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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] [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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Cheng C, Messerschmidt L, Bravo I, Waldbauer M, Bhavikatti R, Schenk C, Grujic V, Model T, Kubinec R, Barceló J. A General Primer for Data Harmonization. Sci Data 2024; 11:152. [PMID: 38297013 PMCID: PMC10831085 DOI: 10.1038/s41597-024-02956-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/11/2024] [Indexed: 02/02/2024] Open
Affiliation(s)
- Cindy Cheng
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany.
| | - Luca Messerschmidt
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | - Isaac Bravo
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | - Marco Waldbauer
- Hochschule für Politik, Technical University of Munich, Richard-Wagner Str. 1, Munich, 80333, Bavaria, Germany
| | | | - Caress Schenk
- School of Humanities and Social Sciences, Nazarbayev University, Kabanbay Batry Ave., 53, Astana, 010000, Kazakhstan
| | - Vanja Grujic
- Faculty of Law, University of Brasilia, Campus Universitário Darcy Ribeiro Asa Norte, Brasília, 10587, Brazil
| | - Tim Model
- Delve, 2225 3rd St, San Francisco, 94107, California, USA
| | - Robert Kubinec
- Division of Social Science, New York University Abu Dhabi, Social Science Building (A5), Abu Dhabi, 129188, United Arab Emirates
| | - Joan Barceló
- Division of Social Science, New York University Abu Dhabi, Social Science Building (A5), Abu Dhabi, 129188, United Arab Emirates
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Swilley-Martinez ME, Coles SA, Miller VE, Alam IZ, Fitch KV, Cruz TH, Hohl B, Murray R, Ranapurwala SI. "We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021. Epidemiol Rev 2023; 45:15-31. [PMID: 37789703 DOI: 10.1093/epirev/mxad010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/31/2023] [Accepted: 09/28/2023] [Indexed: 10/05/2023] Open
Abstract
Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.
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Affiliation(s)
- Monica E Swilley-Martinez
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Serita A Coles
- Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7440, United States
| | - Vanessa E Miller
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Ishrat Z Alam
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Kate Vinita Fitch
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Theresa H Cruz
- Prevention Research Center, Department of Pediatrics, Health Sciences Center, University of New Mexico, Albuquerque, NM 87131, United States
| | - Bernadette Hohl
- Penn Injury Science Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-6021, United States
| | - Regan Murray
- Center for Public Health and Technology, Department of Health, Human Performance and Recreation, University of Arkansas, Fayetteville, AR 72701, United States
| | - Shabbar I Ranapurwala
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
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4
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Elhakeem A, Taylor AE, Inskip HM, Huang JY, Mansell T, Rodrigues C, Asta F, Blaauwendraad SM, Håberg SE, Halliday J, Harskamp-van Ginkel MW, He JR, Jaddoe VWV, Lewis S, Maher GM, Manios Y, McCarthy FP, Reiss IKM, Rusconi F, Salika T, Tafflet M, Qiu X, Åsvold BO, Burgner D, Chan JKY, Gagliardi L, Gaillard R, Heude B, Magnus MC, Moschonis G, Murray D, Nelson SM, Porta D, Saffery R, Barros H, Eriksson JG, Vrijkotte TGM, Lawlor DA. Long-term cardiometabolic health in people born after assisted reproductive technology: a multi-cohort analysis. Eur Heart J 2023; 44:1464-1473. [PMID: 36740401 PMCID: PMC10119029 DOI: 10.1093/eurheartj/ehac726] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/23/2022] [Accepted: 11/23/2022] [Indexed: 02/07/2023] Open
Abstract
AIMS To examine associations of assisted reproductive technology (ART) conception (vs. natural conception: NC) with offspring cardiometabolic health outcomes and whether these differ with age. METHODS AND RESULTS Differences in systolic (SBP) and diastolic blood pressure (DBP), heart rate (HR), lipids, and hyperglycaemic/insulin resistance markers were examined using multiple linear regression models in 14 population-based birth cohorts in Europe, Australia, and Singapore, and results were combined using meta-analysis. Change in cardiometabolic outcomes from 2 to 26 years was examined using trajectory modelling of four cohorts with repeated measures. 35 938 (654 ART) offspring were included in the meta-analysis. Mean age ranged from 13 months to 27.4 years but was <10 years in 11/14 cohorts. Meta-analysis found no statistical difference (ART minus NC) in SBP (-0.53 mmHg; 95% CI:-1.59 to 0.53), DBP (-0.24 mmHg; -0.83 to 0.35), or HR (0.02 beat/min; -0.91 to 0.94). Total cholesterol (2.59%; 0.10-5.07), HDL cholesterol (4.16%; 2.52-5.81), LDL cholesterol (4.95%; 0.47-9.43) were statistically significantly higher in ART-conceived vs. NC offspring. No statistical difference was seen for triglycerides (TG), glucose, insulin, and glycated haemoglobin. Long-term follow-up of 17 244 (244 ART) births identified statistically significant associations between ART and lower predicted SBP/DBP in childhood, and subtle trajectories to higher SBP and TG in young adulthood; however, most differences were not statistically significant. CONCLUSION These findings of small and statistically non-significant differences in offspring cardiometabolic outcomes should reassure people receiving ART. Longer-term follow-up is warranted to investigate changes over adulthood in the risks of hypertension, dyslipidaemia, and preclinical and clinical cardiovascular disease.
