Abstract
Aims
Heart failure with preserved ejection fraction (HF-PEF) can be difficult to diagnose in clinical practice. Myocardial fibrosis is a major determinant of diastolic dysfunction (DD), potentially contributing to the progression of HF-PEF. The aim of this study was to analyse whether serological markers of collagen turnover may predict HF-PEF and DD.
Methods and results
We included 85 Caucasian treated hypertensive patients (DD n = 65; both DD and HF-PEF n = 32). Serum carboxy (PICP), amino (PINP), and carboxytelo (CITP) peptides of procollagen type I, amino (PIIINP) peptide of procollagen type III, matrix metalloproteinases (MMP-1, MMP-2, and MMP-9), and tissue inhibitor of MMP levels were assayed. Using receiver operating characteristic curve analysis, MMP-2 (AUC = 0.91; 95% CI: 0.84, 0.98), CITP (0.83; 0.72, 0.92), PICP (0.82; 0.72, 0.92), B-type natriuretic peptide (BNP) (0.82; 0.73, 0.91), MMP-9 (0.79; 0.68, 0.89), and PIIINP (0.78; 0.66, 0.89) levels were significant predictors of HF-PEF (P < 0.01 for all). Carboxytelo peptides of procollagen type I (AUC = 0.74; 95% CI: 0.62, 0.86), MMP-2 (0.73; 0.62, 0.84), PIIINP (0.73; 0.60, 0.85), BNP (0.69; 0.55, 0.83) and PICP (0.66; 0.54, 0.78) levels were significant predictors of DD (P < 0.05 for all). A cutoff of 1585 ng/mL for MMP-2 provided 91% sensitivity and 76% specificity for predicting HF-PEF and combinations of biomarkers could be used to adjust either sensitivity or specificity.
Conclusion
Markers of collagen turnover identify patients with HF-PEF and DD. Matrix metalloproteinase 2 may be more useful than BNP in the identification of HF-PEF. This suggests that these new biochemical tools may assist in identifying patients with these diagnostically challenging conditions.
Keywords: Diastolic dysfunction, Heart failure with preserved ejection fraction, Markers of collagen turnover, B-type natriuretic peptide
Introduction
A significant proportion of heart failure with preserved ejection fraction (HF-PEF) is secondary to diastolic heart failure, often occurring as a result of hypertensive heart disease (HHD).1 The prevalence of HF-PEF is estimated at 20–50% of all heart failure in clinical practice.2–4 However, the diagnosis of this type of heart failure can present a challenge, often requiring accurate definition of diastolic dysfunction (DD).
Doppler echocardiography is the standard technique for assessing DD, but problems can occur with its application.5 Many physiological factors (heart rate, age, loading conditions), atrial fibrillation, and technical factors (operator skills and echocardiographic windows) influence its evaluation and interpretation. These practical limitations highlight the need for alternative strategies to help define DD in order to confirm a diagnosis of HF-PEF.
Abnormal diastolic filling pressure, the major functional abnormality in HF-PEF as a consequence of DD, results in the release of natriuretic peptides. Previous literature has demonstrated that levels of B-type natriuretic peptide (BNP) and its biologically inactive fragment N-terminal proBNP (NT-proBNP) can reliably detect the presence of HF-PEF and correlate with different degrees of DD.6,7 In particular, BNP was more elevated in the restrictive-like filling phase, the most severe form of DD.6 Nonetheless, these peptides have significant biological variability and elevations may not be specific for this syndrome.8,9
A growing body of evidence has recently emerged in myocardial interstitial fibrosis.10–13 We10 and others11–13 have demonstrated that serological markers of cardiac fibrosis are significantly elevated in patients with DD and HF-PEF. However, the sensitivity and specificity of these markers for the diagnosis of DD and HF-PEF remain uncertain. The aim of this study was therefore to assess the value of markers of collagen turnover in isolation or in tandem with natriuretic peptides in diagnosing DD and HF-PEF.
