Time series clustering of left atrial strain curves for risk stratification in the general population

Objective

Currently, only the peak reservoir left atrial (LA) strain has been in use while a huge amount of useful information during different phases of cardiac cycle has been ignored. In this study, therefore, we tested the hypothesis that an unsupervised time series analysis utilizing the whole LA deformation curves will identify distinct clusters associated to risk factors and improve cardiovascular (CV) risk prediction over the traditionally used covariables in the general population.

Design and method

We prospectively studied 929 community-dwelling individuals (mean age, 51.6 years; 52.9% women), in whom we acquired clinical and echocardiographic data including LA strain traces at baseline and collected cardiac events on average 7.2 years later. We employed two unsupervised time series techniques such as Self-Organizing map and Variational Autoencoder (VAE) with Convolutional Neural Network as building block to cluster spatiotemporal patterns of LA strain curves. Clinical characteristics and cardiac outcome were used to evaluate the validity of the clusters (k).

Results

According to the quantization error value for every k, the optimal number of clusters was 5 for applied methods. Figure 1 (left panel) illustrates the centroids of the clusters using the proposed VAE network. The first three clusters had differences in sex distribution and heart rate, but had similar low CV risk profiles. On the other hand, cluster 5 had the worst CV risk factors combination, and higher prevalence of left ventricular remodelling and diastolic dysfunction (ie, lowest e' velocity and highest E/e') compared to other clusters. We also observed an increase in the risk for incidence of adverse events between cluster 5 and other clusters (Figure 1, right panel). After adjustment for traditional risk factors, cluster 5 had the highest risk of cardiac events as compared to cluster 1, 2 and 3 (HR: 1.36; 95% CI: 1.10–1.69); P=0.0046).

Conclusion

Unsupervised Machine/Deep Learning algorithms employed on time series LA strain curves identifies clinically meaningful clusters of LA deformation, and provides incremental prognostic information over traditional risk stratification.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): KU Leuven

Echocardiographic diversity associated with exercise capacity in heart failure precursor stage B: the Project Baseline Health Study

Background

The current paradigm to stage heart failure (HF) defines precursor stage B (or “pre-HF”) as having any subclinical change in cardiac structure or function. Yet, HF risk factors and type of cardiac abnormality may induce clinically relevant heterogeneity in HF stage B. Therefore, we assessed clinical and echocardiographic heterogeneity within stage B HF and its association with exercise capacity in a large community-based sample.

Methods

2071 participants to the Baseline Health Study (50.4±17.0 years, 56.2% women, 37.8% hypertensive) underwent echocardiography and physical performance testing including 6-minute walk (6MWT) and treadmill exercise test. We defined echocardiographic profiles of left and right heart remodeling and dysfunction using sex-specific internal reference values. We assessed HF stages (0-A-B-C-D) following HF societies recommendations. stage B participants were stratified according to presence/absence of HF risk factors and to the most severe echocardiographic abnormality present (reduced ejection fraction (EF), left ventricular (LV) hypertrophy/diastolic dysfunction or other abnormalities). We reported associations between physical performance metrics and HF (sub)stages.

Results

Stage B HF was present in 516 participants (24.9%). Within stage B HF, we observed a large diversity in echocardiographic profiles. Yet, stage B participants without HF risk factors (n=96, 18.6% of stage B) predominantly presented echo abnormalities other than LV diastolic dysfunction, hypertrophy and reduced EF, while their physical performance profile resembled that of people with normal echocardiography without HF risk factors. In contrast, stage B participants with HF risk factors (n=420) were characterized by LV diastolic dysfunction, hypertrophy or reduced EF, three phenotypes associated with lower 6MWT distance and lower exercise capacity. Concomitant presence of HF risk factors and LV dysfunction/hypertrophy was associated with worst physical performance.

Conclusions

We observed a wide clinical and echocardiographic diversity affecting physical performance in HF precursor stage B when defined by the current staging paradigm. Concomitant presence of HF risk factors and LV dysfunction/hypertrophy may mark individuals at highest risk for progression towards overt HF.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): This research was made supported by an institutional research grant from Verily Inc. (CA, USA) and by the Research Foundation Flanders (Belgium).

Personalized remotely guided preventive exercise therapy for a healthy heart: protocol and design of the PRIORITY study

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation – Flanders (FWO)

Introduction

Approximately half of the heart failure population has heart failure with preserved ejection fraction (HFpEF), a chronic disease starting with cardiovascular risk factors such as hypertension, diabetes and obesity (stage A) which can further emerge in a structural heart disease without (stage B) or with (stage C) signs or symptoms. Structured exercise therapy is recommended as a Class IA intervention in national and international guidelines and, as such, should be offered to all patients. Yet, in current practice, exercise therapy is often only offered within a secondary prevention program. At present, there exists no comprehensive preventive care program that includes structured exercise for patients in the early stages of heart failure, when cardiovascular risk factors are present, but cardiac remodeling and dysfunction might still be reversible or even preventable.