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Affiliation(s)
- Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Amy E Taylor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Hazel M Inskip
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Jonathan Y Huang
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Singapore
- Duke-NUS Medical School, Centre for Quantitative Medicine,Singapore, Singapore
| | - Toby Mansell
- Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC, Australia
- University of Melbourne, Parkville, VIC, Australia
| | - Carina Rodrigues
- EPIUnit—Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
- Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal
| | - Federica Asta
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | - Sophia M Blaauwendraad
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Siri E Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Jane Halliday
- Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC, Australia
- University of Melbourne, Parkville, VIC, Australia
| | - Margreet W Harskamp-van Ginkel
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jian-Rong He
- Division of Birth Cohort Study, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Vincent W V Jaddoe
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Sharon Lewis
- Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC, Australia
- University of Melbourne, Parkville, VIC, Australia
| | - Gillian M Maher
- School of Public Health, University College Cork, Cork, Ireland
- The Irish Centre for Maternal and Child Health Research (INFANT), University College Cork, Cork, Ireland
| | - Yannis Manios
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
- Institute of Agri-Food and Life Sciences, Hellenic Mediterranean University Research Centre, Heraklion, Greece
| | - Fergus P McCarthy
- The Irish Centre for Maternal and Child Health Research (INFANT), University College Cork, Cork, Ireland
- Department of Obstetrics and Gynaecology, University College Cork, Cork, Ireland
| | - Irwin K M Reiss
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Franca Rusconi
- Department of Mother and Child Health, Ospedale Versilia, Viareggio, AUSL Toscana Nord Ovest, Pisa, Italy
| | - Theodosia Salika
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Muriel Tafflet
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Xiu Qiu
- Division of Birth Cohort Study, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Bjørn O Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - David Burgner
- Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Department of Paediatrics, Monash University, Clayton, VIC, Australia
| | - Jerry K Y Chan
- Department of Reproductive Medicine, KK Women’s and Children’s Hospital, Singapore, Singapore
- Academic Clinical Program in Obstetrics and Gynaecology, Duke-NUS Medical School, Singapore, Singapore
| | - Luigi Gagliardi
- Department of Mother and Child Health, Ospedale Versilia, Viareggio, AUSL Toscana Nord Ovest, Pisa, Italy
| | - Romy Gaillard
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Department of Paediatrics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Maria C Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - George Moschonis
- Department of Food, Nutrition and Dietetics, School of Allied Health, Human Services and Sport, College of Science, Health and Engineering, La Trobe University, Melbourne, Australia
| | - Deirdre Murray
- The Irish Centre for Maternal and Child Health Research (INFANT), University College Cork, Cork, Ireland
- Department of Pediatrics and Child Health, University College Cork, Cork, Ireland
| | - Scott M Nelson
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- School of Medicine, University of Glasgow, Glasgow, UK
| | - Daniela Porta
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | - Richard Saffery
- Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, VIC, Australia
- University of Melbourne, Parkville, VIC, Australia
| | - Henrique Barros
- EPIUnit—Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
- Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal
| | - Johan G Eriksson
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Singapore
- Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | - Tanja G M Vrijkotte
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
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Janc M, Jankowska A, Weteska M, Brzozowska A, Hanke W, Jurewicz J, Garí M, Polańska K, Jerzyńska J. REPRO_PL-Polish Mother and Child Cohort-Exposure, Health Status, and Neurobehavioral Assessments in Adolescents-Design and Cohort Update. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14167. [PMID: 36361044 PMCID: PMC9656994 DOI: 10.3390/ijerph192114167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
Early life is a crucial window of opportunity to improve health across the life course. The prospective cohort study design is the most adequate to evaluate the longitudinal effects of exposure, the notification of changes in the exposure level and evaluation of the simultaneous impact of various exposures, as well as the assessment of several health effects and trajectories throughout childhood and adolescence. This paper provides an overview of the Polish Mother and Child cohort (REPRO_PL), with particular emphasis on Phase IV of this study. REPRO_PL is conducted in central Europe, where such longitudinal studies are less frequently implemented. In this population-based prospective cohort, which was established in 2007, three phases covering pregnancy (I), early childhood (II), and early school age (III) periods have already been completed. Phase IV gives a uniform opportunity to follow-up children during adolescence in order to evaluate if the consequences of prenatal and early postnatal exposures still persist at the age of 14. Moreover, we will be able to investigate the associations between simultaneous exposures to a broad spectrum of environmental factors, adolescents' health and neurobehavioral outcomes, and their trajectories within life, which is a novel framework of high scientific, public health and clinical priority.
