To determine associations between arm and ankle systolic blood pressures (SBPs), develop and validate a multivariable model predicting arm SBP from ankle SBP, and investigate associations between ankle SBP, cardiovascular disease and mortality.

Ankle-arm SBP differences were examined in two-stage individual participant data (IPD) meta-analyses using multivariable hierarchical linear regression models. Models were used to derive and validate a prediction model for arm SBP based on ankle SBP. Model performance was assessed using area under the receiver operating characteristic (AUROC) curve analyses. Prognostic associations of ankle SBP with outcomes were examined using Cox proportional hazards models.

Searches identified cohorts for the Inter-arm Blood Pressure Difference IPD (INTERPRESS-IPD) Collaboration from Medline, Old Medline, Medline in process, Embase and CINAHL databases from inception until January 2017; unpublished data were also sought. Required primary outcomes were all-cause mortality, cardiovascular mortality, and/or fatal and non-fatal cardiovascular events.

Prospective studies from community, primary care or general clinic settings, without language restriction, that recorded SBP in both arms were eligible. Adults aged ≥18 years with SBP measured in all four limbs, in a supine position, were included in the current analyses. People with peripheral artery disease were excluded.

Anonymised datasets were individually cleaned and then combined into a single dataset for the INTERPRESS-IPD Collaboration.

The current dataset included 33 710 participants from 14 studies; mean age 58 years, 45% female, mean baseline arm blood pressure 138/80 (SD: 20/12) mm Hg. Mean ankle SBP was 12.0 mm Hg (95% CI 8.8 to 15.2) higher than arm SBP. The multivariable model predicting arm SBP from ankle SBP demonstrated excellent performance (AUROC curves, sensitivities and specificities were >0.82, 0.80 and 0.82, respectively, at all BP thresholds from 130 to 160 mm Hg). Model performance was superior to existing arithmetic formulae.Ankle SBP was neither associated with all-cause nor cardiovascular mortality (HR 1.000 (0.997 to 1.002; p=0.682) and 1.001 (0.996 to 1.005; p=0.840), respectively). However, lower-reading ankle SBP was associated with fatal or non-fatal cardiovascular events (HR 1.005 (1.002 to 1.007; p<0.001).

On average, ankle SBP is 12 mm Hg higher than arm SBP. Estimating individual arm SBP from ankle SBP measurements with a multivariable model is more accurate than existing fixed arithmetic formulae. This model, operationalised in an online calculator (https://ablebp.

exeter.ac.uk/), could facilitate hypertension management and cardiovascular care for people unable to have arm SBP measured.

CRD42015031227.

Elevated lipoprotein(a) (Lp[a]) is a genetically determined cardiovascular risk factor, linked to both atherosclerotic cardiovascular disease and aortic stenosis. Elevated Lp(a) is widely prevalent, and consequently, several cardiovascular societies now recommend performing Lp(a) screening at least once in all adults. While there are presently no approved drugs specifically aimed at lowering Lp(a), several promising candidates are currently in the drug development pipeline, and many of these are now undergoing late phase clinical trials. In this comprehensive review, we outline Lp(a) biology and genetics, describe Lp(a)'s relationship to various cardiovascular clinical phenotypes of interest, highlight novel Lp(a)-lowering therapies, and outline what role these may have in future clinical practice.

Cardiovascular disease remains the leading cause of morbidity and mortality worldwide, especially in regions like Eastern Europe, South Asia, and Latin America. A significant portion of these cases (80%) is linked to atherosclerosis, which can lead to severe conditions like ischemic heart disease and stroke, with atherosclerosis (ATS) responsible for the majority of cases. This review explores the multifaceted relationship between insulin resistance (IR) and ATS, highlighting their roles as both independent and interrelated contributors to cardiovascular risk. ATS is characterized by lipid accumulation and chronic inflammation within arterial walls, driven by factors such as hypertension, dyslipidemia, and genetic predisposition, with endothelial dysfunction as a key early event. The early detection of subclinical ATS is critical and can be achieved through a combination of non-invasive imaging techniques-such as coronary artery calcium scoring and carotid ultrasound-and comprehensive risk profiling. IR, marked by impaired glucose uptake in liver, muscle, and adipose tissue, often precedes early diabetes and is associated with metabolic disturbances, including dyslipidemia and chronic inflammation. The diagnosis of IR relies on surrogate indices such as HOMA-IR, the QUICKI, and the TyG index, which facilitate screening in clinical practice. Compelling evidence indicates that IR independently predicts the progression of atherosclerotic plaques, even in non-diabetic individuals, and operates through both traditional risk factors and direct vascular effects. Understanding and targeting the IR-ATS axis is essential for the effective prevention and management of cardiovascular disease.

Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).

In total, 5057 CSCT examinations were performed on one CT system at Linköping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland-Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis.

The evaluation included 4567 subjects, 2229 (48.8%) male, 2338 (51.2%) female, 50-64 years of age (mean 57.3 ± 4.4). The AS ranged from 0 to 2871 in the cohort, with 2846 subjects having an AS of 0. Mean and median AS were 51.4 and 0.0, respectively. Total AS, VS, MS and number of lesions ICCs were 0.994, 0.994, 0.994, 0.960 (p < 0.001), respectively. Bland-Altman analyses rendered mean differences ± 1.96 SD upper and lower limits of agreement for AS -0.04, -52.5 to 52.4, VS -0.44, -46.51 to 45.63, and MS -0.07, -9.62 to 9.48. Weighted kappa analysis for CV risk category classifications was 0.913, and overall accuracy was 91.2%.

