Knisely and Colleagues Publish Article in Biological Research for Nursing

Mitchell Knisely, assistant professor, recently published an article entitled "Symptom Science: Advocating for Inclusion of Functional Genetic Polymorphisms" in Biological Research for Nursing. Co-authors include Megan Maserati of the University of Pittsburgh, Lacey Heinsberg of the University of Pittsburgh, Lisa Shah of the University of Pittsburgh, Hongjin Li of the University of Pittsburgh, Yehui Zhu of the University of Pittsburgh, Yumi Ma of the University of Pittsburgh, Letitia Graves of the University of Pittsburgh, John Merriman of New York University and Yvette Conley of the University of Pittsburgh. 

Abstract

Incorporating biologically based data into symptom science research can contribute substantially to understanding commonly experienced symptoms across chronic conditions. The purpose of this literature review was to identify functional polymorphisms associated with common symptoms (i.e., pain, sleep disturbance, fatigue, affective and cognitive symptoms) with the goal of identifying a parsimonious list of functional genetic polymorphisms with evidence to advocate for their inclusion in symptom science research. PubMed was searched to identify genes and functional polymorphisms associated with symptoms across chronic conditions, revealing eight functional genetic polymorphisms in seven different genes that showed evidence of association with at least three or more symptoms and/or symptom clusters: BDNFrs6265, COMT rs4680, FKBP5 rs3800373, IL-6 rs1800795, NFKB2 rs1056890, SLC6A4 5-HTTLPR+rs25531, and TNFA rs1799964 and rs1800629. Of these genes, three represent protein biomarkers previously identified as common data elements for symptom science research (BDNF, IL-6, and TNFA), and the polymorphisms in these genes identified through the search are known to impact secretion or level of transcription of these protein biomarkers. Inclusion of genotype data for polymorphisms offers great potential to further advance scientific knowledge of the biological basis of individual symptoms and symptom clusters across studies. Additionally, these polymorphisms have the potential to be used as targets to optimize precision health through the identification of individuals at risk for poor symptom experiences as well as the development of symptom management interventions.