Improve Cardiovascular Disease Risk Prediction Research Articles

Does Adding Single-Nucleotide Polymorphisms to Risk Algorithms Improve Cardiovascular Disease Risk Prediction in Rheumatoid Arthritis? An Internal and External Validation of a Clinical Risk Score.

Current risk algorithms do not accurately predict cardiovascular disease (CVD) risk in rheumatoid arthritis (RA). An area of interest is that of single-nucleotide polymorphisms (SNPs), of which several have been associated with CVD in the general population. We investigated whether these SNPs are associated with CVD in RA and whether SNPs could improve CVD risk prediction in RA. Sixty SNPs were genotyped in 353 patients with RA. Logistic and Cox regression analyses were performed to identify SNPs that were associated with CVD (n = 99). A prediction model with clinical variables was made. SNPs were added to investigate the additional predictive value. Both models were internally validated. External validation was done in a separate cohort (n = 297). rs3184504, rs4773144, rs12190287, and rs445925 were significantly associated with new CVD. The clinical prediction model consisted of age, sex, body mass index, systolic blood pressure, high-density lipoprotein cholesterol (HDLc), and creatinine, with an area under the curve (AUC) of 0.74 (P = 0.03). Internal validation resulted in an AUC of 0.76 (P < 0.01). A new model was made including SNPs and resulted in a model with rs17011666 and rs801426, age, total cholesterol, and HDLc, which performed slightly better with an AUC of 0.77 (P < 0.01). External validation resulted in a good fit for the clinical model, but a poor fit for the SNP model. Several SNPs were associated with CVD in RA. Risk prediction slightly improved after adding SNPs to the models, but the clinical relevance is debatable. However, larger studies are needed to determine more accurately the additional value of these SNPs to CVD risk prediction algorithms.

Sex inequalities in cardiovascular risk prediction.

Evaluate sex differences in cardiovascular disease (CVD) risk prediction, including use of i) optimal sex-specific risk predictors and ii) sex-specific risk thresholds. Prospective cohort study using UK Biobank, including 121,724 and 182,632 healthy men and women, respectively, aged 38-73 years at baseline. There were 11,899 (men) and 9,110 (women) incident CVD cases (hospitalization or mortality) with median 12.1 years follow-up. We used recalibrated Pooled Cohort Equations (PCE, 7.5% 10-year risk threshold as per US guidelines), QRISK3 (10% 10-year risk threshold as per UK guidelines) and Cox survival models using sparse sex-specific variable sets (via LASSO stability selection) to predict CVD risk separately in men and women. LASSO stability selection included 12 variables in common between men and women, with three additional variables selected for men and one for women. C-statistics were slightly lower for PCE than QRISK3 and models using stably-selected variables, but were similar between men and women: 0.67 [0.66-0.68], 0.70 [0.69-0.71], and 0.71 [0.70-0.72] in men and 0.69 [0.68-0.70], 0.72 [0.71-0.73], and 0.72 [0.71-0.73] in women for PCE, QRISK3 and models using stably-selected variables, respectively. At current clinically implemented risk thresholds, test sensitivity was markedly lower in women than men for all models: at 7.5% 10-year risk, sensitivity was 65.1% and 68.2% in men and 24.0% and 33.4% in women for PCE and models using stably-selected variables, respectively; at 10% 10-year risk, sensitivity was 53.7% and 52.3% in men and 16.8% and 20.2% in women for QRISK3 and models using stably-selected variables, respectively. Specificity was correspondingly higher in women than men. However, the sensitivity in women at 5% 10-year risk threshold increased to 50.1%, 58.5% and 55.7% for PCE, QRISK3 and models using stably-selected variables, respectively. Use of sparse sex-specific variables improved CVD risk prediction compared with PCE but not QRISK3. At current risk thresholds, PCE and QRISK3 work less well for women than men but sensitivity was improved in women using a 5% 10-year risk threshold. Use of sex-specific risk thresholds should be considered in any re-evaluation of CVD risk calculators. Cardiovascular disease (CVD) risk prediction is an important component of clinical risk management and disease prevention. We find that at risk prediction thresholds used by currently applied risk prediction algorithms (PCE 7.5% 10-year risk threshold in the US and QRISK3 10% risk threshold in the UK), sensitivity of these risk prediction tools is markedly lower in women than in men. This sex inequality implies that women are proportionately less likely to receive appropriate clinical management including lipid-lowering therapy. If the risk prediction threshold is lowered to 5% 10-year risk in women, then sensitivity in women is substantially increased.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis.

