Clinical features of progressive supranuclear palsy.

Background: Progressive supranuclear palsy (PSP) is a clinically heterogenous atypical parkinsonian syndrome. Therefore, early recognition and correct diagnosis of PSP is challenging but essential. This study aims to characterize the clinical manifestations, magnetic resonance imaging (MRI), and longitudinal MRI changes of PSP in China. Method: Clinical and MRI presentations were compared among 150 cases with PSP. Then the longitudinal MRI changes among 20 patients with PSP were further explored. Additionally, a series of midbrain-based MRI parameters was compared between PSP-P and PD. Results: Throughout the course of the disease, there were differences in the symptoms of the fall and hand tremor between the PSP-RS and PSP-P. There were significant differences in the six midbrain-based MRI parameters between the PSP-RS and the PSP-P, including hummingbird sign, midbrain diameter, midbrain to pons ratio (MTPR), midbrain area, midbrain area to pons area ratio (Ma/Pa), and midbrain tegmental length (MBTegm). Longitudinal MRI studies revealed that the annual rel.ΔMTPR and rel.Δ (Ma/Pa) for PSP were 5.55 and 6.52%, respectively; additionally, PSP-RS presented a higher decline rate than PSP-P. Moreover, MTPR ≤0.56, midbrain diameter ≤ 0.92, midbrain area ≤ 1.00, and third ventricle width ≤ 0.75 could identify PSP-P from PD. Conclusion: PSP-P differs from PSP-RS regarding clinical manifestations, MRI, and longitudinal MRI changes. MRI parameters could be potential imaging markers to identify PSP-P from PD.

Diagnostic value of nerve conduction study in NOTCH2NLC-related neuronal intranuclear inclusion disease.

BACKGROUND AND AIMS: Neuronal intranuclear inclusion disease (NIID) is a rare progressive neurodegenerative disorder mainly caused by abnormally expanded GGC repeats within the NOTCH2NLC gene. Most patients with NIID show polyneuropathy. Here, we aim to investigate diagnostic electrophysiological markers of NIID. METHODS: In this retrospective dual-center study, we reviewed 96 patients with NOTCH2NLC-related NIID, 94 patients with genetically confirmed Charcot-Marie-Tooth (CMT) disease, and 62 control participants without history of peripheral neuropathy, who underwent nerve conduction studies between 2018 and 2022. RESULTS: Peripheral nerve symptoms were presented by 53.1% of patients with NIID, whereas 97.9% of them showed peripheral neuropathy according to electrophysiological examinations. Patients with NIID were characterized by slight demyelinating sensorimotor polyneuropathy; some patients also showed mild axonal lesions. Motor nerve conduction velocity (MCV) of the median nerve usually exceeded 35 m/s, and were found to be negatively correlated with the GGC repeat sizes. Regarding the electrophysiological differences between muscle weakness type (n = 27) and non-muscle weakness type (n = 69) of NIID, nerve conduction abnormalities were more severe in the muscle weakness type involving both demyelination and axonal impairment. Notably, specific DWI subcortical lace sign was presented in only 33.3% of muscle weakness type, thus it was difficult to differentiate them from CMT. Combining age of onset, distal motor latency, and compound muscle action potential of the median nerve showed the optimal diagnostic performance to distinguish NIID from major CMT (AUC = 0.989, sensitivity = 92.6%, specificity = 97.4%). INTERPRETATION: Peripheral polyneuropathy is common in NIID. Our study suggest that nerve conduction study is useful to discriminate NIID.

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

Characteristics of autonomic dysfunction in neuronal intranuclear inclusion disease.

