SEARCH

SEARCH BY CITATION

Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Objective

To investigate the familiality of systemic sclerosis (SSc) in relation to Raynaud's phenomenon (RP) (a marker of vasculopathy), other autoimmune inflammatory disease, and fibrotic interstitial lung disease (ILD).

Methods

A genealogic resource, the Utah Population Database (UPDB), was used to test heritability of RP, other autoimmune disease, and ILD. Diseases were defined by International Classification of Diseases, Ninth Revision codes and identified from statewide discharge data, the University of Utah Health Science Center Enterprise Data Warehouse, and death certificates and were linked to the UPDB for analysis. Familial standardized incidence ratio (FSIR), relative risks (RRs) to first-, second-, third-, and fourth-degree relatives for SSc, RP, other autoimmune disease, and ILD (with 95% confidence intervals [95% CIs]), and population attributable risk (PAR) were calculated.

Results

A software kinship analysis tool was used to analyze 1,037 unique SSc patients. Fifty SSc families had significant FSIRs, ranging from 2.07 to 17.60. The adjusted PAR was ∼8%. The RRs were significant for other autoimmune disease in the first-degree relatives (2.49 [95% CI 1.99–3.41], P = 2.42 × 10−15) and second-degree relatives (1.48 [95% CI 1.34–2.39], P = 0.002), for RP in first-degree relatives (6.38 [95% CI 3.44–11.83], P = 4.04 × 10−9) and second-degree relatives (2.39 [95% CI 1.21–4.74], P = 0.012), and for ILD in first-degree relatives (1.53 [95% CI 1.04–2.26], P = 0.03), third-degree relatives (1.47 [95% CI 1.18–1.82], P = 0.0004), and fourth-degree relatives (1.2 [95% CI 1.06–1.35], P = 0.004).

Conclusion

These data suggest that SSc pedigrees include more RP, autoimmune inflammatory disease, and ILD than would be expected by chance. In SSc pedigrees, genetic predisposition to vasculopathy is the most frequent risk among first-degree relatives.

Systemic sclerosis (SSc; scleroderma) is a heterogeneous chronic illness with variability in clinical manifestations, internal organ involvement, and outcome, due to a complex interplay of inflammation, fibrosis, and vasculopathy. Genetic predispositions to SSc have been described, but the exact underpinnings that may predispose to specific disease presentations are unknown. Although the etiopathogenesis of this disease has not yet been identified, autoimmunity has been thought to be the root cause of SSc (1). As such, permissive genetic background is thought to be essential to the development of this condition. Genetic susceptibility likely involves a combination of polymorphisms at multiple genes, particularly those regulating immune response and/or fibrotic mechanisms. The genes possibly work in epistatic pleiotropy for different phenotypic expressions.

Immune dysregulation is thought to play a key role in the initiation and perpetuation of vascular dysfunction and fibrosis in SSc (2). Despite its phenotypic complexity, almost all patients with SSc have Raynaud's phenomenon (RP) with capillary nailbed changes, which are a marker of the vasculopathy. The leading cause of mortality in this population is interstitial lung disease (ILD), a fibrotic process similar to that observed in the skin of SSc patients. Of interest, RP and ILD are not unique to SSc and are observed in other autoimmune conditions, including systemic lupus erythematous (SLE), Sjögren's syndrome (SS), dermatomyositis (DM), rheumatoid arthritis (RA), and undifferentiated connective tissue disease (UCTD) (3). Thus, it is the fibrotic and/or vasculopathic skin manifestations and SSc-specific serologies that generally help differentiate between SSc and other connective tissue diseases (4).

In a large cohort study of white and black adults in the US, the prevalence and incidence of SSc were estimated to be 24.2 per 100,000 adults per year and 1.93 per 100,000 adults per year, respectively (5). Higher prevalence in the Oklahoma Choctaw Indians is reported, with 66.0 cases per 100,000, suggesting either a higher genetic risk and/or common environmental exposure in this ethnic group (6, 7). Familial aggregation and twin studies in SSc support the notion of a genetic basis for the disease (8). In a twin study of 42 pairs, including 24 monozygotic pairs, there was a reported 4.7% concordance for disease expression (9). Based on a study of 3 large cohorts with a total of 701 affected scleroderma cases (10), a positive family history of SSc is thought to represent the largest risk factor for the disease; although the absolute risk factor for each family member was reported to be <1%, the familial relative risk (RR) was increased ∼15-fold for siblings and 13-fold for first-degree relatives, and 1.6% of the SSc probands had a family member who also had SSc (10). In another study of 710 proband cases, 10 cases of SSc occurred in first-degree relatives (11). Recently, the risk of SSc among family members of individuals with RA in a study population of 447,704 patients (standard incidence ratio [SIR] 1.65 in offspring of proband) was described (12). This supports the notion of a shared autoimmunity component to SSc and RA.

