Metabolic syndrome (MetS) is comprised of endocrine/metabolic disturbances characterized by type 2 diabetes mellitus (T2DM) due to insulin resistance and impaired glucose regulation, hypertension, obesity, and altered lipid profile consisting of elevated levels of triglyceride (TG) and low levels of high-density lipoprotein cholesterol (HDL-C) (1).

Metabolic syndrome is increasingly recognized as a significant public health concern, affecting millions globally. Defined by the presence of at least three of five risk factors—abdominal obesity, elevated triglycerides (TG), low high-density lipoprotein (HDL) cholesterol, hypertension, and elevated fasting glucose—MetS is closely linked to increased risks of cardiovascular disease (CVD) and type 2 diabetes. The prevalence of MetS has risen in parallel with the global obesity epidemic, necessitating effective diagnostic and management strategies (2).

Individuals with MetS exhibit a characteristic pattern of abnormalities in serum lipid levels consisting of low levels of HDL-C and elevated levels of TG. This dyslipidemia is also characterized by increased concentration of small, dense low-density lipoprotein cholesterol (LDL-C) particles (3). Such lipid pattern is termed atherogenic dyslipidemia. Evidence from epidemiologic studies suggests that the co-occurrence of low levels of HDL-C and elevated levels of TG is a strong risk factor for CVD (4,5). Many studies available in literature show the association between lipid profile and MetS associated variables (6,7).

Lipid abnormalities are central to the pathophysiology of MetS. Elevated TG levels, low HDL, high low-density lipoprotein (LDL), and increased total cholesterol (TC) are common findings among individuals diagnosed with MetS. For instance, the mean TG level observed in recent studies underscores the association between hypertriglyceridemia and insulin resistance, a hallmark of MetS (8).

Despite the established importance of lipid profiles in MetS, there are notable gaps in the literature regarding the predictive power of specific lipid markers. While TG and the TC/HDL ratio have shown strong associations with MetS, traditional markers like LDL and HDL have demonstrated limited diagnostic efficacy. For example, studies indicate that while LDL is traditionally viewed as a significant lipid marker, its standalone diagnostic performance is inadequate for MetS . Moreover, HDL levels have been shown to have poor predictive power, suggesting a need for a paradigm shift in how lipid profiles are utilized in clinical practice (9).

This study aims to elucidate the role of specific lipid markers, particularly triglycerides (TG) and the total cholesterol-to-high-density lipoprotein (TC/HDL) ratio, in diagnosing metabolic syndrome (MetS) and to explore their predictive capabilities among subjects with MetS.

This cross-sectional study was conducted at King George's Medical University, Lucknow, from January 2018 to July 2019, in collaboration with the Departments of Physiology, Medicine, and Pathology. A total of 120 subjects were screened through history taking and required investigations. Of these, 60 subjects who met the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria for metabolic syndrome were selected from the Clinical Outpatient Department (OPD) of the Medicine Department.

This study included subjects aged 20-60 years, ensuring a diverse range of participants. Fasting morning blood samples were collected from the antecubital vein under strict aseptic conditions using dry disposable syringes and needles. The samples were then sent to the Department of Pathology for analysis of blood glucose, liver function tests, and lipid profiles.

The inclusion criteria for this study were carefully defined to ensure that only participants with dyslipidemia and other metabolic syndrome characteristics were included. Specifically, participants were required to have triglycerides greater than 150 mg/dl, low HDL cholesterol (less than 40 mg/dl for men and less than 50 mg/dl for women), fasting glucose greater than 100 mg/dl, waist circumference greater than 102 cm for men and greater than 88 cm for women, and blood pressure greater than 130/85 mmHg.

To ensure the accuracy and reliability of the results, several exclusion criteria were established. Participants with documented liver disease or a history of liver disease were excluded, as were those with anatomical deformities that could interfere with anthropometric data. Additionally, participants with a history of bone disease or treatment, documented malignancy or history of treatment, cholestasis, pancreatitis, or other biliary tract diseases were excluded. Furthermore, participants receiving anticonvulsant therapy (e.g., phenytoin) and those with a history of chronic alcoholism were also excluded.

