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Jansen PR, Vos N, van Uhm J, Dekkers IA, van der Meer R, Mannens MMAM, van Haelst MM. The utility of obesity polygenic risk scores from research to clinical practice: A review. Obes Rev 2024; 25:e13810. [PMID: 39075585 DOI: 10.1111/obr.13810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 06/13/2024] [Accepted: 07/10/2024] [Indexed: 07/31/2024]
Abstract
Obesity represents a major public health emergency worldwide, and its etiology is shaped by a complex interplay of environmental and genetic factors. Over the last decade, polygenic risk scores (PRS) have emerged as a promising tool to quantify an individual's genetic risk of obesity. The field of PRS in obesity genetics is rapidly evolving, shedding new lights on obesity mechanisms and holding promise for contributing to personalized prevention and treatment. Challenges persist in terms of its clinical integration, including the need for further validation in large-scale prospective cohorts, ethical considerations, and implications for health disparities. In this review, we provide a comprehensive overview of PRS for studying the genetics of obesity, spanning from methodological nuances to clinical applications and challenges. We summarize the latest developments in the generation and refinement of PRS for obesity, including advances in methodologies for aggregating genome-wide association study data and improving PRS predictive accuracy, and discuss limitations that need to be overcome to fully realize its potential benefits of PRS in both medicine and public health.
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Affiliation(s)
- Philip R Jansen
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands
- Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | - Niels Vos
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Jorrit van Uhm
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Ilona A Dekkers
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Rieneke van der Meer
- Netherlands Obesity Clinic, Huis ter Heide, Netherlands
- Amsterdam UMC, Department of Endocrinology and Metabolism, University of Amsterdam, Amsterdam, Netherlands
| | - Marcel M A M Mannens
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
| | - Mieke M van Haelst
- Amsterdam UMC, Department of Human Genetics, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands
- Amsterdam UMC, Emma Center for Personalized Medicine, University of Amsterdam, Amsterdam, Netherlands
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Goulet D, Boivin M, Gravel CA, Little J, Potter BK, Dubois L. Mediation of genetic susceptibility to obesity through eating behaviours in children. Pediatr Obes 2024:e13180. [PMID: 39390328 DOI: 10.1111/ijpo.13180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 09/09/2024] [Accepted: 09/13/2024] [Indexed: 10/12/2024]
Abstract
BACKGROUND/OBJECTIVES Few studies have examined the putative mediating role of eating behaviours linking genetic susceptibility and body weight. The goal of this study was to investigate the extent to which two polygenic scores (PGSs) for body mass index (BMI), based on child and adult data, predicted BMI through over-eating and fussy eating across childhood. SUBJECTS/METHODS The study sample involved 692 participants from a birth cohort study. Height and weight were measured on six occasions between ages 6 and 13 years. Over-eating and fussy eating behaviours were assessed five times between ages 2 and 6 years. Longitudinal growth curve mediation analysis was used to estimate the contributions of the PGSs to BMI z-scores mediated by over-eating and fussy eating. RESULTS Both PGSs predicted BMI z-scores (PGSchild: β = 0.26, 95% CI: 0.19-0.33; PGSadult: β = 0.34, 95% CI: 0.27-0.41). Over-eating significantly mediated these associations, but this mediation decreased over time from 6 years (PGSchild: 18.0%, 95% CI: 3.1-32.9, p-value = 0.018; PGSadult: 14.2%, 95% CI: 2.8-25.5, p-value = 0.014) to 13 years (PGSchild: 11.4%, 95% CI: -0.4-23.1, p-value = 0.057; PGSadult: 6.2%, 95% CI: 0.4-12.0, p-value = 0.037). Fussy eating did not show any mediation. CONCLUSIONS Our results support the view that appetite is key to translating genetic susceptibility into changes in body weight.
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Affiliation(s)
- Danick Goulet
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Christopher A Gravel
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada
| | - Julian Little
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Beth K Potter
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Lise Dubois
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
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Lu M, Feng R, Li M, Liu L, Xiao Y, Liu Y, Yin C. Causal relationship between gut microbiota and childhood obesity: A Mendelian randomization study and case-control study. Clin Nutr ESPEN 2024; 63:197-206. [PMID: 38963766 DOI: 10.1016/j.clnesp.2024.05.012] [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] [Received: 12/03/2023] [Revised: 03/28/2024] [Accepted: 05/17/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND Gut microbiota and obesity are deeply interconnected. However, the causality in the relationship between these factors remains unclear. Therefore, this study aimed to elucidate the genetic relationship between gut microbiota and childhood obesity. METHODS Genetic summary statistics for the gut microbiota were obtained from the MiBioGen consortium. Genome-wide association studies (GWAS) summary data for childhood obesity were obtained from North American, Australian, and European collaborative genome-wide meta-analyses. Mendelian randomization (MR) analyses were performed using the inverse variance weighting method. 16 children with obesity and 16 without obesity were included for clinical observation, and their weight, body mass index, blood lipid levels, and gut microbiology were assessed. Paired t-test was the primary method of data analysis, and statistical significance was set at P < 0.05. RESULTS MR identified 16 causal relationships between the gut microbiome and childhood obesity. In the case-control study, we found that five gut microorganisms differed between children with and without obesity, whereas three gut microorganisms changed after weight loss in children with obesity. CONCLUSION Our study provides new insights into the genetic mechanisms underlying gut microbiota and childhood obesity. TRIAL REGISTRATION NUMBER ChiCTR2300072179. NAME OF REGISTRY Change of intestinal flora and plasma metabolome in obese children and their weight loss intervention: a randomized controlled tria URL OF REGISTRY: https://www.chictr.org.cn/showproj.html. DATE OF REGISTRATION 2023-06-06. DATE OF ENROLMENT OF THE FIRST PARTICIPANT TO THE TRIAL 2023-06-07.
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Affiliation(s)
- Mengnan Lu
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shanxi, 710054, China
| | - Ruoyang Feng
- Department of Joint Surgery, Xi'an Jiaotong University HongHui Hospital, Xi'an, Shanxi, 710054, China
| | - Meng Li
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shanxi, 710054, China
| | - Lujie Liu
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shanxi, 710054, China
| | - Yanfeng Xiao
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shanxi, 710054, China.
| | - Yuesheng Liu
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shanxi, 710054, China.
| | - Chunyan Yin
- Department of Pediatrics, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shanxi, 710054, China.
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Yeum D, Renier TJ, Masterson TD, Carlson DD, Ballarino GA, Lansigan RK, Loos RJF, Emond JA, Gilbert-Diamond D. Genetic associations with consumption of palatable foods in the absence of hunger in response to food cues in children. Pediatr Obes 2024:e13168. [PMID: 39197865 DOI: 10.1111/ijpo.13168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 07/12/2024] [Accepted: 08/05/2024] [Indexed: 09/01/2024]
Abstract
OBJECTIVE The objective of this study is to evaluate obesity-related genetic factors in relation to excess consumption and assess if food cues modify associations. METHODS Children (9-12 years) completed a randomized crossover experiment. During two visits, children ate a preload and then snacks ad libitum while watching television, embedded with food or non-food advertisements to assess eating in the absence of hunger (EAH). Primary exposures were obesity-associated genotypes, FTO rs9939609 and MC4R rs571312, and a paediatric-specific polygenic risk score (PRS). Outcomes included consumption of all snacks (total EAH) and gummy candy only (gummy candy EAH). Linear mixed-effects models tested whether genetic exposures related to EAH outcomes. We tested for effect modification by food cues using multiplicative interaction terms. RESULTS Among 177 children, each FTO risk allele was associated with a 30% increase in gummy candy EAH (p = 0.025) in adjusted models. Food cue exposure exacerbated associations between the FTO variant with gummy candy EAH (p = 0.046). No statistically significant associations were found between MC4R and EAH. CONCLUSION The results suggest children with the FTO rs9939609 risk allele may be predisposed to excess consumption of candy and that this association may be exacerbated by food cues.
