Next Article in Journal
Translocator Protein-18 kDa (TSPO) Positron Emission Tomography (PET) Imaging and Its Clinical Impact in Neurodegenerative Diseases
Next Article in Special Issue
Skeletal Muscle Nucleo-Mitochondrial Crosstalk in Obesity and Type 2 Diabetes
Previous Article in Journal
Emerging Roles of IL-33/ST2 Axis in Renal Diseases
Previous Article in Special Issue
Metabolic Effect of an Oriental Herbal Medicine on Obesity and Its Comorbidities with Transcriptional Responses in Diet-Induced Obese Mice

Volume 18, Issue 4

► Article Menu
Issue 4 (April) cover image

Article Versions

Related Info

More by Authors

Export Article

Int. J. Mol. Sci. 2017, 18(4), 787; https://doi.org/10.3390/ijms18040787

Review
Gene-Diet Interaction and Precision Nutrition in Obesity
1 and 1,2,*
1
Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
2
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Lynnette Ferguson and Virginia R. Parslow
Received: 22 February 2017 / Accepted: 3 April 2017 / Published: 7 April 2017

Abstract

:
The rapid rise of obesity during the past decades has coincided with a profound shift of our living environment, including unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Growing studies have found that changes in adiposity and metabolic response to low-calorie weight loss diets might be modified by genetic variants related to obesity, metabolic status and preference to nutrients. This review summarized data from recent studies of gene-diet interactions, and discussed integration of research of metabolomics and gut microbiome, as well as potential application of the findings in precision nutrition.
Keywords:
gene-diet interactions; weight loss; obesity

1. Introduction

The increasing epidemic of obesity has coincided with a profound shift of our living environment, such as unhealthy dietary patterns, a sedentary lifestyle, physical inactivity, poor sleeping habits, as well as changes in demographic and cultural background [1]. In a recent study of the US national cohort, the magnitude of association between obesity, as assessed by body mass index (BMI) and genetic risk of obesity was stronger in more recent birth cohorts than in earlier years of birth cohorts, suggesting that such genetic predisposition to obesity may have a greater effect in more recent obesogenic environments [2]. Although the environmental risk factors are largely modifiable and the development of obesity would be essentially preventable, genetic variants associated with adiposity may also influence behavioral responses such as shaping appetite, total energy intake and preferences of macronutrients [3,4,5,6]. Also, food preference patterns (such as high sugar and carbohydrate consumption) [7,8] would partly be genetically determined.
The genetic contribution to obesity has been extensively investigated in genome-wide association studies (GWAS) [9,10,11], which successfully discovered susceptible loci and unveiled mechanisms. However, predicting disease risk from genetic background is complicated by interactions between genetic variants and environmental risk factors. Gene–environment interactions are ubiquitous, and may account for the greater part of disease risk seen across genotypes [12]. With rapid advances in omics technologies and analytic approaches, recent genome-wide analyses have reveled genetics of intermediate phenotypes such as circulating metabolites (metabolomics) and gut microbiome [13,14,15,16,17,18]. Integrating information from studies of metabolomics and gut microbiome [19] will provide new insights into the roles of gene–environment interaction in complex traits including obesity, and contribute to a precision prevention and management of obesity. Here, we highlighted data from recent studies of gene–diet interactions on obesity, and discussed how these findings may inform understanding of more complex architecture of interactions between genes and environment factors in obesity and associated diseases.

2. Studying Gene–Environment Interactions

More and more genetic bases of complex metabolic diseases such as obesity and type 2 diabetes have been revealed [9,10,11,20,21], however the genetic variants identified so far only explain a small proportion of heritability of the diseases, suggesting so-called ‘missing’ heritability [22]. For example, the recent GWAS by the Genetic Investigation of Anthropometric Traits (GIANT) consortium identified a total of 97 BMI-associated loci; however, these loci only account for 2.7% of BMI variation [11]. As we and others described previously [1,23,24,25,26], the importance of studying gene–environment interaction has been well recognized, and the missing heritability of obesity could be partly due to interactions between the genetic variations and environmental factors such as lifestyle and dietary factors. In the following section of our review, we showed several studies on lifestyle and dietary factors that magnified risk of obesity among individuals genetically at high risk. Genes can trigger the occurrence of diseases when a person with a high-risk genetic profile is exposed to high-risk environmental factors in the gene–environment interaction phenomena [27]. Gene–environment interaction may reflect a causal mechanism where the variants and environmental exposures contribute to the causation of a disease or condition in the same individual with the genetic factors influencing the sensitivity to environmental factors. How these two exposures synergistically affect vulnerability to diseases remains unresolved. In particular, obese individuals are characterized by different body shapes and considerable heterogeneity within the spectrum of clinical obesity may exist [28,29,30,31,32,33,34,35,36,37]. A recent GWAS identified genetic variants associated with overall body shape based on a combination of multiple anthropometric traits [38]. To support gene–diet interaction and precision nutrition in obesity, considering different body shapes and subtypes of obesity would be necessary.

3. Dietary and Lifestyle Factors Interact with Genetic Variants on Obesity

Epidemiological studies have consistently shown that particular diets and lifestyles accentuate risk of obesity among adults genetically at high risk (Table 1). For example, replicable evidence has shown that sugar-sweetened beverages [39,40,41], fried food consumption [42], physical activity and sedentary lifestyles [43,44,45] are interacted with genetic variants in the association of obesity.
Intake of free sugars or sugar sweetened beverages is a determinant of body weight [48,49]. We previously reported significant interactions between genetic factors linked to obesity (as assessed by genetic risk score (GRS) based on 32 BMI-associated loci) and intake of sugar-sweetened beverages in two US cohorts of the Nurses’ Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) [39]. The genetic association with obesity was stronger among individuals with higher intake of sugar-sweetened beverages as compared with those with lower intake [39]. In a recent study of Swedish adults, similar findings were observed, and the association of sugar-sweetened beverages with BMI was stronger in people genetically predisposed to obesity [40]. Further, another recent study reported similar interactions between a GRS for obesity and soft drinks consumption in relation to changes in BMI [41]. In a Hispanic population living in Costa Rica, there were significant interactions between intake of sugar-sweetened beverages and the chromosome 9p21 variant on myocardial infarction, and high consumption of sugar-sweetened beverages strengthen the genetic risk [50]. Consumption of sugar-sweetened beverages has been implicated in driving the epidemic of obesity [51]; recent reproducible evidence from these studies in the US and European populations suggests potential interactions in the relationship.
Higher fried food intake, which increases energy intake, is considered as one of unhealthy dietary factors that influence risks of general and central obesity [52]. We previously reported for the first time that fried food consumption interacted with genetic background in relation to obesity in the NHS and HPFS cohorts, highlighting the importance of reducing fried food consumption among individuals genetically predisposed to obesity [42]. Our study indicated that FTO genotype showed the strongest interaction (Pinteraction < 0.001) among all obesity predisposing variants [42]. In addition to fried food, among participants of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) and the Multi-Ethnic Study of Atherosclerosis (MESA) population, higher intake of saturated fatty acids was associated with higher BMI among individuals at a genetically high risk of obesity [46]. In a study of three US cohorts, association of the APOA2 − 265T > C polymorphism and BMI was modified by saturated fat intake [53].
On the other hand, results of the UK Biobank study [45] did not show significant interactions between BMI-GRS and fired-food consumption or fizzy drink intake. Analysis, and the definitions of fizzy drink consumption (such as no data on type were available) and fried-food intake (which was indicated by combined the reported intake of fried chicken and fried potato), were different from other studies [39,40,41,42], and habitual intake of these foods are also differ across study populations.
A recent study including data from 18 cohorts of European ancestry investigated whether a composite score representing healthy diet (which was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes) modified associations of genetic variants associated with obesity using GRSs based on 32 BMI- and 14 waist–hip ratio (WHR)-associated single nucleotide polymorphisms (SNPs) [54]. Their results suggested that associations between genetic predisposition and obesity traits were stronger among individuals with healthier diet scores [54].
A number of studies investigated a gene–physical-activity interaction on obesity [43,44,45,55,56]. A meta-analysis has shown that physical activity attenuated the influence of FTO variants on obesity in adults [55]. Whereas greater leisure time physical activity attenuated the genetic association, a sedentary lifestyle indicated by prolonged TV watching was found to accentuate genetic predisposition to elevated adiposity [43]. In our previous paper, we demonstrated that in both women and men from the NHS and HPFS cohorts, the genetic association with BMI was strengthened with increased hours of TV watching [43]. A recent study of the UK Biobank study also provides similar results, and the effect of genetic risk of obesity on BMI was stronger for people watching at least four hours of TV per day compared with those watching three hours or less [45]. The UK Biobank study also reported that associations of genetic predisposition and measures of adiposity (such as BMI and waist circumference) were modified by a variety of sleep characteristics including sleep duration, chronotype, day napping, shift work, and night-shift work [47]. Their results showed that the association of genetic risk and adiposity was exacerbated by adverse sleeping characteristics [47].
Childhood obesity is a strong risk factor for metabolic abnormalities in later adulthood [57,58]. In line with evidence in adults, studies have shown that FTO rs9939609 genotype was associated with childhood obesity [59,60,61,62], and also suggested an association of the FTO variant and dietary intake and preference [59,61,63]. We previously reported results of a combined analysis of 16,094 boys and girls from 14 studies [63], and found that BMI-increasing allele of the FTO variant was associated with increased total energy intake, but not with protein, carbohydrate, or fat intake. Also, there was a significant interaction between FTO variant and dietary protein intake on BMI, showing that lower protein intake attenuated the association between the FTO variant and BMI, with no heterogeneity among the studies [63]. A study suggests an interaction between the FTO SNP rs9939609 and socioeconomic status on childhood obesity [64], and some other studies reported gene–environment interactions in childhood obesity [53,65,66]. In a population-based longitudinal study in Brazil, Vitamin D status significantly modified FTO effects on weight changes in children, suggesting that FTO SNP rs9939609 may affect childhood weight gain, and genotype effects were more pronounced among children with insufficient vitamin D levels [67].

