Two-Year Change in 18F-Sodium Fluoride Uptake in Major Arteries of Healthy Subjects and Angina Pectoris Patients

doi:10.21203/rs.3.rs-230923/v1

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Research Article

Two-Year Change in 18F-Sodium Fluoride Uptake in Major Arteries of Healthy Subjects and Angina Pectoris Patients

https://doi.org/10.21203/rs.3.rs-230923/v1

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Purpose

To examine 2-year changes in carotid and aortic ¹⁸F-sodium fluoride (NaF) uptake in both healthy controls and angina pectoris patients.

Methods

Twenty-nine healthy subjects and 20 angina pectoris patients underwent 90-min NaF-PET/CT twice two years apart. The carotids and three sections of the aorta (arch, thoracic, abdominal) were manually segmented. NaF uptake was expressed as the mean and total standardized uptake values without and with partial volume correction (SUVmean, SUVtotal and cSUVmean, cSUVtotal).

Results

Insignificant tendencies were higher NaF uptake in angina patients at both time points with less uptake in healthy subjects and higher uptake in angina patients after 2 years. Thus, aortic cSUVmean of angina patients was 1.14±0.35 and 1.29±0.71 at baseline and after 2 years vs. 0.99±0.31 and 0.95±0.28 in healthy subjects. A similar pattern was observed for the carotid cSUVmean. NaF uptake at baseline could not predict a change in CT-calcification after 2 years. NaF uptake in all parts of the aorta correlated positively with age.

Conclusions

Slightly, but consistently, higher arterial NaF uptake in the angina group indicated more ongoing microcalcification at both time points in patients than in healthy subjects. The 2-year changes were very small in both groups, albeit with a tendency of slight decreases among healthy controls and slight increases in angina patients despite statin therapy in half of these.

Cardiac & Cardiovascular Systems

18F-sodium fluoride

NaF

microcalcifications

positron emission tomography/computed tomography

atherosclerosis

disease progression

With increasing life expectancy and proportion of older individuals in the population, there is a growing concern about chronic morbidities such as cardiovascular diseases (CVD). Despite the significant decline in mortality from coronary heart disease and stroke during several decades, CVDs remain the number one cause of mortality worldwide [1]. The cause of decline is probably multifactorial, fueled by progress in both prevention and treatment, including widespread use of statins to lower circulating cholesterol levels and timely use of thrombolysis and stents in acute coronary syndrome. However, there remain many questions about this decline. There is evidence that the rate of decline may have abated and may even be showing early signs of reversal in some population groups [2]. Thus, CVDs are one of the most challenging fields for health care systems, in particular, because symptoms of CVDs tend to appear late in the course of the disease, meaning that treatments must be directed at alleviating symptoms or complications rather than prevention.

This rather long asymptomatic phase of CVDs provides an excellent chance to counteract detectable components of CVD [3-4]. However, diagnosing CVD early in its course is also challenging because atherosclerosis, as the main underlying cause of CVD, must be developed enough to be detected using computed tomography (CT), magnetic resonance imaging or ultrasonography [5]. Hopefully, this limitation may be overcome by using positron emission tomography (PET), offering detection of molecular components of atherosclerosis way before it further develops. In this regard, targeting inflammation or microcalcification as initiation points of atherosclerosis [6] may make PET an effective modality to detect early-phase atherosclerosis [7].

Initially, ¹⁸F-fluorodeoxyglucose (FDG) was utilized to detect increased glucose uptake indicating inflammation [8], however, FDG uptake fluctuation during the course of CVD could make detection and follow-up of atherosclerosis challenging [9], This limitation might be overcome by targeting micro-calcifications instead using ¹⁸F-sodium fluoride (NaF), not only because calcium depositions are somehow correlated with inflammation [10], but also because they tend to have a more steady presence that might predict CVDs better [11]. It has been shown that patients with suspected CVD tend to have increased NaF uptake in the coronary arteries compared to healthy individuals [12]. Although it has been demonstrated that NaF uptake increases with age in the vascular system [13], it is not known, how microcalcification varies between healthy individuals and those with higher CVD risk and how it changes over time. Therefore, we mapped the occurrence and extent of NaF uptake in the carotids and the aorta in a cohort of healthy individuals and patients evaluated for angina pectoris at baseline and at follow-up after two years.

