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Symptom Science: Repurposing Existing Omics Data.

ABSTRACT: Omics approaches, including genomics, transcriptomics, proteomics, epigenomics, microbiomics, and metabolomics, generate large data sets. Once they have been used to address initial study aims, these large data sets are extremely valuable to the greater research community for ancillary investigations. Repurposing available omics data sets provides data to address research questions, generate and test hypotheses, replicate findings, and conduct mega-analyses. Many well-characterized, longitudinal, epidemiological studies collected extensive phenotype data related to symptom occurrence and severity. While the main phenotype of interest for many of these studies was often not symptom related, these data were collected to better understand the primary phenotype of interest. A search for symptom data (i.e., cognitive impairment, fatigue, gastrointestinal distress/nausea, sleep, and pain) in the database of genotypes and phenotypes (dbGaP) revealed many studies that collected symptom and omics data. There is thus a real possibility for nurse scientists to be able to look at symptom data over time from thousands of individuals and use omics data to identify key biological underpinnings that account for the development and severity of symptoms without recruiting participants or generating any new data. The purpose of this article is to introduce the reader to resources that provide omics data to the research community for repurposing, provide guidance on using these databases, and encourage the use of these data to move symptom science forward.

SUBMITTER: Osier ND

PROVIDER: S-EPMC5942510 | biostudies-other | 2017 Jan

REPOSITORIES: biostudies-other

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Symptom Science: Repurposing Existing Omics Data.

Osier Nicole D ND Imes Christopher C CC Khalil Heba H Zelazny Jamie J Johansson Ann E AE Conley Yvette P YP

Biological research for nursing 20160920 1

Omics approaches, including genomics, transcriptomics, proteomics, epigenomics, microbiomics, and metabolomics, generate large data sets. Once they have been used to address initial study aims, these large data sets are extremely valuable to the greater research community for ancillary investigations. Repurposing available omics data sets provides data to address research questions, generate and test hypotheses, replicate findings, and conduct mega-analyses. Many well-characterized, longitudinal ...[more]

PMID: 27613438

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Project description:PurposeBiomarkers as common data elements (CDEs) are important for the characterization of biobehavioral symptoms given that once a biologic moderator or mediator is identified, biologically based strategies can be investigated for treatment efforts. Just as a symptom inventory reflects a symptom experience, a biomarker is an indicator of the symptom, though not the symptom per se. The purposes of this position paper are to (a) identify a "minimum set" of biomarkers for consideration as CDEs in symptom and self-management science, specifically biochemical biomarkers; (b) evaluate the benefits and limitations of such a limited array of biomarkers with implications for symptom science; (c) propose a strategy for the collection of the endorsed minimum set of biologic samples to be employed as CDEs for symptom science; and (d) conceptualize this minimum set of biomarkers consistent with National Institute of Nursing Research (NINR) symptoms of fatigue, depression, cognition, pain, and sleep disturbance.Design and methodsFrom May 2016 through January 2017, a working group consisting of a subset of the Directors of the NINR Centers of Excellence funded by P20 or P30 mechanisms and NINR staff met bimonthly via telephone to develop this position paper suggesting the addition of biomarkers as CDEs. The full group of Directors reviewed drafts, provided critiques and suggestions, recommended the minimum set of biomarkers, and approved the completed document. Best practices for selecting, identifying, and using biological CDEs as well as challenges to the use of biological CDEs for symptom and self-management science are described. Current platforms for sample outcome sharing are presented. Finally, biological CDEs for symptom and self-management science are proposed along with implications for future research and use of CDEs in these areas.FindingsThe recommended minimum set of biomarker CDEs include pro- and anti-inflammatory cytokines, a hypothalamic-pituitary-adrenal axis marker, cortisol, the neuropeptide brain-derived neurotrophic factor, and DNA polymorphisms.ConclusionsIt is anticipated that this minimum set of biomarker CDEs will be refined as knowledge regarding biologic mechanisms underlying symptom and self-management science further develop. The incorporation of biological CDEs may provide insights into mechanisms of symptoms, effectiveness of proposed interventions, and applicability of chosen theoretical frameworks. Similarly, as for the previously suggested NINR CDEs for behavioral symptoms and self-management of chronic conditions, biological CDEs offer the potential for collaborative efforts that will strengthen symptom and self-management science.Clinical relevanceThe use of biomarker CDEs in biobehavioral symptoms research will facilitate the reproducibility and generalizability of research findings and benefit symptom and self-management science.

Project description:Background Coronavirus Disease 2019 (COVID-19) is a multi-systemic infection caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), that has become a pandemic. Although its prevailing symptoms include anosmia, ageusia, dry couch, fever, shortness of brief, arthralgia, myalgia, and fatigue, regional and methodological assessments vary, leading to heterogeneous clinical descriptions of COVID-19. Aging, uncontrolled diabetes, hypertension, obesity, and exposure to androgens have been correlated with worse prognosis in COVID-19. Abnormalities in the renin-angiotensin-aldosterone system (RAAS), angiotensin-converting enzyme-2 (ACE2) and the androgen-driven transmembrane serine protease 2 (TMPRSS2) have been elicited as key modulators of SARS-CoV-2. Main text While safe and effective therapies for COVID-19 lack, the current moment of pandemic urges for therapeutic options. Existing drugs should be preferred over novel ones for clinical testing due to four inherent characteristics: 1. Well-established long-term safety profile, known risks and contraindications; 2. More accurate predictions of clinical effects; 3. Familiarity of clinical management; and 4. Affordable costs for public health systems. In the context of the key modulators of SARS-CoV-2 infectivity, endocrine targets have become central as candidates for COVID-19. The only endocrine or endocrine-related drug class with already existing emerging evidence for COVID-19 is the glucocorticoids, particularly for the use of dexamethasone for severely affected patients. Other drugs that are more likely to present clinical effects despite the lack of specific evidence for COVID-19 include anti-androgens (spironolactone, eplerenone, finasteride and dutasteride), statins, N-acetyl cysteine (NAC), ACE inhibitors (ACEi), angiotensin receptor blockers (ARB), and direct TMPRSS-2 inhibitors (nafamostat and camostat). Several other candidates show less consistent plausibility. In common, except for dexamethasone, all candidates have no evidence for COVID-19, and clinical trials are needed. Conclusion While dexamethasone may reduce mortality in severely ill patients with COVID-19, in the absence of evidence of any specific drug for mild-to-moderate COVID-19, researchers should consider testing existing drugs due to their favorable safety, familiarity, and cost profile. However, except for dexamethasone in severe COVID-19, drug treatments for COVID-19 patients must be restricted to clinical research studies until efficacy has been extensively proven, with favorable outcomes in terms of reduction in hospitalization, mechanical ventilation, and death.

Dataset Information

Symptom Science: Repurposing Existing Omics Data.

Publications

Symptom Science: Repurposing Existing Omics Data.

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