Adjustment For Traditional Risk Factors Research Articles

Discordance Between Very Low-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol Increases Cardiovascular Disease Risk in a Geographically Defined Cohort.

Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low-density lipoprotein cholesterol (VLDL-C) and lipid discordance could be contributors to the residual risk of ASCVD. Cardiovascular disease-free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low-density lipoprotein cholesterol (LDL-C) and VLDL-C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow-up. The association of LDL-C and VLDL-C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL-C and VLDL-C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL-C (per 10-mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05-1.09]; P<0.001]) after adjustment for traditional risk factors. The interaction between LDL-C and VLDL-C was not statistically significant (P=0.11). Discordant individuals with high VLDL-C and low LDL-C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person-years, during follow-up. VLDL-C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.

Prognostic significance of preoperative nutritional status for postoperative acute kidney injury in older patients undergoing major abdominal surgery: a retrospective cohort study.

The association between malnutrition and postoperative acute kidney injury (AKI) has not been well studied. In this study, the authors examined the association between preoperative nutritional status and postoperative AKI in older patients who underwent major abdominal surgery, as well as the predictive value of malnutrition for AKI. The authors retrospectively included patients aged 65 or older who underwent major elective abdominal surgery. The nutritional status of the patient was evaluated using three objective nutritional indices, such as the geriatric nutritional risk index (GNRI), the prognostic nutritional index (PNI), and the controlling nutritional status (CONUT). AKI was determined using the KDIGO criteria. The authors performed logistic regression analysis to investigate the association between preoperative nutritional status and postoperative AKI, as well as the predictive value of nutritional scores for postoperative AKI. A total of 2775 patients were included in the study, of which 707 (25.5%), 291 (10.5%), and 517 (18.6%) had moderate to severe malnutrition according to GNRI, PNI, and CONUT calculations. After surgery, 144 (5.2%) patients developed AKI, 86.1% at stage 1, 11.1% at stage 2, and 2.8% at stage 3 as determined by KDIGO criteria. After adjustment for traditional risk factors, worse nutritional scores were associated with a higher AKI risk. In addition to traditional risk factors, these nutritional indices improved the predictive ability of AKI prediction models, as demonstrated by significant improvements in integrated discrimination and net reclassification. Poor preoperative nutritional status, as assessed by GNRI, PNI, and CONUT scores, was associated with an increased risk of postoperative AKI. Incorporating these scores into AKI prediction models improved their performance. These findings emphasize the need for screening surgical patients for malnutrition risk. Further research is needed to determine whether preoperative malnutrition assessment and intervention can reduce postoperative AKI incidence.

Association of circulating fibroblast growth factor 21 levels with all-cause and cardiovascular mortality: The multi-ethnic study of atherosclerosis

BackgroundFibroblast growth factor 21 (FGF21) levels are often elevated in cardiovascular disease (CVD). However, no study has assessed its association with cardiovascular and all-cause mortality in a population free of clinically evident CVD. MethodsA total of 5543 Multi-Ethnic Study of Atherosclerosis (MESA) participants (mean age 62.7 years, 47.5 % male), free of clinically evident CVD at baseline, were studied. From baseline (2000–2002), 1606 deaths (including 387 CVD deaths) were observed over a median follow-up of 17.7 years. Multivariable Cox regression analysis was performed to assess the association of plasma FGF21 levels with mortality. ResultsFGF21 levels at baseline were associated with all-cause mortality, even after adjustment for traditional risk factors, including demographic, socioeconomic and cardiovascular risk factors (adjusted hazard ratio 1.08 [95% confidence interval 1.01, 1.16] per 1 SD increase in ln-transformed levels; 1.27 for the highest vs, lowest quartile). Baseline FGF21 levels were significantly associated with both CVD and non-CVD mortality in unadjusted models. However, the association with non-CVD mortality, but not CVD mortality, remained statistically significant after adjusting for covariates. Similar results were obtained in FGF21 quartile analyses and also when using competing risk regression or matched case-control cohort in sensitivity analyses. ConclusionsIn subjects without clinically-evident CVD at baseline, over 17.7 years follow-up there is a modest association of baseline FGF21 levels with all-cause mortality. The finding that this is driven primarily by a significant association with non-CVD mortality over almost two decades merits further investigation.

Prognostic value of gut microbe-generated metabolite phenylacetylglutamine in patients with heart failure.

Phenylacetylglutamine (PAGln) is a phenylalanine-derived metabolite produced by gut microbiota with mechanistic links to heart failure (HF)-relevant phenotypes. We sought to investigate the prognostic value of PAGln in patients with stable HF. Fasting plasma PAGln levels were measured by stable-isotope-dilution liquid chromatography-tandem mass spectrometry (LC-MS/MS) in patients with stable HF from two large cohorts. All-cause mortality was assessed at 5-year follow-up in the Cleveland cohort, and HF, hospitalization, or mortality were assessed at 3-year follow-up in the Berlin cohort. Within the Cleveland cohort, median PAGln levels were 4.2 (interquartile range [IQR] 2.4-6.9) μM. Highest quartile of PAGln was associated with 3.09-fold increased mortality risk compared to lowest quartile. Following adjustments for traditional risk factors, as well as race, estimated glomerular filtration rate, amino-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, left ventricular ejection fraction, ischaemic aetiology, and HF drug treatment, elevated PAGln levels remained predictive of 5-year mortality in quartile comparisons (adjusted hazard ratio [HR] [95% confidence interval, CI] for Q4 vs Q1: 1.64 [1.07-2.53]). In the Berlin cohort, a similar distribution of PAGln levels was observed (median 3.2 [IQR 2.0-4.8] μM), and PAGln levels were associated with a 1.92-fold increase in 3-year HF hospitalization or all-cause mortality risk (adjusted HR [95% CI] for Q4 vs Q1: 1.92 [1.02-3.61]). Prognostic value of PAGln appears to be independent of trimethylamine N-oxide levels. High levels of PAGln are associated with adverse outcomes independent of traditional cardiac risk factors and cardio-renal risk markers.

