Recent findings suggest that left atrial (LA) function is more strongly related to adverse prognosis than LA volumes. We aimed to evaluate the associations between LA volumes and Doppler filling indices with LA function. Echocardiographic LA volumes (LAVs), mitral valve early (MV-E) and late (MV-A) peak flow velocities, and mitral atrioventricular plane tissue-Doppler early (TD-e ') and late (TD-a ') peak velocities were obtained in 320 patients with acute myocardial infarction (AMI) free from atrial fibrillation and more than moderate valvular disease. LA function was estimated as the LA emptying fraction (LAEF), that is 100x (LAVmax-LAVmin)/LAVmax. LA reservoir volume was calculated as LAVmax-LAVmin and LA transit volume as LV stroke volume-reservoir volume. In restricted cubic spline regression analyses with multivariable adjustment, a reduced LAEF was strongly associated with smaller reservoir volume, larger transit volume, LAVmax, LAVpreA and especially LAVmin. MV-E linearly increased with a lower LAEF, whereas MV-A decreased but only below LAEF levels of approximately 45%. The resulting E/A ratio showed a sudden increase in LAEF levels below similar to 45%. Lower TD-a ' was linearly associated with a lower LAEF. In conclusion, a reduced atrial function was associated with smaller LA reservoir volume, larger LA transit volume, lower TD-a ', a non-linear decrease in MV-A and a non-linear increase in E/A. Our findings are likely a reflection of the adaptation to sustain LV filling volume and counteracting a rise in pulmonary venous pressure in face of an enhanced LV end-diastolic pressure.

Background: Newer therapeutic agents for type 2 diabetes mellitus can improve cardiovascular outcomes, but diabetes remains underdiagnosed in patients with myocardial infarction (MI). We sought to identify proteomic markers of undetected dysglycaemia (impaired fasting glucose, impaired glucose tolerance, or diabetes mellitus) to improve the identification of patients at highest risk for diabetes.

Materials and methods: In this prospective cohort, 626 patients without known diabetes underwent oral glucose tolerance testing (OGTT) during admission for MI. Proximity extension assay was used to measure 81 biomarkers. Multivariable logistic regression, adjusting for risk factors, was used to evaluate the association of biomarkers with dysglycaemia. Subsequently, lasso regression was performed in a 2/3 training set to identify proteomic biomarkers with prognostic value for dysglycaemia, when added to risk factors, fasting plasma glucose, and glycated haemoglobin A1c. Determination of discriminatory ability was performed in a 1/3 test set.

Results: In total, 401/626 patients (64.1%) met the criteria for dysglycaemia. Using multivariable logistic regression, cathepsin D had the strongest association with dysglycaemia. Lasso regression selected seven markers, including cathepsin D, that improved prediction of dysglycaemia (area under the receiver operator curve [AUC] 0.848 increased to 0.863). In patients with normal fasting plasma glucose, only cathepsin D was selected (AUC 0.699 increased to 0.704).

Conclusions: Newly detected dysglycaemia, including manifest diabetes, is common in patients with acute MI. Cathepsin D improved the prediction of dysglycaemia, which may be helpful in the a priori risk determination of diabetes as a motivation for confirmatory OGTT.

Objectives: This study aimed to derive age-specific multivariate reference regions (MVRs) able to classify subjects into those having normal or abnormal filling patterns and to evaluate the prognostic impact of this classification.

Background: The integration of several parameters is necessary to diagnose disorders of left ventricular (LV) filling because no single measurement accurately describes the complexity of diastolic function. However, no generally accepted validated multiparametric algorithm currently exists.

Methods: A cohort of 1,240 apparently healthy subjects from HUNT (Nord-Trøndelag Health Study) with measured early (E) and late (A) mitral inflow velocity and early mitral annular diastolic tissue velocity (e′) were used to derive univariate 95% reference bands and age-specific MVRs. Subsequently, the prognostic impact of this MVR-based classification was evaluated by Cox regression in a community-based cohort (n = 726) and in a cohort of subjects with recent acute myocardial infarction (n = 551). Both evaluation cohorts were derived from VaMIS (the Västmanland Myocardial Infarction Study).