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Affiliation(s)
- Dabin Yeum
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, USA
| | - Timothy J Renier
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, USA
| | - Travis D Masterson
- Department of Nutritional Sciences, College of Health and Human Development, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Delaina D Carlson
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, USA
| | - Grace A Ballarino
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, USA
| | - Reina K Lansigan
- Department of Pediatrics, Geisel School of Medicine at Dartmouth College, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
| | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jennifer A Emond
- Department of Pediatrics, Geisel School of Medicine at Dartmouth College, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, USA
| | - Diane Gilbert-Diamond
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, USA
- Department of Pediatrics, Geisel School of Medicine at Dartmouth College, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Department of Medicine, Geisel School of Medicine at Dartmouth College, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
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Renier TJ, Yeum D, Emond JA, Lansigan RK, Ballarino GA, Carlson DD, Loos RJF, Gilbert-Diamond D. Elucidating pathways to pediatric obesity: a study evaluating obesity polygenic risk scores related to appetitive traits in children. Int J Obes (Lond) 2024; 48:71-77. [PMID: 37736781 PMCID: PMC10841756 DOI: 10.1038/s41366-023-01385-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 09/05/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023]
Abstract
BACKGROUND/OBJECTIVES Obesity polygenic risk scores (PRS) explain substantial variation in body mass index (BMI), yet associations between PRSs and appetitive traits in children remain unclear. To better understand pathways leading to pediatric obesity, this study aimed to assess the association of obesity PRSs and appetitive traits. SUBJECTS/METHODS This study included 248 unrelated children aged 9-12 years. DNA from the children was genotyped (236 met quality control thresholds) and four weighted polygenic risk scores from previous studies were computed and standardized: a 97 SNP PRS, 266 SNP pediatric-specific PRS, 466 SNP adult-specific PRS, and ~2 million SNP PRS. Appetitive traits were assessed using a parent-completed Child Eating Behavior Questionnaire, which evaluated food approach/avoidance traits and a composite obesogenic appetite score. BMI was directly measured and standardized by age and sex. Three associations were evaluated with linear regression: (1) appetitive traits and BMI, (2) PRSs and BMI, and (3) PRSs and appetitive traits, the primary association of interest. RESULTS Expected positive associations were observed between obesogenic appetitive traits and BMI and all four PRSs and BMI. Examining the association between PRSs and appetitive traits, all PRSs except for the 466 SNP adult PRS were significantly associated with the obesogenic appetite score. Each standard deviation increase in the 266 SNP pediatric PRS was associated with an adjusted 2.1% increase in obesogenic appetite score (95% CI: 0.6%, 3.7%, p = 0.006). Significant partial mediation of the PRS-BMI association by obesogenic appetite score was found for these PRSs; for example, 21.3% of the association between the 266 SNP pediatric PRS and BMI was explained by the obesogenic appetite score. CONCLUSIONS Genetic obesity risk significantly predicted appetitive traits, which partially mediated the association between genetic obesity risk and BMI in children. These findings build a clearer picture of pathways leading to pediatric obesity.
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Affiliation(s)
- Timothy J Renier
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
| | - Dabin Yeum
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Jennifer A Emond
- Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Pediatrics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Reina K Lansigan
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Grace A Ballarino
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Delaina D Carlson
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Ruth J F Loos
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Diane Gilbert-Diamond
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Pediatrics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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Kulisch LK, Arumäe K, Briley DA, Vainik U. Triangulating causality between childhood obesity and neurobehavior: Behavioral genetic and longitudinal evidence. Dev Sci 2023; 26:e13392. [PMID: 36950909 DOI: 10.1111/desc.13392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 02/17/2023] [Accepted: 02/24/2023] [Indexed: 03/24/2023]
Abstract
Childhood obesity is a serious health concern that is not yet fully understood. Previous research has linked obesity with neurobehavioral factors such as behavior, cognition, and brain morphology. The causal directions of these relationships remain mostly untested. We filled this gap by using the Adolescent Brain Cognitive Development study cohort comprising 11,875 children aged 9-10. First, correlations between the age- and sex-specific 95th BMI percentile (%BMIp95) and neurobehavioral measures were cross-sectionally analyzed. Effects were then aggregated by neurobehavioral domain for causal analyses. Behavioral genetic Direction of Causation modeling was used to test the direction of each relationship. Findings were validated by longitudinal cross-lagged panel modeling. %BMIp95 correlated with impulsivity, motivation, psychopathology, eating behavior, and cognitive tests (executive functioning, language, memory, perception, working memory). Greater %BMIp95 was also associated with reduced cortical thickness in frontal and temporal brain areas but with increased thickness in parietal and occipital areas. Similar although weaker patterns emerged for cortical surface area and volume. Behavioral genetic modeling suggested causal effects of %BMIp95 on eating behavior (β = 0.26), cognition (β = 0.05), cortical thickness (β = 0.15), and cortical surface area (β = 0.07). Personality/psychopathology (β = 0.09) and eating behavior (β = 0.16) appeared to influence %BMIp95. Longitudinal evidence broadly supported these findings. Results regarding cortical volume were inconsistent. Results supported causal effects of obesity on brain functioning and morphology. The present study highlights the importance of physical health for brain development and may inform interventions aimed at preventing or reducing pediatric obesity. RESEARCH HIGHLIGHTS: A continuous measure related to obesity, %BMIp95, has correlations with various measures of brain functioning and structure Behavioral genetic and longitudinal modeling suggest causal links from personality, psychopathology, and eating behavior to %BMIp95 Results also indicate directional links from %BMIp95 to eating behavior, cognition, cortical thickness, and cortical surface area Obesity may play a role for healthy brain development during childhood.
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Affiliation(s)
- Leonard Konstantin Kulisch
- Institute of Psychology, University of Tartu, Tartu, Estonia
- Wilhem Wundt Institute for Pschology, Leipzig University, Leipzig, Germany
| | - Kadri Arumäe
- Institute of Psychology, University of Tartu, Tartu, Estonia
| | - Daniel A Briley
- Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Uku Vainik
- Institute of Psychology, University of Tartu, Tartu, Estonia
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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Carnell S, Thapaliya G, Jansen E, Chen L. Biobehavioral susceptibility for obesity in childhood: Behavioral, genetic and neuroimaging studies of appetite. Physiol Behav 2023; 271:114313. [PMID: 37544571 PMCID: PMC10591980 DOI: 10.1016/j.physbeh.2023.114313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 06/06/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Modern food environments are conducive to overeating and weight gain, but not everyone develops obesity. One reason for this may be that individuals differ in appetitive characteristics, or traits, that manifest early in life and go on to influence their behavioral susceptibility to gain and maintain excess weight. Classic studies showing that eating behavior in children can be measured by behavioral paradigms such as tests of caloric compensation and eating in the absence of hunger inspired the development and validation of psychometric instruments to assess appetitive characteristics in children and infants. A large body of evidence now suggests that food approach traits increase obesity risk, while food avoidant traits, such as satiety responsiveness, decrease obesity risk. Twin studies and genetic association studies have demonstrated that appetitive characteristics are heritable, consistent with a biological etiology. However, family environment factors are also influential, with mounting evidence suggesting that genetic and environmental risk factors interact and correlate with consequences for child eating behavior and weight. Further, neuroimaging studies are revealing that individual differences in responses to visual food cues, as well as to small tastes and larger amounts of food, across a number of brain regions involved in reward/motivation, cognitive control and other functions, may contribute to individual variation in appetitive behavior. Growing evidence also suggests that variation on psychometric measures of appetite is associated with regional differences in brain structure, and differential patterns of resting state functional connectivity. Large prospective studies beginning in infancy promise to enrich our understanding of neural and other biological underpinnings of appetite and obesity development in early life, and how the interplay between genetic and environmental factors affects appetitive systems. The biobehavioral susceptibility model of obesity development and maintenance outlined in this narrative review has implications for prevention and treatment of obesity in childhood.
