Association of the Novel Inflammatory Marker GlycA and Incident Heart Failure and Its Subtypes of Preserved and Reduced Ejection Fraction
What Is New?
-
Higher GlycA has been shown to be associated with cardiovascular diseases and may be a more reliable prognostic marker of HF; however, its relation with HF subtypes, specifically, was unknown.
-
In a multiethnic cohort followed for a median of 14 years, we found higher plasma GlycA levels were associated with heart failure with preserved ejection fraction but not heart failure with reduced ejection fraction, independently of traditional cardiovascular disease risk factors and other inflammatory markers.
-
Other inflammatory markers (hsCRP [high-sensitivity C-reactive protein], IL-6 [interleukin-6], and fibrinogen) were not independently associated with either HF type in mutually adjusted models including GlycA.
What are the Clinical Implications?
-
Our findings lend support to the current understanding of the different pathophysiology of heart failure with preserved ejection fraction and heart failure with reduced ejection fraction and suggest that inflammatory markers may offer different prognostic information based on HF subtype.
-
Our findings provide rationale for further study of GlycA as a biomarker to improve HF risk prediction or to direct therapy.
-
Future studies should examine mechanisms that might explain differential association of GlycA with heart failure with preserved ejection fraction versus heart failure with reduced ejection fraction, and whether therapeutic lowering of GlycA can prevent heart failure with preserved ejection fraction development.
Introduction
The global burden of heart failure (HF) is tremendous, affecting over 23 million people worldwide.1 The estimated prevalence of HF is expected to increase by 46% from 2012 to 2030, which translates to >8 million people in the United States alone, with a corresponding increase in total healthcare cost from $21 billion to $53 billion.2 Thus, prevention and early detection of HF is imperative to improve clinical outcomes and reduce healthcare costs. The American College of Cardiology/American Heart Association guidelines have classified HF into 2 broad categories: HF with reduced ejection fraction (HFrEF), and HF with preserved ejection fraction (HFpEF).3
Blood biomarkers may serve as prognostic and diagnostic tools to evaluate the risk of HF development, detect the presence of HF, and guide therapy. To date, numerous biomarkers have been studied and found associated with HF. Some examples are hsCRP (high-sensitivity C-reactive protein), IL-6 (interleukin-6), and TNF (tumor necrosis factor-α) (eg, inflammatory markers), procollagen type III aminopropeptide, and interleukin 33/ST2 (extracellular matrix remodeling marker), natriuretic peptides (wall strain marker), copeptin (neurohormonal activation marker), and cardiac troponins (cardiomyocyte injury marker).4 The different types of biomarkers show the complexity of HF pathogenesis and progression.
Inflammatory activation has been shown to play a key role in HF pathogenesis.5,6
Inflammatory cytokine levels are not only increased in patients with HF but also correlated with the severity of the disease.7 Cytokines like TNF-α can trigger electrolyte imbalance which contributes to adverse cardiac remodeling leading to HF.8 Notably, inflammatory biomarkers are elevated relatively early in the disease progress, which makes them potentially useful in predicting risk of HF development unlike other biomarkers representing neurohormonal activation or wall strain, which are elevated in more advanced disease states.9 Although inflammation can contribute to the whole spectrum of HF phenotypes, inflammation seems to be more strongly associated with HFpEF than with HFrEF.6,10,11 While several studies have shown the prognostic value of the inflammatory biomarkers in HF,12–15 their associations with incident HF have been inconsistent.16 Thus, there is interest in finding a more reliable marker which may better predict inflammatory-mediated HF risk.
GlycA has the potential to be a more superior biomarker for measuring inflammation and predicting cardiovascular disease (CVD) outcomes. GlycA is a novel biomarker for systemic inflammation that reflects the integrated concentrations and glycosylation states of several abundant acute-phase inflammatory proteins, including α1-acid glycoprotein, haptoglobin, α1-antitrypsin, α1-antichymotrypsin, and transferrin.17 Measured by nuclear magnetic resonance spectroscopy, GlycA can serve as a reliable biomarker compared with other common inflammatory markers, because of its low intraindividual variability and greater analytic precision.17 Prior studies from the Multi-Ethnic Study of Atherosclerosis (MESA) and other cohorts have shown higher plasma GlycA levels were associated with increased risk of CVD events, peripheral arterial disease, and mortality, even after adjusting for other inflammatory markers.14,18–22 GlycA is also closely linked to obesity and other metabolic risk factors that predispose to cardiac remodeling.23 However, the association of GlycA with HF subtypes has not been previously evaluated. As HFpEF and HFrEF have different etiologic profiles, comparing associations of GlycA with the different HF subtypes may lead to a discovery of a better prediction marker or therapeutic target for HF.
Therefore, we examined the association of GlycA with HF and its subtypes in a multiethnic cohort free of baseline CVD. Given the stronger association of other inflammatory markers with HFpEF than with HFrEF,6,10,11 we hypothesized that higher plasma GlycA levels will similarly have a stronger association with HFpEF than HFrEF, and that the association of GlycA with HFpEF will be independent of traditional CVD risk factors and other inflammatory markers.
Material and Methods
Transparency and Openness
Requests for access to MESA data can be made through the NIH BioLincc Open program at: https://biolincc.nhlbi.nih.gov/studies/mesa/.
Study Sample
A detailed description of the MESA study design has been previously published.24 MESA is a concurrent cohort study which enrolled ethnically diverse men and women free of known CVD, including HF, at baseline from 6 different field centers in the United States to follow the progression of CVD, study its risk factors, and assess its characteristics. At study entry, MESA recruited a total of 6814 individuals from ages 45 to 84 years old, whose racial/ethnic distribution was 38% White, 28% African, 22% Hispanic, and 12% Asian. Since the initial visit between 2000 and 2002, there have been 5 subsequent visits, at which participant demographics, medical history, and physical examination results were collected, and continuous follow-up for clinical CVD events. Participants excluded from this study were those missing incident HF follow-up data, GlycA measurement at baseline, or covariate data. The primary analysis was comprised of 6507 participants (Figure 1). The MESA study received approval from the institutional review boards at each participating field center and obtained informed consent from each study participant.
Figure 1. Flow diagram illustrating study sample inclusion and exclusion criteria. EF indicates ejection fraction; HF, heart failure; HFpEF, heart failure with preserved EF; HFrEF, heart failure with reduced EF; and MESA, Multi-Ethnic Study of Atherosclerosis.
