Diabetic Retinopathy Enhances Coronary Artery Calcium-Based Cardiovascular Risk Stratification in Patients with Type 2 Diabetes: Insights from the ACCoDiab Study

Diabetic Retinopathy Enhances Coronary Artery Calcium-Based Cardiovascular Risk Stratification in Patients with Type 2 Diabetes: Insights from the ACCoDiab Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diabetic Retinopathy Enhances Coronary Artery Calcium-Based Cardiovascular Risk Stratification in Patients with Type 2 Diabetes: Insights from the ACCoDiab Study Franck Phan, Fabrizio Andreelli, Thomas Broussaud, Samia Boussouar, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7925795/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The Coronary artery calcium (CAC) score is a validated imaging biomarker that is used in clinical practice to improve cardiovascular risk stratification in patients with type 2 diabetes (T2DM). However, residual risk remains, particularly in patients with moderate CAC. Diabetic retinopathy (DR), a microvascular complication of diabetes, may reflect systemic vascular vulnerability and provide additional prognostic information. Methods This observational cohort study was conducted as part of the primary prevention ACCoDiab study. Four hundred and twenty-four patients with T2DM and no prior history of cardiovascular events underwent CAC scoring and clinical assessment, including screening for DR. The study population comprised 175 females and 249 males, with an average age of 60.9 years. Patients were monitored for seven years for cardiovascular events, including nonfatal myocardial infarction, ischemic stroke, hospitalization for heart failure, revascularization of the limbs due to peripheral artery disease and cardiovascular death. Cox regression, Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and net reclassification improvement (NRI) were employed to evaluate the prognostic value of DR alongside CAC score with the aim of developing a simple cardiovascular risk score. Results Fifty patients (11.8%) experienced cardiovascular events. DR was significantly more prevalent among those who experienced events (58% vs. 25%, p < 0.001). Both CAC score and DR were independently associated with cardiovascular events. Combining the CAC and DR models significantly improved the prediction of events over the CAC model alone (AUC 75.3 vs. 67.8, p = 0.011), with even further improvement when adjusted for traditional risk factors (AUC 78.2, vs 71.3 p = 0.014 vs. the CAC model alone). The presence of DR reclassified a significant proportion of patients into higher risk categories, particularly among those with moderate CAC scores. Conclusion The combination of DR and CAC score significantly enhances cardiovascular risk stratification in patients living with T2DM, even after adjustment for traditional risk factors. Trial registration NCT03920683 diabetic retinopathy coronary artery calcium cardiovascular risk type 2 diabetes risk stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 Research Insights What is currently known about this topic? In patients with type 2 diabetes and moderate Coronary Artery Calcium (CAC) score, residual cardiovascular risk remains. Improving cardiovascular risk stratification in patients with type 2 diabetes is needed. What is the key research question? Could diabetic retinopathy improve the prognostic performance of the CAC score in predicting cardiovascular events in patients with type 2 diabetes? What is new? The combination of diabetic retinopathy and CAC score significantly enhances cardiovascular risk stratification in patients living with type 2 diabetes, even after adjustment for traditional risk factors. How might this study influence clinical practice? Our results support the inclusion of diabetic retinopathy in clinical algorithms and risk calculators to improve cardiovascular risk stratification in patients with type 2 diabetes. Introduction Cardiovascular (CV) disease remains the leading cause of morbidity and mortality in individuals with type 2 diabetes (T2DM), yet identifying those most at risk remains a clinical challenge. Indeed, studies including patients with T2DM have reported a 10-year occurrence of major cardiovascular events (MACE) ranging from 6% (indicating a moderate CV risk) to 34% (reflecting a very high CV risk)( 1 – 6 ). Therefore, identifying patients at the greatest risk of developing cardiovascular (CV) events early on is a key objective in preventing CV complications in patients living with T2DM. Measuring the coronary artery calcium (CAC) score plays a key role in reclassifying cardiovascular (CV) risk, especially in patients whose clinical risk assessment remains uncertain( 7 – 11 ). This is because the CAC score directly quantifies subclinical atherosclerosis. The CAC score provides personalized risk assessment and informs treatment decisions, ranging from lifestyle modifications to more intensive pharmacological interventions, such as initiating aspirin or statin therapies as well as determining LDL cholesterol targets, and identifying patients requiring myocardial ischaemia screening( 10 ). However, although higher CAC scores are clearly associated with an increased risk of MACE( 12 , 13 ), patients with moderate or even low CAC scores can still experience CV events( 13 ). Consequently, there is an increasing demand for improved CV risk assessment tools. CAC alone may fail to capture residual CV risk, particularly in patients with low-to-moderate scores and macrovascular disease. There is growing evidence that microangiopathy is an independent risk factor for CV disease in patients with diabetes. Diabetic retinopathy (DR) is a marker of prolonged exposure to chronic hyperglycaemia, leading to microvascular damage and systemic vascular injury. This suggests that DR and macrovascular complications may share underlying mechanisms, such as inflammation( 14 – 16 ), oxidative stress and endothelial dysfunction( 17 – 20 ). Beyond its ophthalmological significance, DR is increasingly recognized as a marker of systemic vascular injury and predictor of adverse CV outcomes. Several studies have demonstrated an independent association between DR and coronary artery disease( 21 – 25 ), congestive heart failure( 26 , 27 ), stroke( 24 , 28 ), and peripheral arterial disease( 29 – 31 ). Given their distinct pathological origins, i.e. macrovascular calcification (CAC score) and microvascular injury (DR), the combined assessment of CAC and DR could enhance CV risk prediction. DR is non-invasive, inexpensive, and routinely performed in diabetes care, making it a practical addition to CAC-based risk models. We hypothesized that DR could improve the prognostic performance of the CAC score in predicting CV events in T2DM patients. We therefore evaluated whether incorporating DR into CAC-based models improves cardiovascular risk discrimination and reclassification, thereby enabling the development of more individualized cardiovascular prevention strategies. Methods Study Design and Population ACCoDiab is a cross-sectional monocentric study with retrospective data collection (ClinicalTrials.gov identifier NCT03920683). The recruitment period extended from January 2014 to May 2017. This study is an ancillary analysis of the ACCoDiab study and included 424 patients with T2DM, enrolled during routine hospital admissions for CV risk assessment at Pitié-Salpêtrière Hospital. Inclusion criteria were: age ≥ 18 years, confirmed T2DM diagnosis, no previous history of CV events, and available computed tomography CAC score and DR screening. All participants provided written informed consent. The study was approved by the institutional ethics committee. Cardiovascular Outcomes The primary outcome was the occurrence of a CV event over a 7-year follow-up period (from January 1st, 2014 to December 31, 2023). Events included nonfatal myocardial infarction, ischemic stroke, hospitalization for heart failure, limb revascularization due to peripheral artery disease, or CV death. Data were retrieved from centralized hospital records and verified via direct contact or national mortality registries (INSEE). Coronary Artery Calcium Scoring All patients with T2DM were evaluated as part of routine clinical care for cardiovascular risk assessment and prevention during scheduled outpatient visits at the Diabetes day-hospital of the Pitié-Salpêtrière Sorbonne University Hospital. CAC assessment was performed as part of standard care in the Department of Cardiovascular and Thoracic Imaging. All patients underwent non-contrast electrocardiogram (ECG)-gated computed tomography (CT) for CAC scoring. Scans were acquired using either a dual-source SOMATOM Definition Flash (majority) or a single-source SOMATOM Edge CT scanner (Siemens Healthineers, Erlangen, Germany). Image acquisition was performed using a prospectively ECG-triggered sequential mode, targeting mid-diastole, without contrast injection. The scan extended from the top of the aortic arch to the cardiac apex, using the following parameters: 120 kVp or equivalent tube voltage, modulated mAs, 3 mm slice thickness. CAC scoring was conducted using the Agatston method. A calcified atherosclerotic lesion was defined as a contiguous pixel area ≥ 1 mm² with a peak attenuation ≥ 130 Hounsfield units (HU). The calcium score per lesion was calculated by multiplying the lesion area by a density weighting factor based on peak HU: 1 for 130–199 HU; 2 for 200–299 HU; 3 for 300–399 HU; 4 for ≥ 400 HU. Total CAC scores were categorized into four strata, based on established risk Agatston units (AU) thresholds: 0 (no detectable calcification), 1–99 (mild), 100–399 (moderate), ≥ 400 AU (severe). Retinopathy Assessment DR was assessed at baseline using standardized digital fundus photography, as part of routine diabetes care. Retinal images were evaluated by trained ophthalmologists in accordance with established international grading criteria, namely the International Clinical Diabetic Retinopathy Disease Severity Scale (ICDR). This classification system defines five stages: no retinopathy, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). For the purposes of statistical analysis in this study, DR was analyzed as a binary variable: present versus absent. Any level of retinopathy (from mild NPDR to PDR) was considered 'present,' while 'absent' corresponded to no visible signs of retinopathy. This binary stratification facilitated clear risk comparisons. Baseline Clinical and Laboratory Variables Baseline variables were selected based on their established relevance in cardiovascular risk prediction models, particularly those outlined in the 2023 European Society of Cardiology (ESC) guidelines on diabetes and cardiovascular disease( 32 ). The variables reported included: Demographic and clinical parameters: age, gender, body mass index (BMI), and duration of diabetes, current smoking status, hypertension. Lipid profile: triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), total cholesterol and low-density lipoprotein cholesterol (LDL-C). Renal function: estimated glomerular filtration rate (eGFR, calculated using the CKD-EPI equation) and albuminuria measured as urinary albumin-to-creatinine ratio (UACR). Nephropathy was defined as either eGFR 30 mg/mmol. Glycemic control and inflammation: glycated hemoglobin (HbA1c) and high-sensitivity C-reactive protein (hs-CRP). Medication use: antihypertensives, statins, and glucose-lowering therapies. These variables were collected systematically at baseline during routine clinical care. Statistical Analysis Categorical variables were compared using chi-squared tests; continuous variables using Mann-Whitney U tests. Kaplan-Meier survival curves and log-rank tests evaluated time-to-event differences across CAC and DR categories. Then, Multivariable Cox proportional hazards models assessed the independent associations of CAC score and DR with CV events. Four models were compared: CAC only, CAC + DR, adjusted CAC, and adjusted CAC + DR. The multivariable adjustment model included age, gender, smoking status, hypertension, dyslipidemia, and nephropathy. Assumptions of Cox regression modeling were checked afterwards for all models with assessment of residuals by applying Schoenfeld tests. We performed a Receiver operating characteristic (ROC) analysis to estimate discriminatory ability of prediction models. To do so, we applied Liu’s criteria which consists in maximizing the product of sensitivity and specificity. As compared to usual Youden criteria, Liu criteria favors more balanced performances with respect to both sensitivity and specificity. Pairwise AUC comparisons were performed with DeLong test or stratified bootstrapping for respectively nested and non-nested prediction models. We also assessed sensitivity, specificity, positive and negative predictive values and also accuracy of models. Net reclassification improvement (NRI) analysis quantified the change in risk classification between models. A two-sided p-value < 0.05 was considered statistically significant. Finally, we provided a simple risk-scoring tool combining Calcium scoring with DR and smoking status to ease patient management in routine clinical setting. Results Baseline Characteristics and Event rates The study population comprised 424 patients (175 females and 249 males) with T2DM and a mean age of 60.9 years. All patients were followed for 7 years or until the occurrence of a first cardiovascular event. During follow-up, 50 patients (11.8%) experienced a first CV event, including non-fatal myocardial infarction (n = 32), ischemic stroke (n = 10), hospitalization for heart failure (n = 5), and lower-limb revascularization (n = 3). No CV deaths were recorded. The median follow-up duration for patients who experienced an event was 1,406 days [IQR: 566–1,876]. As shown in Table 1 , the baseline demographic and clinical characteristics of patients with and without CV events were similar. There were no significant differences in age (63 vs. 61 years, p = 0.10), gender distribution (34% vs. 42% female, p = 0.30), duration of diabetes or BMI. However, active smoking was significantly more prevalent among patients who experienced an event (30% vs. 18%, p = 0.042). Table 1 Baseline demographic, clinical, biochemical, treatment, and imaging characteristics of the study population stratified by occurrence of cardiovascular events. Overall N = 424 No event N = 374 Event N = 50 p-value Characteristics Gender: Female 175 (41%) 158 (42%) 17 (34%) 0.3 Age (years) 61 (54–67) 61 (54–67) 63 (59–69) 0.10 Duration of diabetes (years) 13 ( 7 – 20 ) 13 ( 6 – 20 ) 12 ( 8 – 19 ) 0.7 Weight (kg) 82 (72-92.5) 82 (72–93) 83.5 (73–90) > 0.9 Height (cm) 169.5 (162–175) 169.1 (162–175) 170.2 (156–174) 0.6 BMI (kg/m 2 ) 28.7 (25.5–32) 28.7 (25.4–32) 28.7 (25.6–32.5) 0.6 Hypertension 285 (67%) 248 (66%) 37 (74%) 0.3 Dyslipidemia 306 (72%) 269 (72%) 37 (74%) 0.8 Smoker 82 (19%) 67 (18%) 15 (30%) 0.042 Tobacco 0.066 Never 227 (54%) 207 (55%) 20 (40%) Former 115 (27%) 100 (27%) 15 (30%) Active 82 (19%) 67 (18%) 15 (30%) Complications of Diabetes Nephropathy (eGFR 30 mg/mmol) 41 (9.7%) 34 (9.1%) 7 (14%) 0.3 eGFR (mL/min/1.73m 2 ) 89 (74.5–106) 89 (74–106) 90.5 (79–104) 0.7 Retinopathy 124 (29%) 95 (25%) 29 (58%) < 0.001 Biology HbA1c (%) 7.5 (6.75–8.25) 7.5 (6.8–8.3) 7.2 (6.5–7.9) 0.056 HDL-cholesterol (mmol/L) 1.16 (0.96–1.42) 1.16 (0.96–1.42) 1.18 (0.91–1.34) 0.7 LDL-cholesterol (mmol/L) 2.38 (1.86–2.92) 2.38 (1.86–2.92) 2.20 (1.71–2.97) 0.7 Total cholesterol (mmol/L) 4.40 (3.75–5.07) 4.40 (3.76–5.06) 4.38 (3.74–5.20) 0.8 Triglycerides (mmol/L) 1.38 (0.99–2.11) 1.37 (0.98–2.11) 1.65 (1.02–2.43) 0.3 C-reactive protein (mg/L) 1.86 (0.98–3.46) 1.85 (0.98–3.48) 2.00 (0.98-3) > 0.9 Treatment Insulin 174 (41%) 153 (41%) 21 (42%) 0.9 Metformin 358 (84%) 319 (85%) 39 (78%) 0.2 Sulfonylureas 200 (47%) 177 (47%) 23 (46%) 0.9 Glinides 13 (3.1%) 12 (3.2%) 1 (2.0%) > 0.9 Gliptines 115 (27%) 104 (28%) 11 (22%) 0.4 GLP1 Receptor Agonists 49 (12%) 40 (11%) 9 (18%) 0.13 Statins 251 (59%) 224 (60%) 27 (54%) 0.4 Ezetimibe 23 (5.4%) 18 (4.8%) 5 (10%) 0.2 Fibrates 21 (5.0%) 20 (5.3%) 1 (2.0%) 0.5 ACE inhibitors 109 (26%) 98 (26%) 11 (22%) 0.5 ARB 157 (37%) 133 (36%) 24 (48%) 0.09 Betablocker 46 (11%) 42 (11%) 4 (8.0%) 0.5 Thiazide diuretics 106 (25%) 93 (25%) 13 (26%) 0.9 Calcium channel inhibitors 95 (22%) 82 (22%) 13 (26%) 0.5 Computed Tomography Agatston score (AU) 38.85 (0-222.8) 33.2 (0.00-186.3) 221.7 (19.8-784.7) < 0.001 CAC Class < 0.001 0 130 (31%) 125 (33%) 5 (10%) 1–99 135 (32%) 120 (32%) 15 (30%) 100–399 84 (20%) 72 (19%) 12 (24%) ≥ 400 75 (18%) 57 (15%) 18 (36%) Overall N = 424 No event N = 374 Event N = 50 p-value Cardiovascular Events First Cardiovascular Event < 0.001 Myocardial infarction 32 (7.5%) 0 (0%) 32 (64%) Heart Failure 5 (1.2%) 0 (0%) 5 (10%) Ischaemic Stroke 10 (2.3%) 0 (0%) 10 (20%) Obliterating arteriopathy of the lower limbs 3 (0.7%) 0 (0%) 3 (6.0%) Cardiovascular Death 0 (0%) 0 (0%) 0 (0%) Follow-up (days) 2,556 (2,556-2,556) 2,556 (2,556-2,556) 1,406 (566-1,876) Categorical variables were compared using Fisher's exact test or Pearson's Chi-squared test ; continuous variables using Wilcoxon rank sum test. Values are presented as median (IQR) or number (percentage). Comparisons between patients with and without cardiovascular events are reported along with p-values. BMI: Body Mass Index, eGFR: estimated Glomerular Filtration Rate, UACR: Urinary Albumin Creatinin Ratio, ACE: Angiotensin-Converting Enzyme, ARB: Angiotensin Receptor