SCORE2-Diabetes for Predicting Coronary Artery Disease: A Cardiac CT Study in a Diabetic Moderate-Risk Region Population

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The SCORE2-Diabetes (SCORE2-D) model was developed to offer a more nuanced cardiovascular risk estimate by incorporating continuous variables and individualized risk factor weighting. However, its correlation with the actual presence and severity of CAD in diabetic patients remains under-investigated. Objective This study aims to evaluate the association between SCORE2-D scores and CAD characteristics, as assessed by computed tomography coronary angiography (CCTA), in patients with T2DM and no prior coronary revascularization. Specifically, it investigates the relationship between SCORE2-D risk categories and the presence, morphology, and severity of coronary plaques. Methods A retrospective analysis was conducted on patients aged 40–69 with T2DM, no history of atherosclerotic cardiovascular disease, and no severe target organ damage, who underwent CCTA at a tertiary care center. Clinical data, SCORE2-D values, and imaging results were collected. Patients were stratified into SCORE2-D risk categories, and coronary findings were compared across groups. Results The study included 104 patients (mean age 60.9 years; mean SCORE2-D 12.2 ± 4.9). Higher SCORE2-D scores were significantly associated with the presence of coronary plaques. In the low–moderate risk group, calcified and non-calcified plaques were similarly distributed, while in the high–very high risk group, non-calcified (lipid-rich and mixed) plaques predominated, indicating potentially more vulnerable lesions. Proximal coronary segments, especially the left anterior descending artery, were most frequently involved. A progressive increase in plaque burden and stenosis severity was observed with rising SCORE2-D risk category. Patients at higher risk were more often referred for invasive coronary angiography. Conclusions Higher SCORE2-D scores correlate with greater CAD burden, more severe stenosis, and a predominance of high-risk plaque features in patients with T2DM. These findings suggest that SCORE2-D may be a valuable tool in refining cardiovascular risk stratification and guiding clinical decision-making in diabetic populations. type 2 diabetes mellitus SCORE2-D coronary CT angiography coronary artery disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Type 2 diabetes mellitus (T2DM) remains a leading cause of morbidity and mortality worldwide, primarily through its association with atherosclerotic cardiovascular disease (ASCVD) [ 1 ]. Indeed, diabetics experiencing 2–4 times higher risk of events compared to non-diabetic individuals, and this risk escalates further with worsening glycemic control [ 2 , 3 ]. Contemporary guidelines recognize that not all patients with T2DM share the same risk profile. Patients with long-standing (> 10 years) T2DM or those with diabetes-related target organ damage (combined signs of nephropathy, retinopathy and neuropathy) have a cardiovascular risk approaching that of individuals with established coronary artery disease (CAD). Existing scores for baseline assessment of clinical likelihood of CAD incorporates symptoms at medical evaluation, age and number of risk factors [ 4 , 5 ]. However, dynamic factors like disease duration and glycemic status critically influence an individual diabetic patient’s risk of coronary events, yet traditional CAD pre-test probability models do not fully capture these evolving characteristics [ 6 ]. Risk prediction models such as ADVANCE and DIAL have been proposed to estimate cardiovascular disease (CVD) risk in individuals with diabetes. However, their utility in European populations is limited. These models do not adequately capture the variation in CVD risk across different countries, which may lead to under- or overestimation of individual risk. [ 7 , 8 , 9 ]. To address the need for more detailed risk assessment in diabetes, the European Society of Cardiology recently introduced the SCORE2-Diabetes (SCORE2-D) risk algorithm. SCORE2-D builds upon the conventional SCORE2 10-year cardiovascular risk model by integrating diabetes-specific variables such as age at T2DM diagnosis (duration of disease), glycated hemoglobin (a proxy for metabolic control) and estimated glomerular filtration rate (eGFR, a proxy for microangiopathy) in addition to the traditional risk factors [ 10 ]. By incorporating glycemic control and duration of disease, SCORE2-D aims to improve risk stratification for 10-year cardiovascular events in individuals with T2DM. The tool has been calibrated and validated on large cohorts, demonstrating improved discrimination for cardiovascular events compared to general-population risk models [ 11 ]. However, it is not yet known whether the SCORE2-Diabetes algorithm – beyond predicting long-term cardiovascular outcomes – can also help to estimate the pre-test probability of underlying CAD in patients with T2DM. Although coronary computed tomography angiography (CCTA) is an effective tool for ruling out obstructive CAD in patients with low to intermediate pre-test probability according to conventional risk models, these models may substantially underestimate cardiovascular risk in individuals with T2DM [ 12 , 13 ]. Many diabetic patients, despite being classified as low or intermediate risk by traditional clinical algorithms, harbor a high burden of cardiovascular risk when key disease-specific factors—such as long-standing hyperglycemia and prolonged T2DM duration—are taken into account. These parameters, which are often omitted in standard pre-test probability scores, are strongly associated with the presence and progression of subclinical atherosclerosis and long-term cardiovascular events [ 3 ]. Consequently, in this subset of diabetic patients, the likelihood of significant, obstructive CAD may be higher than expected, and they may derive greater diagnostic and prognostic value from an initial functional assessment (e.g., stress imaging) rather than an anatomical test. However, in addition to the degree and distribution of coronary stenosis, CCTA provides information regarding the characteristics and composition of the atherosclerotic plaque, features that have been shown to impact cardiovascular endpoints such as non-fatal infarction and death [ 14 ]. We hypothesized that the SCORE2-D assessment– although designed for 10-year event prediction – might identify the presence and extent of CAD on CCTA in T2DM patients. Rationale Given that SCORE2-D accounts for disease duration, glycemic control, and renal function—key drivers of atherosclerosis—its application may align more closely with CAD burden and phenotype on CCTA than traditional scores. Uncovering such associations would support integrating SCORE2-D into clinical workflows to improve patient selection for advanced diagnostic imaging and tailor preventive/treatment strategies. Materials and Methods We conducted a retrospective analysis of patients with T2DM who were referred for CCTA due to symptoms or clinical scenarios suggestive of stable CAD from June 2023 to December 2024 in a single center Tertiary Care Hospital. All patients included had clinical indications for CCTA based on current guidelines for the evaluation of stable chest pain or equivalent symptoms. Key inclusion criteria were and age 40–69 years, established T2DM, and no prior history of ASCVD. We excluded individuals with history of previous myocardial infarction, percutaneous coronary intervention, coronary bypass surgery or documented coronary plaques by a previous invasive or non-invasive angiography. The study was approved by a local ethics committee and conducted in compliance with the principles of the Declaration of Helsinki. Written informed consent was collected from all patients at the time of coronary computed tomography angiography. Coronary CT Angiography CCTA was performed using a dual source high pitch CT scanner (SOMATOM Definition Force, Siemens Healthineers, Germany). Patients were prepared with beta-blockers and sublingual nitroglycerin as needed to achieve optimal heart rate control and coronary vasodilation for imaging. Scans were acquired with ECG gating and dose modulation, with typical parameters of Kvp and mA automatically adjusted for body habitus (CARE dose). CT analysis of the coronary arteries, morphological assessment, and quantification of stenosis were performed by radiologists and cardiologists with extensive experience. The degree of stenosis was determined