Purpose

PRIORITY aims to investigate the use of remotely guided exercise therapy as a preventive clinical and cost-effective treatment in the HFpEF continuum. This includes both prevention of progression of asymptomatic diastolic dysfunction towards symptomatic HFpEF (= primary prevention) and delaying progression of symptomatic HFpEF (= secondary prevention).

Methods

A randomized controlled multicenter trial will be conducted in 450 patients (men and women, aged 35-80 years) with heart failure (n = 180 stage A, 180 stage B, 90 stage C). Participants are being recruited from 3 different hospitals and the general population during a 16-month period which started in September 2021. Patients will be randomized (1:1) to usual care or to the PRIORITY exercise intervention (i.e. a combination of supervised with remotely guided home-based training sessions). Training prescription is based on the EXPERT tool and includes person-tailored endurance and dynamic strength training. During one year, participants will receive 18 supervised exercise sessions supplemented with a structured progressive home-based exercise program. Outcomes will be assessed at baseline, 4 months, one and two-years. Primary outcome is the proportion of patients with a clinically relevant improvement in peak oxygen uptake at one-year. Secondary outcomes include vascular health, muscle metabolism, change in electrocardiographic parameters and physical fitness parameters (muscle strength, body composition). Further, big data of physical activity collected during the trial will be used to develop models using machine-learning algorithms which can predict physical activity uptake and changes in fitness to facilitate the creation of more personalized interventions and better tailored exercise prescription.

Conclusion

We anticipate that the PRIORITY study will contribute to better prevention of heart failure thanks to an early easily accessible person-tailored exercise intervention.

Proteomic profiling for detection of early-stage heart failure in the community

Background and purpose

Biomarkers may provide insight into the molecular mechanisms underlying cardiac remodelling and dysfunction. Using a targeted proteomic approach, we aimed to identify circulating biomarkers associated with early-stage heart failure and extract a proteome-based risk classifier for this condition.

Methods

575 community-based participants (mean age, 57 years; 51.7% women) underwent echocardiography and proteomic profiling (CVD II panel, Olink Proteomics). We applied partial least squares-discriminant analysis (PLS-DA) and a machine learning algorithm (extreme gradient boosting, XGBoost) to identify key proteins associated with echocardiographic abnormalities. We used Gaussian Mixture modelling for unbiased clustering to construct phenogroups based on influential proteins in PLS-DA and XGBoost.

Results

Of 87 proteins, 13 were important in PLS-DA and XGBoost modelling for detection of left ventricular (LV) remodelling, LV diastolic dysfunction and/or left atrial reservoir dysfunction: placenta growth factor, kidney injury molecule-1, prostasin, angiotensin-converting enzyme-2, galectin-9, cathepsin L1, matrix metalloproteinase-7, TNFR superfamily members 10A, 10B and 11A, interleukins-6 and 16 and alpha-1-microglobulin/bikunin precursor. Based on these proteins, the clustering algorithm divided the cohort into two distinct phenogroups, with each cluster grouping individuals with a similar protein profile. Participants belonging to the second cluster (n=118) were characterized by an unfavourable cardiovascular risk profile and adverse cardiac structure and function. The adjusted risk of presenting cardiac maladaptation was higher in this phenogroup than in the other cluster (P<0.0001).

Conclusion

We identified proteins reflecting renal function, extracellular matrix remodelling, angiogenesis and inflammation to be associated with echocardiographic signs of early-stage heart failure. Focused proteomic phenomapping discriminated individuals at high risk for cardiac maladaptation in the community.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders

Application of a clinical decision support system to assess the severity of the new coronavirus infection COVID-19

Purpose

To apply the clinical decision support system (CDSS) and evaluate its effectiveness in determining the prognosis of the new coronavirus infection COVID-19.

Methods

7118 outpatient and hospitalized cases with COVID-19 were analyzed, mean age 39.4±18.3 years, 52% men. The data was accumulated in the the Webiomed.DataSet service, which allows to accumulate a base of de-identified biomedical data from electronic health records. To test the severity of the COVID-19, the CDSS was connected to the 8 medical information systems in one region of the Russian Federation. For each risk factor (RF) of the unfavorable COVID-19 outcome the contribution to the risk was determined in points indicated in brackets: age over 60 (1), age over 80 (3), BMI of 30–34.9 kg/m2 (1), BMI of ≥35 kg/m2 (2), arterial hypertension (HTN) (1), diabetes mellitus (DM) (1), coronary artery disease (CAD) (2), cerebrovascular accident (CVA) (1), atrial fibrillation (AF) (1), pulmonary disease (1), cancer (1). Hospitalization and death were considered as unfavorable outcome. Each patient had risk level (high – two or more points, moderate – one, low – zero).

