National Institute of Health Research Biomedical Research Centre for Mental Health, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom.
PLoS One. 2012;7(9):e44260. doi: 10.1371/journal.pone.0044260. Epub 2012 Sep 24.
Changes in brain amyloid burden have been shown to relate to Alzheimer's disease pathology, and are believed to precede the development of cognitive decline. There is thus a need for inexpensive and non-invasive screening methods that are able to accurately estimate brain amyloid burden as a marker of Alzheimer's disease. One potential method would involve using demographic information and measurements on plasma samples to establish biomarkers of brain amyloid burden; in this study data from the Alzheimer's Disease Neuroimaging Initiative was used to explore this possibility. Sixteen of the analytes on the Rules Based Medicine Human Discovery Multi-Analyte Profile 1.0 panel were found to associate with [(11)C]-PiB PET measurements. Some of these markers of brain amyloid burden were also found to associate with other AD related phenotypes. Thirteen of these markers of brain amyloid burden--c-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, cortisol, AXL receptor kinase, interleukin-3, interleukin-13, matrix metalloproteinase-9 total, apolipoprotein E and immunoglobulin E--were used along with co-variates in multiple linear regression, and were shown by cross-validation to explain >30% of the variance of brain amyloid burden. When a threshold was used to classify subjects as PiB positive, the regression model was found to predict actual PiB positive individuals with a sensitivity of 0.918 and a specificity of 0.545. The number of APOE [Symbol: see text] 4 alleles and plasma apolipoprotein E level were found to contribute most to this model, and the relationship between these variables and brain amyloid burden was explored.
脑淀粉样蛋白负担的变化已被证明与阿尔茨海默病病理学有关,并且被认为先于认知能力下降的发生。因此,需要一种廉价且非侵入性的筛选方法,能够准确估计脑淀粉样蛋白负担作为阿尔茨海默病的标志物。一种潜在的方法是使用人口统计学信息和血浆样本测量值来建立脑淀粉样蛋白负担的生物标志物;在这项研究中,使用了阿尔茨海默病神经影像学倡议的数据来探索这种可能性。在规则为基础的医学人类发现多分析物 1.0 面板上的 16 种分析物与 [(11)C]-PiB PET 测量值相关。这些脑淀粉样蛋白负担的一些标志物也与其他与 AD 相关的表型相关。其中 13 种脑淀粉样蛋白负担标志物——肽、纤维蛋白原、α-1-抗胰蛋白酶、胰多肽、补体 C3、玻连蛋白、皮质醇、AXL 受体激酶、白细胞介素-3、白细胞介素-13、基质金属蛋白酶-9 总、载脂蛋白 E 和免疫球蛋白 E——与协变量一起用于多元线性回归,并通过交叉验证表明,它们可以解释脑淀粉样蛋白负担的 30%以上。当使用阈值将受试者分类为 PiB 阳性时,发现回归模型可以以 0.918 的灵敏度和 0.545 的特异性预测实际的 PiB 阳性个体。发现 APOE [符号:见正文]4 等位基因和血浆载脂蛋白 E 水平对该模型的贡献最大,并探讨了这些变量与脑淀粉样蛋白负担之间的关系。