Genetic Differential Susceptibility to Socioeconomic Status and Childhood Obesogenic Behavior: Why Targeted Prevention May Be the Best Societal Investment | Child Development | JAMA Pediatrics | JAMA Network
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1.
Locke  AE, Kahali  B, Berndt  SI,  et al; LifeLines Cohort Study; ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium.  Genetic studies of body mass index yield new insights for obesity biology.  Nature. 2015;518(7538):197-206.PubMedGoogle ScholarCrossref
2.
Fall  T, Ingelsson  E.  Genome-wide association studies of obesity and metabolic syndrome.  Mol Cell Endocrinol. 2014;382(1):740-757.PubMedGoogle ScholarCrossref
3.
Belsky  J.  Variation in susceptibility to environmental influence: an evolutionary argument.  Psychol Inq. 1997;8(3):182-186.Google ScholarCrossref
4.
Boyce  WT, Ellis  BJ.  Biological sensitivity to context, I: an evolutionary-developmental theory of the origins and functions of stress reactivity.  Dev Psychopathol. 2005;17(2):271-301.PubMedGoogle ScholarCrossref
5.
Rowe  DC, Vazsonyi  AT, Figueredo  AJ.  Mating-effort in adolescence: a conditional or alternative strategy.  Pers Individ Dif. 1997;23(1):105-115.Google ScholarCrossref
6.
Caspi  A, McClay  J, Moffitt  TE,  et al.  Role of genotype in the cycle of violence in maltreated children.  Science. 2002;297(5582):851-854.PubMedGoogle ScholarCrossref
7.
Caspi  A, Sugden  K, Moffitt  TE,  et al.  Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene.  Science. 2003;301(5631):386-389.PubMedGoogle ScholarCrossref
8.
Bakermans-Kranenburg  MJ, van Ijzendoorn  MH.  Research review: genetic vulnerability or differential susceptibility in child development: the case of attachment.  J Child Psychol Psychiatry. 2007;48(12):1160-1173.PubMedGoogle ScholarCrossref
9.
Belsky  J, Pluess  M.  Beyond risk, resilience, and dysregulation: phenotypic plasticity and human development.  Dev Psychopathol. 2013;25(4, pt 2):1243-1261.PubMedGoogle ScholarCrossref
10.
Mitchell  C, Hobcraft  J, McLanahan  SS,  et al.  Social disadvantage, genetic sensitivity, and children’s telomere length.  Proc Natl Acad Sci U S A. 2014;111(16):5944-5949.PubMedGoogle ScholarCrossref
11.
Belsky  J, Bakermans-Kranenburg  MJ, van Ijzendoorn  MH.  For better and for worse: differential 16 susceptibility to environmental influences.  Curr Dir Psychol Sci. 2007;16(6):300-304. Google ScholarCrossref
12.
Levitan  RD, Masellis  M, Lam  RW,  et al.  A birth-season/DRD4 gene interaction predicts weight gain and obesity in women with seasonal affective disorder: A seasonal thrifty phenotype hypothesis.  Neuropsychopharmacology. 2006;31(11):2498-2503.PubMedGoogle ScholarCrossref
13.
Levitan  RD, Masellis  M, Basile  VS,  et al.  The dopamine-4 receptor gene associated with binge eating and weight gain in women with seasonal affective disorder: an evolutionary perspective.  Biol Psychiatry. 2004;56(9):665-669.PubMedGoogle ScholarCrossref
14.
Kaplan  AS, Levitan  RD, Yilmaz  Z, Davis  C, Tharmalingam  S, Kennedy  JLA.  A DRD4/BDNF gene-gene interaction associated with maximum BMI in women with bulimia nervosa.  Int J Eat Disord. 2008;41(1):22-28.PubMedGoogle ScholarCrossref
15.
Bakermans-Kranenburg  MJ, van Ijzendoorn  MH.  Differential susceptibility to rearing environment depending on dopamine-related genes: new evidence and a meta-analysis.  Dev Psychopathol. 2011;23(1):39-52.PubMedGoogle ScholarCrossref
16.
O’Donnell  KG, Colalillo  S, Steiner  M,  et al.  The Maternal Adversity Vulnerability and Neurodevelopment (MAVAN) Project: theory and methodology.  Can J Psychiatry. 2014;59(9):497-508.PubMedGoogle Scholar
17.
Silveira  PP, Portella  AK, Kennedy  JL,  et al; MAVAN Study Team.  Association between the seven-repeat allele of the dopamine-4 receptor gene (DRD4) and spontaneous food intake in pre-school children.  Appetite. 2014;73:15-22.PubMedGoogle ScholarCrossref
18.
Ordre Professional des Diététistes du Québec.  Manuel de nutrition clinique. 3rd ed. Quebec, Canada: Ordre Professional des Diététistes du Québec; 2000.
19.
 Low income cut offs for 2005 and low income measures for 2004.  Stat Canada. 2006;4:2006004.Google Scholar
20.
Lichter  JB, Barr  CL, Kennedy  JL, Van Tol  HH, Kidd  KK, Livak  KJ.  A hypervariable segment in the human dopamine receptor D4 (DRD4) gene.  Hum Mol Genet. 1993;2(6):767-773.PubMedGoogle ScholarCrossref
21.
Asghari  V, Sanyal  S, Buchwaldt  S, Paterson  A, Jovanovic  V, Van Tol  HH.  Modulation of intracellular cyclic AMP levels by different human dopamine D4 receptor variants.  J Neurochem. 1995;65(3):1157-1165.PubMedGoogle ScholarCrossref
22.
