A genetic risk tool for obesity predisposition assessment and personalized nutrition implementation based on macronutrient intake

Precision nutrition-based interventions for the management of obesity in children and adolescents up to the age of 19 years

BACKGROUND

Precision nutrition-based methods develop tailored interventions and/or recommendations accounting for determinants of intra- and inter-individual variation in response to the same diet, compared to current 'one-size-fits-all' population-level approaches. Determinants may include genetics, current dietary habits and eating patterns, circadian rhythms, health status, gut microbiome, socioeconomic and psychosocial characteristics, and physical activity. ​​​​In this systematic review, we examined the evidence base for the effect of interventions based on precision nutrition approaches on overweight and obesity in children and adolescents to help inform future research and global guidelines.

OBJECTIVES

To examine the impact of precision nutrition-based interventions for the management of obesity in children and adolescents in all their diversity.

SEARCH METHODS

We searched CENTRAL, MEDLINE, CINAHL, Web of Science Core Collection, BIOSIS Previews, Global Index Medicus (all regions), IBECS, SciELO, PAHO, PAHO IRIS, WHO IRIS, WHOLIS, Bibliomap, and TRoPHI, as well as the WHO ICTRP and ClinicalTrials.gov. We last searched the databases on 23 July 2024. We did not apply any language restrictions.

SELECTION CRITERIA

We included randomised or quasi-randomised controlled trials that evaluated precision nutrition-based interventions (accounting for 'omics' such as phenotyping, genotyping, gut microbiome; clinical data, baseline dietary intake, postprandial glucose response, etc., and/or including artificial intelligence such as machine learning methods) compared to general or one-size-fits-all interventions or no intervention in children and adolescents aged 0 to 9 years or 10 to 19 years with overweight or obesity.

DATA COLLECTION AND ANALYSIS

Two review authors independently conducted study screening, data extraction, and risk of bias and GRADE assessments. We used fixed-effect analyses. Our outcomes of interest were physical and mental well-being, physical activity, health-related quality of life, obesity-associated disability, and adverse events associated with the interventions as defined or measured by trialists, and weight change (reduction, stabilisation or maintenance).

MAIN RESULTS

Two studies (3 references, 105 participants) conducted in Ukraine and Greece met our eligibility criteria. One study reported nonprofit funding sources, whilst the other did not report funding, and the certainty of evidence ranged from very low to low across outcomes (all measured at endpoint). Only one trial (65 participants) contributed data on our primary outcomes of interest. Precision nutrition-based intervention versus one-size-fits-all intervention or standard of care In children 0 to 9 years of age, evidence is very uncertain about the effect of a precision nutrition-based intervention (a computerised Decision Support Tool (DST) that incorporates a variety of participant data and provides personalised diet recommendations based on decision-tree algorithms) on body mass index (BMI) (mean difference (MD) -1.40 kg/m2, 95% confidence interval (CI) -3.48 to 0.68; 1 study, 35 participants; very low-certainty evidence) and on weight (MD -2.60 kg, 95% CI -8.42 to 3.22; 1 study, 35 participants; very low-certainty evidence) compared with a one-size-fits-all control intervention. In children and adolescents 10 to 19 years of age, evidence is very uncertain about the effect of a precision nutrition-based intervention (computerised DST) on BMI (MD 3.00 kg/m2, 95% CI -0.26 to 6.26; 1 study, 30 participants; very low-certainty evidence) and on weight (MD 11.40 kg, 95% CI -0.47 to 23.27; 1 study, 30 participants; very low-certainty evidence) compared with a one-size-fits-all control intervention.

AUTHORS' CONCLUSIONS

Based on data from two small studies with a total of 105 participants, the evidence is very uncertain about the effect of precision nutrition-based interventions on body weight or BMI. This review was limited by the number of available randomised controlled trials in this relatively nascent field. Given these limitations, the two studies do not provide sufficient evidence to adequately inform practice. Future research should report participant outcome data, including outcomes related to mental, emotional, and functional well-being, in addition to biochemical and physical measures, stratified by World Health Organization-defined age groups (children (0 to 9 years), and children and adolescents (10 to 19 years)). Future studies should also report methods related to randomisation, blinding, and compliance, as well as include prespecified analysis plans.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

Assessment of the impact of a personalised nutrition intervention in impaired glucose regulation over 26 weeks: a randomised controlled trial

Dietary interventions can reduce progression to type 2 diabetes mellitus (T2DM) in people with non-diabetic hyperglycaemia. In this study we aimed to determine the impact of a DNA-personalised nutrition intervention in people with non-diabetic hyperglycaemia over 26 weeks. ASPIRE-DNA was a pilot study. Participants were randomised into three arms to receive either (i) Control arm: standard care (NICE guidelines) (n = 51), (ii) Intervention arm: DNA-personalised dietary advice (n = 50), or (iii) Exploratory arm: DNA-personalised dietary advice via a self-guided app and wearable device (n = 46). The primary outcome was the difference in fasting plasma glucose (FPG) between the Control and Intervention arms after 6 weeks. 180 people were recruited, of whom 148 people were randomised, mean age of 59 years (SD = 11), 69% of whom were female. There was no significant difference in the FPG change between the Control and Intervention arms at 6 weeks (- 0.13 mmol/L (95% CI [- 0.37, 0.11]), p = 0.29), however, we found that a DNA-personalised dietary intervention led to a significant reduction of FPG at 26 weeks in the Intervention arm when compared to standard care (- 0.019 (SD = 0.008), p = 0.01), as did the Exploratory arm (- 0.021 (SD = 0.008), p = 0.006). HbA1c at 26 weeks was significantly reduced in the Intervention arm when compared to standard care (- 0.038 (SD = 0.018), p = 0.04). There was some evidence suggesting prevention of progression to T2DM across the groups that received a DNA-based intervention (p = 0.06). Personalisation of dietary advice based on DNA did not result in glucose changes within the first 6 weeks but was associated with significant reduction of FPG and HbA1c at 26 weeks when compared to standard care. The DNA-based diet was effective regardless of intervention type, though results should be interpreted with caution due to the low sample size. These findings suggest that DNA-based dietary guidance is an effective intervention compared to standard care, but there is still a minimum timeframe of adherence to the intervention before changes in clinical outcomes become apparent.Trial Registration: www.clinicaltrials.gov.uk Ref: NCT03702465.

