Abstract
Background
Mitral annular calcification (MAC) is associated with cardiovascular events and mitral valve dysfunction. However, the underlying pathophysiology remains incompletely understood. In this prospective longitudinal study, we used a multi-modality approach including positron emission tomography (PET), computed tomography (CT) and echocardiography to investigate the pathophysiology of MAC and assess factors associated with disease activity and progression.
Methods
A total of 104 patients (age 72±8 years, 30% women) with calcific aortic valve disease, therefore predisposed to MAC, underwent 18F-sodium fluoride (calcification activity) and 18F-Fluorodeoxyglucose (18F-FDG, inflammation activity) PET, CT calcium scoring and echocardiography. Sixty patients underwent repeat CT and echocardiography after 2 years.
Results
MAC (mitral annular calcium score >0) was present in 35 (33.7%) patients who had increased 18F-fluoride (tissue-to-background ratio [TBRmax] 2.32 [95% confidence interval 1.81-3.27]) vs. 1.30 [1.22-1.49], p<0.001) and 18F-FDG activity (TBRmax 1.44 [1.37-1.58]) vs. 1.17 [1.12-1.24]; p<0.001) compared to patients without MAC. Mitral annular calcification activity (18F-fluoride uptake) was closely associated with the local calcium score and 18F-FDG uptake, as well as female sex and renal function. Similarly, MAC progression was closely associated with local factors, in particular, baseline MAC. Traditional cardiovascular risk factors and calcification activity in bone or remote atherosclerotic areas were not associated with disease activity nor progression.
Conclusions
MAC is characterized by increased local calcification activity and inflammation. Baseline MAC burden was associated with disease activity and rate of subsequent progression. This suggests a self-perpetuating cycle of calcification and inflammation that may be the target of future therapeutic interventions.
Subject terms: Nuclear Cardiology and PET, Valvular Heart Disease
Introduction
Mitral annular calcification (MAC) is a common finding on cardiovascular imaging studies with an estimated prevalence ranging from 8% to 42% depending on age of the population studied and analysis method.1, 2 Often associated with aortic, coronary artery and aortic valve calcification (AVC),2 MAC has been linked to increased atherosclerotic burden,2 incident stroke3 and cardiovascular mortality.4 While MAC is associated with endothelial damage, lipid infiltration and progressive valve calcification,5–7 the pathophysiology of MAC remains incompletely understood and medical therapies to halt its progression are lacking. MAC also has functional consequences, helping to drive progressive mitral stenosis and mitral regurgitation, the severe stages of which can only be remedied through surgical or, potentially, percutaneous intervention.8, 9
Several epidemiological studies have investigated risk factors for MAC, finding similar determinants as for calcific aortic valve disease, including age, obesity, smoking, and serum phosphate.10, 11 Important differences have also been observed, with MAC showing female predominance7 and a stronger association with chronic kidney disease and dysregulated mineral metabolism.12, 13 An association with low bone mineral density (BMD) has been suggested, but remains unproven.14 Despite the various investigations into risk factors for MAC incidence and prevalence, no studies to date have evaluated risk factors governing disease activity.
Hybrid positron emission and computed tomography (PET-CT) allows for the simultaneous noninvasive evaluation of disease activity and heart valve anatomy.15, 16 CT provides detailed assessment of calcium burden, and PET can measure the activity of specific disease processes dependent on the availability of suitable tracers. 18F-Sodium fluoride (18F-fluoride) is a marker of vascular and valvular calcification activity17 that has been used to investigate vascular atherosclerosis and aortic stenosis.15, 18 18F-FDG has been utilized to measure vascular inflammation due to its accumulation within tissue macrophages.19
In this clinical imaging study, our aim was to use a state-of-the-art multi-modality imaging approach to investigate mitral annular calcification activity and inflammation in order to elucidate factors associated with disease prevalence, activity and progression.
Methods
Patients aged >50 years with calcific aortic valve disease were recruited as previously described and formed the study cohort.15 All had aortic valve calcification on CT and were therefore deemed prone to developing calcific valve disease. Exclusion criteria included insulin-dependent diabetes, blood glucose >200 mg/dL, end-stage renal disease, metastatic malignancy and life expectancy <2 years. The study cohort was divided into patients who did (score>0) or did not (score=0) have MAC on CT to assess factors associated with MAC prevalence. Clinical data were ascertained based on detailed history and clinical examination. Fasting serum biomarkers were analyzed as previously described. Lipoprotein(a) was measured using chemiluminescent immunoassays as previously described.20 A detailed echocardiographic exam was performed under standardized conditions according to a formal protocol as previously described.15 The Institutional Review Board of the University of Edinburgh approved the protocol, and participants provided written, informed consent. Study data can be made available to other researchers on request to the corresponding author.
