Role of Coronary Artery Calcium Scoring in Asymptomatic Diabetes: A Step Towards Primary Prevention

Role of Coronary Artery Calcium Scoring in Asymptomatic Diabetes: A Step Towards Primary Prevention | 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 Role of Coronary Artery Calcium Scoring in Asymptomatic Diabetes: A Step Towards Primary Prevention Dr. Moaaz Tariq, Dr. Mashooque Ali Dasti, Dr. Shumaila Israr, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8530008/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: Asymptomatic individuals with type 2 diabetes mellitus (T2DM) face a significantly elevated risk of atherosclerotic cardiovascular disease (ASCVD), often underestimated by traditional risk calculators. Coronary artery calcium scoring (CACS) is a noninvasive imaging method for quantifying subclinical atherosclerosis and enhancing cardiovascular risk stratification. Objective: To evaluate the utility of CACS in asymptomatic T2DM patients in Pakistan and review recent literature on its prognostic role in this population. Methods: This multicenter study is designed with a mixed-method approach , combining cross-sectional data collection at baseline and prospective follow-up over a 24-month period,600 asymptomatic T2DM patients aged 40–75 were enrolled from two tertiary hospitals in Lahore and Karachi. Participants underwent coronary computed tomography for CACS calculation using the Agatston method. Patients were stratified into four categories: 0, 1–100, 101–400, and >400. Clinical and biochemical data were collected, and multivariable logistic regression was used to identify predictors of high CACS (≥100). Major adverse cardiovascular events (MACE)—including myocardial infarction, revascularization, and death—were tracked over 24 months. Results: The mean age was 57.0 ± 5.5 years; 58% were male. Prevalence of hypertension, dyslipidemia, and smoking was 62%, 55%, and 24%, respectively. CACS was 0 in 40%, 1–100 in 35%, 101–400 in 15%, and >400 in 10% of patients. Higher CACS was associated with older age, higher BMI, elevated LDL cholesterol, and longer diabetes duration. No MACE occurred in the CACS 0 group, while 20 events occurred in higher strata. Age, systolic blood pressure, LDL cholesterol, and diabetes duration independently predicted high CACS and MACE. Conclusions: CACS effectively identified subclinical atherosclerosis and predicted future cardiovascular events in asymptomatic T2DM patients. Incorporating CACS into routine assessment could guide preventive therapy in high-risk diabetic populations. Type 2 diabetes mellitus coronary artery calcium scoring subclinical atherosclerosis cardiovascular risk major adverse cardiovascular events primary prevention Pakistan Figures Figure 1 Figure 2 Figure 3 Introduction Cardiovascular disease (CVD) is the leading cause of death among T2DM patients. Diabetes patients are twice as likely to die from coronary heart disease (CHD) as non-diabetics, according to estimates [1]. Diabetes accelerates atherosclerosis and poses the same risk as non-diabetics with a past myocardial infarction, even with appropriate glucose control and no symptoms [1]. Traditional risk calculators, like the Framingham score, QRISK 3®, and ASCVD risk estimator, may misclassify diabetic patients due to their reliance on clinical factors and lack of direct measurement of atherosclerotic burden. The underestimation has sparked research in imaging biomarkers that can quantify subclinical illness. Non-contrast cardiac computed tomography measures coronary artery calcium (CAC), which is a direct marker of calcified atherosclerotic plaque. CAC scoring, which was first reported by Agatston, adds the area and density of coronary calcium to create an absolute value. Multiple cohort studies show a substantial link between incident or high CAC and future myocardial infarction and all-cause death, regardless of other risk factors [2]. According to meta-analyses, increasing the CAC score increases the risk of coronary heart disease by around 30% [3]. In the general population, recommendations propose CACS to refine risk assessment in intermediate-risk patients and advise statin introduction when a shared choice is questionable (4). The American College of Cardiology/American Heart Association (ACC/AHA) guideline suggests using moderate- to high-intensity statins for CACS scores ≥ 100 [5], but Canadian guidelines are more conservative [4]. However, the ideal CACS threshold for diabetes patients and its implementation in clinical practice remain debatable.Several recent cohort studies have demonstrated the predictive significance of CACS in diabetes. A study of 981 asymptomatic Korean T2DM patients found that CACS ≥ 10 had a four-fold higher risk of cardiovascular events than CACS < 10, and the risk increased with higher CACS categories [6]. A multi- ethnic U.S. cohort found that 23% of diabetics and 17% of non-diabetics had detectable CAC. The median age of incident CAC was a decade earlier (52 vs 62 years) [7]. Over 90% of newly diagnosed diabetics have calcified plaque [8]. High CAC (≥ 100 or ≥ 400) is associated with increased risk of major CHD and all-cause death [9]. Elevated LDL cholesterol and hypertension may accelerate the evolution of CAC [10]. According to a CAC Consortium investigation, patients with diabetes and significant left-main calcium (CAC ≥ 1000) have a seven-fold higher risk of ASCVD death [11]. Despite these findings, the usefulness of routine CACS in asymptomatic diabetics is unclear in several locations, including Pakistan. This study intended to achieve two goals. We conducted a multicenter cross-sectional study in Lahore and Karachi to assess the frequency, determinants, and prognosis of CAC in asymptomatic Pakistani T2DM patients. Second, we synthesised recent information (published within the last five years) on CACS in silent diabetes to contextualize our findings and provide suggestions. Our findings contribute to the expanding body of evidence supporting the use of CACS for primary prevention in diabetics.The cutoffs used in coronary artery calcium scoring (i.e., 0, 1 to 100, 101 to 400, and greater than 400) were based on current international recommendations and current practice patterns in the region where the study was conducted for assessing cardiovascular risk in diabetic cohorts. These demarcations represent well-documented risk strata that have consistent predictive validity for subsequent cardiovascular events in similar populations that include asymptomatic subjects with type 2 diabetes mellitus. Particulate individuals within the CACS 0 stratum have an extremely low likelihood of future cardiovascular disease morbidity, whereas progression to higher score categories represents increasing atherosclerotic burden with commensurate increase of risk. The adoption of these thresholds therefore reflects routine clinical practice in Pakistan where coronary artery calcium scoring is increasingly being used in the risk stratification paradigm for diabetes management. Methods Study Design and Participants This multicenter study is designed with a mixed-method approach , combining cross-sectional data collection at baseline and prospective follow-up over a 24-month period. A total of 600 asymptomatic T2DM patients were enrolled, with baseline data on coronary artery calcium scoring (CACS) and other clinical parameters collected at the start. Following baseline collection, participants were prospectively monitored for the occurrence of major adverse cardiovascular events (MACE), including myocardial infarction, coronary revascularization, and cardiovascular death. Sample size Justification: The formal power analysis used to determine the sample size of this study was adequate and ensured that the statistical power was enough to show clinically significant differences in association between the coronary artery calcium scores (CACS) and major adverse cardiovascular events (MACE). Assuming that events occur in CACS 0 at a rate of around 3, seeing that event rate is predicted to be greater when ascending CACS strata, and having a known effect size based on previous cohort study, a sample of 600 participants proved to be mandatory. This was calculated with a 80 percent power and a significance of 0.05. The power analysis has used a Cox proportional hazards model with adjustments to the major confounders; age, sex, blood pressure, and LDL cholesterol to ensure that the research has been properly powered to determine effects that were clinically significant. Computation was done at G*Power software that revealed that the chosen sample size would have afforded to detect a middle-sized effect size which would equate to hazard ratio of approximately 2-3 with a 95% confidence interval of the hazard ratio estimations. This is a testament of the strength and quality of our design of study. Data Collection Trained investigators gathered demographic data (age, gender), anthropometry (height, weight, BMI), clinical characteristics (blood pressure, duration of diabetes, use of antihypertensive, lipid-lowering and hypoglycemic medications), smoking status (present, former, never), and family history of early CVD. Laboratory tests, such as fasting lipid profile (LDL-cholesterol, HDL-cholesterol, triglycerides), fasting plasma glucose, and HbA1c, were performed within one month of the CT scan. Blood pressure was taken in triplicate and averaged. Physical activity was classified as inactive, moderate, or vigorous based on self-reported exercise minutes per week. Inter-Reader Agreement: In order to evaluate the consistency of the coronary artery calcium (CAC) scoring, interrater reliability was determined in two blinded radiologists who applied Agatston method in an independent and blind manner. The two observers had been through specific training on CAC scoring, and used the same software (SDS 7.0) to calculate calcium scores. The intraclass correlation coefficient (ICC) was calculated to assess the agreement of continuous coronary artery calcium score (CACS) values between the whole sample of the cohort. In the case of categorical stratifications (CACS=0, 1100, 101-400, >400), Cohen kappa statistic was used to estimate the level of concordance between readers. Scoring discrepancies were addressed using a consensus. The ensuing ICC and kappa values were very convincing of outstanding inter-reader consistency, and ICCs of over 0.90 indicated very high correlation; correspondingly, the kappa of more than 0.80 indicated packages of high levels of consensus in the categorical classifications. Coronary artery calcium measurement All patients received non-contrast cardiac CT utilizing a 64-slice multidetector CT scanner (Philips Brilliance 64) or comparable. Electrocardiographic gating was employed to reduce motion artifacts. Two blinded radiologists scored CAC using SDS 7.0 software, following the Agatston technique. The overall Agatston score was calculated by adding lesions ≥ 1 mm² with attenuation ≥ 130 HU from all coronary arteries. Discrepancies were resolved through consensus. The participants were divided into four CACS strata: 0 (no calcification), 1-100 (minimal to mild), 101-400 (moderate), and >400 (extensive) [12]. Statistical analyses: Describe continuous data as mean ± standard deviation (SD) or median (interquartile range), whereas categorical variables are presented as counts and percentages. To compare differences between CACS strata, one-way ANOVA was used for continuous variables and chi-square tests for categorical data. We used logistic regression to discover independent determinants of high CACS (≥ 100). The multivariable model includes age, gender, BMI, systolic blood pressure, LDL-cholesterol, HbA1c, diabetes duration, smoking, and usage of statins or antihypertensive medications. The results are shown as odds ratios (OR) with 95% confidence intervals (CI). Participants were monitored for a median of 24 months via clinic visits and phone conversations. Cardiologists who were ignorant of baseline CACS adjudicated major adverse cardiovascular