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Nutrigenetic approaches in obesity and weight loss
Chapter 40
Nutrigenetic approaches in obesity and weight loss Omar Ramos-Lopez1, 2 and J. Alfredo Martinez1, 3, 4, 5 1
Department of Nutrition, Food Science and Physiology, University of Navarra, and Center for Nutrition Research, University of Navarra,
Pamplona, Spain; 2Faculty of Medicine and Psychology, Autonomous University of Baja California, Tijuana, BC, Mexico; 3CIBERobn, Physiopathology of Obesity, Carlos III Institute, Madrid, Spain; 4Navarra Institute for Health Research (IdiSNA), Pamplona, Spain; 5Madrid Institute of Advanced Studies (IMDEA Food), Madrid, Spain
Introduction Obesity epidemic is one of the most important health challenges worldwide, as denoted by a recent report of the World Health Organization, revealing that more than 39% of adults presented overweight or obesity (about 1.9 billion people) [1]. According to the Global Burden of Disease study, excessive body weight accounted for 4 million deaths, among adults in 195 Countries over 25 Years [2]. Genetic factors may contribute to the onset and development of excessive adiposity and accompanying comorbidities, by affecting energy homeostasis and body weight regulation [3]. Nutrigenetic studies are enabling to clarify the involvement of gene-diet interactions, in determining specific adiposity phenotypes, and modulating therapy outcomes [4]. Herein, we review genetic variants and biomarkers related to obesity and weight loss, which may serve to understand disease etiology and envisage future therapeutic targets and innovative treatments (Fig. 40.1).
Gene-diet interactions and obesity predisposition Single nucleotide polymorphisms (SNPs) are the most studied genetic variants in the field of precision nutrition [5]. Multiple SNPs are associated with obesity predisposition through interactions with dietary factors (Table 40.1). Relevant interactions between polymorphisms located at lipidmetabolism genes (PPARG, APOA5, APOA2, and APOB) and high-fat diets, were found in relation to greater adiposity markers. Interestingly, SNP rs2301241 in TXN gene (acting as an antioxidant), was associated with higher waist
circumference (WC) values in subjects with low vitamin E intakes. Obesity risk was significantly higher in T-allele carriers of 13,910 C > T polymorphism (rs4988235), upstream LCT gene, only among subjects consuming moderate or high amounts of lactose (Table 40.1). Because the magnitude of associations between individual SNPs and adiposity traits is generally modest, studies using genetic risk scores (GRS), have examined the additive effect of multiple loci and diet interactions [6]. Thus, a validated GRS for obesity was associated with higher BMI and WC values among individuals consuming high amounts of fats, compared to those with low-fat intakes [7]. Also, higher intakes of animal protein, saturated fat, and carbohydrates were positively associated with greater percentages of body fat mass, among individuals carrying a high genetic risk group for obesity [8]. Nominally significant interactions were detected between BMI-associated GRS and protein intake, on obesity and fat mass among women within the Malmö Diet and Cancer Study [9]. Nevertheless, a longitudinal analysis evidenced no relationships between adiposity-associated GRS and dietary protein, in relation to subsequent change in body weight and waist circumference in three different Danish cohorts [10]. The role of micronutrient status in this field has also been explored. For instance, a diet with a high content of ascorbic acid was associated with higher WC gain, among people genetically predisposed to abdominal obesity [11]. On the contrary, a significant interaction between a GRS from six WC-related SNPs and dietary calcium was reported concerning WC changes, where each risk allele was associated with greater WC reductions per 1000 mg of calcium intake [12].
Precision Medicine for Investigators, Practitioners and Providers. https://doi.org/10.1016/B978-0-12-819178-1.00040-X Copyright © 2020 Elsevier Inc. All rights reserved.
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FIGURE 40.1 Nutrigenetic approaches in precision nutrition, for prevention and management of obesity and associated chronic diseases.
TABLE 40.1 Nutrigenetic examples of SNPs-diet interactions involved in obesity predisposition. Gene
Polymorphism
Allele
Diet interaction
Main findings
Reference
FTO
rs8050136
A
High carbohydrate
Increased obesity risk
54
LCT
rs4988235
T
High lactose
Increased obesity risk
55
PPARG
rs1801282
G
High fat
Higher BMI
56
TXN
rs2301241
T
Low vitamin E
Higher WC
57
ADAM17
rs10495563
A
Low n-6 PUFA
Increased obesity risk
58
TNFA
rs1800629
A
High fat
Increased obesity risk
59
APOA5
rs662799
T
High fat
Higher adiposity markers
60
LEPR
rs1137101
G
High SFA/High fat
Increased obesity risk
61
APOB
rs1469513
G
High fat
Increased obesity risk
62
APOA2
rs5082
C
High fat dairy foods
Higher BMI
63
ADAM17, ADAM metallopeptidase domain 17; APOA2, apolipoprotein A2; APOA5, apolipoprotein A5; APOB, apolipoprotein B; BMI, body mass index; FTO, fat mass and obesity associated; LCT, lactase; LEPR, leptin receptor; PPARG, peroxisome proliferator activated receptor gamma; PUFA, polyunsaturated fatty acids; SFA, saturated fatty acids; TNFA, tumor necrosis factor a; TXN, thioredoxin; WC, waist circumference.
The association of a GRS constructed from 32 BMIassociated variants with adiposity, was strengthened with greater intake of fried foods and sugar-sweetened beverages, in three American cohort studies [13,14]. Similarly, an increased risk for obesity was found among individuals with low habitual coffee consumption, who were genetically predisposed to obesity in the basis of a GRS calculated from 77 BMI-related loci [15]. Under the assumption that the assessment of dietary patterns provides more reliable information regarding real food intake, compared to particular macronutrient consumption, higher Mediterranean dietary pattern adherence
was associated with decreased obesity risk, in subjects carrying high GRS according to FTO polymorphisms [16]. Associations between genetic predisposition and obesity traits were stronger with a healthier diet (based on habitual consumption of whole grains, fish, fruits, vegetables, and nuts/seeds), in 18 cohorts of European ancestry [17]. The increment in BMI was smaller among individuals with a strong genetic predisposition to obesity, categorized in the highest tertiles of three diet quality scores (Alternative Healthy Eating Index 2010 [AHEI-2010]; Alternative Mediterranean Diet score [AMED]; and the Dietary Approach to Stop Hypertension [DASH]) [18]. Genetic
Nutrigenetic approaches in obesity and weight loss Chapter | 40
association with weight gain was also significantly attenuated, with increasing adherence to the AHEI-2010 dietary score in two prospective cohort studies [19].
Gene-diet interactions involving weight loss and adiposity outcomes Potential gene-diet interactions have also been reported to influence the heterogeneity of adiposity outcomes (Table 40.2). Investigations include SNPs mapped to genes involved in the regulation of critical physiological processes, such as circadian rhythm, inflammatory response, lipid metabolism, insulin signaling, amino acid breakdown, and blood glucose homeostasis (Table 40.2). Greater body fat losses were reported in highly sensitive carriers of obesity GRS in response to dietary therapy, in a large Korean population [20]. Also, highest diabetessusceptibility loci were associated with greater WC reductions, after 1-year of intensive lifestyle advice within the Look AHEAD (Action for Health in Diabetes) clinical trial [21]. Likewise, potentially modest benefits in weight loss and physical activity were found among participants with higher GRS for coronary artery disease, who received risk-reducing strategies based on diet and exercise [22]. Furthermore, obese and overweight Spanish adolescents with lower obesity GRS, evidenced greater benefits on weight loss and metabolic profile improvements, after 3 months of a multidisciplinary intervention program [23]. On the other hand, changes in body weight over a 5-year lifestyle intervention were not influenced by BMI-
411
related GRS in a middle-aged Danish cohort [24]. Moreover, diabetes genetic risk counseling did not significantly modify mean weight loss, among overweight individuals under a validated diabetes prevention program, designed to improve diet quality and physical activity level [25]. Meanwhile, GRS comprising 15 SNP previously associated with childhood BMI, did not influence changes in BMI or cardiometabolic traits in Danish Children, following a lifestyle intervention [26].
