Disease Risk Score Research Articles

Disease Risk Score Derivation and Validation in Abu Dhabi, United Arab Emirates: A Retrospective Cohort Study.

Cardiovascular disease risk assessment is a key tool in primary prevention. The ADRS (Abu Dhabi Risk Study) is a retrospective cohort study aiming to develop 10-year risk prediction equations for coronary artery disease (CAD), stroke, and atherosclerotic cardiovascular disease (ASCVD), and validate international risk equations. The 8699 participants were examined in the Abu Dhabi cardiovascular screening program from 2011 to 2013 with a subsequent average follow-up of 9.2 years. They were assessed in 2023 for new CAD, admissions for acute coronary syndrome, or stroke. The validation cohort, 2554 subjects, is from the 2016 to 2017 Abu Dhabi community screening program, with 6.67 years average follow-up. Of 8504 ASCVD-free subjects, 250 experienced new CAD events. ASCVD risk factors in this population were age, sex, smoking, high cholesterol/high-density lipoprotein ratio, and diabetes diagnosis, in addition to low vitamin D level and low glomerular filtration rate. Three ADRS prediction models were derived using Cox regression. The ADRS-CAD had a C statistic of 0.899 (0.882-0.916) compared with 0.828 (0.803-0.852) for the Framingham Risk Score in the same sample. ADRS-stroke had a C statistic of 0.904 (0.865-0.944). The ADRS-ASCVD had a C statistic of 0.898 (0.883-0.913) compared with 0.891 (0.875-0.907) of pooled cohort equations and 0.825 (0.802-0.847) for Framingham Risk Score-cardiovascular disease. Applying our formulas to the validation cohort yielded C statistics of 0.825 (0.803-0.846), 0.799 (0.774-0.824), and 0.761 (0.71-0.813) for ASCVD, CAD, and stroke, respectively. The pooled cohort equations in this cohort had a C statistic for ASCVD of 0.824 (0.802-0.846). This study demonstrates the value of tailoring risk assessments to local populations and health care contexts.

Food consumption patterns and Framingham cardiovascular risk score among shift workers: A NOVA-based approach

ObjectiveTo evaluate the association between dietary quantity and variety, by extent and purpose of processing, and Framingham cardiovascular disease (CVD) risk score among rotating shift workers. MethodsThe cross-sectional study included male shift workers. Dietary intake was assessed using a 24-hour recall method conducted by trained interviewers. Food items were classified using two approaches: the first was based on the amount of food consumed in each food group. Fruits, vegetables, and legumes (FVL) intake, is classified as recommended at 400g per day by WHO guidelines. Ultraprocessed foods (UPFs) were analyzed based on tertiles of daily caloric contribution. The second approach, the NOVA dietary diversity score (DDS-NOVA) assessed the variety of consumed items within each food group, assigning points for each unique item consumed, irrespective of quantity or frequency. The CVD-risk score was evaluated using the Framingham coronary heart disease risk score, categorizing participants as low risk (<5%) or intermediate to high risk (>5%). Descriptive, univariate, and multivariate logistic regression were used, with adjustments made for age, skin color, schooling, duration of shift work, physical activity, BMI and total caloric intake. ResultsThe study involved 213 participants, with an average age of 38.67 years (±6.96), a minimum of 26 and a maximum of 60. The majority (62.4%) were between 30 and 40 years old; 49.3% identified themselves as brown; 56.8% had completed high school; and 79.3% were married. The Framingham CVD-risk score indicated that 56.2% were at low risk (<5%) and 43.7% at intermediate to high risk (≥5%). The average consumption of FVL was 272.32g (±350.43), with 70.4% meeting the recommendation of ≥400g/day. Participants consumed an average of 3.46 fresh food items, 3.74 minimally processed food items, 2.54 processed food items and 3.39 ultra-processed food items. In the multivariate model, consuming ≥400g/day of FVL reduced the likelihood of cardiovascular risk ≥5% by 2.12 times (OR: 0.47; 95%CI: 0.23-0.98). Each additional item of fresh food reduced the risk by 49% (OR: 0.67; 95%CI: 1.01-1.66), while each additional item of ultra-processed food increased the risk by 30% (OR: 1.30; 95%CI: 0.52-0.87). There was no significant association between the calorie intake of ultra-processed foods and cardiovascular risk. ConclusionBoth adequate consumption of FVL quantity and a greater variety of fresh-foods were associated with a lower chance of CVD-risk, while a higher variety of consumption of UPF items increased this chance.