Background: This study aimed to investigate the features of autonomic dysfunction (AutD) in a large cohort of patients with neuronal intranuclear inclusion disease (NIID). Methods: A total of 122 patients with NIID and 122 controls were enrolled. All participants completed the Scales for Outcomes in Parkinson's Disease-Autonomic Questionnaire (SCOPA-AUT) and genetic screening for GGC expanded repeats within the NOTCH2NLC gene. All patients underwent neuropsychological and clinical assessments. SCOPA-AUT was performed to compare AutD between patients and controls. The associations between AutD and disease-related characteristics of NIID were studied. Results: 94.26% of patients had AutD. Compared with controls, patients had more severe AutD in total SCOPA-AUT, gastrointestinal, urinary, cardiovascular, thermoregulatory, pupillomotor and sexual domains (all p < 0.05). The area under the curve (AUC) value for the total SCOPA-AUT (AUC = 0.846, sensitivity = 69.7%, specificity = 85.2%, cutoff value = 4.5) was high in differentiating AtuD of patients with NIID from controls. The total SCOPA-AUT was significantly and positively associated with age (r = 0.185, p = 0.041), disease duration (r = 0.207, p = 0.022), Neuropsychiatric Inventory (NPI) (r = 0.446, p < 0.01), and Activities of Daily Living (ADL) (r = 0.390, p < 0.01). Patients with onset-of-AutD had higher SCOPA-AUT scores than patients without onset-of-AutD (p < 0.001), especially in the urinary system (p < 0.001) and male sexual dysfunction (p < 0.05). Conclusion: SCOPA-AUT can be used as a diagnostic and quantitative tool for autonomic dysfunction in NIID. The high prevalence of AutD in patients suggests that NIID diagnosis should be considered in patients with AutD, especially in those with unexplained AutD alone. AutD in patients is related to age, disease duration, impairment of daily living ability, and psychiatric symptoms.

Mendelian randomization analyses of smoking and Alzheimer's disease in Chinese and Japanese populations.

Background: Previous epidemiological studies have reported controversial results on the relationship between smoking and Alzheimer's disease (AD). Therefore, we sought to assess the association using Mendelian randomization (MR) analysis. Methods: We used single nucleotide polymorphisms (SNPs) associated with smoking quantity (cigarettes per day, CPD) from genome-wide association studies (GWAS) of Japanese population as instrumental variables, then we performed two-sample MR analysis to investigate the association between smoking and AD in a Chinese cohort (1,000 AD cases and 500 controls) and a Japanese cohort (3,962 AD cases and 4,074 controls), respectively. Results: Genetically higher smoking quantity showed no statistical causal association with AD risk (the inverse variance weighted (IVW) estimate in the Chinese cohort: odds ratio (OR) = 0.510, 95% confidence interval (CI) = 0.149-1.744, p = 0.284; IVW estimate in the Japanese cohort: OR = 1.170, 95% confidence interval CI = 0.790-1.734, p = 0.434). Conclusion: This MR study, for the first time in Chinese and Japanese populations, found no significant association between smoking and AD.

Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology.

BACKGROUND: Electroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer's disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD. METHODS: A total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants' EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual's cognitive function. RESULTS: The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction. CONCLUSIONS: Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.

GSN gene frameshift mutations in Alzheimer's disease.

BACKGROUND: The pathogenic missense mutations of the gelsolin (GSN) gene lead to familial amyloidosis of the Finnish type (FAF); however, our previous study identified GSN frameshift mutations existed in patients with Alzheimer's disease (AD). The GSN genotype-phenotype heterogeneity and the role of GSN frameshift mutations in patients with AD are unclear. METHOD: In total, 1192 patients with AD and 1403 controls were screened through whole genome sequencing, and 884 patients with AD were enrolled for validation. Effects of GSN mutations were evaluated in vitro. GSN, Aß42, Aß40 and Aß42/40 were detected in both plasma and cerebrospinal fluid (CSF). RESULTS: Six patients with AD with GSN P3fs and K346fs mutations (0.50%, 6/1192) were identified, who were diagnosed with AD but not FAF. In addition, 13 patients with AD with GSN frameshift mutations were found in the validation cohort (1.47%, 13/884). Further in vitro experiments showed that both K346fs and P3fs mutations led to the GSN loss of function in inhibiting Aß-induced toxicity. Moreover, a higher level of plasma (p=0.001) and CSF (p=0.005) GSN was observed in AD cases than controls, and a positive correlation was found between the CSF GSN and CSF Aß42 (r=0.289, p=0.009). Besides, the GSN level was initially increasing and then decreasing with the disease course and cognitive decline. CONCLUSIONS: GSN frameshift mutations may be associated with AD. An increase in plasma GSN is probably a compensatory reaction in AD, which is a potential biomarker for early AD.

