Abstract
Objective:
Rheumatoid arthritis (RA) has been considered an independent risk factor for cardiovascular disease (CVD), where RA and CVD later manifest following genetic predisposition and an extended preclinical phase. The HLA-DRB1 Shared Epitope (SE) alleles predispose for RA and are associated with higher risk of CV mortality in RA patients, but have not been evaluated in a community-living population. Thus, we evaluated whether HLA-DRB1 (SE) alleles were associated with subclinical CVD, CV events, and mortality in the Multi-Ethnic Study of Atherosclerosis (MESA).
Methods:
We performed HLA typing in 955 MESA participants and evaluated associations of SE alleles with coronary artery calcium (CAC) and abdominal aortic calcium (AAC); risk difference for CAC, AAC severity, and carotid intima media thickness (cIMT); and associations of SE with all-cause mortality, CVD and non-CVD death, and CV events.
Results:
Among 955 participants, 30 % were HLA-DRB1 SE positive. SE positivity was not significantly associated with higher risk of CAC, AAC, cIMT, CV events, or mortality. DRB1*10:01 demonstrated 2.63-fold higher risk for CAC (95 % CI 1.17–5.99); DRB1*14:02 demonstrated 42 % higher risk for AAC (95 % CI 1.17–1.74); and DRB1*04:05 demonstrated 24 % lower risk for AAC (95 % CI 0.58–0.99).
Conclusion:
In a multi-ethnic community-living population, HLA-DRB1 SE alleles were not associated with subclinical CVD, CV events, or mortality. However, individual allele-associations with subclinical CVD suggested variability in risk.
Keywords: Autoimmunity, Shared epitope, Cardiovascular disease
1. Introduction
Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that leads to joint damage, significant disability, and lower life expectancy [1,2]. Individuals with RA have a higher risk of developing CVD compared to those without RA. Moreover, they experience a higher incidence of cardiovascular events, including myocardial infarction (MI), stroke, and arterial occlusive events [3]. This higher CVD risk may be present even at the pre-clinical stage of RA. Maradit-Kramers et al. showed that RA patients had a higher risk of MI prior to being diagnosed with RA [4] and Kobayashi et al. found RA patients were at higher risk of hospitalization for acute MI prior to RA diagnosis [5]. Thus, the risk of CVD in patients with RA may precede the diagnosis of RA. This increased CVD risk in the preclinical phase of RA warrants evaluation of the factors contributing to this risk.
A group of Human Leukocyte Antigen (HLA) DRB1 alleles with specific common amino acids referred to as “the shared epitope (SE)”, has been considered to genetically predispose individuals to develop RA. [8–10] Furthermore, they have been associated with endothelial dysfunction in RA patients [6]. HLA genes encode Major Histocompatability Complex (MHC) molecules which act to present peptide antigens to T lymphocytes. The SE refers to a five amino acid sequence motif in positions 70–74 of HLA-DRβ proteins, located on the alpha helix of the molecule, which influences the presentation of peptide antigens [7]. SE alleles have been associated with developing severe RA and increased risk of CV mortality [8–10]. SE-coding alleles include members of the HLA-DRB1 *04 allele group, *01:01, *14:02, and *10:01. HLA-DRB1 amino acid positions 11/13, 71, and 74 are associated with inflammation level and disease activity. These HLA-DRB1 haplotypes have been associated with CV mortality in inflammatory polyarthritis, independent of C-reactive protein (CRP) and anti-cyclic citrullinated peptide (CCP) status [11–15]. Furthermore, the MHC haplotype HLA-DRB1*01 has been associated with ST-elevation myocardial infarction (STEMI) and elevated baseline high sensitivity CRP (hsCRP) levels in the general population as well [16]. However, less is known about HLA-DRB1 SE alleles and their potential association with endothelial dysfunction, consequent subclinical CVD, ischemic events, and mortality in individuals without RA in a multiethnic community-living population.
We sought to determine whether the HLA-DRB1 SE alleles—adjusted for RA related autoimmunity through rheumatoid factor (RF) and anti-cyclic citrullinated protein (CCP2)— were associated with higher risk of CVD such as abnormal coronary artery calcium (CAC), abdominal aortic calcium (AAC), carotid intima media thickness (cIMT), events such as angina, STEMI, and death in the Multi-Ethnic Study of Atherosclerosis (MESA) population.
2. Methods
2.1. The Multi-Ethnic Study of Atherosclerosis (MESA)
The Multi-Ethnic Study of Atherosclerosis (MESA) is a prospective cohort study established in July 2000. It was designed to determine risk factors and progression of subclinical and clinical CVD in Black, Chinese, Hispanic/Latino, and non-Hispanic/Latino white Americans [17]. Approximately 6814 individuals aged 45–84 years with no clinically apparent cardiovascular disease were recruited from six US field centers: Baltimore, MD; Chicago, IL; Los Angeles, CA; New York, NY; St Paul, MN; and Winston-Salem, NC. Individuals were excluded if they had clinical CVD, including physician-diagnosed myocardial infarction, angina, stroke, transient ischemic attack, or heart failure; use of nitroglycerin; current atrial fibrillation, history of procedure related to CVD; or additional co-morbidities that would prevent long-term participation. The details regarding the MESA design, recruitment methods, examination components and data collection have been previously published [17,18].
