Abstract
Background:
Matrix Gla-protein (MGP), a known inhibitor of vascular calcification, becomes biologically active by vitamin K-dependent carboxylation. Circulating levels of dephospho-uncarboxylated-MGP (dpucMGP), the inactive form of MGP, have been associated with large artery stiffening and reduced skeletal muscle mass in heart failure (HF). Whether dpucMGP is related to adverse outcomes in patients with HF is unknown.
Methods:
In this cohort study, we measured plasma dpucMGP among 2,247 Penn HF Study (PHFS) participants. We examined the relationship between dpucMGP and ~5000 other proteins (SomaScan assay) to identify biological pathways associated with dpucMGP. We assessed the association between dpucMGP levels and: 1) death or HF-related hospital admission (DHFA); 2) all-cause death.
Results:
Participants’ median age was 61 years (interquartile range (IQR) [53, 70] years), 64% were male, and 71% were White. dpucMGP exhibited prominent proteomic associations with acute phase response, coagulation, complement system, fibrosis, cell signaling, and metabolic pathways. Greater dpucMGP was associated with older age, renal dysfunction and warfarin use, whereas African American ethnicity was associated with lower dpucMGP. Increased dpucMGP levels were associated with an increased risk of DHFA (standardized HR=1.23; 95%CI=1.17–1.28; P<0.0001) and all-cause death (standardized hazard ratio [HR]=1.32; 95%CI=1.25–1.40; P<0.0001), particularly among participants with non-ischemic HF. Associations between dpucMGP and outcomes were dependent on warfarin use, and higher dpucMGP levels were found to mediate the association between warfarin use and adverse outcomes (death [total effect: P=0.005; indirect effect: P<0.001] and DHFA [total effect: P<0.001; indirect effect: P=0.002]).
Conclusions:
Higher dpucMGP is associated with multiple biological pathways and with an increased risk for adverse outcomes in HF. Greater dpucMGP levels mediated the relationship between warfarin use and adverse outcomes. Further studies are required to determine the role of therapeutic interventions to reduce dpucMGP levels in this patient population.
Keywords: matrix Gla-protein, heart failure, warfarin, outcomes, Penn Heart Failure Study
Introduction
Matrix Gla-protein (MGP), a protein produced mainly by vascular smooth muscle cells and chondrocytes, is a known inhibitor of ectopic calcification.(1,2) Dephospho-uncarboxylated MGP (dpucMGP) is the inactive form of MGP, and becomes biologically active by vitamin K dependent γ-glutamate carboxylation and serine phosphorylation.(3) Increased levels of circulating inactive MGP (dpucMGP) have been reported in heart failure (HF) with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF).(1,4,5) Increased levels of dpucMGP have been associated with large artery stiffness in the general population as well as in various clinical populations, including chronic HF.(1) Large artery stiffness has been implicated in the pathogenesis of HF, due to a variety of mechanisms including the development of systolic hypertension, reduction in coronary perfusion pressure, increased left ventricular afterload, and excessive arterial pulsatility.(5,6) Interestingly, a recent analysis of the Framingham Heart Study demonstrated an association between uncarboxylated MGP levels and the risk of incident HF with preserved ejection fraction (HFpEF) in a general community sample.(3,4) In addition to its association with large artery stiffness, increased levels of dpucMGP have been associated with a reduced skeletal muscle mass in patients with hypertension.(7) Importantly, levels of dpucMGP are modifiable by enhancing activation of MGP (for instance, with vitamin K supplementation), therefore representing a potential therapeutic target. However, whether higher dpucMGP levels are related to adverse outcomes in human HF remains unknown.
In this study, we investigated a large cohort of HF patients (Penn HF Study, PHFS) to assess the clinical and plasma proteomic correlates of plasma dpucMGP, as well as the association between plasma dpucMGP and adverse outcomes. We specifically assessed the association between levels of plasma dpucMGP and the risk of: 1) death or HF-related hospital admission (DHFA); 2) all-cause death.
METHODS
Study population
We performed measurements of dpucMGP levels in stored plasma samples from 2,247 Penn HF Study (PHFS) participants. The PHFS has been described in detail previously (8–12). In brief, the PHFS is a prospective cohort study of ambulatory HF patients enrolled at the University of Pennsylvania (Philadelphia, PA), the University of Wisconsin-Madison (Madison, WI), and Case Western Reserve University (Cleveland, OH) between 2003–2011. Patients with a clinical diagnosis of HF (HFrEF and HFpEF) were enrolled after an assessment by a HF specialist. At the time of enrollment, clinical data were obtained from standardized questionnaires completed by patients and physicians, and venous blood samples were collected and stored at −80 °C. Patients with mechanical circulatory support, a life expectancy of six months or less from a noncardiac condition, or unable to provide informed consent were excluded. The institutional review boards from each institution approved this study. Participants provided written informed consent prior to being enrolled.
