Category-free Net Reclassification Index Research Articles

Plasma proteomics of all-cause and cause-specific mortality in individuals with and without type 2 diabetes: Results from two population-based KORA cohorts

Abstract Background Protein biomarkers may contribute to the identification of vulnerable subgroups for premature mortality in middle-aged and older adults. Purpose Given the distinctive impact of type 2 diabetes (T2D) on mortality rates and protein levels, this study aimed to explore the association of proteins with all-cause and cause-specific mortality separately among individuals with and without baseline T2D and to assess their impact on the prediction of all-cause mortality. Methods Using proximity extension assay technology, we measured 233 cardiovascular (CV) disease- and inflammation-related proteins in two population-based KORA (Cooperative Health Research in the Region of Augsburg) cohorts. The discovery KORA S4 study, with 15.6 years of follow-up, included 1545 participants (T2D group: 116 total, 62 CV, 31 cancer-related and 23 other-cause deaths; non-T2D group: 321 total, 114 CV, 120 cancer-related and 87 other-cause deaths). The validation KORA-Age1 study, with 6.9 years of follow-up, included 1031 participants (T2D group: 76 total, 45 CV, 19 cancer-related and 12 other-cause deaths; non-T2D group: 169 total, 74 CV, 39 cancer-related and 56 other-cause deaths). Cox regression was used to examine associations of proteins with all-cause and cause-specific mortality. Furthermore, eXtreme Gradient Boosting was applied for identification of the most relevant proteins for the prediction of all-cause mortality stratifying by baseline T2D status. The predictive performance of the protein-based model and combined model including both proteins and clinical risk factors was compared to a clinical risk factor-based model using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI). Results After validation, we identified 48 proteins associated with all-cause mortality in the T2D group and 64 in the non-T2D group, respectively (37 overlapping proteins). Out of these, 45, eight, and 32 were associated with CV, cancer-related, and other-cause mortality in the T2D group, while 53, 42, and 48 were associated with the respective cause-specific mortality outcomes in the non-T2D group in the pooled analysis of both studies. The combined model improved the prediction of all-cause mortality in both the KORA S4 and KORA-Age1 cohorts among individuals with baseline T2D (KORA-Age1: ΔC-index: 0.065, 95%=0.009-0.129; cfNRI: 0.370, 95%=0.203-0.812; IDI: 0.129, 95%=0.059-0.253) as well as those without T2D (KORA-Age1: ΔC-index: 0.037, 95%=0.015-0.071; cfNRI: 0.370, 95%=0.236-0.632; IDI: 0.053, 95%=0.028-0.104), respectively, compared to the clinical model. Conclusion This study discovered shared and unique proteins in individuals with and without T2D, unveiling the complex dynamics of mortality. Moreover, our findings support the role of proteins in improving prediction of premature mortality beyond clinical risk factors in different T2D subgroups, providing insights for future prevention strategies.

AI-enabled cardiac chambers volumetry in coronary calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The Multi-Ethnic Study of Atherosclerosis

Abstract Introduction Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC for prediction of incident heart failure (HF) and compared it with 10 known clinical risk factors, NT-proBNP, and the Agatston CAC score. Methods We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% women, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC versus NT-proBNP, Agatston score, and 10 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL, total cholesterol, and hs-CRP) for predicting incident HF over 15 years. Results Over 15 years of follow-up, 256 HF events accrued. The AUC for predicting HF with AI-CAC (0.826) was significantly higher than NT-proBNP (0.742) and Agatston score (0.712) (p<.0001), and comparable to clinical risk factors (0.818, p=0.4141). AI-CAC category-free Net Reclassification Index (NRI) significantly improved on clinical risk factors (0.32), NT-proBNP (0.46), and Agatston score (0.71) for HF prediction at 15 years (p<0.0001). Conclusion AI-CAC significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

Arterial Stiffness Associated with Long-Term Major Adverse Cardiac Events in Patients of Primary Aldosteronism.

