Identification and characterization of novel genetic variants in the first Chinese family of mucopolysaccharidosis IIIC (Sanfilippo C syndrome).

Mucopolysaccharidosis type IIIC (MPS IIIC) is one of inherited lysosomal storage disorders, caused by deficiencies in lysosomal hydrolases degrading acidic mucopolysaccharides. The gene responsible for MPS IIIC is HGSNAT, which encodes an enzyme that catalyses the acetylation of the terminal glucosamine residues of heparan sulfate. So far, few studies have focused on the genetic landscape of MPS IIIC in China, where IIIA and IIIB were the major subtypes. In this study, we utilized whole-exome sequencing (WES) to identify novel compound heterozygous variants in the HGSNAT gene from a Chinese patient with typical MPS IIIC symptoms: c.743G>A; p.Gly248Glu and c.1030C>T; p.Arg344Cys. We performed in silico analysis and experimental validation, which confirmed the deleterious pathogenic nature of both variants, as evidenced by the loss of HGSNAT activity and failure of lysosomal localization. To the best of our knowledge, the MPS IIIC is first confirmed by clinical, biochemical and molecular genetic findings in China. Our study thus expands the spectrum of MPS IIIC pathogenic variants, which is of importance to dissect the pathogenesis and to carry out clinical diagnosis of MPS IIIC. Moreover, this study helps to depict the natural history of Chinese MPS IIIC populations.

AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis.

INTRODUCTION: The cognitive impairment patterns and the association with Alzheimer's disease (AD) in mental disorders remain poorly understood. METHODS: We analyzed data from 486,297 UK Biobank participants, categorizing them by mental disorder history to identify the risk of AD and the cognitive impairment characteristics. Causation was further assessed using Mendelian randomization (MR). RESULTS: AD risk was higher in individuals with bipolar disorder (BD; hazard ratio [HR] = 2.37, P < 0.01) and major depressive disorder (MDD; HR = 1.63, P < 0.001). MR confirmed a causal link between BD and AD (ORIVW = 1.098), as well as obsessive-compulsive disorder (OCD) and AD (ORIVW = 1.050). Cognitive impairments varied, with BD and schizophrenia showing widespread deficits, and OCD affecting complex task performance. DISCUSSION: Observational study and MR provide consistent evidence that mental disorders are independent risk factors for AD. Mental disorders exhibit distinct cognitive impairment prior to dementia, indicating the potential different mechanisms in AD pathogenesis. Early detection of these impairments in mental disorders is crucial for AD prevention. HIGHLIGHTS: This is the most comprehensive study that investigates the risk and causal relationships between a history of mental disorders and the development of Alzheimer's disease (AD), alongside exploring the cognitive impairment characteristics associated with different mental disorders. Individuals with bipolar disorder (BD) exhibited the highest risk of developing AD (hazard ratio [HR] = 2.37, P < 0.01), followed by those with major depressive disorder (MDD; HR = 1.63, P < 0.001). Individuals with schizophrenia (SCZ) showed a borderline higher risk of AD (HR = 2.36, P = 0.056). Two-sample Mendelian randomization (MR) confirmed a causal association between BD and AD (ORIVW = 1.098, P < 0.05), as well as AD family history (proxy-AD, ORIVW = 1.098, P < 0.001), and kept significant after false discovery rate correction. MR also identified a nominal significant causal relationship between the obsessive-compulsive disorder (OCD) spectrum and AD (ORIVW = 1.050, P < 0.05). Individuals with SCZ, BD, and MDD exhibited impairments in multiple cognitive domains with distinct patterns, whereas those with OCD showed only slight declines in complex tasks.

A detection model of cognitive impairment via the integrated gait and eye movement analysis from a large Chinese community cohort.

