Abstract
Aims
Premature advanced subclinical coronary atherosclerosis among young adults is an under-recognized and unique disease phenotype that has not been well characterized.
Methods and results
We used data from 44 047 participants with no prior CVD history (59.8% male) from the Coronary Artery Calcium (CAC) Consortium. We defined advanced disease as CAC ≥ 90th percentile for age, sex, and race and compared the risk factor profile of persons with advanced disease to those without CAC and those with CAC < 90th percentile. Using multivariable-adjusted Cox proportional hazard and competing risks regression, we assessed the association of premature advanced disease with all-cause, cardiovascular, and coronary heart disease (CHD) mortality. Of 44 047 participants, 18 561 (42.2%) had CAC. Among those with CAC, 6680 (36.0%) had CAC ≥ 90th percentile. Notably, 76.4% of those with CAC ≥ 90th percentile had multivessel CAC compared with 40.6% of those with CAC < 90th percentile. After a mean follow-up of 12.5 ± 3.6 years, the incidence per 1000 person-years of all-cause (2.93 vs. 1.85 vs. 1.11), cardiovascular (1.11 vs. 0.39 vs. 0.21), and CHD mortality (0.65 vs. 0.19 vs. 0.08) was highest in the advanced disease group compared with CAC < 90th percentile and the no CAC group. Persons with CAC ≥ 90th percentile had a higher multivariable-adjusted risk of all-cause [HR: 2.17 (1.83–2.57)], cardiovascular [sub-distribution hazard ratios (SHR): 3.89 (2.78–5.44)], and CHD mortality [SHR: 5.45 (3.38–8.78)], compared with those without CAC. In the subgroup analysis, there was no difference in mortality between men and women with advanced CAC.
Conclusion
Premature advanced atherosclerosis is a distinct clinical phenotype that strongly predicts all-cause and cause-specific mortality. Among persons with CAC at young age, those with scores ≥90th percentile have the highest risk of early death and should be identified in future guidelines as a focus for aggressive clinical prevention.
Keywords: Atherosclerotic cardiovascular disease, Coronary artery calcium, Mortality, Advanced disease
Graphical Abstract
Graphical Abstract.
Risk profile and mortality associated with premature advanced coronary atherosclerosis
See the editorial comment for this article ‘Coronary artery calcium score: the perfect tool to predict the risk but one step is still missing’, by G. Lemesle and C. Bauters, https://doi.org/10.1093/eurjpc/zwaf116.
Introduction
Atherosclerosis is a lifelong process which begins at birth.1 Much effort has been made to screen for and control atherosclerosis early in its pathogenesis, since atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of mortality globally and in the USA, being responsible for one-third of deaths in the USA.2,3
Most guidelines recommend using a 10-year ASCVD risk estimation to guide preventive therapies.4 However, this risk estimation relies mostly on age and does not adequately address the risk in young adults.5 Consequently, persons with early ASCVD who lack the classic risk factors or known genetic predispositions are at a high risk of being overlooked.6–8 The coronary artery calcium (CAC) score is now established as an independent predictor of ASCVD, among various subgroups including younger adults who are at risk of subclinical atherosclerosis.9–12 CAC serves as a well-known marker of the burden of coronary atherosclerosis and has received a Class IIA recommendation for further risk stratification among persons with borderline to intermediate 10-year ASCVD risk in the 2019 American College of Cardiology/American Heart Association (ACC/AHA) guidelines.4,13–15
Both the percentile and absolute CAC scores are utilized for ASCVD risk assessment, though the percentile score appears more applicable in young populations due to relatively lower absolute scores and the long-time horizon for prevention.16–19 The ACC/AHA guidelines suggest the 75th percentile or CAC ≥ 100 AU as the cutoff value for consideration of statin therapy in populations at borderline risk, but there is concern for under-recognition and undertreatment of those with advanced premature atherosclerotic disease who are at the highest lifetime risk.4 Kilichap et al. reported that a great proportion of women <55 and men <45 with any CAC fall into the high-risk category. Also, Javaid et al. showed that CAC > 0 places all females aged 30–45, Black males <37, and White males <34 years in >90th percentile.17,20 It is therefore important that the clinical implications of CAC in the highest percentile groups in young people be explored.
