Key Points
Question
What is the ectodermal dysplasias prevalence in Denmark and what are the patient characteristics?
Findings
This nationwide population-based cohort study used Danish registries to identify and characterize 396 validated cases of ectodermal dysplasia, showing a minimum birth prevalence of 14.5 cases per 100 000 live births for all ectodermal dysplasias and 2.8 cases for X-linked hypohidrotic ectodermal dysplasia.
Meaning
The findings of this nationwide cohort study indicate that the prevalence of ectodermal dysplasia was lower than previously reported, and the detailed clinical and molecular data provide a unique resource for future ectodermal research.
Abstract
Importance
Ectodermal dysplasias constitute a group of rare genetic disorders of the skin and skin appendages with hypodontia, hypotrichosis, and hypohidrosis as cardinal features. There is a lack of population-based research into the epidemiology of ectodermal dysplasias.
Objective
To establish a validated population-based cohort of patients with ectodermal dysplasia in Denmark and to assess the disease prevalence and patient characteristics.
Design, Setting, and Participants
This nationwide cohort study used individual-level registry data recorded across the Danish universal health care system to identify patients with ectodermal dysplasias from January 1, 1995, to August 25, 2021. A 3-level search of the Danish National Patient Registry and the Danish National Child Odontology Registry was conducted to identify patients with diagnosis codes indicative of ectodermal dysplasias; patients registered in the Danish RAREDIS Database, the Danish Database of Genodermatoses, and local databases were also added. The search results underwent diagnosis validation and review of clinical data using medical records. Of 844 patient records suggestive of ectodermal dysplasias, 791 patients (93.7%) had medical records available for review. Positive predictive values of the diagnosis coding were computed, birth prevalence was estimated, and patient characteristics were identified. Data analysis was performed from May 4 to December 22, 2023.
Results
The identified and validated study cohort included 396 patients (median [IQR] age at diagnosis, 13 [4-30] years, 246 females [62.1%]), of whom 319 had confirmed ectodermal dysplasias and 77 were likely cases. The combined positive predictive value (PPV) for ectodermal dysplasia−specific diagnosis codes was 67.0% (95% CI, 62.7%-71.0%). From 1995 to 2011, the estimated minimum birth prevalence per 100 000 live births was 14.5 (95% CI, 12.2-16.7) for all ectodermal dysplasias and 2.8 (95% CI, 1.8-3.8) for X-linked hypohidrotic ectodermal dysplasias. A molecular genetic diagnosis was available for 241 patients (61%), including EDA (n = 100), IKBKG (n = 55), WNT10A (n = 21), TRPS1 (n = 18), EDAR (n = 10), P63 (n = 9), GJB6 (n = 9), PORCN (n = 7), and other rare genetic variants.
Conclusions and Relevance
The findings of this nationwide cohort study indicate that the prevalence of ectodermal dysplasias was lower than previously reported. Furthermore, PPVs of the search algorithms emphasized the importance of diagnosis validation. The establishment of a large nationwide cohort of patients with ectodermal dysplasias, including detailed clinical and molecular data, is a unique resource for future research in ectodermal dysplasias.
This nationwide population-based cohort study investigates the prevalence and patient characteristics of ectodermal dysplasias using a large validated cohort in Denmark.
Introduction
Ectodermal dysplasias (EDs) are a heterogeneous group of genetic disorders affecting the ectodermally derived tissues, typically involving the skin and appendages (eg, hair, teeth, nails, and eccrine sweat glands).1 Cardinal features of EDs include hypohidrosis, hypotrichosis, nail dystrophy, and hypodontia.1 In the original clinical classification by Newton Freire-Maia, EDs were divided into 15 subgroups based on different combinations of the 4 cardinal features or other ectodermal derivates involved.2 Historically, more than 180 different EDs have been described based on clinical presentation. However, increasing insights into the genetic background of EDs have prompted changes to classification systems based on the disease-related genes and molecular pathways.1,3,4,5 In 2017, an international working group consolidated the EDs to approximately 100 entities,1 and in 2022, the most recent consensus group updated that number to 49 entities.3
To our knowledge, there are no published nationwide population-based studies of the entire group of EDs, and there are few and diverging reports on ED occurrence, primarily from small studies without denominators and restricted to specific ED subtypes. Previous studies have reported prevalence estimates of 10 to 70 cases per 100 000 births.6,7,8 A Danish registry-based study on X-linked hypohidrotic ED (XLHED) reported a prevalence of 4.2 cases per 100 000 based on molecularly confirmed and clinically diagnosed cases, and 21.9 per 100 000 when including a feature-based search; however, these cases were not validated.9
Given that EDs have been associated with impaired quality of life10,11 and increased mortality and morbidity,12,13,14,15 studies of the occurrence, presentation, and prognosis of EDs are important for improving the management of care and outcomes for these patients. Our objectives were to use the Danish health registries to establish a validated nationwide cohort of patients with ED and to assess ED prevalence and patient characteristics.