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D). We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management.

To review current evidence, discuss key knowledge gaps and identify opportunities for development, validation and application of polysocial risk scores (pSRS) for cardiovascular disease (CVD) risk prediction and population cardiovascular health management. Limited existing evidence suggests that pSRS are promising tools to capture cumulative social determinants of health (SDOH) burden and improve CVD risk prediction beyond traditional risk factors. However, available tools lack generalizability, are cross-sectional in nature or do not assess social risk holistically across SDOH domains. Available SDOH and clinical risk factor data in large population-based databases are under-utilized for pSRS development. Recent advances in machine learning and artificial intelligence present unprecedented opportunities for SDOH integration and assessment in real-world data, with implications for pSRS development and validation for both clinical and healthcare utilization outcomes. pSRS presents unique opportunities to potentially improve traditional "clinical" models of CVD risk prediction. Future efforts should focus on fully utilizing available SDOH data in large epidemiological databases, testing pSRS efficacy in diverse population subgroups, and integrating pSRS into real-world clinical decision support systems to inform clinical care and advance cardiovascular health equity.

Role of Soluble Urokinase-Type Plasminogen Activator Receptor in Cardiovascular Disease.

Chronic inflammation is a major contributor to cardiovascular disease (CVD) risk. Soluble urokinase plasminogen activator receptor (suPAR) is an immune-derived glycoprotein that is strongly associated with atherosclerotic disease. This review summarizes evidence on suPAR's role in CVD pathogenesis and its potential as a prognostic indicator and therapeutic target. Clinical, genetic, and experimental evidence supports suPAR's role as a pathogenic factor in atherosclerosis. suPAR promotes atherosclerosis through modulation of monocyte activation and function. Clinically, elevated suPAR levels are linked to increased cardiovascular risk across diverse populations. Ongoing clinical trials are evaluating therapies targeting suPAR signaling. Current evidence positions suPAR as a regulator of myeloid cell function that contributes to vascular inflammation and subsequent cardiovascular events. Additional research is needed to determine whether suPAR measurement can improve CVD risk prediction and enable personalized management. Overall, suPAR is a promising immune-derived biomarker and target for reducing inflammation and cardiovascular risk.

Abstract 17308: Patient and Primary Care Provider Experiences From an Implementation Study of a Risk Prediction Tool Integrating a Polygenic Risk Score and the QRISK2 Cardiovascular Risk Calculator

Introduction: We have previously shown that combining a polygenic risk score (PRS) for cardiovascular disease (CVD), with standard clinical risk calculators such as QRISK®2, results in improved CVD risk prediction via an integrated risk tool (CVD-IRT). Research Question: The objective of this study was to explore the implementation of CVD-IRT within routine practice in the UK National Health Service (NHS), through participant and healthcare provider (HCP) surveys and interviews. Methods: The Healthcare Evaluation of Absolute Risk Testing Study (HEART) (NCT05294419), a prospective, single-arm pragmatic trial, enrolled 836 participants undergoing health checks across 12 NHS general practices. QRISK2 and CVD-IRT scores were returned to participants via HCPs. The primary outcome of the study was feasibility of CVD-IRT implementation. This was assessed by a mixed methods approach (quantitative and qualitative methods). Results: After the results were reported and discussed, 520 surveys were completed by participants and 824 surveys were completed by HCPs. Subsequently, 21 participants were interviewed and 13 HCPs attended focus groups or interviews to explore their experiences. 23 HCPs completed a final questionnaire (34.8% physician, 21.7% research nurse, 43.5% other). For 90.7% of reports, HCPs indicated that the CVD-IRT could be incorporated into routine primary care in a straightforward manner. 80% of HCPs agreed that having these tests could lead to better health outcomes for patients, and 68.4% believed that the CVD-IRT could help them manage their patients through a shared decision making process covering lifestyle and treatment options. Participants found the report personally useful (98.5%) and easy to understand (94.3%); agreed that genetic measures are important to identify the risks of developing CVD (85.2%); thought that the test should be widely made available (interview summaries); and would recommend to friends and family (86.8%). Conclusion: The implementation of the CVD-IRT into routine health-checks was recommendable, feasible and well received by both HCPs and participants, who felt that the information was useful, could support clinical decisions, and easy to understand.

