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

Measurement of cortical porosity of the proximal femur improves identification of women with nonvertebral fragility fractures

UNLABELLED: We tested whether cortical porosity of the proximal femur measured using StrAx1.0 software provides additional information to areal bone mineral density (aBMD) or Fracture Risk Assessment Tool (FRAX) in differentiating women with and without fracture. Porosity was associated with fracture independent of aBMD and FRAX and identified additional women with fractures than by osteoporosis or FRAX thresholds. INTRODUCTION: Neither aBMD nor the FRAX captures cortical porosity, a major determinant of bone strength. We therefore tested whether combining porosity with aBMD or FRAX improves identification of women with fractures. METHODS: We quantified femoral neck (FN) aBMD using dual-energy X-ray absorptiometry, FRAX score, and femoral subtrochanteric cortical porosity using StrAx1.0 software in 211 postmenopausal women aged 54-94 years with nonvertebral fractures and 232 controls in Tromsø, Norway. Odds ratios (ORs) were calculated using logistic regression analysis. RESULTS: Women with fractures had lower FN aBMD, higher FRAX score, and higher cortical porosity than controls (all p 20%, whereas porosity >80th percentile identified 61 women (29%). Porosity identified 26% additional women with fractures than identified by the osteoporosis threshold and 21% additional women with fractures than by this FRAX threshold. CONCLUSIONS: Cortical porosity is a risk factor for fracture independent of aBMD and FRAX and improves identification of women with fracture

Parental TB associated with offspring asthma and rhinitis

: BACKGROUND: Infections in early life are associated with asthma and allergies in one-generation settings; however, the link between parental infection and offspring phenotype is rarely investigated. We aim to study the association of parental TB before conception of the offspring with offspring asthma and rhinitis.METHODS: We included 2,965 offspring born in 1985-2004 and registered in the Norwegian prescription database to 1,790 parents born after 1960 with a history of TB, and included in the Norwegian TB registry. Offspring asthma (n = 582) and rhinitis (n = 929) were defined based on diagnosis, type of medication and prescribed medication ≥1 year. Associations of parental TB <8 years, ≥8 years but before offspring´s birth year and after birth (reference category) with offspring asthma and rhinitis were analysed using logistic regression.RESULTS: Asthma risk was higher in persons with parental TB in childhood (OR 1.73, 95% CI 1.20-2.50) or later preconception (OR 1.38, 95% CI 1.00-1.91) than in persons with parental TB after offspring´s birth; this was significant only in the maternal line (childhood: OR 1.95, 95% CI 1.13-3.37; later preconception: OR 1.74, 95% CI 1.08-2.80). Associations with rhinitis were not identified.CONCLUSIONS: Parental childhood TB was associated with higher asthma risk in future offspring. We speculate that TB impacts maternal immunity and dysregulates the offspring´s type 2 immunity, and that TB-induced epigenetic reprograming of immune defences are transferred to the offspring

Burden of disease attributable to risk factors in European countries: a scoping literature review

Objectives Within the framework of the burden of disease (BoD) approach, disease and injury burden estimates attributable to risk factors are a useful guide for policy formulation and priority setting in disease prevention. Considering the important differences in methods, and their impact on burden estimates, we conducted a scoping literature review to: (1) map the BoD assessments including risk factors performed across Europe; and (2) identify the methodological choices in comparative risk assessment (CRA) and risk assessment methods. Methods We searched multiple literature databases, including grey literature websites and targeted public health agencies websites. Results A total of 113 studies were included in the synthesis and further divided into independent BoD assessments (54 studies) and studies linked to the Global Burden of Disease (59 papers). Our results showed that the methods used to perform CRA varied substantially across independent European BoD studies. While there were some methodological choices that were more common than others, we did not observe patterns in terms of country, year or risk factor. Each methodological choice can affect the comparability of estimates between and within countries and/or risk factors, since they might significantly influence the quantification of the attributable burden. From our analysis we observed that the use of CRA was less common for some types of risk factors and outcomes. These included environmental and occupational risk factors, which are more likely to use bottom-up approaches for health outcomes where disease envelopes may not be available. Conclusions Our review also highlighted misreporting, the lack of uncertainty analysis and the under-investigation of causal relationships in BoD studies. Development and use of guidelines for performing and reporting BoD studies will help understand differences, avoid misinterpretations thus improving comparability among estimates