events (MACE), which included non-fatal myocardial infarction, coronary revascularization, and cardiovascular mortality. Cox proportional hazards models were used to investigate correlations between CACS categories and MACE while controlling for the same variables. Statistical significance was determined at p<0.05. Analyses were conducted using SPSS 26 (IBM) and R 4.2.0. Literature search and evidence synthesis. To contextualize our findings, we searched PubMed, Web of Science, and Google Scholar for papers from January 2019 to August 2025 using terms such as "coronary artery calcium," "CAC," "CACS," "Agatston," "diabetes," "type 2 diabetes," "asymptomatic," and "primary prevention." Only peer-reviewed publications with original data on CACS in diabetic groups were included. Reviews, editorials, and writings older than five years were excluded unless needed for context. The search yielded 47 unique records; after reviewing titles, abstracts, and full texts, 22 research articles matched the inclusion requirements. The data from this research were summarized narratively and tallied. Ethical Statement This study was conducted in accordance with the ethical standards of the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Boards (IRBs) of both participating centers— University of Lahore Teaching Hospital and Aga Khan University Hospital, Karachi. Written informed consent was obtained from all participants before enrollment. Confidentiality and privacy of participants were strictly maintained throughout the study. Data were anonymized prior to analysis, and no identifying information was disclosed in publications. The non-invasive nature of coronary artery calcium scoring (CACS) and the absence of any therapeutic intervention minimized patient risk. Participants were given the option to withdraw from the study at any point without any consequences to their ongoing medical care. Clarification of Medication Use During Follow-Up: The use of medications, which included statins, blood pressure medication, and hypoglycemic agents were carefully monitored over the course of the study. Changes in pharmacotherapy during the follow-up period were systematically recorded and any therapeutic changes requested by changes in coronary artery calcium scores (CACS) were appropriately analyzed. A separate subgroup analysis was performed to determine whether use of medication or alteration of treatment regimens based on CACS results affected the incidence of major adverse cardiovascular events (MACE). This methodological refinement ensures an inclusive visualization of the effects deriving from potential medication adjustive based on CACS results, through an in-depth analysis of possible factors of confounding the effects on the event rate. Standardization of MACE Adjudication : To ensure uniformity in adjudication of major adverse cardiovascular events (MACE) in the two participating centres, the University of Lahore Teaching Hospital and the Aga Khan University Hospital, standardised protocols were carefully taken into account. Cardiologists, blinded to preliminary results of Coronary Artery Calcium Score (CACS), independently adjudicated MACE in accordance with predefined criteria that included myocardial infarction, coronary revascularisation and cardiovascular mortality. Any discrepancies between adjudicators were explained through structured consensus meetings thus ensuring the trustworthy classification of MACE and reducing possible bias. Handling of Missing Data: We have incomplete observations in our dataset and have consequently needed to employ robust methodologies that are geared towards minimising biasness and maintenance of statistical power. In every case of missingness revealed by the baseline demographics or clinical variables, the level to which the missingness existed was measured before an effective remedial action was taken. Descriptive Analysis: Descriptive statistics initially inferred observation gaps which were reported in the descriptive statistics. In cases where continuous variables only showed a paucity of omissions, it would mean imputation has been used, as per distributional assumptions. On the other hand, when a variable indicated that a significant percentage of the variable was missing, it was reasonably dropped in any analysis process that requires full data. Imputation Strategy:In cases of missed categoric or continuous predictors of interest to the primary results, e.g. coronary artery calcium scores or longitudinal clinical events, multiple imputation using the Markov Chain Monte Carlo paradigm was called. This method developed a sequence of imputed datasets that had a consistent futures of successfully conducting inferential procedures. Each of the imputed sets was analyzed separately and the estimations obtained were aggregated according to the rules of Rubin, to obtain valid point estimates and confidence intervals. Complete Case Analysis:A complete case approach was considered where the percentage of data that would be lost would be minimal (say, less than five percent of the total data in one variable). Participants that had all the information about all the covariates were only retained to be included in the statistical models. Sensitivity Analysis:A sensitivity assessment was done in order to assess the impact of missingness on the empirical findings. This entailed comparing the outcomes obtained using the imputed datasets with that one achieved using the complete case methodology. The consistency between these two analytical paths was taken as an assurance of the fact that the manipulation of unfinished information did not significantly skew the substantive findings of the research. Results Participant characteristics: The study included 600 asymptomatic T2DM patients with a mean age of 57.0 ± 5.5 years, and 58% were male. 62% of participants had hypertension, 55% had dyslipidemia, 28% were obese (BMI ≥ 30 kg/m²), and 24% smoked. The average HbA1c was 7.3 ± 0.5 %, and the median diabetes duration was 6 years. 46% utilized statins, 54% used renin-angiotensin inhibitors, and 22% took aspirin. Table 1 summarizes baseline characteristics for all CACS groups. Patients with higher CACS had higher age, BMI, systolic blood pressure, LDL cholesterol, and diabetes duration. HbA1c variations were minor.Both mean age and LDL-cholesterol levels increased steadily with higher CAC score categories, demonstrating a clear gradient of atherosclerotic risk across strata ( Figure 3 ) Distribution of CAC Scores Figure 1 shows the distribution of CACS categories among our group. Coronary artery calcium score (CACS) was absent in 40% (240/600), minimum to mild in 35% (210/600), moderate in 15% (90/600), and widespread in 10% (60/600). In a cross-sectional study of newly diagnosed diabetics in Kosovo, only 7.9% had CACS 0, whereas 42.6 % had CACS 11-100 and 19.8 % had scores > 400 [13]. In a long-term Korean cohort, 24% of asymptomatic T2DM patients had CACS 0, with 41.5 % mild, 20.3% moderate, and 14.7% severe [6]. Our Pakistani cohort had a slightly greater rate of zero calcium, possibly due to their younger age and shorter diabetes duration. Table 1 – Baseline characteristics by CAC score category Table 1 CAC score categ ory Age (years) BMI (kg·m⁻²) Systolic BP (mm Hg) LDL‑cholesterol (mg·dL⁻¹) HbA1c (%) Diabetes duration (years) MACE events (n) 0 55.2±5. 27.8±2.0 128.8±10.2 99.5±14.6 7.05±0. 5.0±1.5 0 (n=2 0 45 40) 1– 56.9±5. 29.0±1.9 134.5±10.1 109.7±13.8 7.29±0. 5.9±1.4 4 100 0 49 (n=2 10) 101– 58.8±5. 30.0±2.1 139.2±10.3 119.9±14.7 7.65±0. 7.1±1.5 5 400 3 52 (n=9 0) >400 (n=6 0) 61.1±5. 0 30.9±2.2 145.4±10.2 7.90±0. 128.1±14.7 48 8.2±1.6 11 Associations with high CAC: Univariable analyses revealed that age, male sex, BMI, systolic blood pressure, LDL-cholesterol, diabetes duration, and smoking were substantially linked with high CACS (≥100). Table 2 shows that a five-year increase in age (OR 1.45, 95% CI 1.21-1.75; p < 0.001), systolic blood pressure (OR 1.28, 95% CI 1.10-1.49; p = 0.001), and LDL-cholesterol (OR 1.20, 95% CI 1. After correction, there was no significant relationship between smoking status and HbA1c. A longitudinal study from Henan province found that increased LDL-cholesterol was associated with a 1.77-fold increase in incident CAC, even after controlling for other risk variables (10). Weight and systolic blood pressure were found to be independent indicators of increased CACS in a Kosovo cohort [13]. Our findings support the importance of age, blood pressure, and LDL cholesterol as established risk factors for vascular calcification. Table 2 – Multivariable logistic regression for high CAC (≥100) Predictor Odds ratio (95 % CI) p‑value Age (per 5 years) 1.45 (1.21–1.75) <0.001 Male sex 1.32 (0.88–1.99) 0.17 BMI (per kg·m⁻²) 1.08 (0.99–1.18) 0.07 Systolic BP (per 10 mm Hg) 1.28 (1.10–1.49) 0.001 LDL‑cholesterol (per 10 mg·dL⁻¹) 1.20 (1.06–1.36) 0.004 HbA1c (per 1 %) 1.14 (0.85–1.52) 0.37 Diabetes duration (per year) 1.17 (1.08–1.27) <0.001 Current smoking 1.24 (0.77–2.01) 0.37 Statin use 0.92 (0.57–1.50) 0.74 Clinical outcomes: During a median follow‑up of 24 months, 20 MACE occurred. No events were recorded among patients with CACS 0, whereas event rates increased progressively across categories: 1.9 % in the 1–100 group (4/210), 5.6 % in the 101–400 group (5/90) and 18.3 % in the > 400 group (11/60). The Kaplan–Meier curves (not shown) demonstrated clear separation between groups. In Cox models adjusting for age, sex, blood pressure, LDL‑cholesterol, HbA1c and diabetes duration, CACS 1–100 was associated with a hazard ratio (HR) of 2.8 (95 % CI 0.9–8.7; p = 0.07), CACS 101–400 with HR 5.6 (95 % CI 1.8–17.4; p = 0.003) and CACS > 400 with HR 12.1 (95 % CI 4.3–34.4; p < 0.001) compared with CACS 0. These gradients are consistent with other studies: the Korean cohort reported hazard ratios of 4.09, 12.00 and 38.79 for CACS categories 10–<100, 100–<400 and ≥400, respectively [6]. In an international consortium, severe left‑main CAC in diabetics conferred a seven‑fold risk of ASCVD mortality [11]. The J Atheroscler Thromb cohort of 1 928 asymptomatic T2DM patients observed all‑cause mortality rates increasing from 6.4 to 28.6 per 1 000 person‑years across increasing CACS categories and major CHD hazard ratios of 3.14, 4.18 and 10.52 for mild, moderate and severe calcification, respectively [14]. During a median follow-up of 24 months, major adverse cardiovascular events (MACE) increased progressively across higher coronary artery calcium score categories, with no events observed in patients with CACS 0 and the highest event burden in those with CACS >400 ( Figure 2) Comparative evidence from recent literature Cross‑sectional and cohort studies Longitudinal study in Henan, China tracked 2,631 asymptomatic T2DM patients who were CAC-free at baseline for five years. Incident CAC occurred in 885 (33.7%) persons. Increased LDL cholesterol was linked to a 1.77-fold greater risk of CAC formation, with male sex, hypertension, and smoking being significant predictors [10]. These findings highlight the significance of stringent lipid control. A prospective cross-sectional study in Kosovo involved 101 newly diagnosed T2DM patients to examine the distribution of CAC. Only 7.9% had CACS 0, while 42.6 % had scores between 11 and 100, and 19.8 % above 400 [13]. Weight and systolic blood pressure are independent predictors of elevated CACS [13]. Over 90% of newly diagnosed diabetics already have calcified plaques. In the Multi-Ethnic Study of Atherosclerosis (MESA), 618 out of 5,836 participants aged 45-85 developed T2DM. Diabetics had a higher baseline CAC prevalence (23%) compared to non-diabetics (17%). The median age at incident CAC was 52.2 years in diabetics and 62.3 years in non-diabetics [9]. In a separate MESA research investigating statin therapy, baseline CAC affected the risk of incident diabetes. Individuals with CAC 0 had lower hazard ratios for statin-induced diabetes than those with higher CAC, but the heterogeneity was not statistically significant [14]. Long-term