GlycA Assessment
GlycA was measured from EDTA plasma samples drawn after a 12-hour fast during the MESA baseline visit (2000–2002) and stored at −70°C. The GlycA nuclear magnetic resonance signal was quantified by NMR LipoProfile testing as previously described.14,25 This assay detects the level of N-acetyl methyl group protons on the N-acetylglucosamine residues of the glycan portion of several abundant acute phase serum proteins, which are elevated during inflammation.17 The intraassay and inter-assay coefficients of variation for the GlycA assay were 1.9% and 2.6%, respectively. Prior work from MESA found that the intraindividual variability of GlycA, assessed weekly for 5 weeks in 23 healthy volunteers, was 4.3%, lower than for hsCRP which was 29.2%.17 GlycA levels have previously been shown to be similar regardless of the length of storage, type of samples, and fasting state.17
Covariates
Covariates of interest for this analysis were obtained at visit 1 and included demographics (age, sex, race/ethnicity, and MESA site), behavioral factors (smoking status, pack-years of smoking, and physical activity), socioeconomic factors (education, health insurance), body mass index, traditional CVD risk factors (systolic blood pressure, use of antihypertensive medication, total cholesterol, HDL-cholesterol, use of lipid-lowering medication, diabetes mellitus, and estimated glomerular filtration rate), and laboratory markers (NT-proBNP [N-terminal pro-B-type natriuretic peptide]) and other inflammatory markers (hsCRP, IL-6, fibrinogen).
Level of education was categorized into 9 levels: no schooling, grades 1 to 8, grades 9 to 11, completed high school/GED, some college but no degree, technical school certificate, associate degree, bachelor’s degree, graduate or professional school. Height and weight were measured according to the standard protocol, and body mass index was calculated as weight divided by squared height (kg/m2). Total amount of moderate and vigorous physical activity was estimated in metabolic equivalent minutes per week using a 28-item Typical Week Physical Activity Questionnaire.26 Baseline systolic and diastolic blood pressure was measured in the seated position with the Dinamap automated blood pressure device, and the average of the last 2 out of 3 measurements were used in analyses. The chronic kidney disease epidemiology collaboration formula was used to calculate the estimated glomerular filtration rate.27 A positive diabetes status was determined by a fasting blood glucose level ≥126 mg/dL, self-reported diagnosis of diabetes mellitus, or use of diabetes medication, which was assessed through a medication inventory approach. Other inflammatory biomarkers, including hsCRP, IL-6, and fibrinogen were measured from the stored plasma samples obtained at the baseline examination by methods previously reported.28,29 NT-proBNP was measured using the Elecsys proBNP immunoassay (Roche Diagnostics Corporation, Indianapolis, IN).30
Outcomes Assessment
MESA study participants or their next of kin were contacted every 9 to 12 months after enrollment and asked about interim hospitalizations. Hospitalized HF events were adjudicated by a physician committee on the basis of the medical records using standardized criteria.31–33 We evaluated probable or definite HF events, with probable HF defined as a physician diagnosis and medical treatment for HF, and definite HF requiring at least one additional objective findings such as evidence of pulmonary congestion on chest X-ray, reduced left ventricular EF by echocardiography or ventriculography, or evidence of left ventricular diastolic dysfunction.31 HF events were censored in 2015 and dichotomized by EF reported in the hospital record and defined as HFpEF if EF ≥50% and HFrEF if EF <50%. There were not enough events to consider 3 categories of HF subtypes including HF with mid-range EF (40%–49%). HF events with missing EF in the medical records were omitted from the analysis (Figure 1).
Statistical Analysis
We modeled plasma GlycA level in quartiles as well as continuously per 1 SD increment. Descriptive statistics were used to present baseline characteristics by presence of HF or its subtypes, HFpEF or HFrEF. We presented continuous variables as means (SD) or median (interquartile interval) and categorical variables as frequency (percent). Participants were followed from the first visit until the development of a study end point, death, drop-out, or until December 31, 2015. Unadjusted HF incident rates were calculated per 1000-person years. We tested and satisfied the proportional hazards assumption using Schoenfeld residuals. Multivariable-adjusted Cox proportional hazard regression models were used to estimate hazard ratios (HRs) and their 95% CIs for the association of GlycA with incident total HF events and the subtypes of HFpEF or HFrEF.
We evaluated 4 progressively adjusted models. For model 1, we adjusted for demographics (age, sex, race/ethnicity, MESA site). In model 2, we adjusted for all model 1 covariates as well as socioeconomic, behavioral, and adiposity measures (education, health insurance, body mass index, smoking status, pack-years of smoking, and physical activity). For model 3, we adjusted for CVD risk factors (systolic blood pressure, use of antihypertensive medication, total cholesterol, HDL-cholesterol, use of lipid-lowering medication, diabetes mellitus, and estimated glomerular filtration rate) in addition to all covariates adjusted in model 2. In model 4 (our primary model), we further adjusted for other commonly studied inflammatory markers: hsCRP, IL-6, and fibrinogen, which were log-transformed for analysis. Finally, we performed a supplemental analysis (model 5) where we adjusted for the CVD risk factors in model 3 plus NT-proBNP, a biomarker of wall strain which may reflect subclinical HF.
We performed several additional analyses. First, we used restricted cubic splines adjusted for the variables in Model 4 with knots placed at the 5th, 25th, 65th, 95th percentiles to allow a more flexible distribution in characterizing the nonlinear association between GlycA (continuous) levels with incident HF, HFpEF, and HFrEF. Second, we tested for multiplicative effect modification of the association of GlycA with each HF subtype by age, sex, and race/ethnicity, also using Model 4. Third, we performed a sensitivity analysis examining the association of GlycA with HF and its subtypes after excluding individuals who had interim ASCVD (fatal and nonfatal coronary heart disease and stroke events; atherosclerotic CVD), using the same 4 primary models.
The analyses were performed using STATA version 15.0 (StataCorp LP, College Station, TX). P values were 2-sided, with significance level set at 0.05.
Results
Baseline Characteristics
The baseline characteristics of the 6507 participants included in the analyses are shown in Table 1 by HF status. Among the sample, the mean (SD) for age was 62 (10) years with 53% being women, 39% White, 27% Black, 22% Hispanic, and 12% Chinese. The mean (SD) for plasma GlycA level was 375 (82) μmol/L. Over a median (interquartile interval) follow-up time of 14.0 (11.5–14.7) years, a total of 319 participants (5%) experienced HF. Among those who experienced HF with available data on systolic function, we did not have data on the EF of 22 (7%) participants (ie, HF with unknown subtype). Of the remaining 297 participants, 135 (42%) people experienced HFpEF and 162 (51%) experienced HFrEF (Figure 1).