4. Genetic Variants Modify the Response to Interventions

It has been reported that how genetic variants modifies effect of dietary intake on weight loss among overweight and obese individuals. In participants of the Preventing Overweight Using Novel Dietary Strategies (POUNDS Lost) Trial [68] and the Dietary Intervention Randomized Controlled Trial (DIRECT) [69], we have performed a series of analyses on gene–diet interactions in obesity and metabolic risk factors (Table 2) [70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93]. There have been debates about which dietary intervention is more effective in losing body weight. According to a meta-analysis that assessed effectiveness of different popular diets in improving weight loss among overweight and obese individuals [94], significant weight loss was observed with any low-carbohydrate or low-fat diet, and weight loss differences between individual diets were small [94]. On the other hand, our findings have consistently shown that the effect of low-calorie dietary interventions varying macronutrient content differed according to the genetic background including disease susceptibility, metabolic status and preference to foods or nutrients. In addition, considerable inter-individual variation has long been noted in response to dietary interventions, and genetic variations may at least partly account for such inter-individual variance. For example, low-fat dietary intervention was associated with more weight loss among overweight and obese individuals with IRS1 rs2943641 CC genotype [81]. Another study indicated that overweight and obese individuals carrying the T allele of PPM1K rs1440581 might benefit more in weight loss when undertaking a low-carbohydrate diet [87].
In addition, obese individuals are at high risk of progression to type 2 diabetes, and previous GWASs revealed susceptibility loci for type 2 diabetes [95,96,97,98,99,100,101,102,103,104]. We examined associations between weight-loss diets and a GRS for diabetes based on 31 diabetes-associated variants and assessed 2-year changes in markers of insulin resistance and β cell function in the POUNDS Lost trial [72]. We found that the lower GRS was associated with a greater decrease in fasting insulin, HbA1c, and insulin resistance as assessed by HOMA-IR, and a lesser increase in insulin secretion as assessed by HOMA-B, particularly among participants consuming a low-protein diet [72]. We found a significant interaction between the GRS and dietary protein on these outcomes, and the genetic effect was opposite among who consumed a high-protein diet [72]. Furthermore, we previously showed that changes in adiposity and metabolic response to weight loss diets varying macronutrient content were significantly influenced by several other individual genetic variants, such as those relating obesity (FTO and NPY), and type 2 diabetes (TCF7L2 and IRS1, etc.). Also, a genetic variant in FGF21 region determining preference to carbohydrate intake was associated with improving obesity in the POUNDS Lost trial [70]. Our series of studies through assessing gene–diet interaction support a concept of ‘precision dietary interventions’ which takes individual variability, determined by genome, metabolome, microbiome, and other makeup, into consideration in designing interventions. Despite further external replications are necessary, accumulating data suggest that one dietary intervention might be more appropriate than others according to individual variability. Personal information such as genotype is useful to predict inter-individual differences in effectiveness of dietary interventions; however, there are concerns whether provision of such personal information may induce adverse effects for individual’s behaviors. According to results of a randomized controlled trial of healthy middle-aged adults [105], as compared to standard lifestyle advice, additional provision of personalized information about genetic risk (of type 2 diabetes) did not affect behaviors (such as physical activity and dietary habit) among the study participants. Also, provision of personal information about risk of type 2 diabetes did not seem to cause anxiety in their study [105].
In addition to dietary interventions, bariatric surgery is also considered to be an effective treatment for patients with severe and complex obesity [106,107,108]. There is a significant genetic contribution to weight loss after Roux-en-Y gastric bypass (RYGB) surgery [109]. However, only a few GWASs were performed previously to identify genetic variants associated with weight-loss response after gastric bypass [110,111], and more work is needed to understand the role of genetics after bariatric surgery. Whether genetic variants may predict the effectiveness of gastric bypass surgery needs to be further examined.
Further, an increasing number of research studies reveal new genetic variants associated with diseases, and whether or how much modifiable factors would alter the genetic risk need to be further investigated in the future. In a recent meta-analysis of diet/lifestyle intervention trials, the effect of weight-loss interventions was not different according to FTO risk allele [112]. On the other hand, according to results of the Look AHEAD (Action for Health in Diabetes) trial, genetic risk of coronary artery disease significantly predicted cardiovascular morbidity and mortality over nearly 10 years, and their lifestyle intervention did not alter the genetic association [113].

5. Metabolomics Approach in the Gene–Diet Interaction

In addition to classical environmental exposures, circulating metabolites could be used for predicting risk of metabolic diseases [114,115,116] as well as for assessing weight-loss in response to a dietary intervention [91,117]. Metabolomics is systematic study of small-molecules generated by process of metabolisms, and it has made remarkable progress in understanding underlying mechanisms of metabolic diseases and the risk prediction. Among various metabolites, circulating amino acids such as branched-chain and aromatic amino acids concentrations have been consistently associated with metabolic abnormalities like type 2 diabetes and obesity-associated insulin resistance [27,114,118,119]. Also, taurine metabolism disturbance is closely linked to obesity, insulin resistance and diabetes, and we recently reported that effects of diabetes genetic risk (assessed by 31 diabetes-associated variants) on changes in fasting glucose, insulin, and insulin resistance were significantly modified by circulating taurine among overweight and obese participants in the POUNDS Lost trial [92]. We observed that elevated concentrations of taurine were associated with a greater reduction of insulin resistance among individuals with higher genetic risk of diabetes than those with lower genetic risk [92].
Recent GWASs have revealed loci associated with intermediate phenotypes and circulating metabolites [13,14,15], and these variants would be useful to investigate effects of genetic determinant of metabolites on obesity. According to the Mendelian randomization principle, genetic variants can be a better marker than biomarkers in assessing causal inference, and it is less likely to be affected by confounding and reverse causation. We previously examined relations between a genetic variant determining amino acid metabolites and obesity in the POUNDS Lost trial [87]. We identified significant interactions between dietary fat and a genetic variant rs1440581 near PPM1K gene region that was associated with branched-chain amino acids/aromatic amino acids ratio (the Fischer’s ratio) on weight loss and changes in insulin resistance [87]. Our results suggested that biological mechanisms underlying associations of metabolites and the outcomes were different across the participants [87]. More studies are warranted to examine metabolomics approaches in the gene–diet interaction to get insights into potential mechanisms.

6. Potential Interactions of Diet with Gut Microbiome

Gut microbiota may be a potential factor for the treatment of obesity and related metabolic diseases [120,121], and a study has also shown that obese individuals with lower bacterial richness would have greater weight gain [120]. Potential influences of dietary habit on gut microbiota have also been attracting interests [19,122,123]. Long-term dietary habits would influence in determining composition of gut microbiota [124], suggesting the importance of well-designed study to investigate the interplay of long-term dietary intake and gut microbiota on metabolic disease onset. Circulating levels of a microbial metabolite, trimethylamine N-oxide (TMAO), has been associated with an increased risk of cardiovascular diseases and mortality [125,126], and its precursor such as betaine was also associated with cardiovascular diseases and type 2 diabetes [127,128]. While experimental studies in animals suggest the causality of gut microbiota in development of metabolic diseases, prospective cohort studies among healthy individuals are warranted to investigate how altered or changing gut microbiota and their genome (metagenome) are associated with risk of complex diseases.
Recently, several studies identified host genetic variants associated gut microbiota [16,17,18], and a study showed an interaction between host genetics and diet in regulating microbiome composition [16]. The study identified a genome-wide significant variant in LCT region that determines gut microbiome Bifidobacterium abundance, and the variant was also differently associated with dairy intake [16]. In a study of elderly Mediterranean population, an association of the LCT variant and obesity was significantly modified by dairy lactose and milk intake [129], suggesting that changes in the gut microbiota across the LCT genotype might be involved in differences in caloric extraction of ingested food and the risk of obesity [129]. Further studies considering gut-microbiome, those related genetic variants, and dietary habit would be warranted.

