Executive summary of clinical practice guide on fracture risk in lifestyle diseases.

Accumulating evidence has shown that patients with lifestyle diseases such as type 2 diabetes mellitus, chronic kidney disease, and chronic obstructive pulmonary disease are at increased risk of osteoporotic fracture. Fractures deteriorate quality of life, activities of daily living, and mortality as well as a lifestyle disease. Therefore, preventing fracture is an important issue for those patients. Although the mechanism of the lifestyle diseases-induced bone fragility is still unclear, not only bone mineral density (BMD) reduction but also bone quality deterioration are involved in it. Because fracture predictive ability of BMD and FRAX® is limited, especially for patients with lifestyle diseases, the optimal management strategy should be established. Thus, when the intervention of the lifestyle diseases-induced bone fragility is initiated, the deterioration of bone quality should be taken into account. We here review the association between lifestyle diseases and fracture risk and proposed an algorism of starting anti-osteoporosis drugs for patients with lifestyle diseases.

Moving from the Oslerian paradigm to the post-genomic era: are asthma and COPD outdated terms?

In the majority of cases, asthma and chronic obstructive pulmonary disease (COPD) are two clearly distinct disease entities. However, in some patients there may be significant overlap between the two conditions. This constitutes an important area of concern because these patients are generally excluded from randomised controlled trials (mostly because of smoking history in the case of asthma or because of significant bronchodilator reversibility in the case of COPD). As a result, their pathobiology, prognosis and response to therapy are largely unknown. This may lead to suboptimal management and can limit the development of more personalised therapeutic options. Emerging genetic and molecular information coupled with new bioinformatics capabilities provide novel information that can pave the way towards a new taxonomy of airway diseases. In this paper we question the current value of the terms 'asthma' and 'COPD' as still useful diagnostic labels; discuss the scientific and clinical progress made over the past few years towards unravelling the complexity of airway diseases, from the definition of clinical phenotypes and endotypes to a better understanding of cellular and molecular networks as key pathogenic elements of human diseases (so-called systems medicine); and summarise a number of ongoing studies with the potential to move the field towards a new taxonomy of airways diseases and, hopefully, a more personalised approach to medicine, in which the focus will shift from the current goal of treating diseases as best as possible to the so-called P4 medicine, a new type of medicine that is predictive, preventive, personalised and participatory.

Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.

MOTIVATION: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. RESULTS: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. AVAILABILITY: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.