Blockers, CAC: Coronary Artery Calcium Notably, DR was significantly more prevalent among patients who developed CV events (58% vs. 25%, p < 0.001), and CAC scores were markedly higher in this group (median Agatston score: 221.7 vs. 33.2, p < 0.001). The proportion of patients with CAC = 0 was 33% in the control group versus 10% in the event group whereas CAC ≥ 400 was present in 36% of patients in the event group versus only 15% of patients without events. Intermediate CAC categories (1–99 and 100–399) were similarly distributed between the two groups. These findings suggest a marked difference towards more advanced coronary calcification in the event group but with the intermediate CAC categories (1–99 and 100–399) not clearly distinguishing between individuals with and without events. Cox Regression Confirms Independent and Additive Value of DR Multivariable Cox regression models (Table 2 ) confirmed that CAC score and DR were both independently associated with CV outcomes. In the unadjusted CAC model, hazard ratios (HRs) increased progressively with CAC burden vs. having no coronary artery calcium (CAC = 0): HR 2.96 [1.08–8.15] for CAC 1–99, HR 3.97 [1.40–11.27] for CAC 100–399, and HR 7.13 [2.65–19.20] for CAC ≥ 400 (p < 0.001 for trend). Table 2 Cox proportional hazards models for cardiovascular events: stepwise inclusion of Coronary Artery Calcium (CAC), Diabetic Retinopathy (DR) and classical cardiovascular risk factors. CAC CAC + DR Adj. CAC Adj. CAC + DR Variables HR 95% CI p-value HR 95% CI p-value HR 95% CI p-value HR 95% CI p-value CAC < 0.001 0.008 0.003 0.013 1–99 2.96 1.08–8.15 0.036 3.04 1.10–8.36 0.032 2.99 1.07–8.34 0.036 3.02 1.08–8.41 0.035 100–399 3.97 1.40-11.27 0.010 3.55 1.25–10.10 0.017 3.70 1.25–10.99 0.019 3.46 1.16–10.36 0.026 ≥ 400 7.13 2.65–19.20 < 0.001 5.59 2.06–15.18 < 0.001 6.92 2.42–19.75 < 0.001 5.67 1.98–16.23 0.001 Retinopathy 3.11 1.75–5.52 < 0.001 3.56 1.96–6.45 < 0.001 Age (years) 1.00 0.97–1.04 0.800 1.01 0.98–1.05 0.420 Gender : Female 0.96 0.52–1.79 0.903 1.03 0.55–1.93 0.938 Duration of diabetes (years) 0.99 0.96–1.03 0.737 0.98 0.95–1.01 0.185 Hypertension 1.31 0.66–2.58 0.437 1.19 0.61–2.34 0.614 Smoker 2.06 1.08–3.95 0.029 2.34 1.22–4.51 0.011 Dyslipidemia 0.87 0.45–1.68 0.672 0.91 0.47–1.75 0.770 Nephropathy ( eGFR 30 mg/mmol) 1.58 0.70–3.58 0.276 1.34 0.58–3.07 0.494 Hazard ratios (HR), 95% confidence intervals (CI), and p-values are shown for each model, CAC score alone and CAC score + Diabetic Retinopathy, adjusted or not. Each model assesses the independent and additive predictive value of each variable. Adding DR to the model (CAC + DR) revealed that DR was independently associated with CV events (HR 3.11 [1.75–5.52] when DR present, p < 0.001). In the fully adjusted model (adjusted CAC + DR), DR remained a strong and significant predictor (HR 3.56 [1.96–6.45], p < 0.001), as did CAC. Active smoking also emerged as an independent risk factor (HR 2.34 [1.22–4.51], p = 0.011), while age, hypertension, and dyslipidemia were not significant predictors of events. The Kaplan-Meier survival curves highlight the progressive prognostic value of CAC scores and diabetic retinopathy in predicting adverse cardiovascular outcomes First, we used the Kaplan–Meier method to assess CV event-free survival by CAC score category. Figure 1 A illustrates the progressive reduction in survival associated with an increasing CAC burden. The 7-year event-free survival rate decreased progressively across CAC categories. Patients with CAC score of 0 had the most favorable prognosis, while those with CAC score of ≥ 400 experienced the highest event rate over time. These differences remained statistically significant after adjusting for age, gender, hypertension, dyslipidemia, active smoking, and nephropathy (Fig. 1 B), confirming CAC as a robust and independent prognostic biomarker. To evaluate whether DR further improves risk stratification, we performed stratified event-free survival analyses that were stratified by CAC and DR status. Figure 2 A shows the unadjusted event-free survival curves for the eight subgroups defined by the combinations of CAC strata and DR presence. Patients with both CAC ≥ 400 and DR exhibited significantly poorer event-free survival, with the most pronounced decline in event-free probability. Interestingly, patients with CAC = 100–399 and DR exhibited event-free survival curves that closely resembled those of the CAC ≥ 400 group without DR. This suggests that DR effectively "uprisks" patients with moderate CAC categorizing them as high risk. Conversely, patients with CAC 100–399 and no DR demonstrated substantially better event-free survival, resembling the CAC 1–99 group more closely. Similarly, among patients with CAC = 0, the presence of DR substantially reduced event-free survival and brought their risk closer to that of patients with CAC 1–99 without DR. This illustrates the impact of DR even in patients with minimal coronary calcification. After adjusting for traditional cardiovascular risk factors, the same pattern persisted (Fig. 2 B). The addition of DR provided a more granular risk stratification within each CAC category. For instance, among patients with a CAC score of zero, those with DR had significantly lower event-free survival rates than those without DR. Conversely, in higher CAC groups, the presence of DR consistently identified the subpopulation at the highest absolute cardiovascular risk. Overall, these survival analyses demonstrate that DR provides valuable additional prognostic information to CAC, effectively upgrading cardiovascular risk stratification of patients with low-to-moderate CAC scores. DR Improves Predictive Accuracy of CAC-Based Models Receiver operating characteristic (ROC) curve analysis (Fig. 3 ) showed that adding DR to CAC-based models greatly improved their ability to distinguish between patients who would and would not experience CV events. The DR-only CAC-only model yielded an area under the curve AUC of 67.8 [95% CI: 60.5–75.1], indicative of modest but commendable predictive performance. When DR was added (CAC + DR model), the AUC increased significantly to 75.3 [68.9–81.6] (p = 0.011 vs. CAC alone), reflecting improved discrimination through the integration of microvascular disease. Further refinement was achieved by adjusting CAC for traditional cardiovascular risk factors (age, gender, hypertension, dyslipidemia, smoking status, and nephropathy), which improved the AUC to 71.3 [63.6–79.0]. However, the model that was fully adjusted to include both CAC and DR (Adj. CAC + DR) produced the most accurate predictions with an AUC of 78.2 [72.0–84.3]. This represented a statistically significant improvement on the adjusted CAC model alone (p = 0.0139). Table 3 provides a comprehensive breakdown of the models. The adjusted CAC + DR model demonstrated significant improvement in sensitivity (78%) compared to CAC alone (36%) and adjusted CAC (58%), indicating a greater capacity for accurately identify true positive cases. Although specificity decreased (66.8% vs.72.46% for adjusted CAC), the negative predictive value increased substantially (95.8% vs. 92.8%, p = 0.049), indicating superior performance in ruling out events. Positive predictive values were comparable across the models, but the adjusted CAC + DR model achieved the best overall classification accuracy. Table 3 Diagnostic performance of different predictive models evaluated. CAC CAC + DR Adj. CAC Adj. CAC + DR Threshold (%) 14.4 13.7 13.84 10.36 Sensitivity (%) 36 [22.92–50.81] 64 [49.19–77.08] 58 [43.21–71.81] 78 [64.04–88.47] Specificity (%) 84.76 [80.71–88.25] 72.99 [68.19–77.43] 72.46 [67.63–76.93] 66.84 [61.82–71.6] Positive LR 2.36 [1.52–3.67] 2.37 [1.82–3.09] 2.11 [1.58–2.81] 2.35 [1.91–2.89] Negative LR 0.76 [0.61–0.93] 0.49 [0.34–0.72] 0.58 [0.42–0.81] 0.33 [0.19–0.56] PPV (%) 24 [16.9–32.9] 24.06 [19.53–29.26] 21.97 [17.44–27.29] 23.93 [20.38–27.87] NPV (%) 90.83 [88.9-92.45] 93.81 [91.25–95.66] 92.81 [90.26–94.73] 95.79 [93.07–97.47] Accuracy (%) 79.01 [74.82–82.79] 71.93 [67.4-76.16] 70.75 [66.17–75.04] 68.16 [63.49–72.57] AUC 67.8 75.25 71.31 78.17 p-values CAC vs CAC + DR Adj. (CAC vs CAC + DR) Sensitivity (%) 0.003 2.4e-4 Specificity (%) 5.4e-10 0.037 Positive LR 0.041 NS Negative LR NS NS PPV (%) 0.038 0.007 NPV (%) NS 0.049 Models include coronary artery calcium (CAC) score alone (CAC), CAC score combined with Diabetic Retinopathy (CAC + DR), adjusted CAC (Adj. CAC), and adjusted CAC with DR (Adj. CAC + DR). Models are adjusted for age, gender, dyslipidemia, hypertension, smoking status and nephropathy Reported metrics include optimal classification threshold (%), sensitivity, specificity, positive and negative likelihood ratios (LR + and LR−), positive predictive value (PPV), negative predictive value (NPV), overall accuracy, and area under the curve (AUC), each presented with 95% confidence intervals. Statistical comparisons between models were conducted using appropriate pairwise statistical tests (e.g., McNemar's test). A two-sided p-value < 0.05 was considered statistically significant. AUC statistical comparison confirmed that the CAC + DR model outperformed CAC alone (p = 0.011), and that even when adjusted for traditional risk factors, adding DR continued to confer a significant benefit (p = 0.0139). These findings highlight the effectiveness of DR as an additional biomarker alongside CAC, enhancing the accuracy and effectiveness of cardiovascular risk stratification. Net Reclassification Analysis Supports Clinical Utility of DR To further evaluate the clinical added value of DR, we performed net reclassification improvement (NRI) analyses based on predefined 7-year cardiovascular risk categories: low ( 11%). These categories were chosen to represent significant clinical thresholds for preventive decision-making. Table 4 summarizes the categorical NRI outcomes. Table 4 Net Reclassification Index (NRI) for 7-year cardiovascular event risk predicted by models incorporating coronary artery calcium (CAC) score alone and CAC combined to Diabetic Retinopathy (DR) Probability of CV event with CAC + DR model Low risk Moderate risk High risk Total Reclassified to higher risk Reclassified to lower risk Categorial NRI [95% CI] p-value Probability of CV event with CAC model Low risk No Event 97 28 0 125 28 NA 0.576 [0.2178–0.9342] 0.00162 Event 1 4 0 5 4 NA N 98 32 0 130 32 NA Moderate risk No Event 0 97 23 120 23 0 0.4083 [0.1506–0.6661] 0.0019 Event 0 6 9 15 9 0 N 0 103 32 135 32 0 High risk No Event 0 51 78 129 NA 51 0.162 [-0.0113–0.3353] 0.06687 Event 0 7 23 30 NA 7 N 0 58 101 159 NA 58 Total No Event 97 176 101 374 51 51 0.12 [-0.0601–0.3001] 0.19152 Event 1 17 32 50 13 7 N 98 193 133 424 64 58 Probability of CV event with Adj. CAC + DR model Low risk Moderate risk High risk Total Reclassified to higher risk Reclassified to lower risk Categorial NRI [95% CI] p-value Probability of CV event with Adj. CAC model Low risk No Event 78 19 1 98 20 NA 0.3959 [-0.0408–0.8327] 0.07562 Event 2 3 0 5 3 NA N 80 22 1 103 23 NA Moderate risk No Event 33 74 26 133 26 33 0.689 [0.3831–0.9949] 1,00E-05 Event 0 4 7 11 7 0 N 33 78 33 144 33 33 High risk No Event 0 52 91 143 NA 52 0.2754 [0.1517–0.3991] 1,00E-05 Event 0 3 31 34 NA 3 N 0 55 122 177 NA 55 Total No Event 111 145 118 374 46 85 0.2443 [0.0961–0.3925] 0.00123 Event 2 10 38 50 10 3 N 113 155 156 424 56 88 Cardiovascular risk categories: 0 to < 7% (low risk), 7 to 14% (high risk). Models are adjusted for age, gender, dyslipidemia, hypertension, smoking status and nephropathy. NA, non applicable. A two-sided p-value < 0.05 was considered statistically significant. In the low-risk group, we observed a significant categorical NRI of 0.576 [95% CI: 0.218–0.934], p = 0.0016, when comparing the CAC + DR model with CAC alone. This suggests that the model can more accurately classify non-event cases that have already been designated as low risk as non-events, as well as more accurately up-classify cases that have occurred. In the intermediate-risk group, where treatment decisions are often challenging, the categorical NRI was 0.4083 [0.151–0.666], p = 0.0019. Within this subgroup, 9 out of 15 patients who experienced events were correctly reclassified upward from intermediate to high risk, ensuring more aggressive preventive attention. In the high-risk category, 51 out of 129 patients who did not experience an event were reclassified downward from high to moderate risk, potentially avoiding unnecessary treatment. Importantly, the effects of reclassification were even more pronounced in the adjusted models (adjusted CAC vs. adjusted CAC + DR), reclassification effects were even more pronounced. The intermediate-risk category again demonstrated the greatest change in classification accuracy. The categorical NRI reached 0.689 [95% CI: 0.383–0.995], p < 0.00001, indicating that almost 69% of reclassifications improved the concordance between the predicted and observed outcomes. Specifically, 7 of 11 patients who experienced events were correctly reclassified upwards to the high-risk group, ensuring timely identification for aggressive intervention. Conversely, 33 out of 133 patients without events were correctly reclassified downwards to low-risk group, representing a significant improvement in the allocation of preventive measures. Stratified Risk Visualization Confirms Clinical Value of DR To provide a practical tool for clinical stratification, Fig. 4 presents a heat map integrating CAC categories (0, 1–99, 100–399, ≥ 400), DR status (absent or present), and smoking status (non-smoker vs. active smoker). Each combination of these three variables is color-coded into one of four clinical risk categories: low ( 20%) 7-year cardiovascular risk. This visualization reveals several clinically relevant patterns. Firstly, individuals with a CAC score of 0 and no retinopathy consistently demonstrate low cardiovascular risk, irrespective of smoking status. This suggests that the absence of both vascular calcification and retinopathy is a strong negative predictor. In contrast, patients with a score of ≥ 400 were predominantly classified as high or very high risk, with this risk being further amplified in smokers, particularly in the presence of DR. Even within the moderate CAC range (100–399), the presence of DR significantly shifted risk classification upwards. For example, non-smokers with DR in this range were estimated to be at high risk (~ 17.9%), whereas smokers with DR showed a dramatic increase into the very high-risk range (~ 37%). A similar pattern was seen in those with mild CAC (1–99): while most non-smoking individuals with no DR remained low risk, the addition of DR and smoking elevated risk levels into the high-risk or very high-risk categories (up to 33.1%). While smoking alone did increase baseline risk; however, smoking in isolation rarely drove classification into high or very-high risk categories unless coupled with DR and/or elevated CAC scores. Discussion We show, for the first time, that DR significantly enhances cardiovascular risk stratification when combined with CAC score in patients living with T2DM, even after adjustment for traditional risk factors. DR and CAC: Complementary and Synergistic Markers of Systemic Vascular Risk Our data show that the presence of DR increases the risk associated with intermediate CAC scores. Indeed, individuals with moderate CAC (100–399) and DR have outcomes comparable to those with high CAC (≥ 400), However, individuals without DR within the same CAC range have a markedly better prognosis. A similar pattern is observed among patients with low CAC scores (1–99), where the presence of DR is associated with worse outcomes than in those without DR. Adjusted Cox models and Kaplan-Meier survival analyses confirm that DR stratifies residual risk not captured by CAC alone. This supports a dual-marker approach wherein DR and CAC jointly reflect both macro-atherosclerotic load and systemic vascular fragility. Notably, this interaction remains evident even when adjusted for traditional CV risk factors such as age, gender, duration of diabetes, hypertension, dyslipidemia and nephropathy. This suggests that DR also enhances risk discrimination independently of known covariates. Risk Reclassification and Clinical Value From a clinical utility perspective, our study provides compelling evidence that incorporating DR into CAC-based models greatly improves risk prediction and patient reclassification, particularly in the intermediate-risk group, where therapeutic decision-making is most challenging. Receiver operating characteristic (ROC) curve analyses and NRI showed that integrating DR increases the sensitivity and negative predictive value of predictive models, while improving their calibration. Notably, upon inclusion of DR, a substantial proportion of patients who experienced an event were correctly reclassified into higher-risk categories, while those who did not experience an event were appropriately reclassified into lower-risk categories, reducing overtreatment. This refined stratification has immediate clinical implications. Patients with moderate CAC and concomitant DR should be considered for intensified CV prevention strategies, which may include statins, renin-angiotensin system blockade, sodium-glucose cotransporter-2 (SGLT2) inhibitors, and even aspirin. Conversely, patients with low CAC and absence of DR may safely avoid escalating therapy, thereby minimizing polypharmacy and potential complications. Furthermore, these