To further enhance our understanding of the CAC score in young adults, we aimed to explore the implications of advanced coronary atherosclerotic disease by examining the risk profile and mortality associated with the 90th percentile of CAC.
Methods
Study population and study design
We used data from the CAC Consortium, a retrospectively assembled cohort of 66 636 individuals who were 18 years or older without a history of CVD referred for CAC scan between 1991 and 2010 by their clinicians to evaluate for subclinical atherosclerosis. Data on participants were obtained from four study institutions: Cedars-Sinai Medical Center, Los Angeles, CA, USA; Harbor-UCLA Medical Center, Torrance, CA, USA; Previa Health Wellness Diagnostic Center, Columbus, OH, USA; and Minneapolis Heart Institute, Minneapolis, MN, USA. Consent for participation was collected at each study centre. A detailed description of the study design and methods have been previously described.21 For the main analysis in this study, we used data from participants aged 30–55 years for males and 30–65 years for females, giving an analytic sample size of 44 047.
Measurement of coronary artery calcium
Non-contrast cardiac-gated CT scans for CAC scoring were performed at each site according to a common standard protocol. CAC was quantified using the Agatston method. Most patients (93%) were scanned using electron beam tomography (EBT), while the remaining participants (7%) were scanned using multi-detector CT (MDCT). Prior studies have shown no clinically meaningful differences between CAC scores derived from EBT vs. MDCT scanners.22 We stratified CAC as absent, <90th percentile, and ≥90th percentile, with percentiles based on age, sex, and race from the website cac-tools.com.17
Outcome ascertainment
Mortality status was ascertained by linking patient records with the Social Security Administration Death Master File via a validated algorithm. Patient identifiers such as first/last name, date of birth, and social security numbers were used to search everyone in the death index data. The cause of death was obtained via coded death certificates obtained from the National Death Index, which is a service run by the US Centers for Disease Control and Prevention (CDC) for determining the vital status of individuals. Deaths were categorized using International Classification of Diseases (ICD) codes from the primary underlying cause of death on the death certificate. Our outcomes of interest in this study were all-cause, cardiovascular disease mortality [inclusive of coronary heart disease (CHD), stroke, heart failure, and other cardiovascular mortality], and CHD mortality.
Covariate assessment and evaluation of atherosclerotic cardiovascular disease risk factors
For the CAC Consortium, data on patient demographics and ASCVD risk factors were collected at the time of CAC scanning. Diabetes was defined as a previous diagnosis of diabetes or treatment with oral hypoglycaemic drugs or insulin. Similarly, hypertension was present if there was a prior diagnosis of hypertension or treatment with anti-hypertensive therapy. Dyslipidaemia was defined as a prior diagnosis of dyslipidaemia (elevated triglycerides, elevated LDL-C, or low HDL-C), treatment with any lipid-lowering drug, or abnormal lipid parameters on testing (LDL-C > 160 mg/dL, HDL-C < 40 mg/dL in men and <50 mg/dL in women, or fasting triglycerides > 150 mg/dL). Smoking status was categorized as current and non-current. Finally, a family history of CHD was determined by the presence of a first-degree relative with a history of CHD or a family history of premature CHD (<55 years old in a male relative and <65 years old in a female relative). Missing risk factors were imputed using a multivariable model adjusting for age, sex, race, CAC score, and the remaining non-missing traditional risk factors as per the design of the CAC Consortium.21
Statistical analysis
We summarized the baseline characteristics of the study participants using means and proportions for continuous and categorical variables, respectively. The baseline characteristics were summarized first for the entire study sample and then by CAC burden categories (0, <90th percentile, and ≥90th percentile). Differences in proportions were tested using the χ2 test, whereas the differences in means were tested using the analysis of variance test.
Among those with CAC, we additionally summarized the patterns of calcifications including the proportions with multivessel disease, left main (LM) disease, and diffusivity index, as well as the proportion with calcification in other vascular beds. The diffusivity index was calculated as 100 − (CAC in most affected vessel / total CAC) × 100. A diffuse CAC pattern was defined as a diffusivity index ≥75th percentile for a given number of vessels with CAC.