This study was part and continuation of the ‘Cardiovascular Molecular Calcification Assessed by ¹⁸F-NaF PET/CT (CAMONA)’ study, conducted 2012-2014 [11], approved by the Danish National Committee on Health Research Ethics (s-20120056), and registered at ClinicalTrials.gov (NCT01724749). CAMONA was carried out in accordance with the Declaration of Helsinki. All study participants provided written informed consent.

Participant Selection

CAMONA included 89 healthy individuals with low CVD risk who were recruited via a local advertisement or from the blood bank at Odense University Hospital, Odense, Denmark. Individuals with no history of malignant diseases, immunodeficiency syndromes, autoimmune diseases, illicit drug use, alcohol abuse or CVDs were considered healthy and were eligible for inclusion. Adults were preselected by age and gender to make sure a balanced inclusion of both genders aged 20–29, 30–39, 40–49, 50–59, and 60 years or older was guaranteed. Furthermore, 50 patients suspected of having angina pectoris who were referred to the Department of Cardiology at OUH for coronary angiography were included.

Study Design

The included patients were asked to fill a questionnaire about alcohol consumption, smoking habits, past medical history, familial history, and current medical status. Blood pressure after at least 30 minutes of rest was measured three times in the supine position. The mean of the last two measurements was recorded as the systolic and diastolic blood pressure. Laboratory tests included total serum cholesterol, serum low-density lipoprotein, serum high-density lipoprotein, serum triglycerides, fasting plasma glucose and glycated hemoglobin, and glomerular filtration rate, which was calculated using the Modification of Diet and Renal Disease equation [14]. The 10-year risk of developing CVD was estimated using the Framingham Risk Score (FRS) based on age, gender, systolic blood pressure, total serum cholesterol, serum HDL cholesterol, smoking habit, and treatment for hypertension [15]. Then they were offered a whole-body NaF-PET/CT scan at baseline and after two years of follow-up performed at the same PET/CT scanner and at approximately the same time of the day (morning or noon). Of all initially included patients, 29 healthy individuals and 20 patients with angina pectoris attended the 2-year follow-up. So, this subgroup of patients was examined to inspect the change prospectively during two years in NaF uptake in major arteries, including the carotids and the arch, thoracic, and abdominal parts of the aorta.

NaF-PET/CT Protocol

NaF-PET/CT imaging was performed according to previously published methods [16] on hybrid PET/CT systems (General Electric Healthcare using Discovery PET/CT, 690/710, VCT, or XTe) with comparable spatial resolution. All participants underwent PET/CT imaging 90 minutes after they were injected with approximately 2.2 MBq/kg (max 400 MBq) NaF. The acquisition time was 2.5 minutes per bed position. PET/CT system specifications and parameters of image reconstruction are summarized in the Electronic Supplementary Material 1. The 3D acquisition of total-body PET images and reconstruction of them into transverse, coronal and sagittal slices was made by an iterative reconstruction algorithm (VUE Point; GE Healthcare). The correction of PET images for random, scattered coincidences, attenuation and anatomic directions were done by implanting transmission maps produced by a 64-slice CT scan as follows (120 kV, 200 mA, 16 x 2.5 mm collimation, 0.5 seconds per rotation).

Image Analysis

All scans at baseline and follow-up were analyzed and quantified independently without the reader being aware of the participants’ demographic and clinical features. ROVER software version 3.0.4 (ABX GmbH, Radeberg, Germany) was used for quantitative analysis. Initially, PET and CT images were reregistered using DICOM information, allowing us to improve the diagnostic accuracy of both modalities and optimizing the outlining of aortic segments, and then imported into the software. If necessary, additional adjustment of images was made by modification of PET images in transverse, coronal and sagittal planes considering the CT images as the reference point.

