Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets

The USIDNET Consortium

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Common variable immunodeficiency (CVID) is increasingly recognized for its association with autoimmune and inflammatory complications. Despite recent advances in immunophenotypic and genetic discovery, clinical care of CVID remains limited by our inability to accurately model risk for non-infectious disease development. Herein, we demonstrate the utility of unbiased network clustering as a novel method to analyze inter-relationships between non-infectious disease outcomes in CVID using databases at the United States Immunodeficiency Network (USIDNET), the centralized immunodeficiency registry of the United States, and Partners, a tertiary care network in Boston, MA, USA, with a shared electronic medical record amenable to natural language processing. Immunophenotypes were comparable in terms of native antibody deficiencies, low titer response to pneumococcus, and B cell maturation arrest. However, recorded non-infectious disease outcomes were more substantial in the Partners cohort across the spectrum of lymphoproliferation, cytopenias, autoimmunity, atopy, and malignancy. Using unbiased network clustering to analyze 34 non-infectious disease outcomes in the Partners cohort, we further identified unique patterns of lymphoproliferative (two clusters), autoimmune (two clusters), and atopic (one cluster) disease that were defined as CVID non-infectious endotypes according to discrete and non-overlapping immunophenotypes. Markers were both previously described [high serum IgE in the atopic cluster [odds ratio (OR) 6.5] and low class-switched memory B cells in the total lymphoproliferative cluster (OR 9.2)] and novel [low serum C3 in the total lymphoproliferative cluster (OR 5.1)]. Mortality risk in the Partners cohort was significantly associated with individual non-infectious disease outcomes as well as lymphoproliferative cluster 2, specifically (OR 5.9). In contrast, unbiased network clustering failed to associate known comorbidities in the adult USIDNET cohort. Together, these data suggest that unbiased network clustering can be used in CVID to redefine non-infectious disease inter-relationships; however, applicability may be limited to datasets well annotated through mechanisms such as natural language processing. The lymphoproliferative, autoimmune, and atopic Partners CVID endotypes herein described can be used moving forward to streamline genetic and biomarker discovery and to facilitate early screening and intervention in CVID patients at highest risk for autoimmune and inflammatory progression.

Original languageEnglish (US)
Article number1740
JournalFrontiers in immunology
Volume8
Issue numberJAN
DOIs
StatePublished - Jan 9 2018

Fingerprint

Common Variable Immunodeficiency
Cluster Analysis
Odds Ratio
Natural Language Processing
B-Lymphocytes
Electronic Health Records
Tertiary Healthcare
Streptococcus pneumoniae
Datasets
Serum
Autoimmunity
Immunoglobulin E
Registries
Comorbidity
Biomarkers
Databases
Mortality
Antibodies

Keywords

  • Atopy
  • Autoimmunity
  • Common variable immunodeficiency
  • Endotypes
  • Lymphoproliferation
  • Non-infectious complications
  • Unbiased network clustering

