While occupational exposure as a firefighter is considered a dangerous occupation, research on the underlying mechanisms remains limited, particularly in wildland firefighters. Inflammation, a key effect of wildfire exposure, plays a significant role in the development of various diseases. The current study aims to investigate the impact of wildland firefighting exposure on the levels of pro-inflammatory systemic biomarkers. A pre-post study design investigated 59 wildland firefighters comparing data collected after participation in a wildfire event (Phase II) with data obtained before wildfire season (Phase I). Data on demographics, lifestyle, health and occupational-related factors were assessed. Exposure factors, such as fire combat (e.g., exposure duration), were also registered. Inflammatory biomarkers (i.e. interleukin-6 [IL-6], interleukin-8 [IL-8], tumor necrosis factor α [TNF-α] and high-sensitivity C-reactive protein [hs-CRP]) and hydroxylated polycyclic aromatic hydrocarbons metabolites (1-OHNaph+1-OHAce, 2-OHFlu, 1-OHPhen, 1-OHPyr) were analysed in blood and urine samples, respectively. Serum IL-8 and IL-6 levels were significantly increased after wildland fire combat. IL-8 levels were 2.62 times higher (95 % CI: 1.96-3.50; p < 0.01), whereas IL-6 levels were 1.25 times higher (95 % CI: 1.00-1.57; p = 0.04). Furthermore, IL-8 levels were significantly correlated with urinary 2-hydroxyfluorene levels and fire combat duration (>12 h). In addition, the mean hs-CRP level, in both phases, was above 3.0 mg/L, indicating a potential risk for cardiovascular events. Given the long-term health implications of firefighting occupational exposure, biomonitoring and early detection of occupational risks are essential for protecting firefighters' health. Protective measures must be urgently implemented to enhance occupational health and strengthen preventive strategies in this sector.

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.

Cardiovascular disease (CVD) remains the leading cause of death globally, despite significant public health efforts. The identification and targeting of modifiable risk factors - including hypertension, dyslipidemia, diabetes mellitus, smoking and obesity - have led to significant improvements in patient outcomes over the past 60 years. However, current strategies based on this model have been shown to underestimate CVD risk in women and they are less frequently targeted compared to men. In addition, female-specific biological differences known to contribute to CVD are frequently understudied or excluded from risk stratification efforts. This review explores the unique epidemiological burden, pathobiology and outcomes of CVD in women; the influence of traditional and sex-specific risk factors; and both diagnostic and therapeutic strategies that may improve clinical outcomes in the future.

Based on current knowledge, omega-3 fatty acids help to reduce the concentration of C-reactive protein (CRP). However, the dose-response effect and the strength of this effect are not entirely clear.

We systematically searched and screened databases to include eligible studies. This study incorporates a random effect, as well as dose-response meta-analyses using a restricted cubic spline model.

Forty randomized clinical trials were analyzed. Results demonstrated significant non-linear dose-response efficacy in the reduction of CRP concentration in patients with cardiovascular disease, metabolic syndrome, and hypertension up to 1200 mg/day of EPA and DHA. In addition, there was a linear decrease in CRP concentration in the dyslipidemia population. The meta-analysis results did not show any significant reduction of CRP in overweight and obese participants, and the dose-response analysis failed to show any apparent reduction. In type 2 diabetes, pooling the results revealed a significant reduction in CRP; however, the combination of EPA and DHA failed to show significant dose-response efficacy in changing CRP concentration.

1200 mg/day of EPA and DHA may help to reduce CRP concentration in patients with cardiometabolic disorders. This reduction is clinically significant, and thus intervention with omega-3 fatty acids should be considered for this population.

Cardiovascular disease (CVD) are the leading cause of death globally and often go undetected. Modifiable risk factors for CVD include diet and physical activity. This study aimed to evaluate the health status of Polish patients without a prior diagnosis of CVD, diabetes, or chronic kidney disease (CKD), focusing on healthy dietary choices, dietary harm avoidance, daily routine, organized physical exercise, and social and mental balance (HLPCQ). The multicenter study involved patients without previous CVD, CKD, or diabetes, analyzing anthropometric measurements, blood pressure, heart rate, and laboratory test results. Participants also completed the HLPCQ questionnaire. The study included 836 patients, with a mean age of 48 ± 9.3 years. On the SCORE2 risk analysis, 173 (20.6%) met the criteria for very high risk. The average HLPCQ score was 65.6 ± 26.0. Regression analysis revealed that higher scores on the healthy food choices subscale correlated with lower serum non-HDL cholesterol levels (B = -0.009, SE = 0.003, t = -3.196, p = 0.001) and higher HDL cholesterol levels (B = 0.027, SE = 0.008, t = 3.529, p < 0.001). Similar correlations were observed with the Organized Exercise subscale. The study concluded that lifestyle choices significantly impact biochemical parameters, including blood lipid panels, highlighting the importance of educating both patients and medical professionals on the health impacts of lifestyle.

Coronary heart disease (CHD) remains a leading cause of death and has been associated with alterations in plasma lipoprotein particles and inflammation markers. This study aimed to evaluate and compare standard and advanced lipid parameters and inflammatory biomarkers in CHD cases and matched control subjects. We hypothesized that incorporating advanced lipid and inflammatory biomarkers into risk models would improve CHD risk prediction beyond the standard lipid measures.

CHD cases (n = 227, mean age 61 years, 47% female) and matched controls (n = 526) underwent fasting blood collection while off lipid-lowering medications. Automated chemistry analyses were performed to measure total cholesterol (TC), triglycerides (TGs), low-density lipoprotein-C (LDL-C), small dense LDL-C (sdLDL-C), apolipoproteins (apos) A-I and B, lipoprotein(a) (Lp(a)), high-sensitivity C-reactive protein (hsCRP), serum amyloid-A (SAA), myeloperoxidase (MPO), and apoA-I in HDL particles (via 2-dimensional electrophoresis and immunoblotting). Univariate, multivariate, and machine learning analyses compared the CHD cases with the controls.

