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Frontiers in Immunology logoLink to Frontiers in Immunology
. 2023 Jan 26;13:1103533. doi: 10.3389/fimmu.2022.1103533

Risk factors and sequelae of epidermolysis bullosa acquisita: A propensity-matched global study in 1,344 patients

Khalaf Kridin 1,2,3, Artem Vorobyev 4, Cristian Papara 1, David A De Luca 1, Katja Bieber 1, Ralf J Ludwig 1,4,*
PMCID: PMC9910332  PMID: 36776391

Abstract

Identification of risk factors and sequelae of any given disease is of key importance. For common diseases, primary prevention and disease management are based on this knowledge. For orphan diseases, identification of risk factors and sequelae has been challenging. With the advent of large databases, e.g., TriNetX, this can now be addressed. We used TriNetX to identify risk factors and sequelae of epidermolysis bullosa acquisita (EBA), a severe and orphan autoimmune disease. To date, there is only enigmatic information on EBA comorbidity. We recruited 1,344 EBA patients in the Global Collaborative Network of TriNetX. Using the “explore outcomes” function we identified 55 diagnoses with a different prevalence between EBA and no-EBA patients. We next performed propensity-matched, retrospective cohort studies in which we determined the risk of EBA development following any of the identified 55 diseases. Here, 31/55 diseases were identified as risk factors for subsequent EBA. Importantly, the highest risk for EBA were other chronic inflammatory diseases (CID), especially lupus erythematosus and lichen planus. Lastly, we determined the risk to develop any of the identified diseases after EBA diagnosis. Here, 38/55 diseases were identified as sequelae. Notably, EBA patients showed an increased risk for metabolic and cardiovascular disease, and thrombosis. Furthermore, the risk for CIDs, especially lupus erythematosus and lichen planus, was elevated. These insights into risk factors and sequelae of EBA are not only of clinical relevance, e.g., optimizing cardiovascular disease risk, but in addition, point to shared pathogenetic pathways between EBA and other inflammatory diseases.

Keywords: epidermolysis bullosa acquisita, TriNetX, risk factor, sequelae, systemic lupus erythematosus (SLE), inflammatory bowel disease, cardiovascular disease, lichen planus

Introduction

Epidermolysis bullosa acquisita (EBA) is an orphan autoimmune disease that is characterized and caused by autoantibodies targeting type VII collagen (COL7). This short statement is grounded on research that was fronted by Detlef Zillikens: In 2005 his group demonstrated the induction of experimental EBA in mice following the transfer of antibodies targeting COL7 (1). At the same time, similar findings were published by the group of David Woodley (2). With these two landmark publications, the autoimmune pathogenesis of EBA had been firmly established. In parallel to this basic research on EBA, Detlef Zillikens also pursued epidemiological research questions, mainly aimed at the determination of the incidence and prevalence of autoimmune bullous dermatoses, such as EBA. Among other findings, he demonstrated a low EBA incidence and prevalence in Germany (3, 4).

Because EBA is such a rare disease, insights into risk factors and sequelae are sparse. Based on small cohorts, reviews of case reports and case report-series, an association between EBA and inflammatory bowel diseases (IBDs), such as ulcerative colitis (UC) and Crohn’s disease (CD), as well as infectious, cardiovascular, metabolic, malignant, and neurological diseases have been reported (5–12). Whilst no association of EBA with systemic lupus erythematosus (SLE) was observed, antinuclear antibodies (ANAs), a hallmark of SLE, were described to occur at higher frequencies than expected in patients diagnosed with EBA (12). Without exception, these reports are based on case reports, case report series or a meta-analysis of these. Thus, the evidence provided is of rather limited validity. In addition, all previous publications on that topic had reported associations only, with no information of the sequence of events. Hence, insights into the sequence of events are missing.

Understanding risk factors and sequelae of EBA would, however, significantly improve patient outcomes because risk factors could be addressed to prevent disease onset, and screening for diseases that subsequently develop after EBA diagnosis may contribute to their early detection. This is, for example, exemplified in the implementation of screening for metabolic, psychiatric, and cardiovascular comorbidity in psoriasis (13). In the same line, the European League Against Rheumatism (EULAR) recently published recommendations concerning lifestyle behavior to prevent progression of rheumatic diseases. In the context of non-inflammatory diseases, such as cardiovascular diseases, preventive measures to normalize elevated blood pressure or cholesterol are common clinical practice (14, 15). These include smoking cession and weight reduction in obese patients because nicotine dependence and overweight/obesity are known risk factors for the development of rheumatic diseases, being often associated with a more severe disease and a reduced response to treatment (16).

To define the so far enigmatic risk factors and associations of EBA, we assessed the Global Collaborative Network of TriNetX that provides access to anonymized, longitudinal patient data from close to 115 million individuals. First, we determined differences in disease prevalence in patients with EBA to those without. Next, we performed several retrospective cohort studies in which we determined the risk of subsequent EBA development following the diagnosis of any of the diseases found to be different in prevalence between EBA and no-EBA patients. Lastly, we determined the risk to develop any of the identified diseases after the diagnosis of EBA.