Correlation and Agreement Between the SCORE2 and PREVENT 10-Year Atherosclerotic Cardiovascular Disease Risk Scores: Insights from Coronary Computed Tomography Angiography

Correlation and Agreement Between the SCORE2 and PREVENT 10-Year Atherosclerotic Cardiovascular Disease Risk Scores: Insights from Coronary Computed Tomography Angiography

Abstract 4133654: External Validation of the Guha-Stabellini CVD Prostate Cancer-Specific Calculator in Short-Term Follow-Up

Introduction: Conventional cardiovascular disease (CVD) risk scores are inaccurate for prostate cancer patients (PC); the Guha-Stabellini machine learning (GS-ML) score shows promise but lacks external validation. Hypothesis: The GS-ML score is superior to conventional CVD scores in patients with PC. Aim(s): To conduct external validation of the GS-ML score in patients diagnosed with PC. Methods: The validation used holdout data from Seidman Cancer Center’s CAISIS platform (internal validation cohort) and RADICAL PC1 (external validation cohort; a prospective study of men diagnosed with PC within 1 year or starting ADT within 1 month of enrollment), matching covariates with the GS-ML score. With limited follow-up, short-term CVD (2,000 days from PC diagnosis) was the outcome. Performance was assessed via area under the receiver operating characteristic curve (AUC) with assistance from Youden statistic cutoff, comparing metrics with ACC/AHA pooled cohort equations (PCE), SCORE-2, and AHA-PREVENT scores. Atherosclerotic cardiovascular disease (ASCVD) included non-fatal ischemic stroke and myocardial infarction. CVD included ASCVD and heart failure. Results: We included 2,495 patients from RADICAL PC1 and 1,506 from the internal validation cohort ( Table 1 ). In the internal validation cohort, the CVD AUCs were 0.58, 0.49, 0.65, 0,73, and 0.75 for PCE, SCORE2, PREVENT simple, PREVENT enhanced, and GS-ML score, respectively. In RADICAL PC1, the AUCs for CVD were 0.60, 0.43, 0.67, and 0.64 for PCE, SCORE2, PREVENT simple and GS-ML score, respectively. For ASCVD, internal validation cohorts’ AUCs were 0.63, 0.62, 0.66, 0.73, and 0.67 for PCE, SCORE2, PREVENT simple, PREVENT enhanced, and Guha-Stabellini, respectively. The RADICAL PC1 ASCVD AUCs were 0.64, 0.46, 0.65, and 0.61, for PCE, SCORE2, PREVENT simple and GS-ML scores, respectively, Conclusion(s): The GS-ML score was validated for PC patients, showing superior performance to PCE and SCORE2, and similar performance to AHA-PREVENT in predicting CVD. The European Cardio-Oncology guidelines should reconsider using SCORE2 for PC patients. Further improvement and validation with nationally representative datasets are needed to corroborate these findings and enhance the generalizability of the GS-ML score.

Machine-learning-based prediction of cardiovascular events for hyperlipidemia population with lipid variability and remnant cholesterol as biomarkers.

Dyslipidemia poses a significant risk for the progression to cardiovascular diseases. Despite the identification of numerous risk factors and the proposal of various risk scales, there is still an urgent need for effective predictive models for the onset of cardiovascular diseases in the hyperlipidemic population, which are essential for the prevention of CVD. We carried out a retrospective cohort study with 23,548 hyperlipidemia patients in Shenzhen Health Information Big Data Platform, including 11,723 CVD onset cases in a 3-year follow-up. The population was randomly divided into 70% as an independent training dataset and remaining 30% as test set. Four distinct machine-learning algorithms were implemented on the training dataset with the aim of developing highly accurate predictive models, and their performance was subsequently benchmarked against conventional risk assessment scales. An ablation study was also carried out to analyze the impact of individual risk factors to model performance. The non-linear algorithm, LightGBM, excelled in forecasting the incidence of cardiovascular disease within 3years, achieving an area under the 'receiver operating characteristic curve' (AUROC) of 0.883. This performance surpassed that of the conventional logistic regression model, which had an AUROC of 0.725, on identical datasets. Concurrently, in direct comparative analyses, machine-learning approaches have notably outperformed the three traditional risk assessment methods within their respective applicable populations. These include the Framingham cardiovascular disease risk score, 2019 ESC/EAS guidelines for the management of dyslipidemia and the 2016 Chinese recommendations for the management of dyslipidemia in adults. Further analysis of risk factors showed that the variability of blood lipid levels and remnant cholesterol played an important role in indicating an increased risk of CVD. We have shown that the application of machine-learning techniques significantly enhances the precision of cardiovascular risk forecasting among hyperlipidemic patients, addressing the critical issue of disease prediction's heterogeneity and non-linearity. Furthermore, some recently-suggested biomarkers, including blood lipid variability and remnant cholesterol are also important predictors of cardiovascular events, suggesting the importance of continuous lipid monitoring and healthcare profiling through big data platforms.

