Journal of Laboratory Physicians
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ORIGINAL ARTICLE
Year : 2019  |  Volume : 11  |  Issue : 3  |  Page : 244-248

Single point insulin sensitivity estimator as an index for insulin sensitivity for metabolic syndrome: A study in North Indian population


1 Department of Biochemistry, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
2 Department of Community and Family Medicine, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
3 Department of Biochemistry, All India Institute of Medical Sciences, Rishikesh, Uttarakhand; Department of Biochemistry, Lady Hardinge Medical College, New Delhi, India

Correspondence Address:
Rajeev Goyal
Department of Biochemistry, Lady Hardinge Medical College, New Delhi - 110 054
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/JLP.JLP_163_18

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BACKGROUND: Various indices for estimating insulin sensitivity, based on glucose tolerance test and fasting insulin levels, have been devised. However, they are laborious, time-consuming, and costly. Recently, a new index, single point insulin sensitivity estimator (SPISE) based on TG, high-density lipoproteins (HDL), and body mass index (BMI) was proposed in the European population and was found comparable to gold standard test. Decreased insulin sensitivity is a hallmark of metabolic syndrome (MetS). Hence, the current study was planned to determine the optimal cutoff of SPISE with high sensitivity and specificity in MetS patients of the North Indian population. MATERIALS AND METHODS: A community-based cross-sectional study including 229 MetS cases and 248 controls was conducted. MetS was defined according to the South Asian Modified National Cholesterol Education Program criteria. SPISE index was calculated for cases and controls using the formula devised by Paulmichl et al.: SPISE = 600 × HDL-C0.185/(TG0.2 × BMI1.338). Receiver operating characteristic (ROC) curve was plotted for determining optimal cutoff for SPISE in MetS. RESULTS: SPISE was significantly lower in MetS patients (5.35 ± 1.35) than that for controls (7.45 ± 2) with P < 0.05 (confidence interval [CI]: 1.79–2.41). ROC curve showed area under the curve = 0.83 for SPISE (P < 0.05, CI: 0.79–0.86), showing SPISE to have good predictive ability to discriminate MetS cases from controls. The cutoff value of SPISE index for predicting insulin sensitivity in MetS was found out to be 5.82 with sensitivity and specificity of 73% and 80%, respectively. This cutoff is lower than the European population (6.61), indicating higher insulin resistance (IR) in the study population. CONCLUSION: SPISE could be a useful potential low-cost indicator with high sensitivity and specificity for predicting IR in MetS.


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