Potential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity

Precision nutrition (PN) considers multiple individual-level and environmental characteristics or variables to better inform dietary strategies and interventions for optimizing health, including managing obesity and metabolic disorders. Here, we review the evidence on potential mechanisms-including ones to identify individuals most likely to respond-that can be leveraged in the development of PN interventions addressing obesity. We conducted a review of the literature and included laboratory, animal, and human studies evaluating biochemical and genetic data, completed and ongoing clinical trials, and public programs in this review. Our analysis describes the potential mechanisms related to 6 domains including genetic predisposition, circadian rhythms, physical activity and sedentary behavior, metabolomics, the gut microbiome, and behavioral and socioeconomic characteristics, i.e., the factors that can be leveraged to design PN-based interventions to prevent and treat obesity-related outcomes such as weight loss or metabolic health as laid out by the NIH 2030 Strategic Plan for Nutrition Research. For example, single nucleotide polymorphisms can modify responses to certain dietary interventions, and epigenetic modulation of obesity risk via physical activity patterns and macronutrient intake have also been demonstrated. Additionally, we identified limitations including questions of equitable implementation across a limited number of clinical trials. These include the limited ability of current PN interventions to address systemic influences such as supply chains and food distribution, healthcare systems, racial or cultural inequities, and economic disparities, particularly when designing and implementing PN interventions in low- and middle-income communities. PN has the potential to help manage obesity by addressing intra- and inter-individual variation as well as context, as opposed to "one-size fits all" approaches though there is limited clinical trial evidence to date.

Physiological Appetite Regulation and Bariatric Surgery

Obesity remains a common metabolic disorder and a threat to health as it is associated with numerous complications. Lifestyle modifications and caloric restriction can achieve limited weight loss. Bariatric surgery is an effective way of achieving substantial weight loss as well as glycemic control secondary to weight-related type 2 diabetes mellitus. It has been suggested that an anorexigenic gut hormone response following bariatric surgery contributes to weight loss. Understanding the changes in gut hormones and their contribution to weight loss physiology can lead to new therapeutic treatments for weight loss. Two distinct types of neurons in the arcuate hypothalamic nuclei control food intake: proopiomelanocortin neurons activated by the anorexigenic (satiety) hormones and neurons activated by the orexigenic peptides that release neuropeptide Y and agouti-related peptide (hunger centre). The arcuate nucleus of the hypothalamus integrates hormonal inputs from the gut and adipose tissue (the anorexigenic hormones cholecystokinin, polypeptide YY, glucagon-like peptide-1, oxyntomodulin, leptin, and others) and orexigeneic peptides (ghrelin). Replicating the endocrine response to bariatric surgery through pharmacological mimicry holds promise for medical treatment. Obesity has genetic and environmental factors. New advances in genetic testing have identified both monogenic and polygenic obesity-related genes. Understanding the function of genes contributing to obesity will increase insights into the biology of obesity. This review includes the physiology of appetite control, the influence of genetics on obesity, and the changes that occur following bariatric surgery. This has the potential to lead to the development of more subtle, individualised, treatments for obesity.

A genomics perspective of personalized prevention and management of obesity

This review discusses the landscape of personalized prevention and management of obesity from a nutrigenetics perspective. Focusing on macronutrient tailoring, we discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake revealed enrichment of pathways associated with circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodeling and PPAR signaling as potential targets of macronutrients for the management of obesity in relevant genetic backgrounds. Notably, our data-based in silico predictions suggest the potential of repurposing the SYK inhibitor fostamatinib for obesity treatment in relevant genetic profiles. In addition to dietary considerations, we address genetic variations guiding lifestyle changes in weight management, including exercise and chrononutrition. Finally, we emphasize the need for a refined understanding and expanded research into the complex genetic landscape underlying obesity and its management.

Metabolic Syndrome: An Overview on Its Genetic Associations and Gene-Diet Interactions

Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that includes central obesity, hyperglycemia, hypertension, and dyslipidemias and whose inter-related occurrence may increase the odds of developing type 2 diabetes and cardiovascular diseases. MetS has become one of the most studied conditions, nevertheless, due to its complex etiology, this has not been fully elucidated. Recent evidence describes that both genetic and environmental factors play an important role on its development. With the advent of genomic-wide association studies, single nucleotide polymorphisms (SNPs) have gained special importance. In this review, we present an update of the genetics surrounding MetS as a single entity as well as its corresponding risk factors, considering SNPs and gene-diet interactions related to cardiometabolic markers. In this study, we focus on the conceptual aspects, diagnostic criteria, as well as the role of genetics, particularly on SNPs and polygenic risk scores (PRS) for interindividual analysis. In addition, this review highlights future perspectives of personalized nutrition with regard to the approach of MetS and how individualized multiomics approaches could improve the current outlook.

Precision Nutrition to Improve Risk Factors of Obesity and Type 2 Diabetes

PURPOSE OF REVIEW

Existing dietary and lifestyle interventions and recommendations, to improve the risk factors of obesity and type 2 diabetes with the target to mitigate this double global epidemic, have produced inconsistent results due to interpersonal variabilities in response to these conventional approaches, and inaccuracies in dietary assessment methods. Precision nutrition, an emerging strategy, tailors an individual's key characteristics such as diet, phenotype, genotype, metabolic biomarkers, and gut microbiome for personalized dietary recommendations to optimize dietary response and health. Precision nutrition is suggested to be an alternative and potentially more effective strategy to improve dietary intake and prevention of obesity and chronic diseases. The purpose of this narrative review is to synthesize the current research and examine the state of the science regarding the effect of precision nutrition in improving the risk factors of obesity and type 2 diabetes.

RECENT FINDINGS

The results of the research review indicate to a large extent significant evidence supporting the effectiveness of precision nutrition in improving the risk factors of obesity and type 2 diabetes. Deeper insights and further rigorous research into the diet-phenotype-genotype and interactions of other components of precision nutrition may enable this innovative approach to be adapted in health care and public health to the special needs of individuals. Precision nutrition provides the strategy to make individualized dietary recommendations by integrating genetic, phenotypic, nutritional, lifestyle, medical, social, and other pertinent characteristics about individuals, as a means to address the challenges of generalized dietary recommendations. The evidence presented in this review shows that precision nutrition markedly improves risk factors of obesity and type 2 diabetes, particularly behavior change.

Regulation of Macronutrients in Insulin Resistance and Glucose Homeostasis during Type 2 Diabetes Mellitus

Insulin resistance is an important feature of metabolic syndrome and a precursor of type 2 diabetes mellitus (T2DM). Overnutrition-induced obesity is a major risk factor for the development of insulin resistance and T2DM. The intake of macronutrients plays a key role in maintaining energy balance. The components of macronutrients distinctly regulate insulin sensitivity and glucose homeostasis. Precisely adjusting the beneficial food compound intake is important for the prevention of insulin resistance and T2DM. Here, we reviewed the effects of different components of macronutrients on insulin sensitivity and their underlying mechanisms, including fructose, dietary fiber, saturated and unsaturated fatty acids, and amino acids. Understanding the diet-gene interaction will help us to better uncover the molecular mechanisms of T2DM and promote the application of precision nutrition in practice by integrating multi-omics analysis.

