Association Between Cardiovascular Risk Factors and Pan-Coronary Plaque Burden, Phenotype, and Vulnerability: A 3-Vessel Imaging Study

Jakub Chmiel; Stefano Andreaggi; Sekeun Kim; Iris McNulty; Hang Lee; Adrian P Banning; Krzysztof L Bryniarski; Piotr Musialek; Ik-Kyung Jang

doi:10.1016/j.jacadv.2026.102983

. 2026 Jul 23;5(8):102983. doi: 10.1016/j.jacadv.2026.102983

Association Between Cardiovascular Risk Factors and Pan-Coronary Plaque Burden, Phenotype, and Vulnerability

A 3-Vessel Imaging Study

Jakub Chmiel ^a,^b, Stefano Andreaggi ^a, Sekeun Kim ^a, Iris McNulty ^a, Hang Lee ^c, Adrian P Banning ^d,^e, Krzysztof L Bryniarski ^f, Piotr Musialek ^b, Ik-Kyung Jang ^a,^g,^∗

PMCID: PMC13531621 PMID: 42489602

Abstract

Background

Cardiovascular risk factors (RFs) are commonly used in clinical practice to predict future adverse cardiovascular outcomes, including acute coronary syndromes (ACS). A recent study reported an association between RFs and plaque vulnerability in patients with ACS.

Objectives

This study aimed to investigate the association between RFs (modifiable and non-modifiable) and pan-coronary plaque burden, plaque phenotype, and features of vulnerability.

Methods

In patients undergoing 3-vessel optical coherence tomography imaging, modifiable (dyslipidemia, hypertension, diabetes mellitus, obesity, smoking) and non-modifiable (age, sex, family history) RFs were recorded. Plaque number, plaque phenotype, and vulnerable features were analyzed.

Results

A total of 534 plaques from 131 patients (36.6% ACS) were analyzed. As the number of RFs increased, the number of plaques (P trend = 0.001) as well as plaques with a vulnerable phenotype (thin-cap fibroatheromas [TCFAs], P trend = 0.001) increased. An increasing number of RFs was also associated with a higher number of vulnerable features (P trend < 0.001), including more thin fibrous caps (P trend = 0.001), lipid-rich plaques (LRPs), macrophages, microvessels, and cholesterol crystals (all P trend ≤ 0.001). In multivariable analyses, only modifiable RF burden was associated with increased pan-coronary vulnerability (incidence rate ratio [IRR]: 1.36; 95% CI: 1.18-1.57; P < 0.001), including a higher prevalence of TCFAs (IRR: 1.48; 95% CI: 1.20-1.83; P < 0.001), LRPs (IRR: 1.35; 95% CI: 1.15-1.59; P < 0.001), and cholesterol crystals (IRR: 1.50; 95% CI: 1.21-1.87; P < 0.001).

Conclusions

As the number of cardiovascular RFs increased, the number of plaques, plaques with a high-risk phenotype, and vulnerable features also increased. Only increasing modifiable RF burden was associated with greater pan-coronary vulnerability, including more TCFAs and LRPs. Clinical Trial Registration: ClinicalTrials.gov NCT01110538

Key words: cardiovascular risk factors, optical coherence tomography, plaque phenotype, plaque vulnerability

Central Illustration

Cardiovascular risk factors (RFs) are well-established predictors of adverse clinical outcomes, including acute coronary syndrome (ACS).¹ Current guidelines emphasize modifiable RFs—including elevated low-density lipoprotein cholesterol levels, high blood pressure, diabetes mellitus, obesity, and cigarette smoking—as primary targets for cardiovascular disease prevention, although nonmodifiable RFs, such as age, sex, and family history of coronary artery disease (CAD), also contribute substantially to cardiovascular risk.¹^,² Optical coherence tomography (OCT) enables visualization of plaque structure at near-microscopic resolution, including features of plaque vulnerability. These vulnerable features have also been linked to adverse coronary events.3, 4, 5, 6 The increased vulnerability in patients with multiple RFs may explain poor outcomes; however, the association between cardiovascular RFs and coronary vulnerability has not been fully elucidated. Previous OCT studies have primarily examined individual RFs or modifiable and non-modifiable RFs in isolation or have focused on specific clinical cohorts, such as patients with ACS.⁷^,⁸ Moreover, they predominantly analyzed individual lesions or vessels, presenting a partial and incomplete picture of pan-coronary vulnerability.9, 10, 11, 12

In the present study, we investigated the associations between cardiovascular RF burden (modifiable and non-modifiable) and pan-coronary plaque number (plaque burden), plaque phenotype, and vulnerability in patients undergoing 3-vessel OCT imaging.

Methods

Study population

From the Massachusetts General Hospital OCT registry (NCT01110538), we selected patients who underwent OCT imaging of all 3 major epicardial coronary arteries and did not have prior percutaneous coronary intervention (PCI). Demographic, medical history, and laboratory data were recorded from the medical records at each participating site. Cardiovascular RFs were divided into modifiable (dyslipidemia, hypertension, diabetes mellitus, obesity, smoking) and non-modifiable (age ≥62 years, male sex, and family history of CAD). Age ≥62 years (the cohort median) was used as the cutoff for patient age stratification. Definitions for other RFs are provided in the Supplemental Material.

The Massachusetts General Hospital OCT registry was approved by the Massachusetts General Hospital’s Institutional Review Board (IRB) and by the IRB at each participating site. Written informed consent was obtained from all patients before enrollment, and the registry study was conducted in accordance with the Declaration of Helsinki. The current study was deemed “exempt” by the Mass General Brigham IRB.

