, 60]. Several therapeutic strategies have been investigated to modify HDL‐C concentrations. However, the clinical significance of such therapeutic strategies remains an area of ongoing investigation. A recent meta‑analysis of six RCTs of 3399 patients with dyslipidemia showed that obicetrapib, a CETP inhibitor, reduced LDL‑C by 27.66 mg/dL (p < 0.001) and increased HDL‑C by 70.85 mg/dL (p < 0.001) compared with placebo [61].

While low HDL‐C is a well‐established risk marker, a growing body of evidence reveals a paradox, suggesting that high HDL‐C levels do not consistently have protective effects, and extremely high levels may even be harmful. A meta‑analysis of over a million individuals showed that very high levels of HDL‑C (≥ 80 mg/dL) did not associate with reduced cardiovascular mortality (HR = 1.05; 95% CI: 0.94–1.17) [62]. However, dose‑response analysis revealed that HDL‑C levels above 94 mg/dL in men (HR = 1.29; 95% CI: 1.01–1.65) and 116 mg/dL in women (HR = 1.47; 95% CI: 1.01–2.15) were paradoxically linked to increased cardiovascular mortality [62].

Recent studies revealed that relying solely on HDL‐C levels may not be a reliable therapeutic goal for reducing cardiovascular risk [63, 64]. On the other hand, a growing body of evidence suggests that HDL functional characteristics, including cholesterol efflux capacity and anti‐inflammatory effects, may be a viable guide to its role in atheroprotection [65, 66]. In pathological states such as T2DM, chronic kidney disease (CKD), or autoimmune disorders, HDL particles become dysfunctional, characterized by impaired cholesterol efflux capacity, reduced antioxidant activity, and acquisition of pro‐inflammatory properties [67]. This dysfunction is marked by impaired atheroprotective functions and the acquisition of pro‐atherogenic properties, which can contribute to cardiovascular complications despite high HDL‐C levels [68, 69]. Whether improving HDL functionality, rather than increasing HDL‐C concentration, will reduce cardiovascular events remains an area requiring further investigation [70]. Therefore, from the perspective of our review, instead of focusing on isolated HDL‐C measurements, considering functional assessments or integration into ratios (e.g., TG/HDL‐C) may better describe the clinical impact of HDL metabolism. Additional evidence regarding broader clinical implications of TG and HDL‐C abnormalities is summarized in Supporting Information Section 2.

2.4. Mixed Dyslipidemia and Its Implications

Mixed dyslipidemia is defined as the presence of two or more concurrent lipid abnormalities [71, 72]. In a large Indian cohort, 68.9% of newly diagnosed patients with T2DM had mixed dyslipidemia, while a Nepalese study found an even higher prevalence at 88.1% [73, 74]. In a study of 2097 diabetic patients, 97.8% had at least one lipid abnormality, and 73.9% had mixed dyslipidemia [75]. Triple dyslipidemia (concurrently high TG, high LDL‑C, and low HDL‑C) was observed in 24.7% of patients [75]. The triple dyslipidemia pattern was observed in a substantial proportion in other studies, ranging from 24.7% to 44.7% in different populations [73, 74]. Gender differences are also notable, with high TG and low HDL‐C being a frequent combination in males [76]. A high LDL‐C and low HDL‐C pattern is common in both sexes, particularly in females [76, 77]. These findings, along with previous studies, suggest that mixed dyslipidemia is highly prevalent and clinically significant [78].

Mixed dyslipidemia is a cluster of abnormalities, including insulin resistance and overproduction of TG‑rich lipoproteins [79]. The elevated concentration of TG‐rich lipoproteins leads to increased catabolism of HDL and a shift in the LDL phenotype towards sdLDL‐C, which are more atherogenic than LDL [80]. Furthermore, the dysfunction of HDL particles reduces their anti‑inflammatory activity, resulting in pro‐atherogenic processes and endothelial dysfunction [81]. This pattern identifies individuals who remain at elevated cardiovascular risk even after LDL‑C lowering with standard therapies [79]. Therapeutic strategies include lifestyle modifications targeting weight, diet quality, and physical activity, along with lipid‑lowering therapies (e.g., fibrates and omega‑3 fatty acids), to address a broader spectrum of atherogenic lipoproteins and reduce overall cardiovascular risk more effectively [79].