Methods
This nationwide population-based cohort study was approved by the Danish Data Protection Agency (No. 1-16-02-143-21). Central Denmark Region waived informed consent and granted access to medical records data (No. 1-45-70-76-21) in accordance with The Danish Health Care Act. We applied the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Study Setting and Data Sources
Denmark has a universal health care system with free access to tax-financed health care for all residents (approximately 5.8 million inhabitants).16 Routinely collected health data are recorded in nationwide registries using a unique personal identifier that allows for accurate registry linkage in studies with all Danish residents as the source population.17,18
We used linked data from the Danish National Patient Registry (DNPR), the National Child Odontology Registry (SCOR), the Danish National Database of Rare Genetic Diseases (RAREDIS), and the Danish Genodermatosis Database (DGD). The DNPR contains hospital admission records from 1977 to present, and outpatient visits from 1994 to present at all of the hospitals in Denmark. These records include the diagnoses registered by the treating physician per the International Classification of Diseases, Eighth Revision (in use until 1994), and the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) thereafter.19 The SCOR is a nationwide odontological registry containing childhood dental care information from municipal dental clinics (1972 to present).20 RAREDIS is a national clinical database with clinical and genetic data regarding patients with rare diseases; data have been submitted by treating clinicians since 2007. The DGD is a research database containing data from 5 dermatology departments in Denmark from 2014 to the present; it includes clinical and diagnostic information as well as family-case linkage for patients with genodermatoses.
Cohort Identification
We searched the DNPR and SCOR for patients whose records were suggestive of ED from January 1, 1995, to August 25, 2021, using 3 search algorithms (detailed in eTable 1 in Supplement 1). In order of priority, the algorithms searched for patient records that contained (1) specific ICD-10 codes for various ED disorders; (2) 2 or more cardinal ED features in different tissues (hypotrichosis, hypodontia, hypohidrosis, nail dystrophy); or (3) 1 cardinal ED feature and 2 or more minor features. To ensure feasibility, we included algorithm 3 as a sensitivity analysis restricted to only Aarhus University Hospital contacts (visits).
We chose the diagnosis codes by searching the Danish version of the ICD-10 (SKS-browser, version 4.0521), including historical codes. In a subanalysis assessing how changing ED classifications affect ED prevalence, we included specific ICD-10 codes for entities previously considered to be EDs, eg, monilethrix (eTable 1 in Supplement 1). To improve completeness, we also searched RAREDIS and DGD for various ED-related codes from the ICD-10, OMIM (Online Mendelian Inheritance in Man), and ORPHAcodes (Orphanet rare disease nomenclature) (eTable 2 in Supplement 1).
One author (L.K.H.) reviewed the patients’ medical records for validation and detailed patient characterization. Data on patient and family medical history, phenotypical features, patient treatment course and care management, and genetic test results were extracted to a piloted REDCap form22,23 (eTable 3 in Supplement 1) to the extent available. Local databases and family records linked to the identified patients were assessed when available.
We validated cases using the 2022 definition from the International ED Consensus Group.3 Thus, patients with specific ED-like phenotypes classified elsewhere (eg, monilethrix) were excluded.1,3 When uncertain, case validation was determined by consensus agreement among 3 authors (L.K.H., K.R., and M.S.). We included patients based on 2 levels of certainty: confirmed or likely ED. Among likely cases, we included those clinically suggestive of ED but without firm clinical and/or genetic diagnoses and those with a pathogenic gene variant of an ED-associated gene and description of only 1 affected ectodermal derivate if the medical record did not clearly state that other ectodermal derivates were unaffected. We excluded cases that did not fulfill the applied ED definition, including patients with gene variations without ED-related manifestations (including unaffected female carriers of XLHED).
Statistical Analyses
We computed descriptive statistics for the entire cohort and ED subgroups. As an estimate of the validity for the overall sample, for the 3 search algorithms, and for each ED-specific diagnosis code, we computed the positive predictive values (PPVs) with 95% CIs as the number of patients with a confirmed or likely ED diagnosis after validation, divided by those identified by the search. We excluded patients whose medical records were unavailable. We estimated the sensitivity of the DNPR search algorithm 1 based on the number of additional validated patients from our other searches.
We estimated the minimum birth prevalence of ED with 95% CIs for annual birth cohorts and from 1995 to 2011 and 1995 to 2001 for a minimum follow-up for a first-time diagnosis recorded before age 10 and 20 years, respectively. We estimated prevalence proportions using annual live births reported by Statistics Denmark as denominators.24 We performed a sensitivity analysis including patients with missing records born from 1995 to 2011.
We defined the diagnosis date as the date of the patient’s ED-defining contact (visit) registered with any of the listed ED-specific ICD-10 codes or for algorithms 2 and 3 when the second or third code was fulfilled, respectively (eTable 1 in Supplement 1). Statistical testing was not used given the descriptive nature of the study. Data analyses were performed from May 4 to December 22, 2023, using Stata, version 17.0 (StataCorp LLC).
Results
Patient Identification
The database searches identified 844 patients records suggestive of ED, including 787 patients identified by the 3 DNPR-based algorithms (algorithm 3 restricted to Aarhus University Hospital). The numbers of patients uniquely identified from these prioritized searches (populations 1, 2, and 3) were 530, 147, and 110 patients, respectively. The remaining 57 patients were identified from the additional data sources referenced in the Methods (eFigure 1 in Supplement 1 shows the overlap among the different searches). Despite substantial overlap between DGD and RAREDIS using algorithm 1, an additional 26 patients were identified from RAREDIS and an additional 5 from the DGD.
