Nutrigenetics: bridging two worlds to understand type 2 diabetes

Genetics, Nutrition, and Health: A New Frontier in Disease Prevention

The field of nutrition research has traditionally focused on the effects of macronutrients and micronutrients on the body. However, it has become evident that individuals have unique genetic makeups that influence their response to food. Nutritional genomics, which includes nutrigenetics and nutrigenomics, explores the interaction between an individual's genetic makeup, diet, and health outcomes. Nutrigenetics studies the impact of genetic variation on an individual's response to dietary nutrients, while nutrigenomics investigates how dietary components affect gene regulation and expression. These disciplines seek to understand the impact of diet on the genome, transcriptome, proteome, and metabolome. It provides insights into the mechanisms underlying the effect of diet on gene expression. Nutrients can cause the modification of genetic expression through epigenetic changes, such as DNA methylation and histone modifications. The aim of nutrigenomics is to create personalized diets based on the unique metabolic profile of an individual, gut microbiome, and genetic makeup to prevent diseases and promote health. Nutrigenomics has the potential to revolutionize the field of nutrition by combining the practicality of personalized nutrition with knowledge of genetic factors underlying health and disease. Thus, nutrigenomics offers a promising approach to improving health outcomes (in terms of disease prevention) through personalized nutrition strategies based on an individual's genetic and metabolic characteristics.

Nutrigenetics/Nutrigenomics, Personalized Nutrition, and Precision Healthcare

PURPOSE OF REVIEW

The purposes of the present review are to examine the emergence of nutrigenetics/nutrigenomics, to analyze the relationship between nutrigenetics and nutrigenomics, to explore the impact of nutrigenetics/nutrigenomics on healthcare with respect to noncommunicable diseases, and to discuss the challenges facing the implementation of nutrigenetics/nutrigenomics within healthcare.

RECENT FINDINGS

Nutrigenetics/nutrigenomics is certainly a thriving specialty given the sharp increase of publications over the last two decades. The relationship between nutrigenetics and nutrigenomics is proposed as complementary. The current clinical and research literature supports the significant impact nutrigenetics/nutrigenomics has on treating and preventing noncommunicable diseases. Although several challenges face the implementation of nutrigenetics/nutrigenomics into healthcare, they are not insurmountable. Nutrigenetics/nutrigenomics plays an important role not only in treating diseases and illnesses but also in promoting health and wellness through both basic and clinical research; and it is critical for the future of both personalized nutrition and precision healthcare.

Interaction between genes and macronutrient intake on the risk of developing type 2 diabetes: systematic review and findings from European Prospective Investigation into Cancer (EPIC)-InterAct

Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date.Objective: We aimed to identify existing evidence for gene-macronutrient interactions and T2D and to examine the reported interactions in a large-scale study.Design: We systematically reviewed studies reporting gene-macronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e.g., total carbohydrate) or indicators of quality (e.g., dietary fiber) by use of self-report or objective biomarkers of intake. Interactions identified in the review were subsequently examined in the EPIC (European Prospective Investigation into Cancer)-InterAct case-cohort study (n = 21,148, with 9403 T2D cases; 8 European countries). Prentice-weighted Cox regression was used to estimate country-specific HRs, 95% CIs, and P-interaction values, which were then pooled by random-effects meta-analysis. A primary model was fitted by using the same covariates as reported in the published studies, and a second model adjusted for additional covariates and estimated the effects of isocaloric macronutrient substitution.Results: Thirteen observational studies met the eligibility criteria (n < 1700 cases). Eight unique interactions were reported to be significant between macronutrients [carbohydrate, fat, saturated fat, dietary fiber, and glycemic load derived from self-report of dietary intake and circulating n-3 (ω-3) polyunsaturated fatty acids] and genetic variants in or near transcription factor 7-like 2 (TCF7L2), gastric inhibitory polypeptide receptor (GIPR), caveolin 2 (CAV2), and peptidase D (PEPD) (P-interaction < 0.05). We found no evidence of interaction when we tried to replicate previously reported interactions. In addition, no interactions were detected in models with additional covariates.Conclusions: Eight gene-macronutrient interactions were identified for the risk of T2D from the literature. These interactions were not replicated in the EPIC-InterAct study, which mirrored the analyses undertaken in the original reports. Our findings highlight the importance of independent replication of reported interactions.