INTRODUCTION: Whether the integration of eye-tracking, gait, and corresponding dual-task analysis can distinguish cognitive impairment (CI) patients from controls remains unclear. METHODS: One thousand four hundred eighty-one participants, including 724 CI and 757 controls, were enrolled in this study. Eye movement and gait, combined with dual-task patterns, were measured. The LightGBM machine learning models were constructed. RESULTS: A total of 105 gait and eye-tracking features were extracted. Forty-six parameters, including 32 gait and 14 eye-tracking features, showed significant differences between two groups (P < 0.05). Of these, the Gait_3Back-TurnTime and Dual-task cost-TurnTime patterns were significantly correlated with plasma phosphorylated tau 181 (p-tau181) level. A model based on dual-task gait, dual-task smooth pursuit, prosaccade, and anti-saccade achieved the best area under the receiver operating characteristics curve (AUC) of 0.987 for CI detection, while combined with p-tau181, the model discriminated mild cognitive impairment from controls with an AUC of 0.824. DISCUSSION: Combining dual-task gait and dual-task eye-tracking analysis is feasible for the detection of CI. HIGHLIGHTS: This is the first study to report the efficiency of integrated parameters of dual-task gait and eye-tracking for cognitive impairment (CI) detection in a large cohort. We identified 46 gait and eye-tracking features associated with CI, and two were correlated to plasma phosphorylated tau 181. We constructed the model based on dual-task gait, smooth pursuit, prosaccade, and anti-saccade, achieving the best area under the curve of 0.987 for CI detection.

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.

A detection model for cognitive dysfunction based on volatile organic compounds from a large Chinese community cohort.

INTRODUCTION: We explored whether volatile organic compound (VOC) detection can serve as a screening tool to distinguish cognitive dysfunction (CD) from cognitively normal (CN) individuals. METHODS: The cognitive function of 1467 participants was assessed and their VOCs were detected. Six machine learning algorithms were conducted and the performance was determined. The plasma neurofilament light chain (NfL) was measured. RESULTS: Distinguished VOC patterns existed between CD and CN groups. The CD detection model showed good accuracy with an area under the receiver-operating characteristic curve (AUC) of 0.876. In addition, we found that 10 VOC ions showed significant differences between CD and CN individuals (p < 0.05); three VOCs were significantly related to plasma NfL (p < 0.005). Moreover, a combination of VOCs with NfL showed the best discriminating power (AUC = 0.877). DISCUSSION: Detection of VOCs from exhaled breath samples has the potential to provide a novel solution for the dilemma of CD screening.

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.

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.

The prevalence and risk factors study of cognitive impairment: Analysis of the elderly population of Han nationality in Hunan province, China.

OBJECTIVE: A large number of studies have found that the prevalence of cognitive impairment varies in different regions. However, data on cognitive impairment in the Chinese population is still lacking. The goal of this study was to assess the prevalence of cognitive impairment among the elderly in a region of China and explore the associated risk factors. METHODS: We performed a population-based cross-sectional survey from April to June 2022. Residents come from three villages and six urban communities in the county-level city of Liuyang in southern China (N = 3233) and the coverage rate of our study population reached 73%. Participants were assessed with a series of clinical examinations and neuropsychological measures. A total of 2598 participants were selected after filtering out those under 60 years old or with incomplete data. Patients with cognitive impairment included those with mild cognitive impairment (MCI) or dementia who met standard diagnostic criteria. RESULTS: The prevalence of cognitive impairment, MCI, and dementia among participants aged 60 years and older were 21.48% (95% CI, 19.90-23.10), 15.70% (95% CI, 14.30-17.10), and 5.77 (95% CI, 4.90-6.70), respectively. And residents in villagers were more likely to have cognitive impairment than in urban communities (p < 0.001). Age growth and education level were independent influencing factors for cognitive impairment in all populations (p < 0.001). For lifestyles factors, both smoking and drinking reduced the risk of cognitive impairment (p < 0.05), but when further quantified, the link disappeared. Moreover, having cerebrovascular disease and severe vision impairment were risk factors (p < 0.05). CONCLUSION: A representative prevalence of cognitive impairment, MCI, and dementia was found in the elderly Han Chinese population in Southern China. And we further explored the role of known risk factors, particularly in physical activity, smoking, and alcohol consumption.

