Background: All versions of the Montreal Cognitive Assessment (MoCA) lack population-based data of 80-plus individuals. The norms and cut-off scores for mild cognitive impairment (MCI) and dementia of the MoCA are different among five Chinese versions. Objective: To provide the cut-off scores in detecting MCI and dementia of the Peking Medical Union College Hospital version of the MoCA (MoCA-P). Methods: In a cross-sectional survey, Chinese veterans aged ≥60 years completed the MoCA-P and the Mini-Mental State Examination (MMSE). Results: Among 7,445 elderly veterans, 5,085 (68.30%) were aged ≥80 years old, 2,621 (35.20%) had 6 years of education or less, 6,847 (91.97%) were male, and 2,311 (31.04%) and 984 (13.22%) veterans were diagnosed as having MCI and dementia, respectively. Adding two points and one point to the MoCA scores for the primary and middle school groups, respectively, can fully adjust for the notable impact of education but cannot compensate for the effect of age. In the three age groups (60–79, 80–89, and ≥90 years old), the optimal MoCA-P cut-off scores for detecting MCI were ≤25, ≤24, and ≤23, respectively, and for detecting dementia were ≤24, ≤21, and ≤19, respectively, which demonstrated relatively high sensitivities and specificities. The areas under the curves for the MoCA-P for detecting MCI and dementia (0.937 and 0.908, respectively) were greater than those for the MMSE (0.848 and 0.892, respectively). Conclusion: Compared with the MMSE, the MoCA-P is significantly better for detecting MCI in the elderly, particularly in the oldest old population, and it also displays more effectiveness in detecting dementia.

Background: Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery (NTB) by machine learning and develop new classification models for distinguishing mild cognitive impairment (MCI) and dementia among individuals with MMSE ≥ 26. Methods: 375 participants with MMSE ≥ 26 were assigned a diagnosis of cognitively unimpaired (CU) (n = 67), MCI (n = 174), or dementia (n = 134). We compared the performance of five machine learning algorithms, including logistic regression, decision tree, SVM, XGBoost, and random forest (RF), in identifying MCI and dementia. Results: RF performed best in identifying MCI and dementia. Six neuropsychological subtests with high-importance features were selected to form a simplified NTB, and the test time was cut in half. The AUC of the RF model was 0.89 for distinguishing MCI from CU, and 0.84 for distinguishing dementia from nondementia. Conclusions: This simplified cognitive assessment model can be useful for the diagnosis of MCI and dementia in patients with normal MMSE. It not only optimizes the content of cognitive evaluation, but also improves diagnosis and reduces missed diagnosis.

Background: The previous studies have identified several genes in relation to Alzheimer’s disease (AD), such as ABCA7, CR1, etc. A few studies have explored the association between the common variants, mainly in the non-coding regions of these genes, and cerebrospinal fluid (CSF) biomarkers. Fewer studies target the variants in the coding regions. Objective: To illustrate the association between the common variants within or adjacent to the coding regions of AD susceptible genes and CSF biomarkers in AD patients. Methods: 75 sporadic probable AD patients were extracted from the dementia cohort of Peking Union Medical College Hospital. They all had history inquiry, physical examination, blood test, cognitive assessment, brain MRI, CSF testing of Aβ ₄₂ , ¹⁸¹ p-tau, and t-tau, and next-generation DNA sequencing. Sixty-nine common single nucleotide polymorphisms (SNPs) (minor allele frequency > 0.01) within or near the coding region of 13 AD susceptible genes were included in the analysis. Results: The rs7412-CC ( APOE) genotype showed lower CSF Aβ ₄₂ level and higher p-tau/Aβ ₄₂ ratio than the rs7412-CT genotype. The rs3752246-C ( ABCA7) allele correlated with lower CSF Aβ ₄₂ level. The alternate alleles of six ABCA7 SNPs were related to lower CSF p-tau, including rs3745842, rs3764648, rs3764652, rs4147930, rs4147934 and rs881768. The rs11609582-TT ( A2M) genotype showed higher CSF p-tau than the rs11609582-TA genotype. The p-tau/Aβ ₄₂ ratio was higher in the rs490460-TT ( BACE1) genotype relative to the rs490460-GT genotype. Conclusion: Some common variants within or near the coding regions of APOE, ABCA7, A2M, and BACE1 are associated with CSF Aβ ₄₂ , p-tau. or p-tau/Aβ ₄₂ .

Background: Neuronal Intranuclear Inclusion Disease (NIID) is a degenerative disease with heterogeneous clinical manifestations. We aim to analysis the relationship between clinical manifestations, neuroimaging and skin pathology in a Chinese NIID cohort. Methods: Patients were recruited from a Chinese cohort. Detail clinical information were collected. Visual rating scale was used for evaluation of neuroimaging. The relationship between clinical presentations and neuroimaging, as well as skin pathology was statistically analyzed. Results: Thirty-two patients were recruited. The average onset age was 54.3 y/o. 28.1% had positive family history. Dementia, autonomic nervous system dysfunction, episodic attacks were three main presentations. CSF analysis including Aβ₄₂ and tau level was almost normal. The most frequently involved on MRI was periventricular white matter (100%), frontal subcortical and deep white matter (96.6%), corpus callosum (93.1%) and external capsule (72.4%). Corticomedullary junction DWI high intensity was found in 87.1% patients. Frontal and external capsule DWI high intensity connected to form a “kite-like” specific image. Severity of dementia was significantly related to leukoencephalopathy (r = 0.465, p = 0.0254), but not cortical atrophy and ventricular enlargement. Grey matter lesions were significantly associated with encephalopathy like attacks (p = 0.00077) but not stroke like attacks. The density of intranuclear inclusions in skin biopsy was not associated with disease duration, severity of leukoencephalopathy and dementia. Conclusions: Specific distribution of leukoencephalopathy and DWI high intensity were indicative. Leukoencephalopathy and subcortical mechanism were critical in pathogenesis of NIID. Irrelevant of inclusion density and clinical map suggested the direct pathogenic factor need further investigation.

Our study aimed to identify the appropriate evaluation time point and assessment forthe CSF tap test(TT) to predict the shunting responsiveness of patients with idiopathic normal-pressure hydrocephalus (iNPH). Eighty-eight inpatients with clinically possible iNPH who underwent CSF TT at multiple time points (baseline, 8 hours, 24 hours, and 72 hours after CSF TT) at Peking Union Medical College Hospital were recruited. The multidomain assessment included the timed up and go test(TUG), 10-meter walking tests, and a brief executive function battery. Performance in multidomain assessment at the indicated time points were compared. The positive response rate and cumulative positive rate of multidomain assessment at multiple time points were calculated. And their corresponding specificity and sensitivity of predicting shunting response were calculated according to the follow-up results after shunting. The multidomain assessment performance except TUG at 8 hours were significantly improved at each time point after CSF TT compared with baseline (P<0.01). Reduction more than 10% in the 10-meter walking time and number of steps at 24 hours showed the highest specificity (both 85.7%) and sensitivity (37.5% and 46.7%, respectively) for predicting shunting response. Additionally, an improvement of more than 20% in the composite z score at 72 hours showed 100% specificity and 80% sensitivity for predicting shunting response. Multiple time points and multidomain assessment were helpful to identify more shunting responders. Executive function evaluation might be a candidate tool to increase the effectiveness of CSF TT.