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A HIERARCHICAL BAYESIAN LATENT CLASS MODEL FOR THE DIAGNOSTIC PERFORMANCE OF MINI-MENTAL STATE EXAMINATION AND MONTREAL COGNITIVE ASSESSMENT IN SCREENING MILD COGNITIVE IMPAIRMENT DUE TO ALZHEIMER’S DISEASE

 

X. Wang1,2,*, F. Li1,2,*, H. Zhu1,2, Z. Jiang1,2, G. Niu1,2, Q. Gao1,2

1. Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, P. R. China; 2. Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China; * Xiaonan Wang and Fengjie Li contributed equally to this study and were co-first authors.

Corresponding Author: Qi Gao, PhD, Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No. 10, Xi Toutiao You Anmenwai, Beijing 100069, China. Tel.: +010 83911497; E-mail: gaoqi@ccmu.edu.cn

J Prev Alz Dis 2022;
Published online August 29, 2022, http://dx.doi.org/10.14283/jpad.2022.70

 


Abstract

Background: The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are low costing and noninvasive neuropsychological tests in screening Mild Cognitive Impairment (MCI) due to Alzheimer’s disease (AD). There is no consensus on which test performs better in detecting MCI due to AD based on the different imperfect reference standards. Therefore, we conducted a meta-analysis to assess the diagnostic performance of MMSE and MoCA for screening MCI due to AD in the absence of a gold standard.
Methods: Six electronic databases were searched for relevant studies until April, 2022. A hierarchical Bayesian latent class model was used to estimate the pooled sensitivity and specificity of MoCA and MMSE in the absence of a gold standard.
Results: 90 eligible studies covering 21273 individuals for MMSE, 26631 individuals for MoCA were included in this meta-analysis. The pooled sensitivity was 0.71(95%CI: 0.67-0.74) for MMSE and 0.85(95%CI: 0.83-0.88) for MoCA, while the pooled specificity was 0.71(95%CI: 0.68-0.74) for MMSE and 0.79(95%CI: 0.76-0.81) for MoCA. MoCA was useful to “rule in” and “rule out” the diagnosis of MCI due to AD with higher positive likelihood ratio (4.07; 95%CI: 3.60-4.62) and lower negative likelihood ratio (0.18; 95%CI: 0.16-0.22). Moreover, the diagnostic odds ratio of MoCA was 22.08(95%CI: 17.24-28.29), which showed significantly favorable diagnostic performance.
Conclusions: It suggests that MoCA has greater diagnostic performance than MMSE for differentiating MCI due to AD when the gold standard is absent. However, these results should be taken with caution given the heterogeneity observed.

Key words: MCI, MoCA, MMSE, sensitivity and specificity, without a gold standard, meta-analysis.


 

 