data reinforce the additive prognostic value of DR, particularly when combined with lifestyle (smoking) and imaging-based (CAC) markers. The proposed risk heatmap which integrates CAC, DR, and smoking status offers clinicians a simple, visual decision-support tool for clinicians consistent with the principles of personalized medicine. Beyond Coronary Atherosclerosis: A Broader Cardiovascular Perspective Traditional CV risk models, including those integrating CAC, focus primarily on macrovascular events. However, in diabetes, the vascular pathology is systemic and not limited to large vessel atherosclerosis. Notably, while CAC quantifies structural, calcified atherosclerosis, it can underestimate cardiovascular risk in patients with non-calcified or lipid-rich plaques, or with predominant microvascular dysfunction. DR addresses this issue by identifying patients with subclinical or systemic vascular impairment which is potentially caused by diffuse endothelial injury and microcirculatory rarefaction. Recently, the evaluation of retinal microcrovasculature using optical coherence tomography angiography (OCTA) parameters, was found to be closely associated with coronary atherosclerosis( 33 ). However, DR may provide a more comprehensive indication of generalized vascular injury, encompassing both the microvascular and macrovascular beds. Thus, DR highlights risks beyond classic atherothrombotic events, including heart failure. Recent evidence suggests that CAC is not equally predictive across all forms of CV disease. For example, data from the MESA study revealed a significant association between CAC and heart failure with preserved ejection fraction (HFpEF) only in women( 34 ), highlighting sex-specific differences and limitations of CAC as a universal marker. These findings challenge the notion that CAC can be used universally and highlight the importance of microvascular dysfunction, a key factor in diastolic dysfunction and HFpEF, that cannot be quantified by CAC. In contrast, DR has been independently associated with both systolic( 26 , 27 ) and diastolic( 35 ) cardiac dysfunction, carotid intima-media thickness( 36 , 37 ) and stroke( 24 , 28 ), coronary microvascular dysfunction( 21 – 25 ), and peripheral arterial disease (PAD)( 29 – 31 ). Notably, DR has been linked to incident HFpEF even after adjusting for conventional risk factors—including age, sex, diabetes duration, insulin use, blood pressure, lipid profile( 27 ). This suggests that DR may reflect also the microvascular impairment in HFpEF. These findings support the use of DR as a proxy for systemic microangiopathy, driven by shared mechanisms such as oxidative stress, inflammation, and endothelial dysfunction. This microangiopathy is relevant to a range of CV conditions, including coronary and cerebrovascular diseases, PAD and heart failure. Therefore, DR expands the assessment of vascular risk beyond what that can be offered by CAC alone thereby supporting its integration into CV risk models. Common Risk Factors and Shared Pathophysiological Pathways DR and CV diseases share a spectrum of modifiable and non-modifiable risk factors, including hyperglycemia, hypertension, dyslipidemia, obesity, insulin resistance, and chronic inflammation( 38 ). These risk factors act through overlapping pathophysiological mechanisms that promote both microvascular and macrovascular complications in diabetes. Their pathogenesis is rooted in oxidative stress( 17 ) and inflammation( 14 , 15 ). Hyperglycemia triggers mitochondrial ROS overproduction( 39 ), activating polyol, hexosamine, and PKC pathways, and accumulating AGEs( 18 ) , ( 40 ). These processes cause endothelial dysfunction, pericyte loss, and capillary occlusion in DR, while simultaneously promoting plaque instability and atherogenesis in large vessels( 19 ). Endothelial dysfunction, marked by ROS-mediated NO inactivation, eNOS uncoupling, vascular inflammation, impaired vasodilation and platelet hyperaggregability, manifests early as blood-retinal barrier breakdown in DR and as arterial stiffness( 41 ) reduced coronary flow reserve and impaired microcirculatory adaptation in CVD( 19 ) ,20 . Inflammation is another shared axis between DR and CVD. In DR, elevated ICAM-1, VCAM-1, and pro-inflammatory cytokines (IL-6, MCP-1, TNF-α) drive leukostasis, vascular leakage, and neovascularization ( 39 , 42 ). These same mediators promote atherosclerotic plaque infiltration, rupture, and thrombosis in CVD( 15 , 43 ), while foam cell formation—via macrophage uptake of oxidized LDL—sustains vascular inflammation and CVD progression ( 16 , 44 ). The convergence of metabolic, oxidative, and inflammatory pathways suggests that DR is not merely a local retinal disorder, but also an indicator of systemic vascular damage. Therefore, it is biologically plausible and clinically justified to use DR as an indicator of broader CV vulnerability in T2DM. Diabetic Retinopathy: a surrogate marker of myocardial microvascular disease? Once a debated entity, myocardial microcirculatory dysfunction is now recognized as a key mechanism of myocardial dysfunction( 45 ). During increased metabolic demand, arterioles normally dilate to reduce microvascular resistance and boost perfusion. Dysfunction of the coronary microcirculation can therefore cause myocardial ischemia and heart failure( 46 ) , ( 47 ). Interestingly, diabetic microangiopathy affects both retinal capillaries and arterioles (12–200 µm) and also coronary arterioles (30–150 µm). Lipotoxicity and glucotoxicity both play a role in the development of DR and diabetic microangiopathy in the myocardium through identified mechanisms such as oxidative stress caused by mitochondrial dysfunction and the production of reactive oxygen species (ROS) as well as the release of pro-inflammatory cytokines( 48 ) leading to vascular stiffness and impaired rheology and perfusion. In turn, microvascular dysfunction promotes myocardial remodeling and stiffness which can lead to heart failure. Therefore, DR, which was historically considered a localized microvascular complication, emerges as a non-invasive and clinically relevant marker of myocardial microangiopathy( 49 ). In clinical practice, the presence of DR indicates the presence of early and diffuse microvascular dysfunction, which may precede or accompany subclinical myocardial impairment. This concept is reinforced by recent recommendations from ESC 2023( 32 ), which demonstrate that patients with DR exhibit a very high cardiovascular risk, independently of traditional risk factors. Our study provides further evidence to support this view: DR should no longer be regarded solely as an isolated retinal microvascular event but rather as a systemic indicator of heightened cardiovascular risk. Incorporating DR into a comprehensive cardiovascular risk assessment could enable the early identification of high-risk patients and inform targeted preventive strategies, such as strict glycemic control, management of metabolic comorbidities, and potentially, microvascular-directed pharmacological interventions. In conclusion, DR represents a powerful and accessible clinical indicator of systemic microvascular fragility and should therefore be incorporated into the cardiovascular risk stratification of patients with diabetes. Limitations Despite its strengths, our study has several limitations that must be acknowledged. Firstly, the study was conducted in a single tertiary care academic center, which may limit the generalizability of the findings. Second, the observational nature of the study prevents causal inferences and restricts our ability to determine whether the observed associations are directly attributable to the presence of DR or reflect confounding vascular risk factors. There is a potential source of selection bias, as all participants included in the analysis had undergone both CAC scanning and retinal imaging. This dual assessment may reflect a cohort that clinicians already perceive to be at elevated cardiovascular risk or to be more engaged in preventive care, thereby introducing a degree of selection bias. The classification of DR in this study was binary (present versus absent). While this approach ensures reproducibility, it may mask the prognostic nuances conveyed by different stages of DR severity. Future studies should consider integrating the full spectrum of DR, from mild non-proliferative forms to proliferative retinopathy, to improve the granularity of risk stratification Conclusion This study shows that DR, a readily available, non-invasive, and cost-effective marker, significantly improves CV risk prediction when combined with CAC scoring in patients with type 2 diabetes mellitus in a primary prevention setting. Therefore, CAC and DR represent two complementary markers of cardiovascular risk. Notably, the greatest improvement in risk stratification was observed among individuals with intermediate CAC scores, a group for whom clinical decision-making is often the most uncertain. By identifying patients at high- and very high-risk patients who would otherwise be misclassified by CAC alone, including DR enables more precise, personalized CV prevention strategies. Although DR is largely absent from conventional cardiovascular risk prediction models, our results support its inclusion in clinical algorithms and risk calculators. Abbreviations ACE Angiotensin-converting enzyme inhibitor ARB Angiotensin II receptor blocker AUC Area Under the Curve CAC Coronary Artery Calcium CV CardioVascular DBP Diastolic blood pressure DR Diabetic Retinopathy HFpEF Heart Failure with preserved Ejection Fraction eGFR Estimated glomerular filtration rate ESC European Society of Cardiology GLP-1 Glucagon-like peptide-1 HbA1c Hemoglobin A1c HDL High-density lipoprotein HR Hazard Ratio LDL Low-density lipoprotein NPDR Non-Proliferative Diabetic Retinopathy PAD Peripheral Arterial Disease PDR Proliferative Diabetic Retinopathy ROS Reactive Oxygen Species SBP Systolic blood pressure SD Standard deviation T2DM Type 2 Diabetes Mellitus UACR Urinary Albumin Creatinin Ratio Declarations Acknowledgements The authors thank all the patients, investigators, nurses and URC-Pitié-Salpêtrière staff members involved in the ACCoDiab study. Author contributions AR, FA, FP, and OB were involved in the conception and design of the study. ACJ, AG, AT, FA, FP OB, SB and SL recruited participants and collected the data. AR and SB performed cardiac imaging measurements. AR, FA, FP, OB and TB performed the data analysis. AG, AR, FA, FP, OB and TB wrote the first draft of the manuscript, and AG, AR, FA, FP, OB and TB participated in manuscript edition. All authors reviewed and approved the final version of the manuscript. Funding No funding Availability of data and materials The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The study was approved by the local ethics committee (PARIS VI CPP) and registered in ClinicalTrials.gov (Identifiers: NCT03920683). All patients were informed about the study objectives and procedure. Participants gave their written informed consent to participate prior to inclusion. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HAW, Livingstone SJ, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 2004;364(9435):685–96. Keech A, Simes RJ, Barter P, Best J, Scott R, Taskinen MR, et al. Effects of long-term fenofibrate therapy on cardiovascular events in 9795 people with type 2 diabetes mellitus (the FIELD study): randomised controlled trial. Lancet. 2005;366(9500):1849–61. ADVANCE Collaborative Group, Patel A, MacMahon S, Chalmers J, Neal B, Billot L, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358(24):2560–72. Action to Control Cardiovascular Risk in Diabetes Study Group, Gerstein HC, Miller ME, Byington RP, Goff DC, Bigger JT, et al. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358(24):2545–59. BARI 2D Study Group, Frye RL, August P, Brooks MM, Hardison RM, Kelsey SF, et al. A randomized trial of therapies for type 2 diabetes and coronary artery disease. N Engl J Med. 2009;360(24):2503–15. Young LH, Wackers FJT, Chyun DA, Davey JA, Barrett EJ, Taillefer R, et al. Cardiac outcomes after screening for asymptomatic coronary artery disease in patients with type 2 diabetes: the DIAD study: a randomized controlled trial. JAMA. 2009;301(15):1547–55. Elkeles RS, Godsland IF, Feher MD, Rubens MB, Roughton M, Nugara F, et al. Coronary calcium measurement improves prediction of cardiovascular events in asymptomatic patients with type 2 diabetes: the PREDICT study. Eur Heart J. 2008;29(18):2244–51. Agarwal S, Cox AJ, Herrington DM, Jorgensen NW, Xu J, Freedman BI, et al. Coronary calcium score predicts cardiovascular mortality in diabetes: diabetes heart study. Diabetes Care. 2013;36(4):972–7. Malik S, Zhao Y, Budoff M, Nasir K, Blumenthal RS, Bertoni AG, et al. Coronary Artery Calcium Score for Long-term Risk Classification in Individuals With Type 2 Diabetes and Metabolic Syndrome From the Multi-Ethnic Study of Atherosclerosis. JAMA Cardiol. 2017;2(12):1332–40. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, et al. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2020;41(2):255–323. Ichikawa K, Miyoshi T, Osawa K, Miki T, Toda H, Ejiri K, et al. Prognostic value of non-alcoholic fatty liver disease for predicting cardiovascular events in patients with diabetes mellitus with suspected coronary artery disease: a prospective cohort study. Cardiovasc Diabetol. 2021;20(1):8. Budoff MJ, Young R, Burke G, Jeffrey Carr J, Detrano RC, Folsom AR, et al. Ten-year association of coronary artery calcium with atherosclerotic cardiovascular disease (ASCVD) events: the multi-ethnic study of atherosclerosis (MESA). Eur Heart J. 2018;39(25):2401–8. Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358(13):1336–45. Semeraro F, Morescalchi F, Cancarini A, Russo A, Rezzola S, Costagliola C. Diabetic retinopathy, a vascular and inflammatory disease: Therapeutic implications. Diabetes Metab. 2019;45(6):517–27. Kanter JE, Kramer F, Barnhart S, Averill MM, Vivekanandan-Giri A, Vickery T, et al. Diabetes promotes an inflammatory macrophage phenotype and atherosclerosis through acyl-CoA synthetase 1. Proc Natl Acad Sci U S A. 2012;109(12):E715–724. Libby P. Inflammation in atherosclerosis. Nature. 2002;420(6917):868–74. Betteridge DJ. What is oxidative stress? Metabolism. 2000;49(2 Suppl 1):3–8. Hammes HP. Diabetic retinopathy: hyperglycaemia, oxidative stress and beyond. Diabetologia. 2018;61(1):29–38. Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res. 2010;107(9):1058–70. Münzel T, Daiber A, Ullrich V, Mülsch A. Vascular consequences of endothelial nitric oxide synthase uncoupling for the activity and expression of the soluble guanylyl cyclase and the cGMP-dependent protein kinase. Arterioscler Thromb Vasc Biol. 2005;25(8):1551–7. Reaven PD, Emanuele N, Moritz T, Klein R, Davis M, Glander K, et al. Proliferative diabetic retinopathy in type 2 diabetes is related to coronary artery calcium in the Veterans Affairs Diabetes Trial (VADT). Diabetes Care. 2008;31(5):952–7. Targher G, Bertolini L, Zenari L, Lippi G, Pichiri I, Zoppini G, et al. Diabetic retinopathy is associated with an increased incidence of cardiovascular events in Type 2 diabetic patients. Diabet Med. 2008;25(1):45–50. Gimeno-Orna JA, Faure-Nogueras E, Castro-Alonso FJ, Boned-Juliani B. Ability of retinopathy to predict cardiovascular disease in patients with type 2 diabetes mellitus. Am J Cardiol. 2009;103(10):1364–7. Kawasaki R, Tanaka S, Tanaka S, Abe S, Sone H, Yokote K, et al. Risk of cardiovascular diseases is increased even with mild diabetic retinopathy: the Japan Diabetes Complications Study. Ophthalmology. 2013;120(3):574–82. Gerstein HC, Ambrosius WT, Danis R, Ismail-Beigi F, Cushman W, Calles J, et al. Diabetic retinopathy, its progression, and incident cardiovascular events in the ACCORD trial. Diabetes Care. 2013;36(5):1266–71. Wong TY, Rosamond W, Chang PP, Couper DJ, Sharrett AR, Hubbard LD, et al. Retinopathy and risk of congestive heart failure. JAMA. 2005;293(1):63–9. Cheung N, Wang JJ, Rogers SL, Brancati F, Klein R, Sharrett AR, et al. Diabetic retinopathy and risk of heart failure. J Am Coll Cardiol. 2008;51(16):1573–8. Cheung N, Rogers S, Couper DJ, Klein R, Sharrett AR, Wong TY. Is diabetic retinopathy an independent risk factor for ischemic stroke? Stroke. 2007;38(2):398–401. Mohammedi K, Woodward M, Hirakawa Y, Zoungas S, Colagiuri S, Hamet P, et al. Presentations of major peripheral arterial disease and risk of major outcomes in patients with type 2 diabetes: results from the ADVANCE-ON study. Cardiovasc Diabetol. 2016;15(1):129. Chen YW, Wang YY, Zhao D, Yu CG, Xin Z, Cao X, et al. High prevalence of lower extremity peripheral artery disease in type 2 diabetes patients with proliferative diabetic retinopathy. PLoS ONE. 2015;10(3):e0122022. Chen SC, Hsiao PJ, Huang JC, Lin KD, Hsu WH, Lee YL, et al. Abnormally Low or High Ankle-Brachial Index Is Associated with Proliferative Diabetic Retinopathy in Type 2 Diabetic Mellitus Patients. PLoS ONE. 2015;10(7):e0134718. Marx N, Federici M, Schütt K, Müller-Wieland D, Ajjan RA, Antunes MJ, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes. Eur Heart J. 2023;44(39):4043–140. Yang JM, Yang DH, Lee SW, Kwak J, Lee Y, Kim YJ et al. Subclinical Coronary Atherosclerosis and Retinal Optical Coherence Tomography Angiography. JAMA Cardiol. 