Characterization of Metabolically Healthy Obese People and Metabolically Unhealthy Normal-Weight People in a General Population Cohort of the ABCD Study

There is actually no consensus about the possibility that in some instances, obesity may be a benign metabolically healthy (MH) condition as opposed to a normal-weight but metabolically unhealthy (MUH) state. The aim of this study was to characterize MH condition and to investigate possible associations with metabolic and cardiovascular complications. One thousand nineteen people (range of age 18-90 years) of the cohort of the ABCD_2 study were investigated. Participants were classified as normal weight (BMI < 24.9 kg/m²) or overweight-obese (BMI ≥25 kg/m²); they were also classified as MH in the presence of 0-1 among the following conditions: (a) prediabetes/type 2 diabetes, (b) hypertension, (c) hypertriglyceridemia or low HDL cholesterolemia, and (d) hypercholesterolemia. MUH condition was diagnosed if ≥2 of the conditions listed were found. The prevalence of overweight/obese people was 71.1%, of whom 27.4% were found to be MH. In addition, 36.7% of the normal-weight participants were MUH. HOMA-IR, high sensitivity C-reactive protein, and the carotid intima-media thickness were significantly different in the 4 subgroups (P < 0.001), with higher values observed in the MUH normal-weight and obese groups. In conclusion, this study highlights the importance of identifying a MH condition in normal-weight and in obese people in order to offer better treatment.

From Genome-Wide Association Study to Phenome-Wide Association Study: New Paradigms in Obesity Research

Obesity is a condition in which excess body fat has accumulated over an extent that increases the risk of many chronic diseases. The current clinical classification of obesity is based on measurement of body mass index (BMI), waist-hip ratio, and body fat percentage. However, these measurements do not account for the wide individual variations in fat distribution, degree of fatness or health risks, and genetic variants identified in the genome-wide association studies (GWAS). In this review, we will address this important issue with the introduction of phenome, phenomics, and phenome-wide association study (PheWAS). We will discuss the new paradigm shift from GWAS to PheWAS in obesity research. In the era of precision medicine, phenomics and PheWAS provide the required approaches to better definition and classification of obesity according to the association of obese phenome with their unique molecular makeup, lifestyle, and environmental impact.

Nutrigenetics and nutrigenomics insights into diabetes etiopathogenesis

Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide. Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease. The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved. Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, gene-diet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools. In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications. This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM. Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression, how epigenetic changes and micro RNAs (miRNAs) can alter cellular signaling in response to nutrients and the dietary interventions that may help to prevent the onset of DM.

Defining metabolically healthy obesity: role of dietary and lifestyle factors

BACKGROUND

There is a current lack of consensus on defining metabolically healthy obesity (MHO). Limited data on dietary and lifestyle factors and MHO exist. The aim of this study is to compare the prevalence, dietary factors and lifestyle behaviours of metabolically healthy and unhealthy obese and non-obese subjects according to different metabolic health criteria.

METHOD

Cross-sectional sample of 1,008 men and 1,039 women aged 45-74 years participated in the study. Participants were classified as obese (BMI ≥ 30 kg/m(2)) and non-obese (BMI < 30 kg/m(2)). Metabolic health status was defined using five existing MH definitions based on a range of cardiometabolic abnormalities. Dietary composition and quality, food pyramid servings, physical activity, alcohol and smoking behaviours were examined.

RESULTS

The prevalence of MHO varied considerably between definitions (2.2% to 11.9%), was higher among females and generally increased with age. Agreement between MHO classifications was poor. Among the obese, prevalence of MH was 6.8% to 36.6%. Among the non-obese, prevalence of metabolically unhealthy subjects was 21.8% to 87%. Calorie intake, dietary macronutrient composition, physical activity, alcohol and smoking behaviours were similar between the metabolically healthy and unhealthy regardless of BMI. Greater compliance with food pyramid recommendations and higher dietary quality were positively associated with metabolic health in obese (OR 1.45-1.53 unadjusted model) and non-obese subjects (OR 1.37-1.39 unadjusted model), respectively. Physical activity was associated with MHO defined by insulin resistance (OR 1.87, 95% CI 1.19-2.92, p = 0.006).

CONCLUSION

A standard MHO definition is required. Moderate and high levels of physical activity and compliance with food pyramid recommendations increase the likelihood of MHO. Stratification of obese individuals based on their metabolic health phenotype may be important in ascertaining the appropriate therapeutic or intervention strategy.