Rationale: Every year, 200 million children under 5 years of age fail to reach their full cognitive development, the vast majority of cases in low-income countries (LIC). Ten percent of the global burden of disability results from neurodevelopmental impairment arising from perinatal insults. Maternal undernutrition and infections are two prevalent and modifiable pregnancy risks linked with adverse neurodevelopmental outcomes. In this K23 proposal, Dr. Lee will test the hypothesis that perinatal undernutrition and infections place the fetus at greater risk of inflammation, and in turn, risk of white matter injury, poor brain growth and long-term development. Objectives: In a cohort of 300 Bangladeshi mothers and infants, Dr. Lee will: 1) determine the associations of pregnancy undernutrition and infections on fetal inflammatory responses, 2) examine the associations of fetal inflammation and neurodevelopmental outcomes at 2 years, and 3) explore the role of inflammation in infant brain structure and function at 3 months. She will further explore these associations among higher-risk moderate-late preterm and small for gestational age (SGA) infants. Design: In an ongoing longitudinal pregnancy-birth cohort and specimen bio-repository, Dr. Lee will test umbilical cord blood for pro-inflammatory mediators and perform secondary data analysis. She will assess the relationships between maternal nutritional status, dietary intake of potentially immuno-modulatory nutrients, prenatal infections, fetal inflammation, and neurodevelopmental outcomes at 2 years. Multivariate models will be created to determine associations between exposures and outcomes adjusting for other relevant determinants. In a subset of infants, Dr. Lee will also explore the potential effects of inflammation and prematurity on brain structure and function using MRI and visual evoked potentials. Innovation: The effects of maternal nutrition on fetal inflammation, and the interactions between nutrition and infection, have been poorly characterized in pregnancy. The role of inflammation in predicting neurodevelopmental outcomes has been inadequately described in moderate-late preterm and SGA infants, which are highly prevalent in LIC. Dr. Lee will use neuroimaging and electrophysiology in order to study the underlying mechanisms of these processes. Career Development: The K23 award would enable Dr. Lee to expand her investigation into neuro- development and build a new, independent multi-disciplinary research program. Mentored training is proposed in four key areas: 1) neurodevelopmental assessment, including neurobehavior, electrophysiology and imaging (Primary mentor: Inder, co-mentor: Nelson), 2) inflammation (Fichorova), 3) nutrition (Fawzi, Oken), and 4) longitudinal statistical modeling of child outcomes (Rosner, Baqui). The expertise, knowledge, and pilot data gained from the K23 program will enable Dr. Lee to obtain R01 funding to conduct a randomized controlled trial of perinatal interventions targeting fetal inflammation to optimize infant neurodevelopment in LIC.
Globally, 200 million children under 5 years of age fail to reach their full potential in cognitive development, with the vast majority of cases in low-income countries. Early exposures to undernutrition, infections, and inflammation in pregnancy may adversely affect the baby's developing brain and have life-long effects on child development. These risks are common in the poorest countries, where one in four mothers is undernourished and one in seven will have an infection in pregnancy. This project aims to characterize the role of these two prenatal risks for neurodevelopmental impairment in Bangladesh, in order to design effective interventions to optimize child wellbeing and potential in low-income countries and help achieve the 2030 United Nations Sustainable Development Goals.
| Lee, Anne Cc; Kozuki, Naoko; Cousens, Simon et al. (2017) Estimates of burden and consequences of infants born small for gestational age in low and middle income countries with INTERGROWTH-21st standard: analysis of CHERG datasets. BMJ 358:j3677 |