2025;e253036. Sharma K, Al Rifai M, Ahmed HM, Dardari Z, Silverman MG, Yeboah J, et al. Usefulness of Coronary Artery Calcium to Predict Heart Failure With Preserved Ejection Fraction in Men Versus Women (from the Multi-Ethnic Study of Atherosclerosis). Am J Cardiol. 2017;120(10):1847–53. Chung YR, Park SJ, Moon KY, Choi SA, Lim HS, Park SW, et al. Diabetic retinopathy is associated with diastolic dysfunction in type 2 diabetic patients with non-ischemic dilated cardiomyopathy. Cardiovasc Diabetol. 2017;16(1):82. Rema M, Mohan V, Deepa R, Ravikumar R, Chennai Urban Rural Epidemiology Study-2. Association of carotid intima-media thickness and arterial stiffness with diabetic retinopathy: the Chennai Urban Rural Epidemiology Study (CURES-2). Diabetes Care. 2004;27(8):1962–7. Malecki MT, Osmenda G, Walus-Miarka M, Skupien J, Cyganek K, Mirkiewicz-Sieradzka B, et al. Retinopathy in type 2 diabetes mellitus is associated with increased intima-media thickness and endothelial dysfunction. Eur J Clin Invest. 2008;38(12):925–30. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98. Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes. 2005;54(6):1615–25. Wakisaka M, Nagao T. Sodium glucose cotransporter 2 in mesangial cells and retinal pericytes and its implications for diabetic nephropathy and retinopathy. Glycobiology. 2017;27(8):691–5. Rosenbaum D, Kachenoura N, Koch E, Paques M, Cluzel P, Redheuil A, et al. Relationships between retinal arteriole anatomy and aortic geometry and function and peripheral resistance in hypertensives. Hypertens Res. 2016;39(7):536–42. Khalfaoui T, Lizard G, Ouertani-Meddeb A. Adhesion molecules (ICAM-1 and VCAM-1) and diabetic retinopathy in type 2 diabetes. J Mol Histol. 2008;39(2):243–9. Tabas I, García-Cardeña G, Owens GK. Recent insights into the cellular biology of atherosclerosis. J Cell Biol. 2015;209(1):13–22. Libby P. Current concepts of the pathogenesis of the acute coronary syndromes. Circulation. 2001;104(3):365–72. Nakamura M. Lipotoxicity as a therapeutic target in obesity and diabetic cardiomyopathy. J Pharm Pharm Sci. 2024;27:12568. Chilian WM. Coronary microcirculation in health and disease. Summary of an NHLBI workshop. Circulation. 1997;95(2):522–8. Chen X, Shi C, Wang Y, Yu H, Zhang Y, Zhang J, et al. The mechanisms of glycolipid metabolism disorder on vascular injury in type 2 diabetes. Front Physiol. 2022;13:952445. Ahmad FS, Ning H, Rich JD, Yancy CW, Lloyd-Jones DM, Wilkins JT, Hypertension. Obesity, Diabetes, and Heart Failure-Free Survival: The Cardiovascular Disease Lifetime Risk Pooling Project. JACC Heart Fail. 2016;4(12):911–9. Liu J, Chen C, Yu Z, Chen X, Chen Z, Li W, et al. Myocardial Blood Flow and the Retinal Microvasculature Across the Spectrum From Normal to Failing Hearts. J Clin Hypertens (Greenwich). 2025;27(6):e70087. Additional Declarations No competing interests reported. Supplementary Files GA.png Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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16:33:17","extension":"json","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11359,"visible":true,"origin":"","legend":"","description":"","filename":"dbf53c4c0fe24f1892cb400679fc0bb4.json","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/aa8e72c5970c7c399dac7afc.json"},{"id":95170978,"identity":"dc10af71-0aa7-4594-b8c5-d52e3f94d1cc","added_by":"auto","created_at":"2025-11-05 06:30:45","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":214879,"visible":true,"origin":"","legend":"","description":"","filename":"dbf53c4c0fe24f1892cb400679fc0bb41enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/dcd255494812f082583b1b9f.xml"},{"id":95170981,"identity":"09b482dc-648e-4565-aa53-85bb8b78765b","added_by":"auto","created_at":"2025-11-05 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06:30:45","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":225690,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/f76fb7a4d972c8ce35553ad7.html"},{"id":95226102,"identity":"71d70506-2b52-47a3-97ce-d77ace4dc274","added_by":"auto","created_at":"2025-11-05 16:26:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves for cardiovascular events stratified by coronary artery calcium score categories\u003c/strong\u003e \u003cstrong\u003e(CAC)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAC=0\u003c/strong\u003e (dark green), \u003cstrong\u003eCAC=1-99\u003c/strong\u003e(light green), \u003cstrong\u003eCAC=100-399\u003c/strong\u003e (orange), \u003cstrong\u003eCAC≥400\u003c/strong\u003e (dark red)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A\u003c/strong\u003e: Unadjusted survival curves for different CAC categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B\u003c/strong\u003e: Survival curves adjusted for age, gender, dyslipidemia, hypertension, smoking status and nephropathy\u003c/p\u003e\n\u003cp\u003eHazard ratios (HR) with 95% confidence intervals (CI) and corresponding p-values are provided.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/094f51d79509ba110bc7c42d.png"},{"id":95170976,"identity":"b9573e34-4b4d-4e3b-8824-573b9cc826cb","added_by":"auto","created_at":"2025-11-05 06:30:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":195795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival probability over time cardiovascular events, stratified by Coronary Artery Calcium (CAC) score and Diabetic Retinopathy (DR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCAC=0 \u0026amp; DR- (dark green), CAC=0 \u0026amp; DR+ (light green), CAC=1-99 \u0026amp; DR- (yellow), CAC=1-99 \u0026amp; DR+ (orange), CAC=100-399 \u0026amp; DR- (dark blue), CAC=100-399 \u0026amp; DR+ (red), CAC≥400 \u0026amp; DR- (brown), CAC≥400 \u0026amp; DR+ (purple)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel A\u003c/strong\u003e. Unadjusted survival curves\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel B\u003c/strong\u003e. Adjusted survival curves for age, gender, dyslipidemia, hypertension, smoking status and nephropathy.\u003c/p\u003e\n\u003cp\u003eAdjusted hazard ratios (HR) with 95% confidence intervals (CI) and corresponding p-values are displayed.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/11db893f31006461fbe1035f.png"},{"id":95170974,"identity":"7dc0d795-3a8d-4b5f-b5ab-f6a90ef81183","added_by":"auto","created_at":"2025-11-05 06:30:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncremental predictive value of coronary artery calcium (CAC) score, CAC score combined to Diabetic Retinopathy (DR) for cardiovascular events in patients with T2DM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curves displaying the discriminatory performance of various risk models to predict CV events. In black, the CAC score model alone; in orange, the CAC score model combined to DR; in light blue, the CAC score model adjusted on traditional cardiovascular risk factors (age, gender, hypertension, dyslipidemia, smoking status and nephropathy) ; in green, the adjusted CAC score model implemented DR.\u003c/p\u003e\n\u003cp\u003ePairwise AUC comparisons were performed using DeLong’s test for correlated ROC curves. A two-sided p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/af1a83a62f8446082d0c0172.png"},{"id":95170983,"identity":"6a239fcb-5ee4-493a-b16b-7536eaf31bf6","added_by":"auto","created_at":"2025-11-05 06:30:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStratified risk score chart combining Coronary Artery Calcium (CAC) score and presence of Diabetic Retinopathy faceted by smoking status for cardiovascular risk classification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe chart displays risk classes stratified across four CAC categories on the y-axis and retinopathy status (0 = absent, 1 = present) on the x-axis, faceted by smoking status (0 = absent, 1 = present). Each cell is color-coded according to risk class (in % with 95% confidence intervals) : green for low risk (\u0026lt;7%), orange for medium risk (7-11%), red for high risk (11-20%), and dark red for very high risk (\u0026gt;20%). Presence of retinopathy and higher CAC scores were associated with a shift toward higher risk categories. This stratification provides a visual tool for identifying high-risk patients based on combined imaging and clinical parameters.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/fd4a3f12519089d8f9d3126f.png"},{"id":96082750,"identity":"b2530b7a-13b1-40d2-bfdf-f8b45aa1444f","added_by":"auto","created_at":"2025-11-17 11:53:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3272856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/a17dd7a0-34dc-4698-9fb3-9ac6289ec14b.pdf"},{"id":95226224,"identity":"16cf1f32-7f02-48c2-ad20-8877490c54ee","added_by":"auto","created_at":"2025-11-05 16:30:43","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":604787,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-7925795/v1/52d1222973825477fac91b5e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diabetic Retinopathy Enhances Coronary Artery Calcium-Based Cardiovascular Risk Stratification in Patients with Type 2 Diabetes: Insights from the ACCoDiab Study","fulltext":[{"header":"Research Insights","content":"\u003cp\u003e\u003cstrong\u003eWhat is currently known about this topic?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn patients with type 2 diabetes and moderate Coronary Artery Calcium (CAC) score, residual cardiovascular risk remains. Improving cardiovascular risk stratification in patients with type 2 diabetes is needed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the key research question?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCould diabetic retinopathy improve the prognostic performance of the CAC score in predicting cardiovascular events in patients with type 2 diabetes?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is new?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe combination of diabetic retinopathy and CAC score significantly enhances cardiovascular risk stratification in patients living with type 2 diabetes, even after adjustment for traditional risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow might this study influence clinical practice?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results support the inclusion of diabetic retinopathy in clinical algorithms and risk calculators to improve cardiovascular risk stratification in patients with type 2 diabetes.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCardiovascular (CV) disease remains the leading cause of morbidity and mortality in individuals with type 2 diabetes (T2DM), yet identifying those most at risk remains a clinical challenge. Indeed, studies including patients with T2DM have reported a 10-year occurrence of major cardiovascular events (MACE) ranging from 6% (indicating a moderate CV risk) to 34% (reflecting a very high CV risk)(\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, identifying patients at the greatest risk of developing cardiovascular (CV) events early on is a key objective in preventing CV complications in patients living with T2DM.\u003c/p\u003e\u003cp\u003eMeasuring the coronary artery calcium (CAC) score plays a key role in reclassifying cardiovascular (CV) risk, especially in patients whose clinical risk assessment remains uncertain(\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This is because the CAC score directly quantifies subclinical atherosclerosis. The CAC score provides personalized risk assessment and informs treatment decisions, ranging from lifestyle modifications to more intensive pharmacological interventions, such as initiating aspirin or statin therapies as well as determining LDL cholesterol targets, and identifying patients requiring myocardial ischaemia screening(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, although higher CAC scores are clearly associated with an increased risk of MACE(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), patients with moderate or even low CAC scores can still experience CV events(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Consequently, there is an increasing demand for improved CV risk assessment tools. CAC alone may fail to capture residual CV risk, particularly in patients with low-to-moderate scores and macrovascular disease.\u003c/p\u003e\u003cp\u003eThere is growing evidence that microangiopathy is an independent risk factor for CV disease in patients with diabetes. Diabetic retinopathy (DR) is a marker of prolonged exposure to chronic hyperglycaemia, leading to microvascular damage and systemic vascular injury. This suggests that DR and macrovascular complications may share underlying mechanisms, such as inflammation(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), oxidative stress and endothelial dysfunction(\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Beyond its ophthalmological significance, DR is increasingly recognized as a marker of systemic vascular injury and predictor of adverse CV outcomes. Several studies have demonstrated an independent association between DR and coronary artery disease(\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), congestive heart failure(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), stroke(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and peripheral arterial disease(\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven their distinct pathological origins, i.e. macrovascular calcification (CAC score) and microvascular injury (DR), the combined assessment of CAC and DR could enhance CV risk prediction. DR is non-invasive, inexpensive, and routinely performed in diabetes care, making it a practical addition to CAC-based risk models.\u003c/p\u003e\u003cp\u003eWe hypothesized that DR could improve the prognostic performance of the CAC score in predicting CV events in T2DM patients. We therefore evaluated whether incorporating DR into CAC-based models improves cardiovascular risk discrimination and reclassification, thereby enabling the development of more individualized cardiovascular prevention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003eACCoDiab is a cross-sectional monocentric study with retrospective data collection (ClinicalTrials.gov identifier NCT03920683). The recruitment period extended from January 2014 to May 2017. This study is an ancillary analysis of the ACCoDiab study and included 424 patients with T2DM, enrolled during routine hospital admissions for CV risk assessment at Piti\u0026eacute;-Salp\u0026ecirc;tri\u0026egrave;re Hospital. Inclusion criteria were: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, confirmed T2DM diagnosis, no previous history of CV events, and available computed tomography CAC score and DR screening. All participants provided written informed consent. The study was approved by the institutional ethics committee.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCardiovascular Outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the occurrence of a CV event over a 7-year follow-up period (from January 1st, 2014 to December 31, 2023). Events included nonfatal myocardial infarction, ischemic stroke, hospitalization for heart failure, limb revascularization due to peripheral artery disease, or CV death. Data were retrieved from centralized hospital records and verified via direct contact or national mortality registries (INSEE).\u003c/p\u003e\n\u003ch3\u003eCoronary Artery Calcium Scoring\u003c/h3\u003e\n\u003cp\u003eAll patients with T2DM were evaluated as part of routine clinical care for cardiovascular risk assessment and prevention during scheduled outpatient visits at the Diabetes day-hospital of the Piti\u0026eacute;-Salp\u0026ecirc;tri\u0026egrave;re Sorbonne University Hospital. CAC assessment was performed as part of standard care in the Department of Cardiovascular and Thoracic Imaging.\u003c/p\u003e\u003cp\u003eAll patients underwent non-contrast electrocardiogram (ECG)-gated computed tomography (CT) for CAC scoring. Scans were acquired using either a dual-source SOMATOM Definition Flash (majority) or a single-source SOMATOM Edge CT scanner (Siemens Healthineers, Erlangen, Germany).\u003c/p\u003e\u003cp\u003eImage acquisition was performed using a prospectively ECG-triggered sequential mode, targeting mid-diastole, without contrast injection. The scan extended from the top of the aortic arch to the cardiac apex, using the following parameters: 120 kVp or equivalent tube voltage, modulated mAs, 3 mm slice thickness.\u003c/p\u003e\u003cp\u003eCAC scoring was conducted using the Agatston method. A calcified atherosclerotic lesion was defined as a contiguous pixel area\u0026thinsp;\u0026ge;\u0026thinsp;1 mm\u0026sup2; with a peak attenuation\u0026thinsp;\u0026ge;\u0026thinsp;130 Hounsfield units (HU). The calcium score per lesion was calculated by multiplying the lesion area by a density weighting factor based on peak HU: 1 for 130\u0026ndash;199 HU; 2 for 200\u0026ndash;299 HU; 3 for 300\u0026ndash;399 HU; 4 for \u0026ge;\u0026thinsp;400 HU. Total CAC scores were categorized into four strata, based on established risk Agatston units (AU) thresholds: 0 (no detectable calcification), 1\u0026ndash;99 (mild), 100\u0026ndash;399 (moderate), \u0026ge;\u0026thinsp;400 AU (severe).\u003c/p\u003e\n\u003ch3\u003eRetinopathy Assessment\u003c/h3\u003e\n\u003cp\u003eDR was assessed at baseline using standardized digital fundus photography, as part of routine diabetes care. Retinal images were evaluated by trained ophthalmologists in accordance with established international grading criteria, namely the International Clinical Diabetic Retinopathy Disease Severity Scale (ICDR). This classification system defines five stages: no retinopathy, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR).\u003c/p\u003e\u003cp\u003eFor the purposes of statistical analysis in this study, DR was analyzed as a binary variable: present versus absent. Any level of retinopathy (from mild NPDR to PDR) was considered 'present,' while 'absent' corresponded to no visible signs of retinopathy. This binary stratification facilitated clear risk comparisons.\u003c/p\u003e\n\u003ch3\u003eBaseline Clinical and Laboratory Variables\u003c/h3\u003e\n\u003cp\u003eBaseline variables were selected based on their established relevance in cardiovascular risk prediction models, particularly those outlined in the 2023 European Society of Cardiology (ESC) guidelines on diabetes and cardiovascular disease(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The variables reported included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDemographic and clinical parameters: age, gender, body mass index (BMI), and duration of diabetes, current smoking status, hypertension.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLipid profile: triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), total cholesterol and low-density lipoprotein cholesterol (LDL-C).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRenal function: estimated glomerular filtration rate (eGFR, calculated using the CKD-EPI equation) and albuminuria measured as urinary albumin-to-creatinine ratio (UACR). Nephropathy was defined as either eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73 m\u0026sup2; and/or UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/mmol.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGlycemic control and inflammation: glycated hemoglobin (HbA1c) and high-sensitivity C-reactive protein (hs-CRP).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMedication use: antihypertensives, statins, and glucose-lowering therapies.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese variables were collected systematically at baseline during routine clinical care.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were compared using chi-squared tests; continuous variables using Mann-Whitney U tests. Kaplan-Meier survival curves and log-rank tests evaluated time-to-event differences across CAC and DR categories. Then, Multivariable Cox proportional hazards models assessed the independent associations of CAC score and DR with CV events. Four models were compared: CAC only, CAC\u0026thinsp;+\u0026thinsp;DR, adjusted CAC, and adjusted CAC\u0026thinsp;+\u0026thinsp;DR. The multivariable adjustment model included age, gender, smoking status, hypertension, dyslipidemia, and nephropathy. Assumptions of Cox regression modeling were checked afterwards for all models with assessment of residuals by applying Schoenfeld tests. We performed a Receiver operating characteristic (ROC) analysis to estimate discriminatory ability of prediction models. To do so, we applied Liu\u0026rsquo;s criteria which consists in maximizing the product of sensitivity and specificity. As compared to usual Youden criteria, Liu criteria favors more balanced performances with respect to both sensitivity and specificity. Pairwise AUC comparisons were performed with DeLong test or stratified bootstrapping for respectively nested and non-nested prediction models. We also assessed sensitivity, specificity, positive and negative predictive values and also accuracy of models. Net reclassification improvement (NRI) analysis quantified the change in risk classification between models. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Finally, we provided a simple risk-scoring tool combining Calcium scoring with DR and smoking status to ease patient management in routine clinical setting.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics and Event rates\u003c/h2\u003e\u003cp\u003eThe study population comprised 424 patients (175 females and 249 males) with T2DM and a mean age of 60.9 years. All patients were followed for 7 years or until the occurrence of a first cardiovascular event. During follow-up, 50 patients (11.8%) experienced a first CV event, including non-fatal myocardial infarction (n\u0026thinsp;=\u0026thinsp;32), ischemic stroke (n\u0026thinsp;=\u0026thinsp;10), hospitalization for heart failure (n\u0026thinsp;=\u0026thinsp;5), and lower-limb revascularization (n\u0026thinsp;=\u0026thinsp;3). No CV deaths were recorded. The median follow-up duration for patients who experienced an event was 1,406 days [IQR: 566\u0026ndash;1,876].\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the baseline demographic and clinical characteristics of patients with and without CV events were similar. There were no significant differences in age (63 vs. 61 years, p\u0026thinsp;=\u0026thinsp;0.10), gender distribution (34% vs. 42% female, p\u0026thinsp;=\u0026thinsp;0.30), duration of diabetes or BMI. However, active smoking was significantly more prevalent among patients who experienced an event (30% vs. 18%, p\u0026thinsp;=\u0026thinsp;0.042).\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 demographic, clinical, biochemical, treatment, and imaging characteristics of the study population stratified by occurrence of cardiovascular events.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;424\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo event \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;374\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEvent \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;50\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCharacteristics\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender: Female\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158 (42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (54\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (54\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (59\u0026ndash;69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDuration of diabetes (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (72-92.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (72\u0026ndash;93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.5 (73\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeight (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169.5 (162\u0026ndash;175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169.1 (162\u0026ndash;175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170.2 (156\u0026ndash;174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.7 (25.5\u0026ndash;32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.7 (25.4\u0026ndash;32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.7 (25.6\u0026ndash;32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e285 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e248 (66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 (74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e306 (72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e269 (72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 (74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoker\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTobacco\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e227 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComplications of Diabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNephropathy\u003c/b\u003e (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/m\u003csup\u003e2\u003c/sup\u003e and/or UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/mmol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eeGFR (mL/min/1.73m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (74.5\u0026ndash;106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (74\u0026ndash;106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.5 (79\u0026ndash;104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRetinopathy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 (58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBiology\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.5 (6.75\u0026ndash;8.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5 (6.8\u0026ndash;8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2 (6.5\u0026ndash;7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-cholesterol (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16 (0.96\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16 (0.96\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18 (0.91\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-cholesterol (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.38 (1.86\u0026ndash;2.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.38 (1.86\u0026ndash;2.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.20 (1.71\u0026ndash;2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal cholesterol (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.40 (3.75\u0026ndash;5.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.40 (3.76\u0026ndash;5.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.38 (3.74\u0026ndash;5.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglycerides (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.38 (0.99\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37 (0.98\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65 (1.02\u0026ndash;2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC-reactive protein (mg/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.86 (0.98\u0026ndash;3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.85 (0.98\u0026ndash;3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (0.98-3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsulin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetformin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e358 (84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e319 (85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSulfonylureas\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlinides\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGliptines\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGLP1 Receptor Agonists\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStatins\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e251 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEzetimibe\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFibrates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eACE inhibitors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eARB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133 (36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBetablocker\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThiazide diuretics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCalcium channel inhibitors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComputed Tomography\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAgatston score (AU)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.85 (0-222.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.2 (0.00-186.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e221.7 (19.8-784.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCAC Class\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100\u0026ndash;399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNo event\u003c/b\u003e \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eEvent\u003c/b\u003e \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCardiovascular Events\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFirst Cardiovascular Event\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyocardial infarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart Failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschaemic Stroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObliterating arteriopathy of the lower limbs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular Death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up (days)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,556 (2,556-2,556)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,556 (2,556-2,556)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,406 (566-1,876)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eCategorical variables were compared using Fisher's exact test or Pearson's Chi-squared test ; continuous variables using Wilcoxon rank sum test.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eValues are presented as median (IQR) or number (percentage). Comparisons between patients with and without cardiovascular events are reported along with p-values.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eBMI: Body Mass Index, eGFR: estimated Glomerular Filtration Rate, UACR: Urinary Albumin Creatinin Ratio, ACE: Angiotensin-Converting Enzyme, ARB: Angiotensin Receptor Blockers, CAC: Coronary Artery Calcium\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNotably, DR was significantly more prevalent among patients who developed CV events (58% vs. 25%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CAC scores were markedly higher in this group (median Agatston score: 221.7 vs. 33.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of patients with CAC\u0026thinsp;=\u0026thinsp;0 was 33% in the control group versus 10% in the event group whereas CAC\u0026thinsp;\u0026ge;\u0026thinsp;400 was present in 36% of patients in the event group versus only 15% of patients without events. Intermediate CAC categories (1\u0026ndash;99 and 100\u0026ndash;399) were similarly distributed between the two groups. These findings suggest a marked difference towards more advanced coronary calcification in the event group but with the intermediate CAC categories (1\u0026ndash;99 and 100\u0026ndash;399) not clearly distinguishing between individuals with and without events.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCox Regression Confirms Independent and Additive Value of DR\u003c/h2\u003e\u003cp\u003eMultivariable Cox regression models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) confirmed that CAC score and DR were both independently associated with CV outcomes. In the unadjusted CAC model, hazard ratios (HRs) increased progressively with CAC burden vs. having no coronary artery calcium (CAC\u0026thinsp;=\u0026thinsp;0): HR 2.96 [1.08\u0026ndash;8.15] for CAC 1\u0026ndash;99, HR 3.97 [1.40\u0026ndash;11.27] for CAC 100\u0026ndash;399, and HR 7.13 [2.65\u0026ndash;19.20] for CAC\u0026thinsp;\u0026ge;\u0026thinsp;400 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for trend).\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\u003eCox proportional hazards models for cardiovascular events: stepwise inclusion of Coronary Artery Calcium (CAC), Diabetic Retinopathy (DR) and classical cardiovascular risk factors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eCAC\u0026thinsp;+\u0026thinsp;DR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eAdj. CAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eAdj. CAC\u0026thinsp;+\u0026thinsp;DR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.08\u0026ndash;8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e3.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.10\u0026ndash;8.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e2.99\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.07\u0026ndash;8.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e3.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.08\u0026ndash;8.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100\u0026ndash;399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.40-11.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e3.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.25\u0026ndash;10.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e3.70\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.25\u0026ndash;10.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e3.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.16\u0026ndash;10.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e7.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.65\u0026ndash;19.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e5.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.06\u0026ndash;15.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e6.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.42\u0026ndash;19.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e5.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.98\u0026ndash;16.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRetinopathy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e3.11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.75\u0026ndash;5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e3.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.96\u0026ndash;6.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.97\u0026ndash;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.98\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u0026nbsp;: Female\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.52\u0026ndash;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.55\u0026ndash;1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDuration of diabetes\u003c/b\u003e (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.96\u0026ndash;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.95\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.66\u0026ndash;2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.61\u0026ndash;2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoker\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e2.06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.08\u0026ndash;3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e2.