Using Cox proportional hazard models to obtain hazard ratios (HR), we assessed the association of CAC burden categories with all-cause mortality. Also, we used Fine and Gray competing-risk regression models to obtain sub-distribution hazard ratios (SHR) of the association of CAC burden with cardiovascular and CHD mortality. Models were adjusted for age, sex, and ASCVD risk factors. These variables, common to all ASCVD risk scores, were selected for adjustment in the multivariable analysis since they can confound the association between CAC and mortality. Additionally, we assessed the interaction of sex on the association of advanced disease with mortality and presented the results separately for males and females. In a supplementary analysis, we further subclassified CAC percentiles 0, <75th percentile, 75–89th percentile, and ≥90th percentile and assessed the graded association of these CAC burden categories with the outcomes of interest. Additionally, as a sensitivity analysis, we restricted our sample to 23 706 participants aged 30–50 years (mean age 43.9 ± 4.6 years; 74.7% men), as a truly young population, and tested these associations. Finally, we restricted our analysis to participants who were not on statins at baseline and assessed these associations.
All analyses were conducted using Stata 16 software (StataCorp LLC, College Station, TX, USA). A two-sided α of P < 0.05 was considered statistically significant.
Results
Of the 44 047 participants with a mean age of 49.1 ± 7.1 years, 59.8% were male. Among the participants, 11 881 (27%) had a CAC score below the 90th percentile, and 6680 (15.2%) had a CAC score at or above the 90th percentile for age, sex, and race. The risk profile of the study population is presented in Table 1. All included ASCVD risk factors were significantly higher in participants with a CAC ≥ 90th percentile compared with those with a CAC < 90th percentile or a CAC = 0. Specifically, 60% of participants with a CAC ≥ 90th percentile had at least two risk factors, whereas 48.6% of those with a CAC < 90th percentile and 39.2% of those with a CAC = 0 had at least two risk factors (P < 0.001) (Table 1 and Supplementary material online, Figure S1).
Table 1.
Demographic characteristics and ASCVD risk profile of the study population stratified by the burden of CAC
| Characteristic | Total | CAC = 0 | CAC < 90th percentile | CAC ≥ 90th percentile | P-value |
|---|---|---|---|---|---|
| n = 44 047 | n = 25 486 (57.8%) | n = 11 881 (27.0%) | n = 6680 (15.2%) | ||
| Age | 49.1 (±7.1) | 48.0 (±7.4) | 51.4 (±6.1) | 49.2 (±6.9) | <0.001 |
| Sex | |||||
| Male | 26 359 (59.8) | 13 482 (52.9) | 8769 (73.8) | 4108 (61.5) | <0.001 |
| Female | 17 688 (40.2) | 12 004 (47.1) | 3112 (26.2) | 2572 (38.5) | |
| Hypertension | 11 166 (25.4) | 5395 (21.2) | 3466 (29.2) | 2305 (34.5) | <0.001 |
| Hyperlipidaemia | 23 008 (52.2) | 12 134 (47.6) | 6775 (57.0) | 4099 (61.4) | <0.001 |
| Current smoker | 4498 (10.2) | 2318 (9.1) | 1203 (10.1) | 977 (14.6) | <0.001 |
| Diabetes | 2270 (5.2) | 935 (3.7) | 706 (5.9) | 629 (9.4) | <0.001 |
| Family history of CHD | 21 540 (48.9) | 11 972 (47.0) | 5750 (48.4) | 3818 (57.2) | <0.001 |
| Number of risk factors | |||||
| 0 | 8123 (18.4) | 5562 (21.8) | 1855 (15.6) | 706 (10.6) | |
| 1 | 16 157 (36.7) | 9941 (39.0) | 4248 (35.8) | 1968 (29.4) | <0.001 |
| ≥2 | 19 767 (44.9) | 9983 (39.2) | 5778 (48.6) | 4006 (60.0) | |
| Statin use | 3849 (17.7) | 1696 (12.9) | 1203 (22.7) | 950 (29.2) | <0.001 |
ASCVD, atherosclerotic cardiovascular disease; CHD, coronary heart disease; CAC, coronary artery calcium.
The median CAC was 14 (interquartile interval: 4, 40) AU in participants with CAC below the 90th percentile, compared with 178 (interquartile interval: 65, 398) AU in those with CAC at or above the 90th percentile. Multivessel disease and LM coronary artery involvement were significantly more prevalent in participants with a CAC ≥ 90th percentile compared with those with CAC < 90th percentile (76.4% vs. 40.6%, P < 0.001; 21.8% vs. 9.9%, P < 0.001). Additionally, 32.6% of participants with a CAC ≥ 90th percentile had thoracic aortic calcification, which was significantly higher than those below the 90th percentile (25%) or those without CAC (9.3%) (Table 2).
