Search

Background: Despite the availability of alternative homogenous assays for LDL-C measurement, most of the laboratories still use Friedewald Equation (FE). However, various novel equations have shown better performance than FE specific to a particular population. Besides, no equation has been devised for use in Sub-Himalayan population. Methods: A cross-sectional laboratory data-based study was conducted by recruiting lipid profiles of 1851 samples to validate 10 different equations for calculating LDL and to devise a novel Modified Friedewald Equation (MFE) specific for Sub-Himalayan population. Results: The novel MFE is presented as: LDL-C = -2.421 + (0.752 × TC) - (0.047 × TG) - (0.350 × HDL). A significant difference was observed between direct LDL-C (118.84 ± 40.39 mg/dL) and all other equations except MFE (118.84 ± 37.96 mg/dl, P > 0.999) and Puavilai Equation (117.99 ± 49.05 mg/dL, P = 0.138). Additionally, MFE showed lowest mean percentage bias of 0.14% with 95% limits of agreement within ± 2SD on Bland-Altman analysis. On ROC analysis at cut-offs of clinical decision limits of 100 mg/dl, 130 mg/dl, 160 mg/dl, and 190 mg/dl, MFE outperformed all other equations with highest AUC (0.974, 0.978, 0.982, and 0.995) respectively with specificity >95% at higher levels. MFE also showed highest correlation (r = 0.954, P < 0.001) and least rMSE (13.8) with direct LDL although all the equations showed significant positive correlation with direct LDL (p < 0.001). Conclusion: MFE derived in this study showed a better diagnostic performance as compared to other 10 equations taking Direct LDL-C as gold standard for Sub-Himalayan Population and may be used as a substitute for FE in the study population.

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.