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Single point insulin sensitivity estimator as a marker of metabolic syndrome in a South Indian cohort
For correspondence: Dr Subbiah Eagappan, Department of Diabetology and Endocrinology, Madurai Medical College and Govt. Rajaji Hospital, Madurai 625 020, Tamil Nadu, India e-mail: subbiahegappan@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Sreekumar S, Subbiah E, Raghavan KS, Sridhar S. Single point insulin sensitivity estimator as a marker of metabolic syndrome in a South Indian cohort. Indian J Med Res. 2026;163:593-7. DOI: 10.25259/IJMR_2608_2025.
Abstract
Background and objectives
Insulin resistance is an important mechanism linking metabolic syndrome, type 2 diabetes mellitus (T2DM) and cardiovascular disease. Accurate assessment of insulin resistance is vital for early detection, but existing methods have limitations. The gold standard euglycemic-hyperinsulinemic clamp is impractical for large-scale studies, while homeostatic model assessment for insulin resistance (HOMA-IR) requires fasting insulin measurement. This study aimed to validate single point insulin sensitivity estimator (SPISE), a lipid-based non-invasive test for detection of metabolic syndrome by comparing it with HOMA-IR. The primary objective was to determine a suitable cut-off for SPISE in South Indian population.
Methods
Between June and December 2024, we conducted a cross-sectional study among 151 adults (>30 years) living in the semi-urban outskirts of Madurai, Tamil Nadu, using systematic random sampling to select participants. Participants were categorised as individuals with metabolic syndrome (n=76) or individuals without metabolic syndrome (n=75) using South Asian-modified National Cholesterol Education Program (NCEP) criteria. Anthropometry, blood glucose, lipid profile, and insulin levels were measured in fasting state. SPISE and HOMA-IR were calculated using the formulas SPISE=600 x HDL–C 0.185/ (TG 0.2 x BMI 1.338) HOMA-IR=[fasting blood glucose (mg/dL) x fasting insulin (µU/mL)]/405. The values were calculated using photometric method for lipid profile and ECLIA for serum insulin levels, and the ideal cut off, sensitivity, and specificity were found using receiver operating characteristic (ROC) analysis.
Results
Participants with metabolic syndrome showed significantly higher BMI (28.55±3.91 vs. 26.95±4.06) and HOMA-IR (6.56±2.04 vs. 1.89±1.09), and lower SPISE values (4.93±0.82 vs. 5.73±1.48 P<0.05). A SPISE cut-off of 4.05 yielded 89% sensitivity and 88% specificity, outperforming HOMA-IR (sensitivity 64.9%, specificity 70.6%). Area under the curve values were comparable for SPISE (0.662) and HOMA-IR (0.638).
Interpretation and conclusions
The SPISE index is a simple, non-invasive tool for assessing insulin resistance in the South Indian population, exhibiting superior sensitivity (89%) and specificity (88%) compared to HOMA-IR at a cut-off of 4.05. By utilising routine lipid and anthropometric data rather than expensive insulin assays, SPISE serves as a practical alternative for large-scale metabolic syndrome screening in semi-urban settings. SPISE is a surrogate point of care marker for insulin resistance, suitable for large-scale studies in resource-limited settings. Further multicentre studies are warranted to validate population-specific cut-offs.
Keywords
HOMA-IR
Insulin resistance
Metabolic syndrome
South Indian population
Type 2 diabetes mellitus
Insulin resistance is clinically characterised by a reduced biological response to a given concentration of endogenous or exogenous insulin, resulting in impaired glucose uptake and utilisation compared to that observed in individuals with normal insulin sensitivity.1 The pathophysiology of insulin resistance is multifactorial. Insulin resistance is a common pathophysiological thread in non-communicable diseases, including type 2 diabetes mellitus (T2DM), systemic hypertension, and coronary artery disease. Metabolic syndrome includes cluster of metabolic disturbances that markedly elevate the risk of developing T2DM and coronary artery disease.
Metabolic syndrome alters the metabolism by increasing insulin resistance, aggravating atherosclerosis and inducing low-grade inflammation, thus increasing cardiovascular mortality and morbidity.2 The prevalence of T2DM and pre diabetes among the Indian population according to the ICMR-INDIAB (Indian council of medical research - India Diabetes study) study phase 2 was 11.4% and 15.3%, respectively.3 This alarming rise in the prevalence of T2DM highlights the need to identify markers indicating insulin resistance, enabling early clinical intervention to reduce associated mortality and morbidity. The euglycemic hyperinsulinemic clamp test is the gold standard method for assessment of insulin resistance, which is a laborious, invasive, expensive and complicated procedure, making it non-feasible for epidemiological studies.4 Multiple indices were formulated to circumvent the complexity of the hyperinsulinemic clamp method. Although formulas like homeostatic model assessment-insulin resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) serve as simpler alternatives, their dependence on serum insulin measurement, which requires specialised equipment poses a significant barrier in routine clinical use in resource-limited settings. Single point insulin sensitivity estimator (SPISE), developed by Paulmichl et al5 has emerged as a promising tool. Developed as a modification of the triglyceride to HDL (TG/HDL-C) ratio, SPISE offers a non-invasive, lipid based surrogate marker for insulin sensitivity. SPISE showed results comparable to HOMA-IR, and cutoffs of 5.82 and 6.61 were defined for the North Indian and European populations, respectively.6 Since there is a difference in ethnicity, metabolic parameters and dietary habits, the exact cutoff cannot be applied to the whole Indian population. Hence, our study was aimed at validating SPISE with HOMA-IR as a gold standard for identifying insulin resistance in a cohort from South India. The primary objective was to determine a cut-off of SPISE index for the South Indian population and determine its sensitivity and specificity.