A control cohort without evidence of heart valve calcification (CT calcium score of 0 in mitral annulus and aortic valve) was also included in order to determine the normal range of 18F-fluoride PET uptake in the mitral annulus, with the highest 18F-fluoride TBRmax values defining the upper limit of normal and differentiating between study cohort patients that did (PET+) and did not have (PET-) increased PET activity.
PET-CT Imaging
PET-CT scans of the heart and aorta were performed with a hybrid scanner (Biograph mCT, Siemens Medical Systems, Erlangen, Germany). Two scans were performed at least 24 hours apart, 60 minutes after administration of 18F-fluoride 125 MBq and 90 min after 18F-FDG 200 MBq. ECG-gating was not used, and all counts were utilized for analysis. All patients were asked to adhere to a carbohydrate-free diet for 24 hours preceding their 18F-FDG scan in order to suppress myocardial uptake, as previously described.15 Patients were given a list of foods (high in fat, low in carbohydrate) to eat and also those to avoid. An ECG-gated breath-hold CT scan (non-contrast-enhanced, 40 mA/rot [CareDose], 100 kV) of the heart was performed for calcium scoring.
Image Analysis: Computed Tomography
Mitral annulus, aortic valve, coronary artery and aortic CT calcium scores were determined using dedicated analysis software (VScore, Vital Images, Minnetonka, USA & OsiriX Lite version 8.5.1, OsiriX Imaging Software, Geneva, Switzerland). Agatston scores were calculated using a threshold of 130 Hounsfield units.21 MAC on computed tomography (CT-MAC) was defined as calcium score >0 Agatston Units (AU) in the mitral annulus.
Image Analysis: Positron Emission Tomography
Mitral annular 18F-fluoride and 18F-FDG PET activity was quantified according to a standardized protocol using OsiriX. Regions of interest (ROI) were drawn around maximal areas of 18F-fluoride and 18F-FDG activity to obtain the maximum standardized uptake values (SUVmax), which were divided by blood pool uptake values in the right atrium (2 cm2 area) to obtain tissue-to-background ratio (TBRmax) values. Given the difficulty in determining the exact borders of the mitral annulus, SUVmean and TBRmean values were not quantified.
Uptake of 18F-fluoride and 18F-FDG in the aortic valve, aorta, and coronary arteries was measured as previously reported (Data Supplement).15 BMD and 18F-fluoride bone uptake were measured in four thoracic vertebrae as detailed previously.18 Briefly, 0.5 cm2 ROI were drawn within the cancellous bone. The average Hounsfield Unit (HU) density within those regions was used as a relative measure of BMD.22 Maximum 18F-fluoride SUV values were quantified in the same ROI. Myocardial 18F-FDG uptake was assessed by recording the maximum SUV in the left ventricular septum. A diffuse pattern of myocardial 18F-FDG uptake accompanied by SUV ≥5.0 indicated failed myocardial suppression.15 Patients with failed suppression were excluded from analysis of FDG data, but not from analysis of 18F-fluoride data.
Repeatability Studies
All CT and PET quantifications were independently performed in a blinded fashion by two trained observers (M.G.T. and D.M.). Disagreements were resolved by consensus with involvement of a third observer (R.A.).
Image Analysis: Echocardiography
Examination of the mitral valve apparatus was performed in a blinded fashion by one cardiologist (J.A.). At least three diastolic transmitral continuous-wave Doppler envelopes were traced to obtain an average diastolic transmitral gradient. Mitral regurgitation severity was assessed according to American Society of Echocardiography guidelines.23 No adjustment for heart rate was performed because 87% of patients had a heart rate of <80 beats/minute.
Disease Progression Studies
A subset of study participants underwent repeat CT and echocardiography using the same protocol and equipment 2 years after initial imaging. Mitral annular disease progression was assessed using the annualized change in CT calcium score and transmitral pressure gradient.
Statistical Analysis
Continuous variables are reported as mean ± standard deviation or median (interquartile range) and were compared with the unpaired Student t, Wilcoxon rank-sum or Kruskal-Wallis tests, as appropriate. Categorical variables are reported as proportions and analyzed with the chi-squared or Fisher’s exact test. Correlations were calculated using Spearman correlation coefficients. Data are presented by presence or absence of CT-MAC or mitral annular 18F-fluoride activity or were dichotomized at the median CT-MAC calcium score. Bland-Altman mean differences and limits of agreement were obtained. Intraclass correlation coefficients were calculated with two-way mixed-effects models. Multivariable linear and logistic regression models were used to identify predictors of MAC prevalence and 18F-fluoride activity. Logarithmic transformation of 18F-fluoride uptake was performed to achieve a normal distribution. Initially, all variables with p<0.2 in bivariate comparisons were included in the model, as well as important cardiovascular risk factors (age, sex, hypertension, diabetes, smoking, LDL cholesterol and prior CVD). Subsequently, a backward stepwise selection process was utilized with age and sex forced into the model. Separately, 18F-FDG TBRmax was added to the model to identify FDG as predictor of 18F-fluoride uptake. Multiple linear and multinomial logistic regression models were used to identify predictors of MAC progression. All analyses were performed with STATA 14.2 (StataCorp LP, College Station, TX, USA). A two-tailed p<0.05 was used to define statistical significance.
