Plaque characteristics and parameters derived from coronary computed tomography angiography for predicting major adverse cardiovascular events in patients with and without diabetes

Introduction

Coronary artery atherosclerosis is the primary pathological basis for coronary heart disease, with the resulting myocardial ischemia and cardiac lesions posing significant threats to patients’ life and health. Notably, plaque rupture has been identified as a critical factor in triggering major adverse cardiovascular events (MACEs) (1-3). For individuals with diabetes, their unique inflammatory microvascular pathology presents an independent correlation with plaque rupture, further exacerbating the risk of cardiovascular events (4,5). Consequently, the rapid and accurate assessment of cardiovascular function in individuals with diabetes has become a major challenge in clinical decision-making and prognosis improvement.

In recent years, coronary computed tomography (CT) angiography (CCTA) has demonstrated distinct advantages in evaluating the anatomical features of coronary arteries as a noninvasive imaging modality. However, despite its ability to provide detailed anatomical structural information, CCTA has limitations in acquiring vascular functional data (6,7). Furthermore, the distribution of coronary artery calcification in those with diabetes is often more extensive and diffuse, which not only complicates the assessment of the degree of arterial stenosis but also further limits the effectiveness of CCTA in diabetics (8).

In comprehensively assessing the conditions of coronary artery lesions, the coronary artery calcium score (CACS), has been widely used as a method to quantitatively analyze plaque burden and predict the risk of future MACEs (9,10). The CACS not only provides objective data on the degree of coronary artery calcification but also offers important insights for clinical decision-making. However, relying solely on CACS is insufficient for fully reflecting the degree of coronary artery stenosis and its functional status.

On this basis, CT-derived fractional flow reserve (CTFFR), a noninvasive approach combining functional and anatomical assessments, has gradually garnered attention in recent years. CTFFR not only evaluates the degree of coronary artery stenosis but also reflects myocardial blood flow perfusion, providing more accurate diagnostic information for clinical practice (11). It is worth noting that between individuals with and without diabetes, there exist differences in the assessment of MACE risk according to coronary plaque characteristics, CACS, and CTFFR (9,12).

Therefore, this study aimed to characterize the differences in the application value of CCTA-based plaque characteristics and CT-derived parameters in predicting future MACEs between patients with and without diabetes through comparative analysis in order to provide a more precise reference for clinical treatment. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-531/rc).

Methods

Patient selection

A retrospective analysis was conducted on 1,043 patients who underwent CCTA at The Eighth Affiliated Hospital of Southern Medical University between June 2018 and December 2018. CCTA indications included typical or atypical chest pain and electrocardiographic abnormalities suggesting myocardial ischemia. The study analyzed plaque characteristics, CT-derived parameters, and clinical data collected during the abovementioned period. After the exclusion of 168 patients with a history of nonfatal myocardial infarction (MI) or revascularization, 159 patients with incomplete or poor-quality imaging unsuitable for diagnosis, 203 patients without coronary atherosclerotic plaques on CCTA, and 41 patients with incomplete or missing clinical data, a total of 472 patients were included in the final analysis. Of these, 340 patients were assigned to the non-diabetes mellitus (non-DM) group, and 132 patients were assigned to the diabetes mellitus (DM) group. Consecutive cases were included this study. Among the patients included, antihypertensive medications primarily consisted of angiotensin-converting enzyme inhibitors, angiotensin II receptor blocker, beta-blockers, and calcium-channel blockers. Hypoglycemic agents mainly included biguanides, sulfonylureas, and glinides, while lipid-modifying drugs were predominantly statins. This study was approved by Ethics Board of The Eighth Affiliated Hospital of Southern Medical University (approval No. KYLS20231051) and was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The requirement for consent was waived due to the retrospective nature of the analysis.

The inclusion criteria were as follows: (I) age ≥18 years; (II) high-quality CCTA imaging; and (III) the presence of at least one coronary atherosclerotic plaque on CCTA. Meanwhile, the exclusion criteria were as follows: (I) history of MI or revascularization; (II) incomplete or poor-quality CCTA images unsuitable for determining the CACS, CTFFR calculation, or plaque status; and (III) incomplete or missing clinical data. The study flowchart is presented in Figure 1.