The volume of interest (VOI) was formed by stacking manually defined regions of interest (ROIs) using a 5 mm width brush in CT images for each participant. The VOIs included left carotid and right carotid, arch of aorta, thoracic aorta, and abdominal aorta. The arch of the aorta was defined as aorta above the lower level of T5 in a transaxial view until the aortic valve. The carotids were defined from the branching initiation (branching from aorta for left carotid and brachiocephalic artery for right carotid) until the bifurcation (including itself). The thoracic aorta was defined as aorta between the inferior edge of T5 to T12. The abdominal aorta was defined as aorta between the lower level of T12 until the beginning of the bifurcation. A sample of segmented NaF-PET/CT images, including VOIs in 3-dimensional planes, is shown in Figure 1. The manual ROI determination was done in a manner that would contain the whole carotid or aortic wall (intima, media and adventitia), excluding the vertebral bones and their uptake halo from inclusion in defined ROIs. Therefore, in some transaxial slices, where the aorta was adjacent to the vertebral body, the ROI was defined with a lunar shape, unlike all other slices in which the ROI was circular.

Performing a quantitative assessment of PET scans was done by generating standardized uptake values (SUVs) of the VOIs, adjusted to body weight. After segmentation of each ROI, the recorded NaF uptake was expressed as SUVmean (average SUV of all voxels within VOI), SUVmax (the highest SUV of all voxels in the VOI), SUVtotal (sum of the SUVs of all voxels), and as the corresponding measures corrected for partial volume effect (i.e., cSUVmean and cSUVtotal) as described by Hofheinz et al. [17] The measurement of NaF uptake was, therefore, performed in two automated steps, first by approximation of the actual object boundaries with a threshold-based method and determination of the total activity in ROI and then determining activity fraction, which is measured outside the ROI due to spill-out. With this correction approach, accurate knowledge of image resolution is not necessary as it is, for instance, with deconvolution techniques [18-19]. The measurement unit was MBq/ml. Also, the CT-related variable mean density (CTmean) expressed in Hounsfield units was extracted in all corresponding VOIs.

The reproducibility of quantifying arterial wall NaF uptake is reported in a manuscript submitted elsewhere. By repeat determination of aortic uptake performed in 25 randomly selected scans after several months and without knowledge of prior results, one observer (RP) found in the three segments of the aorta a variation in SUV values of maximally 6 percent.

Statistical Analysis

Descriptive statistics were expressed as frequency (percentage), mean ± standard deviation or median (minimum-maximum). Mann-Whitney U and Fisher’s exact test were used to compare demographic, laboratory, and PET/CT variables between healthy and angina groups. Intragroup comparisons over time were performed with Wilcoxon matched pairs signed rank sum test, and change over time was shown as the mean of estimated differences. Aside from the comparison of control and angina groups and in order to compare NaF uptake in different major arteries within groups, Friedman’s test was used, in which the Wilcoxon matched pairs signed rank sum test was performed post hoc. Finally, the non-parametric Spearman’s correlation test was used to examine for correlation between PET/CT variables and age, then Fisher’s r-to-z transformation method was applied to compare them [20]. NaF uptake was plotted against age in a scatter plot, supplemented by fitted lines from linear regression for each group of subjects. A p-value of less than 0.05 was considered statistically significant. All statistical analyses were performed using SPSS 19.0 (SPSS Inc., Chicago, IL, USA).

Participants’ demographics, laboratory test results, medical history and medications are shown in Table 1. The two groups did not significantly differ regarding age, gender, weight and height. Participants in the angina group tended to have higher 10-year FRS compared to those in the healthy group; besides, other differences in laboratory tests, medical history, familial history, and medications were observed. With regard to medication, there was a noteworthy change in that 11 angina pectoris patients had statin medication at 2-year follow-up compared to only 5 at baseline.