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.22\u0026ndash;4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.45\u0026ndash;1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.47\u0026ndash;1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNephropathy (\u003c/b\u003eeGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/m\u0026sup2; and/or UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/mmol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.70\u0026ndash;3.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.58\u0026ndash;3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eHazard ratios (HR), 95% confidence intervals (CI), and p-values are shown for each model, CAC score alone and CAC score\u0026thinsp;+\u0026thinsp;Diabetic Retinopathy, adjusted or not.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eEach model assesses the independent and additive predictive value of each variable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdding DR to the model (CAC\u0026thinsp;+\u0026thinsp;DR) revealed that DR was independently associated with CV events (HR 3.11 [1.75\u0026ndash;5.52] when DR present, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eIn the fully adjusted model (adjusted CAC\u0026thinsp;+\u0026thinsp;DR), DR remained a strong and significant predictor (HR 3.56 [1.96\u0026ndash;6.45], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as did CAC. Active smoking also emerged as an independent risk factor (HR 2.34 [1.22\u0026ndash;4.51], p\u0026thinsp;=\u0026thinsp;0.011), while age, hypertension, and dyslipidemia were not significant predictors of events.\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe Kaplan-Meier survival curves highlight the progressive prognostic value of CAC scores and diabetic retinopathy in predicting adverse cardiovascular outcomes\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFirst, we used the Kaplan\u0026ndash;Meier method to assess CV event-free survival by CAC score category. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA illustrates the progressive reduction in survival associated with an increasing CAC burden. The 7-year event-free survival rate decreased progressively across CAC categories. Patients with CAC score of 0 had the most favorable prognosis, while those with CAC score of \u0026ge;\u0026thinsp;400 experienced the highest event rate over time. These differences remained statistically significant after adjusting for age, gender, hypertension, dyslipidemia, active smoking, and nephropathy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), confirming CAC as a robust and independent prognostic biomarker.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate whether DR further improves risk stratification, we performed stratified event-free survival analyses that were stratified by CAC and DR status. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows the unadjusted event-free survival curves for the eight subgroups defined by the combinations of CAC strata and DR presence. Patients with both CAC\u0026thinsp;\u0026ge;\u0026thinsp;400 and DR exhibited significantly poorer event-free survival, with the most pronounced decline in event-free probability. Interestingly, patients with CAC\u0026thinsp;=\u0026thinsp;100\u0026ndash;399 and DR exhibited event-free survival curves that closely resembled those of the CAC\u0026thinsp;\u0026ge;\u0026thinsp;400 group without DR. This suggests that DR effectively \"uprisks\" patients with moderate CAC categorizing them as high risk. Conversely, patients with CAC 100\u0026ndash;399 and no DR demonstrated substantially better event-free survival, resembling the CAC 1\u0026ndash;99 group more closely. Similarly, among patients with CAC\u0026thinsp;=\u0026thinsp;0, the presence of DR substantially reduced event-free survival and brought their risk closer to that of patients with CAC 1\u0026ndash;99 without DR. This illustrates the impact of DR even in patients with minimal coronary calcification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter adjusting for traditional cardiovascular risk factors, the same pattern persisted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The addition of DR provided a more granular risk stratification within each CAC category. For instance, among patients with a CAC score of zero, those with DR had significantly lower event-free survival rates than those without DR. Conversely, in higher CAC groups, the presence of DR consistently identified the subpopulation at the highest absolute cardiovascular risk.\u003c/p\u003e\u003cp\u003eOverall, these survival analyses demonstrate that DR provides valuable additional prognostic information to CAC, effectively upgrading cardiovascular risk stratification of patients with low-to-moderate CAC scores.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDR Improves Predictive Accuracy of CAC-Based Models\u003c/h2\u003e\u003cp\u003eReceiver operating characteristic (ROC) curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that adding DR to CAC-based models greatly improved their ability to distinguish between patients who would and would not experience CV events. The DR-only CAC-only model yielded an area under the curve AUC of 67.8 [95% CI: 60.5\u0026ndash;75.1], indicative of modest but commendable predictive performance. When DR was added (CAC\u0026thinsp;+\u0026thinsp;DR model), the AUC increased significantly to 75.3 [68.9\u0026ndash;81.6] (p\u0026thinsp;=\u0026thinsp;0.011 vs. CAC alone), reflecting improved discrimination through the integration of microvascular disease.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther refinement was achieved by adjusting CAC for traditional cardiovascular risk factors (age, gender, hypertension, dyslipidemia, smoking status, and nephropathy), which improved the AUC to 71.3 [63.6\u0026ndash;79.0]. However, the model that was fully adjusted to include both CAC and DR (Adj. CAC\u0026thinsp;+\u0026thinsp;DR) produced the most accurate predictions with an AUC of 78.2 [72.0\u0026ndash;84.3]. This represented a statistically significant improvement on the adjusted CAC model alone (p\u0026thinsp;=\u0026thinsp;0.0139).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a comprehensive breakdown of the models. The adjusted CAC\u0026thinsp;+\u0026thinsp;DR model demonstrated significant improvement in sensitivity (78%) compared to CAC alone (36%) and adjusted CAC (58%), indicating a greater capacity for accurately identify true positive cases. Although specificity decreased (66.8% vs.72.46% for adjusted CAC), the negative predictive value increased substantially (95.8% vs. 92.8%, p\u0026thinsp;=\u0026thinsp;0.049), indicating superior performance in ruling out events. Positive predictive values were comparable across the models, but the adjusted CAC\u0026thinsp;+\u0026thinsp;DR model achieved the best overall classification accuracy.\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\u003e\u003cb\u003eDiagnostic performance of different predictive models evaluated.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCAC\u0026thinsp;+\u0026thinsp;DR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdj. CAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdj. CAC\u0026thinsp;+\u0026thinsp;DR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThreshold (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSensitivity (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36 [22.92\u0026ndash;50.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64 [49.19\u0026ndash;77.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58 [43.21\u0026ndash;71.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78 [64.04\u0026ndash;88.47]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpecificity (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.76 [80.71\u0026ndash;88.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.99 [68.19\u0026ndash;77.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72.46 [67.63\u0026ndash;76.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66.84 [61.82\u0026ndash;71.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive LR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.36 [1.52\u0026ndash;3.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.37 [1.82\u0026ndash;3.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.11 [1.58\u0026ndash;2.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.35 [1.91\u0026ndash;2.89]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNegative LR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.76 [0.61\u0026ndash;0.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.49 [0.34\u0026ndash;0.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58 [0.42\u0026ndash;0.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33 [0.19\u0026ndash;0.56]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePPV (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 [16.9\u0026ndash;32.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.06 [19.53\u0026ndash;29.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.97 [17.44\u0026ndash;27.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.93 [20.38\u0026ndash;27.87]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNPV (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90.83 [88.9-92.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.81 [91.25\u0026ndash;95.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.81 [90.26\u0026ndash;94.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e95.79 [93.07\u0026ndash;97.47]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAccuracy (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.01 [74.82\u0026ndash;82.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.93 [67.4-76.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.75 [66.17\u0026ndash;75.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.16 [63.49\u0026ndash;72.57]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCAC vs CAC\u0026thinsp;+\u0026thinsp;DR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdj. (CAC vs CAC\u0026thinsp;+\u0026thinsp;DR)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4e-4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.4e-10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive LR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNegative LR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePPV (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNPV (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eModels include coronary artery calcium (CAC) score alone (CAC), CAC score combined with Diabetic Retinopathy (CAC\u0026thinsp;+\u0026thinsp;DR), adjusted CAC (Adj. CAC), and adjusted CAC with DR (Adj. CAC\u0026thinsp;+\u0026thinsp;DR). Models are adjusted for age, gender, dyslipidemia, hypertension, smoking status and nephropathy\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eReported metrics include optimal classification threshold (%), sensitivity, specificity, positive and negative likelihood ratios (LR\u0026thinsp;+\u0026thinsp;and LR\u0026minus;), positive predictive value (PPV), negative predictive value (NPV), overall accuracy, and area under the curve (AUC), each presented with 95% confidence intervals.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eStatistical comparisons between models were conducted using appropriate pairwise statistical tests (e.g., McNemar's test). A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAUC statistical comparison confirmed that the CAC\u0026thinsp;+\u0026thinsp;DR model outperformed CAC alone (p\u0026thinsp;=\u0026thinsp;0.011), and that even when adjusted for traditional risk factors, adding DR continued to confer a significant benefit (p\u0026thinsp;=\u0026thinsp;0.0139). These findings highlight the effectiveness of DR as an additional biomarker alongside CAC, enhancing the accuracy and effectiveness of cardiovascular risk stratification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eNet Reclassification Analysis Supports Clinical Utility of DR\u003c/h2\u003e\u003cp\u003eTo further evaluate the clinical added value of DR, we performed net reclassification improvement (NRI) analyses based on predefined 7-year cardiovascular risk categories: low (\u0026lt;\u0026thinsp;7%), intermediate (7\u0026ndash;11%), and high (\u0026gt;\u0026thinsp;11%). These categories were chosen to represent significant clinical thresholds for preventive decision-making. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the categorical NRI outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNet Reclassification Index (NRI) for 7-year cardiovascular event risk predicted by models incorporating coronary artery calcium (CAC) score alone and CAC combined to Diabetic Retinopathy (DR)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eProbability of CV event with CAC\u0026thinsp;+\u0026thinsp;DR model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\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\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReclassified to higher risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReclassified to lower risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCategorial NRI [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProbability of CV event with CAC model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow risk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.576 [0.2178\u0026ndash;0.9342]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e0.00162\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvent\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\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate risk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.4083 [0.1506\u0026ndash;0.6661]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e0.0019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh risk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.162 [-0.0113\u0026ndash;0.3353]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.06687\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\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\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.12 [-0.0601\u0026ndash;0.3001]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.19152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvent\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\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eProbability of CV event with\u003c/p\u003e\u003cp\u003eAdj. CAC\u0026thinsp;+\u0026thinsp;DR model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\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\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReclassified to higher risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReclassified to lower risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCategorial NRI [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProbability of CV event with Adj. CAC model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow risk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.3959 [-0.0408\u0026ndash;0.8327]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.07562\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvent\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\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate risk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.689 [0.3831\u0026ndash;0.9949]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e1,00E-05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh risk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.2754 [0.1517\u0026ndash;0.3991]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e1,00E-05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.2443 [0.0961\u0026ndash;0.3925]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e0.00123\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvent\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\u003e10\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\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eCardiovascular risk categories: 0 to \u0026lt;\u0026thinsp;7% (low risk), 7 to \u0026lt;\u0026thinsp;14% (intermediate risk), and \u0026gt;\u0026thinsp;14% (high risk).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eModels are adjusted for age, gender, dyslipidemia, hypertension, smoking status and nephropathy.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNA, non applicable. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the low-risk group, we observed a significant categorical NRI of 0.576 [95% CI: 0.218\u0026ndash;0.934], p\u0026thinsp;=\u0026thinsp;0.0016, when comparing the CAC\u0026thinsp;+\u0026thinsp;DR model with CAC alone. This suggests that the model can more accurately classify non-event cases that have already been designated as low risk as non-events, as well as more accurately up-classify cases that have occurred. In the intermediate-risk group, where treatment decisions are often challenging, the categorical NRI was 0.4083 [0.151\u0026ndash;0.666], p\u0026thinsp;=\u0026thinsp;0.0019. Within this subgroup, 9 out of 15 patients who experienced events were correctly reclassified upward from intermediate to high risk, ensuring more aggressive preventive attention. In the high-risk category, 51 out of 129 patients who did not experience an event were reclassified downward from high to moderate risk, potentially avoiding unnecessary treatment.