Lower serum carotenoid levels are associated with incident cognitive decline independent of traditional risk factors in a prospective study of older men from Northern Ireland

AbstractBackgroundNutritional influences, and antioxidant status in particular, have been implicated in the aetiology of cognitive decline. This study investigated the neuroprotective effects of serum antioxidants in a prospective cohort of older men to identify associations with cognitive outcomes, independent of traditional risk factors.MethodThis cohort study included 873 Northern Irish men nested within PRIME (Prospective Epidemiological Study of Myocardial Infarction) who were followed‐up for at least 15 years. Mini Mental State Examination (MMSE) was carried out at baseline. The Addenbrooke’s Cognitive Examination‐R and MMSE were carried out at follow‐up. A consensus diagnosis of normal cognition for age, mild cognitive impairment (MCI) or dementia was made using accepted clinical criteria. Serum concentrations of retinol, two forms of vitamin E (α‐ and ϒ‐tocopherol) and six carotenoids measured from baseline samples were quantified by high‐performance liquid chromatography and assessed by multiple logistic regression analyses.ResultThe case definition included the 163 men characterised with MCI and 46 men with dementia; 664 participants were considered cognitively normal controls (Table 1). In unadjusted analyses, serum levels of the carotenoids α‐carotene, β‐carotene, lycopene and β‐cryptoxanthin were significantly lower in MCI/dementia cases than controls, in contrast to ϒ‐tocopherol, which was significantly higher (Table 2). Following adjustment for traditional risk factors including height, APOε4 genotype, diastolic blood pressure, smoking status and the presence of (pre)diabetes or depression, the effect size of those carotenoids significant in the unadjusted analysis was attenuated somewhat; only α‐carotene remained significantly associated with MCI/dementia (odds ratio = 0.55; confidence interval: 0.31‐0.99; Table 2).ConclusionOur data implicates lower levels of serum carotenoids as potential early biomarkers of MCI/ dementia before overt manifestation of symptoms, independent of traditional risk factors. These findings are consistent with hypotheses supportive of a neuroprotective role of dietary antioxidants and corroborate previous associations of higher serum carotenoid levels with better cognition in aging subjects. There is emerging evidence that carotenoids, through their potent antioxidant and anti‐inflammatory properties, may positively impact cognitive function. Limiting cognitive decline through lifestyle, exercise, and a higher carotenoid intake, may prove beneficial.

Left atrioventricular coupling index assessed using cardiac CT as a prognostic marker of cardiovascular death

Abstract Background Although the left atrioventricular coupling index (LACI) measured by cardiovascular magnetic resonance (CMR) is a strong predictor of cardiovascular events, the availability to CMR remains limited in clinical routine. Therefore, it would be useful to validate LACI assessment using other imaging methods such as computed tomography (CT). However, the feasibility of LACI measurement using cardiac CT and its prognostic value are not established. Purpose We aimed to investigate the prognostic value of the LACI assessed by cardiac CT, to predict the occurrence of cardiovascular death in consecutive patients without known CVD referred for coronary CT angiography (CCTA). Methods Between 2010 and 2020, we conducted a single-center study with all consecutive patients without known CVD referred for CCTA. LACI was defined as the ratio of LA to LV end-diastolic volumes. The primary outcome was cardiovascular death. Cox regressions were used to evaluate the association of LACI with the primary outcome after adjustment for traditional risk factors and CCTA findings. Results In 1,444 patients (70±12 years, 43% men), 92 (6.4%) patients experienced all-cause death, including 67 (4.3%) patients with cardiovascular death after a median (IQR) follow-up of 6.8 (5.9-9.1) years. After adjustment for risk factors and CCTA findings, LACI was positively associated with the occurrence of cardiovascular death (adjusted hazard ratio [HR], 1.07 [95% CI, 1.05-1.09] per 1% increment, p&amp;lt;0.001), and all-cause death (adjusted HR, 1.05 [95% CI, 1.03-1.07] per 1% increment, p&amp;lt;0.001). After adjustment, a LACI ≥25% showed the best improvement in model discrimination and reclassification above traditional risk factors and CCTA findings (C-statistic improvement: 0.27; NRI=0.826; IDI=0.209, all p&amp;lt;0.001; LR-test p&amp;lt;0.001). Conclusions LACI measured by CCTA is independently associated with cardiovascular death and all-cause death in patients without known CVD referred for CCTA, with an incremental prognostic value over traditional risk factors and CCTA findings.LACI measurement using CCTASurvival curves stratified by LACI value

