Hypertension is a major global public health challenge and is frequently associated with metabolic abnormalities, particularly insulin resistance (IR), which plays a central role in the pathogenesis of cardiovascular disease and type 2 diabetes mellitus. Insulin resistance contributes to endothelial dysfunction, sympathetic overactivity, sodium retention, and vascular remodeling, thereby accelerating the progression of hypertension and its related complications. Early identification of insulin resistance in hypertensive patients is therefore essential for timely intervention and risk stratification. However, direct measurement of insulin resistance using hyperinsulinemic–euglycemic clamp technique, considered the gold standard, is expensive, labor-intensive, and impractical for routine clinical use, especially in resource-limited settings.^[1]

Surrogate biochemical indices have been developed to estimate insulin resistance in clinical practice. The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), which is derived from fasting plasma glucose and fasting insulin levels, is widely used due to its simplicity and reproducibility. Another emerging marker is the Triglyceride-Glucose (TyG) index, calculated using fasting triglyceride and glucose values, which has shown strong correlation with insulin resistance and cardiovascular risk. Both indices offer cost-effective alternatives for estimating insulin sensitivity in large populations. However, variations in laboratory availability and cost may limit their routine application in some healthcare settings.^[2][3]

Glycated hemoglobin (HbA1c), traditionally used to assess long-term glycemic control, has gained attention as a potential indirect marker of insulin resistance. HbA1c reflects average blood glucose levels over the preceding 8–12 weeks and has been shown to correlate with metabolic syndrome components, visceral adiposity, and insulin resistance. Its widespread availability, standardized measurement techniques, and minimal pre-analytical variability make HbA1c an attractive screening tool. Several studies have suggested that HbA1c may serve as a simple surrogate indicator of insulin resistance even in non-diabetic populations, including individuals with hypertension.^[4]

AIM

To comparatively evaluate the diagnostic performance of HbA1c-derived indices and fasting-based indices (HOMA-IR and TyG index) for detecting insulin resistance in hypertensive patients.

OBJECTIVES

1. To assess the prevalence of insulin resistance among hypertensive patients using HOMA-IR and TyG index.
2. To evaluate the association between HbA1c levels and insulin resistance in hypertensive patients.
3. To compare the diagnostic accuracy of HbA1c-derived measures with fasting indices for identifying insulin resistance.

Source of Data The data were obtained from hypertensive patients attending the outpatient department and inpatient wards of the Department of Medicine at the study institution. Clinical details, anthropometric measurements, and biochemical parameters were collected using a structured proforma. Study Design This was a hospital-based cross-sectional analytical study. Study Location The study was conducted at a tertiary care teaching hospital attached to a medical college. Study Duration The study was carried out over a period of 12 months. Sample Size A total of 160 hypertensive patients were included in the study based on inclusion and exclusion criteria. Inclusion Criteria • Adult patients aged ≥18 years diagnosed with primary hypertension • Patients willing to provide informed written consent • Patients attending outpatient or admitted to inpatient medical wards Exclusion Criteria • Patients with known diabetes mellitus or on antidiabetic medications • Secondary hypertension • Pregnant women • Patients with chronic kidney disease, chronic liver disease, or acute infections • Patients on steroid therapy or drugs affecting glucose and lipid metabolism Procedure and Methodology Detailed demographic data, clinical history, and anthropometric measurements including height, weight, waist circumference, and body mass index were recorded. Blood pressure was measured using a standardized sphygmomanometer after adequate rest. Participants were instructed to undergo overnight fasting of 8–10 hours prior to blood sample collection. Venous blood samples were collected under aseptic precautions for estimation of fasting plasma glucose, fasting serum insulin, lipid profile, and HbA1c. Insulin resistance was calculated using the HOMA-IR formula: fasting insulin (µIU/mL) × fasting glucose (mg/dL) / 405. The TyG index was calculated using the formula: ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2]. Standard cut-off values were used to define insulin resistance. Sample Processing Blood samples were centrifuged at 3000 rpm for 10 minutes to separate serum and plasma. Biochemical parameters were analyzed using an automated analyzer. HbA1c estimation was performed using high-performance liquid chromatography (HPLC) or immunoturbidimetric method as per laboratory protocol. Internal quality control procedures were followed regularly to ensure accuracy and reliability of results. Statistical Methods Data were entered into Microsoft Excel and analyzed using Statistical Package for Social Sciences (SPSS) software version 26.0. Continuous variables were expressed as mean ± standard deviation and categorical variables as frequency and percentage. Independent t-test or Mann–Whitney U test was used for comparison of continuous variables. Chi-square test was used for categorical variables. Pearson or Spearman correlation analysis was performed to assess associations between indices. Receiver Operating Characteristic (ROC) curve analysis was used to determine diagnostic accuracy and area under the curve (AUC) for HbA1c, HOMA-IR, and TyG index. A p-value <0.05 was considered statistically significant. Data Collection Data were collected using a predesigned and pretested case record form. All laboratory and clinical parameters were recorded systematically and cross-verified to minimize data entry errors. Confidentiality of patient information was maintained throughout the study.

