Dietary fat and total energy intake modifies the association of genetic profile risk score on obesity: evidence from 48 170 UK Biobank participants

Interaction of genetics risk score and fatty acids quality indices on healthy and unhealthy obesity phenotype

BACKGROUND

The growth in obesity and rates of abdominal obesity in developing countries is due to the dietary transition, meaning a shift from traditional, fiber-rich diets to Westernized diets high in processed foods, sugars, and unhealthy fats. Environmental changes, such as improving the quality of dietary fat consumed, may be useful in preventing or mitigating the obesity or unhealthy obesity phenotype in individuals with a genetic predisposition, although this has not yet been confirmed. Therefore, in this study, we investigated how dietary fat quality indices with metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) based on the Karelis criterion interact with genetic susceptibility in Iranian female adults.

METHODS

In the current cross-sectional study, 279 women with overweight or obesity participated. Dietary intake was assessed using a 147-item food frequency questionnaire and dietary fat quality was assessed using the cholesterol-saturated fat index (CSI) and the ratio of omega-6/omega-3 (N6/N3) essential fatty acids. Three single nucleotide polymorphisms-MC4R (rs17782313), CAV-1 (rs3807992), and Cry-1(rs2287161) were genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique and were combined to produce the genetic risk score (GRS). Body composition was evaluated using a multi-frequency bioelectrical impedance analyzer. Participants were divided into MHO or MUO phenotypes after the metabolic risk assessment based on the Karelis criteria.

RESULTS

We found significant interactions between GRS and N6/N3 in the adjusted model controlling for confounding factors (age, body mass index, energy, and physical activity) (β = 2.26, 95% CI: 0.008 to 4.52, P = 0.049). In addition, we discovered marginally significant interactions between GRS and N6/N3 in crude (β = 1.92, 95% CI: -0.06 to 3.91, P = 0.058) and adjusted (age and energy) (β = 2.00, 95% CI: -0.05 to 4.05, P = 0.057) models on the MUH obesity phenotype. However, no significant interactions between GRS and CSI were shown in both crude and adjusted models.

CONCLUSION

This study highlights the importance of personalized nutrition and recommends further study of widely varying fat intake based on the findings on gene-N6/N3 PUFA interactions.

Relationships between the Planetary Health Diet Index, its food groups, and polygenic risk of obesity in the CARTaGENE cohort

BACKGROUND

The Planetary Health Diet, proposed by the EAT-Lancet Commission, seeks to promote a sustainable and healthy diet for both humans and the environment. However, few studies have investigated relationships between the Planetary Health Diet and the genetic pathway of obesity. The aim of this study was to assess whether adherence to a Planetary Health Diet Index (PHDI) mediated or moderated the genetic susceptibility to obesity.

METHODS

Participants were 7,037 adults (57% females, aged 55.6 ± 7.7) from the Quebec CARTaGENE Biobank. We constructed a primary polygenic risk score (PRS-Khera) for body mass index (BMI) comprised of ~ 2 million SNPs and utilized a secondary 97 SNPs polygenic risk score (PRS-Locke) for sensitivity analyses. The PHDI was based on 16 food groups. General linear models were conducted to assess main effect associations between the PRSs, the Planetary Health Diet Index (PHDI), and the individual food groups that comprise the PHDI on obesity outcomes. Causal mediation analyses (CMA) were used to evaluate mediation and interaction effects. All models were adjusted for age, sex, genetic ancestry, socio-demographic, and lifestyle variables, including those associated with dietary habits.

RESULTS

The overall PHDI was inversely associated with BMI (β = - 0.11, 95% confidence interval (CI): - 0.13, - 0.09), waist circumference (WC) (β = - 0.12, 95% CI: - 0.14, - 0.10), and body fat % (β = - 0.10, 95% CI: - 0.12, - 0.08) for all participants, but did not mediate or moderate obesity polygenic risk. Associations between the PRS-Khera and obesity outcomes in all participants were partly mediated by the intake of red meat (mediation effect BMI: 1.72%, p = 0.01; WC: 2.22%, p = 0.01; body fat %: 2.14%, p = 0.01). Moreover, among males, whole grains intake partly mediated the association between the PRS-Khera and outcomes cross-sectionally (BMI: 1.28%, p = 0.03; WC: 1.71%, p = 0.02; body fat %: 2.19%, p = 0.02) and longitudinally (BMI: 3.80%, p = 0.02; WC: 7.38%, p = 0.04), but some observations were attenuated upon correction for multiple comparisons.

CONCLUSIONS

PHDI adherence was associated with a lower BMI, WC, and body fat % and genetic susceptibility to obesity was partly mediated by the intake of red meat and whole grains. Some components of a plant-based diet could be implicated in mechanisms underlying genetic susceptibility to obesity.

How do lifestyle factors modify the association between genetic predisposition and obesity-related phenotypes? A 4-way decomposition analysis using UK Biobank

BACKGROUND

Obesity and central obesity are multifactorial conditions with genetic and non-genetic (lifestyle and environmental) contributions. There is incomplete understanding of whether lifestyle modifies the translation from respective genetic risks into phenotypic obesity and central obesity, and to what extent genetic predisposition to obesity and central obesity is mediated via lifestyle factors.