Ai-based fully automated left atrioventricular coupling index as a prognostic marker in patients undergoing stress-CMR

Abstract Background The left atrioventricular coupling index (LACI) is a strong and independent predictor of heart failure (HF) in individuals without clinical cardiovascular disease. Its prognostic value is not established in patients with cardiovascular disease. Purpose To determine in patients undergoing stress CMR whether fully automated artificial intelligence-based LACI can provide incremental prognostic value to predict HF. Methods Between 2016 and 2018, we conducted a longitudinal study including all consecutive patients with abnormal (inducible ischemia or late gadolinium enhancement (LGE)) vasodilator stress CMR. Control subjects with normal stress CMR were selected using propensity score-matching. LACI was defined as the ratio of LA to LV end-diastolic volumes. The primary outcome included hospitalization for acute HF or cardiovascular death. Cox regression was used to evaluate the association of LACI with the primary outcome after adjustment for traditional risk factors. Results In 2,134 patients [65±12 years, 77% men, 1:1 matched patients (1,067 with normal and 1,067 with abnormal CMR)], LACI was positively associated with the primary outcome (median follow-up 5.2 (4.8-5.5) years) before and after adjustment for risk factors in the overall propensity-matched population (adjusted hazard ratio [HR], 1.18 [95%CI, 1.13-1.24] per 0.1% increment), patients with abnormal (adjusted HR, 1.22 [95%CI, 1.14-1.30] per 0.1% increment), and normal CMR (adjusted HR, 1.12 [95%CI, 1.05-1.20] per 0.1% increment; all p&amp;lt;0.001). After adjustment, a higher LACI of ≥25% showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-statistic improvement: 0.16; NRI=0.388; IDI=0.153, all p&amp;lt;0.001; LR-test p&amp;lt;0.001). Conclusion LACI is independently associated with hospitalization for HF and cardiovascular death in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors including inducible ischemia and LGE.LACI measurement method using CMRKaplan-Meier curves by LACI value

Incremental prognostic value of coronary plaque features on CT angiography in symptomatic patients with obstructive CAD

Abstract Background Several coronary computed tomography angiography (CCTA) studies have shown the strong prognostic value of the coronary artery disease (CAD) burden and the potential interest of the plaque composition analysis. However, the incremental prognostic value of this plaque analysis above the CAD burden in patients with obstructive CAD is not well established. Purpose To assess the incremental prognostic value of the plaque composition analysis using CCTA to predict major adverse cardiovascular events (MACEs) above traditional risk factors and CAD burden in consecutive symptomatic patients with obstructive CAD. Methods In a large multicentric registry, consecutive symptomatic patients without known CAD referred for CCTA were screened between 2008-2020. All patients with obstructive CAD (at least 1 ≥50% stenosis on CCTA) were included. The primary composite outcome was MACE defined by cardiovascular death or nonfatal myocardial infarction. CCTA data were interpreted using multiplanar reconstruction according to the 16-segment coronary artery model. Coronary segments were scored visually for both degree of luminal stenosis and composition of coronary plaque which were classified as noncalcified, mixed, or calcified. To assess the prognostic value of each CCTA findings, different multivariable Cox regression models were used (Model 1: clinical [traditional risk factors]; Model 2: model 1 + CAD burden [number of proximal segments with stenosis ≥50% + number of vessels with obstructive CAD]; Model 3: model 2 + plaques feature [number of segments with noncalcified plaques]). The additional predictive value of each model for predicting MACEs was calculated using C-statistic increment, continuous net reclassification improvement (NRI), and the integrative discrimination index (IDI). Results Of 2,312 patients referred for CCTA, 2,038 (mean age 70±12 years, 46% men) completed follow-up (median 6.7 years; interquartile range: 5.9-9.1 years), and 319 experienced a MACE (14%). After adjustment for traditional risk factors, number of proximal segments with &amp;gt;50% stenosis, number of vessels with obstructive CAD, number of segments with noncalcified were all strongly associated with MACEs (all p&amp;lt;0.001). After adjustment, the addition of the analysis of plaque features (model 3) showed the best improvement in model discrimination and reclassification above a model including traditional risk factors and CAD burden (model 2) (C-statistic improvement=0.03; NRI=45.0%; IDI=9.8%, Chi-2 global=412, all p&amp;lt;0.001; LR-test p&amp;lt;0.001). Conclusions In a large cohort of symptomatic patients with obstructive CAD on CCTA, the analysis of plaque features had an incremental prognostic value to predict MACEs over and above a model combining traditional risk factors and CAD burden.Survival curves for MACEIncremental prognostic value of CCTA

Cardiovascular and inflammatory biomarkers for non-invasive detection of coronary artery obstruction and prediction of long-term survival in patients with suspected chronic coronary syndrome

