Divergence of Cardiovascular Biomarkers of Lipids and Subclinical Myocardial Injury Among Rheumatoid Arthritis Patients with Increased Inflammation

Abstract

Background:

Patients with rheumatoid arthritis (RA) have a 1.5x excess risk of cardiovascular (CV) disease attributed to chronic inflammation. A decrease in inflammation in RA is associated with increased LDL-C. This study evaluated the changes in lipid levels prospectively among RA patients experiencing changes in inflammation and determined the association with concomitant temporal patterns in markers of myocardial injury.

Methods:

We studied 196 patients in a longitudinal RA cohort with blood samples and hsCRP measured annually, who experienced either a significant increase or decrease in inflammation, defined as hsCRP ≧10 mg/L, in 2 consecutive annual visits. Routine and advanced lipids, markers of inflammation (IL-6, hsCRP, sTNFR2), and markers of subclinical myocardial injury (hs-cTnT, NT-proBNP) were measured

Results:

The mean age was 59 years, 81% female, with mean RA disease duration of 17.9 years. The average hsCRP increase was 36 mg/dl, associated with significant reductions in LDL-C, TG, TC, apoB and apoA1. At baseline in the increase cohort, 45.6% (47/103) had detectable circulating hs-cTnT which further increased during inflammation (p=0.02). In the decrease cohort, hs-cTnT levels remained stable despite a reduction in inflammation. In both cohorts, levels of hs-cTnT associated with overall estimated cardiovascular risk.

Conclusion:

Among RA patients experiencing an increase in inflammation, routine lipids, including LDL-C, were significantly decreased while increases in markers of subclinical myocardial injury were observed. These findings highlight the divergence in biomarkers of CV risk and suggest a role in future studies examining the utility of including hs-cTnT for CV risk stratification in RA.

Background

Patients with rheumatoid arthritis (RA) are at an increased risk for cardiovascular (CV) disease compared to the general population (1,2) . This increased risk of developing CV disease is not fully explained by traditional CV risk factors such as smoking, hyperlipidemia, and hypertension and has been attributed to systemic inflammation. Inflammation is strongly implicated as contributing to atherosclerosis and inflammatory markers, such as high-sensitivity C-reactive protein (hsCRP), have been shown to predict CV risk independently in the general population as well as in RA (3-5). Moreover, recent clinical trials in the general population have demonstrated the importance of inflammation as an independent risk factor for CVD. In the Canakinumab Anti-Inflammatory Thrombosis Outcomes Study (CANTOS) trial, blocking interleukin-1ß prospectively decreased future CV events without changing other CV risk markers (6). However, the magnitude of inflammatory burden over a prolonged period of time in RA distinguishes this group from the general population. As well, the relationship between changes in systemic fluctuating inflammation and the concomitant, temporal changes in cardiac biomarkers in RA has not been elucidated. Understanding these changes can inform efforts to improve CV risk stratification in RA.

In RA, low levels of low density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) levels have been observed to have similar risk to those with the highest LDL-C and TC levels, a term often referred to as the ‘lipid paradox’ (7,8). Studies have also found that the relationship is dynamic, a reduction in inflammation in RA is associated with increased levels of LDL-C, and not specific to a particular class of disease modifying anti-rheumatic drugs (DMARDs) (9-11) Increased LDL-C in the setting of reduced inflammation may not portend worse CV risk as it is also associated with improved anti-atherogenic capacity of high density lipoprotein cholesterol (HDL-C) measured by high density lipoprotein cholesterol efflux capacity (CEC) (10,12,13). There are however limited data on the potential impact of these fluctuations in inflammation on the heart through studies of cardiac biomarkers.

Development of a high-sensitivity assay for cardiac troponin (hs-cTn) has enhanced the ability to measure low levels of circulating cardiac troponin to detect subclinical myocardial injury. Hs-cTn has emerged as the preferred biomarker for noninvasive detection of myocardial injury and is elevated in a host of conditions in the absence of acute coronary syndrome (14,15). Elevated levels of hs-cTn in the blood are associated with increased rate of cardiac events, including coronary artery disease and heart failure (HF), as well as CV and all-cause mortality, independent of the underlying disease (14,16,17). Furthermore, specifically in RA, hs-cTn has been independently associated with occult coronary plaque burden and long-term cardiovascular events, and both hs-cTnT and NT-proBNP have been shown to be higher in RA compared to controls (18-20). Systemic inflammatory disorders such as RA are not static, and patients exhibit periods of increased and decreased periods of inflammation and continue to do so chronically over time. Thus, the objective of this study was to determine the relationships between lipids and subclinical markers of myocardial injury as RA patients experience fluctuations in inflammation.