METHODS

This is a cross-sectional study of 201,466 (out of approximately 502,000) European participants from UK Biobank and tested for interactions and mediation role of lifestyle factors (diet quality; physical activity levels; total energy intake; sleep duration, and smoking and alcohol intake) between genetic risk for obesity and central obesity. BMI-PRS and WHR-PRS are exposures and obesity and central obesity are outcomes.

RESULTS

Overall, 42.8% of the association between genetic predisposition to obesity and phenotypic obesity was explained by lifestyle: 0.9% by mediation and 41.9% by effect modification. A significant difference between men and women was found in central obesity; the figures were 42.1% (association explained by lifestyle), 1.4% (by mediation), and 40.7% (by modification) in women and 69.6% (association explained by lifestyle), 3.0% (by mediation), and 66.6% (by modification) in men.

CONCLUSIONS

A substantial proportion of the association between genetic predisposition to obesity/central obesity and phenotypic obesity/central obesity was explained by lifestyles. Future studies with repeated measures of obesity and lifestyle would be needed to clarify causation.

Plant-based dietary patterns and genetic susceptibility to obesity in the CARTaGENE cohort

OBJECTIVE

The study's objective was to examine whether adherence to three plant-based dietary indices (PDIs) mediated or moderated genetic susceptibility to obesity.

METHODS

Baseline participants were 7037 adults (57% women, aged 55.6 ± 7.7 years) from the CARTaGENE cohort of Quebec adults. Two polygenic risk scores for BMI (PRS-BMI), 92 single-nucleotide polymorphisms and 2 million single-nucleotide polymorphisms, and three plant-based scores were calculated (overall, healthy, and unhealthy). Follow-up participants were 2258 adults with data on obesity outcomes, measured 6 years later. General linear models were used to examine the relationships between PRSs and PDI scores on obesity outcomes. Causal mediation analyses were conducted to assess mediation and interaction models.

RESULTS

The overall- and healthy-PDIs and PRSs were significantly associated with obesity outcomes. Adherence to PDIs did not mediate or moderate genetic susceptibility to obesity. Associations between PRSs and obesity outcomes were partly mediated by meat intake cross-sectionally and whole grains intake among males both cross-sectionally and longitudinally. Higher meat intake had a positive association with obesity outcomes, whereas higher whole grains intake had an inverse association.

CONCLUSIONS

These findings suggest that components of a plant-based diet and a shift away from animal products, specifically meat, might be beneficial for nutrition interventions, particularly among individuals with higher genetic risk of 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.

Associations of oxidative balance score with total abdominal fat mass and visceral adipose tissue mass percentages among young and middle-aged adults: findings from NHANES 2011-2018

Objective

This study aimed to explore the association of the oxidative balance score (OBS) with total abdominal fat mass (TAFM) and visceral adipose tissue mass (VATM) percentages among young and middle-aged U.S. adults.

Methods

Young and middle-aged adults in the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018 were included. Analysis of variance and Rao-Scott adjusted chi-square tests were used to compare the characteristics across quartiles of OBS. Univariate and multivariate weighted logistic regression models were employed to explore the relationship between OBS and the risks of high TAFM or high VATM percentage in the general population and subgroups, while the interaction effects were tested with a likelihood test. Weighted restricted cubic spline analyses were utilized to assess the non-linear association of OBS with TAFM and VATM percentages.

Results

The final sample included 8,734 young and middle-aged non-institutionalized U.S. adults representing 134.7 million adults. Compared with adults in the first quartile of OBS, those with higher OBS were less likely to have a high TAFM percentage; the ORs and 95% CI for adults in the second, third, and highest quartiles of OBS were 0.70 (0.53-0.94), 0.49 (0.36-0.60), and 0.25 (0.18-0.36), respectively. Similar trends were observed in the association between OBS and VATM percentages. Moreover, similar effects were confirmed in the sensitivity analyses and subgroup analyses according to demographic characteristics. Regarding the OBS subclass, higher dietary OBS and lifestyle OBS were also correlated with decreased ORs of high TAFM and VATM percentages.

Conclusion

This study strongly suggests that higher OBS, as well as higher dietary OBS and lifestyle OBS, are significantly correlated with lower risks of abdominal obesity and visceral fat accumulation. The findings highlight the importance of an antioxidant-rich diet and maintaining a healthy lifestyle in reducing the risks.

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.

Interactions Between Genetic Risk Score and Healthy Plant Diet Index on Cardiometabolic Risk Factors Among Obese and Overweight Women

People with higher genetic predisposition to obesity are more susceptible to cardiovascular diseases (CVDs) and healthy plant-based foods may be associated with reduced risks of obesity and other metabolic markers. We investigated whether healthy plant-foods-rich dietary patterns might have inverse associations with cardiometabolic risk factors in participants at genetically elevated risk of obesity. For this cross-sectional study, 377 obese and overweight women were chosen from health centers in Tehran, Iran. We calculated a healthy plant-based diet index (h-PDI) in which healthy plant foods received positive scores, and unhealthy plant and animal foods received reversed scores. A genetic risk score (GRS) was developed based on 3 polymorphisms. The interaction between GRS and h-PDI on cardiometabolic traits was analyzed using a generalized linear model (GLM). We found significant interactions between GRS and h-PDI on body mass index (BMI) (p = 0.02), body fat mass (p = 0.04), and waist circumference (p = 0.056). There were significant gene-diet interactions for healthful plant-derived diets and BMI-GRS on high-sensitivity C-reactive protein (p = 0.03), aspartate aminotransferase (p = 0.04), alanine transaminase (p = 0.05), insulin (p = 0.04), and plasminogen activator inhibitor 1 (p = 0.002). Adherence to h-PDI was more strongly related to decreased levels of the aforementioned markers among participants in the second or top tertile of GRS than those with low GRS. These results highlight that following a plant-based dietary pattern considering genetics appears to be a protective factor against the risks of cardiometabolic abnormalities.

