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Cadman T, Elhakeem A, Vinther JL, Avraam D, Carrasco P, Calas L, Cardol M, Charles MA, Corpeleijn E, Crozier S, de Castro M, Estarlich M, Fernandes A, Fossatti S, Gruszfeld D, Guerlich K, Grote V, Haakma S, Harris JR, Heude B, Huang RC, Ibarluzea J, Inskip H, Jaddoe V, Koletzko B, Lioret S, Luque V, Manios Y, Moirano G, Moschonis G, Nader J, Nieuwenhuijsen M, Andersen AMN, McEachen R, de Moira AP, Popovic M, Roumeliotaki T, Salika T, Santa Marina L, Santos S, Serbert S, Tzorovili E, Vafeiadi M, Verduci E, Vrijheid M, Vrijkotte TGM, Welten M, Wright J, Yang TC, Zugna D, Lawlor D. Associations of Maternal Educational Level, Proximity to Green Space During Pregnancy, and Gestational Diabetes With Body Mass Index From Infancy to Early Adulthood: A Proof-of-Concept Federated Analysis in 18 Birth Cohorts. Am J Epidemiol 2024; 193:753-763. [PMID: 37856700 DOI: 10.1093/aje/kwad206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 04/06/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023] Open
Abstract
International sharing of cohort data for research is important and challenging. We explored the feasibility of multicohort federated analyses by examining associations between 3 pregnancy exposures (maternal education, exposure to green vegetation, and gestational diabetes) and offspring body mass index (BMI) from infancy to age 17 years. We used data from 18 cohorts (n = 206,180 mother-child pairs) from the EU Child Cohort Network and derived BMI at ages 0-1, 2-3, 4-7, 8-13, and 14-17 years. Associations were estimated using linear regression via 1-stage individual participant data meta-analysis using DataSHIELD. Associations between lower maternal education and higher child BMI emerged from age 4 and increased with age (difference in BMI z score comparing low with high education, at age 2-3 years = 0.03 (95% confidence interval (CI): 0.00, 0.05), at 4-7 years = 0.16 (95% CI: 0.14, 0.17), and at 8-13 years = 0.24 (95% CI: 0.22, 0.26)). Gestational diabetes was positively associated with BMI from age 8 years (BMI z score difference = 0.18, 95% CI: 0.12, 0.25) but not at younger ages; however, associations attenuated towards the null when restricted to cohorts that measured gestational diabetes via universal screening. Exposure to green vegetation was weakly associated with higher BMI up to age 1 year but not at older ages. Opportunities of cross-cohort federated analyses are discussed.
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Mikołajczyk-Stecyna J, Zuk E, Seremak-Mrozikiewicz A, Kurzawińska G, Wolski H, Drews K, Chmurzynska A. Genetic risk score for gestational weight gain. Eur J Obstet Gynecol Reprod Biol 2024; 294:20-27. [PMID: 38184896 DOI: 10.1016/j.ejogrb.2023.12.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/09/2024]
Abstract
Gestational weight gain (GWG) involves health consequences for both mother and offspring. Genetic factors seem to play a role in the GWG trait. For small effect sizes of a single genetic polymorphism (SNP), a genetic risk score (GRS) summarizing risk-associated variation from multiple SNPs can serve as an effective approach to genetic association analysis. The aim of the study was to analyze the association between genetic risk score (GRS) and gestational weight gain (GWG). GWG was calculated for a total of 342 healthy Polish women of Caucasian origin, aged 19 to 45 years. The SNPs rs9939609 (FTO), rs6548238 (TMEM18), rs17782313 (MC4R), rs10938397 (GNPDA2), rs10913469 (SEC16B), rs1137101 (LEPR), rs7799039 (LEP), and rs5443 (GNB3) were genotyped using commercial TaqMan SNP assays. A simple genetic risk score was calculated into two ways: GRS1 based on the sum of risk alleles from each of the SNPs, while GRS2 based on the sum of risk alleles of FTO, LEPR, LEP, and GNB3. Positive association between GRS2 and GWG (β = 0.12, p = 0.029) was observed. Genetic risk variants of TMEM18 (p = 0.006, OR = 2.6) and GNB3 (p < 0.001, OR = 3.3) are more frequent in women with increased GWG, but a risk variant of GNPDA2 (p < 0.001, OR = 2.7) is more frequent in women with adequate GWG, and a risk variant of LEPR (p = 0.011, OR = 3.1) in women with decreased GWG. GRS2 and genetic variants of TMEM18, GNB3, GNPDA2, and LEPR are associated with weight gain during pregnancy.
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Affiliation(s)
- Joanna Mikołajczyk-Stecyna
- Department of Human Nutrition and Dietetics, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland
| | - Ewelina Zuk
- Department of Human Nutrition and Dietetics, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland
| | - Agnieszka Seremak-Mrozikiewicz
- Division of Perinatology and Women's Diseases, Poznań University of Medical Sciences, Polna 33, 60-535 Poznań, Poland; Laboratory of Molecular Biology, Division of Perinatology and Women's Diseases, Poznań University of Medical Sciences, Polna 33, 60-535 Poznań, Poland
| | - Grażyna Kurzawińska
- Division of Perinatology and Women's Diseases, Poznań University of Medical Sciences, Polna 33, 60-535 Poznań, Poland; Laboratory of Molecular Biology, Division of Perinatology and Women's Diseases, Poznań University of Medical Sciences, Polna 33, 60-535 Poznań, Poland
| | - Hubert Wolski
- Division of Perinatology and Women's Diseases, Poznań University of Medical Sciences, Polna 33, 60-535 Poznań, Poland; Podhale State College of Applied Sciences in Nowy Targ, Kokoszków 71, 34-400 Nowy Targ, Poland
| | - Krzysztof Drews
- Division of Perinatology and Women's Diseases, Poznań University of Medical Sciences, Polna 33, 60-535 Poznań, Poland; Laboratory of Molecular Biology, Division of Perinatology and Women's Diseases, Poznań University of Medical Sciences, Polna 33, 60-535 Poznań, Poland
| | - Agata Chmurzynska
- Department of Human Nutrition and Dietetics, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland.
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Lawton RI, Stanford FC. The Role of Racism in Childhood Obesity. Curr Obes Rep 2024; 13:98-106. [PMID: 38172479 PMCID: PMC10939728 DOI: 10.1007/s13679-023-00538-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/01/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE OF REVIEW Obesity rates continue to rise among children and have shown persistent racial disparities. Racism plays a potentially essential and actionable role in these disparities. This report reviews some mechanisms through which racism may shape childhood obesity. RECENT FINDINGS From the youngest ages, disparities in childhood obesity prevalence are already present. Racism may shape intergenerational and prenatal factors that affect obesity and various stressors and environments where children grow up. The relationships between clinicians and patients may also be shaped by everyday racism and legacies of past racism, which may affect obesity prevalence and treatment efficacy. Comprehensive data on the extent to which racism shapes childhood obesity is limited. However, compelling evidence suggests many ways through which racism ultimately does affect childhood obesity. Interventions to address racism at multiple points where it shapes childhood obesity, including intergenerational and prenatal mechanisms, may help to close disparities.
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Affiliation(s)
| | - Fatima Cody Stanford
- Harvard Medical School, Boston, MA, USA.
- MGH Weight Center, Department of Medicine-Division of Endocrinology-Neuroendocrine, Massachusetts General Hospital, Weight Center, 50 Staniford Street, 4th Floor, Boston, MA, 02114, USA.
- Department of Pediatrics-Division of Endocrinology, Nutrition Obesity Research Center at Harvard (NORCH), Weight Center, 50 Staniford Street, 4th Floor, Boston, MA, USA.
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D’Urso S, Moen GH, Hwang LD, Hannigan LJ, Corfield EC, Ask H, Johannson S, Njølstad PR, Beaumont RN, Freathy RM, Evans DM, Havdahl A. Intrauterine Growth and Offspring Neurodevelopmental Traits: A Mendelian Randomization Analysis of the Norwegian Mother, Father and Child Cohort Study (MoBa). JAMA Psychiatry 2024; 81:144-156. [PMID: 37878341 PMCID: PMC10600722 DOI: 10.1001/jamapsychiatry.2023.3872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/18/2023] [Indexed: 10/26/2023]
Abstract
Importance Conventional epidemiological analyses have suggested that lower birth weight is associated with later neurodevelopmental difficulties; however, it is unclear whether this association is causal. Objective To investigate the relationship between intrauterine growth and offspring neurodevelopmental difficulties. Design, Setting, and Participants MoBa is a population-based pregnancy cohort that recruited pregnant women from June 1999 to December 2008 included approximately 114 500 children, 95 200 mothers, and 75 200 fathers. Observational associations between birth weight and neurodevelopmental difficulties were assessed with a conventional epidemiological approach. Mendelian randomization analyses were performed to investigate the potential causal association between maternal allele scores for birth weight and offspring neurodevelopmental difficulties conditional on offspring allele scores. Exposures Birth weight and maternal allele scores for birth weight (derived from genetic variants robustly associated with birth weight) were the exposures in the observational and mendelian randomization analyses, respectively. Main Outcomes and Measures Clinically relevant maternal ratings of offspring neurodevelopmental difficulties at 6 months, 18 months, 3 years, 5 years, and 8 years of age assessing language and motor difficulties, inattention and hyperactivity-impulsivity, social communication difficulties, and repetitive behaviors. Results The conventional epidemiological sample included up to 46 970 offspring, whereas the mendelian randomization sample included up to 44 134 offspring (median offspring birth year, 2005 [range, 1999-2009]; mean [SD] maternal age at birth, 30.1 [4.5] years; mean [SD] paternal age at birth, 32.5 [5.1] years). The conventional epidemiological analyses found evidence that birth weight was negatively associated with several domains at multiple offspring ages (outcome of autism-related trait scores: Social Communication Questionnaire [SCQ]-full at 3 years, β = -0.046 [95% CI, -0.057 to -0.034]; SCQ-Restricted and Repetitive Behaviors subscale at 3 years, β = -0.049 [95% CI, -0.060 to -0.038]; attention-deficit/hyperactivity disorder [ADHD] trait scores: Child Behavior Checklist [CBCL]-ADHD subscale at 18 months, β = -0.035 [95% CI, -0.045 to -0.024]; CBCL-ADHD at 3 years, β = -0.032 [95% CI, -0.043 to -0.021]; CBCL-ADHD at 5 years, β = -0.050 [95% CI, -0.064 to -0.037]; Rating Scale for Disruptive Behavior Disorders [RS-DBD]-ADHD at 8 years, β = -0.036 [95% CI, -0.049 to -0.023]; RS-DBD-Inattention at 8 years, β = -0.037 [95% CI, -0.050 to -0.024]; RS-DBD-Hyperactive-Impulsive Behavior at 8 years, β = -0.027 [95% CI, -0.040 to -0.014]; Conners Parent Rating Scale-Revised [Short Form] at 5 years, β = -0.041 [95% CI, -0.054 to -0.028]; motor scores: Ages and Stages Questionnaire-Motor Difficulty [ASQ-MOTOR] at 18 months, β = -0.025 [95% CI, -0.035 to -0.015]; ASQ-MOTOR at 3 years, β = -0.029 [95% CI, -0.040 to -0.018]; and Child Development Inventory-Gross and Fine Motor Skills at 5 years, β = -0.028 [95% CI, -0.042 to -0.015]). Mendelian randomization analyses did not find any evidence for an association between maternal allele scores for birth weight and offspring neurodevelopmental difficulties. Conclusions and Relevance This study found that the maternal intrauterine environment, as proxied by maternal birth weight genetic variants, is unlikely to be a major determinant of offspring neurodevelopmental outcomes.
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Affiliation(s)
- Shannon D’Urso
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Gunn-Helen Moen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Frazer Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Liang-Dar Hwang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Laurie J. Hannigan
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Elizabeth C. Corfield
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Helga Ask
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Stefan Johannson
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Pål Rasmus Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Section for Endocrinology and Metabolism, Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Robin N. Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, Devon, United Kingdom
| | - Rachel M. Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, Devon, United Kingdom
| | - David M. Evans
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Frazer Institute, The University of Queensland, Woolloongabba, Queensland, Australia
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Alexandra Havdahl
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- MRC (Medical Research Council) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
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Borges MC, Clayton GL, Freathy RM, Felix JF, Fernández-Sanlés A, Soares AG, Kilpi F, Yang Q, McEachan RRC, Richmond RC, Liu X, Skotte L, Irizar A, Hattersley AT, Bodinier B, Scholtens DM, Nohr EA, Bond TA, Hayes MG, West J, Tyrrell J, Wright J, Bouchard L, Murcia M, Bustamante M, Chadeau-Hyam M, Jarvelin MR, Vrijheid M, Perron P, Magnus P, Gaillard R, Jaddoe VWV, Lowe WL, Feenstra B, Hivert MF, Sørensen TIA, Håberg SE, Serbert S, Magnus M, Lawlor DA. Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes. BMC Med 2024; 22:32. [PMID: 38281920 PMCID: PMC10823651 DOI: 10.1186/s12916-023-03167-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 11/09/2023] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear. METHODS We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women. RESULTS All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation. CONCLUSIONS Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications. FUNDING Medical Research Council, British Heart Foundation, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.