The data, analytic methods, and study materials are not publicly available. Such data may be made available to other researchers for collaborative research through the establishment of appropriate data-sharing agreements.
Plasma dpucMGP measurements
Plasma dpucMGP was measured in a single run by the Laboratory of Coagulation Profile (Maastricht, the Netherlands) using the commercially available IVD CE-marked chemiluminescent InaKtif MGP assay (IDS-iSYS system, Boldon, UK). Plasma samples and internal calibrators were incubated using magnetic particles that were coated with murine monoclonal antibodies against dp-MGP, acridinium-labelled murine monoclonal antibodies against ucMGP, and an assay buffer. The magnetic particles were captured using a magnet and washed to remove any unbound analyte. Trigger reagents were added, and the resulting light emitted by the acridinium label was directly proportional to the level of dp-ucMGP in the sample. The assay measuring range was between 100 and 12,000 pmol/L and was linear up to 11,651 pmol/L. The within-run and total variations of this assay were 0.8–6.2% and 3.0–8.2%, respectively.
Plasma proteomics and pathway analyses
We utilized the SomaScan® assay version 4, which is a multiplexed, modified aptamer-based binding-assay. This assay includes 4,979 modified aptamer reagents to 4,776 unique protein targets. The SomaScan assay utilizes Slow-Off-rate Modified Aptamer (SOMAmer) reagents, which are chemically modified nucleotides, to bind and quantify target proteins in relative fluorescent units directly proportional to the amount of target protein in the sample. We performed knowledge-based pathway analysis to assess the correlates of dpucMGP, as previously described. (12,13) First, we assessed the correlation between dpucMGP and all proteins measured in the SomaScan assay after Box-Cox transformation. We corrected the alpha level for multiple comparisons based on the principal components underlying the variability of all measured proteins, as previously described (12,14–16). Associations between dpucMGP and individual proteins that were significant, with an adjusted P value <0.01, were then utilized to perform pathway analyses, using Ingenuity® Pathway Analysis (IPA) software (Qiagen; Hilden, Germany; www.qiagen.com/ingenuity). Proteins were identified according to their UniProt ID annotation. The totality of proteins included in the SomaScan assay was used as the reference set and both direct and indirect experimentally confirmed relationships from all species were included. The analysis calculates a P value (Fisher exact test and right tailed), quantifying the overlap, and a Z score, quantifying the likelihood and direction (up or downregulated), between the plasma proteomics pattern and known canonical pathways.
Statistical analysis
Continuous data are shown as mean ± standard deviation (SD) for normally distributed variables and analyzed using ANOVA. Non-normally distributed continuous variables are shown as median (interquartile range) and analyzed using the Kruskal-Wallis test. Categorical variables are shown as total counts with percentages and analyzed using the chi-square test or Fisher’s exact test.
Survival analyses were performed among participants with follow-up event data (n=2,188). We computed Kaplan-Meier survival curves for tertiles of dpucMGP and compared them with the log-rank test. We further assessed the relationship between dpucMGP and the risk of adverse outcomes using Cox regression. For easier interpretation, we express hazard ratios (HRs) per-standard-deviation increase (i.e., 1-point increase in the z-score). We performed unadjusted survival analyses and models adjusted for: 1) the MAGGIC risk score (which incorporates multiple demographic, clinical and laboratory parameters)(17) and NT-proBNP levels and 2) the MAGGIC risk score, estimated glomerular filtration rate (eGFR), ischemic etiology of heart failure, and systolic and diastolic blood pressure. The time of HF diagnosis, one of the components of the MAGGIC risk score, was not available, and no points were given to any subject for this component during the score computation.
Given that treatment with warfarin, a vitamin K antagonist, markedly reduces MGP carboxylation, we assessed the relationship between warfarin use and outcomes, and applied causal mediation analysis with the delta method to assess the extent to which this association could be explained by standardized dpucMGP levels. We applied formal mediation analysis using warfarin use as the exposure, death/DHFA as the outcome, and measured dpucMGP levels as the mediator. Supplemental Figure 1 is a graphical representation of mediation analyses implemented. In the diagram for both pathways, e1 represents any (residual) error under the path of warfarin predicting standardized dpucMGP by using linear regression; e2 captures any error under the path of the indirect effect of warfarin on the outcome via the mediating effect from standardized dpucMGP by applying a Cox proportional hazard model. The interaction between the exposure and the mediator was also included in the Cox proportional hazard model. The direct effect is the extent of the remaining link between warfarin use and the outcome after accounting for the indirect effect of the hypothesized mediator. The product of both the natural indirect effect (NIE) HR and natural direct effect (NDE) HR provides the total effect (TE) HR. As the delta method incorporated comparisons to a standard normal distribution to determine the indirect effect’s level of significance, the P value was calculated in z statistics. Analyses were performed using code developed for the statistical mediator R package.