Precision Nutrition: Recent Advances in Obesity

"Precision nutrition" is an emerging area of nutrition research that focuses on understanding metabolic variability within and between individuals and helps develop customized dietary plans and interventions to maintain optimal individual health. It encompasses nutritional genomic (gene-nutrient interactions), epigenetic, microbiome, and environmental factors. Obesity is a complex disease that is affected by genetic and environmental factors and thus a relevant target of precision nutrition-based approaches. Recent studies have shown significant associations between obesity phenotypes (body weight, body mass index, waist circumference, and central and regional adiposity) and genetic variants, epigenetic factors (DNA methylation and noncoding RNA), microbial species, and environment (sociodemographics and physical activity). Additionally, studies have also shown that the interactions between genetic variants, microbial metabolites, and epigenetic factors affect energy balance and adiposity. These include variants in FTO, MC4R, PPAR, APOA, and FADS genes, DNA methylation in CpG island regions, and specific miRNAs and microbial species such as Firmicutes, Bacteriodes, Clostridiales, etc. Similarly, studies have shown that microbial metabolites, folate, B-vitamins, and short-chain fatty acids interact with miRNAs to influence obesity phenotypes. With the advent of next-generation sequencing and analytical approaches, the advances in precision nutrition have the potential to lead to new paradigms, which can further lead to interventions or customized treatments specific to individuals or susceptible groups of individuals. This review highlights the recent advances in precision nutrition as applied to obesity and projects the importance of precision nutrition in obesity and weight management.

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.

Dietary Mediators of the Genetic Susceptibility to Obesity-Results from the Quebec Family Study

BACKGROUND

Recent studies showed that eating behaviors such as disinhibition, emotional and external eating, and snacking mediate genetic susceptibility to obesity. It remains unknown if diet quality and intake of specific food groups also mediate the genetic susceptibility to obesity.

OBJECTIVE

This study aimed to assess if diet quality and intakes of specific food groups mediate the association between a polygenic risk score (PRS) for BMI and BMI and waist circumference (WC). We hypothesized that poor diet quality, high intakes of energy-dense food groups, and low intakes of nutrient-dense food groups mediate the genetic susceptibility to obesity.

METHODS

This cross-sectional study included 750 participants (56.3% women, aged 41.5 ± 14.9 y, BMI 27.8 ± 7.5 kg/m2) from the Quebec Family Study. A PRSBMI based on >500,000 genetic variants was calculated using LDpred2. Dietary intakes were assessed with a 3-d food record from which a diet quality score (i.e. Nutrient Rich Food Index 6.3) and food groups were derived. Mediation analyses were conducted using a regression-based and bootstrapping approach.

RESULTS

The PRSBMI explained 25.7% and 19.8% of the variance in BMI and WC, respectively. The association between PRSBMI and BMI was partly mediated by poor diet quality (β = 0.33 ± 0.12; 95% CI: 0.13, 0.60), high intakes of fat and high-fat foods (β = 0.46 ± 0.16; 95% CI: 0.19, 0.79) and sugar-sweetened beverages (β = 0.25 ± 0.14; 95% CI: 0.05, 0.60), and low intakes of vegetables (β = 0.15 ± 0.08; 95% CI: 0.03, 0.32), fruits (β = 0.37 ± 0.12; 95% CI: 0.17, 0.64), and dairy products (β = 0.17 ± 0.09; 95% CI: 0.02, 0.37). The same trends were observed for WC.

CONCLUSIONS

The genetic susceptibility to obesity was partly mediated by poor diet quality and intakes of specific food groups. These results suggest that improvement in diet quality may reduce obesity risk among individuals with high genetic susceptibility and emphasize the need to intervene on diet quality among these individuals.

Healthful plant-based dietary patterns, genetic risk of obesity, and cardiovascular risk in the UK biobank study

BACKGROUND & AIMS

People with a higher genetic risk for obesity are more likely to develop cardiovascular disease (CVD), and healthy plant-based dietary patterns may be associated with decreased risks of obesity and cardiovascular events. We investigated whether adherence to healthy plant-foods-rich dietary patterns might attenuate risks of obesity and related cardiovascular abnormalities for people at genetically higher risk of obesity.

METHODS

This study included 121,799 middle-aged adults in UK Biobank who were initially free of metabolic diseases and cancer. We calculated a healthful plant-based diet index (hPDI) based on 17 major food groups as well as a genetic risk score (GRS) for obesity consisting of body mass index (BMI)-associated variants. The incidence of cardiovascular events (myocardial infarction, MI, or stroke) was prospectively followed during a mean (SD) 5.1 (0.9) years.