Performance Characteristics of a Calculated Index Control Method for the phi Multianalyte Assay with Algorithmic Analysis.

Multianalyte assays with algorithmic analysis (MAAAs), such as the Prostate Health Index (phi), are increasingly utilized for generating disease risk scores. Currently, imprecision and bias in phi are not directly monitored by quality control (QC) assessment of the index but rather by QC assessment of individual components. This may not be adequately controlling for imprecision and bias in the calculated multicomponent phi value itself. Inter- and intra-assay phi precision was compared to precision of the individual component assays. QC measurements from total prostate-specific antigen (PSA), free PSA, and p2PSA were used to calculate a single calculated phi QC metric (PHIc). The frequency of QC failure of PHIc, relative to individual components QC by Westgard rules (13S and 22S), was determined. The effects of varying analyte component assay bias on the resulting PHIc metric were also examined. Average measured phi imprecision (6.7% CV) was higher than individual phi analyte component imprecision (3.9-4.5% CV) across 2 Beckman Coulter Unicel DxI 800 instruments. A retrospective examination of PHIc QC over 84 quality control determinations was concurrently carried out for both PHIc and component assay failure patterns, which were dependent on SDs utilized for Westgard evaluation. Finally, reinforcing nonlinear changes in PHIc were observed in select cases of introduced simulated bias of individual component measurements. An additional calculated phi QC measure can be introduced to monitor MAAA precision/bias, and in principle calculated index controls may represent a complementary supplemental QC method that could be applied to other MAAA indices.

GenoSiS: A Biobank-Scale Genotype Similarity Search Architecture for Creating Dynamic Patient-Match Cohorts.

Many patients do not experience optimal benefits from medical advances because clinical research does not adequately represent them. While the diversity of biomedical research cohorts is improving, ensuring that individual patients are adequately represented remains challenging. We propose a new approach, GenoSiS, which leverages machine learning-based similarity search to dynamically find patient-matched cohorts across different populations quickly. These cohorts could serve as reference cohorts to improve a range of clinical analyses, including disease risk score calculations and dosage decisions. While GenoSiS focuses on finding genetic similarity within a biobank, our similarity search architecture can be extended to represent other medically relevant patient characteristics and search other biobanks.

Waist-to-hip ratio as a contributor associated with higher atherosclerotic cardiovascular disease risk assessment in patients with diabetes: a cross-sectional study

IntroductionThis study aimed to identify the factors most strongly associated with an increased atherosclerotic cardiovascular disease (ASCVD) risk score in patients with type 2 diabetes (T2D).MethodsThis cross-sectional study included 4698 patients with T2D over an 11-year period (2010–2021). Patients were categorized into four groups based on their 10-year ASCVD risk score (< 5%, 5–7.5%, 7.5–20%, and > 20%). Multinominal regression analysis was used to evaluate the association between various modifiable and non-modifiable risk factors and the ASCVD risk score.ResultsOf the patients, 35.9% had a 10-year ASCVD risk score below 5%, 12.6% had a score between 5% and 7.5%, 30.8% had a score between 7.5% and 20%, and 19.7% had a score above 20%. Higher ASCVD risk scores were significantly associated with elevated waist-to-hip ratio (WHR > 0.93), pulse pressure, uric acid, triglycerides, and decreased glomerular filtration rate (all p-values < 0.05). WHR demonstrated the strongest association with higher ASCVD risk scores (OR: 4.55, 95% CI: 2.94–7.03, p < 0.001) when comparing patients with ASCVD scores > 5% to those with scores < 5%.ConclusionWHR was independently associated with higher ASCVD risk scores in patients with T2D. Incorporating WHR, along with traditional risk factors, could improve ASCVD risk assessments in this population.