SSc vasculopathy, characterized by both noninflammatory macrovascular and microvascular changes, has been linked to genetic abnormalities in the expression of type I interferon (IFN) and regulator of G protein signaling 5, two molecules associated with vascular rarefaction (13). Thus, a genetic predisposition to abnormal endothelial cell senescence and apoptosis may be important in the pathogenesis of vasculopathy in SSc. Studies have shown increased messenger RNA and protein levels of IFNs and several IFN-stimulated genes in cells and tissue from SSc patients (14). IFNs are well-known immunomodulators and inhibitors of collagen production; thus, in addition to a role in vasculopathy, they may also play a role in the inflammatory and fibrotic aspects of SSc.

Population-level studies are important for investigations of complex autoimmune diseases in order to estimate the likelihood of identifying candidate genes, such as those associated with IFN pathways (15). A valid assessment of familial relative risk may have important clinical utility in triaging persons for more sophisticated screening and informing family members about potential risks (16). In this study we investigated the familiality of the 3 important manifestations of SSc, i.e., vasculopathy, altered immunity, and fibrosis, in founder families of SSc patients. It was hypothesized that the relative contributions of these components to the pathophysiology may be determined by examining their compared heritability in a population-based ascertainment model.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Study population.

A unique genealogic resource, the Utah Population Database (UPDB), was used to examine heritability using records from more than 7.0 million individuals. This resource links the medical records of an individual to his or her family or pedigree structure; the family history information dates back to approximately 1800 AD, and many families have as many as 11 generations included (17). It provides a valid resource for identifying founder mutations and assessing familial components to diseases and has been described in other reports (18–21). The Utah population is genetically representative of northern Europe and has low inbreeding levels, similar to other areas of the US (22, 23).

For this study, diagnoses were defined using International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 codes (SSc 710.1, SLE, 710.0, SS 710.2, DM 710.3, RA 714.0, and UCTD 710.9). Records were collected from any of 3 sources: 1) statewide death certificates, 2) University of Utah Health Science Center Enterprise Data Warehouse (UUHSC), which includes inpatient and outpatient records, or 3) statewide hospital discharge data. The SSc, RP, ILD, SLE, SS, DM, and UCTD capture dates ranged from January 1996 to December 2007 for the statewide inpatient data, from May 1990 to May 2007 for UUHSC outpatient data, and from January 1979 to December 1998 (ICD-9 codes) and January 1979 to December 2007 (ICD-10 codes) for death certificates. These lengths of time between the different databases were disproportionate, and the incidences reported herein are based on the date of first encounter within the respective data system. The Hospital Discharge Data System is a database containing statewide, population-based health care information associated with all hospitals in a state. In Utah, 53 hospitals have submitted their discharge data to the state Department of Health. These data include principal diagnosis and up to 8 other diagnoses. The UUHSC data include information on more than 1.8 million patient records from hospitals and outpatient clinics associated with the University of Utah. There were 21,520 unique patients included in the inpatient discharge data, 20,561 unique patients in the UUHSC data, and 809 unique patients in the death certificate data for all captured diagnoses. The distributions of the ICD-9 codes from each data source are shown in Table 1.

Table 1. Distribution of ICD-9 codes by diagnosis and data source*
Diagnosis (ICD-9)Data sourceTotalUnique
UUHSCInpatientDeath certificates
  • *

    “Unique” refers to the number of distinct patients after correction for the fact that some patients were identified from more than one source (i.e., after elimination of the duplication). ICD-9 = International Classification of Diseases, Ninth Revision; UUHSC = University of Utah Health Science Center Enterprise Data Warehouse; SSc = systemic sclerosis; RP = Raynaud's phenomenon; SLE = systemic lupus erythematous; SS = Sjögren's syndrome; DM = dermatomyositis; UCTD = undifferentiated connective tissue disease; RA = rheumatoid arthritis; ILD = interstitial lung disease.