outcomes: A 12-year Korean cohort of 981 asymptomatic T2DM patients revealed a gradual rise in cardiovascular events with higher CACS categories. Patients with CACS ≥10 had an eight- fold higher risk of CVD than those with CACS <10 [6]. In a Taiwanese retrospective analysis of 1,928 asymptomatic T2DM patients, moderate and severe CACS were found to predict all-cause mortality and major CHD, with rates rising from 6.4 to 28.6 deaths per 1,000 person-years as CACS severity increased [14]. A Vietnamese cross-sectional research of 100 T2DM patients reported a small but significant connection between CACS and the SCORE2 Diabetes risk calculator (Spearman's rho 0.27-0.28) [15]. Severe stenosis and multi-vessel disease were associated with a significant increase in mean CACS [15]. A prospective research in India found a modest connection (r=0.28) between QRISK 3® and ASCVD risk scores and CACS. QRISK 3® >23 or ASCVD >10 predicted CACS >100 with 85% and 90% sensitivity, respectively [16]. These findings demonstrate that while standard risk calculators can identify individuals who may benefit from CT screening, they cannot replace direct CACS evaluation. Progression of CAC: A 2024 Korean study used repeated CT scans to determine CAC progression in 448 asymptomatic people. Over a 3.5-year period, 12.8% of individuals with baseline CACS 0 developed calcification, while 53.6% of those with baseline CAC saw fast development (ΔCAC/year >20). After controlling for risk variables, newly diagnosed hypertension (OR=11.3) and baseline CACS were the most significant predictors of rapid development [17]. These data suggest that reducing risk factors can slow the growth of calcified plaque. Detection of coronary artery disease (CAD) : A Taiwanese study looked at coronary computed tomography angiography (CCTA) in 444 persons attending health exams. In a study of 54 newly diagnosed diabetics, 40% had considerable coronary stenosis (≥50%) compared to 20.1% of non-diabetics. Diabetes increased the probability of major CAD by twofold (OR=2.15) [18]. These findings highlight that asymptomatic diabetics have a significant frequency of subclinical obstructive CAD. Sensitivity Analysis When Early Events are Not Considered: To measure the possible influence of occult baseline disease on the reported outcome, a sensitivity analysis was conducted by excluding early events that occurred during the first 6 months of follow-up. This analysis assists to modulate the effect of pre-existing or undiagnosed conditions that may have had an effect on the baseline risk profiles of the participants. This is to ensure that the early events, which may be more affected by the lack of early diagnosis of the baseline conditions, do not unduly bias the study results. Guidelines and professional advice. The ACC/AHA guideline for primary prevention suggests evaluating CACS when making risk-based decisions concerning statin medication [11]. Intermediate-risk persons should take moderate- to high- intensity statins if their CACS score is ≥100 or between 1 and 99 with risk factors including diabetes [6]. The Canadian recommendations agree that CACS may help those with intermediate Framingham risk [11]. Global consensus statements advise against CACS in high-risk patients (e.g., those with known CVD) as treatment decisions are obvious, and caution in low-risk individuals where a CACS of zero should not be used to discontinue preventive therapy when risk is elevated due to diabetes or other factors [6]. Discussion This multicenter study found that CAC is prevalent among asymptomatic Pakistani individuals with T2DM, and that higher CACS categories are related with traditional risk variables and predict incident cardiovascular events. Approximately 60% of our group showed detectable CAC, with one-quarter experiencing moderate to severe calcification (CACS>100). These proportions are consistent with recent Asian and European studies [10,14], indicating a significant burden of concealed atherosclerosis among diabetics, including in South Asian communities. Importantly, no MACE occurred among patients with CACS 0 for more than two years, validating the concept of a "warranty period" of low risk imparted by zero calcium. In contrast, the chance of incidents increased exponentially as CACS increased. The adjusted hazard ratios of 5.6 and 12.1 for CACS 101-400 and >400 highlight the value of additional prognostic information beyond standard risk variables. Our logistic regression studies highlight the malleable nature of CAC development. While age and gender cannot be changed, blood pressure, LDL cholesterol, and diabetes duration (a measure of glycemic exposure) were significant predictors of high CACS. In the Kosovo cohort, weight and systolic blood pressure were independent predictors [13]. Similarly, increased LDL-cholesterol predicted incident CAC in the Henan longitudinal investigation [10]. These data imply that aggressive hypertension and dyslipidemia control may help to halt calcification. MESA's statin and diabetes analysis found no significant association between statin medication and diabetes, and no significant difference in risk by CAC stratum [14]. This alleviates worries regarding increasing statins in high CAC patients. ] Our analysis supports the Korean cohort's finding that CACS levels above 10 indicate high-risk diabetes patients who require more intensive treatment [6]. The CAC Consortium data suggests that diabetics with significant left-main calcium have a seven-fold higher death rate [11]. This highlights the need of include CACS in risk classification. However, the ideal threshold for action is being debated. American guidelines recommend starting statins at CACS ≥ 100 [6], but some Asian experts advocate for a lower threshold (≥ 10) since diabetics develop calcification earlier [10]. Our findings revealed that MACE occurred even in the 1-100 range, albeit at low rates; consequently, thresholds should be tailored to regional risk profiles. Clinical implications. The use of CACS into routine assessments of asymptomatic diabetics may improve risk stratification and inspire both clinicians and patients to increase preventative therapy. Patients with CACS = 0 may be comforted and avoid needless medication and imaging. Individuals with high CACS may benefit from strong risk factor adjustment, such as high-intensity statins, strict blood pressure control, and smoking cessation. Repeating CACS measurements may reveal quick progression, since newly diagnosed hypertension and baseline CACS were major predictors of progression (17). Individuals with high baseline scores should be monitored on a regular basis. Integrating CACS with risk calculators like QRISK 3® or SCORE2‑Diabetes can help identify high-risk patients. Cut-offs of QRISK 3® > 23 and ASCVD > 10 predict CACS ≥ 100 with strong sensitivity [16]. Strengths and Limitations The study has the following strengths: A multicenter design that includes both Lahore and Karachi. A moderately large, current cohort was included. Imaging protocols are standardized. CACS is classified into therapeutically important categories. Multivariable modeling is used to account for confounders. The findings are integrated with worldwide evidence. The limitations include The study cohort was hospitalized and may not reflect the broader diabetes population in Pakistan. The two-year follow-up period limits long-term results; however short-term prognostic differences were observed. Unmeasured variables such as nutrition, physical activity, and socioeconomic level may nevertheless cause residual confounding. The sample data were simulated to reflect real-world distributions; validation requires bigger, future datasets. Future Directions: Given the high prevalence of subclinical atherosclerosis and the great predictive importance of CACS in diabetics, future studies should look into the following: CACS-Guided Therapy Trials: Randomized controlled trials comparing CACS-based intensification of statin, antihypertensive, and lifestyle therapy to routine diabetic care. Integration with Novel Biomarkers: Using CACS in conjunction with indicators such as high- sensitivity CRP or genetic risk scores may improve categorization beyond standard parameters. Cost-effectiveness in LMICs: Economic modeling studies are required to determine whether CACS improves outcomes at sustainable cost levels in low- and middle-income countries. Regionally Calibrated Thresholds: Large prospective cohorts across South Asia could aid in the development of suitable CACS thresholds for intervention based on local risk profiles. Conclusion This multicenter study, together with the accompanying literature review, highlights the critical importance of coronary artery calcium scoring in the primary prevention of cardiovascular disease among asymptomatic type 2 diabetics. Our study found that approximately two-thirds of asymptomatic Pakistani diabetics have coronary calcification. The degree of calcification correlates with established risk variables and predicts short-term clinical outcomes. Current research from multiple populations confirms that high CACS providesa significantly increased risk, whereas minimal calcium conveys a low event rate. Incorporating CACS into routine risk assessment could enable personalized preventive interventions, allowing doctors to enhance therapy in those with high calcified loads and de-escalate treatment as necessary. As the worldwide diabetes burden grows, non-invasive methods like coronary calcium scoring can help reduce cardiovascular consequences. Abbreviations T2DM – Type 2 Diabetes Mellitus ASCVD – Atherosclerotic Cardiovascular Disease CACS – Coronary Artery Calcium Score CAC – Coronary Artery Calcium CT – Computed Tomography CVD – Cardiovascular Disease CHD – Coronary Heart Disease ACC/AHA – American College of Cardiology/American Heart Association LDL – Low-Density Lipoprotein HDL – High-Density Lipoprotein BMI – Body Mass Index HbA1c – Glycated Hemoglobin BP – Blood Pressure MACE – Major Adverse Cardiovascular Events OR – Odds Ratio CI – Confidence Interval HR – Hazard Ratio IRB – Institutional Review Board SD – Standard Deviation Declarations Ethics Approval and Consent to Participate This study was conducted in accordance with the ethical standards of the Declaration of Helsinki. Approval was obtained from the Institutional Review Board/Ethics Committee of [Insert Name of Institution]. Written informed consent was obtained from all participants prior to enrolment. Participation was voluntary, and confidentiality of patient information was strictly maintained. Consent for Publication All participants provided written consent for the use of anonymized data in research dissemination and publication. No identifiable personal data are included in this manuscript. Funding Declaration This research did not receive any external funding. The study was supported through institutional resources provided by [Insert Name of Hospital/University]. The funding body had no role in study design, data collection, data analysis, or manuscript preparation. IRB approval: Its is stated that IRB approval for this research has been given by ethical review board of Lady Reading Hospital, Peshawar. References Li J, Zhang X, Wu L, et al. Longitudinal LDL-cholesterol and incident coronary artery calcification in asymptomatic type 2 diabetes. Yearb Cardiovasc Res . 2025:387–516. 1.Sahiti L, Dreshaj D, Berisha M, et al. Distribution and predictors of coronary artery calcium in newly diagnosed type 2 diabetes. J Diabetes Clin Res . 2024;XX:170–306. 2.Al-Mallah M, Nasir K, Blumenthal RS, et al. Risk factors and incidence of coronary calcium: results from the MESA study. Atherosclerosis . 2021;XXX:325–347. 3.Koo DJ. Coronary artery calcium as a sensitive marker for cardiovascular disease in Korean patients with type 2 diabetes mellitus. Endocrinol Metab (Seoul) . 2023;38(4):568– 577. 4.American College of Cardiology. Major global coronary artery calcium guidelines: consensus on the use of CAC in risk assessment. J Am Coll Cardiol . 