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Application of the Asthma Phenotype Algorithm from the Severe Asthma Research Program to an Urban Population

Author

Listed:
  • Paru Patrawalla
  • Angeliki Kazeros
  • Linda Rogers
  • Yongzhao Shao
  • Mengling Liu
  • Maria-Elena Fernandez-Beros
  • Shulian Shang
  • Joan Reibman

Abstract

Rationale: Identification and characterization of asthma phenotypes are challenging due to disease complexity and heterogeneity. The Severe Asthma Research Program (SARP) used unsupervised cluster analysis to define 5 phenotypically distinct asthma clusters that they replicated using 3 variables in a simplified algorithm. We evaluated whether this simplified SARP algorithm could be used in a separate and diverse urban asthma population to recreate these 5 phenotypic clusters. Methods: The SARP simplified algorithm was applied to adults with asthma recruited to the New York University/Bellevue Asthma Registry (NYUBAR) to classify patients into five groups. The clinical phenotypes were summarized and compared. Results: Asthma subjects in NYUBAR (n = 471) were predominantly women (70%) and Hispanic (57%), which were demographically different from the SARP population. The clinical phenotypes of the five groups generated by the simplified SARP algorithm were distinct across groups and distributed similarly to those described for the SARP population. Groups 1 and 2 (6 and 63%, respectively) had predominantly childhood onset atopic asthma. Groups 4 and 5 (20%) were older, with the longest duration of asthma, increased symptoms and exacerbations. Group 4 subjects were the most atopic and had the highest peripheral eosinophils. Group 3 (10%) had the least atopy, but included older obese women with adult-onset asthma, and increased exacerbations. Conclusions: Application of the simplified SARP algorithm to the NYUBAR yielded groups that were phenotypically distinct and useful to characterize disease heterogeneity. Differences across NYUBAR groups support phenotypic variation and support the use of the simplified SARP algorithm for classification of asthma phenotypes in future prospective studies to investigate treatment and outcome differences between these distinct groups. Trial Registration: Clinicaltrials.gov NCT00212537

Suggested Citation

  • Paru Patrawalla & Angeliki Kazeros & Linda Rogers & Yongzhao Shao & Mengling Liu & Maria-Elena Fernandez-Beros & Shulian Shang & Joan Reibman, 2012. "Application of the Asthma Phenotype Algorithm from the Severe Asthma Research Program to an Urban Population," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
    • Handle: RePEc:plo:pone00:0044540
    DOI: 10.1371/journal.pone.0044540
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    Cited by:

    1. Yongzhao Shao & Yian Zhang & Mengling Liu & Maria-Elena Fernandez-Beros & Meng Qian & Joan Reibman, 2020. "Gene-Environment Interaction between the IL1RN Variants and Childhood Environmental Tobacco Smoke Exposure in Asthma Risk," IJERPH, MDPI, vol. 17(6), pages 1-16, March.
    2. Yongzhao Shao & Nedim Durmus & Yian Zhang & Sultan Pehlivan & Maria-Elena Fernandez-Beros & Lisette Umana & Rachel Corona & Adrienne Addessi & Sharon A. Abbott & Sheila Smyth-Giambanco & Alan A. Arsla, 2021. "The Development of a WTC Environmental Health Center Pan-Cancer Database," IJERPH, MDPI, vol. 18(4), pages 1-18, February.

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