Evidence on cardiovascular (CV) risk stratification in Bangladeshi patients with type 2 diabetes mellitus (T2DM) who are asymptomatic for cardiovascular disease (CVD) is limited. This study aimed to assess the 10-year CV risk in newly diagnosed patients with T2DM.

In 2023, a cross-sectional study was carried out at endocrinology clinics in tertiary hospitals throughout Bangladesh, involving newly diagnosed patients with T2DM aged 25 to 84 who had no prior history of CVD and were asymptomatic for the condition. CV risk was assessed and classified using QRISK3.

1617 newly diagnosed patients with T2DM (age 44.92 ± 11.84 years, male 49.5 %) were analyzed. Their median QRISK3 score was 11.0 %, with 46.5 % at low, 25.7 % at moderate, and 27.8 % at high 10-year CV risk, respectively. The QRISK3 score increased with age for both men and women, with men consistently scoring higher than women in every age group. Among the age groups 25-39, 40-64, and 65-84, the percentages of patients with high 10-year CV risk were 3.3 %, 34.0 %, and 94.5 %, respectively. The median relative risk (RR) of CVD was 4.3. RR decreased with age for both sexes, and men had a lower RR than women across all age groups. A sleep duration of 6-9 h was associated with a lower 10-year CV risk.

Many newly diagnosed Bangladeshi patients with T2DM have substantial CV risk. QRISK3 can assist clinicians in predicting 10-year CV risk and choosing appropriate treatments to prevent CVD.

Lithium is a first-line maintenance treatment for bipolar-disorder (BD) but has increased risk for chronic-kidney-disease (CKD). There is a paucity of research on risk-model development predicting CKD during/following lithium treatment, and none was conducted in Asian regions. This study aimed to derive and validate 10-year risk prediction model for CKD-stage 3 in first-diagnosed BD patients receiving ≥ 1 prescription of lithium during 2002-2018 in Hong-Kong, using electronic-medical-record database of public-healthcare services. Literature-informed predictor selection included demographics, physical comorbidities, mean lithium serum-levels and non-lithium psychotropic use. The risk-equation was developed using Least-Absolute-Shrinkage-and-Selection-Operator (LASSO) Cox-proportional hazards regression model with 4-fold internal cross-validation over 1,000 iterations. We identified 2,258 lithium-treated BD patients, with CKD incidence of 12.6 per 1000 person-years (95 %CI=11.1-14.4) over a median follow-up of 7.7 years (interquartile range=3.7-12.3). Our results showed that older age at BD-diagnosis, male sex, physical comorbidities, higher mean lithium serum-level, fewer antipsychotic and mood-stabilizing anticonvulsant use, and greater antidepressant exposure were independent risk factors predicting CKD, with an event-per-variable ratio of 25.2. The 10-year risk prediction model had satisfactory area-under-the-curve (AUC) (0.74 [95 %CI=0.66-0.83]), with good calibration (calibration slope=0.88 [95 %CI=0.61-1.15]; observed/expected risk ratio=1.14 [95 %CI=0.86-1.42]), and discrimination performances (Harrell's C-index=0.75 [95 %CI=0.68-0.82]; Royston and Sauerbrei's D statistic=1.45 [95 %CI=0.99-1.92]). In conclusion, this CKD risk-model for lithium-treated BD patients demonstrated satisfactory prediction performance in a predominantly-Chinese population. Further research including external validation is needed to verify model performance to facilitate implementation of this CKD risk prediction tool for individualized clinical decision-making and outcomes in real-world practice.

To compare the performance of the QRESEARCH risk estimator version 3 (QRISK3), the Systematic COronary Risk Evaluation (SCORE) 2, and Predicting Risk of cardiovascular disease EVENTs (PREVENT) equationin a cohort of individuals with chronic inflammatory rheumatic diseases (CIRD) enrolled in the Spanish prospective CARdiovascular in RheuMAtology (CARMA) project.

Between July 2010 and January 2012, the study recruited CIRD patients from 67 hospitals across Spain. It included individuals diagnosed with rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis. At the 10-year follow-up, data for all patients included in the initial cohort were assessed. We estimated four 10-year cardiovascular disease (CVD) incidence risk scores using data recorded at recruitment.

2080 patients were included in this analysis. QRISK3 and PREVENT-CVD predicted an average of approximately 10 % CV events across the entire cohort, while SCORE2 and PREVENT-Atherosclerotic Cardiovascular Disease (ASCVD) predicted an average of only 6.3 %. The linear correlation coefficients between each pair of scales were consistently above 0.8, with an average of 0.9074. Notably, lower correlations were observed between QRISK3 and the other scales. When identifying patients with higher CV risk, the kappa index was higher between SCORE2, PREVENT-CVD, and PREVENT-ASCVD than between QRISK3 and any other scale. These findings suggest that most patients identified as high-risk by SCORE2 would also be classified as high-risk when using PREVENT-CVD or PREVENT-ASCVD.

The higher correlation and reliability observed between SCORE2, PREVENT-CVD, and PREVENT-ASCVD in our series of CIRD patients followed over a 10-year period suggest that these scales may be largely interchangeable for identifying high-risk CIRD patients.

Digital screening may divert lower-risk persons to lower-cost online screening, or offer higher-risk non-responders a more acceptable alternative. Population-based uptake estimates are lacking. We conducted four studies within four weeks by inviting 1700 Londoners (40-74 years, without cardiovascular disease) to a digital Health Check. A six-arm pragmatic unregistered randomised controlled trial (RCT) tested different Short Message Service (SMS) invitations. Uptake varied from 12% (standard SMS) to 20% (shortest SMS, P = 0.009). We tested three sequential reminders (an SMS, a second pragmatic trial [SMS vs postal reminder], and a final SMS). The first SMS reminder increased uptake by +3%. The postal reminder (+7%) was twice as effective as the SMS reminder (+3%, P < 0.0001). The "final reminder" SMS added +7%. Altogether, shorter invites, multi-modal reminders, and a "final reminder" all increased uptake. Adding digital care to in person care may raise uptake from 50 to 60%. Agile evaluations can rapidly improve invitation systems.