Baseline NaF uptake and density in the carotids and aorta (Tables 2 and 3, respectively) were compared between healthy and angina groups. With few exceptions, at baseline, the carotid NaF uptake was slightly higher in angina pectoris patients than in controls. The same trend was invariably present in all three sections of the aorta (Table 2). After 2 years, there was a tendency for a slight decline in NaF uptake (cSUVmean) in both the carotids and the three aortic segments of healthy controls, whereas the opposite trend was present in angina pectoris patients. In these, there was a slight increase in NaF uptake in all segments, which – judged from mean values – were 2 percent in the carotids (1.64±0.44 vs. 1.67±0.63) compared to 19 percent in the aortic arch (1.15±0.42 vs. 1.37±0.86), 12 percent in the thoracic aorta (1.04±0.35 vs. 1.16±0.6) and 6 percent in the abdominal aorta (1.31±0.44 vs. 1.39±0.82). Mean NaF uptake in the different sections of the carotids (Figure 2a) and in the aorta (Figure 2b) at baseline and follow-up are shown in Figure 2. In almost all VOIs, mean NaF uptake decreased in healthy individuals over 2 years, while this trend was opposite in patients with angina.

Baseline and follow-up density of the major arteries quantified by CT scan did not change significantly after two years of follow-up (Table 3). At baseline, there was a tendency for lower CT density in angina patients than healthy controls, except for clearly higher density in the abdominal aorta of angina patients. At 2-year follow-up, there was a slightly higher density in the abdominal aorta in both groups, but otherwise no clear changes.

Since the arteries’ volume increased significantly with age, SUVtotal was age-dependent. The correlation of NaF uptake and density with age in major arteries among all participants is shown and compared in Table 4. NaF uptake in all segments of aorta, namely cSUVmean, had a significantly positive correlation with age. Also, maximum density in almost all the major arteries was positively correlated with age. Baseline SUVmean correlated with change in CTmean in the arch of aorta (r=0.43, p=0.003) but not in the left carotid (r=-0.03, p=0.83) or right carotid (r=0.13, p=0.36), abdominal aorta (r=0.09, p=0.55) and thoracic aorta (r=-0.02, p=0.88),.

A simple linear regression was applied to predict cSUVmean in the whole aorta based on the participants’ age (Figure 3). A significant regression equation was found (F(1,47)=10.721, p=0.002), with an R² of 0.18. Participants’ predicted cSUVmean in the aorta was equal to 0.44 + 0.012 age, where age was measured in years. In stratified analyses by group, the same linear regression was also significant in each group (see Electronic Supplementary Material 2).

Statement of Principal Findings

NaF uptake in the carotids and the aorta of angina patients was higher than in healthy subjects. This difference was most prominent in the arch and abdominal aorta. NaF uptake after two years had decreased slightly in the healthy group and increased slightly in the angina group, even if more angina patients received statin therapy during the follow-up period, i.e., 11 out of 20 compared to only 5 out of 20 at baseline. This change in the angina group during follow-up was in keeping with the positive correlation between age and NaF uptake in these major arteries. On the whole, our results indicate that early-phase atherosclerotic microcalcification is a slow process showing little progress over a time span of 2 years. Surprisingly, arterial macrocalcification, measured in Hounsfield units in the same arterial segments, did not either show any obvious changes during the same time span, and, in fact, there was slightly lower calcification density in angina patients at baseline than in healthy controls, suggesting variation in CT-measurements or macrocalcification or both.

Strengths and Weaknesses of the Study

A main strength of the current study was that we examined both groups prospectively, while a good deal of the previous reports were post-hoc analyses of NaF-PET/CT scans performed for other purposes, in particular search for skeletal metastases in cancer patients on various treatment regimens including chemo- and radiotherapy [21]. The age of our study population ranged from 21 to 75 years of age, providing a suitable age span to investigate changes in arterial NaF uptake with age, and our repeat PET/CT scans were made with the same scanner in each patient and solely to elucidate potential variations with time.

