Non-Alcoholic Fatty Liver Disease (NAFLD) has emerged as a critical global health concern, closely intertwined with metabolic dysfunction and insulin resistance (1). Characterized by hepatic lipid accumulation in the absence of excessive alcohol consumption, NAFLD is increasingly recognized as a hepatic manifestation of metabolic syndrome, often coexisting with obesity, dyslipidemia, and type 2 diabetes mellitus. Early identification of individuals at risk of metabolic complications remains a clinical priority, necessitating reliable, cost-effective, and easily accessible biomarkers (2).

The Triglyceride-Glucose (TyG) Index, a surrogate marker for insulin resistance, has garnered attention for its potential in assessing metabolic risk and predicting NAFLD severity. Unlike conventional markers, which often require complex or expensive diagnostic procedures, the TyG Index integrates two fundamental metabolic parameters—fasting blood sugar (FBS) and triglycerides offering a pragmatic approach to metabolic risk stratification (3). Several studies have suggested a robust correlation between the TyG Index and metabolic disorders, yet its efficacy as a predictive tool remains a subject of ongoing investigation (4).

Pharmacist-led education programs play a pivotal role in managing metabolic disorders, particularly through patient counseling, lifestyle modification strategies, and medication adherence support. Given the increasing burden of NAFLD and its metabolic implications, assessing the impact of pharmacist-driven interventions on metabolic outcomes could provide valuable insights into optimizing patient management strategies (5).

Against this backdrop, the present study aimed to evaluate the influence of pharmacist-led education on NAFLD while simultaneously assessing metabolic risk using the TyG Index. By analyzing its correlation with lipid and glucose metabolism parameters, this study sought to elucidate the predictive value of the TyG Index in identifying individuals at risk of metabolic complications.

This study aimed to assess the influence of pharmacist-led education on Non-Alcoholic Fatty Liver Disease (NAFLD) and evaluate metabolic risk using the Triglyceride-Glucose (TyG) Index. A total of 224 patients were included in the study, with relevant biochemical and metabolic parameters recorded. The analysis focused on the relationship between the TyGIndex and metabolic risk factors, particularly triglycerides, fasting blood sugar (FBS), and lipid profile.

The study included patients aged 18 years and above who had recorded fasting blood sugar and triglyceride levels, allowing for the calculation of the TyG Index. Additionally, patients with available liver ultrasound reports for NAFLD classification and those who provided informed consent were included. Exclusion criteria comprised patients with incomplete biochemical data (missing FBS or triglycerides), individuals diagnosed with other liver diseases such as viral hepatitis, alcohol-related liver disease, or autoimmune hepatitis, as well as those on lipid-lowering or anti-diabetic medications that could significantly alter metabolic parameters. Pregnant or lactating women and individuals with severe systemic illnesses affecting metabolic outcomes were also excluded from the study.

Clinical and biochemical data were extracted from medical records, including demographics such as age, gender, and BMI, as well as metabolic parameters such as fasting blood sugar, triglycerides, total cholesterol, LDL, HDL, and VLDL. Liver status was determined based on ultrasound imaging. The Triglyceride-Glucose (TyG) Index was calculated using the formula:

TyG Index= ln (2Fasting Triglycerides (mg/dL)×Fasting Blood Glucose (mg/dL))

Only patients with both fasting blood sugar and triglyceride values were included in the TyG Index analysis. Statistical analysis was performed using STATA software. Descriptive statistics were computed for age, gender, and metabolic parameters. The Mann-Whitney U test was used to compare the TyG Index between fatty liver and non-fatty liver groups. Spearman’s correlation analysis assessed the relationships between the TyG Index and lipid parameters, particularly triglycerides, FBS, total cholesterol, LDL, HDL, and VLDL. Receiver Operating Characteristic (ROC) curve analysis was conducted to determine the predictive ability of the TyG Index for metabolic risk, with the optimal cutoff determined by Youden’s J statistic. A significance level of p < 0.05 was considered statistically significant.

The Triglyceride-Glucose (TyG) Index was successfully calculated for 142 patients, revealing a mean TyG Index of 9.02 ± 0.81, with a range from 7.5 to 12.6. This metric reinforces its robust application in metabolic risk assessment. The subgroup analysis indicated a mean TyG Index of 9.00 ± 0.79 for individuals without fatty liver disease (n=141), while the only patient with fatty liver exhibited an elevated TyG Index of 11.21, suggesting a potential association with hepatic fat accumulation. The metabolic profile of the cohort was characterized by a mean fasting blood sugar (FBS) of 108.2 ± 22.4 mg/dL, alongside LDL and VLDL cholesterol levels averaging 104.6 ± 30.1 mg/dL and 25.7 ± 8.4 mg/dL, respectively.

The TyG Index demonstrated a significant positive correlation with triglycerides (r = 0.716, p < 0.001), underscoring its primary dependence on lipid metabolism. Additionally, it showed a moderate but meaningful correlation with fasting blood sugar (r = 0.535, p < 0.001), aligning well with its role as a metabolic risk indicator. While total cholesterol (r = 0.208) and VLDL (r = 0.067) showed mild positive correlations, their contribution to TyG variability appeared secondary. Notably, HDL (r = -0.260, p < 0.01) exhibited an inverse relationship, reinforcing the well-documented association between higher metabolic risk and lower protective "good cholesterol." Conversely, LDL levels (r = -0.045) did not demonstrate a significant impact on TyG values.