Introduction

Alzheimer’s disease (AD), the leading cause of death in dementia, is one of the greatest medical challenges in global health with the increasing aging population (1). Mild cognitive impairment (MCI) is conceptualized as a transitional stage between normal aging and dementia, and can be due to a variety of diseases, including AD and non-AD as well as various mental and physical disorders (2). It is a decline in cognitive ability which seriously affects the person’s activities of daily living. The concept of “MCI due to AD” refers to the symptomatic predementia phase of AD (2, 3). It was estimated that the MCI progressed to AD at rates of 10% to 15% per year (2, 4). Notably, subjects with MCI may revert to normal cognition when MCI patients are promptly detected and given timely and effective treatments (5). Therefore, accurate diagnosis of MCI in time is of significant importance for older people.
The international diagnostic guidelines for MCI are Petersen’s criteria (2, 6), recommendations from National Institute on Aging and the Alzheimer’s Association (NIA-AA) (3, 7), National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) (8), International Working Group (IWG) (9) and Diagnostic and Statistical Manual of Mental Disorders (DSM) (10), mainly depending on the clinical diagnosis and biomarkers in clinical and research fields. However, it is difficult to obtain the biomarkers and imaging information for older people who have not been admitted to the hospital. Thus, these criteria are not easily used to detect MCI at early stage. Neuropsychological tests referring to Mini-Mental State Examination (MMSE) (11) and Montreal Cognitive Assessment (MoCA) (12) are simple, low costing and noninvasive methods to detect cognitive status. Therefore, it is necessary to evaluate the performance of MMSE and MoCA in diagnosis of MCI due to AD.
Numerous studies stated that the accuracy of MMSE and MoCA in detecting MCI due to AD were inconsistent when selecting different reference standards as “gold standard” or selecting different cutoff values for positivity. For example, the sensitivity and specificity of MMSE in diagnosis of MCI were 21.7% and 93.4% when adopting IWG criteria as a “gold standard” (13). In contrast, the sensitivity and specificity of MMSE were 83.3% and 42.4% with Petersen’s criteria (14). A similar phenomenon has occurred for MoCA in diagnosis of MCI due to AD. That is, the sensitivity and specificity were 94% and 62% with NIA-AA criteria (15), but the sensitivity and specificity were 54% and 86% based on the DSM-5 (16). In addition, one meta-analysis showed the pooled AUCs varied from 0.71 to 0.99 for MoCA and 0.43 to 0.94 for MMSE in differentiating MCI (17). Difference in positivity thresholds for neuropsychological tests is another source of inconsistency in diagnostic performance (18). To date, there is still no consensus on which neuropsychological test performs better in detecting MCI due to AD, and which international criteria are more suitable as the reference standard in evaluating the accuracy of diagnostic tools.
Actually, the gold standard detecting MCI due to AD is ambiguous. The previous literatures including meta-analysis did not consider the imperfect gold standard bias problem in evaluating the performance of MMSE and MoCA for the diagnosis of MCI due to AD. Simple averaging or pooling across studies may give misleading results in reviews and meta-analysis (18). Therefore, we used a statistical method in meta-analysis for assessing the performance of MMSE and MoCA in diagnosis of MCI due to AD without a gold standard, to justify the imperfect gold standard bias. We recommended the hierarchical summary receiver operating characteristic (HSROC) model to measure the variation in sensitivity and specificity when different cutoff values for positivity were used in the included diagnostic accuracy studies (18). Moreover, Dendukuri et al. extended the HSROC and developed hierarchical Bayesian latent class model to evaluate the accuracy of diagnostic tests in the absence of a gold standard (19). There is no meta-analysis on assessing the accuracy of MMSE and MoCA in diagnosis of MCI due to AD when a reference standard is absent in current studies. Therefore, the objective of this meta-analysis is to evaluate and compare the diagnostic performance of MoCA and MMSE in detecting MCI due to AD from cognitively healthy individuals by a hierarchical Bayesian latent-class model in the absence of a gold standard.

 

Methods

Search strategy

The study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines (20) (Supplementary. Table S1).
We conducted a comprehensive search to identify relevant studies in PubMed, Web of Science, China National Knowledge Infrastructure, Chinese Wan Fang Database, China Science and Technology Journal Database and Cochrane Library through March 31, 2022. Our search strategy items for MoCA included “Mild Cognitive Impairment”, “Alzheimer’s disease”, “Montreal Cognitive Assessment” combined with the abbreviations and “Sensitivity and Specificity”. The similar search strategy was performed for MMSE. Reference lists of identified studies were double checked to discover other potentially relevant studies. The pre-specified search strategy in PubMed is provided in the Supplementary. TableS2.

Inclusion and exclusion criteria

Studies were considered eligible if they satisfied the following criteria: (1) involved patients with MCI due to AD; (2) used MMSE (11) or MoCA (12) to detect MCI from cognitively healthy individuals; (3) recommended an appropriate reference standard to detect MCI, including Petersen’s criteria, the DSM criteria, NINCDS-ADRDA, NIA-AA and IWG criteria; (4) reported sufficient data to reconstruct a 2×2 contingency table for meta-analysis.
Studies were excluded if one of the following existed: (1) case-control studies; (2) patients with cognitive impairment due to a current or history of neurologic, psychiatric, systemic diseases or psychoactive drug use; such as post-stroke, Parkinson’s disease, cerebral infarction, vascular dementia, Lewy bodies dementia, multiple sclerosis and brain injury; REM sleep behavior disorder, obstructive sleep apnea; HIV, chronic obstructive pulmonary disease, systemic lupus erythematosus and so on; (3) used MMSE or MoCA as a part of reference standard; (4) non-English and non-Chinese language publications. Two authors retrieved and screened the primary studies independently. The detailed selection criteria were summarized in the flow diagrams (Figure 1).