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Affiliation(s)
- Susan Carnell
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore MD, USA.
| | - Gita Thapaliya
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore MD, USA
| | - Elena Jansen
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore MD, USA
| | - Liuyi Chen
- Division of Psychiatric Neuroimaging, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore MD, USA
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Pérusse L, Jacob R, Drapeau V, Llewellyn C, Arsenault BJ, Bureau A, Labonté MÈ, Tremblay A, Vohl MC. Understanding gene-lifestyle interaction in obesity: the role of mediation versus moderation. Lifestyle Genom 2022; 15:67-76. [PMID: 35231909 DOI: 10.1159/000523813] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/22/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Obesity results from complex interactions between genetic susceptibility to weight gain and poor eating and lifestyle behaviors. The approach that has been traditionally used in genetics to investigate gene-environment/lifestyle interaction in obesity is based on the concept of moderation, or effect modification. Another approach called mediation analysis can be used to investigate gene-environment interaction in obesity. The objective of this review article is to explain the differences between the concepts of moderation and mediation and summarize the studies that have used mediation analysis to support the role of eating or lifestyle behaviors as putative mediators of genetic susceptibility to obesity. SUMMARY Moderation is used to determine whether the effect of an exposure (genes associated with obesity) on an outcome (obesity phenotype) differs in magnitude and/or direction across the spectrum of environmental exposure. Mediation analysis is used to assess the extent to which the effect of the exposure on the outcome is explained by a given set of hypothesized mediators with the aim of understanding how the exposure could lead to the outcome. In comparison with moderation, relatively few studies used mediation analyses to investigate gene-environment in obesity. Most studies found evidence that traits related to appetite or eating behaviors partly mediated genetic susceptibility to obesity in either children or adults. Key messages: Moderation and mediation represent two complementary approaches to investigate gene-environment interaction in obesity and address different research questions pertaining to the cause-effect relationship between genetic susceptibility to obesity and various obesity outcomes. More studies relying on mediation are needed to better understand the role eating and lifestyle habits in mediating genetic susceptibility to obesity.
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Affiliation(s)
- Louis Pérusse
- Department of Kinesiology, Faculty of Medicine, Université Laval, Québec, Québec, Canada
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Québec, Québec, Canada
| | - Raphaëlle Jacob
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Québec, Québec, Canada
- School of Nutrition, Université Laval, Québec, Québec, Canada
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Québec, Québec, Canada
| | - Vicky Drapeau
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Québec, Québec, Canada
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Québec, Québec, Canada
- Department of Physical Education, Faculty of Education, Université Laval, Québec, Québec, Canada
| | - Clare Llewellyn
- Research Department of Behavioural Science and Health, University College London, London, United Kingdom
| | - Benoit J Arsenault
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Québec, Québec, Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Québec, Québec, Canada
| | - Alexandre Bureau
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Québec, Québec, Canada
- CERVO Brain Research Center, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Québec, Québec, Canada
| | - Marie-Ève Labonté
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Québec, Québec, Canada
- School of Nutrition, Université Laval, Québec, Québec, Canada
| | - Angelo Tremblay
- Department of Kinesiology, Faculty of Medicine, Université Laval, Québec, Québec, Canada
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Québec, Québec, Canada
| | - Marie-Claude Vohl
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Québec, Québec, Canada
- School of Nutrition, Université Laval, Québec, Québec, Canada
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Masip G, Foraita R, Silventoinen K, Adan RAH, Ahrens W, De Henauw S, Hebestreit A, Keski-Rahkonen A, Lissner L, Mehlig K, Molnar D, Moreno LA, Pigeot I, Russo P, Veidebaum T, Bogl LH, Kaprio J. The temporal relationship between parental concern of overeating and childhood obesity considering genetic susceptibility: longitudinal results from the IDEFICS/I.Family study. Int J Behav Nutr Phys Act 2021; 18:139. [PMID: 34732214 PMCID: PMC8567680 DOI: 10.1186/s12966-021-01205-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 09/28/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Many genes and molecular pathways are associated with obesity, but the mechanisms from genes to obesity are less well known. Eating behaviors represent a plausible pathway, but because the relationships of eating behaviors and obesity may be bi-directional, it remains challenging to resolve the underlying pathways. A longitudinal approach is needed to assess the contribution of genetic risk during the development of obesity in childhood. In this study we aim to examine the relationships between the polygenic risk score for body mass index (PRS-BMI), parental concern of overeating and obesity indices during childhood. METHODS The IDEFICS/I.Family study is a school-based multicenter pan-European cohort of children observed for 6 years (mean ± SD follow-up 5.8 ± 0.4). Children examined in 2007/2008 (wave 1) (mean ± SD age: 4.4 ± 1.1, range: 2-9 years), in 2009/2010 (wave 2) and in 2013/2014 (wave 3) were included. A total of 5112 children (49% girls) participated at waves 1, 2 and 3. For 2656 children with genome-wide data we constructed a PRS based on 2.1 million single nucleotide polymorphisms. Z-score BMI and z-score waist circumference (WC) were assessed and eating behaviors and relevant confounders were reported by parents via questionnaires. Parental concern of overeating was derived from principal component analyses from an eating behavior questionnaire. RESULTS In cross-lagged models, the prospective associations between z-score obesity indices and parental concern of overeating were bi-directional. In mediation models, the association between the PRS-BMI and parental concern of overeating at wave 3 was mediated by baseline z-BMI (β = 0.16, 95% CI: 0.10, 0.21) and baseline z-WC (β = 0.17, 95% CI: 0.11, 0.23). To a lesser extent, baseline parental concern of overeating also mediated the association between the PRS-BMI and z-BMI at wave 3 (β = 0.10, 95% CI: 0.07, 0.13) and z-WC at wave 3 (β = 0.09, 95% CI: 0.07, 0.12). CONCLUSIONS The findings suggest that the prospective associations between obesity indices and parental concern of overeating are likely bi-directional, but obesity indices have a stronger association with future parental concern of overeating than vice versa. The findings suggest parental concern of overeating as a possible mediator in the genetic susceptibility to obesity and further highlight that other pathways are also involved. A better understanding of the genetic pathways that lead to childhood obesity can help to prevent weight gain. TRIAL REGISTRATION Registry number: ISRCTN62310987 Retrospectively registered 17 September 2018.
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Affiliation(s)
- Guiomar Masip
- Department of Public Health, University of Helsinki, Helsinki, Finland.
| | - Ronja Foraita
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Karri Silventoinen
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Roger A H Adan
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Stefaan De Henauw
- Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Antje Hebestreit
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | | | - Lauren Lissner
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Kirsten Mehlig
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Dénés Molnar
- Department of Paediatrics, Medical School, University of Pécs, Pécs, Hungary
| | - Luis A Moreno
- GENUD (Growth, Exercise, Nutrition and Development) Research Group, Faculty of Health Sciences, University of Zaragoza Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria de Aragón, Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición CIBEROBN, Instituto de Salud Carlos III, Madrid, Spain
| | - Iris Pigeot
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Paola Russo
- Institute of Food Sciences, National Research Council, Avellino, Italy
| | - Toomas Veidebaum
- Department of Chronic Diseases, National Institute for Health Development, Tallinn, Estonia
| | - Leonie H Bogl
- Department of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
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10
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Shared genetic architecture underlying sleep and weight in children. Sleep Med 2021; 83:40-44. [PMID: 33990065 DOI: 10.1016/j.sleep.2021.04.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 11/20/2022]
Abstract
Meta-analyses suggest shorter sleep as a risk factor for obesity in children. The prevailing hypothesis is that shorter sleep causes obesity by impacting homeostatic processes. Sleep duration and adiposity are both heritable, and the association may reflect shared genetic aetiology. We examined the association between a body mass index (BMI) genetic risk score (GRS) and objectively-measured total sleep time (TST) in a cohort of Norwegian children (enrolled at age four in 2007-2008) using cross-sectional data at age six. The analytical sample included 452 six-year old children with complete genotype and phenotype data. The outcome was actigraphic total sleep time (TST) measured at age six years. Genetic risk of obesity was inferred using a 32-single nucleotide polymorphism (SNP) weighted GRS of BMI. Covariates were BMI-Standard deviation scores (SDS) (which takes into account age and sex) and, in a sensitivity analysis socioeconomic status. Analyses consisted of Pearson's correlations and linear regressions. In our sample, 54% of participants were male; mean (SD) TST, age and BMI were 9.6 (0.8) hours, 6.0 (0.2) years and 15.3 (1.2) kg/m2, respectively. BMI and TST were not correlated, r = -0.003, p = 0.946. However, the BMI GRS was associated with TST after adjusting for BMI-SDS, standardised β = -0.11; 95% confidence interval (CI) = -0.22, -0.01. To our knowledge, this is the first study to establish a relationship between genetic risk of obesity and objective sleep duration in children. Findings suggest some shared genetic aetiology underlying these traits. Future research could identify the common biological pathways through which common genes predispose to both shorter sleep and increased risk of obesity.
