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

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.

Obesogenic sleep patterns among Chinese preschool children: A latent profile and transition analysis of the association sleep patterns and obesity risk

OBJECTIVE

This paper utilized a person-centered approach to examine whether sleep patterns on school and free days are associated with obesity risk in preschool children aged 3-6 years.

METHODS

The cross-sectional analysis included 204 children from the Wuhan Healthy Start Project with valid sleep data in at least four consecutive days gathered via Actigraph GT3X+. Based on three domains of sleep duration, sleep onset, and sleep offset, we used latent profile analysis to identify distinct sleep patterns on school and free days separately. Additionally, we conducted latent transition analysis to explore the probabilities of sleep patterns transitions between school and free days. The multivariate logistic regression model investigated the associations of sleep patterns with overweight/obesity (OWO) (BMI ≥ age- and sex-specific 85th percentile) and abdominal obesity (AO) (WC ≥ age- and sex-specific 75th percentile).

RESULTS

Two sleep patterns were identified for school days: "EL-sc" (early-to-sleep/longer-duration) (n = 119; 58.3%) and "LS-sc" (late-to-sleep/shorter-duration) (n = 85; 41.7%). Similarly, "LES-fr" (late-to-sleep/early-to-wake/shorter-duration) (n = 118; 57.8%) and "ELL-fr" (early-to-sleep/late-to-wake/longer-duration) (n = 86; 42.2%) patterns were identified for free days. LTA categorized the participants into four distinct transition groups, i.e., "EL-sc→ELL-fr" (32.9%), "EL-sc→LES-fr" (24.0%), "LS-sc→LES-fr" (33.8%), and "LS-sc→ELL-fr" (9.3%). Compared with the "ELsc→ELL-fr", the "LS-sc→LES-fr" had a higher risk of OWO (AOR 4.76; 95% CI: 1.39-20.33) and AO (AOR, 2.78; 95% CI, 1.21-6.62), respectively. Neither "EL-sc→LES-fr" (AOR, 1.11; 95% CI, 0.14-6.67) nor "LS-sc→ELL-fr" (AOR, 0.74; 95% CI, 0.03-6.14) was significantly associated with OWO. Likewise, no significant association was observed for "EL-sc→LES-fr" (AOR, 0.96; 95% CI, 0.35-2.62) and "LS-sc→ELL-fr" (AOR, 0.56; 95% CI, 0.11-2.18) with AO.

CONCLUSIONS

"LS-sc→LES-fr" pattern is significantly associated with an increased risk of general and abdominal obesity, indicating its obesogenic nature. Furthermore, although not statistically associated with obesity outcomes, "LS-sc→ELL-fr" and "EL-sc→LES-fr" patterns exhibit a semi-obesogenic characteristic. In addition, we identified a concerning trend that preschool children are at risk of transitioning to and persisting in sleep patterns characterized by delayed and shorter sleep. These findings underscore the importance of implementing interventions and strategies to address sleep patterns as a crucial step to minimize the risk of obesity.

Childhood Overweight and Obesity in Vietnam: A Landscape Analysis of the Extent and Risk Factors

Tackling childhood overweight and obesity is critical not only to improve the health and well-being of children and adolescents, but also for entire populations and future generations. This paper provides the latest evidence on the extent of, and risk factors for, childhood overweight and obesity in Vietnam. The landscape analysis tool developed by the United Nations Children’s Fund (UNICEF) and World Health Organization (WHO) was used. A search for peer-reviewed articles in English on online databases was undertaken. Peer-reviewed Vietnamese articles were also retrieved from a range of sources. The prevalence of overweight among children aged under 5 years increased from 5.6% in 2010 to 7.4% in 2019. For overweight and obesity among children aged 5 to 19 years, prevalence rose from 8.5% and 2.5% in 2010 to 19% and 8.1% in 2020, respectively. Maternal malnutrition, gestational diabetes during pregnancy, and inadequate infant and young child feeding practices are all risk factors for early childhood overweight. Unhealthy diets, insufficient physical activity, and lack of sleep are among the risk factors for overweight and obesity among school aged children and adolescents. The prevention of overweight and obesity among Vietnamese children requires a whole-of-government, cross-sectoral approach to addresses the obesogenic environment that is negatively influencing the nutrition of children.

Obesity risk factors in Turkish preschool children: a cross-sectional study

Amaç: Bu çalışma, Türk okul öncesi çocuklarında obezite ile ilgili potansiyel risk faktörlerini -gebelik öncesi ve sonrası, çocuk, ebeveyn ve çevresel faktörler- belirlemeyi amaçlamaktadır. Gereç ve Yöntem: Özel ve devlete bağlı 25 anaokulunda 3-5 yaş grubu 538 çocuk ve anneleri ile yüz yüze görüşme yöntemi kullanılarak kesitsel bir çalışma gerçekleştirilmiştir. Gebelik, bebeklik ve okul öncesi döneme ait demografik, antropometrik, beslenme, uyku ve fiziksel aktivite bilgileri sorgulanmıştır. İki günlük besin tüketim kayıtları ve antropometrik ölçümleri alınmıştır. Bulgular: Okul öncesi çocuklarda hafif şişmanlık ve obezite sıklığı %27 olarak saptanmıştır. Yirmi sekiz potansiyel risk faktöründen gebelik öncesi obezitesi [1,108 (1,042-1,179)], gebelik sonu obezitesi (OR:4,350, CI:2,053-9,217), gebelikte >200mg/gün kafein alımı (OR:1,588, CI:1,031- 2,446), obezitesi olan babaya sahip olma (OR:1,089 CI:1,027-1,155), devlet okulu yerine özel okula gitme (OR:2,093, CI:1,298-3,376), hızlı yeme (OR:3,355, CI:1,175-9,583), kısa öğle yemeği süresi (OR:0,966, CI:0,934-0,998), günlük uyku süresinin 2 saat ekran süresi (OR:1,560, CI:1,012-2,405) okul öncesi çocukluk obezitesi ile ilişkili bulunmuştur. Sonuç: Ebeveyn obezitesi, gebelikte kafein alımı, yeme hızı, günlük uyku ve ekran süresi Türk okul öncesi çocuklarında obezite risk faktörleri olarak belirlendi. Erken çocukluk döneminde anne ve çocuğa ait risk faktörlerinin belirlenmesi, yaşam tarzının ve obezojenik çevrenin düzenlenmesi, obeziteden koruyucu olabilir.

