Neuroinflammation — using big data to inform clinical practice

Key Points

  • Neurological disorders affect as many as 1 billion people worldwide, including people of all age groups and races, in different geographical locations and of different socioeconomic backgrounds

  • Neuroinflammation is emerging as a key process that is common to the majority of neurological conditions, either as a causative factor or a secondary response to nervous system insult

  • Big data generated with large-scale, high-throughput 'omics' technologies are changing our perception of the breadth and heterogeneity of neuroinflammatory responses and, consequently, how these responses could be targeted to ameliorate disease

  • Appropriate use of big data requires fulfilment of diverse clinical needs, and crosstalk between the scientific and medical disciplines is necessary to allow convergence of clinically targeted science and evidence-generating medicine

  • Big data that pertains to neuroinflammation could aid patient diagnosis and prognosis and help to predict and monitor drug efficacy and safety through the implementation of precision medicine

  • Personalized medicine for patients with neurological disease might involve combination therapy with drugs that inhibit neuroinflammation and promote neuroprotection, as well as systematic health monitoring and management

Abstract

Neuroinflammation is emerging as a central process in many neurological conditions, either as a causative factor or as a secondary response to nervous system insult. Understanding the causes and consequences of neuroinflammation could, therefore, provide insight that is needed to improve therapeutic interventions across many diseases. However, the complexity of the pathways involved necessitates the use of high-throughput approaches to extensively interrogate the process, and appropriate strategies to translate the data generated into clinical benefit. Use of 'big data' aims to generate, integrate and analyse large, heterogeneous datasets to provide in-depth insights into complex processes, and has the potential to unravel the complexities of neuroinflammation. Limitations in data analysis approaches currently prevent the full potential of big data being reached, but some aspects of big data are already yielding results. The implementation of 'omics' analyses in particular is becoming routine practice in biomedical research, and neuroimaging is producing large sets of complex data. In this Review, we evaluate the impact of the drive to collect and analyse big data on our understanding of neuroinflammation in disease. We describe the breadth of big data that are leading to an evolution in our understanding of this field, exemplify how these data are beginning to be of use in a clinical setting, and consider possible future directions.

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Figure 1: Neuroinflammatory disease pathways and the impact of big data.
Figure 2: Big data — from the lab to the clinic and back.

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C.A.D wrote the manuscript and made substantial contributions to discussion of the content. G.M. and L.F. reviewed and/or edited the manuscript before submission.

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Correspondence to Lars Fugger.

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Dendrou, C., McVean, G. & Fugger, L. Neuroinflammation — using big data to inform clinical practice. Nat Rev Neurol 12, 685–698 (2016). https://doi.org/10.1038/nrneurol.2016.171

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