| Total | HF | No HF | P Value | HFpEF | HFrEF | P Value | |
|---|---|---|---|---|---|---|---|
| N | 6507 | 319 (5%) | 6188 (95%) | 135 (42%)* | 162 (51%)* | ||
| Age, y† | 62 (10) | 68 (9) | 62 (10) | <0.001 | 69 (9) | 67 (9) | 0.03 |
| <65 y | 3667 (56%) | 95 (30%) | 3572 (58%) | <0.001 | 34 (25%) | 57 (35%) | 0.06 |
| ≥65 y | 2840 (44%) | 224 (70%) | 2616 (42%) | 101 (75%) | 105 (65%) | ||
| Sex | |||||||
| Men | 3065 (47%) | 191 (60%) | 2874 (46%) | <0.001 | 66 (49%) | 112 (69%) | <0.001 |
| Women | 3442 (53%) | 128 (40%) | 3314 (54%) | 69 (51%) | 50 (31%) | ||
| Race/ethnicity | |||||||
| White | 2532 (39%) | 130 (41%) | 2402 (39%) | 0.04 | 59 (44%) | 62 (38%) | 0.007 |
| Chinese American | 787 (12%) | 23 (7%) | 764 (12%) | 14 (10%) | 4 (2%) | ||
| Black | 1757 (27%) | 96 (30%) | 1661 (27%) | 33 (24%) | 61 (38%) | ||
| Hispanic | 1431 (22%) | 70 (22%) | 1361 (22%) | 29 (21%) | 35 (22%) | ||
| Education | |||||||
| ≥Bachelor’s degree | 2304 (35%) | 96 (30%) | 2208 (36%) | 0.04 | 42 (31%) | 47 (29%) | 0.69 |
| <Bachelor’s degree | 4203 (65%) | 223 (70%) | 3980 (64%) | 93 (69%) | 115 (71%) | ||
| BMI, kg/m2† | 28 (5) | 30 (6) | 28 (5) | <0.001 | 30 (6) | 29 (5) | 0.14 |
| Current smoker | 832 (13%) | 45 (14%) | 787 (13%) | 0.007 | 16 (12%) | 27 (17%) | 0.50 |
| Former smoker | 2390 (37%) | 140(44%) | 2250 (36%) | 62 (46%) | 70 (43%) | ||
| Never smoker | 3285 (50%) | 134 (42%) | 3151 (51%) | 57 (42%) | 65 (40%) | ||
| Pack-years of smoking if >0, median (IQI)‡ | 17 (6–33) | 20 (7–38) | 16 (6–32) | 0.01 | 21 (7–46) | 18 (7–31) | 0.03 |
| Physical activity MET-min/wk‡ | 4028 (1985–7530) | 3540 (1680–6480) | 4050 (1995–7576) | 0.17 | 3255 (2100–5330) | 3966 (1680–7943) | 0.23 |
| Systolic blood pressure, mm Hg† | 126 (22) | 138 (23) | 126 (21) | <0.001 | 139 (23) | 137 (23) | 0.54 |
| eGFR, ml/min per 1.73m2† | 78 (16) | 72 (19) | 78 (16) | <0.001 | 73 (18) | 71 (19) | 0.48 |
| Total cholesterol, mg/dL† | 194 (35) | 190 (35) | 194 (35) | 0.05 | 189 (35) | 190 (37) | 0.88 |
| HDL-C, mg/dL† | 51 (15) | 48 (14) | 51 (15) | 0.001 | 50 (14) | 47 (13) | 0.04 |
| Diabetes mellitus | 810 (12%) | 91 (29%) | 719 (12%) | <0.001 | 36 (27%) | 45 (28%) | 0.97 |
| Antihypertensive medication | 2405 (37%) | 188 (59%) | 2217 (36%) | <0.001 | 80 (59%) | 93 (57%) | 0.75 |
| Lipid-lowering medication | 1065 (16%) | 61 (19%) | 1004 (16%) | 0.17 | 23 (17%) | 34 (21%) | 0.39 |
| NT-proBNP‡ | 53 (24–107) | 117 (59–251) | 51 (23–102) | <0.001 | 117 (61–232) | 125 (59–269) | 0.28 |
| GlycA, μmol/L‡ | 375 (337–419) | 389 (352–438) | 374 (337–419) | <0.001 | 393 (361–447) | 381 (345–427) | 0.01 |
| hsCRP, mg/L‡ | 1.9 (0.8–4.2) | 2.5 (1.1–5.0) | 1.9 (0.8–4.1) | 0.003 | 2.5 (1.1–5.6) | 2.4 (1.2–4.6) | 0.13 |
| IL-6 pg/mL‡ | 1.2 (0.8–1.9) | 1.5 (1.1–2.5) | 1.2 (0.8–1.9) | <0.001 | 1.6 (1.1–2.7) | 1.5 (1.0–2.3) | 0.15 |
| Fibrinogen, mg/dL‡ | 337 (295–388) | 352 (314–406) | 337 (294–387) | <0.001 | 349 (319–408) | 352 (303–402) | 0.38 |
Those who developed incident total HF tended to be older, White, men, and have a higher body mass index, median pack-years of smoking, systolic blood pressure, prevalence of diabetes mellitus, higher median NT-proBNP, and higher median level of inflammatory biomarkers, such as GlycA, hsCRP, IL-6, and fibrinogen. In addition, they had lower average estimated glomerular filtration rate, total cholesterol, and HDL-C. Participants with any HF were also more likely to take medications for hypertension. Among people who developed any HF, those with HFpEF tended to be older, women, white, and have a higher median pack-years of smoking, and HDL-C than those with HFrEF. Moreover, patients with HFpEF showed elevated levels of GlycA compared with those with HFrEF. There was no significant difference in NT-proBNP levels between those with incident HFpEF and HFrEF.
Associations of GlycA with HF Subtypes
The incidence rates and HRs for the association of GlycA with HF subtypes are shown in Table 2. Among our study participants, incidence rate (95% CI) per 1000 person-years for any HF, HFpEF, and HFrEF were 4.0 (3.6–4.5), 1.7 (1.4–2.0), and 2.0 (1.7–2.4), respectively.