7. Challenges and Opportunities for Gene–Diet Interaction Studies

A major challenge of examining gene–diet interactions is whether observations are replicable in other populations [23,24,25]. In previous publications on gene–diet interactions on obesity, results from different populations are presented to demonstrate that the findings are replicable in other cohorts [39,40,54]. On the other hand, large-scale collaboration studies are also needed to provide a higher level of evidence and also to perform more detailed analyses including different types of dietary factors, phenotypes, and different obesity GRSs. Within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, authors have collected results from multiple cohorts and meta-analyzed results to examine gene–diet interactions [130,131]. Population-wide biobanks have been established in several countries such the UK [132] and China [133]. Research based on the large sample size of biobanks with the electronic health records, available data on habitual dietary intake (such as using food frequency questionnaires), and other health data will significantly contribute to identification of gene–diet interactions on various health outcomes. Also, it is of importance to provide robust evidence on gene–environment interaction from a large-scale collaboration study in participants of randomized clinical trials. Different dietary interventions were introduced in each study, and testing gene–diet interactions is also challenging.
Other challenges include imprecise assessment of environmental exposures, difficulty in defining the causal variants, and devising standardized statistical models to detect interactions in different patterns [23,24,25]. A study [45] introduced a negative control variable to control for residual confounding factors, and also considered effects of ‘heteroscedasticity’ since overweight and obese individuals have a wider variance in BMI than non-overweight individuals, and these differences in BMI may create false positive evidence of interaction.

8. Conclusions

The obesity epidemic during the past decades has coincided with a profound shift of unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Increasing evidence has shown the potential effects of gene–environment interactions on obesity. Data from dietary intervention trials suggest that changes in adiposity and metabolic response to low-calorie weight-loss diets could be significantly modified by genetic variants, especially those related to obesity, type 2 diabetes, metabolism and food preference. While further external replication and a large-scale analysis would be necessary to confirm these findings, the positive results obtained thus far tend to support precision dietary interventions considering genetic predisposition to diseases, genetic variants determining dietary preference and metabolites, as well as phenotypes and intermediate metabolites. The idea of precision nutrition and dietary intervention is considered as each dietary habit and advice is individually tailored to prevent chronic diseases on the basis of genomic background, habitual food and beverage consumption, nutrient intake (especially those contributing to risks of diseases), and also a person’s metabolomics, microbiome, and other omics profiles. On the other hand, few studies investigate potential roles of metabolomics mechanisms and gut microbiome that may act at the interface of genetic variation and environment in affecting obesity and health. Research integrating data on genes, dietary habits, metabolites and gut-microbiome in investigation of human health would be one of the most exciting areas in precision nutrition in the near future.

