Dataset Information

A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain.

ABSTRACT: Objectives:The aim is to identify exposures associated with lung cancer mortality and mortality disparities by race and gender using an exposome database coupled to a graph theoretical toolchain. Methods:Graph theoretical algorithms were employed to extract paracliques from correlation graphs using associations between 2162 environmental exposures and lung cancer mortality rates in 2067 counties, with clique doubling applied to compute an absolute threshold of significance. Factor analysis and multiple linear regressions then were used to analyze differences in exposures associated with lung cancer mortality and mortality disparities by race and gender. Results:While cigarette consumption was highly correlated with rates of lung cancer mortality for both white men and women, previously unidentified novel exposures were more closely associated with lung cancer mortality and mortality disparities for blacks, particularly black women. Conclusions:Exposures beyond smoking moderate lung cancer mortality and mortality disparities by race and gender. Policy Implications:An exposome approach and database coupled with scalable combinatorial analytics provides a powerful new approach for analyzing relationships between multiple environmental exposures, pathways and health outcomes. An assessment of multiple exposures is needed to appropriately translate research findings into environmental public health practice and policy.

SUBMITTER: Juarez PD

PROVIDER: S-EPMC5687093 | biostudies-literature | 2017 Apr-Jun

REPOSITORIES: biostudies-literature

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A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain.

Juarez Paul D PD Hood Darryl B DB Rogers Gary L GL Baktash Suzanne H SH Saxton Arnold M AM Matthews-Juarez Patricia P Im Wansoo W Cifuentes Myriam Patricia MP Phillips Charles A CA Lichtveld Maureen Y MY Langston Michael A MA

Environmental disease 20170401 2

<h4>Objectives</h4>The aim is to identify exposures associated with lung cancer mortality and mortality disparities by race and gender using an exposome database coupled to a graph theoretical toolchain.<h4>Methods</h4>Graph theoretical algorithms were employed to extract paracliques from correlation graphs using associations between 2162 environmental exposures and lung cancer mortality rates in 2067 counties, with clique doubling applied to compute an absolute threshold of significance. Factor ...[more]

PMID: 29152601

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Project description:BackgroundMicrobiomes play vital roles in shaping environments and stabilize them based on their compositions and inter-species relationships among its species. Variations in microbial properties have been reported to have significant impact on their host environment. For example, variants in gut microbiomes have been reported to be associated with several chronic conditions, such as inflammatory disease and irritable bowel syndrome. However, how microbial bacteria contribute to pathogenesis still remains unclear and major research questions in this domain remain unanswered.MethodsWe propose a split graph model to represent the composition and interactions of a given microbiome. We used metagenomes from Korean populations in this study. The dataset consists of three different types of samples, viz. mucosal tissue and stool from Crohn's disease patients and stool from healthy individuals. We use the split graph model to analyze the impact of microbial compositions on various host phenotypes. Utilizing the graph model, we have developed a pipeline that integrates genomic information and pathway analysis to characterize both critical informative components of inter-bacterial correlations and associations between bacterial taxa and various metabolic pathways.ResultsThe obtained results highlight the importance of the microbial communities and their inter-relationships and show how these microbial structures are correlated with Crohn's disease. We show that there are significant positive associations between detected taxonomic biomarkers as well as multiple functional modules in the split graph of mucosal tissue samples from CD patients. Bacteria Moraxellaceae and Pseudomonadaceae were detected as taxonomic biomarkers in CD groups. Higher abundance of these bacteria have been reported in previous study and several metabolic pathways associated with these bacteria were characterized in CD samples.ConclusionsThe proposed pipeline provides a new way to approach the analysis of complex microbiomes. The results obtained from this study show great potential in unraveling mechansims in complex biological systems to understand how various components in such complex environments are associated with critical biological functions.

Project description:BACKGROUND:The assumption is that executive dysfunctions (EF), associated with frontal lobe injury, are responsible for behavioral disturbances. Some studies do not find a relationship between EF and behavior following frontal lobe lesions. Our main goal of this study was to use a novel statistical method, graph theory, to analyze this relationship in different brain injury groups; frontal lobe damage, non-frontal lobe damage, and controls. Within the frontal group, we expect to find a pattern of executive nodes that are highly interconnected. METHODS:For each group, we modeled the relationship between executive functions and behavior as a network of interdependent variables. The cognitive tests and the behavioral questionnaire are the "nodes" in the network, while the relationships between the nodes were modeled as the correlations between two nodes corrected for the correlation with all other nodes in the network. Sparse networks were estimated within each group using graphical LASSO. We analyzed the relative importance of the nodes within a network (centrality) and the clustering (modularity) of the different nodes. RESULTS:Network analysis showed distinct patterns of relationships between EF and behavior in the three subgroups. The performance on the verbal learning test is the most central node in all the networks. In the frontal group, verbal memory forms a community with working memory and fluency. The behavioral nodes do not differentiate between groups or form clusters with cognitive nodes. No other communities were found for cognitive and behavioral nodes. CONCLUSION:The cognitive phenotype of the frontal lobe damaged group, with its stability and proportion, might be theoretically interpreted as a potential "buffer" for possible cognitive executive deficits. This might explain some of the ambiguity found in the literature. This alternative approach on cognitive test scores provides a different and possibly complimentary perspective of the neuropsychology of brain-injured patients.

Project description:ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects' clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC.ResultsIn patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model.ConclusionFunctional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM.

Dataset Information

A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain.

Publications

A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain.

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