Body Image Concerns in Adolescent and Young Adult Oncology Patients within Six Months of Upfront Diagnosis.

This cross-sectional study described prevalent body image (BI) concerns among adolescents and young adults (AYAs) with neoplasms who received treatment at a quaternary care children's hospital. Thirty-two AYAs, aged 15-39 years, completed questionnaires assessing BI within six months of diagnosis. The most frequently endorsed questionnaire items included the following: desire for increased physical fitness (62.5%), self-consciousness about hair (45.2%), weight dissatisfaction (40.6%), lack of strength (37.5%), wearing loose clothing to hide one's body (37.5%), decreased agility (34.4%), shape dissatisfaction (32.2%), and self-perception of too much body fat (31.3%). Awareness of AYA BI concerns during treatment may generate early intervention targeting this complex issue.

Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data.

Lifestyle interventions have been shown to prevent or delay the onset of diabetes; however, inter-individual variability in responses to such interventions makes lifestyle recommendations challenging. We analyzed the Japan Diabetes Outcome Intervention Trial-1 (J-DOIT1) study data using a previously published mechanistic simulation model of type 2 diabetes onset and progression to understand the causes of inter-individual variability and to optimize dietary intervention strategies at an individual level. J-DOIT1, a large-scale lifestyle intervention study, involved 2607 subjects with a 4.2-year median follow-up period. We selected 112 individuals from the J-DOIT1 study and calibrated the mechanistic model to each participant's body weight and HbA1c time courses. We evaluated the relationship of physiological (e.g., insulin sensitivity) and lifestyle (e.g., dietary intake) parameters with variability in outcome. Finally, we used simulation analyses to predict individually optimized diets for weight reduction. The model predicted individual body weight and HbA1c time courses with a mean (±SD) prediction error of 1.0 kg (±1.2) and 0.14% (±0.18), respectively. Individuals with the most and least improved biomarkers showed no significant differences in model-estimated energy balance. A wide range of weight changes was observed for similar model-estimated caloric changes, indicating that caloric balance alone may not be a good predictor of body weight. The model suggests that a set of optimal diets exists to achieve a defined weight reduction, and this set of diets is unique to each individual. Our diabetes model can simulate changes in body weight and glycemic control as a result of lifestyle interventions. Moreover, this model could help dieticians and physicians to optimize personalized nutritional strategies according to their patients' goals.

Computational Simulation Model to Predict Behavior Changes in Inflammatory Bowel Disease Patients during the COVID-19 Pandemic: Analysis of Two Regional Japanese Populations.

Managing inflammatory bowel disease (IBD) is a major challenge for physicians and patients during the COVID-19 pandemic. To understand the impact of the pandemic on patient behaviors and disruptions in medical care, we used a combination of population-based modeling, system dynamics simulation, and linear optimization. Synthetic IBD populations in Tokyo and Hokkaido were created by localizing an existing US-based synthetic IBD population using data from the Ministry of Health, Labor, and Welfare in Japan. A clinical pathway of IBD-specific disease progression was constructed and calibrated using longitudinal claims data from JMDC Inc for patients with IBD before and during the COVID-19 pandemic. Key points considered for disruptions in patient behavior (demand) and medical care (supply) were diagnosis of new patients, clinic visits for new patients seeking care and diagnosed patients receiving continuous care, number of procedures, and the interval between procedures or biologic prescriptions. COVID-19 had a large initial impact and subsequent smaller impacts on demand and supply despite higher infection rates. Our population model (Behavior Predictor) and patient treatment simulation model (Demand Simulator) represent the dynamics of clinical care demand among patients with IBD in Japan, both in recapitulating historical demand curves and simulating future demand during disruption scenarios, such as pandemic, earthquake, and economic crisis.

A Computational Platform Integrating a Mechanistic Model of Crohn's Disease for Predicting Temporal Progression of Mucosal Damage and Healing.

INTRODUCTION: Physicians are often required to make treatment decisions for patients with Crohn's disease on the basis of limited objective information about the state of the patient's gastrointestinal tissue while aiming to achieve mucosal healing. Tools to predict changes in mucosal health with treatment are needed. We evaluated a computational approach integrating a mechanistic model of Crohn's disease with a responder classifier to predict temporal changes in mucosal health. METHODS: A hybrid mechanistic-statistical platform was developed to predict biomarker and tissue health time courses in patients with Crohn's disease. Eligible patients from the VERSIFY study (n = 69) were classified into archetypical response cohorts using a decision tree based on early treatment data and baseline characteristics. A virtual patient matching algorithm assigned a digital twin to each patient from their corresponding response cohort. The digital twin was used to forecast response to treatment using the mechanistic model. RESULTS: The responder classifier predicted endoscopic remission and mucosal healing for treatment with vedolizumab over 26 weeks, with overall sensitivities of 80% and 75% and overall specificities of 69% and 70%, respectively. Predictions for changes in tissue damage over time in the validation set (n = 31), a measure of the overall performance of the platform, were considered good (at least 70% of data points matched), fair (at least 50%), and poor (less than 50%) for 71%, 23%, and 6% of patients, respectively. CONCLUSION: Hybrid computational tools including mechanistic components represent a promising form of decision support that can predict outcomes and patient progress in Crohn's disease.

A long-term mechanistic computational model of physiological factors driving the onset of type 2 diabetes in an individual.

A computational model of the physiological mechanisms driving an individual's health towards onset of type 2 diabetes (T2D) is described, calibrated and validated using data from the Diabetes Prevention Program (DPP). The objective of this model is to quantify the factors that can be used for prevention of T2D. The model is energy and mass balanced and continuously simulates trajectories of variables including body weight components, fasting plasma glucose, insulin, and glycosylated hemoglobin among others on the time-scale of years. Modeled mechanisms include dynamic representations of intracellular insulin resistance, pancreatic beta-cell insulin production, oxidation of macronutrients, ketogenesis, effects of inflammation and reactive oxygen species, and conversion between stored and activated metabolic species, with body-weight connected to mass and energy balance. The model was calibrated to 331 placebo and 315 lifestyle-intervention DPP subjects, and one year forecasts of all individuals were generated. Predicted population mean errors were less than or of the same magnitude as clinical measurement error; mean forecast errors for weight and HbA1c were ~5%, supporting predictive capabilities of the model. Validation of lifestyle-intervention prediction is demonstrated by synthetically imposing diet and physical activity changes on DPP placebo subjects. Using subject level parameters, comparisons were made between exogenous and endogenous characteristics of subjects who progressed toward T2D (HbA1c > 6.5) over the course of the DPP study to those who did not. The comparison revealed significant differences in diets and pancreatic sensitivity to hyperglycemia but not in propensity to develop insulin resistance. A computational experiment was performed to explore relative contributions of exogenous versus endogenous factors between these groups. Translational uses to applications in public health and personalized healthcare are discussed.