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Introduction: Carotenoids, which are a reliable biomarker of fruit and vegetable consumption, are positively associated with the lipid profile. Circulating carotenoid concentrations may interact with several omics profiles including genome, transcriptome, and epigenome. Few studies have used multi-omics approaches, and they rarely include environmental factors, such as diet. Objective: The objective of this observational study was to examine the potential role of multi-omics data in the interconnection between diet, represented by total carotenoids, and lipid profile using weighted gene correlation network analysis (WGCNA). Methods: Blood leukocyte DNA methylation levels of 472,245 CpG sites and whole blood gene expression levels of 18,160 transcripts were tested for associations with total carotenoid concentrations using regressions in 48 healthy subjects. WGCNA was used to identify co-omics modules and hub genes related to the lipid profile. Results: Among genes associated with total carotenoid concentrations, a total of 236 genes were identified at both DNA methylation and gene expression levels. Using WGCNA, six modules, consisting of groups of highly correlated genes represented by colors, were identified and linked to the lipid profile. Probes clustered in the turquoise and green modules correlated with plasma lipid concentrations. A total of 28 hub genes were identified. Conclusions: Genome-wide DNA methylation and gene expression levels were both associated with plasma total carotenoid concentrations. Several hub genes, mostly involved in lipid metabolism and inflammatory response with several genetic variants associated with plasma lipid concentrations, came out of the integrative analysis. This provides a comprehensive understanding of the interactive molecular system between carotenoids, omics, and plasma lipid profile.

Chronic diseases, including obesity, are major causes of morbidity and mortality in most countries. The adverse impacts of obesity and associated comorbidities on health remain a major concern due to the lack of effective interventions for prevention and management. Precision nutrition is an emerging therapeutic approach that takes into account an individual's genetic and epigenetic information, as well as age, gender, or particular physiopathological status. Advances in genomic sciences are contributing to a better understanding of the role of genetic variants and epigenetic signatures as well as gene expression patterns in the development of diverse chronic conditions, and how they may modify therapeutic responses. This knowledge has led to the search for genetic and epigenetic biomarkers to predict the risk of developing chronic diseases and personalizing their prevention and treatment. Additionally, original nutritional interventions based on nutrients and bioactive dietary compounds that can modify epigenetic marks and gene expression have been implemented. Although caution must be exercised, these scientific insights are paving the way for the design of innovative strategies for the control of chronic diseases accompanying obesity. This document provides a number of examples of the huge potential of understanding nutrigenetic, nutrigenomic, and nutriepigenetic roles in precision nutrition.

Aims: To test the potential association of cytosine-phosphate-guanine dinucleotides (CpG)-single-nucleotide polymorphisms (SNPs) located within actin-related protein 2/3 complex subunit 3 (ARPC3), a gene recently linked to adipogenesis and lipid accumulation, with metabolic syndrome (MetS) features in severely obese patients. Methods: Prioritized SNPs within the ARPC3 locus were genotyped and tested for associations with MetS features in a cohort of 1,749 obese patients with and without MetS. Association testing with CpG methylation levels was performed in a methylation sub-cohort of 16 obese men. Results: A significant association was found between the CpG-SNP rs3759384 (C>T) and plasma triglyceride (TG) levels (false discovery rate-corrected p = 3.5 × 10-2), with 0.6% of the phenotypic variance explained by the CpG-SNP, and with TT homozygotes showing the highest plasma TG levels (1.89 mmol/l). The carriers of the rs3759384 T allele also showed a significant decrease in methylation levels of the ARPC3 promoter-associated CpG site cg10738648 in both visceral adipose tissue and blood. ARPC3 expression levels showed a strong correlation with plasma TG levels (r = 0.70; p = 0.02). Conclusions: The increased plasma TG levels found in homozygous rs3759384 T allele carriers argue for a relevant role of this CpG-SNP in lipid management among obese individuals, which may be driven by an epigenetic-mediated mechanism.

Background: The small, dense LDL phenotype is associated with an increased cardiovascular disease risk. A genome-wide scan performed on the Quebec Family Study (QFS) revealed a quantitative trait locus for LDL peak particle diameter (LDL-PPD) on the 17q21 region. A positional candidate gene – the fatty acid synthase gene (FASN) – encodes a key enzyme in the biogenesis of membrane lipids. FASN may play a role in the regulation of feeding and may be a potential therapeutic target for obesity and insulin resistance. Methods: Analyses were performed on 592 subjects of the QFS. Dietary fat was estimated by a 3-day food record. LDL-PPD was measured by gradient gel electrophoresis on polyacrylamide gradient gels. Results: Five single nucleotide polymorphisms were genotyped in FASN gene. FASN rs4246444 was associated with LDL-PPD, but only when fat intake was taken into account (p = 0.001). High and low lipid consumers were defined using a cutoff of 35% of dietary fat intake. Carriers of the variant allele showed smaller LDL-PPD only when consuming a high amount of fat. This association remained significant after adjustments for age, sex, body mass index and plasma triglyceride levels. Conclusion: The results suggest that dietary fat intake may modify the effect of the FASN rs4246444 polymorphism on LDL-PPD.