Diagnosis Validation
Figure 1 depicts the validation process. We were able to retrieve medical records for 791 of 844 patients (validation rate, 93.7%). Patients who were excluded due to missing records had been diagnosed in earlier calendar years and had a median (IQR) age of 22 (10-43) years compared with 15 (7-27) years for those with complete medical records available.
Figure 1. Identification of Patients With Ectodermal Dysplasia (ED) and Diagnosis Validation in Denmark.
aOnly for patients of the Aarhus University Hospital.
RAREDIS is the Danish National Database of Rare Genetic Diseases.
Validated ED Cohort
The final validated ED cohort comprised 396 patients (median [IQR] age at diagnosis, 13 [4-30] years; 246 females [62.1%] and 150 males [37.9%]), of whom 319 had confirmed ED and 77 had likely ED. The latter group included patients with a genetic variant suggestive of ED plus involvement of 1 ectoderm derivate noted in their medical records (n = 26), and patients with clinically suggested ED but no definitive clinical and/or genetic diagnosis per the available information (n = 51). Genetic confirmation was available for 241 of 396 patients (61%).
From the 791 patients with available medical records, 395 were excluded: 87 records had coding mistakes (more information follows) and 67 patients had other congenital syndromic disorders (ie, misdiagnosed); 33 had isolated nail dystrophy; 28 had isolated hypotrichosis or alopecia; 91 had isolated tooth agenesis; 17 had been referred on suspicion of ED diagnosis that was rejected after assessment; and the final 90 had other non-ED diagnoses at validation (primarily from populations 2 and 3).
The PPV was 50.1% (95% CI, 46.6%-53.5%) for all data sources combined and 46.6% (95% CI, 43.0%-50.2%) for the DNPR-based searches (Table 1). The PPV was 67.0% (95% CI, 62.7%-71.0%) for search algorithm 1, and markedly lower for the feature-based search algorithms. The PPV of search algorithm 1 varied between 62.1% (95% CI, 57.9%-66.1%) and 69.4% (95% CI, 65.4%-73.2%) in a bias analysis including patients with missing medical records. The ICD-10 codes with the highest PPVs were the specific subcodes of Q82.4 ectodermal dysplasia, with PPVs greater than 90%.
Table 1. Positive Predictive Values of Search Algorithmsa and Specific ICD-10 Codes for the Identification of Patients With Ectodermal Dysplasias (ED).
| Search algorithm | Cases of ectodermal dysplasia, No. | |||||
|---|---|---|---|---|---|---|
| All | Records available | Confirmed or likely cases | Confirmed cases only | |||
| No. | PPV, % (95% CI) | No. | PPV, % (95% CI) | |||
| All searches combined | 844 | 791 | 396 | 50.1 (46.6-53.5) | 319 | 40.3 (37.0-43.8) |
| DNPR searches combined | 787 | 734 | 342 | 46.6 (43.0-50.2) | 276 | 37.6 (34.2-41.2) |
| Algorithm 1−specific ICD-10 codes | 530 | 491 | 329 | 67.0 (62.7-71.0) | 272 | 55.4 (51.0-59.8) |
| Q82.3 Incontinentia pigmenti | 107 | 97 | 73 | 75.3 (65.6-82.9) | 68 | 70.1 (60.2-78.5) |
| Q82.4 ED (subcodes included) | 384 | 358 | 256 | 71.5 (66.6-76.0) | 204 | 57.0 (51.8-62.0) |
| Q82.4 ED (subcodes excluded) | 327 | 302 | 209 | 69.2 (63.7-74.2) | 168 | 55.6 (50.0-61.2) |
| Q82.4A ED, anhidrotic | 43 | 43 | 41 | 95.3 (82.6-98.9) | 38 | 88.4 (74.4-95.2) |
| Q82.4B ED, hidrotic | 49 | 47 | 42 | 89.4 (76.4-95.6) | 31 | 66.0 (51.0-78.3) |
| Q82.4C ED, hypohidrotic | 61 | 61 | 59 | 96.7 (87.5-99.2) | 56 | 91.8 (81.5-96.6) |
| Q87.8E Goltz syndrome | 47 | 44 | 8 | 18.2 (9.2-32.8) | 8 | 18.2 (9.2-32.8) |
| Algorithm 2b | 147 | 134 | 11 | 8.2 (4.6-14.3) | 3 | 2.2 (0.7-6.8) |
| DNPR only | 61 | 59 | 7 | 11.9 (5.7-23.2) | 3 | 5.1 (1.6-14.9) |
| Hypo/oligodontia in SCOR | 86 | 75 | 4 | 5.3 (2.0-13.6) | 0 | |
| Algorithm 3 (AUH only)c | 110 | 109 | 2 | 1.8 (0.5-7.1) | 1 | 0.9 (0.1-6.4) |
| RAREDIS search | 67 | 67 | 65 | 97.0 (88.6-99.3) | 59 | 80.1 (77.7-94.0) |
| DGD search | 45 | 45 | 43 | 95.6 (83.3-98.9) | 41 | 91.1 (78.1-96.7) |
Abbreviations: AUH, Aarhus University Hospital; DGD, Danish Genodermatosis Database; DNPR, Danish National Patient Registry; ICD-10, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision; PPV, positive predictive value; RAREDIS, Danish National Database of Rare Genetic Diseases; SCOR, Danish National Child Odontology Registry.
