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Background and Aim: Circulating level of glutamate, a by-product of the catabolism of branched-chain amino acids, has been positively correlated with visceral adipose tissue accumulation and waist circumference (WC). The aim of the present study was to assess the potential of using glutamate level to identify individuals with abdominal obesity and a high cardiometabolic risk. Methods and results: The study sample included 99 men and 99 women. Fasting serum glutamate was measured using the Biocrates p180 kit. Anthropometric and metabolic variables were used to identify individuals with abdominal obesity (WC ≥95cm in both sexes), the hypertriglyceridemic waist (HTW) phenotype and the metabolic syndrome (MetS). Mean (± SD) age was 34.1±10.1 years, mean BMI was 29.0±6.2 kg/m2 and mean WC was 92.7±16.5 cm. Glutamate was strongly correlated with WC (r=0.66 for men; r =0.76 for women, both p<0.0001) and multiple markers of metabolic dysfunction, particularly fasting triglyceride level (r=0.59 for men; r=0.57 for women, both p<0.0001), HDL-cholesterol level (r=-0.45, p<0.0001 in both sexes) and the HOMA-IR index (r=0.65 for men; r=0.60 for women, both p<0.0001). Logistic regressions showed that glutamate had an excellent accuracy to identify individuals with abdominal obesity (ROC_AUC: 0.90 for both sexes), a good accuracy to identify those with the HTW phenotype (ROC_AUC: 0.81 for men; 0.85 for women) and fair-togood accuracy for the MetS (ROC_AUC: 0.78 for men; 0.89 for women). Conclusion: Glutamate level may represent an interesting potential biomarker of abdominal obesity and metabolic risk.

Elevated plasma branched-chain amino acids (BCAA) and C3 and C5 acylcarnitines (AC) levels observed in individuals with insulin resistance (IR) might be influenced by dietary protein intakes. This study explores the associations between dietary protein sources, plasma BCAA levels and C3 and C5 ACs in normal weight (NW) or overweight (OW) individuals with or without metabolic syndrome (MS). Data from 199 men and women aged 18–55 years with complete metabolite profile were analyzed. Associations between metabolic parameters, protein sources, plasma BCAA and AC levels were tested. OW/MS+ consumed significantly more animal protein (p = 0.0388) and had higher plasma BCAA levels (p < 0.0001) than OW/MS− or NW/MS− individuals. Plasma BCAA levels were not associated with BCAA intakes in the whole cohort, while there was a trend for an association between plasma BCAA levels and red meat or with animal protein in OW/MS+. These associations were of weak magnitude. In NW/MS− individuals, the protein sources associated with BCAA levels varied greatly with adjustment for confounders. Plasma C3 and C5 ACs were associated with plasma BCAA levels in the whole cohort (p < 0.0001) and in subgroups based on OW and MS status. These results suggest a modest association of meat or animal protein intakes and an association of C3 and C5 ACs with plasma BCAA levels, obesity and MS.

Variability in plasma carotenoids may be attributable to several factors including genetic variants and lipid profile. Until now, the impact of DNA methylation on this variability has not been widely studied. Weighted gene correlation network analysis (WGCNA) is a systems biology method used for finding gene clusters (modules) with highly correlated methylation levels and for relating them to phenotypic traits. The objective of the present study was to examine the role of DNA methylation in the relationship between plasma total carotenoid concentrations and lipid profile using WGCNA in 48 healthy subjects. Genome-wide DNA methylation levels of 20,687 out of 472,245 CpG sites in blood leukocytes were associated with total carotenoid concentrations. Using WGCNA, nine co-methylation modules were identified. A total of 2734 hub genes (17 unique top hub genes) were potentially related to lipid profile. This study provides evidence for the potential implications of gene co-methylation in the relationship between plasma carotenoids and lipid profile. Further studies and validation of the hub genes are needed.

Background and aims: Obesity is a major health problem worldwide. Given the heterogeneous obesity phenotype, an optimal obesity stratification would improve clinical management. Since obesity has a strong genetic component, we aimed to develop a polygenic risk score (PRS) to stratify obesity according to the genetic background of the individuals. Methods: A total of 231 single nucleotide polymorphisms (SNP) significantly associated to body mass index (BMI) from 21 genome-wide association studies were genotyped or imputed in 881 subjects from the Quebec Family Study (QFS). The population was randomly split into discovery (80%; n = 704) and validation (20%; n = 177) samples with similar obesity (BMI ≥ 30) prevalence (27.8% and 28.2%, respectively). Familybased associations with obesity were tested for every SNP in the discovery sample and a weighed and continuous PRS231 was constructed. Generalized linear mixed effects models were used to test the association of PRS231 with obesity in the QFS discovery sample and validated in the QFS replication sample. Furthermore, the Fatty Acid Sensor (FAS) Study (n = 141; 27.7% obesity prevalence) was used as an independent sample to replicate the results. Results: The linear trend test demonstrated a significant association of PRS231 with obesity in the QFS discovery sample (ORtrend = 1.19 [95% CI, 1.14-1.24]; P = 2.0x10-16). We also found that the obesity prevalence was significantly greater in the higher PRS231 quintiles compared to the lowest quintile. Significant and consistent results were obtained in the QFS validation sample for both the linear trend test (ORtrend = 1.16 [95% CI, 1.07- 1.26]; P = 6.7x10-4), and obesity prevalence across quintiles. These results were partially replicated in the FAS sample (ORtrend = 1.12 [95% CI, 1.02-1.24]; P = 2.2x10-2). PRS231 explained 7.5%, 3.2%, and 1.2% of BMI variance in QFS discovery, QFS validation, and FAS samples, respectively. Conclusions: These results revealed that genetic ba

