Abnormal cerebral micro-structures in end-stage renal disease patients related to mild cognitive impairment

医学 蒙特利尔认知评估 终末期肾病 内科学 认知 疾病 肾功能 生物标志物 血液透析 痴呆 心脏病学 精神科 生物化学 化学
作者
Jiahui Zheng,Zhuqing Jiao,Jiankun Dai,Tongqiang Liu,Haifeng Shi
出处
期刊:European Journal of Radiology [Elsevier BV]
卷期号:157: 110597-110597 被引量:8
标识
DOI:10.1016/j.ejrad.2022.110597
摘要

To investigate the mediating effect of abnormal brain micro-structures on the relationship between clinical risk factors and mild cognitive impairment (MCI), and further predict individual cognitive function in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis.In total, 40 ESRD patients and 30 healthy controls were prospectively enrolled in our study. All subjects completed diffusion kurtosis imaging (DKI) examinations and Montreal cognitive assessment (MoCA) test. Between-group differences in the DKI metrics were analyzed. In addition, the mediating effects of altered brain micro-structures on the association between clinical risk factors and MCI were determined by mediation analysis. Finally, cognitive function was predicted based on DKI metrics and clinical characteristics by applying the optimized least squares support vector regression machine.We observed disrupted brain micro-structures in ESRD patients with MCI, as indicated by significantly altered DKI parameters. Significant correlations were found between the DKI metrics, clinical characteristics, and MoCA scores. In ESRD patients, low hemoglobin level and high serum creatine level were clinical risk factors for MCI. A decreased axial kurtosis value in the left hippocampus may partially mediate the impact of serum creatine on MCI. Furthermore, the prediction model could predict cognitive scores associated with ESRD with relatively high accuracy.Aberrant micro-structures partially mediated the association between clinical risk factors and MCI, which is a novel insight into the progression of cognitive dysfunction in ESRD patients. Combined DKI metrics and clinical characteristics could be used as features to efficiently predict cognitive function associated with ESRD.
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