Interaction between genetic susceptibility to obesity and food intake on BMI in Finnish school-aged children

Diet modulates the genetic risk of obesity, but the modulation has been rarely studied using genetic risk scores (GRSs) in children. Our objectives were to identify single nucleotide polymorphisms (SNPs) that drive the interaction of specific foods with obesity and combine these into GRSs. Genetic and food frequency data from Finnish Health in Teens study was utilized. In total, 1142 11-year-old subjects were genotyped on the Metabochip array. BMI-GRS with 30 well-known SNPs was computed and the interaction of individual SNPs with food items and their summary dietary scores were examined in relation to age- and sex-specific BMI z-score (BMIz). The whole BMI-GRS interacted with several foods on BMIz. We identified 7-11 SNPs responsible for each interaction and these were combined into food-specific GRS. The most predominant interaction was witnessed for pizza (p < 0.001): the effect on BMIz was b - 0.130 (95% CI - 0.23; - 0.031) in those with low-risk, and 0.153 (95% CI 0.072; 0.234) in high-risk. Corresponding, but weaker interactions were verified for sweets and chocolate, sugary juice drink, and hamburger and hotdog. In total 5 SNPs close to genes NEGR1, SEC16B, TMEM18, GNPDA2, and FTO were shared between these interactions. Our results suggested that children genetically prone to obesity showed a stronger association of unhealthy foods with BMIz than those with lower genetic susceptibility. Shared SNPs of the interactions suggest common differences in metabolic gene-diet interactions, which warrants further investigation.

Dietary approach to stop hypertension and healthy eating index 2015, modify the association between FTO polymorphisms and obesity phenotypes

This study aimed to investigate the interaction of the healthy eating index (HEI) and the dietary approach to stop hypertension (DASH) diet scores with FTO polymorphisms in relation to change in obesity traits. A total of 4480 subjects aged ≥ 18 years were selected from participants of the Tehran lipid and glucose study and followed-up 3 years. Selected polymorphisms (rs1421085, rs1121980, rs8050136) were genotyped and genetic risk score (GRS) was computed. HEI and DASH scores were computed based on dietary data. Changes in body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR) and visceral adiposity index (VAI) were measured. Higher adherence to both DASH and HEI scores were increased with higher ages. Individuals with high GRS had a lower change in BMI when they had higher adherence to HEI, compared to subjects with lower HEI score (P trend = 0.01). Change in WC in participants in the fourth quartile of HEI score in minor allele carriers of FTO variants was lower compared to the first quartile; conversely, higher adherence to the DASH score by this genotypic group was related to increase in WC. No significant interaction was seen between FTO polymorphisms and both diet scores regarding changes in any of obesity traits. In conclusion, in individuals with high GRS higher adherence to HEI score was associated with lower change in BMI and WC, while higher adherence to DASH diet was associated with higher change in WC, compared to individuals with lower adherence to both scores.

Next-Generation Sequencing of a Large Gene Panel for Outcome Prediction of Bariatric Surgery in Patients with Severe Obesity

Obesity is a chronic disease in which abnormal deposition of fat threatens health, leading to diabetes, cardiovascular diseases, cancer, and other chronic illnesses. According to the WHO, 19.8% of the adult population in Italy is obese, and the prevalence is higher among men. It is important to know the predisposition of an individual to become obese and to respond to bariatric surgery, the most up-to-date treatment for severe obesity. To this purpose, we developed an NGS gene panel, comprising 72 diagnostic genes and 244 candidate genes, and we sequenced 247 adult obese Italian patients. Eleven deleterious variants in 9 diagnostic genes and 17 deleterious variants in 11 candidate genes were identified. Interestingly, mutations were found in several genes correlated to the Bardet-Biedl syndrome. Then, 25 patients were clinically followed to evaluate their response to bariatric surgery. After a 12-month follow-up, the patients that carried deleterious variants in diagnostic or candidate genes had a reduced weight loss, as compared to the other patients. The NGS-based panel, including diagnostic and candidate genes used in this study, could play a role in evaluating, diagnosing, and managing obese individuals, and may help in predicting the outcome of bariatric surgery.

In the context of the triple burden of malnutrition: A systematic review of gene-diet interactions and nutritional status

Genetic background interacts with dietary components to modulate nutritional health status. This study aimed to review the evidence for gene-diet interactions in all forms of malnutrition. A comprehensive systematic literature search was conducted through April 2021 to identify observational and intervention studies reporting the effects of gene-diet interactions in over-nutrition, under-nutrition and micronutrient status. Risk of publication bias was assessed using the Quality Criteria Checklist and a tool specifically designed for gene-diet interaction research. 167 studies from 27 populations were included. The majority of studies investigated single nucleotide polymorphisms (SNPs) in overnutrition (n = 158). Diets rich in whole grains, vegetables, fruits and low in total and saturated fats, such as Mediterranean and DASH diets, showed promising effects for reducing obesity risk among individuals who had higher genetic risk scores for obesity, particularly the risk alleles carriers of FTO rs9939609, rs1121980 and rs1421085. Other SNPs in MC4R, PPARG and APOA5 genes were also commonly studied for interaction with diet on overnutrition though findings were inconclusive. Only limited data were found related to undernutrition (n = 1) and micronutrient status (n = 9). The findings on gene-diet interactions in this review highlight the importance of personalized nutrition, and more research on undernutrition and micronutrient status is warranted.

Early nutritional influences of cardiovascular health

INTRODUCTION

Increasing evidence shows that nutritional choices during children's formative years, including prenatally, impacts the development of adult onset cardiovascular diseases (CVDs), such as hypertension, myocardial infarction, or stroke.

AREAS COVERED

This literature review aims to synthesize the current body of evidence on nutritional factors, from conception through adolescence, which may influence a person's risk factors for future development of CVD.

EXPERT OPINION

Given the escalating healthcare costs associated with CVD, it is imperative that medical professionals and scientists remain steadfast in prioritizing and promoting early CVD prevention, even within the first few years of life. Though not the only contributing risk factor, diet is a modifiable risk factor and has been shown to have a profound impact on the reduction of cardiovascular morbidity and mortality in adult literature. Nutritional choices should be targeted on multiple levels: prenatally with the mother, individually with the patient, in conjunction with their family unit, and also within the broader community wherein they reside. Healthcare providers can play a key advocacy role for local and national food environment policy changes.

Nutrigenomics and Life Style Facet- A Modulatory Molecular Evidence in Progression of Breast and Colon Cancer with Emerging Importance

Legitimate nutrition assumes a significant role in preventing diseases and, in this way, nutritional interventions establish vital strategies in the area of public health. Nutrigenomics centres on the different genes and diet in an individual and how an individual's genes influence the reaction to bioactive foodstuff. It targets considering the genetic and epigenetic interactions with nutrients to lead to a phenotypic alteration and consequently to metabolism, differentiation, or even apoptosis. Nutrigenomics and lifestyle factors play a vital role in health management and represent an exceptional prospect for the improvement of personalized diets to the individual at risk of developing diseases like cancer. Concerning cancer as a multifactorial genetic ailment, several aspects need to be investigated and analysed. Various perspectives should be researched and examined regarding the development and prognosis of breast and colon cancer. Malignant growth occurrence is anticipated to upsurge in the impending days, and an effective anticipatory strategy is required. The effect of dietary components, basically studied by nutrigenomics, looks at gene expression and molecular mechanisms. It also interrelates bioactive compounds and nutrients because of different 'omics' innovations. Several preclinical investigations demonstrate the pertinent role of nutrigenomics in breast and colon cancer, and change of dietary propensities is conceivably a successful methodology for reducing cancer risk. The connection between the genomic profile of patients with breast or colon cancer and their supplement intake, it is conceivable to imagine an idea of personalized medicine, including nutrition and medicinal services.