The most significant percent differences between male and female cases versus controls were for hsCRP (+78%, +200%), MPO (+109%, +106%), SAA (+84%, +33%), sdLDL-C (+48%; +43%), Lp(a) (+43%,+70%), apoA-I in very large α-1 HDL (-34%, -26%), HDL-C (-24%, -27%), and apoA-I in very small preβ-1 HDL (+17%; +16%). Total C, non-HDL-C, and direct and calculated LDL-C levels were only modestly higher in the cases. Multivariate models incorporating advanced parameters were statistically superior to a standard model (C statistic: men: 0.913 vs. 0.856; women: 0.903 versus 0.838). Machine learning identified apoA-I in preβ-1-HDL, α-2-HDL, α-1-HDL, α-3-HDL, MPO, and sdLDL-C as the top predictors of CHD.

This study introduces a novel approach to CHD risk assessment by integrating advanced HDL particle analysis and machine learning. By assessing HDL subpopulations (α-1, α-2, preβ-1 HDL), inflammatory biomarkers (MPO, SAA), and small dense LDL, we provide a more refined stratification model. Notably, preβ-1 HDL, an independent risk factor reflecting impaired cholesterol efflux from the artery wall, is highlighted as a critical marker of CHD risk. Our approach allows for earlier identification of high-risk individuals, particularly those with subtle lipid or inflammatory abnormalities, supporting more personalized interventions. These findings demonstrate the potential of advanced lipid profiling and machine learning to enhance CHD risk prediction.

Chronic inflammation is a pivotal driver in the progression of atherosclerosis, significantly contributing to the burden of cardiovascular disease (CVD). Patients with chronic inflammatory diseases, such as inflammatory bowel diseases (IBDs) (e.g., ulcerative colitis and Crohn's disease), rheumatological disorders, as well as individuals with auto-immune diseases (such as systemic lupus erythematosus), present a higher risk of major adverse cardiac events (MACEs). Despite their elevated CVD risk, these populations remain underrepresented in cardiovascular research, leading to a critical underestimation of their cardiovascular risk (CVR) in clinical practice. Furthermore, even recent CVR scores poorly predict the risk of events in these specific populations. This narrative review examines the physiopathological mechanisms linking chronic inflammation, immunomodulation, atherosclerosis, thrombosis and cardiovascular events. We review data from epidemiological studies and clinical trials to explore the potential cardiovascular benefits of anti-inflammatory and immunomodulatory therapies. Despite existing evidence, significant gaps in knowledge remain. Future research is mandatory, focusing on innovative strategies for risk stratification and optimization, including lipidomics, proteomics, advanced inflammatory markers, microbiota profiling, and cardiovascular imaging. Addressing these unmet needs will enhance understanding of cardiovascular risk in chronic inflammatory diseases, enabling tailored interventions and better outcomes.

Inflammation and dyslipidemia are central to the pathogenesis of atherosclerotic cardiovascular disease (ASCVD). While lipid-lowering therapies are the cornerstone of ASCVD prevention and treatment, there are other emerging targets, including inflammation (which has been dubbed the 'residual inflammatory risk'), that can be addressed after LDL cholesterol thresholds have been reached. Research over the past 20 years has identified C-reactive protein (CRP) as a key marker of inflammation with atherosclerosis. The association of more sensitive measures of CRP (high- sensitivity C-reactive protein [hsCRP]) with ASCVD risk in epidemiological studies has also led to its incorporation as a risk enhancer in primary prevention guidelines and its incorporation into risk stratification tools. While there are no formal recommendations related to measurement of hsCRP in secondary prevention, consideration should be given to an individualized approach that addresses inflammatory risk in those with major adverse cardiovascular events, despite maximal lipid-lowering therapy and well-controlled LDL cholesterol levels. The aim of this review is to discuss the role of inflammation in ASCVD, the use of hsCRP as a tool to assess residual inflammatory risk to target upstream pathways such as glucose intolerance and obesity, and to consider use of additional anti-inflammatory medications for ASCVD risk reduction. The authors provide clinical context around when to measure hsCRP in clinical practice and how to address residual inflammatory risk in ASCVD.

Using data from the largest German cohort study, we aimed to investigate sex differences in the relationship of socioeconomic position (SEP) with cardiovascular disease (CVD), CVD risk factors, and estimated CVD risk.

A total of 204 780 (50.5% women) participants from the baseline examination of the population-based NAKO (German National Cohort) were included. Logistic, multinomial, and linear regression models were used to estimate sex-specific odds ratios (ORs) and β coefficients with 95% CIs of CVD, CVD risk factors, and very high-risk score (Systemic Coronary Risk Estimation-2) for CVD associated with SEP. Women-to-men ratios of ORs (RORs) with 95% CIs were estimated. In women compared with men, low versus high SEP (educational attainment and relative income) was more strongly associated with myocardial infarction, hypertension, obesity, overweight, elevated blood pressure, antihypertensive medication, and current alcohol consumption, but less strongly with current and former smoking. In women with the lowest versus highest educational level, the OR for a very high 10-year CVD risk was 3.61 (95% CI, 2.88-4.53) compared with 1.72 (95% CI, 1.51-1.96) in men. The women-to-men ROR was 2.33 (95% CI, 1.78-3.05). For the comparison of low versus high relative income, the odds of having a very high 10-year CVD risk was 2.55 (95% CI, 2.04-3.18) in women and 2.25 (95% CI, 2.08-2.42) in men (women-to-men ROR, 1.31 [95% CI, 1.05-1.63]).