Materials and methods

Study design and database

We performed a global population-based study with a propensity-matched retrospective cohort design. First, the Global Collaborative Network of TriNetX was used to compare differences regarding diagnoses of EBA patients (ICD10:L12.3) to those without EBA. For the latter group, individuals presenting for “Encounter for general examination without complaint, suspected or reported diagnosis” (ICD10CM:Z00) that had no diagnosis of EBA (ICD10CM:L12.3) were included. For the analysis, the “explore outcomes” function of TriNetX was used. Next, only diagnoses with a medium difference (2/3 bars) and a prevalence of 5% or more in EBA patients, and those with a high difference (3/3 bars) and a prevalence of 1% or more in EBA patients were considered further. We then performed the following retrospective cohort studies: First, the risk of EBA development following the diagnosis of any of the selected diseases was assessed. This was followed by the determination of development of any of the selected diseases following an EBA diagnosis ( Figure 1 ).

Figure 1.

Figure 1

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Study flow chart. Within the Global Collaborative Network of TriNetX that encompassed data from close to 115 million patients at the time of access (October 1st, 2022), 1,344 patients with the diagnosis of epidermolysis bullosa acquisita (EBA), defined by ICD10:L12.3 were identified. Patients without a diagnosis of EBA were excluded. Next, the “explore outcomes” function of TriNetX to identify diseases that are different in prevalence (at any given time) between EBA patients (defined by presence of ICD10:L12.3) those without EBA (defined as presence of ICD10:Z00 and absence of ICD10:L12.3). This identified 55 diseases with a different prevalence (at any given time) between EBA and no-EBA patients. Subsequently, 55 propensity-matched studies were performed to explore if any one of the identified diseases had an impact on subsequent EBA development. For this, patients with the disease, i.e., Crohn’s disease (ICD10:K50) were compared to those without the disease, i.g., no-Crohn’s disease (ICD10:Z00 and absence of ICD10:K50). Lastly, a propensity-matched study was performed to delineate the risk of EBA to develop any of the 55 identified diseases. Figure created with BioRender (https://biorender.com).

Study population and definition of eligible patients

The data used in this study was collected between October 1st to 8th, 2022, from the TriNetX Global Collaborative Network, which, at the time of analysis, provided access to electronic medical records (diagnoses, procedures, medications, laboratory values, genomic information) from 113,986,635 million patients from 90 healthcare organizations (HCO). Propensity score matching was performed for the following variables: Age, sex, ethnicity, and race. TriNetX, LLC is compliant with the Health Insurance Portability and Accountability Act (HIPAA), the US federal law which protects the privacy and security of healthcare data, and any additional data privacy regulations applicable to the contributing HCO. TriNetX is certified to the ISO 27001:2013 standard and maintains an Information Security Management System (ISMS) to ensure the protection of the healthcare data it has access to and to meet the requirements of the HIPAA Security Rule, Any data displayed on the TriNetX Platform in aggregate form, or any patient level data provided in a data set generated by the TriNetX Platform, only contains de-identified data as per the de-identification standard defined in Section §164,514(a) of the HIPAA Privacy Rule. The process by which the data is de-identified is attested to through a formal determination by a qualified expert as defined in Section §164,514(b)(1) of the HIPAA Privacy Rule. Because this study used only de-identified patient records and did not involve the collection, use, or transmittal of individually identifiable data, this study was exempted from Institutional Review Board approval.

Statistical analysis

Baseline characteristics were described by means and standard deviations (SDs) for continuous variables, and numbers and percentages for dichotomous variables. Continuous variables were compared using the student t-test, and dichotomous variables by Pearson chi-square test. Survival analyses were conducted by the Kaplan-Meier method. A log-rank test was run to determine if there were differences in the survival distribution for patients in the two investigated groups. Hazard ratios (HR)s for the study outcomes were obtained using the Cox regression model. Nelson-Aalen plots were utilized to test the proportional hazards assumption. Two-tailed P-values less than 0,05 were considered statistically significant.

Results

Cohort description and global results

For EBA-risk factors analysis, detailed cohort descriptions and results are displayed in Supplement Tables 1–55 and Table 1 . Sample sizes for the risk factor analysis ranged from 11,573 (conjunctival scars) to 7,849,544 (hypertensive diseases), with a median of 1,370,800 (845,473 to 2.337,172, 25/75-percentile). For each disease, propensity matching (age, sex, race, and ethnicity) was performed for better comparability of the groups. Globally, 31/55 diseases were associated with a higher risk of subsequent EBA, 23/55 with no impact on EBA risk, while 1/55 disease led to a reduced risk of subsequent EBA ( Figure 2 and Table 1 ). The corresponding data for the sequalae is found in Supplement Table 56 and Table 2 . Overall, we included 1,344 EBA patients (cases) and a similar number of controls. For a better comparability between cases and controls, they were again propensity matched for age, sex, race, and ethnicity. Between groups, no differences regarding these variables were observed. Overall, 37/55 diseases developed more frequently than expected following EBA diagnosis, for 17/55 diseases EBA was not a risk factor, and EBA had a lower risk for 1/55 diseases.