The associations of APP, PSEN1, and PSEN2 genes with Alzheimer's disease: A large case-control study in Chinese population.

AIM: The associations of non-pathogenic variants of APP, PSEN1, and PSEN2 with Alzheimer's disease (AD) remain unclear. This study is aimed at determining the role of these variants in AD. METHODS: Our study recruited 1154 AD patients and 2403 controls. APP, PSEN1, PSEN2, and APOE were sequenced using a targeted panel. Variants were classified into common or rare variants with the minor allele frequencies (MAF) cutoff of 0.01. Common variant (MAF≥0.01)-based association test was performed by PLINK 1.9, and gene-based (MAF <0.01) association analysis was conducted using Sequence Kernel Association Test-Optimal (SKAT-O test). Additionally, using PLINK 1.9, we performed AD endophenotypes association studies. RESULTS: A common variant, PSEN2 rs11405, was suggestively associated with AD risk (p = 1.08 × 10-2 ). The gene-based association analysis revealed that the APP gene exhibited a significant association with AD (p = 1.43 × 10-2 ). In the AD endophenotypes association studies, APP rs459543 was nominally correlated with CSF Aß42 level (p = 7.91 × 10-3 ). CONCLUSION: Our study indicated that non-pathogenic variants in PSEN2 and APP may be involved in AD pathogenesis in the Chinese population.

Age of onset correlates with clinical characteristics and prognostic outcomes in neuromyelitis optica spectrum disorder.

Objective: Neuromyelitis optica spectrum disorder (NMOSD) is an inflammatory disease preferentially affects the optic nerve and the spinal cord. The first attack usually occurs in the third or fourth decade, though patients with disease onset in the fifties or later are not uncommon. This study aimed to investigate the clinical characteristics and prognosis in patients with different age of onset and to explore the correlations between age of onset and clinical characteristics and prognostic outcomes. Method: We retrospectively reviewed the medical records of 298 NMOSD patients diagnosed according to the 2015 updated version of diagnostic criteria. Patients were divided into early-onset NMOSD (EO-NMOSD) (<50 years at disease onset) and late-onset NMOSD (LO-NMOSD) (≥50 years at disease onset) based on the age of disease onset. LO-NMOSD patients were divided into two subgroups: relative-late-onset NMOSD (RLO-NMOSD) (50~70 years at disease onset) and very-late-onset NMOSD (≥70 years at disease onset). Clinical characteristics, laboratory findings, neuroimaging features, and prognostic outcomes were investigated. Results: Compared to EO-NMOSD patients, patients with LO-NMOSD showed more frequent transverse myelitis (TM) (58.20% vs. 36.00%, p = 0.007) while less frequent optic neuritis (ON) (23.10% vs. 34.80%, p = 0.031) and brainstem/cerebral attacks (7.50% vs. 18.30%, p = 0.006) as the first attack. Patients with LO-NMOSD showed less frequent relapses, higher Expanded Disability Status Scale (EDSS) score at the last follow-up, fewer NMOSD-typical brain lesions, and longer segments of spinal cord lesions. Patients with older onset age showed a higher proportion of increased protein levels in cerebrospinal fluid during the acute phase of attacks. Age at disease onset positively correlated with length of spinal cord lesions at first attack and at last follow-up, negatively correlated with ARR-1 (ARR excluding the first attack, calculated from disease onset to final follow-up), irrespective of AQP4-IgG serostatus. Patients with older age at disease onset progressed to severe motor disability sooner, and age of onset positively correlated with EDSS score at the last follow-up, irrespective of AQP4-IgG serostatus. Conclusion: Age of disease onset affects clinical characteristics and prognosis outcomes of patients with NMOSD.