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Affiliation(s)
- Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rachel M Freathy
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Alba Fernández-Sanlés
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ana Gonçalves Soares
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Fanny Kilpi
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Qian Yang
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rosemary R C McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Trust, Bradford, UK
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Xueping Liu
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Line Skotte
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Amaia Irizar
- Department of Preventive Medicine and Public Health, University of the Basque Country, Leioa, Spain
- BIODONOSTIA Health Research Institute, Paseo Dr. Beguiristain, 20014, San Sebastian, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Barbara Bodinier
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Denise M Scholtens
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ellen A Nohr
- Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Tom A Bond
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jane West
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Trust, Bradford, UK
| | - Jessica Tyrrell
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Trust, Bradford, UK
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Mario Murcia
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain
| | - Mariona Bustamante
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Marc Chadeau-Hyam
- Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | | | - Martine Vrijheid
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Patrice Perron
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CR-CHUS), Sherbrooke, Québec, Canada
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Québec, Canada
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Romy Gaillard
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - William L Lowe
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Thorkild I A Sørensen
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Diseases, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Siri E Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Sylvain Serbert
- Center For Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Maria Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
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Wells JCK, Desoye G, Leon DA. Reconsidering the developmental origins of adult disease paradigm: The 'metabolic coordination of childbirth' hypothesis. Evol Med Public Health 2024; 12:50-66. [PMID: 38380130 PMCID: PMC10878253 DOI: 10.1093/emph/eoae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 12/18/2023] [Indexed: 02/22/2024] Open
Abstract
In uncomplicated pregnancies, birthweight is inversely associated with adult non-communicable disease (NCD) risk. One proposed mechanism is maternal malnutrition during pregnancy. Another explanation is that shared genes link birthweight with NCDs. Both hypotheses are supported, but evolutionary perspectives address only the environmental pathway. We propose that genetic and environmental associations of birthweight with NCD risk reflect coordinated regulatory systems between mother and foetus, that evolved to reduce risks of obstructed labour. First, the foetus must tailor its growth to maternal metabolic signals, as it cannot predict the size of the birth canal from its own genome. Second, we predict that maternal alleles that promote placental nutrient supply have been selected to constrain foetal growth and gestation length when fetally expressed. Conversely, maternal alleles that increase birth canal size have been selected to promote foetal growth and gestation when fetally expressed. Evidence supports these hypotheses. These regulatory mechanisms may have undergone powerful selection as hominin neonates evolved larger size and encephalisation, since every mother is at risk of gestating a baby excessively for her pelvis. Our perspective can explain the inverse association of birthweight with NCD risk across most of the birthweight range: any constraint of birthweight, through plastic or genetic mechanisms, may reduce the capacity for homeostasis and increase NCD susceptibility. However, maternal obesity and diabetes can overwhelm this coordination system, challenging vaginal delivery while increasing offspring NCD risk. We argue that selection on viable vaginal delivery played an over-arching role in shaping the association of birthweight with NCD risk.
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Affiliation(s)
- Jonathan C K Wells
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK
| | - Gernot Desoye
- Department of Obstetrics and Gynaecology, Medical University of Graz, Auenbruggerplatz 14, 8036 Graz, Austria
| | - David A Leon
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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7
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Power GM, Sanderson E, Pagoni P, Fraser A, Morris T, Prince C, Frayling TM, Heron J, Richardson TG, Richmond R, Tyrrell J, Warrington N, Davey Smith G, Howe LD, Tilling KM. Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review. Eur J Epidemiol 2023:10.1007/s10654-023-01032-1. [PMID: 37938447 DOI: 10.1007/s10654-023-01032-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/21/2023] [Indexed: 11/09/2023]
Abstract
Diseases diagnosed in adulthood may have antecedents throughout (including prenatal) life. Gaining a better understanding of how exposures at different stages in the lifecourse influence health outcomes is key to elucidating the potential benefits of disease prevention strategies. Mendelian randomisation (MR) is increasingly used to estimate causal effects of exposures across the lifecourse on later life outcomes. This systematic literature review explores MR methods used to perform lifecourse investigations and reviews previous work that has utilised MR to elucidate the effects of factors acting at different stages of the lifecourse. We conducted searches in PubMed, Embase, Medline and MedRXiv databases. Thirteen methodological studies were identified. Four studies focused on the impact of time-varying exposures in the interpretation of "standard" MR techniques, five presented methods for repeat measures of the same exposure, and four described methodological approaches to handling multigenerational exposures. A further 127 studies presented the results of an applied research question. Over half of these estimated effects in a single generation and were largely confined to the exploration of questions regarding body composition. The remaining mostly estimated maternal effects. There is a growing body of research focused on the development and application of MR methods to address lifecourse research questions. The underlying assumptions require careful consideration and the interpretation of results rely on select conditions. Whilst we do not advocate for a particular strategy, we encourage practitioners to make informed decisions on how to approach a research question in this field with a solid understanding of the limitations present and how these may be affected by the research question, modelling approach, instrument selection, and data availability.
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Affiliation(s)
- Grace M Power
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Panagiota Pagoni
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tim Morris
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
| | - Claire Prince
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Jon Heron
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Rebecca Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Jessica Tyrrell
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Nicole Warrington
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Frazer Institute, University of Queensland, Woolloongabba, Queensland, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- NIHR Bristol Biomedical Research Centre Bristol, University Hospitals Bristol and Weston NHS Foundation Trust, University of Bristol, Bristol, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kate M Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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8
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Beaumont RN, Flatley C, Vaudel M, Wu X, Chen J, Moen GH, Skotte L, Helgeland Ø, Solé-Navais P, Banasik K, Albiñana C, Ronkainen J, Fadista J, Stinson SE, Trajanoska K, Wang CA, Westergaard D, Srinivasan S, Sánchez-Soriano C, Bilbao JR, Allard C, Groleau M, Kuulasmaa T, Leirer DJ, White F, Jacques PÉ, Cheng H, Hao K, Andreassen OA, Åsvold BO, Atalay M, Bhatta L, Bouchard L, Brumpton BM, Brunak S, Bybjerg-Grauholm J, Ebbing C, Elliott P, Engelbrechtsen L, Erikstrup C, Estarlich M, Franks S, Gaillard R, Geller F, Grove J, Hougaard DM, Kajantie E, Morgen CS, Nohr EA, Nyegaard M, Palmer CNA, Pedersen OB, Rivadeneira F, Sebert S, Shields BM, Stoltenberg C, Surakka I, Thørner LW, Ullum H, Vaarasmaki M, Vilhjalmsson BJ, Willer CJ, Lakka TA, Gybel-Brask D, Bustamante M, Hansen T, Pearson ER, Reynolds RM, Ostrowski SR, Pennell CE, Jaddoe VWV, Felix JF, Hattersley AT, Melbye M, Lawlor DA, Hveem K, Werge T, Nielsen HS, Magnus P, Evans DM, Jacobsson B, Järvelin MR, Zhang G, Hivert MF, Johansson S, Freathy RM, Feenstra B, Njølstad PR. Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth. Nat Genet 2023; 55:1807-1819. [PMID: 37798380 PMCID: PMC10632150 DOI: 10.1038/s41588-023-01520-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 08/31/2023] [Indexed: 10/07/2023]
Abstract
A well-functioning placenta is essential for fetal and maternal health throughout pregnancy. Using placental weight as a proxy for placental growth, we report genome-wide association analyses in the fetal (n = 65,405), maternal (n = 61,228) and paternal (n = 52,392) genomes, yielding 40 independent association signals. Twenty-six signals are classified as fetal, four maternal and three fetal and maternal. A maternal parent-of-origin effect is seen near KCNQ1. Genetic correlation and colocalization analyses reveal overlap with birth weight genetics, but 12 loci are classified as predominantly or only affecting placental weight, with connections to placental development and morphology, and transport of antibodies and amino acids. Mendelian randomization analyses indicate that fetal genetically mediated higher placental weight is causally associated with preeclampsia risk and shorter gestational duration. Moreover, these analyses support the role of fetal insulin in regulating placental weight, providing a key link between fetal and placental growth.
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Affiliation(s)
- Robin N Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Christopher Flatley
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Marc Vaudel
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Xiaoping Wu
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Jing Chen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Gunn-Helen Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Line Skotte
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Øyvind Helgeland
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Pol Solé-Navais
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Clara Albiñana
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | | | - João Fadista
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Sara Elizabeth Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Katerina Trajanoska
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
| | - Carol A Wang
- School of Medicine and Public Health, College of Medicine, Public Health and Wellbeing, The University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, New South Wales, Australia
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Denmark
- Methods and Analysis, Statistics Denmark, Copenhagen, Denmark
| | - Sundararajan Srinivasan
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | | | - Jose Ramon Bilbao
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Spain
- Biobizkaia Health Research Institute, Barakaldo, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Barcelona, Spain
| | - Catherine Allard
- Centre de recherche du Centre Hospitalier de l'Universite de Sherbrooke, Sherbrooke, Québec, Canada
| | - Marika Groleau
- Département de Biologie, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Teemu Kuulasmaa
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Kuopio, Finland
| | - Daniel J Leirer
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Frédérique White
- Département de Biologie, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Pierre-Étienne Jacques
- Centre de recherche du Centre Hospitalier de l'Universite de Sherbrooke, Sherbrooke, Québec, Canada
- Département de Biologie, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Haoxiang Cheng
- Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ke Hao
- Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Mustafa Atalay
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Kuopio, Finland
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada
- Clinical Department of Laboratory Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay-Lac-St-Jean-Hôpital Universitaire de Chicoutimi, Saguenay, Québec, Canada
| | - Ben Michael Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Jonas Bybjerg-Grauholm
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Cathrine Ebbing
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Line Engelbrechtsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Herlev Hospital, Herlev, Denmark
| | - Christian Erikstrup
- Department Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Marisa Estarlich
- Faculty of Nursing and Chiropody, Universitat de València, C/Menendez Pelayo, Valencia, Spain
- Epidemiology and Environmental Health Joint Research Unit, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region, FISABIO-Public Health, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Stephen Franks
- Institute of Reproductive and Developmental Biology, Imperial College London, London, UK
| | - Romy Gaillard
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Frank Geller
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Jakob Grove
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine-Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - David M Hougaard
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Eero Kajantie
- Research Unit of Clinical Medicine, Medical Research Center, University of Oulu, Oulu, Finland
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Oulu, Finland
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Camilla S Morgen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
| | - Ellen A Nohr
- Institute of Clinical research, University of Southern Denmark, Odense, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Colin N A Palmer
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Ole Birger Pedersen
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sylvain Sebert
- Research Unit of Population Health, University of Oulu, Oulu, Finland
| | - Beverley M Shields
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Lise Wegner Thørner
- Department of Clinical Immunology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | | | - Marja Vaarasmaki
- Research Unit of Clinical Medicine, Medical Research Center, University of Oulu, Oulu, Finland
- Department of Obstetrics and Gynaecology, Oulu University Hospital, Oulu, Finland
| | - Bjarni J Vilhjalmsson
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Timo A Lakka
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Dorte Gybel-Brask
- Psychotherapeutic Outpatient Clinic, Mental Health Services, Capital Region, Copenhagen, Denmark
| | - Mariona Bustamante
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ewan R Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Rebecca M Reynolds
- Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, UK
| | - Sisse R Ostrowski
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Craig E Pennell
- School of Medicine and Public Health, College of Medicine, Public Health and Wellbeing, The University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, New South Wales, Australia
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Mads Melbye
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Danish Cancer Institute, Copenhagen, Denmark
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, Denmark
- Lundbeck Center for Geogenetics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Henriette Svarre Nielsen
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - David M Evans
- Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Ge Zhang
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Stefan Johansson
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
| | - Rachel M Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark.
- Department of Clinical Immunology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.
| | - Pål R Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway.
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9
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Gaillard R, Jaddoe VWV. Maternal cardiovascular disorders before and during pregnancy and offspring cardiovascular risk across the life course. Nat Rev Cardiol 2023; 20:617-630. [PMID: 37169830 DOI: 10.1038/s41569-023-00869-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/21/2023] [Indexed: 05/13/2023]
Abstract
Obesity, hypertension, type 2 diabetes mellitus and dyslipidaemia are highly prevalent among women of reproductive age and contribute to complications in >30% of pregnancies in Western countries. An accumulating body of evidence suggests that these cardiovascular disorders in women, occurring before and during their pregnancy, can affect the development of the structure, physiology and function of cardiovascular organ systems at different stages during embryonic and fetal development. These developmental adaptations might, in addition to genetics and sociodemographic and lifestyle factors, increase the susceptibility of the offspring to cardiovascular disease throughout the life course. In this Review, we discuss current knowledge of the influence of maternal cardiovascular disorders, occurring before and during pregnancy, on offspring cardiovascular development, dysfunction and disease from embryonic life until adulthood. We discuss findings from contemporary, large-scale, observational studies that provide insights into specific critical periods, evidence for causality and potential underlying mechanisms. Furthermore, we focus on priorities for future research, including defining optimal cardiovascular and reproductive health in women and men before their pregnancy and identifying specific embryonic, placental and fetal molecular developmental adaptations from early pregnancy onwards. Together, these approaches will help stop the intergenerational cycle of cardiovascular disease.
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Affiliation(s)
- Romy Gaillard
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, Netherlands.
| | - Vincent W V Jaddoe
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, Netherlands
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Reshetnikova Y, Churnosova M, Stepanov V, Bocharova A, Serebrova V, Trifonova E, Ponomarenko I, Sorokina I, Efremova O, Orlova V, Batlutskaya I, Ponomarenko M, Churnosov V, Eliseeva N, Aristova I, Polonikov A, Reshetnikov E, Churnosov M. Maternal Age at Menarche Gene Polymorphisms Are Associated with Offspring Birth Weight. Life (Basel) 2023; 13:1525. [PMID: 37511900 PMCID: PMC10381708 DOI: 10.3390/life13071525] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
In this study, the association between maternal age at menarche (AAM)-related polymorphisms and offspring birth weight (BW) was studied. The work was performed on a sample of 716 pregnant women and their newborns. All pregnant women underwent genotyping of 50 SNPs of AAM candidate genes. Regression methods (linear and Model-Based Multifactor Dimensionality Reduction (MB-MDR)) with permutation procedures (the indicator pperm was calculated) were used to identify the correlation between SNPs and newborn weight (transformed BW values were analyzed) and in silico bioinformatic examination was applied to assess the intended functionality of BW-associated loci. Four AAM-related genetic variants were BW-associated including genes such as POMC (rs7589318) (βadditive = 0.202/pperm = 0.015), KDM3B (rs757647) (βrecessive = 0.323/pperm = 0.005), INHBA (rs1079866) (βadditive = 0.110/pperm = 0.014) and NKX2-1 (rs999460) (βrecessive = -0.176/pperm = 0.015). Ten BW-significant models of interSNPs interactions (pperm ≤ 0.001) were identified for 20 polymorphisms. SNPs rs7538038 KISS1, rs713586 RBJ, rs12324955 FTO and rs713586 RBJ-rs12324955 FTO two-locus interaction were included in the largest number of BW-associated models (30% models each). BW-associated AAM-linked 22 SNPs and 350 proxy loci were functionally related to 49 genes relevant to pathways such as the hormone biosynthesis/process and female/male gonad development. In conclusion, maternal AMM-related genes polymorphism is associated with the offspring BW.
