Unbiased estimates of cerebrospinal fluid β-amyloid 1-42 cutoffs in a large memory clinic population
Unbiased estimates of cerebrospinal fluid β-amyloid 1-42 cutoffs in a large memory clinic population
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DOI：
10.1186/s13195-016-0233-7
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发表时间：
2017-02-14
影响因子：
9
通讯作者：
Visser, Pieter Jelle
中科院分区：
文献类型：
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作者：
Bertens, Daniela;Tijms, Betty M.;Visser, Pieter Jelle
背景：我们试图通过记忆诊所人群中数据驱动的高斯混合模型来确定脑脊液 (CSF) 中 β-淀粉样蛋白 1-42 的临界值，脑脊液 (CSF) 是阿尔茨海默病 (AD) 的关键标志物。方法：我们进行了一项横断面和前瞻性队列研究相结合。我们从阿姆斯特丹痴呆队列中选择了 2462 名患有主观认知下降、轻度认知障碍、AD 型痴呆和除 AD 以外的痴呆的受试者。我们根据临床诊断、年龄和载脂蛋白 E (APOE) 基因型，通过数据驱动的高斯混合模型在总人群和亚组中定义了 CSF β-淀粉样蛋白 1-42 截止值。我们研究了由数据驱动的截止值定义的异常 β-淀粉样蛋白 1-42 是否比使用 Cox 比例风险回归基于临床诊断的截止值定义的异常 β-淀粉样蛋白 1-42 更能预测 AD 型痴呆的进展。结果：在整个患者组中，我们发现异常 CSF β-淀粉样蛋白 1-42 的截止值为 680 pg/ml（95% CI 660-705皮克/毫升）。在诊断亚组和 APOE 基因型亚组中也发现了类似的截止值。老年受试者的截止值高于年轻受试者。数据驱动的截止值高于我们基于临床诊断的截止值，并且对非痴呆受试者进展为 AD 型痴呆具有更好的预测准确性（HR 7.6 与 5.2，p < 0.01）。结论：混合模型是确定 CSF β-淀粉样蛋白 1-42 截止值的稳健方法。与基于临床诊断的截止值相比，它可能更好地捕获与 AD 相关的生物学变化。
Background: We sought to define a cutoff for beta-amyloid 1-42 in cerebrospinal fluid (CSF), a key marker for Alzheimer's disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population.Methods: We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF beta-amyloid 1-42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal beta-amyloid 1-42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal beta-amyloid 1-42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression.Results: In the total group of patients, we found a cutoff for abnormal CSF beta-amyloid 1-42 of 680 pg/ml (95% CI 660-705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosisbased cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01).Conclusions: Mixture modeling is a robust method to determine cutoffs for CSF beta-amyloid 1-42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis.