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Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES

Overview of attention for article published in Environmental Health, September 2017
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1 tweeter
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1 Google+ user

Citations

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48 Dimensions

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114 Mendeley
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Title
Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES
Published in
Environmental Health, September 2017
DOI 10.1186/s12940-017-0310-9
Pubmed ID
Authors

Sung Kyun Park, Zhangchen Zhao, Bhramar Mukherjee

Abstract

There is growing concern of health effects of exposure to pollutant mixtures. We initially proposed an Environmental Risk Score (ERS) as a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research considering only pollutant main effects. We expand the ERS by consideration of pollutant-pollutant interactions using modern machine learning methods. We illustrate the multi-pollutant approaches to predicting a marker of oxidative stress (gamma-glutamyl transferase (GGT)), a common disease pathway linking environmental exposure and numerous health endpoints. We examined 20 metal biomarkers measured in urine or whole blood from 6 cycles of the National Health and Nutrition Examination Survey (NHANES 2003-2004 to 2013-2014, n = 9664). We randomly split the data evenly into training and testing sets and constructed ERS's of metal mixtures for GGT using adaptive elastic-net with main effects and pairwise interactions (AENET-I), Bayesian additive regression tree (BART), Bayesian kernel machine regression (BKMR), and Super Learner in the training set and evaluated their performances in the testing set. We also evaluated the associations between GGT-ERS and cardiovascular endpoints. ERS based on AENET-I performed better than other approaches in terms of prediction errors in the testing set. Important metals identified in relation to GGT include cadmium (urine), dimethylarsonic acid, monomethylarsonic acid, cobalt, and barium. All ERS's showed significant associations with systolic and diastolic blood pressure and hypertension. For hypertension, one SD increase in each ERS from AENET-I, BART and SuperLearner were associated with odds ratios of 1.26 (95% CI, 1.15, 1.38), 1.17 (1.09, 1.25), and 1.30 (1.20, 1.40), respectively. ERS's showed non-significant positive associations with mortality outcomes. ERS is a useful tool for characterizing cumulative risk from pollutant mixtures, with accounting for statistical challenges such as high degrees of correlations and pollutant-pollutant interactions. ERS constructed for an intermediate marker like GGT is predictive of related disease endpoints.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 114 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 19%
Researcher 13 11%
Student > Master 13 11%
Student > Bachelor 10 9%
Student > Postgraduate 6 5%
Other 22 19%
Unknown 28 25%
Readers by discipline Count As %
Medicine and Dentistry 17 15%
Environmental Science 10 9%
Social Sciences 9 8%
Computer Science 7 6%
Engineering 6 5%
Other 25 22%
Unknown 40 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 September 2017.
All research outputs
#9,466,007
of 15,466,991 outputs
Outputs from Environmental Health
#862
of 1,226 outputs
Outputs of similar age
#151,000
of 278,085 outputs
Outputs of similar age from Environmental Health
#6
of 9 outputs
Altmetric has tracked 15,466,991 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,226 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 278,085 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.