Statistical significance was defined as a 2-tailed P value<0.05. All probability values presented are 2-tailed. Analyses were performed using R Statistical Software v3.5.2 (Foundation for Statistical Computing, Vienna, Austria) and the MATLAB statistics and machine learning toolbox (MATLAB 2016b, the MathWorks; Natick, MA).
Results
Baseline characteristics of the study cohort
dpucMGP measurements were available in 2,247 (94.5%) of PHFS participants. PHFS participants with measured dpucMGP levels were slightly older (median age 61, interquartile range (IQR) [53, 70] vs 58 [48, 66] years; P = 0.002), had a lower proportion of African-American patients (4.6% vs 20.9%; P <0.0001), lower estimated GFR (median 53.3 ([IQR 37.3, 63.4] vs 56.7 [42.5, 70.5] mL/min/1.73 m2; P = 0.0061), lower proportion of angiotensin-converting enzyme inhibitor/aldosterone receptor antagonist use (78.5% vs 84.9%; P = 0.047), higher proportion of calcium channel blocker (16.2% vs 9.0%; P = 0.006) and statin (62.3% vs 51.6%; P = 0.017) use, and had higher proportion of HFpEF (18.9% vs 11.3%; P = 0.009).
Baseline characteristics of the participants with measured dpucMGP levels, stratified by tertiles of dpucMGP, are shown in Table 1. Patients in the highest tertile of dpucMGP levels were on average older, with a higher proportion of males, higher prevalence of diabetes mellitus, hypertension, hyperlipidemia, chronic kidney disease, prior cardiovascular revascularization procedures (percutaneous coronary intervention and coronary artery bypass graft surgery), and atrial arrhythmias. The cohort consisted predominantly of participants with HFrEF, and there was no difference in the prevalence of HFpEF, HFrEF, or HF with recovered EF among the three tertiles.
Table 1.
Baseline characteristics of the study cohort (n=2,247), stratified by tertiles of dpucMGP (pmol/L)
| Characteristics | Lowest tertile (102 to 503 pmol/L) N=749 |
Middle tertile (503 to 923 pmol/L) N=749 |
Highest tertile (923 to 8184 pmol/L) N=749 |
P value |
|---|---|---|---|---|
| Age (years) | 53.4 (41.4, 62.0) | 57.5 (48.6, 65.5) | 62.0 (54.7, 69.7) | <0.0001 |
| Male | 452 (61.8%) | 462 (63.2%) | 534 (73.5%) | <0.0001 |
| Race | <0.0001 | |||
| White | 480 (64.3%) | 566 (76.1%) | 554 (74.2%) | |
| Black | 207 (27.7%) | 131 (17.6%) | 130 (17.4%) | |
| Asian | 9 (1.2%) | 7 (0.9%) | 5 (0.7%) | |
| Other or Unknown | 51 (6.8%) | 40 (5.4%) | 58 (7.8%) | |
| Systolic blood pressure (mmHg) | 114 (100, 130) | 112 (100, 128) | 110 (98, 125) | 0.001 |
| Diastolic blood pressure (mmHg) | 70 (62, 80) | 70 (62, 78) | 68 (60, 76) | <0.0001 |
| Body mass index (kg/m2) | 28.2 (24.7, 33.7) | 29 (25.5, 33.6) | 29 (25.2, 34.0) | 0.05 |
| Medical history | ||||
| LVEF category | 0.11 | |||
| HFrEF | 610 (82.32%) | 580 (78.38%) | 571 (77.27%) | |
| HFpEF | 75 (10.12%) | 91 (12.30%) | 87 (11.77%) | |
| HFrecEF | 56 (7.56%) | 69 (9.32%) | 81 (10.96%) | |
| Diabetes mellitus | 163 (22.2%) | 206 (28.1%) | 262 (35.7%) | <0.0001 |
| History of Hypertension | 384 (52.2%) | 442 (60.2%) | 477 (65.1%) | <0.0001 |
| Hyperlipidemia | 298 (40.5%) | 360 (49.1%) | 403 (55.0%) | <0.0001 |
| Chronic kidney disease | 69 (9.4%) | 88 (12.0%) | 184 (25.1%) | <0.0001 |
| Ischemic heart failure etiology | 156 (21.49%) | 230 (31.55%) | 297 (41.42%) | <0.0001 |
| eGFR (mL/min/1.73 m2) | 64.4 (51.2, 77.5) | 58 (44.8, 70.3) | 46.5 (32.3, 61.7) | <0.0001 |
| Prior percutaneous coronary intervention | 114 (15.5%) | 161 (21.9%) | 212 (28.9%) | <0.0001 |
| Prior coronary artery bypass graft surgery | 71 (9.7%) | 133 (18.1%) | 202 (27.6%) | <0.0001 |
| Atrial fibrillation or atrial flutter | 148 (20.1%) | 230 (31.3%) | 421 (57.4%) | <0.0001 |