Figure 1 Flowchart of patient recruitment and grouping. From June 2018 to December 2018, patients with coronary arteriosclerosis who underwent CCTA at The Eighth Affiliated Hospital of Southern Medical University were divided into four groups according to whether they had diabetes and whether major adverse cardiovascular events occurred. CCTA, coronary computed tomography angiography; DM, diabetes mellitus; IVOCT, intravascular optical coherence tomography; MACE, major adverse cardiovascular event.

Inspection and analytical methods

All patients received sublingual nitroglycerin (0.5 mg) 5 minutes prior to scanning. CCTA and the CACS test were performed via a SOMATOM Definition Flash dual-source CT scanner (Siemens Healthineers, Erlangen, Germany) with scanning parameters of 100–120 kV and 400–700 mA. A bolus of Ultravist 370 (0.769 g/mL iopromide injection; Bayer, Berlin, Germany) was administered intravenously via a pressure injector. Images were acquired with either bolus injection tracking or a low-dose contrast agent test technique and were subsequently uploaded to the picture archiving and communication system (PACS) workstation for analysis.

Two experienced radiologists (each with ≥15 years of CCTA experience) independently analyzed the highest-quality CCTA images and resolved any discrepancies through consensus discussions. Coronary artery stenoses in major epicardial vessels with diameters ≥2 mm were manually measured, and the following parameters were recorded: (I) vessel involvement; (II) high-risk plaque (HRP) characteristics; (III) positive remodeling (PR; i.e., any lesion with a remodeling index ≥1.1); (IV) low-attenuation plaque [LAP; i.e., areas with <30 Hounsfield units (HU) within coronary plaques]; (V) spotty calcification (SC; i.e., calcifications with diameters <3 mm and circumferences <90°); and (VI) napkin-ring sign (NRS; i.e., plaques with a low-attenuation core surrounded by an edge-like high-density area).

CCTA analysis and Coronary Artery Disease Reporting and Data System (CAD-RADS) classification

Plaque characterization was performed via artificial intelligence-assisted Shukun software (Shukun Technology, Beijing, China), which automates plaque segmentation and CACS computation based on deep learning algorithms. The CACS and lesion-specific CTFFR were calculated. CTFFR was measured 2–3 cm distal to the focal stenosis in major epicardial vessels (≥2 mm in diameter) for individualized analysis. Meanwhile, obstructive coronary artery disease was defined as a CAD-RADS classification score ≥3.

Follow-up and grouping

Follow-up was conducted at least once 1 month after CCTA, with a median follow-up duration of 55.10 months [interquartile range (IQR), 25.58–58.26 months] until June 1, 2024. MACEs were defined as a composite of congestive heart failure, hospital readmission for unstable angina, nonfatal MI, or coronary revascularization.

Patients were stratified into four groups based on the presence or absence of diabetes and MACEs.

Statistical analysis

Data analysis was performed with R software version 4.3.1 (The R Foundation for Statistical Computing), and figures were generated via GraphPad Prism version 8.0 (Dotmatics, Boston, MA, USA). The Kolmogorov-Smirnov test was applied to assess the normality of continuous variables. Continuous variables with a normal distribution are expressed as the mean ± standard deviation (x¯±s), while those with a skewed distribution are expressed as the median and IQR.

Comparisons of normally distributed continuous variables were performed via the independent samples t-test, while the Wilcoxon rank-sum test was applied for nonnormally distributed data. Discrete variables are presented as absolute frequencies and percentages, and comparisons were made with Chi-squared tests.

Univariate Cox regression analysis was used to evaluate the prognostic value of CT-derived parameters and clinical features for MACEs. Variables with P<0.1 were included in multivariate Cox regression to identify independent predictors. Forest plots and time-dependent receiver operating characteristic (ROC) curves were generated to calculate the area under the curve (AUC). Kaplan-Meier (KM) survival analysis was used to estimate the cumulative event rates across different CT-derived parameter stratifications in patients with and without diabetes. Statistical significance was defined as P<0.05.