The impact of genetic information disclosure on obesity management The Food4Me European randomized controlled trial revealed that disclosure of information concerning fat-mass and obesity-associated (FTO) genotype risk, had a greater effect on changes in adiposity markers, compared with nonpersonalized intervention group [27]. Similarly, the Coriell Personalized Medicine Collaborative trial reported that individuals receiving their FTO genotype alone had greater intentions to lose body weight at follow-up than those who received no risk genetic advice [28]. This effect was enhanced in participants carrying a high genetic risk. Genetic information has also been included in nutrigenetic tests, aimed at assessing the effect on changing certain obesity-related dietary behaviors. Apolipoprotein E (APOE)-based personalized nutrition resulted in a greater reduction in saturated fat intake (percentage of total energy) than did standard dietary advice, although no differences by APOE genotypes (E4þ vs. E4) were found [29]. Greater
TABLE 40.2 Nutrigenetic trials analyzing SNPs-diet interactions involved in adiposity outcomes in response to nutritional interventions. Gene
Polymorphism
Allele
Diet interaction
Main outcomes
Reference
FTO
rs1558902
A
High protein
Greater weight loss
64
TFAP2B
rs987237
G
High protein
Higher weight regains
65
MTNR1B
rs10830963
G
High protein
Lower weight loss in women
66
IL6
rs2069827
C
Mediterranean diet
Lower weight gains
67
IRS1
rs2943641
C
High carbohydrate
Greater weight loss
68
PPM1K
rs1440581
C
High fat
Less weight loss
69
TCF7L2
rs7903146
C
High fiber
Greater weight loss
70
PPARG
rs1801282
G
High fat
Lower weight loss
71
ADCY3
rs10182181
G
Moderately high-protein
Lower decrease of fat mass, trunk and android fat
72
FGF21
rs838147
C
Low-carbohydrate/highfat
Less reduction of total fat mass and trunk fat
73
ADCY3, adenylate cyclase 3; FGF21, fibroblast growth factor 21; FTO, fat mass and obesity associated; IL6, interleukin 6; IRS1, insulin receptor substrate 1; MTNR1B, melatonin receptor 1B; PPARG, peroxisome proliferator activated receptor gamma; PPM1K, protein phosphatase, Mg2þ/Mn2þ dependent 1K; TCF7L2, transcription factor 7 like 2; TFAP2B, transcription factor AP-2 beta.
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improvements in Mediterranean diet scores were reported among subjects receiving genotypic feedback, targeting specific variants in five nutrient-responsive genes, after 6 months of follow-up [30]. On the other hand, there was no evidence that including phenotypic (anthropometry and blood biomarkers) and genotypic information (5 dietresponsive genetic variants), enhanced the effectiveness of a customized nutritional program based on individual baseline diet [31]. Genotype-based nutritional advice appears to be a useful strategy for the prevention and treatment of obesity by favoring some stable dietary changes and improving adiposity outcomes. These findings could be related to individuals perceiving gene-tailored counseling, as more understandable and useful than general dietary recommendations [32], with counseling more positively scored toward the end of personalized intervention [33].
Other genetic variants Two of the most studied are highly polymorphic copy number variants (CNVs), encompassing salivary (AMY1A) and pancreatic (AMY2A) amylase genes. A pioneering study reported the association between reduced copy number of AMY1A gene and increased BMI [34]. Afterward, significant and positive contributions of AMY1A copy number, to lower obesity risks in Mexican and French children were found [35,36]. However, an independent study involving two East Asian populations of Chinese and Malay ethnicity, was not able to replicate the association between AMY1A gene and obesity or BMI [37]. A significant interaction between AMY1A copy number and starch intake on BMI and body fat percentage was detected, in which BMI tended to decrease with increasing AMY1A copy numbers in the low-starch intake group, and tended to increase with increasing AMY1 copy numbers in the high-starch intake group [38]. Furthermore, a randomized trial revealed greater reductions in body weight and WC, among individuals carrying the A allele of the AMY1A-AMY2A rs11185098 genotype (indicating higher amylase amount and activity), compared to those without the A allele [39]. Possible contributions of CNVs in five genes (LEPR, NEGR1, ARHGEF4, and CPXCR1), and four intergenic regions (12q15c, 15q21.1a, and 22q11.21d), to development of obesity, particularly abdominal obesity, were recently reported in Mexican children [40]. Meanwhile, a significant interaction between APOB Ins/Del polymorphism and dietary n-3 polyunsaturated fatty acid (PUFA) intake, regarding obesity risk in type 2 diabetic patients, was found [41]. Thus, a higher general obesity risk was detected in carriers of the Del allele, than Ins/Ins homozygotes, when dietary n-3 PUFA intake was low [41]. Instead, a variable number of tandem repeats (VNTR)
polymorphism, within the exon III of the DRD4 gene, was not related to success in weight loss in obese children, after 1-year lifestyle intervention [42].
Host genetics, microbiota composition, and obesity risk: potential interactions Emerging evidence suggests complex interactions between host genetic background and gut microbiome, concerning the risk of developing obesity [43]. SNPs located within intronic and untranslated regions of PLD1 gene were associated with abundant levels of genus Akkermansia, which has been shown to affect obesity susceptibility [44]. On the other hand, differences in the abundance of Prevotella genus were related to a human variant adjacent to LYPLAL1, a gene, reported to be associated with body fat distribution, waist-hip ratio, and insulin sensitivity in some populations [45]. Also, analyses of the gut microbial community composition in twins unveiled reduced abundances of Actinobacteria and Bifidobacterium that were significantly linked to the minor allele at the APOA5 SNP rs651821, which is known to be associated with metabolic syndrome [46]. Moreover, elevated levels of the beneficial Bifidobacteria have been found among carriers of the lactase nonpersistence genotype [47], whose protective effect against obesity traits has been documented in some populations of European descent [48,49]. This gut-microbiome-related LCT gene profile was associated with long-term improvements of body fat composition and distribution, among subjects eating a lowcalorie, high-protein diet [50]. In genetically distinct inbred mouse strains, susceptibility to obesity and metabolic syndrome was modulated by interactions between gut microbiota, host genetics, and diet [51]. In the same way, microbiota transplant experiments in animal models revealed that alterations of the gut microbiota composition modified the strain-specific susceptibility to diet-induced metabolic disease [52]. Furthermore, a significant enrichment of Enterobacteriaceae (phylum Proteobacteria) was linked to chromosome 3 locus (rs29982345) in mice [53]. Interestingly, this genomic region containing three amylase genes was subsequently associated with body fat growth during high-fat/highsucrose feeding [53].
Future directions Genomic studies have identified a number of genetic variants associated with obesity predisposition. This knowledge has contributed to the design of genotype-based nutritional strategies to induce long-term weight loss. Nevertheless, recent investigations have revealed the involvement of epigenetic marks and gut microbiota composition in body
Nutrigenetic approaches in obesity and weight loss Chapter | 40
weight regulation. Furthermore, metabolomic analyses have allowed metabolically categorizing individuals in different groups, based on food consumption or in response to dietary prescriptions. The integration of these scientific insights is needed for the implementation of tailored nutritional interventions.