In women and men, there was an inverse relationship between indicators of SEP and the likelihood of having several CVD risk factors and a very high 10-year CVD risk. This association was stronger in women, suggesting that CVD risk is more strongly influenced by SEP in women compared with men.

Partial least squares structural equation modeling is a simple approach that may be used to determine the factors associated with diseases. In the current study, we aimed to explore the most associated high-sensitivity C-reactive protein (hs-CRP) as well as hematologic-inflammatory indices for the risk of cardiovascular disease (CVD). A total of 7362 healthy (non-CVD) participants aged 35-65 years old from baseline investigation were evaluated in the Phase 2 follow-up. Of these, 1022 individuals were found to have CVDs in the second phase (10-year follow-up) of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort study. We used partial least squares structural equation modeling to develop a prediction model for association of CVD risk factors and hs-CRP as well as hematologic-inflammatory indices in the study population. According to the study, age had the most significant impact on the presence of CVD. Increasing in age by one unit raises the risk of CVD by 0.166. Also, serum hs-CRP was found to have the second-highest impact on CVD; increasing in age by one unit raises the risk of CVD by 0.042. The study also discovered a strong and significant correlation between red cell distribution width (RDW) and CVD. Moreover, the study found that several factors such as hemoglobin (HGB), neutrophil (NEUT), neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and platelet-to-lymphocyte ratio (PLR) have indirect effects on CVD that are mediated by hs-CRP while controlling for age, sex and social-economic factors. Generally, the results showed that age, hs-CRP, and RDW were the most important risk factors on CVD.

This study aimed to validate an inflammation-based risk score in patients with ST-segment elevation myocardial infarction (STEMI) by examining their cytokine profiles. Upon admission, patients were evaluated for systemic inflammation using a risk score that assigned points based on specific biomarkers: 1 point for leukocyte count ≥9.3 × 10³ cells/μL, 2 points for high-sensitivity C-reactive protein (hsCRP) ≥13.0 mg/L, and 3 points for serum albumin ≤3.6 g/dL. Patients were categorized into three groups: no inflammation (0 points, n = 13), mild inflammation (1-2 points, n = 35), and severe inflammation (3-6 points, n = 26). Serum levels of 16 key cytokines were measured. Patients with higher risk scores showed elevated interleukin (IL)-6 levels (19.6 vs. 8.5 vs. 6.8 pg/mL; P = 0.021) and decreased interferon-γ-induced protein-10 (IP-10) levels (73.4 vs. 68.8 vs. 112.2 pg/mL; P = 0.011). IL-6 was positively correlated with hsCRP (ρ 0.307) and negatively correlated with albumin (ρ -0.298), while IP-10 was negatively correlated with leukocyte count (ρ -0.301). No other cytokines showed significant association with the risk score. Higher inflammation scores were also associated with an increased incidence of major adverse cardiovascular events, particularly acute heart failure. This study underscores the association between the inflammation-based risk score and cytokine levels, specifically IL-6 and IP-10, in patients with STEMI.

The risk of atherosclerotic cardiovascular disease increases with advancing age. Elevated low-density lipoprotein (LDL)-cholesterol and non-high-density lipoprotein (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.

Risk prediction is important for preventing and managing cardiovascular disease (CVD). CVD risk prediction tools designed for the general population may be inaccurate in people with inflammatory diseases.

To investigate the performance of four cardiovascular risk prediction tools (QRISK3, Framingham Risk Score, Reynolds Risk Score and SCORE) in psoriatic arthritis (PsA) and psoriasis. We also compare performance in participants with no inflammatory conditions and in people with rheumatoid arthritis (RA).

This research utilised the UK Biobank Resource. We identified participants with PsA, psoriasis and RA and calculated their cardiovascular risk using each risk tool. We assessed model calibration by comparing observed and predicted outcomes. Discrimination of 10-year risk prediction was assessed using time-dependent area under ROC curve (AUC), sensitivity, specificity, positive and negative predictive values.

We included 769 individuals with PsA, 8062 with psoriasis and 4772 with RA when assessing the QRISK3 tool. Predictions for individuals with psoriasis were roughly as accurate as those with no inflammatory conditions with time-dependent AUC of 0.74 (95%CI, 0.72, 0.76) and of 0.74 (95%CI, 0.72, 0.77) respectively. In contrast, individuals with PsA obtained the least accurate predictions with an AUC of 0.70 (95%CI, 0.64, 0.76). Individuals with RA also obtained less accurate predictions with AUC of 0.72 (0.69,0.74). For the Framingham risk score, AUCs varied between 0.61 (95%CI, 0.55, 0.68) for participants with PsA and 0.71 (95%CI, 0.68, 0.74) for individuals with no inflammatory condition.

In general, CVD risk prediction accuracy was similar for individuals with psoriasis or no inflammatory condition, but lower for individuals with PsA or RA.

We aimed to evaluate the performance of the revised World Health Organization (WHO) models in predicting the 10-year risk of cardiovascular disease (CVD) in Iran, as part of the Middle East and North Africa (MENA) region.

We analyzed data from the Tehran Lipid and Glucose Study (TLGS), including 5162 participants (2241 men) aged 40-80 years without CVD at baseline (the third examination, 2006-2008), for the occurrence of CVD (myocardial infarction (MI), coronary heart disease (CHD) death, and stroke). We assessed the statistical performance of original and regionally recalibrated models, both laboratory- and non-laboratory-based, using discrimination (C-statistic) calibration (calibration plot and observed-to-expected[O:E] ratio) and clinical performance applying net benefit (NB), a measure of true positives (TP) penalized for a weight of false positives (FP), a decimal value representing the expected proportion of TP outcomes among total population.