Results

64.2% was outpatient, age 34.8±17.3 years. 35.8% was hospitalized (mean age 47.8±15.1 y), 50 patients died (mean age 61.3±14.4 y, mortality 0.7%). Low risk had 74.9% outpatient treated patients, 26.4% – hospitalized, 26% – dead; average risk – 12.6%, 17.3%, 24%, high risk – 12.5%, 56.3%, 50% (respectively for subgroups). The RF incidence of poor prognosis in the groups: age over 60 years old – 9%, over 80 – 0.5%, HTN – 18.9%, DM – 5.2%, CAD – 3.9%, CVA – 1%, AF – 1.4%, COPD/asthma – 1.9%, cancer – 1.7%, obesity – 15.5%. In the hospitalized group: age over 60 years – 11.1%, over 80 – 1.6%, HTN – 13.3%, DM – 4.2%, CAD – 7.6%, CVA – 1.1%, AF – 1%, COPD/asthma – 1.3%, cancer – 1.3%, obesity – 13.2%. Among patient who died: age over 60 years – 54%, over 80 – 6%, AH – 50%, DM – 18%, CAD – 36%, CVA – 4%, AF – 12%, COPD/asthma – 6%, cancer – 4%, obesity – 30%. When comparing the incidence of RF in the high-risk group, a significant difference in hospitalized, dead, and patients treated outpatient was obtained for the following RF: age over 60 years (p<0.001), HTN (p<0.001), DM (0.004), CAD (p<0.001), AF (p<0.001), COPD and AD (p=0.043), obesity (p=0.031). In the moderate-risk group, the main RFs influencing the prognosis were age over 60 years (p<0.001), HTN (p=0.03) and obesity (p=0.004).

Conclusions

The created CDSS allowed to stratify the risk of COVID-19 by the presence of cardiovascular risk factors and diseases, as well as by the presence of bronchopulmonary pathology and oncological diseases. The use of this CDSS allowed to route COVID-19 patients more effective. In addition to clinical criteria of the disease severity, the system allows to assess the prognosis quickly and hospitalize high-risk patients, or organize their careful monitoring in case of outpatient treatment.

Funding Acknowledgement

Type of funding sources: None.

Temporal shift and accuracy of machine learning in heart transplant outcomes

Background

Accurate prediction of outcomes following a heart transplant is critical to explaining risks and benefits to patients and decision-making when considering potential organ offers. Given the large number of potential variables to be considered, this task may be most efficiently performed using machine learning (ML).

Purpose

We trained and tested different ML algorithms to accurately predict outcomes following a cardiac transplant using the United Network of Organ Sharing (UNOS) database.

Methods

We included 67 939 adult and pediatric patients enrolled in the UNOS database between January 1994 and December 2016 who underwent cardiac transplantation (median age 53 [IQR 38 – 60], 72.7% males). In our models, as an input, we included 114 features that have been collected from recipients and donors prior to transplant. The primary outcome was all-cause mortality at one-year post-transplant. We evaluated three different ML methods: XGBoost, Random Forest (RF) and L2 regularized logistic regression. Algorithms were trained and tested using shuffled 10-fold cross-validation (CV) as well as rolling CV. In the rolling CV, to mimic prospective procedure, ML models were trained by incrementally adding patients according to transplant year and testing models on the data in the following year. The hyperparameters, controlling the learning process, were tuned using Bayesian optimization. Prognostic accuracy for one-year all-cause mortality was characterized using the area under the receiver-operating characteristic curve (AUC).

Results

In total, 8,394 patients died within 1 year of transplant. We observed a substantial difference in prognostic accuracy between the shuffled 10-fold CV and the rolling CV. In the 10-fold CV, XGBoost and RF achieved high predictive performance with AUC of 0.848 (95% CI: 0.842–0.854) and 0.891 (95% CI: 0.886–0.896), respectively. In the rolling CV, which is a more realistic setting, AUC dropped to 0.673 (95% CI: 0.661–0.684) for XGBoost and 0.670 (0.657–0.683) for RF. Predictive performance of L2 regularized logistic regression remained stable across the two CV procedures, achieving AUC 0.669 (95% CI: 0.662–0.676) in the 10-fold shuffled CV and 0.665 (95% CI: 0.649–0.680) in the rolling CV procedure (Figure).

Conclusions

Our study suggests that ML models could be used to predict mortality in the first year post-transplant. We also show that the choice of CV procedure is crucial for evaluating ML models, particularly in data collected over a long period of time. The difference between the shuffled and rolling CV in the predictive performance of the tree-based ML models might indicate temporal dataset shift. In the rolling CV, all three methods achieved similar predictive performance.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders (FWO)

Prognostic value of strain rate during isovolumic relaxation in a general population

Background

Left ventricular (LV) strain rate (SR) during isovolumic relaxation (SRIVR) has been shown to correlate with invasive measurements of diastolic function, namely the time constant of LV relaxation (τ), and has thus been proven useful in the assessment of diastolic function. Tissue Doppler imaging (TDI) has an adequate frame rate to resolve the SR during a short-lived mechanical event such as IVR.