Methods
Study population
All subjects gave written informed consent to participate in the study. The Ethics Committee at St Vincent's University Hospital approved the study protocol, which conformed to the principles of the Helsinki Declaration.
Details of the study population have been published previously.10 It consisted of 85 Caucasian treated hypertensive patients. There were 65 patients with DD and 20 patients with no DD. Of the patients with DD, 32 had HF-PEF. DD was assessed using Doppler echocardiography (see below). The diagnosis of HF-PEF as the cause of hospitalization was made by the heart failure team and confirmed by the consultant cardiologist. The diagnosis was based on the presence of all of the following criteria: one hospitalization for proven Class IV heart failure (all patients had chest X-ray confirmation of signs of pulmonary congestion and received IV diuretics) with continued signs or symptoms of heart failure (at least New York class association II level), left ventricular ejection fraction (LVEF) >45% with Doppler abnormalities of DD, and no significant evidence of valvular disease. Patients were excluded if they had established pulmonary disease, anaemia, renal insufficiency (serum creatinine >130 mmol), metabolic bone diseases, malignancy, and conditions known to alter collagen turnover, including chronic liver and connective tissue disorders or those with recent trauma or surgery (<6 months).
A pre-requisite of the study dictated that patients were clinically stable for 1 month (as defined by freedom from hospitalization or change in medication) prior to enrolment.
Biochemical measurements of indices of collagen metabolism
Peripheral venous blood samples were drawn during clinical assessment and immediately underwent (less than 30 min) serum isolation. Each sample was centrifuged for 10 min at 4°C. The serum was then aliquoted and stored at −80°C before simultaneous analysis of markers of collagen turnover, as described below.
Amino-terminal propeptide of procollagen type I (PINP), type III (PIIINP), and carboxy-terminal telopeptide of collagen type I (CITP) were measured by radioimmunoassay (Orion Diagnostica, Espoo, Finland). The intra-assay variations were 7% for PINP, <5% for PIIINP, and<8% for CITP. The sensitivity (lower detection limit) of the assays was 13 µg/L for PINP, 1.9 µg/L for PIIINP, and 0.5 µg/L for CITP. Carboxy-terminal propeptides of procollagen type I (PICP) was measured with a specific enzyme-linked immunosorbent assay (Takara Biochemicals Co, Osaka, Japan). The sensitivity for PICP was 2 ng/mL. All plasma matrix metalloproteinase (MMP) and tissue inhibitor of matrix metalloproteinase (TIMP) levels were measured using two-site sandwich enzyme-linked immunosorbent assays (Amersham Pharmacia Biotech, Buckinghamshire, UK), The sensitivity of the assays was1.7 ng/mL for MMP-1, 0.37 ng/mL for MMP-2, 0.6 ng/mL for MMP-9, and 1.25 ng/mL for TIMP-1. Duplicate measurements were performed, and the intra-assay coefficients of variation were <10% for all assays. Plasma BNP levels were measured in all patients (Biosite, Triage, USA).
Doppler echocardiography study
Two-dimensional echocardiography imaging, targeted M-mode, and Doppler ultrasound measurements were obtained in all patients.14 All Doppler echocardiography data represent the mean of three measurements on sequential cardiac cycles. LVEF was calculated by the Teichholtz method. All measurements were made by observers, from archived images in a blinded fashion. The following pulsed Doppler measurements were obtained in the apical view with a cursor at the tip of the mitral valve leaflets: maximal early (E), late (A) transmitral velocities in diastole and E wave deceleration time (DT). Isovolumic relaxation time (IVRT) was measured in the apical four chamber view by continuous wave Doppler placed between the mitral inflow area and the LV outflow tract. Left ventricular diastolic dysfunction (LVDD) was defined by the presence of alterations in E/A ratio, IVRT, and DT, as previously described by Lubien et al.6 This classification was based on the following criteria: impaired relaxation pattern was defined as E/A < 1 or DT > 240 ms in patients <55 years of age and E/A < 0.8 and DT > 240 ms in patients ≥55 years of age, and/or IVRT > 90 ms. Pseudonormal pattern was defined as an E/A ratio of 1–1.5 and DT > 240 ms. Confirmation included IVRT < 90 ms or by reversal of the E/A ratio to <1.0 by Valsalva manoeuvre. Restrictive-like filling pattern was defined as DT < 160 ms with ≥1 of the following: left atrial size > 5 cm, E/A > 1.5, IVRT < 70 ms. None of the patients studied exhibited left ventricular systolic dysfunction, as defined by an LVEF < 45%.