Table 1.

Risk factors of epidermolysis bullosa acquisita (EBA).

Cases Controls
Disease ICD10 code N of eligible participants N of Outcomes Risk, % N of eligible participants* N of Outcomes Risk, % Risk difference (95% confidence interval), % Hazard ratio (95% confidence interval) P value
Overweight and obesity E66 6,923,027 280 0.004 6,923,063 245 0.004 0.001, 0-0.001 1.257, 1.059-1.493 0.0088
Type 2 diabetes mellitus E11 5,637,675 240 0.004 5,637,665 255 0.005 – – n.s.
Hypothyroidism, unspecified E03.9 3,402,569 145 0.004 3,402,556 133 0.004 – – n.s.
Malnutrition E40-E46 919,769 46 0.005 919,811 33 0.004 0.001, 0-0.003 2.363, 1.505-3.71 0.0001
Systemic lupus erythematosus M32 231,329 29 0.013 231,352 10 0.004 0.008, 0.003-0.014 3.099, 1.466-6.552 0.0018
Hypertensive diseases I10-I16 7,840,452 284 0.004 7,840,405 307 0.004 – – n.s.
Heart failure I50 2,460,148 84 0.003 2,460,164 113 0.005 – – n.s.
Atrial fibrillation and flutter I48 2,440,224 84 0.003 2,440,208 113 0.005 – – n.s.
Nonrheumatic mitral valve disorders I34 1,549,166 64 0.004 1,549,175 57 0.004 – – n.s.
Atherosclerosis I70 1,246,293 57 0.005 1,246,328 50 0.004 – – n.s.
Acute embolism and thrombosis of deep veins of lower extremity I82.4 845,427 53 0.006 845,451 24 0.003 0.003, 0.001-0.005 2.628, 1.622-4.259 < 0.0001
Hypotension I95 1,837,108 89 0.005 1,837,163 84 0.005 0, -0.001-0.002 1.395, 1,035-1,882 0.0282
Cerebral infarction I63 1,567,585 84 0.005 1,567,605 70 0.004 0.001, -0.001-0.002 1,494, 1,008-2,053 0.0126
Diverticular disease of intestine K57 2,337,084 112 0.005 2,337,112 132 0.006 – – n.s.
Gastro-esophageal reflux disease K21 6,973,564 295 0.004 6,973,597 251 0.004 – – n.s.
Ulcer of esophagus K22.1 127,391 11 0.009 127,404 10 0.008 – – n.s.
Gastritis and duodenitis K29 1,869,533 74 0.004 1,869,561 61 0.003 – – n.s.
Stomatitis and related lesions K12 624,417 71 0.011 624,467 14 0.002 0.009, 0.006-0.012 4.943, 2.785-8.773 < 0.0001
Diaphragmatic hernia K44 1,329,587 66 0.005 1,329,616 70 0.005 – – n.s.
Crohn’s disease K50 294,555 20 0.007 294,562 11 0.004 – – n.s.
Other chronic obstructive pulmonary disease J44 2,317,942 72 0.003 2,317,939 114 0.005 -0.002, -0.003-0.001 0.732 0.545-0.984 0.0378
Sleep disorders G47 5,970,685 251 0.004 5,970,732 216 0.004 0.001, 0-0.001 1.231, 1.026-1.477 0.0250
Acute kidney failure and chronic kidney disease N17-N19 3,557,269 169 0.005 3,557,299 158 0.004 0, -0.001-0.001 1.371, 1.103-1.704 0.0043
Malignant melanoma of skin C43 282,444 24 0.008 282,476 13 0.005 – – n.s.
Malignant neoplasm of liver and intrahepatic bile ducts C22 594,423 38 0.006 594,476 21 0.004 0.003, 0-0.005 3.037, 1.737-5.309 < 0.0001
Melanocytic nevi D22 1,370,655 40 0.003 1,370,781 46 0.003 – – n.s.
Other and unspecified malignant neoplasm of skin C44 1,080,602 73 0.007 1,080,674 47 0.004 0.002, 0-0.004 1.504, 1.042-2.17 0.0282
Anemia, unspecified D64.9 4,097,465 190 0.005 4,097,521 148 0.004 0.001, 0-0.002 1.394, 1.124-1.728 0.0024
Elevated white blood cell count D72.82 1,554,558 94 0.006 1,554,612 61 0.004 0.002, 0.001-0.004 2.174, 1.57-3.009 < 0.0001
Purpura and other hemorrhagic conditions D69 1,325,255 79 0.006 1,325,309 50 0.004 0.002, 0.001-0.004 1.977, 1.387-2.82 0.0001
Iron deficiency anemia D50 2,055,291 84 0.004 2,055,347 64 0.003 0.001, 0-0.002 1.473, 1.064-2.04 0.0190
