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日本久山町研究:普通老年人群中视网膜内层厚度与痴呆症患病率及脑萎缩的关联

Association of Inner Retinal Thickness with Prevalent Dementia and Brain Atrophy in a General Older Population: The Hisayama Study.

作者信息

Ueda Emi, Hirabayashi Naoki, Ohara Tomoyuki, Hata Jun, Honda Takanori, Fujiwara Kohta, Furuta Yoshihiko, Shibata Mao, Hashimoto Sawako, Nakamura Shun, Nakazawa Taro, Nakao Tomohiro, Kitazono Takanari, Ninomiya Toshiharu, Sonoda Koh-Hei

机构信息

Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.

Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.

出版信息

Ophthalmol Sci. 2022 Apr 19;2(2):100157. doi: 10.1016/j.xops.2022.100157. eCollection 2022 Jun.

DOI:10.1016/j.xops.2022.100157
PMID:36249677
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9559916/
Abstract

PURPOSE

To assess the association of inner retinal thickness with prevalent dementia and regional brain atrophy in a general older population of Japanese.

DESIGN

Population-based, cross-sectional study.

PARTICIPANTS

A total of 1078 residents aged 65 years or older who participated in an eye examination, a comprehensive survey of dementia, and brain magnetic resonance imaging scanning in 2017.

METHODS

The thicknesses of the inner retinal layers, namely, the ganglion cell-inner plexiform layer (GC-IPL) and retinal nerve fiber layer (RNFL)-were measured by swept-source OCT (SS-OCT). The association of these retinal thicknesses with the risk of the presence of dementia was estimated using restricted cubic splines and logistic regression models. Regional brain volumes were estimated separately by applying 2 different methods: voxel-based morphometry (VBM) and analysis by FreeSurfer software. The associations of GC-IPL and RNFL thickness with each brain regional volume were analyzed using multiple regression analysis.

MAIN OUTCOME MEASURE

Prevalent dementia and regional brain atrophy.

RESULTS

Among the study participants, 61 participants (5.7%) were diagnosed with dementia. The likelihood of the presence of dementia significantly increased with lower GC-IPL thickness after adjusting for potential confounders (odds ratio, 1.62 [95% confidence interval, 1.30-2.01] per 1 standard deviation decrement in the GC-IPL thickness), but no significant association was observed with RNFL thickness. In the VBM analyses with the multivariable adjustment, lower GC-IPL thickness was significantly associated with lower volume of known brain regions related to cognitive functions (i.e., the hippocampus, amygdala, entorhinal area, and parahippocampal gyrus) and visual functions (i.e., the cuneus, lingual gyrus, and thalamus). Meanwhile, the volume of the thalamus significantly decreased with lower RNFL thickness, but none of the brain regions related to cognitive function exhibited a volume change in association with RNFL thickness. The sensitivity analysis using FreeSurfer analysis also showed that lower GC-IPL thickness was significantly associated with lower regional brain volume/intracranial volume of the hippocampus, amygdala, cuneus, lingual gyrus, and thalamus.

CONCLUSIONS

Our findings suggest that the measurement of GC-IPL thickness by SS-OCT, which is a noninvasive, convenient, and reproducible method, might be useful for identifying high-risk individuals with dementia.

摘要

目的

评估日本普通老年人群中视网膜内层厚度与痴呆症患病率及脑区萎缩之间的关联。

设计

基于人群的横断面研究。

参与者

共有1078名65岁及以上的居民参与了2017年的眼部检查、痴呆症综合调查和脑磁共振成像扫描。

方法

通过扫频光学相干断层扫描(SS-OCT)测量视网膜内层厚度,即神经节细胞-内网状层(GC-IPL)和视网膜神经纤维层(RNFL)。使用受限立方样条和逻辑回归模型估计这些视网膜厚度与痴呆症患病风险之间的关联。分别应用两种不同方法估计脑区体积:基于体素的形态计量学(VBM)和FreeSurfer软件分析。使用多元回归分析GC-IPL和RNFL厚度与每个脑区体积之间的关联。

主要观察指标

痴呆症患病率和脑区萎缩。

结果

在研究参与者中,61名参与者(5.7%)被诊断患有痴呆症。在调整潜在混杂因素后,随着GC-IPL厚度降低,痴呆症患病可能性显著增加(每降低1个标准差的GC-IPL厚度,比值比为1.62[95%置信区间,1.30 - 2.01]),但未观察到与RNFL厚度有显著关联。在多变量调整的VBM分析中,较低的GC-IPL厚度与已知与认知功能相关的脑区(即海马体、杏仁核、内嗅区和海马旁回)以及视觉功能相关的脑区(即楔叶、舌回和丘脑)体积减小显著相关。同时,随着RNFL厚度降低,丘脑体积显著减小,但与认知功能相关的脑区均未表现出与RNFL厚度相关的体积变化。使用FreeSurfer分析的敏感性分析也表明,较低的GC-IPL厚度与海马体、杏仁核、楔叶、舌回和丘脑的脑区体积/颅内体积降低显著相关。

结论

我们的研究结果表明,通过SS-OCT测量GC-IPL厚度,这是一种无创、便捷且可重复的方法,可能有助于识别痴呆症高危个体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/c6a2d3531fa7/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/3513ecbfcdc0/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/e6dd64b883ae/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/6a38e6191236/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/c6a2d3531fa7/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/3513ecbfcdc0/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/e6dd64b883ae/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/6a38e6191236/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa6d/9559916/c6a2d3531fa7/gr4.jpg

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