Using Repeated Measurements to Predict Cardiovascular Risk in Patients With Type 2 Diabetes Mellitus

The QRISK cardiovascular disease (CVD) risk assessment model is not currently optimized for patients with type 2 diabetes mellitus (T2DM). We aim to identify if the abundantly available repeatedly measured data for patients with T2D improves the predictive capability of QRISK to support the decision-making process regarding CVD prevention in patients with T2DM. We identified patients with T2DM aged 25 to 85, not on statin treatment and without pre-existing CVD from the IQVIA Medical Research Data United Kingdom primary care database and then followed them up until the first diagnosis of CVD, ischemic heart disease, or stroke/transient ischemic attack. We included traditional, nontraditional risk factors and relevant treatments for our analysis. We then undertook a Cox's hazards model accounting for time-dependent covariates to estimate the hazard rates for each risk factor and calculated a 10-year risk score. Models were developed for males and females separately. We tested the performance of our models using validation data and calculated discrimination and calibration statistics. The study included 198,835 (180,143 male with 11,976 outcomes and 90,466 female with 8,258 outcomes) patients. The 10-year predicted survival probabilities for females was 0.87 (0.87 to 0.87), whereas the observed survival estimates from the Kaplan-Meier curve for all female models was 0.87 (0.86 to 0.87). The predicted and observed survival estimates for males were 0.84 (0.84 to 0.84) and 0.84 (0.83 to 0.84) respectively. The Harrell's C-index of all female models and all male models were 0.71 and 0.69 respectively. We found that including time-varying repeated measures, only mildly improved CVD risk prediction for T2DM patients in comparison to the current practice standard. We advocate for further research using time-varying data to identify if the involvement of further covariates may improve the accuracy of currently accepted prediction models.

Body roundness index improves the predictive value of cardiovascular disease risk in hypertensive patients with obstructive sleep apnea: a cohort study

ABSTRACT Background Obesity, especially visceral obesity, plays an important role in the progression of cardiovascular disease (CVD). The body roundness index (BRI) is a new measure of obesity that is considered to reflect visceral obesity more comprehensively than other measures. This study aims to evaluate the relationship between BRI and CVD risk in hypertensive patients with obstructive sleep apnea (OSA) and explore its superiority in predicting CVD. Methods The Cox proportional hazards model was used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CVD. The area under the curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to assess which measures of obesity had the best predictive value for CVD risk. Results During a median follow-up period of 6.8 years, 324 participants suffered a CVD event. After multivariable adjustment, compared with the reference group (the first tertile), the HRs (95% CI) of CVD were 1.25 (95% CI, 0.93–1.70) and 1.74 (95% CI, 1.30–2.33) for subjects in the tertile 2 and tertile 3 groups, respectively. Compared with other measurement indicators, BRI has the highest predictive value for CVD risk [AUC: 0.627, 95% CI: 0.593–0.661]. The addition of the BRI to the fully adjusted multivariate model improved the predictive power for CVD, which was validated in the continuous NRI and the IDI (all P < .05). Conclusions BRI was significantly associated with the risk of CVD in hypertensive patients with OSA. Furthermore, BRI may improve CVD risk prediction in hypertensive patients with OSA.

