A prediction model for childhood obesity in New Zealand

Prediction models for children/adolescents with obesity/overweight: A systematic review and meta-analysis

The incidence of obesity and overweight in children and adolescents is increasing worldwide and becomes a global health concern. This study aims to evaluate the accuracy of available prediction models in early identification of obesity and overweight in general children or adolescents and identify predictive factors for the models, thus provide a reference for subsequent development of risk prediction tools for obesity and overweight in children or adolescents. Related publications were obtained from several databases such as PubMed, Embase, Cochrane Library, and Web of Science from their inception to September 18th, 2022. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to assess the bias risk of the included studies. R4.2.0 and Stata15.1 softwares were used to conduct meta-analysis. This study involved 45 cross-sectional and/or prospective studies with 126 models. Meta-analyses showed that the overall pooled index of concordance (c-index) of prediction models for children/adolescents with obesity and overweight in the training set was 0.769 (95% CI 0.754-0.785) and 0.835(95% CI 0.792-0.879), respectively. Additionally, a large number of predictors were found to be related to children's lifestyles, such as sleep duration, sleep quality, and eating speed. In conclusions, prediction models can be employed to predict obesity/overweight in children and adolescents. Most predictors are controllable factors and are associated with lifestyle. Therefore, the prediction model serves as an excellent tool to formulate effective strategies for combating obesity/overweight in pediatric patients.

A prediction model for childhood obesity risk using the machine learning method: a panel study on Korean children

Young children are increasingly exposed to an obesogenic environment through increased intake of processed food and decreased physical activity. Mothers' perceptions of obesity and parenting styles influence children's abilities to maintain a healthy weight. This study developed a prediction model for childhood obesity in 10-year-olds, and identify relevant risk factors using a machine learning method. Data on 1185 children and their mothers were obtained from the Korean National Panel Study. A prediction model for obesity was developed based on ten factors related to children (gender, eating habits, activity, and previous body mass index) and their mothers (education level, self-esteem, and body mass index). These factors were selected based on the least absolute shrinkage and selection operator. The prediction model was validated with an Area Under the Receiver Operator Characteristic Curve of 0.82 and an accuracy of 76%. Other than body mass index for both children and mothers, significant risk factors for childhood obesity were less physical activity among children and higher self-esteem among mothers. This study adds new evidence demonstrating that maternal self-esteem is related to children's body mass index. Future studies are needed to develop effective strategies for screening young children at risk for obesity, along with their mothers.

Longitudinal body mass index trajectories at preschool age: children with rapid growth have differential composition of the gut microbiota in the first year of life

Background/Objective

The steep rise in childhood obesity has emerged as a worldwide public health problem. The first 4 years of life are a critical window where long-term developmental patterns of body mass index (BMI) are established and a critical period for microbiota maturation. Understanding how the early-life microbiota relate to preschool growth may be useful for identifying preventive interventions for childhood obesity. We aim to investigate whether longitudinal shifts within the bacterial community between 3 months and 1 year of life are associated with preschool BMI z-score trajectories.

Methods

BMI trajectories from birth to 5 years of age were identified using group-based trajectory modeling in 3059 children. Their association with familial and environmental factors were analyzed. Infant gut microbiota at 3 months and 1 year was defined by 16S RNA sequencing and changes in diversity and composition within each BMIz trajectory were analyzed.

Results

Four BMIz trajectories were identified: low stable, normative, high stable, and rapid growth. Infants in the rapid growth trajectory were less likely to have been breastfed, and gained less microbiota diversity in the first year of life. Relative abundance of Akkermansia increased with age in children with stable growth, but decreased in those with rapid growth, abundance of Ruminococcus and Clostridium at 1 year were elevated in children with rapid growth. Children who were breastfed at 6 months had increased levels of Sutterella, and decreased levels of Ruminococcus and Clostridium.

Conclusion

This study provides new insights into the relationship between the gut microbiota in infancy and patterns of growth in a cohort of preschool Canadian children. We highlight that rapid growth since birth is associated with bacteria shown in animal models to have a causative role in weight gain. Our findings support a novel avenue of research targeted on tangible interventions to reduce childhood obesity.