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11
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Abstract
Eating behaviors may be expressions of genetic risk for obesity and are potential antecedents of later eating disorders. However, childhood eating behaviors are heterogeneous and transient. Here we show associations between polygenic scores for body mass index (BMI-PGS) and anorexia nervosa (AN-PGS) with eating behavior trajectories during the first ten years of life using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), N=7,825. Results indicated that one standard deviation (SD) increase in the BMI-PGS was associated with a 30-37% increased risk for early- and mid-childhood overeating. In contrast, one SD increase in BMI-PGS was associated with a 20% decrease in risk of persistent high levels of undereating and a 15% decrease in risk of persistent fussy eating. There was no evidence for a significant association between AN-PGS and eating behavior trajectories. Our results support the notion that child eating behavior share common genetic variants associated with BMI.
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12
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Silventoinen K, Konttinen H. Obesity and eating behavior from the perspective of twin and genetic research. Neurosci Biobehav Rev 2021; 109:150-165. [PMID: 31959301 DOI: 10.1016/j.neubiorev.2019.12.012] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 11/11/2019] [Accepted: 12/09/2019] [Indexed: 12/21/2022]
Abstract
Obesity has dramatically increased during the last decades and is currently one of the most serious global health problems. We present a hypothesis that obesity is a neuro-behavioral disease having a strong genetic background mediated largely by eating behavior and is sensitive to the macro-environment; we study this hypothesis from the perspective of genetic research. Genetic family and genome-wide-association studies have shown well that body mass index (BMI, kg/m2) is a highly heritable and polygenic trait. New genetic variation of BMI emerges after early childhood. Candidate genes of BMI notably express in brain tissue, supporting that this new variation is related to behavior. Obesogenic environments at both childhood family and societal levels reinforce the genetic susceptibility to obesity. Genetic factors have a clear influence on macro-nutrient intake and appetite-related eating behavior traits. Results on the gene-by-diet interactions in obesity are mixed, but emerging evidence suggests that eating behavior traits partly mediate the effect of genes on BMI. However, more rigorous prospective study designs controlling for measurement bias are still needed.
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Affiliation(s)
- Karri Silventoinen
- Department of Social Research, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland.
| | - Hanna Konttinen
- Department of Social Research, University of Helsinki, Helsinki, Finland
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13
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Abstract
PURPOSE OF REVIEW Eating behaviours are hypothesised to be the behavioural expression of genetic risk of obesity. In this review, we summarise findings from behavioural genetic research on the association between genetic risk for obesity and validated psychometrics measures of eating behaviours in children and adults (published in the past 10 years). RECENT FINDINGS Twin studies have produced some evidence for a shared genetic aetiology underlying body mass index and eating behaviours. Studies using measured genetic susceptibility to obesity have suggested that increased genetic liability for obesity is associated with variation in obesogenic eating behaviours such as emotional and uncontrolled eating. More research on this topic is needed. Especially longitudinal studies using genetically sensitive designs to investigate the direction of genetic pathways between genetic liability of eating behaviours to weight and vice versa, as well as the potential subsequent link to eating disorders.
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Affiliation(s)
- Moritz Herle
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AF, UK.
| | - Andrea D Smith
- Research Department of Behavioural Science and Health, University College London, London, UK
| | | | - Clare Llewellyn
- Research Department of Behavioural Science and Health, University College London, London, UK
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14
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Exclusive breastfeeding can attenuate body-mass-index increase among genetically susceptible children: A longitudinal study from the ALSPAC cohort. PLoS Genet 2020; 16:e1008790. [PMID: 32525877 PMCID: PMC7289340 DOI: 10.1371/journal.pgen.1008790] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 04/22/2020] [Indexed: 02/02/2023] Open
Abstract
Recent discoveries from large-scale genome-wide association studies (GWASs) explain a larger proportion of the genetic variability to BMI and obesity. The genetic risk associated with BMI and obesity can be assessed by an obesity-specific genetic risk score (GRS) constructed from genome-wide significant genetic variants. The aim of our study is to examine whether the duration and exclusivity of breastfeeding can attenuate BMI increase during childhood and adolescence due to genetic risks. A total sample of 5,266 children (2,690 boys and 2,576 girls) from the Avon Longitudinal Study of Parents and Children (ALSPAC) was used for the analysis. We evaluated the role of breastfeeding (exclusivity and duration) in modulating BMI increase attributed to the GRS from birth to 18 years of age. The GRS was composed of 69 variants associated with adult BMI and 25 non-overlapping SNPs associated with pediatric BMI. In the high genetic susceptible group (upper GRS quartile), exclusive breastfeeding (EBF) to 5 months reduces BMI by 1.14 kg/m2 (95% CI, 0.37 to 1.91, p = 0.0037) in 18-year-old boys, which compensates a 3.9-decile GRS increase. In 18-year-old girls, EBF to 5 months decreases BMI by 1.53 kg/m2 (95% CI, 0.76 to 2.29, p<0.0001), which compensates a 7.0-decile GRS increase. EBF acts early in life by delaying the age at adiposity peak and at adiposity rebound. EBF to 3 months or non-exclusive breastfeeding was associated with a significantly diminished impact on reducing BMI growth during childhood. EBF influences early life growth and development and thus may play a critical role in preventing overweight and obesity among children at high-risk due to genetic factors. Previous studies have shown that EBF is associated with lower BMI during childhood and adolescence. Moreover, a GRS based on 97 genetic variants has been derived from large GWASs and is predictive of BMI in adults and children. However, it remains unclear whether EBF can attenuate the increase in BMI attributed to the GRS in children. Our study was able to characterize the effect of the GRS in children from birth to 18 years of age. Our main results showed that EBF to 5 months has substantial effect in decreasing BMI among children at higher genetic risks. EBF to 3 months or non-exclusive breastfeeding had a significantly diminished effect on reducing BMI growth during childhood. Our study suggests that interventions aimed at reducing the risks of overweight and obesity across the lifespan should start in very early childhood to be impactful, which makes EBF a key candidate intervention. While EBF is beneficial to all children, targeting those carrying multiple BMI/obesity alleles should be a priority to reduce obesity and associated non-communicable diseases.
