Search

The combination of multiple omics approaches has emerged as an innovative holistic scope to provide a more comprehensive view of the molecular and physiological events underlying human diseases (including obesity, dyslipidemias, fatty liver, insulin resistance, and inflammation), as well as for elucidating unique and specific metabolic phenotypes. These omics technologies include genomics (polymorphisms and other structural genetic variants), epigenomics (DNA methylation, histone modifications, long non-coding RNA, telomere length), metagenomics (gut microbiota composition, enterotypes), transcriptomics (RNA expression patterns), proteomics (protein quantities), and metabolomics (metabolite profiles), as well as interactions with dietary/nutritional factors. Although more evidence is still necessary, it is expected that the incorporation of integrative omics could be useful not only for risk prediction and early diagnosis but also for guiding tailored dietary treatments and prognosis schemes. Some challenges include ethical and regulatory issues, the lack of robust and reproducible results due to methodological aspects, the high cost of omics methodologies, and high-dimensional data analyses and interpretation. In this review, we provide examples of system biology studies using multi-omics methodologies to unravel novel insights into the mechanisms and pathways connecting the genotype to clinically relevant traits and therapy outcomes for precision nutrition applications in health and disease.

Changes in gut microbiota composition and in epigenetic mechanisms have been proposed to play important roles in energy homeostasis, and the onset and development of obesity. However, the crosstalk between epigenetic markers and the gut microbiome in obesity remains unclear. The main objective of this study was to establish a link between the gut microbiota and DNA methylation patterns in subjects with obesity by identifying differentially methylated DNA regions (DMRs) that could be potentially regulated by the gut microbiota. DNA methylation and bacterial DNA sequencing analysis were performed on 342 subjects with a BMI between 18 and 40 kg/m2. DNA methylation analyses identified a total of 2648 DMRs associated with BMI, while ten bacterial genera were associated with BMI. Interestingly, only the abundance of Ruminococcus was associated with one BMI-related DMR, which is located between the MACROD2/SEL1L2 genes. The Ruminococcus abundance negatively correlated with BMI, while the hypermethylated DMR was associated with reduced MACROD2 protein levels in serum. Additionally, the mediation test showed that 19% of the effect of Ruminococcus abundance on BMI is mediated by the methylation of the MACROD2/SEL1L2 DMR. These findings support the hypothesis that a crosstalk between gut microbiota and epigenetic markers may be contributing to obesity development.

This research was funded by Centro Tecnológico para el Desarrollo Industrial (CDTI) through the program Consorcio de Investigación Empresarial Nacional (Programa CIEN, BIOFOOD Project) and by CIBERobn (Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CB12/03/30002). This study aims to analyze the relationship between gut microbiota composition and health parameters through specific biochemical markers and food consumption patterns in the Spanish population. This research includes 60 Spanish adults aged 47.3 ± 11.2 years old. Biochemical and anthropometric measurements, and a self-referred dietary survey (food frequency questionnaire), were analyzed and compared with the participant's gut microbiota composition analyzed by 16s rDNA sequencing. Several bacterial strains differed significantly with the biochemical markers analyzed, suggesting an involvement in the participant's metabolic health. Lower levels of Lactobacillaceae and Oscillospiraceae and an increase in Pasteurellaceae, Phascolarctobacterium, and Haemophilus were observed in individuals with higher AST levels. Higher levels of the Christensenellaceae and a decrease in Peptococcaceae were associated with higher levels of HDL-c. High levels of Phascolarctobacterium and Peptococcus and low levels of Butyricicoccus were found in individuals with higher insulin levels. This study also identified associations between bacteria and specific food groups, such as an increase in lactic acid bacteria with the consumption of fermented dairy products or an increase in Verrucomicrobiaceae with the consumption of olive oil. In conclusion, this study reinforces the idea that specific food groups can favorably modulate gut microbiota composition and have an impact on host's health.