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Affiliation(s)
- Magdalena Janc
- Department of Environmental and Occupational Health Hazards, Nofer Institute of Occupational Medicine (NIOM), 91-348 Lodz, Poland
| | - Agnieszka Jankowska
- Department of Environmental and Occupational Health Hazards, Nofer Institute of Occupational Medicine (NIOM), 91-348 Lodz, Poland
| | - Monika Weteska
- Department of Pediatrics and Allergy, Copernicus Memorial Hospital, Medical University of Lodz (MUL), 90-329 Lodz, Poland
| | - Agnieszka Brzozowska
- Department of Pediatrics and Allergy, Copernicus Memorial Hospital, Medical University of Lodz (MUL), 90-329 Lodz, Poland
| | - Wojciech Hanke
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine (NIOM), 91-348 Lodz, Poland
| | - Joanna Jurewicz
- Department of Toxicology, Medical University of Lodz (MUL), 90-151 Lodz, Poland
| | - Mercè Garí
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| | - Kinga Polańska
- Department of Environmental and Occupational Health Hazards, Nofer Institute of Occupational Medicine (NIOM), 91-348 Lodz, Poland
| | - Joanna Jerzyńska
- Department of Pediatrics and Allergy, Copernicus Memorial Hospital, Medical University of Lodz (MUL), 90-329 Lodz, Poland
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O'Connor M, Spry E, Patton G, Moreno-Betancur M, Arnup S, Downes M, Goldfeld S, Burgner D, Olsson CA. Better together: Advancing life course research through multi-cohort analytic approaches. ADVANCES IN LIFE COURSE RESEARCH 2022; 53:100499. [PMID: 36652217 DOI: 10.1016/j.alcr.2022.100499] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 06/22/2022] [Accepted: 07/15/2022] [Indexed: 06/17/2023]
Abstract
Longitudinal cohorts can provide timely and cost-efficient evidence about the best points of health service and preventive interventions over the life course. Working systematically across cohorts has the potential to further exploit these valuable data assets, such as by improving the precision of estimates, enhancing (or appropriately reducing) confidence in the replicability of findings, and investigating interrelated questions within a broader theoretical model. In this conceptual review, we explore the opportunities and challenges presented by multi-cohort approaches in life course research. Specifically, we: 1) describe key motivations for multi-cohort work and the analytic approaches that are commonly used in each case; 2) flag some of the scientific and pragmatic challenges that arise when adopting these approaches; and 3) outline emerging directions for multi-cohort work in life course research. Harnessing their potential while thoughtfully considering limitations of multi-cohort approaches can contribute to the robust and granular evidence base needed to promote health and wellbeing over the life span.
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Affiliation(s)
- Meredith O'Connor
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia.