Results

Baseline, imaging data, and features in the DM and non-DM groups

A total of 340 patients without diabetes and 132 patients with diabetes were included in the study, consisting of 256 males and 216 females, with a median age of 64 years (IQR, 57–71 years). No significant differences were observed between DM and non-DM groups in terms of gender, body weight, smoking history, history of hypertension, CACS, SC, LAP, NRS, HRP, triglycerides (TGs), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, remnant cholesterol (RC), apolipoprotein A (Apo A), apolipoprotein B (Apo B), neutrophil-to-lymphocyte ratio (NLR), monocyte count (Mono), or the use of statin medications (P>0.05).

Compared to patients without diabetes, those with diabetes exhibited significantly higher glycosylated hemoglobin type A1c (HbA1c) levels, a higher prevalence of hypertension, increased use of antihypertensive medications, higher rates of CTFFR ≤0.80 and CAD-RADS ≥3, and a higher proportion of PR (P<0.05) (Table 1 and Figures 2,3).

Figure 2 Plaque characteristics and parameters derived from CCTA for patients with diabetes. (A) Diabetic patient 1—MACE(−) with CACS assessment. The yellow region indicates calcified plaque deposition in the LAD. (B) Diabetic patient 1—MACE(−), with CT-FFR measurement performed 2 mm distal to the coronary lesion. (C) Diabetic patient 1—MACE(−), with positive remodeling plaque observed in the proximal RCA. (D) Diabetic patient 2—MACE(+), with low-attenuation plaque visualized within the proximal-mid RCA. (E) Diabetic patient 3—MACE(−), with napkin-ring sign observed in a plaque in the mid-LAD. CACS, coronary artery calcium score; CCTA, coronary computed tomography angiography; CT-FFR, computed tomography-derived fractional flow reserve; LAD, left anterior descending artery; LCX, left circumflex artery; LM, left main artery; MACE, major adverse cardiovascular event; RCA, right coronary artery.

Figure 3 Plaque characteristics and parameters derived from CCTA for patients without diabetes. (A) Nondiabetic patient 1—MACE(+), with CACS assessment. The yellow region indicates the calcified plaque deposition in the LAD. (B) Nondiabetic patient 1—MACE(+), with CT-FFR measurement performed 2 mm distal to the coronary lesion. (C) Nondiabetic patient 1—MACE(+), with positive remodeling observed in the proximal-mid LAD. (D) Nondiabetic patient 2—MACE(−), with low-attenuation observed in the R-PDA. (E) Nondiabetic patient 3—MACE(+), with napkin-ring sign observed in a plaque in the proximal LAD. CACS, coronary artery calcium score; CCTA, coronary computed tomography angiography; CT-FFR, computed tomography-derived fractional flow reserve; LAD, left anterior descending artery; LCX, left circumflex artery; LM, left main artery; MACE, major adverse cardiovascular event; RCA, right coronary artery; R-PDA, right posterior descending artery.

Baseline, imaging data, and features of the MACE and non-MACE groups

Patients in the MACE group had a significantly higher prevalence of LAP and CACS ≥100 compared to those in the non-MACE group. Additionally, the non-MACE group showed a higher prevalence of antihypertensive medication use, PR, CTFFR ≤0.80, CACS ≥100, and CAD-RADS score ≥3 (P<0.05).

In both the DM and non-DM groups, the proportion of patients with CACS ≥100 was significantly higher in the MACE group than in the non-MACE group (P<0.05) (Table 2).

Prognostic value of different CT-derived parameters and clinical indicators in the DM and non-DM groups

Univariate Cox regression analysis identified low-LAP and CACS ≥100 as significant predictors of MACEs in patients with diabetes. In patients without diabetes, significant predictors included PR, CTFFR ≤0.80, CACS ≥100, the level of Apo B, and the use of antihypertensive medications (Table 3).