Detailed descriptions of search algorithms are available in eTables 1 and 2 in Supplement 1.
Excluding patients already identified by algorithm 1.
Excluding patients already identified by algorithms 1 and 2.
The PPV for Goltz syndrome (Q87.8E) was only 18.2% (95% CI, 9.2%-32.8%) due to frequent coding errors of Gorlin-Goltz syndrome (ie, nevoid basal cell carcinoma syndrome) as Goltz-Gorlin syndrome (ie, focal dermal hypoplasia). We also found other coding mistakes, including errors explained by the resemblance of the ICD-10 codes for spastic tetraplegia (G82.4) and ectodermal dysplasia (Q82.4). The sensitivity of ED-specific codes in DNPR (algorithm 1) was 329 of 396, or 83.1% (95% CI, 79.1-86.5), using the entire validated cohort as reference.
Birth Prevalence
There was no substantial calendar trend in ED birth prevalence except for a downward tendency in more recent years, which had a shorter follow-up time (Figure 2). The estimated minimum birth prevalence from 1995 to 2011 (at least 10 years of follow-up to receive a first-time diagnosis) for all ED cases (n = 160) was 14.5 cases per 100 000 live births (95% CI, 12.2-16.7). From 1995 to 2001 with 20 years of follow-up (n = 78), the birth prevalence increased to 16.6 per 100 000 live births (95% CI, 12.9-20.3). In a sensitivity analysis that included all unvalidated (potential) cases in individuals born from 1995 to 2011 (n = 13), the birth prevalence increased from 14.5 to 15.6 cases per 100 000 live births (95% CI, 13.3-18.0). Instead, when including validated cases with diagnoses previously classified as EDs (n = 14; eTable 1 in Supplement 1), the 1995 to 2011 birth prevalence was 15.7 per 100 000 (95% CI, 13.4-18.1).
Figure 2. Annual Prevalence of Ectodermal Dysplasia at Birth in Denmark, 1985 to 2020.
Circles represent the birth prevalence estimates (per 100 000 live births) and bars represent 95% CIs.
The birth prevalence of molecularly confirmed XLHED was 2.8 per 100 000 live births (95% CI, 1.8-3.8) for the 1995 to 2011 birth cohorts (n = 31) and 4.5 per 100 000 live births (95% CI, 2.6-6.4) for the 1995 to 2001 birth cohorts (n = 21). These estimates increased to 4.5 per 100 000 live births (95% CI, 3.3-5.8) and 6.6 per 100 000 live births (95% CI, 4.3-8.9), respectively, when including hypohidrotic ED (HED) cases without genetic confirmation.
Patient Characteristics
The patient characteristics of the cohort and selected subgroups are summarized in Table 2. Genetic testing had been performed for 292 patients (74%), identifying a genetic diagnosis in 241 patients (61% [83% of those tested]). The median (IQR) age at diagnosis was 13 (4-30) years overall and 5 (1-10) years when restricted to those born during the study period beginning on January 1, 1995. The median (IQR) age at the genetic diagnosis was 8 (6-36) years overall and 7 (2-13) years for patients born after 1995. Of DNPR registrations with diagnosis codes included in the search algorithms, 254 patients (64%) had visited a dental or maxillofacial department; 211 (53%), dermatology; 203 (51%), pediatrics; 108 (27%), otorhinolaryngology; 105 (27%), clinical genetics; 48 (12%), orthopedic surgery; and 46 (12%), ophthalmology.
Table 2. Reported Characteristics of the Cohort and Selected Subgroups of Patients With Confirmed or Likely Ectodermal Dysplasia.