Biochemical Analysis

Blood samples were collected from participants after a minimum of 8 hours of overnight fasting. A 4ml blood sample was divided into two parts and collected in separate vials. One part was collected in a fluoride vial containing sodium fluoride-potassium oxalate as an anticoagulant for the estimation of fasting plasma glucose. The other part was collected in a plain vial, allowed to clot for 30 minutes, and then centrifuged to separate the serum. The serum was used for the estimation of lipid profile and liver function tests, and was stored at -20°C for gamma-glutamyl transferase (GGT) estimation using an ELISA kit.

Lipid Profile Analysis

Lipid profile parameters, including total cholesterol (TC) and triglycerides (TG), were measured using enzymatic colorimetric (CHOD-PAP) Trinder-Endpoint methods. Serum high-density lipoprotein (HDL) levels were measured using a direct enzymatic (PVS/PEGME) endpoint method suitable for fully automated analyzers (10).

Written informed consent is taken from each participant on the prescribed consent form obtained from a research cell. Ethical clearance was taken from the ethical committee of K.G. Medical University before the start of the research activity.

Statistical Analysis

Statistical analysis was performed by SPSS 16.0 version for windows and Microsoft excel. Descriptive statistics of lipid profile among subjects with metabolic syndrome Data are expressed as mean, standard deviation, minimum and maximum.  ROC analysis has been done for Lipid Profile (TG (mg/dl), HDL (mg/dl), LDL (mg/dl), Total Cholesterol (mg/dl) and TC/HDL). A two tailed P value of <0.05 was considered significant.

The lipid profile parameters of individuals diagnosed with metabolic syndrome are presented in Table 1 and figure 1. The mean TG level is 351.62 mg/dl, with a moderate variation among participants. The median TG level is 358.55 mg/dl, indicating a symmetric distribution.  The mean HDL level is 53.39 mg/dl, with some variability across subjects. The median HDL is 55.66 mg/dl, indicating a marginal positive skew. The mean LDL level is 193.49 mg/dl, with high variability. The median LDL is 191.21 mg/dl, suggesting a relatively symmetric distribution of values. The mean TC level is 316.60 mg/dl, with a moderate spread in values. The median TC is 317.45 mg/dl, suggesting a normal distribution. The mean ratio TC/HDL is 6.15, with high variability among subjects. The median ratio is 6.13, indicating symmetry in the data.

Table 1: Descriptive statistics of Lipid Profile among Subjects with metabolic syndrome

Figure 1: Box-and-whisker plot shows lipid profile (TG, HDL, LDL, TC and TC/HDL) among Subjects with metabolic syndrome

Table 2: Receiver operating characteristic (ROC) analysis of metabolic syndrome components and Lipid profile

^*Significant (p≤0.05)

Figure 2: Receiver operating characteristic (ROC) curve analysis of TG, HDL, LDL, TC and TC/HDL tests for metabolic syndrome. Each receiver characteristic curve is expressed as a solid line. AUC: area under the curve.

ROC analysis, summarized in Table 2 and figure 2, evaluates the diagnostic performance of lipid profile components in identifying metabolic syndrome. The results indicate that TG are a crucial marker for metabolic syndrome, with an area under the curve (AUC) of 0.000, a standard error of 0.000, and a highly significant p-value (<0.001).

In contrast, HDL does not perform well as a standalone predictor of metabolic syndrome, with an AUC of 0.577, a standard error of 0.053, and a non-significant p-value (0.145).

LDL shows a weak yet statistically significant association with metabolic syndrome, with an AUC of 0.002, a standard error of 0.002, and a significant p-value (<0.001). TC has marginal diagnostic utility for detecting metabolic syndrome, with an AUC of 0.017, a standard error of 0.014, and a significant p-value (<0.001).

Ratio of TC/HDL demonstrates high diagnostic accuracy and precision, with an AUC of 0.000, a standard error of 0.000, and a highly significant p-value (<0.001).

The ROC analysis highlights TG and the ratio TC/HDL as the most significant and precise predictors of metabolic syndrome, with both showing highly significant p-values and precise confidence intervals. While LDL and TC are also statistically significant, their weak AUC values suggest limited standalone diagnostic performance. HDL showed poor predictive power, as indicated by its non-significant p-value and AUC near 0.5. This analysis underscores the importance of specific lipid markers, particularly TG and TC/HDL, in identifying individuals with metabolic syndrome.

