An Increased B-Type Natriuretic Peptide in the Absence of a Cardiac Abnormality Identifies Those Whose Left Ventricular Mass Will Increase Over Time

Objectives The purpose of this study was to identify the relationship of B-type natriuretic peptide (BNP) with evolution of left ventricular mass (LVM) in optimally treated primary prevention patients. Background Patients who have an elevated BNP no cardiac abnormality on echocardiography are common and at increased risk of adverse events. One hypothesis is that an elevated BNP is an early sensitive indicator of who will develop future structural abnormalities such as left ventricular (LV) hypertrophy. Methods We identified optimally treated primary prevention patients with no cardiac abnormality at baseline. In particular, they had no myocardial ischemia, LV hypertrophy, LV dysfunction, or left atrial enlargement. They had a diverse range of plasma BNP levels and underwent cardiac magnetic resonance at baseline and 3 years later on a 3-T scanner. Results Fifty patients with a diverse range of BNP were studied (with BNP ≤10 pg/ml in 25 patients and >10 pg/ml in 25 patients). LVM increased (+4.7 ± 3.5 g) in 24 patients and decreased (–4.9 ± 2.8 g) in 26 patients (p < 0.01). Blood pressure by 24-h monitoring was virtually identical between those whose LVM increased (systolic blood pressure 122 ± 14 mm Hg) and those whose LVM decreased (systolic blood pressure 121 ± 11 mm Hg, p = 0.77). Plasma BNP was nearly 3 times higher in those whose LVM increased versus those in whom LVM decreased (21 ± 9.6 pg/ml vs. 7.9 ± 3.9 pg/ml, p < 0.01). The c-statistic for BNP was 0.88. Conclusions In optimally treated primary prevention patients, plasma BNP levels are able to distinguish between those whose LVM will increase during the next 3 years versus those whose LVM will decrease during the next 3 years. This may explain why individuals with high BNP are at increased risk even if no cardiac abnormality can be detected initially.

of those in the highest tertile of BNP have no apparent cardiac abnormality on phenotyping (1).
BNP is known to be a very strong independent predictor of a poor (cardiac) prognosis in every population ever examined (2)(3)(4)(5)(6)(7). This even seems to be the case in individuals with high BNP levels but no apparent cardiac abnormality on phenotyping (false positives). Several studies show that BNP levels predict prognosis over and above a wide range of baseline echocardiographic abnormalities, although, as would be expected, each echocardiographic abnormality explained part of the BNP risk (4)(5)(6)(7).
The unexplained extra BNP risk not accounted for by echocardiographic abnormalities may be related to BNP being able to predict future but not yet apparent abnormalities in left ventricular (LV) structure or function (5,8). The hypothesis that these data raise is that one of the drivers for future LV abnormalities is intracardiac pressure and that an early subtle elevation in intracardiac pressure can be picked up by BNP before LV abnormalities are either present or detectable on imaging. We therefore set out to see whether in individuals with no apparent cardiac abnormality at baseline a high BNP value could identify those who would develop a cardiac abnormality during the next few years. We focused particularly on left ventricular mass (LVM) to see whether BNP predicted how this would change during the next few years in a population of treated primary prevention patients.