Age-related cataract H25 1,663,835 111 0.007 1,663,880 83 0.005 – – n.s.
Other cataract H26 1,223,272 99 0.008 1,223,308 50 0.004 0.004, 0.002-0.006 1.663, 1.183-2.339 0.0031
Visual disturbances and blindness H53-H54 3,457,195 179 0.005 3,457,247 98 0.003 0.002, 0.001-0.003 1.956, 1.529-2.503 < 0.0001
Glaucoma H40-H42 1,221,974 77 0.006 1,221,991 47 0.004 – – n.s.
Dry eye syndrome H04.12 908,659 57 0.006 908,695 35 0.004 – – n.s.
Presbyopia H52.4 878,532 77 0.009 878,566 41 0.005 0.004, 0.002-0.007 1.569, 1.074-2.294 0.0190
Conjunctival scars H11.2 11,556 14 0.121 11,573 0 0 0.121, 0.058-0.185% n.a. 0.0001
Keratitis H16 362,090 32 0.009 362,108 17 0.005 – – n.s.
Disorders of choroid and retina H30-H36 1,476,209 82 0.006 1,476,231 61 0.004 – – n.s.
Lupus erythematosus L93 92,859 28 0.03 92,904 10 0.011 0.019, 0.006-0.032 26.957, 3.666,198.223 < 0.0001
Pressure ulcer L89 520,244 25 0.005 520,276 23 0.004 0, -0.002-0.003 1.828, 1.035-3.228 0.0348
Pruritus L29 1,719,598 180 0.01 1,719,763 56 0.003 0.007, 0.005-0.009 3.507, 2.598-4.735 < 0.0001
Seborrheic dermatitis L21 667,636 44 0.007 667,680 14 0.002 0.004, 0.002-0.007 2.893, 1.585-5.283 0.0003
Lichen simplex chronicus and prurigo L28 257,992 48 0.019 258,031 11 0.004 0.014, 0.009-0.02 4.446, 2.309-8.561 < 0.0001
Urticaria L50 1,072,233 69 0.006 1,072,291 18 0.002 0.005, 0.003- 0.006 4.094, 2.437-6.88 < 0.0001
Lichen planus L43 74,580 21 0.028 74,609 10 0.013 0.015, 0-0.029 10.198, 2.391-43.499 < 0.0001
Chronic pain, not elsewhere classified G89.2 4,199,569 152 0.004 4,199,663 169 0.004 – – n.s.
Acute pain, not elsewhere classified G89.1 1,261,295 61 0.005 1,261,333 34 0.003 0.002, 0.001-0.004 2.371, 1.554-3.617 < 0.0001
Polyneuropathy, unspecified G62.9 1,013,058 74 0.007 1,013,111 40 0.004 0.003, 0.001-0.005 2.45, 1.647-3.644 < 0.0001
Benign prostatic hyperplasia N40 1,593,540 70 0.004 1,593,550 84 0.005 – – n.s.
Candidiasis B37 1,933,646 107 0.006 1,933,718 46 0.002 0.003, 0.002-0.004 2.311, 1.636-3.266 < 0.0001
Dermatophytosis B35 1,676,899 105 0.006 1,676,983 59 0.004 0.003, 0.001-0.004 1.565, 1.137-2.153 0.0056
Sepsis, unspecified organism A41.9 1,336,969 62 0.005 1,337,011 52 0.004 0.001, -0.001-0.002 1.935 1.333-2.808 0.0004
Nicotine dependence F17 5,515,751 196 0.004 5,515,748 213 0.004 – – n.s.
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The risk of EBA development in individuals diagnosed with any one of the listed diseases cases to those without these diagnoses controls. Data shown displays results from measures of association excluding patients with EBA prior to the time window. For the Hazard ratio. Kaplan-Meier Analysis again excluding patients with outcome prior to the time window with Log-Rank Test was performed. N, number; n.s., not significant; n.a., not applicable; Diseases where presence of any of the diseases increases the risk are highlighted in bold. Diseases where presence of the indicated disease decreases the risk are highlighted in blue and bold. Diseases that do not impose a risk for subsequent EBA development are displayed in gray. Please note that propensity score matching is re-run with each outcomes analysis so that the analysis uses the most current data available on the TriNetX network. Analysis was performed from October 3rd to 8th, 2022.

Figure 2.

