Topiramate protects apoE-deficient mice from kidney damage without affecting plasma lipids.

Topiramate is an anticonvulsant drug also prescribed for migraine prophylaxis that acts through several mechanisms of action. Several studies indicate that topiramate induces weight loss and a moderate reduction of plasma lipids and glucose. Based on these favourable metabolic effects, aim of this study was to evaluate if topiramate could modulate atherosclerosis development and protect target organs of dysmetabolic conditions. Thirty apoE-deficient mice were divided into three groups and fed for 12 weeks a high fat diet (Control) or the same diet containing topiramate at 0.125% and 0.250%. Body weight, water and food intake were monitored throughout the study. Plasma lipids and glucose levels were measured and a glucose tolerance test was performed. Atherosclerosis development was evaluated in the whole aorta and at the aortic sinus. Histological analysis of liver, kidney and adipose tissue was performed. Topiramate did not affect weight gain and food intake. Glucose tolerance and plasma lipids were not changed and, in turn, atherosclerosis development was not different among groups. Topiramate did not modify liver and adipose tissue histology. Conversely, in the kidneys, the treatment reduced the occurrence of glomerular lipidosis by decreasing foam cells accumulation and reducing the expression of inflammatory markers. Blood urea nitrogen levels were also reduced by treatment. Our results indicate that topiramate does not affect atherosclerosis development, but preserves kidney structure and function. The study suggests that topiramate could be investigated in drug repurposing studies for the treatment of glomerular lipidosis.

Hypothyroidism is a Risk Factor for New-Onset Diabetes: A Cohort Study.

OBJECTIVE: To identify risk factors for the development of statin-associated diabetes mellitus (DM). RESEARCH DESIGN AND METHODS: The study was conducted in two phases. Phase one involved high-throughput in silico processing of a large amount of biomedical data to identify risk factors for the development of statin-associated DM. In phase two, the most prominent risk factor identified was confirmed in an observational cohort study at Clalit, the largest health care organization in Israel. Time-dependent Poisson regression multivariable models were performed to assess rate ratios (RRs) with 95% CIs for DM occurrence. RESULTS: A total of 39,263 statin nonusers were matched by propensity score to 20,334 highly compliant statin initiators in 2004-2005 and followed until the end of 2010. Within 59,597 statin users and nonusers in a multivariable model, hypothyroidism and subclinical hypothyroidism carried an increased risk for DM (RR 1.53 [95% CI 1.31-1.79] and 1.75 [1.40-2.18], respectively). Hypothyroidism increased DM risk irrespective of statin treatment (RR 2.06 [1.42-2.99] and 1.66 [1.05-2.64] in statin users and nonusers, respectively). Subclinical hypothyroidism risk for DM was prominent only upon statin use (RR 1.94 [1.13-3.34] and 1.20 [0.52-2.75] in statin users and nonusers, respectively). Patients with hypothyroidism treated with thyroid hormone replacement therapy were not at increased risk for DM. CONCLUSIONS: Hypothyroidism is a risk factor for DM. Subclinical hypothyroidism-associated risk for DM is prominent only upon statin use. Identifying and treating hypothyroidism and subclinical hypothyroidism might reduce DM risk. Future clinical studies are needed to confirm the findings.

Drug repurposing and adverse event prediction using high-throughput literature analysis.

Drug repurposing is the process of using existing drugs in indications other than the ones they were originally designed for. It is an area of significant recent activity due to the mounting costs of traditional drug development and scarcity of new chemical entities brought to the market by bio-pharmaceutical companies. By selecting drugs that already satisfy basic toxicity, ADME and related criteria, drug repurposing promises to deliver significant value at reduced cost and in dramatically shorter time frames than is normally the case for the drug development process. The same process that results in drug repurposing can also be used for the prediction of adverse events of known or novel drugs. The analytics method is based on the description of the mechanism of action of a drug, which is then compared to the molecular mechanisms underlying all known adverse events. This review will focus on those approaches to drug repurposing and adverse event prediction that are based on the biomedical literature. Such approaches typically begin with an analysis of the literature and aim to reveal indirect relationships among seemingly unconnected biomedical entities such as genes, signaling pathways, physiological processes, and diseases. Networks of associations of these entities allow the uncovering of the molecular mechanisms underlying a disease, better understanding of the biological effects of a drug and the evaluation of its benefit/risk profile. In silico results can be tested in relevant cellular and animal models and, eventually, in clinical trials.