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Affiliation(s)
- Yuliya Reshetnikova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Vadim Stepanov
- Research Institute for Medical Genetics, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634050 Tomsk, Russia
| | - Anna Bocharova
- Research Institute for Medical Genetics, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634050 Tomsk, Russia
| | - Victoria Serebrova
- Research Institute for Medical Genetics, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634050 Tomsk, Russia
| | - Ekaterina Trifonova
- Research Institute for Medical Genetics, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634050 Tomsk, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Sorokina
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Olga Efremova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Valentina Orlova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Irina Batlutskaya
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Marina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Vladimir Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Natalya Eliseeva
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Alexey Polonikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
- Department of Biology, Medical Genetics and Ecology and Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
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Jin S, Cui S, Xu J, Zhang X. Associations between prenatal exposure to phthalates and birth weight: A meta-analysis study. Ecotoxicol Environ Saf 2023; 262:115207. [PMID: 37393820 DOI: 10.1016/j.ecoenv.2023.115207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/25/2023] [Accepted: 06/28/2023] [Indexed: 07/04/2023]
Abstract
Previous studies have suggested that phthalates are associated with birth weight. However, most phthalate metabolites have not been fully explored. Therefore, we conducted this meta-analysis to assess the relationship between phthalate exposure and birth weight. We identified original studies that measured phthalate exposure and reported its association with infant birth weight in relevant databases. Regression coefficients (β) with 95% confidence intervals (CIs) were extracted and analyzed for risk estimation. Fixed-effects (I2 ≤ 50%) or random-effects (I2 > 50%) models were adopted according to their heterogeneity. Overall summary estimates indicated negative associations of prenatal exposure to mono-n-butyl phthalate (pooled β = -11.34 g; 95% CI: -20.98 to -1.70 g) and mono-methyl phthalate (pooled β = -8.78 g; 95% CI: -16.30 to -1.27 g). No statistical association was found between the other less commonly used phthalate metabolites and birth weight. Subgroup analyses indicated that exposure to mono-n-butyl phthalate was associated with birth weight in females (β = -10.74 g; 95% CI: -18.70 to -2.79 g). Our findings indicate that phthalate exposure might be a risk factor for low birth weight and that this relationship may be sex specific. More research is needed to promote preventive policies regarding the potential health hazards of phthalates.
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Affiliation(s)
- Shihao Jin
- Department of Maternal, Child and Adolescent Health, School of Public Health, Tianjin Medical University, No. 22 Qixiangtai Road, Tianjin 300070, PR China
| | - Shanshan Cui
- School of Public Health, Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China
| | - Jinghan Xu
- Department of Maternal, Child and Adolescent Health, School of Public Health, Tianjin Medical University, No. 22 Qixiangtai Road, Tianjin 300070, PR China
| | - Xin Zhang
- Department of Maternal, Child and Adolescent Health, School of Public Health, Tianjin Medical University, No. 22 Qixiangtai Road, Tianjin 300070, PR China.
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Wu Y, Zeng F, Li J, Jiang Y, Zhao S, Knibbs LD, Zhang X, Wang Y, Zhang Q, Wang Q, Hu Q, Guo X, Chen Y, Cao G, Wang J, Yang X, Wang X, Liu T, Zhang B. Sex-specific relationships between prenatal exposure to metal mixtures and birth weight in a Chinese birth cohort. Ecotoxicol Environ Saf 2023; 262:115158. [PMID: 37348214 DOI: 10.1016/j.ecoenv.2023.115158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/14/2023] [Accepted: 06/17/2023] [Indexed: 06/24/2023]
Abstract
Birth weight is an indicator linking intrauterine environmental exposures to later-life diseases, and intrauterine metal exposure may affect birth weight in a sex-specific manner. We investigated sex-specific associations between prenatal exposure to metal mixtures and birth weight in a Chinese birth cohort. The birth weight of 1296 boys and 1098 girls were recorded, and 10 metals in maternal urine samples collected during pregnancy were measured using inductively coupled plasma mass spectrometry. Bayesian Kernel Machine Regression was used to estimate the association of individual metals or metal mixtures and birth weight for gestational age (BW for GA). The model showed a sex-specific relationship between prenatal exposure to metal mixtures and BW for GA with a significant negative association in girls and a non-significant positive association in boys. Cadmium (Cd) and nickel (Ni) were positively and negatively associated with BW for GA in girls, respectively. Moreover, increasing thallium (Tl) concentration lowered the positive association between Cd and BW for GA and enhanced the negative association between Ni and BW for GA in girls. When exposure to other metals increased, the positive association with Cd diminished, whereas the negative association with Ni or Tl increased. Our findings provide evidence supporting the complex effects of intrauterine exposure to metal mixtures on the birth weight of girls and further highlight the sex heterogeneity in fetal development influenced by intrauterine environmental factors.
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Affiliation(s)
- Ying Wu
- Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Fulin Zeng
- Guangdong-Hongkong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Public Health, Food Safety and Health Research Center, Guangdong Provincial Key Laboratory of Tropical Disease Research, Southern Medical University, Guangzhou, Guangdong, China
| | - Jinhui Li
- Department of Urology, Stanford University Medical Center, Stanford, CA, USA
| | - Yukang Jiang
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong, China; Southern China Center for Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; Chinese University of Hong Kong (CUHK) Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Luke D Knibbs
- School of Public Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Xiaojun Zhang
- Guangdong-Hongkong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Public Health, Food Safety and Health Research Center, Guangdong Provincial Key Laboratory of Tropical Disease Research, Southern Medical University, Guangzhou, Guangdong, China
| | - Yiding Wang
- Guangdong-Hongkong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Public Health, Food Safety and Health Research Center, Guangdong Provincial Key Laboratory of Tropical Disease Research, Southern Medical University, Guangzhou, Guangdong, China
| | - Qianqian Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Qiong Wang
- School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Qiansheng Hu
- School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaobo Guo
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong, China; Southern China Center for Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yumeng Chen
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Ganxiang Cao
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Jing Wang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China
| | - Xingfen Yang
- Guangdong-Hongkong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Public Health, Food Safety and Health Research Center, Guangdong Provincial Key Laboratory of Tropical Disease Research, Southern Medical University, Guangzhou, Guangdong, China
| | - Xueqin Wang
- Department of Statistics and Finance/International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China; Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou, Guangdong, China.
| | - Bo Zhang
- Guangdong-Hongkong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Public Health, Food Safety and Health Research Center, Guangdong Provincial Key Laboratory of Tropical Disease Research, Southern Medical University, Guangzhou, Guangdong, China.
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13
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Cerf ME. Maternal and Child Health, Non-Communicable Diseases and Metabolites. Metabolites 2023; 13:756. [PMID: 37367913 DOI: 10.3390/metabo13060756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/02/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023] Open
Abstract
Mothers influence the health and disease trajectories of their children, particularly during the critical developmental windows of fetal and neonatal life reflecting the gestational-fetal and lactational-neonatal phases. As children grow and develop, they are exposed to various stimuli and insults, such as metabolites, that shape their physiology and metabolism to impact their health. Non-communicable diseases, such as diabetes, cardiovascular disease, cancer and mental illness, have high global prevalence and are increasing in incidence. Non-communicable diseases often overlap with maternal and child health. The maternal milieu shapes progeny outcomes, and some diseases, such as gestational diabetes and preeclampsia, have gestational origins. Metabolite aberrations occur from diets and physiological changes. Differential metabolite profiles can predict the onset of non-communicable diseases and therefore inform prevention and/or better treatment. In mothers and children, understanding the metabolite influence on health and disease can provide insights for maintaining maternal physiology and sustaining optimal progeny health over the life course. The role and interplay of metabolites on physiological systems and signaling pathways in shaping health and disease present opportunities for biomarker discovery and identifying novel therapeutic agents, particularly in the context of maternal and child health, and non-communicable diseases.
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Affiliation(s)
- Marlon E Cerf
- Grants, Innovation and Product Development, South African Medical Research Council, P.O. Box 19070, Tygerberg, Cape Town 7505, South Africa
- Biomedical Research and Innovation Platform, South African Medical Research Council, P.O. Box 19070, Tygerberg, Cape Town 7505, South Africa
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Semertzidou A, Grout-Smith H, Kalliala I, Garg A, Terzidou V, Marchesi J, MacIntyre D, Bennett P, Tsilidis K, Kyrgiou M. Diabetes and anti-diabetic interventions and the risk of gynaecological and obstetric morbidity: an umbrella review of the literature. BMC Med 2023; 21:152. [PMID: 37072764 PMCID: PMC10114404 DOI: 10.1186/s12916-023-02758-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/27/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Diabetes has reached epidemic proportions in recent years with serious health ramifications. The aim of this study was to evaluate the strength and validity of associations between diabetes and anti-diabetic interventions and the risk of any type of gynaecological or obstetric conditions. METHODS Design: Umbrella review of systematic reviews and meta-analyses. DATA SOURCES PubMed, Medline, Embase, Cochrane Database of Systematic Reviews, manual screening of references. ELIGIBILITY CRITERIA Systematic reviews and meta-analyses of observational and interventional studies investigating the relationship between diabetes and anti-diabetic interventions with gynaecological or obstetric outcomes. Meta-analyses that did not include complete data from individual studies, such as relative risk, 95% confidence intervals, number of cases/controls, or total population were excluded. DATA ANALYSIS The evidence from meta-analyses of observational studies was graded as strong, highly suggestive, suggestive or weak according to criteria comprising the random effects estimate of meta-analyses and their largest study, the number of cases, 95% prediction intervals, I2 heterogeneity index between studies, excess significance bias, small study effect and sensitivity analysis using credibility ceilings. Interventional meta-analyses of randomised controlled trials were assessed separately based on the statistical significance of reported associations, the risk of bias and quality of evidence (GRADE) of included meta-analyses. RESULTS A total of 117 meta-analyses of observational cohort studies and 200 meta-analyses of randomised clinical trials that evaluated 317 outcomes were included. Strong or highly suggestive evidence only supported a positive association between gestational diabetes and caesarean section, large for gestational age babies, major congenital malformations and heart defects and an inverse relationship between metformin use and ovarian cancer incidence. Only a fifth of the randomised controlled trials investigating the effect of anti-diabetic interventions on women's health reached statistical significance and highlighted metformin as a more effective agent than insulin on risk reduction of adverse obstetric outcomes in both gestational and pre-gestational diabetes. CONCLUSIONS Gestational diabetes appears to be strongly associated with a high risk of caesarean section and large for gestational age babies. Weaker associations were demonstrated between diabetes and anti-diabetic interventions with other obstetric and gynaecological outcomes. TRIAL REGISTRATION Open Science Framework (OSF) (Registration https://doi.org/10.17605/OSF.IO/9G6AB ).
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Affiliation(s)
- Anita Semertzidou
- Department of Metabolism, Digestion and Reproduction - Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Harriet Grout-Smith
- Department of Metabolism, Digestion and Reproduction - Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Ilkka Kalliala
- Department of Metabolism, Digestion and Reproduction - Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
- Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Akanksha Garg
- Queen Charlotte's and Chelsea - Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Vasso Terzidou
- Department of Metabolism, Digestion and Reproduction - Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
- Queen Charlotte's and Chelsea - Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Julian Marchesi
- Department of Metabolism, Digestion and Reproduction - Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
- School of Biosciences, Cardiff University, Cardiff, UK
| | - David MacIntyre
- Department of Metabolism, Digestion and Reproduction - Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Phillip Bennett
- Department of Metabolism, Digestion and Reproduction - Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
- Queen Charlotte's and Chelsea - Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Konstantinos Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Maria Kyrgiou
- Department of Metabolism, Digestion and Reproduction - Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
- Queen Charlotte's and Chelsea - Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK.
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15
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Tebbani F, Oulamara H, Agli A. Effect of physical activity and sedentary behaviours on gestational weight gain: What are the reasons of non-practice? NUTR CLIN METAB 2023. [DOI: 10.1016/j.nupar.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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Abstract
Obesity is a disease with a major negative impact on human health. However, people with obesity may not perceive their weight to be a significant problem and less than half of patients with obesity are advised by their physicians to lose weight. The purpose of this review is to highlight the importance of managing overweight and obesity by discussing the adverse consequences and impact of obesity. In summary, obesity is strongly related to >50 medical conditions, with many of them having evidence from Mendelian randomisation studies to support causality. The clinical, social and economic burdens of obesity are considerable, with these burdens potentially impacting future generations as well. This review highlights the adverse health and economic consequences of obesity and the importance of an urgent and concerted effort towards the prevention and management of obesity to reduce the burden of obesity.
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Affiliation(s)
- Benjamin Chih Chiang Lam
- Family and Community Medicine, Khoo Teck Puat Hospital; Integrated Care for Obesity and Diabetes, Khoo Teck Puat Hospital, Singapore
| | - Amanda Yuan Ling Lim
- Singapore Association for the Study of Obesity; Division of Endocrinology, Department of Medicine, National University Hospital, Singapore
| | - Soo Ling Chan
- Division of Endocrinology, Department of Medicine, Ng Teng Fong General Hospital, Singapore
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17
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Ouyang F, Wang X, Wells JC, Wang X, Shen L, Zhang J. Maternal Pre-Pregnancy Nutritional Status and Infant Birth Weight in Relation to 0-2 Year-Growth Trajectory and Adiposity in Term Chinese Newborns with Appropriate Birth Weight-for-Gestational Age. Nutrients 2023; 15. [PMID: 36904121 DOI: 10.3390/nu15051125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 02/25/2023] Open
Abstract
Being born with appropriate weight-for-gestational age (AGA, ~80% of newborns) is often considered as low risk for future obesity. This study examined differential growth trajectories in the first two years by considering pre- and peri-natal factors among term-born AGA infants. We prospectively investigated 647 AGA infants and their mothers enrolled during 2012-2013 in Shanghai, China, and obtained repeated anthropometric measures at ages 42 days, 3, 6, 9, and 18 months from postnatal care records, and onsite measurements at age 1 and 2 years (skinfold thickness, mid-upper arm circumference (MUAC)). Birthweight was classified into sex-and gestational age-specific tertiles. Among mothers, 16.3% were overweight/obese (OWO), and 46.2% had excessive gestational weight gain (GWG). The combination of maternal prepregnancy OWO and high birthweight tertile identified a subset of AGA infants with 4.1 mm higher skinfold thickness (95% CI 2.2-5.9), 1.3 cm higher MUAC (0.8-1.7), and 0.89 units higher weight-for-length z-score (0.54, 1.24) at 2 years of age with adjustment for covariates. Excessive GWG was associated with higher child adiposity measures at 2 years of age. AGA infants manifested differential growth trajectories by the combination of maternal OWO and higher birthweight, suggesting that additional attention is needed for those "at increased risk" of OWO in early intervention.