| GlycA in μmol/L (Median [IQI]) | Quartile 1 (314 [294–327]) | Quartile 2 (358 [348–367]) | Quartile 3 (396 [386–406]) | Quartile 4 (451 [434–477]) | Per 1 SD (61 μmol/L) Increment |
|---|---|---|---|---|---|
| Incidence rates (95% CI) and hazard ratios (95% CI) of total HF events (n=319) by GlycA: N=6507† | |||||
| N | 1647 | 1608 | 1631 | 1621 | 6507 |
| Cases | 58 | 74 | 78 | 109 | 319 |
| Incidence rate‡ | 2.8 (2.2–3.6) | 3.7 (2.9–4.6) | 3.9 (3.1–4.9) | 5.7 (4.7–6.9) | 4.0 (3.6–4.5) |
| Hazard ratio§ | |||||
| Model 1 | 1 (reference) | 1.30 (0.92–1.83) | 1.46 (1.03–2.06)* | 2.46 (1.76–3.43)* | 1.43 (1.29–1.59)* |
| Model 2 | 1 (reference) | 1.21 (0.86–1.71) | 1.26 (0.89–1.79) | 2.03 (1.44–2.86)* | 1.36 (1.21–1.52)* |
| Model 3 | 1 (reference) | 1.12 (0.79–1.59) | 1.13 (0.79–1.61) | 1.69 (1.19–2.40)* | 1.26 (1.13–1.42)* |
| Model 4 | 1 (reference) | 1.09 (0.77–1.55) | 1.06 (0.74–1.53) | 1.48 (1.01–2.18)* | 1.22 (1.07–1.39)* |
| Model 5 | 1 (reference) | 1.17 (0.82–1.66) | 1.13 (0.79–1.61) | 1.60 (1.12–2.28)* | 1.20 (1.07–1.35)* |
| Incidence rates (95% CI) and hazard ratios (95% CI) of HFpEF (n=135) by GlycA: N=6507† | |||||
| N | 1647 | 1608 | 1631 | 1621 | 6507 |
| Cases | 18 | 30 | 36 | 51 | 135 |
| Incidence rate‡ | 0.9 (0.5–1.4) | 1.5 (1.0–2.1) | 1.8 (1.3–2.5) | 2.7 (2.0–3.5) | 1.7 (1.4–2.0) |
| Hazard ratio§ | |||||
| Model 1 | 1 (reference) | 1.67 (0.93–3.00) | 2.03 (1.14–3.60)* | 3.30 (1.88–5.79)* | 1.56 (1.33–1.82)* |
| Model 2 | 1 (reference) | 1.50 (0.83–2.71) | 1.65 (0.92–2.96) | 2.57 (1.44–4.59)* | 1.47 (1.24–1.74)* |
| Model 3 | 1 (reference) | 1.42 (0.78–2.57) | 1.60 (0.88–2.88) | 2.38 (1.32–4.29)* | 1.41 (1.19–1.67)* |
| Model 4 | 1 (reference) | 1.40 (0.77–2.54) | 1.55 (0.85–2.84) | 2.18 (1.15–4.13)* | 1.41 (1.15–1.71)* |
| Model 5 | 1 (reference) | 1.47 (0.81–2.67) | 1.61 (0.89–2.91) | 2.41 (1.33–4.37)* | 1.38 (1.17–1.64)* |
| Incidence rates (95% CI) and hazard ratios (95% CI) of HFrEF (n=162) by GlycA: N=6507† | |||||
| N | 1647 | 1608 | 1631 | 1621 | 6507 |
| Cases | 38 | 38 | 37 | 49 | 162 |
| Incidence rate‡ | 1.8 (1.3–2.5) | 1.9 (1.4–2.6) | 1.9 (1.3–2.6) | 2.6 (1.9–3.4) | 2.0 (1.7–2.4) |
| Hazard ratio§ | |||||
| Model 1 | 1 (reference) | 1.02 (0.65–1.60) | 1.09 (0.69–1.73) | 1.81 (1.16–2.82)* | 1.28 (1.10–1.50)* |
| Model 2 | 1 (reference) | 0.98 (0.62–1.55) | 0.98 (0.61–1.56) | 1.57 (0.99–2.48) | 1.22 (1.04–1.45)* |
| Model 3 | 1 (reference) | 0.91 (0.58–1.44) | 0.84 (0.53–1.35) | 1.24 (0.78–1.98) | 1.12 (0.94–1.33) |
| Model 4 | 1 (reference) | 0.88 (0.55–1.39) | 0.78 (0.48–1.27) | 1.06 (0.63–1.79) | 1.05 (0.86–1.28) |
| Model 5 | 1 (reference) | 0.94 (0.59–1.48) | 0.84 (0.52–1.35) | 1.08 (0.66–1.74) | 1.03 (0.87–1.22) |
After adjusting for demographics, such as age, sex, race/ethnicity, and MESA site (model 1), compared with the lowest GlycA quartile, the adjusted HRs (95% CI) for incident total HF were 1.30 (0.92–1.83), 1.46 (1.03–2.06), and 2.46 (1.76–3.43) for the second, third, and fourth quartiles, respectively, showing statistically significant associations between any incident HF and the top 2 quartiles of GlycA. The highest GlycA quartile remained significantly positively associated with any HF after further adjustment for socioeconomic and behavioral factors (model 2), CVD risk factors (model 3), and other biomarkers for inflammation (model 4), but the association was attenuated.
The adjusted HRs (95% CI) for HFpEF using model 1 were 1.67 (0.93–3.00), 2.03 (1.14–3.60), and 3.30 (1.88–5.79) for the second, third, and fourth quartiles of GlycA, compared with the first quartile. Having a similar pattern with the overall HF analysis, the top 2 quartiles of GlycA showed statistically significant association with HFpEF, and the highest quartile remained positively associated with HFpEF in all models. Notably, in the primary model 4, which was adjusted for CVD risk factors and other inflammatory markers, there was a 2-fold increased risk of incident HFpEF for the highest quartile of GlycA compared with the lowest (2.18 [1.15–4.13]).
The adjusted HRs (95% CI) for HFrEF using model 1 were 1.02 (0.65–1.60), 1.09 (0.69–1.73), and 1.81 (1.16–2.82). Unlike for HFpEF, HFrEF was only significantly positively associated with the highest GlycA quartile, and was no longer statistically significant upon additional adjustments in models 2, 3, and 4.
Of note, in our primary model 4, which adjusted for CVD risk factors and all of the inflammatory markers in the same model, while GlycA was associated with any HF and HFpEF risk, there was no independent association of hsCRP, IL-6, or fibrinogen with either HF outcome in this mutually adjusted model. Additionally, in our exploratory model adjusting for CVD risk factors plus NT-proBNP (model 5), GlycA remained still significantly associated with any HF and with HFpEF.
There were no significant interactions of GlycA with either HF subtypes by sex, age, or race/ethnicity (P values shown in footnote to Table 2). In restricted cubic spline models, the association of GlycA with risk of any HF and its subtypes was generally linear for all outcomes but was stronger for HFpEF than HFrEF (Figure 2).
Figure 2. Adjusted* restricted cubic spline models showing the association of GlycA levels with hazard ratio of (A) heart failure (HF), (B) heart failure with preserved ejection fraction (HFpEF), (C) heart failure with reduced ejection fraction (HFrEF). CRP indictes C reactive protein; eGFR, estimated glomerular filtration rate; HDL, high density lipoprotein; and IL, interleukin. *Spline models adjusted for age, sex, and race/ethnicity, Multi-Ethnic Study of Atherosclerosis site, education, health insurance, body mass index, smoking status, pack-years of smoking, physical activity, systolic blood pressure, use of antihypertensive medication, total cholesterol, HDL-cholesterol, use of lipid-lowering medications, diabetes, eGFR, ln(CRP), ln(IL-6) and ln(fibrinogen). The 4 knots are at 5th, 25th, 65th, and 95th percentiles. Black curves represent the hazard ratio for the type of HF by proportion of population with the respective GlycA concentration. The 95% CI is represented by the gray shadow.