Multivariate Cox regression analysis revealed that the independent predictors of MACE in patients with diabetes were a CACS ≥100 [hazard ratio (HR) =1.98; 95% CI: 1.06–3.72; P=0.033] and LAP (HR =2.13; 95% CI: 1.00–4.53; P=0.049). For patients without diabetes, PR (HR =45.00; 95% CI: 22.69–89.28; P<0.001) was the sole independent predictor of MACEs (Figure 4).

Figure 4 Forest plots of (A) the diabetic group and (B) the nondiabetic group. *, a statistically significant difference. Apo B, apolipoprotein B; CACS, coronary artery calcium score; CAD-RADS, Coronary Artery Disease Reporting and Data System; CI, confidence interval; CTFFR, computed tomography-derived fractional flow reserve; HR, hazard ratio; HRP, high-risk plaque; LAP, low-attenuation plaque; PR, positive remodeling; TG, triglyceride.

Association between different CT-derived parameters and MACEs in the DM and non-DM groups

Among patients without diabetes, those with PR (HR =31.68; 95% CI: 15.23–65.90; P<0.0001) had a significantly higher incidence of MACEs compared to those without PR.

In contrast, among patients with diabetes, the incidence of MACEs was significantly higher in those with a CACS ≥100 (HR =2.09; 95% CI: 1.11–3.95; P=0.0134) or LAP (HR =3.28; 95% CI: 1.02–4.53; P=0.0031) (Figure 5).

Figure 5 Survival curves of patients with diabetes and patients without diabetes. (A) Survival curves of positive remodeling in the nondiabetic group. (B,C) Survival curves of (B) CACS ≥100 and (C) LAP in the diabetic group. CACS, coronary artery calcium score; DM, diabetes mellitus; LAP, low-attenuation plaque; MACE, major adverse cardiovascular event; PR, positive remodeling.

Predictive value of different CT-derived parameters and clinical indicators in the DM and non-DM groups

The combined use of the CACS and LAP demonstrated higher predictive efficacy for MACEs in the DM group as compared to either parameter used alone. The AUCs for the combined predictors were 0.649, 0.603, and 0.668 at 1, 3, and 5 years of follow-up, respectively.

PR showed strong predictive value for cardiovascular prognosis in the non-DM group, with AUCs of 0.792, 0.884, and 0.795 at 1, 3, and 5 years, respectively (Figure 6).

Figure 6 Plaque characteristics and CT-derived parameters demonstrate distinct predictive values for MACEs in diabetic versus non-diabetic patients.. ROC curves predicting the 1-, 3-, and 5-year rates of MACEs in patients with diabetes based on (A) CACS alone, (B) LAP alone, and (C) a combination of CACS and LAP. (D) ROC curves for predicting the 1-, 3-, and 5-year MACE rates in patients without diabetes based on PR. Time-dependent AUC curves assessing cardiovascular prognosis in (E) the diabetic group and (F) nondiabetic group for various parameters. AUC, area under the curve; CACS, coronary artery calcium score; CT, computed tomography; DM, diabetes mellitus; LAP, low-attenuation plaque; MACE, major adverse cardiovascular event; PR, positive remodeling; ROC, receiver operating characteristic.

The value of the CACS for predicting MACEs in the DM group remained stable over 5 years, whereas the predictive value of LAP in the DM group and PR in the non-DM group declined after 4 years (Figure 6).

Discussion

This study evaluated the value of CCTA-based plaque characteristics and CT-based parameters for predicting MACEs in patients with and without diabetes. The results indicated that the combination of CACS and LAP provides superior predictive efficacy for MACEs in patients with diabetes as compared to either parameter alone. In patients without diabetes, PR exhibited the highest values for predicting MACEs.