Data extraction

Two authors independently extracted the data from available eligible studies. We recorded the key characteristics in an evidence table, including first author, year of publication, country, sample size, number of patients with MCI due to AD, number of the healthy individuals, mean age of participants, percentage of female, mean education of participants, reference standard, cognitive screening test and cutoff point. In addition, we extracted the results of true positive (TP), false negative (FN), false positive (FP), true negative (TN), as well as sensitivity and specificity to calculate numbers for reconstruction of a 2×2 contingency table in the meta-analysis. When a study reported results of sensitivity and specificity across different cutoff values for MMSE and MoCA, we only selected the results with the optimal cutoff value or a recommended cutoff by the authors. When pertinent data were not found in the published part, we contacted with the corresponding author online. Otherwise, the article would be discarded if there was no response. Discrepancies in study eligibility and data extraction were resolved by discussion with two other investigators.

Quality assessment

Two authors independently evaluated the risk of bias and concerns about the applicability of diagnostic accuracy studies using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool (21), which assessed the methodological quality of eligible articles from four domains: patient selection; index test; reference standard; flow and timing. The risk of bias and concerns about applicability of eligible articles were judged as ‘low’, ‘unclear’ or ‘high’ results in each domain. Any divergences on the methodological quality of eligible articles reached a consensus through negotiation with two other authors.

Statistical methods

Pooled outcomes were calculated by converting the results of MMSE and MoCA into dichotomous variable (MCI due to AD or cognitively healthy individuals). We calculated TP, FN, FP, TN value to tabulate 2 x 2 contingency tables by sensitivity, specificity and the number of subjects with MCI and cognitively healthy individuals. The gold standard detecting MCI due to AD is unclear. Adopting international diagnostic guidelines as the reference standard to evaluate the accuracy of MMSE and MoCA in diagnosis of MCI due to AD would give biased estimations of sensitivity and specificity. We used the HSROC method of Rutter and Gatsonis (18) to model the variation in diagnostic accuracy and cutoff values for positivity, both well-recognized as sources of heterogeneity across diagnostic studies (19). A hierarchical Bayesian latent class model enabled the calculation of the pooled sensitivity, specificity, positive and negative likelihood ratio (LR+, LR-), diagnostic odds ratios (DOR) and their corresponding 95% confidence intervals (CIs) in the absence of a gold standard. This statistic method provided a more precise estimates of the diagnostic accuracy (19). HSROC curves were drawn to show the diagnostic performance of MoCA and MMSE under different cutoff values.
Likelihood ratios (LRs) compare the probability that a test result is correct to the probability that the test is incorrect for the target disease, including LR+ and LR-. The specific calculation formulas are as follows: LR+=sensitivity/(1-specificity); LR-= (1-sensitivity)/specificity. LRs have unique useful properties for clinician decision-makers with higher LR+ meaning “rule in disease” and lower LR- meaning “rule out disease” in clinical (22). Moreover, according to the Bayes theorem, the post-test probability of MCI due to AD was obtained by multiplying the pre-test odds by the LR of the test. The pre-test probability of MCI was 40% estimated from the prevalence of all included studies. The results were displayed with Fagan plot.
The DOR value of a test is the ratio of the odds of positivity in MCI relative to the odds of positivity in the non-MCI, with higher values indicating better discriminatory test performance (23).
Heterogeneity across studies was assessed by the inconsistency index (I2) and Cochrane Q statistic, with I2 > 50% or p < 0.05 suggesting significant heterogeneity (24). Meta-regression analyses were performed to explore the possible sources of heterogeneity. Deeks’ funnel plot of the DOR were performed to detect the potential publication bias, with p < 0.05 indicating publication bias (25).
Results of the meta-analysis were conducted by Stata (version 14.0), R (version 4.1.0) and the Cochrane Collaboration Review Manager (version 5.4) statistical software.

 

Results

Results of study identification and characteristics

Totally, 90 articles covering at least one of the two cognitive screening tests (MoCA and MMSE) were available in this meta-analysis according to the inclusion criteria (Figure 1). Of these available studies, 53 datasets were for MMSE (Figure 1a) and 63 datasets were for MoCA (Figure 1b) to detect MCI from cognitively healthy individuals.
For all studies, the sample size ranged from 36 to 7970. A total of 21273 individuals for MMSE, 26631 individuals for MoCA were included. Among the included 90 articles, 31 were collected from China, 10 from the United States, 5 from Korea and Brazil respectively and the rest from Singapore, Japan, Poland, Israel, Netherlands and other 24 countries. Actually, 52 studies were located in Asia and 37 were mainly located in North or South America, Africa and Europe. The cutoff values showed great variability in the MMSE and MoCA when screening MCI due to AD, ranging from 19 to 29 and 19 to 27, respectively. The detailed characteristics of the included studies were shown in Supplementary. TableS3.