References [1] World Health Organization. Obesity and overweight. 2018. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-andoverweight. [2] GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, et al. Health effects of overweight and obesity in 195 Countries over 25 years. N. Engl. J. Med. 2017;377:13e27. [3] Goni L, Milagro FI, Cuervo M, Martínez JA. Single-nucleotide polymorphisms and DNA methylation markers associated with central obesity and regulation of body weight. Nutr. Rev. 2014;72:673e90. [4] Heianza Y, Qi L. Gene-diet interaction and precision nutrition in obesity. Int. J. Mol. Sci. 2017. https://doi.org/10.3390/ ijms18040787. [5] Ramos-Lopez O, Milagro FI, Allayee H, et al. Guide for current nutrigenetic, nutrigenomic, and nutriepigenetic approaches for precision nutrition involving the prevention and management of chronic diseases associated with obesity. J. Nutrigenet. Nutrigenomics 2017;10:43e62. [6] Moonesinghe R, Liu T, Khoury MJ. Evaluation of the discriminative accuracy of genomic profiling in the prediction of common complex diseases. Eur. J. Hum. Genet. 2010;18:485e9. [7] Celis-Morales CA, Lyall DM, Gray SR, et al. Dietary fat and total energy intake modifies the association of genetic profile risk score on obesity: evidence from 48 170 UK biobank participants. Int. J. Obes. 2017;41:1761e8. [8] Goni L, Cuervo M, Milagro FI, Martínez JA. A genetic risk tool for obesity predisposition assessment and personalized nutrition implementation based on macronutrient intake. Genes Nutr 2015;10:445. [9] Rukh G, Sonestedt E, Melander O, et al. Genetic susceptibility to obesity and diet intakes: association and interaction analyses in the Malmö diet and cancer study. Genes Nutr 2013;8:535e47. [10] Ankarfeldt MZ, Larsen SC, Ängquist L, et al. Interaction between genetic predisposition to adiposity and dietary protein in relation to subsequent change in body weight and waist circumference. PLoS One 2014;9:e110890. [11] Larsen SC, Angquist L, Ahluwalia TS, et al. Dietary ascorbic acid and subsequent change in body weight and waist circumference: associations may depend on genetic predisposition to obesityda prospective study of three independent cohorts. Nutr. J. 2014;13:43. [12] Larsen SC, Ängquist L, Ahluwalia TS, et al. Interaction between genetic predisposition to obesity and dietary calcium in relation to subsequent change in body weight and waist circumference. Am. J. Clin. Nutr. 2014;99:957e65. [13] Qi Q, Chu AY, Kang JH, et al. Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies. BMJ 2014;348:g1610. [14] Qi Q, Chu AY, Kang JH, et al. Sugar-sweetened beverages and genetic risk of obesity. N. Engl. J. Med. 2012;367:1387e96.
413
[15] Wang T, Huang T, Kang JH, et al. Habitual coffee consumption and genetic predisposition to obesity: gene-diet interaction analyses in three US prospective studies. BMC Med 2017;15:97. [16] Hosseini-Esfahani F, Koochakpoor G, Daneshpour MS, SedaghatiKhayat B, Mirmiran P, Azizi F. Mediterranean dietary pattern adherence modify the association between FTO genetic variations and obesity phenotypes. Nutrients 2017. https://doi.org/10.3390/nu9101064. [17] Nettleton JA, Follis JL, Ngwa JS, et al. Gene dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry. Hum. Mol. Genet. 2015;24:4728e38. [18] Ding M, Ellervik C, Huang T, et al. Diet quality and genetic association with body mass index: results from 3 observational studies. Am. J. Clin. Nutr. 2018;108:1291e300. [19] Wang T, Heianza Y, Sun D, et al. Improving adherence to healthy dietary patterns, genetic risk, and long term weight gain: gene-diet interaction analysis in two prospective cohort studies. BMJ 2018. https://doi.org/10.1136/bmj.j5644. [20] Cha S, Kang JH, Lee JH, et al. Impact of genetic variants on the individual potential for body fat loss. Nutrients 2018. https://doi.org/ 10.3390/nu10030266. [21] Peter I, McCaffery JM, Kelley-Hedgepeth A, et al. Association of type 2 diabetes susceptibility loci with one-year weight loss in the look AHEAD clinical trial. Obesity (Silver Spring) 2012;20:1675e82. [22] Knowles JW, Zarafshar S, Pavlovic A, et al. Impact of a genetic risk score for coronary artery disease on reducing cardiovascular risk: a pilot randomized controlled study. Front. Cardiovasc. Med 2017. https://doi.org/10.3389/fcvm.2017.00053. [23] Moleres A, Rendo-Urteaga T, Zulet MA, et al. Obesity susceptibility loci on body mass index and weight loss in Spanish adolescents after a lifestyle intervention. J. Pediatr. 2012;161:466e470.e2. [24] Sandholt CH, Allin KH, Toft U, et al. The effect of GWAS identified BMI loci on changes in body weight among middle-aged Danes during a five-year period. Obesity (Silver Spring) 2014;22:901e8. [25] Grant RW, O’Brien KE, Waxler JL, et al. Personalized genetic risk counseling to motivate diabetes prevention: a randomized trial. Diabetes Care 2013;36:13e9. [26] Hollensted M, Fogh M, Schnurr TM, et al. Genetic susceptibility for childhood BMI has no impact on weight loss following lifestyle intervention in Danish children. Obesity 2018;26:1915e22. [27] Celis-Morales C, Marsaux CF, Livingstone KM, et al. Can geneticbased advice help you lose weight? Findings from the Food4Me European randomized controlled trial. Am. J. Clin. Nutr. 2017;105:1204e13. [28] Wang C, Gordon ES, Norkunas T, et al. A randomized trial examining the impact of communicating genetic and lifestyle risks for obesity. Obesity 2016;24:2481e90. [29] Fallaize R, Celis-Morales C, Macready AL, et al. The effect of the apolipoprotein E genotype on response to personalized dietary advice intervention: findings from the Food4Me randomized controlled trial. Am. J. Clin. Nutr. 2016;104:827e36. [30] Livingstone KM, Celis-Morales C, Navas-Carretero S, et al. Effect of an internet-based, personalized nutrition randomized trial on dietary changes associated with the Mediterranean diet: the Food4Me study. Am. J. Clin. Nutr. 2016;104:288e97. [31] Celis-Morales C, Livingstone KM, Marsaux CF, et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int. J. Epidemiol. 2017;46:578e88.