2.2. Study population
We utilized data from the Multi-Ethnic Study of Atherosclerosis Abdominal Aortic Calcium Study, which is an ancillary study of the MESA parent study. Participants in this ancillary study were recruited during follow-up visits between August 2002 and September 2005 from 5 of 6 field centers: Chicago, Illinois; Forsyth County, North Carolina; Los Angeles County, California; New York, New York; and St. Paul, Minnesota. These patients were randomly selected and were representative of the larger MESA cohort. Of the 955 participants who completed the abdominal aortic calcium (AAC) ancillary study and were later seen at exam 5 for MRI, we completed human leukocyte antigen (HLA) typing [19,20]. These 955 participants comprise our analysis dataset.
2.3. HLA typing
We completed HLA typing at UC San Diego in the Center for Advanced Laboratory Medicine (CALM). The MESA participant DNA was genotyped to the 2-field allele level from low-resolution HLA genotyping data for HLA-DRB1 using LABType SSO Class II DRB1 reagents (One Lambda, Canoga Park, CA) on a Labscan 100 (Luminex, Austin, TX) and analyzed using HLA Fusion software (One Lambda). Genotypes were determined as allele strings inclusive of potential alleles present based on SSO data and genotypes were assigned as the most common allele present in the allele string [21].
We defined the shared epitope alleles in our study as the HLA-DRB1*04 allele group (*04:01, *04:04, *04:05, *04:08), *01:01, *14:02, and *10:01). These shared epitope alleles were chosen as they have been well established in literature linked to both rheumatoid arthritis and severe disease through genome-wide scanning studies. Furthermore, meta-analysis has also linked these shared epitope alleles with erosive RA [7,14,22].
2.4. Measurement of abdominal aortic calcium (AAC) and coronary artery calcium (CAC)
AAC and CAC were measured using computed tomography (CT) scanners at Northwestern University and University of California, Los Angeles, Columbia University, Wake Forest University, and University of Minnesota at settings previously described [23]. For accuracy, 2 chest scans were performed for everyone. All scans were analyzed centrally using a standard protocol by the MESA CT Reading Center [20]. We classified calcification as a plaque ≥1 m m2 with a density >130 Hounsfield units and quantified using the Agatston scoring method [23]. Any detectable calcium was defined as an AAC or CAC score >0 [24].
2.5. Carotid intima media thickness (cIMT)
CIMT readings were derived from carotid ultrasound images in Exam 1 and Exam 5. This was done by trained, certified sonographers through B-mode ultrasound images of the right and left common, bifurcation, and internal carotid artery segments. The ultrasound images were reviewed and interpreted by the University of Wisconsin Atherosclerosis Imaging Research Program MESA Carotid Ultrasound Reading Center. The images were then loaded with Arterial Health Package software (Siemens Medical, Malvern, PA) for IMT measurement and plaque scoring [25]. For IMT measurements, the distal common carotid artery was defined as the distal 10-mm of the vessel. IMT was measured as the mean of the mean left and right mean far wall distal CCA wall thickness [25].
2.6. Troponin elevation
Concentrations of high sensitivity troponin-T (hs-TnT) at baseline were measured in 4986 MESA participants [26,27]. It was measured from ethylenediaminetetraacetic acid (EDTA) plasma samples using the Cobas e601 analyzer (Roche Diagnostics, Switzerland). For this instrument, the limit of detection of the assay (LOD) is 3 ng/L. Sex-specific 99th percentile upper-reference limits (URLs) of 10 ng/L for women and 15 ng/L for men were used [28].
2.7. All-cause mortality, cardiovascular mortality and cardiovascular events
We defined all-cause mortality as death due to any cause. We defined cardiovascular mortality as death attributed to atherosclerotic coronary heart disease, stroke, primary arrhythmic death, other atherosclerotic disease, other CVD, and heart failure. We defined non-CVD causes of death according to the underlying cause listed on the death certificate. These were not adjudicated [29].
For CVD events, angina was classified as a symptomatic event generally involving ischemic chest, left arm, or jaw pain, or as ‘atypical’ symptoms [30]. Adjudicators within MESA identified angina and graded it as definite, probable, or absent [30]. MI was defined based on symptoms, electrocardiographic findings, and levels of cardiac biomarkers [30], involving chart review for coronary heart disease through family interviews on whether there was chest pain within 72 h before death, history of coronary heart disease (CHD), absence of known non-atherosclerotic or non-cardiac cause of death, and whether death occurred within 28 days after MI [30]. Furthermore, to assess CV events, MESA contacted participants every 9 months for CV follow-up, during which participants self-reported hospitalizations and events. Trained personnel also reviewed hospital records for potential CVD events. Additionally, two physicians independently reviewed records for CV endpoint classification and to assign incidence dates. This review process has been previously published [20,31,32].