Abstract Introduction and methods: The knowledge about diagnostic and prognostic value of cardiac and inflammatory biomarkers in patients with chronic coronary syndrome (CCS) is limited. To address this, we analyzed serum levels of selected biomarkers in 2536 patients with suspected CCS (35% female) from the Leipzig LIFE Heart Study who were admitted for coronary angiography. The median follow-up was 10 years. The following biomarkers were considered: high sensitive troponin T (hsTNT), N-terminal pro B-type natriuretic peptide (NT-proBNP), copeptin, high sensitive C-reactive protein (hsCRP) and interleukin-6 (IL-6). Patients were stratified according to the angiographic severity of coronary artery disease (CAD): CAD0 (no sclerosis), CAD1 (non-obstructive, i.e., stenosis &amp;lt; 50%), CAD2 (≥ one stenosis ≥ 50%). Group comparison (GC) included GC1: CAD0 + 1 vs. CAD2, GC2: CAD0 vs. CAD1 + 2. Using age-, sex and symptom-based pre-test probability (PTP) table for obstructive CAD (i.e. CAD 2) which was published in the current CCS guidelines, patients were further classified into the following three categories: PTP &amp;lt; 5% (n=559), PTP 5-15% (n=545) and PTP &amp;gt; 15% (n=1432). Results CAD0, CAD1, CAD2 were apparent in 999, 529, and 1008 patients, respectively. Upon adjustment for traditional risk factors (TRF) the levels of hsTNT, NT-proBNP and IL-6 showed significant difference in GC1 (hsTNT: p=2.0x10-10, NT-proBNP: p=0.049, IL6: p=0.030). In GC2 only elevated hsTNT remained significant (p=1.9x10-7). In the ROC analysis only hsTNT slightly improved the AUC for the GC1-comparison in addition to TRF (0.729 with vs 0.712 witt hsTNT, p=0.013). Within the PTP subcategories the following results were obtained: PTP &amp;lt; 5%: AUC 0.747 with vs 0.730 without hsTNT, p=0.017; PTP 5-15 %: AUC 0.636 with vs 0.670 without hsTNT, p&amp;lt;0.001; PTP &amp;gt; 15%: AUC 0.699 with vs 0.677 without hsTNT, p= 0.024). In PTP 5-15% subgroup TropT cut-off value &amp;lt; 3 pg/mL (N= 119 / 23.4%) showed sensitivity of 89.7% and specificity of 73.1% for CAD2 detection. Ten years survival in groups CAD0, CAD1, CAD2 were 88.3%, 77.3%, 72.4%, respectively. In the multivariate analysis elevated hsTNT, NT-proBNP, copeptin and IL-6 remained significant mortality predictors in CAD2 patients with hazard ratios of similar magnitude (i.e. 1.5, 1.3, 1.4 and 1.3 per unit on the log-scale of the parameters, respectively). hsCRP did not reach significance. In the model stratified into tertiles according to the effects of classical risk factors and the joint biomarker levels for except hsCRP, hazard rates were 3.13 (tertile 2 vs 1) and 11.2 (tertile 3 vs 1). Conclusions In the present study hsTNT substantially improved the detection of obstructive CAD particularly in patients with intermediate PTP 5-15%. Furthermore, the studied biomarkers enable fast and precise non-invasive prediction of mortality risk in patients with suspected CCS, allowing tailored primary and secondary CAD prevention in this high-risk group.

Incremental prognostic value of fully-automatic machine-learning based global circumferential strain during a stress CMR exam

Abstract Background Left ventricular global circumferential strain using cardiovascular magnetic resonance (CMR) is an accurate indicator to predict cardiovascular events. Although several studies have shown the excellent prognostic value of stress CMR, the prognostic value of stress global circumferential strain (sGCS) remains unknown. Purpose To investigate the prognostic value of sGCS for predicting cardiovascular events using a fully automatic machine learning algorithm without human correction in consecutive patients referred for stress CMR. Methods Between 2016 and 2018, all consecutive patients referred for stress CMR were included and followed for the occurrence of major adverse cardiovascular events (MACE), defined by cardiovascular death or nonfatal myocardial infarction (MI). A fully automatic machine learning algorithm was trained and validated on unseen CMR studies (MAGNETOM Aera and Skyra, Siemens Healthcare, Erlangen, Germany) to assess the sGCS from short-axis cine images at stress. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Cox regressions were performed to determine the prognostic value of sGCS. Results Among 2,906 consecutive patients who underwent stress CMR, the automatic sGCS was successfully computed in 2,859 (98.4%) patients (68% male, mean age 64±12 years). A total of 256 (8.8%) MACEs were observed during a median (IQR) follow-up period of 4.5 (3.7-5.3) years. Using Kaplan-Meier analysis, sGCS and the presence of inducible ischemia were significantly associated with the occurrence of MACE (hazard ratio, HR: 1.12 [95% CI, 1.08-1.17]; and HR: 8.48 [95% CI, 6.05-11.91], both p&amp;lt;0.001; respectively). After adjustment for traditional risk factors, inducible ischemia and late gadolinium enhancement (LGE), sGCS was an independent predictor of a higher incidence of MACE (adjusted HR: 1.12 [95% CI, 1.05-1.20]). Finally, sGCS showed an incremental prognostic value to predict MACE compared to a multivariable model including traditional risk factors, the presence of inducible ischemia and LGE (C-statistic improvement: 0.05, p=0.007; NRI= 0.169; IDI= 0.097; both p&amp;lt;0.001). Conclusions Automatic sGCS has an incremental prognostic value to predict MACE above traditional risk factors, and other stress CMR parameters.Survival curvesIncremental prognostic value

Fully automated assessment of global longitudinal strain by machine learning predicts death in patients undergoing stress CMR