Table 2.
Calcification patterns in the study population by the burden of CAC
| CAC = 0 | CAC < 90th percentile | CAC ≥ 90th percentile | |
|---|---|---|---|
| Median CAC | – | 14 (4, 40) | 178 (65, 398) |
| Multivessel disease (≥2 vessels) | – | 3658 (40.6) | 3888 (76.4) |
| LM disease | – | 891 (9.9) | 1110 (21.8) |
| a Diffusivity index, mean | – | 11.2 (±17.0) | 24.0 (±20.1) |
| a Diffused CAC pattern, mean | – | 46.9 (±7.4) | 50.9 (±7.5) |
| a Diffused distribution, n (%) | – | 961 (10.7) | 925 (18.2) |
| % with TAC | 1588 (9.3) | 1784 (25.0) | 1382 (32.6) |
CAC, coronary artery calcium; LM, left main; TAC, total aortic calcium. The ‘diffusivity index’ was calculated as 100 − (CAC in the most affected vessel / total CAC) * 100. Diffuse CAC pattern was defined as a diffusivity index ≥75th percentile for a given number of vessels with CAC.
aOnly for participants with ≥2-vessel involvement.
After a mean follow-up of 12.5 ± 3.6 years, all-cause mortality rates were higher among participants with a CAC ≥ 90th percentile (2.93 per 1000 person-years) and participants with CAC < 90th percentile (1.85 per 1000 person-years) compared with participants without CAC (1.11 per 1000 person-years). A similar pattern was seen with the CV and CHD mortality rates (see Supplementary material online, Figures S2A, S2B, and S2C). In multivariable-adjusted models, CAC ≥ 90th percentile was associated with significantly higher hazards of all-cause [2.17 (1.83–2.57)], CV [3.89 (2.78–5.44)], and CHD mortality [5.45 (3.38–8.78)] compared with participants without CAC. Similarly, participants with CAC ≥ 90th percentile had significantly higher hazards of all-cause [1.62 (1.36–1.94)], CV [2.85 (2.01–4.03)], and CHD mortality [3.54 (2.19–5.73)] even when compared with persons with CAC < 90th percentile (Table 3 and Figure 1). The risk of CV and CHD mortality did not differ significantly between participants with CAC < 90th percentile and those without CAC (Table 3 and Figure 1).
Table 3.
Association between CAC burden and all-cause mortality, cardiovascular, and coronary heart disease mortality
| Coronary artery calcium | All-cause mortality | Cardiovascular disease mortality | Coronary heart disease mortality | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Incidence rate | HR (95% CI) | P-value | Incidence rate | SHR (95% CI) | P-value | Incidence rate | SHR (95% CI) | P-value | |
| Using absent CAC as reference | |||||||||
| Absent | 1.11 | Ref | 0.21 | Ref | 0.08 | Ref | |||
| CAC < 90th percentile | 1.85 | 1.34 (1.13–1.58) | 0.001 | 0.39 | 1.37 (0.95–1.97) | 0.096 | 0.19 | 1.54 (0.89–2.66) | 0.123 |
| CAC ≥ 90th percentile | 2.93 | 2.17 (1.83–2.57) | <0.001 | 1.11 | 3.89 (2.78–5.44) | <0.001 | 0.65 | 5.45 (3.38–8.78) | <0.001 |
| Using present CAC < 90th percentile as reference | |||||||||
| Absent | 1.11 | 0.75 (0.63–0.88) | 0.001 | 0.21 | 0.73 (0.51–1.06) | 0.096 | 0.08 | 0.65 (0.38–1.12) | 0.123 |
| CAC < 90th percentile | 1.85 | Ref | 0.39 | Ref | 0.19 | Ref | |||
| CAC ≥ 90th percentile | 2.93 | 1.62 (1.36–1.94) | <0.001 | 1.11 | 2.85 (2.01–4.03) | <0.001 | 0.65 | 3.54 (2.19–5.73) | <0.001 |
CAC, coronary artery calcium; CI, confidence interval; HR, hazard ratio; SHR, sub-distribution hazard ratio. Incidence rate is per 1000 person-years. Mean follow-up is 12.5 ± 3.6 years. Models are adjusted for age, sex, and atherosclerotic cardiovascular disease risk factors (hypertension, hyperlipidaemia, cigarette smoking, diabetes, and family history of coronary heart disease).