Methods

Study Design and Population:

We conducted this study in the Brigham and Women’s Hospital Rheumatoid Arthritis Sequential Study (BRASS) (21), a prospective observational RA cohort study. All subjects were age 18 or older and had a rheumatologist diagnosis of RA based on ACR criteria (22,23). Subjects in BRASS had regular clinical assessments for RA disease activity, in person interviews to obtain information on current and past RA therapies, assessments of CV risk factors as well as hsCRP measured annually; blood samples were also banked annually. Details of the BRASS cohort were previously reported (21). Within BRASS, we studied two populations of patients: (1) subjects who experienced an hsCRP increase defined as ≥10mg/ between any two time points one year apart (increase inflammation cohort), (2) subjects who experienced an hsCRP decrease defined as ≥10mg/ between any two time points one year apart (decrease inflammation cohort). The focus of this study is on patients who experience an increase in inflammation. For both cohorts, the first time point was defined as the baseline. As statins are potent LDL-C-lowering agents, subjects on statin therapy one year before baseline or during the follow-up period were excluded; no patients were on PCKS9 therapy. All patients in BRASS have disease activity assessed every 6 months using two validated instruments, the Disease Activity Score 28 (DAS28) and the Clinical Disease Activity Index (CDAI) (24). From a prior study, the American College of Cardiology (ACC)/American Heart Association (AHA) 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk score was calculated in these cohorts (25).

A subset of data on lipids and advanced lipoproteins were previously published from the decrease inflammation cohort only (10). Previously published data included in the manuscript were noted and shown if needed for interpretation. The new data from the decrease inflammation cohort reported in this study include association between the inflammatory markers IL-6 and sTNFR2 with biomarkers of subclinical myocardial injury.

Laboratory measurements

Inflammatory Markers

HsCRP and IL-6 were measured in all subjects at the clinical laboratory of Boston Children's Hospital, Boston, MA using standardized methods as previously reported(26-28). HsCRP was measured using a standard immunoturbimetric assay on the Roche P Modular system (Roche Diagnostics) with reagents and calibrators from Roche (6). Soluble TNFR2 (sTNFR2) levels were measured using the Quantikine ELISA Human TNF RII/TNFRSF1B Immunoassay (R&D Systems, Inc., Minneapolis, MN). For the measurement of sTNFR2, we excluded patients [n=25 increased inflammation cohort and n=20 decreased inflammation cohort] who were actively taking etanercept (Enbrel) which can result in spuriously high measured sTNFR2 values in subjects with RA (29).

Lipids and advanced lipoproteins

Banked blood samples were tested for the following: (1) Routine lipids: TC, HDL-C, LDL-C, triglycerides (increase cohort only); (2) advanced lipoprotein measures: apoA1, apoB, lipoprotein(a) [Lp(a)], HDL cholesterol efflux capacity. TC, HDL-C, LDL-C, triglycerides, apoA1, and apoB measurements were performed according to standardized techniques in the clinical laboratories (30,31). We performed HDL cholesterol efflux capacity per published methods by using J774 cells derived from a murine macrophage cell line (25,32). Lipoprotein(a) was measured by a turbidimetric assay on the Roche Cobas 6000 system (33).

Subclinical markers of myocardial injury

Two myocardial markers were measured, high sensitivity cardiac troponin T (hs-cTnT), and NT-proBNP. The hs-cTnT level was analyzed using the Elecsys 2010 system (Roche Diagnostics GmbH, Mannheim, Germany), which has a 5 ng/L limit of detection and a 3 ng/L limit of blank. The 99th percentile cutoff point was 14 ng/L, and the coefficient of variation is <10% at 13 ng/L (34). Clinical cut-offs for hs-cTnT levels were categorized as per prior studies: category 1 (<5ng/L, undetectable), category 2 (5ng/L-14ng/L, intermediate) and category 3 (>14ng/L, >99^th percentile) (17,35). NT-proBNP was measured using a quantitative sandwich enzyme immunoassay technique on the Roche E Modular system (Roche Diagnostics, Indianapolis, IN) (36). Hs-cTnT and NT-proBNP were measured at Boston Children's Hospital, Boston, MA. All assays were performed in duplicate.