Figure 2

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Risk factors and sequelae of epidermolysis bullosa acquisita. Hazard ratio (HR) event plots for the risk factors and sequelae of epidermolysis bullosa acquisita (EBA). Cohort descriptions and detailed results for risk factors are listed in Supplement Tables 1–55 and Table 1 , respectively. For risk factors, these are shown in Supplement Table 56 and Table 2 . Error bars indicate 5/95-percentiles. If no HR is shown, the indicated disease was not a risk factor / sequela of EBA.

Table 2.

Sequelae of epidermolysis bullosa acquisita (EBA).

Cases Controls
Disease ICD10 code N of eligible participants N of Outcomes Risk, % N of eligible participants* N of Outcomes Risk, % Risk difference (95% confidence interval), % Hazard ratio (95% confidence interval) P value
Overweight and obesity E66 1,031 128 12.415 1,175 130 11.064 1.351, -1.345-4.047 1.360, 1.059-1.747 0.0156
Type 2 diabetes mellitus E11 1,048 93 8.874 1,138 80 7.030 1.844, -0.430-4.118 1.420, (1.045-1.931 0.0245
Hypothyroidism, unspecified E03.9 1,180 44 3.729 1,222 65 5.319 – – n.s.
Malnutrition E40-E46 1,285 44 1,335 1,335 23 1.723 1.701, 0.486-2.916 2.088, 1.250-3.467 0.0036
Systemic lupus erythematosus M32 1,301 11 0.846 1,338 10 0.747 0.098, -0.581-0.777 5.940, 1.311-26.908 0.0087
Hypertensive diseases I10-I16 729 145 19.890 809 147 18.171 1.720, -2.212-5.65 1.289, 1.017-1.632 0.0350
Heart failure I50 1,241 70 5.641 1,286 54 4.199 1.442, -0.246-3.130 1.575, 1.090-2.275 0.0147
Atrial fibrillation and flutter I48 1,234 47 3.809 1,256 49 3.901 – – n.s.
Nonrheumatic mitral valve disorders I34 1,275 40 3.137 1,304 51 3.911 – – n.s.
Atherosclerosis I70 1,284 73 5.685 1,306 60 4.594 1.091, -0.610-2.792 1.441, 1.014-2.050 0.0407
Acute embolism and thrombosis of deep veins of lower extremity I82.4 1,283 38 2.962 1,330 19 1.429 1.533, 0.407-2.659 2.291, 1.301-4.035 0.0032
Hypotension I95 1,235 73 5.911 1,313 57 4.341 1.570, -0.1460-3.286 1.574, 1.102-2.249 0.0119
Cerebral infarction I63 1,255 51 4.064 1,313 31 2.361 1.703, 0.336-3.069 2.036, 1.280-3.238 0.0022
Diverticular disease of intestine K57 1,226 83 6.770 1,285 106 8.249 – – n.s.
Gastro-esophageal reflux disease K21 1,023 137 13.392 1,150 139 12.087 1.305, -1.507-4.117 1.303, 1.024-1.659 0.0312
Ulcer of esophagus K22.1 1,327 12 0.904 1,344 10 0.744 0.160, -0.526-0.846 3.677, 1.156-11.691 0.0195
Gastritis and duodenitis K29 1,265 57 4.506 1,315 47 3.574 0.932, -0.589-2.453 1.578, 1.053-2.365 0.0259
Stomatitis and related lesions K12 1,251 36 2.878 1,339 14 1.046 1.832, 0.757- 2.907 3.108, 1.648-5.862 0.0002
Diaphragmatic hernia K44 1,274 56 4.396 1,311 46 3.509 – – n.s.
Crohn’s disease K50 1,321 10 0.757 1,339 10 0.747 – – n.s.
Other chronic obstructive pulmonary disease J44 1,261 49 3.886 1,287 52 4.040 – – n.s.
Sleep disorders G47 1,069 124 11.6 1,193 126 10.562 – – n.s.
Acute kidney failure and chronic kidney disease N17-N19 1,121 98 8.742 1,249 104 8.327 – – n.s.
Malignant melanoma of skin C43 1,314 28 2.131 1,337 11 0.823 1.308, 0.389-2.227 3.581, 1.683-7.620 0.0004
Malignant neoplasm of liver and intrahepatic bile ducts C22 1,311 54 4.119 1,338 32 2.392 1.727, 0.376- 3.079 2.263, (1.424-3.596 0.0004
Melanocytic nevi D22 1,249 95 7.606 1,327 61 4.597 3.009, 1.157-4.862 2.142, 1.526-3.005 < 0.0001
Other and unspecified malignant neoplasm of skin C44 1,244 69 5.547 1,308 40 3.058 2.489, 0.911-4.066 2.398, 1.582-3.636 < 0.0001
Anemia, unspecified D64.9 1,126 119 10.568 1,261 129 10.23 – – n.s.