Figure 1.
Association between coronary artery calcium burden and all-cause, cardiovascular, and coronary heart disease (CHD) mortality.
In the subgroup analysis by sex, there was no significant difference in the association of advanced subclinical coronary atherosclerotic disease with mortality, indicating that both females aged 30–65 years and males aged 30–55 years have similar mortality risks based on the 90th percentile CAC (Table 4). Sensitivity analyses further subclassifying CAC burden as 0, <75th percentile, 75–89th percentile, and ≥90th percentile (see Supplementary material online, Table S1) and analysis restricting our study sample to participants aged 30–50 years (see Supplementary material online, Table S2), as well as limiting our study sample to those who were not on statins at the time of CAC assessment (see Supplementary material online, Table S3), did not alter our findings.
Table 4.
Association between CAC burden and all-cause mortality, cardiovascular, and coronary heart disease mortality by sex
| Coronary artery calcium | All-cause mortality | Cardiovascular disease mortality | Coronary heart disease mortality | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Incidence rate | HR (95% CI) | P-value | Incidence rate | SHR (95% CI) | P-value | Incidence rate | SHR (95% CI) | P-value | |
| Females (age 30–65 years) | |||||||||
| Absent | 1.37 | Ref | 0.21 | Ref | 0.08 | Ref | |||
| CAC < 90th percentile | 3.22 | 1.46 (1.15–1.85) | 0.002 | 0.56 | 1.40 (0.79–2.46) | 0.244 | 0.19 | 1.06 (0.40–2.81) | 0.899 |
| CAC ≥ 90th percentile | 3.55 | 2.07 (1.63–2.64) | <0.001 | 1.19 | 3.97 (2.40–6.56) | <0.001 | 0.42 | 3.47 (1.57–7.68) | 0.002 |
| Males (age 30–55 years) | |||||||||
| Absent | 0.88 | Ref | 0.22 | Ref | 0.08 | Ref | |||
| CAC < 90th percentile | 1.39 | 1.27 (1.01–1.60) | 0.045 | 0.34 | 1.36 (0.84–2.18) | 0.209 | 0.20 | 1.95 (0.98–3.88) | 0.056 |
| CAC ≥ 90th percentile | 2.57 | 2.31 (1.82–2.95) | <0.001 | 1.06 | 3.97 (2.54–6.22) | <0.001 | 0.79 | 7.27 (3.91–13.52) | <0.001 |
CAC, coronary artery calcium; CI, confidence interval; HR, hazard ratio; SHR, sub-distribution hazard ratio. Incidence rate is per 1000 person-years. Mean follow-up is 12.5 ± 3.6 years. Models are adjusted for age and atherosclerotic cardiovascular disease risk factors (hypertension, hyperlipidaemia, cigarette smoking, diabetes, and family history of coronary heart disease).
Discussion
In this study using a large cohort of men aged 30–55 and women aged 30–65 years with no history of CAD (n = 44 047), we highlight the risk profile and prognostic implications of premature advanced coronary atherosclerosis. In this cohort, 42% of participants had CAC, and among them, more than one-third had CAC scores ≥90th percentile for age, sex, and race—a phenotype that was more likely to have diffused CAC pattern and multivessel disease and was significantly associated with a 5.5-fold increase in CHD mortality, a 3.9-fold increase in CVD mortality, and a 2.2-fold increase in all-cause mortality, compared with persons without CAC. However, CAC < 90th percentile did not display a significant association with CHD or CVD mortality compared with absent CAC.
