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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Systematic Review and Meta-Analysis on Heavy Metals and Trace Elements in Mild Cognitive Impairment
Yaxin Dong1, Lu Ren1, TianRan Tang2
1Shanghai University of Medicine & Health Sciences, No. 279, Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China.
None:
Mild cognitive impairment (MCI) represents a prodromal stage of various neurodegenerative diseases. However, heterogeneity persists regarding heavy metal and trace element levels in MCI patients. PubMed, Web of Science, and Chinese academic databases were systematically searched from 2000 to the present, and 43 case-control/cross-sectional studies on the levels of heavy metals and trace elements in MCI and healthy controls were included, and the standardized mean difference (SMD) of 12 elements (Al, As, Cd, Pb, Cu, Se, Zn, etc.) was evaluated by using a random-effects model. A total of 16,743 samples were included. Al (SMD: 0.73, 95%CI: 0.31-1.14), As (SMD: 0.19, 95%CI: 0.03-0.35), Cd (SMD: 0.91, 95%CI: 0.43-1.38), Cu (SMD: 0.69, 95%CI. 0.27-1.10), Pb (SMD: 0.77, 95%CI: 0.30-1.24) were significantly elevated in the MCI patients, while Se (SMD: -0.28, 95%CI: -0.56-0.01) and Zn (SMD: -0.77, 95%CI: -1.22-0.33) were significantly lower. No statistical differences were observed for the remaining elements (Fe, Mg, etc.). Levels of specific heavy metals and trace elements in MCI patients differ from those in healthy controls. Monitoring and modulating these element levels may provide novel targets for early intervention strategies.
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