Results
Patient Population
The study cohort comprised 104 patients (mean age 72±8 years, 30% women; baseline characteristics are presented in Tables 1 and 2). The median transmitral mean diastolic pressure gradient was 1.4 (IQR 1.0-2.1) mmHg (Data Supplement). In addition, a control cohort of 17 subjects without heart valve calcification was included (68±8 years; Data Supplement). The effective radiation dose per patient was 9.7±1.2 mSv (CT conversion factor 0.014 mSv/mGy/cm). Interobserver reproducibility for MAC-CT calcium scoring (intraclass coefficient 1.00 [95% CI 0.99-1.00]) and PET quantification (18F-fluoride TBRmax 0.99 [0.98-0.99], 18F-FDG TBRmax 0.87 [0.82-0.91]) was good (Data Supplement).
Table 1.
Baseline characteristics by presence of mitral annular calcification (prevalence) and mitral annular 18F-fluoride uptake (disease activity).
| Baseline Characteristics | MAC- (n=69) |
MAC+ (n=35) |
p | 18F-fluoride- (n=66) |
18F-fluoride+ (n=36) |
p* |
|---|---|---|---|---|---|---|
| Age, years | 70.6±7.9 | 75.1±8.2 | 0.011 | 70.8±7.9 | 74.8±8.7 | 0.026 |
| Female, n (%) | 14 (20.3) | 17 (48.6) | 0.003 | 12 (18.2) | 19 (52.8) | <0.001 |
| Body mass index, kg/m2 | 27.6±4.2 | 28.7±4.9 | 0.276 | 27.4±3.9 | 28.9±5.0 | 0.093 |
| Ischemic heart disease, n (%) | 27 (39.1) | 11 (31.4) | 0.441 | 28 (42.4) | 9 (25.0) | 0.080 |
| Cardiovascular disease, n (%) | 31 (44.9) | 11 (31.4) | 0.185 | 32 (48.9) | 9 (25.0) | 0.021 |
| Current smoking, n (%) | 8 (11.6) | 4 (11.4) | 0.980 | 7 (10.6) | 4 (11.1) | 0.937 |
| Diabetes, n (%) | 9 (13.2) | 6 (17.1) | 0.594 | 9 (13.9) | 6 (16.7) | 0.703 |
| Hypertension, n (%) | 41 (59.4) | 23 (65.7) | 0.533 | 39 (59.1) | 23 (63.9) | 0.635 |
| Osteoporosis, n (%) | 2 (2.9) | 0 (0) | 0.309 | 2 (3.0) | 0 (0) | 0.539 |
| Bone mineral density (mean HU) | 160.6±43.2 | 142.1±38.5 | 0.035 | 159.7±41.0 | 144.8±43.6 | 0.096 |
| eGFR, mL/min/1.73m2 | 74.5±17.8 | 63.5±18.9 | 0.004 | 73.0±17.6 | 67.0±20.1 | 0.121 |
| Urea, mg/dL | 20.0±7.1 | 22.3±7.7 | 0.159 | 20.0±5.5 | 22.4±9.8 | 0.187 |
| Calcium, mg/dL | 9.3±0.7 | 9.4±0.3 | 0.119 | 9.2±0.5 | 9.5±0.7 | 0.047 |
| Phosphate, mg/dL | 3.6±1.1 | 3.5±0.5 | 0.606 | 3.5±0.5 | 3.7±1.4 | 0.411 |
| Alkaline phosphatase, U/dL | 78.7±20.2 | 99.1±74.9 | 0.133 | 80.2±22.9 | 95.6±74.3 | 0.255 |
| Total cholesterol, mg/dL | 195.6±52.6 | 183.8±51.5 | 0.280 | 190.2±50.1 | 193.2±56.2 | 0.781 |
| LDL cholesterol, mg/dL | 107.4±44.4 | 101.0±46.2 | 0.511 | 101.1±41.2 | 110.6±49.2 | 0.307 |
| HDL cholesterol, mg/dL | 55.4±23.2 | 50.4±12.0 | 0.146 | 55.8±23.4 | 50.5±12.4 | 0.133 |
| Triglycerides, mg/dL | 75.7±47.2 | 70.7±37.1 | 0.554 | 76.0±46.6 | 71.5±39.6 | 0.621 |
| Lipoprotein(a), ng/dL | 18.6 (8.9-62.9) | 18.1 (9.0-54.9) | 0.845 | 17.6 (8.3-67.4) | 20.5 (9.0-55.6) | 0.660 |
| Statin therapy, n (%) | 39 (56.5) | 521 (60.0) | 0.734 | 40 (60.6) | 18 (50.0) | 0.301 |
| ACE inhibitor therapy, n (%) | 27 (39.1) | 14 (40.0) | 0.932 | 26 (39.4) | 14 (38.9) | 0.960 |
Continuous variables are presented as mean±SD or median (IQR). eGFR, estimated glomerular filtration rate (CKD-EPI).