There was excellent correlation and agreement between AI and semi-automatic evaluations for all calcium scores, number of lesions and lesion location. High degrees of agreement and accuracy were found for the CV risk categorization.

Question Can AI function as a tool for enhancing the efficiency and accuracy of Coronary Artery Calcium Score (CACS) evaluations in clinical radiology practice? Findings This study confirms the robustness of AI-derived CACS results across extensive datasets, though its generalizability is limited by data acquisition from a single CT system. Clinical relevance This study suggests that AI holds significant promise as a tool for enhancing the efficiency and accuracy of CACS evaluations, with implications for improving patient diagnostics and reducing radiologist workload in clinical practice.

A limited understanding exists on the associations of neighborhood environment with subclinical atherosclerosis and its progression.

The purpose of this integrative review was to explore associations of neighborhood environments and socioeconomic status (SES) with subclinical atherosclerosis and its long-term progression.

Three themes were identified: environmental exposure affects the natural history of atherosclerosis, neighborhood characteristics are associated with subclinical atherosclerosis, and individual SES is associated with development and progression of subclinical atherosclerosis more so than neighborhood SES. Some variations in results were noted based on the vascular site examined.

Disadvantaged neighborhoods and low SES are associated with greater subclinical atherosclerosis. Inconsistencies in a few studies seemed to be related to lack of coronary artery progression among the relatively young adults. This suggests further examination is needed of the contextual associations of neighborhood and SES with markers of generalized atherosclerosis, such as carotid intima-media thickness.

The best use of cardiac imaging to guide preventive coronary heart disease (CHD) treatment is debated. Current guidelines recommend the pooled cohort equation, followed by computed tomography for coronary artery calcification (CAC) assessment. We evaluated if this approach could be simplified using a self-report risk algorithm instead of the pooled cohort equation.

A gradient boosting machine model was trained on self-reported factors to calculate the probability of a high CAC score (≥100). This model was part of a self-report-based CHD preventive strategy with 3 steps: (1) calculate the probability of having a high CAC; (2) perform computed tomography for high-risk individuals; and (3) assign treatment eligibility with lipid-lowering therapy if CAC score exceeds a designated threshold. This strategy was tested using data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort (n=4564) and compared with guidelines recommending CAC scanning for intermediate-risk individuals (pooled cohort equation, 7.5% to <20%) by evaluating CHD events over 10-year follow-up in the group defined as treatment eligible by either strategy.

The pooled cohort equation identified 33% of the MESA population as eligible for a CAC scan and 19% as treatment eligible, capturing 48% of all CHD events (103 of 216). The self-report strategy identified 56% of CHD events (120 of 216; P=0.02) with the same number of CAC scans and treatments but required health care visits for only 33% of the population.

A self-report screening strategy, combined with CAC scoring, is more resource efficient and better discriminates high-risk individuals suitable for lipid-lowering therapy compared with current guidelines.

Atherosclerotic cardiovascular disease remains a major cause of death, and it is important to accurately estimate the cardiovascular events risk stratification even in asymptomatic patients. The coronary artery calcium score (CACS), which is quantitatively evaluated by electrocardiogram (ECG)-gated non-contrast chest computed tomography (CT) imaging, has been reported to be useful for cardiovascular event risk stratification in large studies. In the USA and Europe, guidelines recommend the use of the CACS in borderline or intermediate-risk asymptomatic individuals based on a high level of evidence. In Japan, however, the use of CACS in clinical practice is currently limited. Although it has been reported that the prevalence and distribution of coronary artery calcification (CAC) may differ by race and ethnicity, there are few data on its usefulness in stratifying the risk of cardiovascular events in asymptomatic Japanese individuals. While it is important to establish evidence for the usefulness of CACS in the Japanese population, for widespread clinical dissemination it would be beneficial to evaluate CAC and to perform accurate cardiovascular event risk stratification from non-ECG-gated non-contrast chest CT imaging performed during medical check-up and routine clinical practice. There have been reports on the usefulness of CAC assessed by non-ECG-gated chest CT imaging and on the relationship of CAC between ECG-gated and non-ECG-gated chest CT imaging. In recent years, a more accurate method of evaluating CACS from non-ECG-gated chest CT imaging has been developed using artificial intelligence, and further development is expected in the future.

Purpose To estimate the extent in which coronary artery calcium (CAC) score and incident lung cancer diagnosis are associated and determine if use of CAC score to predict lung cancer could improve lung cancer screening (LCS). Materials and Methods This retrospective analysis analyzed data from an ongoing, prospective, population-based cohort study (Heinz Nixdorf Recall study) in which participants aged 45-75 years underwent electron-beam CT of the heart. The association between CAC score and incident lung cancer was assessed using Cox proportional hazard regression models adjusted for potential confounders. The area under the receiver operating characteristic curve (AUC) was used to assess predictive performance of CAC score for lung cancer in all participants, eligible participants, and ineligible participants for LCS. Results The study included 4605 participants (mean age, 59.7 [SD, 7.8] years; 2328 female). During a median follow-up time of 15.2 years, incident lung cancer was diagnosed in 111 participants. CAC score as a continuous variable (log CAC+1) was associated with incident lung cancer (hazard ratio [HR] in the fully adjusted model: 1.21 [95% CI: 1.10, 1.32]). A CAC score of 400 or higher versus 0 was associated with a more than fourfold higher risk of lung cancer (adjusted HR: 4.31 [95% CI: 2.19, 8.51]). CAC score alone showed poor performance for predicting lung cancer in the total study sample (AUC, 0.63) and subgroups of participants eligible (AUC, 0.56) and ineligible (AUC, 0.61) for LCS. Conclusion CAC score was associated with incident lung cancer but did not demonstrate potential to improve the efficiency of LCS. Keywords: Epidemiology, Screening, Arteriosclerosis, Cardiac, Thorax, CT, Lung Cancer Supplemental material is available for this article. © RSNA, 2025.

Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radiation exposure and the need for specialized personnel. This study aims to develop an artificial intelligence (AI) method to automatically detect CAC from non-gated, freely-breathing, low-dose CT images obtained from positron emission tomography/computed tomography scans. A retrospective analysis of 677 PET/CT scans from a medical center was conducted. The dataset was divided into training (88%) and testing (12%) sets. The DLA-3D model was employed for high-resolution representation learning of cardiac CT images. Data preprocessing techniques were applied to normalize and augment the images. Performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity and p-values. The AI model achieved an average AUC of 0.85 on the training set and 0.80 on the testing set. The model demonstrated expert-level performance with a specificity of 0.79, a sensitivity of 0.67, and an overall accuracy of 0.73 for the test group. In real-world scenarios, the model yielded a specificity of 0.8, sensitivity of 0.6, and an accuracy of 0.76. Comparison with human experts showed comparable performance. This study developed an AI method utilizing DLA-3D for automated CAC detection in non-gated PET/CT images. Findings indicate reliable CAC detection in routine PET/CT scans, potentially enhancing both cancer diagnosis and cardiovascular risk assessment. The DLA-3D model shows promise in aiding non-specialist physicians and may contribute to improved cardiovascular risk assessment in oncological imaging, encouraging additional CAC interpretation.

This review examines the figure of Dr. René Favaloro, a pioneer in cardiovascular surgery and advocate for social justice, who devoted his life to making advanced medical care accessible to underserved communities. Despite the increasing incidence of coronary artery disease (CAD), particularly in Asian and Latin American countries, Favaloro envisioned a healthcare system where innovative technology benefits everyone. Building on his ideals, we explore the democratization of healthcare access through innovative tools for cardiovascular risk assessment, specifically Pulse Wave Velocity (PWV) and its association with Coronary Artery Calcium Score (CACs). PWV, a non-invasive and cost-effective method, shows promise as a practical screening tool for CAD, particularly when combined with Computational Intelligence (CI) and the Internet of Medical Things (IoMT). The integration of PWV into a Point-of-Care Testing (POCT) framework could enhance preventive care, especially in underserved populations. By aligning with Favaloro's vision of equitable healthcare, this approach seeks to support CAD screening and risk assessment in low-resource settings, aiming to overcome socio-economic barriers and improve access to preventive cardiac care.

Calcified coronary artery disease is a common clinical finding and is visible angiographically in 25-30% of patients presenting for percutaneous coronary intervention. The presence of coronary calcium, even without coronary artery obstruction, confers an adverse clinical prognosis. Coronary calcium score on CT is additive in predicting risk of cardiovascular events beyond traditional scoring systems. Deposition of calcium in coronary arteries is initiated by the formation of an atherosclerotic plaque. Thereafter, multiple processes and pathways are involved, resulting in initial microcalcifications that coalesce into calcium sheets. Calcified nodules are thought to occur from rupture of these sheets. Calcified coronary stenoses requiring revascularisation result in greater target lesion failure and overall major adverse cardiovascular events than noncalcified lesions, regardless of the mode of revascularisation. Modifying calcium prior to stenting to optimise stent expansion is required and intracoronary imaging can greatly facilitate not only the detection of coronary calcium, but also the confirmation of adequate modification and stent optimisation. In this review, the authors examine the pathophysiology, prevalence, predictors and impact on outcomes of coronary calcium.

Artificial intelligence (AI) has transformed medical imaging by detecting insights and patterns often imperceptible to the human eye, enhancing diagnostic accuracy and efficiency. In cardiovascular imaging, numerous AI models have been developed for cardiac computed tomography (CCT), a primary tool for assessing coronary artery disease (CAD). CCT provides comprehensive, non-invasive assessment, including plaque burden, stenosis severity, and functional assessments such as CT-derived fractional flow reserve (FFRct). Its prognostic value in predicting major adverse cardiovascular events (MACE) has increased the demand for CCT, consequently adding to radiologists' workloads. This review aims to examine AI's role in CCT for ischemic heart disease, highlighting its potential to streamline workflows and improve the efficiency of cardiac care through machine learning and deep learning applications.

Patients with cancer are at increased risk of ischemic heart disease (IHD). The increased risk of IHD in these patients is due to the interaction of shared risk factors, cancer type and stage, and immuno/chemotherapy and radiotherapy regimens. Management of IHD in cancer patients is challenging, due to atypical presentation, increased thrombotic and bleeding risk, and worse outcomes compared to patients without cancer. In this review, we will discuss the trends, outcomes, epidemiology and challenges in the diagnosis and treatment of IHD among cancer patients.

Premature advanced subclinical coronary atherosclerosis among young adults is an under-recognized and unique disease phenotype that has not been well characterized.

