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Abstract Number: 1886

Phenotypic Subgroups in IgG4-Related Disease – a Cluster Analysis

Zachary Wallace1, Yuqing Zhang2, Cory A. Perugino3, Raymond P. Naden4, Hyon K. Choi5 and John H. Stone6, 1Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 2Department of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Boston, MA, 3Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, 4New Zealand Ministry of Health, New Zealand Ministry of Health, Auckland, New Zealand, 5Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston, MA, 6Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA

Meeting: 2018 ACR/ARHP Annual Meeting

Keywords: IgG4 Related Disease and phenotypes

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Session Information

Date: Monday, October 22, 2018

Title: 4M091 ACR Abstract: Vasculitis–Non-ANCA-Assocd & Rel D/Os I: Population-Based Studies(1881–1886)

Session Type: ACR Concurrent Abstract Session

Session Time: 2:30PM-4:00PM

Background/Purpose: IgG4-related disease (IgG4-RD) is a multi-organ condition of uncertain etiology characterized by substantial morbidity if not diagnosed and treated promptly. Identifying IgG4-RD subgroups based on the distribution of organ involvement may facilitate diagnosis, illuminate our understanding of the pathogenesis, and guide management. We sought to identify phenotypic clusters of IgG4-RD using an unbiased method.

Methods: Our study patients consisted of 493 IgG4-RD cases diagnosed by 76 IgG4-RD specialists from North America, South America, Europe, and Asia. For each case, investigators reported age at disease onset and diagnosis, race/ethnicity, organ involvement, and lab results. We performed latent class analysis to identify subgroups with distinct patterns of organ involvement. Cases were assigned to the cluster in which they had the highest probability of membership. We used logistic regression adjusted for baseline covariates and probability of an individual’s cluster membership to estimate the odds ratio (95% CI) comparing the distribution of covariates between clusters, using one cluster as the reference group. To validate our results, we repeated the analysis in a separate cohort.

Results: Of the 493 IgG4-RD cases, the mean age was 59.5 (±14.0) years and 65% were male. Of the cases, 40% were Caucasian, 45% were Asian, and 12% were Hispanic. We identified four clusters (Table 1). Cluster 1 (“Pancreato-Hepatobiliary”) included 31% of patients. Cluster 2 (“Retroperitoneum/Aorta”) included 24% of patients. Cluster 3 (“Head/Neck”) included 24% of patients. Cluster 4 (“Mikulicz/Systemic”) included 22% of patients. Compared to the “Head/Neck” cluster (Table 2), individuals in other clusters were significantly more likely to be older (OR Range 1.17-1.28) and male (OR Range 9.21-11.93). Individuals in other clusters were significantly much less likely to be Asian (OR Range 0.14-0.16) compared to the “Head/Neck” cluster. The “Mikulicz/Systemic” cluster included patients who tended to have quite elevated serum IgG4 levels compared to the “Head/Neck Cluster” [OR 1.12 (1.02-1.22)]. We replicated our results in the second cohort.

Conclusion: Using an unbiased method, we identified four phenotypic clusters of IgG4-RD patients. Besides differences in organ involvement, clusters were distinguished by age at diagnosis as well as race/ethnicity, gender distribution, and serum IgG4 concentrations. These clusters may identify patients with IgG4-RD resulting from different risk factors or exposures and those likely to respond differently to treatment.

Table 1: Clusters of IgG4-RD Using the 2018 Classification Criteria Validation Cohort (N=478) *

Pancreato-Hepato-Biliary

Retroperitoneum and Aorta

Head and Neck

Mikulicz with Systemic Disease

Proportion of Cohort (%)

30.8%

23.7 %

23.9%

21.6%

Average Probability of Cluster Membership (Mean ±SD)**

92.9% (±11.5%)

91.6%

(±13.6%)

89.8% (±14.1%)

90.2% (±15.0%)

Input Variables / Proportion of Organ Involvement in Each Cluster†

Pancreas

87.0%

12.3%

14.5%

46.2%

Liver

12.9%

0.9%

1.6%

5.0%

Biliary

54.5%

0.1%

0.1%

26.9%

Orbital

0.01%

3.3%

22.1%

0.02%

Extra-Ocular Muscle

0.01%

1.0%

13.1%

3.8%

Sinusitis

2.5%

0.2%

16.9%

16.3%

Parotid

1.9%

0.9%

21.6%

48.9%

Submandibular

15.3%

5.2%

50.4%

77.3%

Lacrimal

3.0%

3.0%

6.0%

47.5%

Renal

11.3%

13.0%

5.1%

35.6%

Lung

2.1%

14.8%

6.5%

38.5%

Lymph Node

15.1%

24.8%

28.8%

66.5%

Prostate

0.9%

0.01%

0.01%

14.2%

Thoracic Aorta

0.8%

10.1%

0.9%

3.3%

Abdominal Aorta

2.7%

22.4%

0.1%

13.1%

Retroperitoneum

3.5%

52.8%

1.6%

7.9%

*Only cases with complete covariate data are included in fully-adjusted latent class analysis
**Generally, a probability of cluster membership > 70% is considered very strong
†
All reported proportions are probabilities conditional on latent class membership


Table 2: Logistic Regression Evaluating the Ability of Selected Covariates to Predict Cluster Membership

“Pancreato-Hepato-Biliary”

“Retroperitoneum and Aorta”

“Head and Neck”

“Mikulicz with Systemic Disease”

Male

Proportion of Cluster (%)

79%

74%

24%

78%

Adjusted Odds Ratio (95% CI)

11.93 (5.31-26.78)

10.33 (4.15-25.71)

1 [Ref]

9.21 (2.85-29.77)

Asian

Proportion of Cluster (%)

37%

25%

67%

52%

Adjusted Odds Ratio (95% CI)

0.15 (0.04-0.60)

0.14 (0.04-0.48)

1 [Ref]

0.16 (0.03-0.82)

Age at Diagnosis

Mean (±SD) Years

63 (±13)

58 (±16)

55 (±13)

63 (±13)

Adjusted Odds Ratio (per 5 years; 95% CI)

1.28 (1.13-1.46)

1.17 (1.01-1.36)

1 [Ref]

1.25 (1.01-1.53)

Diagnostic Delay

Mean (±SD) Years

0.9 (±1.8)

1.8 (±4.0)

2.3 (±3.4)

2.0 (±3.6)

Adjusted Odds Ratio (per year; 95% CI)

0.65 (0.51-0.84)

0.88 (0.81-0.95)

1 [Ref]

0.90 (0.75-1.09)

Serum IgG4 Concentration

Median (IQR) mg/dL

316

(147, 622)

177.5

(62.5, 322)

445.0

(183, 888)

1,170.0

(520, 2,178)

Adjusted Odds Ratio (per 100mg/dL; 95% CI)

0.98 (0.89-1.07)

0.84 (0.67-1.05)

1 [Ref]

1.12 (1.02-1.22)


Disclosure: Z. Wallace, None; Y. Zhang, None; C. A. Perugino, None; R. P. Naden, None; H. K. Choi, Takeda, Selecta, Kowa, and Horizon, 5,Selecta and Horizon, 2; J. H. Stone, None.

To cite this abstract in AMA style:

Wallace Z, Zhang Y, Perugino CA, Naden RP, Choi HK, Stone JH. Phenotypic Subgroups in IgG4-Related Disease – a Cluster Analysis [abstract]. Arthritis Rheumatol. 2018; 70 (suppl 9). https://acrabstracts.org/abstract/phenotypic-subgroups-in-igg4-related-disease-a-cluster-analysis/. Accessed .
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