BackgroundMetabolic syndrome over the years have structured definitions to classify an individual with the disease. Literature review suggests insulin résistance is hallmark of these metabolic clustering. While measuring insulin resistance directly or indirectly remains technically difficult in general practice, along with multiple stability issues for insulin, various indirect measures have been suggested by authorities. Fasting triglycerides-glucose (TyG) index is one such marker, which is recently been suggested as a useful diagnostic marker to predict metabolic syndrome. However, limited data is available on the subject with almost no literature from our region on the subject.Objective1. To correlate TyG index with insulin resistance, anthropometric indices, small dense LDLc, HbA1c and nephropathy. 2. To evaluate TyG index as a marker to diagnose metabolic syndrome in comparison to other available markers.Design-cross-sectional analysisPlace and duration of study-From Jun-2016 to July-2017 at PSS HAFEEZ hospital Islamabad.Subjects and methodsFrom a finally selected sample size of 227 male and female subjects we evaluated their anthropometric data, HbA1c, lipid profile including calculated sdLDLc, urine albumin creatinine raito(UACR) and insulin resistance (HOMAIR). TyG index was calculated using formula of Simental-Mendía LE et al. Aforementioned parameters were correlated with TyG index, differences between subjects with and without metabolic syndrome were calculated using Independent sample t-test. Finally ROC curve analysis was carried out to measure AUC for candidate parameters including TyG Index for comparison.ResultsTyG index in comparison to other markers like fasting triglycerides, HOMAIR, HDLc and non-HDLc demonstrated higher positive linear correlation with BMI, atherogenic dyslipidemia (sdLDLc), nephropathy (UACR), HbA1c and insulin resistance. TyG index showed significant differences between various markers among subjects with and without metabolic syndrome as per IDF criteria. AUC (Area Under Curve) demonstrated highest AUC for TyG as [(0.764, 95% CI 0.700–0.828, p-value ≤ 0.001)] followed by fasting triglycerides [(0.724, 95% CI 0.656–0.791, p-value ≤ 0.001)], sdLDLc [(0.695, 95% CI 0.626–0.763, p-value ≤ 0.001)], fasting plasma glucose [(0.686, 95% CI 0.616–0.756, p-value ≤ 0.001)], Non-HDLc [(0.640, 95% CI 0.626–0.763, p-value ≤ 0.001)] and HOMAIR [(0.619, 95% CI 0.545–0.694, p-value ≤ 0.001)].ConclusionTyG index, having the highest AUC in comparison to fasting glucose, triglycerides, sdLDLc, non-HDLc and HOMAIR can act as better marker for diagnosing metabolic syndrome.

Objective: To evaluate glucose tolerance patterns in pregnant ladies undergoing 2-hour oral glucose tolerance test (OGTT) for comparing fasting, 1-hour, 2-hour post-glucose load results, HbA1c, sum of all glucose readings with and without gestational diabetes mellitus (GDM) using International Association of the Diabetes and Pregnancy Study Group (IADPSG) diagnostic criteria.

Objectives: To measure correlation and concordance between measured LDL cholesterol (mLDLc) and Friedewald’s calculated LDL cholesterol (cLDLc). To compare the mLDLc and cLDLc values for various anthropometric measures and biochemical indices including insulin resistance, nephropathy, glycated hemoglobin and triglycerides. Methods: Two hundred thirty two subjects were included in this cross-sectional analysis from Jan-2016 to July-2017 from a target population visiting PNS HAFEEZ hospital. Mean age of the subjects was 46.56(±11.95) years (n=232). These subjects underwent clinical evaluation including measurement of anthropometric measurements, biochemical testing for fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), lipid profile, urine albumin creatinine ratio (UACR), and insulin. Correlation and concordance between mLDLc and Friedewald’s cLDLc were measured. Finally, Comparison of risk evaluation for mLDLc and cLDLc between groups formulated based upon UACR (Based upon a cut off of 2.5 mg/g) and fasting triglycerides (Group-1 :< 1.0 mmol/L, Group-2: 1.0-1.99 mmol/L and Group-3 :> 1.99 mmol/) was carried out. Results: There was significant positive linear correlation between mLDLc and cLDLc [r=0.468, <0.001]. Kendall’s Coefficient of concordance between mLDLc and cLDLc was 0.055 (p<0.001). Differences evaluated by one way ANOVA analysis for mLDLc between various triglycerides groups were only significant between group-1 and group-2 [{Group-1:Mean=2.40, (2.19-2.61), n=43}, {Group-2:Mean=2.81, (2.69-2.92),n=136}, [{Group-3:Mean=2.59,(2.37-2.81), n=53}],(p=0.004) in comparison to cLDLc [{Group-1:Mean=2.63, (2.43-2.84), n=43}, {Group-2:Mean=2.85, (2.76-2.93), n=136}, [{Group-3:Mean=2.75, (2.60-2.90),n=53}]. Calculated method for LDLc showed higher UACR than mLDLc. (p=0.021) Conclusion: cLDLc over estimates LDL-cholesterol in comparison to mLDLc. The correlation between cLDLc and mLDLc was only moderate. However, cLDLc provided better degree of risk prediction for nephropathy and glycated hemoglobin than mLDLc. How to cite this:Khan SH, Niazi NK, Sobia F, Fazal N, Manzoor SM, Nadeem A. Friedewald’s equation for calculating LDL-cholesterol: Is it the time to say “Goodbye” and adopt direct LDL cholesterol methods? Pak J Med Sci. 2019;35(2):---------.   doi: https://doi.org/10.12669/pjms.35.2.679 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.