Statistical analysis
Data are presented as the mean ± standard deviation for continuous variables, while frequencies and percentages summarize categorical variables. Comparisons between the three groups were conducted using Kruskal–Wallis test and Mann–Whitney U test for continuous data. χ2 analysis was used to compare categorical variables (all two sided, α = 0.05). Receiver operating characteristic (ROC) curves were plotted to assess the usefulness of both changes in collagen turnover markers and BNP in predicting HF-PEF. The point on the ROC which lies on a 45° line closest to the top-left corner (0, 1) was chosen as the best operating point to select thresholds for markers with significant area underneath the curve (AUC) for predicting DD and HF-PEF. Univariable and multivariable analyses were conducted using binary logistic regression with the presence or absence of HF-PEF or DD as the outcome variables. For multivariable analysis, the P-value of the partial likelihood ratio test was used to confirm if a covariate was significant and the coefficients of the remaining variables were assessed to determine if important (>20%) changes had occurred on variable exclusion. The P-value of this statistic was used in preference to the Wald statistic if conflict occurred as it is not biased to type II errors when standard errors are large. All statistical calculations were performed using SPSS V.12 software (Statistical Package for Social Sciences, Chicago, IL, 2001).
Results
Baseline characteristics
There were 85 patients included in the study: 20 patients with no DD or HF-PEF, and 65 patients with DD of whom 32 also had HF-PEF. The patients with DD and HF-PEF were older (72 ± 11, range 59–84) in comparison to the no DD and no HF-PEF group (64 ± 10, range 46–68) and the DD and no HF-PEF group (68 ± 9, range 53–81). Patients with DD and HF-PEF had lesser male predominance and higher BNP levels compared to patients with no DD and no HF-PEF (Table 1). There were no other differences noted apart from higher urea and the anticipated higher use of loop diuretics (frusemide or bumetanide) in the DD and HF-PEF group.
Table 1.
Baseline demographics according to the presence or absence of diastolic dysfunction and heart failure with preserved ejection fraction
| Variable | No DD, no HF-PEF | DD, no HF-PEF | DD, HF-PEF | P-value |
|---|---|---|---|---|
| n | 20 | 33 | 32 | |
| Age (years) | 64 ± 10 | 68 ± 9 | 72 ± 11 | 0.01 |
| Gender: male | 15 (75) | 25 (76) | 17 (53) | 0.11 |
| BMI (kg/m2) | 27 ± 5 | 27 ± 4 | 30 ± 6 | 0.11 |
| SBP/DBP (mmHg) | 134/73 ± 22/12 | 149/77 ± 20/11 | 138/73 ± 20/13 | 0.10/0.35 |
| Casual factors | ||||
| Ischaemic | 5 (25) | 11 (33) | 14 (44) | 0.37 |
| Smoking | 1 (5) | 6 (18) | 1 (3) | N/A |
| Raised cholesterol | 11 (55) | 22 (67) | 18 (56) | 0.60 |
| Diabetes mellitus | 0 | 4 (12) | 4 (13) | N/A |
| Biochemical indicators | ||||
| Urea (mmol/L) | 6.2 ± 1.8 | 5.9 ± 1.6 | 8.9 ± 4.7 | 0.01 |
| Creatinine (μmol/L) | 94.4 ± 18.1 | 100.6 ± 26.0 | 115.1 ± 48.2 | 0.14 |
| Sodium (mmol/L) | 139.7 ± 2.9 | 138.6 ± 4.2 | 139.5 ± 4.6 | 0.49 |
| Potassium (mmol/L) | 4.0 ± 0.3 | 4.0 ± 0.5 | 4.1 ± 0.5 | 0.52 |
| BNP (pg/mL) | 91.5 ± 128.6 | 109.3 ± 137.8 | 264.7 ± 181.7 | <0.001 |