Statistical Analysis:

We compared values at baseline and 1-year follow-up using the Wilcoxon signed rank test. For the primary analyses, we tested the correlations between changes in hsCRP and the percentage change in each lipid parameter [(lipid_follow-up – lipid_baseline)/lipid_baseline] by using the Pearson correlation test. Due to the non-normal distribution of hsCRP, we performed all correlation and association studies by using the natural log of the change (increase) in hsCRP (hsCRP_follow-up-hsCRP_baseline). Changes in hs-cTnT were assessed by comparing the proportion of subjects with detectable levels at baseline to the proportion with detectable levels at follow-up. Next, we performed cross-sectional analyses to determine the correlations using the clinically defined cut-offs of hs-cTnT outlined above with demographics, RA specific factors, and the ACC/AHA risk factors and scores. We combined data from both the increase cohort and decrease cohort at time points with elevated inflammation; for the increase cohort we used baseline data, for the decrease cohort we used the follow-up data. To test for presence of a significant trend across the troponin categories, the Jonckheere’s trend test was used for continuous variables and the Cochran-Armitage test for binary variables. The ACC/AHA risk score and risk factors were previously calculated for subjects as described in Yu, et al. (37). All aspects of this study were approved by the Partners Healthcare Institutional Review Board, and the subjects gave informed consent. Analyses were performed by using SAS 9.2 (SAS Institute) and R version 3.6.3.

Results

We studied a total of 196 RA patients, 103 RA patients experienced an increase in inflammation and 93 experienced a decrease in inflammation. For the increase inflammation cohort, the focus of this study, at baseline, the mean age was 59.0 years, 81% were female, 80% were positive for RF or anti-CCP, and had a mean RA disease duration of 17.9 years (Table 1). The mean hsCRP at baseline was 8.7mg/L, and the mean DAS28-CRP was 3.32 (moderate disease activity) (24). The mean hsCRP at 1-year follow-up was 44.6 mg/L (SD 47.7), an absolute increase of 35.9 mg/L (P<0.001). Most subjects (90%) were receiving disease-modifying anti-rheumatic drugs (DMARDs) with the majority on methotrexate (50.5%) or a TNF inhibitor (TNFi) (49.5%). Further details on DMARD use at baseline and follow-up for both cohorts are provided in Table 1. Baseline characteristics of the decreased cohort is also shown, as previously reported (10) (Table 1). We performed systematic chart review of the 103 subjects with increases in inflammation to determine the reasons for this increase from baseline to their 1-year follow-up. The primary reasons for an increase in inflammation were need for tighter control of RA disease (61%), concomitant infection (18%), trauma (8.7%) or other/unknown (~18%). Since subjects on statins were excluded from the study, only one patient had a history of coronary artery disease.

Table 1.

Characteristics of the RA patients^*

	Increased inflammation cohort (n = 103)	Decreased inflammation cohort (n = 93)
Age, mean ± SD years	59.0 ± 12.6	57.6 ± 12.3
Female sex	83 (81)	83 (89)
RA disease duration, mean ± SD years	17.9 ± 12.1	17 ± 12.2
CDAI, mean ± SD	14.8 ± 12.5	26.81 ± 18.39
RF positive	71/99 (72)	67 (72)
Anti-CCP positive	71/102 (70)	69 (78)
hsCRP, median (IQR) mg/liter	4.5 (1.7–8.1)	28.7 (22.1–43.8)
RA treatment
Methotrexate	52 (51)	46 (50)
Tumor necrosis factor inhibitor	51 (50)	42 (45)
Prednisone	27 (26)	35 (38)
Cardiovascular risk factors
Obesity	23/100 (23)	23/92 (25)
Diabetes mellitus	2 (2)	10 (11)
Hyperlipidemia	12/98 (12)	13 (14)
Hypertension	25 (24)	29 (31)

^*Except where indicated otherwise, values are the number (%). RA = rheumatoid arthritis; CDAI = Clinical Disease Activity Index; RF = rheumatoid factor; anti-CCP = anti–cyclic citrullinated peptide; hsCRP = high-sensitivity C-reactive protein; IQR = interquartile range.

