Synergistic interaction between bedtime and eating speed in predicting overweight and obesity in Chinese preschool-aged children

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.

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.

Prevalence and factors associated with overweight or obesity among 2- to 6-year-old children in Hunan, China: a cross-sectional study

OBJECTIVE

To compare the prevalence of overweight or obesity (ow/ob) with WHO BMI cut-off points, International Obesity Task Force (IOTF) cut-off points and Chinese BMI criteria and examine its potential factors among preschool children in Hunan Province.

DESIGN

A cross-sectional survey including anthropometric measurements and questionnaires about children's information, caregivers' socio-demographic characteristics and maternal characteristics. χ2 tests and univariate and multivariate binary logistic regression were performed to evaluate the possible factors of ow/ob.

SETTING

Hunan, China, from September to October 2019.

PARTICIPANTS

In total, 7664 children 2 to 6 years of age.

RESULTS

According to Chinese BMI criteria, about 1 in 7-8 children aged 2-6 years had ow/ob in Hunan, China. The overall estimated prevalence of ow/ob among 2- to 6-year-old children was significantly higher when based on the Chinese BMI criteria compared with the WHO BMI cut-off points and IOTF cut-off points. According to Chinese BMI criteria, ow/ob was associated with residing in urban areas, older age, male sex, eating snacking food more frequently, macrosomia delivery, caesarean birth, heavier maternal prepregnancy weight and pre-delivery weight.

CONCLUSION

The prevalence of ow/ob in preschool children in Hunan Province remains high. More ow/ob children could be screened out according to Chinese BMI cut-offs compared with WHO and IOTF BMI criteria. In the future, targeted intervention studies with matched controls will be needed to assess the long-term effects of intervention measures to provide more information for childhood obesity prevention and treatment.

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.

A Questionnaire-Based Assessment of Hunger, Speed of Eating and Food Intake in Children with Obesity

PURPOSE

The aim of this study was to investigate the hypothesis that obesity in Israeli children is associated with chronically increased hunger and to examine for persistent abnormalities of satiation and between-meal satiety in these children.

SUBJECTS/METHODS

The parents of 200 children with obesity and 100 normal-weight children completed a questionnaire together with their child that rated hunger, food intake at main meal, and speed of eating. Time to hunger from the main meal was also recorded. Children with hunger ratings above 4 on a 7-point scale were considered to have persistent hunger. Food intake ratings at the main meal were used as an approximate indicator of satiation and time from main meal to feeling hunger as an approximate indicator of between-meal satiety.

RESULTS

There were marked differences between children with obesity and controls for hunger, food intake at main meal and speed of eating ratings (all p<0.001). The difference to time to hunger reached significance after adjusting for age and sex (p=0.048). 41% of the children with obesity had the highest rating for persistent hunger versus 5% of controls (p<0.001).

CONCLUSION

Persistent hunger, abnormal food intake at the main meal and rapid eating are common in children with obesity and are often of marked degree. These findings could have implications for understanding how pediatric obesity perpetuates itself and even worsens and its resistance to successful treatment over the long term.

Oral Processing, Satiation and Obesity: Overview and Hypotheses

Increasing the speed of eating or decreasing the amount of chewing of a test meal significantly decreases its satiation, increases concomitant caloric intake, and influences entero-endocrine secretion. Speed of eating is a strong risk factor for obesity and longitudinal studies suggest an etiological relationship. Individuals with obesity have an increase in bite size, less chewing per bite, decreased satiation, and greater food intake. Oral processing in terms of bite size and amount of chewing per gram of food is influenced by food texture and textural complexity. Soft foods increase bite size and decrease chewing per gram of food and meal duration compared to hard foods. An ultra-processed diet can lead to greater weight gain than a non-processed diet and a significant increase in eating rate. Many children with obesity are noted by their parents to have persistent hunger on a questionnaire and this is often extreme. Results of attempts to change eating behavior have been mixed in terms of producing long-term changes in eating behavior and body weight. It is hypothesized that there may be a unidirectional relationship between changes in oral processing, satiation and weight gain. However, the presence of persistent hunger can produce a vicious cycle that may exacerbate obesity and make treatment difficult. The increased energy density of foods as found particularly in ultra-processed foods also influences energy intake and obesity.

Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder

Background: Major depressive disorder (MDD) is often associated with suicidal attempt (SA). Therefore, predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population. Method: A cross-sectional survey among 474 patients was analyzed. All subjects met the diagnostic criteria of MDD according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Multi-factor logistic regression analysis was used to explore demographic information and clinical characteristics associated with SA. A nomogram was further used to predict the risk of SA. Bootstrap re-sampling was used to internally validate the final model. Integrated Discrimination Improvement (IDI) and Akaike Information Criteria (AIC) were used to evaluate the capability of discrimination and calibration, respectively. Decision Curve Analysis (DCA) and the Receiver Operating Characteristic (ROC) curve was also used to evaluate the accuracy of the prediction model. Result: Multivariable logistic regression analysis showed that being married (OR = 0.473, 95% CI: 0.240 and 0.930) and a higher level of education (OR = 0.603, 95% CI: 0.464 and 0.784) decreased the risk of the SA. The higher number of episodes of depression (OR = 1.854, 95% CI: 1.040 and 3.303) increased the risk of SA in the model. The C-index of the nomogram was 0.715, with the internal (bootstrap) validation sets was 0.703. The Hosmer-Lemeshow test yielded a P-value of 0.33, suggesting a good fit of the prediction nomogram in the validation set. Conclusion: Our findings indicate that the demographic information and clinical characteristics of SA can be used in a nomogram to predict the risk of SA in Chinese MDD patients.

Interaction effects of significant risk factors on overweight or obesity among 7222 preschool-aged children from Beijing

OBJECTIVES

We aimed to identify potential risk factors, both individually and interactively, associated with overweight and obesity among preschool-aged children, and further to create a risk prediction nomogram model.

RESULTS

After graded multivariable adjustment, maternal body mass index (BMI) (odds ratio, 95% confidence interval, P under China criteria: 1.07, 1.05 to 1.10, <0.001), maternal pre-pregnancy BMI (1.08, 1.05 to 1.10, <0.001), breastfeeding duration (0.86, 0.76 to 0.98, 0.019), and sleep duration (0.95, 0.90 to 1.00, 0.042) were found to be independently and consistently associated with the significant risk of childhood overweight or obesity under three different growth criteria. Further analyses revealed the four significant factors acted in an additive manner, especially for the interaction between maternal obesity, sleep duration, and breastfeeding. Finally, a risk prediction nomogram model was created for childhood overweight or obesity based on significant and conventional attributes under each criterion.

CONCLUSIONS

Our findings provide evidence that the four significant factors are associated with the risk of childhood overweight or obesity in an additive manner.

METHODS

Using a stratified cluster random sampling strategy, 7222 preschool-aged children were analyzed. Childhood overweight and obesity are defined according to the China criteria and two widely-used international growth criteria.

Interaction between delivery mode and maternal age in predicting overweight and obesity in 1,123 Chinese preschool children

Background

Pediatric overweight/obesity has escalated to epidemic proportions worldwide. In this study, we aimed to assess the association of delivery mode and maternal age, both individually and interactively, with the risk of being overweight or obese among Chinese preschool children.

Methods

We cross-sectionally recruited 1,123 preschool children from five kindergartens in Beijing. Data were collected by a pre-validated self-developed questionnaire. Overweight and obesity are defined according to the World Health Organization (WHO), International Obesity Task Force (IOTF), and China criteria, respectively.

Results

Cesarean delivery was significantly associated with pediatric overweight/obesity under the WHO [adjusted odds ratio (aOR), 95% confidence interval (CI): 1.60, 1.12-2.29], IOTF (1.77, 1.23-2.53), and China (1.43, 1.06-1.94) criteria, respectively. Maternal age <28 years reached statistical significance under both WHO (1.69, 1.09-2.61) and IOTF (1.69, 1.09-2.61) criteria in predicting pediatric overweight/obesity. The interaction between cesarean delivery and maternal age <28 years was remarkably significant under the WHO (2.26, 1.10-4.67), IOTF (2.92, 1.43-5.96), and China (2.36, 1.24-4.50) criteria.

Conclusions

Our findings indicate that the interaction between cesarean delivery and maternal age <28 years can remarkably increase the risk of overweight/obesity among Chinese preschool children.