Pre-bypass critical closing pressure predicts acute kidney injury after cardiopulmonary bypass

ObjectivesOptimal blood pressure goals during cardiopulmonary bypass (CPB) remain uncertain and new metrics to individualize perfusion targets are needed. Critical closing pressure (Pcrit) is a fundamental property of the arterial circulation related to vascular tone and represents the outflow pressure impacting flow across the systemic circulation. We examined Pcrit as a prognostic marker of acute kidney injury (AKI). DesignRetrospective cohort study. SettingSingle tertiary care hospital Participants1038 adult cardiac surgery patients that underwent CPB. InterventionsPcrit was calculated using arterial waveform data before initiation of CPB. Pcrit was examined in relation to incidence of stage 2 or higher postoperative AKI according to standard KDIGO definitions. Measurements and Main ResultsOf the 1038 patients included in the study, 50 (5%) experienced AKI. Patients that suffered AKI had significantly higher preoperative risk factors, including higher incidence of severe chronic kidney disease and higher STS risk score (p<0.01). They also had longer operative times and longer cross clamp times (p<0.01). All patients were maintained at similar mean MAP while on CPB. Patients that suffered AKI had a significantly higher pre-bypass Pcrit than those who did not (49.0 vs 44.1 mmHg, p=0.018). In a multivariate regression, Pcrit remained a significant predictor, representing a 16% increased risk of AKI for each 5 mmHg increase in pre-bypass Pcrit (p=0.011). ConclusionsA higher pre-bypass Pcrit is associated with a significantly higher incidence of postoperative AKI. Future study is warranted to investigate using intra-operative Pcrit to determine a personalized blood pressure goal during CPB.

The modified Baveno classification for obstructive sleep apnoea - Development and evaluation based on the ESADA database.

The "Baveno classification" replaced the apnoea hypopnoea index (AHI) with symptoms and comorbidities for treatment indication in obstructive sleep apnoea (OSA). This study evaluates a modified Baveno classification which adds a validated cardiovascular disease (CVD) risk score and acknowledges severe breathing disturbances. OSA patients from the European Sleep Apnoea Data Base (ESADA) were retrospectively allocated into CVD risk groups 1-3 based on SCORE-2 and the ESC guidelines. AHI ≥30 /h conferred strong treatment indication. When AHI was <30/h, symptoms and CVD risk dictated allocation to weak, intermediate or strong treatment indication group. Change in Epworth Sleepiness Scale (ESS) and office systolic blood pressure (SBP) at follow-up (12-24 months) under positive airway pressure (PAP) were assessed. 8625 patients were analysed (29% female, age 56 [49;64] years, BMI 31.9 [28.4;36.3] kg·m-2). Treatment indication was weak in 501 (6%), intermediate in 2085 (24%) and strong in 6039 (70%). There was a continuous increase in age, SBP, C-reactive protein and glycosylated haemoglobin from weak to strong (p<0.001). PAP prescription increased from 52% to 64% to 93% (weak to strong, p<0.001). The change in ESS score was -2, -4 and -5, respectively (p<0.001). Reductions of ≥3 mmHg of median SBP occurred when AHI was ≥30/h and in symptomatic patients with CVD risk levels>1 when AHI was <30/h. This analysis provides supporting evidence for the key role of CVD risk assessment and severe breathing disturbances in the identification of OSA patients most likely to benefit from treatment.

10-Year Atherosclerotic Cardiovascular Disease (ASCVD) Risk Score Calculation in Out-Patient Department

Objective: To calculate 10-year Atherosclerotic Cardiovascular Disease risk score in outdoor department of a tertiary care hospital and identify individuals at high risk of cardiovascular disease. Study Design: Cross-sectional study. Place and Duration of Study: Combined Military Hospital, Malir, Pakistan, from Oct 2020 to Mar 2021. Methodology: 116 patients presenting to Out-Patient Department of General Medicine of CMH, Malir, Pakistan, without previous evidence of cardiovascular disease were included. Previous history of diabetes, smoking, use of anti-hypertensives, Aspirin and statins was recorded. Systolic and diastolic blood pressures, waist circumference, HbA1c, fasting blood sugar, fasting LDL, HDL and total cholesterol were tested for each patient. 10-year ASCVD risk score was calculated using online ASCVD Risk Estimator calculator. Results: Mean age of the patients was 58.6±9.92 years with 62.1% being males and 37.9% being females. Mean 10-year ASCVD risk score was 13.37±7.1, of which 21 patients (18.1%) had Very High, 66(56.9%) had Intermediate and 29(25%) had Low 10-year ASCVD risk score. Among Very High cases, 80.9% were males, all were in age group 50-79 years, 61.9% were diabetic, 33.3% were smokers and 66.7% were obese. Conclusion: A very high percentage of study group had Intermediate or Very High 10-year ASCVD risk score. Identifying those individuals at high risk early and controlling the modifiable risk factors can help lessen the morbidity and mortality associated with cardiovascular diseases.