Table 1: Baseline profile of hypertensive patients by Insulin Resistance status (HOMA-IR as reference) (N=160)

Table 1 presents the baseline clinical and biochemical characteristics of hypertensive patients stratified according to insulin resistance status using HOMA-IR as the reference standard. The mean age and gender distribution were comparable between the two groups, with no statistically significant difference observed for age (55.8 ± 8.7 vs 54.1 ± 9.4 years; p = 0.233) or male proportion (56.4% vs 59.8%; p = 0.651), indicating demographic homogeneity. However, patients with insulin resistance had significantly higher anthropometric indices, including body mass index (28.1 ± 3.6 vs 25.3 ± 3.2 kg/m²; p < 0.001) and waist circumference (98.4 ± 8.1 vs 91.7 ± 7.6 cm; p < 0.001), suggesting greater adiposity in the insulin-resistant group. Systolic and diastolic blood pressures were also significantly elevated among insulin-resistant patients (p = 0.008 and p = 0.021, respectively). Biochemical parameters showed marked differences, with significantly higher fasting plasma glucose, fasting insulin, HbA1c, triglyceride levels, and TyG index in the insulin-resistant group (all p < 0.001), while HDL cholesterol was significantly lower (p = 0.001).

Table 2: Prevalence of Insulin Resistance using HOMA-IR and TyG index (N=160)

Table 2 illustrates the prevalence and diagnostic agreement of insulin resistance using HOMA-IR and TyG index. Insulin resistance was detected in 48.8% of patients using HOMA-IR and in 45.6% using TyG index, with no statistically significant difference in prevalence between the two methods (p = 0.312). The concordance between HOMA-IR and TyG index was high (82.5%), with a substantial level of agreement demonstrated by a Cohen’s kappa value of 0.65 (p < 0.001). Using HOMA-IR as the reference, TyG index demonstrated high sensitivity (84.6%) and good specificity (80.5%) for detecting insulin resistance, indicating its reliability as an alternative fasting-based surrogate marker in hypertensive patients.

Table 3: Association between HbA1c levels and Insulin Resistance (HOMA-IR reference) (N=160)

1. A) HbA1c categories vs IR prevalence

1. B) Mean HbA1c by IR status

1. C) Correlation (continuous)

Table 3 shows the association between HbA1c levels and insulin resistance. A progressive increase in insulin resistance prevalence was observed with rising HbA1c categories. Only 28.6% of patients with HbA1c <5.7% were insulin resistant, whereas this proportion increased to 53.2% in the 5.7–6.0% group and further to 69.0% in the 6.1–6.4% group, with a highly significant overall association (p < 0.001). Trend analysis demonstrated a significant dose–response relationship, with each increasing HbA1c category associated with more than two-fold higher odds of insulin resistance (OR = 2.05; p < 0.001). Mean HbA1c levels were significantly higher in the insulin-resistant group compared to the non-insulin-resistant group (6.09 ± 0.39 vs 5.69 ± 0.34%; p < 0.001). Correlation analysis further revealed moderate positive correlations between HbA1c and HOMA-IR (r = 0.46) as well as TyG index (r = 0.41), both of which were statistically significant (p < 0.001), supporting the role of HbA1c as a surrogate marker of insulin resistance.

Table 4: Diagnostic accuracy of HbA1c-derived measure vs fasting indices for detecting IR (Reference: HOMA-IR ≥ 2.5) (N=160)

Table 4 compares the diagnostic accuracy of HbA1c-derived measures with fasting indices for identifying insulin resistance using HOMA-IR as the reference standard. The TyG index demonstrated the highest diagnostic performance with an area under the ROC curve (AUC) of 0.81, followed by HbA1c with an AUC of 0.74, and fasting glucose with an AUC of 0.68. At the optimal cut-off of ≥8.80, TyG index showed superior sensitivity (84.6%) and specificity (80.5%) compared to HbA1c (71.8% and 68.3%, respectively). DeLong test revealed a statistically significant difference between the AUCs of TyG index and HbA1c (p = 0.048), indicating better discriminatory power of TyG index.

Baseline Characteristics and Metabolic Profile (Table 1): In the present study, insulin-resistant hypertensive patients exhibited significantly higher body mass index, waist circumference, systolic and diastolic blood pressure, fasting plasma glucose, fasting insulin, triglycerides, HbA1c, and TyG index, along with lower HDL cholesterol levels, compared to non-insulin-resistant patients. These findings are consistent with observations reported by Yadegar A et al.(2025)[5], who emphasized the central role of insulin resistance in clustering obesity, dyslipidemia, hypertension, and impaired glucose metabolism.

Similar associations between central obesity and insulin resistance in hypertensive individuals were reported by Selvi NM et al.(2021)[2], who demonstrated that waist circumference and BMI were strong predictors of insulin resistance and cardiovascular risk. The absence of significant differences in age and sex distribution in the present study aligns with findings by Lee YC et al.(2022)[6], suggesting that metabolic parameters rather than demographic characteristics primarily influence insulin resistance in hypertensive populations.

