Agent-Based Learning Model for the Obesity Paradox in RCC.
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ABSTRACT:
SUBMITTER: Belenchia M
PROVIDER: S-EPMC8116955 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Must Investing more resources to protect every node in a network improve the robustness of the whole network subject to target attacks? To answer this question, we investigate the cascading dynamics in some typical networks. In real networks, the load on a node is generally correlated with the betweenness. Considering the weight of a node, we give a new method to define the initial load on a node by the revised betweenness. Then we present a simple cascading model. We investigate the cascading dynamics by disabling a single key node with the highest load. We find that in BA scale-free networks, the bigger the capacity of every node, the stronger the robustness of the whole network. However, in WS networks and some random networks, when we increase the capacity of every node, instead, the robustness of the whole network is weaker. In US power grid and the China power grid, we also observe this counterintuitive phenomenon. We give a reasonable explanation by a simple illusion. By the analysis, we think that resurrections of some nodes in a ring network structure after removing a node may be the reason of this phenomenon.
| S-EPMC4564763 | biostudies-other
Project description:Prior 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.We 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.We 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.We 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.
| S-EPMC5720539 | biostudies-literature
Project description:BACKGROUND:While 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. METHODS:The 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. RESULTS:Obesity 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). CONCLUSIONS:A 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.
| S-EPMC7405433 | biostudies-literature
Project description:We propose a method for learning multi-agent policies to compete against multiple opponents. The method consists of recurrent neural network-based actor-critic networks and deterministic policy gradients that promote cooperation between agents by communication. The learning process does not require access to opponents' parameters or observations because the agents are trained separately from the opponents. The actor networks enable the agents to communicate using forward and backward paths while the critic network helps to train the actors by delivering them gradient signals based on their contribution to the global reward. Moreover, to address nonstationarity due to the evolving of other agents, we propose approximate model learning using auxiliary prediction networks for modeling the state transitions, reward function, and opponent behavior. In the test phase, we use competitive multi-agent environments to demonstrate by comparison the usefulness and superiority of the proposed method in terms of learning efficiency and goal achievements. The comparison results show that the proposed method outperforms the alternatives.
| S-EPMC6739057 | biostudies-literature
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.
| S-EPMC10960494 | biostudies-literature
Project description: Not available
| S-EPMC10334383 | biostudies-literature
Project description:ObjectivesThis study sought to determine whether pre-heart failure (HF) myocardial injury explains the differential mortality after HF across weight categories.BackgroundObesity is a risk factor for HF, but pre-HF obesity is associated with lower mortality after incident HF. High-sensitivity cardiac troponin T (hs-cTnT) is a sensitive marker of myocardial injury, and predicts incident HF and mortality.MethodsStratifying 1,279 individuals with incident HF hospitalizations by their pre-HF hs-cTnT levels (< and ≥ 14 ng/l), we examined the association of pre-HF body mass index (BMI) with mortality after incident HF hospitalization in the ARIC (Atherosclerosis Risk In Communities) study.ResultsMean age at HF was 74 years (53% women, 27% black). Individuals with pre-HF hs-cTnT ≥14 ng/l had higher mortality after incident HF (hazard ratio [HR]: 1.46; 95% confidence interval [CI]: 1.18 to 1.80) compared to individuals with hs-cTnT <14 ng/l in an adjusted model including BMI. Compared with normal weight subjects, the mortality was lower in overweight (HR: 0.69, 95% CI 0.48-0.98) and obese individuals (HR: 0.50; 95% CI: 0.35 to 0.72) with hs-cTnT <14 ng/l; and in those with hs-cTnT ≥14 ng/l (overweight HR: 0.50; 95% CI: 0.30 to 0.83; obese HR: 0.56; 95% CI: 0.34 to 0.91; interaction: p = 0.154 between BMI and hs-cTnT). The lower mortality risk in obese and overweight subjects remained similar when log hs-cTnT was added as a continuous variable to a multivariable model, and in sensitivity analyses after further adjusting for left ventricular hypertrophy or high-sensitivity C-reactive protein.ConclusionAlthough greater pre-existing subclinical myocardial injury was associated with higher mortality after incident HF hospitalization, it did not explain the obesity paradox in HF, which was observed irrespective of subclinical myocardial injury. (Atherosclerosis Risk In Communities [ARIC]; NCT00005131).