Purpose

The purpose of this study was to assess the additive prognostic value of SRIVR on top of conventional cardiovascular risk factors in a general population.

Methods

We included 670 subjects (age: 51.2±14.2, 48.8% males) that were already recruited in the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO), from May 2005 to February 2009. Subjects were followed up on average 5 years after their recruitment, either by a follow-up visit or by telephone. Exclusion criteria at baseline were atrial fibrillation, presence of an artificial pacemaker, more than mild valvular disease and segmental wall motion abnormalities. All patients underwent echocardiographic examination with a state of the art ultrasound machine. Using an in-house developed software package (SPEQLE), we extracted the velocity, strain and SR curves from the color TDI images (FR >180Hz) and imposed timing information on the IVR based on valve opening/closing as determined from PW Doppler data. The sample volume was positioned at the mid portion of the inferolateral wall, in an apical 3 chamber view, manually tracked over the cardiac cycle and all curves were averaged over 3 subsequent cardiac cycles. Then, SRIVR was estimated as the peak SR value during IVR (Fig. 1). Outcome data consisted of major adverse cardiac events (MACE) during the follow-up period. The hazard ratio (HR) associated with SRIVR values was estimated using Cox regression analysis; we included age, sex, body mass index, systolic blood pressure, smoking and serum cholesterol as co-variables in the model.

Results

An accurate assessment of the SRIVR in the inferolateral wall was not possible in 34 participants, so further analysis was confined to 636 subjects. In total, 65 adverse cardiac events were recorded over the period of 8.7 years of follow-up. Figure 2 demonstrates the cumulative incidence estimates (1-Kaplan-Meier survival estimates) of composite cardiac events in quartiles of SRIVR measured in the inferolateral wall (log-rank test p=0.005). Overall, after adjustment for the important cardiovascular risk factors, SRIVR of the inferolateral wall analyzed as a continuous variable was a significant predictor of fatal and nonfatal cardiac events (HR 1.94 (95% CI 1.09–3.47); p=0.025).

Conclusion

SRIVR measured in the inferolateral wall is an important biomarker not only in assessing diastolic function but also as a significant predictor of future adverse outcomes.

Funding Acknowledgement

Type of funding sources: None.

The association of soluble ACE2 change with metabolic health, body composition and proteome dynamics during a weight loss diet intervention: implications for the COVID-19 pandemic

Background

Angiotensin-converting enzyme 2 (ACE2) serves protective functions in metabolic, cardiovascular, renal and pulmonary diseases and is linked to COVID-19 pathology. We explored the association between soluble AC2 (sACE2) and metabolic health and proteome dynamics during a weight loss diet intervention.

Methods

We analyzed 457 healthy individuals (mean age 39.8±6.6) with BMI 28–40 kg/m2 who participated in the Diet Intervention Examining the Factors Interacting with Treatment Success (DIETFITS). Biochemical markers of metabolic health and 236 proteins measured by Olink CVD II, CVD III and Inflammation arrays were available at baseline and 6 months following dietary intervention. We determined clinical and routine biochemical correlates of the diet-induced change in sACE2 (ΔsACE2) using stepwise linear regression. We then combined feature selection models and multivariable-adjusted linear regression to identify protein dynamics associated with ΔsACE2.

Results

sACE2 decreased significantly on average at 6-months in the diet intervention. A stronger decline in sACE2 during the diet intervention was independently associated with female sex, lower HOMA-IR and LDL cholesterol at baseline, and a stronger decline in HOMA-IR, triglycerides, HDL-cholesterol and fat mass. In line, participants with decreasing HOMA-IR and triglycerides had significantly higher odds for a decrease in sACE2 during the diet intervention than those who did not (P≤0.0073 for both). Feature selection models linked ΔsACE2 to changes in AMBP, E-selectin, HAOX1, KIM-1, MERTK, PGF, thrombomodulin and TRAIL-R2. ΔsACE2 remained independently associated with these protein changes in multivariable-adjusted linear regression.

Conclusion

Decrease in sACE2 during a weight loss diet intervention was associated with improvements in metabolic health, fat mass and markers of angiotensin peptide metabolism, vascular injury, renal function, chronic inflammation and oxidative stress. Our findings may improve the risk stratification, prevention, and management of cardiometabolic and COVID-19-related complications.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIHResearch Foundation Flanders

AB0705 SHORT-TERM OUTCOMES OF COVID-19 IN PATIENTS WITH RHEUMATIC DISEASES WHO ARE TREATED BY BIOLOGICAL AND TARGETED SYNTHETIC DMARDS: OBSERVATIONAL SINGLE-CENTER STUDY

Background:

The course of new coronavirus infection in patients with rheumatic diseases (RD) undergoing treatment with biological and targeted drugs is still poorly understood.