The need for timely detection of the early manifestation of CAD makes CAC a valuable marker for risk assessment particularly in young and middle-aged adults. Multiple studies have established the association between CAC burden and ASCVD in this age group.12,23 A report from the Coronary Artery Risk Development in Young Adults (CARDIA) study showed that among 3043 asymptomatic participants aged 32–46 years (mean age 40.3 years), about 10% had CAC > 0, and CAC was associated with a three-fold increase in CVD and a five-fold increase in CHD events.24 In another study using the Walter Reed Cohort (mean age 43.6 years), CAC > 0 was associated with significantly increased hazards of major adverse cardiovascular events (MACE), with CAC > 100 associated with even more significantly higher risk of MACE, MI, stroke, and all-cause mortality.25 However, all these studies focused on absolute CAC scores rather than calculated percentiles, which may be a better predictor of the lifetime risk trajectory of developing CAD26 and a useful clinical tool for communicating relative risk to patients.27 In a study conducted by Miedema et al., which also uses the CAC Consortium multicentre data, 34.4% of participants aged 30–49 had CAC > 0, of which one-fifth had CAC > 100 AU. They reported that CAC > 100 was associated with a five-fold increase in risk for CHD mortality and a three-fold increase in risk for CVD mortality.23 However, this study was conducted before CAC percentiles were derived for ages 30–45; thus, the 90th percentile could not be identified.
Atherosclerotic plaques in young patients are less likely to have calcium, which must be considered when interpreting the absolute CAC score in this population. We hypothesized that CAC ≥ 90th percentile for age, sex, and race might be a particularly useful cut-point for identifying uniquely advanced diseases across a wide spectrum of young to early middle-aged adults. An analysis of the Multi-Ethnic Study of Atherosclerosis (MESA) data by Budoff et al. previously reported that the absolute CAC score and not percentiles performed better in predicting CHD events in the short term (median follow-up period of 3.75 years).28 However, this study investigated older adults (mean age 62 years) and could not study adults <45 years of age. Additionally, while short-term risk is important to assess, lifetime risk particularly among young and early middle-aged adults is crucial in guiding the initiation and intensification of preventive pharmacotherapies. To our knowledge, no cohort study to date has investigated patterns of calcification and subsequent mortality in young to early middle-aged adults with CAC ≥ 90th percentile, which we believe should be henceforth referred to as ‘premature advanced subclinical atherosclerosis’.
The 2019 ACC/AHA guideline suggested using a CAC score ≥ 100 AU or an age/sex/race percentile ≥ 75th to make decisions on statin therapy in intermediate-risk individuals.4 The 2022 ECDP extended this further by suggesting an LDL threshold of <70 mg/dL in this population, with the option to add ezetimibe in this population. PCSK9i was reserved for consideration only when CAC ≥ 1000 and high-risk individuals who have not achieved the optimal LDL goal despite maximally tolerated statin and ezetimibe therapy.29 We propose that in future guidelines, the 90th percentile should be also considered for the most aggressive preventive therapy particularly in young adults, which would broaden the indications for more aggressive therapy, allowing more of the highest-risk patients to receive treatment earlier in life. Such aggressive therapies include up-titration of statins as tolerated with lower LDL goals; other LDL-lowering therapies such as ezetimibe, PCSK9i, and bempedoic acid; and other preventive pharmacotherapies such as aspirin. It has been established that earlier preventive treatment maximizes life-years gained from preventive therapy.30,31 It should be emphasized, however, that evidence on the role of revascularization among asymptomatic individuals regardless of CAC score is limited and is generally thought to have no role.32,33 In support of the unique aspects of the 90th percentile is the fact that our results showed that CAC ≥ 90th percentile was associated with a significantly higher risk of all-cause, cardiovascular, and CHD mortality compared with absent CAC and CAC < 90th percentile, while persons with CAC < 90th percentile did not differ significantly in cardiovascular and CHD mortality compared with those without CAC. Importantly, our results showed that there is no significant difference in the association of premature advanced subclinical atherosclerosis (CAC ≥ 90th percentile) with all-cause, CVD, and CHD mortality between men and women.
It should be stated, however, that while the role of CAC in risk stratification is undoubtedly robust, its therapeutic implications are not as clear and remain under study. Large prospective randomized controlled trials showing the impact of CAC testing on patient outcomes are limited given that it may be inherently unethical to randomize patients who have severe amounts of CAC into an arm of no therapy.34 While the DANCAVAS trial (among men aged 65–74 years) did not show that cardiovascular screening including CAC led to a significantly lower risk of death, it hinted that among those <70 years, such screening could have therapeutic benefits.35 By extrapolation, the benefits of CAC screening may potentially be even more beneficial among young adults.
