Ellis Voerman1,2, Susana Santos1,2, Hazel Inskip3,4, Pilar Amiano5,6,7, Henrique Barros8,9, Marie-Aline Charles10,11, Leda Chatzi12,13,14, George P Chrousos15, Eva Corpeleijn16, Sarah Crozier3, Myriam Doyon17, Merete Eggesbø18, Maria Pia Fantini19, Sara Farchi20, Francesco Forastiere20, Vagelis Georgiu13, Davide Gori19, Wojciech Hanke21, Irva Hertz-Picciotto22, Barbara Heude10,11, Marie-France Hivert17,23,24, Daniel Hryhorczuk25, Carmen Iñiguez7,26, Anne M Karvonen27, Leanne K Küpers16,28,29,30, Hanna Lagström31, Debbie A Lawlor29,30, Irina Lehmann32, Per Magnus33, Renata Majewska34, Johanna Mäkelä35, Yannis Manios36, Monique Mommers37, Camilla S Morgen38,39, George Moschonis40, Ellen A Nohr41, Anne-Marie Nybo Andersen39, Emily Oken23, Agnieszka Pac34, Eleni Papadopoulou42, Juha Pekkanen27,43, Costanza Pizzi44, Kinga Polanska21, Daniela Porta20, Lorenzo Richiardi44, Sheryl L Rifas-Shiman23, Nel Roeleveld45, Luca Ronfani46, Ana C Santos8,9, Marie Standl47, Hein Stigum48, Camilla Stoltenberg49,50, Elisabeth Thiering47,51, Carel Thijs37, Maties Torrent52, Tomas Trnovec53, Marleen M H J van Gelder45,54, Lenie van Rossem55, Andrea von Berg56, Martine Vrijheid7,57,58, Alet Wijga59, Oleksandr Zvinchuk60, Thorkild I A Sørensen39,61, Keith Godfrey3,4, Vincent W V Jaddoe1,2,62, Romy Gaillard1,2. 1. Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands. 2. Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands. 3. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, England. 4. NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, England. 5. Public Health Division of Gipuzkoa, San Sebastián, Spain. 6. BioDonostia Research Institute, San Sebastián, Spain. 7. CIBER Epidemiología y Salud Pública, Madrid, Spain. 8. EPI Unit-Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal. 9. Department of Public Health and Forensic Sciences and Medical Education, Unit of Clinical Epidemiology, Predictive Medicine and Public Health, University of Porto Medical School, Porto, Portugal. 10. INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center, ORCHAD Team, Villejuif, France. 11. Paris Descartes University, Villejuif, France. 12. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles. 13. Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece. 14. Department of Genetics and Cell Biology, Maastricht University, Maastricht, the Netherlands. 15. First Department of Pediatrics, National and Kapodistrian University of Athens, Medical School, Aghia Sophia Children's Hospital, Athens, Greece. 16. University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands. 17. Centre de Recherche du Centre Hospitalier de l'Universite de Sherbrooke, Sherbrooke, Quebec, Canada. 18. Department of Exposure and Environmental Epidemiology, Norwegian Institute of Public Health, Oslo, Norway. 19. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy. 20. Department of Epidemiology, Lazio Regional Health Service, Rome, Italy. 21. Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Lodz, Poland. 22. Department of Public Health Sciences, School of Medicine, University of California, Davis. 23. Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, Massachusetts. 24. Diabetes Unit, Massachusetts General Hospital, Boston. 25. Center for Global Health, College of Medicine, University of Illinois, Chicago. 26. Department of Statistics and Computational Research, Universitat de València, València, Spain. 27. Department of Health Security, National Institute for Health and Welfare, Kuopio, Finland. 28. Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, the Netherlands. 29. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, England. 30. Population Health Science, Bristol Medical School, University of Bristol, Bristol, England. 31. Department of Public Health, University of Turku, Turku, Finland. 32. Department of Environmental Immunology/Core Facility Studies, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany. 33. Division of Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway. 34. Department of Epidemiology, Jagiellonian University Medical College, Krakow, Poland. 35. Turku Centre for Biotechnology, University of Turku and Abo Akademi University, Turku, Finland. 36. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece. 37. Department of Epidemiology, Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands. 38. National Institute of Public Health, University of Southern Denmark, Copenhagen. 39. Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark. 40. Department of Dietetics, Nutrition, and Sport, La Trobe University, Melbourne, Australia. 41. Research Unit for Gynaecology and Obstetrics, Institute for Clinical Research, University of Southern Denmark, Odense. 