There were also significant limitations, the most critical one being technical in that even with reasonably new PET imaging technology, the spatial resolution of output images is low compared to the size of the arterial walls examined, which significantly hampers manual segmentation. Furthermore, proper co-registration of PET and CT components are often not present, meaning that PET and CT images must be aligned again for every segmentation done for the separate VOIs. Another PET-related challenge is the fact that NaF uptake in the defined background VOI may vary significantly even by few millimeters of dislocation, when one tries to select the VOI with the least NaF uptake. This is the main reason why we chose not to calculate target-to-background values as an expression of NaF uptake. Similarly, the choice of VOI and the algorithm used for partial volume correction is challenging and may cause unexpected variation in the partial volume corrected parameters, which, however, appears not to be as critical due to the much higher numbers. The delineation of smaller arteries such as the carotids using CT without venous contrast is very difficult because of the resemblance of carotids’ density and adjacent anatomical structures in some transaxial slices, in which the exact location of the artery could only be determined with guidance from upper or lower slices.

A further limitation, which may have affected the comparison between groups, was the small study population. Moreover, patients in the angina group were only suspected of having CVD at the time of the baseline scan and, therefore, they might not carry a very high CVD risk rendering the difference between groups correspondingly smaller. It may be that the modest changes observed were an expression that it was the most agile healthy subjects and the least ill angina pectoris patients, who attended the 2-year follow-up, and that, thus, the limited material was not representative of all the original material. We cannot deny that, only state that the arteriosclerotic process, assessed by repeat NaF-PET/CT scans, is slow and more variable than expected.

Strengths and Weaknesses in Relation to Other Studies

Atherosclerosis is mostly known as an inflammatory disease of the arterial wall [22], which is the plausible reason why FDG was the first successful PET tracer utilized to characterize atherosclerosis [23-24]. Although it could detect inflammation, it was unspecific for the detection of atherosclerosis and tracing glucose utilization in the artery wall and as mentioned has been found to be rapidly changing [9], limiting the possibility of making longitudinal studies. Therefore, NaF was proposed to detect microcalcification as a sign of incipient atherosclerosis [21, 25], which may not be as rapidly changing. In vitro and in vivo studies have demonstrate that arterial wall NaF uptake is due to adsorption to calcium deposits [26-27]. Likewise, high NaF uptake appears to be more consistently associated with different CVD risk factors [7]. These shreds of evidence appear to be in line with our study, where NaF uptake was higher in the angina than the healthy group.

Arterial calcification in the shape of micro- or macrocalcifications is considered a hallmark of aging, especially in the presence of diabetes, hypertension and chronic renal diseases [28]. Likewise, it has been shown in many studies that NaF uptake in arteries increases by age. This correlation was also present in our study, where age was positively correlated with NaF uptake in the examined arteries. However, although statistically significant, the correlation was weak. Nonetheless, we expected to find the same increase in NaF uptake after two years, but it was not present in the healthy group and surprisingly small in the angina group. In fact, the finding of slight decline in the healthy subjects was unexpected. However, the same trend we also found for the CT-detectable changes is in line with what has been reported by Meirelles et al. with regard to change in arterial FDG uptake over time.

Meaning of the Study: Possible Mechanisms

Taken together, the mentioned findings seem to suggest that the arteriosclerotic process is somewhat more volatile and varied than one might think, at least in the early, non-symptomatic, stages, albeit with a more constant, but still slow, progression in angina pectoris patients. A probable reason for the small difference between our two groups was that members of the angina group were only suspected of having CVD when entering the CAMONA study, and therefore not have been at very high risk. A more significant reason might be the fact that more than half the angina patients were on statin therapy after two years. Statins are found to be effective in decreasing inflammation in artery walls, which is detectable by FDG-PET [29-31] and possibly also by means of NaF-PET as used in the current study. If this is true, it may reflect that early atherosclerotic changes are reversible, but to what extent remains unknown until large prospective trials in very early stage atherosclerosis have been conducted. Another explanation for the limited change in NaF uptake could be that NaF is mainly a microcalcification tracer rather than a calcium deposition tracer. Thus, in a study by Fiz et al. on the correlation between calcification density and mineral metabolic activity through NaF uptake, it was concluded that NaF-PET is mostly suitable for the detection of atherosclerosis in its early phase because NaF retention progressively decreases by increased calcification density [32]. In other words, NaF uptake among patients in our angina group could have been damped by increases in calcification density during two years. However, according to Table 3, the calcification density process appears also to be a very protracted one, and progression in calcification was not reflected by baseline NaF uptake.