| S-EPMC5425302 | biostudies-literature
Project description:Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, a significant drawback of ABMs is their inability to estimate agent-specific (or "micro") variables, which hinders their ability to make accurate predictions using micro-level data. In this paper, we propose a protocol to learn the latent micro-variables of an ABM from data. We begin by translating an ABM into a probabilistic model characterized by a computationally tractable likelihood. Next, we use a gradient-based expectation maximization algorithm to maximize the likelihood of the latent variables. We showcase the efficacy of our protocol on an ABM of the housing market, where agents with different incomes bid higher prices to live in high-income neighborhoods. Our protocol produces accurate estimates of the latent variables while preserving the general behavior of the ABM. Moreover, our estimates substantially improve the out-of-sample forecasting capabilities of the ABM compared to simpler heuristics. Our protocol encourages modelers to articulate assumptions, consider the inferential process, and spot potential identification problems, thus making it a useful alternative to black-box data assimilation methods.
| S-EPMC10247821 | biostudies-literature
Project description:ObjectiveSuccessful whole-of-community childhood obesity prevention interventions tend to involve community stakeholders in spreading knowledge about and engagement with obesity prevention efforts through the community. This process is referred to by the authors as stakeholder-driven community diffusion (SDCD). This study uses an agent-based model in conjunction with intervention data to increase understanding of how SDCD operates.MethodsThis agent-based model retrospectively simulated SDCD during Romp & Chomp, a 4-year whole-of-community childhood obesity prevention intervention in Victoria, Australia. Stakeholder survey data, intervention records, and expert estimates were used to parameterize the model. Model output was evaluated against criteria derived from empirical data and experts' estimates of the magnitude and timing of community knowledge and engagement change.ResultsThe model was able to produce outputs that met the evaluation criteria: increases in simulated community knowledge and engagement driven by SDCD closely matched expert estimates of magnitude and timing.ConclusionsStrong suggestive evidence was found in support of a hypothesis that SDCD was a key driver of the success of the Romp & Chomp intervention. Model exploration also provided additional insights about these processes (including where additional data collection might prove most beneficial), as well as implications for the design and implementation of future interventions.
| S-EPMC6707874 | biostudies-literature
Project description:BackgroundThe inverse association between body mass index (BMI) and mortality observed in patients treated with maintenance hemodialysis (MHD), also known as the obesity paradox, may be a result of residual confounding. Marginal structural model (MSM) analysis, a technique that accounts for time-varying confounders, may be more appropriate to investigate this association. We hypothesize that after applying MSM, the inverse association between BMI and mortality in MHD patients is attenuated.MethodsWe examined the associations between BMI and all-cause mortality among 123 624 adult MHD patients treated during 2001-6. We examined baseline and time-varying BMI using Cox proportional hazards models and MSM while considering baseline and time-varying covariates, including demographics, comorbidities and markers of malnutrition and inflammation.ResultsThe patients included 45% women and 32% African Americans with a mean age of 61(SD 15) years. In all models, BMI showed a linear incremental inverse association with mortality. Compared with the reference (BMI 25 to <27.5 kg/m(2)), a BMI of <18 kg/m(2) was associated with a 3.2-fold higher death risk [hazard ratio (HR) 3.17 (95% CI 3.05-3.29)], and mortality risks declined with increasing BMI with the greatest survival advantage of 31% lower risk [HR 0.69 (95% CI 0.64-0.75)] observed with a BMI of 40 to <45 kg/m(2).ConclusionsThe linear inverse relationship between BMI and mortality is robust across models including MSM analyses that more completely account for time-varying confounders and biases.
| S-EPMC4967726 | biostudies-literature