Table 2.
Imaging characteristics by presence of mitral annular calcification (prevalence) and mitral annular 18F-fluoride uptake (disease activity).
| Imaging Characteristics | MAC- (n=69) |
MAC+ (n=35) |
p | 18F-fluoride- (n=66) |
18F-fluoride+ (n=36) |
p |
|---|---|---|---|---|---|---|
| Aortic valve by echocardiography | ||||||
| Control, n (%) | 3 (4.4) | 2 (5.7) | 0.034 | 4 (6.1) | 1 (2.8) | 0.012 |
| Sclerosis, n (%) | 16 (23.3) | 2 (5.7) | 17 (25.8) | 1 (2.8) | ||
| Mild stenosis, n (%) | 20 (29.0) | 5 (14.3) | 17 (25.8) | 7 (19.4) | ||
| Moderate stenosis, n (%) | 18 (26.1) | 15 (42.9) | 18 (27.3) | 15 (41.7) | ||
| Severe stenosis, n (%) | 12 (17.4) | 11 (31.4) | 10 (15.2) | 12 (33.3) | ||
| AVC calcium score, AU | 801 (298-2174) | 1501 (600-3314) | 0.030 | 771 (309-2076) | 1598 (1007-3230) | 0.003 |
| MAC calcium score, AU | 0 | 837 (300-2129) | - | 0 | 834 (139-2107) | - |
| Aorta calcium score, AU | 894 (190-2548) | 1733 (396-7984) | 0.058 | 997 (144-3181) | 1378 (374-4036) | 0.170 |
| Aortic valve 18F-fluoride TBRmax | 2.44 (1.91-2.99) | 2.58 (2.21-3.14) | 0.192 | 2.34 (1.96-2.91) | 2.74 (2.38-3.18) | 0.028 |
| Mitral annulus 18F-fluoride TBRmax | 1.30 (1.22-1.49) | 2.32 (1.81-3.27) | <0.001 | 1.29 (1.22-1.41) | 2.30 (1.84-3.07) | <0.001 |
| Coronary artery18F-fluoride TBRmax | 1.50 (1.33-1.75) | 1.60 (1.35-2.09) | 0.274 | 1.50 (1.35-1.76) | 1.59 (1.35-2.02) | 0.590 |
| 18F-fluoride TBRmax in aorta | 2.06 (1.82-2.28) | 2.14 (1.19-2.38) | 0.081 | 2.05 (1.84-2.26) | 2.20 (1.91-2.50) | 0.060 |
| Aortic valve 18F-FDG TBRmax * | 1.52 (1.44-1.63) | 1.39 (1.33-1.63) | 0.072 | 1.51 (1.40-1.63) | 1.46 (1.35-1.68) | 0.83 |
| Mitral annulus 18F-FDG TBRmax * | 1.17 (1.12-1.24) | 1.44 (1.37-1.58) | <0.001 | 1.17 (1.12-1.26) | 1.38 (1.24-1.56) | 0.002 |
| Aorta 18F-FDG TBRmax | 1.84 (1.69-1.94) | 1.68 (1.50-1.78) | 0.002 | 1.83 (1.68-1.92) | 1.69 (1.61-1.83) | 0.116 |
Continuous variables are presented as median (IQR).
n=33 patient with failed myocardial FDG suppression were excluded
Factors Associated with MAC Prevalence
The median baseline MAC-CT calcium score was 0 (IQR 0-316) AU and was higher in women (283 [0-1082] AU) compared with men (0 [0-0] AU, p=0.001). Overall, 35 (33.7%) patients had MAC on CT (CT+; 837 [300-2129] AU), who were older, twice as likely to be female, had more AVC, lower BMD and reduced eGFR compared to patients without MAC (CT-). Both groups had extensive CVD risk factor burden (Table 1). In a multiple logistic regression model, female sex and AVC calcium score were statistically significantly associated with MAC prevalence (Table 3).
Table 3.