Relationship between increase in inflammation and changes in lipid parameters

The median TC at baseline was 188 mg/dl and at follow-up was 175 mg/dl in the increase cohort. The overall median percent change of TC was −9.5% (p < 0.0001) (Figure 1). For LDL-C the median at baseline was 107 mg/dl and at follow-up was 99.3 mg/dl. The overall median percent change was −8.6% (p < 0.0001). The median for triglycerides decreased from 100 mg/dl at baseline to 81 mg/dl at follow-up, with an overall median percent change of −7.7% (p=0.003). Median ApoA decreased from 163.4 mg/dl at baseline to 152.2 mg/dl at follow-up while median ApoB decreased from 91.9 mg/dl to 88.1 mg/dl. The overall median percent change in ApoA was −6.5% (p < 0.0001). The overall median percent change in ApoB was −4.7 (p=0.02). No significant changes in HDL-C, Lp(a), apoB/apoA1, and HDL cholesterol efflux capacity were evident between baseline and 1-year follow-up (Figure 1).

Figure 1.

Changes in lipid parameters at baseline compared to follow-up among RA patients experiencing an increase in inflammation. Boxplots demonstrate the percent change in lipid parameters from baseline to follow-up and the median percent change is shown inside each boxplot. The median change between baseline and follow-up inflammatory markers were: hsCRP + 22.3mg/L. Indicates a p<0.05 from the paired Wilcoxon sign rank test. HDL-C = high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; apoB, apolipoprotein B; apoA1, apolipoprotein A1; Lp(a) lipoprotein(a), hs-CRP, high sensitivity C-reactive protein.

A significant increase in hsCRP was correlated with decreases in total cholesterol, LDL-C, Apo-A1 and ApoB (Figure 2). In keeping with this pattern, a similar relationship existed between these lipid parameter reductions changes and increases in IL-6 and sTNFR2. No significant correlations were observed between hsCRP or IL-6 with TG, HDL-C, Lp(a) and HDL cholesterol efflux. In contrast sTNFR2 was significantly correlated with both lower HDL-C (Figure 2).

Figure 2.

Correlations between the change in lipids and inflammatory markers among RA patients experiencing an increase in inflammation. Numbers shown represent the correlation coefficient. White indicates non-significance, shaded indicates a significant correlation (p<0.05).

Relationship between changes in inflammation with cardiac biomarkers

A high prevalence of detectable hs-cTnT was seen among patients who experienced an increase in inflammation; 45.6% of RA patients (47/103) at baseline had evidence of detectable troponin values (defined as >5ng/L, lower limit of assay detection). At follow-up, 54.4% (56/103) had a detectable troponin, an increase of 8.7% with detectable troponin compared to the baseline measurement (p=0.02). We also observed evidence of increased ventricular wall stress as indicated by NT-proBNP among RA patients experiencing an increase in inflammation. The median NT-proBNP value was 87 (IQR 43.9 - 196.8), which increased to 112 (49.0 - 285.6) at follow-up (p=0.0017), a 28.7% increase. Nearly two-thirds of patients (64%) experienced an increase in NT-proBNP, with a 32% median increase (Figure 3). These data suggest that increases in inflammation in RA patients is associated with both increased myocardial injury and evidence of biochemical LV dysfunction.

Figure 3.

Comparison of subclinical markers of myocardial injury among RA patients who either experience an increase or decrease in inflammation. The boxplots display the distribution of the percent change in the number of patients with detectable hs-cTnT. NT-pro-BNP is the median (and IQR) of the percent change from baseline to follow-up. The median change in inflammatory biomarkers in each cohort is demonstrated in the inset table as a point of reference. *Indicates a p<0.05 from the paired Wilcoxon sign rank test.

Among the RA patients experiencing a decrease in inflammation (n=93), 39/93 (42.4%) had detectable levels of hs-cTnT at baseline. At follow-up there was no significant reductions in hs-cTnT (38/93 [41.3%], p=0.84). Furthermore, there was no change in NT-proBNP following a decrease in inflammation (baseline: 82.3 [IQR 54.3 – 161], and follow-up 74.6 ([IQR 38.1 – 139], p=0.10) (Figure 3). Thus, there was a significant increase in hs-cTnT, translating to an increase of 8.7% of subjects with detectable hs-cTnT compared to baseline, and increased NT-proBNP among RA patients experiencing an increase in inflammation; in contrast, among RA patients experiencing a decrease in inflammation, a corresponding decrease in hs-cTnT and NT-proBNP was not present.