During the 10-year follow-up, 307 CVD events occurred. The cumulative incidence of CVD was 9.0% (95% CI: 8.0%-10.0%) in men and 4.0% (3.0%-5.0%) in women. For the laboratory-based model, the C-statistic was 0.72 (0.68-0.75) in men and 0.83 (0.80-0.86) in women; for the nonlaboratory-based model, it was 0.70 (0.66-0.73) and 0.82 (0.79-0.86) for men and women, respectively. The lab model underpredicted the risk (O:E = 1.20 [1.00-1.33] for men and 1.40 [1.13-1.60] for women). At the risk threshold of 10%, NB for the lab model was 0.03 (0.02-0.04) for men and 0.01 (0.004-0.01) for women; these values became zero or negative for thresholds over 20%. Regionally recalibrated models overestimated the risk (O:E < 1) and showed lower NB.

The loss of specificity was not sufficiently offset by the increase in sensitivity provided by the regionally recalibrated models compared to the original models.

In this study, we assessed the performance of the World Health Organization (WHO) cardiovascular disease (CVD) risk models in Iran, which is part of the Middle East and North Africa (MENA) region. Regarding the statistical performance of the models, both the original and regionally recalibrated WHO models had good discriminative ability. Concerning calibration, another component of statistical performance, the original models underestimated the actual risk, while the recalibrated version overestimated it. Regarding the clinical performance of the models, both the original and regionally recalibrated versions were clinically useful at the risk threshold of 10%.

Cardiovascular diseases are the major cause of death globally. The primary risk factors are high blood lipid levels, hypertension, diabetes, and obesity. Phytosterols are naturally occurring plant bioactive substances. Short-term clinical trials have demonstrated phytosterols' cholesterol-lowering potential, but their effects on cardiovascular risk factors remain controversial, and relevant meta-analyses are limited and incomplete. We conducted a systematic and comprehensive search of PubMed, Web of Science, Embase and Cochrane Library up to December 22, 2023. A total of 109 randomized controlled trials (RCTS) of phytosterols (PS) intervention on cardiovascular risk factor outcomes were included in a preliminary screening of the retrieved literature by Endnote 20. We assessed the quality of all included randomized controlled trials using the Cochrane Collaboration's Risk of Bias tool. Cochrane data conversion tool was used for data conversion, and finally Stata was used for meta-analysis, egger test and sensitivity analysis of the included studies. The results indicated that dietary phytosterols intake could significantly decrease total cholesterol (TC) level (mean difference = -13.41; 95% confidence interval [CI]: -15.19, -11.63, p < 0.001), low density lipoprotein cholesterol (LDL-C) level (mean difference = -12.57; 95% CI: -13.87, -11.26, p < 0.001), triglycerides (TG) level (mean difference = -6.34; 95% CI: -9.43, -3.25, p < 0.001), C-reactive protein (CRP) level (mean difference = -0.05; 95% CI: -0.08, -0.01, p = 0.671), systolic blood pressure (SBP) level (mean difference = -2.10; 95% CI: -3.27, -0.9, p < 0.001), diastolic blood pressure (DBP) level (mean difference = -0.83; 95% CI: -0.58, -0.07, p = 0.032), increased high-density lipoprotein cholesterol (HDL-C) level (mean difference = 0.46; 95% CI: 0.13, 0.78, p = 0.005), but did not alter the levels of blood glucose (GLU) (mean difference = -0.44; 95% CI: -1.64, 0.76, p = 0.471), glycosylated hemoglobin, Type A1C (HbA1c) (mean difference = -0.28; 95% CI: -0.75, 0.20, p = 0.251), interleukin-6 (IL-6) (mean difference = 0.00; 95% CI: -0.02, 0.02, p = 0.980), tumor necrosis factor (TNF-α) (mean difference = 0.08; 95% CI: -0.08, 0.24, p = 0.335), oxidized low-density lipoprotein cholesterol (OXLDL-C) (standard mean difference = 0.16; 95% CI: -0.38, 0.06, p = 0.154), body mass index (BMI) (mean difference = 0.01; 95% CI: -0.07, 0.09, p = 0.886), waist circumference (WC) (mean difference = -0.10; 95% CI: -0.50, 0.30, p = 0.625) and body weight (mean difference = 0.03; 95% CI: -0.18, 0.24, p = 0.787). Our results suggest that phytosterols may be beneficial in reducing the levels of TC, LDL-C, TG, CRP, SBP, and DBP, but have no significant effect on GLU, HbA1c, TNF-α, IL-6, OXLDL-C, BMI, WC, and Weight. However, there were a small number of RCTS included in this study and their small population size may have reduced the quality of the study. And most of the included studies were short-term intervention trials. Therefore, higher quality studies need to be designed in future studies to establish more accurate conclusions.

Studying the association between blood pressure (BP) trajectories during young adulthood and subsequent cardiovascular disease (CVD) risk can provide insights into how long-term BP patterns in early-life influence the development of CVD later in life.

We pooled data from 2 US cohorts (Coronary Artery Risk Development in Young Adults, Framingham Heart Study). We used latent growth curve models to identify distinct BP trajectory groups between ages 18 and 39 years. We then used Cox proportional hazards models to assess the associations between BP trajectories and CVD events (composite of coronary heart disease [CHD], stroke, and heart failure [HF]) after age 40 years.

We included 6,579 participants and identified 4 distinct systolic BP (SBP) trajectory groups during young adulthood. During a median follow-up of 18.2 years after age 40 years, 213 CHD, 139 stroke, 120 HF, and 400 composite CVD events occurred. Individuals in an elevated-increasing vs. low-stable SBP trajectory during young adulthood were associated with a higher risk of CVD after adjusting for traditional CVD risk factors, with hazard ratios (95% confidence interval) of 3.25 (1.63, 6.46) for CHD, 3.92 (1.63, 9.43) for stroke, 8.30 (2.97, 23.17) for HF, and 3.91 (2.38, 6.41) for composite CVD outcomes. Adding BP trajectory to BP at baseline improved model discrimination for all outcomes (changes in Harrell's C-index 0.0084-0.0192).