Abstract Background Several studies described the independent prognostic value of left ventricular global longitudinal strain (GLS) using cardiovascular magnetic resonance (CMR) to predict death and cardiovascular events. While routine GLS evaluation is a time consuming and partially operator-dependent process, the potential interest of GLS using a fully automated method has not yet been established. Purpose To assess the feasibility and prognostic value of GLS to predict all-cause death using a fully automated machine-learning algorithm without human correction in consecutive patients referred for stress CMR. Methods Between 2016 and 2018, all consecutive patients referred for stress CMR were included and followed for the occurrence of all-cause death. A fully automated machine learning algorithm was trained and validated on unseen CMR studies (MAGNETOM Aera and Skyra) to assess the GLS from long-axis cine images acquired at rest. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Cox regressions were performed to determine the prognostic value of GLS. Results In a retrospective study of 9,883 consecutive patients who underwent stress CMR between 2016 and 2018, automated GLS was successfully computed in 9,638 (97.5%) patients (67% male, mean age 66±12 years). A total of 510 (5.3%) deaths were observed during a median follow-up period of 4.5 years (interquartile range: 3.7-5.2). GLS, the presence of inducible ischemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of death (hazard ratio, HR: 1.22 [95% CI, 1.17-1.26]; HR: 2.23 [95% CI, 1.61-3.10]; and HR: 2.04 [95% CI, 1.41-2.95], respectively, all p&amp;lt;0.001). After adjustment for traditional risk factors, inducible ischemia and LGE, GLS was an independent predictor of a higher incidence of death (adjusted HR: 1.14 [95% CI, 1.08-1.20]). Automated GLS showed an incremental prognostic value to predict death compared to traditional risk factors, inducible ischemia and LGE (C-statistic improvement: 0.05; NRI=0.146; IDI=0.244; all p&amp;lt;0.001). Conclusions Fully automated GLS measured at rest has an incremental prognostic value to predict all-cause death above traditional risk factors, and other stress CMR parameters. This finding suggests that unsupervised GLS measurements are accurate and allow improved risk stratification.

Feature-based clustering of left ventricular strain curve for risk stratification in the general population

Abstract Objective Global left ventricular (LV) strain has been identified as an important predictor of adverse cardiovascular (CV) events. However, currently only the peak strain is used from the LV deformation curve, neglecting the temporal information hidden in all phases of cardiac cycle. Therefore, we employed unsupervised machine learning methods on time-series-derived features from LV strain to identify distinct clinical phenogroups associated with the risk of developing adverse events in the general population. Design and Method We prospectively studied 1185 community-dwelling individuals (mean age, 53.2 years; 51.3% women), in whom we acquired clinical and echocardiographic data including LV strain traces at baseline and collected CV events (n=116) on average 8.7 years later. We utilised Gaussian Mixture Model (GMM) on different features derived from LV strain curve, including slope during systole and early diastole, peak strain, slope during late diastole and the duration and height of diastasis (Figure, left panel). We evaluated the clinical significance of the clusters (k) by comparing the clinical characteristics and CV adverse outcome. Results Based on inertia and BIC scores the optimal number of clusters (k) was 4. The first two clusters had differences in heart rate, but had similar low CV risk profiles. Cluster 4 had the worst CV risk factors combination, and higher prevalence of LV remodelling and diastolic dysfunction (i.e. lowest e’ velocity and highest E/e’) compared to other clusters. Kaplan–Meier showed an increased cumulative incidence risk for all CV events across clusters (Figure, right panel). After adjustment for traditional risk factors, the risk was significantly higher in clusters 3 (HR: 1.28; 95% CI: 1.01-1.61; P=0.038) and 4 (HR: 1.20; 95% CI: 1.02-1.43; P=0.034) compared to the average population, while the risk of adverse events did not reach the significant level in subjects with an abnormal peak LV strain (&amp;lt;17%) (HR 1.12; P=0.66). Conclusion Unsupervised machine learning algorithm employed on features derived from time series LV strain curves identifies clinically meaningful clusters, and provides important prognostic information over the peak LV strain.

Abstract 12316: Metrnl Deficiency Destabilized the Atherosclerotic Plaque and Increased the Risk of Acute Coronary Syndrome

Introduction: Metrnl has been recently identified as a myokine/adipokine that acts as an immune/metabolic regulator. Hypothesis: Metrnl might be exert an important role in acute coronary syndrome. Methods: Circulating Metrnl levels were evaluated in 983 subjects with acute coronary syndrome and healthy controls recruited from Jinan Central Hospital. Apolipoprotein E-deficient mice infected with Metrnl-silencing adenovirus were used for investigating the function of Metrnl in atherosclerotic plaques. The relevant molecular mechanisms were then explored in-vitro in siMetrnl transfected and Metrnl treated HUSMC cells. Results: We found circulating Metrnl levels were significantly decreased in patients with ACS as compared to healthy controls. Metrnl were inversely associated with the incident and recurrent risk of acute coronary syndrome after adjustment for traditional risk factors. Metrnl inhibition in HFD-fed ApoE-/- mice resulted in aggravated atherosclerotic lesions with an SMC-poor/macrophage-rich fibrous cap. Further study demonstrated that Metrnl downregulation increased whereas overexpression prevented SMCs apoptosis and lipid trans-differentiation. KIT/SRC/AKT signaling pathway is involved in the role of Metrnl for the regulation of SMCs fate. Conclusions: Our findings demonstrated that decreased circulating Metrnl levels might be a potential biomarker for identification of patients with increased risk of acute coronary syndrome. Metrnl protected against apoptosis and lipid trans-differentiation in SMCs and stabilized the atherosclerotic plaque, suggesting a potential target for the treatment of atherosclerosis.

