Dataset Information

Accelerating Medicines Partnership-Rheumatoid Arthritis (AMP-RA)

ABSTRACT:

This project is analyzing tissue and blood samples from people with RA and lupus to pinpoint genes, proteins, chemical pathways, and networks involved at a single cell level. This type of modular, molecular analysis will allow comparisons across the diseases and will provide insights into key aspects of the disease process. The project will identify differences between those RA patients who respond to therapies and those who do not, as well as provide a better systems level understanding of disease mechanisms in both RA and lupus. This knowledge is essential for the development of targeted therapies and for the application of existing and future therapies to appropriate patient populations.

Additional datasets can be accessed through ImmPort (http://www.immport.org/immport-open/public/home/studySearch), accession: SDY998, SDY999.

PROVIDER: phs001457 | dbGaP |

SECONDARY ACCESSION(S): PRJNA407594PRJNA407593

REPOSITORIES: dbGaP

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	GapExchange_phs001457.v1.p1.xml	Xml
	dbGaPEx2.1.5.xsd	Other
	Study_Report.phs001457.AMP_RA.v1.p1.MULTI.pdf	Pdf
	manifest_phs001457.AMP_RA.v1.p1.c1.HMB.pdf	Pdf
	datadict_v2.xsl	Other

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Project description:Background/Purpose: Prior studies show an independent association between greater physical activity and lower inflammatory markers among adults in the general population, but the impact of physical activity on systemic inflammation in people with rheumatoid arthritis (RA) hasn’t been thoroughly investigated. We therefore aimed to determine whether physical activity behavior associates with differential expression of inflammatory genes in RA. Methods: Data derive from baseline assessments for a prospective observational cohort of patients with RA. At each study visit, we obtained detailed clinical information, collected peripheral whole blood for subsequent RNA sequencing (RNA-seq), and provided participants with actigraphs (GT9X ActiGraph Link device) to objectively measure physical activity by wearing the device at home for 7 consecutive 24-hour periods. Participants were categorized into three physical activity groups based on the highest (“active”), middle (“intermediate”), and lowest (“inactive”) tertiles for percentage of time spent in moderate/vigorous physical activity (metabolic equivalent (MET) level ≥2.00) across the sample. Results: There were 35 patients with complete clinical, actigraphy, and transcriptomic data available for analysis. The participants had a mean age of 56 years, were 90% female, and self-reported the following racial/ethnic identities: 32% white, 11% African American, 8% Asian, 40% Hispanic. Participants spent 48%, 41%, and 12% of time awake in sedentary, light, and moderate physical activity, respectively. None of the participants achieved vigorous activity. The patients in the most active group were younger but did not otherwise significantly differ by clinical and demographic characteristics compared to the other physical activity groups. However, in the differential gene expression analysis comparing the highest versus lowest physical activity tertiles, there were 365 up- and 402 down-regulated genes at an adjusted P value < 0.1. Gene set enrichment analysis revealed that the most physically active patients exhibited downregulation of diverse innate and adaptive immune signaling pathways. Assessment of upstream cytokine activation states demonstrated inhibition of type I, II and III interferons and activation of EPO in the highest physical activity tertile patients. Conclusion: Among a racially and ethnically diverse RA cohort, participants who spent more time in moderately intense physical activity had down-regulation of genes involved in both innate and adaptive immune signaling compared to those who were more sedentary. These findings provide mechanistic evidence to support a disease-modifying effect of physical activity in RA.

Dataset Information

Accelerating Medicines Partnership-Rheumatoid Arthritis (AMP-RA)

Dataset's files

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