As a novel parameter for risk prediction, artery stifiness may hold promise in refining risk assessment strategies, guiding therapeutic interventions, and ultimately improving cardiovascular outcomes in patients with primary aldosteronism (PA). To investigate the correlation between brachial-ankle pulse wave velocity (baPWV), an indicator of arterial stiffness, and the occurrence of major adverse cardiovascular events (MACEs) in patients with PA under a primary prevention design. Among the 830 patients included in the final analysis, 113 (13.6%) developed inciden t MACEs over a median follow-up period of 5.8 years, with a crude rate of 23.2 per 1000 person-years. Multivariable Cox proportional hazards analyses revealed that baPWV was an independent risk factor for incident MACEs, with an adjusted hazard ratio of 1.01 (P = 0.028). The generalized additive model identified a cut-off value of 2000 cm/s for baPWV, which was independently associated with incident MACEs, with a hazard ratio of 1.72 (P = 0.045). Subgroup analyses revealed that PA patients who were mineralocorticoid receptor antagonist (MRA) users and had high baPWV had a significantly higher risk of incident MACEs (HR = 3.34; P < 0.001), while the risk was not significant in patients who underwent adrenalectomy (P = 0.062). Furthermore, the addition of baPWV to the cardiovascular Framingham risk score significantly improved the category-free net reclassification index (0.308, P < 0.001). Our study found that 13.6% of patients with PA developed MACEs after a median follow-up of 5.8 years. Our findings highlight the potential utility of baPWV as a tool for risk stratification in PA patients in primary prevention, whereas adrenalectomy appears to mitigate this risk irrespective of baPWV. The measurement of baPWV could be a valuable addition to hypertension screening programs for primary prevention, providing additional predictive information for the potential occurrence of MACEs.

Association of plasma proteomics with mortality in individuals with and without type 2 diabetes: Results from two population-based KORA cohort studies

BackgroundProtein biomarkers may contribute to the identification of vulnerable subgroups for premature mortality. This study aimed to investigate the association of plasma proteins with all-cause and cause-specific mortality among individuals with and without baseline type 2 diabetes (T2D) and evaluate their impact on the prediction of all-cause mortality in two prospective Cooperative Health Research in the Region of Augsburg (KORA) studies.MethodsThe discovery cohort comprised 1545 participants (median follow-up 15.6 years; 244 with T2D: 116 total, 62 cardiovascular, 31 cancer-related and 23 other-cause deaths; 1301 without T2D: 321 total, 114 cardiovascular, 120 cancer-related and 87 other-cause deaths). The validation cohort comprised 1031 participants (median follow-up 6.9 years; 203 with T2D: 76 total, 45 cardiovascular, 19 cancer-related and 12 other-cause deaths; 828 without T2D: 169 total, 74 cardiovascular, 39 cancer-related and 56 other-cause deaths). We used Cox regression to examine associations of 233 plasma proteins with all-cause and cause-specific mortality and Lasso regression to construct prediction models for all-cause mortality stratifying by baseline T2D. C-index, category-free net reclassification index (cfNRI), and integrated discrimination improvement (IDI) were conducted to evaluate the predictive performance of built prediction models.ResultsThirty-five and 62 proteins, with 29 overlapping, were positively associated with all-cause mortality in the group with and without T2D, respectively. Out of these, in the group with T2D, 35, eight, and 26 were positively associated with cardiovascular, cancer-related, and other-cause mortality, while in the group without T2D, 55, 41, and 47 were positively associated with respective cause-specific outcomes in the pooled analysis of both cohorts. Regulation of insulin-like growth factor (IGF) transport and uptake by IGF-binding proteins emerged as a unique pathway enriched for all-cause and cardiovascular mortality in individuals with T2D. The combined model containing the selected proteins (five and 12 proteins, with four overlapping, in the group with and without T2D, respectively) and clinical risk factors improved the prediction of all-cause mortality by C-index, cfNRI, and IDI.ConclusionsThis study uncovered shared and unique mortality-related proteins in persons with and without T2D and emphasized the role of proteins in improving the prediction of mortality in different T2D subgroups.

AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis.

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths. We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score. During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342). In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis

IntroductionCoronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF). MethodsWe applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000–2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years. ResultsOver 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​< ​0.0001), and comparable to clinical risk factors (0.85, p ​= ​0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p ​< ​0.0001). ConclusionAI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

Estimated pulse wave velocity and risk of new-onset heart failure.

