Increased Dephospho-uncarboxylated Matrix Gla-Protein Is Associated With Lower Axial Skeletal Muscle Mass in Patients With Hypertension.

BACKGROUND: Matrix Gla-protein (MGP) is a well-established inhibitor of vascular calcification that is activated by vitamin K-dependent carboxylation. In the setting of vitamin K2 deficiency, dephospho-uncarboxylated MGP (dpucMGP) levels increase, and have been associated with large artery stiffening. Vitamin K2 is also a mitochondrial electron carrier in muscle, but the relationship of vitamin K2 deficiency and dpucMGP with muscle mass is not well understood. We therefore aimed to examine the association of vitamin K2 deficiency and dpucMGP with skeletal muscle mass in patients with hypertension. METHODS: We studied 155 hypertensive adults without heart failure. Axial skeletal muscle mass was measured using magnetic resonance imaging from axial steady-state free precession images. DpucMGP was measured with ELISA. Carotid-femoral pulse wave velocity (CF-PWV) was measured from high-fidelity arterial tonometry recordings. RESULTS: We found an inverse relationship between dpucMGP levels and axial muscle mass, with progressively rising dpucMGP levels correlating with decreasing axial muscle mass. In an unadjusted linear regression model, correlates of dpucMGP included axial skeletal muscle area factor (ß = -0.32; P < 0.0001) and CF-PWV (ß = 0.31; P = 0.0008). In adjusted analyses, independent correlates of dpucMGP included axial skeletal muscle area factor (ß = -0.30; P = 0.0003) and CF-PWV (ß = 0.20; P = 0.019). CONCLUSIONS: In hypertensive adults, dpucMGP is independently associated with lower axial muscle mass, in addition to increased large artery stiffness. Further studies are required to investigate the role of vitamin K supplementation in this population.

Predictive Ability of Pressure-Corrected Arterial Stiffness Indices: Comparison of Pulse Wave Velocity, Cardio-Ankle Vascular Index (CAVI), and CAVI0.

BACKGROUND: Pulse wave velocity (PWV) is blood pressure (BP) dependent, leading to the development of the BP-corrected metrics cardio-ankle vascular index (CAVI) and CAVI0. We aimed to assess risk prediction by heart-to-ankle PWV (haPWV), CAVI, and CAVI0 in a US population. METHODS: We included 154 subjects (94.8% male; 47.7% African American) with and without heart failure (HF). Left and right haPWV, CAVI, and CAVI0 were measured with the VaSera 1500N device. We prospectively followed participants for a mean of 2.56 years for the composite endpoint death or HF-related hospital admission (DHFA). RESULTS: Left and right haPWV, CAVI, and CAVI0 values did not differ significantly. In unadjusted analyses, haPWV (left standardized hazard ratio [HR] = 1.51, P = 0.007; right HR = 1.66, P = 0.003), CAVI (left HR = 1.45, P = 0.012; right HR = 1.58, P = 0.006), and CAVI0 (left HR = 1.39, P = 0.022; right HR = 1.44, P = 0.014) significantly predicted DHFA. Predictive ability showed a decreasing trend from haPWV to CAVI to CAVI0; in line with the increasing amount of BP correction in these metrics. In Cox models, right-sided metrics showed a trend toward stronger predictive ability than left-sided metrics. After adjustment for baseline HF status, the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score, and systolic BP, right haPWV (HR = 1.58, P = 0.025) and CAVI (HR = 1.44, P = 0.044), but no other stiffness metrics, remained predictive. CONCLUSIONS: Although conceptually attractive, BP-corrected arterial stiffness metrics do not offer better prediction of DHFA than conventional arterial stiffness metrics, nor do they predict DHFA independently of systolic BP. Our findings support PWV as the primary arterial stiffness metric for outcome prediction.

Plasma biomarkers associated with adverse outcomes in patients with calcific aortic stenosis.

AIMS: Enhanced risk stratification of patients with aortic stenosis (AS) is necessary to identify patients at high risk for adverse outcomes, and may allow for better management of patient subgroups at high risk of myocardial damage. The objective of this study was to identify plasma biomarkers and multimarker profiles associated with adverse outcomes in AS. METHODS AND RESULTS: We studied 708 patients with calcific AS and measured 49 biomarkers using a Luminex platform. We studied the correlation between biomarkers and the risk of (i) death and (ii) death or heart failure-related hospital admission (DHFA). We also utilized machine-learning methods (a tree-based pipeline optimizer platform) to develop multimarker models associated with the risk of death and DHFA. In this cohort with a median follow-up of 2.8 years, multiple biomarkers were significantly predictive of death in analyses adjusted for clinical confounders, including tumour necrosis factor (TNF)-α [hazard ratio (HR) 1.28, P < 0.0001], TNF receptor 1 (TNFRSF1A; HR 1.38, P < 0.0001), fibroblast growth factor (FGF)-23 (HR 1.22, P < 0.0001), N-terminal pro B-type natriuretic peptide (NT-proBNP) (HR 1.58, P < 0.0001), matrix metalloproteinase-7 (HR 1.24, P = 0.0002), syndecan-1 (HR 1.27, P = 0.0002), suppression of tumorigenicity-2 (ST2) (IL1RL1; HR 1.22, P = 0.0002), interleukin (IL)-8 (CXCL8; HR 1.22, P = 0.0005), pentraxin (PTX)-3 (HR 1.17, P = 0.001), neutrophil gelatinase-associated lipocalin (LCN2; HR 1.18, P < 0.0001), osteoprotegerin (OPG) (TNFRSF11B; HR 1.26, P = 0.0002), and endostatin (COL18A1; HR 1.28, P = 0.0012). Several biomarkers were also significantly predictive of DHFA in adjusted analyses including FGF-23 (HR 1.36, P < 0.0001), TNF-α (HR 1.26, P < 0.0001), TNFR1 (HR 1.34, P < 0.0001), angiopoietin-2 (HR 1.26, P < 0.0001), syndecan-1 (HR 1.23, P = 0.0006), ST2 (HR 1.27, P < 0.0001), IL-8 (HR 1.18, P = 0.0009), PTX-3 (HR 1.18, P = 0.0002), OPG (HR 1.20, P = 0.0013), and NT-proBNP (HR 1.63, P < 0.0001). Machine-learning multimarker models were strongly associated with adverse outcomes (mean 1-year probability of death of 0%, 2%, and 60%; mean 1-year probability of DHFA of 0%, 4%, 97%; P < 0.0001). In these models, IL-6 (a biomarker of inflammation) and FGF-23 (a biomarker of calcification) emerged as the biomarkers of highest importance. CONCLUSIONS: Plasma biomarkers are strongly associated with the risk of adverse outcomes in patients with AS. Biomarkers of inflammation and calcification were most strongly related to prognosis.