A deep learning system for retinal vessel calibre improves cardiovascular risk prediction in Asians with chronic kidney disease.

Cardiovascular disease (CVD) and mortality is elevated in chronic kidney disease (CKD). Retinal vessel calibre in retinal photographs is associated with cardiovascular risk and automated measurements may aid CVD risk prediction. Retrospective cohort study of 860 Chinese, Malay and Indian participants aged 40-80 years with CKD [estimated glomerular filtration rate (eGFR) <60ml/min/1.73m2] who attended the baseline visit (2004-2011) of the Singapore Epidemiology of Eye Diseases Study. Retinal vessel calibre measurements were obtained by a deep learning system (DLS). Incident CVD [non-fatal acute myocardial infarction (MI) and stroke, and death due to MI, stroke and other CVD] in those who were free of CVD at baseline was ascertained until 31 December 2019. Risk factors (established, kidney, and retinal features) were examined using Cox proportional hazards regression models. Model performance was assessed for discrimination, fit, and net reclassification improvement (NRI). Incident CVD occurred in 289 (33.6%) over mean follow-up of 9.3 (4.3)years. After adjusting for established cardiovascular risk factors, eGFR [adjusted HR 0.98 (95% CI: 0.97-0.99)] and retinal arteriolar narrowing [adjusted HR 1.40 (95% CI: 1.17-1.68)], but not venular dilation, were independent predictors for CVD in CKD. The addition of eGFR and retinal features to established cardiovascular risk factors improved model discrimination with significantly better fit and better risk prediction according to the low (<15%), intermediate (15-29.9%), and high (30% or more) risk categories (NRI 5.8%), and with higher risk thresholds (NRI 12.7%). Retinal vessel calibre measurements by DLS were significantly associated with incident CVD independent of established CVD risk factors. Addition of kidney function and retinal vessel calibre parameters may improve CVD risk prediction among Asians with CKD.

Predictive value of metabolic profiling in cardiovascular risk scores: analysis of 75 000 adults in UK Biobank

BackgroundMetabolic profiling (the extensive measurement of circulating metabolites across multiple biological pathways) is increasingly employed in clinical care. However, there is little evidence on the benefit of metabolic profiling as...

MSR19 An Update on Real-Time Application of Machine Learning Programs to Improve Cardiovascular Risk Prediction in European Population

According to European Society of Cardiology, cardiovascular disease (CVD) accounts for 3.9 million deaths/year in Europe. In recent years, artificial intelligence (AI)-driven machine learning (ML) has shown promise in CVD risk prediction. This study aimed to summarise the composite predictive ability of AI/ML algorithms to improve CVD risk prediction in focused populations.

Abstract 10457: Improved 10-Year Cardiovascular Risk Prediction Combining Serum Metabolites With Traditional Cardiovascular Risk Factors in People With Type 2 Diabetes: Edinburgh Type 2 Diabetes Study