Factors associated with MAC prevalence in a multiple logistic regression model.
| OR | 95% confidence interval | p | |
|---|---|---|---|
| Age (per 10 years) | 1.29 | 0.67-2.50 | 0.45 |
| Sex | 0.25 | 0.11-0.75 | 0.01 |
| Aortic valve calcium (per 100 AU increase) | 1.03 | 1.00-1.06 | 0.03 |
| eGFR (per 10 ml/min) | 0.77 | 0.59-1.01 | 0.06 |
Mitral Annular Inflammatory Activity (18F-FDG PET)
Thirty-three patients (32%) met criteria for failed myocardial suppression of physiological 18F-FDG uptake and were excluded from further analysis of FDG data only. In the remaining patients, median mitral annular 18F-FDG TBRmax was 1.21 [IQR 1.14-1.39], higher in patients with CT-MAC (CT+ 1.44 [1.37-1.58]) compared to those without (CT- 1.17 [1.12-1.24]; p<0.001) or to controls (1.06 [1.04-1.17]; p<0.001). A moderate correlation was observed between mitral annular 18F-FDG TBRmax and CT-MAC scores (r=0.50, p<0.001) (Table 4).
Table 4.
Correlations of mitral annular 18F-fluoride and 18F-FDG PET uptake (disease activity) with imaging findings in the mitral annulus (local factors) and remote regions, as well as serum biomarker levels (remote factors)
| 18F-fluoride TBRmax | 18F-FDG TBRmax | |||
|---|---|---|---|---|
| r | p | r | p | |
| Local Factors | ||||
| Mitral annulus CT calcium score | 0.78 | <0.001 | 0.50 | <0.001 |
| Mitral annulus 18F-fluoride TBRmax | - | - | 0.54 | <0.001 |
| Mitral annulus 18F-FDG TBRmax | 0.54 | <0.001 | - | - |
| Remote Factors | ||||
| Aortic valve CT calcium score | 0.24 | 0.017 | 0.15 | 0.218 |
| Aortic valve 18F-fluoride TBRmax | 0.19 | 0.053 | -0.02 | 0.848 |
| Aortic valve 18F-FDG TBRmax | -0.02 | 0.895 | -0.05 | 0.658 |
| Coronary artery CT calcium score | 0.03 | 0.789 | 0.12 | 0.327 |
| Coronary artery 18F-fluoride TBRmax | 0.14 | 0.159 | 0.08 | 0.518 |
| Aorta CT calcium score | 0.20 | 0.049 | 0.14 | 0.262 |
| Aorta 18F-fluoride TBRmax | 0.23 | 0.025 | -0.02 | 0.884 |
| Aorta 18F-FDG TBRmax | -0.16 | 0.127 | -0.23 | 0.060 |
| Bone mineral density | -0.19 | 0.065 | -0.15 | 0.247 |
| Bone 18F-fluoride TBRmax | 0.02 | 0.861 | 0.01 | 0.989 |
| Serum biomarkers | ||||
| Calcium | 0.15 | 0.126 | 0.04 | 0.774 |
| Phosphate | -0.02 | 0.828 | 0.14 | 0.260 |
| Alkaline phosphatase | 0.11 | 0.264 | -0.02 | 0.887 |
| Creatinine | 0.07 | 0.494 | -0.02 | 0.848 |
| LDL cholesterol | -0.03 | 0.746 | -0.30 | 0.014 |
| HDL cholesterol | -0.04 | 0.677 | -0.01 | 0.919 |
| Total cholesterol | -0.07 | 0.500 | -0.24 | 0.050 |
| Triglycerides | -0.07 | 0.484 | 0.00 | 0.992 |
| Lipoprotein(a) | 0.11 | 0.286 | 0.08 | 0.507 |
Data presented in study cohort patients (n=104). In 18F-FDG analyses, patients with failed myocardial suppression were excluded (n=33).
Mitral annular 18F-FDG TBRmax uptake was negatively correlated with total cholesterol and LDL (r=-0.30, p=0.014) and was higher in women (1.33 [1.16-1.45]) than men (1.19 [1.12-1.32], p=0.037); there was no correlation with other serum biomarkers nor 18F-FDG activity measured in remote areas (aortic valve, r=-0.05, p=0.658; aorta, r=-0.23, p=0.060) (Table 4).
Mitral Annular Calcification Activity (18F-fluoride PET)
Median mitral annular 18F-fluoride TBRmax uptake in the entire study cohort (104 patients) was 1.44 [IQR 1.27-1.89]. Patients with CT-MAC had higher 18F-fluoride uptake (CT+ 2.32 [1.81-3.27]) than those without (CT- 1.30 [1.22-1.49], p<0.001). Mitral annular 18F-fluoride activity appeared most closely related to local markers of disease burden. A strong correlation was observed between mitral annular 18F-fluoride activity and baseline CT-MAC score (r=0.79, p<0.001, Figure 1A), while a moderate correlation was observed with 18F-FDG uptake (r=0.32, p=0.001, Figure 1B). By comparison, modest or no correlations were observed between mitral annular 18F-fluoride uptake and uptake in other areas (aorta, r=0.23, p=0.025; aortic valve, r=0.19, p=0.053; coronary arteries, r=0.14, p=0.159; bone, r=0.02, p=0.861) or serum biomarkers including calcium, alkaline phosphatase and lipid markers (Table 3). Mitral annular 18F-fluoride uptake was higher in women compared with men (2.01 [1.31-2.69] vs. 1.36 [1.26-1.63], p=0.002), and in patients with impaired (eGFR<60 mL/min/1.73m2) compared with preserved renal function (1.39 [1.10-1.61] vs. 1.26 [1.04-1.36], p=0.046).