Abstract 12263: Inflammation and Cholesterol as Predictors of Cardiovascular Events and Risk Reduction With Bempedoic Acid Among Statin Intolerant Patients: An Analysis of the CLEAR Outcomes Trial

Introduction: Among contemporary statin-treated patients, residual inflammatory risk assessed by high-sensitivity C-reactive protein (hsCRP) is a stronger predictor of future cardiovascular (CV) events and all-cause mortality than residual cholesterol risk assessed by low density lipoprotein cholesterol (LDLC). Whether these relationships are present among statin-intolerant patients is unknown but has implications for the choice of preventive therapies including bempedoic acid, an agent that reduces both LDLC and hsCRP. Methods: In the multi-national CLEAR-Outcomes trial, 13,970 statin-intolerant patients were randomly allocated 1:1 to 180 mg oral bempedoic acid daily or matching placebo and followed for incident myocardial infarction (MI), stroke, coronary revascularization, CV death, and all-cause mortality. Quartiles of increasing baseline hsCRP and LDLC were assessed as predictors of future adverse CV events after adjustment for traditional risk factors and treatment (bempedoic acid vs. placebo). Results: Baseline hsCRP was significantly associated with incident composite endpoint of MI, stroke, coronary revascularization, and CV death (highest vs lowest hsCRP quartile, HR=1.43, 95% CI 1.24-1.65), CV mortality (HR=2.00, 95% CI 1.53-2.61), and all-cause mortality (HR=2.21, 95% CI 1.79-2.73). By contrast, the relationship of baseline LDLC quartile (highest vs. lowest) to future CV events was smaller for the composite endpoint (HR=1.19, 95% CI 1.04-1.37) and neutral for CV mortality (HR 0.90, 95% CI 0.70-1.17) and all-cause mortality (HR 0.95, 95% CI 0.78-1.16) such that risks were high for those with elevated hsCRP irrespective of LDLC level. Compared to placebo, bempedoic acid reduced median hsCRP by 21.6% and mean LDLC levels by 21.1% at 6 months and demonstrated similar efficacy in reducing major adverse CV events across all levels of hsCRP and LDLC. Interpretation: Among contemporary statin-intolerant patients, inflammation assessed by hsCRP was a stronger predictor of risk for future CV events and death than was hyperlipidemia assessed by LDLC. Compared to placebo, bempedoic acid had similar efficacy for reducing CV risk across hsCRP and LDLC strata.

Inflammation and Cholesterol as Predictors of Cardiovascular Events Among 13 970 Contemporary High-Risk Patients With Statin Intolerance.

Among patients treated with statin therapy to guideline-recommended cholesterol levels, residual inflammatory risk assessed by high-sensitivity C-reactive protein (hsCRP) is at least as strong a predictor of future cardiovascular events as is residual risk assessed by low-density lipoprotein cholesterol (LDLC). Whether these relationships are present among statin-intolerant patients with higher LDLC levels is uncertain but has implications for the choice of preventive therapies, including bempedoic acid, an agent that reduces both LDLC and hsCRP. The multinational CLEAR-Outcomes trial (Cholesterol Lowering via Bempedoic Acid, an ACL-Inhibiting Regimen Outcomes Trial) randomly allocated 13 970 statin-intolerant patients to 180 mg of oral bempedoic acid daily or matching placebo and followed them for a 4-component composite of incident myocardial infarction, stroke, coronary revascularization, or cardiovascular death, and for all-cause mortality. Quartiles of increasing baseline hsCRP and LDLC were assessed as predictors of future adverse events after adjustment for traditional risk factors and randomized treatment assignment. Compared with placebo, bempedoic acid reduced median hsCRP by 21.6% and mean LDLC levels by 21.1% at 6 months. Baseline hsCRP was significantly associated with the primary composite end point of major cardiovascular events (highest versus lowest hsCRP quartile; hazard ratio [HR], 1.43 [95% CI, 1.24-1.65]), cardiovascular mortality (HR, 2.00 [95% CI, 1.53-2.61]), and all-cause mortality (HR, 2.21 [95% CI, 1.79-2.73]). By contrast, the relationship of baseline LDLC quartile (highest versus lowest) to future events was smaller in magnitude for the primary composite cardiovascular end point (HR, 1.19 [95% CI, 1.04-1.37]) and neutral for cardiovascular mortality (HR, 0.90 [95% CI, 0.70-1.17]) and all-cause mortality (HR, 0.95 [95% CI, 0.78-1.16]). Risks were high for those with elevated hsCRP irrespective of LDLC level. Bempedoic acid demonstrated similar efficacy in reducing cardiovascular events across all levels of hsCRP and LDLC. Among contemporary statin-intolerant patients, inflammation assessed by hsCRP predicted risk for future cardiovascular events and death more strongly than hyperlipidemia assessed by LDLC. Compared with placebo, bempedoic acid had similar efficacy for reducing cardiovascular risk across hsCRP and LDLC strata. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02993406.