Unanswered Questions and Future Research

It is unclear whether changes NaF uptake reflects only changes in arterial wall microcalcification or whether it heralds beginning or developing CT-detectable macrocalcification. Therefore, long-term follow-up and interventional imaging studies are direly needed to investigate the time dependency and inter-relationship between molecular and macroscopic features of atherosclerosis.

Our prospective 2-year follow-up study indicated that the atherosclerotic process is slow and variable in both healthy subjects and angina pectoris patients, albeit with a tendency for slightly higher NaF uptake in the angina group, more consistently so in the arch and abdominal aorta than in the carotid arteries and the thoracic aorta. The 2-year changes in the angina group may have been somewhat blunted by statin therapy. Larger, prospective, and more longitudinal follow-up studies are warranted to elucidate in more detail the time-dependent relationship between arterial wall NaF uptake and atherosclerosis development.

Author Contribution

AA and PFHC conceived and planned the experiments. RP, GL and PR carried out the experiments. RP analyzed images and generated results. RP, OG, AA and PFHC contributed to the interpretation of the results. RP took the lead in writing the manuscript. PFHC supervised the project. All authors provided critical feedback and helped shape the research, analysis and manuscript.

Funding sources

The study was partly funded through a PhD scholarship to Reza Piri by the University of Southern Denmark, Odense, Denmark.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Table 1. Participants’ demographics, laboratory tests, medical history, familial history, and medications.
Variables		Group		P value
Variables		Healthy (n=29)	Angina (n=20)	P value
Sex	Male (%)	16 (55.1)	10 (50)	0.72
Sex	Female (%)	13 (44.8)	10 (50)	0.72
Age (year)		51 (21-75)	57 (23-67)	0.75
Body mass index (kg/m²)		27.17±5.07	27.02±5.11	0.79
White blood cell (10^3cells/ml)		5.79±1.36	7.12±2.24	0.041
Systolic blood pressure (mmHg)		132.12±19.98	132.45±20.62	0.89
Diastolic blood pressure (mmHg)		78.19±10.24	80.53±8.21	0.38
Total cholesterol (mmol/L)		5.08±0.78	5.64±1.01	0.06
Low-density lipoprotein (mmol/L)		3.24±0.66	3.69±0.89	0.08
High-density lipoprotein (mmol/L)		1.38±0.37	1.38±0.33	0.98
Triglyceride (mmol/L)		1.03±0.56	1.27±0.81	0.43
Homocysteine (µmol/L)		8.13±2.42	11.21±3.81	0.002
Fasting blood glucose (mmol/L)		5.68±0.47	5.71±0.44	0.98
Hemoglobin A1c (mmol/L)		34.66±4.71	37.37±2.54	0.009
C-reactive protein (mg/L)		1 (1-8)	1.8 (1-18)	0.18
Fibrinogen (µmol/L)		9.1 (6.6-82)	10.3 (7.1-15.2)	0.11
Creatinine (µmol/L)		77.38±10.2	84.79±19.38	0.17
10-year Framingham score		7 (0.4-30)	12.2 (1.5-30)	0.036
Medical history	Alcohol (%)	25 (86.2)	18 (90)	0.69
	Smoking (%)	1 (3.4)	5 (25)	0.024
	Hypercholesterolemia (%)	2 (6.9)	7 (35)	0.013
	Peripheral artery disease (%)	0 (0)	2 (10)	0.08
	Cardiovascular disease (%)	0 (0)	2 (10)	0.08
	Hypertensive vascular disease (%)	0 (0)	4 (20)	0.012
Medications	Antihypertensive medication (%)	0 (0)	7 (35)	0.001
Medications	Statins (%)	0 (0)	5 (25)	0.001
ACE: Angiotensin converting enzyme.