Figure 1. PRISMA flow chart of study selection for inclusion in the meta-analysis

Results of quality assessment

We used QUADAS-2 tool to evaluate the risk of bias and applicability concerns of each included article. Moderate methodological quality among the included studies was presented (Supplementary. Figure S1). Regarding of the generally low concerns of applicability for patient selection, index test and reference standard, none of the studies was deemed necessary to exclude from the meta-analysis.

Results of meta-analysis

The pooled sensitivity, specificity, LR+, LR- and DOR were estimated by the hierarchical Bayesian latent class model in the absence of a gold standard (Table 1). As it turned out, MoCA showed a better pooled sensitivity with 0.85 (95% CI: 0.83 to 0.88) and specificity with 0.79 (95%CI: 0.76 to 0.81) in screening MCI from cognitively healthy individuals. The pooled sensitivity of MMSE was rather poor with 0.71 (95%CI: 0.67 to 0.74) and its pooled specificity was 0.71 (95%CI: 0.68 to 0.74). Both sensitivity and specificity of MoCA performed much better than that of MMSE. The HSROC curves made it possible to better illustrate the diagnostic accuracy of MoCA and MMSE more intuitively (Figure 2). The estimated sensitivity and specificity of each eligible study was presented in the forest plots (Figure 3). As for LR, the LR+ was 4.07 (95%: 3.60 to 4.62) for MoCA and 2.48 (95%: 2.25 to 2.73) for MMSE, respectively. The LR- was 0.18 (95%: 0.16 to 0.22) for MoCA and 0.41 (95%: 0.37 to 0.45) for MMSE. Moreover, the DOR was 22.08 (95%CI: 17.24 to 28.29) for MoCA and 6.04 (95%CI: 5.17 to 7.05) for MMSE. MoCA showed significantly great diagnostic performance with around four times DOR of MMSE in detecting MCI due to AD.

Table 1. Results of meta-analysis and Bayes analysis

Figure 2. Hierarchical summary receiver operating characteristic (HSROC) curves for screening MCI due to AD

Figure 3. Forest plot for sensitivity and specificity

 

Results of Bayes analysis

Based on Bayes’ theorem, we performed Fagan plot analysis to evaluate the clinical utility of the two cognitive tests in diagnosis of MCI due to AD. The pre-test probability we evaluated was 40% and the corresponding post-test probability was presented with Fagan’s nomograms (Figure 4). The post-test probability of MCI due to AD following a positive test result was 73% for MoCA and 62% for MMSE when the pre-test probability was 40%, suggesting that MoCA was more informative to “rule in” MCI due to AD. Meanwhile, MoCA was more useful to “rule-out” diagnosis with a lower 11% post-test probability of MCI than MMSE (21%), following a “negative” result.

Figure 4. Fagan plot analysis to evaluate the diagnostic accuracy of MMSE and MoCA for diagnosing MCI due to AD

The vertical axis on the left represents the pre-test probability, the middle representing the likelihood ratio (LR), and the right vertical axis represents the post-test probability.

 

Results of heterogeneity exploration and publication bias

The result of Q-test showed significant heterogeneity (p < 0.01) and the Higgins I2 statistics illustrated remarkable degree of inconsistency in terms of the sensitivity (I2MMSE =82.81%, I2MoCA=79.71%) and specificity (I2MMSE =87.91%, I2MoCA =89.92%) of MMSE and MoCA, respectively (Figure 3). We carried out meta-regression analyses to examine the possible sources of potential heterogeneity in sensitivity and specificity. The results showed reference standard, publication year, country location and number of patients was significantly associated with the heterogeneity of sensitivity and specificity of MMSE (Supplementary.TableS4) as well as MoCA (Supplementary. Table S5) with p < 0.05(Supplementary. Figure S2), respectively. Both the sensitivity and specificity of MOCA outperformed MMSE in screening MCI due to AD regardless of which factors adjusted for in meta-regressions, with 0.64-0.74 sensitivity, 0.68-0.77 specificity for MMSE and 0.84-0.91 sensitivity, 0.78-0.85 specificity for MoCA (Supplementary.TableS4-TableS5).
The Deeks’ funnel plot showed that the included studies in this meta-analysis were distributed symmetrically, indicating no publication bias among literatures of MMSE (p = 0.20) and MoCA (p = 0.96) (Supplementary. Figure S3).

