Deep clinical and biological phenotyping of the preterm birth and small for gestational age syndromes: The INTERBIO-21st Newborn Case-Control Study protocol

Abstract

Background: INTERBIO-21st is Phase II of the INTERGROWTH-21st Project, the population-based, research initiative involving nearly 70,000 mothers and babies worldwide coordinated by Oxford University and performed by a multidisciplinary network of more than 400 healthcare professionals and scientists from 35 institutions in 21 countries worldwide. Phase I, conducted 2008-2015, consisted of nine complementary studies designed to describe optimal human growth and neurodevelopment, based conceptually on the WHO prescriptive approach. The studies generated a set of international standards for monitoring growth and neurodevelopment, which complement the existing WHO Child Growth Standards. Phase II aims to improve the functional classification of the highly heterogenous preterm birth and fetal growth restriction syndromes through a better understanding of how environmental exposures, clinical conditions and nutrition influence patterns of human growth from conception to childhood, as well as specific neurodevelopmental domains and associated behaviors at 2 years of age. Methods: In the INTERBIO-21st Newborn Case-Control Study, a major component of Phase II, our objective is to investigate the mechanisms potentially responsible for preterm birth and small for gestational age and their interactions, using deep phenotyping of clinical, growth and epidemiological data and associated nutritional, biochemical, omic and histological profiles. Here we describe the study sites, population characteristics, study design, methodology and standardization procedures for the collection of longitudinal clinical data and biological samples (maternal blood, umbilical cord blood, placental tissue, maternal feces and infant buccal swabs) for the study that was conducted between 2012 and 2018 in Brazil, Kenya, Pakistan, South Africa, Thailand and the UK. Discussion: Our study provides a unique resource for the planned analyses given the range of potentially disadvantageous exposures (including poor nutrition, pregnancy complications and infections) in geographically diverse populations worldwide. The study should enhance current medical knowledge and provide new insights into environmental influences on human growth and neurodevelopment.

PRECISE pregnancy cohort: challenges and strategies in setting up a biorepository in sub-Saharan Africa

“…To ensure that PRECISE sites may participate in future collaborations and consortia, specimen collection has been designed to be harmonised with other similar large pregnancy cohort studies such as AMANHI [10], INTERBIO-21st [11], GAPPS [12] and HeLTI [13]. The PRECISE Network is also aligned with H3Africa goals [3] by building on existing infrastructure and resources to develop a cohort of African scientists focussed on significant global health problems that disproportionately affect women and families in sub-Saharan Africa.…”

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study

“…19,[21][22][23] The generalisability of the algorithm-ie, its ability to yield accurate estimates using fetal biometric measures from a different dataset (no part of which was used for training)-was established using dataset 2, from the INTERBIO-21st Study (phase 2 of the INTERGROWTH-21st Project). 24 The protocol in the longitudinal component of INTERBIO-21st (the Fetal Study) was almost identical to that used in FGLS. However, the population was much more heterogeneous and women were at higher risk of small for gestational age and preterm birth, with the aim of improving the functional classification of preterm birth and fetal growth restriction.…”

“…The flowchart we used to select healthy FGLS participants for analysis (figure 1) is similar to that used by Papageorghiou and colleagues, 5 thus allowing direct comparison of the results of previous analysis with the results obtained with the algorithm presented here. A total of 3076 participants in the INTERBIO-21st Fetal Study 24 with complete data were included. In both datasets 1 and 2, the distribution of ultrasound data displays peaks at about monthly intervals.…”

“…To show generalisability, the algorithm produced by training with FGLS data 20 was used to estimate gestational age using data from the INTERBIO-21st Fetal Study (dataset 2). 24 The accuracy of our approach could be fully explored only over the period spanning 20 to 30 weeks of gestation, for two reasons. First, head circumference, abdominal circumference, and femur length data were available only after 14 weeks of gestation.…”

“…Figure 3 shows there is no substantial degradation in the accuracy of gestational age estimation when the algorithm trained with data from one population 20 is used to derive estimates for members of a different, far more heterogeneous population in dataset 2. 24 As shown in figure 4, the complex and multipeaked nature of probability distributions for fetal biometric measures using standard estimates of gestational age are removed by the accurate estimates of gestational age obtained with our approach. This illustrates the potential effect of improved estimates of gestational age on fetal growth charts.…”