RESULTS

We found significant interactions between GRS and hPDI on adiposity (P_interaction <0.0001); adherence to hPDI was more strongly associated with lower levels of adiposity among participants with higher GRS than those with lower GRS. Further, we found a similar pattern of GRS-hPDI interactions on untreated hypertension (P_interaction = 0.0036). When we tested GRS-hPDI interactions on cardiovascular events, adherence to hPDI was more strongly associated with a decreased risk of MI among people with high GRS (above median) than those with low GRS (P_interaction = 0.006). Among participants with high GRS, high adherence to hPDI (the top tertile of hPDI) was associated with an HR 0.54 (95% CI: 0.39, 0.74) for MI, as compared to low adherence.

CONCLUSIONS

Adherence to healthy plant-based dietary patterns significantly attenuated risks of cardiovascular abnormalities for people at genetically higher risk of obesity. Our results support the precision medicine strategies considering genetics and dietary habits to modify cardiovascular health for people at higher risk of genetically determined obesity.

Three Different Genetic Risk Scores Based on Fatty Liver Index, Magnetic Resonance Imaging and Lipidomic for a Nutrigenetic Personalized Management of NAFLD: The Fatty Liver in Obesity Study

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the global population. The pathogenesis of NAFLD is complex; available data reveal that genetics and ascribed interactions with environmental factors may play an important role in the development of this morbid condition. The purpose of this investigation was to assess genetic and non-genetic determinants putatively involved in the onset and progression of NAFLD after a 6-month weight loss nutritional treatment. A group of 86 overweight/obese subjects with NAFLD from the Fatty Liver in Obesity (FLiO) study were enrolled and metabolically evaluated at baseline and after 6 months. A pre-designed panel of 95 genetic variants related to obesity and weight loss was applied and analyzed. Three genetic risk scores (GRS) concerning the improvement on hepatic health evaluated by minimally invasive methods such as the fatty liver index (FLI) (GRSFLI), lipidomic-OWLiver®-test (GRSOWL) and magnetic resonance imaging (MRI) (GRSMRI), were derived by adding the risk alleles genotypes. Body composition, liver injury-related markers and dietary intake were also monitored. Overall, 23 SNPs were independently associated with the change in FLI, 16 SNPs with OWLiver®-test and 8 SNPs with MRI, which were specific for every diagnosis tool. After adjusting for gender, age and other related predictors (insulin resistance, inflammatory biomarkers and dietary intake at baseline) the calculated GRSFLI, GRSOWL and GRSMRI were major contributors of the improvement in hepatic status. Thus, fitted linear regression models showed a variance of 53% (adj. R2 = 0.53) in hepatic functionality (FLI), 16% (adj. R2 = 0.16) in lipidomic metabolism (OWLiver®-test) and 34% (adj. R2 = 0.34) in liver fat content (MRI). These results demonstrate that three different genetic scores can be useful for the personalized management of NAFLD, whose treatment must rely on specific dietary recommendations guided by the measurement of specific genetic biomarkers.

Differential response to a 6-month energy-restricted treatment depending on SH2B1 rs7359397 variant in NAFLD subjects: Fatty Liver in Obesity (FLiO) Study

PURPOSE

Non-alcoholic fatty liver disease (NAFLD) is worldwide recognized as the most common cause of chronic liver disease. Current NAFLD clinical management relies on lifestyle change, nevertheless, the importance of the genetic make-up on liver damage and the possible interactions with diet are still poorly understood. The aim of the study was to evaluate the influence of the SH2B1 rs7359397 genetic variant on changes in body composition, metabolic status and liver health after 6-month energy-restricted treatment in overweight/obese subjects with NAFLD. In addition, gene-treatment interactions over the course of the intervention were examined.

METHODS

The SH2B1 genetic variant was genotyped in 86 overweight/obese subjects with NAFLD from the FLiO study (Fatty Liver in Obesity study). Subjects were metabolically evaluated at baseline and at 6-months. Liver assessment included ultrasonography, Magnetic Resonance Imaging, elastography, a lipidomic test (OWL^®-test) and specific blood liver biomarkers. Additionally, body composition, general biochemical markers and dietary intake were determined.

RESULTS

Both genotypes significantly improved their body composition, general metabolic status and liver health after following an energy-restricted strategy. Liver imaging techniques showed a greater decrease in liver fat content (- 44.3%, p < 0.001) and in serum ferritin levels (p < 0.001) in the carriers of the T allele after the intervention. Moreover, lipidomic analysis, revealed a higher improvement in liver status when comparing risk vs. no-risk genotype (p = 0.006 vs. p = 0.926, respectively). Gene-treatment interactions showed an increase in fiber intake and omega-3 fatty acid in risk genotype (p interaction = 0.056 and p interaction = 0.053, respectively), while a significant increase in MedDiet score was observed in both genotype groups (p = 0.020). Moreover, no-risk genotype presented a relevant decrease in hepatic iron as well as in MUFA intake (p = 0.047 and p = 0.034, respectively).

CONCLUSION

Subjects carrying the T allele of the rs7359397 polymorphism may benefit more in terms of hepatic health and liver status when prescribed an energy-restricted treatment, where a Mediterranean dietary pattern rich in fiber and other components such as omega-3 fatty acids might boost the benefits.

TRIAL REGISTRATION

The Fatty Liver in Obesity was approved by the Research Ethics Committee of the University of Navarra and retrospectively registered (NCT03183193; www.clinicaltrials.gov ); June 2017.

