Field Epidemiology
Birth weight predicts physical indicators in adulthood: a large population-based study in Chinese twins
Chunxiao Liao, Wenjing Gao, Luanluan Sun, Ying Gao, Weihua Cao, Jun Lyu, Canqing Yu, Shengfeng Wang, Zengchang Pang, Liming Cong, Zhong Dong, Fan Wu, Hua Wang, Xianping Wu, Guohong Jiang, Xiaojie Wang, Binyou Wang, Liming Li
Published 2020-03-10
Cite as Chin J Epidemiol, 2020, 41(3): 310-314. DOI: 10.3760/cma.j.issn.0254-6450.2020.03.006
Abstract
ObjectiveTo quantitate the association between birth weight and phenotypes of physical indicators in adulthood, i.e. BMI and waist circumference (WC) and to what degree genetic or environmental factors affect birth weight-obesity association.
MethodsA total of 6 623 gender matched twin pairs aged 25 to 79 years were recruited through the Chinese National Twin Registry. The twins reported their own birth weight, current height and weight, and WC using a self-administered questionnaire. BMI was calculated according to the self-reports of body height and weight. Within twin-pair design was used to quantitate the association between birth weight and phenotypes related to obesity while bivariate structural equation models were used to decompose the phenotype correlation.
ResultsAfter adjusted for multiple factors, twin-pair analyses within monozygotic (MZ) showed that, on average, a 1.0 kg increase in birth weight corresponded to an increase of 0.33 kg/m2 in BMI and 0.95 cm in WC in adulthood (P<0.001). Bivariate structural equation models showed significant positive unique environmental correlation between birth weight and the two obesity-related phenotypes.
ConclusionThe study supported the role of twin-specific supply line factors on relationship between birth weight and physical indicators in adulthood.
Key words:
Birth weight; Body-mass index; Waist circumference; Twin studies
Contributor Information
Chunxiao Liao
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Wenjing Gao
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Luanluan Sun
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Ying Gao
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Weihua Cao
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Jun Lyu
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Canqing Yu
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Shengfeng Wang
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Zengchang Pang
Qingdao City Center for Diseases Control and Prevention, Qingdao 266033, China
Liming Cong
Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
Zhong Dong
Beijing Center for Disease Prevention and Control, Beijing 100013, China
Fan Wu
Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
Hua Wang
Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
Xianping Wu
Sichuan Provincial Center for Disease Control and Prevention, Chengdu 610041, China
Guohong Jiang
Tianjin Center for Disease Control and Prevention, Tianjin 300011, China
Xiaojie Wang
Qinghai Provincial Center for Disease Control and Prevention, Xining 810007, China
Binyou Wang
Harbin Medical University, Harbin 150081, China
Liming Li
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