Peer effects of depression between left-behind and non-left-behind children: quasi-experimental evidence from rural China.

PURPOSE: The aim of the study is to investigate the interactive influence of depression on left-behind (LB) and non-left-behind (NLB) children from the perspective of peer effects. The roles of teachers, parents, and friends are also explored. METHODS: Data on 1817 children, 1817 parents, and 55 teachers were obtained from a field survey in December 2021. All students in the sample were randomly assigned to classrooms. A peer effect model and OLS methods were used to estimate the peer influence of depression. Robustness tests were conducted by randomly removing schools from the sample. RESULTS: Depression was contagious among different groups of rural children, and the peer effect of the NLB children's depression played a dominant role. Both LB and NLB children were more affected by their NLB classmates' depression. LB children were not significantly affected by depression in other LB children. This conclusion remains robust after robustness testing. In addition, heterogeneity analysis showed that outgoing and cheerful teachers, effective parent-child communication and high-quality friendship all alleviated peer influence on depression. CONCLUSIONS: LB children have more severe depression than NLB children, but LB children are more affected by depression in their NLB peers. Policymakers should train teachers to engage in positive communication with students to improve mental health in children. In addition, this article recommends that children move and live with their parents when family conditions permit.

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.

Cognitive Dysfunction in Repeat Expansion Diseases: A Review.

With the development of the sequencing technique, more than 40 repeat expansion diseases (REDs) have been identified during the past two decades. Moreover, the clinical features of these diseases show some commonality, and the nervous system, especially the cognitive function was affected in part by these diseases. However, the specific cognitive domains impaired in different diseases were inconsistent. Here, we survey literature on the cognitive consequences of the following disorders presenting cognitive dysfunction and summarizing the pathogenic genes, epidemiology, and different domains affected by these diseases. We found that the cognitive domains affected in neuronal intranuclear inclusion disease (NIID) were widespread including the executive function, memory, information processing speed, attention, visuospatial function, and language. Patients with C9ORF72-frontotemporal dementia (FTD) showed impairment in executive function, memory, language, and visuospatial function. While in Huntington's disease (HD), the executive function, memory, and information processing speed were affected, in the fragile X-associated tremor/ataxia syndrome (FXTAS), executive function, memory, information processing speed, and attention were impaired. Moreover, the spinocerebellar ataxias showed broad damage in almost all the cognitive domains except for the relatively intact language ability. Some other diseases with relatively rare clinical data also indicated cognitive dysfunction, such as myotonic dystrophy type 1 (DM1), progressive myoclonus epilepsy (PME), Friedreich ataxia (FRDA), Huntington disease like-2 (HDL2), and cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS). We drew a cognitive function landscape of the related REDs that might provide an aspect for differential diagnosis through cognitive domains and effective non-specific interventions for these diseases.

Cerebrospinal Fluid Synaptosomal-Associated Protein 25 Levels in Patients with Alzheimer's Disease: A Meta-Analysis.