Consistency analysis and conversion model establishment of mini-mental state examination and montreal cognitive assessment in Chinese patients with Alzheimer's disease.

Background: The Chinese version of the Mini-Mental State Examination (MMSE-C) and the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) are the most commonly used scales to screen for Alzheimer's disease (AD) among Chinese patients; however, their consistency varies according to populations and languages. Equivalent conversion of MMSE-C and MoCA-BJ scores is important for meta-analysis. Materials and methods: MMSE-C and MoCA-BJ scoring were performed on the enrolled patients with AD (n = 332). Consistency analysis of MMSE-C and MoCA-BJ scores of patients in the conversion groups was performed. The circle-arc method was used to convert the MMSE-C scores of the conversion groups into MoCA-BJ scores, and the conversion formula was generated. The MMSE-C data of the verification group was converted to MoCA-BJ according to the formula, and the consistency analysis of the original MoCA-BJ of the verification group and the converted MoCA-BJ was performed to verify the conversion model. Results: The results of the consistency analysis of MMSE-C and MoCA-BJ in group A showed that the correlation coefficients of the total group, high education years subgroup, medium education years subgroup, and low education years subgroup were 0.905 (P < 0.001), 0.874 (P < 0.001), 0.949 (P < 0.001), and 0.874 (P < 0.001), respectively, with high consistency and statistical significance. After applying the circle-arc method for equivalent conversion, the consistency analysis results of the original and the converted MoCA-BJ of the patients in group B of the total group, high education years subgroup, medium education years subgroup, and low education years subgroup were 0.891 (P < 0.001), 0.894 (P < 0.001), 0.781 (P < 0.001), 0.909 (P < 0.001), respectively, with high consistency and statistical significance. Conclusion: We established and validated a model of MMSE-C and MoCA-BJ score conversion for Chinese patients with AD using the circle-arc method. This model could be useful for multi-centers clinical trials and meta-analysis.

Clinical characteristics and genotype-phenotype correlation analysis of familial Alzheimer's disease patients with pathogenic/likely pathogenic amyloid protein precursor mutations.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with aging, environmental, and genetic factors. Amyloid protein precursor (APP) is a known pathogenic gene for familial Alzheimer's disease (FAD), and now more than 70 APP mutations have been reported, but the genotype-phenotype correlation remains unclear. In this study, we collected clinical data from patients carrying APP mutations defined as pathogenic/likely pathogenic according to the American college of medical genetics and genomics (ACMG) guidelines. Then, we reanalyzed the clinical characteristics and identified genotype-phenotype correlations in APP mutations. Our results indicated that the clinical phenotypes of APP mutations are generally consistent with typical AD despite the fact that they show more non-demented symptoms and neurological symptoms. We also performed genotype-phenotype analysis according to the difference in APP processing caused by the mutations, and we found that there were indeed differences in onset age, behavioral and psychological disorders of dementia (BPSD) and myoclonus.

Macular Microvascular Density as a Diagnostic Biomarker for Alzheimer's Disease.

BACKGROUND: Some previous studies showed abnormal pathological and vascular changes in the retina of patients with Alzheimer's disease (AD). However, whether retinal microvascular density is a diagnostic indicator for AD remains unclear. OBJECTIVE: This study evaluated the macular vessel density (m-VD) in the superficial capillary plexus and fovea avascular zone (FAZ) area in AD, explored their correlations with clinical parameters, and finally confirmed an optimal machine learning model for AD diagnosis. METHODS: 77 patients with AD and 145 healthy controls (HCs) were enrolled. The m-VD and the FAZ area were measured using optical coherence tomography angiography (OCTA) in all participants. Additionally, AD underwent neuropsychological assessment, brain magnetic resonance imaging scan, cerebrospinal fluid (CSF) biomarker detection, and APOE ɛ4 genotyping. Finally, the performance of machine learning algorithms based on the OCTA measurements was evaluated by Python programming language. RESULTS: The m-VD was noticeably decreased in AD compared with HCs. Moreover, m-VD in the fovea, superior inner, inferior inner, nasal inner subfields, and the whole inner ring declined significantly in mild AD, while it was more serious in moderate/severe AD. However, no significant difference in the FAZ was noted between AD and HCs. Furthermore, we found that m-VD exhibited a significant correlation with cognitive function, medial temporal atrophy and Fazekas scores, and APOE ɛ4 genotypes. No significant correlations were observed between m-VD and CSF biomarkers. Furthermore, results revealed the Adaptive boosting algorithm exhibited the best diagnostic performance for AD. CONCLUSION: Macular vascular density could serve as a diagnostic biomarker for AD.