Objectives:

To study outcomes of COVID-19 in patients with RD receiving treatment biological and targeted synthetic DMARDs.

Methods:

We studied cases of COVID-19 in patients with RD, included in “Moscow regional registry of patients with rheumatic diseases receiving treatment with biological and targeted synthetic drugs” – observational cohort, started in 2018. A total number of patients, included in the registry, is 1048 at December 2020.

Results:

By January 2021, 44 known cases of COVID-19 were registered among patients included in the registry (4,2%). This group included 29 (65,9%) females, 15 (34,1%) males, with mean age 45,09±12,7 (median 47,0 [34,0; 57,0]) y.o. The vast majority of patients had rheumatoid arthritis (19, 43,2%) and ankylosing spondylitis (19, 43,2%), there were 3 (6,8%) patients with psoriatic arthritis, and one patient each (2,3%) with systemic lupus erythematosus, systemic sclerosis, and ANCA-vasculitis. Before COVID-19, 20 (45,5%) patients received TNF inhibitors (adalimumab, infliximab, etanercept, certolizumab, golimumab), 7 (15,9%) – IL-6 receptor inhibitors (tocilizumab, sarilumab), 7 (15,9%) – rituximab (period between last infusion and COVID-19 was 1-4 months), 5 (11,4%) – sekukinumab, 2 (4,5%) – tofacitinib, and one patient each (2,3%) received abatacept and ustekinumab. Also, 22 (50%) received methotrexate, 4 (9,1%) – leflunomide, 3 (6,8%) - mycophenolate mofetil, 1 (2,3%) – sulfasalazine; 12 (27,3%) took oral steroids. COVID-19 presented as mild disease in 23 (52,3%) patients, and 21 (47,7%) had viral interstitial pneumonia verified by computed tomography. 16 (36,4%) patients were hospitalized, only one patient underwent artificial lung ventilation. We found no significant associations between particular diagnosis and treatment on the one hand, and hospitalization for COVID-19 on the other hand. For treatment of COVID-19, two (4,5%) patients did not receive any medications, and the rest of patients received antiviral and antibacterial therapy according to standardized protocol. In addition, corticosteroids were administered for COVID-19 in 15 (34,1%) patients, mainly (12 cases) in hospital, and two (4,5%) patients in hospital were treated by tocilizumab. The outcome in all cases was favorable, all patients successfully recovered from the new coronavirus infection.

Conclusion:

In this observational study, we found no association between biologic and targeted therapy for rheumatic diseases and severe course of new coronavirus infection, as well as with the need for hospitalization for COVID-19. The outcome of COVID-19 was favorable in all patients receiving treatment with biological and targeted synthetic drugs for rheumatic diseases.

Disclosure of Interests:

None declared

Adapting neural network models to predict 10-year CVD development based on regional data calibration

Background

The used tools for prediction the individual risk of developing cardiovascular diseases and their complications using machine learning methods have proven better prognostic value in comparison with commonly used scales (e.g., Framingham, SCORE). To create such methods, the long-term accumulation of large amount of qualitative data are required. Moreover, to improve the accuracy of models, it is necessary to take into account regional characteristics that affect health: ethnic, nutritional characteristics, climatic conditions, living standards and medical care. These regional characteristics could significantly affect the development and outcomes of CVDs. However, the amount of regional data is not enough to build a qualitative model. Therefore, it is proposed to create models based on publicly available data and validate them on regional medical data sufficient for validation and calibration.

Methods

Two models were trained using data from the Framingham study. Model 1 was trained on 2 588 patient data and predicts a 10-year CVD probability according to the following risk factors: age, gender, cholesterol, HDL, smoking, SBP, and BP medications. Model 2 was trained on 4,363 patient data and predicts a 10-year death probability from CVD according to the following criteria: age, gender, cholesterol, smoking, SBP, BMI, heart rate. To retrain the obtained models, we used dataset created from data from patients in the northwestern part of Russia. The dataset consists of 438 patients, including the signs used in the trained models. This dataset includes CVD and death from it during a 10-year follow-up

Evaluation

We used randomized data splitting: divided the dataset into a training and a test set with an 80/20 proportion. The models was implement with keras convolution neural network (CNN) using 3 hidden layers. For data validation was used a 10 K-fold method.

Results

We compared the initial model metrics and those obtained after local data retraining. The accuracy of model 1 before retraining is 78%, after – 81.3%, the area under the ROC curve (AUC) before retraining: 0.77 (at 95% CI: 0.72–0.82C), after – 0.803. The accuracy of model 2 before retraining is 79%, after – 85.6%, the area under the ROC-curve (AUC) before retraining: 0.78 (at 95% CI: 0.72–0.82), after – 0.828.

