Interactions between Caveolin-1 polymorphism and Plant-based dietary index on metabolic and inflammatory markers among women with obesity

Healthy plant-based diets improve dyslipidemias, insulin resistance, and inflammation in metabolic syndrome. A narrative review

Plant-based diets (PBDs) have become very popular in recent years and have been identified as a dietary strategy associated with protection against chronic disease. However, the classifications of PBDs vary depending on the type of diet. Some PBDs have been recognized as healthful for their high content of vitamins, minerals, antioxidants, and fiber, or unhealthful if they are high in simple sugars and saturated fat. Depending on this classification, the type of PBD impacts its protective effects against disease dramatically. Metabolic syndrome (MetS), characterized by the presence of high plasma triglycerides and low HDL cholesterol, impaired glucose metabolism, elevated blood pressure, and increased concentrations of inflammatory biomarkers, also increases the risk for heart disease and diabetes. Thus, healthful plant-based diets could be considered favorable for individuals having MetS. The different types of plant-based diets (vegan, lacto-vegetarian, lacto-ovo-vegetarian, or pescatarian) are discussed with a focus on specific effects of dietary components in maintaining a healthy weight, protecting against dyslipidemias, insulin resistance, hypertension, and low-grade inflammation.

Personalized optimal nutrition lifestyle for self obesity management using metaalgorithms

Precision medicine applies machine learning methods to estimate the personalized optimal treatment decision based on individual information, such as genetic data and medical history. The main purpose of self obesity management is to develop a personalized optimal life plan that is easy to implement and adhere to, thereby reducing the incidence of obesity and obesity-related diseases. The methodology comprises three components. First, we apply catboost, random forest and lasso covariance test to evaluate the importance of individual features in forecasting body mass index. Second, we apply metaalgorithms to estimate the personalized optimal decision on alcohol, vegetable, high caloric food and daily water intake respectively for each individual. Third, we propose new metaalgorithms named SX and SXwint learners to compute the personalized optimal decision and compare their performances with other prevailing metalearners. We find that people who receive individualized optimal treatment options not only have lower obesity levels than others, but also have lower obesity levels than those who receive ’one-for-all’ treatment options. In conclusion, all metaalgorithms are effective at estimating the personalized optimal decision, where SXwint learner shows the best performance on daily water intake.