| Medications | ||||
| ACEI or/and ARB | 14 (70) | 25 (76) | 25 (78) | 0.80 |
| β-blocker | 13 (65) | 18 (55) | 21 (66) | 0.61 |
| Aldosterone antagonists | 0 | 2 | 0 | N/A |
| Statin | 10 (50) | 19 (58) | 15 (47) | 0.68 |
| Loop diuretics | 3 (15) | 0 | 27 (84) | <0.001 |
| Doppler echocardiographic parametersa | ||||
| EF (%) | 67.4 ± 10.0 | 66.9 ± 9.5 | 63.3 ± 13.7 | 0.57 |
| Peak E (cm/s) | 82.9 ± 13.8 | 74.2 ± 25.6 | 86.9 ± 32.6 | 0.17 |
| Peak A (cm/s) | 68.3 ± 17.7 | 75.2 ± 22.8 | 78.3 ± 31.7 | 0.23 |
| E/A ratio (m/s)b | 1.4 ± 0.3 | 1.0 ± 0.6 | 1.5 ± 1.2 | 0.08 |
| DT (m/s) | 173.7 ± 40.5 | 193.5 ± 51.2 | 193.7 ± 66.6 | 0.47 |
| IVRT (m/s) | 110.5 ± 16.5 | 113.2 ± 21.7 | 111.1 ± 24.9 | 0.74 |
| LVDD (mm) | 49.8 ± 5.4 | 50.1 ± 5.3 | 51.1 ± 7.8 | 0.87 |
| DD Phases I/II/III | N/A | 24/4/5 | 14/6/11 | 0.07 |
Values are mean ± SD/n (%).
BMI, body mass index; SBP/DBP, systolic and diastolic blood pressure; BNP, b-type natriuretic peptide; ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin II receptor blocker; EF, ejection fraction; E, maximal early mitral valve inflow; A, maximal late mitral valve inflow; DT, deceleration time; IVRT, isovolumic relaxation time; LVDD, left ventricular diastolic dysfunction; N/A, not applicable; Phase I, impaired relaxation; Phase II, pseudonormalization; Phase III, restrictive-like filling.
aComplete echocardiography data are missing for two patients.
bE/A ratio could not be calculated for 11 patients owing to atrial fibrillation.
Univariable and multivariable predictors of diastolic dysfunction
Receiver operating characteristic analysis
Receiver operating characteristic analysis demonstrates that MMP-2, PIIINP, PICP, CITP, and BNP are significant predictors of DD (Table 2). However, none of these variables had an AUC value ≥ 0.75 (Table 2). Receiver operating characteristic curves were also estimated to identify predictors of restrictive-like filling pattern vs. no restrictive-like filling pattern. Carboxy-terminal propeptides of procollagen type I (AUC = 0.94, P < 0.01, 95% CI: 0.89, 0.99), PIIINP (AUC = 0.80, P < 0.001, 95% CI: 0.68, 0.92), MMP-2 (AUC = 0.78, P = 0.001, 95% CI: 0.66, 0.90), and BNP (AUC = 0.77, P = 0.001, 95% CI: 0.65, 0.89) were all significant for this grade of DD. We observed that a cutoff point of 1445 ng/mL for MMP-2 provided a sensitivity of 77% and a specificity of 40% for predicting DD (Table 3). Using similar sensitivity, the specificity for other collagen biomarkers and BNP ranged from 25 to 60%. Combining both MMP-2 and BNP above, the thresholds shown in Table 3 reduced the sensitivity by 15% and increased the specificity by 20%. When either MMP-2 or BNP were applied, the sensitivity increased to 92% with a consequent reduction in specificity of 30% for predicting DD in this sample. For diagnosing DD in our population, combining MMP-2 ≥ 1445 ng/mL and BNP ≥ 50 pg/mL together compared to BNP alone results in the positive predictive value remaining at 83% and the negative predictive value falling from 41 to 33%. When either MMP-2 ≥ 1445 ng/mL or BNP ≥ 50 pg/mL are used compared to BNP alone in the same population, the positive predictive value does not alter while the negative predictive value increases from 41 to 54%.
