BackgroundDespite substantial research and development investment in Alzheimer’s disease (AD), effective therapeutics remain elusive. Significant emerging evidence has linked cholesterol, β-amyloid and AD, and several studies have shown a reduced risk for AD and dementia in populations treated with statins. However, while some clinical trials evaluating statins in general AD populations have been conducted, these resulted in no significant therapeutic benefit. By focusing on subgroups of the AD population, it may be possible to detect endotypes responsive to statin therapy.MethodsHere we investigate the possible protective and therapeutic effect of statins in AD through the analysis of datasets of integrated clinical trials, and prospective observational studies.ResultsRe-analysis of AD patient-level data from failed clinical trials suggested by trend that use of simvastatin may slow the progression of cognitive decline, and to a greater extent in ApoE4 homozygotes. Evaluation of continual long-term use of various statins, in participants from multiple studies at baseline, revealed better cognitive performance in statin users. These findings were supported in an additional, observational cohort where the incidence of AD was significantly lower in statin users, and ApoE4/ApoE4-genotyped AD patients treated with statins showed better cognitive function over the course of 10-year follow-up.ConclusionsThese results indicate that the use of statins may benefit all AD patients with potentially greater therapeutic efficacy in those homozygous for ApoE4.

In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

Introduction Establishing efficacy of and molecular pathways for statins has the potential to impact incidence of Alzheimer's and age‐related neurodegenerative diseases (NDD). Methods This retrospective cohort study surveyed US‐based Humana claims, which includes prescription and patient records from private‐payer and Medicare insurance. Claims from 288,515 patients, aged 45 years and older, without prior history of NDD or neurological surgery, were surveyed for a diagnosis of NDD starting 1 year following statin exposure. Patients were required to be enrolled with claims data for at least 6 months prior to first statin prescription and at least 3 years thereafter. Computational system biology analysis was conducted to determine unique target engagement for each statin. Results Of the 288,515 participants included in the study, 144,214 patients (mean [standard deviation (SD)] age, 67.22 [3.8] years) exposed to statin therapies, and 144,301 patients (65.97 [3.2] years) were not treated with statins. The mean (SD) follow‐up time was 5.1 (2.3) years. Exposure to statins was associated with a lower incidence of Alzheimer's disease (1.10% vs 2.37%; relative risk [RR], 0.4643; 95% confidence interval [CI], 0.44–0.49; P < .001), dementia 3.03% vs 5.39%; RR, 0.56; 95% CI, 0.54–0.58; P < .001), multiple sclerosis (0.08% vs 0.15%; RR, 0.52; 95% CI, 0.41–0.66; P < .001), Parkinson's disease (0.48% vs 0.92%; RR, 0.53; 95% CI, 0.48–0.58; P < .001), and amyotrophic lateral sclerosis (0.02% vs 0.05%; RR, 0.46; 95% CI, 0.30–0.69; P < .001). All NDD incidence for all statins, except for fluvastatin (RR, 0.91; 95% CI, 0.65‐1.30; P = 0.71), was reduced with variances in individual risk profiles. Pathway analysis indicated unique and common profiles associated with risk reduction efficacy. Discussion Benefits and risks of statins relative to neurological outcomes should be considered when prescribed for at‐risk NDD populations. Common statin activated pathways indicate overarching systems required for risk reduction whereas unique targets could advance a precision medicine approach to prevent neurodegenerative diseases.

The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence—from studies of both COVID-19 and the related disease SARS—that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases.

Data generated by the numerous clinical trials conducted annually worldwide have the potential to be extremely beneficial to the scientific and patient communities. This potential is well recognized and efforts are being made to encourage the release of raw patient-level data from these trials to the public. The issue of sharing clinical trial data has recently gained attention, with many agreeing that this type of data should be made available for research in a timely manner. The availability of clinical trial data is most important for study reproducibility, meta-analyses, and improvement of study design. There is much discussion in the community over key data sharing issues, including the risks this practice holds. However, one aspect that remains to be adequately addressed is that of the accessibility, quality, and usability of the data being shared. Herein, experiences with the two current major platforms used to store and disseminate clinical trial data are described, discussing the issues encountered and suggesting possible solutions.

Age is an important factor when considering phenotypic changes in health and disease. Currently, the use of age information in medicine is somewhat simplistic, with ages commonly being grouped into a small number of crude ranges reflecting the major stages of development and aging, such as childhood or adolescence. Here, we investigate the possibility of redefining age groups using the recently developed Age-Phenome Knowledge-base (APK) that holds over 35,000 literaturederived entries describing relationships between age and phenotype. Clustering of APK data suggests 13 new, partially overlapping, age groups. The diseases that define these groups suggest that the proposed divisions are biologically meaningful. We further show that the number of different age ranges that should be considered depends on the type of disease being evaluated. This finding was further strengthened by similar results obtained from clinical blood measurement data. The grouping of diseases that share a similar pattern of disease-related reports directly mirrors, in some cases, medical knowledge of disease-age relationships. In other cases, our results may be used to generate new and reasonable hypotheses regarding links between diseases.