Given that GlycA has already been demonstrated to be associated with incident CVD events, we performed a sensitivity analysis to determine whether GlycA was still associated with any HF and its subtypes among those who had not had an interim CVD event (Table I in the Data Supplement). With exclusion of participants with interim CVD events (n=893) after the baseline visit, the model 1-adjusted HR (95% CI) per 1 SD higher GlycA was 1.45 (1.21–1.73), 1.41 (1.10–1.82), and 1.39 (1.06–1.82) for any HF, HFpEF, and HFrEF, respectively. After fully adjusting for the respective covariates (in primary model 4), the highest GlycA quartile were statistically significantly associated with any HF from model 1 to model 3, and the top 2 GlycA quartiles with HFpEF in the first model. However, no association was observed for the highest quartile of GlycA, compared with the lowest, for HFrEF in the initial model, and for either HF subtypes in the fully adjusted model, although there were 202 fewer incident HF events than in primary analysis.
Discussion
In this ethnically diverse community-based cohort free of clinical CVD at baseline, we have found that a higher plasma level of the novel composite inflammatory biomarker GlycA was independently associated with total incident hospitalized HF events and more particularly with the subtype of HFpEF, over 14 years of follow-up. Notably, there was >2-fold adjusted risk of HFpEF for the highest GlycA quartile compared with the lowest. These findings were consistent even after adjusting for NT-proBNP, a biomarker of wall strain which might reflect subclinical HF. Upon excluding individuals who developed ASCVD after the baseline visit in the sensitivity analysis, we observed some attenuation of the association in the context of fewer interim HF events; however, findings were generally similar to the primary analysis. To our knowledge, this is the first study evaluating the association of GlycA with HFpEF and HFrEF. Our findings suggest that GlycA may be an important inflammatory biomarker of risk of HF, especially HFpEF, even after taking into account other more commonly measured inflammatory biomarkers such as hsCRP.
Inflammatory activation plays a key role in the progression of HF,5 by impacting the pathogenesis of HF, and its underlying comorbidities including atherosclerosis, diabetes mellitus, and obesity.34–36 Increased concentrations of inflammatory cytokines like IL-6, TNF-α, and IL-1 have been reported in patients with HF.7 The levels of inflammatory biomarkers have been shown to provide prognostic information in patients with HF.12,37 Previous studies have found a stronger association of other inflammatory markers with HFpEF than with HFrEF,6,10,11 and we now confirm a similar relationship with GlycA.
Prior work in MESA has found that GlycA is modestly to moderately correlated with other markers of inflammation (with Pearson correlation coefficient for GlycA with d-dimer [0.09], IL-6 [0.29], hsCRP [0.47], and fibrinogen [0.49]).23 Nevertheless, previous studies have shown that plasma GlycA levels were associated with CVD events and all-cause mortality even after adjusting for other inflammatory markers such as hsCRP, d-dimer, and IL-6.14,18–22 In our analysis when all of the inflammatory markers were mutually adjusted for each other, we found only GlycA to be associated with any HF and HFpEF, whereas the other inflammatory markers were not independently associated with HF (any HF, HFpEF, or HFrEF) outcomes. GlycA is also linked to subclinical atherosclerosis and its progression38–40 and with reduced cardiovascular health by the American Heart Association’s Life Simple 7 metrics.23 Among patients with established HFpEF (n=248) who underwent cardiac catheterization at a single center, higher GlycA levels were associated with increased risk of death or major adverse cardiovascular events, suggesting GlycA might help identify subgroups of patients with HFpEF at greatest risk for adverse events.41
However, whether GlycA levels can predict incident HFpEF among a community-based cohort free of HF at baseline had not been well established before this study. In a prior analysis from MESA,14 Duprez et al examined the associations between GlycA and CVD events, coronary heart disease, stroke, and overall HF and showed that GlycA was predictive of any CVD, including HF. However, that analysis did not examine the association of GlycA with the specific HF subtypes of HFpEF and HFrEF, which we newly demonstrate here.
Differences in clinical characteristics and patients outcomes for HFpEF and HFrEF have been well established.42,43 Patients with HFrEF have a younger median age with greater prevalence of previous myocardial infarction, contributing to ischemic cardiomyopathy.44 Patients with HFpEF have an older median age and show a higher likelihood of with noncardiac comorbidities such as diabetes mellitus, chronic obstructive pulmonary disease, hypertension, and obesity.44 All these comorbidities have the potential to induce systemic inflammation.45 The high prevalence of comorbidities in patients with HFpEF can be translated to differential association of inflammation with HFpEF compared with HFrEF, as suggested by our analyses.
So far, attempts to utilize therapies that have been proven to work in patients with HFrEF (such as angiotensin receptor blockers, ACE inhibitors, angiotensin receptor-neprilysin inhibitors) to reduce mortality and hospitalizations have so far been largely unsuccessful in patients with HFpEF.46–49 The lack of effective therapy for patients with HFpEF delineates the importance of preventing the development of HFpEF, which could be aided by the identification of a stable inflammatory biomarker. Besides providing biological insights, our findings showing differing associations of GlycA with new onset HFpEF and HFrEF implies that prevention strategies might need to be different for HFpEF and HFrEF.
GlycA is currently already a commercially available lab test in the United States through LabCorp. However, for successful use of GlycA as a biomarker for HF risk predictor or therapeutic target, we must next study whether therapeutic lowering of GlycA either by lifestyle adjustments or pharmacotherapy can slow or prevent HFpEF. Prior study has shown that while statin therapy decreased CVD events, statins only minimally decreased GlycA levels.19 Interestingly, immunomodulatory treatments targeting TNF in patients with psoriasis have been shown to reduce GlycA levels and vascular inflammation.40 Such therapies could be potentially used clinically to lower GlycA levels if confirmed in other studies of patients at risk of inflammatory disease.
There were several limitations in our study that should be noted. First, our study was observational, and we cannot infer causality about GlycA and HF risk. Despite our attempt to include all covariates that likely are to influence the development of HF in the statistical models, residual confounding remains a possibility. Second, only baseline GlycA levels were available, and thus we were unable to account for changes in GlycA levels over time. Previous work has shown that a single baseline measure could accurately capture the short-term inflammatory status for a 6-month period.50 However, whether the same accuracy holds for longer periods of time is not clear. Third, there were few individuals with HF who had a mid-range LVEF of 40% to <50% (n=31), so due to reduced statistical power we were unable to use the 3 classification system proposed by the European Society of Cardiology and therefore focused our analysis on HFpEF and HFrEF, dichotomized at LVEF of 50%.
Strengths of this study include a large sample size, long duration of follow-up of 14.0 years, and ethnic and racial diversity. To our knowledge, our study was the first to examine the association of GlycA with both HF subtypes, HFpEF and HFrEF, separately; however, further external validation of our findings in other cohorts is warranted.
