Dataset Information

Identifying Prenatal and Postnatal Determinants of Infant Growth: A Structural Equation Modelling Based Cohort Analysis.

ABSTRACT: The growth and maturation of infants reflect their overall health and nutritional status. The purpose of this study is to examine the associations of prenatal and early postnatal factors with infant growth (IG). A data-driven model was constructed by structural equation modelling to examine the relationships between pre- and early postnatal environmental factors and IG at age 12 months. The IG was a latent variable created from infant weight and waist circumference. Data were obtained on 274 mother-child pairs during pregnancy and the postnatal periods. Maternal pre-pregnancy BMI emerged as an important predictor of IG with both direct and indirect (mediated through infant birth weight) effects. Infants who gained more weight from birth to 6 months and consumed starchy foods daily at age 12 months, were more likely to be larger by age 12 months. Infant physical activity (PA) levels also emerged as a determinant. The constructed model provided a reasonable fit (χ2 (11) = 21.5, p < 0.05; RMSEA = 0.07; CFI = 0.94; SRMR = 0.05) to the data with significant pathways for all examined variables. Promoting healthy weight amongst women of child bearing age is important in preventing childhood obesity, and increasing daily infant PA is as important as a healthy infant diet.

SUBMITTER: Morgan K

PROVIDER: S-EPMC8507693 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Identifying Prenatal and Postnatal Determinants of Infant Growth: A Structural Equation Modelling Based Cohort Analysis.

Morgan Kelly K Zhou Shang-Ming SM Hill Rebecca R Lyons Ronan A RA Paranjothy Shantini S Brophy Sinead T ST

International journal of environmental research and public health 20210929 19

<h4>Background</h4>The growth and maturation of infants reflect their overall health and nutritional status. The purpose of this study is to examine the associations of prenatal and early postnatal factors with infant growth (IG).<h4>Methods</h4>A data-driven model was constructed by structural equation modelling to examine the relationships between pre- and early postnatal environmental factors and IG at age 12 months. The IG was a latent variable created from infant weight and waist circumfere ...[more]

PMID: 34639581

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Project description:BackgroundThe exposure-response association between prenatal and postnatal household air pollution (HAP) and infant growth trajectories is unknown.ObjectivesTo evaluate associations between prenatal and postnatal HAP exposure and stove interventions on growth trajectories over the first year of life.MethodsThe Ghana Randomized Air Pollution and Health Study enrolled n=1,414 pregnant women at ≤24wk gestation from Kintampo, Ghana, and randomized them to liquefied petroleum gas (LPG), improved biomass, or open fire (control) stoves. We quantified HAP exposure by repeated, personal prenatal and postnatal carbon monoxide (CO) and, in a subset, fine particulate matter [PM with an aerodynamic diameter of ≤2.5μm (PM2.5)] assessments. Length, weight, mid-upper arm circumference (MUAC) and head circumference (HC) were measured at birth, 3, 6, 9, and 12 months; weight-for-age, length-for-age (LAZ), and weight-for-length z (WLZ)-scores were calculated. For each anthropometric measure, we employed latent class growth analysis to generate growth trajectories over the first year of life and assigned each child to a trajectory group. We then employed ordinal logistic regression to determine associations between HAP exposures and growth trajectory assignments. Associations with stove intervention arm were also considered.ResultsOf the 1,306 live births, 1,144 had valid CO data and anthropometric variables measured at least once. Prenatal HAP exposure increased risk for lower length [CO odds ratio (OR)= 1.17, 95% CI: 1.01, 1.35 per 1-ppm increase; PM2.5 OR= 1.07, 95% CI: 1.02, 1.13 per 10-μg/m3 increase], lower LAZ z-score (CO OR= 1.15, 95% CI: 1.01, 1.32 per 1-ppm increase) and stunting (CO OR= 1.25, 95% CI: 1.08, 1.45) trajectories. Postnatal HAP exposure increased risk for smaller HC (CO OR= 1.09, 95% CI: 1.04, 1.13 per 1-ppm increase), smaller MUAC and lower WLZ-score (PM2.5 OR= 1.07, 95% CI: 1.00, 1.14 and OR= 1.09, 95% CI: 1.01, 1.19 per 10-μg/m3 increase, respectively) trajectories. Infants in the LPG arm had decreased odds of having smaller HC and MUAC trajectories as compared with those in the open fire stove arm (OR= 0.58, 95% CI: 0.37, 0.92 and OR= 0.45, 95% CI: 0.22, 0.90, respectively).DiscussionHigher early life HAP exposure (during pregnancy and through the first year of life) was associated with poorer infant growth trajectories among children in rural Ghana. A cleaner-burning stove intervention may have improved some growth trajectories. https://doi.org/10.1289/EHP8109.

Project description:PurposeThe MUNICH Preterm and Term Clinical (MUNICH-PreTCl) birth cohort was established to uncover pathological processes contributing to infant/childhood morbidity and mortality. We collected comprehensive medical information of healthy and sick newborns and their families, together with infant blood samples for proteomic analysis. MUNICH-PreTCl aims to identify mechanism-based biomarkers in infant health and disease to deliver more precise diagnostic and predictive information for disease prevention. We particularly focused on risk factors for pregnancy complications, family history of genetically influenced health conditions such as diabetes and paediatric long-term health-all to be further monitored and correlated with proteomics data in the future.ParticipantsNewborns and their parents were recruited from the Perinatal Center at the LMU University Hospital, Munich, between February 2017 and June 2019. Infants without congenital anomalies, delivered at 23-41 weeks of gestation, were eligible.FindingsFindings to date concern the clinical data and extensive personal patient information. A total of 662 infants were recruited, 44% were female (36% in preterm, 46% in term). 90% of approached families agreed to participate. Neonates were grouped according to gestational age: extremely preterm (<28 weeks, N=28), very preterm (28 to <32 weeks, N=36), late preterm (32 to <37 weeks, N=97) and term infants (>37+0 weeks, N=501). We collected over 450 data points per child-parent set, (family history, demographics, pregnancy, birth and daily follow-ups throughout hospitalisation) and 841 blood samples longitudinally. The completion rates for medical examinations and blood samples were 100% and 95% for the questionnaire.Future plansThe correlation of large clinical datasets with proteomic phenotypes, together with the use of medical registries, will enable future investigations aiming to decipher mechanisms of disorders in a systems biology perspective.Trial registration numberDRKS (00024189); Pre-results.

Dataset Information

Identifying Prenatal and Postnatal Determinants of Infant Growth: A Structural Equation Modelling Based Cohort Analysis.

Publications

Identifying Prenatal and Postnatal Determinants of Infant Growth: A Structural Equation Modelling Based Cohort Analysis.

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