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    Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application

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    Name:
    art_3A10.1186_2Fs12874-016-025 ...
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    Final Published Version
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    Author
    Vasquez, Monica M.
    Hu, Chengcheng
    Roe, Denise J.
    Chen, Zhao
    Halonen, Marilyn
    Guerra, Stefano
    Affiliation
    Univ Arizona, Mel & Enid Zuckerman Coll Publ Hlth
    Univ Arizona, Asthma & Airway Dis Res Ctr
    Issue Date
    2016-11-14
    Keywords
    LASSO
    Biomarkers
    High-Dimensional
    Obesity
    Overweight
    
    Metadata
    Show full item record
    Publisher
    BIOMED CENTRAL LTD
    Citation
    Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application 2016, 16 (1) BMC Medical Research Methodology
    Journal
    BMC Medical Research Methodology
    Rights
    This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Background: The study of circulating biomarkers and their association with disease outcomes has become progressively complex due to advances in the measurement of these biomarkers through multiplex technologies. The Least Absolute Shrinkage and Selection Operator (LASSO) is a data analysis method that may be utilized for biomarker selection in these high dimensional data. However, it is unclear which LASSO-type method is preferable when considering data scenarios that may be present in serum biomarker research, such as high correlation between biomarkers, weak associations with the outcome, and sparse number of true signals. The goal of this study was to compare the LASSO to five LASSO-type methods given these scenarios. Methods: A simulation study was performed to compare the LASSO, Adaptive LASSO, Elastic Net, Iterated LASSO, Bootstrap-Enhanced LASSO, and Weighted Fusion for the binary logistic regression model. The simulation study was designed to reflect the data structure of the population-based Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD), specifically the sample size (N = 1000 for total population, 500 for sub-analyses), correlation of biomarkers (0.20, 0.50, 0.80), prevalence of overweight (40%) and obese (12%) outcomes, and the association of outcomes with standardized serum biomarker concentrations (log-odds ratio = 0.05-1.75). Each LASSO-type method was then applied to the TESAOD data of 306 overweight, 66 obese, and 463 normal-weight subjects with a panel of 86 serum biomarkers. Results: Based on the simulation study, no method had an overall superior performance. The Weighted Fusion correctly identified more true signals, but incorrectly included more noise variables. The LASSO and Elastic Net correctly identified many true signals and excluded more noise variables. In the application study, biomarkers of overweight and obesity selected by all methods were Adiponectin, Apolipoprotein H, Calcitonin, CD14, Complement 3, C-reactive protein, Ferritin, Growth Hormone, Immunoglobulin M, Interleukin-18, Leptin, Monocyte Chemotactic Protein-1, Myoglobin, Sex Hormone Binding Globulin, Surfactant Protein D, and YKL-40. Conclusions: For the data scenarios examined, choice of optimal LASSO-type method was data structure dependent and should be guided by the research objective. The LASSO-type methods identified biomarkers that have known associations with obesity and obesity related conditions.
    ISSN
    1471-2288
    PubMed ID
    27842498
    DOI
    10.1186/s12874-016-0254-8
    Version
    Final published version
    Sponsors
    CADET award [HL107188]; R01 award from the National Heart, Lung, and Blood Institute [HL095021]
    Additional Links
    http://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-016-0254-8
    ae974a485f413a2113503eed53cd6c53
    10.1186/s12874-016-0254-8
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