As a potential surrogate of carotid-femoral pulse wave velocity, estimated pulse wave velocity (ePWV) has been confirmed to independently predict the cardiovascular events, but the association between ePWV and heart failure has not been well confirmed. Therefore, we performed this cohort study to evaluate the association between ePWV and risk of new-onset heart failure. A total of 98269 employees (mean age: 51.77±12.56years, male accounted for 79.9%) without prior heart failure who participated in the 2006-2007 health examination were selected as the observation cohort, with an average follow-up of 13.85±1.40years. Area under the receiver operator characteristic curve (AUC) of ePWV was calculated in prediction of heart failure. The adjusted Cox proportional hazard models were used to estimate hazard ratios and 95% confidence intervals. The category-free net reclassification index (NRI) was calculated to evaluate the reclassification performance of cardiovascular risk models after adding ePWV. The AUC of ePWV was 0.74 in prediction of heart failure. After adjusting for the traditional cardiovascular risk factors except for age and blood pressure, the risk of new-onset heart failure increased by 35% [hazard ratio (HR): 1.35, 95% confidence interval (CI): 1.33-1.37] for each 1m/s increase in ePWV. Subgroup analysis showed that ePWV was significantly associated with incident heart failure regardless of THE presence (HR: 1.33, 95% CI: 1.31-1.36, P<0.01) or absence (HR: 1.59, 95% CI: 1.46-1.73, P<0.01) of cardiovascular risk factors, male (HR: 1.33, 95% CI: 1.31-1.36, P<0.01) or female (HR: 1.44, 95% CI: 1.38-1.51, P<0.01), young and middle-aged (<52years) (HR: 1.50, 95% CI: 1.41-1.58, P<0.01), or middle-aged and elderly (≥52years) (HR: 1.23, 95% CI: 1.21-1.26, P<0.01). The addition of ePWV to the traditional cardiovascular risk model including age and mean arterial pressure could significantly improve the reclassification ability by 31.1% (category-free NRI=0.311, P<0.01). ePWV was an independent predictor for new-onset heart failure.

Association of plasma proteomics with incident coronary heart disease in individuals with and without type 2 diabetes: results from the population-based KORA study.

Coronary heart disease (CHD) is a major global health concern, especially among individuals with type 2 diabetes (T2D). Given the crucial role of proteins in various biological processes, this study aimed to elucidate the aetiological role and predictive performance of protein biomarkers on incident CHD in individuals with and without T2D. The discovery cohort included 1492 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 study with 147 incident CHD cases (45 vs. 102 cases in the group with T2D and without T2D, respectively) during 15.6 years of follow-up. The validation cohort included 888 participants from the KORA-Age1 study with 70 incident CHD cases (19 vs. 51 cases in the group with T2D and without T2D, respectively) during 6.9 years of follow-up. We measured 233 plasma proteins related to cardiovascular disease and inflammation using proximity extension assay technology. Associations of proteins with incident CHD were assessed using Cox regression and Mendelian randomization (MR) analysis. Predictive models were developed using priority-Lasso and were evaluated on top of Framingham risk score variables using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI). We identified two proteins associated with incident CHD in individuals with and 29 in those without baseline T2D, respectively. Six of these proteins are novel candidates for incident CHD. MR suggested a potential causal role for hepatocyte growth factor in CHD development. The developed four-protein-enriched model for individuals with baseline T2D (ΔC-index: 0.017; cfNRI: 0.253; IDI: 0.051) and the 12-protein-enriched model for individuals without baseline T2D (ΔC-index: 0.054; cfNRI: 0.462; IDI: 0.024) consistently improved CHD prediction in the discovery cohort, while in the validation cohort, significant improvements were only observed for selected performance measures (with T2D: cfNRI: 0.633; without T2D: ΔC-index: 0.038; cfNRI: 0.465). This study identified novel protein biomarkers associated with incident CHD in individuals with and without T2D and reaffirmed previously reported protein candidates. These findings enhance our understanding of CHD pathophysiology and provide potential targets for prevention and treatment.