Results: Univariate reference bands and MVRs are presented graphically and as regression equations. After adjustment for sex, age, hypertension, body mass index, diabetes, prior ischemic heart disease, LV mass, LV ejection fraction, and left atrial size, the hazard ratio associated with abnormal filling patterns in the community-based cohort was 3.5 (95% confidence interval: 1.7 to 7.0; p < 0.001) and that in the acute myocardial infarction cohort was 1.8 (95% confidence interval: 1.1 to 2.8; p = 0.011).

Conclusions: This study derived age-specific MVRs for identification of abnormal LV filling patterns and showed, in a community-based cohort and in a cohort of patients with recent acute myocardial infarction, that these MVRs conveyed important prognostic information. An MVR-based classification of LV filling patterns could lead to more consistent diagnostic algorithms for identification of different filling disorders.

Background: Despite the growing body of evidence on growth differentiation factor 15 (GDF-15) reference values for patients with existing cardiovascular disease, limited investigation has been dedicated to characterizing the distribution and prognostic impact of GDF-15 in predominantly healthy populations. Furthermore, current cutoff values for GDF-15 fail to account for the well-documented age-dependence of circulating GDF-15. Methods: From 810 community-dwelling older adults, we selected a group of apparently healthy participants (n = 268). From this sample, circulating GDF-15 was modeled using the generalized additive models for location scale and shape (GAMLSS) to develop age-dependent centile values. Unadjusted and adjusted Cox proportional hazards models were used to assess the association between the derived GDF-15 reference values (expressed as centiles) and all-cause mortality. Results: Smoothed centile curves showed increasing GDF-15 with age in the apparently healthy participants. An approximately three-fold difference was observed between the 95th and 5th GDF-15 centiles across ages. In a median 8.0 years of follow-up, 97 all-cause deaths were observed in 806 participants with eligible values. In unadjusted Cox regression analyses, the hazard ratio (95% CI) for all-cause mortality per 25-unit increase in GDF-15 centile was 1.80 (1.48-2.20) and dichotomized at the 95th centile, >= 95th versus <95th, was 3.04 (1.99-4.65). Age-dependent GDF-15 centiles remained a significant predictor of all-cause mortality in all subsequent adjusted models. Conclusions: Age-dependent GDF-15 centile values developed from a population of apparently healthy older adults are independently predictive of all-cause mortality. Therefore, GDF-15 reference values could be a useful tool for risk-stratification in a clinical setting.

Background: The role of systemic atherosclerosis in myocardial infarction (MI) patients is not fully understood. We investigated the incremental prognostic value of coronary and systemic atherosclerosis after acute MI by estimating extra-cardiac artery disease (ECAD) and extent of coronary atherosclerosis.

Methods and results: The study included 544 prospective MI patients undergoing coronary angiography. For all patients, the longitudinal coronary atherosclerotic extent, expressed as Sullivan extent score (SES) was calculated. In addition, the patients underwent non-invasive screening for ECAD in the carotid, aortic, renal and lower limb. SES was found to be associated with ECAD independent of baseline clinical parameters [adjusted odds ratio (OR) 1.04 95% confidence interval (CI) 1.02–1.06, P < 0.001]. Extensive systemic atherosclerosis, defined as the combination of extensive coronary disease (SES ≥ 17) and ECAD, was associated with higher risk for all-cause mortality compared to limited systemic atherosclerosis (SES < 17 and no ECAD) (hazard ratio [HR] 2.9 95% CI 1.9–4.5, P < 0.001, adjusted for Global Registry of Acute Coronary Events risk score parameters 1.8, 95% CI 1.1–3.0, P = 0.019). The risk for the composite endpoint of cardiovascular death or hospitalization was significantly higher in patients with extensive systemic atherosclerosis compared to patients with limited systemic atherosclerosis (HR 3.1, 95% CI 2.1–4.7, P < 0.001, adjusted HR 1.9, 95% CI 1.2–3.1, P < 0.004).

Conclusions: Visual estimation of the longitudinal coronary atherosclerotic extent at the time of MI predicts ECAD. Coexistence of extensive coronary disease and ECAD defines a group with particularly poor prognosis after MI.