\u003c/p\u003e\u003cp\u003eImportantly, the effects of reclassification were even more pronounced in the adjusted models (adjusted CAC vs. adjusted CAC\u0026thinsp;+\u0026thinsp;DR), reclassification effects were even more pronounced. The intermediate-risk category again demonstrated the greatest change in classification accuracy. The categorical NRI reached 0.689 [95% CI: 0.383\u0026ndash;0.995], p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001, indicating that almost 69% of reclassifications improved the concordance between the predicted and observed outcomes. Specifically, 7 of 11 patients who experienced events were correctly reclassified upwards to the high-risk group, ensuring timely identification for aggressive intervention. Conversely, 33 out of 133 patients without events were correctly reclassified downwards to low-risk group, representing a significant improvement in the allocation of preventive measures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStratified Risk Visualization Confirms Clinical Value of DR\u003c/h2\u003e\u003cp\u003eTo provide a practical tool for clinical stratification, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a heat map integrating CAC categories (0, 1\u0026ndash;99, 100\u0026ndash;399, \u0026ge;\u0026thinsp;400), DR status (absent or present), and smoking status (non-smoker vs. active smoker). Each combination of these three variables is color-coded into one of four clinical risk categories: low (\u0026lt;\u0026thinsp;7%), intermediate (7\u0026ndash;11%), high (11\u0026ndash;20%) and very high (\u0026gt;\u0026thinsp;20%) 7-year cardiovascular risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis visualization reveals several clinically relevant patterns. Firstly, individuals with a CAC score of 0 and no retinopathy consistently demonstrate low cardiovascular risk, irrespective of smoking status. This suggests that the absence of both vascular calcification and retinopathy is a strong negative predictor. In contrast, patients with a score of \u0026ge;\u0026thinsp;400 were predominantly classified as high or very high risk, with this risk being further amplified in smokers, particularly in the presence of DR. Even within the moderate CAC range (100\u0026ndash;399), the presence of DR significantly shifted risk classification upwards. For example, non-smokers with DR in this range were estimated to be at high risk (~\u0026thinsp;17.9%), whereas smokers with DR showed a dramatic increase into the very high-risk range (~\u0026thinsp;37%). A similar pattern was seen in those with mild CAC (1\u0026ndash;99): while most non-smoking individuals with no DR remained low risk, the addition of DR and smoking elevated risk levels into the high-risk or very high-risk categories (up to 33.1%). While smoking alone did increase baseline risk; however, smoking in isolation rarely drove classification into high or very-high risk categories unless coupled with DR and/or elevated CAC scores.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe show, for the first time, that DR significantly enhances cardiovascular risk stratification when combined with CAC score in patients living with T2DM, even after adjustment for traditional risk factors.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDR and CAC: Complementary and Synergistic Markers of Systemic Vascular Risk\u003c/h2\u003e\u003cp\u003eOur data show that the presence of DR increases the risk associated with intermediate CAC scores. Indeed, individuals with moderate CAC (100\u0026ndash;399) and DR have outcomes comparable to those with high CAC (\u0026ge;\u0026thinsp;400), However, individuals without DR within the same CAC range have a markedly better prognosis. A similar pattern is observed among patients with low CAC scores (1\u0026ndash;99), where the presence of DR is associated with worse outcomes than in those without DR.\u003c/p\u003e\u003cp\u003eAdjusted Cox models and Kaplan-Meier survival analyses confirm that DR stratifies residual risk not captured by CAC alone. This supports a dual-marker approach wherein DR and CAC jointly reflect both macro-atherosclerotic load and systemic vascular fragility. Notably, this interaction remains evident even when adjusted for traditional CV risk factors such as age, gender, duration of diabetes, hypertension, dyslipidemia and nephropathy. This suggests that DR also enhances risk discrimination independently of known covariates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e\u003cem\u003eRisk Reclassification and Clinical Value\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eFrom a clinical utility perspective, our study provides compelling evidence that incorporating DR into CAC-based models greatly improves risk prediction and patient reclassification, particularly in the intermediate-risk group, where therapeutic decision-making is most challenging.\u003c/p\u003e\u003cp\u003eReceiver operating characteristic (ROC) curve analyses and NRI showed that integrating DR increases the sensitivity and negative predictive value of predictive models, while improving their calibration. Notably, upon inclusion of DR, a substantial proportion of patients who experienced an event were correctly reclassified into higher-risk categories, while those who did not experience an event were appropriately reclassified into lower-risk categories, reducing overtreatment. This refined stratification has immediate clinical implications. Patients with moderate CAC and concomitant DR should be considered for intensified CV prevention strategies, which may include statins, renin-angiotensin system blockade, sodium-glucose cotransporter-2 (SGLT2) inhibitors, and even aspirin. Conversely, patients with low CAC and absence of DR may safely avoid escalating therapy, thereby minimizing polypharmacy and potential complications. Furthermore, these data reinforce the additive prognostic value of DR, particularly when combined with lifestyle (smoking) and imaging-based (CAC) markers. The proposed risk heatmap which integrates CAC, DR, and smoking status offers clinicians a simple, visual decision-support tool for clinicians consistent with the principles of personalized medicine.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eBeyond Coronary Atherosclerosis: A Broader Cardiovascular Perspective\u003c/h2\u003e\u003cp\u003eTraditional CV risk models, including those integrating CAC, focus primarily on macrovascular events. However, in diabetes, the vascular pathology is systemic and not limited to large vessel atherosclerosis. Notably, while CAC quantifies structural, calcified atherosclerosis, it can underestimate cardiovascular risk in patients with non-calcified or lipid-rich plaques, or with predominant microvascular dysfunction. DR addresses this issue by identifying patients with subclinical or systemic vascular impairment which is potentially caused by diffuse endothelial injury and microcirculatory rarefaction. Recently, the evaluation of retinal microcrovasculature using optical coherence tomography angiography (OCTA) parameters, was found to be closely associated with coronary atherosclerosis(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, DR may provide a more comprehensive indication of generalized vascular injury, encompassing both the microvascular and macrovascular beds. Thus, DR highlights risks beyond classic atherothrombotic events, including heart failure. Recent evidence suggests that CAC is not equally predictive across all forms of CV disease. For example, data from the MESA study revealed a significant association between CAC and heart failure with preserved ejection fraction (HFpEF) only in women(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), highlighting sex-specific differences and limitations of CAC as a universal marker. These findings challenge the notion that CAC can be used universally and highlight the importance of microvascular dysfunction, a key factor in diastolic dysfunction and HFpEF, that cannot be quantified by CAC.\u003c/p\u003e\u003cp\u003eIn contrast, DR has been independently associated with both systolic(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and diastolic(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) cardiac dysfunction, carotid intima-media thickness(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and stroke(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), coronary microvascular dysfunction(\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and peripheral arterial disease (PAD)(\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Notably, DR has been linked to incident HFpEF even after adjusting for conventional risk factors\u0026mdash;including age, sex, diabetes duration, insulin use, blood pressure, lipid profile(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This suggests that DR may reflect also the microvascular impairment in HFpEF.\u003c/p\u003e\u003cp\u003eThese findings support the use of DR as a proxy for systemic microangiopathy, driven by shared mechanisms such as oxidative stress, inflammation, and endothelial dysfunction. This microangiopathy is relevant to a range of CV conditions, including coronary and cerebrovascular diseases, PAD and heart failure.\u003c/p\u003e\u003cp\u003eTherefore, DR expands the assessment of vascular risk beyond what that can be offered by CAC alone thereby supporting its integration into CV risk models.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eCommon Risk Factors and Shared Pathophysiological Pathways\u003c/h2\u003e\u003cp\u003eDR and CV diseases share a spectrum of modifiable and non-modifiable risk factors, including hyperglycemia, hypertension, dyslipidemia, obesity, insulin resistance, and chronic inflammation(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These risk factors act through overlapping pathophysiological mechanisms that promote both microvascular and macrovascular complications in diabetes.\u003c/p\u003e\u003cp\u003eTheir pathogenesis is rooted in oxidative stress(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and inflammation(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Hyperglycemia triggers mitochondrial ROS overproduction(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), activating polyol, hexosamine, and PKC pathways, and accumulating AGEs(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). These processes cause endothelial dysfunction, pericyte loss, and capillary occlusion in DR, while simultaneously promoting plaque instability and atherogenesis in large vessels(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Endothelial dysfunction, marked by ROS-mediated NO inactivation, eNOS uncoupling, vascular inflammation, impaired vasodilation and platelet hyperaggregability, manifests early as blood-retinal barrier breakdown in DR and as arterial stiffness(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) reduced coronary flow reserve and impaired microcirculatory adaptation in CVD(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003csup\u003e,20\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInflammation is another shared axis between DR and CVD. In DR, elevated ICAM-1, VCAM-1, and pro-inflammatory cytokines (IL-6, MCP-1, TNF-α) drive leukostasis, vascular leakage, and neovascularization (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). These same mediators promote atherosclerotic plaque infiltration, rupture, and thrombosis in CVD(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), while foam cell formation\u0026mdash;via macrophage uptake of oxidized LDL\u0026mdash;sustains vascular inflammation and CVD progression (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe convergence of metabolic, oxidative, and inflammatory pathways suggests that DR is not merely a local retinal disorder, but also an indicator of systemic vascular damage. Therefore, it is biologically plausible and clinically justified to use DR as an indicator of broader CV vulnerability in T2DM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eDiabetic Retinopathy: a surrogate marker of myocardial microvascular disease?\u003c/h2\u003e\u003cp\u003eOnce a debated entity, myocardial microcirculatory dysfunction is now recognized as a key mechanism of myocardial dysfunction(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). During increased metabolic demand, arterioles normally dilate to reduce microvascular resistance and boost perfusion. Dysfunction of the coronary microcirculation can therefore cause myocardial ischemia and heart failure(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Interestingly, diabetic microangiopathy affects both retinal capillaries and arterioles (12\u0026ndash;200 \u0026micro;m) and also coronary arterioles (30\u0026ndash;150 \u0026micro;m). Lipotoxicity and glucotoxicity both play a role in the development of DR and diabetic microangiopathy in the myocardium through identified mechanisms such as oxidative stress caused by mitochondrial dysfunction and the production of reactive oxygen species (ROS) as well as the release of pro-inflammatory cytokines(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) leading to vascular stiffness and impaired rheology and perfusion. In turn, microvascular dysfunction promotes myocardial remodeling and stiffness which can lead to heart failure. Therefore, DR, which was historically considered a localized microvascular complication, emerges as a non-invasive and clinically relevant marker of myocardial microangiopathy(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). In clinical practice, the presence of DR indicates the presence of early and diffuse microvascular dysfunction, which may precede or accompany subclinical myocardial impairment. This concept is reinforced by recent recommendations from ESC 2023(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), which demonstrate that patients with DR exhibit a very high cardiovascular risk, independently of traditional risk factors.\u003c/p\u003e\u003cp\u003eOur study provides further evidence to support this view: DR should no longer be regarded solely as an isolated retinal microvascular event but rather as a systemic indicator of heightened cardiovascular risk. Incorporating DR into a comprehensive cardiovascular risk assessment could enable the early identification of high-risk patients and inform targeted preventive strategies, such as strict glycemic control, management of metabolic comorbidities, and potentially, microvascular-directed pharmacological interventions.\u003c/p\u003e\u003cp\u003eIn conclusion, DR represents a powerful and accessible clinical indicator of systemic microvascular fragility and should therefore be incorporated into the cardiovascular risk stratification of patients with diabetes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eDespite its strengths, our study has several limitations that must be acknowledged. Firstly, the study was conducted in a single tertiary care academic center, which may limit the generalizability of the findings. Second, the observational nature of the study prevents causal inferences and restricts our ability to determine whether the observed associations are directly attributable to the presence of DR or reflect confounding vascular risk factors. There is a potential source of selection bias, as all participants included in the analysis had undergone both CAC scanning and retinal imaging. This dual assessment may reflect a cohort that clinicians already perceive to be at elevated cardiovascular risk or to be more engaged in preventive care, thereby introducing a degree of selection bias. The classification of DR in this study was binary (present versus absent). While this approach ensures reproducibility, it may mask the prognostic nuances conveyed by different stages of DR severity. Future studies should consider integrating the full spectrum of DR, from mild non-proliferative forms to proliferative retinopathy, to improve the granularity of risk stratification\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that DR, a readily available, non-invasive, and cost-effective marker, significantly improves CV risk prediction when combined with CAC scoring in patients with type 2 diabetes mellitus in a primary prevention setting. Therefore, CAC and DR represent two complementary markers of cardiovascular risk. Notably, the greatest improvement in risk stratification was observed among individuals with intermediate CAC scores, a group for whom clinical decision-making is often the most uncertain. By identifying patients at high- and very high-risk patients who would otherwise be misclassified by CAC alone, including DR enables more precise, personalized CV prevention strategies. Although DR is largely absent from conventional cardiovascular risk prediction models, our results support its inclusion in clinical algorithms and risk calculators.