2.8. RA related autoimmunity: RF and anti-CCP2 antibodies
RF and anti-CCP2 antibodies were measured in frozen serum samples collected during the baseline MESA examination 1 from 2000 to 2002 [33]. Enzyme-linked immunosorbent assay (ELISA) kits from the TheraTest Labs utilized isotype-specific IgM and IgA RF and IgG anti-CCP2. IgM RF levels were expressed in IU/ml with an upper limit of normal of 25. The units for IgA and IgG anti-CCP2 were arbitrary, with the upper limits of normal as 35 and 5, respectively. The RF results were obtained by single point calibration, and anti-CCP2 results were based on a standard curve with each test being calibrated with positive and negative controls [33].
The intra- and interassay coefficients of variability did not exceed 10 %. All tests are cleared by the Food and Drug Administration for routine clinical diagnostic use. The clinical specificity was established on specimens collected from at least 200 random blood bank donors by percentile ranking method [34]. Clinical sensitivity of our lab tests were tested using specimens collected from clinically characterized RA patients that met the 1987 American College of Rheumatology (ACR) clinical criteria [35]. The sensitivity determined was 91 % for IgM RF, 80 % for IgA RF and 72 % for anti-CCP2 [33].
Based on prior work, we defined “high risk profile” (HRP) as being positive for anti-CCP2 and/or both RF isotypes, where the HRP may indicate a later or more specific stage in RA-related autoimmunity [36].
2.9. Covariates
Patient demographics including age, sex, race/ethnicity were collected through standardized questionnaires. We designated cigarette smoking classification as never, current, or former. These classifications were dichotomized in the analyses to ‘ever’ versus ‘never’. Height and weight were measured in participants wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight (kg)/height (m2) . Three resting blood pressures were measured in the seated position using a Dinamap automated sphygmomanometer. The average of second and third readings were used for analysis. Hypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg or current use of hypertensive medications. Diabetes was classified as a fasting blood glucose ≥126 mg/dL or self-reported use of hypoglycemic medications. Impaired fasting glucose status was defined as a fasting glucose of 101–125 mg/dL. Glucose, total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, and creatinine were obtained from blood samples following a 12-hour fast. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation.
IL-6 was measured following a 12-hour fast using ELISA (Quantikine HS Human IL-6 Immunoassay; R&D Systems, Minneapolis MN. The lower limit of detection was <0.094 pg/mL (range 0.156–10.00 pg/mL), with a 6.3 % coefficient of variability [37,38].
2.10. Statistical analysis
We defined shared epitope positive, SE(+), as individuals having genotypes with the HLA-DRB1 SE alleles (04:04, 04:05, 04:01, 04:08, 01:01, 14:02, 10:01) for single or double alleles. Participant characteristics were compared by SE status. We then evaluated associations of SE positivity and each of the individual SE alleles with CAC, AAC, and cIMT. Given that cIMT (evaluated in millimeters) is continuous, linear regression was used.
The prevalence of calcification is high in the MESA cohort (CAC > 0 and AAC > 0), thus odds ratios (ORs) inadequately approximate the relative risk (RR). Therefore, RR estimates are derived from the regression model y = exp(XT β), with the exponentiated parameters β interpreted as relative risks. Gaussian error was assumed, and robust standard error estimates were used. Using this method, we examined the relationship between SE status and calcium presence while adjusting for other risk factors. Among those with detectable CAC and AAC, we evaluated the relationship between calcification quantity [(ln)Agatston score] and SE status with multivariable linear regression, controlling for all other risk factors in the model. The relationship was expressed as a relative difference (RD) in calcification.
We evaluated associations of SE positivity with all-cause mortality, CVD death (defined as secondary to stroke, coronary heart disease, other atherosclerotic death, or other CVD death), non-CVD death, angina, and myocardial infarction (MI) using Cox proportional hazards regression. Schoenfeld residuals were used to test the assumption of the proportionality of hazards.
Using sequential models, we first evaluated unadjusted associations, followed by adjustments for age, sex, and race/ethnicity. The third model adjusted for comorbidities and lifestyle factors, including diabetes mellitus, systolic blood pressure, current smoking, history of cancer, estimated glomerular filtration rate (eGFR), current use of anti-hypertensive medications, nonsteroidal anti-inflammatory drugs, and IL-6 levels. Our final model adjusted for the high-risk autoantibody profile. Additionally, we tested for interactions of SE positivity with sex, race/ethnicity, and high-risk autoantibody profile on mortality and CVD events.
