Macular Microvascular Density as a Diagnostic Biomarker for Alzheimer’s Disease

Article type: Research Article

Authors: Wang, Xin^a | Wang, Yaqin^f | Liu, Hui^a | Zhu, Xiangyu^a | Hao, Xiaoli^a | Zhu, Yuan^a | Xu, Bei^g | Zhang, Sizhe^a | Jia, Xiaoliang^h | Weng, Ling^{a; b; c; d; e} | Liao, Xinxin^{b; c; d; e; i} | Zhou, Yafang^{b; c; d; e; i} | Tang, Beisha^{a; b; c; d; e} | Zhao, Rongchang^h | Jiao, Bin^{a; b; c; d; e; *} | Shen, Lu^{a; b; c; d; e; j; *}

Affiliations: [a] Department of Neurology, Xiangya Hospital, Central South University, Changsha, China | [b] National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China | [c] Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China | [d] Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China | [e] Key Laboratory of Hunan Province inNeurodegenerative Disorders, Central South University, Changsha, China | [f] Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China | [g] Eye Center of Xiangya Hospital, Central South University, Changsha, China | [h] School of Computer Science and Engineering, Central South University, Changsha, China | [i] Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China | [j] Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China

Correspondence: [*] Correspondence to: Dr. Lu Shen, Department of Neurology, Xiangya Hospital, Central South University, Changsha, China. Tel.: +86 13807480061; E-mail:[email protected] and Dr. Bin Jiao, Department of Neurology, Xiangya Hospital, Central South University, Changsha, China. Tel.: +86 13548984561; E-mail: [email protected].

Abstract: 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.

Keywords: Alzheimer’s disease, diagnosis, machine learning, optical coherence tomography angiography, vessel density

DOI: 10.3233/JAD-220482

Journal: Journal of Alzheimer's Disease, vol. 90, no. 1, pp. 139-149, 2022