We evaluated temporal and genetic relationships between 176 food-liking-traits and cardio-metabolic diseases using data from the UK Biobank (N = 182,087) for observational analyses and summary-level GWAS data from FinnGen and other consortia (N = 406,565-977,323) for genetic analyses. Integrating observational and genetic results, we identified two detrimental food-liking-traits (bacon and diet-fizzy-drinks) and three protective food-liking-traits (broccoli, pizza, and lentils/beans). These food-liking-traits are associated with habitual food intake and influence cardio-metabolic proteins and biological processes. Notably, we found three genetic links: diet-fizzy-drinks with heart-failure, bacon with type-2-diabetes, and lentils/beans with type-2-diabetes, identifying 54 pleiotropic single-nucleotide-variants, impacting both phenotypes. Our data show the diet-fizzy-drinks and heart-failure link maybe not direct, as diet-fizzy-drinks liking correlates with sweet food consumption and shares variants linked to BMI, adiposity, platelet count and cardio-metabolic traits. The pleiotropic single-nucleotide-variants map to 251 tissue-specific genes, with four showing high druggability potential, highlighting personalized dietary strategies for cardio-metabolic diseases.

Despite advancements in understanding the interplay between systemic lupus erythematosus (SLE), cardiovascular disease and COVID-19, challenges and knowledge gaps persist. This study aimed to characterize the cardiovascular profiles of SLE patients hospitalized with COVID-19 and to evaluate the influence of SLE on the development of cardiovascular complications.

This was a multicentre, nationwide observational study in which data were sourced from the SEMI-COVID-19 Registry between March 1, 2020, and March 31, 2021, involving 150 Spanish hospitals. SLE patients were matched with non-SLE patients based on sex, age, and hospitalization date.

Of the 20,970 patients included in the SEMI-COVID-19 Registry, 38 were previously diagnosed with SLE. The non-SLE group was composed of 103 patients. The mean age of the SLE patients was 63 years, with 81.6% females and 21.1% non-European patients. SLE patients exhibited a significantly higher frequency of chronic kidney disease (14.4% vs 2.9%; p=0.004), stroke (23.7% vs 2.9%; p<0.001), and increased use of cardiovascular medications. SLE demonstrated an independent association with the occurrence of major cardiovascular events (MACE) (OR: 3.934; 95% CI: 1.247-12.432).

SLE patients hospitalized for COVID-19 are at high risk of having an unfavorable baseline cardiovascular profile and are more prone to MACEs and adverse noncardiovascular outcomes during hospitalization.

The Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network (ASSIGN) risk score, developed in 2006, is used in Scotland for estimating the 10-year risk of first atherosclerotic cardiovascular disease (ASCVD). Rates of ASCVD are decreasing, and an update is required. This study aimed to recalibrate ASSIGN (V.2.0) using contemporary data and to compare recalibration with other potential approaches for updating the risk score.

Data from Scotland-resident participants from UK Biobank (2006-2010) and the Generation Scotland Scottish Family Health Study (2006-2010), aged 40-69 and without previous ASCVD, were used for the derivation of scores. External evaluation was conducted on UK Biobank participants who were not residents of Scotland. The original ASSIGN predictor variables and weights formed the basis of the new sex-specific risk equation to predict the 10-year risk of ASCVD. Different approaches for updating ASSIGN (recalibration, rederivation and regression adjustment) were tested in the evaluation cohort.

The original ASSIGN score overestimated ASCVD risk in the evaluation cohort, with median predicted 10-year risks of 10.6% for females and 15.1% for males, compared with observed risks of 6% and 11.4%, respectively. The derivation cohort included 44 947 (57% females and a mean age of 55) participants. The recalibrated score, ASSIGN V.2.0, improved model fit in the evaluation cohort, predicting median 10-year risk of 4% for females and 8.9% for males. Similar improvements were achieved using the regression-adjusted model. Rederivation of ASSIGN using new beta coefficients offered only modest improvements in calibration and discrimination beyond simple recalibration. At the current risk threshold of20% 10-year risk, the original ASSIGN equation yielded a positive predictive value (PPV) of 16.3% and a negative predictive value (NPV) of 94.4%. Recalibrated ASSIGN V.2.0 showed similar performance at a 10% threshold, with a PPV of 16.8% and an NPV of 94.6%.

The recalibrated ASSIGN V.2.0 will give a more accurate estimation of contemporary ASCVD risk in Scotland.

This study examined whether incorporating free-text entries into structured general practice records improves the detection of long-term conditions (LTCs) and multimorbidity (MM) in New Zealand (NZ) general practices.

Data from 374 071 deidentified individuals in general practices were analysed to identify 61 LTCs. Structured data were extracted using Read codes from a national master list, and clinical raters independently identified condition-related free-text, including synonyms, negation terms and common misspellings in randomised samples. Keywords were categorised and refined through ten iterative tests. Programmatic text classification was developed and assessed against gold-standard clinician ratings, using sensitivity, specificity, positive predictive value (PPV) and F1-score.

A quarter of general practitioner classifications contained either unrecognised Read codes or consisted of free-text only. Clinician inter-rater reliability was high (kappa ≥0.9). Compared with clinical gold standard, text classification yielded an average sensitivity of 88%, specificity of 99% and PPV of 95%, with an F1-score range of 82%-95%. Incorporating free text increased LTC prevalence from 42.1% to 46.3%, reducing misclassification of MM diagnoses by identifying 12 626 additional patients with MM and 15 972 additional patients with at least one LTC.

In the course of workflow, general practitioners face barriers to accurate LTC coding or may simply annotate with text-based descriptions. Programmatic text classification has demonstrated high performance and identified many more patients receiving LTC care.

Combining structured and unstructured data optimises MM detection in NZ general practices and has the potential to improve case management, follow-up care and allocation of healthcare resources.

Patients with an autoimmune or inflammatory disease (AIID) are at increased cardiovascular risk and may benefit more from statin therapy. In the FOURIER trial (Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk), the PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitor evolocumab lowered low-density lipoprotein cholesterol levels, but not hsCRP (high-sensitivity C-reactive protein) levels, and reduced the risk of cardiovascular events.

FOURIER was a randomized trial of evolocumab versus placebo in 27 564 patients with stable atherosclerosis who were taking statins. This analysis focused on the effect of evolocumab in patients with or without an AIID, defined as any autoimmune or chronic inflammatory condition. The primary end point was a composite of cardiovascular death, myocardial infarction, stroke, unstable angina, or coronary revascularization.

