Transforming Big Data into AI-ready data for nutrition and obesity research.

OBJECTIVE: Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions. METHODS: We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed. RESULTS: Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented. CONCLUSIONS: Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

Exercise-induced Changes in Central Adiposity During an RCT: Effect of Exercise Dose and Associations With Compensation.

CONTEXT: Exercise can decrease central adiposity, but the effect of exercise dose and the relationship between central adiposity and exercise-induced compensation is unclear. OBJECTIVE: Test the effect of exercise dose on central adiposity change and the association between central adiposity and exercise-induced weight compensation. METHODS: In this ancillary analysis of a 6-month randomized controlled trial, 170 participants with overweight or obesity (mean ± SD body mass index: 31.5 ± 4.7 kg/m2) were randomized to a control group or exercise groups that reflected exercise recommendations for health (8 kcal/kg/week [KKW]) or weight loss and weight maintenance (20 KKW). Waist circumference was measured, and dual-energy X-ray absorptiometry assessed central adiposity. Predicted weight change was estimated and weight compensation (weight change - predicted weight change) was calculated. RESULTS: Between-group change in waist circumference (control: .0 cm [95% CI, -1.0 to 1.0], 8 KKW: -.7 cm [95% CI, -1.7 to .4], 20 KKW: -1.3 cm [95% CI, -2.4 to -.2]) and visceral adipose tissue (VAT; control: -.02 kg [95% CI, -.07 to .04], 8 KKW: -.01 kg [95% CI, -.07 to .04], 20 KKW: -.04 kg [95% CI, -.10 to .02]) was similar (P ≥ .23). Most exercisers (82.6%) compensated (weight loss less than expected). Exercisers who compensated exhibited a 2.5-cm (95% CI, .8 to 4.2) and .23-kg (95% CI, .14 to .31) increase in waist circumference and VAT, respectively, vs those who did not (P < .01). Desire to eat predicted VAT change during exercise (ß = .21; P = .03). CONCLUSION: In the presence of significant weight compensation, exercise at doses recommended for health and weight loss and weight maintenance leads to negligible changes in central adiposity.

The impact of body composition on the degree of misreporting of food diaries.

BACKGROUND/OBJECTIVES: Accurate assessments of energy intake (EI) are needed in lifestyle interventions to guarantee a negative energy balance (EB), thereby losing weight. This study aimed (1) to compare objectively measured and self-reported EI and (2) to determine the predictors of underreporting divided by sex, adiposity and BMI category. METHODS: Seventy-three participants [mean (SD): 43.7 (9.2) years, BMI = 31.5 (4.5) kg/m2, 37% females] of the Champ4Life intervention were included in this study. EI was measured using the "intake-balance method" and self-reported through 3-day food records. Fat mass (FM) and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry. Bland-Altman analysis was performed to compare both EI assessments. RESULTS: Self-reported EI was lower than measured EI during both neutral (-355 kcal/d) and negative EB (-570 kal/day). While no significant trends were observed for EI evaluation in either neutral (p = 0.315) or negative EB (p = 0.611), limits of agreement were wide (-1720 to 1010 and -1920 to 779 kcal/day, respectively). In females, the degree of misreporting (kcal/day and %) was predicted by weight (p = 0.032 and p = 0.039, respectively) and FM (p = 0.029 and p = 0.037, respectively). In males, only BMI (p = 0.036) was a predictor of misreporting (kcal/day). CONCLUSION: Self-reported EI did not agree with measured EI. Our results show that larger body size was associated with higher levels of underestimation for EI (females only). Nevertheless, misreporting EI is a complex issue involving more associations than merely body composition. A deeper understanding could inform counseling for participants filling out food records in other to reduce misreporting and improve validity.

Raising the U.S. Army Height-Weight (Body Mass Index) Standards: Quantifying Metabolic Risk.