Elevated white blood cell count D72.82 1,237 72 5.821 1,322 52 3.933 1.887, 0.214-3.561 1.87, 1.282-2.727 0.0010
Purpura and other hemorrhagic conditions D69 1,246 61 4.896 1,310 36 2.748 2.148, 0.658-3.637 2.064, 1.348-3.16 0.0007
Iron deficiency anemia D50 1,241 79 6.366 1,297 68 5.243 – – n.s.
Age-related cataract H25 1,233 79 6.407 1,289 91 7.060 – – n.s.
Other cataract H26 1,242 55 4.428 1,316 57 4.331 – – n.s.
Visual disturbances and blindness H53-H54 1,151 85 7.385 1,270 90 7.087 – – n.s.
Glaucoma H40-H42 1,266 38 3.002 1,303 30 2.302 0.699, -0.544-1.943 1.762, 1.057-2.940 0.0280
Dry eye syndrome H04.12 1,286 49 3.81 1,325 34 2.566 1.244, -0.105-2.593 1.923, 1.213-3.05 0.0047
Presbyopia H52.4 1,265 43 3.399 1,318 25 1.897 1.502, 0.262-2.743 2.223, 1.327-3.724 0.0019
Conjunctival scars H11.2 1,327 13 0.98 1,344 0 0 0.98, 0.45-1.51 n.a. 0.0001
Keratitis H16 1,311 22 1.678 1,339 10 0.747 0.931, 0.097-1.766 2.77, 1.247-6.156 0.0091
Disorders of choroid and retina H30-H36 1,259 39 3.098 1,303 55 4.221 – – n.s.
Lupus erythematosus L93 1,288 15 1.165 1,342 10 0.745 0.419, -0.326-1.164 15.546, 2.053-117.693 0.0004
Pressure ulcer L89 1,310 32 2.443 1,336 13 0.973 1.470, 0.482-2.458 2.942, 1.513-5.723 0.0009
Pruritus L29 1,111 112 10.081 1,330 61 4.586 5.495, 3.397-7.592 2.791, 2.010-3.877 < 0.0001
Seborrheic dermatitis L21 1,269 49 3.861 1,334 20 1.499 2.362, 1.117-3.607 2.688, 1.597-4.525 0.0001
Lichen simplex chronicus and prurigo L28 1,284 27 2.103 1,341 10 0.746 1.357, 0.447-2.267 4.314, 1.877-9.914 0.0002
Urticaria L50 1,261 41 3.251 1,335 30 2.247 1.004, -0.257-2.260 1.763, 1.078-2.885 0.0222
Lichen planus L43 1,308 15 1.147 1,342 10 0.745 0.402, -0.336-1.140 6.640, 1.744-25.278 0.0017
Chronic pain, not elsewhere classified G89.2 1,188 164 13.805 1,260 142 11.270 2.535, -0.091-5.161 1.631, 1.290-2.062 < 0.0001
Acute pain, not elsewhere classified G89.1 1,278 49 3.834 1,328 32 2.410 1.424, 0.087-2.762 1.990, 1.244-3.182 0.0035
Polyneuropathy, unspecified G62.9 1,261 58 4.6 1,316 33 2.508 2.092, 0.66-3.524 1.943, 1.258-3.0 0.0023
Benign prostatic hyperplasia N40 1,252 41 3.275 1,262 72 5.705 -2.43, -4.046- -0.815 0.6, 0.407-0.884 0.0090
Candidiasis B37 1,219 73 5.989 1,311 40 3.051 2.937, 1.312-4.563 2.377, 1.592,3.55 < 0.0001
Dermatophytosis B35 1,216 78 6.414 1,303 62 4.758 1.656, -0.142-3.454 1.457, 1.039-2.045 0.0283
Sepsis, unspecified organism A41.9 1,270 51 4.016 1,326 32 2.413 1.602, 0.243-2.962 1.713, 1.097-2.676 0.0166
Nicotine dependence F17 1,131 65 5.747 1,254 56 4.466 – – n.s.
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The risk of individuals diagnosed with EBA cases to those without EBA controls to develop one of the listed diseases. Data shown displays results from measures of association excluding patients with outcome prior to the time window. For the Hazard ratio. Kaplan-Meier Analysis again excluding patients with outcome prior to the time window with Log-Rank Test was performed. N, number; n.s., not significant; n.a., not applicable; Diseases where presence of EBA increases the risk are highlighted in bold. Diseases where presence of EBA decreases the risk are highlighted in blue and bold. Diseases in which EBA does not impose a risk for subsequent disease development are displayed in gray letters. Please note that propensity score matching is re-run with each outcomes analysis so that the analysis uses the most current data available on the TriNetX network. Analysis was performed from October 1st to 3rd, 2022.

