The Montreal cognitive assessment: normative data from a large, population-based sample of Chinese healthy adults and validation for detecting vascular cognitive impairment - Wei, Qiang; Du, Baogen; Liu, Yuanyuan; Cao, Shanshan; Yin, Shanshan; Zhang, Ying; Ye, Rong; Bai, Tongjian; Wu, Xingqi; Tian, Yanghua; Hu, Panpan; Wang, Kai (Frontiers in Neuroscience, 2024)
The Montreal Cognitive Assessment (MoCA) is a valuable tool for detecting cognitive impairment, widely used in many countries. However, there is still a lack of large sample normative data and whose cut-off values for detecting cognitive impairment is considerable controversy. Methods: The assessment conducted in this study utilizes the MoCA scale, specifically employing the Mandarin-8.1 version. This study recruited a total of 3,097 healthy adults aged over 20 years. We performed multiple linear regression analysis, incorporating age, gender, and education level as predictor variables, to examine their associations with the MoCA total score and subdomain scores. Subsequently, we established normative values stratified by age and education level. Finally, we included 242 patients with vascular cognitive impairment (VCI) and 137 controls with normal cognition, and determined the optimal cut-off value of VCI through ROC curves.The participants in this study exhibit a balanced gender distribution, with an average age of 54.46 years (SD=14.38) and an average education period of 9.49 years (SD=4.61). The study population demonstrates an average MoCA score of 23.25 points (SD=4.82). The multiple linear regression analysis indicates that MoCA total score is influenced by age and education level, collectively accounting for 46.8% of the total variance. Higher age and lower education level are correlated with lower MoCA total scores. A score of 22 is the optimal cut-off value for diagnosing Vascular cognitive impairment (VCI).This study offered normative MoCA values specific to the Chinese adults. Furthermore, this study indicated that a score of 26 may not represent the most optimal cut-off value for VCI. And for detecting VCI, a score of 22 may be a better cut-off value.