Dataset Information

A Method for Identifying Prevalent Chemical Combinations in the U.S. Population.

ABSTRACT:

Background

Through the food and water they ingest, the air they breathe, and the consumer products with which they interact at home and at work, humans are exposed to tens of thousands of chemicals, many of which have not been evaluated to determine their potential toxicities. Furthermore, while current chemical testing tends to focus on individual chemicals, the exposures that people actually experience involve mixtures of chemicals. Unfortunately, the number of mixtures that can be formed from the thousands of environmental chemicals is enormous, and testing all of them would be impossible.

Objectives

We seek to develop and demonstrate a method for identifying those mixtures that are most prevalent in humans.

Methods

We applied frequent itemset mining, a technique traditionally used for market basket analysis, to biomonitoring data from the 2009-2010 cycle of the continuous National Health and Nutrition Examination Survey (NHANES) to identify combinations of chemicals that frequently co-occur in people.

Results

We identified 90 chemical combinations consisting of relatively few chemicals that occur in at least 30% of the U.S. population, as well as three supercombinations consisting of relatively many chemicals that occur in a small but nonnegligible proportion of the U.S. population.

Conclusions

We demonstrated how FIM can be used in conjunction with biomonitoring data to narrow a large number of possible chemical combinations down to a smaller set of prevalent chemical combinations. https://doi.org/10.1289/EHP1265.

SUBMITTER: Kapraun DF

PROVIDER: S-EPMC5801475 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

A Method for Identifying Prevalent Chemical Combinations in the U.S. Population.

Kapraun Dustin F DF Wambaugh John F JF Ring Caroline L CL Tornero-Velez Rogelio R Setzer R Woodrow RW

Environmental health perspectives 20170824 8

<h4>Background</h4>Through the food and water they ingest, the air they breathe, and the consumer products with which they interact at home and at work, humans are exposed to tens of thousands of chemicals, many of which have not been evaluated to determine their potential toxicities. Furthermore, while current chemical testing tends to focus on individual chemicals, the exposures that people actually experience involve mixtures of chemicals. Unfortunately, the number of mixtures that can be for ...[more]

PMID: 28858827

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Project description:BackgroundAn important emerging trend in the analysis of microarray data is to incorporate known pathway information a priori. Expression level "summaries" for pathways, obtained from the expression data for the genes constituting the pathway, permit the inclusion of pathway information, reduce the high dimensionality of microarray data, and have the power to elucidate gene-interaction dependencies which are not already accounted for through known pathway identification.ResultsWe present a novel method for the analysis of microarray data that identifies joint differential expression in gene-pathway pairs. This method takes advantage of known gene pathway memberships to compute a summary expression level for each pathway as a whole. Correlations between the pathway expression summary and the expression levels of genes not already known to be associated with the pathway provide clues to gene interaction dependencies that are not already accounted for through known pathway identification, and statistically significant differences between gene-pathway correlations in phenotypically different cells (e.g., where the expression level of a single gene and a given pathway summary correlate strongly in normal cells but weakly in tumor cells) may indicate biologically relevant gene-pathway interactions. Here, we detail the methodology and present the results of this method applied to two gene-expression datasets, identifying gene-pathway pairs which exhibit differential joint expression by phenotype.ConclusionThe method described herein provides a means by which interactions between large numbers of genes may be identified by incorporating known pathway information to reduce the dimensionality of gene interactions. The method is efficient and easily applied to data sets of ~102 arrays. Application of this method to two publicly-available cancer data sets yields suggestive and promising results. This method has the potential to complement gene-at-a-time analysis techniques for microarray analysis by indicating relationships between pathways and genes that have not previously been identified and which may play a role in disease.

Project description:BackgroundMultimorbidity (≥ 2 chronic diseases) is associated with greater disability and higher treatment burden, as well as difficulty coordinating self-management tasks for adults with complex multimorbidity patterns. Comparatively little work has focused on assessing multimorbidity patterns among patients seeking care in community health centers (CHCs).ObjectiveTo identify and characterize prevalent multimorbidity patterns in a multi-state network of CHCs over a 5-year period.DesignA cohort study of the 2014-2019 ADVANCE multi-state CHC clinical data network. We identified the most prevalent multimorbidity combination patterns and assessed the frequency of patterns throughout a 5-year period as well as the demographic characteristics of patient panels by prevalent patterns.ParticipantsThe study included data from 838,642 patients aged ≥ 45 years who were seen in 337 CHCs across 22 states between 2014 and 2019.Main measuresPrevalent multimorbidity patterns of somatic, mental health, and mental-somatic combinations of 22 chronic diseases based on the U.S. Department of Health and Human Services Multiple Chronic Conditions framework: anxiety, arthritis, asthma, autism, cancer, cardiac arrhythmia, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), congestive heart failure, coronary artery disease, dementia, depression, diabetes, hepatitis, human immunodeficiency virus (HIV), hyperlipidemia, hypertension, osteoporosis, post-traumatic stress disorder (PTSD), schizophrenia, substance use disorder, and stroke.Key resultsMultimorbidity is common among middle-aged and older patients seen in CHCs: 40% have somatic, 6% have mental health, and 24% have mental-somatic multimorbidity patterns. The most frequently occurring pattern across all years is hyperlipidemia-hypertension. The three most frequent patterns are various iterations of hyperlipidemia, hypertension, and diabetes and are consistent in rank of occurrence across all years. CKD-hyperlipidemia-hypertension and anxiety-depression are both more frequent in later study years.ConclusionsCHCs are increasingly seeing more complex multimorbidity patterns over time; these most often involve mental health morbidity and advanced cardiometabolic-renal morbidity.

Dataset Information

A Method for Identifying Prevalent Chemical Combinations in the U.S. Population.

Background

Objectives

Methods

Results

Conclusions

Publications

A Method for Identifying Prevalent Chemical Combinations in the U.S. Population.

Similar Datasets

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