Acknowledgments

The study is supported by NIH grants from the National Heart, Lung, and Blood Institute (HL071981, HL034594, HL126024), the National Institute of Diabetes and Digestive and Kidney Diseases (DK091718, DK100383, DK078616), the Boston Obesity Nutrition Research Center (DK46200), and United States–Israel Binational Science Foundation Grant 2011036. Lu Qi was a recipient of the American Heart Association Scientist Development Award (0730094N). Yoriko Heianza was a recipient of a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science. The sponsors had no role in the design or conduct of the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hruby, A.; Manson, J.E.; Qi, L.; Malik, V.S.; Rimm, E.B.; Sun, Q.; Willett, W.C.; Hu, F.B. Determinants and consequences of obesity. Am. J. Public Health 2016, 106, 1656–1662. [Google Scholar] [CrossRef] [PubMed]
  2. Walter, S.; Mejia-Guevara, I.; Estrada, K.; Liu, S.Y.; Glymour, M.M. Association of a genetic risk score with body mass index across different birth cohorts. JAMA 2016, 316, 63–69. [Google Scholar] [CrossRef] [PubMed]
  3. Kilpelainen, T.O.; Carli, J.F.; Skowronski, A.A.; Sun, Q.; Kriebel, J.; Feitosa, M.F.; Hedman, A.K.; Drong, A.W.; Hayes, J.E.; Zhao, J.; et al. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels. Nat. Commun. 2016, 7, 10494. [Google Scholar] [CrossRef] [PubMed]
  4. Tanaka, T.; Ngwa, J.S.; van Rooij, F.J.; Zillikens, M.C.; Wojczynski, M.K.; Frazier-Wood, A.C.; Houston, D.K.; Kanoni, S.; Lemaitre, R.N.; Luan, J.; et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. Am. J. Clin. Nutr. 2013, 97, 1395–1402. [Google Scholar] [CrossRef] [PubMed]
  5. Chu, A.Y.; Workalemahu, T.; Paynter, N.P.; Rose, L.M.; Giulianini, F.; Tanaka, T.; Ngwa, J.S.; Qi, Q.; Curhan, G.C.; Rimm, E.B.; et al. Novel locus including FGF21 is associated with dietary macronutrient intake. Hum. Mol. Genet. 2013, 22, 1895–1902. [Google Scholar] [CrossRef] [PubMed]
  6. Qi, Q.; Kilpelainen, T.O.; Downer, M.K.; Tanaka, T.; Smith, C.E.; Sluijs, I.; Sonestedt, E.; Chu, A.Y.; Renstrom, F.; Lin, X.; et al. FTO genetic variants, dietary intake and body mass index: Insights from 177,330 individuals. Hum. Mol. Genet. 2014, 23, 6961–6972. [Google Scholar] [CrossRef] [PubMed]
  7. Treur, J.L.; Boomsma, D.I.; Ligthart, L.; Willemsen, G.; Vink, J.M. Heritability of high sugar consumption through drinks and the genetic correlation with substance use. Am. J. Clin. Nutr. 2016, 104, 1144–1150. [Google Scholar] [CrossRef] [PubMed]
  8. Pallister, T.; Sharafi, M.; Lachance, G.; Pirastu, N.; Mohney, R.P.; MacGregor, A.; Feskens, E.J.; Duffy, V.; Spector, T.D.; Menni, C. Food preference patterns in a UK twin cohort. Twin Res. Hum. Genet. 2015, 18, 793–805. [Google Scholar] [CrossRef] [PubMed]
  9. Speliotes, E.K.; Willer, C.J.; Berndt, S.I.; Monda, K.L.; Thorleifsson, G.; Jackson, A.U.; Lango Allen, H.; Lindgren, C.M.; Luan, J.; Magi, R.; et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 2010, 42, 937–948. [Google Scholar] [CrossRef] [PubMed]
  10. Fall, T.; Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Mol. Cell. Endocrinol. 2014, 382, 740–757. [Google Scholar] [CrossRef] [PubMed]
  11. Locke, A.E.; Kahali, B.; Berndt, S.I.; Justice, A.E.; Pers, T.H.; Day, F.R.; Powell, C.; Vedantam, S.; Buchkovich, M.L.; Yang, J.; et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015, 518, 197–206. [Google Scholar] [CrossRef] [PubMed]
  12. Grishkevich, V.; Yanai, I. The genomic determinants of genotype x environment interactions in gene expression. Trends Genet. 2013, 29, 479–487. [Google Scholar] [CrossRef] [PubMed]
  13. Kettunen, J.; Demirkan, A.; Wurtz, P.; Draisma, H.H.; Haller, T.; Rawal, R.; Vaarhorst, A.; Kangas, A.J.; Lyytikainen, L.P.; Pirinen, M.; et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat. Commun. 2016, 7, 11122. [Google Scholar] [CrossRef] [PubMed]
  14. Burkhardt, R.; Kirsten, H.; Beutner, F.; Holdt, L.M.; Gross, A.; Teren, A.; Tonjes, A.; Becker, S.; Krohn, K.; Kovacs, P.; et al. Integration of genome-wide SNP data and gene-expression profiles reveals six novel loci and regulatory mechanisms for amino acids and acylcarnitines in whole blood. PLoS Genet. 2015, 11, e1005510. [Google Scholar] [CrossRef] [PubMed]
  15. Demirkan, A.; Henneman, P.; Verhoeven, A.; Dharuri, H.; Amin, N.; van Klinken, J.B.; Karssen, L.C.; de Vries, B.; Meissner, A.; Goraler, S.; et al. Insight in genome-wide association of metabolite quantitative traits by exome sequence analyses. PLoS Genet. 2015, 11, e1004835. [Google Scholar] [CrossRef] [PubMed]
  16. Bonder, M.J.; Kurilshikov, A.; Tigchelaar, E.F.; Mujagic, Z.; Imhann, F.; Vila, A.V.; Deelen, P.; Vatanen, T.; Schirmer, M.; Smeekens, S.P.; et al. The effect of host genetics on the gut microbiome. Nat. Genet. 2016, 48, 1407–1412. [Google Scholar] [CrossRef] [PubMed]
  17. Turpin, W.; Espin-Garcia, O.; Xu, W.; Silverberg, M.S.; Kevans, D.; Smith, M.I.; Guttman, D.S.; Griffiths, A.; Panaccione, R.; Otley, A.; et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 2016, 48, 1413–1417. [Google Scholar] [CrossRef] [PubMed]
  18. Wang, J.; Thingholm, L.B.; Skieceviciene, J.; Rausch, P.; Kummen, M.; Hov, J.R.; Degenhardt, F.; Heinsen, F.A.; Ruhlemann, M.C.; Szymczak, S.; et al. Genome-wide association analysis identifies variation in vitamin d receptor and other host factors influencing the gut microbiota. Nat. Genet. 2016, 48, 1396–1406. [Google Scholar] [CrossRef] [PubMed]
  19. Sonnenburg, J.L.; Backhed, F. Diet-microbiota interactions as moderators of human metabolism. Nature 2016, 535, 56–64. [Google Scholar] [CrossRef] [PubMed]
  20. Scott, R.A.; Lagou, V.; Welch, R.P.; Wheeler, E.; Montasser, M.E.; Luan, J.; Magi, R.; Strawbridge, R.J.; Rehnberg, E.; Gustafsson, S.; et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 2012, 44, 991–1005. [Google Scholar] [CrossRef] [PubMed]
  21. Fuchsberger, C.; Flannick, J.; Teslovich, T.M.; Mahajan, A.; Agarwala, V.; Gaulton, K.J.; Ma, C.; Fontanillas, P.; Moutsianas, L.; McCarthy, D.J.; et al. The genetic architecture of type 2 diabetes. Nature 2016, 536, 41–47. [Google Scholar] [CrossRef] [PubMed]
  22. Manolio, T.A.; Collins, F.S.; Cox, N.J.; Goldstein, D.B.; Hindorff, L.A.; Hunter, D.J.; McCarthy, M.I.; Ramos, E.M.; Cardon, L.R.; Chakravarti, A.; et al. Finding the missing heritability of complex diseases. Nature 2009, 461, 747–753. [Google Scholar] [CrossRef] [PubMed]
  23. Qi, L. Gene-diet interaction and weight loss. Curr. Opin. Lipidol. 2014, 25, 27–34. [Google Scholar] [CrossRef] [PubMed]
  24. Qi, L. Gene-diet interactions in complex disease: Current findings and relevance for public health. Curr. Nutr. Rep. 2012, 1, 222–227. [Google Scholar] [CrossRef] [PubMed]
  25. Qi, L.; Cho, Y.A. Gene-environment interaction and obesity. Nutr. Rev. 2008, 66, 684–694. [Google Scholar] [CrossRef] [PubMed]
  26. Franks, P.W.; Pare, G. Putting the genome in context: Gene-environment interactions in type 2 diabetes. Curr. Diabetes Rep. 2016, 16, 57. [Google Scholar] [CrossRef] [PubMed]
  27. Hunter, D.J. Gene-environment interactions in human diseases. Nat. Rev. Genet. 2005, 6, 287–298. [Google Scholar] [CrossRef] [PubMed]
  28. Fabbrini, E.; Yoshino, J.; Yoshino, M.; Magkos, F.; Tiemann Luecking, C.; Samovski, D.; Fraterrigo, G.; Okunade, A.L.; Patterson, B.W.; Klein, S. Metabolically normal obese people are protected from adverse effects following weight gain. J. Clin. Investig. 2015, 125, 787–795. [Google Scholar] [CrossRef] [PubMed]
  29. Heianza, Y.; Arase, Y.; Tsuji, H.; Fujihara, K.; Saito, K.; Hsieh, S.D.; Tanaka, S.; Kodama, S.; Hara, S.; Sone, H. Metabolically healthy obesity, presence or absence of fatty liver, and risk of type 2 diabetes in japanese individuals: Toranomon hospital health management center study 20 (topics 20). J. Clin. Endocrinol. Metab. 2014, 99, 2952–2960. [Google Scholar] [CrossRef] [PubMed]