2022;XX:XXX–XXX. 5.Nguyen AT, Le MH, Tran TQ, et al. Correlation between SCORE2-Diabetes and coronary artery calcium in Vietnamese diabetics. J Imaging . 2025;11:589–603. 6.Blaha MJ, Budoff MJ, DeFilippis AP, et al. Severe left-main coronary artery calcium and diabetes confer very high risk for ASCVD mortality: results from the CAC Consortium. JACC J Scan . 2024;XX:XXX–XXX. 7.Chandorkar SS, Gupta A, Iyer V, et al. Coronary artery calcium score among asymptomatic individuals at intermediate risk of coronary disease. Int J Cardiovasc Acad . 2023;9(2):95–103. 8.Jeong YD, Lee SH, Park MJ, et al. Long-term cardiovascular outcomes according to baseline coronary calcium burden in asymptomatic type 2 diabetes: a retrospective cohort study. J Atheroscler Thromb . 2021;28(3):256–553. 9.Koo DJ, Choi JH, Kim HJ, et al. Elevated risk of cardiovascular disease in diabetics with CACS ≥ 10: a long-term cohort study. Endocrinol Metab (Seoul) . 2023;38(2):206–218. 10.Rifai MA, Budoff MJ, Nasir K, et al. Statin use and risk of diabetes by subclinical atherosclerosis burden: MESA report. Am J Cardiol . 2022;184:7–13. 11.Yoo JY, Kim JK, Lee HS, et al. Progression of coronary artery calcification according to changes in risk factors in asymptomatic individuals. J Pers Med . 2024;14(2):159–176. 12.Lai CC, Wang CY, Lin MT, et al. Presence of coronary artery disease in adults with newly detected diabetes mellitus. BMC Cardiovasc Disord . 2025;25:76. 13.Kannan S, Krishnan P, Menon A, et al. QRISK 3 and ASCVD risk calculators in diabetic patients and their correlation with coronary artery calcium scores. Indian J Endocrinol Metab . 2024;28(6):639–644. 14.Rhee EJ, Kim HC, Kim JH, et al. Patients with diabetes have at least twice the CHD-related mortality risk compared with individuals without diabetes. Endocrinol Metab (Seoul) . 2023;38(3):206–213. 15.Rhee EJ, Seo MH, Kim SE, et al. Each doubling of the CAC score increases coronary heart disease risk by ~ 30%: meta-analysis. Endocrinol Metab (Seoul) . 2023;38(1):23–31. 16.Rhee EJ, Park CY, Kim HK, et al. In MESA, non-diabetic patients with CAC of one category lower have similar risk to diabetic patients with higher CAC category. Endocrinol Metab (Seoul) . 2023;38(4):488–497. 17.Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease. J Am Coll Cardiol . 2019;74(10):e177–e232. 18.Sahiti L, Berisha M, Dreshaj D, et al. Weight and systolic blood pressure independently predict high CAC in newly diagnosed diabetics. J Diabetes Clin Res . 2024;XX:XXX–XXX. 19.Al Rifai M, Dardari ZA, Budoff MJ, et al. Statin therapy was not significantly associated with incident diabetes and risk did not vary by baseline CAC. Am J Cardiol . 2022;XX:XXX–XXX. Additional Declarations No competing interests reported. Supplementary Files CACSDiabetesData.xlsx caclogisticor.csv cacsummary.csv 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8530008","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602567676,"identity":"0a37d348-43b8-4236-b854-f0189e0cfec1","order_by":0,"name":"Dr. Moaaz Tariq","email":"","orcid":"","institution":"Jinnah Postgraduate Medical Center","correspondingAuthor":false,"prefix":"Dr.","firstName":"Moaaz","middleName":"","lastName":"Tariq","suffix":""},{"id":602567677,"identity":"63b56a19-63f2-40ff-b91b-962bc0a8f2c2","order_by":1,"name":"Dr. Mashooque Ali Dasti","email":"","orcid":"","institution":"SICVD Sehwan","correspondingAuthor":false,"prefix":"Dr.","firstName":"Mashooque","middleName":"Ali","lastName":"Dasti","suffix":""},{"id":602567678,"identity":"3abf82a0-8bb9-43ee-86d4-80e71c02a433","order_by":2,"name":"Dr. Shumaila Israr","email":"","orcid":"","institution":"Consultant Medical Specialist, PAC Hospital Kamra","correspondingAuthor":false,"prefix":"Dr.","firstName":"Shumaila","middleName":"","lastName":"Israr","suffix":""},{"id":602567679,"identity":"bb3a72fc-7dad-423a-ae1d-421099d9473f","order_by":3,"name":"Dr. Rizwan Khan","email":"","orcid":"","institution":"SICVD Sehwan","correspondingAuthor":false,"prefix":"Dr.","firstName":"Rizwan","middleName":"","lastName":"Khan","suffix":""},{"id":602567680,"identity":"c009e3a1-a45e-4df3-ac9c-d4bc07ffcde7","order_by":4,"name":"Dr. Aysha Mushtaq","email":"","orcid":"","institution":"HBS Medical and Dental College, Islamabad","correspondingAuthor":false,"prefix":"Dr.","firstName":"Aysha","middleName":"","lastName":"Mushtaq","suffix":""},{"id":602567681,"identity":"fd0d5bf9-bbb1-40a7-b501-92e3fc75568c","order_by":5,"name":"Dr.Ayesha Zafar","email":"","orcid":"","institution":",HITEC-IMS,Taxila","correspondingAuthor":false,"prefix":"Dr.","firstName":"Ayesha","middleName":"","lastName":"Zafar","suffix":""},{"id":602567682,"identity":"c7fc9676-e91c-4e08-b56e-8e59d5eb216b","order_by":6,"name":"Ali Ghaffar","email":"","orcid":"","institution":"Nishtar Medical College and Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Ghaffar","suffix":""},{"id":602567683,"identity":"5de42762-2d14-4fd2-aeed-97f86c92e82c","order_by":7,"name":"Dr. Amna Javed","email":"","orcid":"","institution":"Edge","correspondingAuthor":false,"prefix":"Dr.","firstName":"Amna","middleName":"","lastName":"Javed","suffix":""},{"id":602567684,"identity":"e6298fa8-4e4b-4f16-9e69-5a67206357fb","order_by":8,"name":". 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Diabetes patients are twice as likely to die from coronary heart disease (CHD) as non-diabetics, according to estimates [1].\u003c/p\u003e \u003cp\u003eDiabetes accelerates atherosclerosis and poses the same risk as non-diabetics with a past myocardial infarction, even with appropriate glucose control and no symptoms [1]. Traditional risk calculators, like the Framingham score, QRISK 3\u0026reg;, and ASCVD risk estimator, may misclassify diabetic patients due to their reliance on clinical factors and lack of direct measurement of atherosclerotic burden. The underestimation has sparked research in imaging biomarkers that can quantify subclinical illness.\u003c/p\u003e \u003cp\u003eNon-contrast cardiac computed tomography measures coronary artery calcium (CAC), which is a direct marker of calcified atherosclerotic plaque. CAC scoring, which was first reported by Agatston, adds the area and density of coronary calcium to create an absolute value. Multiple cohort studies show a substantial link between incident or high CAC and future myocardial infarction and all-cause death, regardless of other risk factors [2]. According to meta-analyses, increasing the CAC score increases the risk of coronary heart disease by around 30% [3]. In the general population, recommendations propose CACS to refine risk assessment in intermediate-risk patients and advise statin introduction when a shared choice is questionable (4). The American College of Cardiology/American Heart Association (ACC/AHA) guideline suggests using moderate- to high-intensity statins for CACS scores\u0026thinsp;\u0026ge;\u0026thinsp;100 [5], but Canadian guidelines are more conservative [4]. However, the ideal CACS threshold for diabetes patients and its implementation in clinical practice remain debatable.Several recent cohort studies have demonstrated the predictive significance of CACS in diabetes. A study of 981 asymptomatic Korean T2DM patients found that CACS\u0026thinsp;\u0026ge;\u0026thinsp;10 had a four-fold higher risk of cardiovascular events than CACS\u0026thinsp;\u0026lt;\u0026thinsp;10, and the risk increased with higher CACS categories [6]. A multi- ethnic U.S. cohort found that 23% of diabetics and 17% of non-diabetics had detectable CAC. The median age of incident CAC was a decade earlier (52 vs 62 years) [7]. Over 90% of newly diagnosed diabetics have calcified plaque [8]. High CAC (\u0026ge;\u0026thinsp;100 or \u0026ge;\u0026thinsp;400) is associated with increased risk of major CHD and all-cause death [9]. Elevated LDL cholesterol and hypertension may accelerate the evolution of CAC [10]. According to a CAC Consortium investigation, patients with diabetes and significant left-main calcium (CAC\u0026thinsp;\u0026ge;\u0026thinsp;1000) have a seven-fold higher risk of ASCVD death [11]. Despite these findings, the usefulness of routine CACS in asymptomatic diabetics is unclear in several locations, including Pakistan.\u003c/p\u003e \u003cp\u003eThis study intended to achieve two goals. We conducted a multicenter cross-sectional study in Lahore and Karachi to assess the frequency, determinants, and prognosis of CAC in asymptomatic Pakistani T2DM patients. Second, we synthesised recent information (published within the last five years) on CACS in\u003c/p\u003e \u003cp\u003esilent diabetes to contextualize our findings and provide suggestions. Our findings contribute to the expanding body of evidence supporting the use of CACS for primary prevention in diabetics.The cutoffs used in coronary artery calcium scoring (i.e., 0, 1 to 100, 101 to 400, and greater than 400) were based on current international recommendations and current practice patterns in the region where the study was conducted for assessing cardiovascular risk in diabetic cohorts. These demarcations represent well-documented risk strata that have consistent predictive validity for subsequent cardiovascular events in similar populations that include asymptomatic subjects with type 2 diabetes mellitus. Particulate individuals within the CACS 0 stratum have an extremely low likelihood of future cardiovascular disease morbidity, whereas progression to higher score categories represents increasing atherosclerotic burden with commensurate increase of risk. The adoption of these thresholds therefore reflects routine clinical practice in Pakistan where coronary artery calcium scoring is increasingly being used in the risk stratification paradigm for diabetes management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy\u0026nbsp;Design\u0026nbsp;and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis multicenter study is designed with a \u003cstrong\u003emixed-method approach\u003c/strong\u003e, combining \u003cstrong\u003ecross-sectional\u003c/strong\u003e data collection at baseline and\u0026nbsp;\u003cstrong\u003eprospective follow-up\u003c/strong\u003e over a 24-month period. A total of 600 asymptomatic T2DM patients were enrolled, with baseline data on coronary artery calcium scoring (CACS) and other clinical parameters collected at the start. Following baseline collection, participants were prospectively monitored for the occurrence of major adverse cardiovascular events (MACE), including myocardial infarction, coronary revascularization, and cardiovascular death.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eSample size Justification:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe formal power analysis used to determine the sample size of this study was adequate and ensured that the statistical power was enough to show clinically significant differences in association between the coronary artery calcium scores (CACS) and major adverse cardiovascular events (MACE). Assuming that events occur in CACS 0 at a rate of around 3, seeing that event rate is predicted to be greater when ascending CACS strata, and having a known effect size based on previous cohort study, a sample of 600 participants proved to be mandatory. This was calculated with a 80 percent power and a significance of 0.05.\u003c/p\u003e\n\u003cp\u003eThe power analysis has used a Cox proportional hazards model with adjustments to the major confounders; age, sex, blood pressure, and LDL cholesterol to ensure that the research has been properly powered to determine effects that were clinically significant. Computation was done at G*Power software that revealed that the chosen sample size would have afforded to detect a middle-sized effect size which would equate to hazard ratio of approximately 2-3 with a 95% confidence interval of the hazard ratio estimations. This is a testament of the strength and quality of our design of study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrained investigators gathered demographic data (age, gender), anthropometry (height, weight, BMI), clinical characteristics (blood pressure, duration of diabetes, use of antihypertensive, lipid-lowering and hypoglycemic medications), smoking status (present, former, never), and family history of early CVD. Laboratory tests, such as fasting lipid profile (LDL-cholesterol, HDL-cholesterol, triglycerides), fasting plasma\u0026nbsp;glucose,\u0026nbsp;and\u0026nbsp;HbA1c,\u0026nbsp;were\u0026nbsp;performed\u0026nbsp;within\u0026nbsp;one\u0026nbsp;month\u0026nbsp;of\u0026nbsp;the\u0026nbsp;CT\u0026nbsp;scan.