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology

Cite this

@article{667612d738c549aaae1b2ded1ac2f549,
title = "Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets",
abstract = "Common variable immunodeficiency (CVID) is increasingly recognized for its association with autoimmune and inflammatory complications. Despite recent advances in immunophenotypic and genetic discovery, clinical care of CVID remains limited by our inability to accurately model risk for non-infectious disease development. Herein, we demonstrate the utility of unbiased network clustering as a novel method to analyze inter-relationships between non-infectious disease outcomes in CVID using databases at the United States Immunodeficiency Network (USIDNET), the centralized immunodeficiency registry of the United States, and Partners, a tertiary care network in Boston, MA, USA, with a shared electronic medical record amenable to natural language processing. Immunophenotypes were comparable in terms of native antibody deficiencies, low titer response to pneumococcus, and B cell maturation arrest. However, recorded non-infectious disease outcomes were more substantial in the Partners cohort across the spectrum of lymphoproliferation, cytopenias, autoimmunity, atopy, and malignancy. Using unbiased network clustering to analyze 34 non-infectious disease outcomes in the Partners cohort, we further identified unique patterns of lymphoproliferative (two clusters), autoimmune (two clusters), and atopic (one cluster) disease that were defined as CVID non-infectious endotypes according to discrete and non-overlapping immunophenotypes. Markers were both previously described [high serum IgE in the atopic cluster [odds ratio (OR) 6.5] and low class-switched memory B cells in the total lymphoproliferative cluster (OR 9.2)] and novel [low serum C3 in the total lymphoproliferative cluster (OR 5.1)]. Mortality risk in the Partners cohort was significantly associated with individual non-infectious disease outcomes as well as lymphoproliferative cluster 2, specifically (OR 5.9). In contrast, unbiased network clustering failed to associate known comorbidities in the adult USIDNET cohort. Together, these data suggest that unbiased network clustering can be used in CVID to redefine non-infectious disease inter-relationships; however, applicability may be limited to datasets well annotated through mechanisms such as natural language processing. The lymphoproliferative, autoimmune, and atopic Partners CVID endotypes herein described can be used moving forward to streamline genetic and biomarker discovery and to facilitate early screening and intervention in CVID patients at highest risk for autoimmune and inflammatory progression.",
keywords = "Atopy, Autoimmunity, Common variable immunodeficiency, Endotypes, Lymphoproliferation, Non-infectious complications, Unbiased network clustering",
author = "{The USIDNET Consortium} and Farmer, {Jocelyn R.} and Ong, {Mei Sing} and Sara Barmettler and Yonker, {Lael M.} and Fuleihan, {Ramsay L} and Sullivan, {Kathleen E.} and Charlotte Cunningham-Rundles and Walter, {Jolan E.} and Patricia Lugar and Daniel Suez and John Routes and Bonilla, {Francisco A.} and Gary Kleiner and Ballas, {Zuhair K.} and Secord, {Elizabeth A.} and Rebecca Buckley and Avni Joshi and Javeed Akhter and Jennifer Puck and Elie Haddad and Leonard Calabrese and Warren Strober and Patel, {Niraj C.} and Ochs, {Hans D.} and Burcin Uygungil and Stein, {Mark R.} and Karin Chen and Mark Ballow and Nicholas Bennett and Heather Lehman and Morna Dorsey and Jim Fernandez and Jason Caldwell and Robert Hostoffer and Adina Knight and Ralph Shapiro and Apter, {Andrea J.} and Bennion, {Jeffrey R.} and Melvin Berger and Jose Calderon and Laurence Cheng and Megan Cooper and Reis, {Patricia Costa} and Christopher George and Gonzalez, {Gabriel E.} and Guillot, {Richard J.} and Gundling, {Katherine E.} and Vivian Hernandez-Trujillo and Kirkpatrick, {Charles H.} and Kobayashi, {Roger H.}",
year = "2018",
month = "1",
day = "9",
doi = "10.3389/fimmu.2017.01740",
language = "English (US)",
volume = "8",
journal = "Frontiers in Immunology",
issn = "1664-3224",
publisher = "Frontiers Media S. A.",
number = "JAN",

}

Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets. / The USIDNET Consortium.

In: Frontiers in immunology, Vol. 8, No. JAN, 1740, 09.01.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets

AU - The USIDNET Consortium

AU - Farmer, Jocelyn R.

AU - Ong, Mei Sing

AU - Barmettler, Sara

AU - Yonker, Lael M.

AU - Fuleihan, Ramsay L

AU - Sullivan, Kathleen E.

AU - Cunningham-Rundles, Charlotte

AU - Walter, Jolan E.

AU - Lugar, Patricia

AU - Suez, Daniel

AU - Routes, John

AU - Bonilla, Francisco A.

AU - Kleiner, Gary

AU - Ballas, Zuhair K.

AU - Secord, Elizabeth A.

AU - Buckley, Rebecca

AU - Joshi, Avni

AU - Akhter, Javeed

AU - Puck, Jennifer

AU - Haddad, Elie

AU - Calabrese, Leonard

AU - Strober, Warren

AU - Patel, Niraj C.

AU - Ochs, Hans D.

AU - Uygungil, Burcin

AU - Stein, Mark R.

AU - Chen, Karin

AU - Ballow, Mark

AU - Bennett, Nicholas

AU - Lehman, Heather

AU - Dorsey, Morna

AU - Fernandez, Jim

AU - Caldwell, Jason

AU - Hostoffer, Robert

AU - Knight, Adina

AU - Shapiro, Ralph

AU - Apter, Andrea J.

AU - Bennion, Jeffrey R.

AU - Berger, Melvin

AU - Calderon, Jose

AU - Cheng, Laurence

AU - Cooper, Megan

AU - Reis, Patricia Costa

AU - George, Christopher

AU - Gonzalez, Gabriel E.

AU - Guillot, Richard J.

AU - Gundling, Katherine E.

AU - Hernandez-Trujillo, Vivian

AU - Kirkpatrick, Charles H.

AU - Kobayashi, Roger H.