Protein Biomarkers and Major Cardiovascular Events in Older People With Advanced CKD: The European Quality (EQUAL) Study

Rationale & ObjectiveCardiovascular disease is the leading cause of morbidity and mortality in chronic kidney disease (CKD). We investigated 184 inflammatory and cardiovascular proteins to determine their potential as biomarkers for major cardiovascular events (MACE). Study DesignEQUAL is an observational cohort study which enrolled people aged ≥65 years with an estimated glomerular filtration rate ≤20 ml/min/1.73m2. Setting & ParticipantsRecruited participants were split into Discovery (n=611) and Replication cohorts (n=292). ExposureOne hundred and eighty four blood protein levels measured at the baseline visit, each protein was analysed individually. OutcomeMACE. Analytical ApproachCox proportional hazard models adjusted for age, sex, eGFR, previous MACE and country were used to determine the risk of MACE. Proteins with false discovery rate adjusted P values <0.05 in the Discovery cohort were tested in the Replication cohort. Sensitivity analyses were performed, adjusting for traditional risk factors, CKD-specific risk factors and level of proteinuria, and with atherosclerotic and non-atherosclerotic MACE segregated. ResultsDuring 2.9 years median follow-up, 349 people (39%) experienced a MACE. Forty-eight proteins were associated with MACE in the Discovery cohort; 9 of these were reproduced in the Replication cohort. Three of these proteins maintained a strong association with MACE after adjustment for traditional and CKD-specific risk factors, and proteinuria. Tenascin (TNC), fibroblast growth factor-23 (FGF-23) and V-set and Immunoglobulin Domain containing protein 2 (VSIG2) were associated with both atherosclerotic and non-atherosclerotic MACE. All replicated proteins except carbonic anhydrase 1 and carbonic anhydrase 3 were associated with non-atherosclerotic MACE. LimitationsSingle protein concentration measurements and limited follow up time. ConclusionsOur findings corroborate previously reported relationships between FGF-23, vascular cell adhesion protein-1, TNC and placental growth factor with cardiovascular outcomes in CKD. We identify 5 proteins not previously linked with MACE in CKD, that may be targets for future therapies.

Implications of Social Disadvantage Score in Cardiovascular Outcomes and Risk Assessment: Findings From the Multi-Ethnic Study of Atherosclerosis.

Social determinants of health contribute to disparate cardiovascular outcomes, yet they have not been operationalized into the current paradigm of cardiovascular risk assessment. Data from the Multi-Ethnic Study of Atherosclerosis, which includes participants from 6 US field centers, were used to create an index of baseline Social Disadvantage Score (SDS) to explore its association with incident atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality and impact on ASCVD risk prediction. SDS, which ranges from 0 to 4, was calculated by tallying the following social factors: (1) household income less than the federal poverty level; (2) educational attainment less than a high school diploma; (3) single-living status; and (4) experience of lifetime discrimination. Cox models were used to examine the association between SDS and each outcome with adjustment for traditional cardiovascular risk factors. Changes in the discrimination and reclassification of ASCVD risk by incorporating SDS into the pooled cohort equations were examined. A total of 6434 participants (mean age, 61.9±10.2 years; female 52.8%; non-white 60.9%) had available SDS 1733 (26.9%) with SDS 0; 2614 (40.6%) with SDS 1; 1515 (23.5%) with SDS 2; and 572 (8.9%) with SDS ≥3. In total, 775 incident ASCVD events and 1573 deaths were observed over a median follow-up of 17.0 years. Increasing SDS was significantly associated with incident ASCVD and all-cause mortality after adjusting for traditional risk factors (ASCVD: per unit increase in SDS hazard ratio, 1.15 [95% CI, 1.07-1.24]; mortality: per unit increase in SDS hazard ratio, 1.13 [95% CI, 1.08-1.19]). Adding SDS to pooled cohort equations components in a Cox model for 10-year ASCVD risk prediction did not significantly improve discrimination (P=0.208) or reclassification (P=0.112). Although SDS is independently associated with incident ASCVD and all-cause mortality, it does not improve 10-year ASCVD risk prediction beyond pooled cohort equations.

Incremental value of epicardial fat volume on predicting obstructive coronary artery disease with myocardial ischemia