Diabetes and Genetics: A Relationship Between Genetic Risk Alleles, Clinical Phenotypes and Therapeutic Approaches

Unveiling human genome through successful completion of Human Genome Project and International HapMap Projects with the advent of state of art technologies has shed light on diseases associated genetic determinants. Identification of mutational landscapes such as copy number variation, single nucleotide polymorphisms or variants in different genes and loci have revealed not only genetic risk factors responsible for diseases but also region(s) playing protective roles. Diabetes is a global health concern with two major types - type 1 diabetes (T1D) and type 2 diabetes (T2D). Great progress in understanding the underlying genetic predisposition to T1D and T2D have been made by candidate gene studies, genetic linkage studies, genome wide association studies with substantial number of samples. Genetic information has importance in predicting clinical outcomes. In this review, we focus on recent advancement regarding candidate gene(s) associated with these two traits along with their clinical parameters as well as therapeutic approaches perceived. Understanding genetic architecture of these disease traits relating clinical phenotypes would certainly facilitate population stratification in diagnosing and treating T1D/T2D considering the doses and toxicity of specific drugs.

Interaction between the genetic risk score and dietary protein intake on cardiometabolic traits in Southeast Asian

Background

Cardiometabolic diseases are complex traits which are influenced by several single nucleotide polymorphisms (SNPs). Thus, analysing the combined effects of multiple gene variants might provide a better understanding of disease risk than using a single gene variant approach. Furthermore, studies have found that the effect of SNPs on cardiometabolic traits can be influenced by lifestyle factors, highlighting the importance of analysing gene-lifestyle interactions.

Aims

In the present study, we investigated the association of 15 gene variants with cardiometabolic traits and examined whether these associations were modified by lifestyle factors such as dietary intake and physical activity.

Methods

The study included 110 Minangkabau women [aged 25–60 years and body mass index (BMI) 25.13 ± 4.2 kg/m²] from Padang, Indonesia. All participants underwent a physical examination followed by anthropometric, biochemical and dietary assessments and genetic tests. A genetic risk score (GRS) was developed based on 15 cardiometabolic disease-related SNPs. The effect of GRS on cardiometabolic traits was analysed using general linear models. GRS-lifestyle interactions on continuous outcomes were tested by including the interaction term (e.g. lifestyle factor*GRS) in the regression model. Models were adjusted for age, BMI and location (rural or urban), wherever appropriate.

Results

There was a significant association between GRS and BMI, where individuals carrying 6 or more risk alleles had higher BMI compared to those carrying 5 or less risk alleles (P = 0.018). Furthermore, there were significant interactions of GRS with protein intake on waist circumference (WC) and triglyceride concentrations (P_interaction = 0.002 and 0.003, respectively). Among women who had a lower protein intake (13.51 ± 1.18% of the total daily energy intake), carriers of six or more risk alleles had significantly lower WC and triglyceride concentrations compared with carriers of five or less risk alleles (P = 0.0118 and 0.002, respectively).

Conclusions

Our study confirmed the association of GRS with higher BMI and further showed a significant effect of the GRS on WC and triglyceride levels through the influence of a low-protein diet. These findings suggest that following a lower protein diet, particularly in genetically predisposed individuals, might be an effective approach for addressing cardiometabolic diseases among Southeast Asian women.

Quantile-dependent heritability of computed tomography, dual-energy x-ray absorptiometry, anthropometric, and bioelectrical measures of adiposity

BACKGROUND/OBJECTIVES

Quantile-dependent expressivity occurs when a gene's phenotypic expression depends upon whether the trait (e.g., BMI) is high or low relative to its distribution. We have previously shown that the obesity effects of a genetic risk score (GRSBMI) increased significantly with increasing quantiles of BMI. However, BMI is an inexact adiposity measure and GRSBMI explains <3% of the BMI variance. The purpose of this paper is to test BMI for quantile-dependent expressivity using a more inclusive genetic measure (h2, heritability in the narrow sense), extend the result to other adiposity measures, and demonstrate its consistency with purported gene-environment interactions.

SUBJECTS/METHODS

Quantile-specific offspring-parent regression slopes (βOP) were obtained from quantile regression for height (ht) and computed tomography (CT), dual-energy x-ray absorptiometry (DXA), anthropometric, and bioelectrical impedance (BIA) adiposity measures. Heritability was estimated by 2βOP/(1 + rspouse) in 6227 offspring-parent pairs from the Framingham Heart Study, where rspouse is the spouse correlation.

RESULTS

Compared to h2 at the 10th percentile, genetic heritability was significantly greater at the 90th population percentile for BMI (3.14-fold greater, P < 10-15), waist girth/ht (3.27-fold, P < 10-15), hip girth/ht (3.12-fold, P = 6.3 × 10-14), waist-to-hip ratio (1.75-fold, P = 0.01), sagittal diameter/ht (3.89-fold, P = 3.7 × 10-7), DXA total fat/ht2 (3.62-fold, P = 0.0002), DXA leg fat/ht2 (3.29-fold, P = 2.0 × 10-11), DXA arm fat/ht2 (4.02-fold, P = 0.001), CT-visceral fat/ht2 (3.03-fold, P = 0.002), and CT-subcutaneous fat/ht2 (3.54-fold, P = 0.0004). External validity was suggested by the phenomenon's consistency with numerous published reports. Quantile-dependent expressivity potentially explains precision medicine markers for weight gain from overfeeding or antipsychotic medications, and the modifying effects of physical activity, sleep, diet, polycystic ovary syndrome, socioeconomic status, and depression on gene-BMI relationships.