Introduction: Cardiovascular disease (CVD) is the leading cause of death for people with type 2 diabetes (T2D). The predictive performance of existing CVD risk scores in T2D populations is suboptimal. Metabolomics is a promising method of identifying novel biomarkers which might improve risk prediction. We aimed to identify a group of metabolites associated with incident CVD in people with T2D and assess its predictive performance over-and-above a current CVD risk score (QRISK3). Methods: In 1,066 individuals with T2D (Edinburgh Type 2 Diabetes Study), a panel of 228 serum metabolites was measured at baseline and incident CVD events were identified over the subsequent 10 years. We applied 100 repeats of Cox LASSO (least absolute shrinkage and selection operator) to select metabolites with frequency &gt;90% as candidate components for a metabolites-based risk score (MRS). The MRS was calculated using the linear predictor in an unpenalized Cox regression model where only candidate metabolites were included. The predictive performance of the MRS was assessed in relation to a reference score which refitted components of QRISK3 plus prevalent CVD and statin use at baseline. Predictive metrics were internally validated using 500-repeat bootstrapping. Results: In 1,021 available individuals (mean age 67.9 years, 51.7% male), 255 people developed CVD (25.0%) during a median of 10.6 years of follow-up. Twelve metabolites relating to fluid balance, ketone bodies, amino acids, fatty acids, glycolysis and lipoproteins were selected to construct the MRS. C-statistics were 0.673 (95%CI 0.642, 0.704) for the MRS alone and 0.718 (95%CI 0.689, 0.747) for the reference score, increasing slightly to 0.736 (95%CI 0.707, 0.764) for the combination of the two. The improved prediction by combining the MRS with the reference score was internally validated in bootstrapping samples, where the C-statistics for the reference score and the combination were 0.679 (95%CI 0.650, 0.708) vs. 0.699 (95%CI 0.671, 0.728) separately. Conclusions: Metabolomics data might improve predictive performance of current CVD risk scores based on traditional risk factors in people with T2D. External validation is warranted to assess the generalizability of improved CVD risk prediction using the MRS.

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP

Abstract Background The 2021 ESC guideline on cardiovascular disease (CVD) prevention qualitatively categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, SCORE2 and SCORE2-OP, to predict CVD risk. Purpose To develop and validate an “Add-on” to incorporate CKD measures into these algorithms, using a validated approach. Methods In 3,054,840 participants from 34 datasets, we developed three Add-ons (eGFR only, eGFR + urinary albumin-to-creatinine ratio [ACR] [the primary Add-on], and eGFR + dipstick proteinuria) for SCORE2 and SCORE2-OP. We validated c-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,995,067 participants from 33 different datasets. Results In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved c-statistic by 0.006 (95% CI 0.005–0.008) and 0.018 (0.012–0.024), respectively, for SCORE2 and 0.012 (0.009–0.015) and 0.023 (0.013–0.032), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57,485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI (e.g., 0.100 [0.062–0.138] for SCORE2) compared to the qualitative approach in the ESC guideline. Conclusion Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): US National Kidney Foundation funding as well as US NIDDK

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Serum Orotidine: A Novel Biomarker of Increased CVD Risk in Type 2 Diabetes Discovered Through Metabolomics Studies.

To identify novel biomarkers of cardiovascular disease (CVD) risk in type 2 diabetes (T2D) via a hypothesis-free global metabolomics study, while taking into account renal function, an important confounder often overlooked in previous metabolomics studies of CVD. We conducted a global serum metabolomics analysis using the Metabolon platform in a discovery set from the Joslin Kidney Study having a nested case-control design comprising 409 individuals with T2D. Logistic regression was applied to evaluate the association between incident CVD events and each of the 671 metabolites detected by the Metabolon platform, before and after adjustment for renal function and other CVD risk factors. Significant metabolites were followed up with absolute quantification assays in a validation set from the Joslin Heart Study including 599 individuals with T2D with and without clinical evidence of significant coronary heart disease (CHD). In the discovery set, serum orotidine and 2-piperidinone were significantly associated with increased odds of incident CVD after adjustment for glomerular filtration rate (GFR) (odds ratio [OR] per SD increment 1.94 [95% CI 1.39-2.72], P = 0.0001, and 1.62 [1.26-2.08], P = 0.0001, respectively). Orotidine was also associated with increased odds of CHD in the validation set (OR 1.39 [1.11-1.75]), while 2-piperidinone did not replicate. Furthermore, orotidine, being inversely associated with GFR, mediated 60% of the effects of declining renal function on CVD risk. Addition of orotidine to established clinical predictors improved (P < 0.05) C statistics and discrimination indices for CVD risk (ΔAUC 0.053, rIDI 0.48, NRI 0.42) compared with the clinical predictors alone. Through a robust metabolomics approach, with independent validation, we have discovered serum orotidine as a novel biomarker of increased odds of CVD in T2D, independent of renal function. Additionally, orotidine may be a biological mediator of the increased CVD risk associated with poor kidney function and may help improve CVD risk prediction in T2D.

