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Tan PY, Moore JB, Bai L, Tang G, Gong YY. In the context of the triple burden of malnutrition: A systematic review of gene-diet interactions and nutritional status. Crit Rev Food Sci Nutr 2022; 64:3235-3263. [PMID: 36222100 PMCID: PMC11000749 DOI: 10.1080/10408398.2022.2131727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Genetic background interacts with dietary components to modulate nutritional health status. This study aimed to review the evidence for gene-diet interactions in all forms of malnutrition. A comprehensive systematic literature search was conducted through April 2021 to identify observational and intervention studies reporting the effects of gene-diet interactions in over-nutrition, under-nutrition and micronutrient status. Risk of publication bias was assessed using the Quality Criteria Checklist and a tool specifically designed for gene-diet interaction research. 167 studies from 27 populations were included. The majority of studies investigated single nucleotide polymorphisms (SNPs) in overnutrition (n = 158). Diets rich in whole grains, vegetables, fruits and low in total and saturated fats, such as Mediterranean and DASH diets, showed promising effects for reducing obesity risk among individuals who had higher genetic risk scores for obesity, particularly the risk alleles carriers of FTO rs9939609, rs1121980 and rs1421085. Other SNPs in MC4R, PPARG and APOA5 genes were also commonly studied for interaction with diet on overnutrition though findings were inconclusive. Only limited data were found related to undernutrition (n = 1) and micronutrient status (n = 9). The findings on gene-diet interactions in this review highlight the importance of personalized nutrition, and more research on undernutrition and micronutrient status is warranted.
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Affiliation(s)
- Pui Yee Tan
- School of Food Science and Nutrition, Faculty of Environment, University of Leeds, Leeds, United Kingdom
| | - J. Bernadette Moore
- School of Food Science and Nutrition, Faculty of Environment, University of Leeds, Leeds, United Kingdom
| | - Ling Bai
- School of Food Science and Nutrition, Faculty of Environment, University of Leeds, Leeds, United Kingdom
- School of Psychology, University of East Anglia, Norwich, United Kingdom
| | - GuYuan Tang
- School of Food Science and Nutrition, Faculty of Environment, University of Leeds, Leeds, United Kingdom
| | - Yun Yun Gong
- School of Food Science and Nutrition, Faculty of Environment, University of Leeds, Leeds, United Kingdom
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Stanislawski MA, Litkowski E, Fore R, Rifas-Shiman SL, Oken E, Hivert MF, Lange EM, Lange LA, Dabelea D, Raghavan S. Genetic Interactions with Intrauterine Diabetes Exposure in Relation to Obesity: The EPOCH and Project Viva Studies. Pediatr Rep 2021; 13:279-288. [PMID: 34205853 PMCID: PMC8293453 DOI: 10.3390/pediatric13020036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/18/2021] [Accepted: 05/21/2021] [Indexed: 11/16/2022] Open
Abstract
To examine whether BMI-associated genetic risk variants modify the association of intrauterine diabetes exposure with childhood BMI z-scores, we assessed the interaction between 95 BMI-associated genetic variants and in utero exposure to maternal diabetes among 459 children in the Exploring Perinatal Outcomes among Children historical prospective cohort study (n = 86 exposed; 373 unexposed) in relation to age- and sex-standardized childhood BMI z-scores (mean age = 10.3 years, standard deviation = 1.5 years). For the genetic variants showing a nominally significant interaction, we assessed the relationship in an additional 621 children in Project Viva, which is an independent longitudinal cohort study, and used meta-analysis to combine the results for the two studies. Seven of the ninety-five genetic variants tested exhibited a nominally significant interaction with in utero exposure to maternal diabetes in relation to the offspring BMI z-score in EPOCH. Five of the seven variants exhibited a consistent direction of interaction effect across both EPOCH and Project Viva. While none achieved statistical significance in the meta-analysis after accounting for multiple testing, three variants exhibited a nominally significant interaction with in utero exposure to maternal diabetes in relation to offspring BMI z-score: rs10733682 near LMX1B (interaction β = 0.39; standard error (SE) = 0.17), rs17001654 near SCARB2 (β = 0.53; SE = 0.22), and rs16951275 near MAP2K5 (β = 0.37; SE = 0.17). BMI-associated genetic variants may enhance the association between exposure to in utero diabetes and higher childhood BMI, but larger studies of in utero exposures are necessary to confirm the observed nominally significant relationships.
