磁共振功能成像技术在糖胖症相关肾损伤中的应用研究进展
Progress in the application of functional magnetic resonance imaging in renal injury associated with glucose obesity
罗姣姣 1刘筱妮 1陈愔音1
作者信息
- 1. 湖南省人民医院(湖南师范大学附属第一医院)肾内科 湖南 长沙 410005
- 折叠
摘要
随着糖尿病及肥胖发病率的增高,两者合并存在的糖胖症及其导致的靶器官损伤已成为大家关注的热点.糖尿病肾病是糖尿病最常见的微血管并发症之一,也是导致慢性肾脏病和终末期肾脏病的重要原因.伴发肥胖的糖尿病肾病患者,可出现糖尿病肾病合并肥胖相关性肾病的病理改变,治疗难度加大、预后不佳.对其进行早期诊断和动态监测是实现精准治疗和改善预后的重要方面.肾纤维化和脂质沉积是糖尿病肾病和肥胖相关性肾病共同的重要病理特征.功能磁共振成像技术是一种非侵入性影像学检查手段,可对组织纤维化及脂质沉积特征进行有效捕捉.随着技术的发展,越来越多包含磁共振成像技术在内的功能影像学手段被应用到糖尿病肾病和肥胖肾的研究中,为疾病的早期诊断和随访监测提供了有力支撑.本文就磁共振功能成像技术在该领域的应用进展做一综述.
Abstract
With the increasing incidence of diabetes and obesity,the co-existing sugar obesity and the resulting target organ damage have become the focus of attention.Diabetic nephropathy is one of the most common microvascular complications of dia-betes,and also an important cause of chronic kidney disease and end-stage kidney disease.Diabetic nephropathy patients with obesity may have pathological changes of diabetic nephropathy combined with obesity-related nephropathy,and the treatment is more difficult and the prognosis is poor.Early diagnosis and dynamic monitoring are important aspects to achieve accurate treat-ment and to improve prognosis.Renal fibrosis and lipid deposition are common pathological features of diabetic nephropathy and obesity-related nephropathy.Functional magnetic resonance imaging(FMRI)is a non-invasive imaging method,which can effec-tively capture the characteristics of tissue fibrosis and lipid deposition.With the development of technology,more and more func-tional imaging means,including magnetic resonance imaging technology,have been applied to the research of diabetic nephropa-thy and obese kidney,which provides strong support for the early diagnosis and follow-up monitoring of diseases.In this paper,the application of functional magnetic resonance imaging in this field is reviewed.
关键词
功能影像学/磁共振成像/糖尿病肾病/肥胖相关性肾病Key words
Functional imaging/Magnetic resonance imaging/Diabetic nephropathy/Obesity-related nephropathy引用本文复制引用
出版年
2024
NETL
NSTL
万方数据