Background Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients’ medical background. Given the aging population, not only are diseases becoming increasingly prevalent but occur more frequently in combinations (“multimorbidity”). We hypothesized the existence of patient subgroups in critical care with distinct multimorbidity states. We further hypothesize that certain multimorbidity states associate with higher rates of organ failure, sepsis, and mortality co-occurring with these clinical problems. Methods We analyzed 36,390 patients from the open source Medical Information Mart for Intensive Care III (MIMIC III) dataset. Morbidities were defined based on Elixhauser categories, a well-established scheme distinguishing 30 classes of chronic diseases. We used latent class analysis to identify distinct patient subgroups based on demographics, admission type, and morbidity compositions and compared the prevalence of organ dysfunction, sepsis, and inpatient mortality for each subgroup. Results We identified six clinically distinct multimorbidity subgroups labeled based on their dominant Elixhauser disease classes. The “cardiopulmonary” and “cardiac” subgroups consisted of older patients with a high prevalence of cardiopulmonary conditions and constituted 6.1% and 26.4% of study cohort respectively. The “young” subgroup included 23.5% of the cohort composed of young and healthy patients. The “hepatic/addiction” subgroup, constituting 9.8% of the cohort, consisted of middle-aged patients (mean age of 52.25, 95% CI 51.85–52.65) with the high rates of depression (20.1%), alcohol abuse (47.75%), drug abuse (18.2%), and liver failure (67%). The “complicated diabetics” and “uncomplicated diabetics” subgroups constituted 9.4% and 24.8% of the study cohort respectively. The complicated diabetics subgroup demonstrated higher rates of end-organ complications (88.3% prevalence of renal failure). Rates of organ dysfunction and sepsis ranged 19.6–69% and 12.5–46.7% respectively in the six subgroups. Mortality co-occurring with organ dysfunction and sepsis ranges was 8.4–23.8% and 11.7–27.4% respectively. These adverse outcomes were most prevalent in the hepatic/addiction subgroup. Conclusion We identify distinct multimorbidity states that associate with relatively higher prevalence of organ dysfunction, sepsis, and co-occurring mortality. The findings promote the incorporation of multimorbidity in healthcare models and the shift away from the current single-disease paradigm in clinical practice, training, and trial design. Electronic supplementary material The online version of this article (10.1186/s13054-019-2486-6) contains supplementary material, which is available to authorized users.

Introduction Exacerbation‐prone asthma subtype has been reported in studies using data‐driven methodologies. However, patterns of severe exacerbations have not been studied. Objective To investigate longitudinal trajectories of severe wheeze exacerbations from infancy to school age. Methods We applied longitudinal k‐means clustering to derive exacerbation trajectories among 887 participants from a population‐based birth cohort with severe wheeze exacerbations confirmed in healthcare records. We examined early‐life risk factors of the derived trajectories, and their asthma‐related outcomes and lung function in adolescence. Results 498/887 children (56%) had physician‐confirmed wheeze by age 8 years, of whom 160 had at least one severe exacerbation. A two‐cluster model provided the optimal solution for severe exacerbation trajectories among these 160 children: “Infrequent exacerbations (IE)” (n = 150, 93.7%) and “Early‐onset frequent exacerbations (FE)” (n = 10, 6.3%). Shorter duration of breastfeeding was the strongest early‐life risk factor for FE (weeks, median [IQR]: FE, 0 [0‐1.75] vs. IE, 6 [0‐20], P < .001). Specific airway resistance (sRaw) was significantly higher in FE compared with IE trajectory throughout childhood. We then compared children in the two exacerbation trajectories with those who have never wheezed (NW, n = 389) or have wheezed but had no severe exacerbations (WNE, n = 338). At age 8 years, FEV1/FVC was significantly lower and FeNO significantly higher among FE children compared with all other groups. By adolescence (age 16), subjects in FE trajectory were significantly more likely to have current asthma (67% FE vs. 30% IE vs. 13% WNE, P < .001) and use inhaled corticosteroids (77% FE vs. 15% IE vs. 18% WNE, P < .001). Lung function was significantly diminished in the FE trajectory (FEV1/FVC, mean [95%CI]: 89.9% [89.3‐90.5] vs. 88.1% [87.3‐88.8] vs. 85.1% [83.4‐86.7] vs. 74.7% [61.5‐87.8], NW, WNE, IE, FE respectively, P < .001). Conclusion We have identified two distinct trajectories of severe exacerbations during childhood with different early‐life risk factors and asthma‐related outcomes in adolescence.