Strengths and limitations
The use of a large sample of participants from the CAC Consortium and the greater number of events allowed for a more robust analysis and assessment of the associations tested by sex. Also, the age cutoff point we chose was an important consideration for these results, as the cutoff point of 55 years for men and 65 years for women aligns with the definition of early-onset CVD and is thus useful and familiar to clinical practice. Additionally, to our knowledge, this is the first study to assess the long-term mortality risk among young and early middle-aged adults using age, sex, and race percentiles.
A key limitation of our study is that the CAC Consortium represents a clinical cohort. Hence, our findings may not be generalizable to a general non-clinical population but rather to the population actively engaged in the health care system and thus are likely to be considered for CAC scanning in everyday clinical practice settings. Another important limitation is that, since patients were referred by their physicians for CAC screening, it is likely that the presence of CAC may have led to preventive measures, but it is unclear how aggressive these preventive measures may have been. We do not have the data to ascertain if CAC detection led to preventive treatments or coronary angiogram/interventions. Even if the case were that individuals in the study who were found to have CAC had initiation of some form of preventive therapies, we expect that this would have yielded a conservative bias in the analyses. Hence, these significant findings go on to further emphasize our call for even more aggressive preventive therapies in patients with CAC, especially those with CAC ≥ 90th percentile. Finally, some studies have shown that specific coronary plaque characteristics such as high plaque burden, low minimal lumen area, thin cap fibroatheroma, high lipid core burden index, and spotty calcification, can predict patient-level and lesion-level adverse cardiovascular outcomes including mortality,36 but we did not explore such plaque characteristics in our study because detection of these requires coronary CT angiography.
Conclusion
In this study, we have described the unique features of premature advanced subclinical atherosclerosis as identified by CAC ≥ 90th percentile, including a tendency towards diffuse calcification and strong association with ∼10-year CHD and CVD mortality, as well as all-cause mortality. We believe that adoption of the 90th percentile in future prevention guidelines will broaden the indication for the most aggressive preventive therapies, which are now reserved for those with absolute CAC scores >1000 AU.
Supplementary Material
Contributor Information
Ellen Boakye, Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
Mohammadmoein Dehesh, Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Zeina Dardari, Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Olufunmilayo H Obisesan, Department of Medicine, MedStar Union Memorial Hospital, Baltimore, MD USA.
Albert D Osei, Division of Cardiovascular Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Omar Dzaye, Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Kunal Jha, Division of Cardiovascular Medicine, University of Louisville, Louisville, KY, USA.
Alan Rozanski, Division of Cardiology, Mount Sinai St. Luke’s Hospital, New York, NY, USA.
Daniel S Berman, Departments of Imaging and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Matthew J Budoff, Harbor-UCLA Medical Center, The Lundquist Institute, Torrance, CA, USA.
Michael D Miedema, Nolan Family Center for Cardiovascular Health, Minneapolis Heart Institute and Foundation, Minneapolis, MN, USA.
Khurram Nasir, Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA.
John A Rumberger, Department of Cardiac Imaging, Princeton Longevity Center, Princeton, NJ, USA.
Leslee J Shaw, Department of Medicine (Cardiology), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Michael J Blaha, Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Supplementary material
Supplementary material is available at European Journal of Preventive Cardiology.
Author contribution
E.B.: Conceptualization, Methodology, Data curation, Formal analysis, Writing—Original draft preparation. M.D.: Conceptualization, Methodology, Data curation, Writing—Original draft preparation. Z.D.: Methodology, Formal analysis, Writing—Review and editing. O.H.O.: Writing—Review and editing. A.D.O.: Writing—Review and editing. O.D.: Writing—Review and editing. K.J.: Writing—Review and editing. A.R.: Methodology, Writing—Review and editing. D.S.B.: Methodology, Writing—Review and editing. M.J.B.: Methodology, Writing—Review and editing. M.D.M.: Methodology, Writing—Review and editing. K.N.: Methodology, Writing—Review and editing. J.A.R.: Methodology, Writing—Review and editing. L.J.S.: Methodology, Writing—Review and editing. M.J.B.: Conceptualization, Methodology, Writing—Review and editing, Supervision, Funding acquisition.
Funding
M.J.B. received support from the National Institutes of Health award L30 hl110027.
Data availability
Data used in the study are from the CAC Consortium and can be shared on a reasonable request to the corresponding author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data used in the study are from the CAC Consortium and can be shared on a reasonable request to the corresponding author.