2.5. Ethnic and Sex Differences in Dyslipidemia Patterns

South Asian populations show a distinctive “atherogenic dyslipidemia” phenotype characterized by hypertriglyceridemia, low HDL‐C, and elevated small dense LDL‐C (sdLDL‐C), often despite near‐normal LDL‐C levels [82, 83]. This pattern contributes to the “South Asian Paradox” of high coronary artery disease (CAD) rates despite conventional risk profiles that appear similar to or better than Western populations [82]. South Asian populations often exhibit higher TG and lower HDL‐C levels compared to Western populations, partly because of genetic variants such as ApoB and LPL polymorphisms [84]. Furthermore, socioeconomic status, dietary habits, and physical inactivity may influence these patterns and are often not included in risk models derived from Western cohorts [84, 85].

In the Bangladeshi adult cohort, dyslipidemia prevalence was not significantly different between males (90.1%) and females (85.7%). However, hypertriglyceridemia (p < 0.001) and low HDL‐C (p = 0.002) were more common in males, whereas hypercholesterolemia (p = 0.035) and elevated LDL‐C (p = 0.01) were more prevalent in females [86]. Among South Asian university students, the prevalence of dyslipidemia was significantly higher in males (82.9%) than in females (65.8%) (p < 0.01), suggesting the importance of sex‐based prevention even in younger populations [87]. Future guidelines should consider ethnic‐specific cutoffs and sex‐specific risk profiles, and prospective studies are needed to assess whether lipid ratio‐based approaches outperform traditional targets across different demographic groups.

2.6. Beyond the Numbers: Clinical Implications of Dysregulated Lipid Ratios

Therapeutic interventions and disease processes can alter lipid levels and disrupt the lipid balance, so they may better capture the overall lipid balance and associated disease risk [88, 89]. Table 3 provides an overview of key lipid ratios, their associated outcomes, clinical significance, thresholds, and applicability. Additional associations regarding reported associations of key lipid ratios with cardiovascular and metabolic outcomes are provided in Supplementary Table S1.

Table 3.

Clinical Characteristics, Thresholds, and Applicability of Key Lipid Ratios.

Lipid ratio	Associated clinical outcome	Clinical thresholds (level of evidence)	Clinical applicabilitya
TG/HDL‐C	CVD, CAD, insulin resistance, metabolic syndrome, MASLD, periodontitis	> 3.5 (men)/> 2.5 (women) [90] (II‐B)	Freeb
LDL‐C/HDL‐C	CVD, CAD, MACE	> 2.5 [91] (III)	Free
Total Cholesterol/HDL‐C	CVD, MACE	> 4.0 (men)/> 3.5 (women) [92] (II)	Free
Non‐HDL‐C/HDL‐C	CVD, CAD, MACE, T2DM, NASH, carotid IMT	> 3.0c [93] (III)	Free
oxLDL‐C/LDL‐C	CAD, metabolic syndrome, carotid IMT	> 0.15d [94] (III)	Expensive (research‐grade assay)
AIP	CAD, metabolic syndrome, hypertension, T2DM, MACE	> 0.2 (moderate risk); > 0.4 (high risk) [95] (III)	Free (needs log calculation)
TyG Index	CVD, CAD, MACE, T2DM, renal disease, peripheral artery disease, hyperuricemia	≥ 8.5 [96] (III)	Low cost (requires fasting glucose)
ApoB/ApoA‐I	CVD, MACE, PSCI, insulin resistance, metabolic syndrome, dysglycemia, inflammation	> 0.90 (men)/> 0.80 (women) [97] (III)	Moderate cost (requires ApoB assay)

Note: No universally standardized or guideline‐endorsed cutoffs are currently available for most lipid ratios. The thresholds presented are largely derived from observational studies, retrospective or cross‐sectional analyses, and ROC‐based approaches, often with population‐specific variability. Moreover, many of these markers lack validation in large prospective cohorts with hard cardiovascular endpoints (e.g., myocardial infarction, stroke, or mortality), and inter‐assay variability (particularly for biomarkers such as ox‐LDL) further limits comparability across studies. Therefore, these thresholds should be interpreted with caution as indicative of risk association rather than definitive clinical targets.