by comparing the area of the stenotic segment with an upstream and downstream segment free of atheroma. Plaque characterization was done using semi-automatic software. Fibrolipidic tissue was identified based on plaque component density. A density below 125 HU was considered fibrolipidic tissue, while above 125 HU was considered calcific tissue. Calcified plaques were defined as those where the calcific component made up at least 60% of the atheroma. If the calcific tissue component was less than 50% the plaque was defined as non-calcific plaque. Plaque with equal calcific and fibrolipidic component was considered as mixed. SCORE2-Diabetes The variables required to calculate the SCORE2-D risk score were extracted from the patients electronic medical records. Specifically, we collected clinical and laboratory data documented within a ± 90-days of the CCTA exam date. These data—obtained from standardized diagnostic reports archived in each patient's electronic health record—included glycated hemoglobin (HbA1c), serum creatinine (for eGFR estimation), lipid profile, blood pressure, smoking status, and age at diabetes diagnosis. The SCORE2-D 10-year cardiovascular risk percentage was then retrospectively calculated for each patient based on these parameters. From the patient's electronic medical record, it was also assessed whether the patient had undergone any additional functional diagnostic tests or coronary angiography and percutaneous or surgical revascularization post CCTA. Statistical Analysis Continuous variables are presented as mean ± standard deviation (SD) or median (interquartile range), depending on distribution. Categorical variables are shown as frequencies and percentages. Comparative differences across SCORE2-D risk categories (low, moderate, high, very high) were assessed using ANOVA or Kruskal–Wallis tests for continuous variables and chi-squared tests for categorical data. Associations between SCORE2-D and plaque presence, composition, and stenosis severity were evaluated using Spearman’s correlation and ordinal logistic regression. A two-sided p-value < 0.05 was considered statistically significant. Analyses were performed using SPSS v27. Results A total of 104 patients (mean age 60.7 ± 5.8 years; 69% male) without prior revascularization were included. Mean HbA1c was 7.1 ± 1.1% (55 mmol/mol), T2DM duration averaged 8 ± 9 years, and eGFR was 84.2 ± 20.6 mL/min. Active smoking was reported in 53%, and TOD was documented in 6%. Mean SCORE2-D score was 12.2 ± 4.9, with distribution: low risk 6 (6%), moderate 31 (30%), high 58 (54%), and very high 10 (10%) ( Tables 1 and 2 ). Table 1 Baseline Population Characteristics. Variable Value Age, years, mean, ±SD 60.7 ± 5.8 Female, n (%) 33_(31.%) Systolic blood pressure, mmHg, mean ± SD 135.8 ± 15.6 Hb1Ac, %, mean ± SD 7.1 ± 1.1 Diabetes duration, years, mean ± SD 8 ± 9 Total cholesterol, mg/dl, mean ± SD 159 ± 49 HDL, mg/dl, mean, ±SD 48 ± 13 Active smokers, n (%) 14 (13%) TOD, n (%) 6(5.7%) eGFR, ml/min, mean, ±SD 84.2 ± 20.6 SCORE 2 Diabetes (median [IQR]) 12.5 [5.3] ± 4.9 Low risk, n (%) 6 (6%) Moderate risk, n (%) 31 (30%) High risk, n (%) 58 (54%) Very high risk, n (%) 10 (10%) eGFR = estimated glomerular filtration rate; HbA1c = glycated hemoglobin; HDL = high density lipoprotein; IQR = interquartile range; SD = standard deviation; TOD = target organ damage. Table 2 Clinical characteristics across different risk classes. Parameter Low risk Moderate risk High risk Very high risk p-value Systolic blood pressure, mmHg, mean ± SD 112.5 ± 9.6 134.3 ± 11.2 134.5 ± 16.2 152.1 ± 22.5 < 0.0011 Total cholesterol, mg/dl, mean, ±SD 153.5 ± 21.1 165.8 ± 60.1 152.8 ± 43.8 161.2 ± 60.3 ns HDL cholesterol, mg/dl, mean, ±SD 54.2 ± 15.9 51.1 ± 15.0 45.0 ± 12.1 50.2 ± 11.6 ns HbA1c mmol/mol, mean ± SD 55.1 ± 8.8 49.2 ± 9.0 55.0 ± 12.8 57.1 ± 14.5 ns HbA1c, %, mean ± SD 7.2 ± 0.8 6.7 ± 0.8 7.2 ± 1.2 7.4 ± 1.3 ns eGFR, mL/min/1.73 m², mean ± SD 107.2 ± 17.9 88.8 ± 14.9 85.6 ± 17.4 68.2 ± 38.1 ns Diabetes duration, years, mean ± SD 2.2 ± 2.1 5.1 ± 4.9 8.1 ± 8.2 23.2 ± 12.3 < 0.0001 Active smokers, n (%) 5 (12.5%) 8 (20.5%) 15 (31%) 3 (25%) ns eGFR = estimated glomerular filtration rate; HbA1c = glycated hemoglobin; HDL = high density lipoprotein; SD = standard deviation; ns = non significative. Coronary plaques were detected in 83 patients (79%). Among them, the distribution by plaque type was: mixed plaques in 37.3%, lipid-rich plaques in 36.1%, and calcified plaques in 26.5%. The severity of coronary stenosis was distributed as follows: no detectable CAD in 21.0%, mild stenosis in 25.7%, moderate stenosis in 21.9%, and severe stenosis in 31.4% of patients ( Table 3 ). Table 3 Plaque presence and composition across different risk classes. Plaque Group (per patient) Low ( 20%) Total Calcific plaque, n 1 7 11 3 22 No CAD, n 2 12 8 0 22 Non-calcific plaque, n 3 12 38 7 61 Total 6 31 57 10 104 CAD = coronary artery disease Patients with coronary plaques exhibited significantly higher SCORE2-D values compared to those without plaques (13.4 ± 6.3% vs. 9.0 ± 3.7%; p < 0.01) ( Fig. 1 ). SCORE2-D values increased progressively with stenosis severity: 9.0 ± 3.7% in patients with no CAD, 13.0 ± 9.1% with mild stenosis, 13.0 ± 3.8% with moderate stenosis, and 14.1 ± 4.7% with severe stenosis (p < 0.019) ( Fig. 2 ). When stratifying patients by SCORE2-D risk categories, no significant difference in the distribution of calcified versus non-calcified plaques (combined soft and mixed) was observed in the low–moderate risk group (p = 0.456). In contrast, within the high–very high risk group, non-calcified plaques were significantly more frequent than calcified ones (p = 0.0045). This suggests a progressive increase in the incidence of non-calcified, potentially more vulnerable plaque phenotypes with increasing cardiovascular risk ( Fig. 3 ). The anatomical distribution of CAD varied notably according to SCORE2-D risk stratification. In the overall cohort, stenoses were most frequently observed in the proximal and mid segments of the left anterior descending (LAD) artery, as well as in the right coronary artery (RCA) and its branches. When stratifying patients by cardiovascular risk, those in the high and very high SCORE2-D risk categories exhibited a broader and more diffuse distribution of stenoses across multiple coronary territories, particularly involving the proximal LAD and proximal RCA. Conversely, in patients classified as low or moderate risk, coronary involvement was more limited and predominantly confined to the proximal LAD, with very low prevalence in other segments. These findings suggest a clear gradient of disease burden, with increasing anatomical complexity and extent of CAD corresponding to higher SCORE2-D risk classes ( Fig. 4 ) Based on SCORE2-D risk stratification, clinical management strategies differed accordingly: patients with high or very high SCORE2-D values were more frequently referred to invasive coronary angiography, whereas those at lower risk were typically managed conservatively with medical therapy, clinical follow-up, or non-invasive functional testing. (40.3% vs 5.6%; p = 0.00045) ( Fig. 5 ). Discussion This study shows a significant association between SCORE2-D values and the presence of coronary plaques, the severity of stenosis, and plaque type on CCTA. By including variables like glycemic exposure, kidney function, and age at T2DM onset, SCORE2-D shows alignment with the anatomical and pathological features of CAD compared to traditional risk models. People with T2DM have a two- to four-times higher risk of developing cardiovascular disease over their lifetime. This includes various conditions such as CAD, stroke, heart failure, atrial fibrillation, and peripheral artery disease (15,16,17). In patients with T2DM, the development of cardiovascular disease is often more multifaceted. Along with traditional risk factors, T2DM introduces additional mechanisms — such as elevated blood sugar, insulin resistance with excess insulin levels, chronic low-grade inflammation, and microvascular damage — that can further raise the risk of developing coronary heart disease (18). However, most of the currently validated models for assessing the risk of obstructive CAD do not take these variables into account and overlook important factors such as the patient’s glycemic control and the duration of T2DM, both of which have been shown to have a major impact on cardiovascular risk (19). In clinical practice, CAD remains the main cause of illness and death in this population. Moreover, T2DM