PY - 2018/1/9

Y1 - 2018/1/9

N2 - Common variable immunodeficiency (CVID) is increasingly recognized for its association with autoimmune and inflammatory complications. Despite recent advances in immunophenotypic and genetic discovery, clinical care of CVID remains limited by our inability to accurately model risk for non-infectious disease development. Herein, we demonstrate the utility of unbiased network clustering as a novel method to analyze inter-relationships between non-infectious disease outcomes in CVID using databases at the United States Immunodeficiency Network (USIDNET), the centralized immunodeficiency registry of the United States, and Partners, a tertiary care network in Boston, MA, USA, with a shared electronic medical record amenable to natural language processing. Immunophenotypes were comparable in terms of native antibody deficiencies, low titer response to pneumococcus, and B cell maturation arrest. However, recorded non-infectious disease outcomes were more substantial in the Partners cohort across the spectrum of lymphoproliferation, cytopenias, autoimmunity, atopy, and malignancy. Using unbiased network clustering to analyze 34 non-infectious disease outcomes in the Partners cohort, we further identified unique patterns of lymphoproliferative (two clusters), autoimmune (two clusters), and atopic (one cluster) disease that were defined as CVID non-infectious endotypes according to discrete and non-overlapping immunophenotypes. Markers were both previously described [high serum IgE in the atopic cluster [odds ratio (OR) 6.5] and low class-switched memory B cells in the total lymphoproliferative cluster (OR 9.2)] and novel [low serum C3 in the total lymphoproliferative cluster (OR 5.1)]. Mortality risk in the Partners cohort was significantly associated with individual non-infectious disease outcomes as well as lymphoproliferative cluster 2, specifically (OR 5.9). In contrast, unbiased network clustering failed to associate known comorbidities in the adult USIDNET cohort. Together, these data suggest that unbiased network clustering can be used in CVID to redefine non-infectious disease inter-relationships; however, applicability may be limited to datasets well annotated through mechanisms such as natural language processing. The lymphoproliferative, autoimmune, and atopic Partners CVID endotypes herein described can be used moving forward to streamline genetic and biomarker discovery and to facilitate early screening and intervention in CVID patients at highest risk for autoimmune and inflammatory progression.

AB - Common variable immunodeficiency (CVID) is increasingly recognized for its association with autoimmune and inflammatory complications. Despite recent advances in immunophenotypic and genetic discovery, clinical care of CVID remains limited by our inability to accurately model risk for non-infectious disease development. Herein, we demonstrate the utility of unbiased network clustering as a novel method to analyze inter-relationships between non-infectious disease outcomes in CVID using databases at the United States Immunodeficiency Network (USIDNET), the centralized immunodeficiency registry of the United States, and Partners, a tertiary care network in Boston, MA, USA, with a shared electronic medical record amenable to natural language processing. Immunophenotypes were comparable in terms of native antibody deficiencies, low titer response to pneumococcus, and B cell maturation arrest. However, recorded non-infectious disease outcomes were more substantial in the Partners cohort across the spectrum of lymphoproliferation, cytopenias, autoimmunity, atopy, and malignancy. Using unbiased network clustering to analyze 34 non-infectious disease outcomes in the Partners cohort, we further identified unique patterns of lymphoproliferative (two clusters), autoimmune (two clusters), and atopic (one cluster) disease that were defined as CVID non-infectious endotypes according to discrete and non-overlapping immunophenotypes. Markers were both previously described [high serum IgE in the atopic cluster [odds ratio (OR) 6.5] and low class-switched memory B cells in the total lymphoproliferative cluster (OR 9.2)] and novel [low serum C3 in the total lymphoproliferative cluster (OR 5.1)]. Mortality risk in the Partners cohort was significantly associated with individual non-infectious disease outcomes as well as lymphoproliferative cluster 2, specifically (OR 5.9). In contrast, unbiased network clustering failed to associate known comorbidities in the adult USIDNET cohort. Together, these data suggest that unbiased network clustering can be used in CVID to redefine non-infectious disease inter-relationships; however, applicability may be limited to datasets well annotated through mechanisms such as natural language processing. The lymphoproliferative, autoimmune, and atopic Partners CVID endotypes herein described can be used moving forward to streamline genetic and biomarker discovery and to facilitate early screening and intervention in CVID patients at highest risk for autoimmune and inflammatory progression.

KW - Atopy

KW - Autoimmunity

KW - Common variable immunodeficiency

KW - Endotypes

KW - Lymphoproliferation

KW - Non-infectious complications

KW - Unbiased network clustering

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