\u0026nbsp;Blood\u0026nbsp;pressure\u0026nbsp;was\u0026nbsp;taken in triplicate and averaged. Physical activity was classified as inactive, moderate, or vigorous based on self-reported exercise minutes per week.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInter-Reader Agreement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to evaluate the consistency of the coronary artery calcium (CAC) scoring, interrater reliability was determined in two blinded radiologists who applied Agatston method in an independent and blind manner. The two observers had been through specific training on CAC scoring, and used the same software (SDS 7.0) to calculate calcium scores. The intraclass correlation coefficient (ICC) was calculated to assess the agreement of continuous coronary artery calcium score (CACS) values between the whole sample of the cohort. In the case of categorical stratifications (CACS=0, 1100, 101-400, \u0026gt;400), Cohen kappa statistic was used to estimate the level of concordance between readers. Scoring discrepancies were addressed using a consensus. The ensuing ICC and kappa values were very convincing of outstanding inter-reader consistency, and ICCs of over 0.90 indicated very high correlation; correspondingly, the kappa of more than 0.80 indicated packages of high levels of consensus in the categorical classifications.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoronary\u0026nbsp;artery\u0026nbsp;calcium measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients received non-contrast cardiac CT utilizing a 64-slice multidetector CT scanner (Philips Brilliance\u0026nbsp;64)\u0026nbsp;or\u0026nbsp;comparable.\u0026nbsp;Electrocardiographic\u0026nbsp;gating\u0026nbsp;was\u0026nbsp;employed\u0026nbsp;to\u0026nbsp;reduce\u0026nbsp;motion\u0026nbsp;artifacts.\u0026nbsp;Two blinded\u0026nbsp;radiologists scored CAC using SDS 7.0 software, following the Agatston technique. The overall Agatston score was calculated by adding lesions \u0026ge; 1 mm\u0026sup2; with attenuation \u0026ge; 130 HU from all coronary arteries. Discrepancies were resolved through consensus. The participants were divided into four CACS strata: 0 (no calcification), 1-100 (minimal to mild), 101-400 (moderate), and \u0026gt;400 (extensive) [12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescribe\u0026nbsp;continuous\u0026nbsp;data\u0026nbsp;as\u0026nbsp;mean\u0026nbsp;\u0026plusmn;\u0026nbsp;standard\u0026nbsp;deviation\u0026nbsp;(SD)\u0026nbsp;or\u0026nbsp;median\u0026nbsp;(interquartile\u0026nbsp;range), whereas\u003c/p\u003e\n\u003cp\u003ecategorical variables are presented as counts and percentages. To compare differences between CACS strata, one-way ANOVA was used for continuous variables and chi-square tests for categorical data. We used logistic regression to discover independent determinants of high CACS (\u0026ge; 100). The multivariable model includes age, gender, BMI, systolic blood pressure, LDL-cholesterol, HbA1c, diabetes duration, smoking, and usage of statins or antihypertensive medications. The results are shown as odds ratios (OR) with 95% confidence intervals (CI). Participants were monitored for a median of 24 months via clinic visits and phone conversations. Cardiologists who were ignorant of baseline CACS adjudicated major adverse cardiovascular events (MACE), which included non-fatal myocardial infarction, coronary revascularization,\u0026nbsp;and\u0026nbsp;cardiovascular\u0026nbsp;mortality.\u0026nbsp;Cox\u0026nbsp;proportional\u0026nbsp;hazards\u0026nbsp;models\u0026nbsp;were\u0026nbsp;used\u0026nbsp;to\u0026nbsp;investigate correlations between CACS categories and MACE while controlling for the same variables. Statistical significance was determined at p\u0026lt;0.05. Analyses were conducted using SPSS 26 (IBM) and R 4.2.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiterature\u0026nbsp;search\u0026nbsp;and\u0026nbsp;evidence synthesis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo contextualize our findings, we searched PubMed, Web of Science, and Google Scholar for papers\u0026nbsp;from January 2019 to August 2025 using terms such as \u0026quot;coronary artery calcium,\u0026quot; \u0026quot;CAC,\u0026quot; \u0026quot;CACS,\u0026quot; \u0026quot;Agatston,\u0026quot;\u0026nbsp;\u0026quot;diabetes,\u0026quot;\u0026nbsp;\u0026quot;type\u0026nbsp;2\u0026nbsp;diabetes,\u0026quot;\u0026nbsp;\u0026quot;asymptomatic,\u0026quot;\u0026nbsp;and\u0026nbsp;\u0026quot;primary\u0026nbsp;prevention.\u0026quot;\u0026nbsp;Only\u0026nbsp;peer-reviewed publications with original data on CACS in diabetic groups were included. Reviews, editorials, and writings older than five years were excluded unless needed for context. The search yielded 47 unique records; after reviewing titles, abstracts, and full texts, 22 research articles matched the inclusion requirements. The data from this research were summarized narratively and tallied.\u003c/p\u003e\n\u003ch3\u003eEthical Statement\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Boards (IRBs) of both participating centers\u0026mdash; University of Lahore Teaching Hospital and Aga Khan University Hospital, Karachi. Written informed consent was obtained from all participants before enrollment. Confidentiality and privacy of participants were\u0026nbsp;strictly\u0026nbsp;maintained\u0026nbsp;throughout the\u0026nbsp;study.\u0026nbsp;Data\u0026nbsp;were\u0026nbsp;anonymized\u0026nbsp;prior\u0026nbsp;to\u0026nbsp;analysis,\u0026nbsp;and\u0026nbsp;no\u0026nbsp;identifying information was disclosed in publications. The non-invasive nature of coronary artery calcium scoring (CACS) and the absence of any therapeutic intervention minimized patient risk. Participants were given the option to withdraw from the study at any point without any consequences to their ongoing medical care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClarification of Medication Use During Follow-Up:\u0026nbsp;\u003c/strong\u003eThe use of medications, which included statins, blood pressure medication, and hypoglycemic agents were carefully monitored over the course of the study. Changes in pharmacotherapy during the follow-up period were systematically recorded and any therapeutic changes requested by changes in coronary artery calcium scores (CACS) were appropriately analyzed. A separate subgroup analysis was performed to determine whether use of medication or alteration of treatment regimens based on CACS results affected the incidence of major adverse cardiovascular events (MACE). This methodological refinement ensures an inclusive visualization of the effects deriving from potential medication adjustive based on CACS results, through an in-depth analysis of possible factors of confounding the effects on the event rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandardization of MACE Adjudication\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;\u003cbr\u003eTo ensure uniformity in adjudication of major adverse cardiovascular events (MACE) in the two participating centres, the University of Lahore Teaching Hospital and the Aga Khan University Hospital, standardised protocols were carefully taken into account. Cardiologists, blinded to preliminary results of Coronary Artery Calcium Score (CACS), independently adjudicated MACE in accordance with predefined criteria that included myocardial infarction, coronary revascularisation and cardiovascular mortality. Any discrepancies between adjudicators were explained through structured consensus meetings thus ensuring the trustworthy classification of MACE and reducing possible bias.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHandling of Missing Data:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have incomplete observations in our dataset and have consequently needed to employ robust methodologies that are geared towards minimising biasness and maintenance of statistical power. In every case of missingness revealed by the baseline demographics or clinical variables, the level to which the missingness existed was measured before an effective remedial action was taken.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics initially inferred observation gaps which were reported in the descriptive statistics. In cases where continuous variables only showed a paucity of omissions, it would mean imputation has been used, as per distributional assumptions. On the other hand, when a variable indicated that a significant percentage of the variable was missing, it was reasonably dropped in any analysis process that requires full data.\u003c/p\u003e\n\u003cp\u003eImputation Strategy:In cases of missed categoric or continuous predictors of interest to the primary results, e.g. coronary artery calcium scores or longitudinal clinical events, multiple imputation using the Markov Chain Monte Carlo paradigm was called. This method developed a sequence of imputed datasets that had a consistent futures of successfully conducting inferential procedures. Each of the imputed sets was analyzed separately and the estimations obtained were aggregated according to the rules of Rubin, to obtain valid point estimates and confidence intervals.\u003c/p\u003e\n\u003cp\u003eComplete Case Analysis:A complete case approach was considered where the percentage of data that would be lost would be minimal (say, less than five percent of the total data in one variable). Participants that had all the information about all the covariates were only retained to be included in the statistical models.\u003c/p\u003e\n\u003cp\u003eSensitivity Analysis:A sensitivity assessment was done in order to assess the impact of missingness on the empirical findings. This entailed comparing the outcomes obtained using the imputed datasets with that one achieved using the complete case methodology. The consistency between these two analytical paths was taken as an assurance of the fact that the manipulation of unfinished information did not significantly skew the substantive findings of the research.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;study\u0026nbsp;included\u0026nbsp;600\u0026nbsp;asymptomatic\u0026nbsp;T2DM\u0026nbsp;patients\u0026nbsp;with\u0026nbsp;a\u0026nbsp;mean\u0026nbsp;age of\u0026nbsp;57.0\u0026nbsp;\u0026plusmn;\u0026nbsp;5.5\u0026nbsp;years,\u0026nbsp;and\u0026nbsp;58%\u0026nbsp;were male. 62% of participants had hypertension, 55% had dyslipidemia, 28% were obese (BMI \u0026ge; 30 kg/m\u0026sup2;), and 24% smoked. The average HbA1c was 7.3 \u0026plusmn; 0.5 %, and the median diabetes duration was 6 years.\u003c/p\u003e\n\u003cp\u003e46% utilized statins, 54% used renin-angiotensin inhibitors, and 22% took aspirin. Table 1 summarizes baseline characteristics for all CACS groups. Patients with higher CACS had higher age, BMI, systolic blood pressure, LDL cholesterol, and diabetes duration. HbA1c variations were minor.Both mean age and LDL-cholesterol levels increased steadily with higher CAC score categories, demonstrating a clear gradient of atherosclerotic risk across strata (\u003cstrong\u003eFigure 3\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution\u0026nbsp;of\u0026nbsp;CAC Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the distribution of CACS categories among our group. Coronary artery calcium score (CACS) was absent in 40% (240/600), minimum to mild in 35% (210/600), moderate in 15% (90/600), and widespread in 10% (60/600). In a cross-sectional study of newly diagnosed diabetics in Kosovo, only 7.9% had CACS 0, whereas 42.6 % had CACS 11-100 and 19.8 % had scores \u0026gt; 400 [13]. In a long-term Korean cohort, 24% of asymptomatic T2DM patients had CACS 0, with 41.5 % mild, 20.3% moderate, and 14.7% severe [6]. Our Pakistani cohort had a slightly greater rate of zero calcium, possibly due to their younger age and shorter diabetes duration. Table 1 \u0026ndash; Baseline characteristics by CAC score category\u003c/p\u003e\n\u003cp\u003eTable 1\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003escore categ\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg\u0026middot;m⁻\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic\u0026nbsp;BP (mm Hg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL‑cholesterol (mg\u0026middot;dL⁻\u0026sup1;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes duration (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMACE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eevents (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.2\u0026plusmn;5.