Machine learning methods for propensity and disease risk score estimation in high-dimensional data: a plasmode simulation and real-world data cohort analysis.

Machine learning (ML) methods are promising and scalable alternatives for propensity score (PS) estimation, but their comparative performance in disease risk score (DRS) estimation remains unexplored. We used real-world data comparing antihypertensive users to non-users with 69 negative control outcomes, and plasmode simulations to study the performance of ML methods in PS and DRS estimation. We conducted a cohort study using UK primary care records. Further, we conducted a plasmode simulation with synthetic treatment and outcome mimicking empirical data distributions. We compared four PS and DRS estimation methods: 1. Reference: Logistic regression including clinically chosen confounders. 2. Logistic regression with L1 regularisation (LASSO). 3. Multi-layer perceptron (MLP). 4. Extreme Gradient Boosting (XgBoost). Covariate balance, coverage of the null effect of negative control outcomes (real-world data) and bias based on the absolute difference between observed and true effects (for plasmode) were estimated. 632,201 antihypertensive users and nonusers were included. ML methods outperformed the reference method for PS estimation in some scenarios, both in terms of covariate balance and coverage/bias. Specifically, XgBoost achieved the best performance. DRS-based methods performed worse than PS in all tested scenarios. We found that ML methods could be reliable alternatives for PS estimation. ML-based DRS methods performed worse than PS ones, likely given the rarity of outcomes.

Long-term validation of AtherosSlerosis CardioVascular Disease (ASCVD) risk score and the role of aspirin for primary Cardiovascular prevention in Korean patients with over ASCVD 20% risk score

Abstract Background Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death worldwide. The ASCVD risk score can be a great tool for preventing ASCVD in the future. There is a mediation recommendation depending on calculated risk called United States Prevention Services Task Force (USPSTF) aspirin (ASA) recommendation. However, the validation studies of long-term clinical outcomes in Korean population using the ASCVD risk score and of the effect of ASA in Korean patients using USPSTF recommendation are limited. Methods A total of 7,249 patients with any chest pain who underwent coronary spasm provocation tests between January 2004 and December 2021 were enrolled. Patients were divided into four groups, based on the ASCVD risk score: &amp;lt; 5% group (Low Risk, n = 2,534), 5 - 10% group (Borderline Risk, n=1,681), 10 - 20% group (Intermediate Risk, n = 1,896) and ≥ 20% group (High Risk, n = 1,138). The primary endpoint was Major Adverse Cerebral and Cardiovascular Event (MACCE1), including stroke and MACE. The secondary endpoint was MACCE 2, defined as composite of MACE and recurrent angina requiring follow-up coronary angiography (CAG). The third endpoint was the effect of aspirin in patients who have over 20% ASCVD risk. Results Long term (10-year) ASCVD risk score was well-validated from the "Low Risk" group to the "High Risk" group by 1.87%, 7.44%, 14.09%, and 30.61%, respectively (P &amp;lt; 0.001). In baseline characteristics, the calculation factors of ASCVD risk score is also well-stratified, showing the more adverse clinical profiles in the "High Risk" group than the "Low Risk" group. In 10-year clinical outcomes, although MACCE1 and MACCE2 showed an increasing trend numerically, stroke was observed to significantly increase according to ASCVD risk score. There was no effect of ASA prescribed in accordance with USPSTF recommendation. The group with the highest rate of 20% or more failed to show the aspirin prevent effect on the occurrence of CV events, but also increased the incidence (MACCE1; HR: 1.647, P-Value: 0.283, 95% CI: 0.662 - 4.100, MACCE2; HR: 1.819, P-Value: 0.025, 95% CI: 1.078 - 3.068). Conclusion Long term ASCVD risk score was associated with higher risk of stroke. However, MACCE1 and MACCE2 only showed an increasing trend numerically. Furthermore, the effect of ASA prescribed based on the USPSTF ASA use recommendation statement were not observed in Korean population. Consequently, additional research is needed to ensure the utility of ASCVD risk score and the effect of ASA in high-risk patients to prevent future CV events in Korean population.