Conclusion

Using this method of retraining predictive models, we can take into account local characteristics of the population and significantly increase the accuracy of predicting events. Expand the population to use the model according to local characteristics.

Funding Acknowledgement

Type of funding source: Private company. Main funding source(s): OOO K-SkAI

Left atrial reservoir strain in relation to metabolic and inflammatory biomarkers: a community-based study

Background

The pathophysiological mechanisms that underlie progressive left atrium (LA) remodelling and dysfunction are only partially understood. Metabolic disturbances and chronic inflammation might mediate LA dysfunction. To date, population data investigating the contribution of these processes to LA reservoir dysfunction remain scarce.

Purpose

In a large population sample, we investigated the association between LA reservoir function and a panel of 38 metabolic and inflammatory biomarkers.

Methods

In 1236 community-dwelling individuals (mean age, 51.0 years; 51.5% women), we echocardiographically assessed LA reservoir strain (LARS) using 2D speckle-tracking analysis. LA reservoir dysfunction was defined as having LARS <23%. We applied partial least squares-discriminant analysis (PLS-DA) to identify biomarkers associated with LA dysfunction. We further explored the associations between LARS and selected biomarkers that were the most influential in PLS-DA, while adjusting for important clinical correlates such as age, sex, body mass index (BMI), heart rate, systolic blood pressure (BP) and antihypertensive treatment. We applied stepwise regression to identify the clinical features and circulating biomarkers most valuable for prediction of abnormal LARS.

Results

The three latent factors constructed from the panel of 38 biomarkers during PLS-DA explained 16.9% of the variation between the normal and the impaired LA function group. The PLS-DA model discriminated between normal and abnormal LA reservoir strain with 79% accuracy (P<0.0001). In PLS-DA, serum uric acid, serum insulin, γ-glutamyl transferase, interleukin-6, D-dimer and triglycerides were the top biomarkers responsible for class discrimination. On average, these top biomarkers were higher in the LA dysfunction group as compared to their normal counterparts (P<0.0001 for all). In multivariable-adjusted continuous analyses, LARS decreased significantly with the level of serum insulin, serum uric acid and γ-glutamyl transferase (P≤0.0035 for all). Of the clinical correlates and the top biomarkers selected in PLS-DA, stepwise regression models highlighted age, BMI, systolic BP, serum insulin, serum uric acid and interleukin-6 as the main predictors of an impaired LA reservoir function (see figure). Conjointly, these clinical and biochemical features identified LA reservoir dysfunction with an overall accuracy of 85%.

Conclusions

Circulating markers of insulin resistance, hyperuricemia and chronic inflammation were independently associated with impaired LA reservoir function. These markers may help to further unravel the pathophysiological processes behind LA maladaptation and improve the management of early LA dysfunction in the community.

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders

Echocardiographic phenogrouping by machine learning for risk stratification in the general population

Background

Improved taxonomical classification of routinely measured echocardiographic parameters is needed for better phenotypic characterisation of the asymptomatic stages of cardiac maladaptation. This would create opportunities to intervene early in the course of the heart disease and prevent progression to more advanced stages and adverse events.

Purpose

We tested the hypothesis that an unbiased clustering analysis utilizing echocardiographic indexes reflecting left heart structure and function and hemodynamic measurements could identify phenotypically distinct groups of asymptomatic individuals in the general population.

Methods

We prospectively studied 1407 community-dwelling individuals (mean age, 51.0 years; 51.5% women), in whom we performed clinical and echocardiographic examination at baseline and collected cardiac events on average 8.5 years later. Cardiac phenotypes that were correlated at r>0.8 were filtered, leaving 15 less redundant echocardiographic and hemodynamic features for phenogrouping. We employed two methods of unsupervised machine learning: agglomerative hierarchical clustering and Gaussian mixture using expectation minimization algorithm. The optimal number of phenogroups was chosen based on the combination of the cohesion/separation approach (silhouette index) and stability. Cox regression was used to demonstrate the clinical validity of phenogroups.

Results

Overall, both methods agreed with respect to cluster assignment (RI=0.75). Unbiased clustering analyses classified study participants into 3 distinct phenogroups that differed markedly in cardiac structure/function indexes and hemodynamics used for cluster analysis (Figure, left panel). Indeed, phenogroup 3 had the worst left ventricular diastolic function (ie, lowest e' velocity and left atrial reservoir strain, but highest E/e', deceleration time, and left atrial volume index), highest left ventricular mass index, as well as highest systolic blood pressure and pulse pressure (Figure, left panel). The phenogroups were also different in other clinical characteristics and incidence of cardiac events (Figure, right panel). Even after adjustment for traditional risk factors, phenogroup 3 had the highest risk of cardiac events as compared to cluster 1 (Figure, right panel).