The application value of the CACS in predicting MACEs in patients with and without diabetes

Our findings suggest that the CACS is an effective predictor of long-term cardiovascular outcomes in patients with diabetes, whereas in patients without diabetes, other indicators, such as PR, demonstrate greater predictive value. A potential mechanism for this difference may lie in the promotion of vascular calcification by hyperglycemia, leading to the formation of multiple, small, calcified plaque deposits. These deposits may destabilize plaques, thereby increasing the risk of cardiovascular events and elevating MACE risk.

In contrast, patients without diabetes typically have larger areas of intravascular calcification areas, which may contribute to plaque stability. However, this does not completely prevent cardiovascular events, suggesting that cardiovascular prognosis in patients without diabetes cannot be accurately assessed based on the CACS alone (13).

Furthermore, Ferket et al. reported that the lifetime cardiovascular disease risk of diabetic patients with a zero CACS overlaps with that of non-diabetic patients. This finding study underscores the differing predictive value of the CACS for cardiovascular events between diabetic and nondiabetic populations (14).

In summary, while the CACS proves valuable in predicting MACEs among patients with diabetes, its predictive utility is less pronounced in patients without diabetes.

Predictive value of plaque characteristics for MACEs in patients with diabetes

This study demonstrated that the presence of LAP is a significant predictor of MACEs in patients with diabetes, whereas its predictive value is weaker in patients without diabetes. This difference may be attributed to variations in plaque stability and tissue composition. LAPs typically indicate high lipid content, which contributes to plaque instability. In patients with diabetes, hyperglycemia induces more complex plaque compositions, characterized by increased lipid deposition, calcification, and other destabilizing factors. These changes make LAPs more prominent in patients with diabetes, establishing them as independent predictors of adverse cardiovascular events (15,16).

Conversely, patients without diabetes generally have fewer lipid-rich coronary plaques, and their cardiovascular risk is often influenced by factors such as cholesterol levels and hypertension. Therefore, LAPs demonstrate weaker predictive value in this population (17). Previous studies have recognized the general predictive value of LAPs for cardiovascular events but have not fully clarified the differences between patients with and without diabetes (18). Tesche et al. found that LAPs are more prevalent in patients with diabetes and exhibit distinct predictive efficacy for cardiovascular prognosis in these two groups (19). This study further highlights the importance of LAPs in cardiovascular disease and underscores the need for tailored evaluation strategies for different patient populations.

Additionally, PR has been widely regarded as a valuable predictor of adverse cardiovascular events, although its efficacy varies between individuals with and without diabetes (18,20). Previous research suggests that hyperglycemia in patients with diabetes can impair vascular dilation and reduce the predictive accuracy of PR for cardiovascular prognosis (21,22). In our study, PR was identified as a reliable predictor of MACEs in patients without diabetes but showed no significant association with cardiovascular outcomes in patients with diabetes. This may be because PR is associated with compensatory vascular dilation, plaque rupture, lipid deposition, and thrombus formation, all of which increase cardiovascular risk (23,24). The reduced predictive value of PR in patients with diabetes likely stems from the long-term effects of hyperglycemia, which exacerbates coronary artery lesions, vascular wall inflammation, and endothelial dysfunction. These factors alter vascular dilation capacity and increase resistance to blood flow, thus diminishing the relevance of PR. Even in the absence of PR, patients with diabetes remain at high risk for MACEs. Consequently, PR is less effective for predicting cardiovascular prognosis in patients with diabetes than in those without it (22).

Stability of the predictive efficacy of plaque characteristics and CT-derived parameters for MACEs in patients with and without diabetes

Our study found that the combined use of the CACS and LAP outperformed either parameter alone in predicting MACEs in patients with diabetes, with the predictive efficacy remaining stable over a 5-year period.

In patients without diabetes, the predictive value of PR declined significantly after 4 years. This decline may be attributed to the limitations of PR as an early marker of vascular disease, especially in assessing long-term vascular function (25). Nevertheless, PR remains a valuable tool for evaluating cardiovascular risk in patients without diabetes, providing critical insights into their prognosis (26,27).