Statistical analyses were performed with SPSS version 28.0 (IBM Corp.) and STATA 18.0 (StataCorp. 2023. Stata Statistical Software: Release 18. College Station, TX: StataCorp LLC).
3. Results
Among the 955 MESA participants, 30 % were positive for SE alleles—27 % carried a single allele, and 3 % carried two alleles (Fig. 1). A summary of participant characteristics is shown in Table 1. In both the SE(+) and SE(−) participants, the average age was 60 ± 9 years old. Participants who were SE(+) compared to SE(−) were mostly white (58 % vs 38 %); but SE(+) participants had a lower proportion individuals who were from Asian (8 % vs 16 %) and Black (8 % vs 23 %) racial groups (Table 1). Both SE(+) and SE(−) participants had similar Hispanic/Latino ethnicity, 26 % and 24 %, respectively. Age, sex, blood pressure, cholesterol, and inflammatory markers (CRP, TNF-α, IL 6) were similar between the SE(+) and SE(−) participants.
Fig. 1.
Distribution of HLA-DRB1 shared epitope (SE) alleles in MESA participants.
Table 1.
Characteristics of MESA Participants by shared epitope (SE) status.
| Shared Epitope Positive (SE +/+ or SE +/−) | Shared Epitope Negative (SE −/−) | Total | |
|---|---|---|---|
| N (%) | 286 (30) | 669 (70) | 955 |
| Age, years | 60 (9) | 60 (9) | 60 (9) |
| Sex, % Female | 134 (47) | 334 (50) | 468 (49) |
| Race/Ethnicity | |||
| White | 167 (58) | 253 (38) | 421 (44) |
| Asian | 22 (8) | 104 (16) | 126 (13) |
| Black | 24 (8) | 155 (23) | 179 (19) |
| Hispanic/Latino | 73 (26) | 157 (24) | 230 (24) |
| Weight, kg | 174 (34) | 170 (34) | 171 (34) |
| Height, cm | 168 (10) | 167 (10) | 168 (10) |
| Waist circumference, cm | 98 (13) | 96 (12) | 96 (13) |
| BMI, kg/m2 | 28.0 (4.7) | 27.4 (4.4) | 27.6 (4.5) |
| Former smoker, % | 117 (41) | 277 (34) | 344 (36) |
| Current smoker, % | 32 (11) | 82 (12) | 114 (12) |
| Hypertension, % | 109 (38) | 272 (41) | 381 (40) |
| Systolic BP, mmHg | 124 (20) | 125 (20) | 124 (20) |
| Diastolic BP, mmHg | 73 (10) | 73 (10) | 73 (10) |
| Pulse pressure, mmHg | 51 (16) | 52 (16) | 51 (16) |
| HTN meds, % | 94 (33) | 213 (32) | 307 (32) |
| Diabetes, % | 20 (7) | 65 (10) | 85 (9) |
| Triglycerides | 122 [80, 172] | 114 [78, 164] | 116 [79, 166] |
| LDL-cholesterol | 117 (31) | 119 (30) | 118 (30) |
| HDL-cholesterol | 51 (15) | 51 (16) | 51 (15) |
| Lipid lowering medications,% | 54 (19) | 84 (13) | 138 (15) |
| NSAID, % | 50 (18) | 117 (18) | 167 (18) |
| Estrogens (excluding vaginal creams), % | 60 (21) | 109 (16) | 169 (18) |
| Premarin (conjugated estrogens), % | 47 (16) | 77 (12) | 124 (13) |
| Oral steroids, % | 4 (1.4) | 9 (1.4) | 13 (1.4) |
| Inhaled steroids for asthma,% | 6 (2.1) | 8 (1.2) | 14 (1.5) |
| Cancer | 23 (8) | 44 (7) | 67 (7) |
| C-reactive protein | 1.63 [0.71, 4.08] | 1.67 [0.77, 3.61] | 1.66 [0.76, 3.78] |
| TNF alpha | 4.48 [3.48, 6.13] | 4.52 [3.41, 5.94] | 4.50 [3.44, 5.95] |
| IL-6 | 1.00 [0.67, 1.58] | 1.06 [0.72, 1.57] | 1.04 [0.70, 1.57] |
| RF positive, % | 52 (18) | 129 (19) | 181 (19) |
| Anti-CCP2 positive, % | 3 (1.1) | 8 (1.2) | 11 (1.2) |
Values are mean (SD) or median [25th, 75th percentile] unless ortherwise noted.
BMI: body mass index, LDL: low density lipprotein, HDL: high density lipoprotein,NSAIDS: Non-steroidal anti-inflammatory drugs, TNF alpha: Tumor Necrosis Factor alpha, IL-6: Interleukin 6, Anti-ccp2: Anti-cyclic citrullinated peptide antibodies, Rf: rheumatoid factor.