Table 2.- NaF uptake in major arteries among the participants
		Healthy (n=29)			Angina (n=20)			Baseline Healthy vs. Angina
		Baseline	Follow up	P-value	Baseline	Follow up	P-value	Baseline Healthy vs. Angina
Left carotid	Mean	0.95±0.18	0.91±0.19	0.17	0.98±0.28	0.98±0.31	0.64	0.85
	cMean	1.43±0.23	1.34±0.27	0.08	1.5±0.44	1.53±0.51	0.87	0.87
	Total	10.36±2.29	9.85±2.28	0.27	10.31±3.17	10.36±3.49	0.78	0.73
	cTotal	15.59±2.99	14.51±3.09	0.17	15.78±4.7	16.19±5.82	0.81	0.95
Right carotid	Mean	0.99±0.2	0.95±0.2	0.16	1.05±0.31	1.09±0.4	0.95	0.82
	cMean	1.62±0.35	1.44±0.36	0.001	1.84±0.56	1.88±0.97	0.15	0.2
	Total	8.55±2.09	8.24±1.86	0.39	8.26±2.61	9.29±4.05	0.2	0.44
	cTotal	13.83±3.09	12.39±3.09	0.005	14.31±3.81	15.68±7.44	0.97	0.81
Carotids	Mean	0.97±0.17	0.93±0.19	0.11	1±0.28	1.02±0.34	0.94	0.87
	cMean	1.52±0.27	1.39±0.29	0.008	1.64±0.44	1.67±0.63	0.6	0.55
	Total	18.9±4.01	18.09±3.84	0.3	18.57±5.59	19.66±7.39	0.66	0.42
	cTotal	23.42±5.36	26.9±5.57	0.022	30.09±7.9	31.87±12.66	0.93	0.94
Arch of aorta	Mean	0.83±0.2	0.79±0.16	0.3	0.88±0.24	1±0.48	0.16	0.31
	cMean	1.13±0.53	1.02±0.44	0.17	1.15±0.42	1.37±0.86	0.1	0.13
	Total	33.83(18.2-61.38)	31.73(16.64-54.31)	0.58	44.37(17.35-68.48)	45.2(14.92-104.39)	0.13	0.15
	cTotal	46.17(4.38-123.75)	41.49(13.25-82.05)	0.24	53.62(14.89-86.25)	50.54(10.45-169.18)	0.15	0.32
Thoracic aorta	Mean	0.92±0.2	0.87±0.2	0.08	0.99±0.24	1.06±0.43	0.98	0.43
	cMean	0.85±0.32	0.84±0.34	0.97	1.04±0.35	1.16±0.6	0.81	0.07
	Total	50.97(23.36-94.58)	53.53(25.17-124.74)	0.6	68.68(25.49-103.46)	62.34(23.3-187.11)	0.94	0.09
	cTotal	45.72(15.98-121.1)	48.3(16.96-155.51)	0.6	73.42(20.6-128.69)	59.98(14.65-223-98)	0.99	0.041
Abdominal aorta	Mean	0.94±0.2	0.92±0.19	0.33	1.04±0.28	1.1±0.53	0.97	0.55
	cMean	1.1±0.3	1.08±0.26	0.4	1.31±0.44	1.39±0.82	0.94	0.92
	Total	28.46(18.44-60.34)	29.66(17.45-63.78)	0.84	34.42(18.4-87.43)	37.94(16.97-174.22)	0.44	0.18
	cTotal	35.5(15.36-77.48)	33.05(17.8-83.09)	0.82	42.9(21.91-130.19)	46.15(16.17-252.7)	0.42	0.06
Whole aorta	Mean	0.89±0.18	0.86±0.17	0.14	0.97±0.23	1.05±0.46	0.66	0.43
	cMean	0.99±0.31	0.95±0.28	0.09	1.14±0.35	1.29±0.71	0.38	0.23
	Total	119.5±31.62	118.33±33.47	0.54	143.01±48.59	160.19±87.36	0.2	0.09
	cTotal	134.04±51.8	131.53±51.42	0.4	170.23±72.68	196.95±130.27	0.17	0.08