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18
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Decina CS, Hopkins R, Bowden J, Shields BM, Lawlor DA, Warrington NM, Evans DM, Freathy RM, Beaumont RN. Investigating a possible causal relationship between maternal serum urate concentrations and offspring birthweight: a Mendelian randomization study. Int J Epidemiol 2023; 52:178-189. [PMID: 36191079 PMCID: PMC9908052 DOI: 10.1093/ije/dyac186] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 09/14/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Higher urate levels are associated with higher systolic blood pressure (SBP) in adults, and in pregnancy with lower offspring birthweight. Mendelian randomization (MR) analyses suggest a causal effect of higher urate on higher SBP and of higher maternal SBP on lower offspring birthweight. If urate causally reduces birthweight, it might confound the effect of SBP on birthweight. We therefore tested for a causal effect of maternal urate on offspring birthweight. METHODS We tested the association between maternal urate levels and offspring birthweight using multivariable linear regression in the Exeter Family Study of Childhood Health (EFSOCH; n = 872) and UK Biobank (UKB; n = 133 187). We conducted two-sample MR to test for a causal effect of maternal urate [114 single-nucleotide polymorphisms (SNPs); n = 288 649 European ancestry] on offspring birthweight (n = 406 063 European ancestry; maternal SNP effect estimates adjusted for fetal effects). We assessed a causal relationship between urate and SBP using one-sample MR in UKB women (n = 199 768). RESULTS Higher maternal urate was associated with lower offspring birthweight with similar confounder-adjusted magnitudes in EFSOCH [22 g lower birthweight per 1-SD higher urate (95% CI: -50, 6); P = 0.13] and UKB [-28 g (95% CI: -31, -25); P = 1.8 × 10-75]. The MR causal effect estimate was directionally consistent, but smaller [-11 g (95% CI: -25, 3); PIVW = 0.11]. In women, higher urate was causally associated with higher SBP [1.7 mmHg higher SBP per 1-SD higher urate (95% CI: 1.4, 2.1); P = 7.8 × 10-22], consistent with that previously published in women and men. CONCLUSION The marked attenuation of the MR result of maternal urate on offspring birthweight compared with the multivariable regression result suggests previous observational associations may be confounded. The 95% CIs of the MR result included the null but suggest a possible small effect on birthweight. Maternal urate levels are unlikely to be an important contributor to offspring birthweight.
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Affiliation(s)
- Caitlin S Decina
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Rhian Hopkins
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Jack Bowden
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Beverly M Shields
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - David M Evans
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Rachel M Freathy
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
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Taylor K, Wootton RE, Yang Q, Oddie S, Wright J, Yang TC, Magnus M, Andreassen OA, Borges MC, Caputo M, Lawlor DA. The effect of maternal BMI, smoking and alcohol on congenital heart diseases: a Mendelian randomisation study. BMC Med 2023; 21:35. [PMID: 36721200 PMCID: PMC9890815 DOI: 10.1186/s12916-023-02731-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/10/2023] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Congenital heart diseases (CHDs) remain a significant cause of infant morbidity and mortality. Epidemiological studies have explored maternal risk factors for offspring CHDs, but few have used genetic epidemiology methods to improve causal inference. METHODS Three birth cohorts, including 65,510 mother/offspring pairs (N = 562 CHD cases) were included. We used Mendelian randomisation (MR) analyses to explore the effects of genetically predicted maternal body mass index (BMI), smoking and alcohol on offspring CHDs. We generated genetic risk scores (GRS) using summary data from large-scale genome-wide association studies (GWAS) and validated the strength and relevance of the genetic instrument for exposure levels during pregnancy. Logistic regression was used to estimate the odds ratio (OR) of CHD per 1 standard deviation (SD) higher GRS. Results for the three cohorts were combined using random-effects meta-analyses. We performed several sensitivity analyses including multivariable MR to check the robustness of our findings. RESULTS The GRSs associated with the exposures during pregnancy in all three cohorts. The associations of the GRS for maternal BMI with offspring CHD (pooled OR (95% confidence interval) per 1SD higher GRS: 0.95 (0.88, 1.03)), lifetime smoking (pooled OR: 1.01 (0.93, 1.09)) and alcoholic drinks per week (pooled OR: 1.06 (0.98, 1.15)) were close to the null. Sensitivity analyses yielded similar results. CONCLUSIONS Our results do not provide robust evidence of an effect of maternal BMI, smoking or alcohol on offspring CHDs. However, results were imprecise. Our findings need to be replicated, and highlight the need for more and larger studies with maternal and offspring genotype and offspring CHD data.
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Affiliation(s)
- Kurt Taylor
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
| | - Robyn E Wootton
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Qian Yang
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
| | - Sam Oddie
- University of York, Heslington, York, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Maria Magnus
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- Division of Mental Health and Addiction, NORMENT Centre, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, Oslo University Hospital and Institute of Clinical Medicine, Oslo, Norway
| | - Maria Carolina Borges
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
| | - Massimo Caputo
- Bristol Medical School, Translational Science, Bristol, UK
| | - Deborah A Lawlor
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Bristol Medical School, Translational Science, Bristol, UK
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Zhao J, Stewart ID, Baird D, Mason D, Wright J, Zheng J, Gaunt TR, Evans DM, Freathy RM, Langenberg C, Warrington NM, Lawlor DA, Borges MC. Causal effects of maternal circulating amino acids on offspring birthweight: a Mendelian randomisation study. EBioMedicine 2023; 88:104441. [PMID: 36696816 PMCID: PMC9879767 DOI: 10.1016/j.ebiom.2023.104441] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/28/2022] [Accepted: 01/06/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Amino acids are key to protein synthesis, energy metabolism, cell signaling and gene expression; however, the contribution of specific maternal amino acids to fetal growth is unclear. METHODS We explored the effect of maternal circulating amino acids on fetal growth, proxied by birthweight, using two-sample Mendelian randomisation (MR) and summary data from a genome-wide association study (GWAS) of serum amino acids levels (sample 1, n = 86,507) and a maternal GWAS of offspring birthweight in UK Biobank and Early Growth Genetics Consortium, adjusting for fetal genotype effects (sample 2, n = 406,063 with maternal and/or fetal genotype effect estimates). A total of 106 independent single nucleotide polymorphisms robustly associated with 19 amino acids (p < 4.9 × 10-10) were used as genetic instrumental variables (IV). Wald ratio and inverse variance weighted methods were used in MR main analysis. A series of sensitivity analyses were performed to explore IV assumption violations. FINDINGS Our results provide evidence that maternal circulating glutamine (59 g offspring birthweight increase per standard deviation increase in maternal amino acid level, 95% CI: 7, 110) and serine (27 g, 95% CI: 9, 46) raise, while leucine (-59 g, 95% CI: -106, -11) and phenylalanine (-25 g, 95% CI: -47, -4) lower offspring birthweight. These findings are supported by sensitivity analyses. INTERPRETATION Our findings strengthen evidence for key roles of maternal circulating amino acids during pregnancy in healthy fetal growth. FUNDING A full list of funding bodies that contributed to this study can be found under Acknowledgments.
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Affiliation(s)
- Jian Zhao
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol NIHR Biomedical Research Centre, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; The Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Maternal and Child Health, School of Public Health, Shanghai Jiao Tong University, Shanghai, China.
| | | | - Denis Baird
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol NIHR Biomedical Research Centre, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - David M Evans
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Rachel M Freathy
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK; Computational Medicine, Berlin Institute of Health (BIH), Charité University Medicine, Berlin, Germany
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol NIHR Biomedical Research Centre, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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21
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Qian Y, Zhang Y, Fan X, Yan H, Li X, Fan Y, Song Y, Ma S, Hu Z, Gao X, Yang J. Nonalcoholic Fatty Liver Disease and Adverse Pregnancy Outcomes in Women With Normal Prepregnant Weight. J Clin Endocrinol Metab 2023; 108:463-471. [PMID: 36181486 DOI: 10.1210/clinem/dgac567] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 09/24/2022] [Indexed: 01/20/2023]
Abstract
CONTEXT Existing studies focusing on the effects of nonalcoholic fatty liver disease (NAFLD) combined with normal prepregnant weight on pregnancy outcomes are limited. OBJECTIVE This study aimed to explore the relationship between maternal NAFLD and adverse pregnancy outcomes in different body mass index (BMI) groups. METHODS Using an antenatal care and delivery database, we retrospectively analyzed women who delivered in Minhang Hospital affiliated to Fudan University, Shanghai, China from January 1, 2013, to June 30, 2020. NAFLD was confirmed by ultrasound in early pregnancy. A logistic regression model with adjustment for confounders was used to examine potential associations between NAFLD and pregnancy outcomes. RESULTS A total of 14 708 pregnant women (mean prepregnant BMI 21.0 [SD, 2.8] kg/m2) were included in our final study, of whom 554 (3.8%) had NAFLD. After fully adjusting for potential confounders, NAFLD significantly increased the risk of gestational diabetes mellitus (adjusted odds ratio 2.477; 95% CI, 1.885-3.254), gestational hypertension (3.054; 2.191-4.257), preeclampsia/eclampsia (3.994; 2.591-6.005), cesarean section (1.569; 1.315-1.872), preterm births (1.831; 1.229-2.727), and macrosomia (1.691; 1.300-2.198). It is notable that 83.9% (12 338) of women were of normal weight at the start of pregnancy (prepregnant 18.5 ≤ BMI < 24 kg/m2), and they still had higher odds of adverse pregnancy outcomes. CONCLUSION Women with NAFLD and a normal weight have a higher risk for adverse pregnancy outcomes. Pregnant women with NAFLD, regardless of obesity status, should be offered a more qualified surveillance to optimize pregnancy outcomes.
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Affiliation(s)
- Yiling Qian
- Department of Endocrinology and Metabolism, Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Yu Zhang
- Department of Endocrinology and Metabolism, Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Xiaofang Fan
- Department of Endocrinology and Metabolism, Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Hongmei Yan
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xuesong Li
- Department of Endocrinology and Metabolism, Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Yujuan Fan
- Department of Endocrinology and Metabolism, Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Yuping Song
- Department of Endocrinology and Metabolism, Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Shuai Ma
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Zheng Hu
- Department of Obstetrics, Minhang Hospital, Fudan University, Shanghai 201199, China
| | - Xin Gao
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jialin Yang
- Department of Endocrinology and Metabolism, Minhang Hospital, Fudan University, Shanghai 201199, China
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22
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Bartels HC, O'Keeffe LM, Yelverton CA, O'Neill KN, Geraghty AA, O'Brien EC, Killeen SL, McDonnell C, McAuliffe FM. Associations between maternal metabolic parameters during pregnancy and fetal and child growth trajectories from 20 weeks' gestation to 5 years of age: Secondary analysis from the ROLO longitudinal birth cohort study. Pediatr Obes 2023; 18:e12976. [PMID: 36102219 PMCID: PMC10078394 DOI: 10.1111/ijpo.12976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 08/09/2022] [Accepted: 08/15/2022] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To examine the association between maternal metabolic parameters in pregnancy and growth trajectories up to 5 years of age. METHODS Data from mother-child pairs who participated in the ROLO study, a randomized trial examining the impact of a low glycaemic index diet on the recurrence of macrosomia, were analysed. Fetal and child growth trajectories were developed from longitudinal measurements from 20 weeks gestation up to 5 years of age. We examined associations between maternal fasting glucose, insulin, HOMA-IR and leptin, taken in early pregnancy (14-16 weeks) and late pregnancy (28 weeks), and weight (kg) and abdominal circumference (cm) trajectories using linear spline multilevel models. RESULTS We found no strong evidence of associations between any maternal metabolic parameters and fetal to childhood weight and abdominal circumference trajectories from 20 weeks gestation to 5 years. CONCLUSION In a cohort of women with obesity with infants at risk of macrosomia, maternal metabolic markers were not strongly associated with trajectories of weight or abdominal circumference from 20 weeks gestation to 5 years of age.
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Affiliation(s)
- Helena C Bartels
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Linda M O'Keeffe
- School of Public Health, University College Cork, Cork, Ireland.,MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Cara A Yelverton
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Kate N O'Neill
- School of Public Health, University College Cork, Cork, Ireland
| | - Aisling A Geraghty
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Eileen C O'Brien
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Sarah Louise Killeen
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Ciara McDonnell
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland.,Department of Pediatric Endocrinology & Diabetes, Children's Health Ireland, Temple Street Hospital, Dublin, Ireland
| | - Fionnuala M McAuliffe
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
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23
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Moen GH, Nivard M, Bhatta L, Warrington NM, Willer C, Åsvold BO, Brumpton B, Evans DM. Using Genomic Structural Equation Modeling to Partition the Genetic Covariance Between Birthweight and Cardiometabolic Risk Factors into Maternal and Offspring Components in the Norwegian HUNT Study. Behav Genet 2023; 53:40-52. [PMID: 36322199 PMCID: PMC9823066 DOI: 10.1007/s10519-022-10116-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/18/2022] [Accepted: 09/26/2022] [Indexed: 11/07/2022]
Abstract
The Barker Hypothesis posits that adverse intrauterine environments result in fetal growth restriction and increased risk of cardiometabolic disease through developmental compensations. Here we introduce a new statistical model using the genomic SEM software that is capable of simultaneously partitioning the genetic covariation between birthweight and cardiometabolic traits into maternally mediated and offspring mediated contributions. We model the covariance between birthweight and later life outcomes, such as blood pressure, non-fasting glucose, blood lipids and body mass index in the Norwegian HUNT study, consisting of 15,261 mother-eldest offspring pairs with genetic and phenotypic data. Application of this model showed some evidence for maternally mediated effects of systolic blood pressure on offspring birthweight, and pleiotropy between birthweight and non-fasting glucose mediated through the offspring genome. This underscores the importance of genetic links between birthweight and cardiometabolic phenotypes and offer alternative explanations to environmentally based hypotheses for the phenotypic correlation between these variables.