 

Discussion

In our meta-analysis, we assessed the diagnostic accuracy of MoCA and MMSE for detecting MCI due to AD when the gold standard was absent. The results showed that MoCA performed a better diagnostic accuracy with sensitivity of 0.85 (95%CI: 0.83 to 0.88), specificity of 0.79 (95%CI: 0.76 to 0.81) and DOR of 22.08 (95%CI: 17.24 to 28.29). The sensitivity, specificity and DOR of MMSE was 0.71 (95%CI: 0.67 to 0.74), 0.71 (95%CI: 0.68 to 0.74) and 6.04 (95%CI: 5.17 to 7.05). As screening tools, MoCA is superior to MMSE in diagnosis of MCI due to AD. The results could verify the conclusion published by Pinto et al. (17) and Ciesielska et al. (26) regardless of the imperfect gold standard bias.
In previous meta-analysis, Mitchell et al. (27) suggested that the MMSE had very limited value and modest rule-out accuracy in diagnosis of MCI against cognitively healthy individuals. Actually, MoCA was developed mainly to assess patients with mild cognitive complaints who usually performed in the normal range on the MMSE (12). However, the diagnostic accuracy of MMSE and MoCA in diagnosis of MCI due to AD was not synthetically evaluated when the gold reference standards was absent. The results of this study have important clinical implications to choose a more accurate diagnostic scale for early detection of MCI.
Applying HSROC model was methodological innovation compared with previous general meta-analyses of diagnostic accuracy tests. Hierarchical Bayesian latent-class meta-analysis was used to justify the imperfect gold standard bias. To the best of our knowledge, several systematic reviews had summarized the diagnostic value of cognitive tests to distinguish certain dementia using bivariate random-effects model and HSROC model (28-30). Our conclusion was consistent with the secondary outcome from a subgroup analysis of Kelvin’s study, indicating the screening tool MoCA was superior to MMSE in the identification of MCI by the HSROC model. Kelvin’s study showed a summary point of 0.62 sensitivity (95% CI: 0.52 to 0.71) and 0.87 specificity (95%CI: 0.80 to 0.92) of MMSE, a summary point of 0.89 sensitivity (95% CI: 0.84 to 0.92) and 0.75 specificity (95%CI: 0.62 to 0.85) of MoCA for the detection of MCI (29). There was a slight difference that the specificity of MMSE was higher than that of our results. Arevalo-Rodriguez et al. and Creavin et al. performed a HSROC method to evaluate the diagnostic accuracy of MMSE for the detection of dementia, but these studies did not analyze the performance of MoCA and MMSE for MCI patients due to insufficient data in the primary studies (28, 30). In the previous meta-analysis, we evaluated the accuracy of MoCA and MMSE in detecting dementia associated with AD using the hierarchical Bayesian latent class model, suggesting that MoCA had better performance than MMSE (31). This meta-analysis focuses on patients with MCI due to AD so the population is different from the previous study. Otherwise, no other than that review evaluated the diagnostic accuracy of MoCA and MMSE simultaneously for discriminating MCI using the HSROC model. This study made up for the limitation.
Majority studies were performed in China and America. The cutoff values used in the original studies of the MMSE for MCI ranged from 24 to 28 in China and 22 to 29 in America. It was mainly concentrated at 26/27 in China, 27/28 in America. As for MoCA, the cutoff values ranged from 22 to 27 in China, 23 to 25 in America and was mainly concentrated at 24/25 in China, 23/24 in America. The results were similar to previous studies that MMSE and MoCA for MCI best detection could be achieved with cutoffs of 24/25 and 27/28, respectively (26). The cutoff values showed great variability in the MMSE and MoCA when screening MCI due to AD. Moreover, education adjusted cutoffs were set at 13/14 for MoCA (32) and MMSE<20 (33) for illiterate individuals. However, the HSROC model in this meta-analysis could measure the variation in sensitivity and specificity when different cutoff values for positivity were used in the included diagnostic accuracy studies.
The methodological evaluation of available eligible studies showed that the “index test” domain as well as “flow and time” domain had greater risk of bias. In most of the cases, selection of optimal cutoff values for positivity might result in risk of bias in “index test” domain. Lack of detailed implementation process in evaluating cognitive state of older people in original studies might contribute to risk of bias in “flow and time” domain. Besides, in order to select the corresponding population of MCI due to AD, not all subjects in some studies were included in this meta-study. This would also lead to risk of bias in “flow and time” domain.
This meta-analysis had several advantages. On the one hand, dozens of primary studies were eligible for MoCA and MMSE in diagnosis of MCI due to AD. On the other hand, HSROC model is suitable for the pooled estimation of different diagnostic thresholds for positivity in each original study (18). We performed a hierarchical Bayesian latent-class model to simultaneously compare the diagnostic accuracy of MoCA and MMSE in the detection of MCI due to AD without a gold standard. Moreover, we constructed exhaustive search strategies and ensured that all possible studies were included. As a result, Deeks’ plots of DOR for MMSE and MoCA suggested that there was no publication bias in this meta-analysis.
Otherwise, this study had several limitations. First, numerous primary studies included in meta-analysis could give rise to a possibility of heterogeneity. As a limitation, this study presented a significant heterogeneity (I2 > 50%). It was worth noting that education level was a factor affecting the accuracy of MOCA and MMSE (34-36). Different education levels lead to different cutoff values of MOCA and MMSE for MCI, affecting their accuracy as well (32, 33). The MoCA showed modest accuracy and was no better than MMSE in detecting MCI patients with low education in some researches (34, 36). Besides, the MMSE had a better sensitivity (37, 38) or a better specificity than MoCA (39-42) in different language versions of scales. These aspects might account for the heterogeneity. However, full exploration was not available since that some sources of heterogeneity such as blind methods, subject selection methods, language versions or educational cutoffs were not clearly described in a large portion of primary studies. For all this, we conducted meta-regressions to explore possible sources of heterogeneity among included studies. Stratifying studies by reference standard, publication year, country location or the number of patients could explain the heterogeneity in the meta-analysis. Both the sensitivity and specificity of MOCA performed better than MMSE in screening MCI due to AD, regardless of the adjustment for any of these factors above in meta-regression analysis. Second, the diagnostic accuracy estimates of MoCA and MMSE are all «within-sample» and no cross validation or external validation in original studies. Therefore, this study just summarized previous results for meta-analysis and the results might be probably overly optimistic. Overall, MoCA performed better in both meta-analysis and meta-regression analyses. Third, NIA-AA created separate sets of diagnostic guidelines for MCI due to AD and biomarkers were significant to define MCI (7). However, information of biomarkers was not obtained from all primary studies so that we could not verify the diagnosis of MCI based on biomarkers. MMSE and MOCA have a much better chance of predicting purely clinical diagnosis of MCI while biomarkers are useful to predicting prodromal-MCI. Further studies are supposed to compare the diagnostic accuracy of different combination of biomarkers in detecting MCI.