Conclusions
In sum, we found that higher plasma GlycA levels were associated with total incident HF, and in particular the subtype of HFpEF but not HFrEF, independently of traditional CVD risk factors and other inflammatory markers. Our findings lend support to the current understanding of the different pathophysiology of HFpEF and HFrEF and suggest that inflammatory markers may offer different prognostic information based on HF subtype. Future study is warranted to examine mechanisms that might explain differential association of GlycA with HFpEF versus HFrEF, and whether therapeutic lowering of GlycA can prevent HFpEF development.
| CVD |
cardiovascular disease |
| HR |
hazard ratio |
| hsCRP |
high sensitivity C-reactive protein |
| HF |
heart failure |
| HFpEF |
heart failure with preserved ejection fraction |
| HFrEF |
heart failure with reduced ejection fraction |
| IL-6 |
interleukin 6 |
| IQI |
interquartile interval |
| MESA |
Multi-Ethnic Study of Atherosclerosis |
| NT-proBNP |
N-terminal pro-B-type natriuretic peptide |
| TNF |
tumor necrosis factor |
Acknowledgments
We thank the other investigators, the staff, and the MESA participants for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.
Sources of Funding
The MESA study was supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute (NHLBI), and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences. Drs. Michos and Zhao are additionally funded by the Blumenthal Scholars Award in Preventive Cardiology at Johns Hopkins University. S. Jang was supported by the Dean’s Research Fund at Johns Hopkins University Medical School.
Disclosures
Dr Otvos is employed by LabCorp. The other authors report no conflicts.
Footnotes
References
- 1.
Ziaeian B, Fonarow GC . Epidemiology and aetiology of heart failure.Nat Rev Cardiol. 2016; 13:368–378. doi: 10.1038/nrcardio.2016.25CrossrefMedlineGoogle Scholar - 2.
Heidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, Ikonomidis JS, Khavjou O, Konstam MA, Maddox TM, ; American Heart Association Advocacy Coordinating Committee; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Stroke Council. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association.Circ Heart Fail. 2013; 6:606–619. doi: 10.1161/HHF.0b013e318291329aLinkGoogle Scholar - 3.
Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Drazner MH, Fonarow GC, Geraci SA, Horwich T, Januzzi JL, ; American College of Cardiology Foundation; American Heart Association Task Force on Practice Guidelines. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines.J Am Coll Cardiol. 2013; 62:e147–e239. doi: 10.1016/j.jacc.2013.05.019CrossrefMedlineGoogle Scholar - 4.
Liquori ME, Christenson RH, Collinson PO, Defilippi CR . Cardiac biomarkers in heart failure.Clin Biochem. 2014; 47:327–337. doi: 10.1016/j.clinbiochem.2014.01.032CrossrefMedlineGoogle Scholar - 5.
Shirazi LF, Bissett J, Romeo F, Mehta JL . Role of Inflammation in heart failure.Curr Atheroscler Rep. 2017; 19:27. doi: 10.1007/s11883-017-0660-3CrossrefMedlineGoogle Scholar - 6.
Murphy SP, Kakkar R, McCarthy CP, Januzzi JL Inflammation in heart failure: JACC state-of-the-art review.J Am Coll Cardiol. 2020; 75:1324–1340. doi: 10.1016/j.jacc.2020.01.014CrossrefMedlineGoogle Scholar - 7.
Torre-Amione G, Kapadia S, Benedict C, Oral H, Young JB, Mann DL . Proinflammatory cytokine levels in patients with depressed left ventricular ejection fraction: a report from the studies of left ventricular dysfunction (SOLVD).J Am Coll Cardiol. 1996; 27:1201–1206. doi: 10.1016/0735-1097(95)00589-7CrossrefMedlineGoogle Scholar - 8.
Tschöpe C, Lam CS . Diastolic heart failure: what we still don’t know. Looking for new concepts, diagnostic approaches, and the role of comorbidities.Herz. 2012; 37:875–879. doi: 10.1007/s00059-012-3719-5CrossrefMedlineGoogle Scholar - 9.
Diwan A, Tran T, Misra A, Mann DL . Inflammatory mediators and the failing heart: a translational approach.Curr Mol Med. 2003; 3:161–182. doi: 10.2174/1566524033361537CrossrefMedlineGoogle Scholar - 10.
Tromp J, Khan MA, Klip IT, Meyer S, de Boer RA, Jaarsma T, Hillege H, van Veldhuisen DJ, van der Meer P, Voors AA . Biomarker profiles in heart failure patients with preserved and reduced ejection fraction.J Am Heart Assoc. 2017; 6:e003989. doi: 10.1161/JAHA.116.003989LinkGoogle Scholar - 11.
Tromp J, Westenbrink BD, Ouwerkerk W, van Veldhuisen DJ, Samani NJ, Ponikowski P, Metra M, Anker SD, Cleland JG, Dickstein K, . Identifying pathophysiological mechanisms in heart failure with reduced versus preserved ejection fraction.J Am Coll Cardiol. 2018; 72:1081–1090. doi: 10.1016/j.jacc.2018.06.050CrossrefMedlineGoogle Scholar - 12.
Chow SL, Maisel AS, Anand I, Bozkurt B, de Boer RA, Felker GM, Fonarow GC, Greenberg B, Januzzi JL, Kiernan MS, ; American Heart Association Clinical Pharmacology Committee of the Council on Clinical Cardiology; Council on Basic Cardiovascular Sciences; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation; Council on Epidemiology and Prevention; Council on Functional Genomics and Translational Biology; Council on Quality of Care and Outcomes Research. Role of biomarkers for the prevention, assessment, and management of heart failure: a scientific statement from the American Heart Association.Circulation. 2017; 135:e1054–e1091. doi: 10.1161/CIR.0000000000000490LinkGoogle Scholar - 13.
de Boer RA, Nayor M, deFilippi CR, Enserro D, Bhambhani V, Kizer JR, Blaha MJ, Brouwers FP, Cushman M, Lima JAC, . Association of cardiovascular biomarkers with incident heart failure with preserved and reduced ejection fraction.JAMA Cardiol. 2018; 3:215–224. doi: 10.1001/jamacardio.2017.4987CrossrefMedlineGoogle Scholar - 14.
Duprez DA, Otvos J, Sanchez OA, Mackey RH, Tracy R, Jacobs DR Comparison of the predictive value of GlycA and other biomarkers of inflammation for total death, incident cardiovascular events, noncardiovascular and noncancer inflammatory-related events, and total cancer events.Clin Chem. 2016; 62:1020–1031. doi: 10.1373/clinchem.2016.255828CrossrefMedlineGoogle Scholar - 15.
