机器学习辅助的多组学整合用于早期糖尿病视网膜病变、糖尿病性黄斑水肿及抗血管内皮生长因子治疗反应的识别

Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.

作者信息

Pang Yuhui, Luo Chaokun, Zhang Qingruo, Zhang Xiongze, Liao Nanying, Ji Yuying, Mi Lan, Gan Yuhong, Su Yongyue, Wen Feng, Chen Hui

机构信息

State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.

出版信息

Transl Vis Sci Technol. 2024 Dec 2;13(12):23. doi: 10.1167/tvst.13.12.23.

DOI:10.1167/tvst.13.12.23

PMID:39671223

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11645727/

Abstract

PURPOSE

Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.

METHODS

We analyzed 108 aqueous humor samples from 78 type 2 diabetes mellitus patients and 30 healthy controls. Ultra-high-performance liquid chromatography-high-resolution-mass-spectrometry detected lipidomics and metabolomics profiles. DME patients received ≥3 anti-VEGF treatments, categorized into strong and weak response groups. Machine learning (ML) screened prospective metabolic features, developing prediction models.

RESULTS

Key metabolic features identified in the metabolomics and lipidomics datasets included n-acetyl isoleucine (odds ratio [OR] = 1.635), cis-aconitic acid (OR = 3.296), and ophthalmic acid (OR = 0.836) for DR. For early-DR, n-acetyl isoleucine (OR = 1.791) and decaethylene glycol (PEG-10) (OR = 0.170) were identified as key markers. L-kynurenine (OR = 0.875), niacinamide (OR = 0.843), and linoleoyl ethanolamine (OR = 0.941) were identified as significant indicators for DME. Trigonelline (OR = 1.441) and 4-methylcatechol-2-sulfate (OR = 1.121) emerged as predictors for strong response to anti-VEGF. Predictive models achieved R² values of 99.9%, 97.7%, 93.9%, and 98.4% for DR, early-DR, DME, and strong response groups in the calibration set, respectively, and validated well with R² values of 96.3%, 96.8%, 79.9%, and 96.3%.

CONCLUSIONS

This research used ML to identify differential metabolic features from metabolomics and lipidomics datasets in DR patients. It implies that metabolic indicators can effectively predict early disease progression and potential weak responders to anti-VEGF therapy in DME eyes.

TRANSLATIONAL RELEVANCE

The identified metabolic indicators may aid in predicting the early progression of DR and optimizing therapeutic strategies for DME.

摘要

目的

确定糖尿病视网膜病变（DR）各阶段的最佳代谢特征和途径，开发风险模型以区分糖尿病性黄斑水肿（DME），并预测抗血管内皮生长因子（anti-VEGF）治疗反应。

方法

我们分析了来自78名2型糖尿病患者和30名健康对照的108份房水样本。采用超高效液相色谱-高分辨率质谱法检测脂质组学和代谢组学谱。DME患者接受了≥3次抗VEGF治疗，分为强反应组和弱反应组。机器学习（ML）筛选潜在的代谢特征，建立预测模型。

结果

在代谢组学和脂质组学数据集中确定的关键代谢特征包括，用于DR的N-乙酰异亮氨酸（优势比[OR]=1.635）、顺乌头酸（OR=3.296）和视黄酸（OR=0.836）。对于早期DR，N-乙酰异亮氨酸（OR=1.791）和十甘醇（PEG-10）（OR=0.170）被确定为关键标志物。L-犬尿氨酸（OR=0.875）、烟酰胺（OR=0.843）和亚油酰乙醇胺（OR=0.941）被确定为DME的显著指标。胡芦巴碱（OR=1.441）和4-甲基儿茶酚-2-硫酸盐（OR=1.121）成为抗VEGF强反应的预测指标。在校准集中，预测模型对DR、早期DR、DME和强反应组的R²值分别达到99.9%、97.7%、93.9%和98.4%，并分别以96.3%、96.8%、79.9%和96.3%的R²值得到了良好验证。