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[32] Nielsen DE, El-Sohemy A. A randomized trial of genetic information for personalized nutrition. Genes Nutr 2012;7:559e66. [33] Nielsen DE, Shih S, El-Sohemy A. Perceptions of genetic testing for personalized nutrition: a randomized trial of DNA-based dietary advice. J. Nutrigenet. Nutrigenomics 2014;7:94e104. [34] Falchi M, El-Sayed Moustafa JS, Takousis P, et al. Low copy number of the salivary amylase gene predisposes to obesity. Nat. Genet. 2014;46:492e7. [35] Mejía-Benítez MA, Bonnefond A, Yengo L, et al. Beneficial effect of a high number of copies of salivary amylase AMY1 gene on obesity risk in Mexican children. Diabetologia 2015;58:290e4. [36] Bonnefond A, Yengo L, Dechaume A, et al. Relationship between salivary/pancreatic amylase and body mass index: a systems biology approach. BMC Med 2017;15:37. [37] Yong RY, Mustaffa SB, Wasan PS, et al. Complex copy number variation of AMY1 does not associate with obesity in two East Asian cohorts. Hum. Mutat. 2016;37:669e78. [38] Rukh G, Ericson U, Andersson-Assarsson J, Orho-Melander M, Sonestedt E. Dietary starch intake modifies the relation between copy number variation in the salivary amylase gene and BMI. Am. J. Clin. Nutr. 2017;106:256e62. [39] Heianza Y, Sun D, Wang T, et al. Starch digestion-related amylase genetic variant affects 2-year changes in adiposity in response to weight-loss diets: the POUNDS lost trial. Diabetes 2017;66:2416e23. [40] Antúnez-Ortiz DL, Flores-Alfaro E, Burguete-García AI, et al. Copy number variations in candidate genes and intergenic regions affect body mass index and abdominal obesity in Mexican children. BioMed Res. Int. 2017. https://doi.org/10.1155/2017/2432957. [41] Rafiee M, Sotoudeh G, Djalali M, et al. Dietary u-3 polyunsaturated fatty acid intake modulates impact of insertion/deletion polymorphism of ApoB gene on obesity risk in type 2 diabetic patients. Nutrition 2016;32:1110e5. [42] Roth CL, Hinney A, Schur EA, Elfers CT, Reinehr T. Association analyses for dopamine receptor gene polymorphisms and weight status in a longitudinal analysis in obese children before and after lifestyle intervention. BMC Pediatr 2013;13:197. [43] Cuevas-Sierra A, Ramos-Lopez O, Riezu-Boj JI, Milagro FI, Martinez JA. Diet, gut microbiota and obesity: links with host genetics and epigenetics and potential applications. Adv. Nutr 2019. https://doi.org/10.1093/advances/nmy078. [44] Davenport ER, Cusanovich DA, Michelini K, Barreiro LB, Ober C, Gilad Y. Genome-wide association studies of the human gut microbiota. PLoS One 2015;10:e0140301. [45] Li J, Fu R, Yang Y, et al. A metagenomic approach to dissect the genetic composition of enterotypes in Han Chinese and two Muslim groups. Syst. Appl. Microbiol. 2018;41:1e12. [46] Lim MY, You HJ, Yoon HS, et al. The effect of heritability and host genetics on the gut microbiota and metabolic syndrome. Gut 2017;66:1031e8. [47] Goodrich JK, Davenport ER, Clark AG, Ley RE. The relationship between the human genome and microbiome comes into view. Annu. Rev. Genet. 2017;51:413e33. [48] Almon R, Álvarez-León EE, Serra-Majem L. Association of the European lactase persistence variant (LCT-13910 C>T polymorphism) with obesity in the Canary Islands. PLoS One 2012;7:e43978.
[49] Manco L, Dias H, Muc M, Padez C. The lactase -13910C>T polymorphism (rs4988235) is associated with overweight/obesity and obesity-related variables in a population sample of Portuguese young adults. Eur. J. Clin. Nutr. 2017;71:21e4. [50] Heianza Y, Sun D, Ma W, et al. Gut-microbiome-related LCT genotype and 2-year changes in body composition and fat distribution: the POUNDS lost trial. Int. J. Obes. 2018;42:1565e73. [51] Ussar S, Griffin NW, Bezy O, et al. Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metab 2015;22:516e30. [52] Kreznar JH, Keller MP, Traeger LL, et al. Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes. Cell Rep 2017;18:1739e50. [53] Parks BW, Nam E, Org E, et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab 2013;17:141e52. [54] Vimaleswaran KS, Bodhini D, Lakshmipriya N, et al. Interaction between FTO gene variants and lifestyle factors on metabolic traits in an Asian Indian population. Nutr. Metab. 2016;13:39. [55] Corella D, Arregui M, Coltell O, et al. Association of the LCT13910C>T polymorphism with obesity and its modulation by dairy products in a Mediterranean population. Obesity 2011;19:1707e14. [56] Memisoglu A, Hu FB, Hankinson SE, et al. Interaction between a peroxisome proliferator-activated receptor gamma gene polymorphism and dietary fat intake in relation to body mass. Hum. Mol. Genet. 2003;12:2923e9. [57] Mansego ML, De Marco G, Ivorra C, et al. The nutrigenetic influence of the interaction between dietary vitamin E and TXN and COMT gene polymorphisms on waist circumference: a case control study. J. Transl. Med. 2015;13:286. [58] Junyent M, Parnell LD, Lai CQ, et al. ADAM17_i33708A>G polymorphism interacts with dietary n-6 polyunsaturated fatty acids to modulate obesity risk in the genetics of lipid lowering drugs and diet network study. Nutr. Metabol. Cardiovasc. Dis. 2010;20:698e705. [59] Joffe YT, van der Merwe L, Carstens M, et al. Tumor necrosis factoralpha gene -308 G/A polymorphism modulates the relationship between dietary fat intake, serum lipids, and obesity risk in black South African women. J. Nutr. 2010;140:901e7. [60] Sánchez-Moreno C, Ordovás JM, Smith CE, Baraza JC, Lee YC, Garaulet M. APOA5 gene variation interacts with dietary fat intake to modulate obesity and circulating triglycerides in a Mediterranean population. J. Nutr. 2011;141:380e5. [61] Domínguez-Reyes T, Astudillo-López CC, Salgado-Goytia L, et al. Interaction of dietary fat intake with APOA2, APOA5 and LEPR polymorphisms and its relationship with obesity and dyslipidemia in young subjects. Lipids Health Dis 2015;14:106. [62] Doo M, Won S, Kim Y. Association between the APOB rs1469513 polymorphism and obesity is modified by dietary fat intake in Koreans. Nutrition 2015;31:653e8. [63] Smith CE, Tucker KL, Arnett DK, et al. Apolipoprotein A2 polymorphism interacts with intakes of dairy foods to influence body weight in 2 U.S. populations. J. Nutr. 2013;143:1865e71. [64] Zhang X, Qi Q, Zhang C, et al. FTO genotype and 2-year change in body composition and fat distribution in response to weight-loss diets: the POUNDS LOST trial. Diabetes 2012;61:3005e11.
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[65] Stocks T, Ängquist L, Hager J, et al. TFAP2B -dietary protein and glycemic index interactions and weight maintenance after weight loss in the DiOGenes trial. Hum. Hered. 2013;75:213e9. [66] Goni L, Cuervo M, Milagro FI, Martínez JA. Gene-gene interplay and gene-diet interactions involving the MTNR1B rs10830963 variant with body weight loss. J. Nutrigenet. Nutrigenomics 2014;7:232e42. [67] Razquin C, Martinez JA, Martinez-Gonzalez MA, FernándezCrehuet J, Santos JM, Marti A. A Mediterranean diet rich in virgin olive oil may reverse the effects of the -174G/C IL6 gene variant on 3-year body weight change. Mol. Nutr. Food Res. 2010;54(Suppl. 1):S75e82. [68] Qi Q, Bray GA, Smith SR, Hu FB, Sacks FM, 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;124:563e71. [69] Xu M, Qi Q, Liang J, et al. Genetic determinant for amino acid metabolites and changes in body weight and insulin resistance in response to weight-loss diets: the Preventing Overweight Using
[70]
[71]
[72]
[73]
415
Novel Dietary Strategies (POUNDS LOST) trial. Circulation 2013;127:1283e9. Heni M, Herzberg-Schäfer S, Machicao F, Häring HU, Fritsche A. Dietary fiber intake modulates the association between variants in TCF7L2 and weight loss during a lifestyle intervention. Diabetes Care 2012;35:e24. Garaulet M, Smith CE, Hernández-González T, Lee YC, Ordovás JM. PPARg Pro12Ala interacts with fat intake for obesity and weight loss in a behavioural treatment based on the Mediterranean diet. Mol. Nutr. Food Res. 2011;55:1771e9. Goni L, Riezu-Boj JI, Milagro FI, et al. Interaction between an ADCY3 genetic variant and two weight-lowering diets affecting body fatness and body composition outcomes depending on macronutrient distribution: a randomized trial. Nutrients 2018. https://doi.org/10.3390/nu10060789. Heianza Y, Ma W, Huang T, et al. 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;39:1909e14.