| Current smoker | 82 (11.2%) | 72 (9.8%) | 47 (6.4%) | 0.005 |
| New York Heart Association (NYHA) Class of Heart Failure | <0.0001 | |||
| NYHA Class 1 | 193 (26.5%) | 127 (17.5%) | 57 (7.9%) | |
| NYHA Class 2 | 346 (47.5%) | 336 (46.2%) | 287 (39.9%) | |
| NYHA Class 3 | 161 (22.1%) | 222 (30.5%) | 303 (42.1%) | |
| NYHA Class 4 | 28 (3.9%) | 42 (5.8%) | 72 (10.0%) | |
| Medications | ||||
| ACEI or ARB | 633 (86.1%) | 632 (86.1%) | 605 (82.5%) | 0.09 |
| Aldosterone antagonist | 226 (30.8%) | 240 (32.7%) | 293 (40.0%) | 0.001 |
| Aspirin | 407 (55.4%) | 448 (61.0%) | 403 (55.0%) | 0.03 |
| Beta-blocker | 651 (88.6%) | 639 (87.1%) | 645 (88.0%) | 0.67 |
| Calcium channel blocker | 58 (7.9%) | 72 (9.8%) | 67 (9.1%) | 0.43 |
| Hydralazine | 46 (6.3%) | 49 (6.7%) | 92 (12.6%) | <0.0001 |
| Nitrate | 82 (11.2%) | 103 (14.0%) | 166 (22.7%) | <0.0001 |
| Statin | 334 (45.4%) | 380 (51.8%) | 422 (57.6%) | <0.0001 |
| Warfarin | 94 (12.8%) | 198 (27.0%) | 545 (74.4%) | <0.0001 |
| Insulin | 75 (10.2%) | 81 (11.0%) | 125 (17.1%) | 0.0001 |
Continuous data are shown as mean ± standard deviation for normally distributed variables and median (interquartile range) for non-normally distributed variables. Categorical variables are shown as total counts (%) for participants with available data for each variable.
ACEI, angiotensin-converting enzyme inhibitor; ARB, aldosterone receptor antagonist; eGFR, estimated glomerular filtration rate; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; HFrecEF, heart failure with recovered ejection fraction; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association
Proteomic Associations of dpucMGP and Pathway analysis
The results of regression analyses between dpucMGP and all proteins in the SomaScan utilized for pathway analysis are provided in Supplemental Files 1 and 2, and shown in Figures 1A and 1B. Figure 1A shows the top proteins associated with dpucMGP in unadjusted analyses, whereas Figure 1B shows the top proteins associated with dpucMGP in analyses that adjusted for age, sex, race, enrollment site, systolic and diastolic blood pressure, history of diabetes, hypertension, dyslipidemia, chronic kidney disease, history of percutaneous coronary intervention, coronary artery bypass graft surgery, atrial fibrillation/flutter, current smoking, NYHA class, aspirin, hydralazine, nitrates, statins, warfarin and insulin use. A total of 1000 and 667 proteins were associated with dpucMGP in unadjusted and adjusted analyses, respectively. The top canonical pathways associated with dpucMGP in these analyses are shown in Figure 2A and 2B and listed in Supplemental Tables 1 and 2.
Figure 1. Volcano plots showing proteins associated with dephospho-uncarboxylated Matrix Gla-protein (dpucMGP).
Panel A shows the results of unadjusted analyses, whereas panel B shows results of analyses adjusted for age, sex, race, enrollment site, systolic and diastolic blood pressure, history of diabetes, hypertension, dyslipidemia, chronic kidney disease, percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG) surgery, atrial fibrillation/flutter, current smoking, New York Heart Association (NYHA) class, aspirin, hydralazine, nitrates, statins, warfarin and insulin use. Black dots represent proteins with nonsignificant associations with dpucMGP at the nominal significance level. Blue dots represent proteins with nonsignificant associations with dpucMGP after alpha correction. Yellow dots represent proteins with significant associations with dpucMGP after alpha correction. Refer to Supplemental Files 1 and 2 for additional information.
