Gene–Diet Interactions and Obesity Indices

The interaction of breastfeeding and genetic factors on childhood obesity

Childhood obesity represents a pressing global public health concern due to its widespread prevalence and its close connection to early-life exposure to risk factors. The onset of obesity is contingent upon the interplay of genetic composition, lifestyle choices, and environmental as well as nutritional elements encountered during both fetal development and early childhood. This paper critically examines research discoveries in this area and concisely outlines the influence of breastfeeding on genetic predispositions associated with childhood obesity. Studies have demonstrated that breastfeeding has the potential to reduce childhood obesity by impacting anthropometric indicators. Moreover, the duration of breastfeeding is directly correlated with the degree to which it alters the risk of childhood obesity. Current explorations into the link between genetic factors transmitted through breast milk and childhood obesity predominantly focus on genes like FTO, Leptin, RXRα, PPAR-γ, and others. Numerous research endeavors have suggested that an extended period of exclusive breastfeeding is tied to a diminished likelihood of childhood obesity, particularly if sustained during the initial six months. The duration of breastfeeding also correlates with gene methylation, which could serve as the epigenetic mechanism underpinning breastfeeding's preventative influence against obesity. In summary, the thorough evaluation presented in this review underscores the intricate nature of the association between breastfeeding, genetic factors, and childhood obesity, providing valuable insights for future research efforts and policy formulation.

A Personalized Diet Approach Study: Interaction between PPAR-γ Pro12Ala and Dietary Insulin Indices on Metabolic Markers in Diabetic Patients

BACKGROUND

The objectives were to investigate the effect of the interaction between peroxisome proliferator-activated receptor gamma (PPAR-γ) Pro12Ala polymorphisms and dietary insulin load and insulin index (DIL and DII) on Cardio-metabolic Markers among diabetic patients.

METHODS

This cross-sectional study was conducted on 393 diabetic patients. Food-frequency questionnaire (FFQ) was used for DIL and DII calculation. PPAR-γ Pro12Ala was genotyped by the PCR-RFLP method. Biochemical markers including TC, LDL, HDL, TG, SOD, CRP, TAC, PTX3, PGF2α. IL18, leptin and ghrelin were measured by standard protocol.

RESULT

Risk-allele carriers (CG, GG) had higher obesity indices WC (P _interaction =0.04), BMI (P _interaction =0.006) and, WC (P _interaction =0.04) compared with individuals with the CC genotype when they consumed a diet with higher DIL and DII respectively. Besides, carriers of the G allele who were in the highest tertile of DIL, had lower HDL (P _interaction =0.04) and higher PGF2α (P _interaction =0.03) and PTX3 (P _interaction =0.03). Moreover, the highest tertile of the DII, showed an increase in IL18 (P _interaction =0.01) and lower SOD (P _interaction =0.03) for risk allele carriers compared to those with CC homozygotes.

CONCLUSION

We revealed PPAR-γ Pro12Ala polymorphism was able to intensify the effect of DIL and DII on CVD risk factors; risk-allele carriers who consumed a diet with high DIL and DII score have more likely to be obese and have higher inflammatory markers. Also, protective factor against CVD risk factors were reduced significantly in this group compared to CC homozygotes. This article is protected by copyright. All rights reserved.

The interaction between dietary patterns and melanocortin-4 receptor polymorphisms in relation to obesity phenotypes

INTRODUCTION

Data shows that interactions between dietary factors and genetic variants can modulate the association of polymorphisms such as the Melanocortin-4 receptor (MC4R) gene with obesity. Considering the limited data available on this topic we aimed to investigate interactions between dietary patterns (DPs) and MC4R polymorphisms in relation to obesity phenotypes.

METHODS

This cohort study was performed in the framework of Tehran Lipid and Glucose Study; for eligible participants in this study (n=3850), the median follow-up was 4 years. DPs were determined using factor analysis. The genotypes of polymorphisms (17782313rs and 12970134rs) were identified and their interaction with DPs were assessed in relation to incidence of obesity phenotypes including central obesity, general obesity and visceral adiposity dysfunction.

RESULTS

The mean age of participants (62.5% females) were 37.0±13.7 years. Two main DPs (healthy and unhealthy) were extracted. C-allele carriers of rs17782313 in higher quartiles of the healthy DP score had a significant decrease in the incidence of general obesity, compared to those who had the TT genotype (HR=0.61, 95% CI=0.42-0.89, P interaction=0.01). For rs12970134 A-allele carriers, subjects in the second compared to the first quartile of the healthy DP score, had a significant decrease in the incidence of general obesity (HR=0.68, 95% CI=0.46-0.99). There were no significant interaction between DPs and MC4R variants in relation to other obesity phenotypes.

CONCLUSION

Our results indicate that the healthy DP could interact with rs17782313 in relation to incidence of general obesity.

The importance of gene-environment interactions in human obesity

The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations.