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Background & Objectives: Diabetes is a known chronic disease affecting more than 400 million people and causing millions of deaths worldwide annually. The World Health Organization predicts that by 2030, diabetes will become the seventh leading cause of death. In the last decades, numerous chemotherapy approaches have been used. However, herbal remedies have accounted for more than 70 percent of the total administered antidiabetic treatments during the last decade. In this study, we aimed to develop a novel natural antidiabetic therapy with Trigonella foenum-graecum (T. foenum-graecum) L. and Citrullus colocynthis (C. colocynthis) (L.). Based on past studies, using C. colocynthis singly can cause diarrhea, abdominal pain, and heartburn, hence in order to reduce these complications, we applied this plant in combination with T. foenum-graecum based on the recommendations of Persian medicine references. Materials & Methods: We admitted 34 diabetics aged 30 -65 years old to design a double-blind parallel randomized controlled clinical trial. We prepared therapeutic capsules containing a combination of 40 mg of C. colocynthis and 350 mg of T. foenum-graecum L. We divided participants into an interventional (n=17) and placebo (n=17) groups and measured fasting blood sugar (FBS) levels, lipid profile, and liver function tests initially. After two weeks, all participants returned for a visit, and FBS and blood sugar (BS) were measured. At the end (12th week), all outcomes were measured and analyzed using an independent t-test and Chi -squared (χ²). Results: The FBS levels decreased significantly in the intervention group compared to that of the control group (171.8±54.9 versus 149.6±46.4, p=0.024), but the improvement s in the lipid profile and liver function were not statistically significant. Conclusion: This study unveiled that combination of T. foenum-graecum L. and C. colocynthis L. at safe levels significantly decreased the FBS levels among T2DM patients. Further studies are needed to provide more efficient antidiabetic effects with the low side effects. [ABSTRACT FROM AUTHOR]

Background: Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area. Methods: This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35–70 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP). Results: Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth. Conclusion: ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension. [ABSTRACT FROM AUTHOR]

Background The triglyceride glucose (TyG) and triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-c) are the indices that can predict the progression of pre-diabetes to type 2 diabetes mellitus (T2DM). This study aimed to examine the relationship between TyG and TG/HDL-c indices with the incidence of T2DM in pre-diabetes patients. Methods A total of 758 pre-diabetic patients aged 35–70 years who were enrolled in a prospective Fasa Persian Adult Cohort were followed up for 60 months. TyG and TG/HDL-C indices were obtained at baseline data and divided into quartiles. The 5-year cumulative incidence of T2DM was analyzed by Cox proportional hazards regression analysis while controlling for baseline covariates. Results During 5 years of follow-up, there were 95 incident cases of T2DM, with an overall incidence rate of 12.53%. After adjusting for age, sex, smoking, marital status, socioeconomic status, body mass index, waist circumference, hip circumference, hypertension, total cholesterol, and dyslipidemia, the multivariate-adjusted hazard ratios (HRs) demonstrated that patients with the highest TyG and TG/HDL-C indices quartile were at higher risk of T2DM (HR=4.42, 95%CI 1.75–11.21) and (HR=2.15, 95%CI 1.04–4.47), respectively, compared to participants in the lowest quartile. As the quantiles of these indices increase, the HR value shows a signifcant increment (P<0.05). Conclusion The results of our study showed that the TyG and TG/HDL-C indices can be important independent predictors for the progression of pre-diabetes to T2DM. Therefore, controlling the components of these indicators in pre-diabetes patients can prevent developing T2DM or delay its occurrence. [ABSTRACT FROM AUTHOR]