Sex-specific association between monocyte to high-density lipoprotein cholesterol and extensive abdominal aortic calcification in humans.

Recent studies have identified monocyte-to-high-density lipoprotein cholesterol ratio (MHR) as a simple marker of atherosclerosis. Abdominal aortic calcification (AAC) is a direct result of vascular atherosclerosis. Our study aims to investigate the association between MHR and the prevalent extensive AAC and assess the value of MHR for identifying prevalent extensive AAC. 2857 subjects (28.07%) from the cross-sectional National Health and Nutrition Examination Survey 2013-2014 were included in our study. AAC was detected through dual-energy x-ray absorptiometry and quantified by Kauppila score. Extensive AAC was identified in 153 (10.44% of 1465) females and 146 (10.49% of 1392) males. With the full adjustment, each SD increase of MHR resulted in an 87.3% additional risk for extensive AAC in females. When dividing into quartiles, the top quartile had a 3.472 times risk of prevalent extensive AAC than the bottom quartile. However, no significant association was observed in males. Furthermore, smooth curve fitting implicated that the significant association was linear in the whole range of MHR among females. Additionally, ROC demonstrated an improvement in the identification of extensive AAC only among females when introducing MHR into established risk factors of atherosclerosis (0.808 vs. 0.864, p < 0.001). Finally, category-free net reclassification index and integrated discrimination index also supported the improvement by MHR in females. Our study revealed a linear association between MHR and prevalent extensive AAC in females. Moreover, our results implicated the potential value of MHR to refine the identification of prevalent extensive AAC in females.

Identification of ischemia-causing lesions using coronary plaque quantification and changes in fractional flow reserve derived from computed tomography across the lesion.

This study sought to evaluate the association between coronary plaque characteristics, changes in the fractional flow reserve (FFR) derived from computed tomography across the lesion (ΔFFRCT), and lesion-specific ischemia using the FFR in patients with suspected or known coronary artery disease. The study assessed coronary computed tomography (CT) angiography stenosis, plaque characteristics, ΔFFRCT, and FFR in 164 vessels of 144 patients. Obstructive stenosis was defined as stenosis ≥50%. An area under the receiver -operating characteristics curve (AUC) analysis was conducted to define the optimal thresholds for ΔFFRCT and the plaque variables. Ischemia was defined as a FFR of ≤0.80. The optimal cut-off value of ΔFFRCT was 0.14. Low-attenuation plaque (LAP) ≥76.23 mm3 and a percentage aggregate plaque volume (%APV) ≥28.91% can be used to predict ischemia independent of other plaque characteristics. The addition of LAP ≥76.23 mm3 and %APV ≥28.91% improved the discrimination (AUC, 0.742 vs. 0.649, P=0.001) and reclassification abilities [category-free net reclassification index (NRI), 0.339, P=0.027; relative integrated discrimination improvement (IDI) index, 0.093, P<0.001] of the assessments compared to the stenosis evaluation alone, and the addition of information about ΔFFRCT ≥0.14 further increased the discrimination (AUC, 0.828 vs. 0.742, P=0.004) and reclassification abilities (NRI, 1.029, P<0.001; relative IDI, 0.140, P<0.001) of the assessments. The addition of the plaque assessment and ΔFFRCT to the stenosis assessments improved the identification of ischemia compared to the stenosis assessment alone.

Serum N-terminal pro-B-type natriuretic peptide and cystatin C for acute kidney injury detection in critically ill adults in China: a prospective, observational study

ObjectiveSerum N-terminal pro-B-type natriuretic peptide (NT-proBNP) and cystatin C (sCysC) are available clinically and beneficial in diagnosing acute kidney injury (AKI). Our purpose is to identify the performance of their...

Incremental utility of circulating biomarkers for cardiovascular risk prediction beyond the updated SCORE2 model

Abstract Background Accurate risk prediction for future cardiovascular disease (CVD) is crucial for timely initiation of preventive measures in high-risk individuals. Most risk scores, such as the recently updated SCORE2 risk-prediction model supported by the European Society of Cardiology, consider only traditional cardiovascular risk factors. Whether the addition of circulating biomarkers to the existing SCORE2 model may improve risk prediction is unclear. Purpose We aimed to evaluate the incremental utility of four widely available circulating biomarkers to improve the prediction of 10-year CVD-risk beyond SCORE2. Methods Data from ten prospective population-based cohorts from seven countries across Europe were collected if information on SCORE2-variables and at least one of the following four investigational biomarkers was available: high-sensitivity cardiac troponin I (hs-cTnI), NT-pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP) and creatinine-based estimated glomerular filtration rate (eGFR). Primary outcome was incidence of CVD at 10 years, defined as the composite of cardiovascular mortality, non-fatal myocardial infarction and non-fatal stroke. We used Fine and Gray models adjusted for competing-risk and SCORE2-variables as well as penalized cubic splines to assess and visualize the association of individual biomarkers with incident CVD. In a multimarker approach, we performed backward selection to identify biomarkers providing independent predictive value beyond SCORE2-components. C-indices and category-free net reclassification index (cfNRI) were used to compare the performance of the original SCORE2 model to the biomarker-extended model. Results In 78'507 individuals, median age was 50 years and 50.3% were females. NT-proBNP, hs-CRP and hs-cTnI but not eGFR showed strong associations with 10-year CVD-risk when adjusted for SCORE2 and provided incremental predictive value when individually added to SCORE2 (Figure 1). In a multimarker approach, all three biomarkers remained independently associated with CVD beyond SCORE2 with strongest association of NT-proBNP, followed by hs-CRP and hs-cTnI (Table 1). The simultaneous addition of these three biomarkers to the SCORE2 model significantly increased discrimination (C-index; 0.782 [95% CI, 0.757, 0.806] versus 0.793 [95% CI, 0.768, 0.817], Delta 0.011 [95% CI, 0.005, 0.016]) and risk reclassification, driven by an improvement in non-events (cfNRIoverall 0.17 [95% CI, 0.12, 0.22], cfNRIevents 0.06 [95% CI, 0.02, 0.11], cfNRInon-events 0.11 [95% CI, 0.10, 0.11]). Conclusion NT-proBNP, hs-CRP and hs-cTnI but not eGFR provide incremental predictive value when added to the SCORE2 risk-prediction model and may help to further improve personalized CVD-risk prediction. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Community's Seventh Framework Programme (FP7/2007-2013)

Value of estimated glucose disposal rate to detect prevalent left ventricular hypertrophy: implications from a general population

ABSTRACT Background Insulin resistance plays a pivotal role in developing left ventricular hypertrophy (LVH). Researchers have identified the estimated glucose disposal rate (eGDR) as a simple and cost-effective surrogate of insulin resistance. Our work aims to investigate the association between eGDR and the prevalent LVH and explore the incremental value of eGDR to detect prevalent LVH. Methods The present work enrolled 3839 subjects from a cross-sectional survey conducted between October 2019 to April 2020 in the rural areas of southeastern China. eGDR was calculated based on waist-to-hip circumference ratio, hypertension, and glycated hemoglobin. Results The prevalence of LVH was 17.30%. After adjusting demographic, anthropometric, laboratory, and medical history co-variates, each standard deviation increase of eGDR decreased a 29.6% risk of prevalent LVH. When dividing eGDR into quartiles, the top quartile had a 38.4% risk compared to the bottom quartile. Moreover, smooth curve fitting revealed that the association between eGDR and prevalent LVH was linear in the whole range of eGDR. Additionally, subgroup analysis demonstrated that our main finding was robust to age, sex, BMI, hypertension, and diabetes subgroups. Finally, ROC analysis exhibited a significant improvement by adding eGDR into LVH risk factors (0.780 vs. 0.803, P < 0.001), and category-free net reclassification index (0.702, P < 0.001) and integrated discrimination index (0.027, P < 0.001) also confirmed the improvement from eGDR to detect prevalent LVH. Conclusion Our analysis revealed a linear, robust association between eGDR and prevalent LVH and demonstrated the incremental value of eGDR to optimize the detection of prevalent LVH.

Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study

BackgroundInsulin resistance is one of the major mechanisms for cardiovascular events. Estimated glucose disposal rate(eGDR) has been demonstrated as a simple, accurate, and cost-effective estimator of insulin resistance. Our study aims to evaluate the correlation between eGDR and the prevalent IHD and assess the incremental value of eGDR for identifying prevalent IHD in the rural general population.MethodsOur study enrolled 10,895 participants from a cross-sectional survey of a metabolic management program. The survey was conducted in the rural areas of southeastern China between October 2019 and April 2020. eGDR = 21.158 − (0.09 * waist circumference) − (3.407 * hypertension) − (0.551 * HbA1c).ResultsThe prevalence of IHD was 4.20%. After adjusting for demographic, anthropometric, laboratory, and medical history covariates, each SD increase of eGDR brought a 25.9% risk reduction for prevalent IHD. After dividing eGDR into groups, the top group had a 58.9% risk reduction than the bottom group. Furthermore, smooth curve fitting demonstrated that the correlation between eGDR and prevalent IHD was linear in the whole range of eGDR. Additionally, AUC suggested that eGDR could significantly improve the identification of prevalent IHD by adding it to cardiovascular risk factors (0.703 vs. 0.711, P for comparison = 0.041). Finally, the category-free net reclassification index and integrated discrimination index also implicated the improvement from eGDR to identify prevalent IHD.ConclusionOur data demonstrated a significant, negative, and linear correlation between eGDR and prevalent IHD. Our findings could suggest the potential usefulness of eGDR to improve the identification of prevalent IHD in the rural general population.

The Perivascular Fat Attenuation Index Improves the Diagnostic Performance for Functional Coronary Stenosis.

Background: Coronary computed tomography angiography (CCTA) is an established first-line test in the investigation of patients with suspected coronary artery disease (CAD), while the perivascular fat attenuation index (FAI) derived from CT seems to be a feasible and efficient tool for the identification of ischemia. The association between the FAI and lesion-specific ischemia as assessed by fractional flow reserve (FFR) remains unclear. Methods: In a total of 261 patients, 294 vessels were assessed for CCTA stenosis, vessel-specific FAI, lesion-specific FAI, and plaque characteristics. The diagnostic accuracies of each parameter and the combined approach were analyzed via the receiver operating characteristic curve (ROC) with FFR as the reference standard. The determinants of FAI were statistically analyzed. Results: The cutoff values of vessel-specific FAI and lesion-specific FAI scores calculated according to the Youden index were −70.97 and −73.95 HU, respectively. No significant differences were noted between them; however, they exhibited a strong correlation. No significant differences were noted between the area under the curve (AUC) scores of vessel-specific FAI (0.677), lesion-specific FAI (0.665), and CCTA (0.607) (p > 0.05 for all) results. The addition of two FAI measures to the CCTA showed improvements in the discrimination (AUC) and reclassification ability (relative integrated discrimination improvement (IDI) and category-free net reclassification index (NRI)), vessel-specific FAI (AUC, 0.696; NRI, 49.6%; IDI, 5.9%), and lesion-specific FAI scores (AUC, 0.676; NRI, 43.3%; IDI, 5.4%); (p < 0.01 for all). Multivariate analysis revealed that low-attenuation plaque (LAP) volume was an independent predictor of two FAI measures. Conclusion: The combined approach of adding vessel-specific FAI or lesion-specific FAI scores could improve the identification of ischemia compared with CCTA alone. The LAP volume was the independent risk factor for both tools.

Pericoronary fat attenuation index and coronary plaque quantified from coronary computed tomography angiography identify ischemia-causing lesions