Biological Variability of Small Dense LDL, HDL3, and Triglyceride-Rich Lipoprotein Cholesterol

Abstract The guideline-recommended lipid panel for cardiovascular disease (CVD) risk assessment measures total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and calculated low-density lipoprotein (LDL) cholesterol. Measured cholesterol in subfractions of HDL and LDL purportedly improve CVD risk prediction. Homogenous enzymatic methods are now available for measurement of the cholesterol within small dense LDL (sLDL), small dense HDL (HDL3), and triglyceride-rich lipoproteins (TRL). For meaningful interpretation of these measurements, an understanding of the potential sources and extent of result variability is needed. The smallest difference between serial measurements within a patient that likely reflects a change in clinical status is called the reference change value (RCV). Biological variability and reference change values (RCV) are well-characterized for basic lipids but there is limited information for sLDL, HDL3 or TRL. The objective of this study was to determine intra- and inter-individual variability for sLDL, HDL3, and TRL in a healthy reference population. Serum samples were collected from 24 healthy subjects (n=14 female/10 male) daily for three days (non-fasting), daily for five days (fasting), weekly for four weeks (fasting), and monthly for 7 months (fasting). sLDL, HDL3, and TRL cholesterol were measured in duplicate by enzymatic colorimetric assays (Denka, Japan) on a Roche Cobas c501. Each source of variability (between subject, within subject, and analytical) was calculated using random-effects regression models to estimate each variance component including the overall variation, standard deviation (SD), coefficient of variation (CV), and proportion of total variance (between-subject, within-subject, and analytical). Using these analytical and biological variances, the reference change value (RCV), index of individuality (IoI), and intraclass correlation coefficient (ICC) were determined. Analytic variability (CVa) from monthly testing was 1.2%, 1.1%, and 1.5% for sLDL, HDL3, and TRL, respectively. Monthly within-subject variability (CVw) was 17.1% for sLDL, 7.4% for HDL3 and 25.7% for TRL. Monthly between-subject variability (CVb) was 32.2%, 13.93%, and 33.4% for sLDL, HDL3, and TRL, respectively. Most of the monthly variation was attributed to between-subject variation for all three tests. Within-subject variation accounted for 37% of TRL variation and 22% for both sLDL and HLD3. Within-subject RCVs for monthly measurements were 16.9mg/dL for sLDL, 5.3mg/dL for HDL3, and 15.1mg/dL for TRL. IoIs for monthly testing were 0.81 for TRL, 0.57 for sLDL, and 0.61 for HDL3. Our data demonstrate that sLDL, HDL3, and TRL show low analytical variability, moderate within-subject variability, but high between-subject variability when measured by homogenous assays in a healthy population. The IoI value (&amp;gt;0.6) for TRL suggests use of a reference interval is appropriate for result interpretation. Conversely, clinical cut-points may be more useful than reference intervals for sLDL and HDL3 which had IoIs ~0.6. These findings may be useful for clinical interpretation, particularly when comparing successive measurements of these analytes.

Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases.

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

Assessing the dyslipidemias: to fast or not to fast?

Nonfasting lipid testing has been introduced into several guidelines over the past decade or so however, the uptake into clinical practice has not been universal. This review highlights some of the prevalent reasons for provider reluctance to use nonfasting testing and the evidence to support nonfasting testing for routine screening in most patients. Several studies have found nonfasting lipids to be as, or more, strongly associated with cardiovascular disease (CVD) risk prediction. In particular, nonfasting tests improve system efficiency, are safe for patients with diabetes, the elderly, children, and in the vast majority of patients, do not need to be followed up with fasting studies due to severe hypertriglyceridemia. Nonfasting lipids are a convenient first test for screening that offers equivalent, if not improved CVD risk prediction. Common misconceptions about nonfasting tests are not supported by the evidence.