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Affiliation(s)
- Gunn-Helen Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. .,Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia. .,Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway. .,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK. .,The University of Queensland Diamantina Institute, The University of Queensland, 4102, Woolloongabba, QLD, Australia.
| | - Michel Nivard
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands ,grid.16872.3a0000 0004 0435 165XAmsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Laxmi Bhatta
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nicole M Warrington
- grid.1003.20000 0000 9320 7537Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, The University of Queensland, 4102 Woolloongabba, QLD Australia
| | - Cristen Willer
- grid.214458.e0000000086837370Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA ,grid.214458.e0000000086837370Department of Internal Medicine, University of Michigan, Ann Arbor, MI US ,grid.214458.e0000000086837370Department of Human Genetics, University of Michigan, Ann Arbor, USA
| | - Bjørn Olav Åsvold
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.52522.320000 0004 0627 3560Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, HUNT Research Centre, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ben Brumpton
- grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.5947.f0000 0001 1516 2393Department of Public Health and Nursing, HUNT Research Centre, NTNU, Norwegian University of Science and Technology, Trondheim, Norway ,grid.52522.320000 0004 0627 3560Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - David M. Evans
- grid.1003.20000 0000 9320 7537Institute of Molecular Biosciences, The University of Queensland, Brisbane, Australia ,grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, The University of Queensland, 4102 Woolloongabba, QLD Australia ,grid.5337.20000 0004 1936 7603Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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24
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Wang J, Kuang Y, Shen S, Price MJ, Lu J, Sattar N, He J, Pittavino M, Xia H, Thomas GN, Qiu X, Cheng KK, Nirantharakumar K. Association of maternal lipid levels with birth weight and cord blood insulin: a Bayesian network analysis. BMJ Open 2022; 12:e064122. [PMID: 36581404 PMCID: PMC9806023 DOI: 10.1136/bmjopen-2022-064122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE To assess the independent association of maternal lipid levels with birth weight and cord blood insulin (CBI) level. SETTING The Born in Guangzhou Cohort Study, Guangzhou, China. PARTICIPANTS Women who delivered between January 2015 and June 2016 and with umbilical cord blood retained were eligible for this study. Those with prepregnancy health conditions, without an available fasting blood sample in the second trimester, or without demographic and glycaemic information were excluded. After random selection, data from 1522 mother-child pairs were used in this study. EXPOSURES AND OUTCOME MEASURES Additive Bayesian network analysis was used to investigate the interdependency of lipid profiles with other metabolic risk factors (prepregnancy body mass index (BMI), fasting glucose and early gestational weight gain) in association with birth weight and CBI, along with multivariable linear regression models. RESULTS In multivariable linear regressions, maternal triglyceride was associated with increased birth weight (adjusted β=67.46, 95% CI 41.85 to 93.06 g per mmol/L) and CBI (adjusted β=0.89, 95% CI 0.06 to 1.72 μU/mL per mmol/L increase), while high-density lipoprotein cholesterol was associated with decreased birth weight (adjusted β=-45.29, 95% CI -85.49 to -5.09 g per mmol/L). After considering the interdependency of maternal metabolic risk factors in the Network analysis, none of the maternal lipid profiles was independently associated with birth weight and CBI. Instead, prepregnancy BMI was the global strongest factor for birth weight and CBI directly and indirectly. CONCLUSIONS Gestational dyslipidaemia appears to be secondary to metabolic dysfunction with no clear association with metabolic adverse outcomes in neonates. Maternal prepregnancy overweight/obesity appears the most influential upstream metabolic risk factor for both maternal and neonatal metabolic health; these data imply weight management may need to be addressed from the preconception period and during early pregnancy.
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Affiliation(s)
- Jingya Wang
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Yashu Kuang
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Research in Structure Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Songying Shen
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Research in Structure Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Malcolm James Price
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jinhua Lu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Research in Structure Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Jianrong He
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Research in Structure Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | | | - Huimin Xia
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - G Neil Thomas
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Xiu Qiu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Department of Women's Health, Guangdong Provincial Key Clinical Specialty of Woman and Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Kar Keung Cheng
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
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25
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Robertson OC, Marceau K, Moding KJ, Knopik VS. Developmental pathways linking obesity risk and early puberty: The thrifty phenotype and fetal overnutrition hypotheses. Developmental Review 2022. [DOI: 10.1016/j.dr.2022.101048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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26
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Monyeki MA, Sedumedi CM, Reilly JJ, Janssen X, Kruger HS, Kruger R, Loechl CU. Birth Weight and Body Composition as Determined by Isotopic Dilution with Deuterium Oxide in 6- to 8-Year-Old South African Children. Children (Basel) 2022; 9. [PMID: 36291533 DOI: 10.3390/children9101597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/11/2022] [Accepted: 10/19/2022] [Indexed: 11/18/2022]
Abstract
Low and high birth weight (BW) are associated with obesity later in life; however, this association has not been extensively studied in African countries. This study determines the association between BW and body composition derived from deuterium oxide (D2O) dilution in 6- to 8-year-old South African children (n = 91; 40 boys, 51 girls). BW was recorded retrospectively from the children’s Road-to-Health cards. Weight and height were measured using standard procedures, and D2O dilution was used to determine total body water and, subsequently, to determine body fat. Fatness was classified using the McCarthy centiles, set at 2nd, 85th, and 95th (underfat, overfat and obese). BW correlated with body composition measures, such as body weight (r = 0.23, p = 0.03), height (r = 0.33, p < 0.001), and fat free mass (FFM; r = 0.27, p = 0.01). When multiple regression analysis was employed, BW significantly and positively associated with FFM (β = 0.24, p = 0.013; 95% CI: 0.032; 0.441) and fat mass (β = 0.21, p = 0.02, 95%CI: 0.001; 0.412) in girls and boys combined. A total of 13% of the children had a low BW, with 21% being overweight and 17% obese. More girls than boys were overweight and obese. Intervention strategies that promote healthy uterine growth for optimal BW are needed in order to curb the global obesity pandemic.
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27
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Yang Q, Magnus MC, Kilpi F, Santorelli G, Soares AG, West J, Magnus P, Wright J, Håberg SE, Sanderson E, Lawlor DA, Tilling K, Borges MC. Investigating causal relations between sleep duration and risks of adverse pregnancy and perinatal outcomes: linear and nonlinear Mendelian randomization analyses. BMC Med 2022; 20:295. [PMID: 36089592 PMCID: PMC9465870 DOI: 10.1186/s12916-022-02494-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/25/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Observational studies have reported maternal short/long sleep duration to be associated with adverse pregnancy and perinatal outcomes. However, it remains unclear whether there are nonlinear causal effects. Our aim was to use Mendelian randomization (MR) and multivariable regression to examine nonlinear effects of sleep duration on stillbirth (MR only), miscarriage (MR only), gestational diabetes, hypertensive disorders of pregnancy, perinatal depression, preterm birth and low/high offspring birthweight. METHODS We used data from European women in UK Biobank (N=176,897), FinnGen (N=~123,579), Avon Longitudinal Study of Parents and Children (N=6826), Born in Bradford (N=2940) and Norwegian Mother, Father and Child Cohort Study (MoBa, N=14,584). We used 78 previously identified genetic variants as instruments for sleep duration and investigated its effects using two-sample, and one-sample nonlinear (UK Biobank only), MR. We compared MR findings with multivariable regression in MoBa (N=76,669), where maternal sleep duration was measured at 30 weeks. RESULTS In UK Biobank, MR provided evidence of nonlinear effects of sleep duration on stillbirth, perinatal depression and low offspring birthweight. Shorter and longer duration increased stillbirth and low offspring birthweight; shorter duration increased perinatal depression. For example, longer sleep duration was related to lower risk of low offspring birthweight (odds ratio 0.79 per 1 h/day (95% confidence interval: 0.67, 0.93)) in the shortest duration group and higher risk (odds ratio 1.40 (95% confidence interval: 1.06, 1.84)) in the longest duration group, suggesting shorter and longer duration increased the risk. These were supported by the lack of evidence of a linear effect of sleep duration on any outcome using two-sample MR. In multivariable regression, risks of all outcomes were higher in the women reporting <5 and ≥10 h/day sleep compared with the reference category of 8-9 h/day, despite some wide confidence intervals. Nonlinear models fitted the data better than linear models for most outcomes (likelihood ratio P-value=0.02 to 3.2×10-52), except for gestational diabetes. CONCLUSIONS Our results show shorter and longer sleep duration potentially causing higher risks of stillbirth, perinatal depression and low offspring birthweight. Larger studies with more cases are needed to detect potential nonlinear effects on hypertensive disorders of pregnancy, preterm birth and high offspring birthweight.
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Affiliation(s)
- Qian Yang
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Maria C Magnus
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Fanny Kilpi
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gillian Santorelli
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Ana Gonçalves Soares
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jane West
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Siri Eldevik Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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28
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Denault WRP, Bohlin J, Page CM, Burgess S, Jugessur A. Cross-fitted instrument: A blueprint for one-sample Mendelian randomization. PLoS Comput Biol 2022; 18:e1010268. [PMID: 36037248 PMCID: PMC9462731 DOI: 10.1371/journal.pcbi.1010268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 09/09/2022] [Accepted: 05/31/2022] [Indexed: 11/18/2022] Open
Abstract
Bias from weak instruments may undermine the ability to estimate causal effects in instrumental variable regression (IVR). We present here a new approach to handling weak instrument bias through the application of a new type of instrumental variable coined ‘Cross-Fitted Instrument’ (CFI). CFI splits the data at random and estimates the impact of the instrument on the exposure in each partition. These estimates are then used to perform an IVR on each partition. We adapt CFI to the Mendelian randomization (MR) setting and term this adaptation ‘Cross-Fitting for Mendelian Randomization’ (CFMR). We show that, even when using weak instruments, CFMR is, at worst, biased towards the null, which makes it a conservative one-sample MR approach. In particular, CFMR remains conservative even when the two samples used to perform the MR analysis completely overlap, whereas current state-of-the-art approaches (e.g., MR RAPS) display substantial bias in this setting. Another major advantage of CFMR lies in its use of all of the available data to select genetic instruments, which maximizes statistical power, as opposed to traditional two-sample MR where only part of the data is used to select the instrument. Consequently, CFMR is able to enhance statistical power in consortia-led meta-analyses by enabling a conservative one-sample MR to be performed in each cohort prior to a meta-analysis of the results across all the cohorts. In addition, CFMR enables a cross-ethnic MR analysis by accounting for ethnic heterogeneity, which is particularly important in meta-analyses where the participating cohorts may have different ethnicities. To our knowledge, none of the current MR approaches can account for such heterogeneity. Finally, CFMR enables the application of MR to exposures that are either rare or difficult to measure, which would normally preclude their analysis in the regular two-sample MR setting. We present a new approach to handling weak instrument bias through the use of a new type of instrumental variable that enables a conservative one-sample Mendelian Randomization. The new method provides the same power as the standard two-sample Mendelian Randomization but does not require summary statistics from a previously published genome-wide association study in an independent cohort to build the instrument. In particular, our method can quantify the effect of exposures that are either rare or difficult to measure, which is almost unfeasible with current Mendelian Randomization methods. Finally, our approach enables a cross-ethnic instrumental variable regression to account for heterogeneity in a multi-ethnic sample and is also well-adapted to a meta-analysis setting whereby summary statistics from many participating cohorts are analyzed jointly.
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Affiliation(s)
- William R. P. Denault
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- * E-mail:
| | - Jon Bohlin
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Christian M. Page
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Astanand Jugessur
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
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29
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Mir SA, Chen L, Burugupalli S, Burla B, Ji S, Smith AAT, Narasimhan K, Ramasamy A, Tan KML, Huynh K, Giles C, Mei D, Wong G, Yap F, Tan KH, Collier F, Saffery R, Vuillermin P, Bendt AK, Burgner D, Ponsonby AL, Lee YS, Chong YS, Gluckman PD, Eriksson JG, Meikle PJ, Wenk MR, Karnani N. Population-based plasma lipidomics reveals developmental changes in metabolism and signatures of obesity risk: a mother-offspring cohort study. BMC Med 2022; 20:242. [PMID: 35871677 PMCID: PMC9310480 DOI: 10.1186/s12916-022-02432-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 06/09/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Lipids play a vital role in health and disease, but changes to their circulating levels and the link with obesity remain poorly characterized in expecting mothers and their offspring in early childhood. METHODS LC-MS/MS-based quantitation of 480 lipid species was performed on 2491 plasma samples collected at 4 time points in the mother-offspring Asian cohort GUSTO (Growing Up in Singapore Towards healthy Outcomes). These 4 time points constituted samples collected from mothers at 26-28 weeks of gestation (n=752) and 4-5 years postpartum (n=650), and their offspring at birth (n=751) and 6 years of age (n=338). Linear regression models were used to identify the pregnancy and developmental age-specific variations in the plasma lipidomic profiles, and their association with obesity risk. An independent birth cohort (n=1935), the Barwon Infant Study (BIS), comprising mother-offspring dyads of Caucasian origin was used for validation. RESULTS Levels of 36% of the profiled lipids were significantly higher (absolute fold change > 1.5 and Padj < 0.05) in antenatal maternal circulation as compared to the postnatal phase, with phosphatidylethanolamine levels changing the most. Compared to antenatal maternal lipids, cord blood showed lower concentrations of most lipid species (79%) except lysophospholipids and acylcarnitines. Changes in lipid concentrations from birth to 6 years of age were much higher in magnitude (log2FC=-2.10 to 6.25) than the changes observed between a 6-year-old child and an adult (postnatal mother) (log2FC=-0.68 to 1.18). Associations of cord blood lipidomic profiles with birth weight displayed distinct trends compared to the lipidomic profiles associated with child BMI at 6 years. Comparison of the results between the child and adult BMI identified similarities in association with consistent trends (R2=0.75). However, large number of lipids were associated with BMI in adults (67%) compared to the children (29%). Pre-pregnancy BMI was specifically associated with decrease in the levels of phospholipids, sphingomyelin, and several triacylglycerol species in pregnancy. CONCLUSIONS In summary, our study provides a detailed landscape of the in utero lipid environment provided by the gestating mother to the growing fetus, and the magnitude of changes in plasma lipidomic profiles from birth to early childhood. We identified the effects of adiposity on the circulating lipid levels in pregnant and non-pregnant women as well as offspring at birth and at 6 years of age. Additionally, the pediatric vs maternal overlap of the circulating lipid phenotype of obesity risk provides intergenerational insights and early opportunities to track and intervene the onset of metabolic adversities. CLINICAL TRIAL REGISTRATION This birth cohort is a prospective observational study, which was registered on 1 July 2010 under the identifier NCT01174875 .