OCT analysis

OCT imaging was performed using a frequency-domain (C7/C8; OCT Intravascular Imaging System, St. Jude Medical) or time-domain (M2/M3; Cardiology Imaging Systems, LightLab Imaging) OCT system. All deidentified OCT images were analyzed at the core laboratory of Massachusetts General Hospital by 2 independent investigators blinded to patient data, using an offline review workstation (St. Jude Medical), and in accordance with previously established criteria and definitions (Supplemental Appendix). Any discordance was resolved by consensus with a third reviewer. OCT-defined features of vulnerability—thin fibrous cap, lipid-rich plaque (LRP), macrophages, microvessels, and cholesterol crystals—were identified according to the literature; definitions are detailed in the Supplemental Material.¹³

Statistical analysis

Continuous data are presented as mean ± SD or median (IQR), and categorical data as absolute frequencies and percentages. The overall number of RFs was the sum of modifiable and non-modifiable RFs. The modifiable and non-modifiable RF burdens were assessed in each patient as the number of modifiable and non-modifiable RFs present, respectively. The cohort was divided into groups according to the number of all, modifiable, or non-modifiable RFs, or into 3 groups with 0 to 2, 3 to 4, or ≥5 RFs. In each plaque, plaque phenotype and the presence of each vulnerable feature were assessed. For patient-level analyses, these plaque-level variables were aggregated across all plaques within each patient. Trends were assessed using the one-way Jonckheere-Terpstra test. P value for the trend test was estimated using a Monte Carlo permutation procedure (100,000 permutations). A trend line based on the least-squares method was fitted to the weighted mean outcome across ordered RF categories. In multigroup analyses, differences between groups were assessed using the Kruskal-Wallis test; when significant, pairwise post hoc comparisons were performed using Dunn’s test with Bonferroni correction. Regression analyses were performed using generalized linear models, with Poisson regression as the default and negative binomial regression used when overdispersion was detected (phi >1.3). Results are reported as incidence rate ratio (IRR) with 95% CIs, and statistical significance was assessed using Wald z-tests. For plaque-level analysis, the generalized estimating equations approach, with log-link Poisson and logit-link binomial model assuming independent correlation structure, was used to account for the clustering of multiple lesions in a single patient. Sensitivity analyses were performed by additionally adjusting the primary models for lipid-lowering therapy and ACS presentation. In exploratory analyses, modifiable RF burden was replaced by individual modifiable RFs, with adjustment for non-modifiable RF burden. Statistical significance was defined as P < 0.05. Statistical analysis was performed using R software (version 4.5.2; R Foundation for Statistical Computing).

Results

Patient characteristics

In the Massachusetts General Hospital OCT registry, 131 patients underwent OCT imaging of the 3 major epicardial coronary arteries and did not have prior PCI. The median age was 62 (51-69) years; 95 (72.5%) were male, and 48 (36.6%) presented with ACS. The patient characteristics are shown in Table 1.

Table 1.

Baseline Characteristics

Variable	Total (N = 131)	0-2 RFs (n = 37)	3-4 RFs (n = 66)	5-6 RFs (n = 28)	P Value
Age, y	62 ± 11	56 ± 10^ab	63 ± 11^a	66 ± 10^b	<0.001
Age ≥62 (%)	66 (50.4%)	9 (24.3%)^a	34 (51.5%)^a	23 (82.1%)^a	<0.001
Male (%)	95 (72.5%)	15 (40.5%)^ab	53 (80.3%)^a	27 (96.4%)^b	<0.001
Clinical presentation
ACS (%)	48 (36.6%)	13 (35.1%)	28 (42.4%)	7 (25.0%)	0.287
NSTE-ACS (%)	25 (19.1%)	10 (27.0%)	11 (16.7%)	4 (14.3%)	0.378
STEMI (%)	23 (17.6%)	3 (8.1%)	17 (25.8%)	3 (10.7%)	0.052
Other risk factors
Family history (%)	22 (16.8%)	3 (8.1%)	10 (15.2%)	9 (32.1%)	0.037
Dyslipidemia (%)	83 (63.4%)	15 (40.5%)^a	41 (62.1%)^b	27 (96.4%)^ab	<0.001
Hypertension (%)	83 (63.4%)	9 (24.3%)^ab	48 (72.7%)^a	26 (92.9%)^b	<0.001
Diabetes mellitus (%)	40 (30.5%)	3 (8.1%)^a	19 (28.8%)^b	18 (64.3%)^ab	<0.001
Obesity (%)	8 (6.1%)	0 (0.0%)^a	1 (1.5%)^b	7 (25.0%)^ab	<0.001
Current smoking (%)	43 (32.8%)	10 (27.0%)	22 (33.3%)	11 (39.3%)	0.572
Laboratory data and medication
WBC × 10⁹/L	7.3 ± 2.4	6.8 ± 1.9	7.5 ± 2.6	7.4 ± 2.4	0.228
Creatinine, mg/dL	0.9 (0.8-1.0)	0.8 (0.7-0.9)^ab	0.9 (0.8-1.0)^a	1.0 (0.8-1.2)^b	<0.001
Total cholesterol, mg/dL	193.2 (154.7-210.5)	193.3 (154.7-225.0)	190.0 (154.7-204.5)	191.0 (152.0-220.0)	0.972
LDL-C, mg/dL	113.0 (77.3-125.0)	77.3 (77.3-118.0)	112.0 (77.3-124.0)	116.0 (77.3-138.0)	0.547
HDL-C, mg/dL	40.0 (38.7-56.0)	38.7 (38.7-56.0)	40.0 (38.7-60.0)	40.5 (38.7-47.0)	0.822
Triglyceride, mg/dL	143.5 (88.6-177.1)	177.1 (88.6-180.0)	126.0 (88.6-177.1)	169.0 (93.0-185.0)	0.250
HbA1c, %/L	5.9 (5.3-6.4)	6.0 (5.0-6.0)^a	5.8 (5.3-6.1)	6.4 (5.4-7.0)^a	0.048
EF, %	65.5 ± 8.5	63.3 ± 7.8	66.3 ± 9.5	66.4 ± 6.8	0.297
ASA (%)	40 (30.5)	5 (13.5)^a	21 (31.8)	14 (50.0)^a	0.007
Statins (%)	27 (20.6)	1 (2.7)^ab	15 (22.7)^a	11 (39.3)^b	<0.001
Beta-adrenergic blocker (%)	21 (16.0)	4 (10.8)	9 (13.6)	8 (28.6)	0.146
ACE inhibitor (%)	32 (24.4)	2 (5.4)^ab	17 (25.8)^a	13 (46.4)^b	<0.001

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Values are mean ± SD, median (IQR), or n (%). Superscript letters indicate significant pairwise differences after Bonferroni correction; values sharing the same superscript letter are significantly different from each other.