| | - Elizabeth Spry
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia; Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia
| | - George Patton
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia
| | - Margarita Moreno-Betancur
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia
| | - Sarah Arnup
- Murdoch Children's Research Institute, Parkville, Australia
| | - Marnie Downes
- Murdoch Children's Research Institute, Parkville, Australia
| | - Sharon Goldfeld
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia; Royal Children's Hospital, Centre for Community Child Health, Parkville, Australia
| | - David Burgner
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia; Royal Children's Hospital, Department of General Medicine, Parkville, Australia; Monash University, Department of Pediatrics, Clayton, Australia
| | - Craig A Olsson
- Murdoch Children's Research Institute, Parkville, Australia; University of Melbourne, Department of Paediatrics, Parkville, Australia; Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia
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7
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Elhakeem A, Hughes RA, Tilling K, Cousminer DL, Jackowski SA, Cole TJ, Kwong ASF, Li Z, Grant SFA, Baxter-Jones ADG, Zemel BS, Lawlor DA. Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies. BMC Med Res Methodol 2022; 22:68. [PMID: 35291947 PMCID: PMC8925070 DOI: 10.1186/s12874-022-01542-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 02/11/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. Our aim was to provide an accessible guide along with applied examples to using four sophisticated modelling procedures for describing nonlinear growth trajectories. METHODS This expository paper provides an illustrative guide to summarising nonlinear growth trajectories for repeatedly measured continuous outcomes using (i) linear spline and (ii) natural cubic spline linear mixed-effects (LME) models, (iii) Super Imposition by Translation and Rotation (SITAR) nonlinear mixed effects models, and (iv) latent trajectory models. The underlying model for each approach, their similarities and differences, and their advantages and disadvantages are described. Their application and correct interpretation of their results is illustrated by analysing repeated bone mass measures to characterise bone growth patterns and their sex differences in three cohort studies from the UK, USA, and Canada comprising 8500 individuals and 37,000 measurements from ages 5-40 years. Recommendations for choosing a modelling approach are provided along with a discussion and signposting on further modelling extensions for analysing trajectory exposures and outcomes, and multiple cohorts. RESULTS Linear and natural cubic spline LME models and SITAR provided similar summary of the mean bone growth trajectory and growth velocity, and the sex differences in growth patterns. Growth velocity (in grams/year) peaked during adolescence, and peaked earlier in females than males e.g., mean age at peak bone mineral content accrual from multicohort SITAR models was 12.2 years in females and 13.9 years in males. Latent trajectory models (with trajectory shapes estimated using a natural cubic spline) identified up to four subgroups of individuals with distinct trajectories throughout adolescence. CONCLUSIONS LME models with linear and natural cubic splines, SITAR, and latent trajectory models are useful for describing nonlinear growth trajectories, and these methods can be adapted for other complex traits. Choice of method depends on the research aims, complexity of the trajectory, and available data. Scripts and synthetic datasets are provided for readers to replicate trajectory modelling and visualisation using the R statistical computing software.
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Affiliation(s)
- Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Rachael A Hughes
- 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
| | - Diana L Cousminer
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Stefan A Jackowski
- College of Kinesiology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Tim J Cole
- UCL Great Ormond Street Institute of Child Health, London, UK
| | - Alex S F Kwong
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Zheyuan Li
- School of Mathematics and Statistics, Henan University, Kaifeng, Henan, China
- Department of Statistics and Actuarial Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Struan F A Grant
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Babette S Zemel
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - 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
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Matsumoto N, Kubo T, Nakamura K, Mitsuhashi T, Takeuchi A, Tsukahara H, Yorifuji T. Trajectory of body mass index and height changes from childhood to adolescence: a nationwide birth cohort in Japan. Sci Rep 2021; 11:23004. [PMID: 34837002 PMCID: PMC8626480 DOI: 10.1038/s41598-021-02464-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 11/17/2021] [Indexed: 11/09/2022] Open
Abstract
To investigate the dynamics of body mass index (BMI) and height changes in childhood leading to obesity in adolescents. BMI Z-scores were calculated using the LMS (lambda-mu-sigma) method based on yearly height and weight information (age 1.5-15 years) from a nationwide Japanese birth cohort that started in 2001 (n = 26,711). We delineated the trajectories of BMI and height changes leading to obesity at age 15 years using mixed effect models. Children who became obese at the age of 15 years kept relatively high BMI z-scores through childhood for both genders, and had an increasing trend over time as opposed to the normal weight group, with an increasing slope during puberty. Early adiposity rebound was associated with overweight or obesity at the age of 15 years. Age at peak height velocity (APHV) occurred earlier in the obese/overweight group at age 15 years than in the normal weight group, and occurred later in the underweight group. Obese adolescents experienced early adiposity rebound timing and maintained a serial BMI z-score increase throughout childhood, with a greater slope at puberty. An earlier peak in height gain during puberty may have contributed to the observed patterns of BMI change.
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Affiliation(s)
- Naomi Matsumoto
- Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan.
| | - Toshihide Kubo
- Department of Pediatrics, National Hospital Organization, Okayama Medical Center, Okayama, Japan
| | - Kazue Nakamura
- Department of Pediatrics, National Hospital Organization, Okayama Medical Center, Okayama, Japan
| | - Toshiharu Mitsuhashi
- Center for Innovative Clinical Medicine, Okayama University Hospital, Okayama, Japan
| | - Akihito Takeuchi
- Department of Pediatrics, National Hospital Organization, Okayama Medical Center, Okayama, Japan
| | - Hirokazu Tsukahara
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Takashi Yorifuji
- Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
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