At baseline, 889 patients (3.2%) had an AIID, most commonly rheumatoid arthritis (33.7%) or psoriasis (15.6%). Median (interquartile range) low-density lipoprotein cholesterol levels were 90.0 mg/dL (79.5-105.5) and 91.5 mg/dL (79.5-108.5) in patients with or without an AIID, respectively (P=0.025), and the placebo-adjusted percent reduction with evolocumab was consistent (60.2% versus 59.0%; P=0.57). Baseline hsCRP was higher in patients with an AIID (median 2.1 versus 1.7 mg/L; P<0.001) and did not significantly change with evolocumab in either group. Compared with placebo, evolocumab reduced the rate of the primary end point by 14% in patients without an AIID (hazard ratio, 0.86 [95% CI, 0.80-0.93]) and by 42% in patients with an AIID (hazard ratio, 0.58 [95% CI, 0.38-0.89]; Pinteraction=0.066). Likewise, evolocumab reduced the key secondary end point of cardiovascular death, myocardial infarction, or stroke by 19% in patients without an AIID (hazard ratio, 0.81 [95% CI, 0.74-0.89]) and 58% in those with an AIID (hazard ratio, 0.42 [95% CI, 0.24-0.74]; Pinteraction=0.022).

Intensive lowering of low-density lipoprotein cholesterol levels with evolocumab may lead to greater relative reduction in cardiovascular events in patients with an AIID.

URL: https://www.clinicaltrials.gov; Unique identifier: NCT01764633.

Cardiovascular diseases (CVDs) rank amongst the leading causes of long-term disability and mortality. Predicting CVD risk and identifying associated genes are crucial for prevention, early intervention, and drug discovery. The recent availability of UK Biobank Proteomics data enables investigation of blood proteins and their association with a variety of diseases. We sought to predict 10 year CVD risk using this data modality and known CVD risk factors.

We focused on the UK Biobank participants that were included in the UK Biobank Pharma Proteomics Project. After applying exclusions, 50,057 participants were included, aged 40-69 years at recruitment. We employed the Explainable Boosting Machine (EBM), an interpretable machine learning model, to predict the 10 year risk of primary coronary artery disease, ischemic stroke or myocardial infarction. The model had access to 2978 features (2923 proteins and 55 risk factors). Model performance was evaluated using 10-fold cross-validation.

The EBM model using proteomics outperforms equation-based risk scores such as PREVENT, with a receiver operating characteristic curve (AUROC) of 0.767 and an area under the precision-recall curve (AUPRC) of 0.241; adding clinical features improves these figures to 0.785 and 0.284, respectively. Our models demonstrate consistent performance across sexes and ethnicities and provide insights into individualized disease risk predictions and underlying disease biology.

In conclusion, we present a more accurate and explanatory framework for proteomics data analysis, supporting future approaches that prioritize individualized disease risk prediction, and identification of target genes for drug development.

To externally validate the SCORE2, AHA/ACC pooled cohort equation (PCE), Framingham Risk Score (FRS), Non-Laboratory INTERHEART Risk Score (NL-IHRS), Globorisk-LAC, and WHO prediction models and compare their discrimination and calibration capacity.

Validation in individuals aged 40-69 years with at least 10 years of follow-up and without baseline use of statins or cardiovascular diseases from the Prospective Urban Rural Epidemiology (PURE)-Colombia prospective cohort study. For discrimination, the C-statistic, and receiver operating characteristic curves with the integrated area under the curve (AUCi) were used and compared. For calibration, the smoothed time-to-event method was used, choosing a recalibration factor based on the integrated calibration index (ICI). In the NL-IHRS, linear regressions were used. In 3802 participants (59.1% women), baseline risk ranged from 4.8% (SCORE2 women) to 55.7% (NL-IHRS). After a mean follow-up of 13.2 years, 234 events were reported (4.8 cases per 1000 person-years). The C-statistic ranged between 0.637 (0.601-0.672) in NL-IHRS and 0.767 (0.657-0.877) in AHA/ACC PCE. Discrimination was similar between AUCi. In women, higher over-prediction was observed in the Globorisk-LAC (61%) and WHO (59%). In men, higher over-prediction was observed in FRS (72%) and AHA/ACC PCE (71%). Overestimations were corrected after multiplying by a factor derived from the ICI.

Six prediction models had a similar discrimination capacity, supporting their use after multiplying by a correction factor. If blood tests are unavailable, NL-IHRS is a reasonable option. Our results suggest that these models could be used in other countries of Latin America after correcting the overestimations with a multiplying factor.

Adults with type 2 diabetes have an increased risk of cardiovascular events (CVEs), the world's leading cause of mortality. The SCORE2-Diabetes model is a tool designed to estimate the 10-year risk of CVE specifically in individuals with type 2 diabetes. However, the performance of such models may vary across different demographic and socioeconomic groups, necessitating validation and assessment in diverse populations. This study aims to externally validate SCORE2-Diabetes and assess its performance across various socioeconomic and migration origins in The Netherlands.

We selected adults with type 2 diabetes, aged 40-79 years and without previous CVE from the Extramural LUMC Academic Network (ELAN) primary care data cohort from 2007 to 2023. ELAN data were linked with Statistics Netherlands registry data to obtain information about the country of origin and socioeconomic status (SES). Cardiovascular event was defined as myocardial infarction, stroke, or CV mortality. Non-CV mortality was considered a competing event. Analyses were stratified by sex, Dutch vs. other non-Dutch countries of origin, and quintiles of SES. Of the 26 544 included adults with type 2 diabetes, 2518 developed CVE. SCORE2-Diabetes showed strong predictive accuracy for CVE in the Dutch population [observed-to-expected ratio (OE) = 1.000, 95% CI = 0.990-1.008 for men, and OE = 1.050, 95% CI = 1.042-1.057 for women]. For non-Dutch individuals, the model underestimated CVE risk (OE = 1.121, 95% CI = 1.108-1.131 for men, and OE = 1.100, 95% CI = 1.092-1.111 for women). The model also underestimated the CVE risk (OE > 1) in low SES groups and overestimated the risk (OE < 1) in high SES groups. Discrimination was moderate across subgroups with c-indices between 0.6 and 0.7.

SCORE2-Diabetes accurately predicted the risk of CVE in the Dutch population. However, it underpredicted the risk of CVE in the low SES groups and non-Dutch origins, while overpredicting the risk in high SES men and women. Additional clinical judgment must be considered when using SCORE2-Diabetes for different SES and countries of origin.

A new study validates the SCORE2-Diabetes model for predicting a 10-year risk of cardiovascular events in type 2 diabetes. Strong accuracy for the Dutch population, but underestimation of the risk for low SES and non-Dutch groups. SCORE2-Diabetes should be used with extra caution across diverse subgroups.

