AIMC Topic: Pediatric Obesity
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Transcriptome combined with Mendelian randomization to identify key genes related to polyamine metabolism in childhood obesity and elucidate their molecular regulatory mechanisms.
Scientific reports
May 22, 2025
Currently, research has found a close correlation between childhood obesity (CO) and elevated levels of polyamines in the bloodstream. Thus, the identification of key genes associated with polyamines metabolism in CO could offer fresh insights for cl...
Meta-analysis of shotgun sequencing of gut microbiota in obese children with MASLD or MASH.
Gut microbes
May 21, 2025
Alterations in the gut microbiome affect the development and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) or metabolic dysfunction-associated steatohepatitis (MASH). We analyzed microbiomes of obese children with and w...
The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.
Medicina (Kaunas, Lithuania)
Feb 19, 2025
Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necess...
Factors associated with underweight, overweight, and obesity in Chinese children aged 3-14 years using ensemble learning algorithms.
Journal of global health
Feb 7, 2025
BACKGROUND: Factors underlying the development of childhood underweight, overweight, and obesity are not fully understood. Traditional models have drawbacks in handling large-scale, high-dimensional, and nonlinear data. In this study, we aimed to ide...
Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.
Frontiers in public health
Jan 7, 2025
BACKGROUND: Mexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analy...
Maternal dietary practices during pregnancy and obesity of neonates: a machine learning approach towards hierarchical and nested relationships in a Tibet Plateau cohort study.
The British journal of nutrition
Sep 26, 2024
Studies on obesity and risk factors from a life-course perspective among residents in the Tibet Plateau with recent economic growth and increasing obesity are important and urgently needed. The birth cohort in this area provides a unique opportunity ...
Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study.
Artificial intelligence in medicine
Aug 20, 2024
Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical s...
Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study.
Endocrine
Jun 10, 2024
OBJECTIVE: This study aimed to develop and evaluate machine-learning models for predicting the onset of overweight in adolescents aged 14‒17, utilizing easily collectible personal information.
Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.
Scientific reports
May 31, 2024
Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wear...
Prediction of adolescent weight status by machine learning: a population-based study.
BMC public health
May 20, 2024
BACKGROUND: Adolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for effective prevention. However, few temporal prediction tools for adolescent four weight status have be...
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