CONCLUSIONS

Genetic heritabilities of anthropometric, CT, and DXA adiposity measures increase with increasing adiposity. Some gene-environment interactions may arise from analyzing subjects by characteristics that distinguish high vs. low adiposity rather than the effects of environmental stimuli on transcriptional and epigenetic processes.

An automatic electronic instrument for accurate measurements of food volume and density

OBJECTIVE

Accurate measurements of food volume and density are often required as 'gold standards' for calibration of image-based dietary assessment and food database development. Currently, there is no specialised laboratory instrument for these measurements. We present the design of a new volume of density (VD) meter to bridge this technological gap.

DESIGN

Our design consists of a turntable, a load sensor, a set of cameras and lights installed on an arc-shaped stationary support, and a microcomputer. It acquires an array of food images, reconstructs a 3D volumetric model, weighs the food and calculates both food volume and density, all in an automatic process controlled by the microcomputer. To adapt to the complex shapes of foods, a new food surface model, derived from the electric field of charged particles, is developed for 3D point cloud reconstruction of either convex or concave food surfaces.

RESULTS

We conducted two experiments to evaluate the VD meter. The first experiment utilised computer-synthesised 3D objects with prescribed convex and concave surfaces of known volumes to investigate different food surface types. The second experiment was based on actual foods with different shapes, colours and textures. Our results indicated that, for synthesised objects, the measurement error of the electric field-based method was <1 %, significantly lower compared with traditional methods. For real-world foods, the measurement error depended on the types of food volumes (detailed discussion included). The largest error was approximately 5 %.

CONCLUSION

The VD meter provides a new electronic instrument to support advanced research in nutrition science.

Interaction between Metabolic Genetic Risk Score and Dietary Fatty Acid Intake on Central Obesity in a Ghanaian Population

Obesity is a multifactorial condition arising from the interaction between genetic and lifestyle factors. We aimed to assess the impact of lifestyle and genetic factors on obesity-related traits in 302 healthy Ghanaian adults. Dietary intake and physical activity were assessed using a 3 day repeated 24 h dietary recall and global physical activity questionnaire, respectively. Twelve single nucleotide polymorphisms (SNPs) were used to construct 4-SNP, 8-SNP and 12-SNP genetic risk scores (GRSs). The 4-SNP GRS showed significant interactions with dietary fat intakes on waist circumference (WC) (Total fat, P_interaction = 0.01; saturated fatty acids (SFA), P_interaction = 0.02; polyunsaturated fatty acids (PUFA), P_interaction = 0.01 and monounsaturated fatty acids (MUFA), P_interaction = 0.01). Among individuals with higher intakes of total fat (>47 g/d), SFA (>14 g/d), PUFA (>16 g/d) and MUFA (>16 g/d), individuals with ≥3 risk alleles had a significantly higher WC compared to those with <3 risk alleles. This is the first study of its kind in this population, suggesting that a higher consumption of dietary fatty acid may have the potential to increase the genetic susceptibility of becoming centrally obese. These results support the general dietary recommendations to decrease the intakes of total fat and SFA, to reduce the risk of obesity, particularly in individuals with a higher genetic predisposition to central 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.

Associations of air pollution with obesity and body fat percentage, and modification by polygenic risk score for BMI in the UK Biobank

Air pollution has consistently been associated with cardiometabolic outcomes, although associations with obesity have only been recently reported. Studies of air pollution and adiposity have mostly relied on body mass index (BMI) rather than body fat percentage (BF%), and most have not accounted for noise as a possible confounder. Additionally, it is unknown whether genetic predisposition for obesity increases susceptibility to the obesogenic effects of air pollution. To help fill these gaps, we used the UK Biobank, a large, prospective cohort study in the United Kingdom, to explore the relationship between air pollution and adiposity, and modification by a polygenic risk score for BMI. We used 2010 annual averages of air pollution estimates from land use regression (NO₂, NO_X, PM_2.5, PM_{2.5absorbance}, PM_2.5-10, PM₁₀), traffic intensity (TI), inverse distance to road (IDTR), along with examiner-measured BMI, waist-hip-ratio (WHR), and impedance measures of BF%, which were collected at enrollment (2006-2010, n = 473,026) and at follow-up (2012-2013, n = 19,518). We estimated associations of air pollution with BMI, WHR, and BF% at enrollment and follow-up, and with obesity, abdominal obesity, and BF%-obesity at enrollment and follow-up. We used linear and logistic regression and controlled for noise and other covariates. We also assessed interactions of air pollution with a polygenic risk score for BMI. On average, participants at enrollment were 56 years of age, 54% were female, and 32% had completed college or a higher degree. Almost all participants (~95%) were white. All air pollution measures except IDTR were positively associated with at least one continuous measure of adiposity at enrollment. However, NO₂ was negatively associated with BMI but positively associated with WHR at enrollment, and IDTR was also negatively associated with BMI. At follow-up (controlling for enrollment adiposity), we observed positive associations for PM_2.5-10 with BMI, PM₁₀ with BF%, and TI with BF% and BMI. Associations were similar for binary measures of adiposity, with minor differences for some pollutants. Associations of NO_X, NO₂, PM_{2.5absorbance}, PM_2.5 and PM₁₀, with BMI at enrollment, but not at follow-up, were stronger among individuals with higher BMI polygenic risk scores (interaction p <0.05). In this large, prospective cohort, air pollution was associated with several measures of adiposity at enrollment and follow-up, and associations with adiposity at enrollment were modified by a polygenic risk score for obesity.