Figure 2. Pathways associated with dephospho-uncarboxylated Matrix Gla-protein (dpucMGP).
Panel A shows the results of unadjusted analyses, whereas panel B shows analyses adjusted for age, sex, race, enrollment site, systolic and diastolic blood pressure, history of diabetes, hypertension, dyslipidemia, chronic kidney disease, percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG) surgery, atrial fibrillation/flutter, current smoking, New York Heart Association (NYHA) class, aspirin, hydralazine, nitrates, statins, warfarin and insulin use. Figure 2B shows only the top 35 pathways associated with dpucMGP in adjusted analyses. For a list of top pathways, please refer to Supplemental Tables 1 and 2. Pathways with z scores significantly different from zero (i.e., clearly directional relationships) are shown in blue and the z score value is shown next to the corresponding bar. The P value represents the overall representation of proteins from a given pathway. The z score takes into account biological function of individual proteins in the pathway to assess whether there is directionality of association.
In unadjusted analyses (Figure 2A), the top canonical pathway associated with dpucMGP levels was the coagulation pathway, followed by lipid metabolism pathways (liver X receptor/retinoid X receptor [LXR/RXR] activation), and acute phase response signaling. Various related pathways, such as intrinsic and extrinsic prothrombin activation pathways, the complement system, and some fibrosis and metabolic pathways were also associated with dpucMGP. In analyses that adjusted for age, sex, race, enrollment site, systolic and diastolic blood pressure, history of diabetes, hypertension, dyslipidemia, chronic kidney disease, percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG) surgery, atrial fibrillation/flutter, current smoking, New York Heart Association (NYHA) class, aspirin, hydralazine, nitrates, statins, warfarin and insulin use (Figure 2B), the coagulation pathway, LXR/RXR activation, and acute phase response signaling, as well as various related pathways remained associated with dpucMGP. In addition, cell signaling pathways (such as ephrin receptor signaling, insulin-like growth factor 1 (IGF-1) signaling, glycoprotein 6 (GP6) signaling, insulin receptor signaling) and metabolic pathways (such as fatty acid beta oxidation and leptin signaling) emerged as independent correlates of dpucMGP.
Independent clinical correlates of dpucMGP
A multivariable regression model showing the correlates of dpucMGP is shown in Figure 3. This model included variables that were significantly different across tertiles of dpucMGP (Table 1) in order to identify its independent correlates. The figure shows standardized regression coefficients for each variable along with 95% confidence intervals. As shown, older age, a history of chronic kidney disease, prior coronary artery bypass graft surgery, atrial fibrillation/flutter, NYHA class greater than 1, and warfarin use were independent associated with higher dpucMGP levels, whereas African-American ethnicity and greater estimated glomerular filtration rate were associated with lower dpucMGP levels. The most prominent correlate in this model was warfarin use (β=0.46; 95%CI=0.42=0.50; P<0.0001), consistent with the vitamin K-dependent carboxylation of MGP.
Figure 3. Multivariable linear regression model showing the independent correlates of dpucMGP.
Standardized regression coefficients for each variable are shown as point estimates along with 95% confidence intervals. BP, blood pressure; CABG, coronary artery bypass grafting; eGFR, estimated glomerular filtration rate; NYHA, New York Heart Association; PCI, percutaneous coronary intervention.
Association of dpucMGP levels with death and DHFA
The participant median follow-up time (time to event or censoring) was 4.09 years for death and 2.53 years for DHFA. During this follow-up period, 560 patients died and 1142 patients reached the composite endpoint of DHFA. Measured dpucMGP levels were associated with an increased risk of death (Figure 4A) and DHFA (Figure 4B), with the highest incidence of death and DHFA observed in the patients in the highest tertile of dpucMGP levels.
Figure 4.
Kaplan-Meier curves of the incidence of (A) death and (B) death or heart failure-related hospital admission (DHFA) by tertiles of dephospho-uncarboxylated Matrix Gla-protein (dpucMGP).
Using Cox regression modeling, increased dpucMGP levels were associated with an increased risk of DHFA (standardized HR=1.23; 95% CI=1.17 to 1.28; P<0.0001) and all-cause death (standardized HR =1.32; 95% CI=1.25 to 1.40; P<0.0001). There was no interaction between dpucMGP and warfarin for both outcomes (DHFA: P = 0.82; death: P = 0.31). In analyses that adjusted for the MAGGIC risk score and NTpro-BNP levels, dpucMGP levels remained independently associated with an increased risk of DHFA (standardized HR =1.07; 95% CI=1.01 to 1.13; P=0.03) and all-cause death (standardized HR =1.08; 95% CI=1.002 to 1.17; P=0.044).
