Early identification of children at high risk of obesity can provide clinicians with the information needed to provide targeted lifestyle counseling to high-risk children at a critical time to change the disease course.
This study aimed to develop predictive models of childhood obesity, applying advanced machine learning methods to a large unaugmented electronic health record (EHR) dataset. This work improves on other studies that have (i) relied on data not routinely available in EHRs (like prenatal data), (ii) focused on single-age predictions, or (iii) not been rigorously validated.
A customized sequential deep-learning model to predict the development of obesity was built, using EHR data from 36,191 diverse children aged 0–10 years. The model was evaluated using extensive discrimination, calibration, and utility analysis; and was validated temporally, geographically, and across various subgroups.
Our results are mostly better or comparable to similar studies. Specifically, the model achieved an AUROC above 0.8 in all cases (with most cases around 0.9) for predicting obesity within the next 3 years for children 2–7 years of age. Validation results show the model's robustness and top predictors match important risk factors of obesity.
Our model can predict the risk of obesity for young children at multiple time points using only routinely collected EHR data, greatly facilitating its integration into clinical care. Our model can be used as an objective screening tool to provide clinicians with insights into a patient's risk for developing obesity so that early lifestyle counseling can be provided to prevent future obesity in young children.
This review investigates the side effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) like liraglutide, semaglutide, and tirzepatide, medications known for their efficacy in promoting weight loss among individuals with obesity. The rationale is rooted in understanding the balance between their therapeutic benefits and associated risks.
This was a comprehensive clinical review, including systematic reviews, meta-analyses, randomized controlled trials (RCTs), and cohort studies. Data were extracted from databases such as PubMed, Scopus, Embase, MEDLINE, and Google Scholar, focusing on the tolerability, severity, and risks of these medications.
GLP-1RAs demonstrated significant weight loss outcomes. In clinical trials, liraglutide showed a placebo-corrected weight loss of around 5 %, semaglutide 12 %, and tirzepatide 18 %. Common side effects were predominantly gastrointestinal, including nausea, diarrhea, constipation, and vomiting. Rare serious adverse events included gallbladder disorders and acute pancreatitis. In, addition, multiple studies identify new risks associated with GLP-1RAs including increased aspiration risk during anesthesia due to delayed gastric emptying and challenges with bowel preparation for colonoscopies.
While GLP-1RAs are effective in managing obesity, their use is associated with gastrointestinal side effects and rare but serious adverse events. The findings underscore the importance of individualized dosing and thorough patient assessment. Continuous research and vigilant monitoring are essential to optimize their safe use. Further studies are needed to refine guidelines, particularly regarding new concerns such as delayed gastric emptying and its implications for anesthesia.
The uptake of obesity treatments remains disproportionally low in people living with the disease, even with the advent and availability of GLP-1 agonists in recent years. Efforts to understand this discrepancy have centred on literature syntheses and Healthcare Professionals’ (HCPs) perspectives on the barriers to obesity treatment. This study focuses on patient perspectives on the risks of obesity treatment.
This qualitative study consisted of online focus groups with 30 adults with obesity from Europe and North America. The focus group discussions were recorded, transcribed verbatim and analysed thematically.
Patients identified three risks associated with obesity treatment: (a) the risk that they can’t access treatment; (b) the risk that they would fail to meet treatment expectations – their own, their HCPs and societal expectations, and (c) the risk that the treatment would be ‘successful’ but that they would lose their sense of self, their coping mechanisms and identity along with weight.
Understanding patient concerns about the risks of obesity treatment is essential to addressing obesity treatment inertia.