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Affiliation(s)
- Maggie A. Stanislawski
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Correspondence:
| | - Elizabeth Litkowski
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO 80045, USA;
| | - Ruby Fore
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (R.F.); (S.L.R.-S.); (E.O.); (M.-F.H.)
| | - Sheryl L. Rifas-Shiman
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (R.F.); (S.L.R.-S.); (E.O.); (M.-F.H.)
| | - Emily Oken
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (R.F.); (S.L.R.-S.); (E.O.); (M.-F.H.)
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (R.F.); (S.L.R.-S.); (E.O.); (M.-F.H.)
- Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO 80045, USA
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO 80045, USA;
| | - Dana Dabelea
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO 80045, USA;
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO 80045, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Sridharan Raghavan
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Veterans Affairs Eastern Colorado Healthcare System, Aurora, CO 80045, USA
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Development of a Genetic Risk Score to predict the risk of overweight and obesity in European adolescents from the HELENA study. Sci Rep 2021; 11:3067. [PMID: 33542408 PMCID: PMC7862459 DOI: 10.1038/s41598-021-82712-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 01/22/2021] [Indexed: 11/08/2022] Open
Abstract
Obesity is the result of interactions between genes and environmental factors. Since monogenic etiology is only known in some obesity-related genes, a genetic risk score (GRS) could be useful to determine the genetic predisposition to obesity. Therefore, the aim of our study was to build a GRS able to predict genetic predisposition to overweight and obesity in European adolescents. A total of 1069 adolescents (51.3% female), aged 11-19 years participating in the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study were genotyped. The sample was divided in non-overweight (non-OW) and overweight/obesity (OW/OB). From 611 single nucleotide polymorphisms (SNP) available, a first screening of 104 SNPs univariately associated with obesity (p < 0.20) was established selecting 21 significant SNPs (p < 0.05) in the multivariate model. Unweighted GRS (uGRS) was calculated by summing the number of risk alleles and weighted GRS (wGRS) by multiplying the risk alleles to each estimated coefficient. The area under curve (AUC) was calculated in uGRS (0.723) and wGRS (0.734) using tenfold internal cross-validation. Both uGRS and wGRS were significantly associated with body mass index (BMI) (p < .001). Both GRSs could potentially be considered as useful genetic tools to evaluate individual's predisposition to overweight/obesity in European adolescents.
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Santos C, Bustamante A, Hedeker D, Vasconcelos O, Garganta R, Katzmarzyk PT, Maia J. Correlates of Overweight in Children and Adolescents Living at Different Altitudes: The Peruvian Health and Optimist Growth Study. J Obes 2019; 2019:2631713. [PMID: 31467705 PMCID: PMC6701273 DOI: 10.1155/2019/2631713] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 06/26/2019] [Indexed: 12/19/2022] Open
Abstract
Background and Aim Overweight prevalence in children and adolescents shows great variability which is related to individual-level and environmental-level factors. The present study aimed to determine the prevalence of and factors associated with overweight in Peruvian children and adolescents living at different altitudes. Methods 8568 subjects, aged 6-16 y, from the sea level, Amazon, and high-altitude regions were sampled. Overweight was identified using BMI; biological maturation and physical fitness were measured; school characteristics were assessed via an objective audit. Results Overweight prevalence decreased with age (28.3% at 6 y to 13.9% at 16 y); it was higher in girls (21.7%) than boys (19.8%) and was higher at the sea level (41.3%), compared with Amazon (18.8%) and high-altitude (6.3%) regions. Approximately 79% of the variance in overweight was explained by child-level characteristics. In Model 1, all child-level predictors were significant (p < 0.001); in Model 2, six out of nine added school-level predictors (number of students, existence of policies and practices for physical activity, multisports-roofed, duration of Physical Education classes, and extracurricular activities) were significant (p < 0.001); in Model 3, subjects living at high altitudes were less likely to be overweight than those living at the sea level. Conclusions Child- and school-level variables played important roles in explaining overweight variation. This information should be taken into account when designing more efficient strategies to combat the overweight and obesity epidemic.