not only increases cardiovascular risk but also influences management decisions, including whether or not to perform cardiovascular imaging tests (20). Large randomized trials did not show that routine screening with cardiovascular imaging tests reduces major cardiovascular events in asymptomatic diabetic patients. (21,22). However, these findings are influenced by the characteristics of the study population, which consisted of asymptomatic individuals with virtually no prior cardiovascular risk stratification. For this reason, current guidelines recommend a more selective approach, based on estimating pre-test probability, to decide who might benefit most from further imaging. To our knowledge, this is the first study to demonstrate the association between SCORE2-D and CAD in a population of diabetic patients referred for CCTA. In this context, our results suggest that SCORE2-D, thanks to its inclusion of diabetes-specific variables, may help improve risk estimation and better identify which diabetic patients could benefit from coronary imaging. We observed that patients with higher scores not only had more plaques and more severe stenosis but also had a greater proportion of non-calcific plaques (lipid-rich and mixed), which are considered more vulnerable and prone to acute events (23,24) These data may be useful for defining a more appropriate and tailored diagnostic-therapeutic strategy for each individual patient, helping to identify those who require a more intensive treatment approach and closer follow-up. Another interesting finding concerns the subsequent diagnostic pathway of the study population. In fact, most of the patients who underwent coronary angiography based on CT results or intermediate functional tests were individuals with high SCORE2-D values. Finally, an interesting finding concerns the distribution of CAD according to SCORE2-D levels. Patients with higher SCORE2-D values showed a greater incidence of multivessel involvement, including a higher prevalence of disease in more distal coronary segments. This observation is particularly relevant when considering the choice of the most appropriate diagnostic test, since CCTA, if not performed with the most advanced scanners, may have limitations in accurately assessing the true extent of CAD, especially in calcific disease and more distal segments (25). This suggests that the SCORE2-D score could potentially serve as a supportive tool for risk stratification, helping to guide the choice of the most appropriate non-invasive test, with high- or very-high-risk individuals possibly benefiting from functional testing as an initial approach. From a clinical point of view, these findings suggest that SCORE2-D could be used not only as a prognostic tool but also to help to decide who should undergo further tests, like CCTA or stress testing, particularly among patients with intermediate or high scores. This might allow earlier optimization of therapy — for example, starting statins, intensifying glycemic control, or considering antiplatelet therapy — while patients at lower risk could be managed conservatively, focusing on lifestyle changes. Limitations This study has limitations. It is a retrospective, single-center study with a relatively small sample size, so its generalizability is limited. Moreover, we did not collect longitudinal outcome data, so our conclusions are based only on anatomical findings. Moreover, the study population consists of diabetic patients with an indication for CCTA, and therefore patients with at most low-to-moderate pre-test probability, with evaluations often carried out at other centers as well. Consequently, a more accurate clinical stratification of patients, especially based on symptom assessment, would be ideal. Finally, plaque characterization was not performed using dedicated software for component quantification, but rather through manual quantitative analysis of each segment affected by atherosclerosis. Further research with larger, multi-center cohorts and prospective follow-up is needed to understand whether SCORE2-D-based strategies can truly improve outcomes and be cost-effective in clinical practice Conclusions In T2DM patients without prior revascularization, SCORE2-D score correlates significantly with coronary plaque presence, stenosis severity, and plaque phenotype by CCTA. These associations underscore its possible role in refining risk stratification and guiding imaging-based evaluation. Integration of SCORE2-D into clinical workflows may enable tailored diagnostic pathways, optimized follow-up, and personalized preventive strategies. Future prospective studies are warranted to validate these findings and assess their impact on clinical outcomes and resource utilization. The research leading to these results has received funding from the European Union – NextGenerationEU, through the Italian Ministry of University and Research, under PNRR – M4C2-I1.3 Project PE_00000019 “HEAL ITALIA”, Spoke 1, awarded to Massimo Federici. Rocco Mollace is the recipient of a fixed-term researcher position (Ricercatore a Tempo Determinato A) funded by PNRR – M4C2-I1.3 Project PE_00000019 “HEAL ITALIA”. This research was also co-funded by the Italian Complementary National Plan PNC-I.1 “Research initiatives for innovative technologies and pathways in the health and welfare sector” (D.D. 931 of 06/06/2022), “DARE – DigitAl lifelong pRevEntion” initiative, code PNC0000002, CUP: B53C22006470001, Spoke 3, awarded to Massimo Federici Declarations Author Contribution R.M. Conceptualization, Writing -original draft, review & editing.M.F. 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PMID: 38879449; PMCID: PMC11255726. Muhlestein JB, Lappé DL, Lima JAC, et al. Effect of Screening for Coronary Artery Disease Using CT Angiography on Mortality and Cardiac Events in High-Risk Patients With Diabetes: The FACTOR-64 Randomized Clinical Trial. JAMA. 2014;312(21):2234–2243. doi:10.1001/jama.2014.15825 Young LH, Wackers FJ, Chyun DA, Davey JA, Barrett EJ, Taillefer R, Heller GV, Iskandrian AE, Wittlin SD, Filipchuk N, Ratner RE, Inzucchi SE; DIAD Investigators. Cardiac outcomes after screening for asymptomatic coronary artery disease in patients with type 2 diabetes: the DIAD study: a randomized controlled trial. JAMA. 2009 Apr 15;301(15):1547-55. doi: 10.1001/jama.2009.476. PMID: 19366774; PMCID: PMC2895332. Fujimoto D, Kinoshita D, Suzuki K, Niida T, Yuki H, McNulty I, Lee H, Otake H, Shite J, Ferencik M, Dey D, Kakuta T, Jang IK. Relationship Between Calcified Plaque Burden, Vascular Inflammation, and Plaque Vulnerability in Patients With Coronary Atherosclerosis. JACC Cardiovasc Imaging. 2024 Oct;17(10):1214-1224. doi: 10.1016/j.jcmg.2024.07.013. Epub 2024 Sep 4. PMID: 39243232. Fujimoto D, Kinoshita D, Suzuki K, Niida T, Yuki H, McNulty I, Lee H, Otake H, Shite J, Ferencik M, Dey D, Alfonso F, Kakuta T, Jang IK. Coronary spotty calcification, compared with macro calcification, is associated with a higher level of vascular inflammation and plaque vulnerability in patients with stable angina. Atherosclerosis. 2025 Jun;405:119237. doi: 10.1016/j.atherosclerosis.2025.119237. Epub 2025 May 12. PMID: 40359877. Serruys PW, Kotoku N, Nørgaard BL, Garg S, Nieman K, Dweck MR, Bax JJ, Knuuti J, Narula J, Perera D, Taylor CA, Leipsic JA, Nicol ED, Piazza N, Schultz CJ, Kitagawa K, Bruyne B, Collet C, Tanaka K, Mushtaq S, Belmonte M, Dudek D, Zlahoda-Huzior A, Tu S, Wijns W, Sharif F, Budoff MJ, Mey J, Andreini D, Onuma Y. Computed tomographic angiography in coronary artery disease. EuroIntervention. 2023 Apr 3;18(16):e1307-e1327. doi: 10.4244/EIJ-D-22-00776. PMID: 37025086; PMCID: PMC10071125. Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.png Graphical abstract Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Revision requested 05 Sep, 2025 Reviews received at journal 04 Sep, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers invited by journal 11 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 11 Aug, 2025 First submitted to journal 07 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7321171","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499185705,"identity":"a416c317-6e72-461e-af18-9d6e8ea54481","order_by":0,"name":"Rocco 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University","correspondingAuthor":false,"prefix":"","firstName":"Giulio","middleName":"","lastName":"Stefanini","suffix":""},{"id":499185746,"identity":"eb5c753d-90c6-44f0-be24-8b8152bdd850","order_by":23,"name":"Erika Bertella","email":"","orcid":"","institution":"Humanitas Gavazzeni","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"","lastName":"Bertella","suffix":""},{"id":499185748,"identity":"dcd3ae6e-7f0d-489e-b87d-d793137ef960","order_by":24,"name":"Massimo Federici","email":"","orcid":"","institution":"University of Rome Tor Vergata","correspondingAuthor":false,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Federici","suffix":""}],"badges":[],"createdAt":"2025-08-07 17:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7321171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7321171/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-025-03000-3","type":"published","date":"2025-12-29T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89397729,"identity":"3911b001-a41e-44b6-a64c-79161bc9ed5e","added_by":"auto","created_at":"2025-08-19 13:49:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSCORE2-Diabetes values according to the presence or absence of coronary artery disease (CAD).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7321171/v1/e8a1f99813f9e8633fe14191.png"},{"id":89397730,"identity":"7fec854f-a222-4f2e-ac24-1ea1c063a8c2","added_by":"auto","created_at":"2025-08-19 13:49:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Stenosis Degree by SCORE2 Diabetes Risk Class.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7321171/v1/7214398b0ba4b136eadf25df.png"},{"id":89399115,"identity":"7eab0cdb-8cc0-41ed-a23a-b6cfaa862c83","added_by":"auto","created_at":"2025-08-19 13:57:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSCORE2-D Values According to Plaque Type (Calcific vs Non-calcific) in Low–Moderate and High–Very High Risk Groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7321171/v1/a7972a453fd1b68b50f76d64.png"},{"id":89397733,"identity":"146dcd71-ffcf-4550-bfa3-aaf62991070b","added_by":"auto","created_at":"2025-08-19 13:49:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution and\u003c/strong\u003e \u003cstrong\u003eprelevance of coronary artery disease across SCORE2-Diabetes risk categories. Panel A: entire study population; Panel B: high–very high SCORE2-D risk group; Panel C: low–moderate SCORE2-D risk group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLM= Left Main; LAD= Left Anterior Descending Artery; D1= First Diagonal Branch; D2= Second Diagonal Branch; IB= Intermediate Branch (Ramus Intermedius); CFX= Left Circumflex Artery; OM1= First Obtuse Marginal Branch; OM2= Second Obtuse Marginal Branch; RCA= Right Coronary Artery; PDA= Posterior Descending Artery; PLB= Posterolateral Branch; PROX= proximal segment; MED: medium segment; DIST; distal segment;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7321171/v1/8c9b45cb65c4b32280fa1877.png"},{"id":89397736,"identity":"20b0f16d-53da-4086-9564-57da8808904f","added_by":"auto","created_at":"2025-08-19 13:49:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between SCORE 2D and clinical approach based on CCTA results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eICA= invasive coronary angiography; N= No CAD; NO: Non-Obstructive CAD; M = Moderate stenosis; S = Severe Stenosis\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7321171/v1/896f6446b9c620115a7d2103.png"},{"id":99545273,"identity":"1147681a-aa35-4432-b0fb-383fdd79199a","added_by":"auto","created_at":"2026-01-05 16:05:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7321171/v1/f2ea1fc9-c307-44c1-8965-b15dcb4434b1.pdf"},{"id":89399114,"identity":"d5c7b486-b3a8-471b-ad98-8827ba3667f5","added_by":"auto","created_at":"2025-08-19 13:57:54","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":298568,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"Graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-7321171/v1/3970b482e24540a2864a3e40.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"SCORE2-Diabetes for Predicting Coronary Artery Disease: A Cardiac CT Study in a Diabetic Moderate-Risk Region Population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) remains a leading cause of morbidity and mortality worldwide, primarily through its association with atherosclerotic cardiovascular disease (ASCVD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Indeed, diabetics experiencing 2\u0026ndash;4 times higher risk of events compared to non-diabetic individuals, and this risk escalates further with worsening glycemic control [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Contemporary guidelines recognize that not all patients with T2DM share the same risk profile. Patients with long-standing (\u0026gt;\u0026thinsp;10 years) T2DM or those with diabetes-related target organ damage (combined signs of nephropathy, retinopathy and neuropathy) have a cardiovascular risk approaching that of individuals with established coronary artery disease (CAD). Existing scores for baseline assessment of clinical likelihood of CAD incorporates symptoms at medical evaluation, age and number of risk factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, dynamic factors like disease duration and glycemic status critically influence an individual diabetic patient\u0026rsquo;s risk of coronary events, yet traditional CAD pre-test probability models do not fully capture these evolving characteristics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Risk prediction models such as ADVANCE and DIAL have been proposed to estimate cardiovascular disease (CVD) risk in individuals with diabetes. However, their utility in European populations is limited. These models do not adequately capture the variation in CVD risk across different countries, which may lead to under- or overestimation of individual risk. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To address the need for more detailed risk assessment in diabetes, the European Society of Cardiology recently introduced the SCORE2-Diabetes (SCORE2-D) risk algorithm. SCORE2-D builds upon the conventional SCORE2 10-year cardiovascular risk model by integrating diabetes-specific variables such as age at T2DM diagnosis (duration of disease), glycated hemoglobin (a proxy for metabolic control) and estimated glomerular filtration rate (eGFR, a proxy for microangiopathy) in addition to the traditional risk factors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. By incorporating glycemic control and duration of disease, SCORE2-D aims to improve risk stratification for 10-year cardiovascular events in individuals with T2DM. The tool has been calibrated and validated on large cohorts, demonstrating improved discrimination for cardiovascular events compared to general-population risk models [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, it is not yet known whether the SCORE2-Diabetes algorithm \u0026ndash; beyond predicting long-term cardiovascular outcomes \u0026ndash; can also help to estimate the pre-test probability of underlying CAD in patients with T2DM.\u003c/p\u003e\u003cp\u003eAlthough coronary computed tomography angiography (CCTA) is an effective tool for ruling out obstructive CAD in patients with low to intermediate pre-test probability according to conventional risk models, these models may substantially underestimate cardiovascular risk in individuals with T2DM [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Many diabetic patients, despite being classified as low or intermediate risk by traditional clinical algorithms, harbor a high burden of cardiovascular risk when key disease-specific factors\u0026mdash;such as long-standing hyperglycemia and prolonged T2DM duration\u0026mdash;are taken into account. These parameters, which are often omitted in standard pre-test probability scores, are strongly associated with the presence and progression of subclinical atherosclerosis and long-term cardiovascular events [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, in this subset of diabetic patients, the likelihood of significant, obstructive CAD may be higher than expected, and they may derive greater diagnostic and prognostic value from an initial functional assessment (e.g., stress imaging) rather than an anatomical test. However, in addition to the degree and distribution of coronary stenosis, CCTA provides information regarding the characteristics and composition of the atherosclerotic plaque, features that have been shown to impact cardiovascular endpoints such as non-fatal infarction and death [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe hypothesized that the SCORE2-D assessment\u0026ndash; although designed for 10-year event prediction \u0026ndash; might identify the presence and extent of CAD on CCTA in T2DM patients.