Jack Clifford R, Vemuri Prashanthi, Wiste Heather J, Weigand Stephen D, Lesnick Timothy G, Lowe Val, Kantarci Kejal, Bernstein Matt A, Senjem Matthew L, Gunter Jeffrey L, Boeve Bradley F, Trojanowski John Q, Shaw Leslie M, Aisen Paul S, Weiner Michael W, Petersen Ronald C, Knopman David S
Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota, USA.
Arch Neurol. 2012 Jul;69(7):856-67. doi: 10.1001/archneurol.2011.3405.
To characterize the shape of the trajectories of Alzheimer disease biomarkers as a function of Mini-Mental State Examination (MMSE) score.
Longitudinal registries from the Mayo Clinic and the Alzheimer's Disease Neuroimaging Initiative.
Two different samples (n = 343 and n = 598) were created that spanned the cognitive spectrum from normal to Alzheimer disease dementia. Subgroup analyses were performed in members of both cohorts (n = 243 and n = 328) who were amyloid positive at baseline.
The shape of biomarker trajectories as a function of MMSE score, adjusted for age, was modeled and described as baseline (cross-sectional) and within-subject longitudinal effects. Biomarkers evaluated were cerebrospinal fluid (CSF) Aβ42 and tau levels, amyloid and fluorodeoxyglucose positron emission tomography imaging, and structural magnetic resonance imaging.
Baseline biomarker values generally worsened (ie, nonzero slope) with lower baseline MMSE score. Baseline hippocampal volume, amyloid positron emission tomography, and fluorodeoxyglucose positron emission tomography values plateaued (ie, nonlinear slope) with lower MMSE score in 1 or more analyses. Longitudinally, within-subject rates of biomarker change were associated with worsening MMSE score. Nonconstant within-subject rates (deceleration) of biomarker change were found in only 1 model.
Biomarker trajectory shapes by MMSE score were complex and were affected by interactions with age and APOE status. Nonlinearity was found in several baseline effects models. Nonconstant within-subject rates of biomarker change were found in only 1 model, likely owing to limited within-subject longitudinal follow-up. Creating reliable models that describe the full trajectories of Alzheimer disease biomarkers will require significant additional longitudinal data in individual participants.
将阿尔茨海默病生物标志物轨迹的形状表征为简易精神状态检查表（MMSE）评分的函数。
来自梅奥诊所和阿尔茨海默病神经影像倡议组织的纵向登记处。
创建了两个不同的样本（n = 343和n = 598），涵盖从正常到阿尔茨海默病痴呆的认知范围。对两个队列中基线时淀粉样蛋白呈阳性的成员（n = 243和n = 328）进行了亚组分析。
将生物标志物轨迹的形状作为MMSE评分的函数进行建模，并针对年龄进行调整，描述为基线（横断面）和个体内纵向效应。评估的生物标志物包括脑脊液（CSF）Aβ42和tau水平、淀粉样蛋白和氟脱氧葡萄糖正电子发射断层扫描成像以及结构磁共振成像。
基线生物标志物值通常随着基线MMSE评分降低而恶化（即非零斜率）。在1项或多项分析中，基线海马体积、淀粉样蛋白正电子发射断层扫描和氟脱氧葡萄糖正电子发射断层扫描值随着MMSE评分降低而趋于平稳（即非线性斜率）。纵向来看，个体内生物标志物变化率与MMSE评分恶化相关。仅在1个模型中发现生物标志物变化的个体内速率不恒定（减速）。
按MMSE评分划分的生物标志物轨迹形状很复杂，且受与年龄和APOE状态的相互作用影响。在几个基线效应模型中发现了非线性。仅在1个模型中发现生物标志物变化的个体内速率不恒定，这可能是由于个体内纵向随访有限所致。创建描述阿尔茨海默病生物标志物完整轨迹的可靠模型将需要个体参与者更多的纵向数据。