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Affiliation(s)
- Carla Santos
- CIFI D, Faculty of Sport, University of Porto, Porto, Portugal
| | - Alcibíades Bustamante
- Faculty of Physical Culture and Sports, National University of Education Enrique Guzmán y Valle, Lima, Peru
| | - Donald Hedeker
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | | | - Rui Garganta
- CIFI D, Faculty of Sport, University of Porto, Porto, Portugal
| | - Peter T. Katzmarzyk
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA
| | - José Maia
- CIFI D, Faculty of Sport, University of Porto, Porto, Portugal
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5
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Gene-Environment Interactions on Body Fat Distribution. Int J Mol Sci 2019; 20:ijms20153690. [PMID: 31357654 PMCID: PMC6696304 DOI: 10.3390/ijms20153690] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 07/18/2019] [Accepted: 07/19/2019] [Indexed: 02/08/2023] Open
Abstract
The prevalence of obesity has been increasing markedly in the U.S. and worldwide in the past decades; and notably, the obese populations are signified by not only the overall elevated adiposity but also particularly harmful accumulation of body fat in the central region of the body, namely, abdominal obesity. The profound shift from “traditional” to “obesogenic” environments, principally featured by the abundance of palatable, energy-dense diet, reduced physical activity, and prolonged sedentary time, promotes the obesity epidemics and detrimental body fat distribution. Recent advances in genomics studies shed light on the genetic basis of obesity and body fat distribution. In addition, growing evidence from investigations in large cohorts and clinical trials has lent support to interactions between genetic variations and environmental factors, e.g., diet and lifestyle factors, in relation to obesity and body fat distribution. This review summarizes the recent discoveries from observational studies and randomized clinical trials on the gene–environment interactions on obesity and body fat distribution.
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Beyerlein A, Uusitalo UM, Virtanen SM, Vehik K, Yang J, Winkler C, Kersting M, Koletzko S, Schatz D, Aronsson CA, Larsson HE, Krischer JP, Ziegler AG, Norris JM, Hummel S. Intake of Energy and Protein is Associated with Overweight Risk at Age 5.5 Years: Results from the Prospective TEDDY Study. Obesity (Silver Spring) 2017; 25:1435-1441. [PMID: 28650578 PMCID: PMC5529234 DOI: 10.1002/oby.21897] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 05/03/2017] [Accepted: 05/09/2017] [Indexed: 12/15/2022]
Abstract
OBJECTIVE The associations of energy, protein, carbohydrate, and fat intake with weight status up to the age of 5.5 years were prospectively assessed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. METHODS Food record data (over 3 days) and BMI measurements between 0.25 and 5.5 years were available from 5,563 children with an increased genetic risk for type 1 diabetes followed from shortly after birth. Odds ratios (ORs) were calculated for overweight and obesity by previous intake of energy, protein, carbohydrate, and fat with adjustment for potential confounders. RESULTS Having overweight or obesity at the age of 5.5 years was positively associated with mean energy intake in previous age intervals (e.g., adjusted OR [95% CI] for overweight: 1.06 [1.04-1.09] per 100 kcal intake at the age of 4.5-5.0 years) and with protein intake after the age of 3.5 and 4.5 years, respectively (e.g., adjusted OR for overweight: 1.06 [1.03-1.09] per 1% of energy intake at the age of 4.5-5.0 years). The respective associations with carbohydrate and fat intake were less consistent. CONCLUSIONS These findings indicate that energy and protein intake are positively associated with increased risk for overweight in childhood but yield no evidence for potential programming effects of protein intake in infancy.