42. Department of Environmental Exposures and Epidemiology, Domain of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway. 43. Department of Public Health, University of Helsinki, Helsinki, Finland. 44. Department of Medical Sciences, University of Turin, Turin, Italy. 45. Department for Health Evidence, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands. 46. Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy. 47. Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. 48. Department of Noncommunicable Diseases, Norwegian Institute of Public Health, Oslo, Norway. 49. Norwegian Institute of Public Health, Oslo, Norway. 50. Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway. 51. Dr von Hauner Children's Hospital, Ludwig-Maximilians-University Munich, Munich, Germany. 52. Ib-Salut, Area de Salut de Menorca, Palma, Spain. 53. Department of Environmental Medicine, Slovak Medical University, Bratislava, Slovakia. 54. Radboud Reshape Innovation Center, Radboud University Medical Center, Nijmegen, the Netherlands. 55. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands. 56. Research Institute, Department of Pediatrics, Marien-Hospital Wesel, Wesel, Germany. 57. ISGlobal, Institute for Global Health, Barcelona, Spain. 58. Universitat Pompeu Fabra, Barcelona, Spain. 59. National Institute for Public Health and the Environment, Bilthoven, the Netherlands. 60. Department of Medical and Social Problems of Family Health, Institute of Pediatrics, Obstetrics and Gynecology, Kyiv, Ukraine. 61. Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 62. Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.
Abstract
Importance: Both low and high gestational weight gain have been associated with adverse maternal and infant outcomes, but optimal gestational weight gain remains uncertain and not well defined for all prepregnancy weight ranges. Objectives: To examine the association of ranges of gestational weight gain with risk of adverse maternal and infant outcomes and estimate optimal gestational weight gain ranges across prepregnancy body mass index categories. Design, Setting, and Participants: Individual participant-level meta-analysis using data from 196 670 participants within 25 cohort studies from Europe and North America (main study sample). Optimal gestational weight gain ranges were estimated for each prepregnancy body mass index (BMI) category by selecting the range of gestational weight gain that was associated with lower risk for any adverse outcome. Individual participant-level data from 3505 participants within 4 separate hospital-based cohorts were used as a validation sample. Data were collected between 1989 and 2015. The final date of follow-up was December 2015. Exposures: Gestational weight gain. Main Outcomes and Measures: The main outcome termed any adverse outcome was defined as the presence of 1 or more of the following outcomes: preeclampsia, gestational hypertension, gestational diabetes, cesarean delivery, preterm birth, and small or large size for gestational age at birth. Results: Of the 196 670 women (median age, 30.0 years [quartile 1 and 3, 27.0 and 33.0 years] and 40 937 were white) included in the main sample, 7809 (4.0%) were categorized at baseline as underweight (BMI <18.5); 133 788 (68.0%), normal weight (BMI, 18.5-24.9); 38 828 (19.7%), overweight (BMI, 25.0-29.9); 11 992 (6.1%), obesity grade 1 (BMI, 30.0-34.9); 3284 (1.7%), obesity grade 2 (BMI, 35.0-39.9); and 969 (0.5%), obesity grade 3 (BMI, ≥40.0). Overall, any adverse outcome occurred in 37.2% (n = 73 161) of women, ranging from 34.7% (2706 of 7809) among women categorized as underweight to 61.1% (592 of 969) among women categorized as obesity grade 3. Optimal gestational weight gain ranges were 14.0 kg to less than 16.0 kg for women categorized as underweight; 10.0 kg to less than 18.0 kg for normal weight; 2.0 kg to less than 16.0 kg for overweight; 2.0 kg to less than 6.0 kg for obesity grade 1; weight loss or gain of 0 kg to less than 4.0 kg for obesity grade 2; and weight gain of 0 kg to less than 6.0 kg for obesity grade 3. These gestational weight gain ranges were associated with low to moderate discrimination between those with and those without adverse outcomes (range for area under the receiver operating characteristic curve, 0.55-0.76). Results for discriminative performance in the validation sample were similar to the corresponding results in the main study sample (range for area under the receiver operating characteristic curve, 0.51-0.79). Conclusions and Relevance: In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights. The estimates of optimal gestational weight gain may inform prenatal counseling; however, the optimal gestational weight gain ranges had limited predictive value for the outcomes assessed.