Parker  G, Tupling  H, Brown  LB.  A parental bonding instrument.  Br J Med Psychol. 1979;52:1-10.Google ScholarCrossref
23.
Kramer  MS, Platt  RW, Wen  SW,  et al; Fetal/Infant Health Study Group of the Canadian Perinatal Surveillance System.  A new and improved population-based Canadian reference for birth weight for gestational age.  Pediatrics. 2001;108(2):E35.PubMedGoogle ScholarCrossref
24.
Kraemer  HC, Stice  E, Kazdin  A, Offord  D, Kupfer  D.  How do risk factors work together? mediators, moderators, and independent, overlapping, and proxy risk factors.  Am J Psychiatry. 2001;158(6):848-856.PubMedGoogle ScholarCrossref
25.
Silveira  PP, Agranonik  M, Faras  H, Portella  AK, Meaney  MJ, Levitan  RD; Maternal Adversity, Vulnerability and Neurodevelopment Study Team.  Preliminary evidence for an impulsivity-based thrifty eating phenotype.  Pediatr Res. 2012;71(3):293-298.PubMedGoogle ScholarCrossref
26.
Bakermans-Kranenburg  MJ, Van IJzendoorn  MH, Pijlman  FT, Mesman  J, Juffer  F.  Experimental evidence for differential susceptibility: dopamine D4 receptor polymorphism (DRD4 VNTR) moderates intervention effects on toddlers’ externalizing behavior in a randomized controlled trial.  Dev Psychol. 2008;44(1):293-300.PubMedGoogle ScholarCrossref
27.
Eisenmann  JC, Heelan  KA, Welk  GJ.  Assessing body composition among 3- to 8-year-old children: anthropometry, BIA, and DXA.  Obes Res. 2004;12(10):1633-1640.PubMedGoogle ScholarCrossref
28.
de Beer  M, Vrijkotte  TGM, Fall  CHD, van Eijsden  M, Osmond  C, Gemke  RJBJ.  Associations of infant feeding and timing of linear growth and relative weight gain during early life with childhood body composition.  Int J Obes (Lond). 2015;39(4):586-592.PubMedGoogle ScholarCrossref
29.
Koyama  S, Sairenchi  T, Shimura  N, Arisaka  O.  Association between timing of adiposity rebound and body weight gain during infancy.  J Pediatr. 2015;166(2):309-312.PubMedGoogle ScholarCrossref
30.
Uher  R, Treasure  J, Heining  M, Brammer  MJ, Campbell  IC.  Cerebral processing of food-related stimuli: effects of fasting and gender.  Behav Brain Res. 2006;169(1):111-119.PubMedGoogle ScholarCrossref
31.
Wang  GJ, Volkow  ND, Telang  F,  et al.  Evidence of gender differences in the ability to inhibit brain activation elicited by food stimulation.  Proc Natl Acad Sci U S A. 2009;106(4):1249-1254.PubMedGoogle ScholarCrossref
32.
Cooke  LJ, Wardle  J.  Age and gender differences in children’s food preferences.  Br J Nutr. 2005;93(5):741-746.PubMedGoogle ScholarCrossref
33.
Wendland  BE, Atkinson  L, Steiner  M,  et al; MAVAN Study Team.  Low maternal sensitivity at 6 months of age predicts higher BMI in 48 month old girls but not boys.  Appetite. 2014;82:97-102.PubMedGoogle ScholarCrossref
34.
Levitan  RD, Rivera  J, Silveira  PP,  et al; MAVAN Study Team.  Gender differences in the association between stop-signal reaction times, body mass indices and/or spontaneous food intake in pre-school children: an early model of compromised inhibitory control and obesity.  Int J Obes (Lond). 2015;39(4):614-619.PubMedGoogle ScholarCrossref
35.
VanZomeren-Dohm  AA, Pitula  CE, Koss  KJ, Thomas  K, Gunnar  MR.  FKBP5 moderation of depressive symptoms in peer victimized, post-institutionalized children.  Psychoneuroendocrinology. 2015;51:426-430.PubMedGoogle ScholarCrossref
36.
Grier  SA, Kumanyika  SK.  The context for choice: health implications of targeted food and beverage marketing to African Americans.  Am J Public Health. 2008;98(9):1616-1629.PubMedGoogle ScholarCrossref
37.
Horowitz  CR, Colson  KA, Hebert  PL, Lancaster  K.  Barriers to buying healthy foods for people with diabetes: evidence of environmental disparities.  Am J Public Health. 2004;94(9):1549-1554.PubMedGoogle ScholarCrossref
38.
Powell  LM, Slater  S, Mirtcheva  D, Bao  Y, Chaloupka  FJ.  Food store availability and neighborhood characteristics in the United States.  Prev Med. 2007;44(3):189-195.PubMedGoogle ScholarCrossref
39.
Dubé  L, Pingali  P, Webb  P.  Paths of convergence for agriculture, health, and wealth.  Proc Natl Acad Sci U S A. 2012;109(31):12294-12301.PubMedGoogle ScholarCrossref
40.
Heckman  JJ.  Skill formation and the economics of investing in disadvantaged children.  Science. 2006;312(5782):1900-1902.PubMedGoogle ScholarCrossref
41.
Belsky  J, van Ijzendoorn  MH.  What works for whom? genetic moderation of intervention efficacy.  Dev Psychopathol. 2015;27(1):1-6.PubMedGoogle ScholarCrossref
42.
van Ijzendoorn  MH, Bakermans-Kranenburg  MJ.  Genetic differential susceptibility on trial: meta-analytic support from randomized controlled experiments.  Dev Psychopathol. 2015;27(1):151-162.PubMedGoogle ScholarCrossref
Original Investigation
April 2016