Clinical features of NOTCH2NLC-related neuronal intranuclear inclusion disease.

BACKGROUND: Abnormal expanded GGC repeats within the NOTCH2HLC gene has been confirmed as the genetic mechanism for most Asian patients with neuronal intranuclear inclusion disease (NIID). This cross-sectional observational study aimed to characterise the clinical features of NOTCH2NLC-related NIID in China. METHODS: Patients with NOTCH2NLC-related NIID underwent an evaluation of clinical symptoms, a neuropsychological assessment, electrophysiological examination, MRI and skin biopsy. RESULTS: In the 247 patients with NOTCH2NLC-related NIID, 149 cases were sporadic, while 98 had a positive family history. The most common manifestations were paroxysmal symptoms (66.8%), autonomic dysfunction (64.0%), movement disorders (50.2%), cognitive impairment (49.4%) and muscle weakness (30.8%). Based on the initial presentation and main symptomology, NIID was divided into four subgroups: dementia dominant (n=94), movement disorder dominant (n=63), paroxysmal symptom dominant (n=61) and muscle weakness dominant (n=29). Clinical (42.7%) and subclinical (49.1%) peripheral neuropathies were common in all types. Typical diffusion-weighted imaging subcortical lace signs were more frequent in patients with dementia (93.9%) and paroxysmal symptoms types (94.9%) than in those with muscle weakness (50.0%) and movement disorders types (86.4%). GGC repeat sizes were negatively correlated with age of onset (r=-0.196, p<0.05), and in the muscle weakness-dominant type (median 155.00), the number of repeats was much higher than in the other three groups (p<0.05). In NIID pedigrees, significant genetic anticipation was observed (p<0.05) without repeat instability (p=0.454) during transmission. CONCLUSIONS: NIID is not rare; however, it is usually misdiagnosed as other diseases. Our results help to extend the known clinical spectrum of NOTCH2NLC-related NIID.

Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer's disease.

AIMS: We mainly evaluate retinal alterations in Alzheimer's disease (AD) patients, investigate the associations between retinal changes with AD biomarkers, and explore an optimal machine learning (ML) model for AD diagnosis based on retinal thickness. METHODS: A total of 159 AD patients and 299 healthy controls were enrolled. The retinal parameters of each participant were measured using optical coherence tomography (OCT). Additionally, cognitive impairment severity, brain atrophy, and cerebrospinal fluid (CSF) biomarkers were measured in AD patients. RESULTS: AD patients demonstrated a significant decrease in the average, superior, and inferior quadrant peripapillary retinal nerve fiber layer, macular retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL) thicknesses, as well as total macular volume (TMV) (all p < 0.05). Moreover, TMV was positively associated with Mini-Mental State Examination and Montreal Cognitive Assessment scores, IPL thickness was correlated negatively with the medial temporal lobe atrophy score, and the GCL thickness was positively correlated with CSF Aß42 /Aß40 and negatively associated with p-tau level. Based on the significantly decreased OCT variables between both groups, the XGBoost algorithm exhibited the best diagnostic performance for AD, whose four references, including accuracy, area under the curve, f1 score, and recall, ranged from 0.69 to 0.74. Moreover, the macular retinal thickness exhibited an absolute superiority for AD diagnosis compared with other enrolled variables in all ML models. CONCLUSION: We identified the retinal alterations in AD patients and found that macular thickness and volume were associated with AD severity and biomarkers. Furthermore, we confirmed that OCT combined with ML could serve as a potential diagnostic tool for AD.

Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer's disease.

The relationships between multiple visual rating scales based on structural magnetic resonance imaging (sMRI) with disease severity and cerebrospinal fluid (CSF) biomarkers in patients with Alzheimer's disease (AD) were ambiguous. In this study, a total of 438 patients with clinically diagnosed AD were recruited. All participants underwent brain sMRI scan, and medial temporal lobe atrophy (MTA), posterior atrophy (PA), global cerebral atrophy-frontal sub-scale (GCA-F), and Fazekas rating scores were visually evaluated. Meanwhile, disease severity was assessed by neuropsychological tests such as the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). Among them, 95 patients were tested for CSF core biomarkers, including Aß1-42, Aß1-40, Aß1-42/Aß1-40, p-tau, and t-tau. As a result, the GCA-F and Fazekas scales showed positively significant correlations with onset age (r = 0.181, p < 0.001; r = 0.411, p < 0.001, respectively). Patients with late-onset AD (LOAD) showed higher GCA-F and Fazekas scores (p < 0.001, p < 0.001). With regard to the disease duration, the MTA and GCA-F were positively correlated (r = 0.137, p < 0.05; r = 0.106, p < 0.05, respectively). In terms of disease severity, a positively significant association emerged between disease severity and the MTA, PA GCA-F, and Fazekas scores (p < 0.001, p < 0.001, p < 0.001, p < 0.05, respectively). Moreover, after adjusting for age, gender, and APOE alleles, the MTA scale contributed to moderate to severe AD in statistical significance independently by multivariate logistic regression analysis (p < 0.05). The model combining visual rating scales, age, gender, and APOE alleles showed the best performance for the prediction of moderate to severe AD significantly (AUC = 0.712, sensitivity = 51.5%, specificity = 84.6%). In addition, we observed that the MTA and Fazekas scores were associated with a lower concentration of Aß1-42 (p < 0.031, p < 0.022, respectively). In summary, we systematically analyzed the benefits of multiple visual rating scales in predicting the clinical status of AD. The visual rating scales combined with age, gender, and APOE alleles showed best performance in predicting the severity of AD. MRI biomarkers in combination with CSF biomarkers can be used in clinical practice.

CYLD variants identified in Alzheimer's disease and frontotemporal dementia patients.

OBJECTIVES: CYLD was a novel causative gene for frontotemporal dementia (FTD) and amyotrophic lateral sclerosis. Given the clinical and pathological overlap of FTD and Alzheimer's disease (AD), it is necessary to screen CYLD in AD patients and FTD patients in the Chinese population. METHODS: In our study, using a targeted sequencing panel, we sequenced the CYLD gene in a large cohort of 2485 participants in the Chinese population, including 1008 AD patients, 105 FTD patients, and 1372 controls. RESULTS: In the present study, the average onset age of AD and FTD patients was 66.84 ± 30.42 years old and 60 ± 10.00 years old, respectively. Our study reported three novel CYLD variants: p.Phe288Leu (patient No. 1, AD), p.Tyr485Phe (patients No. 6-9, all AD) and p.Thr951Ala (patient No. 10, AD), plus a previously reported variant: p.Arg397Ser (patient No. 2-5, AD and No. 11, FTD). These variants were absent in our in-house controls and predicted to be deleterious according to the MutationTaster. The variant carriers were composed of 10 AD patients and one FTD patient, and the average onset age was 61.2 ± 10.9 years. The frequency of CYLD variants in AD was similar to that in FTD, which was 0.99% (10/1008) and 0.95% (1/105), respectively. INTERPRETATION: Our finding extended the genotype and phenotype of the CYLD gene and demonstrated that CYLD rare damaging variants may be implicated in AD and FTD pathogenesis.

A 31-Year-Old Man With Bilaterally Limited Mobility of Joints.

A 31-year-old man with limited mobility of joints in his hands, feet, and ankles presented for evaluation with no noted erythema, edema, or Raynaud phenomenon. What is your diagnosis?