ACS = acute coronary syndrome; ASA = acetylsalicylic acid; EF = ejection fraction; HbA1c = glycosylated hemoglobin; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; NSTE-ACS = non-ST-elevation acute coronary syndromes, including unstable angina and non-ST-segment elevation myocardial infarction; RF = risk factor; STEMI = ST-segment elevation myocardial infarction; WBC = white blood cell.

A total of 534 plaques were identified in 131 patients, including 144 (27.0%) fibrous plaques, 215 (40.2%) thick-cap fibroatheromas (ThCFAs), 104 (19.5%) thin-cap fibroatheromas (TCFAs), and 71 (13.3%) fibrocalcific plaques. Across all plaques, 1,012 vulnerable features were identified, including 104 (10.3%) thin fibrous caps, 319 (31.5%) LRPs, 254 (25.1%) macrophages, 262 (25.9%) microvessels, and 73 (7.2%) cholesterol crystals. TCFAs showed the highest mean number of vulnerable features (3.7 ± 1.0), followed by ThCFAs (2.3 ± 0.9), while fibrocalcific and fibrous plaques on average exhibited fewer than one vulnerable feature (0.7 ± 0.8 and 0.7 ± 0.7, respectively; P < 0.001).

The average patient had 3.4 (±1.3) RFs, including 2.0 (±1.0) modifiable and 1.4 (±0.7) non-modifiable RFs (Supplemental Figure 1). Patients with a higher number of RFs had higher serum creatinine (P < 0.001) and were more likely on aspirin (P = 0.007), statins (P < 0.001), and angiotensin-converting enzyme inhibitors (P < 0.001; Table 1). With an increasing number of modifiable RFs, white blood cell count (P trend = 0.032), glucose (P trend <0.001), hemoglobin A1c (P trend <0.001), low-density lipoprotein cholesterol (P trend = 0.001), and creatinine (P trend = 0.003) also increased. With an increasing number of non-modifiable RFs, serum creatinine level increased (P trend <0.001).

Plaque burden and phenotype according to the number of cardiovascular RFs

Figure 1 shows trend analyses of plaque number and phenotype according to the number of cardiovascular RFs. As the number of RFs increased, the total number of plaques also increased (P trend = 0.001), driven predominantly by higher numbers of TCFAs (P trend = 0.001) and ThCFAs (P trend <0.001). In contrast, with the increasing RF burden, the number of fibrous plaques decreased (P trend <0.001).

**Number of Plaques and Plaque Phenotypes Across Increasing Risk Factor Burden**

Mean number of plaques and each plaque phenotype across increasing numbers of (A) all cardiovascular risk factors, (B) modifiable risk factors, and (C) non-modifiable risk factors. As the number of cardiovascular risk factors increased, the total number of plaques, including high-risk thin-cap fibroatheromas, increased, whereas the number of stable fibrous plaques decreased. Although the increasing trend in the total number of plaques was observed with both modifiable and non-modifiable risk factors, higher numbers of high-risk thin-cap fibroatheromas were observed only with increasing numbers of modifiable risk factors. Trend lines are shown, and Jonckheere-Terpstra trend test P values are provided. TCFA = thin-cap fibroatheroma; ThCFA = thick-cap fibroatheroma.

In the categorical analyses, patients with more RFs had a higher total number of plaques (P = 0.004), including more TCFAs (P = 0.011) and ThCFAs (P < 0.001), and fewer fibrous plaques (P = 0.010) (Supplemental Table 1, Supplemental Figure 2).

Plaque burden and phenotype according to the number of modifiable and non-modifiable cardiovascular RFs

As the number of both modifiable and non-modifiable RFs increased, the total number of plaques increased (P trend = 0.006 and P trend = 0.019, respectively), as did the number of ThCFAs (P trend <0.001 and P trend = 0.037), whereas the number of fibrous plaques decreased (P trend = 0.001 and P trend = 0.047). An increase in the number of TCFAs was observed only with increasing numbers of modifiable RFs (P trend <0.001), whereas an increase in the number of fibrocalcific plaques was observed only with increasing numbers of non-modifiable RFs (P trend = 0.001) (Figure 1).

Vulnerable features according to the number of cardiovascular RFs

Figure 2 shows trend analyses of vulnerable features according to the number of cardiovascular RFs. As the number of RFs increased, the total number of vulnerable features increased (P trend <0.001, Central Illustration), together with each individual vulnerable feature, including thin fibrous caps (P trend = 0.001), LRPs, macrophages, microvessels, and cholesterol crystals (all P trend <0.001, Figure 2).

**Number of Vulnerable Features Across Increasing Cardiovascular Risk Factor Burden**

Mean number of vulnerable features and each vulnerable feature across increasing numbers of (A) all cardiovascular risk factors, (B) modifiable risk factors, and (C) non-modifiable risk factors. As the number of cardiovascular risk factors, both modifiable and non-modifiable, increased, the total number of vulnerable features increased, as did the number of lipid-rich plaques. However, an increase in the number of thin fibrous caps was observed only with increasing numbers of modifiable risk factors. Trend lines are shown, and Jonckheere-Terpstra trend test P values are provided. VF = vulnerable feature.

Central Illustration — **Association Between Cardiovascular Risk Factor Burden and Pan-Coronary Vulnerability**

Among patients with coronary artery disease who underwent three-vessel optical coherence tomography (n = 131), pan-coronary vulnerability, as reflected by the patient-level prevalence of vulnerable plaque features including thin fibrous caps, lipid-rich plaques, macrophages, microvessels, and cholesterol crystals, increased in proportion to the number of cardiovascular risk factors (P trend <0.001). OCT = optical coherence tomography; PCI = percutaneous coronary intervention.

