RESUMO
Background and objective: To investigate the use of high-resolution magnetic resonance imaging (HR-MRI) to identify the characteristics of culprit plaques in intracranial arteries, and to evaluate the predictive value of the characteristics of culprit plaques combined with the modified Essen score for the recurrence risk of high-risk non-disabling ischemic cerebrovascular events (HR-NICE) patients. Methods: A retrospective analysis was conducted on 180 patients with HR-NICE at the First Affiliated Hospital of Xinxiang Medical University, including 128 patients with no recurrence (non-recurrence group) and 52 patients with recurrence (recurrence group). A total of 65 patients with HR-NICE were collected from the Sixth Affiliated Hospital of Shanghai Jiaotong University as a validation group, and their modified Essen scores, high-resolution magnetic resonance vessel wall images, and clinical data were collected. The culprit plaques were analyzed using VesselExplorer2 software. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for recurrence, and a nomogram was constructed using R software to evaluate the discrimination of the model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to evaluate the model performance. Calibration curves and Decision Curve Analysis (DCA) were used to evaluate the model efficacy. Results: Intra-plaque hemorrhage (OR = 3.592, 95% CI = 1.474-9.104, p = 0.006), homocysteine (OR = 1.098, 95% CI = 1.025-1.179, p = 0.007), and normalized wall index (OR = 1.114, 95% CI = 1.027-1.222, p = 0.015) were significantly higher in the recurrent stroke group than in the non-recurrent stroke group, and were independent risk factors for recurrent stroke. The performance of the nomogram model (AUC = 0.830, 95% CI: 0.769-0.891; PR-AUC = 0.628) was better than that of the modified Essen scoring model (AUC = 0.660, 95% CI: 0.583-0.738) and the independent risk factor combination model (AUC = 0.827, 95% CI: 0.765-0.889). The nomogram model still had good model performance in the validation group (AUC = 0.785, 95% CI: 0.671-0.899), with a well-fitting calibration curve and a DCA curve indicating good net benefit efficacy for patients. Conclusion: High-resolution vessel wall imaging combined with a modified Essen score can effectively assess the recurrence risk of HR-NICE patients, and the nomogram model can provide a reference for identifying high-risk populations with good clinical application prospects.
RESUMO
Seipin deficiency is responsible for type 2 congenital generalized lipodystrophy with severe loss of adipose tissue and can lead to hepatic steatosis, insulin resistance (IR), and dyslipidemia in humans. Adipose tissue secretes many adipokines that are central to the regulation of metabolism. In this study, we investigated whether transplantation of normal adipose tissue could ameliorate severe hepatic steatosis, IR, and dyslipidemia in lipoatrophic seipin knockout (SKO) mice. Normal adipose tissue from wild-type mice was transplanted into 6-wk-old SKO mice. At 4 mo after adipose tissue transplantation (AT), the transplanted fat survived with detectable blood vessels, and the reduced levels of plasma leptin, a major adipokine, were dramatically increased. Severe hepatic steatosis, IR, and dyslipidemia in SKO mice were ameliorated after AT. In addition, abnormal hepatic lipogenesis and ß-oxidation gene expression in SKO mice were improved after AT. Our results suggest that AT may be an effective treatment to improve lipodystrophy-associated metabolic disorders.