A polygenic risk score (PRS) summarizes in one number an individual's estimated genetic association with a specific trait or disease based on the common DNA variants included in the score. Disease PRSs have the potential to positively affect population health by improving disease risk prediction, thereby also potentially improving disease prevention, early intervention and treatment. However, given the potential psychological, behavioural and other harms, there are also concerns about integrating PRSs into clinical tools and healthcare systems. Here we assess five arguments against implementing PRSs for physical disease in clinical practice that revolve around psychological and behavioural considerations. For each argument, we consider a counterargument, the evidence and underlying theory, any gaps in the evidence base and possible future directions and research priorities. We conclude that, although there may be other barriers to implementation, there is currently little evidence of psychological or behavioural harms from integrating PRSs into practice.

There is no consensus on the optimal time horizon for predicting cardiovascular disease (CVD) risk to inform treatment decisions. New Zealand and Australia recommend 5 years, whereas most countries recommend 10 years. We compared predicted risk and treatment-eligible groups using 5-year and 10-year equations.

Individual-level linked administrative data sets identified 1 746 665 New Zealanders without CVD, aged 30-74 years in 2006, with follow-up to 2018. Participants were randomly allocated to derivation and validation cohorts. Sex-specific 5-year and 10-year risk prediction models were developed in the derivation cohort and applied in the validation cohort. There were 28 116 (3.2%) and 62 027 (7.1%) first CVD events that occurred during 5-year and 10-year follow-ups, respectively (cumulative risk, derivation cohort). Median predicted 10-year CVD risk (3.8%) was approximately 2.5 times 5-year risk (1.6%), and 95% of individuals in the top quintile of 5-year risk were also in the top quintile of 10-year risk, across age/gender groups (validation cohort). Using common guideline-recommended treatment thresholds (5% 5-year and 10% 10-year risk), approximately 14% and 28% of women and men, respectively, were identified as treatment-eligible applying 5-year equations compared with 17% and 32% of women and men applying 10-year equations. Older age was the major contributor to treatment eligibility in both sexes.

Predicted 10-year CVD risk was approximately 2.5 times 5-year risk. Both equations identified mostly the same individuals in the highest risk quintile. Conversely, commonly used treatment thresholds identified more treatment-eligible individuals using 10-year equations, and both equations identified approximately twice as many treatment-eligible men as women. The treatment threshold, rather than the risk horizon, is the main determinant of treatment eligibility.

Systemic lupus erythematosus (SLE) patients have a significantly increased risk of developing cardiovascular disease (CVD). Despite the implementation of preventive measures and treatment of lipid disorders, as well as reduced use of glucocorticoids, CVD remains one of the leading causes of death in this patient group. It is crucial to develop an appropriate CVD risk assessment strategy that considers the distinctive characteristics of this patient population. This paper provides a comprehensive analysis of the methods used to assess CVD risk in SLE patients. It also presents effective strategies for the reduction of the effects of traditional and non-traditional risk factors for atherosclerosis.

Cardiovascular diseases (CVD) are the leading cause of death and disability worldwide, and their prevention is a major public health priority. Detecting health issues early and assessing risk levels can significantly improve the chances of reducing mortality. Mobile apps can help estimate and manage CVD risks by providing users with personalized feedback, education, and motivation. Incorporating visual analysis into apps is an effective method for educating society. However, the usability evaluation and inclusion of visualization of these apps are often unclear and variable.

The primary objective of this study is to review and compare the usability of existing apps designed to estimate CVD risk using the mHealth App Usability Questionnaire (MAUQ). This is not a traditional usability study involving user interaction design, but rather an assessment of how effectively these applications meet usability standards as defined by the MAUQ.

First, we used predefined criteria to review 16 out of 2238 apps to estimate CVD risk in the Google Play Store and the Apple App Store. Based on the apps' characteristics (ie, developed for health care professionals or patient use) and their functions (single or multiple CVD risk calculators), we conducted a descriptive analysis. Then we also compared the usability of existing apps using the MAUQ and calculated the agreement among 3 expert raters.

Most apps used the Framingham Risk Score (8/16, 50%) and Atherosclerotic Cardiovascular Disease Risk (7/16, 44%) prognostic models to estimate CVD risk. The app with the highest overall MAUQ score was the MDCalc Medical Calculator (mean 6.76, SD 0.25), and the lowest overall MAUQ score was obtained for the CardioRisk Calculator (mean 3.96, SD 0.21). The app with the highest overall MAUQ score in the "ease-of-use" domain was the MDCalc Medical Calculator (mean 7, SD 0); in the domain "interface and satisfaction," it was the MDCalc Medical Calculator (mean 6.67, SD 0.33); and in the domain "usefulness," it was the ASCVD Risk Estimator Plus (mean 6.80, SD 0.32).

We found that the Framingham Risk Score is the most widely used prognostic model in apps for estimating CVD risk. The "ease-of-use" domain received the highest ratings. While more than half of the apps were suitable for both health care professionals and patients, only a few offered sophisticated visualizations for assessing CVD risk. Less than a quarter of the apps included visualizations, and those that did were single calculators. Our analysis of apps showed that they are an appropriate tool for estimating CVD risk.

Cancer prediction algorithms are used in the UK to identify individuals at high probability of having a current, as yet undiagnosed cancer with the intention of improving early diagnosis and treatment. Here we develop and externally validate two diagnostic prediction algorithms to estimate the probability of having cancer for 15 cancer types. The first incorporates multiple predictors including age, sex, deprivation, smoking, alcohol, family history, medical diagnoses and symptoms (both general and cancer-specific symptoms). The second additionally includes commonly used blood tests (full blood count and liver function tests). We use multinomial logistic regression to develop separate equations in men and women to predict the absolute probability of 15 cancer types using a population of 7.46 million adults aged 18 to 84 years in England. We evaluate performance in two separate validation cohorts (total 2.64 million patients in England and 2.74 million from Scotland, Wales and Northern Ireland). The models have improved performance compared with existing models with improved discrimination, calibration, sensitivity and net benefit. These algorithms provide superior prediction estimates in the UK compared with existing scores and could lead to better clinical decision-making and potentially earlier diagnosis of cancer.

England is short of GPs, and GP consultation rates, consultation duration, and workload are increasing. Electronic clinical decision support tools assist decision making for screening, diagnosis, and risk management. Cancer detection is one area in which tools are designed to support GPs, with some electronic risk assessment tools (eRATs) estimating the risk of current cancer based on symptoms.

