Explorations on risk profiles for overweight and obesity in 9501 preschool-aged children

Changes in Body Weight and Risk Factors for Overweight and Obesity in 5-6-Year-Old Children Attending School in Geneva

Background: The prevalence of obesity and overweight in children is increasing in industrialized countries. Monitoring the evolution of these phenomena is essential for understanding prevention and health promotion programs. This study aims to present the analysis of anthropometric data collected by school nurses from the School Health Service of Geneva (Service de santé de l'enfance et de la jeunesse) for children aged 5 to 6 years during the 2021-2022 school year, as well as describe the trends in overweight and obesity from 2003-2004 to 2021-2022. Risk factors were also assessed in the 2021-2022 sample. Methods: This study included a random sample of 958 (479 girls and 479 boys) primary school pupils aged 5 to 6 years in Geneva. Data on weight, height and socioeconomic status were collected. BMI was analyzed using the Cole standard. A multivariate analysis was conducted to assess the influence of socioeconomic factors on overweight and obesity. We compared these results with BMI trends in students of the same age since 2003. Results: In 2021-2022, overall prevalence of overweight was 12.73%, and obesity was 5.64%. Girls had higher rates of overweight (14.20%) and obesity (6.68%) compared to boys (11.27% and 4.59%, respectively) (p < 0.0001). Overweight in boys significantly increased since the 2013-2014 and 2019-2020 measurements (p = 0.003). The trend for girls was similar but not statistically significant. Obesity rates have not significantly increased since 2019-2020 in both genders, but there is a significantly increasing trend for girls since 2013-2014 p = 0.045). Socioeconomic factors, particularly the socioeconomic class of parents, played a predictive role in overweight and obesity. Conclusions: The School Health Service of Geneva and the Directorate General of Health have a crucial role in monitoring and preventing childhood obesity. The prevalence of overweight and obesity has remained high since 2010, justifying continuous efforts for prevention. A significant increase in prevalence has been observed since 2020, particularly among overweight boys, and could be related to COVID-19 confinement measures.

Eating Habits and Lifestyle Factors Related to Childhood Obesity Among Children Aged 5-6 Years: Cluster Analysis of Panel Survey Data in Korea

BACKGROUND

Childhood obesity has emerged as a major health issue due to the rapid growth in the prevalence of obesity among young children worldwide. Establishing healthy eating habits and lifestyles in early childhood may help children gain appropriate weight and further improve their health outcomes later in life.

OBJECTIVE

This study aims to classify clusters of young children according to their eating habits and identify the features of each cluster as they relate to childhood obesity.

METHODS

A total of 1280 children were selected from the Panel Study on Korean Children. Data on their eating habits (eating speed, mealtime regularity, consistency of food amount, and balanced eating), sleep hours per day, outdoor activity hours per day, and BMI were obtained. We performed a cluster analysis on the children's eating habits using k-means methods. We conducted ANOVA and chi-square analyses to identify differences in the children's BMI, sleep hours, physical activity, and the characteristics of their parents and family by cluster.

RESULTS

At both ages (ages 5 and 6 years), we identified 4 clusters based on the children's eating habits. Cluster 1 was characterized by a fast eating speed (fast eaters); cluster 2 by a slow eating speed (slow eaters); cluster 3 by irregular eating habits (poor eaters); and cluster 4 by a balanced diet, regular mealtimes, and consistent food amounts (healthy eaters). Slow eaters tended to have the lowest BMI (P<.001), and a low proportion had overweight and obesity at the age of 5 years (P=.03) and 1 year later (P=.005). There was a significant difference in sleep time (P=.01) and mother's education level (P=.03) at the age of 5 years. Moreover, there was a significant difference in sleep time (P=.03) and the father's education level (P=.02) at the age of 6 years.

CONCLUSIONS

Efforts to establish healthy eating habits in early childhood may contribute to the prevention of obesity in children. Specifically, providing dietary guidance on a child's eating speed can help prevent childhood obesity. This research suggests that lifestyle modification could be a viable target to decrease the risk of childhood obesity and promote the development of healthy children. Additionally, we propose that future studies examine long-term changes in obesity resulting from lifestyle modifications in children from families with low educational levels.

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.

BMI growth trajectory from birth to 5 years and its sex-specific association with prepregnant BMI and gestational weight gain

Objective

The purpose of the study was to identify the latent body mass index (BMI) z-score trajectories of children from birth to 5 years of age and evaluate their sex-specific association with prepregnant BMI and gestational weight gain (GWG).

Methods

This was a retrospective longitudinal cohort study performed in China. In total, three distinct BMI-z trajectories from birth to 5 years of age were determined for both genders using the latent class growth modeling. The logistic regression model was used to assess the associations of maternal prepregnant BMI and GWG with childhood BMI-z growth trajectories.

Results

Excessive GWG increased the risks of children falling into high-BMI-z trajectory relative to adequate GWG (OR = 2.04, 95% CI: 1.29, 3.20) in boys; girls born to mothers with prepregnancy underweight had a higher risk of low-BMI-z trajectory than girls born to mothers with prepregnancy adequate weight (OR = 1.85, 95% CI: 1.22, 2.79).

Conclusion

BMI-z growth trajectories of children from 0 to 5 years of age have population heterogeneity. Prepregnant BMI and GWG are associated with child BMI-z trajectories. It is necessary to monitor weight status before and during pregnancy to promote maternal and child health.

