Predicting Disease Progression in Scleroderma with Skin and Blood Biomarkers

Abstract

Scleroderma (Systemic Sclerosis, SSc) is a chronic, incurable autoimmune disease associated with high morbidity and mortality primarily due to lung disease. There is a large variability in individual patients courses and current predictors of disease progression are inadequate. The overall objective of the proposed research is to develop reliable predictors for clinical outcomes in scleroderma, utilizing the biospecimens and longitudinal clinical data in the GENISOS cohort combining data from multiple areas to develop robust prediction models for ILD progression. The GENISOS cohort is a unique and valuable resource for biomarker development; no other early SSc cohort exists that has serial biological samples linked to longitudinal clinical data and genetic markers. GENISOS is an inception cohort that avoids survival bias inherent in studies of prevalent cases (mean disease duration at GENISOS entry = 2.5 years, eligibility criterion mandates disease duration 5 years). The cohort was originally established in 1998 and this funding support permitted us to enroll additional patients, continue follow-upon previously enrolled subjects, collect bio-specimens (DNA, RNA in PAX gene tubes, serum, plasma, skin biopsies), perform laboratory studies and analysis as detailed in the Overall Project Summary section of the final report.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2016
Accession Number
AD1027262

Entities

People

  • Maureen D Mayes

Organizations

  • University of Texas Health Science Center at Houston

Tags

DTIC Thesaurus Topics

  • Cardiovascular Physiological Phenomena
  • Cardiovascular System
  • Cells
  • Chemistry
  • Connective Tissue Diseases
  • Genetics
  • Health Services
  • Lung Diseases
  • Medical Personnel

Fields of Study

  • Medicine

Readers

  • Gulf War Illness and Chronic Multisymptom Illness in Veterans.
  • Immunology and Pathology
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • Biotechnology