Cross-sectional relationships between hs-cTnT with demographics, RA factors, CV risk factors and the ACC/AHA ASCVD risk score

Combining hs-cTnT data from both cohorts, we observed that cross-sectionally patients with detectable levels of hs-cTnT at baseline were older and had longer RA disease duration (Table 2). NT-proBNP levels correlated positively with increasing levels of hs-cTnT, consistent with prior findings from the general population (38,39) (Table 2). However, there was no significant difference in cardiovascular risk factors of hypertension, hyperlipidemia, obesity, and smoking among patients across categories. A statistically significant difference toward higher levels of inflammation as indicated by hs-CRP and IL-6 measurements, with the strongest trend for elevated levels of sTNFR2, in RA patients with elevated troponin was observed. No statistically significant differences were observed in terms of RA treatment.

Table 2.

Association between troponin categories and ACC/AHA risk among RA patients in both cohorts^*

	Troponin category 1, ≤5 ng/ml (n = 91)	Troponin category 2, >5–14 ng/ml (n = 66)	Troponin category 3, >14 ng/ml (n = 29)	P †
Age, mean ± SD years	51.4 ± 11.4	65.2 ± 8.7	69.1 ± 9.9	<0.0001
Female	82 (90)	53 (80)	22 (76)	0.2
RA disease duration, mean ± SD years	16.2 ± 11.2	19.6 ± 13.4	22.1 ± 12.3	0.02
NT-proBNP, median (IQR) ng/liter	67.9 (42.5–112.1)	123.6 (62.4–273.3)	482.4 (247.4–799.7)	<0.0001
Inflammation markers, median (IQR)
hsCRP, mg/liter	26 (18.7–42)	29 (23.9–46.5)	28.7 (21.9–59.5)	0.05
IL-6, pg/ml	13.8 (7.5–44.2)	17.5 (10–40.7)	32.3 (15.1–67.8)	0.03
sTNFRII, pg/ml‡	3,361 (3,010–4,735)	3,823 (3,040–5,545)	4,947 (3,814–6,925)	<0.0001
RA treatment
Current steroid use	29 (32)	25 (38)	14 (48)	0.2
DMARDs	77 (84)	57 (88)	23 (79)	0.94
TNFi	42 (47)	33 (49)	8 (28)	0.25
Routinely measured lipids, mean ± SD mg/dl
HDL cholesterol	61.5 ± 19.9	60.8 ± 19	55.6 ± 16	0.3
LDL cholesterol	103.8 ± 28	101.4 ± 33.3	95.2 ± 30.2	0.17
Triglycerides§	109.4 ± 73.6	95.2 ± 48.7	96.7 ± 29.3	0.8
Total cholesterol	185 ± 34	181.2 ± 45.4	172.7 ± 39.7	0.17
Cardiovascular risk factors
Obesity	23/90 (26)	16/64 (25)	4/28 (14)	0.5
Diabetes mellitus	5 (6)	5 (8)	1 (3)	0.9
Hyperlipidemia	5 (6)	8 (12)	2 (7)	0.7
Hypertension	19 (21)	18 (27)	11 (38)	0.2
Active smoker	2/78 (3)	2/57 (4)	2/28 (7)	0.8
ACC/AHA ASCVD risk score, median (IQR)	2.7 (0.8–5.4)	8.2 (3.8–13.9)	11.4 (5.7–21)	<0.0001

^*Except where indicated otherwise, values are the number (%). ACC = American College of Cardiology; AHA = American Heart Association; RA = rheumatoid arthritis; NT-proBNP = N-terminal pro–brain natriuretic peptide; IQR = interquartile range; hsCRP = high-sensitivity C-reactive protein; IL-6 = interleukin-6; sTNFRII = soluble tumor necrosis factor receptor type II; DMARDs = disease-modifying antirheumatic drugs; TNFi = tumor necrosis factor inhibitor; HDL = high-density lipoprotein; LDL = low-density lipoprotein; ASCVD = atherosclerotic cardiovascular disease.

