Background and purposeThe role of the gut microbiome in the pathogenesis of Parkinson disease (PD) is under intense investigation, and the results presented are still very heterogeneous. These discrepancies arise not only from the highly heterogeneous pathology of PD, but also from widely varying methodologies at all stages of the workflow, from sampling to final statistical analysis. The aim of the present work is to harmonize the workflow across studies to reduce the methodological heterogeneity and to perform a pooled analysis to account for other sources of heterogeneity.MethodsWe performed a systematic review to identify studies comparing the gut microbiota of PD patients to healthy controls. A workflow was designed to harmonize processing across all studies from bioinformatics processing to final statistical analysis using a Bayesian random‐effects meta‐analysis based on individual patient‐level data.ResultsThe results show that harmonizing workflows minimizes differences between statistical methods and reveals only a small set of taxa being associated with the pathogenesis of PD. Increased shares of the genera Akkermansia and Bifidobacterium and decreased shares of the genera Roseburia and Faecalibacterium were most characteristic for PD‐associated microbiota.ConclusionsOur study summarizes evidence that reduced levels of butyrate‐producing taxa in combination with possible degradation of the mucus layer by Akkermansia may promote intestinal inflammation and reduced permeability of the gut mucosal layer. This may allow potentially pathogenic metabolites to transit and enter the enteric nervous system.

Reproducibility is a major issue in microbiome studies, which is partly caused by missing consensus about data analysis strategies. The complex nature of microbiome data, which are high-dimensional, zero-inflated, and compositional, makes them challenging to analyze, as they often violate assumptions of classic statistical methods. With advances in human microbiome research, research questions and study designs increase in complexity so that more sophisticated data analysis concepts are applied. To improve current practice of the analysis of microbiome studies, it is important to understand what kind of research questions are asked and which tools are used to answer these questions. We conducted a systematic literature review considering all publications focusing on the analysis of human microbiome data from June 2018 to June 2019. Of 1,444 studies screened, 419 fulfilled the inclusion criteria. Information about research questions, study designs, and analysis strategies were extracted. The results confirmed the expected shift to more advanced research questions, as one-third of the studies analyzed clustered data. Although heterogeneity in the methods used was found at any stage of the analysis process, it was largest for differential abundance testing. Especially if the underlying data structure was clustered, we identified a lack of use of methods that appropriately addressed the underlying data structure while taking into account additional dependencies in the data. Our results confirm considerable heterogeneity in analysis strategies among microbiome studies; increasingly complex research questions require better guidance for analysis strategies. IMPORTANCE The human microbiome has emerged as an important factor in the development of health and disease. Growing interest in this topic has led to an increasing number of studies investigating the human microbiome using high-throughput sequencing methods. However, the development of suitable analytical methods for analyzing microbiome data has not kept pace with the rapid progression in the field. It is crucial to understand current practice to identify the scope for development. Our results highlight the need for an extensive evaluation of the strengths and shortcomings of existing methods in order to guide the choice of proper analysis strategies. We have identified where new methods could be designed to address more advanced research questions while taking into account the complex structure of the data.

Many cohort studies have investigated the link between diet and plasma TMAO levels, reporting incongruent results, while gut microbiota were only recently included into analyses. In these studies, taxonomic data were recorded that are not a good proxy for TMA formation, as specific members of various taxa exhibit genes catalyzing this reaction, demanding function-based technologies for accurate quantification of TMA-synthesizing bacteria.

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Aim To explore whether adjunctive antibiotics can relevantly influence long‐term microbiota changes in stage III–IV periodontitis patients. Materials and Methods This is a secondary analysis of a randomized clinical trial on periodontal therapy with adjunctive 500 mg amoxicillin and 400 mg metronidazole or placebo thrice daily for 7 days. Subgingival plaque samples were taken before and 2, 8, 14 and 26 months after mechanical therapy. The V4‐hypervariable region of the 16S rRNA gene was sequenced with Illumina MiSeq 250 base pair paired‐end reads. Changes at the ribosomal sequence variant (RSV) level, diversity and subgingival‐microbial dysbiosis index (SMDI) were explored with a negative binomial regression model and non‐parametric tests. Results Overall, 50.2% of all raw reads summed up to 72 RSVs (3.0%) that were generated from 163 stage III–IV periodontitis patients. Of those, 16 RSVs, including Porphyromonas gingivalis, Tannerella forsythia and Aggregatibacter actinomycetemcomitans, changed significantly over 26 months because of adjunctive systemic antibiotics. SMDI decreased significantly more in the antibiotic group at all timepoints, whereas the 2‐month differences in alpha and beta diversity between groups were not significant at 8 and 14 months, respectively. Conclusions Mechanical periodontal therapy with adjunctive antibiotics induced a relevant and long‐term sustainable change towards an oral microbiome more associated with oral health.