  30. Stefan, N.; Kantartzis, K.; Machann, J.; Schick, F.; Thamer, C.; Rittig, K.; Balletshofer, B.; Machicao, F.; Fritsche, A.; Haring, H.U. Identification and characterization of metabolically benign obesity in humans. Arch. Intern. Med. 2008, 168, 1609–1616. [Google Scholar] [CrossRef] [PubMed]
  31. Neeland, I.J.; Turer, A.T.; Ayers, C.R.; Powell-Wiley, T.M.; Vega, G.L.; Farzaneh-Far, R.; Grundy, S.M.; Khera, A.; McGuire, D.K.; de Lemos, J.A. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA 2012, 308, 1150–1159. [Google Scholar] [CrossRef] [PubMed]
  32. Kramer, C.K.; Zinman, B.; Retnakaran, R. Are metabolically healthy overweight and obesity benign conditions? A systematic review and meta-analysis. Ann. Intern. Med. 2013, 159, 758–769. [Google Scholar] [CrossRef] [PubMed]
  33. Bell, J.A.; Kivimaki, M.; Hamer, M. Metabolically healthy obesity and risk of incident type 2 diabetes: A meta-analysis of prospective cohort studies. Obes. Rev. 2014, 15, 504–515. [Google Scholar] [CrossRef] [PubMed]
  34. Roberson, L.L.; Aneni, E.C.; Maziak, W.; Agatston, A.; Feldman, T.; Rouseff, M.; Tran, T.; Blaha, M.J.; Santos, R.D.; Sposito, A.; et al. Beyond BMI: The “metabolically healthy obese” phenotype & its association with clinical/subclinical cardiovascular disease and all-cause mortality—A systematic review. BMC Public Health 2014, 14, 14. [Google Scholar]
  35. Zheng, R.; Zhou, D.; Zhu, Y. The long-term prognosis of cardiovascular disease and all-cause mortality for metabolically healthy obesity: A systematic review and meta-analysis. J. Epidemiol. Community Health 2016, 70, 1024–1031. [Google Scholar] [CrossRef] [PubMed]
  36. Padwal, R.; Leslie, W.D.; Lix, L.M.; Majumdar, S.R. Relationship among body fat percentage, body mass index, and all-cause mortality: A cohort study. Ann. Intern. Med. 2016, 164, 532–541. [Google Scholar] [CrossRef] [PubMed]
  37. Sahakyan, K.R.; Somers, V.K.; Rodriguez-Escudero, J.P.; Hodge, D.O.; Carter, R.E.; Sochor, O.; Coutinho, T.; Jensen, M.D.; Roger, V.L.; Singh, P.; et al. Normal-weight central obesity: Implications for total and cardiovascular mortality. Ann. Intern. Med. 2015, 163, 827–835. [Google Scholar] [CrossRef] [PubMed]
  38. Ried, J.S.; Jeff, M.J.; Chu, A.Y.; Bragg-Gresham, J.L.; van Dongen, J.; Huffman, J.E.; Ahluwalia, T.S.; Cadby, G.; Eklund, N.; Eriksson, J.; et al. A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape. Nat. Commun. 2016, 7, 13357. [Google Scholar] [CrossRef] [PubMed]
  39. Qi, Q.; Chu, A.Y.; Kang, J.H.; Jensen, M.K.; Curhan, G.C.; Pasquale, L.R.; Ridker, P.M.; Hunter, D.J.; Willett, W.C.; Rimm, E.B.; et al. Sugar-sweetened beverages and genetic risk of obesity. N. Engl. J. Med. 2012, 367, 1387–1396. [Google Scholar] [CrossRef] [PubMed]
  40. Brunkwall, L.; Chen, Y.; Hindy, G.; Rukh, G.; Ericson, U.; Barroso, I.; Johansson, I.; Franks, P.W.; Orho-Melander, M.; Renstrom, F. Sugar-sweetened beverage consumption and genetic predisposition to obesity in 2 swedish cohorts. Am. J. Clin. Nutr. 2016, 104, 809–815. [Google Scholar] [CrossRef] [PubMed]
  41. Olsen, N.J.; Angquist, L.; Larsen, S.C.; Linneberg, A.; Skaaby, T.; Husemoen, L.L.; Toft, U.; Tjonneland, A.; Halkjaer, J.; Hansen, T.; et al. Interactions between genetic variants associated with adiposity traits and soft drinks in relation to longitudinal changes in body weight and waist circumference. Am. J. Clin. Nutr. 2016, 104, 816–826. [Google Scholar] [CrossRef] [PubMed]
  42. Qi, Q.; Chu, A.Y.; Kang, J.H.; Huang, J.; Rose, L.M.; Jensen, M.K.; Liang, L.; Curhan, G.C.; Pasquale, L.R.; Wiggs, J.L.; et al. Fried food consumption, genetic risk, and body mass index: Gene-Diet interaction analysis in three us cohort studies. BMJ 2014, 348, g1610. [Google Scholar] [CrossRef] [PubMed]
  43. Qi, Q.; Li, Y.; Chomistek, A.K.; Kang, J.H.; Curhan, G.C.; Pasquale, L.R.; Willett, W.C.; Rimm, E.B.; Hu, F.B.; Qi, L. Television watching, leisure time physical activity, and the genetic predisposition in relation to body mass index in women and men. Circulation 2012, 126, 1821–1827. [Google Scholar] [CrossRef] [PubMed]
  44. Ahmad, S.; Rukh, G.; Varga, T.V.; Ali, A.; Kurbasic, A.; Shungin, D.; Ericson, U.; Koivula, R.W.; Chu, A.Y.; Rose, L.M.; et al. Gene × physical activity interactions in obesity: Combined analysis of 111,421 individuals of european ancestry. PLoS Genet. 2013, 9, e1003607. [Google Scholar] [CrossRef] [PubMed]
  45. Tyrrell, J.; Wood, A.R.; Ames, R.M.; Yaghootkar, H.; Beaumont, R.N.; Jones, S.E.; Tuke, M.A.; Ruth, K.S.; Freathy, R.M.; Davey Smith, G.; et al. Gene-obesogenic environment interactions in the uk biobank study. Int. J. Epidemiol. 2017. [Google Scholar] [CrossRef] [PubMed]
  46. Casas-Agustench, P.; Arnett, D.K.; Smith, C.E.; Lai, C.Q.; Parnell, L.D.; Borecki, I.B.; Frazier-Wood, A.C.; Allison, M.; Chen, Y.D.; Taylor, K.D.; et al. Saturated fat intake modulates the association between an obesity genetic risk score and body mass index in two us populations. J. Acad. Nutr. Diet. 2014, 114, 1954–1966. [Google Scholar] [CrossRef] [PubMed]
  47. Celis-Morales, C.; Lyall, D.M.; Guo, Y.; Steell, L.; Llanas, D.; Ward, J.; Mackay, D.F.; Biello, S.M.; Bailey, M.E.; Pell, J.P.; et al. Sleep characteristics modify the association of genetic predisposition with obesity and anthropometric measurements in 119,679 UK biobank participants. Am. J. Clin. Nutr. 2017. [Google Scholar] [CrossRef] [PubMed]
  48. Malik, V.S.; Pan, A.; Willett, W.C.; Hu, F.B. Sugar-sweetened beverages and weight gain in children and adults: A systematic review and meta-analysis. Am. J. Clin. Nutr. 2013, 98, 1084–1102. [Google Scholar] [CrossRef] [PubMed]
  49. Te Morenga, L.; Mallard, S.; Mann, J. Dietary sugars and body weight: Systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 2012, 346, e7492. [Google Scholar] [CrossRef] [PubMed]
  50. Zheng, Y.; Li, Y.; Huang, T.; Cheng, H.L.; Campos, H.; Qi, L. Sugar-sweetened beverage intake, chromosome 9p21 variants, and risk of myocardial infarction in hispanics. Am. J. Clin. Nutr. 2016, 103, 1179–1184. [Google Scholar] [CrossRef] [PubMed]
  51. Bray, G.A.; Popkin, B.M. Dietary sugar and body weight: Have we reached a crisis in the epidemic of obesity and diabetes?: Health be damned! Pour on the sugar. Diabetes Care 2014, 37, 950–956. [Google Scholar] [CrossRef] [PubMed]
  52. Guallar-Castillon, P.; Rodriguez-Artalejo, F.; Fornes, N.S.; Banegas, J.R.; Etxezarreta, P.A.; Ardanaz, E.; Barricarte, A.; Chirlaque, M.D.; Iraeta, M.D.; Larranaga, N.L.; et al. Intake of fried foods is associated with obesity in the cohort of spanish adults from the european prospective investigation into cancer and nutrition. Am. J. Clin. Nutr. 2007, 86, 198–205. [Google Scholar] [PubMed]
  53. Corella, D.; Peloso, G.; Arnett, D.K.; Demissie, S.; Cupples, L.A.; Tucker, K.; Lai, C.Q.; Parnell, L.D.; Coltell, O.; Lee, Y.C.; et al. Apoa2, dietary fat, and body mass index: Replication of a gene-diet interaction in 3 independent populations. Arch. Intern. Med. 2009, 169, 1897–1906. [Google Scholar] [CrossRef] [PubMed]
  54. Nettleton, J.A.; Follis, J.L.; Ngwa, J.S.; Smith, C.E.; Ahmad, S.; Tanaka, T.; Wojczynski, M.K.; Voortman, T.; Lemaitre, R.N.; Kristiansson, K.; et al. Gene x dietary pattern interactions in obesity: Analysis of up to 68 317 adults of european ancestry. Hum. Mol. Genet. 2015, 24, 4728–4738. [Google Scholar] [CrossRef] [PubMed]
  55. Kilpelainen, T.O.; Qi, L.; Brage, S.; Sharp, S.J.; Sonestedt, E.; Demerath, E.; Ahmad, T.; Mora, S.; Kaakinen, M.; Sandholt, C.H.; et al. Physical activity attenuates the influence of FTO variants on obesity risk: A meta-analysis of 218,166 adults and 19,268 children. PLoS Med. 2011, 8, e1001116. [Google Scholar] [CrossRef] [PubMed]
  56. Young, A.I.; Wauthier, F.; Donnelly, P. Multiple novel gene-by-environment interactions modify the effect of FTO variants on body mass index. Nat. Commun. 2016, 7, 12724. [Google Scholar] [CrossRef] [PubMed]
  57. Zhang, T.; Zhang, H.; Li, Y.; Li, S.; Fernandez, C.; Bazzano, L.; He, J.; Xue, F.; Chen, W. Long-term impact of temporal sequence from childhood obesity to hyperinsulinemia on adult metabolic syndrome and diabetes: The bogalusa heart study. Sci. Rep. 2017, 7, 43422. [Google Scholar] [CrossRef] [PubMed]