Abbreviations: ASCVD, Atherosclerotic Cardiovascular Disease; CAD, Coronary Artery Disease; CVD, Cardiovascular Disease; MACE, Major Adverse Cardiovascular Events; MASLD, Metabolic Dysfunction‐Associated Steatotic Liver Disease; NASH, non‐Alcoholic Steatohepatitis; NSTEMI, non‐ST‐elevation Myocardial Infarction; PSCI, Poststroke Cognitive Impairment; T2DM, Type 2 Diabetes Mellitus.

^a Clinical applicability categorization is qualitative and based on test availability and routine clinical practice rather than formal health economic evaluation.

^b Calculated from standard lipid panel.

^c Proposed thresholds are derived from observational ROC‐based studies, with reported optimal cutoffs around 3.39 for men and 2.89 for women.

^d Measurement of oxidized LDL (ox‐LDL) varies depending on the ELISA assay used and the specific antibodies employed. As a result, reported ox‐LDL/LDL‑C ratios may differ across studies, and no universally standardized cutoff has been established.

While most lipid ratios, AIP, and TyG index can be readily derived from routine laboratory measurements, the implementation of advanced biomarkers differs. ApoB is increasingly available in clinical practice and supported by several guidelines, whereas ox‐LDL remains mostly a research biomarker because of limited assay standardization, uncertain clinical thresholds, and restricted laboratory availability [48, 98]. However, recent evidence supports the potential clinical utility of oxLDL‐C and aims to facilitate its future integration into cardiovascular risk assessment and routine clinical practice [99]. Further evidence supporting the clinical associations of lipid ratios is provided in Supporting Information Section 4.

2.7. TG/HDL‐C Ratio

The TG/HDL‐C ratio is recognized as a valuable marker for characterizing dyslipidemia and stratifying cardiovascular risk [100]. In a prospective cohort study, higher TG/HDL‐C was associated with a higher risk of cardiovascular events (highest vs. lowest tertile: HR, 3.75; 95% CI, 1.04–13.50), independent of other factors [101]. In patients with metabolic syndrome, this ratio is identified as the most effective clinical marker for diagnosis, compared with other lipid ratios [22]. A high TG/HDL‑C ratio was found to be a reliable surrogate insulin resistance index in patients with T2DM, demonstrating a high discriminative ability for CAD (area under the curve [AUC] = 0.721) [102].

2.8. LDL‐C/HDL‐C Ratio

The LDL‐C/HDL‐C ratio indicates the balance between atherogenic LDL‐C and protective HDL‐C fractions, and has been found to be a powerful marker for cardiovascular risk [103]. A higher ratio is linked to a higher risk of ischemic stroke and CVD, especially in men [104]. Studies have shown that this ratio can serve as a predictor of CAD severity, even better than LDL‑C alone (AUC: 0.668 vs. 0.574; 95% CI for difference: 0.032–0.156; p < 0.01) [103]. Interestingly, a prospective cohort of elderly hypertensive patients showed that the relationship between the LDL‑C/HDL‑C ratio and all‑cause mortality followed a U‑shaped pattern, indicating that a moderate, balanced ratio was linked to the lowest risk [105].

2.9. TC/HDL‐C Ratio

An increased TC/HDL‐C ratio indicates that a greater proportion of the total cholesterol is present in forms that contribute to atherosclerosis, while a lower ratio suggests a more favorable lipid profile [106]. The ARIC study, a prospective cohort of 14,403 primary prevention participants with a median of 24 years of follow‐up, indicated that individuals with discordantly high TC/HDL‐C (i.e., TC/HDL‐C at or above the median despite LDL‑C or non‑HDL‑C below the median) had a significantly higher risk of ASCVD events [107]. Moreover, 48% of diabetics with LDL‑C below the median had TC/HDL‑C at or above the median, noting that TC/HDL‑C provides additional risk information beyond traditional cholesterol measures [107]. Thus, using TC/HDL‐C alongside standard lipid measures may better identify patients who need more intensive lipid modification.

