基于质谱的代谢和脂质分析分层糖尿病视网膜病变的阶段
Junwei Fang1, Hanying Wang1, Tian Niu1
1National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
Diabetes research and clinical practice
|August 24, 2025
概括
代谢和脂质分析显示了糖尿病视网膜病变进展的关键代谢变化. 机器学习为早期诊断和分期这种糖尿病并发症确定了新的生物标志物.
科学领域:
- 生物化学
- 代谢学
- 脂质组学
- 数据科学
背景情况:
- 糖尿病视网膜病变 (DR) 是视力丧失的主要原因,也是糖尿病的显著微血管并发症.
- 了解DR进展的代谢基础对于开发有效的诊断和治疗策略至关重要.
研究的目的:
- 研究与糖尿病视网膜病变进展相关的代谢和脂质学变化.
- 通过综合代谢和脂质学数据确定糖尿病视网膜病变的新生物标志物.
主要方法:
- 来自167名患者的血清样本 (45名没有视网膜病变,69名非增殖性DR,53名增殖性DR) 分析了962个分子 (653个脂质,309个代谢物).
- 用单个体质数据的统计分析和机器学习算法来整合新陈代谢和脂质体质资料.
- 进行特征选择以识别区分疾病阶段的关键分子.
主要成果:
- 在糖尿病视网膜病变的不同阶段观察到 purin,sphingolipid,tyrosine 和 glycine-serine-threonine 代谢的显著变化.
- 糖尿病视网膜病变的进展与斯芬哥米林,酸盐胆和酸盐胆水平的降低有关.
- 在分类疾病阶段方面,K-近邻算法表现出卓越的性能,其中脂质ST 24:1;O4和代谢物β-基酸被确定为主要贡献者.
结论:
- 综合代谢学和脂质学,加上机器学习,提供了糖尿病视网膜病变的准确分层.
- 在这项研究中发现的新型分子标记可能有助于早期诊断和针对性干预糖尿病视网膜病变.
- 这种方法有望改善糖尿病视网膜病变的治疗结果.
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