Pharmacological Therapy Determines the Gut Microbiota Modulation by a Pomegranate Extract Nutraceutical in Metabolic Syndrome: A Randomized Clinical Trial

SCOPE

Poly-pharmacological therapy shapes the gut microbiota (GM) in metabolic syndrome (MetS) patients. The effects of polyphenols in poly-medicated MetS patients are unknown.

METHODS AND RESULTS

A randomized, placebo-controlled, double-blinded, and crossover trial in poly-medicated MetS patients (n=50) explored whether the effects of a pomegranate extract nutraceutical (PE, 320 mg phenolics/day for 1 month) are affected by the drug therapy. Considering the lipid-lowering (LL-), anti-hypertensive (HP-) and(or) anti-diabetic (AD-) treatments: GM (16S rRNA sequencing), short-chain fatty acids, 40 inflammatory-metabolic and endotoxemia-related biomarkers, associations between biomarkers and GM with 53 cardiometabolic dysfunctions-related single-nucleotide polymorphisms (SNPs), and urolithin metabotypes (UMs) influence are evaluated. Representative SNPs-GM associations after PE include Lactococcus and ClostridiumXIVa with rs5443-GNB3 (G-protein-β-polypeptide-3) and ClostridiumXIVa with rs7903146-TCF7L2 (transcription-factor-7-like-2) and rs1137101-LEPR (leptin-receptor). PE decreases sICAM-1 in LL-patients and the lipopolysaccharide-binding protein in all the patients. PE does not affect the other patients' markers as a group or stratifying by UMs. After PE, Lactococcus increases in AD-, LL-, and HP-patients, Bifidobacterium increases in LL- and AD-, while Clostridium XIVa decreases in non-LL- and non-HP-patients.

CONCLUSION

The prebiotic effect of PE depends on the medication, mainly on HP-treatments. Targeting GM can complement MetS therapy, but the patients' drug therapy should be considered individually.

Gene-obesogenic environment interactions on body mass indices for older black and white men and women from the Health and Retirement Study

BACKGROUND

Gene-obesogenic environment interactions influence body mass index (BMI) across the life course; however, limited research examines how these interactions may differ by race and sex.

METHODS

Utilizing mixed-effects models, we examined the interaction effects of a polygenic risk score (PGS) generated from BMI-associated single-nucleotide polymorphisms, and environmental factors, including age, physical activity, alcohol intake, and childhood socioeconomic status on measured longitudinal BMI from the Health and Retirement Study (HRS). HRS is a population representative survey of older adults in the United States. This study used a subsample of genotyped Black (N = 1796) and White (N = 4925) men and women (50-70 years) with measured BMI.

RESULTS

Higher PGS was associated with higher BMI. The association between PGS and BMI weakened as individuals aged among White men (Pinteraction = 0.0383) and White women (Pinteraction = 0.0514). The mean BMI difference between the 90th and 10th PGS percentile was 4.25 kg/m2 among 50-year-old White men, and 3.11 kg/m2 among the 70 years old's, i.e., a 1.14 kg/m2 (95% CI: -0.27, 2.82) difference. The difference among 50- and 70-year-old White women was 1.34 kg/m2 (95% CI: 0.09, 2.60). In addition, the protection effect of physical activity was stronger among White women with higher PGS (Pinteraction = 0.0546). Vigorous physical activity (compared with never) was associated with 1.66 kg/m2 (95% CI: 1.06, 2.29) lower mean BMI among those in the 90th PGS percentile, compared with 0.83 kg/m2 (95% CI: 0.37, 1.29) lower among those in the 10th PGS percentile. Interactions were also observed between both PGS and alcohol intake among White men (Pinteraction = 0.0034) and women (Pinteraction = 0.0664) and Black women (Pinteraction = 0.0108), and PGS and childhood socioeconomic status among White women (Pinteraction = 0.0007).

CONCLUSIONS

Our findings reinforce the importance of physical activity among those with an elevated genetic risk; additionally, other detected interactions may underscore the influence of broader social environments on obesity-promoting genes.

Sex-Specific Associations between Gut Prevotellaceae and Host Genetics on Adiposity

The gut microbiome has been recognized as a tool for understanding adiposity accumulation and for providing personalized nutrition advice for the management of obesity and accompanying metabolic complications. The genetic background is also involved in human energy homeostasis. In order to increase the value of nutrigenetic dietary advice, the interplay between genetics and microbiota must be investigated. The purpose of the present study was to evaluate interactive associations between gut microbiota composition and 95 obesity-related single nucleotide polymorphisms (SNPs) searched in the literature. Oral mucosa and fecal samples from 360 normal weight, overweight and obese subjects were collected. Next generation genotyping of these 95 SNPs and fecal 16S rRNA sequencing were performed. A genetic risk score (GRS) was constructed with 10 SNPs statistically or marginally associated with body mass index (BMI). Several microbiome statistical analyses at family taxonomic level were applied (LEfSe, Canonical Correspondence Analysis, MetagenomeSeq and Random Forest), and Prevotellaceae family was found in all of them as one of the most important bacterial families associated with BMI and GRS. Thus, in this family it was further analyzed the interactive association between BMI and GRS with linear regression models. Interestingly, women with higher abundance of Prevotellaceae and higher GRS were more obese, compared to women with higher GRS and lower abundance of Prevotellaceae. These findings suggest relevant interrelationships between Prevotellaceae and the genetic background that may determine interindividual BMI differences in women, which opens the way to new precision nutrition-based treatments for obesity.

Contribution of macronutrients to obesity: implications for precision nutrition

The specific metabolic contribution of consuming different energy-yielding macronutrients (namely, carbohydrates, protein and lipids) to obesity is a matter of active debate. In this Review, we summarize the current research concerning associations between the intake of different macronutrients and weight gain and adiposity. We discuss insights into possible differential mechanistic pathways where macronutrients might act on either appetite or adipogenesis to cause weight gain. We also explore the role of dietary macronutrient distribution on thermogenesis or energy expenditure for weight loss and maintenance. On the basis of the data discussed, we describe a novel way to manage excessive body weight; namely, prescribing personalized diets with different macronutrient compositions according to the individual's genotype and/or enterotype. In this context, the interplay of macronutrient consumption with obesity incidence involves mechanisms that affect appetite, thermogenesis and metabolism, and the outcomes of these mechanisms are altered by an individual's genotype and microbiota. Indeed, the interactions of the genetic make-up and/or microbiota features of a person with specific macronutrient intakes or dietary pattern consumption help to explain individualized responses to macronutrients and food patterns, which might represent key factors for comprehensive precision nutrition recommendations and personalized obesity management.

Precision nutrition: hype or hope for public health interventions to reduce obesity?