To explore any association between the impact of eRATs and GP workload and workflow during consultations.

Observational sub-study.

Thirteen practices in England participating in a cluster randomised controlled trial of eRATs were recruited to the study. Using mixed-effects regression models, the average duration of consulting sessions and individual consultations in which eRATs were, or were not, activated were compared.

There was no evidence that consulting sessions in which an eRAT was activated were, on average, longer than sessions in which an eRAT had not been activated. However, after adjusting for a range of session and consultation characteristics, individual consultations involving an eRAT were longer, on average, by 3.96 minutes (95% confidence interval = 3.45 to 4.47; P<0.001) when compared with consultations with no eRAT.

There was no evidence to suggest that eRATs should not be used to support GPs in early cancer diagnosis from a workload perspective. Activation of eRATs was not associated with increased workload across a consulting session, despite a small increase in time observed in individual consultations involving eRATs. Ultimately, therefore, it should be definitive findings regarding the clinical effectiveness of eRATs, not the related workload/workflow implications, that determine whether the use of eRATs should be rolled out more widely.

Food insecurity is associated with prevalent cardiovascular disease (CVD), but studies have been limited to cross-sectional data.

To study whether food insecurity is associated with incident CVD and to determine whether this association varies by sex, education, or race.

This prospective cohort study was conducted among US adults without preexisting CVD participating in the CARDIA (Coronary Artery Risk Development in Young Adults) study from 2000 to August 31, 2020. Data analysis was conducted from December 2022 to April 2024.

Food insecurity, defined as endorsing limitations in household food variety and/or food quantity, assessed in the period 2000-2001.

The primary outcome was CVD events, consisting of fatal and nonfatal coronary heart disease, heart failure, stroke, transient ischemic attack, or peripheral arterial disease, identified annually through August 31, 2020.

Of 3616 total participating adults, mean (SD) age was 40.1 (3.6) years, and 2027 participants (56%) were female. Of 3616 participants, 1696 (47%) self-reported Black race and 529 participants (15%) had food insecurity at baseline. Individuals with food insecurity were more likely to self-identify as Black and report lower educational attainment. The mean (SD) follow-up period was 18.8 (3.4) years, during which 255 CVD events occurred: 57 events (11%) in food-insecure participants and 198 events (6%) in food-secure participants over the study period. After adjusting for age, sex, and field center, food insecurity was associated with incident CVD (adjusted hazard ratio [aHR], 1.90; 95% CI, 1.41-2.56). The association persisted (aHR, 1.47; 95% CI, 1.08-2.01) after further adjustment for the socioeconomic factors of education, marital status, and usual source of medical care.

In this prospective cohort study among participants in the CARDIA study, food insecurity was associated with incident CVD even after adjustment for socioeconomic factors, suggesting that food insecurity may be an important social deprivation measure in clinical assessment of CVD risk. Whether interventions to reduce food insecurity programs can potentially alleviate CVD should be further studied.

Cardiovascular (CV) disease is the leading cause of death among patients with SLE. This study aimed to compare the performance of QRESEARCH risk estimator version 3 (QRISK3) and the Systematic Coronary Risk Evaluation-2 (SCORE2) scores to identify SLE patients at high risk of CV events, as indicated by the presence of carotid plaques.

Subclinical atherosclerosis was evaluated using carotid US in 365 SLE patients. QRISK3 and SCORE2 were calculated. The relationship between these scores and the presence of carotid plaques was analysed by treating the scores as continuous and categorical variables, as well as separately and in combination. Logistic regression and area under the receiver operating characteristic curve (AUC) analyses were used to assess their predictive accuracy.

The discriminatory capacity of QRISK3, with an AUC of 0.770 (95% CI 0.720, 0.821), and SCORE2, with an AUC of 0.800 (95% CI 0.753, 0.843), for carotid plaque was similar, with no statistically significant difference (P = 0.070). However, when examining the association of both calculators considered continuously and together with their interaction, the discriminatory capacity of this combination was significantly greater than that of QRISK3 alone (P = 0.034) but did not differ from SCORE2 (P = 0.71).

QRISK3 and SCORE2 are equally reliable predictors of carotid plaques in SLE patients. The combination of both calculators offers significantly better discrimination than QRISK3 alone but shows no significant difference when compared with SCORE2 alone. Therefore, SCORE2 alone, without the need for additional tools, can be used to identify patients with SLE who are at high risk of CV events.

Intravenous adenosine induces stable myocardial hyperemia for coronary microvascular function testing. Iodinated radiographic contrast media induce transient, submaximal hyperemia. We assessed the feasibility, diagnostic value, and potential cost-effectiveness of contrast-derived indices of microvascular function.

Coronary flow reserve, index of microvascular resistance, and microvascular resistance reserve were assessed using a diagnostic guidewire. Intracoronary bolus thermodilution injections were performed at rest, immediately after an 8-mL bolus of iohexol, repeated after a second 8-mL bolus, and during intravenous adenosine infusion. Receiver operating characteristic analyses assessed the discriminatory ability of the contrast-derived indices (contrast-derived coronary flow reserve, contrast-derived index of microcirculatory resistance, contrast-derived microvascular resistance reserve) to detect abnormal adenosine-derived indices (coronary flow reserve <2.0, index of microvascular resistance ≥25, and microvascular resistance reserve <2.1).

Among 106 coronary arteries from 93 patients (median age 63 years; 62% female; 13% with diabetes), 88% of assessments were undertaken in the left anterior descending artery. Median fractional flow reserve was 0.88 (interquartile range, 0.85-0.92). Contrast-derived coronary flow reserve <2.0 (area under the curve 0.81; sensitivity 67%, specificity 80%, positive predictive value 40%, negative predictive value 92%), contrast-derived index of microcirculatory resistance >47 (area under the curve 0.82; 80%, 79%, 60%, 91%), and contrast-derived microvascular resistance reserve <1.9 (area under the curve 0.82; 67%, 89%, 35%, 97%) were best for predicting their adenosine-derived counterpart indices. There was good correlation on repeatability testing from the second contrast bolus. A hybrid approach reduced adenosine use by 40%, saving $30 800 (USA) or £8000 (UK) per 1000 vessels assessed.

Contrast-derived indices have high specificity and negative predictive value, enabling rapid exclusion of microvascular dysfunction. This method is feasible, clinically useful and cost-saving compared with routine adenosine testing.