We found no significant interaction between the presence of a reduced ejection fraction and dpucMGP as predictors of either DHFA (P=0.60) or death (P=0.23). We found a significant interaction between dpucMGP and ischemic etiology as a predictor of DHFA (P=0.0008) and death (P=0.0009). In analyses that adjusted for the MAGGIC risk score and NT-proBNP, dpucMGP was associated with the risk of death (standardized HR =1.13; 95% CI=1.04 to 1.23; P=0.0054) and DHFA (standardized HR =1.11; 95% CI=1.05 to 1.19; P=0.0009) in patients with non-ischemic heart failure. In contrast, it was not associated with either death (standardized HR =0.97; 95% CI=0.84 to 1.11; P=0.65) or DHFA (standardized HR =0.95; 95% CI=0.85 to 1.69; P=0.43) among those with ischemic heart failure. However, when adjusted for warfarin use, the strongest clinical correlate of dpucMGP, dpucMGP was not associated with either death (standardized HR =1.08; 95% CI=0.98 to 1.20; P=0.13) or DHFA (standardized HR =1.04; 95% CI=0.97 to 1.13; P=0.26) among those with non-ischemic heart failure. Similarly, in the overall study population, in analyses that adjusted for warfarin use in addition to the MAGGIC risk score and NT-proBNP, there was no longer an association between dpucMGP and death (P=0.18) or with DHFA (P=0.98).
Mediation analysis: warfarin use, dpucMGP levels and outcomes
Levels of dpucMGP were markedly higher in participants taking warfarin at the time of enrollment (1167 pmol/L; IQR=766, 1775; n=837) compared to those not taking warfarin (515 pmol/L; IQR=401, 724; n= 1365, P<0.0001). In mediation analyses (Table 2), higher dpucMGP levels were found to mediate the association between warfarin use and death (total effect: HR: 1.46; 95% CI: 1.20–1.78, P=0.005; indirect effect: HR: 1.22; 95% CI: 1.12–1.32, P<0.001) and DHFA (total effect: HR: 1.65; 95% CI: 1.46–1.85, P<0.001; indirect effect: HR: 1.11; 95% CI: 1.04–1.18, P=0.002).
Table 2.
Mediation analyses for the association between warfarin use (exposure) and the death or DHFA via standardized dpucMGP
| Association between warfarin and potential mediator | |||||
|---|---|---|---|---|---|
| Mediator | Estimate (SE) | P-value | Pathway 1 (Outcome: DHFA) | HR (95% CI) | P-value |
| Standardized dpucMGP | 0.99 (0.04) | < 0.001 | Indirect | 1.11 (1.04, 1.18) | 0.002 |
| Direct | 1.48 (1.29, 1.71) | <0.001 | |||
| Total Effect | 1.65 (1.46, 1.85) | <0.001 | |||
| Mediator | Estimate (SE) | P-value | Pathway 2 (Outcome: Death) | HR (95% CI) | P-value |
| Standardized dpucMGP | 0.99 (0.04) | < 0.001 | Indirect | 1.22 (1.12, 1.32) | <0.001 |
| Direct | 1.20 (0.91, 1.59) | 0.31 | |||
| Total Effect | 1.46 (1.20, 1.78) | 0.005 | |||
DHFA, death or heart failure-related hospitalization; dpucMGP, dephospho-uncarboxylated MGP; SE, standard error
Discussion
In this study of a large cohort of patients with HF, we assessed the clinical and plasma proteomic correlates of plasma dpucMGP and the association of dpucMGP with adverse outcomes (death and DHFA). We found that dpucMGP is associated with biological pathways related to coagulation, inflammation, complement, fibrosis, metabolism, and cell signaling. Greater dpucMGP levels were associated with various clinical factors, including renal dysfunction, older age, whereas African-American ethnicity was associated with lower dpucMGP levels. However, consistent with the known role of vitamin K on MGP carboxylation/activation, warfarin use was associated with markedly higher dpucMGP levels. Circulating levels of dpucMGP were associated with the risk of death and DHFA, even after adjustment of NT-proBNP and the MAGGIC risk score, particularly among participants with non-ischemic heart failure. However, the relationship disappeared after adjustment for warfarin use. Interestingly, in mediation analyses, dpucMGP was found to mediate the relationship between warfarin use and the risk of death and DHFA.