Methods
This cross-sectional study was carried out by the department of Diabetology, Madurai Medical College and Govt. Rajaji Hospital, Madurai, Tamil Nadu, India. The study was conducted among apparently healthy adults aged more than 30 years residing in four semi urban areas in the outskirts of Madurai, Tamil Nadu, South India between June 2024 and December 2024. Institutional Ethics Committee approved the study protocol. Written informed consent was obtained from all the participants prior to participation. The study adhered to the ethical standards outlined in the Declaration of Helsinki, as revised in 2013. The CTR number is given as CTRI/2025/01/079387.
Recruitment of participants
Participants were recruited through community-based screening conducted in the semi-urban areas of Madurai. Systematic random sampling was utilised, with every fifth household in the selected community clusters approached for participant recruitment. All the eligible adults who provided informed consent were enrolled during study period. The required sample size was calculated using the formula n=Z2(1-α/2) sensitivity (1- sensitivity)/ d2p, where Z 1-α/2 = 3.814 p represents expected prevalence (11%), d=0.15, sensitivity=0.90. This calculation yielded a sample size of 140 rounded off to 150 giving a sample size of 75 participants per group.
Anthropometric measures were recorded using electronic weighing scales and stadiometers. Waist circumference, neck circumference, hip circumference, arm circumference, thigh circumference, and mid-calf circumference were measured using non-stretchable tapes as mentioned in standard techniques.7 Body mass index was computed using the formula weight (kg)/height (m2) using standard World Health Organization (WHO) guidelines. Blood pressure (BP) was recorded on two separate occasions in the non-dominant upper limb, while the participants were in a seated position. If a single BP value is elevated, a third measurement is taken on a separate occasion to confirm hypertension.
Inclusion and exclusion criteria
Metabolic syndrome was defined according to National Cholesterol Education Program (NCEP) guidelines. Individuals with age more than 30 years with any three of the five risk factors (South Asian Modified -NCEP criteria) among the following were included as cases with metabolic syndrome: (i) waist Circumference ≥80 cm in women and ≥90 cm in men; (ii) triglycerides (TG) ≥150 mg/dL or drug treatment for elevated TG, (iii) HDL-C <50 mg/dL in women and <40 mg/dL in men, (iv) systolic blood pressure (BP) ≥130 mmHg or diastolic BP ≥85 mmHg or drug treatment for elevated BP, and (v) fasting blood glucose (FBS)>100 mg/dL or drug treatment for elevated glucose.8 Individuals with Cushing syndrome, hypothyroidism, secondary hypertension, type 1 diabetes, hepatobiliary disease, nephritic syndrome, or pregnancy were excluded. Fasting venous blood sample was collected from each participant for biochemical analysis for FBS, triglycerides, and HDL-cholesterol and analysed using the AU5800 clinical chemistry automated system (Beckman Coulter, Inc.). Fasting insulin was measured in a Roche Cobas e411 – ECLIA analyser.
The SPISE index was computed according to the formula shown below:5
SPISE=600 x HDL–C 0.185/(TG 0.2 x BMI 1.338)
HOMA-IR was computed using the formula9: HOMA-IR= [fasting blood glucose (mg/dL) x fasting insulin (µU/mL)]/405
Statistical analysis
Statistical analysis was conducted using SPSS version 22.0. (SPSS Inc., Armonk, USA). Descriptive statistics are reported as mean±standard deviation or number (%) as appropriate. Parametric tests (independent sample t test) were employed to compare continuous data between groups. Receiver operating characteristic (ROC) curves were constructed for the SPISE, HOMA-IR to determine the optimal cutoff for identifying metabolic syndrome.