Non-communicable inflammatory diseases are risk factors and sequelae of EBA

A total of 9 non-communicable inflammatory diseases (CID) were amongst the 55 diseases with a different prevalence contrasting EBA to no-EBA patients ( Tables 1 , 2 and Figure 2 ). Of these 9 CIDs, 6 were identified as risk factors for EBA. Considering that 23 diseases were identified as risk factors for EBA, CIDs are the most prevalent risk factor for subsequent EBA development. Lichen planus (HR 10.20, CI 2.39-43.50, p<0.0001), cutaneous lupus erythematosus (HR 26.96, CI 3.67-198.22, p<0.0001) and systemic lupus erythematosus (SLE, HR 3.10, CI 1.31-26.91, p=0.0087) were among the disease with the highest risk for future EBA development. Interestingly, “other chronic obstructive pulmonary disease” (COPD) was associated with a decreased risk of subsequent EBA development (HR 0.73, CI 0.55-0.98, p=0.0378).

Likewise, EBA was a risk factor for subsequent CID development. Of the 37 diseases for which EBA is a risk factor, 7 were CIDs. Again, lichen planus (HR 6.64, CI 1.74-25.28, p<0.0017), cutaneous lupus erythematosus (HR 15.55, CI 2.05-117.69, p=0.0004) and SLE (HR 5.94, CI 1.47-6.55, p=0.0018) were among the diseases manifesting following an EBA diagnosis. Albeit not a classical CID, pruritus was found to be both a risk factor (HR 3.51, CI 2.60-4.74, p<0.0001) and a sequel of EBA (HR 2.79, CI 2.01-3.88, p<0.0001).

Of note, inflammatory bowel disease, such as ulcerative colitis and Crohn’s disease, that had been previously assumed to be associated with EBA (12, 17–19) were not identified as risk factors or sequelae of EBA. Ulcerative colitis was not among the 55 diseases with a different prevalence when comparing EBA to no-EBA patients. Crohn’s disease was identified to have a different prevalence in EBA compared to no-EBA patients. However, in neither of the two, propensity matched, case-control studies, Crohn’s disease was identified as a risk factor or a sequela of EBA.

Infectious diseases are risk factors and sequelae of EBA

Sepsis, dermatophytosis and candidiasis were among the 55 diseases with a different prevalence between EBA and no-EBA patients ( Tables 1 , 2 ; Figure 2 ). Given that unspecific immunosuppression is the treatment of choice for EBA (12, 20–22), infectious diseases can be expected as sequelae of EBA. Indeed, sepsis, dermatophytosis and candidiasis were identified as sequelae of EBA. Interestingly, all were also risk factors for future EBA development: Sepsis (HR 1.94, CI 1.33-2.81, p=0.0004), dermatophytosis (HR 1.57, CI 1.14-2.15, p=0.0056) and candidiasis (HR 2.31, CI 1.64-3.27, p<0.0001).

Malignant melanoma, non-melanoma skin cancer and neoplasms of the liver and intrahepatic bile ducts are risk factors and sequelae of EBA

Regarding malignancy, 3/55 identified diagnoses fell into this category; namely, non-melanoma skin cancer, melanoma and malignant neoplasm of the liver and intrahepatic bile ducts. Whilst non-melanoma skin cancer and malignant neoplasm of the liver and intrahepatic bile ducts were both risk factors and sequelae of EBA, melanoma was not associated with a higher risk to develop EBA, but developed more frequently after an EBA diagnosis ( Tables 1 , 2 and Figure 2 ).

EBA has a considerable metabolic and cardiovascular disease risk

Metabolic and cardiovascular comorbidity significantly contributes to morbidity and mortality of CIDs (23–25). The potential metabolic and cardiovascular disease burden in EBA has, so far, not been addressed. Following the diagnosis of EBA, cerebral infarction (HR 2.04, CI 1.28-3.24, p=0.0004), deep vein thrombosis (HR 2.29, CI 11.30-4.04, p=0.0032), atherosclerosis (HR 1.44, CI 1.01-2.05, p=0.0407), heart failure (HR 1.58, CI 1.09-2.28, p=0.0147), hypertensive diseases (HR 1.29, CI 1.02-1.62, p=0.0350), type 2 diabetes mellitus (HR 1.42, CI 1.05-1.93, p=0.0245), and overweight & obesity (HR 1.36, CI 1.06-1.76, p=0.0156) developed more frequently than expected.

Discussion

We here determined risk factors and sequelae of EBA using a large-scale database. This led to the identification of so far unrecognized disease trajectories, as well as to the refutation of long-held beliefs, such as an association of inflammatory bowel disease with EBA.