Additionally, we found that CT-FFR was not an independent predictor of MACEs. This is likely attributable to CT-FFR’s reliance on pressure gradient calculations derived from stenosis in epicardial vessels, whereas myocardial ischemia in patients with diabetes is primarily driven by microcirculatory dysfunction (e.g., endothelial dysfunction and capillary rarefaction). Even with a normal epicardial CT-FFR value (>0.80), microvascular resistance can still induce myocardial ischemia and MACEs. In the DM group of this study, despite a significantly higher proportion of patients with CT-FFR ≤0.80, its HR did not reach statistical significance in multivariate regression analysis. This observation corroborates the confounding effect of microvascular impairment on the predictive utility of CT-FFR.

Limitations

This study involved several limitations that should be addressed. First, the endpoint event, MACE occurrence, includes multiple composite outcomes, and no subgroup analysis was conducted for individual events. Future long-term follow-up studies are needed to evaluate the predictive value of plaque characteristics and CT-derived parameters for specific cardiovascular event subgroups. Additionally, larger sample sizes and multicenter clinical studies are needed to validate the reliability and generalizability of the results.

Conclusions

The application of CCTA-derived plaque characteristics and CT-derived parameters for predicting MACEs differs between diabetic and nondiabetic populations. In patients with diabetes, the combination of the CACS and LAP provides an effective risk assessment for MACE. In contrast, PR demonstrates greater predictive value for MACE in patients without diabetes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-531/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-531/dss