In the categorical analyses, patients with a greater number of RFs had a higher total number of vulnerable features (P < 0.001), as well as higher numbers of thin fibrous caps (P = 0.011), LRPs, macrophages, microvessels, and cholesterol crystals (all P < 0.001) (Supplemental Table 2, Supplemental Figure 3). Plaque-level analyses are provided in the Supplemental Appendix.

Vulnerable features according to the number of modifiable and non-modifiable RFs

As the number of both modifiable and non-modifiable RFs increased, the total number of vulnerable features increased (P trend <0.001 and P trend = 0.013, respectively), as did the numbers of macrophages (P trend <0.001 and P trend = 0.005), microvessels (both P trend < 0.001), and cholesterol crystals (P trend <0.001 and P trend = 0.046). By contrast, increasing trends in thin fibrous caps were observed only with increasing numbers of modifiable RFs (P trend <0.001; Figure 2).

Differential associations of modifiable and non-modifiable RFs with plaque number and phenotype

Figure 3 shows the multivariable associations of modifiable and non-modifiable RF burdens with total plaque count and plaque counts by phenotype at the patient level. An increasing number of modifiable RFs was independently associated with higher numbers of TCFAs (IRR: 1.48; 95% CI: 1.20-1.83; P < 0.001) and ThCFAs (IRR: 1.28; 95% CI: 1.06-1.54; P = 0.011), and with a lower number of fibrous plaques (IRR: 0.78; 95% CI: 0.62-0.97; P = 0.027). An increasing number of non-modifiable RFs was independently associated with a higher total number of plaques (IRR: 1.12; 95% CI: 1.00-1.26; P = 0.047) and a higher number of fibrocalcific plaques (IRR: 2.08; 95% CI: 1.28-3.39; P = 0.003) (Supplemental Table 3).

**Associations of Risk Factor Burdens With Optical Coherence Tomography-Defined Plaque Characteristics**

Forest plot of multivariable associations of modifiable and non-modifiable cardiovascular risk factor burdens with (A) total plaque count and plaque counts by phenotype and (B) total vulnerable feature counts and individual vulnerable feature counts. An increasing number of non-modifiable risk factors was associated with higher numbers of plaques, particularly fibrocalcific plaques, whereas an increasing number of modifiable risk factors was associated with higher numbers of high-risk thin-cap fibroatheromas and thick-cap fibroatheromas, as well as fewer fibrous plaques. In contrast to non-modifiable risk factors, an increasing number of modifiable risk factors was associated with greater pan-coronary vulnerability, driven by higher numbers of all analyzed vulnerability features. Abbreviations as in Figures 1 and 2.

Differential associations of modifiable and non-modifiable RFs with vulnerable features

Figure 3 shows the multivariable associations of modifiable and non-modifiable RF burdens with total and individual vulnerable feature counts at the patient level. Only an increasing number of modifiable RFs was independently associated with a higher total number of vulnerable features (IRR: 1.36; 95% CI: 1.18-1.57; P < 0.001), as well as with higher numbers of each individual vulnerable feature, including thin fibrous caps (IRR: 1.48; 95% CI: 1.20-1.83; P < 0.001), LRP (IRR: 1.35; 95% CI: 1.15-1.59; P < 0.001), macrophages (IRR: 1.27; 95% CI: 1.08-1.50; P = 0.004), microvessels (IRR: 1.25; 95% CI: 1.08-1.45; P = 0.003), and cholesterol crystals (IRR: 1.50; 95% CI: 1.21-1.87; P < 0.001) (Supplemental Table 4). By contrast, the number of non-modifiable RFs was independently associated with only 2 of the 5 vulnerable features: macrophages (IRR: 1.26; 95% CI: 1.01-1.57; P = 0.042) and microvessels (IRR: 1.34; 95% CI: 1.10-1.63; P = 0.004). These associations remained significant after adjustments for lipid-lowering therapy and ACS presentation (Supplemental Table 5).

Discussion

The main findings of our study are: 1) as the number of RFs increased, plaque burden and pan-coronary vulnerability increased; 2) modifiable RF burden was strongly associated with pan-coronary vulnerability and with an unfavorable shift in pan-coronary atherosclerosis morphology—fewer stable fibrous plaques and more high-risk TCFAs and LRPs; 3) non-modifiable RF burden was not associated with pan-coronary vulnerability but was associated with a greater plaque burden, driven by a higher number of fibrocalcific plaques.

To date, epidemiological studies linking cardiovascular RFs to clinical events and intravascular imaging studies of coronary atherosclerosis biology have largely progressed in parallel. Our study bridges these 2 lines of evidence by providing a comprehensive assessment of the association between increasing cardiovascular RF burden and unfavorable changes in pan-coronary atherosclerosis morphology on OCT.

Vulnerability: plaque, pan-coronary, patient

The prognostic relevance of plaque morphology is supported by prospective outcome data. First, intravascular ultrasound-based studies identified LRPs, particularly those with thinner fibrous caps, as predictors of adverse cardiovascular events.14, 15, 16 Later, in CLIMA, OCT-identified vulnerable features were linked with future coronary events at 1- and 5-year follow-up, particularly when multiple high-risk features coexisted.³^,⁴ OCT-identified TCFAs, even when physiologically nonobstructive, were associated with an increased risk of subsequent coronary events.⁵^,⁶

