Search

BackgroundsWe aimed to investigate the demographic characteristics, vascular involvement, angiographic patterns, complications, and associations of these variables in a large sample of TAK patients at a national referral center in China.MethodsThe medical records of TAK patients discharged from 2008 to 2020 were retrieved from the hospital discharge database using ICD-10 codes. Demographic data, vascular lesions, Numano classifications and complications were collected and analyzed.ResultsThe median age at onset was 25 years in 852 TAK patients (670 female, 182 male). Compared with the females, the male patients were more likely to have type IV and were more likely to have iliac (24.7% vs. 10.0%) and renal artery (62.7% vs. 53.9%) involvement. They also had a higher prevalence of systemic hypertension (62.1% vs. 42.4%), renal dysfunction (12.6% vs. 7.8%) and aortic aneurysm (AA) (8.2% vs. 3.6%). The childhood-onset group was more likely to have involvement of the abdominal aorta (68.4% vs. 52.1%), renal artery (69.0% vs. 51.8%) and superior mesenteric artery (41.5% vs. 28.5%), and they were more likely to have type IV, V and hypertension than the adult-onset group. After adjusting for sex and age at onset, the patients with type II were associated with an increased risk of cardiac dysfunction (II vs. I: OR = 5.42; II vs. IV: OR = 2.63) and pulmonary hypertension (II vs. I: OR = 4.78; II vs. IV: OR = 3.95) compared with those with types I and IV. Valvular abnormalities (61.0%) were observed to be most prevalent in patients with type IIa. The patients with Type III were associated with a higher risk of aortic aneurysm (23.3%) than the patients with types IV (OR = 11.00) and V (OR = 5.98). The patients with types III and IV were more commonly complicated with systemic hypertension than the patients with types I, II and V. P

Background: Carotid artery stenosis (CAS) is caused by the formation of atherosclerotic plaques inside the arterial wall and accounts for 20–30% of all strokes. The development of an early, noninvasive diagnostic method and the identification of high-risk patients for ischemic stroke is essential to the management of CAS in clinical practice.Methods: We used the data-independent acquisition (DIA) technique to conduct a urinary proteomic study in patients with CAS and healthy controls. We identified the potential diagnosis and risk stratification biomarkers of CAS. And Ingenuity pathway analysis was used for functional annotation of differentially expressed proteins (DEPs). Furthermore, receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic values of DEPs.Results: A total of 194 DEPs were identified between CAS patients and healthy controls by DIA quantification. The bioinformatics analysis showed that these DEPs were correlated with the pathogenesis of CAS. We further identified 32 DEPs in symptomatic CAS compared to asymptomatic CAS, and biological function analysis revealed that these proteins are mainly related to immune/inflammatory pathways. Finally, a biomarker panel of six proteins (ACP2, PLD3, HLA-C, GGH, CALML3, and IL2RB) exhibited potential diagnostic value in CAS and good discriminative power for differentiating symptomatic and asymptomatic CAS with high sensitivity and specificity.Conclusions: Our study identified novel potential urinary biomarkers for noninvasive early screening and risk stratification of CAS.

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.