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Affiliation(s)
- Sartaj Ahmad Mir
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117596, Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Li Chen
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore.,Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore
| | - Satvika Burugupalli
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Bo Burla
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Shanshan Ji
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Adam Alexander T Smith
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Kothandaraman Narasimhan
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore
| | - Adaikalavan Ramasamy
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore
| | - Karen Mei-Ling Tan
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore
| | - Kevin Huynh
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Corey Giles
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Ding Mei
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Gerard Wong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore
| | - Fabian Yap
- KK Women's and Children's Hospital, Singapore, Singapore
| | - Kok Hian Tan
- KK Women's and Children's Hospital, Singapore, Singapore
| | - Fiona Collier
- School of Medicine, Deakin University, Geelong, Australia.,Child Health Research Unit, Barwon Health, Geelong, Australia.,Murdoch Children's Research Institute, University of Melbourne, Parkville, Australia
| | - Richard Saffery
- Murdoch Children's Research Institute, University of Melbourne, Parkville, Australia.,The Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Peter Vuillermin
- School of Medicine, Deakin University, Geelong, Australia.,Child Health Research Unit, Barwon Health, Geelong, Australia.,Murdoch Children's Research Institute, University of Melbourne, Parkville, Australia
| | - Anne K Bendt
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - David Burgner
- Murdoch Children's Research Institute, University of Melbourne, Parkville, Australia.,The Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Anne-Louise Ponsonby
- Murdoch Children's Research Institute, University of Melbourne, Parkville, Australia.,The Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Yung Seng Lee
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore.,Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Johan G Eriksson
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Folkhalsan Research Center, Helsinki, Finland.,Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
| | - Peter J Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC, 3004, Australia.
| | - Markus R Wenk
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117596, Singapore. .,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore.
| | - Neerja Karnani
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117596, Singapore. .,Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, 117609, Singapore. .,DataHub Division, Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, Singapore.
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30
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Mitchell LE. Maternal genetic factors in the development of congenital heart defects. Curr Opin Genet Dev 2022; 76:101961. [PMID: 35882070 DOI: 10.1016/j.gde.2022.101961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 06/20/2022] [Accepted: 06/25/2022] [Indexed: 11/24/2022]
Abstract
Congenital heart defects (CHDs) are among the most common, serious birth defects. However, the cause of CHDs is unknown for approximately half of affected individuals and there are few prevention strategies. Although not extensively investigated, maternal genes may contribute to CHD etiology by modifying the effects of maternal exposures (e.g. medications, nutrients), contributing to maternal phenotypes that are associated with an increased risk of CHDs in offspring (e.g. diabetes), or acting as maternal effect genes. Since maternal genes could serve as a target for the primary prevention of CHDs, efforts to further define the contribution of the maternal genome to CHD etiology are warranted.
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31
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Hwang LD, Moen GH, Evans DM. Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes. eLife 2022; 11:73671. [PMID: 35822614 PMCID: PMC9323003 DOI: 10.7554/elife.73671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
Maternal genetic effects can be defined as the effect of a mother's genotype on the phenotype of her offspring, independent of the offspring's genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We assume that in biological families, offspring phenotypes are influenced prenatally by their mother's genotype and postnatally by both parents' genotypes, whereas adopted individuals' phenotypes are influenced prenatally by their biological mother's genotype and postnatally by their adoptive parents' genotypes. Our SEM framework allows us to model the (potentially) unobserved genotypes of biological and adoptive parents as latent variables, permitting us in principle to leverage the thousands of adopted singleton individuals in the UK Biobank. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347980 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce asymptotically unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases.
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32
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Yang Q, Sanderson E, Tilling K, Borges MC, Lawlor DA. Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization. Eur J Epidemiol 2022; 37:683-700. [PMID: 35622304 PMCID: PMC9329407 DOI: 10.1007/s10654-022-00874-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 04/18/2022] [Indexed: 12/19/2022]
Abstract
With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.
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Affiliation(s)
- Qian Yang
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
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Lin J, Guo H, Wang B, Zhu Q. Association of maternal pre-pregnancy body mass index with birth weight and preterm birth among singletons conceived after frozen-thawed embryo transfer. Reprod Biol Endocrinol 2022; 20:86. [PMID: 35689242 PMCID: PMC9185967 DOI: 10.1186/s12958-022-00957-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/15/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND To explore the effect of pre-pregnancy body mass index (BMI) on neonatal outcomes among singletons born after frozen embryo transfer (FET). METHODS This large retrospective cohort study included 18,683 singleton infants born after FET during the period from Jan 1, 2007 to Dec 31, 2019. The main outcomes were large for gestational age (LGA) and preterm birth. Logistic regression models with generalized estimating equations for clustering by patients to estimate odds ratios of LGA and preterm birth. RESULTS Overweight was positively associated with LGA overall (adjusted OR 1.78 [95%CI 1.60-1.98]), and this association was consistent across age categories. The underweight was inversely associated with LGA among mothers younger than 35 years (adjusted OR 0.49 [95%CI 0.39-0.62] among mothers younger than 30 years; adjusted OR 0.47 [95%CI 0.37-0.60] among mothers aged 30-34 years), but this association was no significant among mothers 35 years or older. Overweight was positively and significantly associated with preterm birth overall (adjusted OR 1.52 [95%CI 1.30-1.77]) and consistently across age categories. The underweight mothers younger than 30 years had a decreased risk of preterm birth (adjusted OR 0.70 [95%CI 0.51-0.97]), but the underweight was no significantly associated with preterm birth among women aged 30 years of older. CONCLUSIONS The risks of LGA and preterm birth were increased in singletons born to overweight mothers, regardless of the maternal age. Underweight decreased the risk of LGA and preterm birth for younger mothers. These findings are important for providing preconceptional counseling to specifically targeted women at high risk of LGA and preterm birth.
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Affiliation(s)
- Jiaying Lin
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital affiliated to JiaoTong University School of Medicine, Zhizaoju Road No. 639, Shanghai, China
| | - Haiyan Guo
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital affiliated to JiaoTong University School of Medicine, Zhizaoju Road No. 639, Shanghai, China
| | - Bian Wang
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital affiliated to JiaoTong University School of Medicine, Zhizaoju Road No. 639, Shanghai, China
| | - Qianqian Zhu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital affiliated to JiaoTong University School of Medicine, Zhizaoju Road No. 639, Shanghai, China.
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34
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Barry CJS, Lawlor DA, Shapland CY, Sanderson E, Borges MC. Using Mendelian Randomisation to Prioritise Candidate Maternal Metabolic Traits Influencing Offspring Birthweight. Metabolites 2022; 12:537. [PMID: 35736469 PMCID: PMC9231269 DOI: 10.3390/metabo12060537] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/06/2022] [Accepted: 06/07/2022] [Indexed: 12/27/2022] Open
Abstract
Marked physiological changes in pregnancy are essential to support foetal growth; however, evidence on the role of specific maternal metabolic traits from human studies is limited. We integrated Mendelian randomisation (MR) and metabolomics data to probe the effect of 46 maternal metabolic traits on offspring birthweight (N = 210,267). We implemented univariable two-sample MR (UVMR) to identify candidate metabolic traits affecting offspring birthweight. We then applied two-sample multivariable MR (MVMR) to jointly estimate the potential direct causal effect for each candidate maternal metabolic trait. In the main analyses, UVMR indicated that higher maternal glucose was related to higher offspring birthweight (0.328 SD difference in mean birthweight per 1 SD difference in glucose (95% CI: 0.104, 0.414)), as were maternal glutamine (0.089 (95% CI: 0.033, 0.144)) and alanine (0.137 (95% CI: 0.036, 0.239)). In additional analyses, UVMR estimates were broadly consistent when selecting instruments from an independent data source, albeit imprecise for glutamine and alanine, and were attenuated for alanine when using other UVMR methods. MVMR results supported independent effects of these metabolites, with effect estimates consistent with those seen with the UVMR results. Among the remaining 43 metabolic traits, UVMR estimates indicated a null effect for most lipid-related traits and a high degree of uncertainty for other amino acids and ketone bodies. Our findings suggest that maternal gestational glucose and glutamine are causally related to offspring birthweight.
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Affiliation(s)
- Ciarrah-Jane Shannon Barry
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Deborah A. Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
- NIHR Bristol Biomedical Research Centre, Bristol BS8 2BN, UK
| | - Chin Yang Shapland
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (D.A.L.); (C.Y.S.); (E.S.); (M.C.B.)
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
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35
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Thompson WD, Beaumont RN, Kuang A, Warrington NM, Ji Y, Tyrrell J, Wood AR, Scholtens DM, Knight BA, Evans DM, Lowe Jr WL, Santorelli G, Azad R, Mason D, Hattersley AT, Frayling TM, Yaghootkar H, Borges MC, Lawlor DA, Freathy RM. Fetal alleles predisposing to metabolically favorable adiposity are associated with higher birth weight. Hum Mol Genet 2022; 31:1762-1775. [PMID: 34897462 PMCID: PMC9169452 DOI: 10.1093/hmg/ddab356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Higher birthweight is associated with higher adult body mass index (BMI). Alleles that predispose to greater adult adiposity might act in fetal life to increase fetal growth and birthweight. Whether there are fetal effects of recently identified adult metabolically favorable adiposity alleles on birthweight is unknown. AIM We aimed to test the effect on birthweight of fetal genetic predisposition to higher metabolically favorable adult adiposity and compare that with the effect of fetal genetic predisposition to higher adult BMI. METHODS We used published genome wide association study data (n = upto 406 063) to estimate fetal effects on birthweight (adjusting for maternal genotype) of alleles known to raise metabolically favorable adult adiposity or BMI. We combined summary data across single nucleotide polymorphisms (SNPs) with random effects meta-analyses. We performed weighted linear regression of SNP-birthweight effects against SNP-adult adiposity effects to test for a dose-dependent association. RESULTS Fetal genetic predisposition to higher metabolically favorable adult adiposity and higher adult BMI were both associated with higher birthweight (3 g per effect allele (95% CI: 1-5) averaged over 14 SNPs; P = 0.002; 0.5 g per effect allele (95% CI: 0-1) averaged over 76 SNPs; P = 0.042, respectively). SNPs with greater effects on metabolically favorable adiposity tended to have greater effects on birthweight (R2 = 0.2912, P = 0.027). There was no dose-dependent association for BMI (R2 = -0.0019, P = 0.602). CONCLUSIONS Fetal genetic predisposition to both higher adult metabolically favorable adiposity and BMI is associated with birthweight. Fetal effects of metabolically favorable adiposity-raising alleles on birthweight are modestly proportional to their effects on future adiposity, but those of BMI-raising alleles are not.
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Affiliation(s)
- William D Thompson
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Alan Kuang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Nicole M Warrington
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane QLD 4102, Australia
- Department of Public Health and Nursing, NTNU, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, Postboks 8905, N-7491, Norway
| | - Yingjie Ji
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Jessica Tyrrell
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Andrew R Wood
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Denise M Scholtens
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Bridget A Knight
- NIHR Exeter Clinical Research Facility, Royal Devon and Exeter NHS Foundation Trust, Exeter EX2 5DW, UK
| | - David M Evans
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane QLD 4102, Australia
| | - William L Lowe Jr
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Gillian Santorelli
- Bradford Institute for Health Research, Bradford Royal Infirmary, Duckworth Lane, Bradford BD9 6RJ, UK
| | - Raq Azad
- Department of Biochemistry, Bradford Royal Infirmary, Bradford BD9 6DA, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Royal Infirmary, Duckworth Lane, Bradford BD9 6RJ, UK
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Timothy M Frayling
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Hanieh Yaghootkar
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
- Population Health, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
- Bristol NIHR Biomedical Research Centre, Bristol BS8 2BN, UK
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
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Kuang A, Hayes MG, Hivert M, Balasubramanian R, Lowe WL, Scholtens DM. Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies. Metabolites 2022; 12:512. [PMID: 35736446 PMCID: PMC9229972 DOI: 10.3390/metabo12060512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/27/2022] [Accepted: 05/27/2022] [Indexed: 02/04/2023] Open
Abstract
The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.
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Bulik CM, Coleman JRI, Hardaway JA, Breithaupt L, Watson HJ, Bryant CD, Breen G. Genetics and neurobiology of eating disorders. Nat Neurosci 2022; 25:543-54. [PMID: 35524137 DOI: 10.1038/s41593-022-01071-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 04/01/2022] [Indexed: 12/14/2022]
Abstract
Eating disorders (anorexia nervosa, bulimia nervosa and binge-eating disorder) are a heterogeneous class of complex illnesses marked by weight and appetite dysregulation coupled with distinctive behavioral and psychological features. Our understanding of their genetics and neurobiology is evolving thanks to global cooperation on genome-wide association studies, neuroimaging, and animal models. Until now, however, these approaches have advanced the field in parallel, with inadequate cross-talk. This review covers overlapping advances in these key domains and encourages greater integration of hypotheses and findings to create a more unified science of eating disorders. We highlight ongoing and future work designed to identify implicated biological pathways that will inform staging models based on biology as well as targeted prevention and tailored intervention, and will galvanize interest in the development of pharmacologic agents that target the core biology of the illnesses, for which we currently have few effective pharmacotherapeutics.
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Zeng J, Shen F, Zou ZY, Yang RX, Jin Q, Yang J, Chen GY, Fan JG. Association of maternal obesity and gestational diabetes mellitus with overweight/obesity and fatty liver risk in offspring. World J Gastroenterol 2022; 28:1681-1691. [PMID: 35581961 PMCID: PMC9048784 DOI: 10.3748/wjg.v28.i16.1681] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 10/30/2021] [Accepted: 03/16/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Childhood obesity and fatty liver are associated with adverse outcomes such as diabetes, metabolic syndrome, and cardiovascular diseases in adulthood. It is very important to identify relevant risk factors and intervene as early as possible. At present, the relationship between maternal and offspring metabolic factors is conflicting.