The concept of the “vulnerable plaque”—a rupture-prone atherosclerotic lesion—was initially promoted as a promising predictor of myocardial infarction and cardiovascular mortality. These predictions proved largely correct, but the clinical implications of these findings have been shown to be limited. While plaque vulnerability does predict a high likelihood of rupture, only a small fraction of ruptures translates into clinically meaningful events, and the question of which vulnerable plaque will become symptomatic remains unanswered.¹⁵^,¹⁷ Those observations once again underscore that the pathogenesis of myocardial infarction is more complex than presumed; the interplay between plaque morphology, plaque burden, and thrombogenic milieu was suggested.¹⁷ Accordingly, the “vulnerable plaque” should be considered within broader risk profiling and interpreted as an imaging-derived coronary risk marker rather than a plaque-specific predictor of myocardial infarction. This concept is supported by evidence that nonculprit lesions in patients with ACS have more vulnerable features than those without ACS and that multiple TCFAs are associated with higher rates of major adverse coronary events than a single TCFA.¹⁸^,¹⁹ Thus, each individual “vulnerable plaque” represents one component of a patient’s pan-coronary vulnerability; following this logic—the more “vulnerable plaques” present, the greater the chance that one of them will trigger a clinical event. Rather than arising solely from plaque disruption, an acute coronary event may also require specific thrombosis-promoting conditions. However, a greater number of “vulnerable plaques” increases the frequency of plaque disruptions, thereby increasing the likelihood that a disruption coincides with a thrombosis-prone milieu and results in a clinical event.¹⁹^,²⁰ A single-vessel OCT assessment to evaluate pan-coronary vulnerability proved insufficient; it misclassifies about 25% of patients as TCFA-free and misses more than 50% of TCFAs.²¹ Considerable expectations for pan-coronary vulnerability assessment rest on noninvasive imaging modalities.²²

In line with this evidence, we aimed to address patients’ pan-coronary vulnerability and did so by assessing all plaques across the 3 major coronary vessels. Beyond addressing each vulnerable feature individually, we also assessed them collectively at the pan-coronary level. Although this approach was previously used only at the plaque level, it more accurately reflects broad pan-coronary vulnerability than assessment of an individual plaque or vessel.¹⁹^,²⁰ Our results show increasing pan-coronary vulnerability, including higher numbers of TCFAs and LRPs, across an increasing number of cardiovascular RFs; the average patient with a high number of RFs (at least 5) had over twice as many TCFAs and LRPs compared to the average patient with a low number of RFs (2 or fewer) (Supplemental Tables 1 and 2). Our results implicate a gradual increase in the plaque-driven risk of acute coronary events with the number of modifiable cardiovascular RFs.

Cardiovascular risk factors and OCT-findings

OCT-defined features of vulnerability in relation to cardiovascular RFs have been partially explored previously. Previous studies showed that current smoking, dyslipidemia, and elevated hemoglobin A1c levels were associated with a higher prevalence of TCFA.⁹^,¹⁰ Suzuki et al¹¹ showed a higher prevalence of LRPs in diabetic patients. Seegers et al¹² showed increasing prevalence of TCFAs and LRPs in culprit lesions with age, but only in women. Vergallo et al²³ investigated the relationship between nonculprit lesions in the 3 major coronary arteries and Framingham Risk Score categories, finding more plaques, macrophages, cholesterol crystals, and microvessels, but no LRPs or TCFAs, with increasing risk category.

Recent studies from our laboratory addressed the relationship between cardiovascular RFs and culprit-vessel vulnerability in ACS patients. Covani et al⁷ showed that a higher number of modifiable RFs was associated with greater plaque vulnerability, including a higher prevalence of TCFAs, LRPs, macrophages, microvessels, and cholesterol crystals, particularly in culprit lesions. In contrast, Scalamera et al⁸ found no association between the number of non-modifiable RFs and the prevalence of vulnerable features.

The present study substantially expands on these prior observations. First, we assessed plaques across the 3 major coronary arteries rather than restricting the analysis to the culprit vessel. Second, rather than focusing solely on vulnerable ACS patients, we included a broader cohort; however, generalizability is limited to patients without prior PCI and eligible for 3-vessel OCT. Third, we evaluated not only vulnerable plaque features but also overall plaque number and phenotype. Fourth, we analyzed both modifiable and non-modifiable RFs, allowing comparisons of their independent contributions. Fifth, we also included obesity as a major modifiable RF. Our study provides a broader and more comprehensive assessment of the relationship between RF burden and OCT-defined coronary plaque characteristics.

Our findings support and extend the observations in ACS presented by Covani et al⁷ by showing that the association between modifiable RF burden and vulnerability is evident and independent at the pan-coronary level in all-comer patients. This association remained consistent after adjustment for lipid-lowering therapy and ACS presentation (Supplemental Table 5). Moreover, we demonstrated parallel unfavorable changes in plaque phenotypes associated with modifiable RFs—fewer stable fibrous plaques and more high-risk TCFAs (Figure 3).

Non-modifiable RFs were associated with more plaques, particularly fibrocalcific plaques. In contrast to Scalamera et al⁸, we observed a positive trend between the number of cardiovascular RFs and vulnerable features (Figure 2). This difference likely reflects both the study population and the imaging strategy discussed above. Notably, in multivariable analysis, non-modifiable RFs were not associated with the total number of vulnerable features but remained associated with a higher prevalence of macrophages and microvessels. This implies that a positive trend between non-modifiable RFs and overall number of vulnerable features (Figure 2) was driven by the modifiable RFs. In the plaque-level analysis (Supplemental Table 6), an increasing number of non-modifiable RFs was associated only with a higher prevalence of microvessels. This suggests that the association between non-modifiable RFs and macrophages observed at the pan-coronary level (Figure 3) was driven solely by the number of plaques—rather than by a change in plaque morphology.

Overall, we observed a clear dissociation between modifiable and non-modifiable RFs—modifiable RFs were not associated with an increased number of plaques but with a shift toward more vulnerable morphology of coronary atherosclerosis, while nonmodifiable RFs were associated with an increased number of plaques, particularly fibrocalcific plaques, and were not associated with vulnerability.