Funding: This research was supported by Foshan Medical Imaging Artificial Intelligence Engineering Technology Application Research Center (No. 361077); Clinical Research Start Plan of The Eighth Affiliated Hospital of Southern Medical University (No. CRSP2022005); Scientific Research Start Plan of The Eighth Affiliated Hospital of Southern Medical University (Nos. SRSP2021021 and SRSP2024013); Scientific Research of Lecong Hospital of Shunde, Foshan (No. A202303); Guangdong Medical Science and Technology Research Fund (Nos. A2023204, A2024338, and A2024582); Traditional Chinese Medicine Research Project of Guangdong (No. 20241312); Characteristic Innovation Projects of Universities in Guangdong Province (No. 2023KTSCX022); Special Project of Key Fields in Ordinary Colleges and Universities of Guangdong Province (No. 2024ZDZX2029); Foshan Self-funded Science and Technology Project (No. 2220001004195); and The National Natural Science Foundation of China (No. 82372079).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-531/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was approved by Ethics Board of The Eighth Affiliated Hospital of Southern Medical University (No. KYLS20231051). Individual consent for this retrospective analysis was waived. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Mátyás BB, Benedek I, Blîndu E, Gerculy R, Roșca A, Rat N, Kovács I, Opincariu D, Parajkó Z, Szabó E, Benedek B, Benedek T. Elevated FAI Index of Pericoronary Inflammation on Coronary CT Identifies Increased Risk of Coronary Plaque Vulnerability after COVID-19 Infection. Int J Mol Sci 2023;24:7398. [Crossref] [PubMed]
2. Baichoo R, Fu J, Xiong X, Zhou Q, Guan X, Xu X. The role of cardiac adipose tissue in coronary artery disease: a narrative review of cardiac imaging studies. Quant Imaging Med Surg 2024;14:9685-97. [Crossref] [PubMed]
3. Xu Q, Shen H, Tu C, Xie Y, Yang R, Yuan X, Ran Z, Zhang J. Coronary computed tomography angiography for assessing the coronary artery and predicting adverse cardiovascular events in patients with thoracic malignancies. Quant Imaging Med Surg 2024;14:9193-206. [Crossref] [PubMed]
4. Zhang W, Singh S, Liu L, Mohammed AQ, Yin G, Xu S, Lv X, Shi T, Feng C, Jiang R, Mohammed AA, Mareai RM, Xu Y, Yu X, Abdu FA, Che W. Prognostic value of coronary microvascular dysfunction assessed by coronary angiography-derived index of microcirculatory resistance in diabetic patients with chronic coronary syndrome. Cardiovasc Diabetol 2022;21:222. [Crossref] [PubMed]
5. Yu MM, Zhao X, Chen YY, Tao XW, Ge JB, Jin H, Zeng MS. Evolocumab attenuate pericoronary adipose tissue density via reduction of lipoprotein(a) in type 2 diabetes mellitus: a serial follow-up CCTA study. Cardiovasc Diabetol 2023;22:121. [Crossref] [PubMed]
6. Skalsky K, Shiyovich A, Hochwald N, Levi A, Zreik L, Tamir S, Shafir G, Briger A, Rahamimov R, Kornowski R, Hamdan A. Coronary Computed Tomography Angiography and Abdominal Aortic Calcification Screening among High-Risk Living Kidney Donors. J Clin Med 2023;12:4541. [Crossref] [PubMed]
7. Yuvaraj J, Cheng K, Lin A, Psaltis PJ, Nicholls SJ, Wong DTL. The Emerging Role of CT-Based Imaging in Adipose Tissue and Coronary Inflammation. Cells 2021;10:1196. [Crossref] [PubMed]
8. Wang J, Huang X, Fu C, Sheng Q, Liu P. Association between triglyceride glucose index, coronary artery calcification and multivessel coronary disease in Chinese patients with acute coronary syndrome. Cardiovasc Diabetol 2022;21:187. [Crossref] [PubMed]
9. Saraste A, Knuuti J, Bax J. Screening for Coronary Artery Disease in Patients with Diabetes. Curr Cardiol Rep 2023;25:1865-71. [Crossref] [PubMed]
10. Napoli G, Pergola V, Basile P, De Feo D, Bertrandino F, Baggiano A, Mushtaq S, Fusini L, Fazzari F, Carrabba N, Rabbat MG, Motta R, Ciccone MM, Pontone G, Guaricci AI. Epicardial and Pericoronary Adipose Tissue, Coronary Inflammation, and Acute Coronary Syndromes. J Clin Med 2023;12:7212. [Crossref] [PubMed]
11. Tang CX, Liu CY, Lu MJ, Schoepf UJ, Tesche C, Bayer RR 2nd, et al. CT FFR for Ischemia-Specific CAD With a New Computational Fluid Dynamics Algorithm: A Chinese Multicenter Study. JACC Cardiovasc Imaging 2020;13:980-90. [Crossref] [PubMed]
12. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, Himmelfarb CD, Khera A, Lloyd-Jones D, McEvoy JW, Michos ED, Miedema MD, Muñoz D, Smith SC Jr, Virani SS, Williams KA Sr, Yeboah J, Ziaeian B. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2019;140:e596-646. [Crossref] [PubMed]