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15
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Doumatey AP, Ekoru K, Adeyemo A, Rotimi CN. Genetic Basis of Obesity and Type 2 Diabetes in Africans: Impact on Precision Medicine. Curr Diab Rep 2019; 19:105. [PMID: 31520154 DOI: 10.1007/s11892-019-1215-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
PURPOSE OF REVIEW Recent advances in genomics provide opportunities for novel understanding of the biology of human traits with the goal of improving human health. Here, we review recent obesity and type 2 diabetes (T2D)-related genomic studies in African populations and discuss the implications of limited genomics studies on health disparity and precision medicine. RECENT FINDINGS Genome-wide association studies in Africans have yielded genetic discovery that would otherwise not be possible; these include identification of novel loci associated with obesity (SEMA-4D, PRKCA, WARS2), metabolic syndrome (CA-10, CTNNA3), and T2D (AGMO, ZRANB3). ZRANB3 was recently demonstrated to influence beta cell mass and insulin response. Despite these promising results, genomic studies in African populations are still limited and thus genomics tools and approaches such as polygenic risk scores and precision medicine are likely to have limited utility in Africans with the unacceptable possibility of exacerbating prevailing health disparities. African populations provide unique opportunities for increasing our understanding of the genetic basis of cardiometabolic disorders. We highlight the need for more coordinated and sustained efforts to increase the representation of Africans in genomic studies both as participants and scientists.
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Affiliation(s)
- Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive, Building 12A, Room 4047, Bethesda, MD, 20862, USA
| | - Kenneth Ekoru
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive, Building 12A, Room 4047, Bethesda, MD, 20862, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive, Building 12A, Room 4047, Bethesda, MD, 20862, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive, Building 12A, Room 4047, Bethesda, MD, 20862, USA.
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16
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Wang AA, Harrison K, Musaad S, Donovan SM, Teran-Garcia M. Genetic risk scores demonstrate the cumulative association of single nucleotide polymorphisms in gut microbiome-related genes with obesity phenotypes in preschool age children. Pediatr Obes 2019; 14:e12530. [PMID: 30972961 DOI: 10.1111/ijpo.12530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 02/25/2019] [Indexed: 12/25/2022]
Abstract
BACKGROUND Childhood obesity is a nutrition-related disease with multiple underlying aetiologies. While genetic factors contribute to obesity, the gut microbiome is also implicated through fermentation of nondigestible polysaccharides to short-chain fatty acids (SCFA), which provide some energy to the host and are postulated to act as signalling molecules to affect expression of gut hormones. OBJECTIVE To study the cumulative association of causal, regulatory, and tagged single nucleotide polymorphisms (SNPs) within genes involved in SCFA recognition and metabolism with obesity. DESIGN Study participants were non-Hispanic White (NHW, n = 270) and non-Hispanic Black (NHB, n = 113) children (2-5 years) from the Synergistic Theory and Research on Obesity and Nutrition Group (STRONG) Kids 1 Study. SNP variables were assigned values according to the additive, dominant, or recessive inheritance models. Weighted genetic risk scores (GRS) were constructed by multiplying the reassigned values by independently generated β-coefficients or by summing the β-coefficients. Ethnicity-specific SNPs were selected for inclusion in GRS by cohort. RESULTS GRS were directly associated with body mass index (BMI) z-score. The models explained 3.75%, 12.9%, and 26.7% of the variance for NHW/NHB, NHW, and NHB (β = 0.89 [CI: 0.43-1.35], P = 0.0002; β = 0.78 [CI: 0.54-1.03], P < 0.0001; β = 0.74 [CI: 0.51-0.97], P < 0.0001). CONCLUSION This analysis supports the cumulative association of several candidate genetic variants selected for their role in SCFA signalling, transport, and metabolism with early-onset obesity. These data strengthen the concept that microbiome influences obesity development through host genes interacting with SCFA.
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Affiliation(s)
- Anthony A Wang
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Kristen Harrison
- Institute for Social Research, University of Michigan, Ann Arbor, Michigan
| | - Salma Musaad
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Sharon M Donovan
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois.,Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Margarita Teran-Garcia
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois.,Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Urbana, Illinois
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17
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Russell CG, Russell A. A biopsychosocial approach to processes and pathways in the development of overweight and obesity in childhood: Insights from developmental theory and research. Obes Rev 2019; 20:725-749. [PMID: 30768750 DOI: 10.1111/obr.12838] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 12/16/2018] [Accepted: 01/07/2019] [Indexed: 12/12/2022]
Abstract
Childhood obesity has reached alarming proportions in many countries. There is consensus that both biological (especially genetic) and environmental (including psychosocial) factors contribute to weight gain and obesity in childhood. Research has identified extensive risk or predictive factors for childhood obesity from both of these domains. There is less consensus about the developmental processes or pathways showing how these risk factors lead to overweigh/obesity (OW/OB) in childhood. We outline a biopsychosocial process model of the development of OW/OB in childhood. The model and associated scholarship from developmental theory and research guide an analysis of research on OW/OB in childhood. The model incorporates biological factors such as genetic predispositions or susceptibility genes, temperament, and homeostatic and allostatic processes with the psychosocial and behavioral factors of parenting, parental feeding practices, child appetitive traits, food liking, food intakes, and energy expenditure. There is an emphasis on bidirectional and transactional processes linking child biology and behavior with psychosocial processes and environment. Insights from developmental theory and research include implications for conceptualization, measurement, research design, and possible multiple pathways to OW/OB. Understanding the developmental processes and pathways involved in childhood OW/OB should contribute to more targeted prevention and intervention strategies in childhood.
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Affiliation(s)
- Catherine G Russell
- Faculty of Health, School of Exercise and Nutrition Sciences, Centre for Advanced Sensory Science, Deakin University, Burwood, Australia
| | - Alan Russell
- College of Education, Psychology and Social Work, Flinders University, Bedford Park, South Australia
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18
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de Lauzon-Guillain B, Koudou YA, Botton J, Forhan A, Carles S, Pelloux V, Clément K, Ong KK, Charles MA, Heude B. Association between genetic obesity susceptibility and mother-reported eating behaviour in children up to 5 years. Pediatr Obes 2019; 14:e12496. [PMID: 30702799 DOI: 10.1111/ijpo.12496] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 11/05/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND Many genetic polymorphisms identified by genome-wide association studies for adult body mass index (BMI) have been suggested to regulate food intake. OBJECTIVE The objective was to study the associations between a genetic obesity risk score, appetitive traits, and growth of children up to age 5 years, with a longitudinal design. METHODS In 1142 children from the Etude des Déterminants pre et post natals de la santé de l'ENfant (EDEN) birth cohort, a combined obesity risk-allele score (BMI genetic risk score [GRS]) was related to appetitive traits (energy intake up to 12 mo, a single item on appetite from 4 mo to 3 y, a validated appetite score at 5 y) using Poisson regressions with robust standard errors. The potential mediation of appetitive traits on the association between BMI-GRS and growth was assessed by the Sobel test. RESULTS Children with a high BMI-GRS were more likely to have high energy intake at 1 year and high appetite at 2 and 5 years. High energy intake in infancy and high appetite from 1 year were related to higher subsequent BMI. High 2-year appetite seemed to partially mediate the associations between BMI-GRS and BMI from 2 to 5 years (all P ≤ 0.05). CONCLUSIONS Genetic susceptibility to childhood obesity seems to be partially explained by appetitive traits in infancy, followed by an early childhood rise in BMI.