Genetic Differential Susceptibility to Socioeconomic Status and Childhood Obesogenic Behavior: Why Targeted Prevention May Be the Best Societal Investment

Author Affiliations
  • 1Department of Pediatrics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
  • 2Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Montreal, Quèbec, Canada
  • 3Department of Psychology, Ryerson University, Toronto, Ontario, Canada
  • 4Department of Psychology, University of Toronto, Toronto, Ontario, Canada
  • 5Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
  • 6Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
  • 7Department of Psychiatry, University of Toronto and Centre for Addiction and Mental Health, Toronto, Ontario, Canada
  • 8Desautels Faculty of Management, McGill Center for the Convergence of Health and Economics, McGill University, Montreal, Quebec, Canada
JAMA Pediatr. 2016;170(4):359-364. doi:10.1001/jamapediatrics.2015.4253
Abstract

Importance  Genes may work by modulating the way individuals respond to environmental variation, and these discrete and differential genes vs environmental interactions may not be readily captured in simple association studies.

Objective  To determine whether children carrying the 7-repeat allele of the DRD4 gene living under adverse economic conditions have worse-than-average fat intake compared with those living in a healthy environment.

Design, Setting, and Participants  Data from an established prospective birth cohort (Maternal Adversity, Vulnerability, and Neurodevelopment) were used to study 4-year-old children from Montreal, Quebec, Canada and Hamilton, Ontario, Canada. A total of 190 children (94 girls and 96 boys) had height and weight measured and complete food diaries and were therefore eligible for the study. The study is derived from a birth cohort started in June 2003 and still ongoing. The last age of follow-up was at 6 years.

Exposures  Social environment was characterized based on the gross family income, and DNA was genotyped for the 7-repeat allele of the DRD4 gene.

Main Outcomes and Measures  Fat intake.

Results  The 5 steps to distinguish the differential susceptibility from other types of interaction were followed, and the study confirms that differential susceptibility is a relevant model to address the association between the 7-repeat allele of DRD4 and food choices in girls. Of the 190 children, 112 did not have the DRD4 7-repeat allele and 78 did. Baseline characteristics did not differ in these 2 groups. Although not different in several confounders, such as maternal educational level, maternal smoking during gestation, birth weight, and breastfeeding duration, girls carrying the 7-repeat allele of the DRD4 gene and living in adverse socioeconomic conditions have increased fat intake compared with girls who are noncarriers (DRD4 7+ mean, 33.95% of calories derived from fat; 95% CI, 28.76%-39.13%; DRD4 7− mean, 28.76%; 95% CI, 26.77%-30.83%). However, girls carrying the 7-repeat allele of the same gene and living in better socioeconomic conditions have decreased fat intake compared with noncarriers (DRD4 7+ mean, 29.03% of calories derived from fat; 95% CI, 26.69%-31.51%; DRD4 7− mean, 31.88%; 95% CI, 30.28%-33.58%).

Conclusions and Relevance  Alleles previously considered to be obesity risk alleles might in fact function as plasticity alleles, determining openness to environmental modification and/or intervention, as seen in the girls in this study. This finding has important implications for obesity prevention and social pediatrics.

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