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 LU
and Hastie, Trevor
(2019)
In PLoS ONE
14(3).
- Abstract
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,... (More)
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.
(Less)
- author
- Rowland, Charles M.
; Shiffman, Dov
; Caulfield, Michael
; Garcia, Veronica
; Melander, Olle
LU
and Hastie, Trevor
- organization
- publishing date
- 2019-03-07
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS ONE
- volume
- 14
- issue
- 3
- article number
- e0213172
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- pmid:30845215
- scopus:85062603675
- ISSN
- 1932-6203
- DOI
- 10.1371/journal.pone.0213172
- language
- English
- LU publication?
- yes
- id
- fbe2c0a9-5e77-41ff-a1e7-44ff2c15f820
- date added to LUP
- 2019-03-20 08:34:26
- date last changed
- 2024-03-13 10:18:07
@article{fbe2c0a9-5e77-41ff-a1e7-44ff2c15f820,
abstract = {{<p>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.</p>}},
author = {{Rowland, Charles M. and Shiffman, Dov and Caulfield, Michael and Garcia, Veronica and Melander, Olle and Hastie, Trevor}},
issn = {{1932-6203}},
language = {{eng}},
month = {{03}},
number = {{3}},
publisher = {{Public Library of Science (PLoS)}},
series = {{PLoS ONE}},
title = {{Association of cardiovascular events and lipoprotein particle size : Development of a risk score based on functional data analysis}},
url = {{http://dx.doi.org/10.1371/journal.pone.0213172}},
doi = {{10.1371/journal.pone.0213172}},
volume = {{14}},
year = {{2019}},
}