Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer’s Disease Neuroimaging Initiative cohort

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10–8 and 4.3 × 10–7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

阿尔茨海默病（AD）是一种通常会导致痴呆的神经退行性疾病。由于大脑功能障碍在临床症状出现前20年就已开始，因此确定用于早期检测AD的生物标志物是一项新的需求。为此，我们使用MS - DIAL重新分析了来自参与阿尔茨海默病神经影像学倡议（ADNI）队列的905名患者的非靶向代谢组学质谱数据，其中包含39108种独特生物分子的1304633个光谱。确定了93种亲水性代谢物的代谢谱。此外，我们整合了靶向脂质组学数据（来自1524名患者的4873个样本），以利用基线代谢组探索用于预测在两年内被诊断为AD的患者中进展性轻度认知障碍（pMCI）的候选生物标志物。麦角硫因水平较低的患者AD进展率高12%，P = 0.012（ Wald检验）具有显著性。此外，在AD患者中证实了神经节苷脂（GM3）增加以及缩醛磷脂减少，其中许多与载脂蛋白E多态性有关，并且溶血磷脂酰胆碱（18:1）和GM3 d18:1/20:0水平较高分别显示AD进展率高19%和17%（Wald检验：P = 3.9×10⁻⁸和4.3×10⁻⁷）。棕榈油酰胺、油酰胺、二酰基甘油和醚脂也被确定为pMCI患者基线时显著改变的代谢物。代谢物和基因组学数据的综合分析表明，结合代谢物和基因型信息可提高AD进展的预测性能，这表明代谢组学对于补充基因组数据至关重要。总之，对多组学数据的重新分析为检测AD病理的早期发展以及部分了解与年龄相关的AD发病中的代谢变化提供了新的见解。