Systems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective.

摘要: Big Data, and in particular Electronic Health Records, provide the medical community with a great opportunity to analyze multiple pathological conditions at an unprecedented depth for many complex diseases, including diabetes. How can we infer on diabetes from large heterogeneous datasets? A possible solution is provided by invoking next-generation computational methods data analytics tools within systems medicine approaches. By deciphering multi-faceted complexity of biological systems, potential emerging diagnostic therapeutic functions be ultimately revealed. In diabetes, multidimensional approach analysis needed better understand disease conditions, trajectories associated comorbidities. Elucidation multidimensionality comes factors such as phenotypes, marker types, motifs while seeking make use levels information genetics, omics, clinical data, environmental lifestyle factors. Examining synergy between dimensions represents challenge. regard, role Data fuels rise Precision Medicine allowing increasing number descriptions captured individuals. Thus, curations analyses should designed deliver highly accurate predicted risk profiles treatment recommendations. It important establish linkages precision order translate their principles into practice. Equivalently, realize full potential, involved must able process ensuring inter-exchange, reducing ambiguities redundancies, improving health care solutions introducing decision support focused reclassified phenotypes (or digital biomarkers) community-driven patient stratifications.