Conclusions

Unsupervised learning algorithms integrating routinely measured cardiac imaging and hemodynamic data can provide a clinically meaningful classification of cardiac health in asymptomatic individuals. This approach might facilitate early detection of cardiac maladaptation and improve risk stratification.

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders

Subclinical left atrial dysfunction profiles for prediction of cardiac outcome in the general population

Background

Echocardiographic screening for subclinical left atrial (LA) dysfunction might enhance the prediction of cardiac diseases such as atrial fibrillation (AFib) in the community. To date, however, outcome-driven definitions of early-stage LA dysfunction remain scarce.

Purpose

In a large population sample, we sought to extract outcome-driven thresholds for echocardiographic indexes of LA function to define subclinical left atrial (LA) dysfunction and assess the prognostic value of these echocardiographic criteria for prediction of cardiac events.

Methods

In 1306 community-dwelling individuals (mean age, 50.7 years; 51.6% women), we assessed LA function and left ventricular (LV) global longitudinal strain (LS) by echocardiography. Using receiver-operating curve threshold analysis, we derived outcome-driven cut-offs for LA emptying fraction (LAEF) and LA reservoir strain (LARS) that best balanced the cardiac event prediction (i.e. cut-offs yielding the highest Youden index (=sensitivity+specificity-1)). Next, we constructed LA dysfunction profiles and integrative LA/LV strain profiles based on the extracted cut-offs for LAEF and LARS and a validated definition of impaired LV global LS. We assessed the prognostic performance of these profiles in predicting the incidence of cardiac events and AFib (mean follow-up, 8.5 years).

Results

During follow-up, 93 participants experienced a cardiac event (8.3 events/1000 person-years) and 27 developed AFib (2.3 events/1000 person-years). LAEF<55% and LARS<23% yielded the highest Youden indexes and thus provided the most balanced prediction of incident AFib. When applying these cut-offs, abnormal LAEF and LARS were respectively present in 27.0% and 18.1% of the cohort. Abnormal LARS was independently associated with higher risk for cardiac events (hazard ratio (HR) versus normal LA phenotype: 2.11, P=0.0021). Both abnormal LAEF (HR: 2.57) and abnormal LARS (HR: 3.28) predicted incident AFib (P≤0.029). As compared to subjects free from any LA dysfunction, those with both LAEF<55% and LARS<23% had a significantly higher risk to develop cardiac events (HR: 2.10; P=0.014) and AFib (HR: 6.45; P=0.0036). Of the integrative LA/LV strain profiles, the concomitant presence of an impaired LARS and LV global LS independently elevated the risk for cardiac events (HR: 2.81; P=0.0012) and AFib (HR: 4.36, P=0.0071) as compared to normal counterparts. Both the degree of LA dysfunction and the integrative LA/LV strain profiles improved the prognostic accuracy beyond clinical risk models and risk scores.

Conclusions

We validated population-based and outcome-driven definitions of subclinical LA dysfunction predicting cardiac events independent of conventional risk factors. Echocardiographic screening for subclinical LA and LV systolic dysfunction might enhance the prediction of cardiac diseases such as AFib in the community, empowering clinicians to timely intervene with the disease development.

Prediction of cardiac events

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders, Internal Funds KU Leuven

Epicardial fat thickness as arterial hypertension predictor in normotensive patients with abdominal obesity

Objective

Arterial hypertension (HTN) is one of the most common diseases associated with obesity. Visceral obesity (VO) with dysfunctional visceral adipose tissue plays the main role in obesity induced HTN. Direct criteria of VO including echocardiographic epicardial fat thickness (EFT) may become an additional predictor of HTN.

Purpose

The aim was to assess the role of echocardiographic EFT (EEFT) as a predictor of HTN in normotensive patients with abdominal obesity (AO).

Methods

526 normotensive men (according to ambulatory blood pressure monitoring (ABPM) without therapy) with AO (waist circumference (WC) >94 cm) and SCORE <5%, without cardiovascular diseases and diabetes mellitus were examined (age 45.1±5.0 years). The lipid and glucose profiles, creatinine, uric acid and C-reactive protein blood levels, albuminuria evaluation, echocardiography, carotid ultrasound, bifunctional ABPM were performed. The values of EEFT ≥75 percentile for persons 35–45 years and 46–55 years were 4.8 mm and 5.8 mm respectively. These values used as epicardial VO criteria. Patients with subclinical carotid atherosclerosis due to the lipid-lowering therapy administration (n=98) were excluded from the follow-up. Re-examination with ABPM was conducted on average through 46.3±5.1 months. Data were summarized as mean ± standard error, statistical analysis conducted with paired two-tailed t-tests, Pearson χ2 criterion and multivariate regression analysis.

