The Gut Microbiome in Polycystic Ovary Syndrome (PCOS) and its Association with Metabolic Traits

This work was funded by Estonian Research Council grants PUT 1371 (to E.O.), EMBO Installation grant 3573 (to E.O.) … E.O. was supported by European Regional Development Fund Project No. 15-0012 GENTRANSMED and Estonian Center of Genomics/Roadmap II project No 16-0125. S.A. was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and European Regional Development Fund (FEDER): grants RYC-2016-21199 and ENDORE (SAF2017-87526-R); and by FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento: MENDO (B-CTS-500-UGR18).Purpose: Despite gut microbiome being widely studied in metabolic diseases, its role in polycystic ovary syndrome (PCOS) has been scarcely investigated. The aim of our study was to test for possible associations between gut microbiome and PCOS in late fertile age women and investigate whether changes in the gut microbiome correlate with PCOS-related metabolic parameters. Methods: We compared the 16S rRNA sequenced gut microbiome of 102 PCOS women with 201 age- and body mass index (BMI) matched non-PCOS women. Clinical and biochemical characteristics of the participants were assessed at ages 31 and 46 and analyzed in the context of gut microbiome data at the age of 46. Results: Bacterial diversity indices did not differ significantly between PCOS and controls. We identified four genera whose balance helps to differentiate between PCOS and non-PCOS. In the whole cohort, the abundance of two genera from the order Clostridiales was correlated with several PCOS-related markers. When investigating the gut microbiome composition in PCOS women with different BMI and glucose tolerance groups, prediabetic PCOS women had significantly lower alpha diversity and markedly increased abundance of genus Dorea compared to women with normal glucose tolerance. Conclusions: Our data indicate that PCOS and non-PCOS women at late fertile age with similar BMI do not signficantly differ in gut microbiota. However, there are significant microbial changes in PCOS individuals depending on their metabolic health. Further studies are needed in order to further understand these changes in more detail.Estonian Research Council grants PUT 1371EMBO Installation grant 3573European Regional Development Fund Project No. 15-0012 GENTRANSMEDEstonian Center of Genomics/Roadmap II project No 16-0125Spanish Ministry of Economy, Industry and Competitiveness (MINECO) European Regional Development Fund (FEDER) RYC-2016-21199 and ENDORE (SAF2017-87526-R)FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento: MENDO (B-CTS-500-UGR18

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

The gut microbiome in polycystic ovary syndrome and its association with metabolic traits

Abstract Context: Despite the gut microbiome being widely studied in metabolic diseases, its role in polycystic ovary syndrome (PCOS) has been scarcely investigated. Objective: Compare the gut microbiome in late fertile age women with and without PCOS and investigate whether changes in the gut microbiome correlate with PCOS-related metabolic parameters. Design: Prospective, case–control study using the Northern Finland Birth Cohort 1966. Setting: General community. Participants: A total of 102 PCOS women and 201 age- and body mass index (BMI)-matched non-PCOS control women. Clinical and biochemical characteristics of the participants were assessed at ages 31 and 46 and analyzed in the context of gut microbiome data at the age of 46. Intervention: (s): None Main outcome measure(s): Bacterial diversity, relative abundance, and correlations with PCOS-related metabolic measures. Results: Bacterial diversity indices did not differ significantly between PCOS and controls (Shannon diversity P = .979, unweighted UniFrac P = .175). Four genera whose balance helps to differentiate between PCOS and non-PCOS were identified. In the whole cohort, the abundance of 2 genera from Clostridiales, Ruminococcaceae UCG-002, and Clostridiales Family XIII AD3011 group, were correlated with several PCOS-related markers. Prediabetic PCOS women had significantly lower alpha diversity (Shannon diversity P = .018) and markedly increased abundance of genus Dorea (false discovery rate = 0.03) compared with women with normal glucose tolerance. Conclusions: PCOS and non-PCOS women at late fertile age with similar BMI do not significantly differ in their gut microbial profiles. However, there are significant microbial changes in PCOS individuals depending on their metabolic health