Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
Alzheimers Dement. 2023 Apr;19(4):1428-1439. doi: 10.1002/alz.12792. Epub 2022 Sep 27.
Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi-modality data-driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well-known risk factors for Alzheimer's disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD-related comorbidities along with other AD-relevant features and biomarkers.
A total of 325 MCI subjects with 32 AD-relevant comorbidities and features were considered. Mixed-data clustering is applied to discover and compare MCI subtypes with and without including AD-related comorbidities. Finally, the relevance of each comorbidity-driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates.
We identified four (five) MCI subtypes: poor-, average-, good-, and best-AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity-driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity-driven MCI subtypes. Among the four comorbidity-driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test-related and plasma features, and by the proportion of comorbidities.
Our study indicates that AD comorbidities should be considered along with other diverse AD-relevant characteristics to better understand MCI heterogeneity.
轻度认知障碍（MCI）是一种异质性疾病，其临床结局存在高度个体差异，受患者人口统计学特征、遗传学、大脑结构特征、血液生物标志物和合并症等因素影响。多模态数据驱动方法已被用于发现 MCI 亚型；然而，尽管包括高血压在内的多种疾病都是阿尔茨海默病（AD）的已知危险因素，但疾病合并症并未作为一种模态纳入其中。本研究旨在探讨 AD 相关合并症以及其他 AD 相关特征和生物标志物背景下 MCI 的异质性。
共纳入 325 例 MCI 患者，其中有 32 例与 AD 相关的合并症和特征。混合数据聚类用于发现和比较包含和不包含 AD 相关合并症的 MCI 亚型。最后，通过检查每个与合并症相关的亚型的 MCI 向 AD 疾病的预后、描述性统计和转化率，确定其相关性。
我们通过纳入合并症（不包括合并症）确定了四个（五个）MCI 亚型：预后不良、平均、良好和最佳 AD。我们证明了合并症驱动的 MCI 亚型与未纳入合并症信息的亚型不同。我们进一步证明了合并症驱动的 MCI 亚型的临床相关性。在四个合并症驱动的 MCI 亚型中，有相当一部分参与者恢复正常功能、保持稳定或转化为 AD 的比例存在显著差异。这些组表现出不同的行为，具有显著不同的 MCI 向 AD 预后、认知测试相关和血浆特征的均值，以及合并症的比例。
我们的研究表明，AD 合并症应与其他多样化的 AD 相关特征一起考虑，以更好地理解 MCI 的异质性。