We used data from 44,047 participants with no prior CVD history (59.8% male) from the Coronary Artery Calcium (CAC) Consortium. We defined advanced disease as CAC ≥90th percentile for age, sex, and race, and compared risk factor profile of persons with advanced disease to those without CAC and those with CAC <90th percentile. Using multivariable-adjusted Cox proportional hazard and competing risks regression, we assessed the association of premature advanced disease with all-cause, cardiovascular, and CHD mortality.

Of 44,047 participants, 18,561 (42.2%) had CAC. Among those with CAC, 6,680 (36.0%) had CAC ≥90th percentile. Notably, 76.4% of those with CAC ≥90th percentile had multivessel CAC compared to 40.6% of those with CAC <90th percentile. After a mean follow-up of 12.5±3.6 years, the incidence per 1,000 person-years of all-cause (2.93 vs 1.85 vs 1.11), cardiovascular (1.11 vs 0.39 vs 0.21), and CHD mortality (0.65 vs 0.19 vs 0.08) was highest in the advanced disease group compared to CAC <90th percentile and the no CAC group. Persons with CAC ≥90th percentile had a higher multivariable-adjusted risk of all-cause (HR:2.17[1.83-2.57]), cardiovascular (SHR:3.89[2.78-5.44]), and CHD mortality (SHR:5.45[3.38-8.78]), compared to those without CAC. In the subgroup analysis, there was no difference in mortality between men and women with advanced CAC.

Premature advanced atherosclerosis is a distinct clinical phenotype that strongly predicts all-cause and cause-specific mortality. Among persons with CAC at young age, those with scores ≥ 90th percentile have the highest risk of early death and should be identified in future guidelines as a focus for aggressive clinical prevention.

To review the current evidence and highlight future strategies regarding consideration of coronary artery calcium (CAC) density in cardiovascular disease (CVD) risk stratification.

Expressed as the product of plaque area and a peak calcium density weighting factor, the Agatston method is the gold-standard for measuring CAC on noncontrast cardiac computed tomography. Over the last decade, observational data have suggested that calcium density is inversely associated with CVD events and confers additional prognostic information independent of traditional risk factors and Agatston CAC scores. Specific density measures have been assessed including peak calcium density, mean CAC density, and CAC area-density discordance. Beyond calcium density, the number of affected arteries and regional distribution of CAC which may be correlated with CAC density have also improved the predictive utility of the Agatston score.

Calcium density is inversely associated with CVD risk after considering plaque area and/or volume. Calcium markers including density, vessel involvement, and regional distribution confer additional prognostic information for the prediction of incident CVD among those with prevalent subclinical atherosclerosis. A future area of study includes calcium radiomics ('calcium-omics') and whether the artificial intelligence-derived automated measurement of calcium markers beyond the Agatston score may be of value in CVD risk stratification among individuals with early to advanced subclinical atherosclerosis.

To investigate whether coronary artery disease (CAD) burden is associated with plasma levels of the myocardial biomarkers Troponin I (TropI) and NT-proBNP in a large population-based sample using a cross-sectional design. Coronary computerized tomography (CT) angiography was performed in 25,859 subjects without a history of atherosclerotic disease from SCAPIS study (age 50-65, 52% women). TropI and NT-proBNP were measured in plasma. Segment involvement score (SIS) was the primary exposure and TropI the primary outcome. Both SIS and coronary artery calcium score, were associated with TropI levels following adjustment for age, sex and multiple confounders (p < 0.001), with similar relationships in men and women. Proximal segments from all three coronary arteries were related to TropI levels independently of one another. Adding TropI to traditional risk factors marginally increased discrimination of atherosclerosis as compared to risk factors alone (C-statistics + 0.0005, p = 0.014). SIS was related also to NT-proBNP levels, mainly in men, but with lower estimates than TropI. The burden of CAD was related to TropI levels in both men and women. All three major coronary arteries contributed to this relationship. Adding TropI to traditional risk factors resulted in only marginally improved discrimination of coronary atherosclerosis.

The continuous technical development of cardiac computed tomography (CT) over the last decades has led to an improvement in image quality and diagnostic accuracy, while simultaneously reducing radiation exposure. Despite these advancements, certain patient-related factors remain a challenge to conduct a high-quality diagnostic examination.

What factors can negatively affect the image quality of cardiac CT and how can these be addressed?

Analysis of the available literature on cardiac CT and identification of the quality-limiting factors, discussion, and possible solutions.

Tachycardia, arrhythmias, high coronary calcification, the presence of stents and coronary artery bypasses, as well as obesity and anxiety were identified as primary factors that limit image quality and diagnostic accuracy. These issues primarily arise from a lack of response or the presence of contraindications to premedication, blooming artifacts, variations in postoperative anatomy, as well as other personal factors. Suggested solutions include optimizing premedication, scanner modifications, the selection of the most suitable acquisition mode, new scanner technologies, and innovative image reconstruction methods including artificial intelligence.

Certain factors continue to pose a major challenge for cardiac CT. Knowledge of alternative premedication, scanner modifications, as well as the use of postprocessing software and new technologies can help overcome these limitations, enabling successful and safe cardiac CTs even in challenging patients.

The SCORE2 algorithm is recommended to estimate risk of cardiovascular disease (CVD). Coronary artery calcification (CAC) score is expensive but improves the risk prediction. This study aims to determine and compare the additive value of CAC-score and 19 biomarkers in risk prediction.