En présence d’un environnement favorisant la sédentarité et l’accessibilité aux aliments, certains individus sont plus susceptibles au gain de poids. Cette différence individuelle peut s’expliquer par des interactions gène-environnement. En effet, il a été démontré que certains individus présentent une prédisposition génétique à l’obésité. De plus, une composante génétique a également été associée aux comportements alimentaires, tels que la restriction cognitive, la désinhibition et la susceptibilité à la faim, ces derniers étant également liés à l’obésité. L’objectif de cet article de revue est de présenter l’état des connaissances en ce qui a trait à l’héritabilité de certains comportements alimentaires, puis d’identifier les gènes associés à ces comportements. Les résultats démontrent que la désinhibition et la susceptibilité à la faim sont deux comportements alimentaires particulièrement héritables et que plusieurs gènes candidats sont maintenant associés aux comportements alimentaires. Ces résultats indiquent que l’on est loin d’être tous égaux face aux aliments et peuvent aider les professionnels à mieux comprendre la variabilité observée en contexte d’intervention. L’identification des gènes associés aux comportements alimentaires s’avère primordiale afin de mieux identifier les individus plus à risque de gain de poids et de mieux intervenir auprès de ces derniers., When exposed to an environment that promotes a sedentary lifestyle and food accessibility, some individuals are at a higher risk of weight gain. This individual difference can be explained by gene–environment interactions. It has been shown that some individuals have a genetic predisposition to obesity. In addition, a genetic component has been associated with eating behaviour traits such as cognitive restraint, disinhibition, and susceptibility to hunger, all of which are also linked to obesity. The purpose of this review article is to present the current state of knowledge on the heritability of specific eating behaviour traits and to then identify the genes associated with these behaviours. The findings show that disinhibition and susceptibility to hunger are 2 eating behaviour traits that are particularly heritable and that several candidate genes are now associated with eating behaviour traits. These findings indicate that we are far from equal when it comes to food and can help professionals better understand the variability observed in an intervention context. Identifying genes associated with eating behaviour traits is crucial to better identify and intervene with people at higher risk of weight gain.

Background: Data on the relationship between protein intake and the risk of type 2 diabetes are conflicting.Objective: We studied prospective associations between the intake of total, plant-based, and animal protein and the risk of pre-diabetes and diabetes in 4 population-based studies included in the PREVIEW project.Methods: Analyses were conducted with the use of data from 3 European cohorts and 1 Canadian cohort, including 78,851 participants. Protein intake was assessed through the use of harmonized data from food-frequency questionnaires or 3-d dietary records. Cohort-specific incidence ratios (IRs) were estimated for pre-diabetes and diabetes, adjusting for general characteristics, lifestyle and dietary factors, disease history, and body mass index (BMI) and waist circumference; results were pooled based on a random-effects meta-analysis.Results: Higher total protein intake (g · kg-1 · d-1) was associated with lower incidences of pre-diabetes and diabetes (pooled IRs: 0.84; 95% CI: 0.82, 0.87 and 0.49; 95% CI: 0.28, 0.83, respectively); plant-based protein intake was the main determinant (pooled IRs: 0.83; 95% CI: 0.81, 0.86 and 0.53; 95% CI: 0.36, 0.76, respectively). Substituting 2 energy percentage (E%) protein at the expense of carbohydrates revealed increased risks of pre-diabetes and diabetes (pooled IRs: 1.04; 95% CI: 1.01, 1.07 and 1.09; 95% CI: 1.01, 1.18, respectively). Except for the associations between intakes of total protein and plant-based protein (g · kg-1 · d-1) and diabetes, all other associations became nonsignificant after adjustment for BMI and waist circumference.Conclusions: Higher protein intake (g · kg-1 · d-1) was associated with a lower risk of pre-diabetes and diabetes. Associations were substantially attenuated after adjustments for BMI and waist circumference, which demonstrates a crucial role for adiposity and may