整合血液生物标志物与基因组变异识别可从多领域干预中获益的轻度认知障碍个体
Integration of blood-based biomarkers and genomic variants identifies individuals with mild cognitive impairment who benefit from multidomain intervention.
文献信息
| PMID | 42890602 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Tetsuaki Kimura |
| 作者单位 | Medical Genome Center Research Institute, National Center for Geriatrics and Gerontology Obu Aichi Japan. |
| 期刊 | Alzheimer's & dementia (New York, N. Y.) |
| SCI 分区 | Q1 |
| IF | 6.2 |
| 研究类型 | 临床研究 · 临床 |
| 所属专科 | 耳科 |
中文摘要
引言： 我们近期的日本预防痴呆多模式干预试验（J-MINT）提示，多领域生活方式干预可能延缓认知衰退。然而，个体间治疗反应存在显著差异，识别更可能从干预中获益的个体仍具挑战性。血液生物标志物（BBMs）提供了一种微创评估阿尔茨海默病（AD）相关病理过程的方法，可能有助于识别轻度认知障碍（MCI）个体中生物学上不同的亚组。
方法： 在J-MINT参与者中测量了血液生物标志物，包括淀粉样蛋白-β42/40比值、磷酸化tau-217、神经丝轻链和胶质纤维酸性蛋白（GFAP）。基于BBM谱的层次聚类用于识别生物学定义的亚组。使用全基因组关联分析（GWAS）分析全基因组测序数据，以识别与基于BBM的亚组相关的遗传变异。利用相关遗传变异、年龄、性别和载脂蛋白E4（APOE4）等位基因计数构建亚组分配的预测模型。模型性能通过重复1000次的五折交叉验证进行评估。
结果： 基于BBM的聚类识别出一个以GFAP升高和多种生物标志物异常为特征的亚组。在该亚组内，从基线到18个月认知表现的变化在干预组和对照组之间差异显著，对照组观察到更明显的衰退。GWAS识别出四个与该亚组提示相关的变异，其中两个位于与听力损失相关基因（PCDH15和ATP11A）的内含子区域。
讨论： 基于BBM的聚类在MCI个体中识别出生物学上不同的亚组。以多种生物标志物异常和GFAP升高为特征的亚组在干预组和对照组之间表现出不同的认知轨迹。这些发现提示，BBMs可能结合遗传信息，有助于识别具有更进展性AD相关生物学特征、可能从多领域干预中获得更大获益的个体。
英文摘要
INTRODUCTION: Our recent Japan-Multimodal Intervention Trial for the Prevention of Dementia (J-MINT) trial suggested that multidomain lifestyle interventions may delay cognitive decline. However, substantial interindividual variability in response persists, and identifying individuals more likely to benefit from the intervention remains challenging. Blood-based biomarkers (BBMs), which provide a minimally invasive means of assessing Alzheimer's disease (AD)-related pathological processes, may help identify biologically distinct subgroups of individuals with mild cognitive impairment (MCI).
METHODS: Blood-based biomarkers-including amyloid-β42/40 ratio, phosphorylated tau-217, neurofilament light chain, and glial fibrillary acidic protein (GFAP)-were measured in J-MINT participants. Hierarchical clustering based on BBM profiles was used to identify biologically defined subgroups. Whole-genome sequencing data were analyzed using genome-wide association analysis (GWAS) to identify genetic variants associated with the BBM-based subgroup. A prediction model for the subgroup assignment was constructed using the associated incorporating the genetic variants, age, sex, and apolipoprotein E4 (APOE4) allele count. Model performance was evaluated using five-fold cross-validation repeated 1000 times.
RESULTS: BBM-based clustering identified a subgroup characterized by elevated GFAP and multiple biomarker abnormalities. Within this subgroup, the change in cognitive performance from baseline to 18 months differed significantly between intervention and control groups, with greater decline observed in the control group. GWAS identified four variants showing suggestive association with the subgroup, two of which were located in intronic regions of genes associated with hearing loss (PCDH15 and ATP11A).
DISCUSSION: BBM-based clustering identified biologically distinct subgroups among individuals with MCI. The subgroup characterized by multiple biomarker abnormalities and elevated GFAP showed differential cognitive trajectories between the intervention and control groups. These findings suggest that BBMs, potentially in combination with genetic information, may help identify individuals with a more progressive AD-related biological profile who may derive greater benefit from multidomain intervention.