| Characteristic | No. (%) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| All cases | Confirmed cases | Likely cases | Common subgroups | ||||||
| Males with XLHED | Females with XLHED | IP | OODD/SSPS | TRPS | AEC/EEC/LM/RH syndrome | ||||
| Total patients | 396 | 319 (81) | 77 (19) | 50 | 50 | 75 | 21 | 20 | 21 |
| Female sex | 246 (62) | 203 (64) | 43 (56) | NA | 50 | 71 (95) | 17 (81) | 13 (65) | 10 (48) |
| Male sex | 150 (38) | 116 (36) | 34 (44) | 50 | NA | 4 (5) | 4 (19) | 7 (35) | 11 (52) |
| Age at diagnosis, median (IQR), y | 13 (4-30) | 13 (3-31) | 13 (7-21) | 6 (1-24) | 30 (12-50) | 3 (0-29) | 21 (5-38) | 15 (8-26) | 12 (4-22) |
| Born during study period (≥1995) | 5 (1-10) | 3 (0-9) | 8 (6-12) | 2 (1-2) | 8 (5-12) | 0 (0-0) | 6 (3-11) | 13 (4-15) | 7 (3-14) |
| Genetic test (any type) | 292 (74) | 255 (80) | 37 (48) | 50 (100) | 50 (100) | 59 (79) | 21 (100) | 19 (95) | 12 (57) |
| Genetic diagnosis | 241 (61) | 215 (67) | 26 (34) | 50 (100) | 50 (100) | 54 (72) | 21 (100) | 18 (90) | 9 (43) |
| Genetic department visit | 105 (27) | 90 (28) | 15 (19) | 13 (26) | 19 (38) | 26 (35) | 12 (57) | 5 (25) | 3 (14) |
| Age at genetic diagnosis, median (IQR), y | 18 (6-36) | 17 (6-32) | 22 (9-49) | 9 (2-35) | 30 (17-49) | 18 (1-31) | 24 (13-43) | 15 (8-26) | 15 (8-21) |
| Positive family history | 251 (63) | 214 (67) | 37 (48) | 38 (76) | 44 (88) | 40 (53) | 16 (76) | 15 (75) | 11 (52) |
| Sweat gland involvement | 135 (34) | 132 (41) | 3 (4) | 45 (90) | 20 (40) | <3 | 8 (38) | 0 | 3 (14) |
| Hypohidrosis | 123 (31) | 120 (38) | 3 (4) | 38 (76) | 20 (40) | <3 | 7 (35) | 0 | 3 (14) |
| Anhidrosis | 12 (3) | 12 (4) | 0 | 8 (16) | 0 | 0 | 0 | 0 | 0 |
| Heat intolerance | 74 (19)a | 74 (23) | <3 | 26 (52) | 9 (18) | 0 | 4 (19) | 0 | <3 |
| Scalp hair involvement | 235 (59) | 218 (68) | 17 (22) | 42 (84) | 25 (50) | 18 (24) | 18 (86) | 19 (95) | 12 (57) |
| Hypotrichosis | 168 (42) | 160 (50) | 8 (10) | 35 (70) | 19 (38) | 6 (8) | 14 (67) | 17 (85) | 9 (43) |
| Fragile hair | 69 (17) | 62 (19) | 7 (9) | 9 (18) | 12 (24) | <3 | 7 (33) | 5 (25) | <3 |
| Alopecia totalis | 12 (3) | 12 (4) | 0 | 3 (6) | 0 | 0 | 0 | 0 | 3 (14) |
| Focal alopecia | 27 (7) | 22 (7) | 5 (6) | 0 | 0 | 10 (13) | <3 | <3 | <3 |
| Other/not specified | 25 (6)a | 25 (8) | <3 | <3 | <3 | 3 (4) | 7 (33) | <3 | <3 |
| Abnormal eyebrows | 114 (29) | 108 (34) | 6 (8) | 29 (58) | 16 (32) | <3 | 3 (14) | 8 (40) | 8 (38) |
| Abnormal eyelashes | 59 (15) | 56 (18) | 3 (4) | 19 (38) | 3 (6) | 0 | <3 | <3 | 8 (38) |
| Skin involvement | 229 (59) | 204 (64) | 25 (32) | 23 (46) | 14 (28) | 70 (93) | 14 (67) | 5 (25) | 9 (43) |
| Dry skin | 128 (32) | 113 (35) | 15 (19) | 21 (42) | 11 (22) | <3 | 14 (67) | <3 | 6 (32) |
| Eczema | 76 (19) | 68 (21) | 8 (10) | 18 (36) | 8 (16) | <3 | 6 (29) | <3 | 3 (14) |
| IP lesions | 70 (18) | 65 (20) | 5 (6) | 0 | 0 | 70 (93) | 0 | 0 | 0 |
| Thin translucent skin | 23 (6)a | 23 (7) | <3 | 4 (8) | 3 (6) | 0 | <3 | <3 | <3 |
| Focal dermal hypoplasia | 7 (2) | 7 (2) | 0 | 0 | 0 | 0 | 0 | 0 | <3 |
| Palmoplantar keratoderma | 16 (4) | 13 (4) | 3 (4) | <3 | 0 | 0 | <3 | 0 | <3 |
| Acne vulgaris | 8 (2) | 8 (3) | 0 | <3 | <3 | 0 | 0 | <3 | 0 |
| Other/not specified | 43 (11) | 38 (12) | 5 (6) | 3 (6) | 5 (10) | 3 (4) | <3 | <3 | <3 |
| Periorbital hyperpigmentation | 61 (15) | 57 (18) | 4 (5) | 30 (60) | 5 (10) | 0 | <3 | <3 | 0 |
| Tooth involvement | 319 (81) | 255 (80) | 64 (83) | 49 (98) | 46 (92) | 44 (59) | 19 (90) | 10 (50) | 20 (95) |
| Hypodontia (1-5 missing teeth) | 145 (37) | 111 (35) | 34 (44) | 7 (14) | 31 (62) | 28 (37) | 7 (33) | 3 (15) | 11 (52) |
| Oligodontia (≥6 missing teeth) | 148 (37) | 122 (38) | 26 (34) | 41 (42) | 13 (26) | 11 (15) | 11 (52) | 0 | 7 (33) |
| Anodontia | 6 (2) | 6 (2) | 0 | <3 | <3 | 0 | <3 | 0 | 0 |