Figure 1.
Mitral annular calcification activity (18F-fluoride TBRmax) increased with the burden of baseline MAC (box plots by categories of baseline CT-MAC calcium score: zero/below median [<837 AU]/above median [≥837 AU]) (A) and was correlated with inflammatory activity (18F-FDG TBRmax), (B). Baseline CT-MAC was virtually absent in patients without 18F-fluoride activity (C).
Among the control cohort, the highest 18F-fluoride TBRmax value was 1.64. This cutoff was used to categorize patients in the study cohort as having increased 18F-fluoride uptake (>1.64, PET+) or not (≤1.64, PET-). Overall, 36 (35.6%) patients had increased 18F-fluoride uptake (median TBRmax 2.30 [1.84-3.07]). PET+ patients had a median CT-MAC calcium score of 834 (139-2107), while PET- patients had no MAC (Figure 1C). Compared with PET- patients, PET+ patients were older, more likely to be female, had more AVC, lower BMD and eGFR (Table 1). In a multiple linear regression model, CT-MAC and AVC calcium scores, female sex and eGFR demonstrated a statistically significant association with MAC disease activity. When 18F-FDG TBRmax was added to the model, significant predictors of MAC 18F-fluoride activity were baseline CT-MAC and 18F-FDG TBRmax in the subset of patients with successful myocardial suppression (Table 5).
Table 5. Factors associated with disease activity in MAC.
Predictors of log-transformed 18F-fluoride TBRmax in multiple linear regression model.
| Model 1 (n=98) | Model 2 (n=68) | |||||
|---|---|---|---|---|---|---|
| beta | 95% CI | p | beta | 95% CI | p | |
| Age (per 10 years) | -0.002 | -0.070, 0.066 | 0.953 | 0.065 | -0.019, 0.148 | 0.127 |
| Sex | -0.172 | -0.289, -0.054 | 0.005* | -0.082 | -0.229, 0.066 | 0.273 |
| AVC (per 100 AU) | 0.003 | -0.000, 0.005 | 0.052 | 0.002 | -0.002, 0.005 | 0.318 |
| eGFR (per 10 ml/min) | -0.032 | -0.061, -0.003 | 0.030* | -0.001 | -0.039, 0.038 | 0.988 |
| MAC (per 100 AU) | 0.014 | 0.011, 0.018 | <0.001* | 0.010 | 0.005, 0.015 | <0.001* |
| 18F-FDG TBRmax (per 0.1) | -- | -- | -- | 0.049 | 0.021, 0.077 | 0.001* |
Model 1 includes age, sex, hypertension, diabetes, smoking, LDL, prior CVD, and to variables with p>0.2 in bivariate comparisons, followed by backwards stepwise elimination process.
Model 2 includes 18F-FDG TBRmax in addition to variables in model 1.
Disease Progression in Mitral Annular Calcification
Sixty patients in the study cohort underwent repeat echocardiography and CT after a median of 741 [IQR 726-751] days (Figure 2 includes examples of three patients). The annual progression rate of CT-MAC calcium score was 2 (0-166) AU per year. The strongest associations of MAC progression were observed with baseline CT-MAC (r=0.82, p<0.001, Figure 3A), 18F-fluoride (r=0.75, p<0.001, Figure 3B) and 18F-FDG activity (r=0.48, p<0.002). Women tended to have a higher rate of MAC progression (34 [0-409] AU/year) than men (0 [0-68] AU/year, p=0.083). There was no association between baseline eGFR and MAC progression (r=-0.13, p=0.308) nor differences in the rate of MAC progression between those with and without advanced chronic kidney disease (p=0.933). There were no associations with MAC progression for LDL (r=-0.10, p=0.444), HDL (r=-0.08, p=0.524) or lipoprotein(a) (r=0.07, p=0.629).
Figure 2.
Baseline CT-MAC, 18F-fluoride PET activity and 2-year progression in 3 patients
First row: mild mitral annular calcification (MAC) at baseline (A), associated with mild mitral annular 18F-fluoride uptake (B), and modest progression after 2 years (change in CT-MAC 69 AU) (C).
Second row: moderate MAC at baseline (A), moderate 18F-fluoride uptake (B), and intermediate progression after 2 years (change in CT-MAC 2404 AU) (C).
