Literature DB >> 32581064

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

Priyanka Parmar1, Estelle Lowry2, Florianne Vehmeijer3,4,5, Hanan El Marroun5,6,7, Alex Lewin8, Mimmi Tolvanen1, Evangelia Tzala9, Leena Ala-Mursula1, Karl-Heinz Herzig10,11, Jouko Miettunen1,10, Inga Prokopenko12,13, Nina Rautio1,14, Vincent Wv Jaddoe3,4,5, Marjo-Riitta Järvelin15,9,16,17, Janine Felix3,4,5, Sylvain Sebert15,9.   

Abstract

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. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  Ageing; Biostatistics; Birth defects; Birth weight; Cardiovascular disease; Child health; Cohort studies; Diabetes; Disability; Epidemiology of chronic non communicable diseases; Life course epidemiology; Maternal health

Mesh:

Year:  2020        PMID: 32581064      PMCID: PMC7577640          DOI: 10.1136/jech-2019-213154

Source DB:  PubMed          Journal:  J Epidemiol Community Health        ISSN: 0143-005X            Impact factor:   3.710


  33 in total

1.  Birth weight and cognitive function in the British 1946 birth cohort: longitudinal population based study.

Authors:  M Richards; R Hardy; D Kuh; M E Wadsworth
Journal:  BMJ       Date:  2001-01-27

2.  The pre-eclampsia community guideline (PRECOG): how to screen for and detect onset of pre-eclampsia in the community.

Authors:  Fiona Milne; Chris Redman; James Walker; Philip Baker; Julian Bradley; Carol Cooper; Michael de Swiet; Gillian Fletcher; Mervi Jokinen; Deirdre Murphy; Catherine Nelson-Piercy; Vicky Osgood; Stephen Robson; Andrew Shennan; Angela Tuffnell; Sara Twaddle; Jason Waugh
Journal:  BMJ       Date:  2005-03-12

3.  Blood pressure is elevated in normotensive pregnant women with intrauterine growth restriction.

Authors:  Andrea Luigi Tranquilli; Stefano Raffaele Giannubilo
Journal:  Eur J Obstet Gynecol Reprod Biol       Date:  2005-09-01       Impact factor: 2.435

Review 4.  The cost of dichotomising continuous variables.

Authors:  Douglas G Altman; Patrick Royston
Journal:  BMJ       Date:  2006-05-06

5.  Maternal prepregnant body mass index, duration of breastfeeding, and timing of complementary food introduction are associated with infant weight gain.

Authors:  Jennifer L Baker; Kim F Michaelsen; Kathleen M Rasmussen; Thorkild I A Sørensen
Journal:  Am J Clin Nutr       Date:  2004-12       Impact factor: 7.045

6.  Catch-up growth in childhood and death from coronary heart disease: longitudinal study.

Authors:  J G Eriksson; T Forsén; J Tuomilehto; P D Winter; C Osmond; D J Barker
Journal:  BMJ       Date:  1999-02-13

7.  Moderate alcohol consumption during pregnancy and the risk of low birth weight and preterm birth. The generation R study.

Authors:  Vincent W V Jaddoe; Rachel Bakker; Albert Hofman; Johan P Mackenbach; Henriëtte A Moll; Eric A P Steegers; Jacqueline C M Witteman
Journal:  Ann Epidemiol       Date:  2007-06-28       Impact factor: 3.797

8.  Understanding the complexity of glycaemic health: systematic bio-psychosocial modelling of fasting glucose in middle-age adults; a DynaHEALTH study.

Authors:  Sylvain Sebert; Marjo-Riitta Järvelin; Estelle Lowry; Nina Rautio; Ville Karhunen; Jouko Miettunen; Leena Ala-Mursula; Juha Auvinen; Sirkka Keinänen-Kiukaanniemi; Katri Puukka; Inga Prokopenko; Karl-Heinz Herzig; Alexandra Lewin
Journal:  Int J Obes (Lond)       Date:  2018-08-17       Impact factor: 5.095

Review 9.  Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity: a systematic review and meta-analysis.

Authors:  Zhangbin Yu; Shuping Han; Jingai Zhu; Xiaofan Sun; Chenbo Ji; Xirong Guo
Journal:  PLoS One       Date:  2013-04-16       Impact factor: 3.240

10.  Does pre-pregnancy BMI determine blood pressure during pregnancy? A prospective cohort study.

Authors:  Ary I Savitri; Peter Zuithoff; Joyce L Browne; Dwirani Amelia; Mohammad Baharuddin; Diederick E Grobbee; Cuno S P M Uiterwaal
Journal:  BMJ Open       Date:  2016-08-11       Impact factor: 2.692

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