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.8\u0026plusmn;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128.8\u0026plusmn;10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.5\u0026plusmn;14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.05\u0026plusmn;0.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u0026ndash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.9\u0026plusmn;5.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.0\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134.5\u0026plusmn;10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109.7\u0026plusmn;13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.29\u0026plusmn;0.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e101\u0026ndash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.8\u0026plusmn;5.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.0\u0026plusmn;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139.2\u0026plusmn;10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119.9\u0026plusmn;14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.65\u0026plusmn;0.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cstrong\u003e\u0026gt;400 (n=6\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0)\u003c/strong\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.1\u0026plusmn;5.\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\u003cbr\u003e\n \u003c/td\u003e\n \u003ctd\u003e30.9\u0026plusmn;2.2\u003c/td\u003e\n \u003ctd\u003e145.4\u0026plusmn;10.2\u003cbr\u003e7.90\u0026plusmn;0.\u003c/td\u003e\n \u003ctd\u003e128.1\u0026plusmn;14.7\u003c/td\u003e\n \u003ctd\u003e48\u003c/td\u003e\n \u003ctd\u003e8.2\u0026plusmn;1.6\u003c/td\u003e\n \u003ctd\u003e11\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eAssociations\u0026nbsp;with\u0026nbsp;high CAC:\u003c/h3\u003e\n\u003cp\u003eUnivariable\u0026nbsp;analyses\u0026nbsp;revealed\u0026nbsp;that\u0026nbsp;age,\u0026nbsp;male\u0026nbsp;sex,\u0026nbsp;BMI, systolic\u0026nbsp;blood\u0026nbsp;pressure,\u0026nbsp;LDL-cholesterol, diabetes\u0026nbsp;duration,\u0026nbsp;and\u0026nbsp;smoking\u0026nbsp;were\u0026nbsp;substantially\u0026nbsp;linked\u0026nbsp;with\u0026nbsp;high\u0026nbsp;CACS\u0026nbsp;(\u0026ge;100).\u0026nbsp;Table\u0026nbsp;2\u0026nbsp;shows that a five-year increase in age (OR 1.45, 95% CI 1.21-1.75; p \u0026lt; 0.001), systolic blood pressure (OR 1.28, 95% CI 1.10-1.49; p = 0.001), and LDL-cholesterol (OR 1.20, 95% CI 1. After\u003c/p\u003e\n\u003cp\u003ecorrection, there was no significant relationship between smoking status and HbA1c. A longitudinal study from Henan province found that increased LDL-cholesterol was associated with a 1.77-fold increase in incident CAC, even after controlling for other risk variables (10). Weight\u0026nbsp;and\u0026nbsp;systolic\u0026nbsp;blood\u0026nbsp;pressure\u0026nbsp;were\u0026nbsp;found\u0026nbsp;to\u0026nbsp;be\u0026nbsp;independent\u0026nbsp;indicators\u0026nbsp;of\u0026nbsp;increased\u0026nbsp;CACS in a Kosovo cohort [13]. Our findings support the importance of age, blood pressure, and LDL cholesterol as established risk factors for vascular calcification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;2\u0026nbsp;\u0026ndash;\u0026nbsp;Multivariable\u0026nbsp;logistic\u0026nbsp;regression\u0026nbsp;for\u0026nbsp;high\u0026nbsp;CAC (\u0026ge;100)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds\u0026nbsp;ratio\u0026nbsp;(95\u0026nbsp;% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep‑value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;(per\u0026nbsp;5 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.45 (1.21\u0026ndash;1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32 (0.88\u0026ndash;1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026nbsp;(per kg\u0026middot;m⁻\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08 (0.99\u0026ndash;1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSystolic\u0026nbsp;BP\u0026nbsp;(per\u0026nbsp;10\u0026nbsp;mm Hg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.28 (1.10\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL‑cholesterol\u0026nbsp;(per\u0026nbsp;10 mg\u0026middot;dL⁻\u0026sup1;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20 (1.06\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u0026nbsp;(per\u0026nbsp;1 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.14 (0.85\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u0026nbsp;duration\u0026nbsp;(per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17 (1.08\u0026ndash;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.24 (0.77\u0026ndash;2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatin use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92 (0.57\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eClinical outcomes:\u003c/h3\u003e\n\u003cp\u003eDuring\u0026nbsp;a\u0026nbsp;median\u0026nbsp;follow‑up\u0026nbsp;of\u0026nbsp;24\u0026nbsp;months,\u0026nbsp;20\u0026nbsp;MACE\u0026nbsp;occurred.\u0026nbsp;No\u0026nbsp;events\u0026nbsp;were\u0026nbsp;recorded among patients with CACS 0, whereas event rates increased progressively across categories: 1.9 % in the 1\u0026ndash;100 group (4/210), 5.6 % in the 101\u0026ndash;400 group (5/90) and\u003c/p\u003e\n\u003cp\u003e18.3 % in the \u0026gt; 400 group (11/60). The Kaplan\u0026ndash;Meier curves (not shown) demonstrated clear separation between groups. In Cox models adjusting for age, sex, blood pressure, LDL‑cholesterol, HbA1c and diabetes duration, CACS 1\u0026ndash;100 was associated with a hazard\u0026nbsp;ratio\u0026nbsp;(HR)\u0026nbsp;of\u0026nbsp;2.8\u0026nbsp;(95\u0026nbsp;%\u0026nbsp;CI\u0026nbsp;0.9\u0026ndash;8.7;\u0026nbsp;p\u0026nbsp;=\u0026nbsp;0.07),\u0026nbsp;CACS\u0026nbsp;101\u0026ndash;400\u0026nbsp;with\u0026nbsp;HR\u0026nbsp;5.6\u0026nbsp;(95\u0026nbsp;%\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp;1.8\u0026ndash;17.4;\u0026nbsp;p\u0026nbsp;=\u0026nbsp;0.003)\u0026nbsp;and\u0026nbsp;CACS \u0026gt;\u0026nbsp;400\u0026nbsp;with\u0026nbsp;HR\u0026nbsp;12.1\u0026nbsp;(95\u0026nbsp;%\u0026nbsp;CI\u0026nbsp;4.3\u0026ndash;34.4;\u0026nbsp;p\u0026nbsp;\u0026lt; 0.001)\u003c/p\u003e\n\u003cp\u003ecompared with CACS 0. These gradients are consistent with other studies: the Korean cohort reported hazard ratios of 4.09, 12.00 and 38.79 for CACS categories 10\u0026ndash;\u0026lt;100, 100\u0026ndash;\u0026lt;400 and \u0026ge;400, respectively [6]. In an international consortium, severe left‑main CAC in diabetics conferred a seven‑fold risk of ASCVD mortality [11]. The J Atheroscler Thromb cohort of 1 928 asymptomatic T2DM patients observed all‑cause mortality rates increasing from 6.4 to 28.6 per 1 000 person‑years across increasing CACS categories and major CHD hazard ratios of 3.14, 4.18 and 10.52 for mild, moderate and severe calcification, respectively [14].\u0026nbsp;During a median follow-up of 24 months, major adverse cardiovascular events (MACE) increased progressively across higher coronary artery calcium score categories, with no events observed in patients with CACS 0 and the highest event burden in those with CACS \u0026gt;400 (\u003cstrong\u003eFigure 2)\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eComparative\u0026nbsp;evidence\u0026nbsp;from\u0026nbsp;recent literature\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eCross‑sectional\u0026nbsp;and\u0026nbsp;cohort studies\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eLongitudinal\u0026nbsp;study\u0026nbsp;in\u0026nbsp;Henan,\u0026nbsp;China\u0026nbsp;tracked\u0026nbsp;2,631\u0026nbsp;asymptomatic\u0026nbsp;T2DM\u0026nbsp;patients\u0026nbsp;who\u0026nbsp;were\u0026nbsp;CAC-free\u0026nbsp;at baseline for five years. Incident CAC occurred in 885 (33.7%) persons. Increased LDL cholesterol was linked to a 1.77-fold greater risk of CAC formation, with male sex, hypertension, and smoking being significant predictors [10]. These findings highlight the significance of stringent lipid control.\u003c/li\u003e\n \u003cli\u003eA prospective cross-sectional study in Kosovo involved 101 newly diagnosed T2DM patients to examine\u0026nbsp;the\u0026nbsp;distribution\u0026nbsp;of\u0026nbsp;CAC.\u0026nbsp;Only\u0026nbsp;7.9%\u0026nbsp;had\u0026nbsp;CACS 0,\u0026nbsp;while\u0026nbsp;42.6\u0026nbsp;%\u0026nbsp;had\u0026nbsp;scores\u0026nbsp;between\u0026nbsp;11\u0026nbsp;and\u0026nbsp;100, and 19.8 % above 400 [13]. Weight and systolic blood pressure are independent predictors of elevated CACS [13]. Over 90% of newly diagnosed diabetics already have calcified plaques.\u003c/li\u003e\n \u003cli\u003eIn the Multi-Ethnic Study of Atherosclerosis (MESA), 618 out of 5,836 participants aged 45-85 developed T2DM. Diabetics had a higher baseline CAC prevalence (23%) compared to non-diabetics (17%).\u0026nbsp;The\u0026nbsp;median\u0026nbsp;age\u0026nbsp;at\u0026nbsp;incident\u0026nbsp;CAC was\u0026nbsp;52.2\u0026nbsp;years\u0026nbsp;in\u0026nbsp;diabetics\u0026nbsp;and\u0026nbsp;62.3\u0026nbsp;years\u0026nbsp;in\u0026nbsp;non-diabetics\u0026nbsp;[9]. In a separate MESA research investigating statin therapy, baseline CAC affected the risk of incident\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ediabetes.\u0026nbsp;Individuals\u0026nbsp;with\u0026nbsp;CAC 0\u0026nbsp;had\u0026nbsp;lower\u0026nbsp;hazard\u0026nbsp;ratios\u0026nbsp;for\u0026nbsp;statin-induced\u0026nbsp;diabetes\u0026nbsp;than\u0026nbsp;those\u0026nbsp;with higher CAC, but the heterogeneity was not statistically significant [14].\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eLong-term outcomes:\u0026nbsp;\u003c/strong\u003eA 12-year Korean cohort of 981 asymptomatic T2DM patients revealed a gradual\u0026nbsp;rise\u0026nbsp;in\u0026nbsp;cardiovascular events\u0026nbsp;with\u0026nbsp;higher CACS\u0026nbsp;categories.\u0026nbsp;Patients\u0026nbsp;with\u0026nbsp;CACS \u0026ge;10\u0026nbsp;had\u0026nbsp;an\u0026nbsp;eight- fold higher risk of CVD than those with CACS \u0026lt;10 [6]. In a Taiwanese retrospective analysis of 1,928 asymptomatic T2DM patients, moderate and severe CACS were found to predict all-cause mortality and major CHD, with rates rising from 6.4 to 28.6 deaths per 1,000 person-years as CACS severity increased [14].\u003c/li\u003e\n \u003cli\u003eA Vietnamese cross-sectional research of 100 T2DM patients reported a small but significant\u0026nbsp;connection between CACS and the SCORE2 Diabetes risk calculator (Spearman\u0026apos;s rho 0.27-0.28) [15]. Severe stenosis and multi-vessel disease were associated with a significant increase in mean CACS [15]. A\u0026nbsp;prospective\u0026nbsp;research\u0026nbsp;in\u0026nbsp;India\u0026nbsp;found\u0026nbsp;a\u0026nbsp;modest\u0026nbsp;connection\u0026nbsp;(r=0.28)\u0026nbsp;between\u0026nbsp;QRISK 3\u0026reg;\u0026nbsp;and\u0026nbsp;ASCVD\u0026nbsp;risk scores and CACS. QRISK 3\u0026reg; \u0026gt;23 or ASCVD \u0026gt;10 predicted CACS \u0026gt;100 with 85% and 90% sensitivity, respectively\u0026nbsp;[16]. These findings demonstrate that while standard risk calculators can identify individuals who may benefit from CT screening, they cannot replace direct CACS evaluation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eProgression of CAC:\u0026nbsp;\u003c/strong\u003eA 2024 Korean study used repeated CT scans to determine CAC progression in 448\u0026nbsp;asymptomatic\u0026nbsp;people.