Importance: Both low and high gestational weight gain have been associated with adverse maternal and infant outcomes, but optimal gestational weight gain remains uncertain and not well defined for all prepregnancy weight ranges. Objectives: To examine the association of ranges of gestational weight gain with risk of adverse maternal and infant outcomes and estimate optimal gestational weight gain ranges across prepregnancy body mass index categories. Design, Setting, and Participants: Individual participant-level meta-analysis using data from 196 670 participants within 25 cohort studies from Europe and North America (main study sample). Optimal gestational weight gain ranges were estimated for each prepregnancy body mass index (BMI) category by selecting the range of gestational weight gain that was associated with lower risk for any adverse outcome. Individual participant-level data from 3505 participants within 4 separate hospital-based cohorts were used as a validation sample. Data were collected between 1989 and 2015. The final date of follow-up was December 2015. Exposures: Gestational weight gain. Main Outcomes and Measures: The main outcome termed any adverse outcome was defined as the presence of 1 or more of the following outcomes: preeclampsia, gestational hypertension, gestational diabetes, cesarean delivery, preterm birth, and small or large size for gestational age at birth. Results: Of the 196 670 women (median age, 30.0 years [quartile 1 and 3, 27.0 and 33.0 years] and 40 937 were white) included in the main sample, 7809 (4.0%) were categorized at baseline as underweight (BMI <18.5); 133 788 (68.0%), normal weight (BMI, 18.5-24.9); 38 828 (19.7%), overweight (BMI, 25.0-29.9); 11 992 (6.1%), obesity grade 1 (BMI, 30.0-34.9); 3284 (1.7%), obesity grade 2 (BMI, 35.0-39.9); and 969 (0.5%), obesity grade 3 (BMI, ≥40.0). Overall, any adverse outcome occurred in 37.2% (n = 73 161) of women, ranging from 34.7% (2706 of 7809) among women categorized as underweight to 61.1% (592 of 969) among women categorized as obesity grade 3. Optimal gestational weight gain ranges were 14.0 kg to less than 16.0 kg for women categorized as underweight; 10.0 kg to less than 18.0 kg for normal weight; 2.0 kg to less than 16.0 kg for overweight; 2.0 kg to less than 6.0 kg for obesity grade 1; weight loss or gain of 0 kg to less than 4.0 kg for obesity grade 2; and weight gain of 0 kg to less than 6.0 kg for obesity grade 3. These gestational weight gain ranges were associated with low to moderate discrimination between those with and those without adverse outcomes (range for area under the receiver operating characteristic curve, 0.55-0.76). Results for discriminative performance in the validation sample were similar to the corresponding results in the main study sample (range for area under the receiver operating characteristic curve, 0.51-0.79). Conclusions and Relevance: In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights. The estimates of optimal gestational weight gain may inform prenatal counseling; however, the optimal gestational weight gain ranges had limited predictive value for the outcomes assessed.
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