The Challenge of Stratifying Obesity: Attempts in the Quebec Family Study

Background and aims: Obesity is a major health problem worldwide. Given the heterogeneous obesity phenotype, an optimal obesity stratification would improve clinical management. Since obesity has a strong genetic component, we aimed to develop a polygenic risk score (PRS) to stratify obesity according to the genetic background of the individuals. Methods: A total of 231 single nucleotide polymorphisms (SNP) significantly associated to body mass index (BMI) from 21 genome-wide association studies were genotyped or imputed in 881 subjects from the Quebec Family Study (QFS). The population was randomly split into discovery (80%; n = 704) and validation (20%; n = 177) samples with similar obesity (BMI ≥ 30) prevalence (27.8% and 28.2%, respectively). Family-based associations with obesity were tested for every SNP in the discovery sample and a weighed and continuous PRS₂₃₁ was constructed. Generalized linear mixed effects models were used to test the association of PRS₂₃₁ with obesity in the QFS discovery sample and validated in the QFS replication sample. Furthermore, the Fatty Acid Sensor (FAS) Study (n = 141; 27.7% obesity prevalence) was used as an independent sample to replicate the results. Results: The linear trend test demonstrated a significant association of PRS₂₃₁ with obesity in the QFS discovery sample (OR_trend = 1.19 [95% CI, 1.14-1.24]; P = 2.0x10^-16). We also found that the obesity prevalence was significantly greater in the higher PRS₂₃₁ quintiles compared to the lowest quintile. Significant and consistent results were obtained in the QFS validation sample for both the linear trend test (OR_trend = 1.16 [95% CI, 1.07-1.26]; P = 6.7x10^-4), and obesity prevalence across quintiles. These results were partially replicated in the FAS sample (OR_trend = 1.12 [95% CI, 1.02-1.24]; P = 2.2x10^-2). PRS₂₃₁ explained 7.5%, 3.2%, and 1.2% of BMI variance in QFS discovery, QFS validation, and FAS samples, respectively. Conclusions: These results revealed that genetic background in the form of a 231 BMI-associated PRS has a significant impact on obesity, but a limited potential to accurately stratify it. Further studies are encouraged on larger populations.

Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants

Obesity is a worldwide health problem that is closely linked to many metabolic disorders. Regular physical exercise has been found to attenuate the genetic predisposition to obesity. However, it remains unknown what kinds of exercise can modify the genetic risk of obesity. This study included 18,424 unrelated Han Chinese adults aged 30–70 years who participated in the Taiwan Biobank (TWB). A total of 5 obesity measures were investigated here, including body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Because there have been no large genome-wide association studies on obesity for Han Chinese, we used the TWB internal weights to construct genetic risk scores (GRSs) for each obesity measure, and then test the significance of GRS-by-exercise interactions. The significance level throughout this work was set at 0.05/550 = 9.1x10^-5 because a total of 550 tests were performed. Performing regular exercise was found to attenuate the genetic effects on 4 obesity measures, including BMI, BFP, WC, and HC. Among the 18 kinds of self-reported regular exercise, 6 mitigated the genetic effects on at least one obesity measure. Regular jogging blunted the genetic effects on BMI, BFP, and HC. Mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga also attenuated the genetic effects on BMI. Exercises such as cycling, stretching exercise, swimming, dance dance revolution, and qigong were not found to modify the genetic effects on any obesity measure. Across all 5 obesity measures, regular jogging consistently presented the most significant interactions with GRSs. Our findings show that the genetic effects on obesity measures can be decreased to various extents by performing different kinds of exercise. The benefits of regular physical exercise are more impactful in subjects who are more predisposed to obesity.

The complex interplay of genetics and lifestyle makes obesity a challenging issue. Previous studies have found performing regular physical exercise could blunt the genetic effects on body mass index (BMI). However, BMI does not take into account lean body mass or identify central obesity. Moreover, it remains unclear what kinds of exercise could more effectively attenuate the genetic effects on obesity measures. With a sample of 18,424 unrelated Han Chinese adults, we comprehensively investigated gene-exercise interactions on 5 obesity measures: BMI, body fat percentage, waist circumference, hip circumference, and waist-to-hip ratio. Moreover, we tested whether the genetic effects on obesity measures could be modified by any of 18 kinds of self-reported regular exercise. Because no large genome-wide association studies on obesity have been done for Han Chinese, we constructed genetic risk scores with internal weights for analyses. Among these exercises, regular jogging consistently presented the strongest evidence to mitigate the genetic effects on all 5 obesity measures. Moreover, mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga attenuated the genetic effects on BMI. The benefits of regularly performing these 6 kinds of exercise are more impactful in subjects who are more predisposed to obesity.

