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.

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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.

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.

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.