The Application of Deep Brain Stimulation for Progressive Supranuclear Palsy: A Systematic Review.

Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease, and currently no effective symptomatic or neuroprotective treatment is available for PSP. Deep brain stimulation (DBS), as a neurosurgical procedure, plays a role in a range of neurological and psychiatric disorders, and a series of case reports have applied DBS in PSP patients. However, there are no systematic investigations about the application of DBS in PSP patients; we therefore performed a systematic review to evaluate the efficacy of DBS for PSP. PubMed, EMBASE and the Cochrane library were systematically searched from database inception to July 31, 2021. Additionally, the reference lists of included studies were searched manually. Of 155 identified studies, 14 were eligible and were included in our analysis (N = 39 participants). We assessed the data between DBS-OFF and DBS-ON conditions, as measured by the Unified Parkinson's Disease Rating Scale (UPDRS) and other clinical rating scales. A reduction of UPDRS III scores under DBS-ON conditions in most PSP cases was observed, but the differences yielded no statistical significance. There was no sufficient evidence proving DBS was effective for PSP patients, though part of PSP cases could benefit from DBS and our findings could provide up-to-date information about the possible role of DBS in PSP, which would provide design strategies for following clinical trials and might ultimately help to promote the clinical application of DBS in PSP patients.

Identification of a Novel Heterozygous Mutation in the EIF2B4 Gene Associated With Vanishing White Matter Disease.

Vanishing white matter disease (VWM) is one of the most common childhood inherited leukoencephalopathies with autosomal recessive inheritance. Mutations in five genes, EIF2B1-5, have been identified as the major cause of VWM. In this study, a targeted gene capture sequencing panel comprising 160 known pathogenic genes associated with leukoencephalopathies was performed in a large Han Chinese family affected by adult-onset VWM, and a novel heterozygous missense mutation (c.1337G > A [p. R446H]) in EIF2B4 (NM_001034116.2) was detected. Further functional studies in HEK 293 cells showed dramatically reduced EIF2Bδ protein levels in the mutated group compared with the wild-type group. This study revealed that a heterozygous missense mutation (c.1337G > A [p. R446H]) in EIF2B4 was potentially associated with the adult-onset mild phenotype of VWM. In contrast to previous reports, autosomal dominant inheritance was also observed in adult-onset VWM.

Genetics of progressive supranuclear palsy in a Chinese population.

BACKGROUND: Genetics plays an important role in progressive supranuclear palsy (PSP) and remains poorly understood. A detailed literature search identified 19 PSP-associated genes: MAPT, LRRK2, LRP10, DCTN1, GRN, NPC1, PARK, TARDBP, TBK1, BSN, GBA, STX6, EIF2AK3, MOBP, DUSP10, SLCO1A2, RUNX2, CXCR4, and APOE. To date, genetic studies on PSP have focused on Caucasian population. The gaps in PSP genetic study on East Asian populations need to be filled. METHODS: Exon and flanking regions of the PSP-associated genes were sequenced in 104 patients with PSP and 488 healthy controls. Common variant-based association analysis and gene-based association tests of rare variants were performed using PLINK 1.9 and the sequence kernel association test-optimal, respectively. Additionally, the association of APOE and MAPT genotypes with PSP was evaluated. The above association analyses were repeated among probable PSP patients. Finally, PLINK 1.9 was used to test variants associated with the onset age of PSP. RESULTS: A rare non-pathogenic variant of MAPT (c.425C > T,p.A142V) was detected in a PSP patient. No common variants were significantly associated with PSP. In both the rare-variant and the rare-damaging-variant groups, the combined effect for GBA reached statistical significance (p = 1.43 × 10-3, p = 4.98 × 10-4). The result between APOE, MAPT genotypes and PSP risk were inconsistent across all PSP group and probably PSP group. CONCLUSIONS: The pathogenic variant in MAPT were uncommon in PSP patients. Moreover, GBA gene was likely to increase the risk of PSP, and GBA-associated diseases were beyond α-synucleinopathies. The association between APOE, MAPT and PSP is still unclear among the non-Caucasian population.