SE positivity was not significantly associated with a higher risk of CAC, AAC, or cIMT (Table 2). However, individual SE allele DRB1*10:01 demonstrated a 2.63-fold higher risk for CAC (95 % CI: 1.17–5.99); SE allele DRB1*14:02 demonstrated a 42 % higher risk for AAC, (95 % CI: 1.17–1.74); and SE allele DRB1*04:05 demonstrated 24 % lower risk for AAC (95 % CI: 0.58–0.99) in fully adjusted analysis. Associations between the shared epitope, CAC, AAC, and carotid IMT did not differ by sex or race/ethnicity.
Table 2.
Associations of HLA-DRB1 shared epitope (SE) alleles with coronary artery calcium, abdominal aortic calcium, and carotid intima media thickness.
|
| ||||||
|---|---|---|---|---|---|---|
| Coronary artery calcium (CAC > 0) |
Abdominal aortic calcium (AAC > 0) |
Carotid IMT | ||||
| RR (95 % CI) | RD (95 % CI) | RR (95 % CI) | RD (95 % CI) | RD (95 % CI) | ||
|
| ||||||
| Shared Epitopea | ||||||
| Unadjusted SE +/+ vs SE −/− | 1.15 (0.79, 1.66) | 2.55 (1.10, 5.89) | 1.17 (0.93, 1.46) | 1.04 (0.90, 1.19) | 1.02 (0.96, 1.09) | |
| Model 1* | 1.01 (0.71, 1.43) | 2.04 (0.91, 4.57) | 1.10 (0.93, 1.29) | 1.00 (0.88, 1.15) | 1.02 (0.96, 1.08) | |
| Model 2** | 1.08 (0.81, 1.43) | 1.94 (0.89, 4.21) | 1.11 (0.94, 1.32) | 1.02 (0.90, 1.15) | 1.02 (0.97, 1.08) | |
| Model 3† | 1.07 (0.81, 1.42) | 1.92 (0.88, 4.17) | 1.11 (0.94, 1.32) | 1.02 (0.90, 1.15) | 1.02 (0.97, 1.08) | |
| Unadjusted SE +/− vs SE −/− | 1.01 (0.86, 1.19) | 1.33 (0.91, 1.95) | 1.13 (0.93, 1.46) | 0.99 (0.93, 1.06) | 1.01 (0.98, 1.04) | |
| Model 1* | 0.97 (0.84, 1.11) | 1.17 (0.81, 1.69) | 1.08 (0.99, 1.17) | 0.98 (0.92, 1.05) | 1.02 (0.99, 1.05) | |
| Model 2** | 0.93 (0.80, 1.07) | 1.10 (0.75, 1.61) | 1.06 (0.97, 1.14) | 0.98 (0.91, 1.04) | 1.02 (0.99, 1.04) | |
| Model 3† | 0.93 (0.80, 1.07) | 1.11 (0.76, 1.61) | 1.06 (0.97, 1.15) | 0.97 (0.91, 1.04) | 1.02 (0.99, 1.05) | |
| Individual Alleles2 | N (%) | Coronary artery calcium (CAC > 0) |
Abdominal aortic calcium (AAC > 0) |
Carotid IMT | ||
| RR (95 % CI) | RD (95 % CI) | RR (95 % CI) RD (95 % CI) | RD (95 % CI) | |||
|
| ||||||
| 4:01 | 71 (7) | 0.93 (0.73–1.19) | 0.97 (0.51–1.87) | 1.1 (0.96–1.25) | 0.97 (0.88–1.07) | 0.98 (0.94–1.03) |
| 4:04 | 51 (5) | 1.03 (0.81–1.33) | 0.82 (0.38–1.78) | 1.1 (0.95–1.27) | 1.08 (0.98–1.19) | 1.02 (0.97–1.08) |
| 4:05 | 27 (3) | 0.98 (0.72–1.33) | 1.24 (0.64–2.40) | 0.76 (0.58–0.99) | 1.02 (0.89–1.17) | 1.00 (0.94–1.05) |
| 4:08 | 3 (0.3) | 1.12 (0.75–1.66) | 0.31 (0.03–3.30) | 0.87 (0.65–1.17) | 0.98 (0.90–1.06) | 1.05 (0.86–1.27) |
| 1:01 | 102 (11) | 0.94 (0.78–1.14) | 1.41 (0.83–2.38) | 1.1 (0.99–1.22) | 0.96 (0.87–1.05) | 1.04 (1.00–1.09) |
| 14:02 | 15 (1.6) | 1.28 (0.90–1.81) | 1.7 (0.73–3.94) | 1.42 (1.17–1.74) | 1.1 (0.96–1.26) | 1.04 (0.96–1.13) |
| 10:01 | 20 (2) | 0.74 (0.34–1.61) | 2.63 (1.17–5.99) | 0.88 (0.67–1.17) | 0.87 (0.67–1.14) | 1.02 (0.95–1.09) |
|
| ||||||
Total frequency N(%) for SE +/+: 75 (8 %), (+)/(−): 360 (38 %), −/−: 521 (55 %).
