Cargando…

Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus

Background and aims: Patients with systemic lupus erythematosus (SLE) have a significantly higher incidence of atherosclerosis than the general population. Studies on atherosclerosis prediction models specific for SLE patients are very limited. This study aimed to build a risk prediction model for a...

Descripción completa

Detalles Bibliográficos
Autores principales: Xing, Haiping, Pang, Haiyu, Du, Tian, Yang, Xufei, Zhang, Jing, Li, Mengtao, Zhang, Shuyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085548/
https://www.ncbi.nlm.nih.gov/pubmed/33936038
http://dx.doi.org/10.3389/fimmu.2021.622216
_version_ 1783686364933390336
author Xing, Haiping
Pang, Haiyu
Du, Tian
Yang, Xufei
Zhang, Jing
Li, Mengtao
Zhang, Shuyang
author_facet Xing, Haiping
Pang, Haiyu
Du, Tian
Yang, Xufei
Zhang, Jing
Li, Mengtao
Zhang, Shuyang
author_sort Xing, Haiping
collection PubMed
description Background and aims: Patients with systemic lupus erythematosus (SLE) have a significantly higher incidence of atherosclerosis than the general population. Studies on atherosclerosis prediction models specific for SLE patients are very limited. This study aimed to build a risk prediction model for atherosclerosis in SLE. Methods: RNA sequencing was performed on 67 SLE patients. Subsequently, differential expression analysis was carried out on 19 pairs of age-matched SLE patients with (AT group) or without (Non-AT group) atherosclerosis using peripheral venous blood. We used logistic least absolute shrinkage and selection operator regression to select variables among differentially expressed (DE) genes and clinical features and utilized backward stepwise logistic regression to build an atherosclerosis risk prediction model with all 67 patients. The performance of the prediction model was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses. Results: The 67 patients had a median age of 42.7 (Q1–Q3: 36.6–51.2) years, and 20 (29.9%) had atherosclerosis. A total of 106 DE genes were identified between the age-matched AT and Non-AT groups. Pathway analyses revealed that the AT group had upregulated atherosclerosis signaling, oxidative phosphorylation, and interleukin (IL)-17-related pathways but downregulated T cell and B cell receptor signaling. Keratin 10, age, and hyperlipidemia were selected as variables for the risk prediction model. The AUC and Hosmer–Lemeshow test p-value of the model were 0.922 and 0.666, respectively, suggesting a relatively high discrimination and calibration performance. The prediction model had a higher net benefit in the decision curve analysis than that when predicting with age or hyperlipidemia only. Conclusions: We built an atherosclerotic risk prediction model with one gene and two clinical factors. This model may greatly assist clinicians to identify SLE patients with atherosclerosis, especially asymptomatic atherosclerosis.
format Online
Article
Text
id pubmed-8085548
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-80855482021-05-01 Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus Xing, Haiping Pang, Haiyu Du, Tian Yang, Xufei Zhang, Jing Li, Mengtao Zhang, Shuyang Front Immunol Immunology Background and aims: Patients with systemic lupus erythematosus (SLE) have a significantly higher incidence of atherosclerosis than the general population. Studies on atherosclerosis prediction models specific for SLE patients are very limited. This study aimed to build a risk prediction model for atherosclerosis in SLE. Methods: RNA sequencing was performed on 67 SLE patients. Subsequently, differential expression analysis was carried out on 19 pairs of age-matched SLE patients with (AT group) or without (Non-AT group) atherosclerosis using peripheral venous blood. We used logistic least absolute shrinkage and selection operator regression to select variables among differentially expressed (DE) genes and clinical features and utilized backward stepwise logistic regression to build an atherosclerosis risk prediction model with all 67 patients. The performance of the prediction model was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses. Results: The 67 patients had a median age of 42.7 (Q1–Q3: 36.6–51.2) years, and 20 (29.9%) had atherosclerosis. A total of 106 DE genes were identified between the age-matched AT and Non-AT groups. Pathway analyses revealed that the AT group had upregulated atherosclerosis signaling, oxidative phosphorylation, and interleukin (IL)-17-related pathways but downregulated T cell and B cell receptor signaling. Keratin 10, age, and hyperlipidemia were selected as variables for the risk prediction model. The AUC and Hosmer–Lemeshow test p-value of the model were 0.922 and 0.666, respectively, suggesting a relatively high discrimination and calibration performance. The prediction model had a higher net benefit in the decision curve analysis than that when predicting with age or hyperlipidemia only. Conclusions: We built an atherosclerotic risk prediction model with one gene and two clinical factors. This model may greatly assist clinicians to identify SLE patients with atherosclerosis, especially asymptomatic atherosclerosis. Frontiers Media S.A. 2021-04-16 /pmc/articles/PMC8085548/ /pubmed/33936038 http://dx.doi.org/10.3389/fimmu.2021.622216 Text en Copyright © 2021 Xing, Pang, Du, Yang, Zhang, Li and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Xing, Haiping
Pang, Haiyu
Du, Tian
Yang, Xufei
Zhang, Jing
Li, Mengtao
Zhang, Shuyang
Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus
title Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus
title_full Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus
title_fullStr Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus
title_full_unstemmed Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus
title_short Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus
title_sort establishing a risk prediction model for atherosclerosis in systemic lupus erythematosus
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085548/
https://www.ncbi.nlm.nih.gov/pubmed/33936038
http://dx.doi.org/10.3389/fimmu.2021.622216
work_keys_str_mv AT xinghaiping establishingariskpredictionmodelforatherosclerosisinsystemiclupuserythematosus
AT panghaiyu establishingariskpredictionmodelforatherosclerosisinsystemiclupuserythematosus
AT dutian establishingariskpredictionmodelforatherosclerosisinsystemiclupuserythematosus
AT yangxufei establishingariskpredictionmodelforatherosclerosisinsystemiclupuserythematosus
AT zhangjing establishingariskpredictionmodelforatherosclerosisinsystemiclupuserythematosus
AT limengtao establishingariskpredictionmodelforatherosclerosisinsystemiclupuserythematosus
AT zhangshuyang establishingariskpredictionmodelforatherosclerosisinsystemiclupuserythematosus