AIM To estimate the association of maternal obesity and gestational diabetes mellitus (GDM) with overweight/obesity and fatty liver risk in offspring at 8 years of age.
METHODS The prospective study included mothers who all had a 75-g oral glucose tolerance test at 24-28 wk of gestation and whose offspring completed follow-up at 8 years of age. Offspring birth weight, sex, height, weight, and body mass index (BMI) were measured and calculated. FibroScan-502 examination with an M probe (Echosens, Paris, France) was prospectively conducted in offspring aged 8 years from the Shanghai Prenatal Cohort Study.
RESULTS A total of 430 mother-child pairs were included in the analysis. A total of 62 (14.2%) mothers were classified as obese, and 48 (11.1%) were classified as having GDM. The mean age of the offspring at follow-up was 8 years old. Thirty-seven (8.6%) offspring were overweight, 14 (3.3%) had obesity, and 60 (14.0%) had fatty liver. The prevalence of overweight, obesity and fatty liver in offspring increased significantly across maternal BMI quartiles (all P < 0.05). Among offspring of mothers with GDM, 12 (25.0%) were overweight, 4 (8.3%) were obese, and 12 (25.0%) had fatty liver vs. 25 (6.5%), 10 (2.6%) and 48 (12.6%), respectively, for offspring of mothers without GDM (all P < 0.05). In multiple logistic regression, after adjustment for variables, the OR for fatty liver in offspring was 8.26 (95%CI: 2.38-28.75) for maternal obesity and GDM.
CONCLUSION This study showed that maternal obesity can increase the odds of overweight/obesity and fatty liver in offspring, and GDM status also increases the odds of overweight/obesity in offspring. Weight management and glycemic control before and during pregnancy need to be highlighted in primary prevention of pediatric obesity and fatty liver.
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Affiliation(s)
- Jing Zeng
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Feng Shen
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Zi-Yuan Zou
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Rui-Xu Yang
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Qian Jin
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Jing Yang
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Guang-Yu Chen
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Jian-Gao Fan
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Shanghai Key Lab of Pediatric Gastroenterology and Nutrition, Shanghai 200092, China
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Gan Y, Lu D, Yan C, Zhang J, Zhao J. Maternal Polycystic Ovary Syndrome and Offspring Birth Weight: A Mendelian Randomization Study. J Clin Endocrinol Metab 2022; 107:1020-1029. [PMID: 34849988 DOI: 10.1210/clinem/dgab843] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Indexed: 02/05/2023]
Abstract
CONTEXT Observational associations between maternal polycystic ovary syndrome (PCOS) and offspring birth weight (BW) have been inconsistent and the causal relationship is still uncertain. OBJECTIVE We conducted a 2-sample Mendelian randomization (MR) study to estimate the causal effect of maternal PCOS on offspring BW. METHODS We constructed genetic instruments for PCOS with 14 single nucleotide polymorphisms (SNPs) which were identified in a genome-wide association study (GWAS) meta-analysis including 10 074 PCOS cases and 103 164 controls of European ancestry from 7 cohorts. The genetic associations of these SNPs with the offspring BW were extracted from summary statistics estimated by the Early Growth Genetics consortium (n = 406 063 European ancestry individuals) using the weighted linear model, an approximation method of structural equation model, which separated maternal genetic effects from fetal genetic effects. We used a 2-sample MR design to examine the causal relationship between maternal PCOS and offspring BW. Sensitivity analyses were conducted to assess the robustness of the MR results. RESULTS We found little evidence for a causal effect of maternal PCOS on offspring BW (-6.1 g, 95% CI -16.8 g, 4.6 g). Broadly consistent results were found in the sensitivity analyses. CONCLUSION Despite the large scale of this study, our results suggested little causal effect of maternal PCOS on offspring BW. MR studies with a larger sample size of women with PCOS or more genetic instruments that would increase the variation of PCOS explained are needed in the future.
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Affiliation(s)
- Yuexin Gan
- Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Donghao Lu
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, 17177, Sweden
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Chonghuai Yan
- Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Jun Zhang
- Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Jian Zhao
- Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Institute of Early Life Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
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Yu W, Jacobson DL, Williams PL, Patel K, Geffner ME, Van Dyke RB, Kacanek D, DiMeglio LA, Jao J. Growth patterns of uninfected children born to women living with perinatally versus nonperinatally acquired HIV. AIDS 2022; 36:593-603. [PMID: 34860195 PMCID: PMC8881380 DOI: 10.1097/qad.0000000000003136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to compare long-term growth between HIV-exposed uninfected children (CHEU) born to women with perinatally acquired HIV (CHEU-PHIV) and CHEU born to women with nonperinatally acquired HIV (CHEU-NPHIV). DESIGN A longitudinal analysis of anthropometric measurements from a U.S.-based multisite prospective cohort study enrolling CHEU and their mothers since April 2007. METHODS CHEU were evaluated for growth annually from birth through age 5 and again at age 7 years. Z-scores were calculated using U.S. growth references for weight (WTZ), height (HTZ), and weight-for-length or BMI-for-age (WLZ/BMIZ). Mid-upper arm circumference (MUACZ) and triceps skinfold thickness (TSFZ) Z-scores were obtained from ages 1 and 2, respectively, through age 7 years. Piecewise mixed-effects models, overall and stratified by race and sex, were fit to assess differential growth patterns across age by maternal PHIV status. RESULTS One thousand four hundred fifty-four singleton infants (286 CHEU-PHIV and 1168 CHEU-NPHIV) were included. CHEU-PHIV had slower growth rates than CHEU-NPHIV for WTZ and WLZ/BMIZ at earlier ages and continued to have lower mean WTZ [-0.27, 95% confidence interval (95% CI): -0.50, -0.04] and WLZ/BMIZ (-0.39, 95% CI: -0.67, -0.11) through age 7. Among non-Black boys, CHEU-PHIV had slightly lower WTZ and WLZ/BMIZ at birth than CHEU-NPHIV and these growth deficits persisted through age 7 years. CONCLUSION Compared with CHEU-NPHIV, CHEU-PHIV had diminished growth in early childhood with differences most pronounced among non-Black male children. Further longitudinal follow-up of CHEU-PHIV into young adulthood is needed to understand whether these early effects of maternal PHIV status on growth persist and have other health consequences.
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Affiliation(s)
- Wendy Yu
- Center for Biostatistics in AIDS Research
| | | | - Paige L Williams
- Center for Biostatistics in AIDS Research, Departments of Biostatistics and Epidemiology
| | - Kunjal Patel
- Center for Biostatistics in AIDS Research, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Mitchell E Geffner
- The Saban Research Institute of Children's Hospital Los Angeles, Keck School of Medicine of USC, Los Angeles, California
| | - Russell B Van Dyke
- Tulane University School of Medicine, Department of Pediatrics, New Orleans, Los Angeles
| | | | - Linda A DiMeglio
- Indiana University School of Medicine, Department of Pediatrics, Indianapolis, Indiana
| | - Jennifer Jao
- Northwestern University Feinberg School of Medicine, Department of Pediatrics, Department of Medicine, Chicago, Illinois, USA
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Chen Y, Han L, Su W, Wu T, Lyu F, Chen Z, Huang B, Wang L, Song H, Shi X, Li X. Breastfeeding on childhood obesity in children were large-for-gestational age: retrospective study from birth to 4 years. Sci Rep 2022; 12:4226. [PMID: 35273323 DOI: 10.1038/s41598-022-08275-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 02/25/2022] [Indexed: 01/12/2023] Open
Abstract
Our aim was to assess effects of breast-feeding (BF) in the association between large-for-gestational age (LGA) and body mass index (BMI) trajectories on childhood overweight from 1 to 4 years old. A total of 1649 healthcare records of mother–child pairs had detailed records of feeding practices and were included in this retrospective cohort study. Data were available in Medical Birth Registry of Xiamen between January 2011 and March 2018. Linear and logistic regression models were used to access the difference between BF and no-BF group. For offspring were LGA and BF was significantly associated with a lower BMI Z-score from 1 to 4 years old after adjustment confounders in Model 1 to 3 [difference in BMI Z-score in Model 1: estimated β: −0.07 [95%CI: −0.13 to −0.01]; Model 2: estimated β: −0.07 (−0.13 to −0.004); Model 3: estimated β: −0.06 (−0.12 to −0.001); P = 0.0221, 0.0371, 0.0471]. A significantly lower risk of childhood overweight was observed in Model 1 [odd ratio (OR): 0.85 (95%CI, 0.73 to 1.00)], P = 0.0475) with adjustment for maternal pre-pregnancy BMI. Furthermore, Model 2 and Model 3 showed LGA-BF infants had a lower risk for childhood overweight then LGA-no-BF infants [OR: 0.87 and 0.87 (95%CI, 0.73 to 1.03; 0.74 to 1.03)], however, there was no statistical significance (P = 0.1099, and 0.1125)]. BF is inversely related to BMI Z-score and risk for overweight in children were LGA from 1 to 4 years old. Adjustment for maternal pre-pregnancy BMI, the protective association between BF and childhood overweight was more significant.
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Bond TA, Richmond RC, Karhunen V, Cuellar-Partida G, Borges MC, Zuber V, Couto Alves A, Mason D, Yang TC, Gunter MJ, Dehghan A, Tzoulaki I, Sebert S, Evans DM, Lewin AM, O'Reilly PF, Lawlor DA, Järvelin MR. Exploring the causal effect of maternal pregnancy adiposity on offspring adiposity: Mendelian randomisation using polygenic risk scores. BMC Med 2022; 20:34. [PMID: 35101027 PMCID: PMC8805234 DOI: 10.1186/s12916-021-02216-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 12/13/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Greater maternal adiposity before or during pregnancy is associated with greater offspring adiposity throughout childhood, but the extent to which this is due to causal intrauterine or periconceptional mechanisms remains unclear. Here, we use Mendelian randomisation (MR) with polygenic risk scores (PRS) to investigate whether associations between maternal pre-/early pregnancy body mass index (BMI) and offspring adiposity from birth to adolescence are causal. METHODS We undertook confounder adjusted multivariable (MV) regression and MR using mother-offspring pairs from two UK cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB). In ALSPAC and BiB, the outcomes were birthweight (BW; N = 9339) and BMI at age 1 and 4 years (N = 8659 to 7575). In ALSPAC only we investigated BMI at 10 and 15 years (N = 4476 to 4112) and dual-energy X-ray absorptiometry (DXA) determined fat mass index (FMI) from age 10-18 years (N = 2659 to 3855). We compared MR results from several PRS, calculated from maternal non-transmitted alleles at between 29 and 80,939 single nucleotide polymorphisms (SNPs). RESULTS MV and MR consistently showed a positive association between maternal BMI and BW, supporting a moderate causal effect. For adiposity at most older ages, although MV estimates indicated a strong positive association, MR estimates did not support a causal effect. For the PRS with few SNPs, MR estimates were statistically consistent with the null, but had wide confidence intervals so were often also statistically consistent with the MV estimates. In contrast, the largest PRS yielded MR estimates with narrower confidence intervals, providing strong evidence that the true causal effect on adolescent adiposity is smaller than the MV estimates (Pdifference = 0.001 for 15-year BMI). This suggests that the MV estimates are affected by residual confounding, therefore do not provide an accurate indication of the causal effect size. CONCLUSIONS Our results suggest that higher maternal pre-/early-pregnancy BMI is not a key driver of higher adiposity in the next generation. Thus, they support interventions that target the whole population for reducing overweight and obesity, rather than a specific focus on women of reproductive age.
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Affiliation(s)
- Tom A Bond
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Center for Life-course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Gabriel Cuellar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
- 23andMe, Inc., Sunnyvale, CA, USA
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Alexessander Couto Alves
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Dan Mason
- Born in Bradford, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Tiffany C Yang
- Born in Bradford, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Marc J Gunter
- Section of Nutrition and Metabolism, IARC, Lyon, France
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sylvain Sebert
- Center for Life-course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - David M Evans
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Alex M Lewin
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Paul F O'Reilly
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life-course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
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Ouidir M, Zeng X, Chatterjee S, Zhang C, Tekola-Ayele F. Ancestry-Matched and Cross-Ancestry Genetic Risk Scores of Type 2 Diabetes in Pregnant Women and Fetal Growth: A Study in an Ancestrally Diverse Cohort. Diabetes 2022; 71:340-349. [PMID: 34789498 PMCID: PMC8914278 DOI: 10.2337/db21-0655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/11/2021] [Indexed: 02/03/2023]
Abstract
Maternal genetic variants associated with offspring birth weight and adult type 2 diabetes (T2D) risk loci show some overlap. Whether T2D genetic risk influences longitudinal fetal weight and the gestational timing when these relationships begin is unknown. We investigated the associations of T2D genetic risk scores (GRS) with longitudinal fetal weight and birth weight among 1,513 pregnant women from four ancestral groups. Women had up to five ultrasonography examinations. Ancestry-matched GRS were constructed separately using 380 European- (GRSeur), 104 African- (GRSafr), and 189 East Asian- (GRSeas) related T2D loci discovered in different population groups. Among European Americans, the highest quartile GRSeur was significantly associated with 53.8 g higher fetal weight (95% CI 19.2-88.5) over the pregnancy. The associations began at gestational week 24 and continued through week 40, with a 106.8 g (95% CI 6.5-207.1) increase in birth weight. The findings were similar in analysis further adjusted for maternal glucose challenge test results. No consistent association was found using ancestry-matched or cross-ancestry GRS in non-Europeans. In conclusion, T2D genetic susceptibility may influence fetal growth starting at midsecond trimester among Europeans. Absence of similar associations in non-Europeans urges the need for further genetic T2D studies in diverse ancestries.
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Abstract
Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.