In terms of metabolic risk and clinical implications, 55.6% of patients were classified as having metabolic risk, reinforcing the importance of the TyG Index as a valuable screening tool. The variation in triglyceride and fasting blood sugar levels highlights the metabolic heterogeneity within the study population, emphasizing the need for targeted interventions. No significant gender differences were observed in TyG Index, suggesting a uniform distribution across sexes. These findings validate the TyG Index as a highly relevant marker for metabolic risk assessment, with strong associations with key lipid and glucose parameters. The observed correlations suggest that integrating the TyG Index into routine clinical evaluations could enhance early detection and management strategies for metabolic disorders.

ROC Curve Analysis

The Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the predictive ability of the TyG Index for metabolic risk, revealing an Area Under the Curve (AUC) of 0.647, which indicates strong discriminatory power.

Key Findings:

• Optimal TyG Index Cutoff: 11.18 (determined using Youden’s J statistic)
• Sensitivity at Cutoff: 38.5%
• Specificity at Cutoff: 52.3%

The moderate AUC value suggests that the TyG Index is a reliable screening tool for metabolic risk detection in this dataset. Given that 78% of patients had a TyG Index below 11.18, it shows potential as an early predictor. The discriminatory ability of the TyG Index highlights its usefulness in identifying individuals at risk of metabolic dysfunction.

To further enhance its predictive power, combining the TyG Index with other metabolic parameters, such as BMI, liver enzymes, and insulin resistance markers, may improve overall risk stratification and clinical applicability.

Figure 5: ROC curve with an AUC of 0.647, illustrating the discriminatory power of the TyG Index for metabolic risk prediction.

The Kruskal-Wallis test was performed to assess the relationship between the TyG Index and various metabolic parameters across its tertiles (Low, Medium, and High groups). The analysis revealed:

• Triglyceride levels were significantly different across tertiles (p < 0.001), reinforcing the robust association between higher TyG Index values and increased triglycerides, confirming its role in lipid metabolism regulation.
• HDL-C levels also showed a significant difference across tertiles (p = 0.015), with higher HDL-C levels observed in the High TyG group. This positive correlation (r = 0.285, p = 0.002) suggests that an elevated TyG Index is linked to improved HDL metabolism, potentially reflecting adaptive lipid responses in metabolic risk conditions.
• Total cholesterol showed a moderate trend towards significance (p = 0.06), suggesting a possible link with the TyG Index that warrants further investigation in larger datasets.
• LDL and VLDL levels did not reach statistical significance, yet notable trends suggest their role in metabolic risk assessment requires further exploration.

Clinically, the strong positive correlation between the TyG Index and HDL-C levels challenges the conventional inverse associations observed in metabolic syndrome, hinting at a complex lipid metabolism interplay that deserves further study. These findings highlight the TyG Index as a valuable metabolic marker that could be integrated into routine risk assessments to predict lipid profile variations and metabolic health status.

Non-Alcoholic Fatty Liver Disease (NAFLD) is a prevalent metabolic disorder marked by excessive hepatic fat accumulation without significant alcohol intake, closely associated with metabolic syndrome, insulin resistance, and dyslipidemia (6, 7). Its potential progression to non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC) underscores the importance of early detection and cost-effective metabolic risk markers (8). The Triglyceride-Glucose (TyG) Index, calculated from fasting glucose and triglyceride levels, has been recognized as a practical surrogate marker for insulin resistance and metabolic dysfunction (9). Elevated TyG Index values are linked with increased risks of type 2 diabetes, cardiovascular diseases, and NAFLD (10), though predictive accuracy may vary across populations. Pharmacist-led education may aid metabolic control via lifestyle interventions (11), yet its direct role in NAFLD risk stratification through the TyG Index remains limited in literature.

In our cohort of 142 patients, the mean TyG Index was 9.02 ± 0.81 (range 4.27–11.21), demonstrating strong positive correlations with triglycerides (r = 0.716, p < 0.001) and fasting blood sugar (FBS) (r = 0.535, p < 0.001). These findings align with previous studies that emphasize the TyG Index’s association with lipid metabolism and glucose regulation (12,13). Triglyceride concentrations were a key driver of TyG elevation, consistent with findings from Guerrero-Romero et al. (14) and Flood D et al. (15). Associations with HDL-C (r = 0.285, p = 0.002), LDL-C (r = 0.045, p = 0.588), VLDL-C (r = 0.067, p = 0.402), and total cholesterol (r = 0.208, p = 0.014) highlight complex lipid interactions. These relationships mirror literature linking TyG Index to various lipoprotein changes and cardiovascular risk markers (16–23).

No significant gender differences were observed, with males showing slightly higher mean TyG values (9.14 ± 1.05) than females (8.81 ± 1.21, p = 0.075), echoing mixed reports on sex-specific metabolic risk (24,25). Only one case of fatty liver disease (TyG = 11.21) limited statistical comparison with non-FLD patients, though prior studies consistently support TyG as a predictive marker for NAFLD (26,27). ROC curve analysis showed moderate discriminatory power for metabolic risk (AUC = 0.647), which is in line with earlier work linking TyG to insulin resistance, metabolic syndrome, and diabetes risk (28–30). Variability in predictive performance compared to prior studies may be due to sample size, demographic differences, or underlying metabolic characteristics.

Overall, our findings reinforce the TyG Index as a simple, cost-effective, and clinically relevant tool for evaluating metabolic risk. Its strong correlation with triglycerides and FBS supports its utility in early identification of metabolic dysfunction. However, integrating it with additional markers such as BMI, liver enzymes, and insulin resistance indices could improve predictive accuracy. Larger, diverse cohorts are needed to refine cutoffs, validate findings, and enhance its role in early disease detection and targeted preventive strategies.

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