\u003c/p\u003e\n\u003ch3\u003eRationale\u003c/h3\u003e\n\u003cp\u003eGiven that SCORE2-D accounts for disease duration, glycemic control, and renal function\u0026mdash;key drivers of atherosclerosis\u0026mdash;its application may align more closely with CAD burden and phenotype on CCTA than traditional scores. Uncovering such associations would support integrating SCORE2-D into clinical workflows to improve patient selection for advanced diagnostic imaging and tailor preventive/treatment strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eWe conducted a retrospective analysis of patients with T2DM who were referred for CCTA due to symptoms or clinical scenarios suggestive of stable CAD from June 2023 to December 2024 in a single center Tertiary Care Hospital. All patients included had clinical indications for CCTA based on current guidelines for the evaluation of stable chest pain or equivalent symptoms. Key inclusion criteria were and age 40\u0026ndash;69 years, established T2DM, and no prior history of ASCVD. We excluded individuals with history of previous myocardial infarction, percutaneous coronary intervention, coronary bypass surgery or documented coronary plaques by a previous invasive or non-invasive angiography.\u003c/p\u003e\u003cp\u003e The study was approved by a local ethics committee and conducted in compliance with the principles of the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e Written informed consent was collected from all patients at the time of coronary computed tomography angiography.\u003c/p\u003e\n\u003ch3\u003eCoronary CT Angiography\u003c/h3\u003e\n\u003cp\u003eCCTA was performed using a dual source high pitch CT scanner (SOMATOM Definition Force, Siemens Healthineers, Germany). Patients were prepared with beta-blockers and sublingual nitroglycerin as needed to achieve optimal heart rate control and coronary vasodilation for imaging. Scans were acquired with ECG gating and dose modulation, with typical parameters of Kvp and mA automatically adjusted for body habitus (CARE dose). CT analysis of the coronary arteries, morphological assessment, and quantification of stenosis were performed by radiologists and cardiologists with extensive experience. The degree of stenosis was determined by comparing the area of the stenotic segment with an upstream and downstream segment free of atheroma. Plaque characterization was done using semi-automatic software. Fibrolipidic tissue was identified based on plaque component density. A density below 125 HU was considered fibrolipidic tissue, while above 125 HU was considered calcific tissue. Calcified plaques were defined as those where the calcific component made up at least 60% of the atheroma. If the calcific tissue component was less than 50% the plaque was defined as non-calcific plaque. Plaque with equal calcific and fibrolipidic component was considered as mixed.\u003c/p\u003e\n\u003ch3\u003eSCORE2-Diabetes\u003c/h3\u003e\n\u003cp\u003eThe variables required to calculate the SCORE2-D risk score were extracted from the patients electronic medical records. Specifically, we collected clinical and laboratory data documented within a\u0026thinsp;\u0026plusmn;\u0026thinsp;90-days of the CCTA exam date. These data\u0026mdash;obtained from standardized diagnostic reports archived in each patient's electronic health record\u0026mdash;included glycated hemoglobin (HbA1c), serum creatinine (for eGFR estimation), lipid profile, blood pressure, smoking status, and age at diabetes diagnosis. The SCORE2-D 10-year cardiovascular risk percentage was then retrospectively calculated for each patient based on these parameters. From the patient's electronic medical record, it was also assessed whether the patient had undergone any additional functional diagnostic tests or coronary angiography and percutaneous or surgical revascularization post CCTA.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range), depending on distribution. Categorical variables are shown as frequencies and percentages. Comparative differences across SCORE2-D risk categories (low, moderate, high, very high) were assessed using ANOVA or Kruskal\u0026ndash;Wallis tests for continuous variables and chi-squared tests for categorical data. Associations between SCORE2-D and plaque presence, composition, and stenosis severity were evaluated using Spearman\u0026rsquo;s correlation and ordinal logistic regression. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Analyses were performed using SPSS v27.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 104 patients (mean age 60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 years; 69% male) without prior revascularization were included. Mean HbA1c was 7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1% (55 mmol/mol), T2DM duration averaged 8\u0026thinsp;\u0026plusmn;\u0026thinsp;9 years, and eGFR was 84.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6 mL/min. Active smoking was reported in 53%, and TOD was documented in 6%. Mean SCORE2-D score was 12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9, with distribution: low risk 6 (6%), moderate 31 (30%), high 58 (54%), and very high 10 (10%) \u003cb\u003e(\u003c/b\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Population Characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years, mean, \u0026plusmn;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33_(31.%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure, mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb1Ac, %, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes duration, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol, mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e159\u0026thinsp;\u0026plusmn;\u0026thinsp;49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL, mg/dl, mean, \u0026plusmn;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive smokers, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (13%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6(5.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR, ml/min, mean, \u0026plusmn;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCORE 2 Diabetes (median [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.5 [5.3]\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow risk, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate risk, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (30%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh risk, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (54%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery high risk, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eeGFR\u0026thinsp;=\u0026thinsp;estimated glomerular filtration rate; HbA1c\u0026thinsp;=\u0026thinsp;glycated hemoglobin; HDL\u0026thinsp;=\u0026thinsp;high density lipoprotein; IQR\u0026thinsp;=\u0026thinsp;interquartile range; SD\u0026thinsp;=\u0026thinsp;standard deviation; TOD\u0026thinsp;=\u0026thinsp;target organ damage.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical characteristics across different risk classes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery high risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure, mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e152.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol, mg/dl, mean, \u0026plusmn;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165.8\u0026thinsp;\u0026plusmn;\u0026thinsp;60.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e152.8\u0026thinsp;\u0026plusmn;\u0026thinsp;43.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e161.2\u0026thinsp;\u0026plusmn;\u0026thinsp;60.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL cholesterol, mg/dl, mean, \u0026plusmn;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c mmol/mol, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c, %, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR, mL/min/1.73 m\u0026sup2;, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;38.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes duration, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive smokers, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eeGFR\u0026thinsp;=\u0026thinsp;estimated glomerular filtration rate; HbA1c\u0026thinsp;=\u0026thinsp;glycated hemoglobin; HDL\u0026thinsp;=\u0026thinsp;high density lipoprotein; SD\u0026thinsp;=\u0026thinsp;standard deviation; ns\u0026thinsp;=\u0026thinsp;non significative.