13. Pugliese L, Spiritigliozzi L, Di Tosto F, Ricci F, Cavallo AU, Di Donna C, De Stasio V, Presicce M, Benelli L, D'Errico F, Pasqualetto M, Floris R, Chiocchi M. Association of plaque calcification pattern and attenuation with instability features and coronary stenosis and calcification grade. Atherosclerosis 2020;311:150-7. [Crossref] [PubMed]
14. Ferket BS, Hunink MGM, Masharani U, Max W, Yeboah J, Burke GL, Fleischmann KE. Lifetime Cardiovascular Disease Risk by Coronary Artery Calcium Score in Individuals With and Without Diabetes: An Analysis From the Multi-Ethnic Study of Atherosclerosis. Diabetes Care 2022;45:975-82. [Crossref] [PubMed]
15. Suzuki K, Niida T, Yuki H, Kinoshita D, Fujimoto D, Lee H, McNulty I, Takano M, Nakamura S, Kakuta T, Mizuno K, Jang IK. Coronary Plaque Characteristics and Underlying Mechanism of Acute Coronary Syndromes in Different Age Groups of Patients With Diabetes. J Am Heart Assoc 2023;12:e031474. [Crossref] [PubMed]
16. Liu Y, Dai L, Dong Y, Ma C, Cheng P, Jiang C, Liao H, Li Y, Wang X, Xu X. Coronary inflammation based on pericoronary adipose tissue attenuation in type 2 diabetic mellitus: effect of diabetes management. Cardiovasc Diabetol 2024;23:108. [Crossref] [PubMed]
17. Zhang CY, Liu ZS, Zhang TY, Meng K. Application of optical coherence tomography in the treatment of plaque characteristics in patients with coronary heart disease and diabetes. Chinese Journal of Evidence-Based Cardiovascular Medicine 2020;12:91-4+99.
18. Williams MC, Moss AJ, Dweck M, Adamson PD, Alam S, Hunter A, Shah ASV, Pawade T, Weir-McCall JR, Roditi G, van Beek EJR, Newby DE, Nicol ED. Coronary Artery Plaque Characteristics Associated With Adverse Outcomes in the SCOT-HEART Study. J Am Coll Cardiol 2019;73:291-301. [Crossref] [PubMed]
19. Tesche C, Baquet M, Bauer MJ, Straube F, Hartl S, Leonard T, Jochheim D, Fink D, Brandt V, Baumann S, Schoepf UJ, Massberg S, Hoffmann E, Ebersberger U. Prognostic Utility of Coronary Computed Tomography Angiography-derived Plaque Information on Long-term Outcome in Patients With and Without Diabetes Mellitus. J Thorac Imaging 2023;38:179-85. [Crossref] [PubMed]
20. Gallone G, Bellettini M, Gatti M, Tore D, Bruno F, Scudeler L, et al. Coronary Plaque Characteristics Associated With Major Adverse Cardiovascular Events in Atherosclerotic Patients and Lesions: A Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging 2023;16:1584-604. [Crossref] [PubMed]
21. Su G, Zhang T, Duan ZY, Gao MX, Yang HX, Mi SH. Value of urinary 8-iso-prostaglandin F2αto risk assessment of plaque rupture in type 2 diabetes mellitus patients with unstable angina pectoris. Journal of Chinese Practical Diagnosis and Therapy 2021;35:23-7.
22. Xue JX, Gong TY, Liang XF, Wang F, Zhang XH. Mechanism of Vascular Remodeling Action in Diabetes Mellitus. Advances in Cardiovascular Diseases 2019;44:738-42.
23. Kinoshita D, Suzuki K, Usui E, Hada M, Yuki H, Niida T, Minami Y, Lee H, McNulty I, Ako J, Ferencik M, Kakuta T, Jang IK. High-Risk Plaques on Coronary Computed Tomography Angiography: Correlation With Optical Coherence Tomography. JACC Cardiovasc Imaging 2024;17:382-91. [Crossref] [PubMed]
24. Nurmohamed NS, Gaillard EL, Malkasian S, de Groot RJ, Ibrahim S, Bom MJ, Kaiser Y, Earls JP, Min JK, Kroon J, Planken RN, Danad I, van Rosendael AR, Choi AD, Stroes ESG, Knaapen P. Lipoprotein(a) and Long-Term Plaque Progression, Low-Density Plaque, and Pericoronary Inflammation. JAMA Cardiol 2024;9:826-34. [Crossref] [PubMed]
25. Liu WB, Tang Y, Guo DH, He YY, Ding QH, Ma C, Jia RF, Li TX, He YK. Distinguish the two different phenomena of positive vascular remodeling and vascular luminal dilatational remodeling. Chinese Journal of Cerebrovascular Diseases 2019;20:200-4.
26. Ichikawa K, Miyoshi T, Osawa K, Nakashima M, Miki T, Nishihara T, Toda H, Yoshida M, Ito H. High pericoronary adipose tissue attenuation on computed tomography angiography predicts cardiovascular events in patients with type 2 diabetes mellitus: post-hoc analysis from a prospective cohort study. Cardiovasc Diabetol 2022;21:44. [Crossref] [PubMed]
27. Tzolos E, McElhinney P, Williams MC, Cadet S, Dweck MR, Berman DS, Slomka PJ, Newby DE, Dey D. Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography. J Cardiovasc Comput Tomogr 2021;15:81-4. [Crossref] [PubMed]