SSc (710.1)8336211351,5891,316
RP (443.0)1,4461,00552,4562,206
Other autoimmune disease     
 SLE (710.0)2,8563,6591376,652
 SS (710.2)1,992590142,596
 DM (710.3)33415116501
 UCTD (710.9)1,05221691,277
 RA (714)8,7599,87437219,005
Total other autoimmune disease30,03123,591
ILD (515)3,8907,36017511,42510,579
Total21,16223,47686345,50137,692
Unique20,56121,52080942,89037,692

Cases of SSc were mapped to pedigrees from the UPDB for analysis. To be included in this pedigree analysis, the proband had to have parents and/or children with accessible medical records. The total number of SSc patient records used in this study from the combined data sets was 1,589. There were 621 unique patients identified from the inpatient data, 833 from the UUHSC data, and 135 from death certificates. However, some patients were identified from more than one source. For example, 244 of the 833 UUHSC patients were also found in the inpatient discharge data. From 1,589 records, we identified 1,316 unique patients (Table 1). Of these patients, 1,037 had either a parent or child (or both) in the UPDB and could be analyzed with our software. Ten matched controls for each SSc patient were selected from the statewide UPDB population file without replacement in a Monte Carlo method, to simulate random sampling. The controls were matched on sex, birth year, and whether they were born in Utah or not. The controls had to be living at the time of their matched case's diagnosis, and had not been diagnosed as having SSc, RP, SLE, SS, DM, RA, UCTD, or ILD from any of the 3 data sources used.

The use of this data resource for this study was approved by the University of Utah Institutional Review Board and by the Utah Resource for Genetic and Epidemiology Research (17).

Statistical analysis.

The 1,037 distinct SSc patients identified in the statewide population file with accessible genealogic records were used with kinship analysis software tools (KAT) to compute the familial SIR (FSIR) and population attributable risk (PAR). The 50 families showing highest risk for SSc were identified for pedigree analysis by first filtering out families in which the number of members with SSc was not statistically significant (at P ≤ 0.05) relative to the number that would be expected in the general population, and then sorting by FSIR for special analysis.

We additionally computed the RR for SSc, RP, ILD, and other autoimmune disease, including SLE, SS, DM, UCTD, and RA, among specific kinship classes of the 1,037 SSc patients: first-degree relatives, second-degree relatives, first cousins (third-degree relatives), and second cousins (fourth-degree relatives). For these calculations the numerator was the risk of the designated type of disease (SSc, RP, ILD, or other autoimmune disease) among specific kinship classes of the cases, and the denominator was the risk of the same type of disease among kinship classes of the control group. We used only cousins for third- and fourth-degree relatives because they were in the same cohort as the probands and within the narrow range of years covered by electronic medical records; hence, they had a higher probability of being included in the records.

The key measures for this study were the PAR, RR, and FSIR. The PAR estimates the proportion of disease in a population that is attributable to familial factors, while the RR was used to compare the risk of SSc among family members of an individual with SSc versus risk of SSc among the matched controls. The FSIR computes an individual family's risk for the given disease, accounting for the number of biologic relatives, their degree of relatedness to the proband, and their time at risk (24). It is calculated by the ratio of observed to expected numbers for SSc among family members, weighting the contribution of each member by the probability that the member shares an allele by common descent. Two refinements of FSIR were used in this analysis. The first uses the natural logarithm of the FSIR, log(1 + FSIR), to improve its behavior as a covariate in the conditional logistic regression model. The second transformation is an empirical Bayes adjustment for uncertainty (25). A standard error was computed for each FSIR, as a function of both the variation in risk among relatives in a family and the number of family members observed. Empirical Bayes was used to adjust for measurement error by moving the individual FSIR estimates closer to the mean in proportion to the magnitude of the standard error, using the expectation-maximization method described by Dempster and colleagues (26). The PAR was calculated using a method described by Bruzzi et al (27). Using conditional logistic regression to predict RR as a function of FSIR, individual probabilities of causation (PAC) for each case were computed as PAC = (RR – 1)/RR. The PAR is calculated as the mean of the PAC across all cases. The raw PAR uses the log of the FSIR to compute the RR, and the adjusted PAR uses the empirical Bayes–adjusted FSIR to compute the RR. We used the adjusted PAR because it accounts for observations that are missing due to lack of family or followup data as mentioned above.

