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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. [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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]