| Microdontia | 4 (1)a | 4 (1) | <3 | 0 | 0 | 0 | 0 | 0 | 0 |
| Cone-shaped teeth | 64 (16) | 56 (18) | 8 (10) | 7 (14) | 8 (16) | 15 (20) | 4 (19) | 0 | <3 |
| Enamel dysplasia | 18 (5) | 15 (5) | 3 (4) | 0 | 0 | <3 | 0 | <3 | 3 (14) |
| Other/not specified | 16 (4)a | 16 (5) | <3 | <3 | 0 | 3 (4) | 0 | 8 (40) | 0 |
| Xerostomia (dry mouth) | 48 (12) | 42 (13) | 6 (8) | 18 (36) | 10 (20) | 0 | <3 | 0 | 0 |
| Nail involvement | 105 (27) | 93 (29) | 12 (16) | 4 (8) | 5 (10) | 12 (16) | 10 (48) | 8 (40) | 6 (29) |
| Nail dystrophy | 96 (24) | 84 (26) | 11 (14) | 3 (6) | 5 (10) | 12 (16) | 10 (48) | 7 | 6 (29) |
| Anonychia | <3a | <3 | <3 | 0 | 0 | <3 | 0 | 0 | 0 |
| Onycholysis | 8 (2) | 8 (3) | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Koilonychia | 8 (2) | 8 (3) | 0 | <3 | 0 | <3 | 0 | <3 | <3 |
| Pachyonychia | 8 (2) | 8 (3) | 0 | 0 | 0 | 0 | 0 | 0 | 3 (14) |
| Micronychia | 5 (1) | 5 (2) | 0 | <3 | 0 | 0 | 0 | <3 | 0 |
| Other/not specified | 8 (2)a | 8 (3) | <3 | 0 | 0 | <3 | 0 | 0 | 0 |
| Eye involvement | 70 (17) | 64 (20) | 6 (8) | 5 (10) | <3 | 22 (29) | 0 | <3 | 15 (71) |
| Dry eyes | 15 (4)a | 15 (5) | <3 | 3 (6) | <3 | <3 | 0 | 0 | 6 (29) |
| Lacrimal duct dysgenesis | 12 (3) | 12 (4) | 0 | 0 | 0 | 0 | 0 | 0 | 9 (43) |
| Cataract | 6 (2) | 6 (2) | <3 | <3 | 0 | <3 | 0 | 0 | <3 |
| Other/not specified | 48 (12) | 44 (14) | 4 (5) | <3 | 0 | 21 (28) | 0 | <3 | 6 (29) |
| Hearing loss | 20 (5)a | 20 (6) | <3 | <3 | <3 | <3 | 0 | 0 | 10 (48) |
| Craniofacial dysmorphology | 148 (37) | 128 (40) | 20 (26) | 30 (60) | 10 (20) | 5 (7) | 4 (19) | 18 (90) | 17 (81) |
| Tall forehead (high anterior hairline) | 30 (8)a | 30 (9) | <3 | 8 (16) | 3 (6) | 0 | 0 | 11 (55) | <3 |
| Hypertelorism | 6 (2)a | 6 (2) | <3 | 3 (6) | 0 | 0 | 0 | <3 | 0 |
| Low-set, prominent ears | 47 (12) | 41 (13) | 6 (8) | 14 (28) | 5 (10) | <3 | 0 | 4 (20) | 5 (24) |
| Dysplastic ears | 8 (2)a | 8 (3) | <3 | 0 | <3 | 0 | 0 | <3 | <3 |
| Midfacial hypoplasia | 51 (13) | 43 (13) | 8 (10) | 19 (38) | 5 (10) | 0 | <3 | <3 | 4 (19) |
| Facial asymmetry | 8 (2)a | 8 (3) | <3 | 0 | 0 | <3 | <3 | 0 | <3 |
| Bulbous nose | 29 (7) | 26 (8) | 3 (4) | 3 (6) | 0 | 0 | 0 | 18 (90) | <3 |
| Long philtrum | 12 (3) | 12 (4) | 0 | 0 | 0 | 0 | 0 | 11 (55) | 0 |
| Cleft lip and/or palate | 13 (3)a | 13 (4) | <3 | 0 | 0 | 0 | <3 | 0 | 11 (52) |
| Thick/everted lower lip | 19 (5) | 16 (5) | 3 (4) | 8 (16) | <3 | 0 | 0 | <3 | 0 |
| Retrognathia | 12 (3) | 9 (3) | 3 (4) | 4 (8) | 0 | 0 | 0 | 0 | <3 |
| Other/not specified | 40 (10) | 37 (12) | 3 (4) | 10 (20) | <3 | 3 (4) | <3 | <3 | 3 (14) |
| Breast aplasia | 12 (3) | 12 (4) | 0 | 0 | 3 (6) | <3 | 0 | 0 | 4 (19) |
| Nipple aplasia (athelia) | 6 (2)a | 6 (2) | <3 | <3 | 0 | 0 | 0 | 0 | <3 |
| Skeletal malformation | 53 (13) | 42 (13) | 11 (14) | 0 | <3 | 3 (4) | <3 | 16 (80) | 9 (43) |
| Syndactyly | 18 (5) | 11 (3) | 7 (9) | 0 | 0 | <3 | 0 | <3 | 4 (19) |
| Clinodactyly | 20 (5)a | 20 (6) | <3 | 0 | 0 | <3 | 0 | 15 (75) | <3 |
| Brachydactyly | 6 (2) | 6 (2) | <3 | 0 | 0 | <3 | 0 | <3 | 0 |
| Ectrodactyly (split hand/foot) | 8 (2)a | 8 (3) | <3 | 0 | 0 | 0 | 0 | 0 | 6 (29) |
| Other/not specified | 9 (2)a | 9 (3) | <3 | 0 | <3 | <3 | <3 | <3 | <3 |
| Growth retardation | 29 (7) | 21 (7) | 8 (11) | <3 | <3 | <3 | 0 | 10 (50) | <3 |
| Intellectual disability | 21 (5) | 18 (6) | 3 (4) | <3 | 0 | 6 (8) | 0 | <3 | 3 (14) |
| Congenital heart anomaly | 6 (2)a | 6 (2) | <3 | 0 | 0 | 0 | 0 | 3 (15) | 0 |
| Immunodeficiency | 4 (1) | 4 (1) | 0 | 0 | <3 | 0 | 0 | 0 | <3 |
| Psychiatric comorbidity | 31 (8)a | 31 (10) | <3 | <3 | 3 (6) | 6 (8) | <3 | 0 | 3 (15) |
Abbreviations: AEC, ankyloblepharon-ectodermal defects-cleft lip/palate; DNPR, Danish National Patient Registry; EEC, ectrodactyly, ectodermal dysplasia, and cleft lip/palate; IP, incontinentia pigment; LM, limb mammary; NA, not applicable; OODD, odontoonychodermal dysplasia; RH, Rapp-Hodgkin; SSPS, Schöpf-Schulz-Passarge syndrome; TRPS, trichorhinophalangeal syndrome; XLHED, X-linked hypohidrotic ectodermal dysplasia.