 

Conclusion

In conclusion, this meta-analysis suggests that MoCA has favorable diagnostic performance for differentiating MCI due to AD from cognitively healthy individuals when the gold standard for MCI due to AD is absent in clinical. MoCA is a promising method with higher sensitivity and specificity than that of MMSE. Future high-quality studies with lower heterogeneity and more sources of heterogeneity among studies are supposed to explore to verify the pooled results.

Acknowledgements & Funding: This study was funded by National Natural Science Foundation of China (Grant No. 81903408), and Beijing Excellent Talents Training Funding Project (Grant No. 2018000020124G136). The funder/sponsor had no role in the design and conduct of the study, collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Conflicts of Interest: The authors have no conflict of interest to report.

Author’s contribution: X.N.W: Conceptualization; Funding acquisition; Methodology; Project administration; Resources; Software; Supervision; Validation; Writing – review & editing. F.J.L: Data curation; Formal analysis; Investigation; Methodology; Project administration; Software; Validation; Visualization; Writing – original draft; Writing – review & editing; H.P.Z: Conceptualization; Project administration; Supervision; Validation; Writing – review & editing. Z.J: Data curation; Formal analysis; Investigation; Software; Supervision; Validation. G.Y.N: Data curation; Investigation; Software; Supervision; Validation; Visualization. Q.G: Conceptualization; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing – review & editing. All authors read and approved the final manuscript.

 

SUPPLEMENTARY MATERIAL1

SUPPLEMENTARY MATERIAL2

 

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