METHODS
STUDY POPULATION. This is a substudy of a previously reported larger study from our center (1). The original study recruited 300 primary prevention patients with well-treated primary risk factors between April 2008 and July 2010 from local general practitioner surgeries and from the cardiovascular (CV) risk clinic at Ninewells Hospital, Dundee, United Kingdom. Patients included in the original study were 50 years or older, and were eligible for primary prevention only with no previous known CV events.
They had to be stable on therapy for at least 1 year and to have reached target for their primary risk factor, for example, office blood pressure (BP) #140/90 mm Hg.
We excluded those with previously known CV disease, known renal impairment (estimated glomerular filtration rate [eGFR] <60 ml/min), atrial fibrillation, and significant (defined as more than mild) valvular heart disease. All study subjects underwent clinical assessment, biochemical measurements (including BNP), electrocardiography, transthoracic echocardiography, dobutamine stress echocardiography to detect myocardial ischemia, and 24-hour ambulatory BP measurement. BIOCHEMICAL ASSAYS. Biochemical measurements including BNP and high-sensitivity troponin T (hs-TnT) were made by trained laboratory staff blinded to the clinical and echocardiographic data. BNP was measured using Triage BNP assay (Biosite Inc., San Diego, California). The interassay percentage coefficient of variation was 8.8% to 11.6%. The detection limit was 5 pg/ml and upper measuring limit was 5,000 pg/ml. hs-TnT was measured using a highly sensitive assay on an automated platform (Elecsys E170, Roche Diagnostics, Indianapolis, Indiana) with lower limit of blank (3 ng/l) and interassay percentage coefficient of variation #10%.
CARDIAC MAGNETIC RESONANCE. Cardiac magnetic resonance was performed at baseline and at 36 months on a 3-T Magnetom Trio scanner (Siemens, Erlangen, Germany) using body array and spine matrix radiofrequency coils as described in detail previously (9,10). CMR images were analyzed offline by an independent, blinded, magnetic resonance physicist (S.J.G.) using commercial software (Argus, Siemens Multi-modality Work Platform, version VB 15, Siemens). Electronic region-of-interest contours were placed around endocardial and epicardial LV borders on all CMR image slices at end-diastole and end-systole that were identified to contain 50% or more full-thickness myocardium. Papillary muscles were included in the LVM if the muscle structure was indistinguishable from the myocardial wall, but otherwise assigned to the LV blood pool. The process of contour placement was repeated such that every patient dataset at both time points was analyzed twice to optimize the measurement precision. The intraobserver variability was 2.02% at baseline and 1.97% at follow-up.  ACEi ¼ angiotensin-converting enzyme inhibitor; ARB ¼ angiotensin receptor blocker; BMI ¼ body mass index; BNP ¼ B-type natriuretic peptide; CMR ¼ cardiac magnetic resonance; CVD ¼ cardiovascular disease; DBP ¼ diastolic blood pressure; E/A ¼ ratio of the early to late ventricular filling velocities; E/e 0 ¼ ratio of the early diastolic transmitral flow velocity to the mitral annular velocity; eGFR ¼ estimated glomerular filtration rate; HDL ¼ high-density lipoprotein; hs-TnT ¼ high-sensitivity troponin T; LVEDV ¼ left ventricular end-diastolic volume; LVEF ¼ left ventricular ejection fraction; LVESV ¼ left ventricular end-systolic volume; LVM ¼ left ventricular mass; SBP ¼ systolic blood pressure.  Table 1, and the change in LV data on CMR are summarized in Table 2. Not surprisingly, LV filling (LV end-diastolic volume) was reduced in those whose LVM increased with time.
No significant differences were noticed in demographics and prevalence of underlying primary risk factor(s) between the 2 groups except that the patients in whom an increase in LVM was observed were significantly more likely to be active smokers  Table 3 Table 3). This was the case whether the BNP tertiles were calculated on the basis of the tertiles of the original study (n ¼ 300) or the tertiles of this substudy (n ¼ 50) (Figure 2). It is worth noting that the difference between tertile 1 and tertile 3 is large at nearly 12% of the mean baseline LVMI.
The independent predictive value of the interaction between DLVM and baseline BNP levels for explaining the evolution of LVM with time (dependent variable) was investigated by multiple linear regression analysis. We investigated 5 different models ( Table 3). Model 1 was composed of previously reported clinical predictors of LVM such as age, sex, BP, body mass index, and history of smoking. Subsequent models explored the additional predictive value of adding total cholesterol and uric acid and then adding baseline hs-TnT or BNP on top of model 1. Table 3, both hs-TnT and BNP offered additional predictive value when added to the model by improving the c-statistics significantly. In a logistic regression analysis, BNP stood out as a strong predictor of a future rise in LVM. A receiver-operating characteristic analysis yielded a c-statistic of 0.88 for BNP with a sensitivity and specificity of 70% and 88%, respectively, at a BNP level of 17 pg/ml.

DISCUSSION
Our main finding is that, in well-controlled primary prevention patients, a high BNP in the absence of any cardiac abnormality is able to identify those individuals whose LVM will increase during the next 3 years, that is, an elevated BNP is able to predict future increases in LVM. This may partly explain why, in so many studies, BNP predicts prognosis independent of echocardiographic abnormalities. Abbreviations as in Table 1. The Framingham study has already shown that the tendency for LVM to increase with aging in a population is highly variable from one individual to the next (11,12). Increases in LVM in treated hypertension are, however, far from innocent (13). Serial changes in LVM predict CV events, independent of baseline LVM and independent of baseline BP or the degree of BP reduction (14). It now appears from our data that BNP can identify those whose serial LVM will increase with time, and we know that such individuals are at increased risk and that they are currently inadequately identified by either baseline LVM or any BP parameter (14).
A major strength of our study is that the population studied was comprehensively phenotyped at baseline, that is, they were all assessed for LVM, LV systolic dysfunction, left atrial enlargement, LV diastolic dysfunction, and most importantly, for silent myocardial ischemia. All 5 of these abnormalities are known to increase BNP and to herald a poor prognosis (1,15). However we know with certainty that in the population we studied here, none of these cardiac abnormalities were present at baseline, meaning that the BNP elevation at baseline was truly unexplained by any prevalent cardiac abnormality. This makes our study unique as most data on BNP being of prognostic significance over and above echocardiographic abnormalities did not do the comprehensive phenotyping that we did and in particular did not screen for myocardial ischemia, which is known to independently increase BNP (4)(5)(6)(7)16). In previous studies, some of the prognostic significance of BNP over and above resting echocardiographic abnormalities could be attributable to silent myocardial ischemia, which was not tested for in those studies. However, we have here demonstrated an additional explanation for why BNP is prognostic beyond echocardiographic abnormalities at baseline, which is that baseline BNP can identify those whose LVM will increase with time.
A lot of recent data are suggesting that measuring BNP might one day establish a role for itself in better managing patients with treated hypertension abnormalities. This study extends that information to now show that in those with no cardiac abnormality at baseline, the elevated residual risk identified by BNP is likely to be also related to future increases in LVM (18,19).
There are a few likely explanations for our results.
A time effect is likely in that a raised intracardiac pressure genuinely precedes the increase in LVM.
BNP is a much more sensitive marker for this   BNP and Evolution of LVM J A N U A R Y 2 0 1 5 : 8 7 -9 3