Abstract 15670: Age-related Differences in the Contribution of Systolic Blood Pressure and Biomarkers to Cardiovascular Disease Risk Prediction: The Atherosclerosis Risk in Communities (ARIC) Study

I ntroduction: Systolic blood pressure (SBP) is an important component of all cardiovascular disease (CVD) risk prediction equations but its biological variability and impact on estimated risk is a concern. Furthermore, predictive value of SBP may differ in older individuals where traditional risk factors (TRF) are less predictive. Hypothesis: Biomarkers reflecting hypertension-related end organ injury (hsTnT, NT-proBNP, eGFR), improve CVD risk prediction in older but not middle age adults as compared to SBP. Methods: Using data from visits 2 (1990-92) and 5 (2011-13) of ARIC, we developed 3 models- Model 1 included all TRF; Model 2- all TRF except SBP + individual biomarkers and Model 3 all TRF + individual biomarkers. C-statistics were used to assess risk discrimination for coronary heart disease, stroke, heart failure, and CVD. Results: After excluding those with prevalent CVD, there were 12,567 individuals at visit 2 (mean age 57, SD 6 years; 43% men) and 4,508 individuals at visit 5 (mean age 76, SD 5 years; 37% men). Over a median (IQR) follow-up time of 22 (12.4–26.7) years and 6.2 (5.4–6.8) years, the incidence rates of CVD events (per 1000 person-years) were 19.0 and 21.8 at visits 2 and 5, respectively. At visit 2, the model with SBP and biomarkers resulted in the largest improvement in C-statistic and SBP contributed to all models. However, at visit 5, removing SBP from the models with the biomarkers had no impact on C-statistic while the addition of the biomarkers (especially hsTnT and NT-proBNP) significantly improved C-statistics for most outcomes ( Table ). Among the biomarkers eGFR had the least additive value. Conclusions: HsTnT and NT-proBNP significantly improve risk discrimination of CHD, stroke, and HF among middle and older adults, while SBP has value in middle age but not in older age. Biomarkers should be considered in risk prediction equations in older individuals where the value of TRF such as SBP decrease.

Grip Strength and Walking Pace and Cardiovascular Disease Risk Prediction in 406,834 UK Biobank Participants

ObjectiveTo investigate whether the addition of grip strength and/or self-reported walking pace to established cardiovascular disease (CVD) risk scores improves their predictive abilities. Patients and MethodsA total of 406,834 participants from the UK Biobank, with baseline measurements between March 13, 2006, and October 1, 2010, without CVD at baseline were included in this study. Associations of grip strength and walking pace with CVD outcomes were investigated using Cox models adjusting for classical risk factors (as included in established risk scores), and predictive utility was determined by changes in C-index and categorical net reclassification index. ResultsOver a median of 8.87 years of follow-up (interquartile range 3, 8.25-9.47 years), there were 7274 composite fatal/nonfatal events (on the basis of the American College of Cardiology/American Heart Association [ACC/AHA] outcome) and 1955 fatal events (on the basis of the Systematic Coronary Risk Evaluation [SCORE] risk score). Both grip strength and walking pace were inversely associated with CVD outcomes after adjusting for classical risk factors. Addition of grip strength (change in C-index: ACC/AHA, +0.0017; SCORE, +0.0047), usual walking pace (ACC/AHA, +0.0031; SCORE, +0.0130), and both combined (ACC/AHA, +0.0041; SCORE, +0.0148) improved the C-index and also improved the net reclassification index (grip, +0.55%; walking pace, +0.53%; combined, 1.12%). ConclusionThe present study has found that the addition of grip strength or usual walking pace to existing risk scores results in improved CVD risk prediction, with an additive effect when both are added. As both these measures are cheap and easy to administer, these tools could provide an important addition to CVD risk screening, although further external validation is required.