BACKGROUND & OBJECTIVES: The U.S. Army fell 25% short of its recruitment goal in 2022 and therefore, increasing the eligibility pool for potential recruits is of interest. Raising the body mass index (BMI) standards for eligibility presents a path to increase the recruitable population; however, there may be additional costs incurred due to attendant health risks that may be present in individuals with higher BMI. METHODS: We filtered the 2017-2020 National Health and Nutrition Examination Survey by age (17-25 years) and BMI (up to 30 kg/m2). A k-means cluster analysis was performed on the filtered dataset for the variables used to determine metabolic syndrome. Metabolic syndrome Clusters were characterized through summary statistics and compared over clinical measurements and questionnaire responses. RESULTS: Five distinct clusters were identified and mean BMI in two clusters (Clusters1 and 3) exceeded the current U.S. Army BMI thresholds. Of these two clusters, Cluster 1 members had metabolic syndrome. Cluster 3 members were at higher risk for metabolic syndrome compared to members of Clusters 2, 4, and 5. Mean waist circumference was slightly lower in Cluster 3 compared to Cluster 1. None of the clusters had significant differences in depression scores, poverty index, or frequency of dental visits. CONCLUSIONS: Potential recruits from Cluster 1 have excessive health risk and may incur substantial cost to the U.S. Army if enlisted. However, potential recruits from Cluster 3 appear to add little risk and offer an opportunity to increase the pool for recruiting.

Post-zygotic rescue of meiotic errors causes brain mosaicism and focal epilepsy.

Somatic mosaicism is a known cause of neurological disorders, including developmental brain malformations and epilepsy. Brain mosaicism is traditionally attributed to post-zygotic genetic alterations arising in fetal development. Here we describe post-zygotic rescue of meiotic errors as an alternate origin of brain mosaicism in patients with focal epilepsy who have mosaic chromosome 1q copy number gains. Genomic analysis showed evidence of an extra parentally derived chromosome 1q allele in the resected brain tissue from five of six patients. This copy number gain is observed only in patient brain tissue, but not in blood or buccal cells, and is strongly enriched in astrocytes. Astrocytes carrying chromosome 1q gains exhibit distinct gene expression signatures and hyaline inclusions, supporting a novel genetic association for astrocytic inclusions in epilepsy. Further, these data demonstrate an alternate mechanism of brain chromosomal mosaicism, with parentally derived copy number gain isolated to brain, reflecting rescue in other tissues during development.

Genetically modified IL2 bone-marrow-derived myeloid cells reprogram the glioma immunosuppressive tumor microenvironment.

Gliomas are one of the leading causes of cancer-related death in the adolescent and young adult (AYA) population. Two-thirds of AYA glioma patients are affected by low-grade gliomas (LGGs), but there are no specific treatments. Malignant progression is supported by the immunosuppressive stromal component of the tumor microenvironment (TME) exacerbated by M2 macrophages and a paucity of cytotoxic T cells. A single intravenous dose of engineered bone-marrow-derived myeloid cells that release interleukin-2 (GEMys-IL2) was used to treat mice with LGGs. Our results demonstrate that GEMys-IL2 crossed the blood-brain barrier, infiltrated the TME, and reprogrammed the immune cell composition and transcriptome. Moreover, GEMys-IL2 extended survival in an LGG immunocompetent mouse model. Here, we report the efficacy of an in vivo approach that demonstrates the potential for a cell-mediated innate immunotherapy designed to enhance the recruitment of activated effector T and natural killer cells within the glioma TME.

Body shape and performance on the US Army Combat Fitness Test: Insights from a 3D body image scanner.

OBJECTIVE: To identify relationships between body shape, body composition, sex and performance on the new US Army Combat Fitness Test (ACFT). METHODS: Two hundred and thirty-nine United States Military Academy cadets took the ACFT between February and April of 2021. The cadets were imaged with a Styku 3D scanner that measured circumferences at 20 locations on the body. A correlation analysis was conducted between body site measurements and ACFT event performance and evaluated using Pearson correlation coefficients and p-values. A k-means cluster analysis was performed over the circumference data and ACFT performance were evaluated between clusters using t-tests with a Holm-Bonferroni correction. RESULTS: The cluster analysis resulted in 5 groups: 1. "V" shaped males, 2. larger males, 3. inverted "V" shaped males and females, 4. "V" shaped smaller males and females, and 5. smallest males and females. ACFT performance was the highest in Clusters 1 and 2 on all events except the 2-mile run. Clusters 3 and 4 had no statistically significant differences in performance but both clusters performed better than Cluster 5. CONCLUSIONS: The association between ACFT performance and body shape is more detailed and informative than considering performance solely by sex (males and females). These associations may provide novel ways to design training programs from baseline shape measurements.

Translating digital anthropometry measurements obtained from different 3D body image scanners.