The lipid profile parameters of individuals diagnosed with MetS reveal significant abnormalities that align with findings from various published studies. The data presented indicates elevated TG levels, suboptimal HDL levels, high LDL levels, and increased TC levels, all of which are consistent with the characteristics of MetS.

The mean TG level of 351.62 mg/dl is notably high. Studies indicate that elevated TG levels are a common feature in MetS, often associated with insulin resistance and increased cardiovascular risk (11,12). For instance, Grundy (1999) emphasized that hypertriglyceridemia is a valuable clinical marker of MetS, linking it to insulin resistance and cardiovascular disease (CVD) risk (12). The mean HDL level of 53.39 mg/dl suggests a marginally low HDL, which is consistent with findings that low HDL levels are prevalent in MetS patients. Research indicates that low HDL is a significant risk factor for CVD, and many patients with MetS exhibit this dyslipidemia (13, 14). The European Society of Cardiology guidelines recommend monitoring HDL levels alongside other lipid parameters to assess CVD risk more accurately (13). The mean LDL level of 193.49 mg/dl indicates a high risk for atherogenic complications. Elevated LDL levels are frequently observed in MetS patients, and reliance solely on LDL levels can overlook other critical lipid abnormalities that contribute to CVD risk (13,15). The association between high LDL and increased cardiovascular events is well-documented, reinforcing the need for comprehensive lipid profiling in MetS management (6, 13). The mean TC level of 316.60 mg/dl reflects hypercholesterolemia, a common finding in MetS. Elevated TC is linked to increased risk of atherosclerosis and cardiovascular events, necessitating effective management strategies to lower these levels (6,13,15). The TC/HDL ratio of 6.15 is indicative of increased cardiovascular risk. A higher ratio is associated with a greater likelihood of coronary artery disease, highlighting the importance of considering this ratio in clinical assessments of MetS patients (13,14).

Furthermore, the findings regarding the predictive power of lipid profiles in diagnosing MetS emphasize the significance of specific lipid markers, particularly TG and the ratio TC/HDL. The analysis indicates that these markers exhibit strong associations with MetS, supported by robust statistical measures such as p-values and confidence intervals. In contrast, other lipid parameters like LDL and TC show limited diagnostic efficacy, while HDL demonstrates poor predictive power.

Identified as a critical marker, with studies showing that elevated TG levels correlate strongly with MetS. For instance, a study reported that a high TG/HDL-C ratio significantly increased the odds of MetS, with odds ratios ranging from 3.50 to 6.00 across different cohorts (17,18). This ratio TC/HDL has been highlighted as a reliable predictor of MetS. Research indicates that it effectively discriminates between individuals with and without MetS, with an area under the curve (AUC) suggesting a strong predictive capability (6,13). Although, LDL is traditionally viewed as a significant lipid marker, its standalone diagnostic performance is limited. Studies have shown that while LDL levels are associated with cardiovascular risk, they do not provide sufficient discrimination for MetS when evaluated independently (13,19). The analysis indicates that HDL levels have poor predictive power for MetS, with AUC values near 0.5, suggesting no better than chance in distinguishing between affected and unaffected individuals (6,18). Historically, lipid profiles have been integral to cardiovascular risk assessment, but recent studies have shifted focus towards specific ratios and markers that better reflect metabolic health. The traditional reliance on LDL as a primary marker has been challenged by findings that suggest a more nuanced approach is necessary, particularly in populations with MetS (6,15). Current guidelines from the European Society of Cardiology advocate for the inclusion of non-HDL cholesterol and apolipoprotein B as secondary targets, reflecting a broader understanding of lipid metabolism in the context of MetS (13,19). This shift highlights the importance of comprehensive lipid profiling rather than isolated measurements.

The lipid profile findings in individuals with MetS align closely with existing literature, underlining the role of lipid abnormalities in the syndrome's pathophysiology. Finding highlights the critical role of specific lipid markers, particularly TG and TC/HDL ratios, in diagnosing metabolic syndrome, while also indicating the limitations of traditional markers like LDL and HDL. Future research should explore the integration of lipid profiles with other metabolic markers, such as inflammatory markers and genetic predispositions, to enhance diagnostic accuracy and also focus on the therapeutic implications of these lipid abnormalities and the potential for targeted interventions to mitigate cardiovascular risk in MetS populations.

Funding: Nil

Conflicts of Interest: The authors declare that there are no conflicts of interest.

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