KAT was also used to identify families that contained more cases of SSc than would be expected by chance. This tool controls for the proportion of Type I errors adaptively, utilizing correlation and distribution characteristics of the observed data. We defined family founders as the oldest ancestors in a familial line in the UPDB, for whom we had no record of parents. We also compared the RR for RP in relatives of patients with SSc and their controls, in order to assess the heritability of this marker of vasculopathy. Similarly, the autoimmune or inflammatory aspect of SSc was examined by ascertaining the RR of developing other autoimmune disease in relatives of patients with SSc compared with controls. Finally, to study the hereditability of fibrosis in the context of SSc, we compared the RR of ILD in relatives of patients with SSc and their controls.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

The cumulative incidence and mortality rate for SSc were examined using the appropriate data source and number of years of available data from each source. The mortality rate was 1.5 per 100,000 person-years from the death certificates group. The incidence rate was 3.3 per 100,000 person-years from the inpatient data and 2.8 per 100,000 person-years from the UUHSC group. The UUHSC data include outpatient records and may be the best estimate of the population incidence rate.

KAT was applied to the data on the 1,037 patients with a parent and/or child in the UPDB genealogic records, in order to identify families that contained more cases of SSc than would be expected by chance. In the 50 families with a significantly increased number of SSc cases (number affected ≥4; P ≤ 0.05), the FSIR ranged from 2.066 to 17.60. In other words, these families had 2–17 times as many members with SSc than would be predicted from a uniform distribution corrected for age, sex, and demographics. These families ranged in size from 1,840 family members to 80,787, and the number of affected descendants (i.e., diagnosed as having SSc) ranged from 4 to 24. The adjusted PAR, which is a measure of familial risk of a specific disease and accounts for missing observations, was ∼8%. PAR reflects familial contribution to risk, also accounting for degree of relatedness and length of time at risk. The familial contribution can be inherited or environmental.

Risk by relatedness.

The increased RRs for SSc were significant for all kinship classes examined except second cousins (Table 2). The RRs did not follow the expected trend from first- to fourth-degree relatives: the RR for second-degree relatives was higher than that for first-degree relatives. This is probably an artifact caused by the small number of affected individuals. The 95% confidence intervals (95% CIs) for first- and second-degree relatives overlapped almost completely, rendering the RRs statistically indistinguishable; in other words, the RRs were significant within their kinship classes but not between them.

Table 2. Relative risk for SSc among relatives of individuals with SSc, by specific kinship class*
RelationshipCasesControlsRR (95% CI)P
AffectedUnaffectedAffectedUnaffected
  • *

    SSc = systemic sclerosis; RR = relative risk; 95% CI = 95% confidence interval.

First-degree64,6232149,2913.07 (1.25−7.57)0.0148
Second-degree810,22123109,8563.8 (1.7−8.46)0.00111
Third-degree1210,32764116,6332.14 (1.16−3.95)0.01496
Fourth-degree3663,75758128,6061.26 (0.83−1.91)0.2708

Risk of vasculopathy.

When we compared the RR for RP in first- through fourth-degree relatives of patients with SSc and their controls, we found that first- and second-degree relatives had a significantly increased risk. First-degree relatives were 6 times more likely to have RP than first-degree relatives of controls, and second-degree relatives were twice as likely to have RP than second-degree relatives of controls (Table 3).

Table 3. Relative risk for Raynaud's phenomenon among relatives of individuals with SSc, by specific kinship class*
RelationshipCasesControlsRR (95% CI)P
AffectedUnaffectedAffectedUnaffected
  • *

    SSc = systemic sclerosis; RR = relative risk; 95% CI = 95% confidence interval.

First-degree164,6132749,2856.38 (3.44−11.83)4.04 × 10−9
Second-degree1010,21945109,8342.39 (1.21−4.74)0.01224
Third-degree910,33071116,6261.43 (0.71−2.86)0.315
Fourth-degree3963,75480129,5130.99 (0.68−1.45)0.955

Risk of altered immunity as determined by presence of another autoimmune disease.

Comparison of the RR for overlapping autoimmune conditions in relatives of patients with SSc versus controls revealed a significant increase in risk among first- and second-degree relatives of SSc patients (Table 4). First-degree relatives were 2.5 times more likely to have another autoimmune disease than first-degree relatives of controls; second-degree relatives had a 1.5 times increased risk.