Objective: This study aimed to investigate the association between epicardial fat volume (EFV) and obstructive coronary artery disease (CAD) with myocardial ischemia, and evaluate the incremental value of EFV on top of traditional risk factors and coronary artery calcium (CAC) in predicting obstructive CAD with myocardial ischemia. Methods: This study was a retrospective cross-sectional study. Patients with suspected CAD who underwent coronary angiography (CAG) and single photon emission computerized tomography-myocardial perfusion imaging (SPECT-MPI) at the Third Affiliated Hospital of Soochow University from March 2018 to November 2019 were consecutively enrolled. EFV and CAC were measured by non-contrast chest computed tomography (CT) scan. Obstructive CAD was defined as coronary artery stenosis≥50% in at least one of the major epicardial coronary arteries, and myocardial ischemia was defined as reversible perfusion defects in stress and rest MPI. Obstructive CAD with myocardial ischemia was defined in patients with coronary stenosis severity≥50% and reversible perfusion defects in the corresponding areas of SPECT-MPI. Patients with myocardial ischemia bot without obstructive CAD were defined as none-obstructive CAD with myocardial ischemia group. We collected and compared the general clinical data, CAC and EFV between the two groups. Multivariable logistic regression analysis was performed to identify the relationship between EFV and obstructive CAD with myocardial ischemia. ROC curves were performed to determine whether addition of EFV improved predictive value beyond traditional risk factors and CAC for obstructive CAD with myocardial ischemia. Results: Among the 164 patients with suspected CAD, 111 patients were males, and average age was (61.4±9.9) years old. 62 (37.8%) patients were included into the obstructive CAD with myocardial ischemia group. 102 (62.2%) patients were included into the none-obstructive CAD with myocardial ischemia group. EFV was significantly higher in obstructive CAD with myocardial ischemia group than in none-obstructive CAD with myocardial ischemia group ((135.63±33.29)cm3 and (105.18±31.16)cm3, P<0.01). Univariate regression analysis showed the risk of obstructive CAD with myocardial ischemia increased by 1.96 times for each SD increase in EFV(OR 2.96; 95%CI, 1.89-4.62; P<0.01). After adjustment for traditional risk factors and CAC, EFV remained as an independent predictor for obstructive CAD with myocardial ischemia (OR, 4.48, 95%CI, 2.17-9.23; P<0.01). Addition of EFV to CAC and traditional risk factors was related to larger AUC for predicting obstructive CAD with myocardial ischemia (0.90 vs. 0.85, P=0.04, 95%CI: 0.85-0.95) and the global chi-square increased by 21.81 (P<0.05). Conclusions: EFV is an independent predictor for obstructive CAD with myocardial ischemia. Addition of EFV to traditional risk factors and CAC has incremental value for predicting obstructive CAD with myocardial ischemia in this patient cohort.

Effector T cell chemokine IP-10 predicts cardiac recovery and clinical outcomes post-myocardial infarction.

Preclinical data suggest that activation of the adaptive immune system is critical for myocardial repair processes in acute myocardial infarction. The aim of the present study was to determine the clinical value of baseline effector T cell chemokine IP-10 blood levels in the acute phase of ST-segment elevation myocardial infarction (STEMI) for the prediction of the left ventricular function changes and cardiovascular outcomes after STEMI. Serum IP-10 levels were retrospectively quantified in two independent cohorts of STEMI patients undergoing primary percutaneous coronary intervention. We report a biphasic response of the effector T cell trafficking chemokine IP-10 characterized by an initial increase of its serum levels in the acute phase of STEMI followed by a rapid reduction at 90min post reperfusion. Patients at the highest IP-10 tertile presented also with more CD4 effector memory T cells (CD4 TEM cells), but not other T cell subtypes, in blood. In the Newcastle cohort (n=47), patients in the highest IP-10 tertile or CD4 TEM cells at admission exhibited an improved cardiac systolic function 12 weeks after STEMI compared to patients in the lowest IP-10 tertile. In the Heidelberg cohort (n=331), STEMI patients were followed for a median of 540 days for major adverse cardiovascular events (MACE). Patients presenting with higher serum IP-10 levels at admission had a lower risk for MACE after adjustment for traditional risk factors, CRP and high-sensitivity troponin-T levels (highest vs. rest quarters: HR [95% CI]=0.420 [0.218-0.808]). Increased serum levels of IP-10 in the acute phase of STEMI predict a better recovery in cardiac systolic function and less adverse events in patients after STEMI.

Left atrioventricular coupling index assessed using cardiac CT as a prognostic marker of cardiovascular death

PurposeThe purpose of this study was to investigate the prognostic value of left atrioventricular coupling index (LACI) assessed by cardiac computed tomography (CT), to predict cardiovascular death in consecutive patients referred for cardiac CT with coronary analysis. Materials and methodsBetween 2010 and 2020, we conducted a single-centre study with all consecutive patients without known cardiovascular disease referred for cardiac CT. LACI was defined as the ratio of left atrial to left ventricle end-diastolic volumes. The primary outcome was cardiovascular death. Cox regressions were used to evaluate the association between LACI and primary outcome after adjustment for traditional risk factors and cardiac CT angiography findings. ResultsIn 1,444 patients (mean age, 70 ± 12 [standard deviation] years; 43% men), 67 (4.3%) patients experienced cardiovascular death after a median follow-up of 6.8 (Q1, Q3: 5.9, 9.1) years. After adjustment, LACI was positively associated with the occurrence of cardiovascular death (adjusted hazard ratio [HR], 1.07 [95% CI: 1.05–1.09] per 1% increment; P < 0.001), and all-cause death (adjusted HR, 1.05 [95% CI: 1.03–1.07] per 1% increment; P <0.001). After adjustment, a LACI ≥ 25% showed the best improvement in model discrimination and reclassification for predicting cardiovascular death above traditional risk factors and cardiac CT findings (C-statistic improvement: 0.27; Nnet reclassification improvement = 0.826; Integrative discrimination index =0.209, all P < 0.001; likelihood-ratio-test, P < 0.001). ConclusionLACI measured by cardiac CT is independently associated with cardiovascular death and all-cause death in patients without known cardiovascular disease referred for cardiac CT, with an incremental prognostic value over traditional risk factors and cardiac CT findings.