We identified several biological pathways associated with dpucMGP in HF. First, we found that plasma dpucMGP was linked to coagulation pathways. This is consistent which the important role of vitamin K in the activation of MGP and of several coagulation factors. Second, we found that dpucMGP was associated with inflammatory pathways, including the acute phase response and the complement pathway. Inflammation and activation of the immune system are established contributors to the pathogenesis of HF(18), and prior research has linked dpucMGP with markers of inflammation in patients with cardiovascular disease.(19) Interestingly, while pathways related to fibrosis were linked to dpucMGP, and fibrosis is a main contributor to the pathophysiology of HF,(20) no prior studies have a relationship between dpucMGP and myocardial fibrosis. Several mechanisms may mediate this association, such as renal dysfunction, or increased pulsatile left ventricular afterload as a result of large artery stiffening, which has been shown to be associated with dpucMGP.(5) Moreover, the relationship with fibrotic biomarkers may be related to fibrosis in tissues other than the myocardium, including the kidneys or skeletal muscle. We also found that dpucMGP was associated with pathways related to lipid metabolism. Whereas prior research identified a correlation between MGP and adipogenesis (21), whether this relationship is causal in patients with heart failure is unknown. In addition, several of the pathways associated with dpucMGP in our study have been previously linked to cardiovascular disease and HF, including ephrin receptor signaling,(22) IGF-1 signaling,(23) GP6 signaling,(24), and insulin signaling.(25) Finally, we found that individual proteins related to renal dysfunction (cystatin C and alpha-1 microglobulin) (26,27) were associated with dpucMGP, consistent with the relationship between dpucMGP and both a history of chronic kidney disease and the estimated glomerular filtration rate found in our study.
Our study is the first to assess the link between vitamin K antagonist use, higher dpucMGP levels, and adverse outcomes in HF patients. Prior studies have demonstrated greater levels of dpucMGP in both HFpEF and HFrEF, and greater dpucMGP levels were also found to be directly associated with increased large artery stiffness in patients with HF.(1,4) In addition, a recent study suggested a causal relationship between dpucMGP and large artery stiffness in HFpEF, possibly mediated by regulation of calcification and fibrotic pathways by MGP.(3,4) Furthermore, this prior study identified associations between higher dpucMGP levels and future increases in systolic blood pressure and incident HF in HFpEF patients.(4) Our study adds to the previous literature because it demonstrates that dpucMGP is prognostic in established HF. Whether large artery stiffening is involved in this association should be assessed in future research. Interestingly, while we found that systolic blood pressures were well controlled across all dpucMGP tertiles, systolic blood pressures were slightly lower in patients in the highest dpucMGP tertile. While the well-controlled blood pressures across tertiles could be due to appropriate medical therapy for heart failure, the relationship between dpucMGP, vascular properties, and stroke volume remains to be assessed.
The relationship between vitamin K antagonist use, higher dpucMGP levels and adverse outcomes in HF patients is likely multifactorial. Increased large artery stiffness due to calcification and fibrosis may contribute to HF hospitalizations and death through similar mechanisms described previously, such as increased left ventricular afterload, kidney disease, and metabolic dysfunction.(5,28,29) Of note, the use of warfarin has been shown to be associated with aortic calcification(30,31), consistent with the important role of MGP as a factor preventing calcification in the arterial wall. In addition, vitamin K2 has an important role in skeletal muscle mitochondria as an electron carrier to maintain ATP production,(32,33) and a recent study by our group demonstrated an independent association between higher dpucMGP and lower axial skeletal muscle mass in hypertensive patients.(34) Our analyses demonstrated that dpucMGP levels mediated the association between warfarin use and adverse outcomes. The precise mechanisms underlying these associations require further study. Moreover, whether low vitamin K levels among participants who are not treated with vitamin K antagonists are associated with outcomes in heart failure, and whether targeting vitamin K deficiency in appropriately selected patients with HF may have an impact on the risk of adverse outcomes remains to be determined. Similarly, further studies are required to assess the effect of vitamin K2 supplementation on mechanistic phenotypes, such as large artery stiffness, in this patient population.
Our study should be considered in the context of its strengths and limitations. Strengths include a large, well-characterized multi-center cohort, with long-term follow-up and a large number of well-adjudicated events, as well as the use of a broad protein panel to assess relationships with biologic pathways. This analysis also utilized the principal component analysis (PCA) method of corrections for multiple comparisons, which was more conservative than the false discovery rate (FDR) method of correction. Our study also has several limitations. Our cohort was comprised predominantly of participants with HFrEF, and therefore our findings may not be generalizable to HFpEF. The majority of participants were White and male, which may also limit the generalizability of our findings. Our cohort was enrolled prior to the availability of direct oral anticoagulants, and therefore we were unable to assess the relationship between dpucMGP levels and direct oral anticoagulant use. We did not adjudicate cause-specific death and thus were unable to assess whether dpucMGP is related to ischemic/thrombotic vs. hemorrhagic fatal events. Despite statistical adjustments to the extent possible in our proteome-wide association study, we cannot rule out residual confounding. Moreover, the causality of our findings in our study cannot be established using its observational design. Randomized trials will be required to assess whether enhancing dpucMGP activation can lead to improved outcomes in patients with HF.