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eACE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAngiotensin-converting enzyme inhibitor\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAngiotensin II receptor blocker\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArea Under the Curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoronary Artery Calcium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCV\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardioVascular\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiabetic Retinopathy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHFpEF\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeart Failure with preserved Ejection Fraction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eeGFR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEstimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eESC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEuropean Society of Cardiology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGLP-1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlucagon-like peptide-1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHemoglobin A1c\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-density lipoprotein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHazard Ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLow-density lipoprotein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNPDR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon-Proliferative Diabetic Retinopathy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePAD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral Arterial Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePDR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProliferative Diabetic Retinopathy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReactive Oxygen Species\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSystolic blood pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandard deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT2DM\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eType 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUACR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUrinary Albumin Creatinin Ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the patients, investigators, nurses and URC-Piti\u0026eacute;-Salp\u0026ecirc;tri\u0026egrave;re staff members involved in the ACCoDiab study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAR, FA, FP, and OB were involved in the conception and design of the study. ACJ, AG, AT, FA, FP OB, SB and SL recruited participants and collected the data. AR and SB performed cardiac imaging measurements. AR, FA, FP, OB and TB performed the data analysis. AG, AR, FA, FP, OB and TB wrote the first draft of the manuscript, and AG, AR, FA, FP, OB and TB participated in manuscript edition. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the local ethics committee (PARIS VI CPP) and registered in ClinicalTrials.gov (Identifiers: NCT03920683). All patients were informed about the study objectives and procedure. Participants gave their written informed consent to participate prior to inclusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eColhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HAW, Livingstone SJ, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 2004;364(9435):685\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeech A, Simes RJ, Barter P, Best J, Scott R, Taskinen MR, et al. Effects of long-term fenofibrate therapy on cardiovascular events in 9795 people with type 2 diabetes mellitus (the FIELD study): randomised controlled trial. Lancet. 2005;366(9500):1849\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eADVANCE Collaborative Group, Patel A, MacMahon S, Chalmers J, Neal B, Billot L, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358(24):2560\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAction to Control Cardiovascular Risk in Diabetes Study Group, Gerstein HC, Miller ME, Byington RP, Goff DC, Bigger JT, et al. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358(24):2545\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBARI 2D Study Group, Frye RL, August P, Brooks MM, Hardison RM, Kelsey SF, et al. A randomized trial of therapies for type 2 diabetes and coronary artery disease. N Engl J Med. 2009;360(24):2503\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoung LH, Wackers FJT, Chyun DA, Davey JA, Barrett EJ, Taillefer R, et al. Cardiac outcomes after screening for asymptomatic coronary artery disease in patients with type 2 diabetes: the DIAD study: a randomized controlled trial. JAMA. 2009;301(15):1547\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElkeles RS, Godsland IF, Feher MD, Rubens MB, Roughton M, Nugara F, et al. Coronary calcium measurement improves prediction of cardiovascular events in asymptomatic patients with type 2 diabetes: the PREDICT study. Eur Heart J. 2008;29(18):2244\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgarwal S, Cox AJ, Herrington DM, Jorgensen NW, Xu J, Freedman BI, et al. Coronary calcium score predicts cardiovascular mortality in diabetes: diabetes heart study. Diabetes Care. 2013;36(4):972\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalik S, Zhao Y, Budoff M, Nasir K, Blumenthal RS, Bertoni AG, et al. Coronary Artery Calcium Score for Long-term Risk Classification in Individuals With Type 2 Diabetes and Metabolic Syndrome From the Multi-Ethnic Study of Atherosclerosis. JAMA Cardiol. 2017;2(12):1332\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, et al. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2020;41(2):255\u0026ndash;323.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIchikawa K, Miyoshi T, Osawa K, Miki T, Toda H, Ejiri K, et al. Prognostic value of non-alcoholic fatty liver disease for predicting cardiovascular events in patients with diabetes mellitus with suspected coronary artery disease: a prospective cohort study. Cardiovasc Diabetol. 2021;20(1):8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBudoff MJ, Young R, Burke G, Jeffrey Carr J, Detrano RC, Folsom AR, et al. Ten-year association of coronary artery calcium with atherosclerotic cardiovascular disease (ASCVD) events: the multi-ethnic study of atherosclerosis (MESA). Eur Heart J. 2018;39(25):2401\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDetrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358(13):1336\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSemeraro F, Morescalchi F, Cancarini A, Russo A, Rezzola S, Costagliola C. Diabetic retinopathy, a vascular and inflammatory disease: Therapeutic implications. Diabetes Metab. 2019;45(6):517\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanter JE, Kramer F, Barnhart S, Averill MM, Vivekanandan-Giri A, Vickery T, et al. Diabetes promotes an inflammatory macrophage phenotype and atherosclerosis through acyl-CoA synthetase 1. Proc Natl Acad Sci U S A. 2012;109(12):E715\u0026ndash;724.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLibby P. Inflammation in atherosclerosis. Nature. 2002;420(6917):868\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBetteridge DJ. What is oxidative stress? Metabolism. 2000;49(2 Suppl 1):3\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHammes HP. Diabetic retinopathy: hyperglycaemia, oxidative stress and beyond. Diabetologia. 2018;61(1):29\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res. 2010;107(9):1058\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;nzel T, Daiber A, Ullrich V, M\u0026uuml;lsch A. Vascular consequences of endothelial nitric oxide synthase uncoupling for the activity and expression of the soluble guanylyl cyclase and the cGMP-dependent protein kinase. Arterioscler Thromb Vasc Biol. 2005;25(8):1551\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReaven PD, Emanuele N, Moritz T, Klein R, Davis M, Glander K, et al. Proliferative diabetic retinopathy in type 2 diabetes is related to coronary artery calcium in the Veterans Affairs Diabetes Trial (VADT). Diabetes Care. 2008;31(5):952\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTargher G, Bertolini L, Zenari L, Lippi G, Pichiri I, Zoppini G, et al. Diabetic retinopathy is associated with an increased incidence of cardiovascular events in Type 2 diabetic patients. Diabet Med. 2008;25(1):45\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGimeno-Orna JA, Faure-Nogueras E, Castro-Alonso FJ, Boned-Juliani B. Ability of retinopathy to predict cardiovascular disease in patients with type 2 diabetes mellitus. Am J Cardiol. 2009;103(10):1364\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawasaki R, Tanaka S, Tanaka S, Abe S, Sone H, Yokote K, et al. Risk of cardiovascular diseases is increased even with mild diabetic retinopathy: the Japan Diabetes Complications Study. Ophthalmology. 2013;120(3):574\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerstein HC, Ambrosius WT, Danis R, Ismail-Beigi F, Cushman W, Calles J, et al. Diabetic retinopathy, its progression, and incident cardiovascular events in the ACCORD trial. Diabetes Care. 2013;36(5):1266\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWong TY, Rosamond W, Chang PP, Couper DJ, Sharrett AR, Hubbard LD, et al. Retinopathy and risk of congestive heart failure. JAMA. 2005;293(1):63\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheung N, Wang JJ, Rogers SL, Brancati F, Klein R, Sharrett AR, et al. Diabetic retinopathy and risk of heart failure. J Am Coll Cardiol. 2008;51(16):1573\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheung N, Rogers S, Couper DJ, Klein R, Sharrett AR, Wong TY. Is diabetic retinopathy an independent risk factor for ischemic stroke? Stroke. 2007;38(2):398\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohammedi K, Woodward M, Hirakawa Y, Zoungas S, Colagiuri S, Hamet P, et al. Presentations of major peripheral arterial disease and risk of major outcomes in patients with type 2 diabetes: results from the ADVANCE-ON study. Cardiovasc Diabetol. 2016;15(1):129.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen YW, Wang YY, Zhao D, Yu CG, Xin Z, Cao X, et al. High prevalence of lower extremity peripheral artery disease in type 2 diabetes patients with proliferative diabetic retinopathy. PLoS ONE. 2015;10(3):e0122022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen SC, Hsiao PJ, Huang JC, Lin KD, Hsu WH, Lee YL, et al. Abnormally Low or High Ankle-Brachial Index Is Associated with Proliferative Diabetic Retinopathy in Type 2 Diabetic Mellitus Patients. PLoS ONE. 2015;10(7):e0134718.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarx N, Federici M, Sch\u0026uuml;tt K, M\u0026uuml;ller-Wieland D, Ajjan RA, Antunes MJ, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes. Eur Heart J. 2023;44(39):4043\u0026ndash;140.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang JM, Yang DH, Lee SW, Kwak J, Lee Y, Kim YJ et al. Subclinical Coronary Atherosclerosis and Retinal Optical Coherence Tomography Angiography. JAMA Cardiol. 2025;e253036.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma K, Al Rifai M, Ahmed HM, Dardari Z, Silverman MG, Yeboah J, et al. Usefulness of Coronary Artery Calcium to Predict Heart Failure With Preserved Ejection Fraction in Men Versus Women (from the Multi-Ethnic Study of Atherosclerosis). Am J Cardiol. 2017;120(10):1847\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung YR, Park SJ, Moon KY, Choi SA, Lim HS, Park SW, et al. Diabetic retinopathy is associated with diastolic dysfunction in type 2 diabetic patients with non-ischemic dilated cardiomyopathy. Cardiovasc Diabetol. 2017;16(1):82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRema M, Mohan V, Deepa R, Ravikumar R, Chennai Urban Rural Epidemiology Study-2. Association of carotid intima-media thickness and arterial stiffness with diabetic retinopathy: the Chennai Urban Rural Epidemiology Study (CURES-2). Diabetes Care. 2004;27(8):1962\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalecki MT, Osmenda G, Walus-Miarka M, Skupien J, Cyganek K, Mirkiewicz-Sieradzka B, et al. Retinopathy in type 2 diabetes mellitus is associated with increased intima-media thickness and endothelial dysfunction. Eur J Clin Invest. 2008;38(12):925\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes. 2005;54(6):1615\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWakisaka M, Nagao T. Sodium glucose cotransporter 2 in mesangial cells and retinal pericytes and its implications for diabetic nephropathy and retinopathy. Glycobiology. 2017;27(8):691\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenbaum D, Kachenoura N, Koch E, Paques M, Cluzel P, Redheuil A, et al. Relationships between retinal arteriole anatomy and aortic geometry and function and peripheral resistance in hypertensives. Hypertens Res. 2016;39(7):536\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhalfaoui T, Lizard G, Ouertani-Meddeb A. Adhesion molecules (ICAM-1 and VCAM-1) and diabetic retinopathy in type 2 diabetes. J Mol Histol. 2008;39(2):243\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTabas I, Garc\u0026iacute;a-Carde\u0026ntilde;a G, Owens GK. Recent insights into the cellular biology of atherosclerosis. J Cell Biol. 2015;209(1):13\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLibby P. Current concepts of the pathogenesis of the acute coronary syndromes. Circulation. 2001;104(3):365\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakamura M. Lipotoxicity as a therapeutic target in obesity and diabetic cardiomyopathy. J Pharm Pharm Sci. 2024;27:12568.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChilian WM. Coronary microcirculation in health and disease. Summary of an NHLBI workshop. Circulation. 1997;95(2):522\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, Shi C, Wang Y, Yu H, Zhang Y, Zhang J, et al. The mechanisms of glycolipid metabolism disorder on vascular injury in type 2 diabetes. Front Physiol. 2022;13:952445.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmad FS, Ning H, Rich JD, Yancy CW, Lloyd-Jones DM, Wilkins JT, Hypertension. Obesity, Diabetes, and Heart Failure-Free Survival: The Cardiovascular Disease Lifetime Risk Pooling Project. JACC Heart Fail. 2016;4(12):911\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Chen C, Yu Z, Chen X, Chen Z, Li W, et al. Myocardial Blood Flow and the Retinal Microvasculature Across the Spectrum From Normal to Failing Hearts. J Clin Hypertens (Greenwich). 2025;27(6):e70087.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"diabetic retinopathy, coronary artery calcium, cardiovascular risk, type 2 diabetes, risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-7925795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7925795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe Coronary artery calcium (CAC) score is a validated imaging biomarker that is used in clinical practice to improve cardiovascular risk stratification in patients with type 2 diabetes (T2DM). However, residual risk remains, particularly in patients with moderate CAC. Diabetic retinopathy (DR), a microvascular complication of diabetes, may reflect systemic vascular vulnerability and provide additional prognostic information.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis observational cohort study was conducted as part of the primary prevention ACCoDiab study. Four hundred and twenty-four patients with T2DM and no prior history of cardiovascular events underwent CAC scoring and clinical assessment, including screening for DR. The study population comprised 175 females and 249 males, with an average age of 60.9 years. Patients were monitored for seven years for cardiovascular events, including nonfatal myocardial infarction, ischemic stroke, hospitalization for heart failure, revascularization of the limbs due to peripheral artery disease and cardiovascular death. Cox regression, Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and net reclassification improvement (NRI) were employed to evaluate the prognostic value of DR alongside CAC score with the aim of developing a simple cardiovascular risk score.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFifty patients (11.8%) experienced cardiovascular events. DR was significantly more prevalent among those who experienced events (58% vs. 25%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both CAC score and DR were independently associated with cardiovascular events. Combining the CAC and DR models significantly improved the prediction of events over the CAC model alone (AUC 75.3 vs. 67.8, p\u0026thinsp;=\u0026thinsp;0.011), with even further improvement when adjusted for traditional risk factors (AUC 78.2, vs 71.3 p\u0026thinsp;=\u0026thinsp;0.014 vs. the CAC model alone). The presence of DR reclassified a significant proportion of patients into higher risk categories, particularly among those with moderate CAC scores.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe combination of DR and CAC score significantly enhances cardiovascular risk stratification in patients living with T2DM, even after adjustment for traditional risk factors.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eNCT03920683\u003c/p\u003e","manuscriptTitle":"Diabetic Retinopathy Enhances Coronary Artery Calcium-Based Cardiovascular Risk Stratification in Patients with Type 2 Diabetes: Insights from the ACCoDiab Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 06:30:40","doi":"10.21203/rs.3.rs-7925795/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38bad59a-83c3-4064-a857-f5fdedfd909f","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T17:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 06:30:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7925795","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7925795","identity":"rs-7925795","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}