  58. Zhang, T.; Zhang, H.; Li, Y.; Sun, D.; Li, S.; Fernandez, C.; Qi, L.; Harville, E.; Bazzano, L.; He, J.; et al. Temporal relationship between childhood body mass index and insulin and its impact on adult hypertension: The bogalusa heart study. Hypertension 2016, 68, 818–823. [Google Scholar] [CrossRef] [PubMed]
  59. Cecil, J.E.; Tavendale, R.; Watt, P.; Hetherington, M.M.; Palmer, C.N. An obesity-associated FTO gene variant and increased energy intake in children. N. Engl. J. Med. 2008, 359, 2558–2566. [Google Scholar] [CrossRef] [PubMed]
  60. Xi, B.; Shen, Y.; Zhang, M.; Liu, X.; Zhao, X.; Wu, L.; Cheng, H.; Hou, D.; Lindpaintner, K.; Liu, L.; et al. The common rs9939609 variant of the fat mass and obesity-associated gene is associated with obesity risk in children and adolescents of Beijing, China. BMC Med. Genet. 2010, 11, 107. [Google Scholar] [CrossRef] [PubMed]
  61. Yang, M.; Xu, Y.; Liang, L.; Fu, J.; Xiong, F.; Liu, G.; Gong, C.; Luo, F.; Chen, S.; Xu, C.; et al. The effects of genetic variation in FTO rs9939609 on obesity and dietary preferences in chinese han children and adolescents. PLoS ONE 2014, 9, e104574. [Google Scholar] [CrossRef] [PubMed]
  62. Muller, T.D.; Hinney, A.; Scherag, A.; Nguyen, T.T.; Schreiner, F.; Schafer, H.; Hebebrand, J.; Roth, C.L.; Reinehr, T. ‘Fat mass and obesity associated’ gene (FTO): No significant association of variant rs9939609 with weight loss in a lifestyle intervention and lipid metabolism markers in german obese children and adolescents. BMC Med. Genet. 2008, 9, 85. [Google Scholar] [CrossRef] [PubMed]
  63. Qi, Q.; Downer, M.K.; Kilpelainen, T.O.; Taal, H.R.; Barton, S.J.; Ntalla, I.; Standl, M.; Boraska, V.; Huikari, V.; Kiefte-de Jong, J.C.; et al. Dietary intake, FTO genetic variants, and adiposity: A combined analysis of over 16,000 children and adolescents. Diabetes 2015, 64, 2467–2476. [Google Scholar] [CrossRef] [PubMed]
  64. Foraita, R.; Gunther, F.; Gwozdz, W.; Reisch, L.A.; Russo, P.; Lauria, F.; Siani, A.; Veidebaum, T.; Tornaritis, M.; Iacoviello, L.; et al. Does the FTO gene interact with the socioeconomic status on the obesity development among young european children? Results from the idefics study. Int. J. Obesity 2015, 39, 1–6. [Google Scholar] [CrossRef] [PubMed]
  65. Song, J.Y.; Song, Q.Y.; Wang, S.; Ma, J.; Wang, H.J. Physical activity and sedentary behaviors modify the association between melanocortin 4 receptor gene variant and obesity in chinese children and adolescents. PLoS ONE 2017, 12, e0170062. [Google Scholar] [CrossRef] [PubMed]
  66. Riedel, C.; von Kries, R.; Fenske, N.; Strauch, K.; Ness, A.R.; Beyerlein, A. Interactions of genetic and environmental risk factors with respect to body fat mass in children: Results from the alspac study. Obesity 2013, 21, 1238–1242. [Google Scholar] [CrossRef] [PubMed]
  67. Lourenco, B.H.; Qi, L.; Willett, W.C.; Cardoso, M.A.; Team, A.S. FTO genotype, vitamin d status, and weight gain during childhood. Diabetes 2014, 63, 808–814. [Google Scholar] [CrossRef] [PubMed]
  68. Sacks, F.M.; Bray, G.A.; Carey, V.J.; Smith, S.R.; Ryan, D.H.; Anton, S.D.; McManus, K.; Champagne, C.M.; Bishop, L.M.; Laranjo, N.; et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N. Engl. J. Med. 2009, 360, 859–873. [Google Scholar] [CrossRef] [PubMed]
  69. Shai, I.; Schwarzfuchs, D.; Henkin, Y.; Shahar, D.R.; Witkow, S.; Greenberg, I.; Golan, R.; Fraser, D.; Bolotin, A.; Vardi, H.; et al. Weight loss with a low-carbohydrate, mediterranean, or low-fat diet. N. Engl. J. Med. 2008, 359, 229–241. [Google Scholar] [CrossRef] [PubMed]
  70. Heianza, Y.; Ma, W.; Huang, T.; Wang, T.; Zheng, Y.; Smith, S.R.; Bray, G.A.; Sacks, F.M.; Qi, L. Macronutrient intake-associated FGF21 genotype modifies effects of weight-loss diets on 2-year changes of central adiposity and body composition: The pounds lost trial. Diabetes Care 2016, 39, 1909–1914. [Google Scholar] [CrossRef] [PubMed]
  71. Huang, T.; Huang, J.; Qi, Q.; Li, Y.; Bray, G.A.; Rood, J.; Sacks, F.M.; Qi, L. PCSK7 genotype modifies effect of a weight-loss diet on 2-year changes of insulin resistance: The pounds lost trial. Diabetes Care 2015, 38, 439–444. [Google Scholar] [CrossRef] [PubMed]
  72. Huang, T.; Ley, S.H.; Zheng, Y.; Wang, T.; Bray, G.A.; Sacks, F.M.; Qi, L. Genetic susceptibility to diabetes and long-term improvement of insulin resistance and beta cell function during weight loss: The preventing overweight using novel dietary strategies (pounds lost) trial. Am. J. Clin. Nutr. 2016, 104, 198–204. [Google Scholar] [CrossRef] [PubMed]
  73. Huang, T.; Qi, Q.; Li, Y.; Hu, F.B.; Bray, G.A.; Sacks, F.M.; Williamson, D.A.; Qi, L. FTO genotype, dietary protein, and change in appetite: The preventing overweight using novel dietary strategies trial. Am. J. Clin. Nutr. 2014, 99, 1126–1130. [Google Scholar] [CrossRef] [PubMed]
  74. Lin, X.; Qi, Q.; Zheng, Y.; Huang, T.; Lathrop, M.; Zelenika, D.; Bray, G.A.; Sacks, F.M.; Liang, L.; Qi, L. Neuropeptide y genotype, central obesity, and abdominal fat distribution: The pounds lost trial. Am. J. Clin. Nutr. 2015, 102, 514–519. [Google Scholar] [CrossRef] [PubMed]
  75. Ma, W.; Huang, T.; Heianza, Y.; Wang, T.; Sun, D.; Tong, J.; Williamson, D.A.; Bray, G.A.; Sacks, F.M.; Qi, L. Genetic variations of circulating adiponectin levels modulate changes in appetite in response to weight-loss diets. J. Clin. Endocrinol. Metab. 2017, 102, 316–325. [Google Scholar] [CrossRef] [PubMed]
  76. Ma, W.; Huang, T.; Wang, M.; Zheng, Y.; Wang, T.; Heianza, Y.; Sun, D.; Smith, S.R.; Bray, G.A.; Sacks, F.M.; et al. Two-year changes in circulating adiponectin, ectopic fat distribution and body composition in response to weight-loss diets: The pounds lost trial. Int. J. Obes. 2016, 40, 1723–1729. [Google Scholar] [CrossRef] [PubMed]
  77. Ma, W.; Huang, T.; Zheng, Y.; Wang, M.; Bray, G.A.; Sacks, F.M.; Qi, L. Weight-loss diets, adiponectin, and changes in cardiometabolic risk in the 2-year pounds lost trial. J. Clin. Endocrinol. Metab. 2016, 101, 2415–2422. [Google Scholar] [CrossRef] [PubMed]
  78. Mattei, J.; Qi, Q.; Hu, F.B.; Sacks, F.M.; Qi, L. TCF7L2 genetic variants modulate the effect of dietary fat intake on changes in body composition during a weight-loss intervention. Am. J. Clin. Nutr. 2012, 96, 1129–1136. [Google Scholar] [CrossRef] [PubMed]
  79. Mirzaei, K.; Xu, M.; Qi, Q.; de Jonge, L.; Bray, G.A.; Sacks, F.; Qi, L. Variants in glucose- and circadian rhythm-related genes affect the response of energy expenditure to weight-loss diets: The pounds lost trial. Am. J. Clin. Nutr. 2014, 99, 392–399. [Google Scholar] [CrossRef] [PubMed]
  80. Qi, Q.; Bray, G.A.; Hu, F.B.; Sacks, F.M.; Qi, L. Weight-loss diets modify glucose-dependent insulinotropic polypeptide receptor rs2287019 genotype effects on changes in body weight, fasting glucose, and insulin resistance: The preventing overweight using novel dietary strategies trial. Am. J. Clin. Nutr. 2012, 95, 506–513. [Google Scholar] [CrossRef] [PubMed]
  81. Qi, Q.; Bray, G.A.; Smith, S.R.; Hu, F.B.; Sacks, F.M.; Qi, L. Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial: The preventing overweight using novel dietary strategies (pounds lost) trial. Circulation 2011, 124, 563–571. [Google Scholar] [CrossRef] [PubMed]
  82. Qi, Q.; Durst, R.; Schwarzfuchs, D.; Leitersdorf, E.; Shpitzen, S.; Li, Y.; Wu, H.; Champagne, C.M.; Hu, F.B.; Stampfer, M.J.; et al. CETP genotype and changes in lipid levels in response to weight-loss diet intervention in the pounds lost and direct randomized trials. J. Lipid Res. 2015, 56, 713–721. [Google Scholar] [CrossRef] [PubMed]
  83. Qi, Q.; Xu, M.; Wu, H.; Liang, L.; Champagne, C.M.; Bray, G.A.; Sacks, F.M.; Qi, L. IRS1 genotype modulates metabolic syndrome reversion in response to 2-year weight-loss diet intervention: The pounds lost trial. Diabetes Care 2013, 36, 3442–3447. [Google Scholar] [CrossRef] [PubMed]