Symptoms and ASCVD score fail to identify majority of the patients at risk of first myocardial infarction

Abstract Background Cardiovascular disease screening has long relied on the presence of symptoms like chest pain or on the Atherosclerotic Cardiovascular Disease (ASCVD) risk score, which is typically initiated at age 40. However, current research suggests that the ASCVD risk score may not incorporate a comprehensive range of factors and should be started earlier. Furthermore, the absence of symptoms such as angina in some patients prior to acute coronary syndrome (ACS) casts doubt on the reliability of symptom-based screening methods. Purpose This study aimed to evaluate the effectiveness of the ASCVD risk score and chest pain as predictors for ACS. Methods We retrospectively analyzed the data of all patients of 65 or younger who presented with their first ACS event in a single US center from January 2020 to January 2024. We collected the demographic and clinical data, including age, sex, lipid panel, blood pressure, medical history, onset of symptoms, and calculated their individual ASCVD risk score. We explored whether these patients, if assessed one week before their ACS event, would have been identified as at risk and thus prescribed lipid lowering or recommended for further diagnostic tests. Results Among 166 patients presenting with ACS, 64 (39%) were considered low risk with an ASCVD risk score of &amp;lt;5 or too low to calculate. 20 (12%) patients were assessed as borderline risk with an estimated risk score of 5-7.5%. In the intermediate risk category, 20 patients (12%) had a score of 7.5% -10% and 42 patients (25%) were estimated to have a score of &amp;gt;10 to &amp;lt;20%. 14 patients (8%) fell in the high-risk category, and lastly 6 patients had an LDL that was above 190 mg/dl. Regarding symptoms of chest pain or shortness of breath, 14 patients (8%) did not experience any symptoms prior to their presentation, 98 (59%) patients experienced their first episode of symptoms within 48 hours, 19 patients (11%) within 2 days to a week, 10 patients (6%) had CP leading up to their event from a week to a month, 6 patients (4%) in the range of a month to 3 months and 19 patients (11%) had symptoms longer than 3 months prior to their presentation. To summarize if there were seen 1 week prior to their first ACS events, 51% of them were not recommended for statin therapy based on their ASCVD risk score and 67% of patients either had no chest pain until the event or chest pain within 48hrs of onset of their ACS and consequently would not have been routinely screened for coronary artery disease (CAD) by any anatomical or functional methods. Conclusion(s) ASCVD risk score and symptoms fail to identify majority of patient below 65 years of age with their first MI. Given high prevalence of CAD and associated mortality, improved screening tools, in addition to symptomatology or risk scores, would be more effective in identifying individuals at risk.

Validation of digital cardiovascular risk score (DiCAVA) in the United States All of Us dataset

Abstract Introduction The cardiovascular disease (CVD) risk score DiCAVA was developed in 2021 using UK Biobank data with the aim to be used for population health screening by deriving its predictions only from variables readily available to every individual, omitting difficult-to-obtain measurements such as blood pressure and invasive blood tests for metrics like cholesterol or HbA1c, which are commonly used in clinical CVD risk assessment tools. The original set of variables was reduced to 30 in the DiCAVA Lite score to further improve usability in a digital solution accessible to the general population, while maintaining its discrimination ability. Purpose Like many countries, the US has a large burden of CVD and would benefit from easily accessible screening tools. However, to ensure generalisability of the UK-developed score, it is necessary to evaluate its performance in a population where the score is intended to be used. Methods Data from a large real-world modern observational study in the US, All of Us, was used to evaluate discrimination and calibration metrics of the risk score. Participants with pre-existing CVD and without available electronic health records were excluded from the study. Variables from the All of Us dataset were matched to the original variables and any missing values were imputed using multiple imputation. Results 216,985 participants with a median follow-up time of 3.44 years were included in the study. The median age was 51 years and 63% of the population were female. Discrimination ability in the All of Us dataset (C-index 0.76) slightly surpassed the discrimination achieved at training with UK Biobank (C-index 0.73), and that of Framingham risk score calculated for the All of Us population (C-index 0.72), demonstrating the score’s usability in the US. The score showed very good discrimination in both males (C-index 0.74) and females (C-index 0.77), all ethnic groups and age groups, with the exception of the population over 65 years of age, where the C-index dropped to 0.65 as expected (similar to that observed in the UK Biobank). Calibration of the original model was assessed at 3 years and showed under-estimation of the predicted risk (calibration-in-the-large of 2.37%). Following re-calibration, the algorithm demonstrated excellent results with the average predicted 3-year risk showing 0.59% difference from the observed CVD rate (calibration plot in Figure). Conclusions Overall, the DiCAVA Lite score has shown very good discrimination in the US, showing its generalisability in different demographic populations. Its re-calibrated form is now validated for use in the US population and because of its ability to be utilised outside clinical settings, it can be deployed widely in the general population as an initial screening tool and potentially boost awareness of cardiovascular health in an effort to drive preventative care of CVD.Re-calibration of the DiCAVA Lite score