Cardiovascular risk factors and clinical outcome

The Framingham Heart Study provided foundational evidence linking cardiovascular RFs to hard cardiovascular endpoints, identifying the major “traditional” modifiable cardiovascular RFs, including elevated blood pressure, cholesterol levels, diabetes, and smoking.¹ Contemporary guidelines also recognize obesity as clinically important for cardiovascular prevention.² Although some studies attributed much of the risk associated with obesity to its clustering with other modifiable RFs, substantial evidence supports an independent association between obesity and cardiovascular risk.¹^,²⁴^,²⁵ Recently, an analysis of over 1.5 million participants showed that 5 modifiable RFs—dyslipidemia, hypertension, diabetes, obesity, and current smoking—accounted for over 50% of incident cardiovascular disease and about 20% of all-cause mortality.²⁵ Considering its rising prevalence and clinical relevance, we therefore decided to include obesity among the modifiable cardiovascular RFs.²^,²⁴^,²⁵

The average patient in our study had 2.0 modifiable RFs, and the most prevalent were dyslipidemia and hypertension, affecting 6 out of 10 patients, followed by current smoking and diabetes present in 3 out of 10 patients, and obesity, which affected only 6% of the study cohort. The Framingham Heart Study also identified older age and male sex as strong predictors of hard cardiovascular outcomes.¹ Evidence for family history of CAD is less robust; however, it is considered a risk-modifier in current guidelines.² The average patient in our study had 1.4 nonmodifiable RFs, 7 out of 10 were male, half were classified as older according to the study design, and 1 in 6 had a family history of CAD. Consistent with prior studies, we evaluated cardiovascular RFs jointly as a composite measure.⁷^,⁸

Modifiable risk factors, non-modifiable risk factors, and vulnerability

CAD is an inflammatory disease, and modifiable RFs have been shown to promote inflammation at the systemic and vascular levels.26, 27, 28 Inflammation promotes plaque progression and is indirectly associated with increased plaque vulnerability.29, 30, 31 In our cohort, as the burden of modifiable RFs increased, white blood cell count also increased. Cardiovascular RFs likely contribute to patient vulnerability not only by shaping plaque biology but also by promoting a prothrombotic milieu.³² Current guidelines emphasize modifiable RFs as primary targets for cardiovascular disease prevention.² By addressing modifiable cardiovascular RFs, primary and secondary prevention of myocardial infarction have been shown to significantly reduce its first occurrence and recurrence.² In our study, modifiable RFs were associated with increased vulnerability—in patients with at least 4 modifiable RFs, 1 in 2 plaques was a TCFA and 4 in 5 were LRPs (Supplemental Figure 5), averaging 2 TCFAs and 2 other LRPs per patient (Figures 1 and 2). Notably, TCFAs and LRPs themselves were shown to be modifiable—lipid-lowering therapy thickens the fibrous cap and reduces lipid burden.³³^,³⁴ Therefore, in line with our findings, TCFAs and LRPs may represent the modifiable imaging-derived biological manifestation through which, beyond the prothrombotic state, modifiable RFs are associated with a higher incidence of myocardial infarction. These findings further strengthen the rationale for early, intensive, and sustained control of modifiable RFs to mitigate future coronary events.

Non-modifiable RFs, particularly older age, are more commonly associated with chronic low-grade inflammatory pathways and vascular remodeling.³⁵ We did not observe an increase in pan-coronary vulnerability with an increasing non-modifiable RF burden. However, non-modifiable RFs were associated with more plaques, particularly fibrocalcific plaques, in line with the findings of Scalamera et al.⁸ This may be explained by a chronic lower-grade inflammatory milieu that contributes to slower plaque growth and, over time, to calcific remodeling.³⁶^,³⁷

Study Limitations

Our study has several limitations. First, although the data were prospectively collected, the analysis was performed retrospectively. In addition, 3-vessel OCT was performed at the discretion of the operator in patients with stable hemodynamics and appropriate anatomy for OCT imaging. Furthermore, patients with prior PCI were excluded by design. These limitations reduce the generalizability of our findings to an all-comer cath-lab population. Therefore, our findings should be interpreted primarily in PCI-naive patients without chronic total occlusion in whom 3-vessel OCT is feasible. Second, although OCT imaging was performed in all 3 major coronary arteries, the far distal segment with a lumen diameter smaller than the size of the OCT catheter (0.9 mm) was not assessed. Third, because of the shallow penetration of OCT, plaque volume and vascular remodeling were not assessed. Fourth, light attenuation from LRP might have hampered the accurate identification of some vulnerable features.

Conclusions

An increasing number of cardiovascular RFs was associated with greater coronary plaque burden and vulnerability. Modifiable RF burden was associated with greater coronary vulnerability, whereas nonmodifiable RF burden showed no association. These findings emphasize the importance of focusing on modifiable RFs for primary and secondary prevention.

Perspectives.

COMPETENCY IN MEDICAL KNOWLEDGE: In patients with CAD, pan-coronary vulnerability increases in proportion to the number of RFs especially modifiable cardiovascular RFs. Individuals with multiple modifiable RFs commonly harbor multiple high-risk coronary plaques, underscoring the need for intensive primary and secondary prevention.

TRANSLATIONAL OUTLOOK: Large-scale prospective studies comparing RFs, plaque characteristics, and clinical outcomes will provide definitive answers for causal relationships. Whether patients with multiple RFs derive additional benefit from intensified primary and secondary prevention warrants further investigation.

Funding support and author disclosures

Dr Chmiel was supported by National Science Centre, Krakow, Poland (2020/39/O/NZ5/0286) and Polish National Agency for Academic Exchange (NAWA), Warsaw, Poland. Dr Andreaggi has received a grant (Borsa di studio Michele Pighi 2024-2025) from the University of Verona, Verona, Italy. Dr Jang’s research has been supported by the Allan and Gill Gray Foundation and Mukesh and Priti Chatter through the Chatter Foundation. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Footnotes

The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.

Appendix

For an expanded Methods section as well as supplemental tables and figures, please see the online version of this paper.

