Stagaman Keaton, Kmiecik Matthew J, Wetzel Madeleine, Aslibekyan Stella, Sonmez Teresa Filshtein, Fontanillas Pierre, Tung Joyce, Holmes Michael V, Walk Seth T, Houser Madelyn C, Norcliffe-Kaufmann Lucy
23andMe, Inc., Sunnyvale, CA, USA.
Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA.
Commun Med (Lond). 2024 Oct 23;4(1):209. doi: 10.1038/s43856-024-00630-8.
Early detection of Parkinson's disease (PD), a neurodegenerative disease with central and peripheral nerve involvement, ensures timely treatment access. Microbes influence nervous system health and are altered in PD.
We examined gut and mouth microbiomes from recently diagnosed patients in a geographically diverse, matched case-control, shotgun metagenomics study.
Here, we show greater alpha-diversity in 445 PD patients versus 221 controls. The microbial signature of PD includes overabundance of 16 OTUs, including Streptococcus mutans and Bifidobacterium dentium, and depletion of 28 OTUs. Machine learning models indicate that subspecies level oral microbiome abundances best distinguish PD with reasonably high accuracy (area under the curve: 0.758). Microbial networks are disrupted in cases, with reduced connectivity between short-chain fatty acid-producing bacteria the the gut. Importantly, microbiome diversity metrics are associated with non-motor autonomic symptom severity.
Our results provide evidence that predictive oral PD microbiome signatures could possibly be used as biomarkers for the early detection of PD, particularly when there is peripheral nervous system involvement.
帕金森病(PD)是一种累及中枢和外周神经的神经退行性疾病,早期检测可确保及时获得治疗。微生物会影响神经系统健康,且在帕金森病中会发生改变。
在一项涵盖不同地理区域、匹配病例对照的鸟枪法宏基因组学研究中,我们检测了近期诊断患者的肠道和口腔微生物群。
在此,我们发现445例帕金森病患者的α多样性高于221例对照。帕金森病的微生物特征包括16个操作分类单元(OTU)丰度过高,其中有变形链球菌和龋齿双歧杆菌,以及28个OTU丰度降低。机器学习模型表明,亚种水平的口腔微生物群丰度能以相当高的准确率(曲线下面积:0.758)最佳地区分帕金森病。病例组的微生物网络遭到破坏,肠道中产生短链脂肪酸的细菌之间的连通性降低。重要的是,微生物群多样性指标与非运动自主神经症状的严重程度相关。
我们的结果提供了证据,表明预测性的帕金森病口腔微生物特征可能可用作帕金森病早期检测的生物标志物,尤其是在存在外周神经系统受累的情况下。 }