Sani CM, Pogue EPL, Hrabia JB, Zayachkowski AG, Zawadka MM, Poniatowski AG, Długosz D, Leśniak W, Kruszelnicka O, Chyrchel B, . Association between low-grade chronic inflammation and depressed left atrial compliance in heart failure with preserved ejection fraction: a retrospective analysis.Folia Med Cracov. 2018; 58:45–55. doi: 10.24425/fmc.2018.124657MedlineGoogle Scholar - 16.
Ohkuma T, Jun M, Woodward M, Zoungas S, Cooper ME, Grobbee DE, Hamet P, Mancia G, Williams B, Welsh P, ; ADVANCE Collaborative Group. Cardiac stress and inflammatory markers as predictors of heart failure in patients with type 2 diabetes: the ADVANCE trial.Diabetes Care. 2017; 40:1203–1209. doi: 10.2337/dc17-0509CrossrefMedlineGoogle Scholar - 17.
Otvos JD, Shalaurova I, Wolak-Dinsmore J, Connelly MA, Mackey RH, Stein JH, Tracy RP . GlycA: a composite nuclear magnetic resonance biomarker of systemic inflammation.Clin Chem. 2015; 61:714–723. doi: 10.1373/clinchem.2014.232918CrossrefMedlineGoogle Scholar - 18.
Akinkuolie AO, Buring JE, Ridker PM, Mora S . A novel protein glycan biomarker and future cardiovascular disease events.J Am Heart Assoc. 2014; 3:e001221. doi: 10.1161/JAHA.114.001221LinkGoogle Scholar - 19.
Akinkuolie AO, Glynn RJ, Padmanabhan L, Ridker PM, Mora S . Circulating N-linked glycoprotein side-chain biomarker, rosuvastatin therapy, and incident cardiovascular disease: an analysis from the JUPITER trial.J Am Heart Assoc. 2016; 5:e003822. doi: 10.1161/JAHA.116.003822LinkGoogle Scholar - 20.
Fashanu OE, Oyenuga AO, Zhao D, Tibuakuu M, Mora S, Otvos JD, Stein JH, Michos ED . GlycA, a novel inflammatory marker and its association with peripheral arterial disease and carotid plaque: the multi-ethnic study of atherosclerosis.Angiology. 2019; 70:737–746. doi: 10.1177/0003319719845185CrossrefMedlineGoogle Scholar - 21.
Muhlestein JB, May HT, Galenko O, Knowlton KU, Otvos JD, Connelly MA, Lappe DL, Anderson JL . GlycA and hsCRP are independent and additive predictors of future cardiovascular events among patients undergoing angiography: the intermountain heart collaborative study.Am Heart J. 2018; 202:27–32. doi: 10.1016/j.ahj.2018.04.003CrossrefMedlineGoogle Scholar - 22.
Gruppen EG, Riphagen IJ, Connelly MA, Otvos JD, Bakker SJ, Dullaart RP . GlycA, a pro-inflammatory glycoprotein biomarker, and incident cardiovascular disease: relationship with C-reactive protein and renal function.PLoS One. 2015; 10:e0139057. doi: 10.1371/journal.pone.0139057CrossrefMedlineGoogle Scholar - 23.
Benson EA, Tibuakuu M, Zhao D, Akinkuolie AO, Otvos JD, Duprez DA, Jacobs DR, Mora S, Michos ED . Associations of ideal cardiovascular health with GlycA, a novel inflammatory marker: the multi-ethnic study of atherosclerosis.Clin Cardiol. 2018; 41:1439–1445. doi: 10.1002/clc.23069CrossrefMedlineGoogle Scholar - 24.
Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, Greenland P, Jacob DR, Kronmal R, Liu K, . Multi-ethnic study of atherosclerosis: objectives and design.Am J Epidemiol. 2002; 156:871–881. doi: 10.1093/aje/kwf113CrossrefMedlineGoogle Scholar - 25.
Duprez DA, Otvos J, Tracy RP, Feingold KR, Greenland P, Gross MD, Lima JA, Mackey RH, Neaton JD, Sanchez OA, . High-density lipoprotein subclasses and noncardiovascular, noncancer chronic inflammatory-related events versus cardiovascular events: the multi-ethnic study of atherosclerosis.J Am Heart Assoc. 2015; 4:e002295. doi: 10.1161/JAHA.115.002295LinkGoogle Scholar - 26.
Vella CA, Allison MA, Cushman M, Jenny NS, Miles MP, Larsen B, Lakoski SG, Michos ED, Blaha MJ . Physical activity and adiposity-related Inflammation: the MESA.Med Sci Sports Exerc. 2017; 49:915–921. doi: 10.1249/MSS.0000000000001179CrossrefMedlineGoogle Scholar - 27.
Levey AS, Stevens LA . Estimating GFR using the CKD epidemiology collaboration (CKD-EPI) creatinine equation: more accurate GFR estimates, lower CKD prevalence estimates, and better risk predictions.Am J Kidney Dis. 2010; 55:622–627. doi: 10.1053/j.ajkd.2010.02.337CrossrefMedlineGoogle Scholar - 28.
Whelton SP, Narla V, Blaha MJ, Nasir K, Blumenthal RS, Jenny NS, Al-Mallah MH, Michos ED . Association between resting heart rate and inflammatory biomarkers (high-sensitivity C-reactive protein, interleukin-6, and fibrinogen) (from the multi-ethnic study of atherosclerosis).Am J Cardiol. 2014; 113:644–649. doi: 10.1016/j.amjcard.2013.11.009CrossrefMedlineGoogle Scholar - 29.
Osibogun O, Ogunmoroti O, Tibuakuu M, Benson EM, Michos ED . Sex differences in the association between ideal cardiovascular health and biomarkers of cardiovascular disease among adults in the United States: a cross-sectional analysis from the multiethnic study of atherosclerosis.BMJ Open. 2019; 9:e031414. doi: 10.1136/bmjopen-2019-031414CrossrefMedlineGoogle Scholar - 30.
Ying W, Zhao D, Ouyang P, Subramanya V, Vaidya D, Ndumele CE, Sharma K, Shah SJ, Heckbert SR, Lima JA, . Sex hormones and change in N-terminal Pro-B-type natriuretic peptide levels: the multi-ethnic study of atherosclerosis.J Clin Endocrinol Metab. 2018; 103:4304–4314. doi: 10.1210/jc.2018-01437CrossrefMedlineGoogle Scholar - 31.
Chahal H, Bluemke DA, Wu CO, McClelland R, Liu K, Shea SJ, Burke G, Balfour P, Herrington D, Shi P, . Heart failure risk prediction in the multi-ethnic study of atherosclerosis.Heart. 2015; 101:58–64. doi: 10.1136/heartjnl-2014-305697CrossrefMedlineGoogle Scholar - 32.