  84. Qi, Q.; Zheng, Y.; Huang, T.; Rood, J.; Bray, G.A.; Sacks, F.M.; Qi, L. Vitamin d metabolism-related genetic variants, dietary protein intake and improvement of insulin resistance in a 2 year weight-loss trial: Pounds lost. Diabetologia 2015, 58, 2791–2799. [Google Scholar] [CrossRef] [PubMed]
  85. Wang, T.; Huang, T.; Zheng, Y.; Rood, J.; Bray, G.A.; Sacks, F.M.; Qi, L. Genetic variation of fasting glucose and changes in glycemia in response to 2-year weight-loss diet intervention: The pounds lost trial. Int. J. Obes. 2016, 40, 1164–1169. [Google Scholar] [CrossRef] [PubMed]
  86. Xu, M.; Ng, S.S.; Bray, G.A.; Ryan, D.H.; Sacks, F.M.; Ning, G.; Qi, L. Dietary fat intake modifies the effect of a common variant in the lipc gene on changes in serum lipid concentrations during a long-term weight-loss intervention trial. J. Nutr. 2015, 145, 1289–1294. [Google Scholar] [CrossRef] [PubMed]
  87. Xu, M.; Qi, Q.; Liang, J.; Bray, G.A.; Hu, F.B.; Sacks, F.M.; Qi, L. Genetic determinant for amino acid metabolites and changes in body weight and insulin resistance in response to weight-loss diets: The preventing overweight using novel dietary strategies (pounds lost) trial. Circulation 2013, 127, 1283–1289. [Google Scholar] [CrossRef] [PubMed]
  88. Zhang, X.; Qi, Q.; Bray, G.A.; Hu, F.B.; Sacks, F.M.; Qi, L. APOA5 genotype modulates 2-y changes in lipid profile in response to weight-loss diet intervention: The pounds lost trial. Am. J. Clin. Nutr. 2012, 96, 917–922. [Google Scholar] [CrossRef] [PubMed]
  89. Zhang, X.; Qi, Q.; Liang, J.; Hu, F.B.; Sacks, F.M.; Qi, L. Neuropeptide y promoter polymorphism modifies effects of a weight-loss diet on 2-year changes of blood pressure: The preventing overweight using novel dietary strategies trial. Hypertension 2012, 60, 1169–1175. [Google Scholar] [CrossRef] [PubMed]
  90. Zhang, X.; Qi, Q.; Zhang, C.; Smith, S.R.; Hu, F.B.; Sacks, F.M.; Bray, G.A.; Qi, L. FTO genotype and 2-year change in body composition and fat distribution in response to weight-loss diets: The pounds lost trial. Diabetes 2012, 61, 3005–3011. [Google Scholar] [CrossRef] [PubMed]
  91. Zheng, Y.; Ceglarek, U.; Huang, T.; Li, L.; Rood, J.; Ryan, D.H.; Bray, G.A.; Sacks, F.M.; Schwarzfuchs, D.; Thiery, J.; et al. Weight-loss diets and 2-y changes in circulating amino acids in 2 randomized intervention trials. Am. J. Clin. Nutr. 2016, 103, 505–511. [Google Scholar] [CrossRef] [PubMed]
  92. Zheng, Y.; Ceglarek, U.; Huang, T.; Wang, T.; Heianza, Y.; Ma, W.; Bray, G.A.; Thiery, J.; Sacks, F.M.; Qi, L. Plasma taurine, diabetes genetic predisposition, and changes of insulin sensitivity in response to weight-loss diets. J. Clin. Endocrinol. Metab. 2016, 101, 3820–3826. [Google Scholar] [CrossRef] [PubMed]
  93. Zheng, Y.; Huang, T.; Zhang, X.; Rood, J.; Bray, G.A.; Sacks, F.M.; Qi, L. Dietary fat modifies the effects of FTO genotype on changes in insulin sensitivity. J. Nutr. 2015, 145, 977–982. [Google Scholar] [CrossRef] [PubMed]
  94. Johnston, B.C.; Kanters, S.; Bandayrel, K.; Wu, P.; Naji, F.; Siemieniuk, R.A.; Ball, G.D.; Busse, J.W.; Thorlund, K.; Guyatt, G.; et al. Comparison of weight loss among named diet programs in overweight and obese adults: A meta-analysis. JAMA 2014, 312, 923–933. [Google Scholar] [CrossRef] [PubMed]
  95. Morris, A.P.; Voight, B.F.; Teslovich, T.M.; Ferreira, T.; Segre, A.V.; Steinthorsdottir, V.; Strawbridge, R.J.; Khan, H.; Grallert, H.; Mahajan, A.; et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 2012, 44, 981–990. [Google Scholar] [CrossRef] [PubMed]
  96. Voight, B.F.; Scott, L.J.; Steinthorsdottir, V.; Morris, A.P.; Dina, C.; Welch, R.P.; Zeggini, E.; Huth, C.; Aulchenko, Y.S.; Thorleifsson, G.; et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 2010, 42, 579–589. [Google Scholar] [CrossRef] [PubMed]
  97. Lyssenko, V.; Nagorny, C.L.; Erdos, M.R.; Wierup, N.; Jonsson, A.; Spegel, P.; Bugliani, M.; Saxena, R.; Fex, M.; Pulizzi, N.; et al. Common variant in mtnr1b associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat. Genet. 2009, 41, 82–88. [Google Scholar] [CrossRef] [PubMed]
  98. Bouatia-Naji, N.; Bonnefond, A.; Cavalcanti-Proenca, C.; Sparso, T.; Holmkvist, J.; Marchand, M.; Delplanque, J.; Lobbens, S.; Rocheleau, G.; Durand, E.; et al. A variant near mtnr1b is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat. Genet. 2009, 41, 89–94. [Google Scholar] [CrossRef] [PubMed]
  99. Rung, J.; Cauchi, S.; Albrechtsen, A.; Shen, L.; Rocheleau, G.; Cavalcanti-Proenca, C.; Bacot, F.; Balkau, B.; Belisle, A.; Borch-Johnsen, K.; et al. Genetic variant near irs1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat. Genet. 2009, 41, 1110–1115. [Google Scholar] [CrossRef] [PubMed]
  100. Zeggini, E.; Scott, L.J.; Saxena, R.; Voight, B.F.; Marchini, J.L.; Hu, T.; de Bakker, P.I.; Abecasis, G.R.; Almgren, P.; Andersen, G.; et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 2008, 40, 638–645. [Google Scholar] [CrossRef] [PubMed]
  101. Zeggini, E.; Weedon, M.N.; Lindgren, C.M.; Frayling, T.M.; Elliott, K.S.; Lango, H.; Timpson, N.J.; Perry, J.R.; Rayner, N.W.; Freathy, R.M.; et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 2007, 316, 1336–1341. [Google Scholar] [CrossRef] [PubMed]
  102. Scott, L.J.; Mohlke, K.L.; Bonnycastle, L.L.; Willer, C.J.; Li, Y.; Duren, W.L.; Erdos, M.R.; Stringham, H.M.; Chines, P.S.; Jackson, A.U.; et al. A genome-wide association study of type 2 diabetes in finns detects multiple susceptibility variants. Science 2007, 316, 1341–1345. [Google Scholar] [CrossRef] [PubMed]
  103. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research; Saxena, R.; Voight, B.F.; Lyssenko, V.; Burtt, N.P.; de Bakker, P.I.; Chen, H.; Roix, J.J.; Kathiresan, S.; Hirschhorn, J.N.; et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007, 316, 1331–1336. [Google Scholar] [PubMed]
  104. Sladek, R.; Rocheleau, G.; Rung, J.; Dina, C.; Shen, L.; Serre, D.; Boutin, P.; Vincent, D.; Belisle, A.; Hadjadj, S.; et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007, 445, 881–885. [Google Scholar] [CrossRef] [PubMed]
  105. Godino, J.G.; van Sluijs, E.M.; Marteau, T.M.; Sutton, S.; Sharp, S.J.; Griffin, S.J. Lifestyle advice combined with personalized estimates of genetic or phenotypic risk of type 2 diabetes, and objectively measured physical activity: A randomized controlled trial. PLoS Med. 2016, 13, e1002185. [Google Scholar] [CrossRef] [PubMed]
  106. Courcoulas, A.P.; Yanovski, S.Z.; Bonds, D.; Eggerman, T.L.; Horlick, M.; Staten, M.A.; Arterburn, D.E. Long-term outcomes of bariatric surgery: A national institutes of health symposium. JAMA Surg. 2014, 149, 1323–1329. [Google Scholar] [CrossRef] [PubMed]
  107. Madsbad, S.; Dirksen, C.; Holst, J.J. Mechanisms of changes in glucose metabolism and bodyweight after bariatric surgery. Lancet Diabetes Endocrinol. 2014, 2, 152–164. [Google Scholar] [CrossRef]
  108. Carlsson, L.M.; Sjoholm, K.; Karlsson, C.; Jacobson, P.; Andersson-Assarsson, J.C.; Svensson, P.A.; Larsson, I.; Hjorth, S.; Neovius, M.; Taube, M.; et al. Long-term incidence of microvascular disease after bariatric surgery or usual care in patients with obesity, stratified by baseline glycaemic status: A post-hoc analysis of participants from the swedish obese subjects study. Lancet Diabetes Endocrinol. 2017, 5, 271–279. [Google Scholar] [CrossRef]
  109. Hatoum, I.J.; Greenawalt, D.M.; Cotsapas, C.; Reitman, M.L.; Daly, M.J.; Kaplan, L.M. Heritability of the weight loss response to gastric bypass surgery. J. Clin. Endocrinol. Metab 2011, 96, E1630–E1633. [Google Scholar] [CrossRef] [PubMed]
  110. Rinella, E.S.; Still, C.; Shao, Y.; Wood, G.C.; Chu, X.; Salerno, B.; Gerhard, G.S.; Ostrer, H. Genome-wide association of single-nucleotide polymorphisms with weight loss outcomes after roux-en-y gastric bypass surgery. J. Clin. Endocrinol. Metab. 2013, 98, E1131–E1136. [Google Scholar] [CrossRef] [PubMed]