2.10. Non‐HDL‐C/HDL‐C Ratio

This ratio reflects the balance between all atherogenic lipoproteins (non‐HDL‐C, which is calculated as TC minus HDL‐C and includes LDL‐C, VLDL‐C, intermediate‐density lipoprotein, and Lp(a)) and the protective HDL‐C fraction. In patients with ACS undergoing PCI, a higher non‐HDL‐C/HDL‐C ratio has been linked to a higher risk of non‐culprit coronary lesion progression [108]. A higher ratio is also strongly associated with NASH prevalence (adjusted risk increase: 54.4%), especially in males and those with BMI > 24 (3‑fold higher risk) [109]. Furthermore, epidemiological research revealed that an elevated ratio is linked to an increased risk of developing T2DM in various demographic groups [110]. This finding suggests that this ratio may be a robust predictor of diabetes, independent of traditional lipid measurements.

2.11. OxLDL‐C/LDL‐C Ratio

A case‑control study showed that ox‑LDL and the ox‑LDL/LDL‑C ratio are more useful indicators than conventional lipid profiles for distinguishing CAD patients from healthy individuals (AUC: 0.966 and 0.957, respectively; p < 0.01) [94]. In T2DM, the oxLDL‐C/LDL‐C ratio is higher and correlates with the presence of metabolic syndrome, characterized by insulin resistance, dyslipidemia, and central obesity [111, 112]. The oxLDL‐C/LDL‐C ratio was also associated negatively with HDL‐C and positively with TG levels, indicating its role in a broader lipid profile context [113, 114]. Therefore, this ratio could be a useful adjunct marker for risk stratification in the management of dyslipidemia.

2.12. AIP

The AIP is a valuable marker used to assess the metabolism of lipids and predict cardiovascular risk [115]. It is calculated as the logarithm of the ratio of TG to HDL‐C [115]. It has been found that elevated AIP levels associate with the severity of CAD, based on the SYNTAX and Gensini scores [115, 116, 117]. In a 9‑year longitudinal study of 7670 Taiwanese adults, AIP was a predictor of metabolic syndrome, hypertension, and T2DM, particularly in middle‑aged individuals (40–64 years), where significant associations persisted after full adjustment [118]. In a study of 404 chronic coronary syndrome (CCS) patients with a median of 35 months follow‐up, AIP independently predicted MACE (adjusted HR: 7.89, 95% CI: 1.82–34.27). These findings suggest that AIP can be used for risk stratification in these patients [119].

2.13. TyG Index

TyG index is defined as Ln (fasting TG (mg/dL) × fasting blood glucose (mg/dL)/2). Based on a meta‑analysis of 49,325 participants, this index showed 80% sensitivity and 81% specificity for detecting metabolic syndrome, and is increasingly used in the definition of dyslipidemia beyond traditional lipid measurements [120]. A meta‐analysis of 44,848 participants indicated that a higher TyG index is correlated with a notably increased incidence of MACE in diabetic patients, with a pooled HR of 4.14 (95% CI: 3.42–5.01) [121]. Compared with conventional lipid ratios, the TyG index integrates lipid and glucose metabolism, making it useful for identifying insulin resistance and metabolic dysfunction.

2.14. ApoB/ApoA‐I Ratio

ApoB/ApoA‐I is linked to insulin resistance and metabolic syndrome components, independent of other risk factors [122]. It is more closely associated with dysglycemia and inflammation than LDL‐C [123]. This ratio provides a more precise cholesterol balance, better cardiovascular risk assessment than traditional cholesterol ratios, and a better indicator of statin therapy effectiveness [124]. The ApoB/ApoA‐I ratio was found to strongly predict the risk of CVD, outperforming traditional lipid measures such as LDL‐C [124, 125, 126, 127]. Notably, it does not require fasting samples. Collectively, these findings suggest that the ApoB/ApoA‐I ratio provides a broad assessment of lipid balance and may improve cardiovascular and metabolic risk stratification, although its role in guiding treatment decisions requires further assessment.