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Affiliation(s)
- Andreas Beyerlein
- Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Forschergruppe Diabetes e.V., Neuherberg, Germany
| | - Ulla M. Uusitalo
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Suvi M. Virtanen
- Unit of Nutrition, National Institute for Health and Welfare, Helsinki; University of Tampere, Tampere, School of Health Sciences; Center for Child Health Research, University of Tampere and Tampere University Hospital, Tampere; and The Science Center of Pirkanmaa Hospital District, Tampere, Finland
| | - Kendra Vehik
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Jimin Yang
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Christiane Winkler
- Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Forschergruppe Diabetes e.V., Neuherberg, Germany
| | - Mathilde Kersting
- Research Institute of Child Nutrition (FKE), Pediatric University Clinic, Ruhr-University Bochum, Germany
| | - Sibylle Koletzko
- Dr. v. Hauner Children’s Hospital, University Munich Medical Center, Munich, Germany
| | - Desmond Schatz
- Departments of Pediatrics and Pathology and Laboratory Medicine, University of Florida, Gainesville, Florida
| | - Carin Andrén Aronsson
- Department of Clinical Sciences, Lund University, Skåne University Hospital SUS, Malmö, Sweden
| | - Helena Elding Larsson
- Department of Clinical Sciences, Lund University, Skåne University Hospital SUS, Malmö, Sweden
| | - Jeffrey P. Krischer
- Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Anette-G. Ziegler
- Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Forschergruppe Diabetes e.V., Neuherberg, Germany
| | - Jill M. Norris
- Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Sandra Hummel
- Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Forschergruppe Diabetes e.V., Neuherberg, Germany
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The importance of gene-environment interactions in human obesity. Clin Sci (Lond) 2017; 130:1571-97. [PMID: 27503943 DOI: 10.1042/cs20160221] [Citation(s) in RCA: 108] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/23/2016] [Indexed: 12/16/2022]
Abstract
The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations.
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Heianza Y, Qi L. Gene-Diet Interaction and Precision Nutrition in Obesity. Int J Mol Sci 2017; 18:ijms18040787. [PMID: 28387720 PMCID: PMC5412371 DOI: 10.3390/ijms18040787] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Revised: 03/30/2017] [Accepted: 04/03/2017] [Indexed: 02/06/2023] Open
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.
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Affiliation(s)
- Yoriko Heianza
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
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Magzamen S, Amato MS, Imm P, Havlena JA, Coons MJ, Anderson HA, Kanarek MS, Moore CF. Quantile regression in environmental health: Early life lead exposure and end-of-grade exams. ENVIRONMENTAL RESEARCH 2015; 137:108-19. [PMID: 25531815 DOI: 10.1016/j.envres.2014.12.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Revised: 11/14/2014] [Accepted: 12/02/2014] [Indexed: 05/22/2023]
Abstract
Conditional means regression, including ordinary least squares (OLS), provides an incomplete picture of exposure-response relationships particularly if the primary interest resides in the tail ends of the distribution of the outcome. Quantile regression (QR) offers an alternative methodological approach in which the influence of independent covariates on the outcome can be specified at any location along the distribution of the outcome. We implemented QR to examine heterogeneity in the influence of early childhood lead exposure on reading and math standardized fourth grade tests. In children from two urban school districts (n=1,076), lead exposure was associated with an 18.00 point decrease (95% CI: -48.72, -3.32) at the 10th quantile of reading scores, and a 7.50 point decrease (95% CI: -15.58, 2.07) at the 90th quantile. Wald tests indicated significant heterogeneity of the coefficients across the distribution of quantiles. Math scores did not show heterogeneity of coefficients, but there was a significant difference in the lead effect at the 10th (β=-17.00, 95% CI: -32.13, -3.27) versus 90th (β=-4.50, 95% CI: -10.55, 4.50) quantiles. Our results indicate that lead exposure has a greater effect for children in the lower tail of exam scores, a result that is masked by conditional means approaches.
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Affiliation(s)
- Sheryl Magzamen
- Department of Environmental and Radiological Health Sciences, Colorado State University, 1681 Campus Delivery, Fort Collins, CO 80523-1681, United States.