BACKGROUND: Several studies have shown increased levels of cerebrospinal fluid (CSF) synaptosomal-associated protein 25 (SNAP-25) in patients with Alzheimer's disease (AD). However, results have been inconsistent thus far. OBJECTIVE: We conducted meta-analyses summarizing the associations of CSF SNAP-25 levels with AD to assess the utility of SNAP-25 as a novel biomarker for AD. METHODS: We conducted a meta-analysis of differences in CSF SNAP-25 levels in patients with AD or mild cognitive impairment (MCI) and in cognitively healthy controls (HC). We calculated pooled correlation coefficients comparing SNAP-25 levels and total tau (T-tau) or hyperphosphorylated tau (P-tau) in CSF. RESULTS: Eight studies enrolling 1,162 individuals (423 AD, 275 MCI, 464 HC) were included for quantitative analysis. Patients with AD (ratio of means [RoM] = 1.50, 95% confidence interval [CI]: 1.30,1.74) and MCI (RoM = 1.45, 95% CI: 1.12,1.87) had increased levels of CSF SNAP-25 as compared to HC. The difference in CSF SNAP-25 levels when comparing AD and MCI (RoM = 1.05, 95% CI: 0.96,1.14) was not statistically significant but showed a trend toward significance. Statistically significant correlations were found when comparing CSF SNAP-25 with CSF T-tau (Spearman correlation coefficient, ρ=0.78; ρ=0.66; ρ=0.69, respectively) and P-tau (ρ=0.77; ρ=0.70; ρ=0.62, respectively) levels in patients with AD, MCI, and HC. CONCLUSION: Increased CSF SNAP-25 levels differentiated patients with AD or MCI from controls, suggesting the utility of this biomarker in the early diagnosis of AD.

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.

The macular inner plexiform layer thickness as an early diagnostic indicator for Parkinson's disease.

Whether structural alterations of intraretinal layers are indicators for the early diagnosis of Parkinson's disease (PD) remains unclear. We assessed the retinal layer thickness in different stages of PD and explored whether it can be an early diagnostic indicator for PD. In total, 397 [131, 146, and 120 with Hoehn-Yahr I (H-Y I), H-Y II, and H-Y III stages, respectively] patients with PD and 427 healthy controls (HCs) were enrolled. The peripapillary retinal nerve fiber layer (pRNFL), total macular retinal thickness (MRT), and macular volume (TMV) were measured by high-definition optical coherence tomography, and the macular intraretinal thickness was analyzed by the Iowa Reference Algorithms. As a result, the PD group had a significantly lower average, temporal quadrant pRNFL, MRT, and TMV than the HCs group (all p < 0.001). Moreover, the ganglion cell layer (GCL), inner plexiform layer (IPL), and outer nuclear layer were thinner in patients with PD with H-Y I, and significantly decreased as the H-Y stage increased. In addition, we observed that GCL and IPL thicknesses were both correlated with Movement Disorder Society-Unified Parkinson's Disease Rating Scale III (MDS-UPDRS III) scores and non-motor symptoms assessment scores. Furthermore, macular IPL thickness in the superior inner (SI) quadrant (IPL-SI) had the best diagnostic performance in patients with PD with H-Y I versus HCs, with a sensitivity and specificity of 75.06% and 81.67%, respectively. In conclusion, we confirmed the retinal structure was significantly altered in patients with PD in different clinical stages, and that GCL and IPL changes occurred during early PD disease and were correlated with MDS-UPDRS III scores and non-motor symptoms assessment scores. Furthermore, macular IPL-SI thickness might be performed as an early diagnostic indicator for PD.

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.

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.

Variants in the Niemann-pick type C genes are not associated with Alzheimer's disease: a large case-control study in the Chinese population.

Despite the similar clinical and pathological features between Niemann-Pick type C (NPC) disease and Alzheimer's disease (AD), few studies have investigated the role of NPC genes in AD. To elucidate the role of NPC genes in AD, we sequenced NPC1 and NPC2 in 1192 AD patients and 2412 controls. Variants were divided into common variants and rare variants according to minor allele frequency (MAF). Common variant (MAF≥0.01) based association analysis was conducted by PLINK 1.9. Gene-based aggregation testing of rare variants was performed by Sequence Kernel Association Test-Optimal (SKAT-O test), respectively. Age at onset (AAO) and mini-mental state examination (MMSE) association studies were also performed with PLINK 1.9. Six common variants were identified and exhibited no association with AD. Gene-based aggregation testing revealed that both NPC1 and NPC2 were not associated with AD risk. Additionally, AAO and MMSE association studies revealed that no common variants were linked with AD endophenotypes. Taken together, our study indicated that NPC1 and NPC2 may not be implicated in AD pathogenesis in the Chinese population.

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.