High-income countries are experiencing an obesity epidemic that follows a socioeconomic gradient, affecting groups of lower socioeconomic status disproportionately. Recent clinical findings have suggested new perspectives for the prevention and treatment of obesity, using personalized dietary approaches. Precision nutrition (PN), also called personalized nutrition, has been developed to deliver more preventive and practical dietary advice than ‘one-size-fits-all’ guidelines. With interventions becoming increasingly plausible at a large scale thanks to artificial intelligence and smartphone applications, some have begun to view PN as a novel way to deliver the right dietary intervention to the right population. We argue that large-scale PN, if taken alone, might be of limited interest from a public health perspective. Building on Geoffrey Rose’s theory regarding the differences in individual and population causes of disease, we show that large-scale PN can only address some individual causes of obesity (causes of cases). This individual-centred approach is likely to have a small impact on the distribution of obesity at a population level because it ignores the population causes of obesity (causes of incidence). The latter are embedded in the populations’ social, cultural, economic and political contexts that make environments obesogenic. Additionally, the most socially privileged groups in the population are the most likely to respond to large-scale PN interventions. This could have the undesirable effect of widening social inequalities in obesity. We caution public health actors that interventions based only on large-scale PN are unlikely, despite current expectations, to improve dietary intake or reduce obesity at a population level.

The Effects of the Type of Exercise and Physical Activity on Eating Behavior and Body Composition in Overweight and Obese Subjects

The aim of this study was to examine whether a type of exercise favors better compliance with a prescribed diet, higher eating-related motivation, healthier diet composition or greater changes in body composition in overweight and obese subjects. One hundred and sixty-two (males n = 79), aged 18-50 years, were randomized into four intervention groups during 24 weeks: strength, endurance, combined strength + endurance and guideline-based physical activity; all in combination with a 25-30% caloric restriction diet. A food frequency questionnaire and a "3-day food and drink record" were applied pre- and post-intervention. Diet and exercise-related motivation levels were evaluated with a questionnaire developed for this study. Body composition was assessed by DXA and habitual physical activity was measured by accelerometry. Body weight, body mass index (BMI) and body fat percentage decreased and lean body mass increased after the intervention, without differences by groups. No interactions were observed between intervention groups and time; all showing a decreased in energy intake (p < 0.001). Carbohydrate and protein intakes increased, and fat intake decreased from pre- to post-intervention without significant interactions with intervention groups, BMI category or gender (p < 0.001). Diet-related motivation showed a tendency to increase from pre- to post-intervention (70.0 ± 0.5 vs 71.0 ± 0.6, p = 0.053), without significant interactions with intervention groups, BMI or gender. Regarding motivation for exercise, gender x time interactions were observed (F(1,146) = 7.452, p = 0.007): Women increased their motivation after the intervention (pre: 17.6 ± 0.3, post: 18.2 ± 0.3), while men maintained it. These findings suggest that there are no substantial effects of exercise type on energy intake, macronutrient selection or body composition changes. After a six-month weight loss program, individuals did not reduce their motivation related to diet or exercise, especially women. Individuals who initiate a long-term exercise program do not increase their energy intake in a compensatory fashion, if diet advices are included.

Lipid metabolism genetic risk score interacts with the Brazilian Healthy Eating Index Revised and its components to influence the odds for dyslipidemia in a cross-sectional population-based survey in Brazil

BACKGROUND

Dyslipidemia can be influenced by genetic and dietary risk factors.

AIM

This study set out to investigate diet and genetic variations in Brazilian people in a cross-sectional population-based survey and to analyze the relationship between single nucleotide polymorphisms (SNPs) of genes involved in lipid metabolism and cardiometabolic-related phenotypes using a genetic risk score (GRS).

METHODS

We recruited 228 adults (mean age 36.5 years) who participated in the Health Survey of São Paulo (HS-SP), Brazil. Clinical and anthropometric parameters, as well as the interaction between the GRS and the Brazilian Healthy Eating Index Revised (BHEI-R) were evaluated. We analyzed the relationship between SNPs in APOA5 (rs662799), APOB (rs693, rs1367117), LDLR (rs688, rs5925) and LIPC (rs2070895, rs1800588) and cardiometabolic-related phenotypes using a GRS.

RESULTS

High-density lipoprotein cholesterol (HDLC) levels were associated with the BHEI-R ( p=0.026; β= -0.183) and with its SoFAAS component (solid fats, alcoholic beverages and added sugars) ( p=0.007; β=0.279). Non-HDL cholesterol levels were associated with the BHEI-R vegetable component ( p=0.015; β=0.002) and the meat, eggs and beans component ( p=0.003; β=0.007). Triacylglycerol levels were associated with the BHEI-R vegetable component ( p=0.027; β=0.003); the meat, eggs and beans component ( p=0.041; β=0.001); and the total protein component ( p=0.013; β=0.032). Significant effects were observed for the interactions between the GRS and both the BHEI-R oils component ( p=0.019) and the SoFAAS component ( p<0.001) on the dyslipidemia risk.

CONCLUSIONS

The evaluation of dietary quality, especially fat quality, together with the lipid metabolism GRS could be a useful tool to manage cardiometabolic risk.

Dietary Fatty Acids and the Metabolic Syndrome: A Personalized Nutrition Approach

Dietary fatty acids are present in a wide variety of foods and appear in different forms and lengths. The different fatty acids are known to have various effects on metabolic health. The metabolic syndrome (MetS) is a constellation of risk factors of chronic diseases. The etiology of the MetS is represented by a complex interplay of genetic and environmental factors. Dietary fatty acids can be important contributors of the evolution or in prevention of the MetS; however, great interindividual variability exists in the response to fatty acids. The identification of genetic variants interacting with fatty acids might explain this heterogeneity in metabolic responses. This chapter reviews the mechanisms underlying the interactions between the different components of the MetS, dietary fatty acids and genes. Challenges surrounding the implementation of personalized nutrition are also covered.

Addressing the Nutritional Phenotype Through Personalized Nutrition for Chronic Disease Prevention and Management

The primary focus of public health recommendations related to the prevention of food-related chronic disease has been on the adoption of healthy dietary patterns; however, implementation has been challenging. There has been increasing recognition that an individual's diet and environment may impact disease susceptibility by affecting the expression of genes involved in critical metabolic pathways. Precision nutrition (PN) has emerged to translate discoveries about diversity in nutrient metabolism between subgroups and the inter-individual variability in the responses to dietary interventions. The overarching goals of PN are to deliver individualized, actionable dietary therapy based on an individual's nutritional phenotype, created from the integration of genetics, metabolic profile, and environmental factors in order to prevent and treat chronic disease. This review addresses the developments of genome- and omic-driven PN and how they have been used to prevent and treat disease, as well as how they might be integrated into broader clinical practice.

Prediction of Blood Lipid Phenotypes Using Obesity-Related Genetic Polymorphisms and Lifestyle Data in Subjects with Excessive Body Weight

BACKGROUND AND AIM

Individual lipid phenotypes including circulating total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and triglycerides (TG) determinations are influenced by gene-environment interactions. The aim of this study was to predict blood lipid level (TC, LDL-c, HDL-c, and TG) variability using genetic and lifestyle data in subjects with excessive body weight-for-height.