Table 2.
Area underneath the curve statistics for collagen markers and b-type natriuretic peptide in predicting diastolic dysfunction and heart failure with preserved ejection fraction
| Variables | Diastolic dysfunction |
Heart failure with preserved ejection fraction |
||
|---|---|---|---|---|
| AUC (95% CI) | P-value | AUC (95% CI) | P-value | |
| >MMP-2 (ng/mL) | 0.73 (0.62, 0.84) | 0.002 | 0.91 (0.84, 0.98) | <0.001 |
| CITP (µg/L) | 0.74 (0.62, 0.86) | 0.001 | 0.83 (0.72, 0.92) | <0.001 |
| BNP (pg/mL) | 0.69 (0.55, 0.83) | 0.01 | 0.82 (0.73, 0.91) | <0.001 |
| PICP (ng/mL) | 0.66 (0.54, 0.78) | 0.03 | 0.82 (0.73, 0.91) | <0.001 |
| MMP-9 (ng/mL) | 0.58 (0.45, 0.71) | 0.27 | 0.79 (0.68, 0.89) | <0.001 |
| PIIINP (µg/L) | 0.73 (0.60, 0.85) | 0.002 | 0.78 (0.66, 0.89) | <0.001 |
| PINP (µg/L) | 0.54 (0.38, 0.69) | 0.64 | 0.61 (0.48, 0.74) | 0.09 |
| TIMP (ng/mL) | 0.59 (0.46, 0.72) | 0.22 | 0.57 (0.43, 0.70) | 0.30 |
| MMP-1 (ng/mL) | 0.60 (0.46, 0.75) | 0.18 | 0.55 (0.42, 0.68) | 0.42 |
AUC, area underneath the curve; CI, confidence interval.
Table 3.
Sensitivity and specificity of markers of collagen turnover and b-type natriuretic peptide in predicting diastolic dysfunction
| Variable | Cutoff | Sensitivity (%) | Specificity (%) | PPV | NPV |
|---|---|---|---|---|---|
| MMP-2 (ng/mL) | ≥1445 | 77 | 40 | 0.80 | 0.35 |
| BNP (pg/mL) | ≥50 | 77 | 50 | 0.83 | 0.41 |
| PICP (µg/L) | ≥204 | 77 | 25 | 0.76 | 0.26 |
| CITP (µg/L) | ≥3.9 | 77 | 45 | 0.82 | 0.38 |
| PIIINP (µg/L) | ≥3.7 | 77 | 60 | 0.86 | 0.45 |
| MMP-2 (ng/mL) and BNP (pg/mL) | ≥1445, ≥50 | 62 | 60 | 0.83 | 0.33 |
| MMP-2 (ng/mL) or BNP (pg/mL) | ≥1445, ≥50 | 92 | 30 | 0.81 | 0.54 |
PPV, positive predictive value; NPV, negative predictive value.
Logistic regression
Unadjusted predictors of DD are shown in Table 4. Multivariable analysis (adjusting for age, gender, and urea levels) identified MMP-2, CITP, and PIIINP as independent predictors of DD (Table 4).
Table 4.