Supplementary materials

Supplemental_Material

mmc1.docx^{(5.1MB, docx)}

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental_Material

mmc1.docx^{(5.1MB, docx)}

[bib1] 1.Pencina M.J., D'Agostino R.B., Sr., Larson M.G., Massaro J.M., Vasan R.S. Predicting the 30-year risk of cardiovascular disease: the framingham heart study. Circulation. 2009;119:3078–3084. doi: 10.1161/CIRCULATIONAHA.108.816694. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib2] 2.Visseren F.L.J., Mach F., Smulders Y.M., et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2021;42:3227–3337. doi: 10.1093/eurheartj/ehab484. [DOI] [PubMed] [Google Scholar]

[bib3] 3.Prati F., Romagnoli E., Gatto L., et al. Relationship between coronary plaque morphology of the left anterior descending artery and 12 months clinical outcome: the CLIMA study. Eur Heart J. 2020;41:383–391. doi: 10.1093/eurheartj/ehz520. [DOI] [PubMed] [Google Scholar]

[bib4] 4.Biccirè F.G., Fabbiocchi F., Gatto L., et al. Long-term prognostic impact of OCT-derived high-risk plaque features: extended Follow-Up of the CLIMA study. JACC Cardiovasc Interv. 2025;18:1361–1372. doi: 10.1016/j.jcin.2025.04.044. [DOI] [PubMed] [Google Scholar]

[bib5] 5.Del Val D., Berta B., Roleder T., et al. Vulnerable plaque features and adverse events in patients with diabetes mellitus: a post hoc analysis of the COMBINE OCT-FFR trial. EuroIntervention. 2024;20:e707–e717. doi: 10.4244/EIJ-D-23-00628. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib6] 6.Mol J.Q., Volleberg R.H.J.A., Belkacemi A., et al. Fractional flow reserve-negative high-risk plaques and clinical outcomes after myocardial infarction. JAMA Cardiol. 2023;8:1013–1021. doi: 10.1001/jamacardio.2023.2910. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib7] 7.Covani M., Niccoli G., Fujimoto D., et al. Plaque vulnerability and cardiovascular risk factor burden in acute coronary syndrome: an optical coherence tomography analysis. J Am Coll Cardiol. 2025;86:77–89. doi: 10.1016/j.jacc.2025.04.070. [DOI] [PubMed] [Google Scholar]

[bib8] 8.Scalamera R., Covani M., Andreaggi S., et al. Non-modifiable cardiovascular risk factors and coronary plaque characteristics in patients with acute coronary syndromes. Eur Heart J Cardiovasc Imaging. 2026;27:733–742. doi: 10.1093/ehjci/jeag023. [DOI] [PubMed] [Google Scholar]

[bib9] 9.Abtahian F., Yonetsu T., Kato K., et al. Comparison by optical coherence tomography of the frequency of lipid coronary plaques in current smokers, former smokers, and nonsmokers. Am J Cardiol. 2014;114:674–680. doi: 10.1016/j.amjcard.2014.05.056. [DOI] [PubMed] [Google Scholar]

[bib10] 10.Ueyama H., Yasumura K., Okamoto N., et al. Relationship between hemoglobin A1C and characteristics of plaque vulnerability in stable coronary disease: an optical coherence tomography study. Int J Cardiovasc Imaging. 2022;38:473–482. doi: 10.1007/s10554-021-02297-x. [DOI] [PubMed] [Google Scholar]

[bib11] 11.Suzuki K., Niida T., Yuki H., et al. Coronary plaque characteristics and underlying mechanism of acute coronary syndromes in different age groups of patients with diabetes. J Am Heart Assoc. 2023;12 doi: 10.1161/JAHA.123.031474. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib12] 12.Seegers L.M., Araki M., Nakajima A., et al. Sex differences in culprit plaque characteristics among different age groups in patients with acute coronary syndromes. Circ Cardiovasc Interv. 2022;15 doi: 10.1161/CIRCINTERVENTIONS.121.011612. [DOI] [PubMed] [Google Scholar]

[bib13] 13.Vergallo R., Park S.J., Stone G.W., et al. Vulnerable or high-risk plaque: a JACC: cardiovascular imaging position statement. JACC Cardiovasc Imaging. 2025;18:709–740. doi: 10.1016/j.jcmg.2024.12.004. [DOI] [PubMed] [Google Scholar]

[bib14] 14.Stone G.W., Maehara A., Lansky A.J., et al. A prospective natural-history study of coronary atherosclerosis. N Engl J Med. 2011;364(3):226–235. doi: 10.1056/NEJMoa1002358. [DOI] [PubMed] [Google Scholar]

[bib15] 15.Erlinge D., Maehara A., Ben-Yehuda O., et al. Identification of vulnerable plaques and patients by intracoronary near-infrared spectroscopy and ultrasound (PROSPECT II): a prospective natural history study. Lancet. 2021;397:985–995. doi: 10.1016/S0140-6736(21)00249-X. [DOI] [PubMed] [Google Scholar]

[bib16] 16.Cheng J.M., Garcia-Garcia H.M., de Boer S.P., et al. In vivo detection of high-risk coronary plaques by radiofrequency intravascular ultrasound and cardiovascular outcome: results of the ATHEROREMO-IVUS study. Eur Heart J. 2014;35(10):639–647. doi: 10.1093/eurheartj/eht484. [DOI] [PubMed] [Google Scholar]

[bib17] 17.Arbab-Zadeh A., Fuster V. The myth of the "vulnerable plaque": transitioning from a focus on individual lesions to atherosclerotic disease burden for coronary artery disease risk assessment. J Am Coll Cardiol. 2015;65:846–855. doi: 10.1016/j.jacc.2014.11.041. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib18] 18.Kato K., Yonetsu T., Kim S.J., et al. Nonculprit plaques in patients with acute coronary syndromes have more vulnerable features compared with those with non-acute coronary syndromes: a 3-vessel optical coherence tomography study. Circ Cardiovasc Imaging. 2012;5:433–440. doi: 10.1161/CIRCIMAGING.112.973701. [DOI] [PubMed] [Google Scholar]