Nutrigenetic approaches in obesity and weight loss Omar Ramos-Lopez1, 2 and J. Alfredo Martinez1, 3, 4, 5 1
Department of Nutrition, Food Science and Physiology, University of Navarra, and Center for Nutrition Research, University of Navarra,
Pamplona, Spain; 2Faculty of Medicine and Psychology, Autonomous University of Baja California, Tijuana, BC, Mexico; 3CIBERobn, Physiopathology of Obesity, Carlos III Institute, Madrid, Spain; 4Navarra Institute for Health Research (IdiSNA), Pamplona, Spain; 5Madrid Institute of Advanced Studies (IMDEA Food), Madrid, Spain
Introduction Obesity epidemic is one of the most important health challenges worldwide, as denoted by a recent report of the World Health Organization, revealing that more than 39% of adults presented overweight or obesity (about 1.9 billion people) [1]. According to the Global Burden of Disease study, excessive body weight accounted for 4 million deaths, among adults in 195 Countries over 25 Years [2]. Genetic factors may contribute to the onset and development of excessive adiposity and accompanying comorbidities, by affecting energy homeostasis and body weight regulation [3]. Nutrigenetic studies are enabling to clarify the involvement of gene-diet interactions, in determining specific adiposity phenotypes, and modulating therapy outcomes [4]. Herein, we review genetic variants and biomarkers related to obesity and weight loss, which may serve to understand disease etiology and envisage future therapeutic targets and innovative treatments (Fig. 40.1).
Gene-diet interactions and obesity predisposition Single nucleotide polymorphisms (SNPs) are the most studied genetic variants in the field of precision nutrition [5]. Multiple SNPs are associated with obesity predisposition through interactions with dietary factors (Table 40.1). Relevant interactions between polymorphisms located at lipidmetabolism genes (PPARG, APOA5, APOA2, and APOB) and high-fat diets, were found in relation to greater adiposity markers. Interestingly, SNP rs2301241 in TXN gene (acting as an antioxidant), was associated with higher waist
circumference (WC) values in subjects with low vitamin E intakes. Obesity risk was significantly higher in T-allele carriers of 13,910 C > T polymorphism (rs4988235), upstream LCT gene, only among subjects consuming moderate or high amounts of lactose (Table 40.1). Because the magnitude of associations between individual SNPs and adiposity traits is generally modest, studies using genetic risk scores (GRS), have examined the additive effect of multiple loci and diet interactions [6]. Thus, a validated GRS for obesity was associated with higher BMI and WC values among individuals consuming high amounts of fats, compared to those with low-fat intakes [7]. Also, higher intakes of animal protein, saturated fat, and carbohydrates were positively associated with greater percentages of body fat mass, among individuals carrying a high genetic risk group for obesity [8]. Nominally significant interactions were detected between BMI-associated GRS and protein intake, on obesity and fat mass among women within the Malmö Diet and Cancer Study [9]. Nevertheless, a longitudinal analysis evidenced no relationships between adiposity-associated GRS and dietary protein, in relation to subsequent change in body weight and waist circumference in three different Danish cohorts [10]. The role of micronutrient status in this field has also been explored. For instance, a diet with a high content of ascorbic acid was associated with higher WC gain, among people genetically predisposed to abdominal obesity [11]. On the contrary, a significant interaction between a GRS from six WC-related SNPs and dietary calcium was reported concerning WC changes, where each risk allele was associated with greater WC reductions per 1000 mg of calcium intake [12].
Precision Medicine for Investigators, Practitioners and Providers. https://doi.org/10.1016/B978-0-12-819178-1.00040-X Copyright © 2020 Elsevier Inc. All rights reserved.
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FIGURE 40.1 Nutrigenetic approaches in precision nutrition, for prevention and management of obesity and associated chronic diseases.
TABLE 40.1 Nutrigenetic examples of SNPs-diet interactions involved in obesity predisposition. Gene
Polymorphism
Allele
Diet interaction
Main findings
Reference
FTO
rs8050136
A
High carbohydrate
Increased obesity risk
54
LCT
rs4988235
T
High lactose
Increased obesity risk
55
PPARG
rs1801282
G
High fat
Higher BMI
56
TXN
rs2301241
T
Low vitamin E
Higher WC
57
ADAM17
rs10495563
A
Low n-6 PUFA
Increased obesity risk
58
TNFA
rs1800629
A
High fat
Increased obesity risk
59
APOA5
rs662799
T
High fat
Higher adiposity markers
60
LEPR
rs1137101
G
High SFA/High fat
Increased obesity risk
61
APOB
rs1469513
G
High fat
Increased obesity risk
62
APOA2
rs5082
C
High fat dairy foods
Higher BMI
63
ADAM17, ADAM metallopeptidase domain 17; APOA2, apolipoprotein A2; APOA5, apolipoprotein A5; APOB, apolipoprotein B; BMI, body mass index; FTO, fat mass and obesity associated; LCT, lactase; LEPR, leptin receptor; PPARG, peroxisome proliferator activated receptor gamma; PUFA, polyunsaturated fatty acids; SFA, saturated fatty acids; TNFA, tumor necrosis factor a; TXN, thioredoxin; WC, waist circumference.
The association of a GRS constructed from 32 BMIassociated variants with adiposity, was strengthened with greater intake of fried foods and sugar-sweetened beverages, in three American cohort studies [13,14]. Similarly, an increased risk for obesity was found among individuals with low habitual coffee consumption, who were genetically predisposed to obesity in the basis of a GRS calculated from 77 BMI-related loci [15]. Under the assumption that the assessment of dietary patterns provides more reliable information regarding real food intake, compared to particular macronutrient consumption, higher Mediterranean dietary pattern adherence
was associated with decreased obesity risk, in subjects carrying high GRS according to FTO polymorphisms [16]. Associations between genetic predisposition and obesity traits were stronger with a healthier diet (based on habitual consumption of whole grains, fish, fruits, vegetables, and nuts/seeds), in 18 cohorts of European ancestry [17]. The increment in BMI was smaller among individuals with a strong genetic predisposition to obesity, categorized in the highest tertiles of three diet quality scores (Alternative Healthy Eating Index 2010 [AHEI-2010]; Alternative Mediterranean Diet score [AMED]; and the Dietary Approach to Stop Hypertension [DASH]) [18]. Genetic
Nutrigenetic approaches in obesity and weight loss Chapter | 40
association with weight gain was also significantly attenuated, with increasing adherence to the AHEI-2010 dietary score in two prospective cohort studies [19].
Gene-diet interactions involving weight loss and adiposity outcomes Potential gene-diet interactions have also been reported to influence the heterogeneity of adiposity outcomes (Table 40.2). Investigations include SNPs mapped to genes involved in the regulation of critical physiological processes, such as circadian rhythm, inflammatory response, lipid metabolism, insulin signaling, amino acid breakdown, and blood glucose homeostasis (Table 40.2). Greater body fat losses were reported in highly sensitive carriers of obesity GRS in response to dietary therapy, in a large Korean population [20]. Also, highest diabetessusceptibility loci were associated with greater WC reductions, after 1-year of intensive lifestyle advice within the Look AHEAD (Action for Health in Diabetes) clinical trial [21]. Likewise, potentially modest benefits in weight loss and physical activity were found among participants with higher GRS for coronary artery disease, who received risk-reducing strategies based on diet and exercise [22]. Furthermore, obese and overweight Spanish adolescents with lower obesity GRS, evidenced greater benefits on weight loss and metabolic profile improvements, after 3 months of a multidisciplinary intervention program [23]. On the other hand, changes in body weight over a 5-year lifestyle intervention were not influenced by BMI-
411
related GRS in a middle-aged Danish cohort [24]. Moreover, diabetes genetic risk counseling did not significantly modify mean weight loss, among overweight individuals under a validated diabetes prevention program, designed to improve diet quality and physical activity level [25]. Meanwhile, GRS comprising 15 SNP previously associated with childhood BMI, did not influence changes in BMI or cardiometabolic traits in Danish Children, following a lifestyle intervention [26].