Traditional cardiovascular (CV) risk factors, CAC-score, and a wide range of biomarkers (including lipids, calcium-phosphate metabolism, troponin, inflammation, kidney function and ankle brachial index (ABI)) were collected from 1211 randomly selected middle-aged men and women in this multicenter prospective cohort in 2009-2010. 10-year follow-up data on CV-events were obtained via the Danish Health Registries. CV-event was defined as stroke, myocardial infarction, hospitalization for heart failure, coronary artery revascularization or death from CVD. The association between SCORE2, CAC-score, biomarkers, and CV-events was assessed using cox proportional hazard rates (HR) and compared using AUC-calculation of ROC-curves. Finally, net reclassification improvement (NRI) was calculated.

92 participants had CV-events. Adjusted for risk factors, CAC-score was significantly associated with events (adjusted HR 1.9 (95%CI:1.1; 3.3), 3.6 (95%CI:1.9; 6.8), and 5. (95%CI:2.6; 10.3) for CAC-score 1-99, CAC-score 100-399 and CAC-score ≥400, respectively. HR for the highest quartile of CRP was 2.3 (95%CI:1.2; 4.5), while none of the remaining biomarkers improved HR. Adjusted for SCORE2, the CAC-score improved AUC (AUCCAC: 0.72, AUCSCORE2: 0.67, p<0.01). A combination of selected biomarkers (total cholesterol, low-density lipoprotein, phosphate, troponin, CRP, and creatinine) borderline improved AUC (AUCBiomarkers + SCORE2: 0.71, AUCSCORE2: 0.67, p=0.06). NRI for CAC score was 63 % (p<0.0001).

CAC-score improved prediction of CV-events, however the selected biomarkers did not.

This study evaluated the association between serum albumin levels and coronary artery calcification (CAC) progression in asymptomatic adults without hypoalbuminaemia at baseline.

Observational cohort study.

Data from the Korea Initiatives on Coronary Artery Calcification (KOICA) which is a retrospective, single ethnicity, multicentre and observational registry were analysed.

A total of 12 344 Korean adults with baseline albumin level of ≥3.5 g/dL (51.7±8.5 years; 84.3% male) were included. The median interscan period was 3.0 (2.0-4.8) years. All participants were stratified into three groups based on serum albumin tertile.

Association of serum albumin with the risk of CAC progression was analysed using multivariate logistic regression models with adjustment of interscan period. CAC progression was defined as a square root (√) transformed difference between the baseline and follow-up coronary artery calcium score (CACS) (Δ√transformed CACS) of ≥2.5. Annualised Δ√transformed CACS was defined as Δ√transformed CACS divided by interscan period.

With increasing serum albumin tertiles, the annualised Δ√transformed CACS (I (lowest): 0.16 (0-1.24) vs II: 0 (0-1.09) vs III (highest): 0 (0-1.01)) and the incidence of CAC progression (I: 36.6% vs II: 31.3% vs III: 25.0%) were decreased despite higher prevalence of hypertension, diabetes and hyperlipidaemia (all p<0.05). Serum albumin levels were inversely related to the annualised Δ√transformed CACS and the risk of CAC progression among overall participants. After adjusting for age, sex, hypertension, diabetes, hyperlipidaemia, obesity, current smoking, alcohol consumption, serum creatinine levels, baseline CACS and interscan period, this inverse association between serum albumin levels (per-1 g/dL increase) and the risk of CAC progression was consistently observed, especially in baseline CACS of 1-10 (OR: 0.392, 95% CI: 0.234 to 0.658) and 11-100 (OR: 0.580, 95% CI: 0.381 to 0.883) (all p<0.05).

Serum albumin levels were inversely associated with the risk of CAC progression. This phenomenon was predominantly observed in CACS of 1-100 at baseline.

Both insulin resistance and hyperglycemia are important risk factors for atherosclerosis. While the characteristics of atherosclerosis are obviously different according to established diabetes, little has been known regarding the risk of coronary artery calcification (CAC) progression related to the biomarkers of atherogenic index of plasma (AIP), triglyceride glucose (TyG) index, and hemoglobin A1C (HbA1C) in conditions with and without diabetes.

We analyzed 12,326 asymptomatic Korean adults (mean age 51.7 ± 8.5 years; 84.2% males; 15.8% with diabetes) over a median follow-up period of 3.0 years. AIP was defined as the base-10 logarithm of the ratio of triglyceride concentration (mmol/L) to high-density lipoprotein cholesterol (mmol/L). The TyG index was calculated as ln (fasting triglycerides [mg/dL] × fasting glucose [mg/ dL]/2). CAC progression was defined using the SQRT method, as a difference of ≥ 2.5 between the square roots (√) of baseline and follow-up coronary artery calcium scores (CACS) (Δ√transformed CACS). Logistic regression models adjusted for interscan periods were used to estimate the odds ratio (OR).