Results
In the present study, a total of 151 participants (76 in the case group (with metabolic sundrome) and 75 in the control group without metabolic syndrome) were enrolled. Male: female distribution in cases was 23:53 and in controls was 44:31. The mean age of individuals with metabolic syndrome and individuals without metabolic syndrome was 50.8±11.3 and 49.3±9.9, years, respectively (P=0.396). The two groups are further compared in Table.
| Parameter | Individuals with metabolic syndrome (n=76) | Individuals without metabolic syndrome (n=75) | P value |
|---|---|---|---|
| Age (yr) | 50.8±11.33 | 49.3±9.89 | 0.396 |
| Weight (kg) | 67.6±10.71 | 68.8±12.15 | 0.534 |
| BMI (Kg/m2) | 28.5±3.91 | 26.9±4.06 | 0.018 |
| Neck circumference (cm) | 34.8±3.52 | 33.3±2.52 | 0.003 |
| Arm circumference (cm) | 30.8±3.81 | 30.0±3.05 | 0.127 |
| Waist circumference (cm) | 92.6±7.45 | 90.1±10.04 | 0.617 |
| Waist hip ratio | 1±8.74 | 1±10.66 | 0.000 |
| HOMA-IR | 6.6±2.04 | 1.9±1.09 | 0.000 |
| SPISE | 4.9±0.82 | 5.7±1.48 | 0.000 |
BMI, body mass index; HOMA-IR, homeostatic model assessment for insulin resistance; SPISE, single point insulin sensitivity estimator
The receiver-operator -curve (ROC) curve of SPISE for a cut-off value of 4.05 showed a sensitivity of 89% and a specificity of 88% ( Figure). Further, HOMA-IR with a cutoff value of 1.99 showed a sensitivity of 64.9% and a specificity of 70.6%. Area under the curve (AUC) for the SPISE index and HOMA-IR was 0.662 [CI-0.551-0.774] and 0.638 [CI-0.557-0.719], respectively with SPISE demonstrating comparable performance to HOMA-IR. The discriminatory performance of the two indices was evaluated using ROC analysis. SPISE showed an AUC of 0.662 (SE=0.057; 95% CI: 0.551–0.774; P=0.035), indicating a statistically significant ability to predict metabolic syndrome in comparison with HOMA-IR, which demonstrated an AUC of 0.638 (SE=0.041; 95% CI: 0.557–0.719; P=0.064).
Discussion
In the present study, both SPISE and HOMA-IR effectively differentiated participants with metabolic syndrome from individuals without metabolic syndrome. At cut-off value of 4.05, SPISE showed high sensitivity (89%) and specificity (88%), outperforming HOMA-IR (sensitivity 64.9%, specificity 70.6%) This suggests SPISE is better screening tool in identifying insulin resistance and metabolic syndrome in this South Indian cohort.
The lower SPISE cut-off value observed in our work compared with European and North Indian studies5,6 likely reflects ethnic and lifestyle differences, including higher carbohydrate consumption, central adiposity, and reduced lean body mass characteristic of South Indian populations.10-14 Similar ethnic variations in SPISE have also been observed in U.S. and Asian cohorts.15 Such findings emphasise the importance of establishing population-specific reference thresholds for insulin resistance indices.
The area under curve for SPISE, HOMA-IR demonstrated a curve above the line of reference, indicating that SPISE can be utilised as a predictor of insulin resistance like HOMA-IR in the current population. However, SPISE can be utilised in epidemiological settings and comparatively requires less cost than HOMA-IR index. Given its simplicity, high sensitivity, and independence from insulin assays, SPISE appears to be a practical and less expensive tool for large-scale screening of insulin resistance in community and primary-care settings. A recent meta-analysis also supports its robust diagnostic accuracy for metabolic syndrome across populations.14 Compared to previous studies,6,16 our study was a field study conducted at the community level within a semi-urban population, ensuring equitable representation of this demographic group. However, the single-centre, cross-sectional design of the present study limits external validity. Future multicentre, longitudinal studies are required to validate SPISE across diverse Indian populations, establish age and gender-specific cut-offs, and assess its ability to monitor therapeutic response and predict long-term cardiometabolic outcomes. Future studies should focus on longitudinal prediction of this index with Type-2DM, cardiovascular outcomes, and stroke, and correlation with other anthropometric parameters.
We have demonstrated that the early recognition of insulin resistance in the South Indian population is feasible using single-point blood sampling in the fasting state and BMI. The SPISE index can be used as a surrogate point-of-care marker for primordial identification of this vital harbinger of chronic diseases and can serve as a valuable measure in epidemiological and clinical settings within our population.
Author contributions
Sh.Sr.: Conception and design of the study, data acquisition, analysis, interpretation, manuscript writing; SE: Supervised the overall study, data collection, statistical analysis, interpretation of results, manuscript writing; KSR: Study design, interpretation of findings, manuscript writing; Su.Sr.: Supervised the overall study, provided expert guidance, manuscript writing. All authors have read and approved the final printed version of the manuscript.
Financial support and sponsorship
None.
Conflicts of Interest
None.
Use of Artificial Intelligence (AI)-Assisted Technology for manuscript preparation
The authors confirm that there was no use of AI-assisted technology for assisting in the writing of the manuscript and no images were manipulated using AI.
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