Triglyceride and Glucose Index (TyG) is a Reliable Biomarker to Predict Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a common liver disease worldwide. There is no specific biomarker for the diagnosis of NAFLD. Trigly-ceride and glucose index (TyG) may predict the subsequent occurrence of NAFLD in later life. This cross sectional study was aimed to evaluate the effectiveness of triglyceride and glucose index (TyG) as a possible biomarker of NAFLD. The study was conducted at the Department of Laboratory Medicine, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh from March 2019 to February 2020. A total of 124 subjects were taken as study population following selection criteria. Among them 62 were diagnosed patients of NAFLD and 62 were healthy subjects as control group. Fasting plasma glucose was measured by glucose oxidase method and serum triglyceride was measured by enzymatic-colorimetric method, while TyG index was calculated accordingly. The mean age was 39.5 ± 11.27 years in NAFLD patients and 37.10 ± 12.28 years in control subjects with male female ratio 1:1.7 and 1:1.8 respectively. Major portion of NAFLD patients (62.9%) were overweight (BMI ≥ 25). The mean fasting plasma glucose level was 5.73 ± 1.47 mmol/L in NAFLD patients and 5.27 ± 0.69 mmol/L in control group (p < 0.027). The mean serum triglyceride level was 237.19 ± 96.47 mg/dl in NAFLD patients and 117.32 ± 53.07 mg/dl in control group (p < 0.001). The triglyceride and glucose index (TyG) was 9.36 ± 0.47 in NAFLD group and 8.53 ± 0.42 in control group. TyG index was significantly higher in NAFLD patients in comparison to control group (p < 0.001). In ROC analysis, cut off value of TyG index was 8.85 with sensitivity 93.5% and specificity 79%. As a fast and effective method, TyG index can be used as a diagnostic tool to predict NAFLD.

Detection of the adulteration of extra virgin olive oil by near-infrared spectroscopy and chemometric techniques

Introduction and Objectives:Due to the value of extra virgin olive oil(EVOO),adulteration has become an important issue in the industry,which has created demand for quick and inexpensive fraud detection testing.In contrast to many current food fraud detection methods,near-infrared spectroscopy(NIRS)can be inexpensive and convenient by minimizing sample preparation and measurement times.In this study,we developed a method using NIRS and chemometrics to detect adulteration of EVOO with other edible oil types that does not require sample preparation and can be completed in less than 10 min.Methods,Results,and Discussions:First,a single EVOO was adulterated with corn oil from 2.7%to 25%w/w.Spectra for the unadulterated sample and its adulterated counterparts were measured.A principal component analysis(PCA)scores plot showed separation between the adulterated mixtures and the unadulterated sample,which demonstrated that the developed method could detect as low as 2.7%w/w adulteration if an unadulterated sample of the oil in question is provided.To study adulteration detection without an unadulterated sample for reference,the spectra of unadulterated samples and samples adulterated with corn,sunflower,soybean,and canola oils were measured.A PCA with soft independent modelling of class analogy was used for adulteration detection.Lower limits of adulteration detection for corn,sunflower,soybean,and canola oils were found to be approximately 20%,20%,15%,and 10%,respectively.Conclusions:These results demonstrate that the developed method can be used to rapidly screen for adulterated olive oils.