3. Discussion

Current clinical approaches to dyslipidemia mainly rely on achieving numerical targets of traditional lipid parameters. However, this approach may not fully capture the complexity of lipid homeostasis, which is influenced by qualitative lipoprotein abnormalities, lipid interactions, metabolic dysfunction, pharmacological interventions, and systemic inflammation [21, 128, 129]. Lipid subfractions such as sdLDL‐C and oxLDL‐C are involved in atherogenesis and have been related to cardiovascular risk and cancer risk, even in patients with apparently “controlled” LDL‐C levels [47, 130, 131, 132]. Although most evidence remains observational, lipid ratios may help identify residual metabolic and cardiovascular risk not fully captured by standard lipid parameters [101, 133]. Beyond CVD, lipid ratios, such as TG/HDL‐C and TG/LDL‐C in locally advanced breast cancer patients and lipoprotein cholesterol‐apolipoprotein score (based on HDL‐C/LDL‐C and ApoA1/ApoB ratio) in colorectal cancer patients, are potential prognostic factors and early detectors of these types of cancer [134, 135, 136].

Two bodies of conflicting evidence, the U‐shaped risk associated with LDL‐C and the HDL paradox, are examples of limitations of the traditional dyslipidemia definition. The LDL paradox shows that residual risk may persist despite achieved LDL‐C levels because of other factors such as inflammation and Lp(a) [31, 137]. Furthermore, although low HDL‐C is an established risk marker in observational studies, Mendelian randomization studies have not confirmed a causal protective role for genetically increased HDL‑C in CVD [138, 139]. These findings suggest that HDL functionality, particularly cholesterol efflux capacity and anti‐inflammatory properties, may be more clinically relevant than HDL‐C concentration alone [67]. Moreover, the AIM‐HIGH trial indicated that although adding niacin to intensive statin therapy improved HDL‐C and TG levels, these improvements did not lead to a significant decrease in cardiovascular outcomes (HR 1.02, 95% CI: 0.87–1.21), suggesting that forcing lipid levels into “better” numeric targets does not effectively restore the complex physiological balance needed for cardiovascular protection [139, 140].

Despite these limitations, traditional lipid measurements are still the cornerstone of clinical management because of their low cost, standardization, wide availability, and support by extensive randomized trial evidence and guidelines. Altogether, the evidence reviewed here suggests that dyslipidemia may be more appropriately viewed not merely as numeric alterations in lipid values, but as a dysfunctional lipid balance that contributes to long‐term health complications. A more comprehensive view could enhance earlier detection of high‐risk patients and guide targeted interventions.

Of note, for some emerging lipid markers and ratios, most of the evidence is observational. These studies cannot establish causation due to the potential for residual confounding (e.g., by obesity, smoking, or physical inactivity) and reverse causality (e.g., pre‐existing disease may alter lipid metabolism before diagnosis). Therefore, the reported predictive values in observational studies should be interpreted as hypothesis‐generating rather than clinically established unless supported by mechanistic studies or Mendelian randomization analyses. Furthermore, clinical implementation of multidimensional lipid assessment faces challenges, including limited availability of advanced lipid testing, cost considerations, and lack of standardized treatment guidelines for non‐traditional lipid markers.

4. Conclusion

The traditional clinical concept of dyslipidemia, which focuses on isolated lipid abnormalities, may not fully capture the complexity of lipid imbalances and their clinical implications. This manuscript highlights the need to reevaluate dyslipidemia by considering interactions among lipid parameters. Mixed lipid abnormalities, as well as novel markers including AIP and lipid ratios, have predictive value for cardiovascular events, metabolic syndrome, and even autoimmune conditions. Each lipid abnormality, whether isolated or in combination, is associated with distinct risks of metabolic conditions and complications. Considering these ratios and mixed lipid abnormalities may offer a more comprehensive understanding of dyslipidemia, more individualized risk assessment, and future therapeutic strategies.

Author Contributions

Ali Mansoursamaei: data curation, visualization, writing – review and editing, writing – original draft, investigation. Amirhossein Yadegar: data curation, visualization, writing – review and editing. Fatemeh Mohammadi: conceptualization, writing – review and editing. Seyed Arsalan Seyedi: writing – review and editing, conceptualization. Soghra Rabizadeh: conceptualization, writing – review and editing. Sahar Karimpour Reyhan: conceptualization, writing – review and editing. Alireza Esteghamati: conceptualization, supervision, writing – review and editing. Manouchehr Nakhjavani: conceptualization, supervision, writing – review and editing.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparency Statement

The lead author, Manouchehr Nakhjavani, affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Supporting information

Supporting File

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.