^†By Jonckheere-Terpstra test.

^‡Patients currently receiving etanercept therapy were excluded from this analysis (n = 134).

^§Triglyceride values were available for the increased inflammation cohort only (n = 103).

In patients with undetectable troponin, the median ASCVD 10-year risk score was calculated to be 2.52 (IQR 0.92-6.54, n=45) at baseline and 2.04 (IQR 1.03-5.63, n=37) at follow-up during increased inflammation. In contrast, RA patients with detectable troponin had a median ACC/AHA ASCVD risk score of 9.6 (IQR 4.2-15.8, n=39) at baseline and 8.54 (IQR 3.85-14.16, n=47) at follow up during increased inflammation. Patients with undetectable troponin fell into a low risk category based on ACC/AHA ASCVD risk (40), whereas patients with elevated troponin had an intermediate risk..

Discussion

In summary, we observed a divergence between lipid and myocardial biomarkers among RA patients experiencing an increase in inflammation. Specifically, increased inflammation was associated with an increase in patients with detectable hs-cTnT levels while a reduction in LDL-C was observed, a change typically associated with reduced CV risk. However, in contrast, among RA patients experiencing a decrease in inflammation, a reduction of circulating hs-cTnT levels was not observed. These results suggest that the subclinical injury occurring with increased inflammation may not be readily reversible with control of inflammation. Additionally, we observed a relatively high prevalence (45%) of RA patients with detectable hs-cTnT at baseline in both cohorts despite a low prevalence of cardiovascular risk factors.

For routine lipids, an increase in inflammation was generally associated with a significant decrease in LDL-C, TG, and TC levels. Among the advanced lipoproteins a decrease in apoB levels was observed with increases in inflammation, in line with the decrease in LDL-C levels observed. No significant change in HDL-C levels was observed, however, a significant reduction in apoA1 was present, without a change in HDL CEC. As a comparison, our previous studied showed that a reduction in inflammation was also associated with no change in HDL-C or apoA1, but was associated with significant improvement in HDL CEC (10). Thus, for apoA1 and HDL CEC, changes occurring as a result of an increase in inflammation were not necessarily mirrored with a decrease in inflammation. Lp(a) levels did not change with increased inflammation, in line with evidence that Lp(a) levels is under strong genetic control by the lipoprotein A (LPA) gene locus (41).

We additionally tested the association with lipids and two inflammatory markers representing two key inflammatory pathways in RA, namely IL-6 and sTNFR2. IL-6 had similar associations with hsCRP. We observed an inverse association between IL-6 with TC, LDL-C, and apoB. Interestingly, the relationships between changes in sTNFR2 differed from hsCRP and IL-6. Higher levels of sTNFR2 were also significantly correlated with lower HDL-C and ApoA1, whereas these associations were not observed with the other inflammatory biomarkers. These results suggest possible specificity regarding the impact of inflammatory pathways on apoA1 versus apoB containing lipoproteins. Supporting this hypothesis is that TNF-alpha has been demonstrated to attenuate the expression of ABCA1 expression and cholesterol efflux to apoA1 (42).

Despite decreased levels of LDL-C, subjects had evidence of increased myocardial injury reflected by higher hs-cTnT after an increase in inflammation. RA patients who experienced a significant reduction in inflammation continued to have evidence of myocardial injury detected by the presence of hs-cTnT and NT-proBNP. The lack of change in the decrease inflammation cohort could represent the fact that there are a high proportion of patients that have coronary atherosclerosis given the long disease duration of 18 years. Levels of hs-cTnT have been shown to associate with coronary plaque features and if the levels are a reflection of coronary plaque than meaningful plaque stabilization will likely take longer than the one-year follow-up in this study (43,44). These findings support the hypothesis that transient increases in inflammation may promote subclinical cardiac damage over time, and that cumulative damage may contribute to an elevated CV risk.