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Affiliation(s)
- Blandine de Lauzon-Guillain
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Early Origin of the Child's Health and Development Team (ORCHAD), Paris, France.,Paris Descartes University, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Paris, France.,INRA, U1125 Epidemiology and Biostatistics Sorbonne Paris Cité Center (CRESS), Early Origin of the Child's Health and Development Team (ORCHAD), Paris, France
| | - Yves Akoli Koudou
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Early Origin of the Child's Health and Development Team (ORCHAD), Paris, France
| | - Jérémie Botton
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Early Origin of the Child's Health and Development Team (ORCHAD), Paris, France.,Faculty of Pharmacy, University of Paris-Sud, Université Paris-Saclay, France
| | - Anne Forhan
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Early Origin of the Child's Health and Development Team (ORCHAD), Paris, France.,Paris Descartes University, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Paris, France
| | - Sophie Carles
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Early Origin of the Child's Health and Development Team (ORCHAD), Paris, France
| | - Véronique Pelloux
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France.,INSERM, UMRS 1166, Nutriomic Team 6, Paris, France.,Sorbonne Universités, UPMC Université Paris 06, UMRS1166, Paris, France
| | - Karine Clément
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France.,INSERM, UMRS 1166, Nutriomic Team 6, Paris, France.,Sorbonne Universités, UPMC Université Paris 06, UMRS1166, Paris, France
| | - Ken K Ong
- Medical Research Council Epidemiology Unit and Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Marie Aline Charles
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Early Origin of the Child's Health and Development Team (ORCHAD), Paris, France.,Paris Descartes University, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Paris, France
| | - Barbara Heude
- INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Early Origin of the Child's Health and Development Team (ORCHAD), Paris, France.,Paris Descartes University, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, Paris, France
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19
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Song M, Zheng Y, Qi L, Hu FB, Chan AT, Giovannucci EL. Associations between genetic variants associated with body mass index and trajectories of body fatness across the life course: a longitudinal analysis. Int J Epidemiol 2019; 47:506-515. [PMID: 29211904 DOI: 10.1093/ije/dyx255] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/13/2017] [Indexed: 12/19/2022] Open
Abstract
Background The genetic associations with trajectories of body fatness over the life course remain unknown. Methods We used a group-based modelling approach to identify trajectories of body fatness from age 5 years up to 65 for 7277 women from the Nurses' Health Study and 4645 men from the Health Professionals Follow-up Study. We created a genetic risk score (GRS) based on 97 variants associated with adulthood body mass index (BMI) and estimated its association with trajectories using logistic regression. Results We identified four distinct trajectories: lean-medium, medium-medium, lean-heavy and medium-heavy. The GRS increased across the four groups in that order (P < 0.001); 47% of women and 45% of men in the first decile of the GRS were in the lean-medium group, and these proportions reduced to 26% and 28%, respectively, for the highest decile. The corresponding proportions in the medium-heavy group were 8% and 5%, increasing to 21% and 14%, respectively. For women, compared with the odds of being in the lean-medium group, a 10-allele increment in the GRS was associated with a 40% [95% confidence interval (CI), 27-54%], 43% (30-58%), and 115% (91-143%) increase in the odds of being in the medium-medium, lean-heavy and medium-heavy groups, respectively. For men, the corresponding increases in the odds were 26% (12-42%), 27% (13-43%), and 81% (53-115%), respectively. Conclusions Individuals with genetic variants for adulthood BMI were more likely to maintain a heavy body shape and gain weight throughout life. These findings support a persistent effect of genetic variants on body fatness across the lifespan.
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Affiliation(s)
- Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yan Zheng
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lu Qi
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Epidemiology, Tulane University, New Orleans, LA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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20
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Genetic risk score based on fat mass and obesity-associated, transmembrane protein 18 and fibronectin type III domain containing 5 polymorphisms is associated with anthropometric characteristics in South Brazilian children and adolescents. Br J Nutr 2018; 121:93-99. [DOI: 10.1017/s0007114518002738] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
AbstractThe prevalence of childhood obesity has increased worldwide. Although it is considered a polygenic inheritance disease, little is known about its susceptibility when the additive effect is considered. The aim of this study is to investigate whether the genetic risk score (GRS) based on previously associated obesity polymorphisms (SNP) rs9939609 (fat mass and obesity-associated (FTO)), rs6548238 (transmembrane protein 18 (TMEM18)) and rs16835198 (fibronectin type III domain containing 5 (FNDC5)) could serve as a predictor for anthropometric characteristics in a sample of Brazilian children and adolescents. This is a cross-sectional study with 1471 children and adolescents aged 6–17 years. BMI, waist circumference (WC) and percentage of body fat and metabolic parameters were verified. In all, three SNP were genotyped by TaqMan™ allelic discrimination. The metabolic and anthropometric parameters were compared between the genotypes, and the unweighted and weighted GRS (GRS and wGRS, respectively) were created to test the additive effect of these genetic polymorphisms on anthropometric parameters. The prevalence of overweight plus obesity was 41 %. Significant associations were identified forFTOrs9939609,TMEM18rs6548238 andFNDC5rs16835198 and for GRS and wGRS with anthropometric phenotypes. The higher score of wGRS was associated with obesity (OR: 2·65, 95 % CI 1·40, 5·04,P=0·003) and with greater WC (OR: 2·91, 95 % CI 1·57, 5·40,P=0·001). Our results suggest that these genetic variants contribute to obesity susceptibility in children and adolescents and reinforce the idea that the additive effect may be useful to elucidate the genetic component of obesity.
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21
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Steinsbekk S, Wichstrøm L. Cohort Profile: The Trondheim Early Secure Study (TESS)—a study of mental health, psychosocial development and health behaviour from preschool to adolescence. Int J Epidemiol 2018; 47:1401-1401i. [DOI: 10.1093/ije/dyy190] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2018] [Indexed: 01/17/2023] Open
Affiliation(s)
- Silje Steinsbekk
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lars Wichstrøm
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- NTNU Social Science, Trondheim, Norway
- Department of Child and Adolescent Psychiatry, St Olavs Hospital, Trondheim, Norway
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Munthali RJ, Sahibdeen V, Kagura J, Hendry LM, Norris SA, Ong KK, Day FR, Lombard Z. Genetic risk score for adult body mass index associations with childhood and adolescent weight gain in an African population. GENES AND NUTRITION 2018; 13:24. [PMID: 30123368 PMCID: PMC6090951 DOI: 10.1186/s12263-018-0613-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 07/13/2018] [Indexed: 11/10/2022]
Abstract
Background Ninety-seven independent single nucleotide polymorphisms (SNPs) are robustly associated with adult body mass index (BMI kg/m2) in Caucasian populations. The relevance of such variants in African populations at different stages of the life course (such as childhood) is unclear. We tested whether a genetic risk score composed of the aforementioned SNPs was associated with BMI from infancy to early adulthood. We further tested whether this genetic effect was mediated by conditional weight gain at different growth periods. We used data from the Birth to Twenty Plus Cohort (Bt20+), for 971 urban South African black children from birth to 18 years. DNA was collected at 13 years old and was genotyped using the Metabochip (Illumina) array. The weighted genetic risk score (wGRS) for BMI was constructed based on 71 of the 97 previously reported SNPs. Results The cross-sectional association between the wGRS and BMI strengthened with age from 5 to 18 years. The significant associations were observed from 11 to 18 years, and peak effect sizes were observed at 13 and 14 years of age. Results from the linear mixed effects models showed significant interactions between the wGRS and age on longitudinal BMI but no such interactions were observed in sex and the wGRS. A higher wGRS was associated with an increased relative risk of belonging to the early onset obese longitudinal BMI trajectory (relative risk = 1.88; 95%CI 1.28 to 2.76) compared to belonging to a normal longitudinal BMI trajectory. Adolescent conditional relative weight gain had a suggestive mediation effect of 56% on the association between wGRS and obesity risk at 18 years. Conclusions The results suggest that genetic susceptibility to higher adult BMI can be tracked from childhood in this African population. This supports the notion that prevention of adult obesity should begin early in life. The genetic risk score combined with other non-genetic risk factors, such as BMI trajectory membership in our case, has the potential to be used to screen for early identification of individuals at increased risk of obesity and other related NCD risk factors in order to reduce the adverse health risk outcomes later. Electronic supplementary material The online version of this article (10.1186/s12263-018-0613-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Richard J Munthali
- 1Faculty of Science, School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa.,2Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, The Mount, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2193 South Africa.,3MRC/Wits Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Johannesburg, South Africa
| | - Venesa Sahibdeen
- 2Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, The Mount, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2193 South Africa.,4Faculty of Health Sciences, Division of Human Genetics, School of Pathology, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa
| | - Juliana Kagura
- 3MRC/Wits Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Johannesburg, South Africa
| | - Liesl M Hendry
- 1Faculty of Science, School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa.,2Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, The Mount, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2193 South Africa
| | - Shane A Norris
- 3MRC/Wits Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Johannesburg, South Africa
| | - Ken K Ong
- 3MRC/Wits Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Johannesburg, South Africa.,5MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Felix R Day
- 5MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Zané Lombard
- 1Faculty of Science, School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa.,2Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, The Mount, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2193 South Africa.,4Faculty of Health Sciences, Division of Human Genetics, School of Pathology, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa
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Song M, Zheng Y, Qi L, Hu FB, Chan AT, Giovannucci EL. Longitudinal Analysis of Genetic Susceptibility and BMI Throughout Adult Life. Diabetes 2018; 67:248-255. [PMID: 29212779 PMCID: PMC5780056 DOI: 10.2337/db17-1156] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 11/21/2017] [Indexed: 01/01/2023]
Abstract
Little is known about the genetic influence on BMI trajectory throughout adulthood. We created a genetic risk score (GRS) comprising 97 adult BMI-associated variants among 9,971 women and 6,405 men of European ancestry. Serial measures of BMI were assessed from 18 (women) or 21 (men) years to 85 years of age. We also examined BMI change in early (from 18 or 21 to 45 years of age), middle (from 45 to 65 years of age), and late adulthood (from 65 to 80 years of age). GRS was positively associated with BMI across all ages, with stronger associations in women than in men. The associations increased from early to middle adulthood, peaked at 45 years of age in men and at 60 years of age in women (0.91 and 1.35 kg/m2 per 10-allele increment, respectively) and subsequently declined in late adulthood. For women, each 10-allele increment in the GRS was associated with an average BMI gain of 0.54 kg/m2 in early adulthood, whereas no statistically significant association was found for BMI change in middle or late adulthood or for BMI change in any life period in men. Our findings indicate that genetic predisposition exerts a persistent effect on adiposity throughout adult life and increases early adulthood weight gain in women.