The significantly higher triglyceride levels and lower HDL cholesterol observed among insulin-resistant patients in the present study corroborate the lipid abnormalities described by Suleiman RR et al.(2023)[7], reinforcing the interrelationship between insulin resistance and atherogenic dyslipidemia.

Prevalence and Agreement Between HOMA-IR and TyG Index (Table 2): The prevalence of insulin resistance in hypertensive patients was 48.8% using HOMA-IR and 45.6% using TyG index, indicating that nearly half of the hypertensive population exhibited underlying insulin resistance. These estimates are comparable to those reported by Yadegar A et al.(2025)[5], who documented insulin resistance prevalence ranging from 40% to 55% among hypertensive cohorts.

The substantial agreement between HOMA-IR and TyG index (κ = 0.65) observed in this study is consistent with findings by Suleiman RR et al.(2023)[7], who reported strong concordance between TyG index and clamp-derived insulin sensitivity. Furthermore, the high sensitivity (84.6%) and specificity (80.5%) of TyG index in detecting insulin resistance reinforce its reliability as a practical fasting-based surrogate marker, particularly in settings where insulin assays are not routinely available.

Association Between HbA1c and Insulin Resistance (Table 3): The present study demonstrated a significant graded relationship between HbA1c levels and insulin resistance, with increasing HbA1c categories showing progressively higher prevalence of insulin resistance. These findings are in agreement with Saha S et  al.(2017)[8], who reported that higher HbA1c levels, even within the non-diabetic range, were strongly associated with metabolic risk and insulin resistance.

The moderate positive correlation between HbA1c and HOMA-IR (r = 0.46) observed in this study is comparable to results reported by An VD et al.(2022)[3], who identified HbA1c as a reliable indirect indicator of insulin resistance in non-diabetic adults. The significantly higher mean HbA1c levels among insulin-resistant hypertensive patients further support the potential role of HbA1c as a simple and stable biomarker reflecting long-term glycemic exposure and metabolic dysfunction.

Diagnostic Accuracy of HbA1c and Fasting Indices (Table 4): Receiver operating characteristic analysis demonstrated that TyG index had the highest diagnostic accuracy (AUC = 0.81), followed by HbA1c (AUC = 0.74) and fasting glucose (AUC = 0.68). These findings are consistent with Aliyu U et al.(2025)[9], who reported superior discriminatory performance of TyG index compared to fasting glucose alone.

The statistically significant difference between TyG index and HbA1c AUCs observed in the present study suggests that TyG index remains a stronger predictor of insulin resistance. However, the acceptable performance of HbA1c highlights its clinical relevance as a readily available and standardized marker. Similar observations were reported by Karadeniz Y et al.(2025)[10], who emphasized the utility of HbA1c in cardiometabolic risk stratification beyond glycemic monitoring.

The present study demonstrated a high prevalence of insulin resistance among hypertensive patients, highlighting the substantial metabolic burden in this high-risk population. Insulin-resistant hypertensive individuals exhibited significantly higher anthropometric indices, adverse lipid profiles, elevated fasting glucose, increased HbA1c levels, and greater blood pressure values, confirming the close interrelationship between hypertension and metabolic dysfunction. Among the evaluated surrogate markers, the TyG index showed superior diagnostic performance with higher sensitivity, specificity, and area under the receiver operating characteristic curve compared to HbA1c and fasting glucose. This indicates that TyG index is a robust and reliable fasting-based marker for detecting insulin resistance in hypertensive patients. However, HbA1c also demonstrated acceptable diagnostic accuracy and showed a strong association with both HOMA-IR and TyG index, supporting its potential role as a simple, stable, and widely available screening marker. Given the ease of measurement, minimal pre-analytical variability, and widespread laboratory availability of HbA1c, it may serve as a practical alternative for initial screening of insulin resistance, particularly in routine outpatient settings and resource-limited environments. Overall, the findings suggest that while TyG index offers superior diagnostic accuracy, HbA1c-derived measures provide clinically meaningful complementary information for early detection and risk stratification of insulin resistance in hypertensive patients. LIMITATIONS OF THE STUDY The cross-sectional design of the study limited the ability to establish causal relationships between insulin resistance and clinical or biochemical parameters. The hyperinsulinemic–euglycemic clamp technique, considered the gold standard for insulin resistance assessment, was not used due to feasibility constraints. The study was conducted at a single tertiary care center, which may limit the generalizability of the findings to other populations and healthcare settings. Lifestyle factors such as physical activity, dietary habits, and socioeconomic status were not quantitatively assessed, which could have influenced insulin sensitivity. Potential effects of antihypertensive medications on glucose and lipid metabolism were not analyzed separately. The study population did not include long-term follow-up data, preventing assessment of cardiovascular outcomes related to insulin resistance. The sample size, although adequate for primary objectives, may not have been sufficient to perform extensive subgroup analyses.

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