URL: https://www.clinicaltrials.gov; Unique identifier: NCT04674449.

Individuals with type 2 diabetes (T2D) frequently exhibit impaired lung function, potentially accelerating the onset of cardiovascular disease (CVD), although prospective studies remain limited. We aimed to explore the relationship between lung function impairment and risk of CVD and mortality within this high-risk population.

This prospective study included 16,242 participants with T2D and free of CVD from the UK Biobank. Obstructive physiology (OP), restrictive physiology (RP), and preserved ratio impaired spirometry (PRISm) were defined using spirometry, including forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC). Fine-Gray subdistribution hazards models and Cox proportional hazards models were used to estimate risks of CVD and all-cause mortality, respectively.

During a median follow-up of 13.9 years, 2,825 incident cases of CVD and 2,811 deaths were documented. Lower FEV1, FVC, FEV1/FVC ratio, FEV1 percent predicted, and FVC percent predicted were related to higher risks of CVD and all-cause mortality. Compared with preserved lung function, the adjusted subdistribution hazard ratios (HRs) for CVD were 1.19 (95% CI 1.05-1.35) for OP and 1.47 (95% CI 1.33-1.65) for RP. Compared with the control group, the subdistribution HRs for CVD were 1.20 (95% CI 1.06-1.36) for OP and 1.43 (95% CI 1.29-1.59) for PRISm. These associations were consistent across subgroups and sensitivity analyses. Adding lung function measurements significantly enhanced the performance of CVD prediction beyond the SCORE2-Diabetes model.

Lung function impairment was associated with increased risks of CVD and all-cause mortality among individuals with T2D.

The timely and rational institution of therapy is a key step towards reducing the global burden of chronic kidney disease (CKD). CKD is a heterogeneous entity with varied aetiologies and diverse trajectories, which include risk of kidney failure but also cardiovascular events and death. Developments in the past decade include substantial progress in CKD risk prediction, driven in part by the accumulation of electronic health records data. In addition, large randomized clinical trials have demonstrated the effectiveness of sodium-glucose co-transporter 2 inhibitors, glucagon-like peptide 1 receptor agonists and mineralocorticoid receptor antagonists in reducing adverse events in CKD, greatly expanding the options for effective therapy. Alongside angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, these classes of medication have been proposed to be the four pillars of CKD pharmacotherapy. However, all of these drug classes are underutilized, even in individuals at high risk. Leveraging prognostic estimates to guide therapy could help clinicians to prescribe CKD-related therapies to those who are most likely to benefit from their use. Risk-based CKD management thus aligns patient risk and care, allowing the prioritization of absolute benefit in determining therapeutic selection and timing. Here, we discuss CKD prognosis tools, evidence-based management and prognosis-guided therapies.

Osteoporosis, a chronic metabolic bone disease, increases the predisposition to fragility fractures and is associated with considerable morbidity, high health care cost as well as mortality. An elevation in the rate of incident fragility fractures will be observed proportional with the increase in the number of older people worldwide. Severe osteoporosis is currently defined as having a bone density determined by dual-energy X-ray absorptiometry that is more than 2.5 standard deviations (SD) below the young adult mean with one or more past fractures due to osteoporosis. Nutrition, physical activity and adequate vitamin D are essential for optimal bone strength throughout life. Hormone (oestrogen/sex steroid) status is also a major determinant of bone health. This review explores mechanisms involved in bone homeostasis, followed by the assessment and management of severe osteoporosis, including an overview of several treatment options in older people that range from anti-resorptive to anabolic therapies.

Earlier studies evaluated the association between systolic blood pressure variability (SBPV) measured during a single period and risk of health outcomes. This study expanded upon existing evidence by examining the association between changes in SBPV over time and clinical outcomes in primary care settings.

Visit-to-visit SBPV was determined as standard deviation of ≥3 systolic blood pressure values measured at 5-10 (Period 1) and 0-5 (Period 2) years before enrolment in the UK Biobank. Cox proportional hazards models were used to evaluate associations of absolute changes in SBPV and SBPV change patterns between these two periods with risk of cardiovascular disease (CVD), coronary heart disease (CHD), stroke, atrial fibrillation and flutter (AF), heart failure (HF), chronic kidney disease (CKD), dementia, and overall mortality.

A total of 36 251 participants were included with a median follow-up time of 13.9 years. In the fully adjusted models, an increased SBPV from Period 1 to Period 2 was significantly associated with an increased risk of CVD, CHD, stroke, CKD, and overall mortality (all P for trend < .005), reflecting a 23%-33% increased risk comparing participants with an increase in SBPV above Tertile 3 with those below Tertile 1. An increase in SBPV from Period 1 to Period 2 appeared to be associated with an increased risk of AF, HF, and dementia; however, the associations did not reach statistical significance at P < .005. The restricted cubic spline analysis did not reveal non-linear associations, as all P-values for non-linearity were >.05. Regarding SBPV change patterns, compared with the participants with consistently low SBPV, participants with a consistently high SBPV during the two periods had an increased risk of CVD, CHD, stroke, AF, HF, CKD, and overall mortality, with a risk evaluation of 28%-46%. The observed associations remained largely unchanged across subgroup and sensitivity analyses.

An increase in SBPV over time was associated with an elevated risk of CVD, CKD, and overall mortality. These findings provide compelling evidence to inform the importance for the management of SBPV in clinical practice.

Nomograms are increasingly recognized as indispensable clinical prediction tools, offering individualized risk estimates through interpretable and visually intuitive formats. Their integration into urologic oncology has significantly advanced precision medicine by enabling refined risk stratification for patients with urological malignancies. This review provides a concise yet comprehensive overview of the development, clinical application, and future prospects of nomograms in urologic oncology. We first outline the essential methodological framework for constructing valid prediction models, including outcome definition, predictor selection, model building, and statistical evaluation using discrimination and calibration metrics. Internal validation techniques such as cross-validation and bootstrapping are highlighted as safeguards against overfitting, while external validation is emphasized to ensure generalizability across diverse clinical contexts. Twelve representative nomograms are examined, classified by display type and implementation format, to illustrate their clinical relevance and limitations. While regression-based models remain widely used, emerging approaches incorporating artificial intelligence and machine learning offer enhanced predictive accuracy but pose challenges in interpretability and integration into electronic health records. Interactive decision-support tools are also gaining prominence, promoting real-time, patient-centered care. Despite existing limitations, such as static outputs, dependence on retrospective data, and inconsistent methodological standards, nomograms continue to facilitate precise and evidence-based decision-making. Looking ahead, future models must prioritize transparency, dynamic updating, multimodal data integration, and adherence to established reporting guidelines. Through rigorous development and thoughtful implementation, nomograms will remain pivotal instruments in delivering personalized, high-quality care in urologic oncology.