  111. Hatoum, I.J.; Greenawalt, D.M.; Cotsapas, C.; Daly, M.J.; Reitman, M.L.; Kaplan, L.M. Weight loss after gastric bypass is associated with a variant at 15Q26.1. Am. J. Human Genet. 2013, 92, 827–834. [Google Scholar] [CrossRef] [PubMed]
  112. Livingstone, K.M.; Celis-Morales, C.; Papandonatos, G.D.; Erar, B.; Florez, J.C.; Jablonski, K.A.; Razquin, C.; Marti, A.; Heianza, Y.; Huang, T.; et al. FTO genotype and weight loss: Systematic review and meta-analysis of 9563 individual participant data from eight randomised controlled trials. BMJ 2016, 354, i4707. [Google Scholar] [CrossRef] [PubMed]
  113. The Look AHEAD Research Group. Prospective association of a genetic risk score and lifestyle intervention with cardiovascular morbidity and mortality among individuals with type 2 diabetes: The look ahead randomised controlled trial. Diabetologia 2015, 58, 1803–1813. [Google Scholar]
  114. Wang, T.J.; Larson, M.G.; Vasan, R.S.; Cheng, S.; Rhee, E.P.; McCabe, E.; Lewis, G.D.; Fox, C.S.; Jacques, P.F.; Fernandez, C.; et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 2011, 17, 448–453. [Google Scholar] [CrossRef] [PubMed]
  115. Wurtz, P.; Tiainen, M.; Makinen, V.P.; Kangas, A.J.; Soininen, P.; Saltevo, J.; Keinanen-Kiukaanniemi, S.; Mantyselka, P.; Lehtimaki, T.; Laakso, M.; et al. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care 2012, 35, 1749–1756. [Google Scholar] [CrossRef] [PubMed]
  116. Cheng, S.; Rhee, E.P.; Larson, M.G.; Lewis, G.D.; McCabe, E.L.; Shen, D.; Palma, M.J.; Roberts, L.D.; Dejam, A.; Souza, A.L.; et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 2012, 125, 2222–2231. [Google Scholar] [CrossRef] [PubMed]
  117. Shah, S.H.; Crosslin, D.R.; Haynes, C.S.; Nelson, S.; Turer, C.B.; Stevens, R.D.; Muehlbauer, M.J.; Wenner, B.R.; Bain, J.R.; Laferrere, B.; et al. Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss. Diabetologia 2012, 55, 321–330. [Google Scholar] [CrossRef] [PubMed]
  118. Park, S.; Sadanala, K.C.; Kim, E.K. A metabolomic approach to understanding the metabolic link between obesity and diabetes. Mol. Cells 2015, 38, 587–596. [Google Scholar] [CrossRef] [PubMed]
  119. Guasch-Ferré, M.; Hruby, A.; Toledo, E.; Clish, C.B.; Martínez-González, M.A.; Salas-Salvadó, J.; Hu, F.B. Metabolomics in prediabetes and diabetes: A systematic review and meta-analysis. Diabetes Care 2016, 39, 833–846. [Google Scholar] [CrossRef] [PubMed]
  120. Le Chatelier, E.; Nielsen, T.; Qin, J.; Prifti, E.; Hildebrand, F.; Falony, G.; Almeida, M.; Arumugam, M.; Batto, J.M.; Kennedy, S.; et al. Richness of human gut microbiome correlates with metabolic markers. Nature 2013, 500, 541–546. [Google Scholar] [CrossRef] [PubMed]
  121. Turnbaugh, P.J.; Hamady, M.; Yatsunenko, T.; Cantarel, B.L.; Duncan, A.; Ley, R.E.; Sogin, M.L.; Jones, W.J.; Roe, B.A.; Affourtit, J.P.; et al. A core gut microbiome in obese and lean twins. Nature 2009, 457, 480–484. [Google Scholar] [CrossRef] [PubMed]
  122. Zhernakova, A.; Kurilshikov, A.; Bonder, M.J.; Tigchelaar, E.F.; Schirmer, M.; Vatanen, T.; Mujagic, Z.; Vila, A.V.; Falony, G.; Vieira-Silva, S.; et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 2016, 352, 565–569. [Google Scholar] [CrossRef] [PubMed]
  123. Cotillard, A.; Kennedy, S.P.; Kong, L.C.; Prifti, E.; Pons, N.; Le Chatelier, E.; Almeida, M.; Quinquis, B.; Levenez, F.; Galleron, N.; et al. Dietary intervention impact on gut microbial gene richness. Nature 2013, 500, 585–588. [Google Scholar] [CrossRef] [PubMed]
  124. Wu, G.D.; Chen, J.; Hoffmann, C.; Bittinger, K.; Chen, Y.Y.; Keilbaugh, S.A.; Bewtra, M.; Knights, D.; Walters, W.A.; Knight, R.; et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 2011, 334, 105–108. [Google Scholar] [CrossRef] [PubMed]
  125. Wang, Z.; Klipfell, E.; Bennett, B.J.; Koeth, R.; Levison, B.S.; Dugar, B.; Feldstein, A.E.; Britt, E.B.; Fu, X.; Chung, Y.M.; et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2011, 472, 57–63. [Google Scholar] [CrossRef] [PubMed]
  126. Tang, W.H.; Wang, Z.; Levison, B.S.; Koeth, R.A.; Britt, E.B.; Fu, X.; Wu, Y.; Hazen, S.L. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 2013, 368, 1575–1584. [Google Scholar] [CrossRef] [PubMed]
  127. Wang, Z.; Tang, W.H.; Buffa, J.A.; Fu, X.; Britt, E.B.; Koeth, R.A.; Levison, B.S.; Fan, Y.; Wu, Y.; Hazen, S.L. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide. Eur. Heart J. 2014, 35, 904–910. [Google Scholar] [CrossRef] [PubMed]
  128. Walford, G.A.; Ma, Y.; Clish, C.; Florez, J.C.; Wang, T.J.; Gerszten, R.E.; Diabetes Prevention Program Research Group. Metabolite profiles of diabetes incidence and intervention response in the diabetes prevention program. Diabetes 2016, 65, 1424–1433. [Google Scholar] [CrossRef] [PubMed]
  129. Corella, D.; Arregui, M.; Coltell, O.; Portoles, O.; Guillem-Saiz, P.; Carrasco, P.; Sorli, J.V.; Ortega-Azorin, C.; Gonzalez, J.I.; Ordovas, J.M. Association of the LCT-13910C>T polymorphism with obesity and its modulation by dairy products in a mediterranean population. Obesity 2011, 19, 1707–1714. [Google Scholar] [CrossRef] [PubMed]
  130. Dashti, H.S.; Follis, J.L.; Smith, C.E.; Tanaka, T.; Garaulet, M.; Gottlieb, D.J.; Hruby, A.; Jacques, P.F.; Kiefte-de Jong, J.C.; Lamon-Fava, S.; et al. Gene-environment interactions of circadian-related genes for cardiometabolic traits. Diabetes Care 2015, 38, 1456–1466. [Google Scholar] [CrossRef] [PubMed]
  131. Smith, C.E.; Follis, J.L.; Nettleton, J.A.; Foy, M.; Wu, J.H.; Ma, Y.; Tanaka, T.; Manichakul, A.W.; Wu, H.; Chu, A.Y.; et al. Dietary fatty acids modulate associations between genetic variants and circulating fatty acids in plasma and erythrocyte membranes: Meta-Analysis of nine studies in the charge consortium. Mol. Nutr. Food Res. 2015, 59, 1373–1383. [Google Scholar] [CrossRef] [PubMed]
  132. UK Biobank. Available online: http://www.ukbiobank.ac.uk/ (accessed on 28 March 2017).
  133. Lv, J.; Yu, C.; Guo, Y.; Bian, Z.; Yang, L.; Chen, Y.; Tang, X.; Zhang, W.; Qian, Y.; Huang, Y.; et al. Adherence to healthy lifestyle and cardiovascular diseases in the chinese population. J. Am. Coll. Cardiol. 2017, 69, 1116–1125. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Unfavorable lifestyle and dietary factors that may accelerate risk of obesity among individuals genetically at high risk.
Table 1. Unfavorable lifestyle and dietary factors that may accelerate risk of obesity among individuals genetically at high risk.
FactorsReferences
High intake of sugar-sweetened beverages[39,40,41]
High intake of fried food[42]
High saturated fatty acids intake[46]
A sedentary lifestyle (indicated by prolonged TV watching)[43,45]
Sleep characteristics[47]
Physically inactive lifestyle[43,44,45]
Table
Table 2. Genetic variant that may alter effect of low-fat/high-carbohydrate and high-protein weight-loss diets on obesity and metabolic risk factors among overweight and obese individuals.
Table 2. Genetic variant that may alter effect of low-fat/high-carbohydrate and high-protein weight-loss diets on obesity and metabolic risk factors among overweight and obese individuals.
Low-Fat/High-Carbohydrate Diet or High-Fat/Low-Carbohydrate DietHigh- or Low-Protein Diet
Genetic VariantsOutcomesGenetic VariantsOutcomes
Diabetes genetic risk score [85]Glycemic traitsDiabetes genetic risk score [72]Insulin resitence; Insulin secretion
IRS1 rs1522813, rs2943641 [81,83]Insulin resistance; Metabolic syndrome; Body weight;DHCR7 rs12785878 [84]Insulin resitence
FTO rs1558902 [93]Insulin resistanceFTO rs9939609, rs1558902 [73,90]Body composition and fat distribution; Appetite
GIPR rs2287019 [80]Glycemic traits; Insulin resistance
CRY2 rs11605924, MTNR1B rs10830963 [79]Energy expenditure
TCF7L2 rs12255372 [78]Body composition
PCSK7 rs236918 [71]Insulin resistance
APOA5 rs964184 [88]Lipid profiles
LIPC rs2070895 [86]Lipid profiles
CETP rs3764261 [82]Lipid profiles
NPY rs16147 [89]Blood pressure
PPM1K rs1440581 [87]Insulin resistance; body weight
FGF21 rs838147 [70]Body composition
Adiponectin GRS [75]Appetite

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top