# Association of Gestational Weight Trajectories With Neonatal Outcomes Among Pregnant Slum‐Dwelling Women, India

## TL;DR

This study shows that tracking weight changes during pregnancy in Indian slum-dwelling women can help predict and improve neonatal outcomes.

## Contribution

The study identifies distinct gestational weight gain patterns and their associations with adverse neonatal outcomes in a slum-dwelling population.

## Key findings

- Underweight women had the highest gestational weight gain rates, while obese women had the lowest.
- Women in certain weight trajectory clusters had higher risks of preterm and small for gestational age births.
- Predictive modeling of weight trajectories could help counsel expectant mothers, but larger studies are needed for generalization.

## Abstract

The influence of early pregnancy weight and gestational weight gain (GWG) on neonatal outcomes among Indian slum‐dwellers remains understudied. A prospective cohort study summarised maternal weight trajectories using the longitudinal clustering technique and explored associations between these clusters and neonatal outcomes (low birthweight, small for gestational age [SGA] and preterm births) among 423 pregnant slum‐dwelling women in Pune, India. Sociodemographic data, height and weight were measured at enrolment (< 12 weeks, ‘early pregnancy’). Weight was additionally measured at 23 ± 1 (‘mid‐pregnancy’), 33 ± 1 (‘late pregnancy’), 36–37 and 39–40 weeks. The mean age was 24.7 (95% CI, 23.3, 25.1) years and the mean BMI at enrolment was 22.3 (95% CI, 21.9, 22.7) kg/m2. Underweight women had the highest GWG rates and total GWG, while obese women had the lowest. Four clusters were identified: Cluster 1 (n = 124, 97% normal and overweight women, GWG rate: 0.27 (95% CI, 0.24, 0.30) kg/week early‐late pregnancy) was the reference group. Women in Cluster 2 (n = 146, 93% underweight and normal weight women, GWG rate: 0.31 (95% CI, 0.28, 0.34) kg/week early‐late pregnancy) had a higher risk of having SGA and preterm newborns and women in Cluster 3 (n = 68, 100% overweight and obese women, GWG rate: 0.17, 95% CI, 0.12, 0.22 kg/week early‐late pregnancy) had a higher risk of having preterm newborns than Cluster 1. The women in Cluster 4 (n = 85, 100% underweight and normal weight, mean early‐late pregnancy GWG rate of 0.47, 95% CI, 0.44, 0.50 kg/week) showed no higher risk of adverse neonatal outcomes. This study highlights the need to monitor both pre‐pregnancy BMI and weight throughout pregnancy to enhance the possibility of favourable neonatal outcomes.

This prospective study among pregnant slum‐dwelling Indian women highlights the need to address both pre‐pregnancy BMI and regular weight monitoring throughout pregnancy to enhance the likelihood of favourable neonatal outcomes.

The mean rates of gestational weight gain at mid‐ and late‐pregnancy, as well as total GWG, were highest among underweight women and lowest among obese women in this cohort of urban slum‐dwelling women.Our study highlights the need to address both pre‐pregnancy BMI and regular weight monitoring throughout pregnancy to enhance the likelihood of favourable neonatal outcomes.Given the pre‐pregnancy weight and weight during the first trimester, we can predict the weight trajectory for the later part of the pregnancy and predict the risk of adverse neonatal outcomes. Such predictive modelling can be valuable for counselling expectant mothers. However, to ensure the generalisability of this approach, larger‐scale studies are necessary.

The mean rates of gestational weight gain at mid‐ and late‐pregnancy, as well as total GWG, were highest among underweight women and lowest among obese women in this cohort of urban slum‐dwelling women.

Our study highlights the need to address both pre‐pregnancy BMI and regular weight monitoring throughout pregnancy to enhance the likelihood of favourable neonatal outcomes.

Given the pre‐pregnancy weight and weight during the first trimester, we can predict the weight trajectory for the later part of the pregnancy and predict the risk of adverse neonatal outcomes. Such predictive modelling can be valuable for counselling expectant mothers. However, to ensure the generalisability of this approach, larger‐scale studies are necessary.