Model 1: Age, sex, race/ethnicity.
Model 2: Age, sex, race/ethnicity, diabetes mellitus, systolic blood pressure, current smoking, eGFR, current use of anti-hypertensive medications, nonsteroidal anti-inflammatory drugs, current use of lipid lowering medications, and IL-6.
Model 3: Model 2 + high risk profile (RA related autoimmunity).
Approximately 4 % of participants were positive for the high-risk autoantibody profile. Participants who were positive for the high-risk profile and a single SE allele had a significantly 47 % lower risk for AAC severity (95 % CI 0.33–0.86), compared to participants who were negative for the high-risk profile (Table 3). There were no participants who were positive for both the high-risk profile group and double SE alleles.
Table 3.
Effect of the high-risk profile (HRP) on associations between the shared epitope, coronary artery calcium, abdominal aortic calcium, and carotid intima media thickness.
|
| ||||||
|---|---|---|---|---|---|---|
| Outcome: CAC > 0 |
Outcome: CAC severity |
|||||
| HRP(−) RR (95 % CI) | HRP (+) RR (95 % CI) | Interaction P-value | HRP (−) RR (95 % CI) | HRP (+) RR (95 % CI) | Interaction P-value | |
|
| ||||||
| Shared Epitope | ||||||
| SE +/+ vs SE −/− | 1.08 (0.84, 1.39) | - | - | 1.92 (0.89, 4.15) | - | - |
| SE +/− vs SE −/− | 0.91 (0.79, 1.05) | 0.03 (0.001, 7.75) | 0.131 | 1.13 (0.77, 1.66) | - | 0.665 |
| Outcome: AAC > 0 |
Outcome: AAC severity |
|
||||
| HRP (−) RR (95 % CI) | HRP (+) RR (95 % CI) | Interaction P-value | HRP (−) RR (95 % CI) | HRP (+) RR (95 % CI) | Interaction P-value | |
|
| ||||||
| Shared Epitope | ||||||
| SE +/+ vs SE −/− | 1.12 (0.94, 1.32) | - | - | 1.02 (0.90, 1.16) | - | - |
| SE +/− vs SE −/− | 1.05 (0.97, 1.14) | 0.84 (0.53, 1.34) | 0.419 | 0.98 (0.92, 1.05) | 0.53 (0.33, 0.86) | 0.377 |
| Carotid IMT |
|
|||||
| HRP (−) RD (95 % CI) | HRP (+) RD (95 % CI) | Interaction P-value | ||||
|
|
||||||
| Shared Epitope | ||||||
| SE +/+ vs SE −/− | 1.02 (0.96, 1.08) | - | - | |||
| SE +/− vs SE − / − | 1.02 (0.99, 1.05) | 0.93 (0.80, 1.08) | 0.076 | |||
|
| ||||||
P values in bold face are statistically significant. Analyses are fully adjusted for age, sex, race/ethnicity, diabetes mellitus, systolic blood pressure, current smoking, eGFR, current use of anti-hypertensive medications, nonsteroidal anti-inflammatory drugs, current use of lipid lowering medications, and IL-6. SE: Shared Epitope alleles include 04:04, 04:05, 0401, 04:08, 01:01, 14:02, 10:01, CAC: coronary artery calcium score, AAC: abdominal aortic calcium, RR: relative risk, CI: confidence interval, CAC: coronary artery calcium, AAC: abdominal aortic calcium, Carotid IMT: carotid intima media thickness, HRP: high risk profile defined as being positive for anti-CCP2 and/or both RF isotypes.
In fully adjusted analysis, being positive for the SE was not significantly associated with a higher risk of all-cause mortality, CVD death, non-CVD death, angina, or MI compared to those who were SE negative (Table 4). The mean follow-up time was 15.3 ± 0.9 years. Associations of the SE with cardiovascular ischemic events or mortality did not differ by high-risk autoantibody profile status. Furthermore, associations between the shared epitope and CVD events and mortality were not modified by sex, race/ethnicity, or the high-risk profile, suggesting that there were no differential associations across sex, race/ethnicity, or autoantibody status.
Table 4.
Associations of HLA-DRB1 shared epitope (SE) alleles with Mortality and CVD Events.