BACKGROUND: Body image scanners are used in industry and research to reliably provide a wealth of anthropometric measurements within seconds. The demonstrated utility of the scanners drives the current proliferation of more commercially available devices that rely on their own reference body sites and proprietary algorithms to output anthropometric measurements. Since each scanner relies on its own algorithms, measurements obtained from different scanners cannot directly be combined or compared. OBJECTIVES: To develop mathematical models that translate anthropometric measurements between the three popular commercially available scanners. METHODS: A unique database that contained 3D scanner measurements in the same individuals from three different scanners (Styku, Human Solutions, and Fit3D) was used to develop linear regression models that translate anthropometric measurements between each scanner. A limits of agreement analysis was performed between Fit3D and Styku against Human Solutions measurements and the coefficient of determination, bias, and 95% confidence interval were calculated. The models were then applied to normalized scanner data from four different studies to compare the results of a k-means cluster analysis between studies. A scree plot was used to determine the optimal number of clusters derived from each study. RESULTS: Correlations ranged between R2 = 0.63 (Styku and Human Solutions mid-thigh circumference) to R2 = 0.97 (Human Solutions and Fit3D neck circumference). In general, Fit3D had better agreement with Human Solutions compared to Styku. The widest disagreement was found in chest circumference (Fit3D (bias = 2.30, 95% CI = [-3.83, 8.43]) and Styku (bias = -5.60, 95% CI = [-10.98, -0.22]). The optimal number of body shape clusters in each of the four studies was consistently 5. CONCLUSIONS: The newly developed models that translate measurements between the scanners Styku and Fit3D to predict Human Solutions measurements make it possible to standardize data between scanners allowing for data pooling and comparison.

2021 updates to the World Health Organization classification of adult-type and pediatric-type diffuse gliomas: a clinical practice review.

In 2021, the World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) underwent significant restructuring to incorporate additional molecular diagnostics, several newly recognized tumor types, and new grading schemes for existing tumor types. The 2021 CNS WHO classification further elaborates and integrates histopathologic and molecular diagnostic criteria to improve diagnostic classification. Furthermore, it is the hope that identification of molecular alterations in pediatric and adult tumors facilitates improved prognostic information and development of novel targeted therapies for adults and children with CNS tumors. In one of the largest changes in the new WHO classification, diffuse gliomas are divided into pediatric-type and adult-type gliomas to highlight our expanding knowledge of their different molecular drivers and prognostic associations. Several new pediatric-type diffuse low-grade gliomas are defined including (I) diffuse astrocytoma, MYB- or MYBL1-altered, (II) polymorphous low-grade neuroepithelial tumor of the young (PLNTY), and (III) diffuse low-grade glioma, MAPK-pathway altered. In addition, several new pediatric-type diffuse high-grade gliomas are recognized including (I) diffuse hemispheric glioma, H3 G34R-mutant (II) diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype, and (III) infant-type hemispheric glioma. These new tumor types have associated clinical, genetic and epigenetic features that are distinct from adult-type diffuse gliomas. This review provides an overview of updates in the 2021 CNS WHO classification specific to diffuse gliomas, with a particular focus on the histopathology and molecular findings of the newly described pediatric-type low-grade and high-grade gliomas.

Machine learning modeling practices to support the principles of AI and ethics in nutrition research.

BACKGROUND: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias. METHODS: Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages. RESULTS: Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research. CONCLUSION: The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.

Social influences on physical activity for establishing criteria leading to exercise persistence.

Despite well-documented health benefits from exercise, a study on national trends in achieving the recommended minutes of physical activity guidelines has not improved since the guidelines were published in 2008. Peer interactions have been identified as a critical factor for increasing a population's physical activity. The objective of this study is for establishing criteria for social influences on physical activity for establishing criteria that lead to exercise persistence. A system of differential equations was developed that projects exercise trends over time. The system includes both social and non-social influences that impact changes in physical activity habits and establishes quantitative conditions that delineate population-wide persistence habits from domination of sedentary behavior. The model was generally designed with parameter values that can be estimated to data. Complete absence of social or peer influences resulted in long-term dominance of sedentary behavior and a decline of physically active populations. Social interactions between sedentary and moderately active populations were the most important social parameter that influenced low active populations to become and remain physically active. On the other hand, social interactions encouraging moderately active individuals to become sedentary drove exercise persistence to extinction. Communities should focus on increasing social interactions between sedentary and moderately active individuals to draw sedentary populations to become more active. Additionally, reducing opportunities for moderately active individuals to engage with sedentary individuals through sedentary social activities should be addressed.

Effect of a Personalized Diet to Reduce Postprandial Glycemic Response vs a Low-fat Diet on Weight Loss in Adults With Abnormal Glucose Metabolism and Obesity: A Randomized Clinical Trial.

