Understanding the cumulative risk of maternal prenatal biopsychosocial factors on birth weight: a DynaHEALTH study on two birth cohorts

• Published in: Journal of Epidemiology and Community Health
• Main Authors: Parmar, P, Lowry, E, Vehmeijer, F, El Marroun, H, Lewin, A, Tolvanen, M, Tzala, E, Ala-Mursula, L, Herzig, K-H, Miettunen, J, Prokopenko, I, Rautio, N, Jaddoe, VWV, Jarvelin, M-R, Felix, J, Sebert, S
• Other Authors: UNIVERSITY OF OULU, Commission of the European Communities
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMJ Publishing Group 2020
• Subjects: Science & Technology; Life Sciences & Biomedicine; Public; Environmental & Occupational Health; Ageing; Diabetes; Disability; Child health; Maternal health; Birth defects; Cardiovascular disease; Birth weight; Epidemiology of chronic non communicable diseases; Life course epidemiology; Cohort studies; Biostatistics; BODY-MASS INDEX; GESTATIONAL-AGE; PREGNANCY; HEALTH; DETERMINANTS; OBESITY; GROWTH; UPDATE; LENGTH; Epidemiology; 1117 Public Health and Health Services; 1604 Human Geography; Northern Finland
• Online Access: http://hdl.handle.net/10044/1/85447; https://doi.org/10.1136/jech-2019-213154

Background There are various maternal prenatal biopsychosocial (BPS) predictors of birth weight, making it difficult to quantify their cumulative relationship. Methods We studied two birth cohorts: Northern Finland Birth Cohort 1986 (NFBC1986) born in 1985–1986 and the Generation R Study (from the Netherlands) born in 2002–2006. In NFBC1986, we selected variables depicting BPS exposure in association with birth weight and performed factor analysis to derive latent constructs representing the relationship between these variables. In Generation R, the same factors were generated weighted by loadings of NFBC1986. Factor scores from each factor were then allocated into tertiles and added together to calculate a cumulative BPS score. In all cases, we used regression analyses to explore the relationship with birth weight corrected for sex and gestational age and additionally adjusted for other factors. Results Factor analysis supported a four-factor structure, labelled closely to represent their characteristics as ‘Factor1-BMI’ (body mass index), ‘Factor2-DBP’ (diastolic blood pressure), ‘Factor3-Socioeconomic-Obstetric-Profile’ and ‘Factor4-Parental-Lifestyle’. In both cohorts, ‘Factor1-BMI’ was positively associated with birth weight, whereas other factors showed negative association. ‘Factor3-Socioeconomic-Obstetric-Profile’ and ‘Factor4-Parental-Lifestyle’ had the greatest effect size, explaining 30% of the variation in birth weight. Associations of the factors with birth weight were largely driven by ‘Factor1-BMI’. Graded decrease in birth weight was observed with increasing cumulative BPS score, jointly evaluating four factors in both cohorts. Conclusion Our study is a proof of concept for maternal prenatal BPS hypothesis, highlighting the components snowball effect on birth weight in two different European birth cohorts.