Table 4. Relative risk for other autoimmune diseases among relatives of individuals with SSc, by specific kinship class*
RelationshipCasesControlsRR (95% CI)P
AffectedUnaffectedAffectedUnaffected
  • *

    SSc = systemic sclerosis; RR = relative risk; 95% CI = 95% confidence interval.

First-degree954,53441748,8952.49 (1.99−3.41)2.42 × 10−15
Second-degree7510,154554109,3241.48 (1.34−2.39)0.002
Third-degree11210,2271,262115,4351.01 (0.83−1.22)0.945
Fourth-degree65363,1401,312126,8971.01 (0.92−1.11)0.848

Risk of fibrotic disease.

Finally, we compared the RR for ILD in relatives of patients with SSc and their controls (Table 5). First-, third-, and fourth-degree relatives had significantly elevated relative risks for ILD. Excluding second-degree relatives, the RRs among the other kinship classes followed the expected decreasing trend for inherited risk.

Table 5. Relative risk for interstitial lung disease among relatives of individuals with SSc, by specific kinship class*
RelationshipCasesControlsRR (95% CI)P
AffectedUnaffectedAffectedUnaffected
  • *

    SSc = systemic sclerosis; RR = relative risk; 95% CI = 95% confidence interval.

First-degree294,60020349,1091.53 (1.04−2.26)0.0309
Second-degree2510,204301109,5780.91 (0.61−1.37)0.6635
Third-degree9610,243754115,9431.47 (1.18−1.820)0.000441
Fourth-degree41063,383706127,8261.2 (1.06−1.35)0.00373

Seven SSc pedigrees were compiled from the UPDB for further analysis. Their founders were among the 50 highest-risk families we identified, and these families were chosen because the relative risk for RP, ILD, and other autoimmune disease among relatives of the SSc patient was significant (P < 0.05). The number of descendants ranged from 1,804 to 9,151, and the number of generations ranged from 5 to 7. One particular founder family with 3,720 descendants demonstrated the power of the UPDB as a resource for identifying multiplex families. This family's pedigree revealed the presence of RP, ILD, and other autoimmune disease in multiple family members, and demonstrated how compilation of a pedigree can link seemingly sporadic cases of disease together to reveal a familial syndrome (Figure 1).

thumbnail image

Figure 1. Pedigree of 1 of 7 families with statistically significant increases in the number of members with systemic sclerosis (SSc), other autoimmune disease (OD), interstitial lung disease (ILD), and Raynaud's phenomenon (RP). The pedigree has been “trimmed” to show only the descendant paths that lead to affected individuals. Diagonal lines indicate family members who are deceased.

Download figure to PowerPoint

DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

This population-based study examined the familiality of SSc in the setting of a family history of vasculopathy (RP), autoimmune inflammatory disease (SLE, SS, DM, UCTD, and RA), and fibrosis (ILD), in order to examine the relative genetic influence of these components of SSc pathophysiology. In the UPDB, which is linked to data on more than 7 million individuals, 1,316 distinct SSc patients and 50 SSc families with the highest FSIR were examined in detail. These families were 2–17 times more frequently affected with SSc than would be predicted from a uniform distribution corrected for age, sex, and demographics, and had excess RP, autoimmunity (as represented by other autoimmune disease), and fibrosis (as represented by ILD). The PAR, a measure of the familial predisposition to SSc, was ∼8%. The study population is genetically representative of northern Europe, with low inbreeding levels (22, 23). The present results have important implications regarding counseling of SSc patients about familial risk. The cumulative incidence rate for SSc was 3.3 per 100,000 person-years from the inpatient data and 2.8 per 100,000 person-years from the UUHSC data, both of which are higher than, but close to, the incidence rate of 1.9 per 100,000 per year (95% CI 1.24–3.02) previously reported by Mayes et al, from a large cohort study in the Detroit tri-county area (5). The higher incidence in our study could be due to the fact that localized scleroderma (morphea and linear disease) were excluded in the study by Mayes and colleagues, whereas these entities could have possibly been coded in our data sources under ICD-9 code 710.1.