Small sample sizes (<3) were omitted from totals when required to obscure the values.
The most frequently altered gene was EDA (n = 100) followed by IKBKG (n = 55), WNT10A (n = 21), TRPS1 (n = 18), EDAR (n = 10), TP63 (n = 9), GJB6 (n = 9), PORCN (n = 7), and other rare genetic variants (eFigure 2 in Supplement 1). The cohort also included rare subtypes, such as HED with immunodeficiency, Clouston syndrome, ulnar-mammary syndrome, keratitis-ichthyosis-deafness syndrome, and oculodentodigital dysplasia (eTable 4 in Supplement 1 provides genetic diagnoses according to the 2022 ED classification).
In addition, hypohidrosis or anhidrosis was noted in 135 patients (34%); hypodontia or oligodontia in 293 (74%); nail involvement in 105 (27%); and hair involvement in 235 (59%), most frequently hypotrichosis (42%) and fragile hair (17%) (Table 2). Dry skin (128 [32%]) and dry mouth (48 [12%]) were also reported. Of note, female patients with XLHED had several cardinal features, eg, 20 (40%) had hypohidrosis and 46 (92%) had tooth involvement. Oligodontia was more common among males with XLHED, and females with XLHED generally had fewer missing teeth. A high frequency of craniofacial dysmorphology (148 [37%]) was found in the cohort, particularly midfacial hypoplasia (Table 2).
Discussion
Using Danish health registries to identify and characterize a nationwide cohort of patients with ED, this study provides what is, to our knowledge, the first validated population-based epidemiologic findings of the entire group of EDs. Given the estimated overall ED birth prevalence of 14.5 cases per 100 000 live births, we propose that the true disease prevalence is lower than previously considered. Thus, the estimated birth prevalence in Denmark from 1995 to 2011 was approximately 5 times lower than the frequently cited estimate of 70 cases per 100 000 live births.6,13 This difference may be partly explained by the new and more stringent definition of EDs, which has reduced the total number of disease subtypes substantially.3 However, bias analyses, including unvalidated cases as well as previous ED entities, produced results that were still well below previously reported prevalence estimates. Diagnostic delays may affect prevalence estimates. We considered the age at diagnosis (Table 2) to support the 1995 to 2011 estimate, allowing for a 10-year delay. The prevalence estimate restricted to 1995 to 2001 was slightly higher; however, this short period contained 2 outliers (Figure 2).
We found a prevalence of XLHED at birth of 2.8 cases per 100 000 live births. Another registry-based Danish study, conducted in 2012, reported a prevalence of 4.2 per 100 000 based on molecularly confirmed cases and clinically diagnosed HED, including cases registered with the ICD-10 code Q82.4.9 However, that unvalidated case definition may have overestimated results, given the limited validity it showed in our study (PPV = 71.5%). In contrast to our study methods, the 2012 study9 may have included female carriers of an EDA variant that did not fulfill a clinical diagnosis of ED.1
Coding Quality
The low PPVs of ED diagnoses in our sample emphasize the importance of diagnosis validation in registry-based studies of rare diseases given that even a small number of misclassified cases can greatly affect associations of interest.25,26 The low validity of specific ICD diagnosis codes for ED in the DNPR may simply be affected by the rarity of ED in the background population.19,27 However, the common coding of Gorlin-Goltz syndrome and spastic tetraplegia as EDs was noticeable and represents true miscoding.
