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Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers.

A large number of different dietary approaches have been studied in the attempt to achieve healthy, sustainable weight loss among individuals with overweight and obesity. Restriction of energy intake is the primary method of producing a negative energy balance leading to weight loss. However, owing to the different metabolic roles of proteins, carbohydrates and lipids in energy homeostasis, diets of similar overall energy content but with different macronutrient distribution can differentially affect metabolism, appetite and thermogenesis. Evidence increasingly suggests that the fuel values of calories provided by distinct macronutrients should be considered separately, as metabolism of specific molecular components generates differences in energy yield and. The causes of variation in individual responses to various diets are currently under debate, and some evidence suggests that differences are associated with specific genotypes. This narrative Review discusses all available systematic reviews and meta‐analyses, and summarizes the results of relevant randomized controlled intervention trials assessing the influence of macronutrient composition on weight management. The initial findings of research into personalized nutrition, based on the interactions of macronutrient intake and genetic background and its potential influence on dietary intervention strategies, are also discussed.

BACKGROUND: We sought to separately examine the effects of either weight loss or diets varying in protein content and glycemic index without further changes in body weight on cardiovascular risk factors within the Diet, Obesity, and Genes study (DiOGenes). METHODS AND RESULTS: DiOGenes is a pan-European controlled dietary intervention study in 932 overweight adults who first lost body weight on an 8-week low-calorie diet and were then randomized to 1 of 5 ad libitum diets for 26 weeks. The diets were either high or low protein or high or low glycemic index in 4 combinations or control. Weight loss (-11.23 kg; 95% confidence interval, -11.54 to -10.92; P

Aims/hypothesis: Diets with different amounts of protein and glycemic index (GI) may be of importance in the control of body weight and metabolic consequences, especially those related to insulin sensitivity. Methods: In DIOGenes, overweight/obese adults in 8 European countries who lost ≥ 8% of initial body-weight (BW) after following a low calorie diet (LCD) were later randomly assigned into five ad libitum interventional groups during 6 months: Low Protein [LP]/Low GI [LGI]; LP/High GI [HGI]; High Protein [HP]/LGI; HP/HGI and a healthy control diet. BW, fasting plasma glucose and insulin as well as an oral glucose tolerance test (OGTT), HOMA-IR, adiponectin and fructosamine levels were determined at the three time points. Results: The LCD period was initiated by 932 adults, 773 were randomised to the 5 diets and 548 completed the intervention. The HP and LGI diets were related to weight-loss management (p

Elevated plasma concentrations of remnant-like particle cholesterol (RLP-C) are atherogenic. However, factors that determine RLP-C are not fully understood. This study evaluates which factors affect RLP-C in the fasting and postprandial state, using multiple regression analyses in a large cohort of lean and obese participants. All participants (n = 740) underwent a test meal challenge containing 95 energy % (en%) fat (energy content 50% of predicted daily resting metabolic rate). Fasting and postprandial concentrations of circulating metabolites were measured over a 3-h period. Obese participants (n = 613) also participated in a 10-wk weight loss program (-2510 kJ/d), being randomized to either a low-fat or a high-fat diet (20-25 vs. 40-45en% fat). Postprandial RLP-C was associated with fasting RLP-C, waist:hip ratio (WHR), HOMA(IR) (homeostasis model assessment index for insulin resistance) (P < 0.001), and age, independently of BMI and gender [adjusted R(2) (adj. R(2)) = 0.70). These factors were also related to fasting RLP-C (P < 0.010), along with gender and physical activity (adj. R(2) = 0.23). The dietary intervention resulted in significantly lower fasting RLP-C concentrations, independently mediated by weight loss, improvements in HOMA(IR), and the fat content of the prescribed diet. However, after inclusion of plasma triglyceride (TG), HDL-cholesterol, and FFA concentrations in the models, HOMA(IR) and WHR no longer significantly predicted fasting RLP-C, although WHR remained a predictor of postprandial RLP-C (P = 0.002). Plasma TG was strongly associated with both fasting and postprandial RLP-C (P < 0.001). In conclusion, plasma RLP-C concentrations are mainly associated with plasma TG concentrations. Interestingly, the high-fat diet was more effective at decreasing fasting RLP-C concentrations in obese participants than the low-fat diet.