METHODS

This cross-sectional study enrolled 304 unrelated overweight/obese adults of self-reported European ancestry. A total of 95 single nucleotide polymorphisms (SNPs) related to obesity and weight loss were analyzed by a targeted next-generation sequencing system. Relevant genotypes of each SNP were coded as 0 (nonrisk) and 1 (risk). Four genetic risk scores (GRS) for each lipid phenotype were calculated by adding the risk genotypes. Information concerning lifestyle (diet, physical activity, alcohol drinking, and smoking) was obtained using validated questionnaires. Total body fat (TFAT) and visceral fat (VFAT) were determined by dual-energy X-ray absorptiometry.

RESULTS

Overall, 45 obesity-related genetic variants were associated with some of the studied blood lipids. In addition to conventional factors (age, sex, dietary intakes, and alcohol consumption), the calculated GRS significantly contributed to explain their corresponding plasma lipid trait. Thus, HDL-c, TG, TC, and LDL-c serum concentrations were predicted by approximately 28% (optimism-corrected adj. R 2 = 0.28), 25% (optimism-corrected adj. R 2 = 0.25), 24% (optimism-corrected adj. R 2 = 0.24), and 21% (optimism-corrected adj. R 2=0.21), respectively. Interestingly, GRS were the greatest contributors to TC (squared partial correlation (PC2) = 0.18) and LDL-c (PC2 = 0.18) features. Likewise, VFAT and GRS had a higher impact on HDL-c (PC2 = 0.09 and PC2 = 0.06, respectively) and TG levels (PC2 = 0.20 and PC2 = 0.07, respectively) than the rest of variables.

CONCLUSIONS

Besides known lifestyle influences, some obesity-related genetic variants could help to predict blood lipid phenotypes.

Changes in lysophospholipids and liver status after weight loss: the RESMENA study

Background

Obesity and comorbidities such as non-alcoholic fatty liver disease (NAFLD) are major public health burdens. Alterations in lipid metabolism are involved in hepatic diseases. The objective of this study was to assess the influence of weight loss on lysophospholipid (LP) metabolism and liver status in obese subjects as well as to provide new evidence regarding the interaction of LP metabolism as a key factor in the onset and management of obesity-related diseases such as liver damage.

Methods

Thirty-three subjects from the RESMENA (Reduction of Metabolic Syndrome in Navarra, NCT01087086) study were selected based on their Fatty Liver Index (FLI). Plasma lipid species (lysophosphatidilcholine: LPC, lysophosphatidilethanolamines: LPE and lysophosphatidylinositols: LPI specifically) were determined by LC-MS, while waist circumference (WC) and other non-invasive liver markers such as, FLI and BAAT scores as well as dietary records, anthropometrical measurements, body composition by DXA and other metabolic determinants were analyzed before and after a six-month hypocaloric nutritional intervention.

Results

Computed Z-scores of total LP (LPC, LPE, and LPI) were significantly decreased after 6-months of following a hypocaloric diet. Specifically, LPC14:0, LPC15:0, LPC16:1, LPC18:4, LPC20:4, showed clear relationships with weight loss. Changes in FLI score, WC and BAAT score revealed associations with general changes in LPC score. Interestingly the BAAT score was statistically associated with the LPC score after adjustment for weight loss.

Conclusion

The lipidomic LPC profile analysis revealed a generalized decrease in circulating lysophospholipids after weight loss. The involvement of particular LP in liver metabolism and obesity merit further attention, as some of these specific non-invasive liver markers were reduced independently of weight loss.

Trial registration

NCT01087086. Registered 15 March 2010, retrospectively registry.

An integrated transcriptomic and epigenomic analysis identifies CD44 gene as a potential biomarker for weight loss within an energy-restricted program

PURPOSE

The interindividual variable response to weight-loss treatments requires the search for new predictive biomarkers for improving the success of weight-loss programs. The aim of this study is to identify novel genes that distinguish individual responses to a weight-loss dietary treatment by using the integrative analysis of mRNA expression and DNA methylation arrays.

METHODS

Subjects from Metabolic Syndrome Reduction in Navarra (RESMENA) project were classified as low (LR) or high (HR) responders depending on their weight loss. Transcriptomic (n = 24) and epigenomic (n = 47) patterns were determined by array-based genome-wide technologies in human white blood cells at the baseline of the treatment period. CD44 expression was validated by qRT-PCR and methylation degree of CpGs of the gene was validated by MassARRAY^® EpiTYPER™ in a subsample of 47 subjects. CD44 protein levels were measured by ELISA in human plasma.

RESULTS

Different expression and DNA methylation profiles were identified in LR in comparison to HR. The integrative analysis of both array data identified four genes: CD44, ITPR1, MTSS1 and FBXW5 that were differently methylated and expressed between groups. CD44 showed higher expression and lower DNA methylation levels in LR than in HR. Although differences in CD44 protein levels between LR and HR were not statistically significant, a positive association was observed between CD44 mRNA expression and protein levels.

CONCLUSIONS

In summary, the combination of a genome-wide methylation and expression array dataset can be a useful strategy to identify novel genes that might be considered as predictors of the dietary response. CD44 gene transcription and methylation may be a possible candidate biomarker for weight-loss prediction.

Phenotype and genotype predictors of BMI variability among European adults

Background/Objective

Obesity is a complex and multifactorial disease resulting from the interactions among genetics, metabolic, behavioral, sociocultural and environmental factors. In this sense, the aim of the present study was to identify phenotype and genotype variables that could be relevant determinants of body mass index (BMI) variability.

Subjects/Methods

In the present study, a total of 1050 subjects (798 females; 76%) were included. Least angle regression (LARS) analysis was used as regression model selection technique, where the dependent variable was BMI and the independent variables were age, sex, energy intake, physical activity level, and 16 polymorphisms previously related to obesity and lipid metabolism.

Results

The LARS analysis obtained the following formula for BMI explanation: (64.7 + 0.10 × age [years] + 0.42 × gender [0, men; 1, women] + −40.6 × physical activity [physical activity level] + 0.004 × energy intake [kcal] + 0.74 × rs9939609 [0 or 1–2 risk alleles] + −0.72 × rs1800206 [0 or 1–2 risk alleles] + −0.86 × rs1801282 [0 or 1–2 risk alleles] + 0.87 × rs429358 [0 or 1–2 risk alleles]. The multivariable regression model accounted for 21% of the phenotypic variance in BMI. The regression model was internally validated by the bootstrap method (r² original data set = 0.208, mean r² bootstrap data sets = 0.210).

Conclusion

In conclusion, age, physical activity, energy intake and polymorphisms in FTO, APOE, PPARG and PPARA genes are significant predictors of the BMI trait.