The exact genetic contribution to vasculopathy, inflammation, and fibrosis in SSc remains unknown. When KAT was used to identify the relative risks for RP, ILD, and overlapping autoimmune diseases in SSc families, the risk for each of these conditions was found to be increased in first-degree relatives. First-degree relatives were 6 times more likely to have RP than controls. Risk for RP was also significantly increased in second-degree relatives, although the risk tended to decrease with more distant relationship, as expected for heritable conditions. The presence of RP in an SSc family member seems to imply that there may be increased risk for SSc, consistent with the hypothesis that places vascular injury at the center of SSc pathogenesis (13). Additionally, this supports the importance of capillaroscopy, which examines vascular nailbed changes in the setting of RP, as an important diagnostic test for SSc, with a reported sensitivity of 100%, specificity of 81%, and positive predictive value of 90% (28). This is a valuable tool for screening for and diagnosing SSc (29).

Only first- and second-degree relatives of SSc patients had a statistically significant increase in risk for other autoimmune diseases. First-degree relatives were 2.5 times more likely to have another autoimmune disease than were first-degree relatives of controls. Second-degree relatives were 1.5 times more at risk for an overlapping autoimmune condition. These findings are consistent with other data indicating that immune dysregulation, possibly related to highly pleiotropic cytokines, also has a heritable component (30).

With the exception of second-degree relatives, the relative risk for ILD in the kinship classes also followed the expected decreasing trend for inherited risk. First-degree relatives were 1.5 times more likely to have ILD. These results suggest that in the clinical setting, screening questions for the presence of RP, ILD, or another autoimmune condition in a first-degree relative are important when considering the diagnosis of SSc. Additionally, these findings provide evidence of the important role of RP in the pathophysiology of SSc. Improved inpatient coding for RP may be warranted. Institution of potent therapies in patients with RP could possibly reduce the likelihood of development of SSc, although this proposition remains untested to date.

There are limitations to the study approach in which a population database is used. Our study population was primarily of northern European descent, and the results should be generalized only to other populations of similar origin. Nonetheless, the findings do suggest a genetic contribution to vasculopathy, immune dysfunction, and fibrosis, and provide the background for additional investigations.

The identification of the cases was based on billing codes, which may entail some misclassification. Due to the de-identified nature of the data, we were unable to validate the diagnoses. However, this approach does allow for more valid estimates of familial risk than would be obtained using data that are based on patient self-reports. Not every patient with SSc in our study had RP listed as a concomitant diagnosis, even though it is almost universally present in SSc (31). This suggests that perhaps only the most severe features of the disease were recorded. There may have been ascertainment biases for each disease that was studied. It is possible that a relative of someone with SSc is more likely to be evaluated if they have RP or pulmonary symptoms. The ICD-9 code for postinflammatory disease/pulmonary fibrosis may not capture all cases of ILD. Additionally, we did not investigate other fibrotic and vasculopathic manifestations of SSc, such as primary biliary cirrhosis or pulmonary hypertension. Similarly, we did not include other more common autoimmune inflammatory conditions, such as thyroid disease. It is likely that the SSc cases were accurately diagnosed based on the unique physical properties of the disease, while less accuracy is expected for other autoimmune disorders. We pooled the other autoimmune disorders in order to mitigate any effect of this lower accuracy for a specific diagnosis. Analysis of each autoimmune disorder separately may have better characterized the genetic component of each disease, but this was not done, in order to allow large enough numbers for calculation of RRs.

In summary, we used the unique UPDB resource to examine the familial nature of SSc. We estimated RRs in relatives of patients with SSc, with a focus on RP, ILD, and other autoimmune diseases. Our results show that the presence of SSc confers a significantly increased risk of SSc, RP, ILD, and other autoimmune diseases in a first-degree relative. Moreover, they suggest that vasculopathy is the most important heritable component, and fibrosis is less polygenic. This finding warrants attention with regard to screening strategies aimed at early identification of SSc, with a focus on a personal and family history of RP and ILD. Further studies on the high-risk pedigrees that have been identified in the UPDB could provide increased understanding of the specific roles of these three components to SSc predisposition. Additionally, studying outpatient records for the presence of RP and subsequently examining cases of SSc might better clarify the risk imparted by RP on familial likelihood of developing SSc.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Frech had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Frech, Mineau, Sawitzke.

Acquisition of data. Frech, Mineau, Pimentel, Sawitzke.

Analysis and interpretation of data. Frech, Khanna, Markewitz, Mineau, Pimentel, Sawitzke.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES