4.4 Article

Association between small dense low-density lipoprotein cholesterol and neuroimaging markers of cerebral small vessel disease in middle-aged and elderly Chinese populations

期刊

BMC NEUROLOGY
卷 21, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12883-021-02472-6

关键词

LDL subclasses; Small and dense LDL; Cerebral small vessel disease; Burden; MRI markers

资金

  1. Nanjing Medical Science and Technique Development Foundation [QRX17032, YKK20203]
  2. Clinical Medical Science and Technology Development Fund of Jiangsu University [JLY2021153]
  3. Kangda College of Nanjing Medical University Science and Research Development Project [KD2019KYJJYB047, KD2020KYJJZD010]
  4. Nanjing Medical University Science and Technology Development Project [NMUB2019240]

向作者/读者索取更多资源

The study identified small and dense low-density lipoprotein cholesterol (sdLDL-C) as an independent risk factor for cerebral small vascular disease (CSVD). A new prediction model based on LDL-C3 and LDL-C4 was established to help clinicians identify high-risk CSVD patients. Levels of sdLDL-C should be considered in the assessment and management of CSVD.
Background Cerebral small vascular disease (CSVD) is one of the leading causes of death in the aged population and is closely related to abnormalities in low-density lipoprotein cholesterol (LDL-C). Our study aims to clarify the relationship between small and dense low-density lipoprotein cholesterol (sdLDL-C) (a subcomponent of LDL-C) and neuroimaging markers of CSVD. Methods In total, 1211 Chinese adults aged >= 45 years with cranial magnetic resonance imaging (MRI) were recruited in this retrospective study from January 2018 to May 2021. Serum lipids and other baseline characteristics were investigated in relation to the occurrence of CSVD. A logistic regression model was performed to analyze the relationships between LDL subtypes and CSVD risk, and the Pearson correlation coefficient was used to analyze the correlation between clinical characteristics and CSVD risk. ROC curves and AUCs were created and depicted to predict the best cutoff value of LDL-C subtypes for CSVD risk. Based on these data, we performed comprehensive analyses to investigate the risk factors for CSVD. Results Ultimately, 623 eligible patients were included in the present study. Of the 623 eligible patients, 487 were included in the CSVD group, and 136 were included in the group without CSVD (control group). We adjusted for confounders in the multivariate logistic regression model, and LDL-C3 was still higher in the CSVD patients than in the group of those without CSVD (OR (95% CI), 1.22(1.08-1.38), P < 0.05). Pearson correlation showed that there was a positive correlation between the levels of LDL-C3, LDL-C4, LDL-C5, glucose, age, hypertension, previous ischemic stroke and CSVD risk (r > 0.15, P < 0.01). Moreover, the best cutoff value of LDL-C3 to predict CSVD was 9.5 mg/dL with 68.4% sensitivity and 72.8% specificity, and the best cutoff value of LDL-C4 to predict CSVD was 5.5 mg/dL with 50.5% sensitivity and 90.4% specificity. Conclusion The results indicate that LDL-C3 is an independent risk factor for CSVD. A new prediction model based on LDL-C3 and LDL-C4 can help clinicians identify high-risk CSVD, even in people with normal LDL-C levels. The levels of sdLDL-C should be considered in the assessment and management of CSVD.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据