| Shared Epitope Negative (SE −/−) | Shared Epitope Positive (SE+/+ or SE +/−) | |
|---|---|---|
|
All-cause mortality
Events/P-Y |
35/669 | 13/286 |
| Event rate (per 1000P-Y) | 3.43 | 2.97 |
| Model 1* | 1.00 (ref) | 0.84 (0.44, 1.63) |
| Model 2** | 1.00 (ref) | 0.78 (0.39, 1.55) |
| Model 3† | 1.00 (ref) | 0.78 (0.39, 1.55) |
|
CVD death
Events/P-Y |
9/669 | 2/286 |
| Event rate (per 1000P-Y) | 0.88 | 0.46 |
| Model 1* | 1.00 (ref) | 0.69 (0.15, 3.18) |
| Model 2** | 1.00 (ref) | 0.39 (0.05, 3.16) |
| Model 3† | 1.00 (ref) | 0.38 (0.05, 3.06) |
|
Non-CVD death
Events/P-Y |
26/669 | 11/286 |
| Event rate (per 1000P-Y) | 2.55 | 2.52 |
| Model 1* | 1.00 (ref) | 0.88 (0.42, 1.82) |
| Model 2** | 1.00 (ref) | 0.87 (0.51, 1.87) |
| Model 3† | 1.00 (ref) | 0.63 (0.14, 2.72) |
|
CVD Events Angina
Events/P-Y |
30/669 | 20/286 |
| Event rate (per 1000P-Y) | 3.03 | 4.79 |
| Model 1* | 1.00 (ref) | 1.38 (0.79, 2.42) |
| Model 2** | 1.00 (ref) | 1.32 (0.73, 2.39) |
| Model 3† | 1.00 (ref) | 1.33 (0.73, 2.40) |
|
Myocardial infarction
Events/P-Y |
21/669 | 11/286 |
| Event rate (per 1000P-Y) | 2.09 | 2.55 |
| Model 1* | 1.00 (ref) | 1.09 (0.53, 2.25) |
| Model 2** | 1.00 (ref) | 1.10 (0.51, 2.35) |
| Model 3† | 1.00 (ref) | 1.10 (0.51, 2.36) |
Shared Epitope alleles include 04:04, 04:05, 0401, 04:08, 01:01, 14:02, 10:01.
The mean follow-up time was 15.3 ± 0.9 years.
Model 1: adjusted for age, sex, race/ethnicity
Model 2: Age, sex, race/ethnicity, diabetes mellitus, systolic blood pressure, current smoking, ever having cancer, eGFR, current use of anti-hypertensive medications, nonsteroidal anti-inflammatory drugs, and IL-6
Model 3: Model 2 + high risk profile (RA related autoimmunity)
4. Discussion
SE alleles are recognized for their contribution in hereditary susceptibility for RA disease severity and patients with RA are known to experience poor CVD outcomes. It is not known, however, whether SE alleles themselves are associated with CVD in the absence of RA.
Therefore, we evaluated whether SE alleles are associated with CVD in a multi-ethnic community-living population without RA. Our rationale for this study was to determine if there’s genetic predisposition to CVD for individuals who are positive for the shared epitope alleles as patients with RA have a higher risk for CVD. We found that being positive for SE alleles of DRB1 was not associated with CVD events, mortality, or measures of CAC, AAC, or cIMT in a multiethnic cohort, with approximately one-third of the population being positive for the SE. Furthermore, evaluation of individual SE alleles demonstrated that allele DRB1*10:01 had a higher risk of prevalent CAC, DRB1*14:02 had a higher risk of prevalent AAC, and DRB1*04:05 had a lower risk of prevalent AAC.
Approximately 30 % of MESA participants were positive for SE alleles, with 27 % being positive for a single SE allele, and 3 % being positive for both SE alleles. Thus, in a multi-ethnic community-living population who do not have clinically apparent RA, a noticeable proportion carry these alleles and its significance in MESA is not yet known. In contrast to our hypothesis, being positive for SE alleles was not associated with CVD events, CAC, AAC, or cIMT in community-living individuals; and this is independent of autoimmunity and markers of inflammation, suggesting SE alleles are not independently associated with CVD. Consistent with our findings, GWAS data of families with coronary artery disease have revealed no evidence of linkage to the HLA region [39]. Thus, there are likely other processes that predispose individuals to CVD risk and events in individuals with shared epitope positivity and warrants further investigation.
Associations of individual alleles and their associations with subclinical CVD such as CAC, AAC, cIMT were mixed depending on the SE allele. We discovered some SE alleles were more atherogenic with a higher risk of prevalent CAC (DRB1*10:01) and prevalent AAC (DRB1*14:02), and one with a lower risk of prevalent AAC (DRB1*04:05). While the finding of increased risk of prevalent CAC with SE allele 10:01 is novel, this notion of increased CVD risk associated with DRB1 genotype (DRB1*01) has been suggested prior. Paakkanen, et al found the DRB1*01 genotype associated with STEMI and elevated baseline hsCRP levels in the general population (adjusted for age, sex, body mass index, risk factors, haplotypes and the use of statin) [16], which supports our finding that SE allele DRB1*10:01 may be uniquely associated with CVD.
