Data-driven identification of endophenotypes of Alzheimer's disease progression: implications for clinical trials and therapeutic interventions
Data-driven identification of endophenotypes of Alzheimer's disease progression: implications for clinical trials and therapeutic interventions
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DOI：
10.1186/s13195-017-0332-0
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发表时间：
2018-01-15
影响因子：
9
通讯作者：
Brinton, Roberta Diaz
中科院分区：
文献类型：
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作者：
Geifman, Nophar;Kennedy, Richard E.;Brinton, Roberta Diaz
背景资料：鉴于阿尔茨海默病（AD）的复杂性和渐进性，一个精确的医学方法的诊断和治疗需要识别患者亚组的生物医学上不同的和可操作的表型definitions.Methods：纵向患者水平的数据为1160 AD患者接受安慰剂或没有治疗的后续长达18个月的综合临床试验数据集提取。我们使用潜在类混合模型（LCMM）来识别患者亚组，这些患者亚组表现出疾病严重程度随时间变化的不同模式，如阿尔茨海默病评估量表认知子量表评分所测量的。最佳的子组（类）的数量选择的模型，具有最低的贝叶斯信息准则。其他患者水平的变量被用来定义这些亚组的区别特征，并调查患者的特点和疾病progress.Results模式之间的相互作用：LCMM导致在三个不同的亚组的患者，10.3%的1级，76.5%的2级和13.2%的3级。虽然所有类别都表现出一定程度的认知下降，但每个类别都表现出不同的认知评分变化模式，可能反映了AD患者的不同亚型。第一类代表认知能力随着时间的推移急剧下降的快速下降者，他们往往更年轻，受过更好的教育。2级代表缓慢下降者，而3级代表严重受损的缓慢下降者：下降速率与2级相似但基线认知评分更差的患者。第2类表现出显着较高比例的患者使用他汀类药物的历史;第3类表现出较低水平的血液单核细胞和血清钙，和较高的血糖levels.Conclusions：我们的研究结果，“学习”从临床数据，表明存在至少三个亚组的阿尔茨海默氏症患者，每个表现出不同的轨迹的疾病进展。这种假设生成方法检测到不同的AD亚组，可能被证明是与特定病因相关的离散内表型。这些发现可以在临床试验或研究背景下进行分层，这可能有助于确定新的干预目标并指导更好的护理。
Background: Given the complex and progressive nature of Alzheimer's disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions.Methods: Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer's Disease Assessment Scale-cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups' distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression.Results: The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels.Conclusions: Our results, 'learned' from clinical data, indicate the existence of at least three subgroups of Alzheimer's patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.