Table 3. CT density in major arteries of health subjects and angina patients at baseline and 2-year follow-up.
Variable		CT (HU)
		Healthy (n=29)			Angina (n=20)			Baseline Angina vs. Healthy
		Baseline	Follow up	P-value	Baseline	Follow up	P-value	Baseline Angina vs. Healthy
Left carotid	Mean	24.39(8.5-39.36)	23.37(9.1-36.47)	0.6	18.17(3.13-41.93)	17.83(4.52-36.46)	0.47	0.009
Right carotid	Mean	38.02(13.86-47.51)	37.17(12.94-50.1)	0.85	34.98(22.34-47.61)	37.37(21.87-49.24)	0.049	0.43
Carotids	Mean	15.98(7.3-22.8)	16.67(6.63-22.28)	0.68	15.53(8.71-22.89)	17.34(9.9-23.31)	0.014	0.43
Arch of aorta	Mean	2.27(-21.42-21.6)	1.8(-25.43-19.29)	0.13	-5.34(-19.79-11.28)	-1.89(-37.63-13.1)	0.4	0.001
Thoracic aorta	Mean	4.46(-15.64-21.97)	4.33(-13.21-16.76)	0.84	1.14(-11.37-15.78)	3.73(-15.9-16.89)	0.42	0.8
Abdominal aorta	Mean	22(-3.07-63.29)	22.67(-3.88-74.66)	0.42	27.37(-3.3-95.98)	26.08(19-114.18)	0.34	0.23
Whole aorta	Mean	10.23(-99.47-20.35)	8.17(-4.6-23.51)	0.6	6.98(-4.53-26.29)	7.71(-6.39-31.23)	0.9	0.49

Table 4. Correlation of age with NaF uptake (SUV and TBR) and density (CT) in major arteries.
			SUV	CT	Z-score*	Pvalue
Right carotid	Mean	Correlation coefficient	0.12	0.09	0.67	0.25
	Mean	Sig. (2-tailed)	0.4	0.56
	cMean	Correlation coefficient	0.22
	cMean	Sig. (2-tailed)	0.12
Left carotid	Mean	Correlation coefficient	0.05	-0.03	0.89	0.19
	Mean	Sig. (2-tailed)	0.76	0.82
	cMean	Correlation coefficient	0.18
	cMean	Sig. (2-tailed)	0.22
Carotids	Mean	Correlation coefficient	0.12	0.11	0.39	0.35
	Mean	Sig. (2-tailed)	0.43	0.44
	cMean	Correlation coefficient	0.20
	cMean	Sig. (2-tailed)	0.17
Arch of aorta	Mean	Correlation coefficient	0.27	-0.13	2.45	0.007
	Mean	Sig. (2-tailed)	0.06	0.36
	cMean	Correlation coefficient	0.39
	cMean	Sig. (2-tailed)	0.006
Thoracic aorta	Mean	Correlation coefficient	0.37	0.08	2.07	0.019
	Mean	Sig. (2-tailed)	0.01	0.58
	cMean	Correlation coefficient	0.50
	cMean	Sig. (2-tailed)	<0.001
Abdominal aorta	Mean	Correlation coefficient	0.33	0.40	0.06	0.48
	Mean	Sig. (2-tailed)	0.02	0.004
	cMean	Correlation coefficient	0.41
	cMean	Sig. (2-tailed)	0.003
Whole aorta	Mean	Correlation coefficient	0.38	0.21	1.54	0.06
	Mean	Sig. (2-tailed)	0.007	0.14
	cMean	Correlation coefficient	0.50
	cMean	Sig. (2-tailed)	<0.001
*The calculated Z-score refers to the comparison of age and cSUVmean correlation vs. age and CTmean correlation.

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Editor invited by journal
26 Mar, 2021
Reviews received at journal
14 Feb, 2021
Reviewers invited by journal
12 Feb, 2021
Editor assigned by journal
11 Feb, 2021
First submitted to journal
10 Feb, 2021

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Two-Year Change in 18F-Sodium Fluoride Uptake in Major Arteries of Healthy Subjects and Angina Pectoris Patients

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