An elevated-increasing BP trajectory during young adulthood is associated with a higher risk of CVD later in life, highlighting the importance of maintaining a low-stable BP trajectory throughout the young adulthood period for prevention of CVD in later life.

A substantial share of patients at risk of developing cardiovascular disease (CVD) fail to achieve control of CVD risk factors, but clinicians lack a structured approach to identify these patients. We applied machine learning to longitudinal data from two completed randomized controlled trials among 1502 individuals with diabetes in urban India and Pakistan. Using commonly available clinical data, we predict each individual's risk of failing to achieve CVD risk factor control goals or meaningful improvements in risk factors at one year after baseline. When classifying those in the top quartile of predicted risk scores as at risk of failing to achieve goals or meaningful improvements, the precision for not achieving goals was 73% for HbA1c, 30% for SBP, and 24% for LDL, and for not achieving meaningful improvements 88% for HbA1c, 87% for SBP, and 85% for LDL. Such models could be integrated into routine care and enable efficient and targeted delivery of health resources in resource-constrained settings.

This opinion paper highlights the advancements in artificial intelligence (AI) technology for cardiovascular disease (CVD), presents best practices and transformative impacts, and addresses current concerns that must be resolved for broader adoption.

With the evolution of digitization in data collection, large amounts of data have become available, surpassing the human capacity for processing and analysis, thus enabling the application of AI. These models can learn complex spatial and temporal patterns from large amounts of data, providing patient-specific outputs. These advantages have resulted, at the moment, in more than 900 AI-based devices being approved, today, by regulatory entities, for clinical use, with similar to improved performance and efficiency compared to traditional technologies. However, issues such as model generalization, bias, transparency, interpretability, accountability, and data privacy remain significant barriers for broad adoption of these technologies. AI shows great promise in enhancing CVD care through more accurate and efficient approaches. Yet, widespread adoption is hindered by unresolved concerns of interested stakeholders. Addressing these challenges is crucial for fully integrating AI into clinical practice and shaping the future of CVD prevention, diagnosis and treatment.

Cardiovascular risk prediction tools developed for the general population often underperform for individuals with RA, and their predictive accuracy are unclear for other inflammatory conditions that also have increased cardiovascular risk. We investigated the performance of QRISK-3, the Framingham Risk Score (FRS) and the Reynolds Risk Score (RRS) in RA, psoriatic disease (PsA and psoriasis) and AS. We considered OA as a non-inflammatory comparator.

We utilized primary care records from the Clinical Practice Research Datalink (CPRD) Aurum database to identify individuals with each condition and calculated 10-year cardiovascular risk using each prediction tool. The discrimination and calibration of each tool was assessed for each disease.

The time-dependent area under the curve (AUC) for QRISK3 was 0.752 for RA (95% CI 0.734-0.777), 0.794 for AS (95% CI 0.764-0.812), 0.764 for PsA (95% CI 0.741-0.791), 0.815 for psoriasis (95% CI 0.789-0.835) and 0.698 for OA (95% CI 0.670-0.717), indicating reasonably good predictive performance. The AUCs for the FRS were similar, and slightly lower for the RRS. The FRS was reasonably well calibrated for each condition but underpredicted risk for patients with RA. The RRS tended to underpredict CVD risk, while QRISK3 overpredicted CVD risk, especially for the most high-risk individuals.

CVD risk for individuals with RA, AS and psoriatic disease was generally less accurately predicted using each of the three CVD risk prediction tools than the reported accuracies in the original publications. Individuals with OA also had less accurate predictions, suggesting inflammation is not the sole reason for underperformance. Disease-specific risk prediction tools may be required.

Whether including additional environmental risk factors improves cardiovascular disease (CVD) prediction is unclear. We attempted to improve CVD mortality prediction performance beyond traditional CVD risk factors by additionally using metals measured in the urine and blood and with statistical machine learning methods.

Our sample included 7,085 U.S. adults aged 40 years or older from the National Health and Nutrition Examination Survey 2003-2004 through 2015-2016, linked with the National Death Index through December 31, 2019. Data were randomly split into a 50/50 training dataset used to construct CVD mortality prediction models (n = 3542) and testing dataset used as validation to assess prediction performance (n = 3543). Relative to the traditional risk factors (age, sex, race/ethnicity, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, hypertension, and diabetes), we compared models with an additional 17 blood and urinary metal concentrations. To build the prediction models, we used Cox proportional hazards, elastic-net (ENET) penalized Cox, and random survival forest methods.

420 participants died from CVD with 8.8 mean years of follow-up. Blood lead, cadmium, and mercury were associated (p < 0.005) with CVD mortality. Including these blood metals in a Cox model, initially containing only traditional risk factors, raised the C-index from 0.845 to 0.847. Additionally, the Net Reclassification Index showed that 23% of participants received a more accurate risk prediction. Further inclusion of urinary metals improved risk reclassification but not risk discrimination.

Incorporating blood metals slightly improved CVD mortality risk discrimination, while blood and urinary metals enhanced risk reclassification, highlighting their potential utility in improving cardiovascular risk assessments.

