Gut microbiome and Mediterranean diet in the context of obesity. Current knowledge, perspectives and potential therapeutic targets.

Mediterranean Diet has been recognized as one of the healthiest and sustainable dietary patterns worldwide, based on the food habits of people living in the Mediterranean region. It is focused on a plant-based cuisine combining local agricultural products and moderate intake of fish. As eating habits seem to exert a major impact on the composition of gut microbiota, numerous studies show that an adherence to the Mediterranean diet positively influences the microbiome ecosystem network. This has a profound effect on multiple host metabolic pathways and plays a major role in immune and metabolic homeostasis. Among metabolic disorders, obesity represents a major health issue where Mediterranean Dietary regime could possibly slowdown its spread. The aim of this review is to emphasize the interaction between diet and gut microbiota and the potential beneficial effects of Mediterranean diet on metabolic disorders like obesity, which is responsible for the development of many noncommunicable diseases.

Estimation of low-density lipoprotein cholesterol by machine learning methods.

BACKGROUND: Accurate determination of low-density lipoprotein cholesterol (LDL) is important for coronary heart disease risk assessment and atherosclerosis. Apart from direct determination of LDL values, models (or equations) are used. A more recent approach is the use of machine learning (ML) algorithms. METHODS: ML algorithms were used for LDL determination (regression) from cholesterol, HDL and triglycerides. The methods used were multivariate Linear Regression (LR), Support Vector Machines (SVM), Extreme Gradient Boosting (XGB) and Deep Neural Networks (DNN), in both larger and smaller data sets. Also, LDL values were classified according to both NCEP III and European Society of Cardiology guidelines. RESULTS: The performance of regression was assessed by the Standard Error of the Estimate. ML methods performed better than established equations (Friedewald and Martin). The performance all ML methods was comparable for large data sets and was affected by the divergence of the train and test data sets, as measured by the Jensen-Shannon divergence. Classification accuracy was not satisfactory for any model. CONCLUSIONS: Direct determination of LDL is the most preferred route. When not available, ML methods can be a good substitute. Not only deep neural networks but other, less computationally expensive methods can work as well as deep learning.