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Affiliation(s)
- Rebecca C Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol BS1 3NU, United Kingdom
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Liu Z, Han N, Su T, Ji Y, Bao H, Zhou S, Luo S, Wang H, Liu J, Wang HJ. Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study. Front Pediatr 2022; 10:899954. [PMID: 36440327 PMCID: PMC9691849 DOI: 10.3389/fped.2022.899954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/24/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Predicting birth weight and identifying its risk factors are clinically important. This study aims to use interpretable machine learning to predict birth weight and identity important predictors. METHODS This prospective cohort study was conducted in Tongzhou Maternal and Child Health Care Hospital of Beijing, China, recruiting pregnant women between June 2018 and February 2019. We used 24 features to predict infant birth weight, including gestational age, mother's age, parity, history of macrosomia delivery, pre-pregnancy body mass index (BMI), height, father's BMI, lifestyle (diet, physical activity, smoking), and biomarker (fasting glucose and lipids) features. Study outcome was birth weight of infant. We used 8 supervised learning models including 4 individual [linear regression, ridge regression, lasso regression, support vector machines regression (SVR)], and 4 ensemble estimators (random forest, AdaBoost, gradient boosted trees, and voting ensemble for regression) to predict birth weight. Model accuracy was measured by root mean squared error (RMSE) of 10-fold cross validation on the training set and RMSE of prediction on the test set. We used permutation importance algorithm to understand the prediction from the models and what affected them. RESULT This study included 4,754 mother-child dyads. RMSEs were lower in voting ensemble for regression, linear regression, and SVR than random forest, AdaBoost, and gradient boosted tree. The 5 most important predictors for infant birth weight were gestational age, fetal sex, preterm birth, mother's height, and pre-pregnancy BMI. After adding ultrasound-measured indicators of fetal growth into predictors, mother's height and pre-pregnancy BMI remained the most important predictors in predicting the outcome. CONCLUSION Mother's height and pre-pregnancy BMI were identified as important predictors for infant birth weight. Interpretable machine learning is a promising tool in the prediction of birth weight.
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Affiliation(s)
- Zheng Liu
- Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China
| | - Na Han
- Tongzhou Maternal and Child Health Care Hospital of Beijing, Beijing, China
| | - Tao Su
- Tongzhou Maternal and Child Health Care Hospital of Beijing, Beijing, China
| | - Yuelong Ji
- Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China
| | - Heling Bao
- Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China
| | - Shuang Zhou
- Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China
| | - Shusheng Luo
- Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China
| | - Hui Wang
- Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China
| | - Jue Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Hai-Jun Wang
- Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China
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Workalemahu T, Rahman ML, Ouidir M, Wu J, Zhang C, Tekola-Ayele F. Associations of maternal blood pressure-raising polygenic risk scores with fetal weight. J Hum Hypertens 2022; 36:69-76. [PMID: 33536548 PMCID: PMC8329099 DOI: 10.1038/s41371-021-00483-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 12/12/2020] [Accepted: 01/13/2021] [Indexed: 01/31/2023]
Abstract
Maternal blood pressure (BP) is associated with variations in fetal weight, an important determinant of neonatal and adult health. However, the association of BP-raising genetic risk with fetal weight is unknown. We tested the associations of maternal BP-raising polygenic risk scores (PRS) with estimated fetal weights (EFWs) at 13, 20, 27, and 40 weeks of gestation. This study included 622 White, 637 Black, 568 Hispanic, and 238 Asian pregnant women with genotype data from the NICHD Fetal Growth Studies. PRS of systolic (SBP) and diastolic BP (DBP) were calculated for each participant based on summary statistics from a recent genome-wide association study. Linear regression models were used to compare mean EFW differences between the highest versus lowest tertile of PRS, adjusting for maternal age, education, parity, genetic principal components and fetal sex. Hispanics in the highest DBP PRS tertile, compared to those in the lowest, had 8.1 g (95% CI: -15.1, -1.1), 32.4 g (-58.4, -6.4) and 119.4 g (-218.1, -20.7) lower EFW at 20, 27 and 40 weeks, respectively. Similarly, Asians in the highest DBP PRS tertile had 137.2 g (-263.5, -10.8) lower EFW at week 40, and those in the highest tertile of SBP PRS had 3.2 g (-5.8, -0.7), 12.9 g (-23.5, -2.4), and 39.8 g (-76.9, -2.7) lower EFWs at 13, 20, and 27 weeks. The findings showed that pregnant women's genetic susceptibility to high BP contributes to reduced fetal growth, suggesting a potential future clinical application in perinatal health.
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Affiliation(s)
- Tsegaselassie Workalemahu
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Mohammad L. Rahman
- Harvard Medical School, Department of Population Medicine and Harvard Pilgrim Healthcare Institute, Boston, MA, USA
| | - Marion Ouidir
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Jing Wu
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Cuilin Zhang
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
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47
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Costa-Júnior JM, Ferreira SM, Kurauti MA, Bernstein DL, Ruano EG, Kameswaran V, Schug J, Freitas-Dias R, Zoppi CC, Boschero AC, Oliveira CAM, Santos GJ, Carneiro EM, Kaestner KH. Paternal Exercise Improves the Metabolic Health of Offspring via Epigenetic Modulation of the Germline. Int J Mol Sci 2021; 23:1. [PMID: 35008427 DOI: 10.3390/ijms23010001] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/02/2021] [Accepted: 12/05/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND/AIMS Epigenetic regulation is considered the main molecular mechanism underlying the developmental origin of health and disease's (DOHAD) hypothesis. Previous studies that have investigated the role of paternal exercise on the metabolic health of the offspring did not control for the amount and intensity of the training or possible effects of adaptation to exercise and produced conflicting results regarding the benefits of parental exercise to the next generation. We employed a precisely regulated exercise regimen to study the transgenerational inheritance of improved metabolic health. METHODS We subjected male mice to a well-controlled exercise -training program to investigate the effects of paternal exercise on glucose tolerance and insulin sensitivity in their adult progeny. To investigate the molecular mechanisms of epigenetic inheritance, we determined chromatin markers in the skeletal muscle of the offspring and the paternal sperm. RESULTS Offspring of trained male mice exhibited improved glucose homeostasis and insulin sensitivity. Paternal exercise modulated the DNA methylation profile of PI3Kca and the imprinted H19/Igf2 locus at specific differentially methylated regions (DMRs) in the skeletal muscle of the offspring, which affected their gene expression. Remarkably, a similar DNA methylation profile at the PI3Kca, H19, and Igf2 genes was present in the progenitor sperm indicating that exercise-induced epigenetic changes that occurred during germ cell development contributed to transgenerational transmission. CONCLUSION Paternal exercise might be considered as a strategy that could promote metabolic health in the offspring as the benefits can be inherited transgenerationally.
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48
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Thompson WD, Beaumont RN, Kuang A, Warrington NM, Ji Y, Tyrrell J, Wood AR, Scholtens DM, Knight BA, Evans DM, Lowe WL, Santorelli G, Azad R, Mason D, Hattersley AT, Frayling TM, Yaghootkar H, Borges MC, Lawlor DA, Freathy RM. Higher maternal adiposity reduces offspring birthweight if associated with a metabolically favourable profile. Diabetologia 2021; 64:2790-2802. [PMID: 34542646 PMCID: PMC8563674 DOI: 10.1007/s00125-021-05570-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/14/2021] [Indexed: 11/30/2022]
Abstract
AIMS/HYPOTHESIS Higher maternal BMI during pregnancy is associated with higher offspring birthweight, but it is not known whether this is solely the result of adverse metabolic consequences of higher maternal adiposity, such as maternal insulin resistance and fetal exposure to higher glucose levels, or whether there is any effect of raised adiposity through non-metabolic (e.g. mechanical) factors. We aimed to use genetic variants known to predispose to higher adiposity, coupled with a favourable metabolic profile, in a Mendelian randomisation (MR) study comparing the effect of maternal 'metabolically favourable adiposity' on offspring birthweight with the effect of maternal general adiposity (as indexed by BMI). METHODS To test the causal effects of maternal metabolically favourable adiposity or general adiposity on offspring birthweight, we performed two-sample MR. We used variants identified in large, published genetic-association studies as being associated with either higher adiposity and a favourable metabolic profile, or higher BMI (n = 442,278 and n = 322,154 for metabolically favourable adiposity and BMI, respectively). We then extracted data on the metabolically favourable adiposity and BMI variants from a large, published genetic-association study of maternal genotype and offspring birthweight controlling for fetal genetic effects (n = 406,063 with maternal and/or fetal genotype effect estimates). We used several sensitivity analyses to test the reliability of the results. As secondary analyses, we used data from four cohorts (total n = 9323 mother-child pairs) to test the effects of maternal metabolically favourable adiposity or BMI on maternal gestational glucose, anthropometric components of birthweight and cord-blood biomarkers. RESULTS Higher maternal adiposity with a favourable metabolic profile was associated with lower offspring birthweight (-94 [95% CI -150, -38] g per 1 SD [6.5%] higher maternal metabolically favourable adiposity, p = 0.001). By contrast, higher maternal BMI was associated with higher offspring birthweight (35 [95% CI 16, 53] g per 1 SD [4 kg/m2] higher maternal BMI, p = 0.0002). Sensitivity analyses were broadly consistent with the main results. There was evidence of outlier SNPs for both exposures; their removal slightly strengthened the metabolically favourable adiposity estimate and made no difference to the BMI estimate. Our secondary analyses found evidence to suggest that a higher maternal metabolically favourable adiposity decreases pregnancy fasting glucose levels while a higher maternal BMI increases them. The effects on neonatal anthropometric traits were consistent with the overall effect on birthweight but the smaller sample sizes for these analyses meant that the effects were imprecisely estimated. We also found evidence to suggest that higher maternal metabolically favourable adiposity decreases cord-blood leptin while higher maternal BMI increases it. CONCLUSIONS/INTERPRETATION Our results show that higher adiposity in mothers does not necessarily lead to higher offspring birthweight. Higher maternal adiposity can lead to lower offspring birthweight if accompanied by a favourable metabolic profile. DATA AVAILABILITY The data for the genome-wide association studies (GWAS) of BMI are available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files . The data for the GWAS of body fat percentage are available at https://walker05.u.hpc.mssm.edu .
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Affiliation(s)
- William D Thompson
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Alan Kuang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Nicole M Warrington
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yingjie Ji
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Jessica Tyrrell
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Andrew R Wood
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Denise M Scholtens
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Bridget A Knight
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - David M Evans
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia
| | - William L Lowe
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Gillian Santorelli
- Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, UK
| | - Rafaq Azad
- Department of Biochemistry, Bradford Royal Infirmary, Bradford, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, UK
| | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Timothy M Frayling
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Hanieh Yaghootkar
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
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49
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Sato N, Fudono A, Imai C, Takimoto H, Tarui I, Aoyama T, Yago S, Okamitsu M, Mizutani S, Miyasaka N. Placenta mediates the effect of maternal hypertension polygenic score on offspring birth weight: a study of birth cohort with fetal growth velocity data. BMC Med 2021; 19:260. [PMID: 34732167 PMCID: PMC8567693 DOI: 10.1186/s12916-021-02131-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Low birth weight (LBW) and fetal growth restriction are associated with the development of cardio-metabolic diseases later in life. A recent Mendelian randomization study concluded that the susceptibility of LBW infants to develop hypertension during adulthood is due to the inheritance of hypertension genes from the mother and not to an unfavorable intrauterine environment. Therein, a negative linear association has been assumed between genetically estimated maternal blood pressure (BP) and birth weight, while the observed relationship between maternal BP and birth weight is substantially different from that assumption. As many hypertension genes are likely involved in vasculature development and function, we hypothesized that BP-increasing genetic variants could affect birth weight by reducing the growth of the placenta, a highly vascular organ, without overtly elevating the maternal BP. METHODS Using a birth cohort in the Japanese population possessing time-series fetal growth velocity data as a target and a GWAS summary statistics of BioBank Japan as a base data, we performed polygenic score (PGS) analyses for systolic BP (SBP), diastolic BP, mean arterial pressure, and pulse pressure. A causal mediation analysis was performed to assess the meditation effect of placental weight on birth weight reduced by maternal BP-increasing PGS. Maternal genetic risk score constituted of only "vasculature-related" BP single nucleotide polymorphisms (SNPs) was constructed to examine the involvement of vascular genes in the mediation effect of placental weight. We identified gestational week in which maternal SBP-increasing PGS significantly decreased fetal growth velocity. RESULTS We observed that maternal SBP-increasing PGS was negatively associated with offspring birth weight. A causal mediation analysis revealed that a large proportion of the total maternal PGS effect on birth weight was mediated by placental weight. The placental mediation effect was remarkable when genetic risk score was constituted of "vasculature-related" BP SNPs. The inverse association between maternal SBP PGS and fetal growth velocity only became apparent in late gestation. CONCLUSIONS Our study suggests that maternal hypertension genes are strongly associated with placental growth and that fetal growth inhibition is induced through the intrauterine environment established by the placenta.
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Affiliation(s)
- Noriko Sato
- Department of Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University (TMDU), 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan. .,Institute of Advanced Biomedical Engineering and Science, The Public Health Research Foundation, Tokyo, Japan.
| | - Ayako Fudono
- Comprehensive Reproductive Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Chihiro Imai
- Department of Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University (TMDU), 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Hidemi Takimoto
- Department of Nutritional Epidemiology, National Institute of Health and Nutrition, Tokyo, Japan
| | - Iori Tarui
- Department of Nutritional Epidemiology, National Institute of Health and Nutrition, Tokyo, Japan
| | - Tomoko Aoyama
- Department of Nutritional Epidemiology, National Institute of Health and Nutrition, Tokyo, Japan
| | - Satoshi Yago
- Child and Family Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Motoko Okamitsu
- Child and Family Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Shuki Mizutani
- Institute of Advanced Biomedical Engineering and Science, The Public Health Research Foundation, Tokyo, Japan
| | - Naoyuki Miyasaka
- Comprehensive Reproductive Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
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50
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Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, Timpson NJ, Higgins JPT, Dimou N, Langenberg C, Loder EW, Golub RM, Egger M, Davey Smith G, Richards JB. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ 2021; 375:n2233. [PMID: 34702754 PMCID: PMC8546498 DOI: 10.1136/bmj.n2233] [Citation(s) in RCA: 344] [Impact Index Per Article: 114.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/02/2021] [Indexed: 12/15/2022]
Affiliation(s)
| | - Rebecca C Richmond
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Benjamin A R Woolf
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Psychological Science, University of Bristol, Bristol, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- K G Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sonja A Swanson
- Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
| | - Tyler J VanderWeele
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Julian P T Higgins
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Claudia Langenberg
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | | | - Robert M Golub
- JAMA, Chicago, IL, USA
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - J Brent Richards
- Departments of Medicine, Human Genetics, Epidemiology & Biostatistics, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Twin Research and Genetic Epidemiology, King's College London, University of London, London, UK
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