The impact of genetic information disclosure on obesity management The Food4Me European randomized controlled trial revealed that disclosure of information concerning fat-mass and obesity-associated (FTO) genotype risk, had a greater effect on changes in adiposity markers, compared with nonpersonalized intervention group [27]. Similarly, the Coriell Personalized Medicine Collaborative trial reported that individuals receiving their FTO genotype alone had greater intentions to lose body weight at follow-up than those who received no risk genetic advice [28]. This effect was enhanced in participants carrying a high genetic risk. Genetic information has also been included in nutrigenetic tests, aimed at assessing the effect on changing certain obesity-related dietary behaviors. Apolipoprotein E (APOE)-based personalized nutrition resulted in a greater reduction in saturated fat intake (percentage of total energy) than did standard dietary advice, although no differences by APOE genotypes (E4þ vs. E4) were found [29]. Greater
TABLE 40.2 Nutrigenetic trials analyzing SNPs-diet interactions involved in adiposity outcomes in response to nutritional interventions. Gene
Polymorphism
Allele
Diet interaction
Main outcomes
Reference
FTO
rs1558902
A
High protein
Greater weight loss
64
TFAP2B
rs987237
G
High protein
Higher weight regains
65
MTNR1B
rs10830963
G
High protein
Lower weight loss in women
66
IL6
rs2069827
C
Mediterranean diet
Lower weight gains
67
IRS1
rs2943641
C
High carbohydrate
Greater weight loss
68
PPM1K
rs1440581
C
High fat
Less weight loss
69
TCF7L2
rs7903146
C
High fiber
Greater weight loss
70
PPARG
rs1801282
G
High fat
Lower weight loss
71
ADCY3
rs10182181
G
Moderately high-protein
Lower decrease of fat mass, trunk and android fat
72
FGF21
rs838147
C
Low-carbohydrate/highfat
Less reduction of total fat mass and trunk fat
73
ADCY3, adenylate cyclase 3; FGF21, fibroblast growth factor 21; FTO, fat mass and obesity associated; IL6, interleukin 6; IRS1, insulin receptor substrate 1; MTNR1B, melatonin receptor 1B; PPARG, peroxisome proliferator activated receptor gamma; PPM1K, protein phosphatase, Mg2þ/Mn2þ dependent 1K; TCF7L2, transcription factor 7 like 2; TFAP2B, transcription factor AP-2 beta.
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improvements in Mediterranean diet scores were reported among subjects receiving genotypic feedback, targeting specific variants in five nutrient-responsive genes, after 6 months of follow-up [30]. On the other hand, there was no evidence that including phenotypic (anthropometry and blood biomarkers) and genotypic information (5 dietresponsive genetic variants), enhanced the effectiveness of a customized nutritional program based on individual baseline diet [31]. Genotype-based nutritional advice appears to be a useful strategy for the prevention and treatment of obesity by favoring some stable dietary changes and improving adiposity outcomes. These findings could be related to individuals perceiving gene-tailored counseling, as more understandable and useful than general dietary recommendations [32], with counseling more positively scored toward the end of personalized intervention [33].
Other genetic variants Two of the most studied are highly polymorphic copy number variants (CNVs), encompassing salivary (AMY1A) and pancreatic (AMY2A) amylase genes. A pioneering study reported the association between reduced copy number of AMY1A gene and increased BMI [34]. Afterward, significant and positive contributions of AMY1A copy number, to lower obesity risks in Mexican and French children were found [35,36]. However, an independent study involving two East Asian populations of Chinese and Malay ethnicity, was not able to replicate the association between AMY1A gene and obesity or BMI [37]. A significant interaction between AMY1A copy number and starch intake on BMI and body fat percentage was detected, in which BMI tended to decrease with increasing AMY1A copy numbers in the low-starch intake group, and tended to increase with increasing AMY1 copy numbers in the high-starch intake group [38]. Furthermore, a randomized trial revealed greater reductions in body weight and WC, among individuals carrying the A allele of the AMY1A-AMY2A rs11185098 genotype (indicating higher amylase amount and activity), compared to those without the A allele [39]. Possible contributions of CNVs in five genes (LEPR, NEGR1, ARHGEF4, and CPXCR1), and four intergenic regions (12q15c, 15q21.1a, and 22q11.21d), to development of obesity, particularly abdominal obesity, were recently reported in Mexican children [40]. Meanwhile, a significant interaction between APOB Ins/Del polymorphism and dietary n-3 polyunsaturated fatty acid (PUFA) intake, regarding obesity risk in type 2 diabetic patients, was found [41]. Thus, a higher general obesity risk was detected in carriers of the Del allele, than Ins/Ins homozygotes, when dietary n-3 PUFA intake was low [41]. Instead, a variable number of tandem repeats (VNTR)
polymorphism, within the exon III of the DRD4 gene, was not related to success in weight loss in obese children, after 1-year lifestyle intervention [42].
Host genetics, microbiota composition, and obesity risk: potential interactions Emerging evidence suggests complex interactions between host genetic background and gut microbiome, concerning the risk of developing obesity [43]. SNPs located within intronic and untranslated regions of PLD1 gene were associated with abundant levels of genus Akkermansia, which has been shown to affect obesity susceptibility [44]. On the other hand, differences in the abundance of Prevotella genus were related to a human variant adjacent to LYPLAL1, a gene, reported to be associated with body fat distribution, waist-hip ratio, and insulin sensitivity in some populations [45]. Also, analyses of the gut microbial community composition in twins unveiled reduced abundances of Actinobacteria and Bifidobacterium that were significantly linked to the minor allele at the APOA5 SNP rs651821, which is known to be associated with metabolic syndrome [46]. Moreover, elevated levels of the beneficial Bifidobacteria have been found among carriers of the lactase nonpersistence genotype [47], whose protective effect against obesity traits has been documented in some populations of European descent [48,49]. This gut-microbiome-related LCT gene profile was associated with long-term improvements of body fat composition and distribution, among subjects eating a lowcalorie, high-protein diet [50]. In genetically distinct inbred mouse strains, susceptibility to obesity and metabolic syndrome was modulated by interactions between gut microbiota, host genetics, and diet [51]. In the same way, microbiota transplant experiments in animal models revealed that alterations of the gut microbiota composition modified the strain-specific susceptibility to diet-induced metabolic disease [52]. Furthermore, a significant enrichment of Enterobacteriaceae (phylum Proteobacteria) was linked to chromosome 3 locus (rs29982345) in mice [53]. Interestingly, this genomic region containing three amylase genes was subsequently associated with body fat growth during high-fat/highsucrose feeding [53].
Future directions Genomic studies have identified a number of genetic variants associated with obesity predisposition. This knowledge has contributed to the design of genotype-based nutritional strategies to induce long-term weight loss. Nevertheless, recent investigations have revealed the involvement of epigenetic marks and gut microbiota composition in body
Nutrigenetic approaches in obesity and weight loss Chapter | 40
weight regulation. Furthermore, metabolomic analyses have allowed metabolically categorizing individuals in different groups, based on food consumption or in response to dietary prescriptions. The integration of these scientific insights is needed for the implementation of tailored nutritional interventions.