Because EBA is such a rare disease, we believe that implementation of preventive measures based on the identification of risk factors is, for the most part, impracticable. For example, the identified EBA risk factors dermatophytosis and candidiasis are common diseases (26). The effort to screen for EBA in these patient populations would not justify the potential benefit. We, however, see some exceptions. These are pruritus, conjunctival scars, and visual disturbances. If in any of these, a definite cause cannot be determined, EBA should be excluded as a potential differential diagnosis following current diagnostic recommendations (20, 27). By contrast, the identified metabolic and cardiovascular risk profile of EBA patients warrants clinical implementation. Compared to controls, EBA patients were more prone to develop hypertensive diseases, atherosclerosis, cerebral infarction, heart failure, deep vein thrombosis, type 2 diabetes mellitus and overweight & obesity – for some, the risk was increased over 2-fold ( Table 2 and Figure 2 ). This profile is in line with several other CIDs, for example SLE, rheumatoid arthritis and psoriasis (28–30). The finding of an increased frequency of deep vein thrombosis in EBA has also been made in bullous pemphigoid, where a prothrombotic state and an increased risk for venous thromboembolism had been noted (31, 32). Consequently, stringent control of known cardiovascular risk factors, and frequent screening for cardiovascular and metabolic disease should be implemented in the management of EBA patients.

We also found other CIDs to be risk factors and sequelae of EBA. Among these, lichen planus, SLE and cutaneous lupus were associated with the highest risk for both, future EBA development, and the probability of manifestation following an EBA diagnosis. Thus, it is tempting to speculate that EBA, lichen planus, SLE and cutaneous lupus share similar disease-driving pathomechanisms. These findings are also in line with previous clinical observations that noted a high prevalence of antinuclear autoantibodies in EBA patients (12, 33), and the co-occurrence of SLE with EBA, which coined the term bullous SLE (34–36).

Interestingly, inflammatory bowel diseases were not identified as risk factors or sequelae of EBA. An association of EBA with these had long been noted, with several subsequent publications supporting this assumption (5, 12, 17–19, 37). However, most of these observations were made before the establishment of the current diagnostic EBA criteria. Furthermore, these observations were based on single case reports, case report series and meta-analysis thereof. Considering the findings from our study, an association of EBA with inflammatory bowel disease seems rather unlikely.

Regarding malignancy, we think the increased risk of EBA patients to develop non-melanoma skin cancer and melanoma is due to regular and prolonged dermatological care. This is certainly not the case for malignant neoplasms of the liver and intrahepatic bile ducts, which were identified as a risk factor (HR 3.07, CI 1.74-5.31, p<0.0001) and a sequela of EBA (2.26, CI 1.42-3.60, p=0.0004). This necessitates regular screening for this malignancy, underscoring the need for a multidisciplinary care for EBA patients (38). The recently noted expression of COL7 in the liver (of mice) may explain this so far unnoticed association (39) – albeit this finding is not sustained by other reports (40). In the human protein atlas, COL7A1 expression has, however, been reported for the gallbladder – and to a minor extent also in the liver. Thus, autoantibody-induced inflammation in the bile ducts may promote the emergence of malignant neoplasms of the liver and intrahepatic bile ducts.

Our study has several limitations to be acknowledged. First, patient electronic health record data may suffer from misdiagnosis and/or miscoding and do not encompass all possible confounding factors. In our dataset this is underscored by the diagnosis of benign prostatic hyperplasia, where close to 0.5% of individuals with this diagnosis were coded to be female ( Supplement Table 51 ). Second, the TriNetX database provides access to medical data from individuals who had medical encounters with healthcare systems. Thus, our analysis does not include patients with low access to healthcare facilities. Third, in the risk factor analysis, the results must be interpreted with caution if the number of EBA cases is low.

In conclusion, the use of TriNetx allowed to define risk factors and sequalae of an orphan disease, here exemplified by EBA. The identified interactions have clinical implications for the management of EBA and point towards shared pathogenic pathways among different CIDs. We envision that the here described methodology will serve as a blueprint to identify risk factors and sequelae for numerus orphan diseases.

This work has only been possible because of Detlef Zillikens. Since 2004, he continuously and methodically developed a research infrastructure for pemphigus and pemphigoid diseases at the University of Lübeck. In doing so, he spread his enthusiasm for pemphigus and pemphigoid research to improve the diagnosis and treatment of patients suffering from these diseases to those around him. All authors decided to come to Lübeck because of Detlef Zillikens. We truly miss him as an inspiring and always motivating mentor and friend, and now seek to continue his mission in pemphigus and pemphigoid.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

Author contributions

Conceptualization: KK, RL; Investigation: All authors; Illustrations: AV, KB; Project Administration: RL; Resources: RL; Writing - Original Draft Preparation: RL; Writing - Review and Editing: All authors. All authors contributed to the article and approved the submitted version

Funding Statement

This research was funded by the Cluster of Excellence “Precision Medicine in Chronic Inflammation” (EXC 2167), the Collaborative Research Centre “Pathomechanisms of Antibody-mediated Autoimmunity” (SFB 1526), the Research Training Group “Autoimmune Pre-Disease” (GRK 2633), all from the Deutsche Forschungsgemeinschaft and the Schleswig-Holstein Excellence-Chair Program from the State of Schleswig Holstein.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.1103533/full#supplementary-material

Click here for additional data file. (47.6KB, docx)

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Associated Data

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Supplementary Materials

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Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

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