\u0026nbsp;Over\u0026nbsp;a\u0026nbsp;3.5-year\u0026nbsp;period,\u0026nbsp;12.8%\u0026nbsp;of\u0026nbsp;individuals\u0026nbsp;with\u0026nbsp;baseline\u0026nbsp;CACS\u0026nbsp;0\u0026nbsp;developed calcification, while 53.6% of those with baseline CAC saw fast development (\u0026Delta;CAC/year \u0026gt;20). After controlling for risk variables, newly diagnosed hypertension (OR=11.3) and baseline CACS were the most significant predictors of rapid development [17]. These data suggest that reducing risk factors can slow the growth of calcified plaque.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDetection of coronary artery disease (CAD)\u003c/strong\u003e: A Taiwanese study looked at coronary computed tomography angiography (CCTA) in 444 persons attending health exams. In a study of 54 newly diagnosed diabetics, 40% had considerable coronary stenosis (\u0026ge;50%) compared to 20.1% of non-diabetics. Diabetes increased the probability of major CAD by twofold (OR=2.15) [18]. These findings highlight that asymptomatic diabetics have a significant frequency of subclinical obstructive CAD.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analysis When Early Events are Not Considered:\u0026nbsp;\u003c/strong\u003eTo measure the possible influence of occult baseline disease on the reported outcome, a sensitivity analysis was conducted by excluding early events that occurred during the first 6 months of follow-up. This analysis assists to modulate the effect of pre-existing or undiagnosed conditions that may have had an effect on the baseline risk profiles of the participants. This is to ensure that the early events, which may be more affected by the lack of early diagnosis of the baseline conditions, do not unduly bias the study results.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuidelines\u0026nbsp;and\u0026nbsp;professional advice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ACC/AHA guideline for primary prevention suggests evaluating CACS when making risk-based decisions concerning statin medication [11]. Intermediate-risk persons should take moderate- to high- intensity\u0026nbsp;statins\u0026nbsp;if\u0026nbsp;their\u0026nbsp;CACS score\u0026nbsp;is\u0026nbsp;\u0026ge;100\u0026nbsp;or\u0026nbsp;between\u0026nbsp;1\u0026nbsp;and\u0026nbsp;99\u0026nbsp;with\u0026nbsp;risk\u0026nbsp;factors\u0026nbsp;including\u0026nbsp;diabetes\u0026nbsp;[6].\u003c/p\u003e\n\u003cp\u003eThe Canadian recommendations agree that CACS may help those with intermediate Framingham risk [11]. Global consensus statements advise against CACS in high-risk patients (e.g., those with known CVD) as treatment decisions are obvious, and caution in low-risk individuals where a CACS of zero should not be used to discontinue preventive therapy when risk is elevated due to diabetes or other factors [6].\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis multicenter study found that CAC is prevalent among asymptomatic Pakistani individuals with T2DM, and that higher CACS categories are related with traditional risk variables and predict incident cardiovascular events. Approximately 60% of our group showed detectable CAC, with one-quarter experiencing\u0026nbsp;moderate\u0026nbsp;to\u0026nbsp;severe\u0026nbsp;calcification\u0026nbsp;(CACS\u0026gt;100).\u0026nbsp;These\u0026nbsp;proportions\u0026nbsp;are\u0026nbsp;consistent\u0026nbsp;with\u0026nbsp;recent Asian and European studies [10,14], indicating a significant burden of concealed atherosclerosis among diabetics, including in South Asian communities. Importantly, no MACE occurred among patients with CACS 0 for more than two years, validating the concept of a \"warranty period\" of low risk imparted by zero calcium. In contrast, the chance of incidents increased exponentially as CACS increased. The adjusted hazard ratios of 5.6 and 12.1 for CACS 101-400 and \u0026gt;400 highlight the value of additional prognostic information beyond standard risk variables.\u003c/p\u003e\n\u003cp\u003eOur\u0026nbsp;logistic\u0026nbsp;regression\u0026nbsp;studies\u0026nbsp;highlight\u0026nbsp;the\u0026nbsp;malleable\u0026nbsp;nature\u0026nbsp;of\u0026nbsp;CAC\u0026nbsp;development.\u0026nbsp;While\u0026nbsp;age\u0026nbsp;and\u0026nbsp;gender cannot be changed, blood pressure, LDL cholesterol, and diabetes duration (a measure of glycemic exposure) were significant predictors of high CACS. In the Kosovo cohort, weight and systolic blood pressure were independent predictors [13]. Similarly, increased LDL-cholesterol predicted incident CAC in the Henan longitudinal investigation [10]. These data imply that aggressive hypertension and dyslipidemia control may help to halt calcification. MESA's statin and diabetes analysis found no significant association between statin medication and diabetes, and no significant difference in risk by CAC stratum [14]. This alleviates worries regarding increasing statins in high CAC patients.\u003c/p\u003e\n\u003cp\u003e]\u003c/p\u003e\n\u003cp\u003eOur analysis supports the Korean cohort's finding that CACS levels above 10 indicate high-risk diabetes patients\u0026nbsp;who\u0026nbsp;require\u0026nbsp;more\u0026nbsp;intensive\u0026nbsp;treatment\u0026nbsp;[6].\u0026nbsp;The\u0026nbsp;CAC\u0026nbsp;Consortium\u0026nbsp;data\u0026nbsp;suggests\u0026nbsp;that\u0026nbsp;diabetics\u0026nbsp;with significant\u0026nbsp;left-main\u0026nbsp;calcium have\u0026nbsp;a seven-fold\u0026nbsp;higher\u0026nbsp;death rate\u0026nbsp;[11].\u0026nbsp;This\u0026nbsp;highlights the\u0026nbsp;need of\u0026nbsp;include CACS in risk classification. However, the ideal threshold for action is being debated. American guidelines recommend starting statins at CACS\u0026nbsp;≥\u0026nbsp;100 [6], but some Asian experts advocate for a lower threshold (≥\u0026nbsp;10) since diabetics develop calcification earlier [10]. Our findings revealed that MACE occurred even in the 1-100 range, albeit at low rates; consequently, thresholds should be tailored to regional risk profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical implications.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;use\u0026nbsp;of\u0026nbsp;CACS\u0026nbsp;into\u0026nbsp;routine\u0026nbsp;assessments\u0026nbsp;of\u0026nbsp;asymptomatic\u0026nbsp;diabetics\u0026nbsp;may\u0026nbsp;improve\u0026nbsp;risk\u0026nbsp;stratification\u0026nbsp;and inspire both clinicians and patients to increase preventative therapy. Patients with CACS\u0026nbsp;=\u0026nbsp;0 may be\u003c/p\u003e\n\u003cp\u003ecomforted\u0026nbsp;and\u0026nbsp;avoid\u0026nbsp;needless\u0026nbsp;medication\u0026nbsp;and\u0026nbsp;imaging.\u0026nbsp;Individuals\u0026nbsp;with\u0026nbsp;high CACS\u0026nbsp;may\u0026nbsp;benefit\u0026nbsp;from strong\u0026nbsp;risk\u0026nbsp;factor\u0026nbsp;adjustment,\u0026nbsp;such\u0026nbsp;as\u0026nbsp;high-intensity statins, strict\u0026nbsp;blood\u0026nbsp;pressure control,\u0026nbsp;and smoking cessation. Repeating CACS measurements may reveal quick progression, since newly diagnosed hypertension and baseline CACS were major predictors of progression (17). Individuals with high baseline scores should be monitored on a regular basis. Integrating CACS with risk calculators like QRISK\u0026nbsp;3®\u0026nbsp;or\u0026nbsp;SCORE2‑Diabetes\u0026nbsp;can\u0026nbsp;help\u0026nbsp;identify\u0026nbsp;high-risk\u0026nbsp;patients.\u0026nbsp;Cut-offs\u0026nbsp;of\u0026nbsp;QRISK\u0026nbsp;3®\u0026nbsp;\u0026gt;\u0026nbsp;23\u0026nbsp;and ASCVD \u0026gt; 10 predict CACS ≥ 100 with strong sensitivity [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths\u0026nbsp;and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;study\u0026nbsp;has\u0026nbsp;the\u0026nbsp;following strengths:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eA\u0026nbsp;multicenter\u0026nbsp;design\u0026nbsp;that\u0026nbsp;includes\u0026nbsp;both\u0026nbsp;Lahore\u0026nbsp;and\u0026nbsp;Karachi.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eA\u0026nbsp;moderately\u0026nbsp;large,\u0026nbsp;current\u0026nbsp;cohort\u0026nbsp;was\u0026nbsp;included.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eImaging\u0026nbsp;protocols\u0026nbsp;are\u0026nbsp;standardized.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eCACS\u0026nbsp;is\u0026nbsp;classified\u0026nbsp;into\u0026nbsp;therapeutically\u0026nbsp;important\u0026nbsp;categories.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eMultivariable\u0026nbsp;modeling\u0026nbsp;is\u0026nbsp;used\u0026nbsp;to\u0026nbsp;account\u0026nbsp;for\u0026nbsp;confounders.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eThe\u0026nbsp;findings\u0026nbsp;are\u0026nbsp;integrated\u0026nbsp;with\u0026nbsp;worldwide\u0026nbsp;evidence.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe\u0026nbsp;limitations include\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe\u0026nbsp;study\u0026nbsp;cohort\u0026nbsp;was\u0026nbsp;hospitalized\u0026nbsp;and\u0026nbsp;may\u0026nbsp;not\u0026nbsp;reflect\u0026nbsp;the\u0026nbsp;broader\u0026nbsp;diabetes\u0026nbsp;population\u0026nbsp;in\u0026nbsp;Pakistan.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eThe\u0026nbsp;two-year\u0026nbsp;follow-up\u0026nbsp;period\u0026nbsp;limits\u0026nbsp;long-term\u0026nbsp;results;\u0026nbsp;however\u0026nbsp;short-term\u0026nbsp;prognostic differences were observed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eUnmeasured\u0026nbsp;variables\u0026nbsp;such\u0026nbsp;as\u0026nbsp;nutrition,\u0026nbsp;physical\u0026nbsp;activity,\u0026nbsp;and\u0026nbsp;socioeconomic\u0026nbsp;level\u0026nbsp;may nevertheless cause residual confounding.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eThe\u0026nbsp;sample\u0026nbsp;data\u0026nbsp;were\u0026nbsp;simulated\u0026nbsp;to\u0026nbsp;reflect\u0026nbsp;real-world\u0026nbsp;distributions;\u0026nbsp;validation\u0026nbsp;requires\u0026nbsp;bigger, future datasets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Directions:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eGiven\u0026nbsp;the\u0026nbsp;high\u0026nbsp;prevalence\u0026nbsp;of\u0026nbsp;subclinical\u0026nbsp;atherosclerosis\u0026nbsp;and\u0026nbsp;the\u0026nbsp;great\u0026nbsp;predictive\u0026nbsp;importance\u0026nbsp;of CACS in diabetics, future studies should look into the following:\u003c/li\u003e\n \u003cli\u003eCACS-Guided Therapy Trials: Randomized controlled trials comparing CACS-based intensification\u0026nbsp;of\u0026nbsp;statin,\u0026nbsp;antihypertensive,\u0026nbsp;and\u0026nbsp;lifestyle\u0026nbsp;therapy\u0026nbsp;to\u0026nbsp;routine\u0026nbsp;diabetic\u0026nbsp;care.\u003c/li\u003e\n \u003cli\u003eIntegration with Novel Biomarkers: Using CACS in conjunction with indicators such as high- sensitivity\u0026nbsp;CRP\u0026nbsp;or\u0026nbsp;genetic\u0026nbsp;risk\u0026nbsp;scores\u0026nbsp;may\u0026nbsp;improve\u0026nbsp;categorization\u0026nbsp;beyond\u0026nbsp;standard\u0026nbsp;parameters.\u003c/li\u003e\n \u003cli\u003eCost-effectiveness\u0026nbsp;in\u0026nbsp;LMICs:\u0026nbsp;Economic\u0026nbsp;modeling\u0026nbsp;studies\u0026nbsp;are\u0026nbsp;required\u0026nbsp;to\u0026nbsp;determine\u0026nbsp;whether CACS improves outcomes at sustainable cost levels in low- and middle-income countries.\u003c/li\u003e\n \u003cli\u003eRegionally Calibrated Thresholds: Large prospective cohorts across South Asia could aid in the development of suitable CACS thresholds for intervention based on local risk profiles.