Impact of Genetic Variants on the Individual Potential for Body Fat Loss

The past decade has witnessed the discovery of obesity-related genetic variants and their functions through genome-wide association studies. Combinations of risk alleles can influence obesity phenotypes with different degrees of effectiveness across various individuals by interacting with environmental factors. We examined the interaction between genetic variation and changes in dietary habits or exercise that influences body fat loss from a large Korean cohort (n = 8840). Out of 673 obesity-related SNPs, a total of 100 SNPs (37 for carbohydrate intake; 19 for fat intake; 44 for total calories intake; 25 for exercise onset) identified to have gene-environment interaction effect in generalized linear model were used to calculate genetic risk scores (GRS). Based on the GRS distribution, we divided the population into four levels, namely, “very insensitive”, “insensitive”, “sensitive”, and “very sensitive” for each of the four categories, “carbohydrate intake”, “fat intake”, “total calories intake”, and “exercise”. Overall, the mean body fat loss became larger when the sensitivity level was increased. In conclusion, genetic variants influence the effectiveness of dietary regimes for body fat loss. Based on our findings, we suggest a platform for personalized body fat management by providing the most suitable and effective nutrition or activity plan specific to an individual.

Obesity, An Enemy of Male Fertility: A Mini Review

Obesity is a highly prevalent non-communicable disease worldwide and is commonly associated with male infertility. Several etiopathological theories have been mentioned in the literature by which obesity affects spermatogenesis, thus affecting the male fertility potential. Mechanisms for explaining the effect of obesity on male infertility include endocrinopathy, increased aromatization activity, associated erectile dysfunction, psychological and thermal effects, obstructive sleep apnea, increased leptin and oxygen free radicals, and associated inflammatory and obstructive elements of epididymitis. Treatment of such a complex problem includes weight reduction (by lifestyle modification and increased physical activity), optimization of altered testosterone-to-estradiol ratio using aromatase inhibitors and/or gonadotropins, treatment of associated comorbidities by phosphodiesterase inhibitors for erectile dysfunction, and insulin-sensitizing agents for the management of diabetes. The aim of this mini-review is to highlight the pathological basis of this problem and to focus on obesity as an etiology of male infertility.

Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome

The translation of the growing increase of findings emerging from basic nutritional science into meaningful and clinically relevant dietary advices represents nowadays one of the main challenges of clinical nutrition. From nutrigenomics to deep phenotyping, many factors need to be taken into account in designing personalized and unbiased nutritional solutions for individuals or population sub-groups. Likewise, a concerted effort among basic, clinical scientists and health professionals will be needed to establish a comprehensive framework allowing the implementation of these new findings at the population level. In a world characterized by an overwhelming increase in the prevalence of obesity and associated metabolic disturbances, such as type 2 diabetes and cardiovascular diseases, tailored nutrition prescription represents a promising approach for both the prevention and management of metabolic syndrome. This review aims to discuss recent works in the field of precision nutrition analyzing most relevant aspects affecting an individual response to lifestyle/nutritional interventions. Latest advances in the analysis and monitoring of dietary habits, food behaviors, physical activity/exercise and deep phenotyping will be discussed, as well as the relevance of novel applications of nutrigenomics, metabolomics and microbiota profiling. Recent findings in the development of precision nutrition are highlighted. Finally, results from published studies providing examples of new avenues to successfully implement innovative precision nutrition approaches will be reviewed.

Guide for Current Nutrigenetic, Nutrigenomic, and Nutriepigenetic Approaches for Precision Nutrition Involving the Prevention and Management of Chronic Diseases Associated with Obesity

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.

Genetics of fat intake in the determination of body mass

Body mass and fat intake are multifactorial traits that have genetic and environmental components. The gene with the greatest effect on body mass is FTO (fat mass and obesity-associated), but several studies have shown that the effect of FTO (and of other genes) on body mass can be modified by the intake of nutrients. The so-called gene–environment interactions may also be important for the effectiveness of weight-loss strategies. Food choices, and thus fat intake, depend to some extent on individual preferences. The most important biological component of food preference is taste, and the role of fat sensitivity in fat intake has recently been pointed out. Relatively few studies have analysed the genetic components of fat intake or fatty acid sensitivity in terms of their relation to obesity. It has been proposed that decreased oral fatty acid sensitivity leads to increased fat intake and thus increased body mass. One of the genes that affect fatty acid sensitivity is CD36 (cluster of differentiation 36). However, little is known so far about the genetic component of fat sensing. We performed a literature review to identify the state of knowledge regarding the genetics of fat intake and its relation to body-mass determination, and to identify the priorities for further investigations.

Application of CRS-PCR-RFLP to identify CYP1A1 gene polymorphism

BACKGROUND

Cytochrome P4501A1 (CYP1A1) is a member of the cytochrome P450 gene family and plays an important role in the metabolism of exogenous and endogenous material. In recent research, it has been shown that its genetic polymorphisms are associated with many diseases. But the isoschizomers such as the BsrDI enzyme required for the detection of this polymorphism are expensive.

METHODS

The study used an improved PCR-RFLP method with mismatched base for detection of the single-nucleotide polymorphism rs1048943. A new restriction enzyme cutting site was created by created restriction site PCR (CRS-PCR), and the restriction enzyme StyI for RFLP was cheaper than other enzymes. A total of 320 samples from Han Chinese were tested to evaluate this novel method. The PCR results were confirmed by DNA sequencing.

RESULTS

After detecting 320 Chinese Han individuals, the genotype frequencies were 63.74% for AA, 31.54% for AG, and 4.72% for GG. The allelic frequencies were 75.48% for A and 24.52% for G. The x² test showed the genotype and allele frequencies of CYP1A1 do not deviate from Hardy-Weinberg equilibrium, and the sequences of amplified products were consistent with the one published in GenBank with the exception of mismatched base.

CONCLUSIONS

Based on PCR with mismatched primers we designed, the CYP1A1 polymorphism could be identified effectively with low cost.

Ethical considerations in the implementation of nutrigenetics/nutrigenomics

Awareness of ethical issues that may be raised by the implementation of nutrigenetic/nutrigenomic (NGx) testing and personalized nutrition, at an individual or a public health level, is crucial to ensure the latter's sound and effective implementation. NGx tests that are currently offered or developed have different natures and scopes. We provide an example of NGx testing on the MTHFR gene to illustrate the current challenges when it comes to grasp the meaning of the results of such testing. In addition, NGx testing is developed within an evolving landscape of new genomic technologies and occurs at a time when public health policies mainly focus on preventive and predictive healthcare, with an emphasis on increased individual responsibility. The ethical issues raised by such a context and the genetic nature of NGx testing both should be carefully evaluated.

Effect of interactions of polymorphisms in the Melanocortin-4 receptor gene with dietary factors on the risk of obesity and Type 2 diabetes: a systematic review

AIM

To perform a systematic review of the effect of interaction between Melanocortin-4 receptor (MC4R) single nucleotide polymorphisms and diet on the development of obesity and Type 2 diabetes.

BACKGROUND

Environmental factors, such as nutrient intakes or feeding behaviours, can modulate the association of polymorphism in the MC4R gene with obesity and Type 2 diabetes mellitus.