Several types of EDs do not have a designated ICD-10 code (eg, trichorhinophalangeal syndrome and ankyloblepharon-ectodermal defects cleft lip/palate syndrome) and may be missed if not registered with an ICD-10 code for ED unspecified. Furthermore, not all physicians are aware of the specific ICD-10 ED codes or will use broader codes (eg, Z84.0, the code for “family history of diseases of the skin and subcutaneous tissue”). We tried to mitigate for these missed cases by using several data sources and broad feature-based search algorithms, which revealed a sensitivity of 83.1% for the use of ED-specific ICD-10 codes only. This type of omission emphasizes the importance of dedicated disease classifications for rare diseases (ie, ORPHAcode and OMIM).28,29
Patient Characteristics
Our findings on clinical characteristics in patients with ED highlight the heterogeneity of EDs, which was also underscored by the many different specialties involved. This knowledge can support clinicians in providing diagnostic services and care for patients with ED. Notably, a large proportion (29 of 50) of female patients with EDA variants in our cohort also had 2 or more ectodermal features. Although symptoms were generally milder among females than males with XLHED, clinicians and geneticists should be aware of clinical presentations in female relatives.15,30 We identified a female predominance (62.1%) in the cohort that may partly be influenced by the nature of incontinentia pigmenti (X-linked), with most IKBKG variants being lethal in males.
We noticed different patient pathways in the health care system in Denmark. Some patients were referred by a general practitioner to a hospital clinic. Other patients were referred by a local dentist to a maxillofacial department. In the centralized units, some were only evaluated by a dental specialist, and not all were referred to a genodermatosis clinic as part of the diagnostic process. Some patients were diagnosed in adulthood, whereas more severe cases of ED were often diagnosed at birth or during early childhood. A genetic diagnosis was made in 61% of the cases in our study, and only 27% of patients had visited a clinical genetics department. Given the importance of molecular genetics in the current ED classification,3 the importance of genetic evaluation to support timely and accurate diagnosis of ED cannot be overstated.
The low PPVs and range of medical specialties involved underscore the importance of consensus on ED diagnostic and treatment guidelines that ensure a uniform but still multidisciplinary approach. Furthermore, awareness of the diagnosis among primary health care practitioners is important for proper ED diagnosis and early referral to a rare disease or genodermatosis clinic.
Strengths and Limitations
The use of a nationwide population-based design within a universal health care system, broad search algorithms, multiple data sources, and diagnosis validation constitute key strengths of this study. We had a high validation rate (94%), and it is unlikely that missingness of records depended on case validity.
Our study also had limitations. Prevalence estimates represent a minimum proportion because we may have missed mild ED cases without hospital or health care visits, as well as patients with rare subtypes, and patients deceased before proper diagnosis. Only 1 author validated all the patients included in the study; however, we applied consensus agreement to uncertain cases to minimize potential misclassification. We did not perform a systematic clinical evaluation of all putative cases; therefore, some may have been misclassified with only 1 affected ectodermal derivate, possibly underestimating the true prevalence. Although misclassification and missing medical records may have excluded some ED cases, we estimated that this bias did not substantially affect the prevalence estimate. Given that frequencies of specific features (Table 2) are based solely on positive findings noted in the patient record, they also represent minimum estimates. Not all patients had undergone genetic testing (74%), and the diagnostic yield was incomplete (61%). A genetic diagnosis for all patients would have definitively supported validation; however, the absence of these results reflects clinical practice.
Conclusions
The findings of this nationwide population-based cohort study indicate that ED prevalence is lower than previously reported. The establishment of a large nationwide cohort of patients with ED, including detailed clinical and molecular data, provides a unique resource for future research in ectodermal dysplasias. The low PPVs and incomplete identification of ED using the ICD-10 codes in our study emphasize the importance of ED diagnosis validation and accurate disease registration.
eFigure 1. Venn diagram of the sources used to identify the patients suspected with ED.
eFigure 2. Genetic diagnoses in the Danish ED cohort by genes and involved pathways.
eTable 1. The three DNPR search algorithms used for patient identification
eTable 2. ICD-10, Orphanet, and OMIM code list for search in RAREDIS and DGD
eTable 3. REDCap codebook for collection of clinical data
eTable 4. Genetic diagnoses in the cohort according to the 2022 ED classification
eReferences.
Data Sharing Statement
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eFigure 1. Venn diagram of the sources used to identify the patients suspected with ED.
eFigure 2. Genetic diagnoses in the Danish ED cohort by genes and involved pathways.
eTable 1. The three DNPR search algorithms used for patient identification
eTable 2. ICD-10, Orphanet, and OMIM code list for search in RAREDIS and DGD
eTable 3. REDCap codebook for collection of clinical data
eTable 4. Genetic diagnoses in the cohort according to the 2022 ED classification
eReferences.
Data Sharing Statement