Third row: severe MAC at baseline (A), bifocal high-intensity 18F-fluoride uptake (B), and rapid progression (change in CT-MAC 9446 AU) (C). Note de-novo areas of MAC that developed at the site of intense 18F-fluoride uptake in the lateral annulus.
Figure 3.
Mitral annular calcification progression (AU/year) increased with the burden of baseline MAC (box plots by categories of baseline CT-MAC calcium score: zero/below median [<837 AU]/above median [≥837 AU]) (A) and was virtually absent in patients without 18F-fluoride activity (B). A steady increase in MAC progression was observed on moving from 18F-fluoride PET-CT- to PET-CT+, to PET+CT- and finally to PET+CT+ patients (C).
All 22 (36.7%) patients with baseline CT-MAC (CT+) demonstrated progression in their CT-MAC scores (median progression rate 199 [63-480] AU/year). Eight (21.1%) of the 38 patients without baseline CT-MAC (CT-) developed new MAC (CT-MAC score at second exam 135 [40-291] AU). MAC regression was not observed. In a multiple linear regression model, baseline CT-MAC calcium score (β=0.048 per 100 AU, p=0.013) was an independent predictor of log-transformed MAC progression after adjustment for age (β=0.008 per year, p=0.847), sex (β=-0.580, p=0.368) and eGFR (β=-0.063 per 10 ml/min, p=0.718).
Patients with increased mitral annular 18F-fluoride PET uptake demonstrated faster progression than patients without (CT-MAC progression: PET+ 200 (47-480) vs. PET- 0 [0-3] AU/year, p<0.001). In multinomial logistic regression models adjusted for age and sex, there was a stronger association of positive 18F-fluoride PET uptake (PET+) with a MAC progression rate above median (OR=100.03, CI 10.88-919.62, p<0.001), than below median (OR=17.25, CI 2.76-107.92, p=0.002). Similar results were obtained with 18F-fluoride uptake as continuous variable (MAC progression above median: OR=1.95 per 0.1 increment in TBRmax, CI 1.38-2.75, p<0.001; MAC progression below median: OR=1.71, CI 1.23-2.37, p=0.001).
When considering PET and CT data together, PET-CT- patients did not demonstrate MAC progression (median MAC progression 0 (0-0) AU/year, n=32), while MAC progression was highest in PET+CT+ patients (270 (68-493) AU/year, n=18). Intermediate progression was observed in PET+CT- (47 (0-95) AU/year, n=5) and PET-CT+ patients (102 (39-166) AU/year, n=4) (Figure 3C).
Discussion
We used state-of-the-art multimodality imaging to investigate MAC, providing novel insights into the pathophysiology of this common condition and factors associated with its prevalence, disease activity and progression. We confirmed that MAC is characterized by both calcification and inflammatory activity that increases proportionally to the baseline MAC burden. Importantly, while female gender, renal dysfunction and local inflammatory activity were associated with MAC disease activity, the strongest correlate was the local burden of calcium already present within the valve annulus. Similar observations were made with respect to progression, with the fastest progression observed in patients with the largest baseline burden of MAC. We therefore suggest that once established, MAC activity and progression are characterized by a vicious cycle of established calcium, injury and inflammation within the valve that prompts further calcification activity. These findings support the concept that therapeutic strategies targeting MAC will need focus on breaking this vicious calcification cycle.
Despite its high prevalence, contribution to mitral valve dysfunction and adverse prognosis,4 the pathobiology of MAC remains incompletely understood. Moreover, therapeutic options are limited since effective medical therapy is lacking and surgical intervention is made complicated by its presence.24 There is therefore an urgent need to illuminate the pathophysiology underlying MAC and to identify novel therapeutic strategies to prevent its clinical sequelae.9 We describe a new multimodality imaging approach to help address this need. First, we have applied CT calcium scoring to define the presence of MAC and to quantify disease prevalence, burden and progression. Second, we used 18F-FDG to measure inflammatory activity. While 18F-FDG was only interpretable in two thirds of patients, our data clearly demonstrate that MAC is an inflammatory condition with the 18F-FDG PET signal increasing in proportion to baseline disease severity. Finally, we used 18F-fluoride PET as marker of calcification activity demonstrating a close association with subsequent progression and building upon a growing body of literature using 18F-fluoride to image developing cardiovascular microcalcification. The use of a cohort of patients with calcific aortic valve disease provided a patient population at high risk of developing MAC, as evidenced by the particularly high prevalence. This gave us the opportunity to assess disease activity and progression in patients with established MAC, but also in patients who subsequently developed MAC during follow-up. It also provided insights into why certain patients with aortic stenosis develop MAC, while others do not, with female sex, renal impairment and advanced AVC appearing to be of particular importance in this population.