The levels of AIP, TyG index, and HbA1C were significantly higher in diabetics than in non-diabetics. CAC progression was more frequently observed in diabetics (46.9%) than in non-diabetics (28.0%). After adjusting for age, sex, hypertension, hyperlipidemia, obesity, current smoking status, serum creatinine levels, baseline CACS, and interscan period, AIP (per-0.1 unit increase) was associated with CAC progression in only non-diabetics (OR: 1.04, 95% confidence interval [CI]: 1.02 - 1.06; P < 0.001). In contrast, HbA1C level (per-1% increase) was significantly associated with CAC progression in only diabetics (OR: 1.19, 95% CI: 1.08 - 1.32; P = 0.001). The TyG index (per-1 unit increase) was associated with CAC progression in both non-diabetics (OR: 1.32, 95% CI: 1.19 - 1.46; P < 0.001) and diabetics (OR: 1.33, 95% CI: 1.10 - 1.60; P = 0.003).

The associations between AIP, TyG index, and HbA1C levels with CAC progression vary according to established diabetes. Of these biomarkers, TyG index is independently associated with CAC progression irrespective of established diabetes.

Flavonoids may play a role in mitigating atherosclerotic cardiovascular diseases, with evidence suggesting effects may differ between vascular beds. Studies examining associations with subclinical markers of atherosclerosis between subpopulations with different underlying risks of atherosclerosis are lacking.

Among 5599 participants from the MESA (Multi-Ethnic Study of Atherosclerosis), associations between dietary flavonoid intakes (estimated from a food frequency questionnaire) and subclinical measures of atherosclerosis (ankle-brachial index, carotid plaques and intima-media thickness, and coronary artery calcification) were examined using repeated measures models. Exposures and outcomes were measured at exam 1 (2000-2002) and exam 5 (2010-2011). Stratified analyses and interaction terms were used to explore effect modification by time, sex, race/ethnicity, and smoking status.

In the analytic population, at baseline, ≈46% were men with a median age of 62 (interquartile range, 53-70) years and total flavonoid intakes of 182 (interquartile range, 98-308) mg/d. After multivariable adjustments, participants with the highest (quartile 4) versus lowest (quartile 1) total flavonoid intakes had 26% lower odds of having an ankle-brachial index <1 (odds ratio, 0.74 [95% CI, 0.60-0.92]) and 18% lower odds of having a carotid plaque (odds ratio, 0.82 [95% CI, 0.69-0.99]), averaged over exams 1 and 5. Moderate (quartile 3) to high (quartile 4) intakes of flavonols, flavanol monomers, and anthocyanins were associated with 19% to 34% lower odds of having an ankle-brachial index <1 and 18% to 20% lower odds of having carotid plaque. Participants with the highest intakes of anthocyanins (quartile 4) at baseline had a marginally slower rate of carotid plaque progression than those with moderate intakes (quartiles 2 and 3). There were no significant associations with intima-media thickness or coronary artery calcification. Observed associations did not differ by sex, race/ethnicity, or smoking status.

In this multi-ethnic population, higher dietary flavonoid intakes were associated with lower odds of peripheral and carotid artery atherosclerosis. Increasing intakes of healthy, flavonoid-rich foods may protect against atherosclerosis in the peripheral and carotid arteries.

Diabetes mellitus is an independent risk factor for asymptomatic cardiovascular disease (CVD). The QRISK 3® score and atherosclerotic cardiovascular disease (ASCVD) risk scores determine the risk of developing CVD over 10 years. The CT coronary artery calcium score (CACS) is a non-invasive imaging modality that identifies sub-clinical atherosclerosis. We studied the correlation between the QRISK 3® and ASCVD scores and the CACS and determined the cut-off for QRISK 3® and ASCVD scores that corresponded to a moderate or accentuated CACS (≥100).

In this prospective study, outpatients with diabetes and no prior coronary artery disease (CAD) or their equivalents, or having symptoms suggestive of angina or heart failure, had their QRISK 3® and ASCVD scores calculated. They subsequently underwent 256 slice cardiac CT, and CACS was calculated by the Agatston method.

A statistically significant correlation (r = 0.28) was found between QRISK 3® and ASCVD with CACS (P = 0.004 and P = 0.007, respectively). A QRISK® score >23 and ASCVD score >10 predicted a CACS score >100 with sensitivities of 85% and 90%, respectively.

The QRISK 3® and ASCVD scores can be used to triage patients who require further evaluation with CACS to determine the risk of future CVD.

Cardiac hybrid positron emission tomography/computed tomography (PET/CT) has become a valid screening modality for cardiac allograft vasculopathy (CAV) following heart transplantation (HT). Visually estimated coronary artery calcium (VECAC) can be quantified from CT images obtained as part of PET/CT and has been shown to be associated with adverse cardiovascular outcomes in coronary artery disease. We investigated the prognostic value of VECAC following HT.

A retrospective analysis of 430 consecutive adult HT patients who underwent 13N-ammonia cardiac PET/CT from 2016 to 2019 with follow-up through October 15, 2022, was performed. VECAC categories included: VECAC 0, VECAC 1-9, VECAC 10-99, and VECAC 100+. The association between VECAC categories and outcomes was assessed using univariable and multivariable proportional hazards regression. The primary outcome was death/retransplantation.

The cohort was 73% male, 33% had diabetes, 67% had estimated glomerular filtration rate <60 ml/min, median age was 61 years, and median time since HT was 7.5 years. VECAC alone was insufficiently sensitive to screen for CAV. During a median follow-up of 4.2 years ninety patients experienced death or retransplantation. Compared with those with VECAC 0, patients VECAC 10-99 (HR 2.25, 95% CI 1.23-4.14, p = 0.009) and VECAC 100+ (HR 3.42, 95% CI 1.96-5.99, p < 0.001) experienced an increased risk of death/retransplantation. The association was similar for cardiovascular death and cardiovascular hospitalization. After adjusting for other predictors of death/retransplantation, VECAC 10-99 (VECAC 10-99: aHR 1.95, 95% CI 1.03-3.71 p = 0.04) and VECAC 100+ (VECAC 100+: aHR 2.33, 95% CI 1.17-4.63, p = 0.02) remained independently associated with death/retransplantation.