Importance: Interindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss. Objective: To compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity. Design, Setting, and Participants: The Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A1c level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes. Interventions: Participants were randomized to either a low-fat diet (<25% of energy intake; standardized group) or a personalized diet that estimates PPGR to foods using a machine learning algorithm (personalized group). Participants in both groups received a total of 14 behavioral counseling sessions and self-monitored dietary intake. In addition, the participants in the personalized group received color-coded meal scores on estimated PPGR delivered via a mobile app. Main Outcomes and Measures: The primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling. Results: Of a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was -4.31% (95% CI, -5.37% to -3.24%) for the standardized group and -3.26% (95% CI, -4.25% to -2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05% [95% CI, -0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05). Conclusions and Relevance: A personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure. Trial Registration: ClinicalTrials.gov Identifier: NCT03336411.

Predicting energy intake in adults who are dieting and exercising.

BACKGROUND: When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question. OBJECTIVE: The objective was to evaluate the performance of an energy balance differential equation model to predict EI in an intervention that includes both calorie restriction and increases in PAEE. DESIGN: The Antonetti energy balance model that predicts body weight trajectories during weight loss was solved and inverted to estimate EI during weight loss. Using data from a calorie restriction study that included interventions with and without prescribed PAEE, we tested the validity of the Antonetti weight predictions against measured weight and the Antonetti EI model against measured EI using the intake-balance method at 168 days. We then evaluated the predicted EI from the model against measured EI in a study that prescribed both calorie restriction and increased PAEE. RESULTS: Compared with measured body weight at 168 days, the mean (±SD) model error was 1.30 ± 3.58 kg. Compared with measured EI at 168 days, the mean EI (±SD) model error in the intervention that prescribed calorie restriction and did not prescribe increased PAEE, was -84.9 ± 227.4 kcal/d. In the intervention that prescribed calorie restriction combined with increased PAEE, the mean (±SD) EI model error was -155.70 ± 205.70 kcal/d. CONCLUSION: The validity of the newly developed EI model was supported by experimental observations and can be used to determine EI during weight loss.

Perioperative cardiovascular outcome in patients with coronary artery disease undergoing major vascular surgery: A retrospective cohort study.

Background: Major adverse cardiac events (MACE) are a major contributor to morbidity and mortality in patients undergoing major vascular surgeries. We aim to assess the incidence, risk factors, and outcome of MACE in patients with coronary artery disease (CAD) undergoing aortic surgeries. Methods: In this retrospective observational study, we included patients with CAD who underwent elective major vascular surgery, namely, thoracoabdominal aortic aneurysm repairs and vascular bypass surgeries for aorto-occlusive disease, in our institute from January 2010 to December 2019. The association of preoperative risk factors including revised cardiac risk index factors, functional status of patients, severity of CAD, and its treatment status and technique of anesthesia with occurrence of MACE was analyzed. Results: Medical records of 141 patients were studied. The incidence of perioperative MACE was 11.3% (16/141) and overall in-hospital mortality was 6.4% (9/141), all of them related to MACE; implicating a 56.2% mortality in patients who develop MACE. The odds of a patient who had undergone preoperative coronary revascularization to develop a MACE was higher than a nonrevascularized patient (odds ratio: 3.9; 95% confidence interval [CI], 1.34-11.34). There was found to be no benefit in the addition of epidural analgesia to general anesthesia in reducing perioperative MACE. Conclusions: Major vascular surgeries in patients with CAD are a highly morbid procedure and a perioperative MACE places them at a significantly high risk of mortality. Early detection of CAD and preoperative medical optimization can play a major role in reducing the risk of MACE.

Detection of brain somatic variation in epilepsy-associated developmental lesions.