In the present study, stratifying patients on degree of troponin elevation demonstrated a trend of increased CV risk estimated by the 10-year ACC/AHA ASCVD risk score. Higher levels of troponin were also associated with increased age and male sex, both strong risk factors and components of the ASCVD risk score. The troponin-negative RA patients had a median risk score of 2.7, which would be considered ‘low-risk’; however, troponin positive patients fell in an ‘intermediate’ category of risk in our analysis. We believe that these data highlight the need for future work to investigate the role of hs-cTnT in RA CV risk classification. Indeed, this is a rapidly evolving field. Previous work has demonstrated that there is a high-prevalence of circulating hs-cTnT in a population of ambulatory community-dwelling individuals aged 65 years or older and demonstrated that a >50% increase in cTnT levels was associated with a 60% higher risk of both heart failure and CV death, whereas a decrease in cTnT levels by >50% was associated with a 30% decreased risk in both heart failure and CV death (17). Furthermore, a recent study that sought to investigate whether low-level positive hs-cTnI values could complement the AHA/ACC management guidelines to improve the ASCVD risk classification in patients with established CVD found that patients with a lower-risk ASCVD score and hs-cTnI levels >6ng/l had the same rate of cardiovascular events as patients classified as high-risk ASCVD. Similarly, patients with a very high risk ASCVD risk score but undetectable troponin had event rates similar to those of patients classified as lower risk ASCVD. Although this study focused on a high risk population with established CVD, it highlights the way in which hs-cTn could be utilized as a way to risk stratify and identify patients at higher risk of CV outcomes (45). We focused on hs-cTnT in this study rather than NT-proBNP due to the availability of clinical cut-offs for the former, facilitating interpretation. Future studies are needed to understand if elevated hsTnT in RA patients confers a higher risk of major adverse cardiovascular events (MACE) and CV mortality and the utility of hsTnT as part of CV risk prediction models.

Current CV risk estimators underestimate CV risk in RA by 2-fold in women with RA (2). Indeed, the European League Against Rheumatism (EULAR) recommends a 1.5 multiplication factor to the ASCVD risk score and recommends to assess the lipid profile when a patient has low disease activity or is in remission (46). Furthermore, RA-adapted scores which incorporate inflammation, RA specific factors, or disease activity (QRISK2, ERS-RA, ATACC-RA) have not been shown to improve CV risk prediction in some cohorts and not others (47-50). Thus, the addition of further information that incorporates CV imaging or serum biomarkers could be utilized to improve the robustness of CV risk prediction in RA.

The role of statin use for the primary prevention of CV events is still not firmly established in RA patients, which may help explain significant underuse of statins in RA patients who otherwise fulfill general population thresholds for statin treatment (51). All RA patients in this study were not on statin treatment as part of the inclusion criteria to interpret the changes in lipids with inflammation. Limited evidence exists for the use of statins for primary prevention of CV events in RA (52). It is also unknown whether statins might lead to a reduction in hs-cTnT levels among RA patients.

There are several limitations in this study. Lipid measurements were not performed in the fasting state. However, this is not standard of care in routine cardiovascular practice as data suggests that the variation between fasting and non-fasting states for TC, LDL-C, and HDL-C do not significantly impact CV risk estimates (53). We could not account for patients who underwent lifestyle modifications that could result in lipid modifications; prior studies indicate changes in lipoproteins in response to lifestyle are modest (54,55). Heterogeneous reasons for increased inflammation in RA may lead to complex interactions between inflammation and lipids that were not measured in this study.

Additionally, the focus of this study was to examine the impact of inflammation in lipid, inflammatory and cardiac biomarker parameters. Thus, it could not test associations between individual drug classes with these changes. Based on prior clinical trial data, the effect of RA treatments on lipids was consistent throughout drug classes, whereby treatment with methotrexate, triple therapy, or TNFi resulted in increases in both LDL-C and TC (9,11). While there are limited data on glucocorticoids and lipids, steroid use was associated with increased HDL-C and no change in LDL-C or TC/HDL ratio compared to no glucocorticoid use in RA patients (56,57).

In conclusion, we observed that RA patients who experience an increase in inflammation have a reduction in routine lipids, LDL-C, TG, and TC while also manifesting increases in hs-cTnT and NT-pro-BNP. Concomitantly, RA patients who have a decrease in inflammation did not show a decrease in these same biomarkers, suggesting that subclinical myocardial injury and stress may accrue over time and lead to increased CV risk. The high prevalence of RA subjects with circulating hs-cTnT, and findings that elevated hs-cTnT persists with increased inflammation, suggests that hs-cTnT may be a useful biomarker to include in future efforts to improve CV risk stratification in patients with RA.