Aims/hypothesis Multiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes. Methods We combined data from six prospective epidemiological studies of 30-77-year-old individuals with type 2 diabetes in whom 80 circulating proteins were measured by proximity extension assay. Multivariable-adjusted Cox regression was used in a discovery/replication design to identify biomarkers for incident MACE. We used gradient-boosted machine learning and lasso regularised Cox regression in a random 75% training subsample to assess whether adding proteins to risk factors included in the Swedish National Diabetes Register risk model would improve the prediction of MACE in the separate 25% test subsample. Results Of 1211 adults with type 2 diabetes (32% women), 211 experienced a MACE over a mean (+/- SD) of 6.4 +/- 2.3 years. We replicated associations (< 5% false discovery rate) between risk of MACE and eight proteins: matrix metalloproteinase (MMP)-12, IL-27 subunit alpha (IL-27a), kidney injury molecule (KIM)-1, fibroblast growth factor (FGF)-23, protein S100-A12, TNF receptor (TNFR)-1, TNFR-2 and TNF-related apoptosis-inducing ligand receptor (TRAIL-R)2. Addition of the 80-protein assay to established risk factors improved discrimination in the separate test sample from 0.686 (95% CI 0.682, 0.689) to 0.748 (95% CI 0.746, 0.751). A sparse model of 20 added proteins achieved a C statistic of 0.747 (95% CI 0.653, 0.842) in the test sample. Conclusions/interpretation We identified eight protein biomarkers, four of which are novel, for risk of MACE in community residents with type 2 diabetes, and found improved risk prediction by combining multiplex proteomics with an established risk model. Multiprotein arrays could be useful in identifying individuals with type 2 diabetes who are at highest risk of a cardiovascular event.

Identifying cardiac disease in patients with extracardiac artery disease (ECAD) is essential for clinical decision-making. Electrocardiography (ECG) is an easily accessible tool to unmask subclinical cardiac disease and to risk stratify patient with or without manifest cardiovascular disease (CV). We aimed to examine the prevalence and prognostic impact of ECG changes in outpatients with ECAD. Outpatients with carotid or lower extremity artery disease (n = 435) and community-based controls (n = 397) underwent resting ECG. The patients were followed during a median of 4.8 years for CV events (hospitalization or death caused by ischaemic heart disease, cardiac arrest, heart failure, or stroke). ECG abnormalities were classified according to the Minnesota Code. Major (33% versus 15%, P<0.001) but not minor ECG abnormalities (23% versus 26%, P = 0.42) were significantly more common in patients versus controls. During the follow-up, 141 patients experienced CV events. Both major ECG abnormalities [hazard ratio (HR) 1.58, 95% confidence interval (CI) 1.11-2.25, P = 0.012] and any ECG abnormalities (HR 1.57, 95% CI 1.06-2.33, P = 0.024) were significantly associated with CV events after adjustment for potential risk factors. In conclusion, ECG abnormalities were common in these outpatients with ECAD. Major and any ECG abnormalities were independent predictors of CV events. Addition of easily accessible ECG information might be useful in risk stratification for such patients.

Background The Proximity Extension Assay proteomics chip provides a large-scale analysis of 92 biomarkers linked to cardiovascular disease or inflammation. We aimed to identify the biomarkers that best predicted long-term all-cause mortality in patients with acute myocardial infarction. Methods In this prospective cohort study, 92 biomarkers were analysed in 847 consecutive patients from the Vastmanland Myocardial Infarction Study with a median follow-up of 6.9 years. Results The mean ( standard deviation) age of the patients was 70 (11.8) years and 32.7% were female. Two hundred and seven patients had died after follow-up. The biomarkers most strongly linked to all-cause mortality were growth differentiation factor 15 (GDF-15) and tumour necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL-R2). Cox regression analysis showed that GDF-15 (hazard ratio 1.25 per unit change, 95% confidence interval, 1.02-1.53, p=0.031) and TRAIL-R2 (hazard ratio 1.37 per unit change, 95% confidence interval 1.12-1.67, p=0.002) were independent predictors of long-term all-cause mortality after adjusting for age, gender, diabetes, previous myocardial infarction, stroke, heart failure, hypertension, smoking, hypercholesterolaemia, body mass index, ST-elevation myocardial infarction, left ventricular ejection fraction, troponin I, estimated glomerular filtration rate, N-terminal pro-brain natriuretic peptide and C-reactive protein. The combination of GDF-15 and TRAIL-R2 with established risk factors and biomarkers showed a discriminating accuracy of separating survivors from non-survivors with a cross-validated area under the receiving operating characteristics curve of 0.88 within five years. Conclusion GDF-15 and TRAIL-R2 were the most powerful Proximity Extension Assay chip biomarkers in predicting long-term all-cause mortality in patients with acute myocardial infarction.