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCoronary plaques were detected in 83 patients (79%). Among them, the distribution by plaque type was: mixed plaques in 37.3%, lipid-rich plaques in 36.1%, and calcified plaques in 26.5%. The severity of coronary stenosis was distributed as follows: no detectable CAD in 21.0%, mild stenosis in 25.7%, moderate stenosis in 21.9%, and severe stenosis in 31.4% of patients \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePlaque presence and composition across different risk classes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaque Group (per patient)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow (\u0026lt;\u0026thinsp;5%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate (5\u0026ndash;10%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh (10\u0026ndash;20%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVery high (\u0026gt;\u0026thinsp;20%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcific plaque, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo CAD, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-calcific plaque, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eCAD\u0026thinsp;=\u0026thinsp;coronary artery disease\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePatients with coronary plaques exhibited significantly higher SCORE2-D values compared to those without plaques (13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3% vs. 9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSCORE2-D values increased progressively with stenosis severity: 9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7% in patients with no CAD, 13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1% with mild stenosis, 13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8% with moderate stenosis, and 14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7% with severe stenosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.019) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen stratifying patients by SCORE2-D risk categories, no significant difference in the distribution of calcified versus non-calcified plaques (combined soft and mixed) was observed in the low\u0026ndash;moderate risk group (p\u0026thinsp;=\u0026thinsp;0.456). In contrast, within the high\u0026ndash;very high risk group, non-calcified plaques were significantly more frequent than calcified ones (p\u0026thinsp;=\u0026thinsp;0.0045). This suggests a progressive increase in the incidence of non-calcified, potentially more vulnerable plaque phenotypes with increasing cardiovascular risk \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe anatomical distribution of CAD varied notably according to SCORE2-D risk stratification. In the overall cohort, stenoses were most frequently observed in the proximal and mid segments of the left anterior descending (LAD) artery, as well as in the right coronary artery (RCA) and its branches. When stratifying patients by cardiovascular risk, those in the high and very high SCORE2-D risk categories exhibited a broader and more diffuse distribution of stenoses across multiple coronary territories, particularly involving the proximal LAD and proximal RCA. Conversely, in patients classified as low or moderate risk, coronary involvement was more limited and predominantly confined to the proximal LAD, with very low prevalence in other segments. These findings suggest a clear gradient of disease burden, with increasing anatomical complexity and extent of CAD corresponding to higher SCORE2-D risk classes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on SCORE2-D risk stratification, clinical management strategies differed accordingly: patients with high or very high SCORE2-D values were more frequently referred to invasive coronary angiography, whereas those at lower risk were typically managed conservatively with medical therapy, clinical follow-up, or non-invasive functional testing. (40.3% vs 5.6%; p\u0026thinsp;=\u0026thinsp;0.00045) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study shows a significant association between SCORE2-D values and the presence of coronary plaques, the severity of stenosis, and plaque type on CCTA. By including variables like glycemic exposure, kidney function, and age at T2DM onset, SCORE2-D shows alignment with the anatomical and pathological features of CAD compared to traditional risk models.\u003c/p\u003e\u003cp\u003ePeople with T2DM have a two- to four-times higher risk of developing cardiovascular disease over their lifetime. This includes various conditions such as CAD, stroke, heart failure, atrial fibrillation, and peripheral artery disease (15,16,17). In patients with T2DM, the development of cardiovascular disease is often more multifaceted. Along with traditional risk factors, T2DM introduces additional mechanisms \u0026mdash; such as elevated blood sugar, insulin resistance with excess insulin levels, chronic low-grade inflammation, and microvascular damage \u0026mdash; that can further raise the risk of developing coronary heart disease (18). However, most of the currently validated models for assessing the risk of obstructive CAD do not take these variables into account and overlook important factors such as the patient\u0026rsquo;s glycemic control and the duration of T2DM, both of which have been shown to have a major impact on cardiovascular risk (19). In clinical practice, CAD remains the main cause of illness and death in this population. Moreover, T2DM not only increases cardiovascular risk but also influences management decisions, including whether or not to perform cardiovascular imaging tests (20). Large randomized trials did not show that routine screening with cardiovascular imaging tests reduces major cardiovascular events in asymptomatic diabetic patients. (21,22). However, these findings are influenced by the characteristics of the study population, which consisted of asymptomatic individuals with virtually no prior cardiovascular risk stratification. For this reason, current guidelines recommend a more selective approach, based on estimating pre-test probability, to decide who might benefit most from further imaging.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is the first study to demonstrate the association between SCORE2-D and CAD in a population of diabetic patients referred for CCTA. In this context, our results suggest that SCORE2-D, thanks to its inclusion of diabetes-specific variables, may help improve risk estimation and better identify which diabetic patients could benefit from coronary imaging. We observed that patients with higher scores not only had more plaques and more severe stenosis but also had a greater proportion of non-calcific plaques (lipid-rich and mixed), which are considered more vulnerable and prone to acute events (23,24) These data may be useful for defining a more appropriate and tailored diagnostic-therapeutic strategy for each individual patient, helping to identify those who require a more intensive treatment approach and closer follow-up. Another interesting finding concerns the subsequent diagnostic pathway of the study population. In fact, most of the patients who underwent coronary angiography based on CT results or intermediate functional tests were individuals with high SCORE2-D values.\u003c/p\u003e\u003cp\u003eFinally, an interesting finding concerns the distribution of CAD according to SCORE2-D levels. Patients with higher SCORE2-D values showed a greater incidence of multivessel involvement, including a higher prevalence of disease in more distal coronary segments. This observation is particularly relevant when considering the choice of the most appropriate diagnostic test, since CCTA, if not performed with the most advanced scanners, may have limitations in accurately assessing the true extent of CAD, especially in calcific disease and more distal segments \u003cb\u003e(25).\u003c/b\u003e This suggests that the SCORE2-D score could potentially serve as a supportive tool for risk stratification, helping to guide the choice of the most appropriate non-invasive test, with high- or very-high-risk individuals possibly benefiting from functional testing as an initial approach.\u003c/p\u003e\u003cp\u003eFrom a clinical point of view, these findings suggest that SCORE2-D could be used not only as a prognostic tool but also to help to decide who should undergo further tests, like CCTA or stress testing, particularly among patients with intermediate or high scores. This might allow earlier optimization of therapy \u0026mdash; for example, starting statins, intensifying glycemic control, or considering antiplatelet therapy \u0026mdash; while patients at lower risk could be managed conservatively, focusing on lifestyle changes.