Conclusions
In patients with heart failure, dpucMGP was associated with important clinical variables (such as older age, renal dysfunction and warfarin use) and with biologic pathways related to the acute phase response, complement activation, coagulation, fibrosis, cell signaling, and metabolism. Higher dpucMGP levels were associated with an increased risk for all-cause death and DHFA, particularly in participants with non-ischemic heart failure. This relationship was linked to warfarin use. Increased dpucMGP levels, a well-known consequence of vitamin K antagonism, appeared to mediate the relationship between warfarin use and adverse outcomes. Further studies are required to determine the role of vitamin K supplementation in this patient population.
Supplementary Material
- What is new? Authors should state in 100 words or less what their research contributes to the field that had not been previously known. Repetition of the abstract should be avoided.
- In a large cohort of patients with heart failure (HF), we found that elevated dephospho-uncarboxylated-MGP (dpucMGP) levels are associated with increased risks of all-cause death and death or HF-related hospital admission.
- Elevated dpucMGP levels mediated the relationship between warfarin use and adverse outcomes.
- Elevated dpucMGP levels are associated with multiple biological pathways, including acute phase response, coagulation, complement activation, fibrosis, cell signaling, and metabolic pathways.
- What are the clinical implications? Authors should state in 100 words or less why their research is important to patient care and what direct implication the findings have to clinical practice. Duplication of text from the manuscript should be avoided.
- The findings from this study suggest that dpucMGP is prognostic in heart failure, and mediates the relationship between warfarin use and adverse outcomes. Further studies are required to determine whether enhancing dpucMGP activation can improve outcomes in patients with heart failure.
Disclosures:
J.A.C. is supported by NIH grants U01-TR003734, U01-TR003734-01S1, UO1-HL160277, R33-HL-146390, R01-HL153646, K24-AG070459, R01-AG058969, R01-HL157108, R01-HL155599, R01-HL104106 and R01HL155764. He has recently consulted for Bayer, Fukuda-Denshi, Bristol-Myers Squibb, Biohaven Pharmaceuticals, Johnson & Johnson, Edwards Life Sciences, Merck, and NGM Biopharmaceuticals. He received University of Pennsylvania research grants from National Institutes of Health, Fukuda-Denshi, Bristol-Myers Squibb, Microsoft and Abbott. He is named as inventor in a University of Pennsylvania patent for the use of inorganic nitrates/nitrites for the treatment of Heart Failure and Preserved Ejection Fraction and for the use of biomarkers in heart failure with preserved ejection fraction. He has received payments for editorial roles from the American Heart Association, the American College of Cardiology, Elsevier and Wiley, and payments for academic roles from the University of Texas, Boston University, and Virginia Commonwealth University. He has received research device loans from Atcor Medical, Fukuda-Denshi, Unex, Uscom, NDD Medical Technologies, Microsoft and MicroVision Medical.
L.J.S. is supported by MSCA EU grants ITN722609, ITN764474, and ITN813409. He has received grants from Gnosis by Lesaffre, Boehringer Ingelheim, and Bayer and consultancy fees from Immunodiagnostic Systems, not related to the submitted work. He is shareholder of Coagulation Profile BV.
P.Z. is supported by R01 HL149722, R01 HL155599, R01 HL157264, U01-HL160277, and UH3 DK128298. PZ also receives research support from Amgen and the Institute for Translational Medicine and Therapeutics at the University of Pennsylvania. He has consulted for Pfizer and Vyaire.
V.vE. is supported by non-restricted grants from Boehringer Ingelheim, Roche, Vifor Pharme, Pfizer and Astra Zeneca, and consultancy fees from Boehringer Ingelheim, Novartis, Janssen and Novo Nordisk, all paid to the institute, not related to the submitted work
Sources of Funding:
The proteomics work in this study was funded by Bristol-Myers-Squibb. The PHFS was funded by NIH grant R01HL088577 (TC).
Abbreviations
- DHFA
death or HF-related hospital admission
- dpucMGP
dephospho-uncarboxylated-MGP
- HF
heart failure
- MGP
Matrix Gla-protein
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