Risk factors for obesity and overweight in Chinese children: a nationwide survey

OBJECTIVE

This study aimed to analyze a comprehensive set of potential risk factors for obesity and overweight among Chinese children with a full range of ages and with wide geographical coverage.

METHODS

In the Prevalence and Risk Factors for Obesity and Diabetes in Youth (PRODY) study (2017-2019), the authors analyzed 193,997 children aged 3 to 18 years from 11 provinces, autonomous regions, and municipalities that are geographically representative of China. All participants underwent physical examinations, and their caregivers completed questionnaires including dietary, lifestyle, familial, and perinatal information of participants. A multilevel multinomial logistic regression model was used to evaluate the potential risk factors.

RESULTS

Among the actionable risk factors that were measured, higher consumption frequencies of animal offal (odds ratios [OR] for an additional time/day = 0.91, 95% CI: 0.88-0.95, same unit for OR below unless specified otherwise), dairy products (0.91, 95% CI: 0.88-0.94), freshwater products (0.94, 95% CI: 0.91-0.96), staple foods (0.94, 95% CI: 0.92-0.96), and coarse food grain (OR for every day vs. rarely = 0.92, 95% CI: 0.86-0.98) were associated with lower relative risk of obesity. However, higher restaurant-eating frequency (OR for >4 times/month vs. rarely = 1.21, 95% CI: 1.15-1.29) and longer screen-viewing duration (OR for >2 hours vs. <30 minutes = 1.16, 95% CI: 1.10-1.22) were associated with higher relative risk of obesity. Increased exercise frequency was associated with the lowest relative risk of obesity (OR for every day vs. rarely = 0.72, 95% CI: 0.68-0.77).

CONCLUSIONS

Changes in lifestyle and diet of Chinese children may help relieve their obesity burden.

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.

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

BACKGROUND

Childhood obesity places a major burden on global public health. We aimed to identify and characterize potential factors, both individually and jointly, in association with overweight and obesity in Chinese preschool-aged children.

METHODS

We cross-sectionally recruited 9501 preschool-aged children from 30 kindergartens in Beijing and Tangshan. Overweight and obesity are defined according to the World Health Organization (WHO), International Obesity Task Force (IOTF), and China criteria.

RESULTS

After multivariable adjustment, eating speed, sleep duration, birthweight, and paternal body mass index (BMI) were consistently and significantly associated with childhood overweight and obesity under three growth criteria at a significance level of 5%. Additional fast food intake frequency, maternal BMI, gestational weight gain (GWG) and maternal pre-pregnancy BMI were significant factors for overweight (WHO criteria) and obesity (both IOTF and China criteria). Importantly, there were significant interactions between parental obesity and eating speed for childhood obesity. Finally, for practical reasons, risk nomogram models were constructed for childhood overweight and obesity based on significant factors under each criterion, with good prediction accuracy.

CONCLUSION

Our findings indicated a synergistic association of lifestyle, fetal and neonatal, and family-related factors with the risk of experiencing overweight and obesity among preschool-aged children.

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.

Adult Body Height and Cardiometabolic Disease Risk: The China National Health Survey in Shaanxi

OBJECTIVES

Based on data from the China National Health Survey, we aimed to examine the association between body height and cardiometabolic disease (CMD) in a large adult population from Shaanxi province, and further to test whether this association was hinged upon other population characteristics.

METHODS

This population-based study was conducted in 2014 in Shaanxi Province, China. Utilizing a multi-stage stratified cluster sampling method, total 5,905 adults with complete data were eligible for analysis, and 1,151 (19.5%) of them had CMD. Of 1,151 CMD patients, 895 (15.1%) had one disorder and 256 (4.4%) had ≥2 disorders.

RESULTS

Using the bi-directional stepwise method and all-subsets regression, five factors-age, body mass index, family histories of CMD, exercise, and height-constituted the optimal model when predicting CMD risk. Restricted cubic spline regression showed a reduced tendency towards CMD with the increase of body height, with per 10 cm increment in body height corresponding to 14% reduced risk. Ordinal Logistic regression supported the contribution of body height on both continuous and categorical scales to CMD risk before and after adjustment, yet this contribution was significantly confounded by exercise and education, especially by exercise, which can explain 65.4% of total impact. For example, short stature was associated with an increased risk of CMD after multivariable adjustment not including exercise and education (odds ratio, 95% confidence interval, P: 1.42, 1.21 to 1.66, <0.001), and tall stature was associated with a reduced risk (0.77, 0.64 to 0.92, 0.003).

CONCLUSIONS

Our findings indicate short stature was a risk factor, yet tall stature was a protective factor for CMD in Chinese. Notably, the prediction of short and tall stature for CMD may be mediate in part by exercise.