Insight into the Predictive Power of Surrogate Diagnostic Indices for Identifying Individuals with Metabolic Syndrome.

BACKGROUND: This study aimed to assess the diagnostic capability of insulin surrogate measurements in identifying individuals with metabolic syndrome (MetS) and propose applicable indices derived from fasting values, particularly in large study populations. METHODS: Data were collected from the datasets of the Surveillance of Risk Factors of NCDs in Iran Study (STEPS). MetS was defined based on the National Cholesterol Education Program (NCEP) criteria. Various insulin surrogate indices, including Homeostasis Model Assessment (HOMA), Quantitative Insulin Sensitivity Check Index (QUICKI), Fasting glucose to insulin ratio (FGIR), Reynaud, Reciprocal insulin, McAuley, Metabolic Score for Insulin Resistance (METS-IR), Triglyceride-glucose index (TyG), TG/ HDL-C, TG/ BMI, and TG/ WC ratio were assessed. Receiver Operating Characteristic (ROC) curves were used to assess pathologic conditions and determine the optimal cut-off through the highest score of the Youden index. Also, Area Under the Curve (AUC) values were established for each index totally and according to sex, age, and BMI differences. RESULTS: The study population consisted of 373 individuals (49.9% women; 75.1% middle age, 39.1% obese, and 27.3% overweight), of whom 117 (31.4%) had MetS. The METS-IR (AUC: 0.856; 95% CI: 0.817-0.895), TG/ HDL-C (AUC: 0.820; 95% CI: 0.775-0.886), TyG (AUC: 0.808; 95% CI: 0.759-0.857), and McAuley (AUC: 0.804; 95% CI: 0.757-0.852) indices provided the greatest AUC respectively for detection of MetS. The values of AUC for all the indices were higher in men than women. This trend was consistent after data stratification based on BMI categories, middle age, and senile individuals. CONCLUSION: The present study indicated that indices of insulin, including METS-IR, TG/HDLC, TyG, and McAuley, have an equal or better capacity in determining the risk of MetS than HOMA-IR, are capable of identifying individuals with MetS and may provide a simple approach for identifying populations at risk of insulin resistance.

Association between ELMO1 gene polymorphisms and diabetic kidney disease: A systematic review and meta-analysis.

BACKGROUND: Previous research has suggested that the ELMO1 gene may play a role in the development of diabetic kidney disease. Diabetic kidney disease (DKD) is a serious complication of diabetes and the leading cause of chronic kidney disease and end-stage renal disease (ESRD). OBJECTIVE AND RATIONALE: This study aim was to systematically review and explore the association between ELMO1 gene polymorphisms and diabetic kidney disease. A comprehensive systematic review provides a clear conclusion and high-level evidence for the association between ELMO1 gene and DKD for future application in personalized medicine. METHODS: A comprehensive search of electronic databases, per PRISMA instructions, was conducted in Scopus, EMBASE, Web of Science, and PubMed databases from 1980 to January 2023. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using appropriate models. Subgroup and sensitivity analyses were performed to explore potential sources of heterogeneity and assess the robustness of the findings. RESULTS: A total of 5794 diabetes patients with DKD, 4886 diabetes patients without DKD, and 2023 healthy controls were included in the 17 studies that made up this systematic review. In the investigation of DM (Diabetes Mellitus) with DKD vs. DM without DKD, the susceptibility for DKD for the EMLO1 rs741301 polymorphism indicated a significant difference under the dominant, homozygote, and recessive genetic models. The susceptibility for DKD for the EMLO1 rs1345365, rs10255208, and rs7782979 polymorphisms demonstrated a significant difference under the allele genetic models in the analysis of DM with DKD vs. DM without DKD groups. There was a considerable increase in DKD risk in the Middle East when the population was stratified by the region. CONCLUSION: The findings of the meta-analysis show that there are a significant connection between the EMLO1 rs741301 polymorphism and DKD susceptibility in overall analyses; as well as rs1345365, rs10255208, and rs7782979 polymorphisms; especially in the Middle East region.

Comprehensive investigation of insulin-induced amyloidosis lesions in patients with diabetes at clinical and histological levels: A systematic review.

INTRODUCTION: Insulin-derived amyloidosis (AIns), a skin complication in patients with diabetes, causes impaired insulin absorption. This systematic review aims to get a better understanding of this overlooked condition. METHODS: Comprehensive literature searches were performed in Scopus, PubMed, EMBASE, and Web of Science databases until June 17, 2023. From 19,343 publications, duplicate and irrelevant records were eliminated by title, and the full texts of the remaining studies were examined for validity. Clinical, pathological, and therapeutic findings were extracted from 44 papers. RESULTS: Forty-four articles were studied that covered 127 insulin-treated patients with diabetes. From the 62 patients with reported age and sex, males had a mean age of 58 years, and females 68.5 years. While AIns were twice as likely to develop in men (66.13 %) as in women (33.87 %), the administered insulin dose was significantly higher in males (p = 0.017). The most common insulin injection site was the abdominal wall (77.63 %). Histological findings showed the presence of amorphous material with the occasional presence of lymphocytes, plasma cells, macrophages, adipocytes, histocytes, and giant cells. The mean HbA1c level was 8.8 % and the need for receiving insulin was increased in AIns. Changing the site of insulin injections and/or surgically removing the nodules were the most common treatments to obtain better insulin uptake and controlled serum glucose levels. CONCLUSION: This study highlights the importance of AIns, proper rotation of insulin injection site, and post-treatment patient follow-up to recognize and prevent the development of amyloid nodules.

Plasma acylcarnitines and amino acids in dyslipidemia: An integrated metabolomics and machine learning approach.

Purpose: The Discovery of underlying intermediates associated with the development of dyslipidemia results in a better understanding of pathophysiology of dyslipidemia and their modification will be a promising preventive and therapeutic strategy for the management of dyslipidemia. Methods: The entire dataset was selected from the Surveillance of Risk Factors of Noncommunicable Diseases (NCDs) in 30 provinces of Iran (STEPs 2016 Country report in Iran) that included 1200 subjects and was stratified into four binary classes with normal and abnormal cases based on their levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C.Plasma concentrations of 20 amino acids and 30 acylcarnitines in each class of dyslipidemia were evaluated using Tandem mass spectrometry. Then, these attributes, along with baseline characteristics data, were used to check whether machine learning (ML) algorithms could classify cases and controls. Results: Our ML framework accurately predicts TG binary classes. Among the models tested, the SVM model stood out, performing slightly better with an AUC of 0.81 and a standard deviation of test accuracy at 0.04. Consequently, it was chosen as the optimal model for TG classification. Moreover, the findings showed that alanine, phenylalanine, methionine, C3, C14:2, and C16 had great power in differentiating patients with high TG from normal TG controls. Conclusions: The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-024-01384-9.