VECAC is an independent prognostic marker of death/retransplantation following HT and merits inclusion as a part of post-HT surveillance PET/CT.

Coronary artery disease (CAD) is the leading global cause of mortality, accounting for approximately 30% of all deaths. It is primarily characterized by the accumulation of atherosclerotic plaques within the coronary arteries, leading to reduced blood flow to the heart muscle. Early detection of atherosclerotic plaques is crucial to prevent major adverse cardiac events. Notably, recent studies have shown that 15% of myocardial infarctions occur in patients with non-obstructive CAD, underscoring the importance of comprehensive plaque assessment beyond merely identifying obstructive lesions. Cardiac Computed Tomography Angiography (CCTA) has emerged as a cost-effective and efficient technique for excluding obstructive CAD, particularly in patients with a low-to-intermediate clinical likelihood of the disease. Recent advancements in CCTA technology, such as improved resolution and reduced scan times, have mitigated many technical challenges, allowing for precise quantification and characterization of both calcified and non-calcified atherosclerotic plaques. This review focuses on two critical physiological aspects of atherosclerotic plaques: the burden of calcifications, assessed via the coronary artery calcium score (CACs), and perivascular fat attenuation index (pFAI), an emerging marker of vascular inflammation. The CACs, obtained through non-contrast CT scans, quantifies calcified plaque burden and is widely used to stratify cardiovascular risk, particularly in asymptomatic patients. Despite its prognostic value, the CACs does not provide information on non-calcified plaques or inflammatory status. In contrast, the pFAI, derived from CCTA, serves as an indirect marker of coronary inflammation and has shown potential in predicting adverse cardiac events. Combining both CACs and pFAI assessment could offer a comprehensive risk stratification approach, integrating the established calcification burden with novel inflammatory markers to enhance CAD prevention and management strategies.

Evidence on the impact of screening for cardiovascular diseases (CVDs) on health-related quality of life (HRQoL) is important for policy decisions about screening implementation and to uncover teachable moments to motivate healthy lifestyle choices. It is unknown whether screening by cardiac computed tomography (CT) scan has a stronger impact on HRQoL than screening by traditional risk prediction models. The study aims to investigate differences in HRQoL across the screening process between participants who were randomized to CVD risk estimation by coronary artery calcium score or Systematic COronary Risk Evaluation.

A subset of 2687 ROBINSCA participants filled in questionnaires at (T0) randomization, (T1) invitation, (T2) 1-3 days before screening, (T3) 1-3 days after, and (T4) screening result. Generic HRQoL (SF-12; EQ-5D) and anxiety (STAI-6) were measured. We investigated the differences in changes in HRQoL across the screening process with linear mixed models. We found comparable levels of HRQoL at all screening moments for the two intervention groups. Mental health scores were worse at invitation and randomization than at the later time points, irrespective of screening group (all P < 0.001). A result indicating a heightened CVD risk was associated with increased anxiety in the CT screening group.

Computed tomography screening for CVD risk has no detrimental impact on HRQoL and anxiety levels compared to screening by traditional risk assessment. Receiving an invitation to screenning or a result implying increased CVD risk could function as teachable moments for high-risk individuals.

ROBINSCA trial registration number: NTR6471 in Dutch Trial Register (NTR).

Physical activity is recommended for the prevention of primary and secondary cardiovascular (CV) disease as it is linked to a number of health benefits, especially CV. However, recent research suggests that high-volume, long-term endurance exercise may hasten rather than slow the coronary atherosclerosis progression. This contentious theory has generated a great discussion and is still a major source of doubt when it comes to the clinical treatment of coronary artery disease (CAD) in athletes. CAD is the primary cause of sudden cardiac death in athletes over 35 years. Thus, recent studies evaluated the prevalence of CAD in athletes and its clinical and prognostic implications. Indeed, many studies have shown a relationship between endurance sports and higher volumes of coronary calcified plaque as determined by computed tomography. However, the precise pathogenetic substrate for the existence of an increased coronary calcification burden among endurance athletes remains unclear. Moreover, the idea that coronary plaques in elite athletes present a benign morphology has been cast into doubt by some recent studies showing potential association with adverse cardiovascular events. This review aims to analyze the association between physical activity and CAD, explaining possible underlying mechanisms of atherosclerotic progression and non-ischemic coronary lesions, focusing primarily on clinical and prognostic implications, multimodal evaluation, and management of CAD in endurance athletes.

High coronary artery calcification (CAC) burden is a significant risk factor for adverse cardiovascular and kidney outcomes. However, it is unknown whether changes in the coronary atherosclerotic burden can accompany changes in kidney disease progression. Here, we evaluated the relationship between CAC progression and the risk of kidney failure with replacement therapy (KFRT).

We analyzed 1173 participants with chronic kidney disease (CKD) G1 to G5 without kidney replacement therapy from the KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease (KNOW-CKD). Participants were categorized into three groups according to the change in the CAC score between enrollment and year 4 (non-progressors, ≤0 AU; moderate progressors, 1-199 AU; and severe progressors, ≥200 AU). The primary outcome was the development of KFRT.