Univariable and multivariable predictors of diastolic dysfunction
| Variable | P-value | Odds ratio | 95% CI |
|---|---|---|---|
| Univariable models | |||
| Age | 0.02 | 1.059 | (1.007, 1.112) |
| Gender: female | 0.38 | 0.609 | (0.196, 1.889) |
| Ischaemic | 0.26 | 1.875 | (0.606, 5.797) |
| Current smoker | 0.41 | 2.293 | (0.265, 19.852) |
| Raised cholesterol | 0.60 | 1.309 | (0.475, 3.604) |
| MMP-2 | 0.01 | 1.004 | (1.001, 1.007) |
| MMP-9 | 0.08 | 1.005 | (0.999, 1.012) |
| PICP | 0.01 | 1.004 | (1.001, 1.008) |
| PIIINP | 0.01 | 2.023 | (1.182, 3.463) |
| PINP | 0.99 | 1.000 | (0.980, 1.020) |
| TIMP-1 | 0.14 | 1.002 | (0.999, 1.005) |
| MMP-1 | 0.13 | 1.056 | (0.979, 1.139) |
| CITP | 0.01 | 1.570 | (1.102, 2.258) |
| BNP | 0.03 | 1.004 | (1.000, 1.009) |
| Urea | 0.11 | 1.170 | (0.938, 1.458) |
| Creatinine | 0.09 | 1.018 | (0.994, 1.042) |
| Multivariable modela | |||
| MMP-2 | 0.02 | 1.004 | (1.001, 1.007) |
| PIIINP | 0.01 | 2.098 | (1.181, 3.727) |
| CITP | 0.02 | 1.490 | (1.002, 2.217) |
aAdjusted for age, gender and urea levels. No significant interactions were observed between MMP-2, CITP, or PIIINP and age, gender, or urea levels. MMP-2 and PIIINP were modelled separately and both were significant predictors of outcome, respectively, when adjusted for age, gender, and urea.
Univariable and multivariable predictors of heart failure with preserved ejection fraction
Receiver operating characteristic analysis
Receiver operating characteristic analysis (Table 2, Figure 1) showed that MMP-2, MMP-9, PICP, CITP, PIIINP, and BNP levels were significant predictors of HF-PEF. Matrix metalloproteinase 2 performed better than all other markers with AUC of 0.91 (Table 2). We observed that a cutoff of 1585 ng/mL for MMP-2 provided 91% sensitivity and 76% specificity for predicting HF-PEF (Table 5). Using a similar sensitivity, the specificity of the other cardiac fibrotic markers ranged from 35 to 52%. Interestingly, combining MMP-2 and BNP above the thresholds shown in Table 5 reduced the sensitivity to 81% and increased specificity to 83%. Similarly, using either MMP-2 or BNP increased sensitivity to 100% with specificity at 39% for predicting HF-PEF in this dataset. For diagnosing HF-PEF, combining MMP-2 ≥ 1585 ng/mL and BNP ≥ 60 pg/mL together compared to BNP alone results in the positive predictive value increasing from 50 to 74%, while the negative predictive value remains high at 88%. Moreover, using either MMP-2 ≥ 1585 ng/mL or BNP ≥ 60 pg/mL together compared to BNP results in the positive predictive value remaining at 50% with an increase in the negative predictive value to 100%.
Figure 1.
Receiver operating characteristic curve comparing the collagen markers and b-type natriuretic peptide in predicting heart failure with preserved ejection fraction.
Table 5.
Sensitivity and specificity of collagen turnover markers at specific cutoff points for the prediction of heart failure with preserved ejection fraction
| Variable | Cutoff | Sensitivity (%) | Specificity (%) | PPV | NPV |
|---|---|---|---|---|---|
| MMP-2 (ng/mL) | ≥1585 | 91 | 76 | 0.69 | 0.93 |
| BNP (pg/mL) | ≥60 | 91 | 46 | 0.50 | 0.90 |
| PICP (µg/L) | ≥220 | 91 | 43 | 0.48 | 0.89 |
| PIIINP (µg/L) | ≥3.6 | 91 | 37 | 0.46 | 0.87 |
| MMP-9 (ng/mL) | ≥65 | 91 | 35 | 0.45 | 0.87 |
| CITP (µg/L) | >4.3 | 91 | 52 | 0.53 | 0.91 |
| BNP (pg/mL) and MMP-2 (ng/mL) | ≥60, ≥1585 | 81 | 83 | 0.74 | 0.88 |
| BNP (pg/mL) or MMP-2 (ng/mL) | ≥60, ≥1585 | 100 | 39 | 0.49 | 1.00 |
PPV, positive predictive value; NPV, negative predictive value.