Factors associated with disease activity in MAC
Using 18F-fluoride PET, we demonstrated that calcification activity in the mitral annulus is closely related to the local inflammatory signal provided by 18F-FDG imaging. This is consistent with histological studies of excised mitral valves demonstrating increased expression of pro-calcific cells and mediators adjacent to T-lymphocytic infiltrates, and suggests that calcium deposition is closely related to inflammatory activity.5, 6 However, mitral annular calcification activity was in fact most closely associated with the baseline CT-MAC calcium score. Similar results were observed for progression: patients with rapid disease progression and highest disease activity were those with the highest baseline CT calcium scores. Indeed, baseline MAC was the strongest predictor of MAC progression, here replicating the findings from the Multiethnic Study of Atherosclerosis.7
We believe our concordant data on MAC disease activity and progression have important therapeutic implications. The findings are remarkably similar to observations made in aortic stenosis, where it has been suggested that calcium within the valve increases mechanical stress and injury leading to inflammation and increased calcification activity.25 A similar self-perpetuating cycle of calcification inducing further calcification might also underlie MAC. The development of effective medical therapy in both conditions is therefore likely to require strategies that interrupt this cycle without impacting bone health. Studies are currently underway testing such therapies in patients with aortic stenosis (SALTIRE2 NCT02132026), providing an opportunity to investigate their impact on bystander MAC.
Study Limitations
Our study cohort comprised participants with calcific aortic valve disease. While this ensured high proportions of prevalent and incident MAC, our results may not directly apply to patients with isolated mitral valve disease or other conditions known to be associated with MAC. Moreover, our sample size was modest, precluding more detailed examination of determinants and consequences of microcalcification and inflammation. In addition, one third of patients met criteria for failed myocardial FDG suppression and were excluded from analysis of FDG data. Further studies exploring the role of PET-CT in larger samples and different patient populations are warranted. Such studies may benefit from the use of contrast computed tomography to better investigate the spatial distribution of PET uptake within the mitral annulus and to improve interobserver reproducibility. In addition, advanced imaging processing technologies such as adaptive thresholding may improve uptake delineation, and ECG-gating of the PET acquisition may reduce image blurring due to cardiac motion.
Conclusion
In this cohort, while female sex, renal dysfunction and local inflammatory activity emerged as important determinants of disease activity in MAC, the strongest determinant was the baseline CT-MAC calcium score. Moreover, the higher the baseline burden of MAC, the higher the disease activity and the faster the rate of progression. This may reflect a vicious cycle of established calcium begetting further calcification within the mitral annulus that may be a suitable target of future therapies.
Supplementary Material
Clinical Perspective Summary.
Mitral annular calcification (MAC) is associated with cardiovascular events and mitral valve dysfunction, but its pathophysiology is incompletely understood. We employed a multi-modality imaging approach including 18F-fluorodeoxyglucose and 18F-sodium fluoride PET, CT calcium scoring and echocardiography to investigate the pathology of MAC and elucidate the factors associated with its prevalence, disease activity and disease progression. Patients who had MAC (34% of patients) had increased inflammatory and calcification activity by PET imaging in the mitral annulus. Furthermore, calcification activity was most closely associated with CT-MAC calcium score, inflammation, female sex and renal dysfunction. Similarly, MAC progression on repeat CT scans after two years was closely associated with baseline MAC, with the fastest rate of progression found in those with high baseline CT-MAC scores and the highest calcification activity. By contrast, traditional cardiovascular risk factors and calcification activity in bone or remote atherosclerotic areas were not associated with disease activity nor progression. This suggests that MAC activity and progression are characterized by a vicious cycle of established calcium, injury and inflammation within the valve that prompts further calcification activity. These findings support the concept that therapeutic strategies targeting MAC will need to focus on breaking this vicious calcification cycle.
Sources of Funding
DM was supported by The Glorney-Raisbeck Fellowship Program, Corlette Glorney Foundation and The New York Academy of Medicine; MGT was supported by the KL2 TR001435 from the Institute for Translational Science, Icahn School of Medicine, Mount Sinai; JPMA and ARC were supported by British Heart Foundation (BHF) Clinical Research Training Fellowship no. FS/17/51/33096 and FS/16/75/32533; JRK was supported by K24 Hl135413 from the National Heart, Lung, and Blood Institute; DEN was supported by the BHF (CH/09/002, RE/13/3/30183 and RM/13/2/30158) and has received a Wellcome Trust Senior Investigator Award (WT103782AIA); MRD was supported by the BHF (FS/14/78/31020) and is the recipient of the Sir Jules Thorn Award for Biomedical Research 2015.
Footnotes
Disclosures: JRK reports stock ownership in Amgen, Gilead Sciences, Johnson & Johnson, and Pfizer. All other authors declare that they have no conflicts of interest.
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