Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Br J Ophthalmol. 2022 Feb;106(2):267-274. doi: 10.1136/bjophthalmol-2020-317454. Epub 2020 Nov 18.
To use machine learning (ML) to determine the relative contributions of modifiable and non-modifiable clinical, metabolic, genetic, lifestyle and socioeconomic factors on the risk of major eye diseases.
We conducted analyses in a cross-sectional multi-ethnic population-based study (n=10 033 participants) and determined a range of modifiable and non-modifiable risk factors of common eye diseases, including diabetic retinopathy (DR), non-diabetic-related retinopathy (NDR); early and late age-related macular degeneration (AMD); nuclear, cortical and posterior subcapsular (PSC) cataract; and primary open-angle (POAG) and primary angle-closure glaucoma (PACG). Risk factors included individual characteristics, metabolic profiles, genetic background, lifestyle patterns and socioeconomic status (n~100 risk factors). We used gradient boosting machine to estimate the relative influence (RI) of each risk factor.
Among the range of risk factors studied, the highest contributions were duration of diabetes for DR (RI=22.1%), and alcohol consumption for NDR (RI=6.4%). For early and late AMD, genetic background (RI20%) and age (RI15%) contributed the most. Axial length was the main risk factor of PSC (RI=30.8%). For PACG, socioeconomic factor (mainly educational level) had the highest influence (20%). POAG was the disease with the highest contribution of modifiable risk factors (cumulative RI~35%), followed by PACG (cumulative RI ~30%), retinopathy (cumulative RI between 20% and 30%) and late AMD (cumulative RI ~20%).
This study illustrates the utility of ML in identifying factors with the highest contributions. Risk factors possibly amenable to interventions were intraocular pressure (IOP) and Body Mass Index (BMI) for glaucoma, alcohol consumption for NDR and levels of HbA1c for DR.
利用机器学习（ML）确定可改变和不可改变的临床、代谢、遗传、生活方式和社会经济因素对主要眼部疾病风险的相对贡献。
我们在一项横断面多民族人群研究（n=10033 名参与者）中进行了分析，并确定了一系列常见眼部疾病的可改变和不可改变的风险因素，包括糖尿病视网膜病变（DR）、非糖尿病相关视网膜病变（NDR）；早发性和晚发性年龄相关性黄斑变性（AMD）；核性、皮质性和后囊下（PSC）白内障；以及原发性开角型（POAG）和原发性闭角型青光眼（PACG）。风险因素包括个体特征、代谢谱、遗传背景、生活方式和社会经济状况（n~100 个风险因素）。我们使用梯度提升机来估计每个风险因素的相对影响（RI）。
在所研究的一系列风险因素中，DR 中糖尿病持续时间的影响最大（RI=22.1%），NDR 中饮酒的影响最大（RI=6.4%）。对于早发性和晚发性 AMD，遗传背景（RI20%）和年龄（RI15%）的贡献最大。眼轴是 PSC 的主要危险因素（RI=30.8%）。对于 PACG，社会经济因素（主要是教育水平）的影响最大（20%）。POAG 是可改变风险因素贡献最高的疾病（累积 RI35%），其次是 PACG（累积 RI30%）、视网膜病变（累积 RI 在 20%至 30%之间）和晚发性 AMD（累积 RI~20%）。
本研究说明了 ML 在识别具有最高贡献的因素方面的效用。可能适合干预的风险因素有青光眼的眼压（IOP）和体重指数（BMI）、NDR 的饮酒和 DR 的糖化血红蛋白（HbA1c）水平。
