Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Valencia, Spain; Unidad Mixta de Investigación en Nanomedicina y Sensores, Instituto de Investigación Sanitaria La Fe (IISLAFE) - Universitat Politècnica de València, Valencia, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain.
Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Valencia, Spain; Unidad Mixta de Investigación en Nanomedicina y Sensores, Instituto de Investigación Sanitaria La Fe (IISLAFE) - Universitat Politècnica de València, Valencia, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain; Departamento de Química Física, Universitat de València, Valencia, Spain.
Talanta. 2024 Jun 1;273:125826. doi: 10.1016/j.talanta.2024.125826. Epub 2024 Feb 25.
Primary Open-Angle Glaucoma (POAG) is the most prevalent glaucoma type, and the leading cause of irreversible visual impairment and blindness worldwide. Identification of early POAG biomarkers is of enormous value, as there is not an effective treatment for the glaucomatous optic nerve degeneration (OND). In this pilot study, a metabolomic analysis, by using proton (H) nuclear magnetic resonance (NMR) spectroscopy was conducted in tears, in order to determine the changes of specific metabolites in the initial glaucoma eyes and to discover potential diagnostic biomarkers. A classification model, based on the metabolomic fingerprint in tears was generated as a non-invasive tool to support the preclinical and clinical POAG diagnosis. H NMR spectra were acquired from 30 tear samples corresponding to the POAG group (n = 11) and the control group (n = 19). Data were analysed by multivariate statistics (partial least squares-discriminant analysis: PLS-DA) to determine a model capable of differentiating between groups. The whole data set was split into calibration (65%)/validation (35%), to test the performance and the ability for glaucoma discrimination. The calculated PLS-DA model showed an area under the curve (AUC) of 1, as well as a sensitivity of 100% and a specificity of 83.3% to distinguish POAG group versus control group tear data. This model included 11 metabolites, potential biomarkers of the disease. When comparing the study groups, a decrease in the tear concentration of phenylalanine, phenylacetate, leucine, n-acetylated compounds, formic acid, and uridine, was found in the POAG group. Moreover, an increase in the tear concentration of taurine, glycine, urea, glucose, and unsaturated fatty acids was observed in the POAG group. These results highlight the potential of tear metabolomics by H NMR spectroscopy as a non-invasive approach to support early POAG diagnosis and in order to prevent visual loss.
原发性开角型青光眼（POAG）是最常见的青光眼类型，也是全球范围内导致不可逆性视力损害和失明的主要原因。识别早期 POAG 生物标志物具有巨大的价值，因为目前尚无针对青光眼视神经变性（OND）的有效治疗方法。在这项初步研究中，我们通过质子（H）核磁共振（NMR）光谱对眼泪进行了代谢组学分析，以确定初始青光眼眼中特定代谢物的变化，并发现潜在的诊断生物标志物。基于眼泪代谢组指纹的分类模型被作为一种非侵入性工具来支持临床前和临床 POAG 诊断。从 30 个对应于 POAG 组（n=11）和对照组（n=19）的眼泪样本中获取 H NMR 光谱。通过多元统计（偏最小二乘判别分析：PLS-DA）分析数据，以确定能够区分组别的模型。整个数据集分为校准（65%）/验证（35%），以测试性能和青光眼鉴别能力。计算出的 PLS-DA 模型显示曲线下面积（AUC）为 1，以及区分 POAG 组与对照组眼泪数据的 100%敏感性和 83.3%特异性。该模型包括 11 种代谢物，是疾病的潜在生物标志物。在比较研究组时，我们发现 POAG 组眼泪中苯丙氨酸、苯乙酸、亮氨酸、N-乙酰化化合物、甲酸和尿苷的浓度降低，而牛磺酸、甘氨酸、尿素、葡萄糖和不饱和脂肪酸的浓度升高。这些结果突出了 H NMR 光谱法通过眼泪代谢组学作为支持早期 POAG 诊断和预防视力丧失的非侵入性方法的潜力。