This study aimed to screen relevant interactions between DRD2/ANKK1 TaqIA polymorphism and dietary intakes with reference to phenotypical features in patients with T2D from western Mexico. In this cross-sectional study, a total of 175 T2D patients were enrolled. Dietary intake was evaluated using 3-day food records and appropriate software. Glycemic and blood lipid profiles were measured by standardized methods. Genotyping of the DRD2/ANKK1 TaqIA polymorphism was performed by the RFLP method. Gene-diet interactions regarding anthropometric and metabolic phenotypes were screened by adjusted multiple linear regression analyses. Genotype frequencies of the DRD2/ANKK1 TaqIA polymorphism were A1A1 (16.0%), A1A2 (52.6%), and A2A2 (31.4%). Statistically significant interactions between the DRD2/ANKK1 TaqIA genotypes and dietary factors in relation to blood triglyceride (TG) levels were found. Carriers of the A1 allele (A1A1 homozygotes plus A1A2 heterozygotes) were protected from TG increases by maltose intake (P int. = 0.023). Instead, A2A2 homozygotes were susceptible to TG rises through consumptions of total fat (P int. = 0.041), monounsaturated fatty acids (P int. = 0.001), and dietary cholesterol (P int. = 0.019). This study suggests that the interactions between DRD2/ANKK1 TaqIA polymorphism and dietary factors (sugar and fats) influence TG levels in diabetic patients.

This study aimed to nutrigenetically screen gene-diet and gene-metabolic interactions influencing insulin resistance (IR) phenotypes. A total of 232 obese or overweight adults were categorized by IR status: non-IR (HOMA-IR (homeostatic model assessment - insulin resistance) index &le, 2.5) and IR (HOMA-IR index &gt, 2.5). A weighted genetic risk score (wGRS) was constructed using 95 single nucleotide polymorphisms related to energy homeostasis, which were genotyped by a next generation sequencing system. Body composition, the metabolic profile and lifestyle variables were evaluated, where individuals with IR showed worse metabolic outcomes. Overall, 16 obesity-predisposing genetic variants were associated with IR (p &lt, 0.10 in the multivariate model). The wGRS strongly associated with the HOMA-IR index (adj. R squared = 0.2705, p &lt, 0.0001). Moreover, the wGRS positively interacted with dietary intake of cholesterol (P int. = 0.002), and with serum concentrations of C-reactive protein (P int. = 0.008) regarding IR status, whereas a negative interaction was found regarding adiponectin blood levels (P int. = 0.006). In conclusion, this study suggests that interactions between an adiposity-based wGRS with nutritional and metabolic/endocrine features influence IR phenotypes, which could facilitate the prescription of personalized nutrition recommendations for precision prevention and management of IR and diabetes.

Some high-carbohydrate diets may lead to obesity and multiple metabolic disorders, including hypertriglyceridemia (HTG). This lipid abnormality is considered an important risk factor for cardiovascular disease and type 2 diabetes. The sweet taste receptor TAS1R2 polymorphism (Ile191Val) has been reported to be associated with carbohydrate intake. The aim of this study was to analyze the association of the TAS1R2 gene polymorphism with carbohydrate intake and HTG among the population of West Mexico. In a cross-sectional study, 441 unrelated subjects were analyzed for TAS1R2 genotypes (Ile/Ile, Ile/Val and Val/Val) by an allelic discrimination assay. Biochemical tests and a three-day food record were assessed. The Val/Val genotype carriers had a higher intake of total carbohydrates, fiber and servings of cereals and vegetables than the other genotype carriers. The Val/Val genotype conferred a higher risk for HTG than the Ile/Val and Ile/Ile genotypes (OR = 3.26, 95%CI 1.35–7.86, p = 0.006 and OR = 2.61, 95%CI 1.12–6.07, p = 0.02, respectively). Furthermore, the Val/Val genotype was associated with approximately 30% higher triglycerides compared with Ile/Val and Ile/Ile genotypes (β = 44.09, 95%CI 9.94–78.25, p = 0.01 and β = 45.7, 95%CI 10.85–80.54, p = 0.01, respectively). In conclusion, the Val/Val genotype of TAS1R2 was associated with a higher carbohydrate intake and HTG.