Nedelec, R., Miettunen, J., Männikkö, M. et al. Maternal and infant prediction of the child BMI trajectories; studies across two generations of Northern Finland birth cohorts. Int J Obes (2020). https://doi.org/10.1038/s41366-020-00695-0
Maternal and infant prediction of the child BMI trajectories : studies across two generations of Northern Finland birth cohorts
|Author:||Nedelec, Rozenn1; Miettunen, Jouko1,2; Männikkö, Minna3;|
1Center for Life Course Health Research, University of Oulu, Oulu, Finland
2Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
3Infrastructure for Population Studies, Northern Finland Birth Cohorts, Faculty of Medicine, University of Oulu, Oulu, Finland Minna Männikkö
4Unit of Primary Care, Oulu University Hospital, Oulu, Finland
5Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
6MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
7Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020101484111
|Publish Date:|| 2021-04-11
Background/objective: Children BMI is a longitudinal phenotype, developing through interplays between genetic and environmental factors. Whilst childhood obesity is escalating, we require a better understanding of its early origins and variation across generations to prevent it.
Subjects/methods: We designed a cross-cohort study including 12,040 Finnish children from the Northern Finland Birth Cohorts 1966 and 1986 (NFBC1966 and NFBC1986) born before or at the start of the obesity epidemic. We used group-based trajectory modelling to identify BMI trajectories from 2 to 20 years. We subsequently tested their associations with early determinants (mother and child) and the possible difference between generations, adjusted for relevant biological and socioeconomic confounders.
Results: We identified four BMI trajectories, ‘stable-low’ (34.8%), ‘normal’ (44.0%), ‘stable-high’ (17.5%) and ‘early-increase’ (3.7%). The ‘early-increase’ trajectory represented the highest risk for obesity. We analysed a dose-response association of maternal pre-pregnancy BMI and smoking with BMI trajectories. The directions of effect were consistent across generations and the effect sizes tended to increase from earlier generation to later. Respectively for NFBC1966 and NFBC1986, the adjusted risk ratios of being in the early-increase group were 1.08 (1.06–1.10) and 1.12 (1.09–1.15) per unit of pre-pregnancy BMI and 1.44 (1.05–1.96) and 1.48 (1.17–1.87) in offspring of smoking mothers compared to non-smokers. We observed similar relations with infant factors including birthweight for gestational age and peak weight velocity. In contrast, the age at adiposity peak in infancy was associated with the BMI trajectories in NFBC1966 but did not replicate in NFBC1986.
Conclusions: Exposures to adverse maternal predictors were associated with a higher risk obesity trajectory and were consistent across generations. However, we found a discordant association for the timing of adiposity peak over a 20-year period. This suggests the role of residual environmental factors, such as nutrition, and warrants additional research to understand the underlying gene–environment interplay.
International journal of obesity
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3142 Public health care science, environmental and occupational health
This work was supported by European Union’s Horizon 2020 research and innovation programme [DYNAHEALTH 633595, LIFECYCLE 733206, EUCANCONNECT 824989, LongITools 874739, EarlyCause 848458], Academy of Finland [EGEA 285547] and the JPI-HDHL programme [PREcise—MRC-UK P75416].
|EU Grant Number:||
(633595) DYNAHEALTH - Understanding the dynamic determinants of glucose homeostasis and social capability to promote Healthy and active aging
(733206) LIFECYCLE - Early-life stressors and LifeCycle health
(824989) EUCAN-Connect - A federated FAIR platform enabling large-scale analysis of high-value cohort data connecting Europe and Canada in personalized health
|Academy of Finland Grant Number:||
285547 (Academy of Finland Funding decision)
© The Author(s), under exclusive licence to Springer Nature Limited 2020. This is a post-peer-review, pre-copyedit version of an article published in Int J Obes. The final authenticated version is available online at https://doi.org/10.1038/s41366-020-00695-0.