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Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition

Physiological dysregulation may underlie aging and many chronic diseases, but is challenging to quantify because of the complexity of the underlying systems. Recently, we described a measure of physiological dysregulation, D(M), that uses statistical distance to assess the degree to which an individ...

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Autores principales: Cohen, Alan A., Li, Qing, Milot, Emmanuel, Leroux, Maxime, Faucher, Samuel, Morissette-Thomas, Vincent, Legault, Véronique, Fried, Linda P., Ferrucci, Luigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4395377/
https://www.ncbi.nlm.nih.gov/pubmed/25875923
http://dx.doi.org/10.1371/journal.pone.0122541
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author Cohen, Alan A.
Li, Qing
Milot, Emmanuel
Leroux, Maxime
Faucher, Samuel
Morissette-Thomas, Vincent
Legault, Véronique
Fried, Linda P.
Ferrucci, Luigi
author_facet Cohen, Alan A.
Li, Qing
Milot, Emmanuel
Leroux, Maxime
Faucher, Samuel
Morissette-Thomas, Vincent
Legault, Véronique
Fried, Linda P.
Ferrucci, Luigi
author_sort Cohen, Alan A.
collection PubMed
description Physiological dysregulation may underlie aging and many chronic diseases, but is challenging to quantify because of the complexity of the underlying systems. Recently, we described a measure of physiological dysregulation, D(M), that uses statistical distance to assess the degree to which an individual’s biomarker profile is normal versus aberrant. However, the sensitivity of D(M) to details of the calculation method has not yet been systematically assessed. In particular, the number and choice of biomarkers and the definition of the reference population (RP, the population used to define a “normal” profile) may be important. Here, we address this question by validating the method on 44 common clinical biomarkers from three longitudinal cohort studies and one cross-sectional survey. D(M)s calculated on different biomarker subsets show that while the signal of physiological dysregulation increases with the number of biomarkers included, the value of additional markers diminishes as more are added and inclusion of 10-15 is generally sufficient. As long as enough markers are included, individual markers have little effect on the final metric, and even D(M)s calculated from mutually exclusive groups of markers correlate with each other at r~0.4-0.5. We also used data subsets to generate thousands of combinations of study populations and RPs to address sensitivity to differences in age range, sex, race, data set, sample size, and their interactions. Results were largely consistent (but not identical) regardless of the choice of RP; however, the signal was generally clearer with a younger and healthier RP, and RPs too different from the study population performed poorly. Accordingly, biomarker and RP choice are not particularly important in most cases, but caution should be used across very different populations or for fine-scale analyses. Biologically, the lack of sensitivity to marker choice and better performance of younger, healthier RPs confirm an interpretation of D(M) physiological dysregulation and as an emergent property of a complex system.
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spelling pubmed-43953772015-04-21 Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition Cohen, Alan A. Li, Qing Milot, Emmanuel Leroux, Maxime Faucher, Samuel Morissette-Thomas, Vincent Legault, Véronique Fried, Linda P. Ferrucci, Luigi PLoS One Research Article Physiological dysregulation may underlie aging and many chronic diseases, but is challenging to quantify because of the complexity of the underlying systems. Recently, we described a measure of physiological dysregulation, D(M), that uses statistical distance to assess the degree to which an individual’s biomarker profile is normal versus aberrant. However, the sensitivity of D(M) to details of the calculation method has not yet been systematically assessed. In particular, the number and choice of biomarkers and the definition of the reference population (RP, the population used to define a “normal” profile) may be important. Here, we address this question by validating the method on 44 common clinical biomarkers from three longitudinal cohort studies and one cross-sectional survey. D(M)s calculated on different biomarker subsets show that while the signal of physiological dysregulation increases with the number of biomarkers included, the value of additional markers diminishes as more are added and inclusion of 10-15 is generally sufficient. As long as enough markers are included, individual markers have little effect on the final metric, and even D(M)s calculated from mutually exclusive groups of markers correlate with each other at r~0.4-0.5. We also used data subsets to generate thousands of combinations of study populations and RPs to address sensitivity to differences in age range, sex, race, data set, sample size, and their interactions. Results were largely consistent (but not identical) regardless of the choice of RP; however, the signal was generally clearer with a younger and healthier RP, and RPs too different from the study population performed poorly. Accordingly, biomarker and RP choice are not particularly important in most cases, but caution should be used across very different populations or for fine-scale analyses. Biologically, the lack of sensitivity to marker choice and better performance of younger, healthier RPs confirm an interpretation of D(M) physiological dysregulation and as an emergent property of a complex system. Public Library of Science 2015-04-13 /pmc/articles/PMC4395377/ /pubmed/25875923 http://dx.doi.org/10.1371/journal.pone.0122541 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
Cohen, Alan A.
Li, Qing
Milot, Emmanuel
Leroux, Maxime
Faucher, Samuel
Morissette-Thomas, Vincent
Legault, Véronique
Fried, Linda P.
Ferrucci, Luigi
Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition
title Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition
title_full Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition
title_fullStr Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition
title_full_unstemmed Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition
title_short Statistical Distance as a Measure of Physiological Dysregulation Is Largely Robust to Variation in Its Biomarker Composition
title_sort statistical distance as a measure of physiological dysregulation is largely robust to variation in its biomarker composition
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4395377/
https://www.ncbi.nlm.nih.gov/pubmed/25875923
http://dx.doi.org/10.1371/journal.pone.0122541
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