Objective: The effect of sex and gender-related variables on the evaluation of cardiovascular (CV) risk in rheumatoid arthritis patients has been poorly explored. We investigated the differences in CV risk features and scores according to sex in a wide rheumatoid arthritis (RA) cohort. Methods: This is a cross-sectional analysis of a consecutive RA cohort. Disease-specific clinical and serologic variables, traditional CV risk factors and the 10-year CV risk calculated by the SCORE-2, Progetto CUORE and Expanded Risk Score-RA algorithms were compared in males and females. Results: A total of 820 patients (193 men, 627 women) were included. Disease activity was similar between the two sexes. A significantly higher prevalence of traditional CV risk factors and higher mean CV risk scores were detected in male compared to female patients. In the multiple linear regression analysis, a higher HAQ, csDMARD use and ACPA positivity were significantly associated with an increased CV risk in females, while b/tsDMARDs was associated with a lower CV risk in males according to different algorithms. Conclusions: The distribution of traditional CV risk factors and the 10-year risk of CV disease significantly differed in female and male patients despite similar disease activity. Disease-specific variables may contribute differently to CV risk according to sex. The CV screening in RA should also take into account the different distribution of CV risk factors between sexes.

Most patients undergoing coronary computed tomography angiography (CCTA) to diagnose coronary artery disease (CAD) are referred from general practitioners (GPs). The burden of contacts to GP in relation to investigation of suspected CAD is unknown.

All patients undergoing CCTA in Western Denmark from 2014 to 2022 were included. CCTA stenosis was defined as diameter stenosis of ≥50%. Patients with and without stenosis were matched, in each group, 1:5 to a reference population based on birth year, gender, and municipality using data from national registries. All GP visits were registered up to 5 years preceding and 1 year after the CTA and stratified by gender and age. Charlson comorbidity index (CCI) was calculated in all groups.Of the 62 512 patients included, 12 886 had a stenosis, while 49 626 did not. Patients in both groups had a substantially higher GP visit frequency compared with reference populations. In the year of coronary CTA, the median GP contacts in patients with stenosis were 11 (6-17) vs. 6 (2-11) in the reference population (P < 0.001), and in patients without stenosis, the median GP contacts were 10 (6-17) vs. 5 (2-11) (P < 0.001). These findings were consistent across age and gender. CCI was higher among both patients with and without stenosis compared with reference groups.

In patients undergoing CCTA to diagnose CAD, a substantially increased frequency of contacts to GP was observed in the 5-year period prior to examination compared with the reference populations, regardless of the CCTA findings. Obtaining the CCTA result did not seem to substantially affect the GP visit frequency.

To explore patient characteristics associated with response to intramuscular methylprednisolone (MP) therapy in hand OA.

We performed an exploratory monocentric retrospective study. Patients with a clinical diagnosis of hand OA who visited our outpatient clinic between July 2016 and June 2021 and received at least once an intramuscular MP injection were included. Clinical data, including laboratory and radiologic results, were retrieved from electronic patient records (EPRs). Patients' reported response to MP and its duration in the first 6 months after injection was based on free text from the EPRs. Response was categorized into three groups: no response or worsening of symptoms, modest response and good response. Duration of response was categorized as short-term (<2 weeks) or long-term (≥2 weeks). Multivariable logistic regression models were performed to determine factors associated with good response to therapy with MP.

Data from 262 hand OA patients (76% female) were analysed. A good response was experienced by 150 patients (57.2%). Among those with modest-good response, the perceived response of 162 patients (80.6%) lasted ≥2 weeks. Univariate regression analysis indicated that the level of CRP was associated with good response [odds ratio 1.08 (95% CI 1.00, 1.17)]. However, multivariate regression analysis showed no statistically significant associations.

In this retrospective study, more than half of hand OA patients displayed good response to intramuscular MP administration. The possible relation between the presence of low-grade inflammation and the response to this therapy warrants further investigation.

Cardiovascular risk assessment is a critical component of healthcare, guiding preventive and therapeutic strategies. In this study, we developed and evaluated an image-based electrocardiogram (ECG) analyzing an artificial intelligence (AI) model that estimates biological age and mortality risk.

Using a dataset of 978 319 ECGs from 250 145 patients at Seoul National University Bundang Hospital, we developed a deep-learning model utilizing printed 12-lead ECG images to estimate patients' age (ECG-Age) and 1- and 5-year mortality risks. The model was validated externally using the CODE-15% dataset from Brazil.

The ECG-Age showed a high correlation with chronological age in both the internal and external validation datasets (Pearson's R = 0.888 and 0.852, respectively). In the internal validation, the direct mortality risk prediction models showed area under the curves (AUCs) of 0.843 and 0.867 for 5- and 1-year all-cause mortality, respectively. For 5- and 1-year cardiovascular mortality, the AUCs were 0.920 and 0.916, respectively. In the CODE-15%, the mortality risk predictions showed AUCs of 0.818 and 0.836 for the prediction of 5- and 1-year all-cause mortality, respectively. Compared to the neutral Delta-Age (ECG-Age - chronological age) group, hazard ratios for deaths were 1.88 [95% confidence interval (CI): 1.14-3.92], 2.12 (95% CI: 1.15-3.92), 4.46 (95% CI: 2.22-8.96) and 7.68 (95% CI: 3.32-17.76) for positive Delta-Age groups (5-10, 10-15, 15-20, >20), respectively.

An image-based AI-ECG model is a feasible tool for estimating biological age and assessing all-cause and cardiovascular mortality risks, providing a practical approach for utilizing standardized ECG images in predicting long-term health outcomes.

Nocturnal blood pressure (BP) is associated with cardiovascular disease independently of awake BP. However, nocturnal BP measured using an ambulatory monitoring device has limited reproducibility because it is a single-day measurement. We investigated the association between sleep BP measured on multiple days using a timer-equipped home BP monitor and cardiovascular diseases in a general population.

The study population comprised 5814 community residents. Participants were required to sleep with wrapping cuffs on their upper arm and BP was measured automatically at 0 : 00, 2 : 00, and 4 : 00. Actigraph was used to determine BP measured during sleep. Participants were also measured home morning and evening BP manually using the same device.