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study has limitations. It is a retrospective, single-center study with a relatively small sample size, so its generalizability is limited. Moreover, we did not collect longitudinal outcome data, so our conclusions are based only on anatomical findings. Moreover, the study population consists of diabetic patients with an indication for CCTA, and therefore patients with at most low-to-moderate pre-test probability, with evaluations often carried out at other centers as well. Consequently, a more accurate clinical stratification of patients, especially based on symptom assessment, would be ideal. Finally, plaque characterization was not performed using dedicated software for component quantification, but rather through manual quantitative analysis of each segment affected by atherosclerosis. Further research with larger, multi-center cohorts and prospective follow-up is needed to understand whether SCORE2-D-based strategies can truly improve outcomes and be cost-effective in clinical practice\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn T2DM patients without prior revascularization, SCORE2-D score correlates significantly with coronary plaque presence, stenosis severity, and plaque phenotype by CCTA. These associations underscore its possible role in refining risk stratification and guiding imaging-based evaluation. Integration of SCORE2-D into clinical workflows may enable tailored diagnostic pathways, optimized follow-up, and personalized preventive strategies. Future prospective studies are warranted to validate these findings and assess their impact on clinical outcomes and resource utilization.\u003c/p\u003e\u003cp\u003eThe research leading to these results has received funding from the European Union \u0026ndash; NextGenerationEU, through the Italian Ministry of University and Research, under PNRR \u0026ndash; M4C2-I1.3 Project PE_00000019 \u0026ldquo;HEAL ITALIA\u0026rdquo;, Spoke 1, awarded to Massimo Federici. Rocco Mollace is the recipient of a fixed-term researcher position (Ricercatore a Tempo Determinato A) funded by PNRR \u0026ndash; M4C2-I1.3 Project PE_00000019 \u0026ldquo;HEAL ITALIA\u0026rdquo;.\u003c/p\u003e\u003cp\u003eThis research was also co-funded by the Italian Complementary National Plan PNC-I.1 \u0026ldquo;Research initiatives for innovative technologies and pathways in the health and welfare sector\u0026rdquo; (D.D. 931 of 06/06/2022), \u0026ldquo;DARE \u0026ndash; DigitAl lifelong pRevEntion\u0026rdquo; initiative, code PNC0000002, CUP: B53C22006470001, Spoke 3, awarded to Massimo Federici\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.M. Conceptualization, Writing -original draft, review \u0026amp; editing.M.F. Conceptualization \u0026amp; review M.A. and M.N review \u0026amp; editing, prepared figures.All authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang, B., Fu, Y., Tan, X. \u003cem\u003eet al.\u003c/em\u003e Assessing the impact of type 2 diabetes on mortality and life expectancy according to the number of risk factor targets achieved: an observational study. \u003cem\u003eBMC Med\u003c/em\u003e 22, 114 (2024). https://doi.org/10.1186/s12916-024-03343-w\u003c/li\u003e\n\u003cli\u003eRaghavan S, Vassy JL, Ho YL, Song RJ, Gagnon DR, Cho K, Wilson PWF, Phillips LS. Diabetes Mellitus-Related All-Cause and Cardiovascular Mortality in a National Cohort of Adults. J Am Heart Assoc. 2019 Feb 19;8(4):e011295. doi: 10.1161/JAHA.118.011295. PMID: 30776949; PMCID: PMC6405678.\u003c/li\u003e\n\u003cli\u003eChang-Sheng Sheng, Jingyan Tian, Ya Miao, Yi Cheng, Yulin Yang, Peter D. Reaven, Zachary T. Bloomgarden, Guang Ning; Prognostic Significance of Long-term HbA\u003csub\u003e1c\u003c/sub\u003e Variability for All-Cause Mortality in the ACCORD Trial. \u003cem\u003eDiabetes Care\u003c/em\u003e 1 June 2020; 43 (6): 1185\u0026ndash;1190. https://doi.org/10.2337/dc19-2589\u003c/li\u003e\n\u003cli\u003eChristiaan Vrints, Felicita Andreotti, Konstantinos C Koskinas, Xavier Rossello, Marianna Adamo, James Ainslie, Adrian Paul Banning, Andrzej Budaj, Ronny R Buechel, Giovanni Alfonso Chiariello, Alaide Chieffo, Ruxandra Maria Christodorescu, Christi Deaton, Torsten Doenst, Hywel W Jones, Vijay Kunadian, Julinda Mehilli, Milan Milojevic, Jan J Piek, Francesca Pugliese, Andrea Rubboli, Anne Grete Semb, Roxy Senior, Jurrien M ten Berg, Eric Van Belle, Emeline M Van Craenenbroeck, Rafael Vidal-Perez, Simon Winther, ESC Scientific Document Group , 2024 ESC Guidelines for the management of chronic coronary syndromes: Developed by the task force for the management of chronic coronary syndromes of the European Society of Cardiology (ESC) \u003cem\u003eEndorsed by the European Association for Cardio-Thoracic Surgery (EACTS)\u003c/em\u003e, \u003cem\u003eEuropean Heart Journal\u003c/em\u003e, Volume 45, Issue 36, 21 September 2024, Pages 3415\u0026ndash;3537, https://doi.org/10.1093/eurheartj/ehae177\u003c/li\u003e\n\u003cli\u003eJoseph JJ, Deedwania P, Acharya T, Aguilar D, Bhatt DL, Chyun DA, Di Palo KE, Golden SH, Sperling LS; American Heart Association Diabetes Committee of the Council on Lifestyle and Cardiometabolic Health; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Clinical Cardiology; and Council on Hypertension. Comprehensive Management of Cardiovascular Risk Factors for Adults With Type 2 Diabetes: A Scientific Statement From the American Heart Association. Circulation. 2022 Mar;145(9):e722-e759. doi: 10.1161/CIR.0000000000001040. Epub 2022 Jan 10. PMID: 35000404.\u003c/li\u003e\n\u003cli\u003eMader, A., Haeberli, D., Larcher, B. \u003cem\u003eet al.\u003c/em\u003e Contribution of type 2 diabetes to major adverse cardiovascular events (MACE) in a long-term observational study with different stages of atherosclerosis. \u003cem\u003eSci Rep\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 2792 (2025). https://doi.org/10.1038/s41598-024-84985-x\u003c/li\u003e\n\u003cli\u003eKengne AP, Patel A, Marre M, Travert F, Lievre M, Zoungas S, et al. Contemporary modelfor cardiovascular risk prediction in people with type 2 diabetes. Eur J Cardiovasc Prevent Rehabil 2011;18:393\u0026ndash;398. https://doi.org/10.1177/1741826710394270\u003c/li\u003e\n\u003cli\u003eBerkelmans GF, Gudbj\u0026ouml;rnsdottir S, Visseren FL, Wild SH, Franzen S, Chalmers J, et al. Prediction of individual life-years gained without cardiovascular events from lipid, blood pressure, glucose, and aspirin treatment based on data of more than 500 000 patients with type 2 diabetes mellitus. Eur Heart J 2019;40:2899\u0026ndash;2906. https://doi. org/10.1093/eurheartj/ehy839\u003c/li\u003e\n\u003cli\u003eVisseren FL, Mach F, Smulders YM, Carballo D, Koskinas KC, B\u0026auml;ck M, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contribution of the European Association of Preventive Cardiology (EAPC). 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Coronary CT angiography as a diagnostic and prognostic tool: perspective from a multicenter randomized controlled trial: PROMISE. \u003cem\u003eCurr Cardiol Rep\u003c/em\u003e 2016;18:40\u003c/li\u003e\n\u003cli\u003eJuhani Knuuti, Haitham Ballo, Luis Eduardo Juarez-Orozco, Antti Saraste, Philippe Kolh, Anne Wilhelmina Saskia Rutjes, Peter J\u0026uuml;ni, Stephan Windecker, Jeroen J Bax, William Wijns, The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability, \u003cem\u003eEuropean Heart Journal\u003c/em\u003e, Volume 39, Issue 35, 14 September 2018, Pages 3322\u0026ndash;3330, https://doi.org/10.1093/eurheartj/ehy267\u003c/li\u003e\n\u003cli\u003eWilliams MC, Moss AJ, Dweck M, Adamson PD, Alam S, Hunter A, et al. Coronary artery plaque characteristics associated with adverse outcomes in the SCOT-HEART study. J Am Coll Cardiol 2019;73:291\u0026ndash;301. 10.1016/j.jacc.2018.10.066 \u003c/li\u003e\n\u003cli\u003eEmerging Risk Factors Collaboration; Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, Stampfer M, Stehouwer CD, Lewington S, Pennells L, Thompson A, Sattar N, White IR, Ray KK, Danesh J. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010 Jun 26;375(9733):2215-22. doi: 10.1016/S0140-6736(10)60484-9. Erratum in: Lancet. 2010 Sep 18;376(9745):958. Hillage, H L [corrected to Hillege, H L]. PMID: 20609967; PMCID: PMC2904878.