BackgroundThe association between pericoronary fat attenuation index (FAI), plaque characteristics, and lesion-specific ischemia identified by fractional flow reserve (FFR) remains unclear. MethodsCoronary computed tomography angiography (CCTA) stenosis, FAI, plaque characteristics, FFR derived from computed tomography (FFRCT) and FFR were assessed in 280 vessels of 247 patients. Stenosis ≥50% was considered obstructive. Optimal thresholds of FAI and plaque variables were defined by the area under the receiver-operating characteristics curve (AUC) analysis. Ischemia was defined by FFR ≤ 0.80. ResultsFAI ≥ −71.9 HU, low-attenuation plaque (LAP) ≥ 49.62 mm3 and aggregate plaque volume (APV) ≥ 28.91% predicted ischemia independent of other plaque characteristics. The addition of FAI ≥ −71.9 HU improved discrimination (AUC, 0.720 vs. 0.674, P = 0.035) and reclassification abilities (category-free net reclassification index [NRI], 0.470, P < 0.001; relative integrated discrimination improvement [IDI], 0.047, P < 0.001) of ischemia compared with stenosis evaluation alone, with further discrimination (AUC, 0.772 vs. 0.720, P = 0.028) and reclassification abilities (NRI, 0.385, P = 0.001; relative IDI, 0.077, P < 0.001) of ischemia by adding information regarding LAP ≥49.62 mm3 + APV ≥ 28.91%. And the diagnostic performance of combination approach was comparable to that of FFRCT alone (AUC, 0.772 vs. 0.762, P = 0.771). ConclusionsStenosis severity, FAI, plaque characteristics predicted lesion-specific ischemia. The combination of FAI and plaque assessment improved the discrimination of ischemia compared with stenosis assessment alone.

Incremental Prognostic Value of Pericoronary Adipose Tissue Thickness Measured Using Cardiac Magnetic Resonance Imaging After Revascularization in Patients With ST-Elevation Myocardial Infarction.

Background and AimPericoronary adipose tissue (PCAT) reflects pericoronary inflammation and is associated with coronary artery disease. We aimed to identify the association between local PCTA thickness using cardiac magnetic resonance (CMR) and prognosis of patients with ST-elevation myocardial infarction (STEMI), and to investigate the incremental prognostic value of PCAT thickness in STEMI after reperfusion.MethodsA total of 245 patients with STEMI (mean age, 55.61 ± 10.52 years) who underwent CMR imaging within 1 week of percutaneous coronary intervention therapy and 35 matched controls (mean age, 53.89 ± 9.45 years) were enrolled. PCAT thickness indexed to body surface area at five locations, ventricular volume and function, infarct-related parameters, and global strain indices were evaluated using CMR. Associations between PCAT thickness index and 1-year major adverse cardiovascular events (MACE) after STEMI were calculated. The prognostic value of the standard model based on features of clinical and CMR and updated model including PACT thickness index were further assessed.ResultsPatients with MACE had a more significant increase in PCAT thickness index at superior interventricular groove (SIVGi) than patients without MACE. The SIVGi was significantly associated with left ventricular ejection fraction (LVEF), infarct size, and global deformation. SIVGi > 4.98 mm/m2 was an independent predictor of MACE (hazard ratio, 3.2; 95% CI: 1.6–6.38; p < 0.001). The updated model significantly improved the power of prediction and had better discrimination ability than that of the standard model for predicting 1-year MACE (areas under the ROC curve [AUC] = 0.8 [95% CI: 0.74–0.87] vs. AUC = 0.76 [95% CI: 0.68–0.83], p < 0.05; category-free net reclassification index [cfNRI] = 0.38 [95% CI: 0.1–0.53, p = 0.01]; integrated discrimination improvement [IDI] = 0.09 [95% CI: 0.01–0.18, p = 0.02]).ConclusionsThis study demonstrated SIVGi as an independent predictor conferred incremental value over standard model based on clinical and CMR factors in 1-year MACE predictions for STEMI.

Sex-specific prediction value of common carotid artery diameter for stroke risk in a hypertensive population: a cross-sectional study.

