Collaborative Framework to Advance Symptom Science: An Intramural Perspective.

PURPOSE

To describe the collaborative framework used by National Institute of Nursing Research (NINR) investigators to advance symptom science and to provide a research exemplar.

MODEL

The National Institutes of Health (NIH) Symptom Science Model (SSM) was developed to guide symptom science researchers to understand the molecular underpinnings of symptoms using innovative "omics" approaches. The process begins with a review of the literature to understand the state of the science of the symptoms of interest and is followed by cross-sectional, case-controlled, or longitudinal studies to identify potential biological correlates of these symptoms. The final steps include validation of these potential symptom biomarkers using multidisciplinary, collaborative, preclinical experiments, and proof-of-concept clinical trials.

RESEARCH EXEMPLAR

Using the NIH SSM as a guide, the identification of biologic correlates of symptoms using omics and bioinformatic strategies depends on determining the distinct phenotype of the symptoms of interest. The identified biologic correlates of these symptoms are then validated for their functional relevance using in vitro and ex vivo experiments, or through proof-of-concept clinical trials. NINR investigators observed that activation of specific receptors in neural networks can trigger inflammation-related fatigue development and predispose patients to develop chronicity of symptoms. Specifically targeting these neural receptors can reduce symptom intensity.

CONCLUSIONS

Through using the NIH SSM as a guide, NINR investigators quickly generate data and discoveries that significantly advance symptom science by simultaneously collaborating with multiple experts and core laboratories to identify more correlates and validate their functional relevance in order to further understand the biological underpinnings of the symptoms of interest.

CLINICAL RELEVANCE

The collaborative framework used by NINR investigators as guided by the NIH SSM identifies functionally relevant clinical markers that can explain the biological underpinnings of symptoms and can be targeted to optimize symptom management.