Logistic regression
Age, female sex, MMP-2, MMP-9, PICP, PIIINP, CITP, BNP, and urea levels were significant unadjusted predictors of HF-PEF (Table 6). Multivariable analysis (adjusting for age, gender, and urea levels) identified three independent predictors of HF-PEF namely, MMP-2, MMP-9, and PIIINP.
Table 6.
Univariable and multivariable models predicting patients with heart failure with preserved ejection fraction
| Variable | P-value | Odds ratio | 95% CI |
|---|---|---|---|
| Univariable models | |||
| Age | 0.04 | 1.050 | (1.004, 1.106) |
| Gender: female | 0.03 | 2.783 | (1.094, 7.077) |
| Ischaemic | 0.19 | 1.847 | (0.743, 4.591) |
| Current smoker | 0.16 | 0.217 | (0.025, 1.848) |
| Raised cholesterol | 0.66 | 0.818 | (0.337, 1.987) |
| Diabetes mellitus | 0.44 | 1.786 | (0.414, 7.697) |
| MMP-2 | <0.01 | 1.010 | (1.006, 1.015) |
| MMP-9 | <0.01 | 1.016 | (1.008, 1.024) |
| PICP | <0.01 | 1.010 | (1.006, 1.015) |
| PIIINP | <0.01 | 2.127 | (1.414, 3.200) |
| CITP | <0.001 | 1.637 | (1.251, 2.143) |
| PINP | 0.08 | 1.02 | (0.990, 1.040) |
| TIMP-1 | 0.18 | 1.002 | (0.999, 1.004) |
| MMP-1 | 0.21 | 1.036 | (0.981, 1.094) |
| BNP | <0.01 | 1.007 | (1.003, 1.011) |
| Urea | <0.01 | 1.473 | (1.160, 1.871) |
| Creatinine | 0.06 | 1.017 | (1.000, 1.034) |
| Multivariable modela | |||
| MMP-2 | 0.002 | 1.013 | (1.005, 1.021) |
| MMP-9 | 0.006 | 1.037 | (1.011, 1.065) |
| PIIINP | 0.03b | 2.020 | (1.000, 4.133) |
aNo significant interactions were observed between MMP-2, MMP-9, and PIIINP levels, or between these markers and age, gender, or urea levels.
bP-value of the partial likelihood ratio test used in preference to Wald P-value.
Discussion
The main finding of this study is that serological markers of collagen turnover are predictors of DD and HF-PEF. Matrix metalloproteinase 2 was the most sensitive and specific biomarker for the identification of HF-PEF. Interestingly, MMP-2 was superior to BNP, the most widely used biochemical tool for the diagnosis of HF-PEF. We also found that combining MMP-2 along with BNP can be used to improve either the positive predictive or negative predictive power for detecting HF-PEF. While the cardiac fibrotic markers studied were less effective for the detection of overall DD, PICP was a very powerful predictor for restrictive-like filling, the most severe form of DD. These findings support the emerging potential role of markers of collagen turnover in diagnosing HF-PEF and severe DD.
Heart failure with preserved ejection failure is a frequent syndrome, but its diagnosis can present a challenge in routine clinical practice.15 The major limitation in the diagnosis of HF-PEF is the identification of DD, which at present is predominantly reliant on Doppler echocardiographic studies. However, different technical and physiological factors can make the evaluation and interpretation of these studies difficult. Furthermore, the presence of atrial fibrillation, which can be present in as many as 37% of the population, negates the use of many of the Doppler echocardiographic parameters used in the diagnosis of DD.16 This underlies the need for another diagnostic tool for DD.