Geospatial clustering of auto-antibodies against apolipoprotein A-1, heavy metals, cardiovascular and liver diseases in the general population

Abstract Aims Heavy metals (HM) are combustion-related environmental factors predisposing to cardiovascular (CVD), autoimmune and liver diseases, even below regulatory concentrations. Pro-atherogenic and pro-steatotic auto-antibodies against apolipoprotein A1 (AAA1) are prevalent in the general population for unclear reasons. We explored a putative interplay between HM exposure, AAA1, CVD risk factors and a non-alcoholic fatty liver disease (NAFLD) risk score, using a geospatial clustering approach at the general population level. Methods Getis-Ord, Moran I2 clustering and geographically weighted regression (GWR) geosptatial analyses were carried out on 6361 individuals recruited in the CoLaus/PsyCoLaus general population cohort. Geospatial dependences between serum AAA1, urinary HM (cadmium, arsenic, cobalt) concentrations, SCORE2 CVD risk, and fatty liver index (FLI) were assessed. Results Cadmium and cobalt were found to be associated with higher SCORE2 risk (p&amp;lt;0.04). AAA1 were associated with higher cadmium, cobalt, and cigarette consumption, independently of SCORE2 (p&amp;lt;0.04). AAA1 hotspots overlapped significantly with those of cadmium and FLI, independently of SCORE2. According to GWR, correlations between cadmium and AAA1 were the highest in railways proximity regions, increasing up to 10-fold compared to global associations. AAA1 were not associated with other HM. Conclusions These geospatial footprints highlight independent associations between Cadmium, AAA1, and NAFLD risk score in the general population. We hypothesize that environmental cadmium exposure may facilitate the occurrence of AAA1 in humans. Ongoing experimental studies will confirm/refute a possible causal link. Whether AAA1 could represent an early biological signature of cadmium exposure and subsequent health hazards remains to be shown.

Non-alcoholic fatty liver disease risk with GLP-1 receptor agonists and SGLT-2 inhibitors in type 2 diabetes: a nationwide nested case–control study

BackgroundNon-alcoholic fatty liver diseases (NAFLDs)/non-alcoholic steatohepatitis (NASH) are the most common liver disorders among patients with type 2 diabetes. Newer classes of glucose-lowering agents (GLAs), such as glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is), have been shown to improve liver-related biomarkers. However, their effects on the development of NAFLD/NASH remain inconclusive.MethodsA nested case–control study was conducted using Taiwan’s National Health Insurance Research Database for 2011–2018. Patients aged ≥ 40 years, diagnosed with type 2 diabetes, having stable non-insulin GLA use, and without NAFLD/NASH history were included. Patients with incident NAFLD/NASH were matched up to 10 randomly sampled controls based on individual’s age, gender, cohort entry date, type 2 diabetes diagnosis date, and disease risk score. Conditional logistic regression analyses were employed to estimate the association between liver risk and treatment exposure. Dose-response analysis was also performed.ResultsThere were 621,438 study patients included for analysis. During 1.8 years of median follow-up, the incidence of NAFLD/NASH was 2.7 per 1000 person-years. After matching, 5,730 incident NAFLD cases (mean age: 57.6 years, male: 53.2%) and 45,070 controls (57.7 years, 52.7%) were identified. Using GLP-1RAs or SGLT2is was associated with an insignificantly lower NAFLD/NASH risk (i.e., odds ratios [95% CIs]: 0.84 [0.46–1.52] and 0.85 [0.63–1.14], respectively) and increased cumulative SGLT2i doses were significantly associated with a reduced NAFLD/NASH risk (0.61 [0.38–0.97]).ConclusionGLP-1RA and SGLT2i therapies in type 2 diabetes patients might prevent NAFLD/NASH development, with a significantly lower risk related to greater treatment exposure.

Visceral Obesity and Its Association with Severe Coronary Artery Calcification in Patients with Metabolic Dysfunction-Associated Steatotic Liver Disease.

