Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs

Updated risk factors should be used to predict development of diabetes

Aims: Predicting incident diabetes could inform treatment strategies for diabetes prevention, but the incremental benefit of recalculating risk using updated risk factors is unknown. We used baseline and 1-year data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial to compare diabetes risk prediction using historical or updated clinical information. Methods: Among non-diabetic participants reaching 1 year of follow-up in NAVIGATOR, we compared the performance of the published baseline diabetes risk model with a “landmark” model incorporating risk factors updated at the 1-year time point. The C-statistic was used to compare model discrimination and reclassification analyses to demonstrate the relative accuracy of diabetes prediction. Results: A total of 7527 participants remained non-diabetic at 1 year, and 2375 developed diabetes during a median of 4 years of follow-up. The C-statistic for the landmark model was higher (0.73 [95% CI 0.72–0.74]) than for the baseline model (0.67 [95% CI 0.66–0.68]). The landmark model improved classification to modest (< 20%), moderate (20%–40%), and high (> 40%) 4-year risk, with a net reclassification index of 0.14 (95% CI 0.10–0.16) and an integrated discrimination index of 0.01 (95% CI 0.003–0.013). Conclusions: Using historical clinical values to calculate diabetes risk reduces the accuracy of prediction. Diabetes risk calculations should be routinely updated to inform discussions about diabetes prevention at both the patient and population health levels

A novel risk classification paradigm for patients with impaired glucose tolerance and high cardiovascular risk.

We used baseline data from the NAVIGATOR trial to (1) identify risk factors for diabetes progression in those with impaired glucose tolerance and high cardiovascular risk, (2) create models predicting 5-year incident diabetes, and (3) provide risk classification tools to guide clinical interventions. Multivariate Cox proportional hazards models estimated 5-year incident diabetes risk and simplified models examined the relative importance of measures of glycemia in assessing diabetes risk. The C-statistic was used to compare models; reclassification analyses compare the models' ability to identify risk groups defined by potential therapies (routine or intensive lifestyle advice or pharmacologic therapy). Diabetes developed in 3,254 (35%) participants over 5 years median follow-up. The full prediction model included fasting and 2-hour glucose and hemoglobin A1c (HbA1c) values but demonstrated only moderate discrimination for diabetes (C = 0.70). Simplified models with only fasting glucose (C = 0.67) or oral glucose tolerance test values (C = 0.68) had higher C statistics than models with HbA1c alone (C = 0.63). The models were unlikely to inappropriately reclassify participants to risk groups that might receive pharmacologic therapy. Our results confirm that in a population with dysglycemia and high cardiovascular risk, traditional risk factors are appropriate predictors and glucose values are better predictors than HbA1c, but discrimination is moderate at best, illustrating the challenges of predicting diabetes in a high-risk population. In conclusion, our novel risk classification paradigm based on potential treatment could be used to guide clinical practice based on cost and availability of screening tests