Aim: To evaluate whether familial history of obesity (FHO) interacts with dietary fat intake (DFI) on obesity-related phenotypes. Methods: We recruited 664 participants aged between 18 and 55 years. A positive FHO (FHO+) was defined as having at least 1 obese first-degree relative and a negative FHO (FHO–) as no obese first-degree relative. Dietary intakes were collected from a food-frequency questionnaire. Body mass index, weight and waist girth were recorded using standard procedures. Fat mass and fat-free mass were assessed by electrical bioimpedance. Results: Significant interaction effects (FHO·DFI) were observed for body mass index, weight, waist girth and fat mass (p interaction = 0.05, 0.04, 0.04, 0.02, respectively). Among FHO+ individuals, indices of obesity increased with an increasing amount of DFI, whereas these associations were not observed in FHO– individuals. We also found that FHO+ individuals consuming a high-fat diet were at higher risk of obesity than FHO– individuals consuming a low-fat diet (3.6, CI 2.1–6.2). Conclusion: These results suggest a stronger relationship between DFI and obesity-related phenotypes in individuals with FHO+.

Introduction: Complex traits such as obesity are modulated by genetic and environmental factors and lead to varied clinical presentations.The aim of this study was to investigate associations between candidate genes and obesity-related phenotypes using a sample of 252 lumberjacks issued from a founder population and sharing a common and circumscribed environment. Methods: Thirty-seven variants in 18 genes were genotyped. The restriction fragment length polymorphism method and the template-directed dye-terminator incorporation assay with fluorescence polarization detection were employed for the genotyping assays. Multivariate logistic regression models were built in order to calculate the relative odds of exhibiting obesity-related phenotypes associated with the presence of the studied polymorphism. Among them, 21 single nucleotide polymorphisms were tested for associations with obesity phenotypes. Results: Significant associations were found between carriers of the minor alleles of APOE-ε2, FABP2-A54T, UCP1-L229M, LPL-HindIII, LPL-S447X and LPL-T1973C, patients bearing a combination of LPL-D9N, LPL-N291S and LPL-P207L and obesity-related phenotypes. Conclusion: The present results suggest that a particular population such as lumberjacks, sharing the same environment, could help target genes involved in complex traits.

Objectives: The purpose of this project was to evaluate the potential of the downward hierarchical clustering analysis (DHCA) for studying genetic heterogeneity, i.e. differences in allele frequency in subpopulations, such as the 15 public health regions of the province of Québec (Canada). Methods: The study relied on an anonymized sample of 1,680 individuals who had participated in the Québec Heart Health Survey in 1990–1991. The genotyping of 11 variants in 8 candidate genes known to be involved in chronic inflammatory diseases, namely asthma and cardiovascular diseases, was performed using the amplification refractory mutation system and restriction fragment length polymorphism techniques. Only variants showing an allelic frequency >2% in the Québec Heart Health Survey (n = 8) were selected. DHCA techniques were then applied to model the geographical distribution of these 8 genetic variants in 15 Québec public health regions and to study genetic heterogeneity. Results: The DHCA allowed to group public health regions and gene variants on the basis of genetic variability. For both asthma and cardiovascular diseases, 3 significant clusters of public health regions and 1 cluster of gene variants were identified. Discussion: This study suggests that DHCA might be useful in studying genetic heterogeneity at the population level and for public health activities.

This special issue of the Journal of Nutrigenetics and Nutrigenomics covers an emerging topic that has been implicated in disease risk and prevention: nutritional epigenomics. Although the impact of epigenetics has been addressed for the last 3 decades in cancer research, only in recent years has interest surfaced in other fields, including cardiovascular and neurodegenerative diseases, obesity, diabetes and nutrition. Many definitions have been proposed for epigenetics in the literature, but most often epigenetics refers to heritable changes in gene expression that are not accompanied by alterations in DNA sequence [1] . Although there is some debate on which processes fall into this definition, DNA methylation, histone posttranslational modifications and more recently microRNAs are considered the main epigenetic phenomena. Methylation of cytosines, acetylation and methylation of lysine residues in histone proteins and microRNAs influence chromatin architecture and thus gene expression. They are involved in many biological processes including DNA-protein interactions, suppression of transposable element mobility, cellular differentiation, embryogenesis, Xchromosome inactivation and genomic imprinting. Accumulating evidence shows that these epigenetic processes can be influenced by nutritional components. For example, folate and vitamin B 12 participate in the 1-carbon metabolism and are necessary for chromatin methylation reactions. Furthermore, several bioactive food components have been shown to modulate the activity of enzymes that integrate the epigenetic machinery, including DNA methyltransferases and histone deacetylases and acetyltransferases. Thus, nutritional modulation of epigenetic processes adds a further layer of complexity to gene-nutrient interactions and should be considered for the definition of strategies for health promotion and disease prevention. Because epigenetic marks are potentially reversible and are implicated in the pathogenesis of diverse non-communicable diseases representing major public health problems in both developed and developing countries, the epigenome becomes an attractive target for Published online: February 22, 2012