Because of a potential differential effect of autoimmunity in individuals with SE positivity, we evaluated interactions between autoantibodies and SE alleles, and found that MESA participants who were positive for the high-risk profile and a single SE allele had a significantly lower AAC severity compared to participants who were negative for the high-risk profile. This finding contrasted with our hypothesis—and suggested that being positive for SE allele and the high-risk autoantibody profile may not contribute to subclinical CVD. Thus, the inheritance of SE alleles is not independently associated with CVD disease in this population with a high-risk autoantibody profile. However, we lacked participants with both SE alleles positive and high-risk profile positive to evaluate this further. We speculate that contributing factors such as RA disease and duration may directly contribute to the increased risk of CVD in those with RA [40] as Masuda, et al revealed a longer duration of RA was associated with cardiovascular events [40]; and other studies have suggested RA duration is linked to CAC, and CIMT as well.
While prior studies have investigated SE alleles and risk for CVD in individuals with RA, limited literature exists on community-living individuals without RA. Prior work in the WHI cohort by Birru Talabi, et al presented a theory of a protective component of SE alleles in a cohort of women who were CCP-negative and without clinical RA. They found that the presence of SE alleles in CCP-negative women had lower risk of CVD, malignancy, and protective cytokines such as IL-10, IL-12, and G-CSF. The authors speculated that in non-RA (anti-CCP negative) individuals, SE alleles may enhance immune surveillance that leads to early detection and destruction of malignant cells [41]. This study suggested a protective notion to SE alleles that was also denoted in some parts of our results as well—the SE allele DRB1*04:05 had a 24 % lower risk of AAC > 0. However, of note, these authors demonstrated these results in a CCP-negative population. Contrastingly, in our cohort, being positive for the high-risk profile (thus, RF and/or CCP positivity) and the shared epitope, instead, had a significantly lower AAC severity compared to participants who were negative for the high-risk profile. Furthermore, no protective signal was seen with CVD events. This should be evaluated in additional cohorts.
Our study has several limitations. SE-associated DRB1 2-field genotypes was based on low-resolution HLA genotyping data, which presents the possibility that the true allele-level genotyping may differ from the assigned typing. While the assigned genotypes represent the statistically most likely based on population frequency, the potential for less common genotypes encoding different amino acid sequences could potentially affect the binding of peptide antigens which could in turn affect the pathobiology of RA. High-resolution HLA genotyping would be necessary to examine this. Furthermore, at the time of analysis, we did not know whether MESA participants had been diagnosed with RA. Thus, we used self-reported arthritis as well as anti-CCP2 and RF assays to determine whether individuals had RA-related autoantibodies, which provided more insight into autoimmunity, reflected by the high-risk profile [36]. For CVD endpoints of all-cause mortality and cardiovascular events, we analyzed SE alleles collectively as we did not have enough events for individual allele analysis. Finally, we did not know the cause of death for non-CVD, and they were not adjudicated by MESA. Given these limitations, the strength of this study is the longitudinal data on autoimmunity and cardiovascular outcomes in an ethnically diverse, sex-balanced population.
In conclusion, approximately 30% of MESA participants in our study were positive for the shared epitope. In contrast to our hypothesis, being positive for the shared epitope was not significantly associated with coronary or abdominal aortic calcification, CV events, or mortality in fully adjusted analysis. When stratified by the high-risk autoantibody profile, however, those who were positive for the shared epitope and the high-risk profile had lower risk for AAC severity. These findings suggest that the presence of the shared epitope is not independently associated with CVD in a community-living population and warrant further investigation into whether the shared epitope influences associations between other aspects of RA and cardiovascular disease.
5. Support
Support for this project was provided by K01 HL122394 (JHA), R03 HL146875 (JHA), and R01 HL104047. This research was supported by contracts 75N92020D00001, HHSN268201500003I, N01HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org [mesa-nhlbi.org]. This paper has been reviewed and approved by the MESA Publications and Presentations Committee.
6. Statement of ethics and consent
All research was performed in accordance with relevant guidelines and regulations. All participants provided informed written consent, and all MESA sites obtained Institutional Review Board approval [Wake Forest University, Columbia University, Johns Hopkins University, University of Minnesota, Northwestern University, University of Los Angeles, and University of Washington Institutional Review Boards]. This study was approved by the UC San Diego Institutional Review Board (IRB# 190001).
Acknowledgements
The authors thank the other investigators, the staff, and the participants of MESA for their valuable contributions. A full list of participating MESA investigators and institutions can be found at www.mesa-nhlbi.or.
Footnotes
CRediT authorship contribution statement
Manmeet Kaur: Writing – review & editing, Writing – original draft, Visualization, Software, Resources, Methodology, Investigation, Conceptualization. Ronit Katz: Writing – review & editing, Validation, Software, Methodology, Formal analysis, Data curation. Michael H. Criqui: Writing – review & editing, Resources, Project administration, Funding acquisition, Data curation. Maripat Corr: Writing – review & editing, Methodology. Wendy S. Post: Writing – review & editing, Data curation. Matthew Budoff: Writing – review & editing, Data curation, Conceptualization. Gerald P. Morris: Writing – review & editing, Data curation. Jan M. Hughes-Austin: Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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