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Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus.

ABSTRACT: 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.

SUBMITTER: Xing H

PROVIDER: S-EPMC8085548 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Systemic lupus erythematosus (SLE) is a latent, insidious autoimmune disease, and with the development of gene sequencing in recent years, our study aims to develop a gene-based predictive model to explore the identification of SLE at the genetic level. First, gene expression datasets of SLE whole blood samples were collected from the Gene Expression Omnibus (GEO) database. After the datasets were merged, they were divided into training and validation datasets in the ratio of 7:3, where the SLE samples and healthy samples of the training dataset were 334 and 71, respectively, and the SLE samples and healthy samples of the validation dataset were 143 and 30, respectively. The training dataset was used to build the disease risk prediction model, and the validation dataset was used to verify the model identification ability. We first analyzed differentially expressed genes (DEGs) and then used Lasso and random forest (RF) to screen out six key genes (OAS3, USP18, RTP4, SPATS2L, IFI27 and OAS1), which are essential to distinguish SLE from healthy samples. With six key genes incorporated and five iterations of 10-fold cross-validation performed into the RF model, we finally determined the RF model with optimal mtry. The mean values of area under the curve (AUC) and accuracy of the models were over 0.95. The validation dataset was then used to evaluate the AUC performance and our model had an AUC of 0.948. An external validation dataset (GSE99967) with an AUC of 0.810, an accuracy of 0.836, and a sensitivity of 0.921 was used to assess the model's performance. The external validation dataset (GSE185047) of all SLE patients yielded an SLE sensitivity of up to 0.954. The final high-throughput RF model had a mean value of AUC over 0.9, again showing good results. In conclusion, we identified key genetic biomarkers and successfully developed a novel disease risk prediction model for SLE that can be used as a new SLE disease risk prediction aid and contribute to the identification of SLE.

Project description:ObjectiveThe Systemic Lupus International Collaborating Clinics (SLICC) frailty index (FI) predicts mortality and damage accrual in systemic lupus erythematosus (SLE), but its association with hospitalizations has not been described. Our objective was to estimate the association of baseline SLICC-FI values with future hospitalizations in the SLICC inception cohort.MethodsBaseline SLICC-FI scores were calculated. The number and duration of inpatient hospitalizations during follow-up were recorded. Negative binomial regression was used to estimate the association between baseline SLICC-FI values and the rate of hospitalizations per patient-year of follow-up. Linear regression was used to estimate the association of baseline SLICC-FI scores with the proportion of follow-up time spent in the hospital. Multivariable models were adjusted for relevant baseline characteristics.ResultsThe 1,549 patients with SLE eligible for this analysis were mostly female (88.7%), with a mean ± SD age of 35.7 ± 13.3 years and a median disease duration of 1.2 years (interquartile range 0.9-1.5) at baseline. Mean ± SD baseline SLICC-FI was 0.17 ± 0.08. During mean ± SD follow-up of 7.2 ± 3.7 years, 614 patients (39.6%) experienced 1,570 hospitalizations. Higher baseline SLICC-FI values (per 0.05 increment) were associated with more frequent hospitalizations during follow-up, with an incidence rate ratio of 1.21 (95% confidence interval [95% CI] 1.13-1.30) after adjustment for baseline age, sex, glucocorticoid use, immunosuppressive use, ethnicity/location, SLE Disease Activity Index 2000 score, SLICC/American College of Rheumatology Damage Index score, and disease duration. Among patients with ≥1 hospitalization, higher baseline SLICC-FI values predicted a greater proportion of follow-up time spent hospitalized (relative rate 1.09 [95% CI 1.02-1.16]).ConclusionThe SLICC-FI predicts future hospitalizations among incident SLE patients, further supporting the SLICC-FI as a valid health measure in SLE.

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Establishing a Risk Prediction Model for Atherosclerosis in Systemic Lupus Erythematosus.

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