References [1] World Health Organization. Obesity and overweight. 2018. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-andoverweight. [2] GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, et al. Health effects of overweight and obesity in 195 Countries over 25 years. N. Engl. J. Med. 2017;377:13e27. [3] Goni L, Milagro FI, Cuervo M, Martínez JA. Single-nucleotide polymorphisms and DNA methylation markers associated with central obesity and regulation of body weight. Nutr. Rev. 2014;72:673e90. [4] Heianza Y, Qi L. Gene-diet interaction and precision nutrition in obesity. Int. J. Mol. Sci. 2017. https://doi.org/10.3390/ ijms18040787. [5] Ramos-Lopez O, Milagro FI, Allayee H, et al. Guide for current nutrigenetic, nutrigenomic, and nutriepigenetic approaches for precision nutrition involving the prevention and management of chronic diseases associated with obesity. J. Nutrigenet. Nutrigenomics 2017;10:43e62. [6] Moonesinghe R, Liu T, Khoury MJ. Evaluation of the discriminative accuracy of genomic profiling in the prediction of common complex diseases. Eur. J. Hum. Genet. 2010;18:485e9. [7] Celis-Morales CA, Lyall DM, Gray SR, et al. Dietary fat and total energy intake modifies the association of genetic profile risk score on obesity: evidence from 48 170 UK biobank participants. Int. J. Obes. 2017;41:1761e8. [8] Goni L, Cuervo M, Milagro FI, Martínez JA. A genetic risk tool for obesity predisposition assessment and personalized nutrition implementation based on macronutrient intake. Genes Nutr 2015;10:445. [9] Rukh G, Sonestedt E, Melander O, et al. Genetic susceptibility to obesity and diet intakes: association and interaction analyses in the Malmö diet and cancer study. Genes Nutr 2013;8:535e47. [10] Ankarfeldt MZ, Larsen SC, Ängquist L, et al. Interaction between genetic predisposition to adiposity and dietary protein in relation to subsequent change in body weight and waist circumference. PLoS One 2014;9:e110890. [11] Larsen SC, Angquist L, Ahluwalia TS, et al. Dietary ascorbic acid and subsequent change in body weight and waist circumference: associations may depend on genetic predisposition to obesityda prospective study of three independent cohorts. Nutr. J. 2014;13:43. [12] Larsen SC, Ängquist L, Ahluwalia TS, et al. Interaction between genetic predisposition to obesity and dietary calcium in relation to subsequent change in body weight and waist circumference. Am. J. Clin. Nutr. 2014;99:957e65. [13] Qi Q, Chu AY, Kang JH, et al. Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies. BMJ 2014;348:g1610. [14] Qi Q, Chu AY, Kang JH, et al. Sugar-sweetened beverages and genetic risk of obesity. N. Engl. J. Med. 2012;367:1387e96.
413
[15] Wang T, Huang T, Kang JH, et al. Habitual coffee consumption and genetic predisposition to obesity: gene-diet interaction analyses in three US prospective studies. BMC Med 2017;15:97. [16] Hosseini-Esfahani F, Koochakpoor G, Daneshpour MS, SedaghatiKhayat B, Mirmiran P, Azizi F. Mediterranean dietary pattern adherence modify the association between FTO genetic variations and obesity phenotypes. Nutrients 2017. https://doi.org/10.3390/nu9101064. [17] Nettleton JA, Follis JL, Ngwa JS, et al. Gene dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry. Hum. Mol. Genet. 2015;24:4728e38. [18] Ding M, Ellervik C, Huang T, et al. Diet quality and genetic association with body mass index: results from 3 observational studies. Am. J. Clin. Nutr. 2018;108:1291e300. [19] Wang T, Heianza Y, Sun D, et al. Improving adherence to healthy dietary patterns, genetic risk, and long term weight gain: gene-diet interaction analysis in two prospective cohort studies. BMJ 2018. https://doi.org/10.1136/bmj.j5644. [20] Cha S, Kang JH, Lee JH, et al. Impact of genetic variants on the individual potential for body fat loss. Nutrients 2018. https://doi.org/ 10.3390/nu10030266. [21] Peter I, McCaffery JM, Kelley-Hedgepeth A, et al. Association of type 2 diabetes susceptibility loci with one-year weight loss in the look AHEAD clinical trial. Obesity (Silver Spring) 2012;20:1675e82. [22] Knowles JW, Zarafshar S, Pavlovic A, et al. Impact of a genetic risk score for coronary artery disease on reducing cardiovascular risk: a pilot randomized controlled study. Front. Cardiovasc. Med 2017. https://doi.org/10.3389/fcvm.2017.00053. [23] Moleres A, Rendo-Urteaga T, Zulet MA, et al. Obesity susceptibility loci on body mass index and weight loss in Spanish adolescents after a lifestyle intervention. J. Pediatr. 2012;161:466e470.e2. [24] Sandholt CH, Allin KH, Toft U, et al. The effect of GWAS identified BMI loci on changes in body weight among middle-aged Danes during a five-year period. Obesity (Silver Spring) 2014;22:901e8. [25] Grant RW, O’Brien KE, Waxler JL, et al. Personalized genetic risk counseling to motivate diabetes prevention: a randomized trial. Diabetes Care 2013;36:13e9. [26] Hollensted M, Fogh M, Schnurr TM, et al. Genetic susceptibility for childhood BMI has no impact on weight loss following lifestyle intervention in Danish children. Obesity 2018;26:1915e22. [27] Celis-Morales C, Marsaux CF, Livingstone KM, et al. Can geneticbased advice help you lose weight? Findings from the Food4Me European randomized controlled trial. Am. J. Clin. Nutr. 2017;105:1204e13. [28] Wang C, Gordon ES, Norkunas T, et al. A randomized trial examining the impact of communicating genetic and lifestyle risks for obesity. Obesity 2016;24:2481e90. [29] Fallaize R, Celis-Morales C, Macready AL, et al. The effect of the apolipoprotein E genotype on response to personalized dietary advice intervention: findings from the Food4Me randomized controlled trial. Am. J. Clin. Nutr. 2016;104:827e36. [30] Livingstone KM, Celis-Morales C, Navas-Carretero S, et al. Effect of an internet-based, personalized nutrition randomized trial on dietary changes associated with the Mediterranean diet: the Food4Me study. Am. J. Clin. Nutr. 2016;104:288e97. [31] Celis-Morales C, Livingstone KM, Marsaux CF, et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int. J. Epidemiol. 2017;46:578e88.