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, and accurately predicting individual risk is critical for prevention. Here we aimed to integrate unmodifiable risk factors, such as age and genetics, with modifiable risk factors, such as clinical and biometric measurements, into a meta-prediction framework that produces actionable and personalized risk estimates. In the initial development of the model, ~2,000 predictive features were considered, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: first, a prevalent CAD cohort used to train predictive models for cross-sectional prediction at baseline and prospective estimation of contributing risk factor levels and diagnoses (baseline models) and, second, an incident CAD cohort using, in part, these baseline models as meta-features to train a final CAD incident risk prediction model. The resultant 10-year incident CAD risk model, composed of 15 derived meta-features with multiple embedded polygenic risk scores, achieves an area under the curve of 0.84. In an independent test cohort from the All of Us research program, this model achieved an area under the curve of 0.81 for predicting 10-year incident CAD risk, outperforming standard clinical scores and previously developed integrative models. Moreover, this framework enables the generation of individualized risk reduction profiles by quantifying the potential impact of standard clinical interventions. Notably, genetic risk influences the extent to which these interventions reduce overall CAD risk, allowing for tailored prevention strategies.

Cardiovascular disease (CVD) is a leading cause of death in women with systemic lupus erythematosus (SLE) due to accelerated atherosclerosis that is not predicted by established CVD risk scores. This study aimed to develop, validate, and test a female-focused predictive atherosclerosis risk signature based on serum metabolites in patients with SLE.

Female patients with SLE were assessed for the presence (SLE-P; n=18) or absence (SLE-NP; n=26) of subclinical atherosclerosis using vascular ultrasound for carotid/femoral intima-media thickness. CVD risk was assessed using QRISK3 (which includes SLE diagnosis as a risk factor) and Framingham Risk Score. Serum metabolomics (n≥250) was performed and analyzed using machine learning pipelines. Despite having subclinical atherosclerosis, 44.8% to 100% of patients with SLE-P had low CVD risk according to QRISK3/Framlingham Risk Score scores. Using a lipid-focused metabolomic analysis, an improved atherosclerosis risk predictive signature was developed comprising 35 metabolites/5 clinical traits that classified patients with SLE-P and outperformed CVD risk assessment tools, lipid profiles measured in routine care, and clinical features alone. This "atherosclerosis risk signature" was validated in a second adult female SLE cohort (n=98) that predicted plaque status with moderate accuracy (area under the receiver operating characteristic curve, 0.79). The signature was then refined into a 5-feature subclinical plaque-predictive score that not only stratified the combined SLE-P/SLE-NP cohorts (n=142; area under the receiver operating characteristic curve, 0.84) but also predicted 3-year atherosclerosis progression in female postpubertal patients with juvenile-onset SLE (n=36; area under the receiver operating characteristic curve, 0.79). Finally, the 5-feature score identified distinct high and low subclinical atherosclerosis risk subgroups in a "real-world" setting of unscanned adult patients with SLE (n=38).

This atherosclerosis risk score could improve CVD risk assessment/management in female patients with SLE across age. Validation in non-SLE and healthy cohorts could further substantiate these findings.

The Pooled Cohort Equations (PCEs), developed by the American Heart Association (AHA) and American College of Cardiology, have been widely used since 2013 to estimate 10-year atherosclerotic cardiovascular disease (ASCVD) risk and guide statin therapy. Recently, the AHA introduced the Predicting Risk of CVD EVENTs (PREVENT) equations to improve ASCVD risk estimation. However, the effect of using PREVENT instead of PCEs on risk classification and statin eligibility remains unclear. This retrospective cohort study analyzed 261,303 UK Biobank participants, aged 40 to 69 years, who were free from cardiovascular disease and not on statin therapy. The PCEs and the base PREVENT equations were used to estimate 10-year ASCVD risk, categorize risk levels, and determine statin eligibility based on a common risk threshold of 7.5%. The median 10-year ASCVD risk was 5.2% (2.2%, 10.6%) using the PCEs and 3.5% (1.8%, 5.8%) with the PREVENT equations. The PREVENT equations classified 14.0% of participants as high-risk (ASCVD risk >7.5%), compared to 36.9% classified by PCEs. Among participants classified as intermediate-risk by PCEs, 75.3% were reclassified as low-risk by PREVENT. The proportion of individuals eligible for statin use by the PREVENT equation was 19.9%, and by the PCEs was 40.7%. The corresponding difference was 20.8% (95% confidence intervals [CI]: 20.6% to 20.9%). More men (33.0% [95% CI: 32.7% to 33.3%]) than women (11.5% [95% CI: 11.3% to 11.7%]) and more individuals in the older age group (60 to 69 years: 34.0% [95% CI: 33.7% to34.3%]) than in the younger age group (40 to 49 years: 3.5% [95% CI: 3.3% to 3.6%]) would not be recommended for statin consideration with the PREVENT equations. In conclusion, based on the common risk threshold of 7.5%, replacing the PCEs with the base PREVENT equation would reduce statin eligibility in the UK Biobank participants by ∼20%, especially among men and older adults.

Migraine is a primary headache disorder, with a prevalence estimated at approximately 15% globally. According to the International Classification of Headache Disorders, 3rd edition (ICHD3), there are three significant types of migraine: migraine without aura (MO), migraine with aura (MA), and chronic migraine (CM), the former being the most common. Migraine diagnosis is based on official criteria specific to each type. Although a lot is already known about the origin of migraine aura, its pathophysiology is still an object of research.Long-term discussions have been held about MO and MA, with some evidence for the same underlying pathogenesis of both and other arguments against it. In this narrative review, we decided to analyse multiple factors from the perspective of similarities and differences between these two types of migraine. The aim was to understand better the bases underlying both types of migraine.Aspects such as genetics, molecular bases, relation with hormones, epidemiological and clinical features, neuroimaging, neurophysiology, treatment response, and migraine complications are covered to find similarities and differences between MO and MA. Although epidemiology shares similarities for both types, there are slight alterations in sex and age distribution. Genetics and pathogenesis showed some crucial differences. Conditions, such as vestibular symptoms and depression, were found to correlate similarly with both types of migraine. For some features, including increased cardiovascular risk, the tendency appeared to be the same; however, migraine types differ in the strength of correlation. Finally, in cases such as hormones, the influence has shown opposite directions. Therefore, although migraine with and without aura are considered two types of the same disease, more research should focus on their differences, thus finally enabling better specific treatment options for both types of migraine.