During the 7.3-year mean follow-up period, we observed 117 cases of cardiovascular diseases. The association between sleep BP (per 10 mmHg hazard ratio = 1.31, P  < 0.001) and cardiovascular events remained significant (hazard ratio = 1.22, P  = 0.036) even after adjusting for office BP and confounding factors, such as sleep-disordered breathing. Individuals with sleep-only hypertension ( n  = 1047; hazard ratio = 2.23, P  = 0.005) had a significant cardiovascular risk. Daytime-only hypertension ( n  = 264; hazard ratio = 3.57, P  = 0.001) and combined sleep and daytime hypertension ( n  = 1216; hazard ratio = 3.69, P  < 0.001) was associated with cardiovascular events to the same extent. Sleep BP dipping was not identified as a significant determinant of cardiovascular events.

Sleep BP measured using a home BP monitor was independently associated with the incidence of cardiovascular disease in a general population.

The spectrum of cardiorenal and metabolic diseases comprises many disorders, including obesity, type 2 diabetes (T2D), chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD), heart failure (HF), dyslipidemias, hypertension, and associated comorbidities such as pulmonary diseases and metabolism dysfunction-associated steatotic liver disease and metabolism dysfunction-associated steatohepatitis (MASLD and MASH, respectively, formerly known as nonalcoholic fatty liver disease and nonalcoholic steatohepatitis [NAFLD and NASH]). Because cardiorenal and metabolic diseases share pathophysiologic pathways, two or more are often present in the same individual. Findings from recent outcome trials have demonstrated benefits of various treatments across a range of conditions, suggesting a need for practice recommendations that will guide clinicians to better manage complex conditions involving diabetes, cardiorenal, and/or metabolic (DCRM) diseases. To meet this need, we formed an international volunteer task force comprising leading cardiologists, nephrologists, endocrinologists, and primary care physicians to develop the DCRM 2.0 Practice Recommendations, an updated and expanded revision of a previously published multispecialty consensus on the comprehensive management of persons living with DCRM. The recommendations are presented as 22 separate graphics covering the essentials of management to improve general health, control cardiorenal risk factors, and manage cardiorenal and metabolic comorbidities, leading to improved patient outcomes.

Cardiovascular disease (CVD) can often lead to serious consequences such as death or disability. This study aims to identify a tree-based machine learning method with the best performance criteria for the detection of CVD. This study analyzed data collected from 9,499 participants, with a focus on 38 different variables. The target variable was the presence of cardiovascular disease (CVD) and the villages were considered as the cluster variable. The standard tree, random forest, Generalized Linear Mixed Model tree (GLMM tree), and Generalized Mixed Effect random forest (GMERF) were fitted to the data and the estimated prediction power indices were compared to identify the best approach. According to the analysis of important variables in all models, five variables (age, LDL, history of cardiac disease in first-degree relatives, physical activity level, and presence of hypertension) were identified as the most influential in predicting CVD. Fitting the decision tree, random forest, GLMM tree, and GMERF, respectively, resulted in an area under the ROC curve of 0.56, 0.73, 0.78, and 0.80. The GMERF model demonstrated the best predictive performance among the fitted models based on evaluation criteria. Regarding the clustered structure of the data, using relevant machine-learning approaches that account for this clustering may result in more accurate predicting indices and targeted prevention frameworks.

Background and Objectives: In the context of female cardiovascular risk categorization, we aimed to assess the inter-model agreement between nine risk prediction models (RPM): the novel Predicting Risk of cardiovascular disease EVENTs (PREVENT) equation, assessing cardiovascular risk using SIGN, the Australian CVD risk score, the Framingham Risk Score for Hard Coronary Heart Disease (FRS-hCHD), the Multi-Ethnic Study of Atherosclerosis risk score, the Pooled Cohort Equation (PCE), the QRISK3 cardiovascular risk calculator, the Reynolds Risk Score, and Systematic Coronary Risk Evaluation-2 (SCORE2). Materials and Methods: A cross-sectional study was conducted on 6527 40-65-year-old women with diagnosed metabolic syndrome from a single tertiary university hospital in Lithuania. Cardiovascular risk was calculated using the nine RPMs, and the results were categorized into high-, intermediate-, and low-risk groups. Inter-model agreement was quantified using Cohen's Kappa coefficients. Results: The study uncovered a significant diversity in risk categorization, with agreement on risk category by all models in only 1.98% of cases. The SCORE2 model primarily classified subjects as high-risk (68.15%), whereas the FRS-hCHD designated the majority as low-risk (94.42%). The range of Cohen's Kappa coefficients (-0.09-0.64) reflects the spectrum of agreement between models. Notably, the PREVENT model demonstrated significant agreement with QRISK3 (κ = 0.55) and PCE (κ = 0.52) but was completely at odds with the SCORE2 (κ = -0.09). Conclusions: Cardiovascular RPM selection plays a pivotal role in influencing clinical decisions and managing patient care. The PREVENT model revealed balanced results, steering clear of the extremes seen in both SCORE2 and FRS-hCHD. The highest concordance was observed between the PREVENT model and both PCE and QRISK3 RPMs. Conversely, the SCORE2 model demonstrated consistently low or negative agreement with other models, highlighting its unique approach to risk categorization. These findings accentuate the need for additional research to assess the predictive accuracy of these models specifically among the Lithuanian female population.

Robust and accurate prediction of cardiovascular disease (CVD) risk facilitates early intervention to benefit patients. The intricate relationship between mental health disorders and CVD is widely recognized. However, existing models often overlook psychological factors, relying on a limited set of clinical and lifestyle parameters, or being developed on restricted population subsets.

This study aims to assess the impact of integrating psychological data into a novel machine learning (ML) approach on enhancing CVD prediction performance.