METHODS

A systematic literature search was conducted in the PubMed, Scopus and Google Scholar databases, with a combination of the following keywords: Diet*, nutr*, melanocortin receptor, melanocortin 4 receptor and MC4R. To assess the quality of observational studies, we used a 12-item quality checklist, derived from the STREGA statement.

RESULTS

A total of 14 articles were selected based on the inclusion and exclusion criteria. Consumption of highly salty foods and adherence to a Mediterranean dietary pattern can modulate the association between MC4R polymorphisms and the risk of obesity or Type 2 diabetes. Despite the highly contradictory results of intervention studies, after short-term lifestyle interventions, children with variant alleles of MC4R single nucleotide polymorphisms can lose more body weight, compared with non-carriers, although they may have difficulty in maintaining this weight loss in the long-term. To interpret the results of studies on adults, we need further studies.

CONCLUSIONS

The interaction between MC4R genes with dietary factors plays a significant role in the development of obesity or Type 2 diabetes phenotypes. Early detection of MC4R risk alleles in individuals and modification of their diet based on these results could be an efficient strategy to prevent obesity or diabetes in these subgroups.

An Improved PCR-RFLP Assay for the Detection of a Polymorphism rs2289487 of PLIN1 Gene

BACKGROUND

In recent research, it has been shown that rs2289487 within the PLIN1 gene has different variants that have been associated with obesity, type 2 diabetes, and other diseases. However, the isochizomers such as the BsmI enzyme required for detection of this polymorphism through polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method are expensive. In this study, we aimed to explore a novel PCR-RFLP method for identifying the single-nucleotide polymorphism (SNP) of rs2289487 of PLIN1 gene.

METHODS

A new restriction enzyme site was created through created restriction site PCR. In the forward primer, a deoxynucleotide A was substituted with C, and after PCR a new restriction enzyme site for BstUI was introduced into the PCR products. A total of 108 samples from Han Chinese were tested to evaluate this new method.

RESULTS

Allele frequencies in the Asian population were 0.326 for allele A and 0.674 for allele G, and the genotype frequencies were 12.8% for AA, 39.5% for AG, and 47.7% for GG.

CONCLUSION

The PCR-RFLP with new site for detecting the SNP of rs2289487 is an improved method with low cost and high accuracy.

Fat mass- and obesity-associated genotype, dietary intakes and anthropometric measures in European adults: the Food4Me study

The interplay between the fat mass- and obesity-associated (FTO) gene variants and diet has been implicated in the development of obesity. The aim of the present analysis was to investigate associations between FTO genotype, dietary intakes and anthropometrics among European adults. Participants in the Food4Me randomised controlled trial were genotyped for FTO genotype (rs9939609) and their dietary intakes, and diet quality scores (Healthy Eating Index and PREDIMED-based Mediterranean diet score) were estimated from FFQ. Relationships between FTO genotype, diet and anthropometrics (weight, waist circumference (WC) and BMI) were evaluated at baseline. European adults with the FTO risk genotype had greater WC (AA v. TT: +1·4 cm; P=0·003) and BMI (+0·9 kg/m2; P=0·001) than individuals with no risk alleles. Subjects with the lowest fried food consumption and two copies of the FTO risk variant had on average 1·4 kg/m2 greater BMI (Ptrend=0·028) and 3·1 cm greater WC (Ptrend=0·045) compared with individuals with no copies of the risk allele and with the lowest fried food consumption. However, there was no evidence of interactions between FTO genotype and dietary intakes on BMI and WC, and thus further research is required to confirm or refute these findings.

Current Applications of Genetic Risk Scores to Cardiovascular Outcomes and Subclinical Phenotypes

Genetic risk scores are a useful tool for examining the cumulative predictive ability of genetic variation on cardiovascular disease. Important considerations for creating genetic risk scores include the choice of genetic variants, weighting, and comparability across ethnicities. Genetic risk scores that use information from genome-wide meta-analyses can successfully predict cardiovascular outcomes and subclinical phenotypes, yet there is limited clinical utility of these scores beyond traditional cardiovascular risk factors in many populations. Novel uses of genetic risk scores include evaluating the genetic contribution of specific intermediate traits or risk factors to cardiovascular disease, risk prediction in high-risk populations, gene-by-environment interaction studies, and Mendelian randomization studies. Though questions remain about the ultimate clinical utility of the genetic risk score, further investigation in high-risk populations and new ways to combine genetic risk scores with traditional risk factors may prove to be fruitful.

Gene-Gene Interplay and Gene-Diet Interactions Involving the MTNR1B rs10830963 Variant with Body Weight Loss

BACKGROUND/AIMS

Investigation of the genetic makeup may facilitate the implementation of more personalized nutritional interventions. The aims were to examine whether the rs10830963 MTNR1B polymorphism affects weight loss in response to a hypocaloric diet and to find potential gene-gene interplays and gene-diet interactions.

METHODS

167 subjects enrolled in a personalized nutritional intervention for weight loss (3-6 weeks) were examined for anthropometric measurements, dietary habits and physical activity at baseline and at the first follow-up visit. Three polymorphisms, which have previously been associated with body weight regulation, rs10830963 (MTNR1B), rs9939609 (FTO) and rs17782313 (MC4R), were analyzed using the Luminex® 100/200™ System.

RESULTS

After adjusting for covariates, females with the rs10830963 CG/GG genotype showed lower weight loss than those with the CC genotype. In the total population, carriers of variant alleles of both FTO and MC4R showed a significant association with MTNR1B and weight loss outcome. Moreover, among women, higher total protein and animal protein intakes were associated with a lower weight loss in G allele carriers of the MTNR1B variant.

CONCLUSIONS

Our data evidenced that rs10830963 MTNR1B polymorphism could be associated with individual differences in weight loss induced by a hypocaloric diet. This association was influenced by FTO and MC4R loci and modified by baseline protein intake.

Future Perspectives of Personalized Weight Loss Interventions Based on Nutrigenetic, Epigenetic, and Metagenomic Data

As obesity has become a major global public health challenge, a large number of studies have analyzed different strategies aimed at inducing a negative energy balance and, consequently, body weight loss. However, most existing weight loss programs are generally unsuccessful, so several interventions have been carried out to identify physiologic and behavioral factors concerning this variability in order to implement more personalized treatment. Nowadays, an individualized approach is being proposed through so-called personalized nutrition, whereby not only the phenotype but also the genotype is used for customized nutrition treatment. Regarding body weight regulation, ∼70 polymorphisms have been identified in or near genes related to energy expenditure, appetite, adipogenesis, insulin resistance, and lipid metabolism. Although personalized nutrition refers mainly to genetic makeup, recent advances in the investigation of the epigenome and the microbiome open the door to implement more personalized recommendations for body weight management. In this context, recent studies have demonstrated the existence of several epigenetic markers that may modify gene expression and could be involved in the outcome of weight loss interventions. Moreover, different studies have shown that dietary interventions could affect the composition of gut microbiota and have an impact on body weight. The integration of nutrigenetic, epigenetic, and metagenomic data may lead to the design of more personalized dietary treatments to prevent chronic diseases and to optimize the individual's response to dietary interventions.