\u003c/li\u003e\n\u003cli\u003eChan JCN, Lim LL, Wareham NJ, Shaw JE, Orchard TJ, Zhang P, Lau ESH, Eliasson B, Kong APS, Ezzati M, Aguilar-Salinas CA, McGill M, Levitt NS, Ning G, So WY, Adams J, Bracco P, Forouhi NG, Gregory GA, Guo J, Hua X, Klatman EL, Magliano DJ, Ng BP, Ogilvie D, Panter J, Pavkov M, Shao H, Unwin N, White M, Wou C, Ma RCW, Schmidt MI, Ramachandran A, Seino Y, Bennett PH, Oldenburg B, Gagliardino JJ, Luk AOY, Clarke PM, Ogle GD, Davies MJ, Holman RR, Gregg EW. The Lancet Commission on diabetes: using data to transform diabetes care and patient lives. Lancet. 2021 Dec 19;396(10267):2019-2082. doi: 10.1016/S0140-6736(20)32374-6. Epub 2020 Nov 12. Erratum in: Lancet. 2021 Dec 19;396(10267):1978. doi: 10.1016/S0140-6736(20)32679-9. PMID: 33189186.\u003c/li\u003e\n\u003cli\u003eMartha Rosana, Em Yunir, Ninik Saragih, Lusiani Rusdi, Dyah Purnamasari, Tri Juli Edi Tarigan, Dicky Levenus Tahapary, Pradana Soewondo, Risk factors for peripheral arterial disease in type 2 diabetes mellitus patients: A systematic review and meta-analysis, Diabetes Research and Clinical Practice, Volume 224, 2025, 12170, ISSN 0168-8227, https://doi.org/10.1016/j.diabres.2025.112170.\u003c/li\u003e\n\u003cli\u003eZhao, M. et al. Associations of type 2 diabetes onset age with cardiovascular disease and mortality: The Kailuan study. \u003cem\u003eDiabetes Care\u003c/em\u003e\u003cstrong\u003e44\u003c/strong\u003e, 1426\u0026ndash;1432 (2021).\u003c/li\u003e\n\u003cli\u003eLi F-R, Yang H-L, Zhou R, et al. Diabetes duration and glycaemic control as predictors of cardiovascular disease and mortality. \u003cem\u003eDiabetes Obes Metab\u003c/em\u003e. 2021; 23: 1361\u0026ndash;1370. https://doi.org/10.1111/dom.14348\u003c/li\u003e\n\u003cli\u003eGami A, Blumenthal RS, McGuire DK, Sarkar S, Kohli P. New Perspectives in Management of Cardiovascular Risk Among People With Diabetes. J Am Heart Assoc. 2024 Jun 18;13(12):e034053. doi: 10.1161/JAHA.123.034053. Epub 2024 Jun 15. PMID: 38879449; PMCID: PMC11255726.\u003c/li\u003e\n\u003cli\u003eMuhlestein JB, Lapp\u0026eacute; DL, Lima JAC, et al. Effect of Screening for Coronary Artery Disease Using CT Angiography on Mortality and Cardiac Events in High-Risk Patients With Diabetes: The FACTOR-64 Randomized Clinical Trial. \u003cem\u003eJAMA.\u003c/em\u003e 2014;312(21):2234\u0026ndash;2243. doi:10.1001/jama.2014.15825\u003c/li\u003e\n\u003cli\u003eYoung LH, Wackers FJ, Chyun DA, Davey JA, Barrett EJ, Taillefer R, Heller GV, Iskandrian AE, Wittlin SD, Filipchuk N, Ratner RE, Inzucchi SE; DIAD Investigators. Cardiac outcomes after screening for asymptomatic coronary artery disease in patients with type 2 diabetes: the DIAD study: a randomized controlled trial. JAMA. 2009 Apr 15;301(15):1547-55. doi: 10.1001/jama.2009.476. PMID: 19366774; PMCID: PMC2895332.\u003c/li\u003e\n\u003cli\u003eFujimoto D, Kinoshita D, Suzuki K, Niida T, Yuki H, McNulty I, Lee H, Otake H, Shite J, Ferencik M, Dey D, Kakuta T, Jang IK. Relationship Between Calcified Plaque Burden, Vascular Inflammation, and Plaque Vulnerability in Patients With Coronary Atherosclerosis. JACC Cardiovasc Imaging. 2024 Oct;17(10):1214-1224. doi: 10.1016/j.jcmg.2024.07.013. Epub 2024 Sep 4. PMID: 39243232.\u003c/li\u003e\n\u003cli\u003eFujimoto D, Kinoshita D, Suzuki K, Niida T, Yuki H, McNulty I, Lee H, Otake H, Shite J, Ferencik M, Dey D, Alfonso F, Kakuta T, Jang IK. Coronary spotty calcification, compared with macro calcification, is associated with a higher level of vascular inflammation and plaque vulnerability in patients with stable angina. Atherosclerosis. 2025 Jun;405:119237. doi: 10.1016/j.atherosclerosis.2025.119237. Epub 2025 May 12. PMID: 40359877.\u003c/li\u003e\n\u003cli\u003eSerruys PW, Kotoku N, N\u0026oslash;rgaard BL, Garg S, Nieman K, Dweck MR, Bax JJ, Knuuti J, Narula J, Perera D, Taylor CA, Leipsic JA, Nicol ED, Piazza N, Schultz CJ, Kitagawa K, Bruyne B, Collet C, Tanaka K, Mushtaq S, Belmonte M, Dudek D, Zlahoda-Huzior A, Tu S, Wijns W, Sharif F, Budoff MJ, Mey J, Andreini D, Onuma Y. Computed tomographic angiography in coronary artery disease. EuroIntervention. 2023 Apr 3;18(16):e1307-e1327. doi: 10.4244/EIJ-D-22-00776. PMID: 37025086; PMCID: PMC10071125.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"type 2 diabetes mellitus, SCORE2-D, coronary CT angiography, coronary artery disease","lastPublishedDoi":"10.21203/rs.3.rs-7321171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7321171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrent approaches to estimating the probability of coronary artery disease (CAD) in patients with type 2 diabetes mellitus (T2DM) often fail to reflect the clinical complexity of the condition, as they tend to oversimplify it by neglecting its progressive nature, variability in glycemic control, and the influence of disease duration. The SCORE2-Diabetes (SCORE2-D) model was developed to offer a more nuanced cardiovascular risk estimate by incorporating continuous variables and individualized risk factor weighting. However, its correlation with the actual presence and severity of CAD in diabetic patients remains under-investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to evaluate the association between SCORE2-D scores and CAD characteristics, as assessed by computed tomography coronary angiography (CCTA), in patients with T2DM and no prior coronary revascularization. Specifically, it investigates the relationship between SCORE2-D risk categories and the presence, morphology, and severity of coronary plaques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective analysis was conducted on patients aged 40–69 with T2DM, no history of atherosclerotic cardiovascular disease, and no severe target organ damage, who underwent CCTA at a tertiary care center. Clinical data, SCORE2-D values, and imaging results were collected. Patients were stratified into SCORE2-D risk categories, and coronary findings were compared across groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 104 patients (mean age 60.9 years; mean SCORE2-D 12.2 ± 4.9). Higher SCORE2-D scores were significantly associated with the presence of coronary plaques. In the low–moderate risk group, calcified and non-calcified plaques were similarly distributed, while in the high–very high risk group, non-calcified (lipid-rich and mixed) plaques predominated, indicating potentially more vulnerable lesions. Proximal coronary segments, especially the left anterior descending artery, were most frequently involved. A progressive increase in plaque burden and stenosis severity was observed with rising SCORE2-D risk category. Patients at higher risk were more often referred for invasive coronary angiography.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigher SCORE2-D scores correlate with greater CAD burden, more severe stenosis, and a predominance of high-risk plaque features in patients with T2DM. These findings suggest that SCORE2-D may be a valuable tool in refining cardiovascular risk stratification and guiding clinical decision-making in diabetic populations.\u003c/p\u003e","manuscriptTitle":"SCORE2-Diabetes for Predicting Coronary Artery Disease: A Cardiac CT Study in a Diabetic Moderate-Risk Region Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 13:49:50","doi":"10.21203/rs.3.rs-7321171/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-05T17:49:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T11:49:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T17:32:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151639830960246494112472608743651012092","date":"2025-08-12T06:31:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339330168099805659968639697101103349719","date":"2025-08-11T17:25:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T17:17:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T10:22:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T07:08:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2025-08-07T17:46:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9cbd9567-c894-45b3-b1f5-700f37da362e","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:00:00+00:00","versionOfRecord":{"articleIdentity":"rs-7321171","link":"https://doi.org/10.1186/s12933-025-03000-3","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2025-12-29 15:57:29","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-08-19 13:49:50","video":"","vorDoi":"10.1186/s12933-025-03000-3","vorDoiUrl":"https://doi.org/10.1186/s12933-025-03000-3","workflowStages":[]},"version":"v1","identity":"rs-7321171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7321171","identity":"rs-7321171","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}