Abstract：
OBJECTIVE: To investigate the xenobiotic profiles of patients with neovascular age-related macular degeneration (nAMD) undergoing anti-vascular endothelial growth factor (anti-VEGF) intravitreal therapy (IVT) to identify biomarkers indicative of clinical phenotypes through advanced AI methodologies.
METHODS: In this cross-sectional observational study, we analyzed 156 peripheral blood xenobiotic features in a cohort of 46 nAMD patients stratified by choroidal neovascularization (CNV) control under anti-VEGF IVT. We employed Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for measurement and leveraged an AI-driven iterative Random Forests (iRF) approach for robust pattern recognition and feature selection, aligning molecular profiles with clinical phenotypes.
RESULTS: AI-augmented iRF models effectively refined the metabolite spectrum by discarding non-predictive elements. Perfluorooctanesulfonate (PFOS) and Ethyl β-glucopyranoside were identified as significant biomarkers through this process, associated with various clinically relevant phenotypes. Unlike single metabolite classes, drug metabolites were distinctly correlated with subretinal fluid presence.
CONCLUSIONS: This study underscores the enhanced capability of AI, particularly iRF, in dissecting complex metabolomic data to elucidate the xenobiotic landscape of nAMD and environmental impact on the disease. The preliminary biomarkers discovered offer promising directions for personalized treatment strategies, although further validation in broader cohorts is essential for clinical application.
METHODS: In this cross-sectional observational study, we analyzed 156 peripheral blood xenobiotic features in a cohort of 46 nAMD patients stratified by choroidal neovascularization (CNV) control under anti-VEGF IVT. We employed Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for measurement and leveraged an AI-driven iterative Random Forests (iRF) approach for robust pattern recognition and feature selection, aligning molecular profiles with clinical phenotypes.
RESULTS: AI-augmented iRF models effectively refined the metabolite spectrum by discarding non-predictive elements. Perfluorooctanesulfonate (PFOS) and Ethyl β-glucopyranoside were identified as significant biomarkers through this process, associated with various clinically relevant phenotypes. Unlike single metabolite classes, drug metabolites were distinctly correlated with subretinal fluid presence.
CONCLUSIONS: This study underscores the enhanced capability of AI, particularly iRF, in dissecting complex metabolomic data to elucidate the xenobiotic landscape of nAMD and environmental impact on the disease. The preliminary biomarkers discovered offer promising directions for personalized treatment strategies, although further validation in broader cohorts is essential for clinical application.
摘要：
目的：研究接受抗血管内皮生长因子（抗VEGF）玻璃体腔治疗（IVT）的新生血管性年龄相关性黄斑变性（nAMD）患者的外源性生物特征，以确定指示临床表型的生物标志物。通过先进的AI方法。
方法：在这项横断面观察研究中，我们分析了46例nAMD患者队列中的156例外周血异种生物特征,这些患者在抗VEGFIVT下通过脉络膜新生血管(CNV)对照进行分层.我们采用液相色谱-串联质谱（LC-MS/MS）进行测量，并利用AI驱动的迭代随机森林（iRF）方法进行稳健的模式识别和特征选择。将分子谱与临床表型进行比对。
结果：AI增强的iRF模型通过丢弃非预测元素有效地改善了代谢物谱。全氟辛烷磺酸（PFOS）和乙基β-吡喃葡萄糖苷通过这一过程被确定为重要的生物标志物，与各种临床相关表型相关。与单一代谢物类别不同，药物代谢产物与视网膜下液的存在明显相关。
结论：这项研究强调了人工智能能力的增强，特别是iRF，在解剖复杂的代谢组学数据，以阐明nAMD的异种生物景观和环境对疾病的影响。初步发现的生物标志物为个性化治疗策略提供了有希望的方向，尽管在更广泛的队列中进一步验证对于临床应用至关重要。
方法：在这项横断面观察研究中，我们分析了46例nAMD患者队列中的156例外周血异种生物特征,这些患者在抗VEGFIVT下通过脉络膜新生血管(CNV)对照进行分层.我们采用液相色谱-串联质谱（LC-MS/MS）进行测量，并利用AI驱动的迭代随机森林（iRF）方法进行稳健的模式识别和特征选择。将分子谱与临床表型进行比对。
结果：AI增强的iRF模型通过丢弃非预测元素有效地改善了代谢物谱。全氟辛烷磺酸（PFOS）和乙基β-吡喃葡萄糖苷通过这一过程被确定为重要的生物标志物，与各种临床相关表型相关。与单一代谢物类别不同，药物代谢产物与视网膜下液的存在明显相关。
结论：这项研究强调了人工智能能力的增强，特别是iRF，在解剖复杂的代谢组学数据，以阐明nAMD的异种生物景观和环境对疾病的影响。初步发现的生物标志物为个性化治疗策略提供了有希望的方向，尽管在更广泛的队列中进一步验证对于临床应用至关重要。