Unhealthy lifestyle behaviors are prevalent among people with mental illness (MI), affecting their physical and mental health. Most research has focused on the isolated effects of lifestyle behaviors, leaving the interconnectedness between these behaviors and health outcomes unexplored. This study aimed to examine these relationships and identify the most strongly connected lifestyle behavior or health outcome within a network.

We conducted a cross-sectional study with 423 inpatients with MI, receiving care as usual. Lifestyle behaviors, physical and mental health outcomes were assessed through questionnaires and routine data. A Gaussian Graphical Model was estimated, and strength centrality was calculated to identify the most influential nodes.

Mean age was 55.5 years, 42% were female, and 41% were diagnosed with schizophrenia. Psychological and physical quality of life (QoL), nighttime sleep problems, and overall sleep quality were the most strongly connected nodes. Sleep was strongly associated with physical QoL. Furthermore, there were negative associations between healthy food intake and cholesterol ratio, and positive associations between daily doses of antipsychotics and length of hospital stay. Node strength was stable (CS(cor = 0.7) = 0.75). No clear pattern emerged among other lifestyle behaviors and health outcomes.

This study offers insights into the interrelatedness of lifestyle behaviors and health outcomes. Addressing sleep problems could enhance QoL and potentially influence other health outcomes. Psychological and physical QoL were also strongly associated, emphasizing the importance of perceived well-being in health outcomes. Future research could explore causal pathways to identify treatment targets to improve care.

The risk of atherosclerotic cardiovascular disease increases with advancing age. Elevated LDL-cholesterol and non-HDL-cholesterol levels remain predictive of incident atherosclerotic cardiovascular events among individuals older than 75 years. Risk prediction among older individuals is less certain because most current risk calculators lack specificity in those older than 75 years and do not adjust for co-morbidities, functional status, frailty, and cognition which significantly impact prognosis in this age group. Data on the benefits and risks of lowering LDL-cholesterol with statins in older patients without atherosclerotic cardiovascular disease are also limited since most primary prevention trials have included mostly younger patients. Available data suggest that statin therapy in older primary prevention patients may reduce atherosclerotic cardiovascular events and that benefits from lipid-lowering with statins outweigh potential risks such as statin-associated muscle symptoms and incident Type 2 diabetes mellitus. While some evidence suggests the possibility that statins may be associated with incident cognitive impairment in older adults, a preponderance of literature indicates neutral or even protective statin-related cognitive effects. Shared decision-making which is recommended for all patients when considering statin therapy is particularly important in older patients. Randomized clinical trial data evaluating the use of non-statin lipid-lowering therapy in older patients are sparse. Deprescribing of lipid-lowering agents may be appropriate for select patients older than 75 years with life-limiting diseases. Finally, a patient-centered approach should be taken when considering primary prevention strategies for older adults.

Atherosclerotic cardiovascular disease (ASCVD) remains the biggest killer of patients with lupus erythematosus (LE) and the general non-autoimmune population. In this literature update on LE and ASCVD, we focused on published work since our earlier review article, meaning from 2021 to the present, with an emphasis on cutaneous LE. Several themes emerged. First, new work shows that patients with lupus still exhibit a high burden of conventional risk factors for ASCVD events. Second, recent studies continue to implicate possible effects of lupus disease activity to worsen rates of ASCVD events beyond predictions from conventional risk factors. Third, new work on estimating the risk of future ASCVD events in patients with lupus supports arterial-wall imaging, inclusion of lupus-specific factors, estimators of ASCVD event risk that take lupus status into account and considering lupus as a diabetes equivalent or even as a diabetes-plus-smoking equivalent in this context. Technologies for arterial-wall imaging continue to improve and will likely play an increasing role in ASCVD assessment and management. Fourth, purported cardiovascular benefits from certain disease-modifying antirheumatic drugs such as antimalarials have become less clear. Fifth, earlier treatment of atherosclerosis, which is a lifelong disease, can be accomplished with diet, exercise, smoking cessation and new classes of safe and effective medications for lipid-lowering and blood pressure control. Benefits on subclinical arterial disease by imaging and on ASCVD events have been reported, supporting the concept that ASCVD is eminently manageable in this autoimmune condition. Sixth, despite the heightened risk for ASCVD events in patients with lupus, available therapeutic approaches remain unused or underused and, accordingly, event rates remain high.Raising awareness among patients and healthcare providers about ASCVD assessment and management in patients with LE is essential. Greater vigilance is needed to prevent ASCVD events in patients with lupus by addressing dyslipidaemias, hypertension, smoking, obesity and physical inactivity.

Cardiovascular diseases (CVDs) persist as formidable contributors to global mortality and pose substantial challenges to public health. Most mortality estimates have been attributed to heart attack and stroke. Despite increased public awareness, the burden of CVDs continues to increase.

This review describes the burden of CVDs and risk factors in adults, according to the World Health Organization's (WHO) defined regions.

A mapping review methodology was used. PubMed, Scopus, Wiley, the WHO Global Health Observatory data repository, American Heart Association, National Forum for Heart Disease and Stroke Prevention, Centers for Disease Control and Prevention, European Commission, Eurostat, European Society of Cardiology, World Heart Federation, and Google Scholar were searched using free text search terms: cardiovascular diseases/CVDs, burden, incidence, prevalence, prevention, and risk factor.

Ischemic heart disease predominated in the Americas, Europe, and Eastern Mediterranean, whereas stroke was more common in Africa, Southeast Asia, and the Western Pacific. Premature deaths occur in populations with low socioeconomic status. Several well-known risk factors are preventable, including hypertension, dyslipidemia, diabetes, air pollution, obesity, smoking, lack of physical activity, and unhealthy dietary intake. Emerging risk factors include excessive or lack of sleep, depression, social isolation, air/noise pollution, and exposure to extreme sunshine, arsenic, lead, cadmium, and copper.

The burden of CVDs and its risk factors vary greatly according to demographics and geographical region. Addressing CVDs requires multifaceted strategies, including region-specific interventions, addressing socioeconomic inequalities, adopting life-course risk management, strengthening the healthcare workforce, and improving health literacy.