Secular trends and sociodemographic determinants of thinness, overweight and obesity among Chinese children and adolescents aged 7-18 years from 2010 to 2018

Background

Most studies have focused on overweight/obesity and its secular trend, with insufficient studies on the factors influencing thinness and trends recently. To examine the trends of prevalence and sociodemographic determinants of thinness, overweight, and obesity among Chinese children and adolescents aged 7 to 18 years from 2010 to 2018.

Methods

This study was based on cross-sectional data of 11,234 children and adolescents aged 7 to 18 years from the Chinese Family Panel Studies (CFPS) in 2010, 2014, and 2018, including anthropometric and sociodemographic characteristics variables. The nutritional status of each individual was determined according to China and WHO criteria. The demographic characteristics of different subgroups were tested by chi-square, and log-binomial regression was used to analyze the trend of prevalence and the relationship between sociodemographic characteristics and different nutritional statuses.

Results

After adjusting for age, from 2010 to 2018, the overall prevalence of thinness decreased, and the prevalence of overweight increased in Chinese children and adolescents. The overall prevalence of obesity declined in boys and increased in girls, but in adolescents aged 16-18 years, it increased significantly. Log-binomial regression analysis showed that among all subjects, time (years), 16-18 years were negatively associated with thinness, while 13-15 years, walking to school, large family size, and paternal age at childbirth older than 30 years old were positively associated with thinness; 10-12/13-15/16-18 years, boarding at school, medium and large family sizes, and mother's education at junior middle school/junior high school and above were negatively associated with overweight/obesity, while time (years), boys were positively associated with overweight/obesity in the multivariate model by adjusting for the statistically significant factors (all p < 0.05).

Conclusion

Chinese children and adolescents are facing a double burden of malnutrition. Future public health policies and interventions should prioritize high-risk groups specifically young age groups, boys, larger family sizes and so on.

Factors of Obesity and Metabolically Healthy Obesity in Asia

The East Asian region (China, Japan, and South Korea) is comprised of almost 1.5 billion people and recent industrialization has brought with it a pandemic of rising obesity, even in children. As these countries are rapidly aging and functioning at sub-replacement birthrates, the burgeoning costs of obesity-related care may threaten socialized healthcare systems and quality of life. However, a condition called metabolically healthy obesity (MHO) has been found to be without immediate cardiopulmonary or diabetic risk. Thus, maintenance of the MHO condition for the obese in East Asia could buffer the burden of long-term obesity care on medical systems and knowledge of the biochemical, genetic, and physiological milieu associated with it could also provide new targets for intervention. Diverse physiological, psychological, environmental, and social factors play a role in obesogenesis and the transition of MHO to a metabolically unhealthy obesity. This review will give a broad survey of the various causes of obesity and MHO, with special emphasis on the East Asian population and studies from that region.

Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques

OBJECTIVES

We adopted the machine-learning algorithms and deep-learning sequential model to determine and optimize most important factors for overweight and obesity in Chinese preschool-aged children.

METHODS

This is a cross-sectional survey conducted in 2020 at Beijing and Tangshan. Using a stratified cluster random sampling strategy, children aged 3-6 years were enrolled. Data were analyzed using the PyCharm and Python.

RESULTS

A total of 9478 children were eligible for inclusion, including 1250 children with overweight or obesity. All children were randomly divided into the training group and testing group at a 6:4 ratio. After comparison, support vector machine (SVM) outperformed the other algorithms (accuracy: 0.9457), followed by gradient boosting machine (GBM) (accuracy: 0.9454). As reflected by other 4 performance indexes, GBM had the highest F1 score (0.7748), followed by SVM with F1 score at 0.7731. After importance ranking, the top 5 factors seemed sufficient to obtain descent performance under GBM algorithm, including age, eating speed, number of relatives with obesity, sweet drinking, and paternal education. The performance of the top 5 factors was reinforced by the deep-learning sequential model.

CONCLUSIONS

We have identified 5 important factors that can be fed to GBM algorithm to better differentiate children with overweight or obesity from the general children, with decent prediction performance.

Identifying factors associated with central obesity in school students using artificial intelligence techniques

OBJECTIVES

We, in a large survey of school students from Beijing, aimed to identify the minimal number of promising factors associated with central obesity and the optimal machine-learning algorithm.

METHODS

Using a cluster sampling strategy, this cross-sectional survey was conducted in Beijing in early 2022 among students 6-14 years of age. Information was gleaned via online questionnaires and analyzed by the PyCharm and Python.

RESULTS

Data from 11,308 children were abstracted for analysis, and 3,970 of children had central obesity. Light gradient boosting machine (LGBM) outperformed the other 10 models. The accuracy, precision, recall, F1 score, area under the receiver operating characteristic of LGBM were 0.769982, 0.688312, 0.612323, 0.648098, and 0.825352, respectively. After a comprehensive evaluation, the minimal set involving top 6 important variables that can predict central obesity with descent performance was ascertained, including father's body mass index (BMI), mother's BMI, picky for foods, outdoor activity, screen, and sex. Validation using the deep-learning model indicated that prediction performance between variables in the minimal set and in the whole set was comparable.

CONCLUSIONS

We have identified and validated a minimal set of six important factors that can decently predict the risk of central obesity when using the optimal LGBM model relative to the whole set.