Using a comprehensive UK Biobank data set (n = 375,145), the correlation between CVD and traditional and psychological risk factors was examined. CVD included hypertensive disease, ischemic heart disease, heart failure, and arrhythmias. An ensemble ML model containing 5 constituent algorithms (decision tree, random forest, XGBoost, support vector machine, and deep neural network) was tested for its ability to predict CVD based on 2 training data sets: using traditional CVD risk factors alone, or using a combination of traditional and psychological risk factors.

A total of 375,145 subjects with normal health status and with CVD were included. The ensemble ML model could predict CVD with 71.31% accuracy using traditional CVD risk factors alone. However, by adding psychological factors to the training data, accuracy increased to 85.13%. The accuracy and robustness of the ensemble ML model outperformed all 5 constituent learning algorithms.

Incorporating mental health assessment data within an ensemble ML model results in a significantly improved, highly accurate, CVD prediction model, outperforming traditional risk factor prediction alone.

No data are currently available regarding the association between Lp(a) and the cardiovascular outcomes in patients with coronary artery disease (CAD) according to their family history (FHx) of CAD. This study aimed to evaluate the significance of Lp(a) in predicting major adverse cardiovascular events (MACEs) in patients with chronic coronary syndrome (CCS) with or without FHx.

A total of 6056 patients with CCS were enrolled. Information on FHx was collected, and the plasma Lp(a) levels were measured. All patients were followed up regularly. The independent and joint associations of Lp(a) and FHx with the risk of MACEs, including cardiovascular death, nonfatal myocardial infarction, and stroke, were analyzed.

With over an average of 50.35±18.58 months follow-up, 378 MACEs were recorded. A Cox regression analysis showed an elevated Lp(a) level to be an independent predictor for MACEs in patients with [hazard ratio (HR): 2.77, 95% confidence interval (CI): 1.38-5.54] or without FHx (HR: 1.35, 95% CI: 1.02-1.77). In comparison to subjects with non-elevated Lp(a) and negative FHx, patients with elevated Lp(a) alone were at a nominally higher risk of MACEs (HR: 1.26, 95% CI: 0.96-1.67), while those with both had the highest risk (HR: 1.93, 95% CI: 1.14-3.28). Moreover, adding Lp(a) to the original model increased the C-statistic by 0.048 in subjects with FHx (p=0.004) and by 0.004 in those without FHx (p=0.391).

The present study is the first to suggest that Lp(a) could be used to predict MACEs in CCS patients with or without FHx; however, its prognostic significance was more noteworthy in patients with FHx.

High-density lipoprotein cholesterol (HDL-C) is one of 5 components [high blood pressure, glucose, triglycerides, waist circumference, low HDL-C], 3 of which, needed to diagnose metabolic syndrome (MetS). Evolving research shows that higher HDL-C is not necessarily cardioprotective in midlife women, supporting a need to re-evaluate HDL-C's contribution to risks related to MetS. We tested whether risk of future diabetes and higher carotid intima-media thickness (cIMT) differ by HDL-C status in midlife women diagnosed with MetS based on the other 4 components.

1) no MetS, 2) MetS with HDL-C ≥ 50 mg/dL (MetS hiHDL), and 3) MetS with HDL-C < 50 mg/dL (MetS loHDL). cIMT was measured 13.8 ± 0.6 years post baseline. Incident diabetes was assessed yearly.

Among 2773 women (1350 (48 %) of them had cIMT), 2383 (86 %) had no MetS, 117 (4 %) had MetS hiHDL, 273 (10 %) had MetS loHDL. Compared with no MetS, both MetS- hiHDL and loHDL groups had higher cIMT and diabetes risk. Risk of having high cIMT did not differ between MetS loHDL vs. hiHDL groups. Adjusting for levels of MetS criteria other than HDL-C at baseline explained the associations of each of the two MetS groups with cIMT. Conversely, after adjustment, associations of MetS hiHDL and MetS loHDL with incident diabetes persisted.

In midlife women, HDL-C status matters for predicting risk of incident diabetes but not higher cIMT beyond other MetS components.

Cardiovascular disease (CVD) is the most common cause of death worldwide, with coronary atherosclerotic heart disease (CHD) accounting for the majority of events. Evidence demonstrates that inflammation plays a vital role in the development of CHD. The association between C-reactive protein (CRP), a representative inflammatory biomarker, and atherosclerosis (AS), CHD, and inflammation has attracted attention. Therefore, we conducted an extensive search on PubMed using the aforementioned terms as search criteria and identified a total of 1246 articles published from January 2000 to April 2024. Both review and research-based articles consistently indicate CRP as a risk enhancer for CVD, contributing to the refinement of risk stratification and early identification of apparently healthy at-risk populations. Additionally, CRP reflects disease progression and predicts the prognosis of recurrent cardiovascular events. Anti-inflammatory therapeutic strategies targeting CRP also provide new treatment options for patients. This review focuses on the link between CRP and CHD, highlighting how CRP is involved in the pathological progression of AS and its potential value for clinical applications.

Cardiovascular diseases remain the number one cause of death globally. Cardiovascular disease risk scores are an integral tool in primary prevention, being used to identify individuals at the highest risk and guide the assignment of preventive interventions. Available risk scores differ substantially in terms of the population sample data sources used for their derivation and, consequently, in the absolute risks they assign to individuals. Differences in cardiovascular disease epidemiology between the populations contributing to the development of risk scores, and the target populations in which they are applied, can result in overestimation or underestimation of cardiovascular disease risks for individuals, and poorly informed clinical decisions. Given the wide plethora of cardiovascular disease risk scores available, identification of an appropriate risk score for a target population can be challenging. This Review provides an up-to-date overview of guideline-recommended cardiovascular disease risk scores from global, regional, and national contexts, evaluates their comparative characteristics and qualities, and provides guidance on selection of an appropriate risk score.