Childhood obesity: how long should we wait to predict weight?

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers

Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child's body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child's current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child's growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child's obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.

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.

Early-life childhood obesity risk prediction: A Danish register-based cohort study exploring the predictive value of infancy weight gain

BACKGROUND

Information on postnatal weight gain is important for predicting later overweight and obesity, but it is unclear whether inclusion of this postnatal predictor improves the predictive performance of a comprehensive model based on prenatal and birth-related predictors.

OBJECTIVES

To compare performance of prediction models based on predictors available at birth, with and without information on infancy weight gain during the first year when predicting childhood obesity risk.

METHODS

A Danish register-based cohort study including 55.041 term children born between January 2004 and July 2011 with birthweight >2500 g registered in The Children's Database was used to compare model discrimination, reclassification, sensitivity and specificity of two models predicting risk of childhood obesity at school age. Each model consisted of eight predictors available at birth, one additionally including information on weight gain during the first 12 months of life.

RESULTS

The area under the receiving operating characteristic curve increased from 0.785 (95% confidence interval (CI) [0.773-0.798]) to 0.812 (95% CI [0.801-0.824]) after adding weight gain information when predicting childhood obesity. Adding this information correctly classified 30% more children without obesity and 21% with obesity and improved sensitivity from 0.42 to 0.48. Specificity remained unchanged at 0.91.

CONCLUSION

Adding infancy weight gain information improves discrimination, reclassification and sensitivity of a comprehensive prediction model based on predictors available at birth.

Is overweight/obesity a risk factor for atopic allergic disease in prepubertal children? A case-control study

OBJECTIVES

It is unclear whether body weight status (underweight/normal weight/overweight/obese) is associated with allergic disease. Our objective was to investigate the relationship between body weight status (body mass index; BMI) and atopic allergic disease in prepubertal children, and to compare children with atopic allergic diseases with non atopic healthy children.

METHODS

A prospective cross sectional study of 707 prepubertal children aged 3-10 years was performed; the participants were 278 atopic children with physician-diagnosed allergic disease (allergic rhinitis and asthma) (serum total IgE level >100 kU/l and eosinophilia >4%, or positivity to at least one allergen in skin test) and 429 non atopic healthy age- and sex-matched controls. Data were collected between December 2019 and November 2020 at the Pediatric General and Pediatric Allergy Outpatient Clinics of Bezmialem Vakıf University Hospital.

RESULTS

Underweight was observed in 11.6% of all participants (10.8% of atopic children, 12.2% of healthy controls), and obesity in 14.9% of all participants (18.0% of atopic children, 12.8% of controls). Obese (OR 1.71; 95% CI: 1.08-2.71, p=0.021), and overweight status (OR 1.62; 95% CI: 1.06-2.50, p=0.026) were associated with an increased risk of atopic allergic disease compared to normal weight in pre-pubertal children. This association did not differ by gender. There was no relationship between underweight status and atopic allergic disease (OR 1.03; 95% CI: 0.63-1.68, p=0.894).

CONCLUSIONS

Overweight and obesity were associated with an increased risk of atopic allergic disease compared to normal weight among middle-income and high-income pre pubertal children living in Istanbul.

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.

Acceptability of early childhood obesity prediction models to New Zealand families

OBJECTIVE

While prediction models can estimate an infant's risk of developing obesity at a later point in early childhood, caregiver receptiveness to such information is largely unknown. We aimed to assess the acceptability of these models to New Zealand caregivers.

METHODS

An anonymous questionnaire was distributed online. The questionnaire consisted of multiple choice and Likert scale questions. Respondents were parents, caregivers, and grandparents of children aged ≤5 years.

RESULTS

1,934 questionnaires were analysed. Responses were received from caregivers of various ethnicities and levels of education. Nearly two-thirds (62.1%) of respondents would "definitely" or "probably" want to hear if their infant was at risk of early childhood obesity, although "worried" (77.0%) and "upset" (53.0%) were the most frequently anticipated responses to such information. With lower mean scores reflecting higher levels of acceptance, grandparents (mean score = 1.67) were more receptive than parents (2.10; p = 0.0002) and other caregivers (2.13; p = 0.021); males (1.83) were more receptive than females (2.11; p = 0.005); and Asian respondents (1.68) were more receptive than those of European (2.05; p = 0.003), Māori (2.11; p = 0.002), or Pacific (2.03; p = 0.042) ethnicities. There were no differences in acceptance according to socioeconomic status, levels of education, or other ethnicities.

CONCLUSIONS

Almost two-thirds of respondents were receptive to communication regarding their infant's risk of childhood obesity. While our results must be interpreted with some caution due to their hypothetical nature, findings suggest that if delivered in a sensitive manner to minimise caregiver distress, early childhood obesity risk prediction could be a useful tool to inform interventions to reduce childhood obesity in New Zealand.

Prediction Models for Early Childhood Obesity: Applicability and Existing Issues

Statistical models have been developed for the prediction or diagnosis of a wide range of outcomes. However, to our knowledge, only 7 published studies have reported models to specifically predict overweight and/or obesity in early childhood. These models were developed using known risk factors and vary greatly in terms of their discrimination and predictive capacities. There are currently no established guidelines on what constitutes an acceptable level of risk (i.e., risk threshold) for childhood obesity prediction models, but these should be set following consideration of the consequences of false-positive and false-negative predictions, as well as any relevant clinical guidelines. To date, no studies have examined the impact of using early childhood obesity prediction models as intervention tools. While these are potentially valuable to inform targeted interventions, the heterogeneity of the existing models and the lack of consensus on adequate thresholds limit their usefulness in practice.

Association between early antibiotic exposure and risk of childhood weight gain and obesity: a systematic review and meta-analysis

Background Recent studies have shown that antibiotic exposure during infancy is associated with increased body mass in healthy children. This study was performed to investigate the association between early-life antibiotic exposure and risk of childhood obesity. Methods A systematic review and meta-analysis was performed to comprehensively and quantitatively determine the association between early antibiotic exposure and risk of childhood obesity. Various databases such as PubMed, Embase, Scopus, Web of Science, ProQuest, Cochrane and Google Scholar were searched. A random-effects meta-analysis was performed to pool the statistical estimates. Additionally, a subgroup analysis was performed based on the time of follow-up. Results Nineteen studies involving at least 671,681 participants were finally included. Antibiotic exposure in early life was significantly associated with risk of childhood weight gain and obesity (odds ratio [OR]: 1.05, 95% confidence interval [CI]: 1.04-1.06). Conclusions Antibiotic exposure in early life significantly increases the risk of childhood weight gain and obesity.