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Affiliation(s)
- Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Yan Zheng
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Lu Qi
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
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Seyednasrollah F, Mäkelä J, Pitkänen N, Juonala M, Hutri-Kähönen N, Lehtimäki T, Viikari J, Kelly T, Li C, Bazzano L, Elo LL, Raitakari OT. Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study. ACTA ACUST UNITED AC 2018; 10:CIRCGENETICS.116.001554. [PMID: 28620069 DOI: 10.1161/circgenetics.116.001554] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 12/06/2016] [Indexed: 12/21/2022]
Abstract
BACKGROUND Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. METHODS AND RESULTS A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P<0.0001) and validation data (AUC=0.769 versus AUC=0.747, P=0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P<0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P=0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index. CONCLUSIONS WGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity.
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Affiliation(s)
- Fatemeh Seyednasrollah
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Johanna Mäkelä
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.).
| | - Niina Pitkänen
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Markus Juonala
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Nina Hutri-Kähönen
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Terho Lehtimäki
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Jorma Viikari
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Tanika Kelly
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Changwei Li
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Lydia Bazzano
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Laura L Elo
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Olli T Raitakari
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
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de Lauzon-Guillain B, Clifton EA, Day FR, Clément K, Brage S, Forouhi NG, Griffin SJ, Koudou YA, Pelloux V, Wareham NJ, Charles MA, Heude B, Ong KK. Mediation and modification of genetic susceptibility to obesity by eating behaviors. Am J Clin Nutr 2017; 106:996-1004. [PMID: 28814400 PMCID: PMC6186415 DOI: 10.3945/ajcn.117.157396] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 07/13/2017] [Indexed: 01/22/2023] Open
Abstract
Background: Many genetic variants show highly robust associations with body mass index (BMI). However, the mechanisms through which genetic susceptibility to obesity operates are not well understood. Potentially modifiable mechanisms, including eating behaviors, are of particular interest to public health.Objective: Here we explore whether eating behaviors mediate or modify genetic susceptibility to obesity.Design: Genetic risk scores for BMI (BMI-GRSs) were calculated for 3515 and 2154 adults in the Fenland and EDEN (Etude des déterminants pré et postnatals de la santé et du développement de l'enfant) population-based cohort studies, respectively. The eating behaviors-emotional eating, uncontrolled eating, and cognitive restraint-were measured through the use of a validated questionnaire. The mediating effect of each eating behavior on the association between the BMI-GRS and measured BMI was assessed by using the Sobel test. In addition, we tested for interactions between each eating behavior and the BMI-GRS on BMI.Results: The association between the BMI-GRS and BMI was mediated by both emotional eating (EDEN: P-Sobel = 0.01; Fenland: P-Sobel = 0.02) and uncontrolled eating (EDEN: P-Sobel = 0.04; Fenland: P-Sobel = 0.0006) in both sexes combined. Cognitive restraint did not mediate this association (P-Sobel > 0.10), except among EDEN women (P-Sobel = 0.0009). Cognitive restraint modified the relation between the BMI-GRS and BMI among men (EDEN: P-interaction = 0.0001; Fenland: P-interaction = 0.04) and Fenland women (P-interaction = 0.0004). By tertiles of cognitive restraint, the association between the BMI-GRS and BMI was strongest in the lowest tertile of cognitive restraint, and weakest in the highest tertile.Conclusions: Genetic susceptibility to obesity was partially mediated by the "appetitive" eating behavior traits (uncontrolled and emotional eating) and, in 3 of the 4 population groups studied, was modified by cognitive restraint. High levels of cognitive control over eating appear to attenuate the genetic susceptibility to obesity. Future research into interventions designed to support restraint may help to protect genetically susceptible individuals from weight gain.
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Affiliation(s)
- Blandine de Lauzon-Guillain
- Early Origin of Child Health and Development (ORCHAD) Team 6, Center of Research in Epidemiology and UMR 1153 Statistics Sorbonne Paris Cité (CRESS), National Institute of Health and Medical Research (INSERM), Paris, France
- Paris Descartes University, Paris, France
| | | | - Felix R Day
- MRC Epidemiology Unit, Institute of Metabolic Science, and
| | - Karine Clément
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
- NutriOmics Team 6, UMRS 1166, National Institute of Health and Medical Research (INSERM), Paris, France; and
- Pierre and Marie Curie University, Sorbonne Universities, Paris, France
| | - Soren Brage
- MRC Epidemiology Unit, Institute of Metabolic Science, and
| | - Nita G Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, and
| | - Simon J Griffin
- MRC Epidemiology Unit, Institute of Metabolic Science, and
- Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Yves Akoli Koudou
- Early Origin of Child Health and Development (ORCHAD) Team 6, Center of Research in Epidemiology and UMR 1153 Statistics Sorbonne Paris Cité (CRESS), National Institute of Health and Medical Research (INSERM), Paris, France
| | - Véronique Pelloux
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
- NutriOmics Team 6, UMRS 1166, National Institute of Health and Medical Research (INSERM), Paris, France; and
- Pierre and Marie Curie University, Sorbonne Universities, Paris, France
| | | | - Marie-Aline Charles
- Early Origin of Child Health and Development (ORCHAD) Team 6, Center of Research in Epidemiology and UMR 1153 Statistics Sorbonne Paris Cité (CRESS), National Institute of Health and Medical Research (INSERM), Paris, France
- Paris Descartes University, Paris, France
| | - Barbara Heude
- Early Origin of Child Health and Development (ORCHAD) Team 6, Center of Research in Epidemiology and UMR 1153 Statistics Sorbonne Paris Cité (CRESS), National Institute of Health and Medical Research (INSERM), Paris, France
- Paris Descartes University, Paris, France
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, and
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Effects of single genetic variants and polygenic obesity risk scores on disordered eating in adolescents - The HUNT study. Appetite 2017; 118:8-16. [PMID: 28694222 DOI: 10.1016/j.appet.2017.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 05/24/2017] [Accepted: 07/06/2017] [Indexed: 02/06/2023]
Abstract
PURPOSE Improving the understanding of the role of genetic risk on disordered eating (DE). METHODS A case-control study including 1757 (F: 979, M: 778) adolescents (aged 13-19 years) from the Nord-Trøndelag Health Study (HUNT), an ethnically homogenous Norwegian population based study. Cases and controls were defined using a shortened version of the Eating Attitude Test. Logistic regression was employed to test for associations between DE phenotypes and 24 obesity and eating disorder susceptibility SNPs, and the joint effect of a subset of these in a genetic risk score (GRS). RESULTS COMT was shown to be associated with poor appetite/undereating (OR: 0.6, CI 95%: 0.43-0.83, p = 0.002). Independent of obesity associations, the weighted GRS was associated to overeating in 13-15 year old females (OR: 2.07, CI 95%: 1.14-3.76, p = 0.017). Additionally, a significant association was observed between the GRS and loss of control over eating in the total sample (OR: 1.62, CI 95%: 1.01-2.61, p = 0.046). CONCLUSIONS The COMT variant (rs4680) was associated with poor appetite/undereating. Our study further confirms prior findings that obesity risk also confers risk for loss of control over eating; and overeating amongst girls.