OBJECTIVE: Epilepsy-associated developmental lesions, including malformations of cortical development and low-grade developmental tumors, represent a major cause of drug-resistant seizures requiring surgical intervention in children. Brain-restricted somatic mosaicism has been implicated in the genetic etiology of these lesions; however, many contributory genes remain unidentified. METHODS: We enrolled 50 children who were undergoing epilepsy surgery into a translational research study. Resected tissue was divided for clinical neuropathologic evaluation and genomic analysis. We performed exome and RNA sequencing to identify somatic variation and we confirmed our findings using high-depth targeted DNA sequencing. RESULTS: We uncovered candidate disease-causing somatic variation affecting 28 patients (56%), as well as candidate germline variants affecting 4 patients (8%). In agreement with previous studies, we identified somatic variation affecting solute carrier family 35 member A2 (SLC35A2) and mechanistic target of rapamycin kinase (MTOR) pathway genes in patients with focal cortical dysplasia. Somatic gains of chromosome 1q were detected in 30% (3 of 10) of patients with Type I focal cortical dysplasia (FCD)s. Somatic variation in mitogen-activated protein kinase (MAPK) pathway genes (i.e., fibroblast growth factor receptor 1 [FGFR1], FGFR2, B-raf proto-oncogene, serine/threonine kinase [BRAF], and KRAS proto-oncogene, GTPase [KRAS]) was associated with low-grade epilepsy-associated developmental tumors. RNA sequencing enabled the detection of somatic structural variation that would have otherwise been missed, and which accounted for more than one-half of epilepsy-associated tumor diagnoses. Sampling across multiple anatomic regions revealed that somatic variant allele fractions vary widely within epileptogenic tissue. Finally, we identified putative disease-causing variants in genes not yet associated with focal cortical dysplasia. SIGNIFICANCE: These results further elucidate the genetic basis of structural brain abnormalities leading to focal epilepsy in children and point to new candidate disease genes.

Technical report: an online international weight control registry to inform precision approaches to healthy weight management.

BACKGROUND: Personalizing approaches to prevention and treatment of obesity will be a crucial aspect of precision health initiatives. However, in considering individual susceptibility to obesity, much remains to be learned about how to support healthy weight management in different population subgroups, environments and geographical locations. SUBJECTS/METHODS: The International Weight Control Registry (IWCR) has been launched to facilitate a deeper and broader understanding of the spectrum of factors contributing to success and challenges in weight loss and weight loss maintenance in individuals and across population groups. The IWCR registry aims to recruit, enroll and follow a diverse cohort of adults with varying rates of success in weight management. Data collection methods include questionnaires of demographic variables, weight history, and behavioral, cultural, economic, psychological, and environmental domains. A subset of participants will provide objective measures of physical activity, weight, and body composition along with detailed reports of dietary intake. Lastly, participants will be able to provide qualitative information in an unstructured format on additional topics they feel are relevant, and environmental data will be obtained from public sources based on participant zip code. CONCLUSIONS: The IWCR will be a resource for researchers to inform improvements in interventions for weight loss and weight loss maintenance in different countries, and to examine environmental and policy-level factors that affect weight management in different population groups. This large scale, multi-level approach aims to inform efforts to reduce the prevalence of obesity worldwide and its associated comorbidities and economic impacts. TRIAL REGISTRATION: NCT04907396 (clinicaltrials.gov) sponsor SB Roberts; Tufts University IRB #13075.

The potential epidemiologic, clinical, and economic impact of requiring schools to offer Physical Education (PE) classes in Mexico City.

BACKGROUND: Many schools have been cutting physical education (PE) classes due to budget constraints, which raises the question of whether policymakers should require schools to offer PE classes. Evidence suggests that PE classes can help address rising physical inactivity and obesity prevalence. However, it would be helpful to determine if requiring PE is cost-effective. METHODS: We developed an agent-based model of youth in Mexico City and the impact of all schools offering PE classes on changes in weight, weight-associated health conditions and the corresponding direct and indirect costs over their lifetime. RESULTS: If schools offer PE without meeting guidelines and instead followed currently observed class length and time active during class, overweight and obesity prevalence decreased by 1.3% (95% CI: 1.0%-1.6%) and was cost-effective from the third-party payer and societal perspectives ($5,058 per disability-adjusted life year [DALY] averted and $5,786/DALY averted, respectively, assuming PE cost $50.3 million). When all schools offered PE classes meeting international guidelines for PE classes, overweight and obesity prevalence decreased by 3.9% (95% CI: 3.7%-4.3%) in the cohort at the end of five years compared to no PE. Long-term, this averted 3,183 and 1,081 obesity-related health conditions and deaths, respectively and averted ≥$31.5 million in direct medical costs and ≥$39.7 million in societal costs, assuming PE classes cost ≤$50.3 million over the five-year period. PE classes could cost up to $185.5 million and $89.9 million over the course of five years and still remain cost-effective and cost saving respectively, from the societal perspective. CONCLUSION: Requiring PE in all schools could be cost-effective when PE class costs, on average, up to $10,340 per school annually. Further, the amount of time students are active during class is a driver of PE classes' value (e.g., it is cost saving when PE classes meet international guidelines) suggesting the need for specific recommendations.