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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 Aβ42/Aβ40 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. [ABSTRACT FROM AUTHOR]