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Monnereau C, Jansen PW, Tiemeier H, Jaddoe VWV, Felix JF. Influence of genetic variants associated with body mass index on eating behavior in childhood. Obesity (Silver Spring) 2017; 25:765-772. [PMID: 28245097 PMCID: PMC5496668 DOI: 10.1002/oby.21778] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 12/23/2016] [Accepted: 12/26/2016] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Childhood eating behaviors are associated with body mass index (BMI). Recent genome-wide association studies have identified many single-nucleotide polymorphisms (SNPs) associated with adult and childhood BMI. This study hypothesized that these SNPs also influence eating behavior. METHODS In a population-based prospective cohort study among 3,031 children (mean age [standard deviation]: 4.0 [0.1] years), two weighted genetic risk scores, based on 15 childhood and 97 adult BMI SNPs, and ten individual appetite- and/or satiety-related SNPs were tested for association with food fussiness, food responsiveness, enjoyment of food, satiety responsiveness, and slowness in eating. RESULTS The 15 SNP-based childhood BMI genetic risk score was not associated with the eating behavior subscales. The 97 SNP-based adult BMI genetic risk score was nominally associated with satiety responsiveness (β: -0.007 standard deviation, 95% confidence interval [CI] -0.013, 0.000). Of the 10 individual SNPs, rs11030104 in BDNF and rs10733682 in LMX1B were nominally associated with satiety responsiveness (β: -0.057 standard deviation, 95% CI -0.112, -0.002). CONCLUSIONS These findings do not strongly support the hypothesis that BMI-associated SNPs also influence eating behavior at this age. A potential role for BMI SNPs in satiety responsiveness during childhood was observed; however, no associations with the other eating behavior subscales were found.
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Affiliation(s)
- Claire Monnereau
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Pauline W Jansen
- Institute of Psychology, Erasmus University, Rotterdam, the Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Psychiatry, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
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29
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De Decker A, Verbeken S, Sioen I, Moens E, Braet C, De Henauw S. Fat Tissue Accretion in Children and Adolescents: Interplay between Food Responsiveness, Gender, and the Home Availability of Snacks. Front Psychol 2017; 7:2041. [PMID: 28101078 PMCID: PMC5209336 DOI: 10.3389/fpsyg.2016.02041] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 12/16/2016] [Indexed: 11/13/2022] Open
Abstract
The appetitive trait “food responsiveness” is assumed to be a risk factor for adiposity gain primarily in obesogenic environments. So far, the reported results are inconsistent in school-aged children, possibly because these studies did not take into account important moderators such as gender and the food-environment. In order to better inform caregivers, clinicians and the developers of targeted obesity-prevention interventions on the conditions in which food responsiveness precedes adiposity gain, the current study investigated if this relationship is stronger in girls and in children exposed to a higher home availability of energy-dense snacks. Age- and sex-independent Fat and Lean Mass Index z-scores were computed based on air-displacement plethysmography at baseline and after 2 years in a community sample of 129 children (48.8% boys) aged 7.5–14 years at baseline. Parents reported at baseline on children's food responsiveness and the home availability of energy-dense snacks. Food responsiveness was a significant predictor of increases in Fat Mass Index z-scores over 2 years in girls but not boys. The home availability of energy-dense snacks did not significantly moderate the relation of food responsiveness with Fat Mass Index z-score changes. The results suggest that food responsiveness precedes accelerated fat tissue accretion in girls, and may inform targeted obesity-prevention interventions. Further, future research should investigate to which food-environmental parameters children high in food responsiveness mainly respond.
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Affiliation(s)
- Annelies De Decker
- Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University Ghent, Belgium
| | - Sandra Verbeken
- Department of Developmental, Personality and Social Psychology, Faculty of Psychology and Educational Sciences, Ghent University Ghent, Belgium
| | - Isabelle Sioen
- Department of Public Health, Faculty of Medicine and Health Sciences, Ghent UniversityGhent, Belgium; Department of Food Safety and Food Quality, Faculty of Bioscience Engineering, Ghent UniversityGhent, Belgium
| | - Ellen Moens
- Department of Developmental, Personality and Social Psychology, Faculty of Psychology and Educational Sciences, Ghent University Ghent, Belgium
| | - Caroline Braet
- Department of Developmental, Personality and Social Psychology, Faculty of Psychology and Educational Sciences, Ghent University Ghent, Belgium
| | - Stefaan De Henauw
- Department of Public Health, Faculty of Medicine and Health Sciences, Ghent UniversityGhent, Belgium; Department of Health Sciences, Vesalius, University College GhentGhent, Belgium
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30
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Elvsaas IKØ, Giske L, Fure B, Juvet LK. Multicomponent Lifestyle Interventions for Treating Overweight and Obesity in Children and Adolescents: A Systematic Review and Meta-Analyses. J Obes 2017; 2017:5021902. [PMID: 29391949 PMCID: PMC5748119 DOI: 10.1155/2017/5021902] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 09/27/2017] [Accepted: 10/19/2017] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Treatment of childhood obesity is important in preventing development of obesity-related diseases later in life. This systematic review evaluates the effect of multicomponent lifestyle interventions for children and adolescents from 2 to 18 years. METHODS AND RESULTS We performed systematic searches in nine databases. Thirty-nine studies met the criteria for meta-analyses. We found a significant difference in body mass index (BMI) after 6 months (MD -0.99 (95% CI -1.36 to -0.61)), 12 months (MD -0.67 (95% CI -1.01 to -0.32)), and 24 months (MD -0.96 (95% CI -1.63 to -0.29)) in favour of multicomponent lifestyle interventions compared to standard, minimal, and no treatment. We also found a significant difference in BMI Z scores after 6 months (MD -0.12 (95% CI -0.17 to -0.06)), 12 months (MD -0.16 (95% CI -0.21 to -0.11)), and 24 months (MD -0.16 (95% CI -0.21 to -0.10)) in favour of multicomponent lifestyle interventions. Subgroup analyses suggested an increased effect in specialist health care with a group treatment component included in the intervention. CONCLUSION Multicomponent lifestyle interventions have a moderate effect on change in BMI and BMI Z score after 6, 12, and 24 months compared with standard, minimal, and no treatment.
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Affiliation(s)
| | - L. Giske
- Norwegian Institute of Public Health, Oslo, Norway
| | - B. Fure
- Norwegian Institute of Public Health, Oslo, Norway
- The Arctic University of Norway, Tromsø, Norway
| | - L. K. Juvet
- Norwegian Institute of Public Health, Oslo, Norway
- University College of Southeast Norway, Notodden, Norway
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