| | - Michael S Amato
- Department of Psychology, University of Wisconsin, 1202 West Johnson Street, Madison, WI 53706, United States
| | - Pamela Imm
- Bureau of Environmental and Occupational Health, Wisconsin Department of Health Services, 1 West Wilson Street, Madison, WI 53703, United States
| | - Jeffrey A Havlena
- Department of Surgery, University of Wisconsin, 600 Highland Ave, Madison, WI 53792, United States
| | - Marjorie J Coons
- Bureau of Environmental and Occupational Health, Wisconsin Department of Health Services, 1 West Wilson Street, Madison, WI 53703, United States
| | - Henry A Anderson
- Bureau of Environmental and Occupational Health, Wisconsin Department of Health Services, 1 West Wilson Street, Madison, WI 53703, United States
| | - Marty S Kanarek
- Department of Population Health Sciences, University of Wisconsin, 707 WARF, 610 Walnut Street, Madison, WI 53726, United States; Nelson Institute for Environmental Studies, University of Wisconsin, 550 North Park Street, 122 Science Hall, Madison, WI 53706, United States
| | - Colleen F Moore
- Department of Psychology, University of Wisconsin, 1202 West Johnson Street, Madison, WI 53706, United States; Department of Psychology, Montana State University, PO Box 173440, Bozeman, MT 59717, United States
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Liu SY, Walter S, Marden J, Rehkopf DH, Kubzansky LD, Nguyen T, Glymour MM. Genetic vulnerability to diabetes and obesity: does education offset the risk? Soc Sci Med 2014; 127:150-8. [PMID: 25245452 DOI: 10.1016/j.socscimed.2014.09.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Revised: 08/29/2014] [Accepted: 09/03/2014] [Indexed: 01/09/2023]
Abstract
The prevalence of type 2 diabetes (T2D) and obesity has recently increased dramatically. These common diseases are likely to arise from the interaction of multiple genetic, socio-demographic and environmental risk factors. While previous research has found genetic risk and education to be strong predictors of these diseases, few studies to date have examined their joint effects. This study investigates whether education modifies the association between genetic background and risk for type 2 diabetes (T2D) and obesity. Using data from non-Hispanic Whites in the Health and Retirement Study (HRS, n = 8398), we tested whether education modifies genetic risk for obesity and T2D, offsetting genetic effects; whether this effect is larger for individuals who have high risk for other (unobserved) reasons, i.e., at higher quantiles of HbA1c and BMI; and whether effects differ by gender. We measured T2D risk using Hemoglobin A1c (HbA1c) level, and obesity risk using body-mass index (BMI). We constructed separate genetic risk scores (GRS) for obesity and diabetes respectively based on the most current available information on the single nucleotide polymorphism (SNPs) confirmed as genome-wide significant predictors for BMI (29 SNPs) and diabetes risk (39 SNPs). Linear regression models with years of schooling indicate that the effect of genetic risk on HbA1c is smaller among people with more years of schooling and larger among those with less than a high school (HS) degree compared to HS degree-holders. Quantile regression models show that the GRS × education effect systematically increased along the HbA1c outcome distribution; for example the GRS × years of education interaction coefficient was -0.01 (95% CI = -0.03, 0.00) at the 10th percentile compared to -0.03 (95% CI = -0.07, 0.00) at the 90th percentile. These results suggest that education may be an important socioeconomic source of heterogeneity in responses to genetic vulnerability to T2D.
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Affiliation(s)
- S Y Liu
- Harvard Center for Population and Development Studies, 9 Bow Street, Cambridge, MA 02138, USA
| | - S Walter
- UCSF School of Medicine, Department of Epidemiology & Biostatistics, 185 Berry Street, San Francisco, CA 94107, USA
| | - J Marden
- Harvard School of Public Health, Department of Social and Behavioral Sciences, 677 Huntington Avenue, Kresge, 6th Floor, Boston, MA 02115, USA
| | - D H Rehkopf
- Stanford University, School of Medicine, Department of Medicine, Division of General Medical Disciplines, 251 Campus Drive, Stanford, CA 94305, USA
| | - L D Kubzansky
- Harvard School of Public Health, Department of Social and Behavioral Sciences, 677 Huntington Avenue, Kresge, 6th Floor, Boston, MA 02115, USA
| | - T Nguyen
- Harvard School of Public Health, Department of Social and Behavioral Sciences, 677 Huntington Avenue, Kresge, 6th Floor, Boston, MA 02115, USA
| | - M M Glymour
- UCSF School of Medicine, Department of Epidemiology & Biostatistics, 185 Berry Street, San Francisco, CA 94107, USA; Harvard School of Public Health, Department of Social and Behavioral Sciences, 677 Huntington Avenue, Kresge, 6th Floor, Boston, MA 02115, USA.
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