During a follow-up period of 4690 person-years (median, 4.2 years), the primary outcome occurred in 230 (19.6 %) participants. The incidence of KFRT was 37.6, 54.3, and 80.9 per 1000 person-years in the non-, moderate, and severe progressors, respectively. In the multivariable cause-specific hazard model, the hazard ratios (HRs) for the moderate and severe progressors were 1.71 (95 % confidence interval [CI], 1.02-2.87) and 2.55 (95 % CI, 1.07-6.06), respectively, compared with non-progressors. A different definition of CAC progression with a threshold of 100 AU yielded similar results in a sensitivity analysis.

CAC progression is associated with an increased risk of KFRT in patients with CKD. Our findings suggest that coronary atherosclerosis changes increase the risk of CKD progression.

The utility of assessment of cardiovascular calcifications for predicting stroke incidence remains unclear. This study assessed the relationship between cardiovascular calcifications including coronary artery calcification (CAC), aortic valve (AVC), and aortic root (ARC) assessed by coronary computed tomography (CT) and stroke incidence in patients with suspected CAD. In this multicenter prospective cohort study, 1187 patients suspected of CAD who underwent coronary CT were enrolled. Cardiovascular events including stroke were documented. Hazard ratio (HR) and confidence interval (CI) were assessed by Cox proportional hazard model adjusted for the Framingham risk score. C statistics for stroke incidence were also examined by models including cardiovascular calcifications. A total of 980 patients (mean age, 65 ± 7 years; females, 45.8%) were assessed by the CAC, AVC, and ARC Agatston scores. During a median follow-up of 4.0 years, 19 patients developed stroke. Cox proportional hazard model showed severe CAC (Agatston score ≥ 90th percentile [580.0 value]) and presence of AVC and ARC were associated with stroke incidence (HR; 10.33 [95% CI; 2.08-51.26], 3.08 [1.19-7.98], and 2.75 [1.03-7.30], respectively). C statistic in the model with CAC and AVC severity for predicting stroke incidence was 0.841 (95% CI; 0.761-0.920), which was superior to the model with CAC alone (0.762 [95% CI; 0.665-0.859], P < 0.01). CAC, AVC, and ARC were associated with stroke incidence in patients suspected of CAD. Assessment of both CAC and AVC may be useful for prediction of stroke incidence.

Recent studies have demonstrated that coronary plaque burden carries greater prognostic value in predicting adverse atherosclerotic cardiovascular disease outcomes than myocardial ischemia, thereby challenging the existing paradigm. Advances in plaque quantification through both noncontrast and contrast-enhanced computed tomography (CT) methods have led to earlier and more cost-effective detection of coronary disease compared with traditional stress testing. The 2 principal techniques of noninvasive coronary plaque quantification assessment are coronary artery calcium scoring by noncontrast CT and coronary CT angiography, both of which correlate with disease burden on invasive angiography. Plaque quantification from these imaging modalities has shown utility in risk stratification and prognostication of adverse cardiovascular events, leading to increased incorporation into clinical practice guidelines and preventive care pathways. Furthermore, due to their expanding clinical value, emerging technologies such as artificial intelligence are being integrated into plaque quantification platforms, placing more advanced measures of plaque burden at the forefront of coronary plaque evaluation. In this review, we summarize recent clinical data on coronary artery calcium scoring and coronary CT angiography plaque quantification in the evaluation of adverse atherosclerotic cardiovascular disease in patients with and without chest pain, highlight how these methods compare to invasive quantification approaches, and directly compare the performance characteristics of coronary artery calcium scoring and coronary CT angiography.

Short telomeres are associated with cardiovascular disease (CVD). We aimed to investigate, if genetically determined telomere-length effects CVD-risk in the Heinz-Nixdorf-Recall study (HNRS) population. We selected 14 single-nucleotide polymorphisms (SNPs) associated with telomere-length (p<10-8) from the literature and after exclusion 9 SNPs were included in the analyses. Additionally, a genetic risk score (GRS) using these 9 SNPs was calculated. Incident CVD was defined as fatal and non-fatal myocardial infarction, stroke, and coronary death. We included 3874 HNRS participants with available genetic data and had no known history of CVD at baseline. Cox proportional-hazards regression was used to test the association between the SNPs/GRS and incident CVD-risk adjusting for common CVD risk-factors. The analyses were further stratified by CVD risk-factors. During follow-up (12.1±4.31 years), 466 participants experienced CVD-events. No association between SNPs/GRS and CVD was observed in the adjusted analyses. However, the GRS, rs10936599, rs2487999 and rs8105767 increase the CVD-risk in current smoker. Few SNPs (rs10936599, rs2487999, and rs7675998) showed an increased CVD-risk, whereas rs10936599, rs677228 and rs4387287 a decreased CVD-risk, in further strata. The results of our study suggest different effects of SNPs/GRS on CVD-risk depending on the CVD risk-factor strata, highlighting the importance of stratified analyses in CVD risk-factors.

Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors.

Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS.

The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors.

A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.

In this review, the authors summarize the role of coronary computed tomography angiography and coronary artery calcium scoring in different clinical presentations of chest pain and preventative care and discuss future directions and new technologies such as pericoronary fat inflammation and the growing footprint of artificial intelligence in cardiovascular medicine.