Overcoming the Bitter Taste of Oils Enriched in Fatty Acids to Obtain Their Effects on the Heart in Health and Disease

Fatty acids come in a variety of structures and, because of this, create a variety of functions for these lipids. Some fatty acids have a role to play in energy metabolism, some help in lipid storage, cell structure, the physical state of the lipid, and even in food stability. Fatty acid metabolism plays a particularly important role in meeting the energy demands of the heart. It is the primary source of myocardial energy in control conditions. Its role changes dramatically in disease states in the heart, but the pathologic role these fatty acids play depends upon the type of cardiovascular disease and the type of fatty acid. However, no matter how good a food is for one's health, its taste will ultimately become a deciding factor in its influence on human health. No food will provide health benefits if it is not ingested. This review discusses the taste characteristics of culinary oils that contain fatty acids and how these fatty acids affect the performance of the heart during healthy and diseased conditions. The contrasting contributions that different fatty acid molecules have in either promoting cardiac pathologies or protecting the heart from cardiovascular disease is also highlighted in this article.

Associations of ADIPOQ and LEP Gene Variants with Energy Intake: A Systematic Review

This systematic review aims to evaluate the association of adiponectin (ADIPOQ) and leptin (LEP) gene variants with energy intake. Cross-sectional, cohort, and case–control studies that reported an association of leptin and/or adiponectin gene variants with energy intake were included in this review. Human studies without any age restrictions were considered eligible. Detailed individual search strategies were developed for each of the following bibliographic databases: Cochrane, Latin American and Caribbean Center on Health Sciences Information (LILACS), PubMed/MEDLINE, Scopus, and Web of Science. Risk of bias assessment was adapted from the Downs and Black scale and was used to evaluate the methodology of the included studies. Seven studies with a pooled population of 2343 subjects were included. The LEP and ADIPOQ gene variants studied were LEP-rs2167270 (k = 1), LEP-rs7799039 (k = 5), ADIPOQ-rs2241766 (k = 2), ADIPOQ-rs17300539 (k = 1), and ADIPOQ marker D3S1262 (k = 1). Two of the seven studies reviewed demonstrated a positive association between the LEP-rs7799039 polymorphism and energy intake. Two other studies—one involving a marker of the ADIPOQ gene and one examining the ADIPOQ-rs17300539 polymorphism—also reported associations with energy intake. More research is needed to further elucidate the contributions of genetic variants to energy metabolism.

Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes

We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank (N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R² = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.

Information of genetic architectures of complex traits can be leveraged for predicting phenotypes. Here, the authors develop CTPR (Cross-Trait Penalized Regression), a method for multi-trait polygenic risk prediction using individual-level genotypes and/or summary statistics from large cohorts.

Correlation of the Homeostasis Model Assessment Index and Adiponectin, Leptin and Insulin Levels to Body Mass Index-Associated Gene Polymorphisms in Adolescents

Objectives

This study aimed to describe correlations between glucose, insulin and adipokine levels and the homeostasis model assessment (HOMA) index with regards to the presence/absence of fat mass and obesity-associated (FTO) rs9939609 and peroxisome proliferator-activated receptor (PPAR)-y rs1801282 single nucleotide polymorphisms (SNPs) as indicators of body mass index in adolescents.

Methods

This cross-sectional study was conducted between September and December 2016 in Toluca, Mexico. A total of 71 students between 14-18 years old were included. Various anthropometric and laboratory measurements were collected, including lipid profile, glucose, insulin and adipokine levels and HOMA index. The degree of association between variables was evaluated with regards to the presence/absence of the SNPs.

Results

Leptin levels were significantly higher among female students (P = 0.001), although adiponectin levels did not differ significantly (P = 0.060). There were significant positive correlations between insulin levels and HOMA index with FTO (r = 0.391; P = 0.007 and r = 0.413; P = 0.005, respectively) and PPARγ (r = 0.529; P = 0.007 and r = 0.537; P = 0.007, respectively) SNPs. Leptin showed a significant positive correlation in the presence of PPARγ (r = 0.483; P = 0.007) or in the absence of both SNPs (r = 0.627; P = 0.039). However, adiponectin was significantly negatively correlated in the presence of FTO, either alone (r = -0.333; P = 0.024) or in combination with PPARγ (r = -0.616; P = 0.043).

Conclusion

The presence of FTO and/or PPARγ SNPs might be related to a genetic predisposition to metabolic syndrome.

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.

A Global Overview of Precision Medicine in Type 2 Diabetes

The detailed characterization of human biology and behaviors is now possible at scale owing to innovations in biomarkers, bioimaging, and wearable technologies; "big data" from electronic medical records, health insurance databases, and other platforms becoming increasingly accessible; and rapidly evolving computational power and bioinformatics methods. Collectively, these advances are creating unprecedented opportunities to better understand diabetes and many other complex traits. Identifying hidden structures within these complex data sets and linking these structures to outcome data may yield unique insights into the risk factors and natural history of diabetes, which in turn may help optimize the prevention and management of the disease. This emerging area is broadly termed "precision medicine." In this Perspective, we give an overview of the evidence and barriers to the development and implementation of precision medicine in type 2 diabetes. We also discuss recently presented paradigms through which complex data might enhance our understanding of diabetes and ultimately our ability to tackle the disease more effectively than ever before.