Background/Objectives: The role of body composition parameters in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) with presence and severity of coronary artery calcification (CAC) is still not fully elucidated. We aimed to evaluate the impact of computed tomography (CT)-based body composition parameters in patients with MASLD with CAC severity. Methods: In this multicenter study, 1870 individuals underwent cardiac CT for the detection of CAC as well as ultrasonography for the diagnosis of hepatic steatosis. The presence of CAC was defined by a CAC score threshold of >0, while severe CAC was defined by a threshold of >300. Using the abdominal cross-sectional CT images at the L3 vertebra level, we analyzed the skeletal muscle index, visceral to subcutaneous adipose tissue ratio, and muscle density using the Hounsfield unit. Results: Of 648 patients with MASLD, the proportions of presence of CAC and severe CAC were 45.2% and 9.9%, respectively. Visceral obesity was not associated with the presence of CAC after adjustment for age, sex, smoking, statin therapy, type 2 diabetes, and advanced fibrosis (adjusted odds ratio (aOR), 1.38; 95% confidence interval (CI), 0.86-2.23; p = 0.180). However, visceral obesity was independently associated with severe CAC after adjustment for several metabolic risk factors (aOR, 3.54; 95% CI, 1.25-14.90; p = 0.039), and adjustment for atherosclerotic cardiovascular disease risk scores (aOR, 3.74; 95% CI, 1.31-15.79; p = 0.032). Conclusions: Visceral obesity may serve as a novel prognostic CT-based radiological biomarker for patients with MASLD with severe CAC.

Predictive role of peak VO2 for short- and long-term major adverse cardiac events in patients with high cardiovascular risk.

The objective of this study was to assess the accuracy of VO2 measurements in predicting long-term major adverse cardiac events (MACEs) in patients with high cardiovascular risk. Based on a10-year atherosclerotic cardiovascular disease risk score, 333 patients with high cardiovascular risk were included in this retrospective analysis. The study endpoint was MACEs, comprising all-cause mortality, cardiovascular mortality, non-fatal myocardial infarction or stroke, and coronary revascularization. The study cohort was divided into two groups according to the frequency of MACE occurrence. Measurements of VO2 were assessed for the prediction of MACEs. The best predictive accuracy for 1‑year MACEs was determined to be aVO2 max value of ≥ 20.3 mL/kg/min, with 60% specificity and 60% sensitivity (area under the curve [AUC]: 0.61; 95% confidence interval [CI]: 0.51-0.71; p < 0.001), and for 5‑year MACEs it was ≥ 19.9 mL/kg/min, with 69% specificity and 64% sensitivity (AUC: 0.69; 95% CI: 0.62-0.76; p < 0.001). Multivariable Cox regression analysis, after adjusting for univariable factors, showed that VO2 max was independently associated with both short- and long-term MACEs in patients at high cardiovascular risk (hazard ratio [HR]: 0.900, 95% CI: 0.858-0.943, p < 0.001). According to the results of this pilot study, VO2 max can predict both short- and long-term MACEs in patients at high cardiovascular risk.

Impact of provision of abdominal aortic calcification results on fruit and vegetable intake: 12-week randomized phase 2 controlled trial

Provision of non-invasive vascular imaging results to individuals has been shown to improve cardiovascular disease risk factor control: its impact on diet remains uncertain. In this two-arm, single-blind, parallel, 12-week randomized controlled trial, 240 participants, 57.5% females aged 60–80 y had abdominal aortic calcification and clinical assessments performed at a hospital clinic. Participants were randomized 1:1 to receive (intervention n = 121) or not (control n = 119) their calcification results. Both groups received educational resources on cardiovascular disease risk control and were unblinded to the intervention. Outcome measures were performed at baseline and 12 weeks. The primary outcomes of the study were changes in fruit and vegetable intake measures over 12 weeks assessed using plasma carotenoid concentrations (biomarkers of FV intake) and a food frequency questionnaire. Secondary outcomes included 12-week changes in other aspects of the diet, physical activity, body weight, blood pressure, heart rate, lipid profile, glucose concentrations, estimated cardiovascular disease risk score, and medication use. Between-group differences were tested using linear mixed-effects regression. There were no between-group differences in the primary outcomes at 12 weeks: plasma carotenoids (mean difference +0.03 µg/mL [95%CI −0.06, 0.13]) and fruit and vegetable intakes (+18 g/d [−37, 72]). However, the provision of calcification results led to between-group differences in serum total (−0.22 mmol/L [−0.41, −0.04]) and non-HDL (−0.19 mmol/L [−0.35, −0.03]) cholesterol, and estimated cardiovascular disease risk score (−0.24% [−0.47, −0.02]). No between-group differences were seen for other secondary outcomes. In this work, providing vascular imaging results did not improve diet but did improve some cardiovascular disease risk factors (Australian and New Zealand Clinical Trials Registry ACTRN12618001087246).