The importance of sex as a risk factor for stroke has been established. This study aimed to assess sex-related disparities in carotid artery diameter and stroke in a hypertensive population. The cross-sectional survey was conducted in rural areas of northeast China. A multistage cluster sampling method was employed to select a representative population. The study comprised 3,245 individuals with hypertension. The common carotid artery (CCA) interadventitial diameter was measured by ultrasound. A linear model of restricted cubic spline function was used to characterize the concentration-response (C-R) relationship between CCA diameter and stroke. The overall prevalence of stroke was 8.9% among hypertensive individuals, with a higher rate in men than in women (10.8% vs. 7.6%). When the women's CCA diameters were divided into quartiles, the top quartile (>8.10 mm) had a 2.49 (95% CI: 1.36-4.56) times greater risk of stroke compared to the bottom quartile (≤6.80 mm) after adjustment was made for other variables. The C-R relationship further confirmed a positive association between CCA diameter and stroke prevalence in women. Moreover, a category-free net reclassification index (0.325; 95% CI: 0.173-0.476; P<0.001) and an integrated discrimination index (0.008; 95% CI: 0.004-0.012, P<0.001) showed improvement in predicting the probability of stroke from CCA diameter. However, no significant relationship between CCA diameter and prevalence of stroke was found in men. The risk of stroke increased proportionally with the enlargement of the CCA diameter in women, supporting the sex-specific value of CCA diameter in optimizing the risk stratification of stroke.

Prospective association of serum adipocyte fatty acid-binding protein with heart failure hospitalization in diabetes.

AimsAdipocyte fatty acid‐binding protein (AFABP) is associated with cardiovascular diseases in type 2 diabetes. Whether circulating AFABP levels are associated with the risk of heart failure (HF) in type 2 diabetes remains undefined. We investigated the prospective association of circulating AFABP levels with incident HF hospitalization in type 2 diabetes, and its relationship to the use of sodium glucose co‐transporter 2 inhibitors (SGLT2i) which reduce HF risk.Methods and resultsBaseline serum AFABP level was measured in 3322 Chinese participants without known history of cardiovascular diseases or hospitalization for HF, recruited from the Hong Kong West Diabetes Registry. Its association with incident HF hospitalization was evaluated using multivariable Cox regression analysis. Use of SGLT2i was included as a time‐dependent covariate. Among these 3322 participants (52.9% men; mean age 60.0 ± 12.6), 176 (5.3%) developed HF hospitalization over a median follow‐up of 8 years. Seven hundred and thirty‐one (22%) were started on SGLT2i during the study period (empagliflozin 55.1%, dapagliflozin 44.2%, canagliflozin 0.4%, and ertugliflozin 0.3%). Serum AFABP levels were significantly higher in participants who developed HF hospitalization than those who did not (men: 14.8 vs. 8.3 ng/mL; women: 21.5 vs. 14.6 ng/mL; all: 18.6 vs. 10.9 ng/mL, P < 0.001). In multivariable Cox regression analysis, baseline serum AFABP level was significantly associated with incident HF hospitalization [hazard ratio (HR) 1.38, 95% confidence interval (CI) 1.06–1.80, P = 0.019] independent of the use of SGLT2i, in a model also consisting of age; sex; body mass index; smoking status; duration of diabetes; hypertension, dyslipidaemia; atrial fibrillation; presence of chronic kidney disease and albuminuria; glycated haemoglobin and high‐sensitivity C‐reactive protein levels; and use of metformin, insulin, aspirin, furosemide, and beta‐blockers at baseline. High cumulative defined daily dose (cDDD) of SGLT2i was protective of incident HF hospitalization (HR 0.10, 95% CI 0.01–0.68, P = 0.019). The addition of circulating AFABP level to a clinical model of conventional HF risk factors provided significant improvement in the category‐free net reclassification index (11.5%, 95% CI 1.6–22.1, P = 0.02) and integrated discrimination improvement (0.3%, 95% CI 0.1–1.7, P = 0.04). A dose‐dependent reduction in cumulative incidence of HF hospitalization in response to SGLT2i, based on cDDD, was more clearly observed in participants with a higher baseline AFABP level above the sex‐specific median (P for trend <0.01).ConclusionsCirculating AFABP level is independently associated with incident HF hospitalization in type 2 diabetes and is potentially helpful in risk stratification for the prevention of HF hospitalization.

Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study.

It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD)to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo ); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PLMetabo ); validation of two comprehensive polygenic risk scores: GRSGola based on Metabochip data, and GRSKhera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDIrel ) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRSMetabo and PLMetabo , GRSGola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDIrel : 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRSKhera yielded slightly worse prediction results than GRSGola .