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[32] Nielsen DE, El-Sohemy A. A randomized trial of genetic information for personalized nutrition. Genes Nutr 2012;7:559e66. [33] Nielsen DE, Shih S, El-Sohemy A. Perceptions of genetic testing for personalized nutrition: a randomized trial of DNA-based dietary advice. J. Nutrigenet. Nutrigenomics 2014;7:94e104. [34] Falchi M, El-Sayed Moustafa JS, Takousis P, et al. Low copy number of the salivary amylase gene predisposes to obesity. Nat. Genet. 2014;46:492e7. [35] Mejía-Benítez MA, Bonnefond A, Yengo L, et al. Beneficial effect of a high number of copies of salivary amylase AMY1 gene on obesity risk in Mexican children. Diabetologia 2015;58:290e4. [36] Bonnefond A, Yengo L, Dechaume A, et al. Relationship between salivary/pancreatic amylase and body mass index: a systems biology approach. BMC Med 2017;15:37. [37] Yong RY, Mustaffa SB, Wasan PS, et al. Complex copy number variation of AMY1 does not associate with obesity in two East Asian cohorts. Hum. Mutat. 2016;37:669e78. [38] Rukh G, Ericson U, Andersson-Assarsson J, Orho-Melander M, Sonestedt E. Dietary starch intake modifies the relation between copy number variation in the salivary amylase gene and BMI. Am. J. Clin. Nutr. 2017;106:256e62. [39] Heianza Y, Sun D, Wang T, et al. Starch digestion-related amylase genetic variant affects 2-year changes in adiposity in response to weight-loss diets: the POUNDS lost trial. Diabetes 2017;66:2416e23. [40] Antúnez-Ortiz DL, Flores-Alfaro E, Burguete-García AI, et al. Copy number variations in candidate genes and intergenic regions affect body mass index and abdominal obesity in Mexican children. BioMed Res. Int. 2017. https://doi.org/10.1155/2017/2432957. [41] Rafiee M, Sotoudeh G, Djalali M, et al. Dietary u-3 polyunsaturated fatty acid intake modulates impact of insertion/deletion polymorphism of ApoB gene on obesity risk in type 2 diabetic patients. Nutrition 2016;32:1110e5. [42] Roth CL, Hinney A, Schur EA, Elfers CT, Reinehr T. Association analyses for dopamine receptor gene polymorphisms and weight status in a longitudinal analysis in obese children before and after lifestyle intervention. BMC Pediatr 2013;13:197. [43] Cuevas-Sierra A, Ramos-Lopez O, Riezu-Boj JI, Milagro FI, Martinez JA. Diet, gut microbiota and obesity: links with host genetics and epigenetics and potential applications. Adv. Nutr 2019. https://doi.org/10.1093/advances/nmy078. [44] Davenport ER, Cusanovich DA, Michelini K, Barreiro LB, Ober C, Gilad Y. Genome-wide association studies of the human gut microbiota. PLoS One 2015;10:e0140301. [45] Li J, Fu R, Yang Y, et al. A metagenomic approach to dissect the genetic composition of enterotypes in Han Chinese and two Muslim groups. Syst. Appl. Microbiol. 2018;41:1e12. [46] Lim MY, You HJ, Yoon HS, et al. The effect of heritability and host genetics on the gut microbiota and metabolic syndrome. Gut 2017;66:1031e8. [47] Goodrich JK, Davenport ER, Clark AG, Ley RE. The relationship between the human genome and microbiome comes into view. Annu. Rev. Genet. 2017;51:413e33. [48] Almon R, Álvarez-León EE, Serra-Majem L. Association of the European lactase persistence variant (LCT-13910 C>T polymorphism) with obesity in the Canary Islands. PLoS One 2012;7:e43978.
[49] Manco L, Dias H, Muc M, Padez C. The lactase -13910C>T polymorphism (rs4988235) is associated with overweight/obesity and obesity-related variables in a population sample of Portuguese young adults. Eur. J. Clin. Nutr. 2017;71:21e4. [50] Heianza Y, Sun D, Ma W, et al. Gut-microbiome-related LCT genotype and 2-year changes in body composition and fat distribution: the POUNDS lost trial. Int. J. Obes. 2018;42:1565e73. [51] Ussar S, Griffin NW, Bezy O, et al. Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metab 2015;22:516e30. [52] Kreznar JH, Keller MP, Traeger LL, et al. Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes. Cell Rep 2017;18:1739e50. [53] Parks BW, Nam E, Org E, et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab 2013;17:141e52. [54] Vimaleswaran KS, Bodhini D, Lakshmipriya N, et al. Interaction between FTO gene variants and lifestyle factors on metabolic traits in an Asian Indian population. Nutr. Metab. 2016;13:39. [55] Corella D, Arregui M, Coltell O, et al. Association of the LCT13910C>T polymorphism with obesity and its modulation by dairy products in a Mediterranean population. Obesity 2011;19:1707e14. [56] Memisoglu A, Hu FB, Hankinson SE, et al. Interaction between a peroxisome proliferator-activated receptor gamma gene polymorphism and dietary fat intake in relation to body mass. Hum. Mol. Genet. 2003;12:2923e9. [57] Mansego ML, De Marco G, Ivorra C, et al. The nutrigenetic influence of the interaction between dietary vitamin E and TXN and COMT gene polymorphisms on waist circumference: a case control study. J. Transl. Med. 2015;13:286. [58] Junyent M, Parnell LD, Lai CQ, et al. ADAM17_i33708A>G polymorphism interacts with dietary n-6 polyunsaturated fatty acids to modulate obesity risk in the genetics of lipid lowering drugs and diet network study. Nutr. Metabol. Cardiovasc. Dis. 2010;20:698e705. [59] Joffe YT, van der Merwe L, Carstens M, et al. Tumor necrosis factoralpha gene -308 G/A polymorphism modulates the relationship between dietary fat intake, serum lipids, and obesity risk in black South African women. J. Nutr. 2010;140:901e7. [60] Sánchez-Moreno C, Ordovás JM, Smith CE, Baraza JC, Lee YC, Garaulet M. APOA5 gene variation interacts with dietary fat intake to modulate obesity and circulating triglycerides in a Mediterranean population. J. Nutr. 2011;141:380e5. [61] Domínguez-Reyes T, Astudillo-López CC, Salgado-Goytia L, et al. Interaction of dietary fat intake with APOA2, APOA5 and LEPR polymorphisms and its relationship with obesity and dyslipidemia in young subjects. Lipids Health Dis 2015;14:106. [62] Doo M, Won S, Kim Y. Association between the APOB rs1469513 polymorphism and obesity is modified by dietary fat intake in Koreans. Nutrition 2015;31:653e8. [63] Smith CE, Tucker KL, Arnett DK, et al. Apolipoprotein A2 polymorphism interacts with intakes of dairy foods to influence body weight in 2 U.S. populations. J. Nutr. 2013;143:1865e71. [64] Zhang X, Qi Q, Zhang C, et al. FTO genotype and 2-year change in body composition and fat distribution in response to weight-loss diets: the POUNDS LOST trial. Diabetes 2012;61:3005e11.
Nutrigenetic approaches in obesity and weight loss Chapter | 40
[65] Stocks T, Ängquist L, Hager J, et al. TFAP2B -dietary protein and glycemic index interactions and weight maintenance after weight loss in the DiOGenes trial. Hum. Hered. 2013;75:213e9. [66] Goni L, Cuervo M, Milagro FI, Martínez JA. Gene-gene interplay and gene-diet interactions involving the MTNR1B rs10830963 variant with body weight loss. J. Nutrigenet. Nutrigenomics 2014;7:232e42. [67] Razquin C, Martinez JA, Martinez-Gonzalez MA, FernándezCrehuet J, Santos JM, Marti A. A Mediterranean diet rich in virgin olive oil may reverse the effects of the -174G/C IL6 gene variant on 3-year body weight change. Mol. Nutr. Food Res. 2010;54(Suppl. 1):S75e82. [68] Qi Q, Bray GA, Smith SR, Hu FB, Sacks FM, 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;124:563e71. [69] Xu M, Qi Q, Liang J, et al. Genetic determinant for amino acid metabolites and changes in body weight and insulin resistance in response to weight-loss diets: the Preventing Overweight Using
[70]
[71]
[72]
[73]
415
Novel Dietary Strategies (POUNDS LOST) trial. Circulation 2013;127:1283e9. Heni M, Herzberg-Schäfer S, Machicao F, Häring HU, Fritsche A. Dietary fiber intake modulates the association between variants in TCF7L2 and weight loss during a lifestyle intervention. Diabetes Care 2012;35:e24. Garaulet M, Smith CE, Hernández-González T, Lee YC, Ordovás JM. PPARg Pro12Ala interacts with fat intake for obesity and weight loss in a behavioural treatment based on the Mediterranean diet. Mol. Nutr. Food Res. 2011;55:1771e9. Goni L, Riezu-Boj JI, Milagro FI, et al. Interaction between an ADCY3 genetic variant and two weight-lowering diets affecting body fatness and body composition outcomes depending on macronutrient distribution: a randomized trial. Nutrients 2018. https://doi.org/10.3390/nu10060789. Heianza Y, Ma W, Huang T, et al. 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;39:1909e14.