Li Zhuoxin, Zhou Jie, Liang Hao, Ye Li, Lan Liuyan, Lu Fang, Wang Qing, Lei Ting, Yang Xiping, Cui Ping, Huang Jiegang
Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Universities Key Laboratory of Prevention and Control of Highly Prevalent Disease, Nanning, China.
School of Public Health, Guangxi Medical University, Nanning, China.
Front Neurosci. 2022 Jun 28;16:879318. doi: 10.3389/fnins.2022.879318. eCollection 2022.
Neurological diseases are difficult to diagnose in time, and there is currently a lack of effective predictive methods. Previous studies have indicated that a variety of neurological diseases cause changes in the gut microbiota. Alpha diversity is a major indicator to describe the diversity of the gut microbiota. At present, the relationship between neurological diseases and the alpha diversity of the gut microbiota remains unclear.
We performed a systematic literature search of Pubmed and Bioproject databases up to January 2021. Six indices were used to measure alpha diversity, including community richness (observed species, Chao1 and ACE), community diversity (Shannon, Simpson), and phylogenetic diversity (PD). Random-effects meta-analyses on the standardized mean difference (SMD) were carried out on the alpha diversity indices. Subgroup analyses were performed to explore the sources of interstudy heterogeneity. Meta-analysis was performed on articles by matching the age, sex, and body mass index (BMI) of the disease group with the control group. Meanwhile, subgroup analysis was performed to control the variability of the sequencing region, platform, geographical region, instrument, and diseases. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was calculated to assess the prediction effectiveness of the microbial alpha diversity indices.
We conducted a meta-analysis of 24 published studies on 16S rRNA gene amplified sequencing of the gut microbiota and neurological diseases from the Pubmed and Bioproject database (patients, = 1,469; controls, = 1,289). The pooled estimate demonstrated that there was no significant difference in the alpha diversity between patients and controls ( < 0.05). Alpha diversity decreased only in Parkinson's disease patients, while it increased in anorexia nervosa patients compared to controls. After adjusting for age, sex, BMI, and geographical region, none of the alpha diversity was associated with neurological diseases. In terms of Illumina HiSeq 2000 and the V3-V5 sequencing region, the results showed that alpha diversity increased significantly in comparison with the controls, while decreased in Illumina HiSeq 2500. ROC curves suggested that alpha diversity could be used as a biomarker to predict the AD (Simpson, AUC= 0.769, = 0.0001), MS (observed species, AUC= 0.737, = 0.001), schizophrenia (Chao1, AUC = 0.739, = 0.002).
Our review summarized the relationship between alpha diversity of the gut microbiota and neurological diseases. The alpha diversity of gut microbiota could be a promising predictor for AD, schizophrenia, and MS, but not for all neurological diseases.
神经疾病难以及时诊断,目前缺乏有效的预测方法。先前的研究表明,多种神经疾病会导致肠道微生物群发生变化。α多样性是描述肠道微生物群多样性的主要指标。目前,神经疾病与肠道微生物群α多样性之间的关系仍不明确。
我们对截至2021年1月的Pubmed和生物项目数据库进行了系统的文献检索。使用六个指标来衡量α多样性,包括群落丰富度(观测物种数、Chao1和ACE)、群落多样性(香农指数、辛普森指数)和系统发育多样性(PD)。对α多样性指数进行标准化均数差(SMD)的随机效应荟萃分析。进行亚组分析以探索研究间异质性的来源。通过匹配疾病组与对照组的年龄、性别和体重指数(BMI)对文章进行荟萃分析。同时,进行亚组分析以控制测序区域、平台、地理区域、仪器和疾病的变异性。计算受试者工作特征(ROC)曲线的曲线下面积(AUC)值,以评估微生物α多样性指数的预测有效性。
我们对来自Pubmed和生物项目数据库的24项关于肠道微生物群16S rRNA基因扩增测序与神经疾病的已发表研究进行了荟萃分析(患者 = 1469;对照组 = 1289)。汇总估计表明,患者与对照组之间的α多样性没有显著差异(P < 0.05)。仅帕金森病患者的α多样性降低,而神经性厌食症患者与对照组相比α多样性增加。在调整年龄、性别、BMI和地理区域后,没有一种α多样性与神经疾病相关。就Illumina HiSeq 2000和V3 - V5测序区域而言,结果表明与对照组相比α多样性显著增加,而在Illumina HiSeq 2500中则降低。ROC曲线表明,α多样性可作为预测阿尔茨海默病(辛普森指数,AUC = 0.769,P = 0.0001)、多发性硬化症(观测物种数,AUC = 0.737,P = 0.001)、精神分裂症(Chao1,AUC = 0.739,P = 0.002)的生物标志物。
我们的综述总结了肠道微生物群α多样性与神经疾病之间的关系。肠道微生物群的α多样性可能是阿尔茨海默病、精神分裂症和多发性硬化症的有前景的预测指标,但并非对所有神经疾病都如此。