B-type natriuretic peptide and NT-proBNP are cardiac neurohormones secreted from the ventricles in response to ventricular volume expansion and pressure overload.17 These cardiac peptides are the only biochemical tools currently used for the diagnosis and screening of HF-PEF.18,19 Lubien et al.6 demonstrated that a BNP value of 60 pg/mL had a sensitivity and specificity over 80% for detecting DD in patients with a history of HF-PEF. In our study, we observed similar results. The elevated levels of BNP in DD and HF-PEF may not simply reflect increased pressure and volume overload. A growing body of data has linked BNP with myocardial interstitial fibrosis, suggesting that BNP has antifibrotic properties.20,21 However, the role of this peptide in the diagnosis of HF-PEF may be limited by biological variability and the fact that elevated levels may not be specific for this syndrome.8,9
More recently, direct serum analysis of collagen turnover has become possible. We10 and others11–13 have previously demonstrated that serological markers of cardiac fibrosis and BNP are significantly elevated in hypertensive populations with DD. Querejeta et al.13 showed that serum PICP, a marker of collagen type I synthesis, is a highly sensitive and specific parameter in the identification of severe myocardial fibrosis in patients with HHD. However, the sensitivity and specificity of these specific cardiac fibrotic markers remain uncertain in HF-PEF. In our study, we demonstrated that MMP-2 is the most powerful marker of HF-PEF and superior to BNP in the identification of this population.
An emerging trend is evolving for the use of multiple biomarkers in the diagnosis of heart failure.22 Adopting this approach, the combination of MMP-2 and BNP can, as demonstrated, improve sensitivity or specificity depending on whether the biomarkers are used together or as alternatives. The explanation of why MMP-2 is the most powerful predictor of HF-PEF is unclear. As an enzyme responsible for collagen degradation, MMP-2 may represent a response to excess myocardial fibrosis.10,23,24 Furthermore, it may also reflect loss of elastin and other components of the myocardial extracellular matrix, which in turn may promote ventricular stiffness, DD, and HF-PEF.25 The diagnostic accuracy of these markers for DD is less impressive than for HF-PEF. This may be explained by reduced specificity and sensitivity in lesser degrees of DD. In the small cohort of patients with restrictive-like filling stage, PICP is shown to be a powerful predictor of this condition which may indicate an exaggerated synthesis of collagen type I contributing to the left ventricular stiffness characteristic of this population.
Blockade of the renin–angiotensin–aldosterone system (RAAS) may alter the natural history of DD and HF-PEF associated with DD through an influence on collagen turnover.26 Therefore, it is possible that that such therapy may alter the diagnostic accuracy of markers of collagen turnover. An analysis of this issue within this small data set would seem to suggest that RAAS modulating therapies do not have an influence. However, a larger prospective dataset would be required to clarify the role of such therapies on these biomarkers.
In interpreting these data, certain limitations need to be taken into consideration. Firstly, this is a small selected cohort from a hypertension population and, in particular, we excluded patients with active inflammation or fibrotic disease. Secondly, although we included Doppler echocardiographic evidence of DD, we did not carry out tissue Doppler, pulmonary venous flow measurements, or invasive confirmation of DD. Finally, strict criteria were used to diagnose HF-PEF, which may exclude patients with less severe forms of this syndrome.
Conclusion
This study provides original data on the predictive power of collagen markers in the diagnosis of DD and HF-PEF. Several cardiac fibrotic markers were found to be effective screening tools for these conditions. Interestingly, MMP-2 was shown to be more predictive than BNP in the identification of patients with HF-PEF. This and other similar studies have set the stage for larger trials of serological markers of collagen turnover in the diagnosis of HF-PEF and DD.
Conflict of interest: K.M. has received horonaria from Biosyn Limited.
References
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