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Abstract Background Fundamentally defined by an imbalance in energy consumption and energy expenditure, obesity is a significant risk factor of several musculoskeletal conditions including osteoarthritis (OA). High-fat diets and sedentary lifestyle leads to increased adiposity resulting in systemic inflammation due to the endocrine properties of adipose tissue producing inflammatory cytokines and adipokines. We previously showed serum levels of specific adipokines are associated with biomarkers of bone remodelling and cartilage volume loss in knee OA patients. Whilst more recently we find the metabolic consequence of obesity drives the enrichment of pro-inflammatory fibroblast subsets within joint synovial tissues in obese individuals compared to those of BMI defined ‘health weight’. As such this present study identifies obesity-associated genes in OA joint tissues which are conserved across species and conditions. Methods The study utilised 6 publicly available bulk and single-cell transcriptomic datasets from human and mice studies downloaded from Gene Expression Omnibus (GEO). Machine learning models were employed to model and statistically test datasets for conserved gene expression profiles. Identified genes were validated in OA tissues from obese and healthy weight individuals using quantitative PCR method (N = 38). Obese and healthy-weight patients were categorised by BMI > 30 and BMI between 18 and 24.9 respectively. Informed consent was obtained from all study participants who were scheduled to undergo elective arthroplasty. Results Principal component analysis (PCA) was used to investigate the variations between classes of mouse and human data which confirmed variation between obese and healthy populations. Differential gene expression analysis filtered on adjusted p-values of p

[This corrects the article DOI: 10.1371/journal.pone.0263390.].

Abstract Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity to develop a further particular disease in the future is one of the key challenges in multimorbidity research. In this paper we are using a network-based approach to analyze multimorbidity data and develop methods for predicting diseases that a patient is likely to develop. The multimorbidity data is represented using a temporal bipartite network whose nodes represent patients and diseases and a link between these nodes indicates that the patient has been diagnosed with the disease. Disease prediction then is reduced to a problem of predicting those missing links in the network that are likely to appear in the future. We develop a novel link prediction method for static bipartite network and validate the performance of the method on benchmark datasets. By using a probabilistic framework, we then report on the development of a method for predicting future links in the network, where links are labelled with a time-stamp. We apply the proposed method to three different multimorbidity datasets and report its performance measured by different performance metrics including AUC, Precision, Recall, and F-Score.

Background Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics. Methods A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation. Results For pure, two locus interactions, PLINK’s implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e − 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e − 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e − 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e − 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.

Recent advances in emergency medicine and the co-ordinated delivery of trauma care mean more critically-injured patients now reach the hospital alive and survive life-saving operations. Indeed, between 2008 and 2017, the odds of surviving a major traumatic injury in the UK increased by nineteen percent. However, the improved survival rates of severely-injured patients have placed an increased burden on the healthcare system, with major trauma a common cause of intensive care unit (ICU) admissions that last ≥10 days. Improved understanding of the factors influencing patient outcomes is now urgently needed. We investigated the serum metabolomic profile of fifty-five major trauma patients across three post-injury phases: acute (days 0–4), intermediate (days 5–14) and late (days 15–112). Using ICU length of stay (LOS) as a clinical outcome, we aimed to determine whether the serum metabolome measured at days 0–4 post-injury for patients with an extended (≥10 days) ICU LOS differed from that of patients with a short (10 days ICU LOS. New Injury Severity Score (NISS), testosterone, and the metabolites cadaverine, urea, isoleucine, acetoacetate, dimethyl sulfone, syringate, creatinine, xylitol, and acetone form the integrated biomarker set. Using metabolic enrichment analysis, we found valine, leucine and isoleucine biosynthesis, glutathione metabolism, and glycine, serine and threonine metabolism were the top three pathways differentiating ICU LOS with a p < 0.05. A combined model of NISS and testosterone and all nine selected metabolites achieved an AUROC of 0.824. Differences exist in the serum metabolome of major trauma patients who subsequently experience a short or prolonged ICU LOS in the acute post-injury setting. Combining metabolomic data with anatomical scoring systems allowed us to discriminate between these two groups with a greater degree of accuracy than that of either variable alone.