Rao VN, Zhao D, Allison MA, Guallar E, Sharma K, Criqui MH, Cushman M, Blumenthal RS, Michos ED . Adiposity and incident heart failure and its subtypes: MESA (multi-ethnic study of atherosclerosis).JACC Heart Fail. 2018; 6:999–1007. doi: 10.1016/j.jchf.2018.07.009CrossrefMedlineGoogle Scholar - 33.
Fliotsos M, Zhao D, Rao VN, Ndumele CE, Guallar E, Burke GL, Vaidya D, Delaney JCA, Michos ED . Body mass index from early-, mid-, and older-adulthood and risk of heart failure and atherosclerotic cardiovascular disease: MESA.J Am Heart Assoc. 2018; 7:e009599. doi: 10.1161/JAHA.118.009599LinkGoogle Scholar - 34.
Libby P, Hansson GK . Inflammation and immunity in diseases of the arterial tree: players and layers.Circ Res. 2015; 116:307–311. doi: 10.1161/CIRCRESAHA.116.301313LinkGoogle Scholar - 35.
Lumeng CN, Saltiel AR . Inflammatory links between obesity and metabolic disease.J Clin Invest. 2011; 121:2111–2117. doi: 10.1172/JCI57132CrossrefMedlineGoogle Scholar - 36.
Tall AR, Yvan-Charvet L . Cholesterol, inflammation and innate immunity.Nat Rev Immunol. 2015; 15:104–116. doi: 10.1038/nri3793CrossrefMedlineGoogle Scholar - 37.
Zhang Y, Bauersachs J, Langer HF . Immune mechanisms in heart failure.Eur J Heart Fail. 2017; 19:1379–1389. doi: 10.1002/ejhf.942CrossrefMedlineGoogle Scholar - 38.
Ezeigwe A, Fashanu OE, Zhao D, Budoff MJ, Otvos JD, Thomas IC, Mora S, Tibuakuu M, Michos ED . The novel inflammatory marker GlycA and the prevalence and progression of valvular and thoracic aortic calcification: the multi-ethnic study of atherosclerosis.Atherosclerosis. 2019; 282:91–99. doi: 10.1016/j.atherosclerosis.2019.01.011CrossrefMedlineGoogle Scholar - 39.
Tibuakuu M, Fashanu OE, Zhao D, Otvos JD, Brown TT, Haberlen SA, Guallar E, Budoff MJ, Palella FJ, Martinson JJ, . GlycA, a novel inflammatory marker, is associated with subclinical coronary disease.AIDS. 2019; 33:547–557. doi: 10.1097/QAD.0000000000002079CrossrefMedlineGoogle Scholar - 40.
Joshi AA, Lerman JB, Aberra TM, Afshar M, Teague HL, Rodante JA, Krishnamoorthy P, Ng Q, Aridi TZ, Salahuddin T, . GlycA is a novel biomarker of inflammation and subclinical cardiovascular disease in psoriasis.Circ Res. 2016; 119:1242–1253. doi: 10.1161/CIRCRESAHA.116.309637LinkGoogle Scholar - 41.
Kelly JP, Hunter WG, McGarrah RW, Craig D, Haynes C, Velazquez EJ, Felker GM, Hernandez AF, Newgard CB, Shah SH , . Novel protein glycan inflammatory biomarkers predict adverse events in heart failure with preserved ejection fraction.J Cardiac Fail. 2015; 21:S89. doi: 10.1016/j.cardfail.2015.06.266CrossrefMedlineGoogle Scholar - 42.
Abebe TB, Gebreyohannes EA, Tefera YG, Abegaz TM . Patients with HFpEF and HFrEF have different clinical characteristics but similar prognosis: a retrospective cohort study.BMC Cardiovasc Disord. 2016; 16:232. doi: 10.1186/s12872-016-0418-9CrossrefMedlineGoogle Scholar - 43.
Brouwers FP, de Boer RA, van der Harst P, Voors AA, Gansevoort RT, Bakker SJ, Hillege HL, van Veldhuisen DJ, van Gilst WH . Incidence and epidemiology of new onset heart failure with preserved vs. reduced ejection fraction in a community-based cohort: 11-year follow-up of PREVEND.Eur Heart J. 2013; 34:1424–1431. doi: 10.1093/eurheartj/eht066CrossrefMedlineGoogle Scholar - 44.
Bursi F, Weston SA, Redfield MM, Jacobsen SJ, Pakhomov S, Nkomo VT, Meverden RA, Roger VL . Systolic and diastolic heart failure in the community.JAMA. 2006; 296:2209–2216. doi: 10.1001/jama.296.18.2209CrossrefMedlineGoogle Scholar - 45.
Kalogeropoulos A, Georgiopoulou V, Psaty BM, Rodondi N, Smith AL, Harrison DG, Liu Y, Hoffmann U, Bauer DC, Newman AB, ; Health ABC Study Investigators. Inflammatory markers and incident heart failure risk in older adults: the health ABC (health, aging, and body composition) study.J Am Coll Cardiol. 2010; 55:2129–2137. doi: 10.1016/j.jacc.2009.12.045CrossrefMedlineGoogle Scholar - 46.
Kanwar M, Walter C, Clarke M, Patarroyo-Aponte M . Targeting heart failure with preserved ejection fraction: current status and future prospects.Vasc Health Risk Manag. 2016; 12:129–141. doi: 10.2147/VHRM.S83662CrossrefMedlineGoogle Scholar - 47.
Massie BM, Carson PE, McMurray JJ, Komajda M, McKelvie R, Zile MR, Anderson S, Donovan M, Iverson E, Staiger C, ; I-PRESERVE Investigators. Irbesartan in patients with heart failure and preserved ejection fraction.N Engl J Med. 2008; 359:2456–2467. doi: 10.1056/NEJMoa0805450CrossrefMedlineGoogle Scholar - 48.
Zakeri R, Cowie MR . Heart failure with preserved ejection fraction: controversies, challenges and future directions.Heart. 2018; 104:377–384. doi: 10.1136/heartjnl-2016-310790CrossrefMedlineGoogle Scholar - 49.
Solomon SD, McMurray JJV, Anand IS, Ge J, Lam CSP, Maggioni AP, Martinez F, Packer M, Pfeffer MA, Pieske B, ; PARAGON-HF Investigators and Committees. Angiotensin-neprilysin inhibition in heart failure with preserved ejection fraction.N Engl J Med. 2019; 381:1609–1620. doi: 10.1056/NEJMoa1908655CrossrefMedlineGoogle Scholar - 50.
Navarro SL, Brasky TM, Schwarz Y, Song X, Wang CY, Kristal AR, Kratz M, White E, Lampe JW . Reliability of serum biomarkers of inflammation from repeated measures in healthy individuals.Cancer Epidemiol Biomarkers Prev. 2012; 21:1167–1170. doi: 10.1158/1055-9965.EPI-12-0110CrossrefMedlineGoogle Scholar