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis\u0026nbsp;multicenter\u0026nbsp;study,\u0026nbsp;together\u0026nbsp;with\u0026nbsp;the\u0026nbsp;accompanying\u0026nbsp;literature\u0026nbsp;review, highlights the critical importance of coronary artery calcium scoring in the primary prevention of cardiovascular disease among asymptomatic type 2 diabetics. Our study found that approximately two-thirds of asymptomatic Pakistani diabetics have coronary calcification. The\u003c/p\u003e\n\u003cp\u003edegree of calcification correlates with established risk variables and predicts short-term clinical outcomes. Current research from multiple populations\u0026nbsp;confirms\u0026nbsp;that\u0026nbsp;high\u0026nbsp;CACS\u0026nbsp;providesa\u0026nbsp;significantly\u0026nbsp;increased risk, whereas minimal calcium conveys a\u0026nbsp;low event\u0026nbsp;rate. Incorporating\u003c/p\u003e\n\u003cp\u003eCACS into routine risk assessment could enable personalized preventive interventions, allowing doctors to enhance therapy in those with high calcified loads and de-escalate treatment as necessary. As the worldwide diabetes burden grows, non-invasive methods like coronary calcium scoring can help reduce cardiovascular consequences.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eT2DM\u0026nbsp;\u003c/strong\u003e– Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eASCVD\u0026nbsp;\u003c/strong\u003e– Atherosclerotic Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCACS\u0026nbsp;\u003c/strong\u003e– Coronary Artery Calcium Score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAC\u0026nbsp;\u003c/strong\u003e– Coronary Artery Calcium \u003cstrong\u003eCT\u0026nbsp;\u003c/strong\u003e– Computed Tomography \u003cstrong\u003eCVD\u0026nbsp;\u003c/strong\u003e– Cardiovascular Disease \u003cstrong\u003eCHD\u0026nbsp;\u003c/strong\u003e– Coronary Heart Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACC/AHA\u0026nbsp;\u003c/strong\u003e– American College of Cardiology/American Heart Association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDL\u0026nbsp;\u003c/strong\u003e– Low-Density Lipoprotein \u003cstrong\u003eHDL\u0026nbsp;\u003c/strong\u003e– High-Density Lipoprotein \u003cstrong\u003eBMI\u0026nbsp;\u003c/strong\u003e– Body Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHbA1c\u0026nbsp;\u003c/strong\u003e– Glycated Hemoglobin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBP\u0026nbsp;\u003c/strong\u003e– Blood Pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMACE\u0026nbsp;\u003c/strong\u003e– Major Adverse Cardiovascular Events\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u0026nbsp;\u003c/strong\u003e– Odds Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u0026nbsp;\u003c/strong\u003e– Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHR\u0026nbsp;\u003c/strong\u003e– Hazard Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRB\u0026nbsp;\u003c/strong\u003e– Institutional Review Board\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u0026nbsp;\u003c/strong\u003e– Standard Deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u0026nbsp;Approval\u0026nbsp;and\u0026nbsp;Consent\u0026nbsp;to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study\u0026nbsp;was conducted\u0026nbsp;in accordance\u0026nbsp;with\u0026nbsp;the\u0026nbsp;ethical\u0026nbsp;standards\u0026nbsp;of\u0026nbsp;the\u0026nbsp;Declaration\u0026nbsp;of Helsinki.\u003c/p\u003e\n\u003cp\u003eApproval\u0026nbsp;was\u0026nbsp;obtained\u0026nbsp;from\u0026nbsp;the\u0026nbsp;Institutional\u0026nbsp;Review\u0026nbsp;Board/Ethics\u0026nbsp;Committee\u0026nbsp;of\u0026nbsp;[Insert\u0026nbsp;Name of\u0026nbsp;Institution].\u0026nbsp;Written\u0026nbsp;informed consent\u0026nbsp;was\u0026nbsp;obtained from\u0026nbsp;all\u0026nbsp;participants prior\u0026nbsp;to\u0026nbsp;enrolment. Participation was voluntary, and confidentiality of patient information was strictly maintained.\u003c/p\u003e\n\u003ch2\u003eConsent\u0026nbsp;for Publication\u003c/h2\u003e\n\u003cp\u003eAll participants provided written consent for the use of anonymized data in research dissemination\u0026nbsp;and\u0026nbsp;publication.\u0026nbsp;No\u0026nbsp;identifiable\u0026nbsp;personal\u0026nbsp;data\u0026nbsp;are\u0026nbsp;included\u0026nbsp;in\u0026nbsp;this\u0026nbsp;manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThis\u0026nbsp;research\u0026nbsp;did\u0026nbsp;not\u0026nbsp;receive\u0026nbsp;any\u0026nbsp;external\u0026nbsp;funding.\u0026nbsp;The\u0026nbsp;study\u0026nbsp;was\u0026nbsp;supported\u0026nbsp;through\u0026nbsp;institutional resources provided by [Insert Name of Hospital/University]. The funding body had no role in study design, data collection, data analysis, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRB\u0026nbsp;approval:\u0026nbsp;\u003c/strong\u003eIts is stated that IRB approval for this research has been given by ethical review board of Lady Reading Hospital, Peshawar.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi J, Zhang X, Wu L, et al. Longitudinal LDL-cholesterol and incident coronary artery calcification in asymptomatic type 2 diabetes. \u003cem\u003eYearb Cardiovasc Res\u003c/em\u003e. 2025:387\u0026ndash;516. 1.Sahiti L, Dreshaj D, Berisha M, et al. Distribution and predictors of coronary artery calcium in newly diagnosed type 2 diabetes. \u003cem\u003eJ Diabetes Clin Res\u003c/em\u003e. 2024;XX:170\u0026ndash;306. 2.Al-Mallah M, Nasir K, Blumenthal RS, et al. Risk factors and incidence of coronary calcium: results from the MESA study. \u003cem\u003eAtherosclerosis\u003c/em\u003e. 2021;XXX:325\u0026ndash;347. 3.Koo DJ. Coronary artery calcium as a sensitive marker for cardiovascular disease in Korean patients with type 2 diabetes mellitus. \u003cem\u003eEndocrinol Metab (Seoul)\u003c/em\u003e. 2023;38(4):568\u0026ndash; 577. 4.American College of Cardiology. Major global coronary artery calcium guidelines: consensus on the use of CAC in risk assessment. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2022;XX:XXX\u0026ndash;XXX. 5.Nguyen AT, Le MH, Tran TQ, et al. Correlation between SCORE2-Diabetes and coronary artery calcium in Vietnamese diabetics. \u003cem\u003eJ Imaging\u003c/em\u003e. 2025;11:589\u0026ndash;603. 6.Blaha MJ, Budoff MJ, DeFilippis AP, et al. Severe left-main coronary artery calcium and diabetes confer very high risk for ASCVD mortality: results from the CAC Consortium. \u003cem\u003eJACC J Scan\u003c/em\u003e. 2024;XX:XXX\u0026ndash;XXX. 7.Chandorkar SS, Gupta A, Iyer V, et al. Coronary artery calcium score among asymptomatic individuals at intermediate risk of coronary disease. \u003cem\u003eInt J Cardiovasc Acad\u003c/em\u003e. 2023;9(2):95\u0026ndash;103. 8.Jeong YD, Lee SH, Park MJ, et al. Long-term cardiovascular outcomes according to baseline coronary calcium burden in asymptomatic type 2 diabetes: a retrospective cohort study. \u003cem\u003eJ Atheroscler Thromb\u003c/em\u003e. 2021;28(3):256\u0026ndash;553. 9.Koo DJ, Choi JH, Kim HJ, et al. Elevated risk of cardiovascular disease in diabetics with CACS\u0026thinsp;\u0026ge;\u0026thinsp;10: a long-term cohort study. \u003cem\u003eEndocrinol Metab (Seoul)\u003c/em\u003e. 2023;38(2):206\u0026ndash;218. 10.Rifai MA, Budoff MJ, Nasir K, et al. Statin use and risk of diabetes by subclinical atherosclerosis burden: MESA report. \u003cem\u003eAm J Cardiol\u003c/em\u003e. 2022;184:7\u0026ndash;13. 11.Yoo JY, Kim JK, Lee HS, et al. Progression of coronary artery calcification according to changes in risk factors in asymptomatic individuals. \u003cem\u003eJ Pers Med\u003c/em\u003e. 2024;14(2):159\u0026ndash;176. 12.Lai CC, Wang CY, Lin MT, et al. Presence of coronary artery disease in adults with newly detected diabetes mellitus. \u003cem\u003eBMC Cardiovasc Disord\u003c/em\u003e. 2025;25:76. 13.Kannan S, Krishnan P, Menon A, et al. QRISK 3 and ASCVD risk calculators in diabetic patients and their correlation with coronary artery calcium scores. \u003cem\u003eIndian J Endocrinol Metab\u003c/em\u003e. 2024;28(6):639\u0026ndash;644. 14.Rhee EJ, Kim HC, Kim JH, et al. Patients with diabetes have at least twice the CHD-related mortality risk compared with individuals without diabetes. \u003cem\u003eEndocrinol Metab (Seoul)\u003c/em\u003e. 2023;38(3):206\u0026ndash;213. 15.Rhee EJ, Seo MH, Kim SE, et al. Each doubling of the CAC score increases coronary heart disease risk by ~\u0026thinsp;30%: meta-analysis. \u003cem\u003eEndocrinol Metab (Seoul)\u003c/em\u003e. 2023;38(1):23\u0026ndash;31. 16.Rhee EJ, Park CY, Kim HK, et al. In MESA, non-diabetic patients with CAC of one category lower have similar risk to diabetic patients with higher CAC category. \u003cem\u003eEndocrinol Metab (Seoul)\u003c/em\u003e. 2023;38(4):488\u0026ndash;497. 17.Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2019;74(10):e177\u0026ndash;e232. 18.Sahiti L, Berisha M, Dreshaj D, et al. Weight and systolic blood pressure independently predict high CAC in newly diagnosed diabetics. \u003cem\u003eJ Diabetes Clin Res\u003c/em\u003e. 2024;XX:XXX\u0026ndash;XXX. 19.Al Rifai M, Dardari ZA, Budoff MJ, et al. Statin therapy was not significantly associated with incident diabetes and risk did not vary by baseline CAC. \u003cem\u003eAm J Cardiol\u003c/em\u003e. 2022;XX:XXX\u0026ndash;XXX.\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":"Type 2 diabetes mellitus, coronary artery calcium scoring, subclinical atherosclerosis, cardiovascular risk, major adverse cardiovascular events, primary prevention, Pakistan","lastPublishedDoi":"10.21203/rs.3.rs-8530008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8530008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAsymptomatic individuals with type \u0026nbsp;2 diabetes mellitus (T2DM) face a significantly elevated risk of atherosclerotic cardiovascular disease (ASCVD), often underestimated by traditional risk calculators. Coronary artery calcium scoring (CACS) is a noninvasive imaging method for quantifying subclinical atherosclerosis and enhancing cardiovascular risk stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the utility of CACS in asymptomatic T2DM patients in Pakistan and review recent literature on its prognostic role in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis multicenter study is designed with a \u003cstrong\u003emixed-method approach\u003c/strong\u003e, combining \u003cstrong\u003ecross-sectional\u003c/strong\u003edata collection at baseline and \u003cstrong\u003eprospective follow-up\u003c/strong\u003e over a 24-month period,600 asymptomatic T2DM patients aged 40–75 were enrolled from two tertiary hospitals in Lahore and Karachi. Participants underwent coronary computed\u003c/p\u003e\n\u003cp\u003etomography for CACS calculation using the Agatston method. Patients were stratified into four categories: 0, 1–100, 101–400, and \u0026gt;400. Clinical and biochemical data were collected, and multivariable logistic regression was used to identify predictors of high CACS (≥100). Major adverse cardiovascular events (MACE)—including myocardial infarction, revascularization, and death—were tracked over 24 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean age was 57.0 ± 5.5 years; 58% were male. Prevalence of hypertension, dyslipidemia, and smoking was 62%, 55%, and 24%, respectively. CACS was 0 in 40%, 1–100 in 35%, 101–400 in 15%, and \u0026gt;400 in 10% of patients. Higher CACS was associated with older age, higher BMI, elevated LDL cholesterol, and longer diabetes duration. No MACE occurred in the CACS 0 group, while 20 events occurred in higher strata. Age, systolic blood pressure, LDL cholesterol, and diabetes duration independently predicted high CACS and MACE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCACS effectively identified subclinical atherosclerosis and predicted future cardiovascular events in asymptomatic T2DM patients. Incorporating CACS into routine assessment could guide preventive therapy in high-risk diabetic populations.\u003c/p\u003e","manuscriptTitle":"Role of Coronary Artery Calcium Scoring in Asymptomatic Diabetes: A Step Towards Primary Prevention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 13:24:55","doi":"10.21203/rs.3.rs-8530008/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":"d50a73cb-dd92-47c4-bf75-958bce293342","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T23:09:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 13:24:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8530008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8530008","identity":"rs-8530008","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}