Cargando…

Association of cardiovascular events and lipoprotein particle size: Development of a risk score based on functional data analysis

BACKGROUND: Functional data is data represented by functions (curves or surfaces of a low-dimensional index). Functional data often arise when measurements are collected over time or across locations. In the field of medicine, plasma lipoprotein particles can be quantified according to particle diam...

Descripción completa

Detalles Bibliográficos
Autores principales: Rowland, Charles M., Shiffman, Dov, Caulfield, Michael, Garcia, Veronica, Melander, Olle, Hastie, Trevor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6405139/
https://www.ncbi.nlm.nih.gov/pubmed/30845215
http://dx.doi.org/10.1371/journal.pone.0213172
_version_ 1783401024917078016
author Rowland, Charles M.
Shiffman, Dov
Caulfield, Michael
Garcia, Veronica
Melander, Olle
Hastie, Trevor
author_facet Rowland, Charles M.
Shiffman, Dov
Caulfield, Michael
Garcia, Veronica
Melander, Olle
Hastie, Trevor
author_sort Rowland, Charles M.
collection PubMed
description BACKGROUND: Functional data is data represented by functions (curves or surfaces of a low-dimensional index). Functional data often arise when measurements are collected over time or across locations. In the field of medicine, plasma lipoprotein particles can be quantified according to particle diameter by ion mobility. GOAL: We wanted to evaluate the utility of functional analysis for assessing the association of plasma lipoprotein size distribution with cardiovascular disease after adjustment for established risk factors including standard lipids. METHODS: We developed a model to predict risk of cardiovascular disease among participants in a case-cohort study of the Malmö Prevention Project. We used a linear model with 311 coefficients, corresponding to measures of lipoprotein mass at each of 311 diameters, and assumed these coefficients varied smoothly along the diameter index. The smooth function was represented as an expansion of natural cubic splines where the smoothness parameter was chosen by assessment of a series of nested splines. Cox proportional hazards models of time to a first cardiovascular disease event were used to estimate the smooth coefficient function among a training set consisting of one half of the participants. The resulting model was used to calculate a functional risk score for the remaining half of the participants (test set) and its association with events was assessed in Cox models that adjusted for traditional cardiovascular risk factors. RESULTS: In the test set, participants with a functional risk score in the highest quartile were found to be at increased risk of cardiovascular events compared with the lowest quartile (Hazard ratio = 1.34; 95% Confidence Interval: 1.05 to 1.70) after adjustment for established risk factors. CONCLUSION: In an independent test set of Malmö Prevention Project participants, the functional risk score was found to be associated with cardiovascular events after adjustment for traditional risk factors including standard lipids.
format Online
Article
Text
id pubmed-6405139
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-64051392019-03-17 Association of cardiovascular events and lipoprotein particle size: Development of a risk score based on functional data analysis Rowland, Charles M. Shiffman, Dov Caulfield, Michael Garcia, Veronica Melander, Olle Hastie, Trevor PLoS One Research Article BACKGROUND: Functional data is data represented by functions (curves or surfaces of a low-dimensional index). Functional data often arise when measurements are collected over time or across locations. In the field of medicine, plasma lipoprotein particles can be quantified according to particle diameter by ion mobility. GOAL: We wanted to evaluate the utility of functional analysis for assessing the association of plasma lipoprotein size distribution with cardiovascular disease after adjustment for established risk factors including standard lipids. METHODS: We developed a model to predict risk of cardiovascular disease among participants in a case-cohort study of the Malmö Prevention Project. We used a linear model with 311 coefficients, corresponding to measures of lipoprotein mass at each of 311 diameters, and assumed these coefficients varied smoothly along the diameter index. The smooth function was represented as an expansion of natural cubic splines where the smoothness parameter was chosen by assessment of a series of nested splines. Cox proportional hazards models of time to a first cardiovascular disease event were used to estimate the smooth coefficient function among a training set consisting of one half of the participants. The resulting model was used to calculate a functional risk score for the remaining half of the participants (test set) and its association with events was assessed in Cox models that adjusted for traditional cardiovascular risk factors. RESULTS: In the test set, participants with a functional risk score in the highest quartile were found to be at increased risk of cardiovascular events compared with the lowest quartile (Hazard ratio = 1.34; 95% Confidence Interval: 1.05 to 1.70) after adjustment for established risk factors. CONCLUSION: In an independent test set of Malmö Prevention Project participants, the functional risk score was found to be associated with cardiovascular events after adjustment for traditional risk factors including standard lipids. Public Library of Science 2019-03-07 /pmc/articles/PMC6405139/ /pubmed/30845215 http://dx.doi.org/10.1371/journal.pone.0213172 Text en © 2019 Rowland et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rowland, Charles M.
Shiffman, Dov
Caulfield, Michael
Garcia, Veronica
Melander, Olle
Hastie, Trevor
Association of cardiovascular events and lipoprotein particle size: Development of a risk score based on functional data analysis
title Association of cardiovascular events and lipoprotein particle size: Development of a risk score based on functional data analysis
title_full Association of cardiovascular events and lipoprotein particle size: Development of a risk score based on functional data analysis
title_fullStr Association of cardiovascular events and lipoprotein particle size: Development of a risk score based on functional data analysis
title_full_unstemmed Association of cardiovascular events and lipoprotein particle size: Development of a risk score based on functional data analysis
title_short Association of cardiovascular events and lipoprotein particle size: Development of a risk score based on functional data analysis
title_sort association of cardiovascular events and lipoprotein particle size: development of a risk score based on functional data analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6405139/
https://www.ncbi.nlm.nih.gov/pubmed/30845215
http://dx.doi.org/10.1371/journal.pone.0213172
work_keys_str_mv AT rowlandcharlesm associationofcardiovasculareventsandlipoproteinparticlesizedevelopmentofariskscorebasedonfunctionaldataanalysis
AT shiffmandov associationofcardiovasculareventsandlipoproteinparticlesizedevelopmentofariskscorebasedonfunctionaldataanalysis
AT caulfieldmichael associationofcardiovasculareventsandlipoproteinparticlesizedevelopmentofariskscorebasedonfunctionaldataanalysis
AT garciaveronica associationofcardiovasculareventsandlipoproteinparticlesizedevelopmentofariskscorebasedonfunctionaldataanalysis
AT melanderolle associationofcardiovasculareventsandlipoproteinparticlesizedevelopmentofariskscorebasedonfunctionaldataanalysis
AT hastietrevor associationofcardiovasculareventsandlipoproteinparticlesizedevelopmentofariskscorebasedonfunctionaldataanalysis