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

OBJECTIVE: The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. MATERIALS AND METHODS: This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. RESULTS: We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. CONCLUSION: We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

Identification of echocardiographic indices for the early detection of left-ventricular systolic dysfunction in beta-thalassaemia via Self-Organizing Maps: a data-exploration study.

Congestive heart failure (CHF) remains the primary cause of death in patients suffering from beta-thalassaemia major. Its early detection allows the prompt initiation of aggressive chelation therapy, when the condition can still be reversed. We aimed at identifying echocardiographic indices for the early detection of left ventricular (LV) systolic dysfunction, the physiological abnormality underlying CHF, in these patients. We used Self-Organizing Maps (SOMs)--an artificial neural network--for identifying novel correlations within our Electronic Healthcare Record (EHCR) database on beta-thalassaemia. We sought echocardiographic parameters that are correlated to future deterioration of the LV ejection fraction and therefore constitute early signs of LV systolic dysfunction. At the same time, we evaluated SOMs as tools for exploring clinical datasets and make recommendations on the setup of the SOM algorithm that is appropriate for such tasks. We found that high values of the LV end-systolic diameter index and of the E/A ratio are early indications of LV systolic dysfunction. From a technical point of view, zero-mean unit-variance normalization of the input data, a large initial neighbourhood radius and a rectangular SOM grid produced optimal maps for the purpose of detecting clinical correlations. We have successfully used SOMs for exploring a clinical dataset and for creating novel medical hypotheses. A clinical study has been launched to confirm these hypotheses, and initial results are encouraging.

Novel biological networks modulated by complement.

The almost complete deciphering of the human genome has paved the way for the application of new technology platforms in understanding the contribution of complex biological pathways to human pathophysiology and disease. In the post-genomic era, the concept of systems biology has gained significant momentum and biomedical research is now being conducted on an integrated and cross-disciplinary platform that pulls together its resources from diverse fields such as computational biology, bioinformatics, functional genomics, structural biology, and proteomics. In this perspective, the identity of established biologic systems is being re-examined in the light of novel findings that suggest novel associations between otherwise unrelated pathways and individual proteins. Complement exemplifies such a system that, transcending its innate immune identity, has forged functional associations with multiple pathways and networks in modulating basic biologic processes. In the present article, we provide a global overview of these unusual system associations of complement with the aid of a powerful and high-throughput bioinformatics platform. Using a novel approach called systems literature analysis that allows the rapid extraction of text-based associations between genes and pathways from the ever expanding scientific article database, we have selected a broad range of biologic processes modulated by complement proteins and have constructed an integrated map of complement-mediated networks that incorporates well over 85 diverse biologic pathways. Expanding the complement cascade beyond its approximately 35 designated components, we discuss protein-protein interactions involving novel ligands and associations with signaling cascades and cellular networks that affect both inflammatory and non-inflammatory processes. This integrated consideration of complement within a unified 'systems biology' framework underscores the concept that innate immunity goes well beyond the protection of 'self' extending links to critical developmental, homeostatic, and metabolic processes.

Systems literature analysis.

Systems literature analysis (SLA) is the literature-driven version of systems biology. It treats collections of scientific literature as a system of millions of interconnections between research parameters, such as genes, diseases, tissues, cell events, model organisms, experiment types, and reagents. SLA aims to replace the traditional keyword-based querying of literature databases, which return sorted lists of papers, with a systems-based approach that returns integrated networks of relationships. Major applications include literature-based discovery of novel targets that link, for example, previously unlinked diseases and phenotypes to common genes and cellular events, and experiment design.