[bib19] 19.Dai J., Zhao J., Xu X., et al. Long-term prognostic implications of non-culprit lesions in patients presenting with an acute myocardial infarction: is it the angiographic stenosis severity or the underlying high-risk morphology? Circulation. 2025;151:1098–1110. doi: 10.1161/CIRCULATIONAHA.124.071855. [DOI] [PubMed] [Google Scholar]

[bib20] 20.Vergallo R., Uemura S., Soeda T., et al. Prevalence and predictors of multiple coronary plaque ruptures: in vivo 3-Vessel optical coherence tomography imaging study. Arterioscler Thromb Vasc Biol. 2016;36:2229–2238. doi: 10.1161/ATVBAHA.116.307891. [DOI] [PubMed] [Google Scholar]

[bib21] 21.Scalamera R., Andreaggi S., Kim S., et al. Is Imaging of a Single Vessel Sufficient to Assess Pan-Coronary Vulnerability? Catheter Cardiovasc Interv. Published online May 27, 2026 doi: 10.1002/ccd.70680. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib22] 22.Oikonomou E.K., Marwan M., Desai M.Y., et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 2018;392:929–939. doi: 10.1016/S0140-6736(18)31114-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[bib24] 24.Yusuf S., Hawken S., Ounpuu S., et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364:937–952. doi: 10.1016/S0140-6736(04)17018-9. [DOI] [PubMed] [Google Scholar]

[bib25] 25.Magnussen C., Ojeda F.M., Leong D.P., et al. Global effect of modifiable risk factors on cardiovascular disease and mortality. N Engl J Med. 2023;389:1273–1285. doi: 10.1056/NEJMoa2206916. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib26] 26.Ridker P.M., Everett B.M., Thuren T., et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med. 2017;377:1119–1131. doi: 10.1056/NEJMoa1707914. [DOI] [PubMed] [Google Scholar]

[bib27] 27.Ray K.K., Cannon C.P., Cairns R., et al. Relationship between uncontrolled risk factors and C-reactive protein levels in patients receiving standard or intensive statin therapy for acute coronary syndromes in the PROVE IT-TIMI 22 trial. J Am Coll Cardiol. 2005;46:1417–1424. doi: 10.1016/j.jacc.2005.08.024. [DOI] [PubMed] [Google Scholar]

[bib28] 28.Libby P., Ridker P.M., Maseri A. Inflammation and atherosclerosis. Circulation. 2002;105:1135–1143. doi: 10.1161/hc0902.104353. [DOI] [PubMed] [Google Scholar]

[bib29] 29.Araki M., Sugiyama T., Nakajima A., et al. Level of vascular inflammation is higher in acute coronary syndromes compared with chronic coronary disease. Circ Cardiovasc Imaging. 2022;15 doi: 10.1161/CIRCIMAGING.122.014191. [DOI] [PubMed] [Google Scholar]

[bib30] 30.Yuki H., Sugiyama T., Suzuki K., et al. Coronary inflammation and plaque vulnerability: a coronary computed tomography and optical coherence tomography study. Circ Cardiovasc Imaging. 2023;16 doi: 10.1161/CIRCIMAGING.122.014959. [DOI] [PubMed] [Google Scholar]

[bib31] 31.Goeller M., Achenbach S., Cadet S., et al. Pericoronary adipose tissue computed tomography attenuation and high-risk plaque characteristics in acute coronary syndrome compared with stable coronary artery disease. JAMA Cardiol. 2018;3:858–863. doi: 10.1001/jamacardio.2018.1997. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib32] 32.Ząbczyk M., Ariëns R.A.S., Undas A. Fibrin clot properties in cardiovascular disease: from basic mechanisms to clinical practice. Cardiovasc Res. 2023;119:94–111. doi: 10.1093/cvr/cvad017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib33] 33.Räber L., Koskinas K.C., Yamaji K., et al. Changes in coronary plaque composition in patients with acute myocardial infarction treated with high-intensity statin therapy (IBIS-4): a serial optical coherence tomography study. JACC Cardiovasc Imaging. 2019;12:1518–1528. doi: 10.1016/j.jcmg.2018.08.024. [DOI] [PubMed] [Google Scholar]

[bib34] 34.Kini A.S., Vengrenyuk Y., Liu J., et al. Intracoronary imaging and transcriptomic characteristics before and after maximum lipid-lowering therapy: YELLOW-III study of evolocumab and maximum statin therapy in chronic coronary disease. JACC Cardiovasc Imaging. 2026;19:516–534. doi: 10.1016/j.jcmg.2025.12.013. [DOI] [PubMed] [Google Scholar]

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PERMALINK

Association Between Cardiovascular Risk Factors and Pan-Coronary Plaque Burden, Phenotype, and Vulnerability

Jakub Chmiel, MD

Stefano Andreaggi, MD

Sekeun Kim, PhD

Iris McNulty, RN

Hang Lee, PhD

Adrian P Banning, MD

Krzysztof L Bryniarski, MD, PhD

Piotr Musialek, MD, DPhil

Ik-Kyung Jang, MD, PhD

Abstract

Background

Objectives

Methods

Results

Conclusions

Central Illustration

Methods

Study population

OCT analysis

Statistical analysis

Results

Patient characteristics

Table 1.

Plaque burden and phenotype according to the number of cardiovascular RFs

Figure 1.

Plaque burden and phenotype according to the number of modifiable and non-modifiable cardiovascular RFs

Vulnerable features according to the number of cardiovascular RFs

Figure 2.

Central Illustration.

Vulnerable features according to the number of modifiable and non-modifiable RFs

Differential associations of modifiable and non-modifiable RFs with plaque number and phenotype

Figure 3.

Differential associations of modifiable and non-modifiable RFs with vulnerable features

Discussion

Vulnerability: plaque, pan-coronary, patient

Cardiovascular risk factors and OCT-findings

Cardiovascular risk factors and clinical outcome

Modifiable risk factors, non-modifiable risk factors, and vulnerability

Study Limitations

Conclusions

Perspectives.

Funding support and author disclosures

Footnotes

Supplementary materials

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases