Botello-Marabotto Marina, Martínez-Bisbal M Carmen, Calero Miguel, Bernardos Andrea, Pastor Ana B, Medina Miguel, Martínez-Máñez Ramón
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 (CIBER-BBN), 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; Departamento de Química-Física, Universitat de València, Valencia, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain; Unidad Mixta UPV-CIPF de Investigación en Mecanismos de Enfermedades y Nanomedicina, Universitat Politècnica de València, Centro de Investigación Príncipe Felipe, Valencia, Spain.
Neurobiol Dis. 2023 Oct 15;187:106312. doi: 10.1016/j.nbd.2023.106312. Epub 2023 Sep 26.
Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15 years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance (H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n = 51), mild cognitive impairment (MCI, n = 27), and cognitively healthy controls (HC, n = 50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n = 9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n = 14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n = 4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.
阿尔茨海默病是老年人中最常见的痴呆类型。它是一种进行性退行性疾病,在临床症状出现前可能长达15年就开始发展。识别早期生物标志物对于实现早期诊断和启动有效干预至关重要。在这项研究中,我们使用质子核磁共振(H NMR)光谱对经神经病理学确诊的阿尔茨海默病患者(AD,n = 51)、轻度认知障碍患者(MCI,n = 27)和认知健康对照者(HC,n = 50)的血清样本进行了代谢组学研究,以寻找可作为生物标志物的代谢物。患者和对照者接受了长达六年的年度临床随访。MCI组包括来自具有不同疾病进展率的三个亚组受试者的样本。第一个亚组包括在研究期间处于MCI阶段临床稳定的受试者(稳定MCI,S-MCI,n = 9)。第二个亚组包括在采血时被诊断为MCI,但在随后几年进展为临床痴呆的受试者(MCI转痴呆,MCI-D,n = 14)。最后一个亚组由在样本采集时首次被诊断为痴呆的受试者组成(早期痴呆,Incp-D,n = 4)。建立了偏最小二乘判别分析(PLS-DA)模型。获得了三个模型,一个用于以高灵敏度(93.75%)和特异性(94.75%)区分AD和HC样本,另一个模型用于区分AD和MCI样本(100%灵敏度和82.35%特异性),最后一个模型用于以较低的灵敏度和特异性(67%和50%)区分HC和MCI。对MCI组内的差异进行了进一步研究,以试图确定那些未来可能发展为AD型痴呆的MCI受试者。研究了代谢物的相对浓度和代谢途径。比较HC和MCI-D患者时,发现丙氨酸、天冬氨酸和谷氨酸代谢途径、泛酸和辅酶A生物合成途径以及β-丙氨酸代谢途径存在改变。相比之下,S-MCI组与HC组比较时未发现任何途径受到干扰。这些结果突出了H NMR代谢组学以侵入性较小的方式支持痴呆诊断的潜力,并为研究潜在生物标志物以识别未来有发展为AD风险的MCI或HC受试者奠定了基础。