Dataset Information

Agent-Based Learning Model for the Obesity Paradox in RCC.

ABSTRACT: A recent study on the immunotherapy treatment of renal cell carcinoma reveals better outcomes in obese patients compared to lean subjects. This enigmatic contradiction has been explained, in the context of the debated obesity paradox, as the effect produced by the cell-cell interaction network on the tumor microenvironment during the immune response. To better understand this hypothesis, we provide a computational framework for the in silico study of the tumor behavior. The starting model of the tumor, based on the cell-cell interaction network, has been described as a multiagent system, whose simulation generates the hypothesized effects on the tumor microenvironment. The medical needs in the immunotherapy design meet the capabilities of a multiagent simulator to reproduce the dynamics of the cell-cell interaction network, meaning a reaction to environmental changes introduced through the experimental data.

SUBMITTER: Belenchia M

PROVIDER: S-EPMC8116955 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Agent-Based Learning Model for the Obesity Paradox in RCC.

Belenchia Matteo M Rocchetti Giacomo G Maestri Stefano S Cimadamore Alessia A Montironi Rodolfo R Santoni Matteo M Merelli Emanuela E

Frontiers in bioengineering and biotechnology 20210429

A recent study on the immunotherapy treatment of renal cell carcinoma reveals better outcomes in obese patients compared to lean subjects. This enigmatic contradiction has been explained, in the context of the debated obesity paradox, as the <i>effect</i> produced by the cell-cell interaction network on the tumor microenvironment during the immune response. To better understand this hypothesis, we provide a computational framework for the <i>in silico</i> study of the tumor behavior. The startin ...[more]

PMID: 33996779

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Project description:BackgroundPrior work suggests that obesity may confer a survival advantage among persons with cardiovascular disease (CVD). This obesity "paradox" is frequently studied in the context of prevalent disease, a stage in the disease process when confounding from illness-related weight loss and selective survival are especially problematic. Our objective was to examine the association of obesity with mortality among persons with incident CVD, where biases are potentially reduced, and to compare these findings with those based on prevalent disease.MethodsWe used data from the Health and Retirement Study, an ongoing, nationally representative longitudinal survey of U.S. adults age 50 years and older initiated in 1992 and linked to Medicare claims. Cox proportional hazard models were used to estimate the association between weight status and mortality among persons with specific CVD diagnoses. CVD diagnoses were established by self-reported survey data as well as Medicare claims. Prevalent disease models used concurrent weight status, and incident disease models used pre-diagnosis weight status.ResultsWe examined myocardial infarction, congestive heart failure, stroke, and ischemic heart disease. A strong and significant obesity paradox was consistently observed in prevalent disease models (hazard of death 18-36% lower for obese class I relative to normal weight), replicating prior findings. However, in incident disease models of the same conditions in the same dataset, there was no evidence of this survival benefit. Findings from models using survey- vs. claims-based diagnoses were largely consistent.ConclusionWe observed an obesity paradox in prevalent CVD, replicating prior findings in a population-based sample with longer-term follow-up. In incident CVD, however, we did not find evidence of a survival advantage for obesity. Our findings do not offer support for reevaluating clinical and public health guidelines in pursuit of a potential obesity paradox.

Project description:BackgroundWhile obesity confers an increased risk of death in the general population, numerous studies have reported an association between obesity and improved survival among critically ill patients. This contrary finding has been referred to as the obesity paradox. In this retrospective study, two causal inference approaches were used to address whether the survival of non-obese critically ill patients would have been improved if they had been obese.MethodsThe study cohort comprised 6557 adult critically ill patients hospitalized at the Intensive Care Unit of the Ghent University Hospital between 2015 and 2017. Obesity was defined as a body mass index of ≥ 30 kg/m2. Two causal inference approaches were used to estimate the average effect of obesity in the non-obese (AON): a traditional approach that used regression adjustment for confounding and that assumed missingness completely at random and a robust approach that used machine learning within the targeted maximum likelihood estimation framework along with multiple imputation of missing values under the assumption of missingness at random. 1754 (26.8%) patients were discarded in the traditional approach because of at least one missing value for obesity status or confounders.ResultsObesity was present in 18.9% of patients. The in-hospital mortality was 14.6% in non-obese patients and 13.5% in obese patients. The raw marginal risk difference for in-hospital mortality between obese and non-obese patients was - 1.06% (95% confidence interval (CI) - 3.23 to 1.11%, P = 0.337). The traditional approach resulted in an AON of - 2.48% (95% CI - 4.80 to - 0.15%, P = 0.037), whereas the robust approach yielded an AON of - 0.59% (95% CI - 2.77 to 1.60%, P = 0.599).ConclusionsA causal inference approach that is robust to residual confounding bias due to model misspecification and selection bias due to missing (at random) data mitigates the obesity paradox observed in critically ill patients, whereas a traditional approach results in even more paradoxical findings. The robust approach does not provide evidence that the survival of non-obese critically ill patients would have been improved if they had been obese.

Project description:BackgroundSarcopenic obesity, a clinical and functional condition characterized by the coexistence of obesity and sarcopenia, has not been investigated in relation to dementia risk and its onset.MethodsWe included 208,867 participants from UK biobank, who aged 60 to 69 years at baseline. Dementia diagnoses were identified using hospital records and death register data. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models to evaluate the associations of obesity, sarcopenia, and sarcopenic obesity with dementia risk, stratified by sex. Stratified analyses were performed across dementia-related polygenic risk score (PRS). Restricted mean survival time models were established to estimate the difference and 95%CIs of dementia onset across different status. Additionally, linear regression models were employed to estimate associations of different status with brain imaging parameters. The mediation effects of chronic diseases were also examined.ResultsObese women with high PRS had a decreased risk (HR = 0.855 [0.761-0.961]), but obese men with low PRS had an increased risk (HR = 1.223 [1.045-1.431]). Additionally, sarcopenia was associated with elevated dementia risk (HRwomen = 1.323 [1.064-1.644]; HRmen = 2.144 [1.753-2.621]) in those with low PRS. Among those with high PRS, however, the association was only significant in early-life (HRwomen = 1.679 [1.355-2.081]; HRmen = 2.069 [1.656-2.585]). Of note, sarcopenic obesity was associated with higher dementia risk (HRwomen = 1.424 [1.227-1.653]; HRmen = 1.989 [1.702-2.323]), and results remained similar stratified by PRS. Considering dementia onset, obesity was associated with dementia by 1.114 years delayed in women, however, 0.170 years advanced in men. Sarcopenia (women: 0.080 years; men: 0.192 years) and sarcopenic obesity (women: 0.109 years; men: 0.511 years) respectively advanced dementia onset. Obesity, sarcopenia, and sarcopenic obesity were respectively related to alterations in different brain regions. Association between sarcopenic obesity and dementia was mediated by chronic diseases.ConclusionsSarcopenic obesity and sarcopenia were respectively associated with increased dementia risk and advanced dementia onset to vary degree. The role of obesity in dementia may differ by sex and genetic background.

Dataset Information

Agent-Based Learning Model for the Obesity Paradox in RCC.

Publications

Agent-Based Learning Model for the Obesity Paradox in RCC.

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