Results

Data of 406 persons were available for analysis. HTN as average daily blood pressure ≥130/80 mm Hg was detected in 157 (38.7%) patients. These patients were characterized by initially higher values of age (45.9±4.6 years vs 44.3±4.9 years, p<0.001), waist circumference (106.9±7.3 cm vs 104.2±7.3 cm, p<0.001), body mass index (BMI) (32.0±3.3 kg/m2 vs 30.9±3.2 kg/m2, p<0.001), average daily systolic and diastolic blood pressure (120.7/74.5±4.6/3.4 mm Hg vs 118.2/73.2±5.5/3.9 mm Hg, p<0.001), EEFT (5.2±0.7 mm vs 4.4±1.0 mm, p<0.001). The epicardial VO was initially detected in 95 (23.3%) patients. In patients with HTN the initial prevalence of epicardial VO was greater (58.0% vs 23.3%, p<0.001). As predictors for the multivariate regression analysis the clinical and laboratory examinations data and EEFT were evaluated. According to the results a mathematical model for estimating the probability HTN was obtained: 0.696*fasting blood glucose + 0.198*systolic BP + 2.844*EFT – 40.166 (constant). Among these predictors EEFT was characterized by the highest standardized regression coefficient (0.302, p<0.001) (0.295, p<0.01 for fasting blood glucose, 0.035, p<0.001 for systolic BP). The Hosmer-Lemeshow test value was 0.863, the total percentage of correct classifications was 86%, the area under the ROC-curve was 0.913.

Conclusions

EEFT (4.8 mm for persons 35–45 years and 5.8 mm for persons 46–55 years) may be an additional predictor of HTN in normotensive patients with AO.

Funding Acknowledgement

Type of funding source: None

Hypospadias in a Sheltie puppy: A case report

Hypospadias in dogs is a rare pathology in the veterinary practice. The manifestation of hypospadias in dogs is diverse, since there is a varying degree of damage to the urogenital apparatus. The owners of a Sheltie puppy at the age of 3 days came to the clinic due to the difficulty of determining sex, the presence of inflammation of the anus and abdominal skin, defecation and urination violations. Clinical examination of the puppy showed a blind-closed preputial sac, absence of the ventral wall of the prepuce and an open urogenital urine trough was located in its place in the abdominal wall area. On examination of the puppy at the age of 28 days, hyperaemia and swelling of the anus were noted, as well as prolapse of the rectum. Findings of the examination at the age of 4 months consisted of drying of the mucous part of the open urogenital canal chute and accumulation of pus in the underdeveloped preputial sac. Bilateral cryptorchidism and the absence of the scrotum were also found out. A decision on the surgical treatment was made. The anus and the opening of the urethra were separated to form a urethrostomy in the scrotum and restore the integrity of the anus. On the 5th post operative day, oedema and stricture of the reconstructed urethra resulted in difficulty urinating, followed by the formation of urinary fistula in the perineal region below the anus opening. As a result of the chosen surgical treatment approach, the problem with contact dermatitis of the perineum and pollakiuria was solved.

P2488The 2013 ACC/AHA pooled cohort equations and insulin resistance status for detection of early-stage heart failure in the community

Objectives

Detection of heart failure (HF) in its subclinical phase would allow timely initiation of preventive measures that counter its pathophysiology. Here, we assessed the usefulness of traditional cardiovascular (CV) risk assessment and insulin resistance status to detect early-stage HF.

Methods

In 984 participants (mean age, 57.0 years, 52.3% women), we derived echocardiographic indexes of left ventricular (LV) structure and function and calculated the 10-year risk for a first atherosclerotic CV disease (ASCVD) using the 2013 ACC/AHA risk score. We assessed the discriminatory value of this risk score to detect LV maladaptation and the improvements in reclassification by insulin resistance status (HOMA-IR).

Results

The probability for LV maladaptation rose progressively with the 10-year ASCVD risk increasing. Participants at high 10-year ASCVD risk (>7.5%) had indeed significantly higher odds for LV concentric remodeling (odds ratio, 4.84), LV hypertrophy (OR, 5.93), abnormal LV longitudinal strain (OR, 2.04) and LV diastolic dysfunction (OR, 25.3) as compared to those at low ASCVD risk (<2.5%; P≤0.0003). Adding markers of insulin resistance to the ACC/AHA risk score moderately improved the integrated discrimination and net reclassification of all LV maladaptive phenotypes (P≤0.022) except LV diastolic dysfunction (P≥0.059). LV remodeling and abnormal LS was particularly more likely in insulin-resistant participants with a 10-year ASCVD risk between 5% and 15% than in their insulin-sensitive counterparts.

Prediction of early-stage HF profiles 2013 ACC/AHA risk score Addition of insulin resistance status to the 2013 ACC/AHA risk score AUC (95% CI) Integrated Discrimination Improvement Net Reclassification Improvement Absolute IDI (%) P value NRI (95% CI) P value LV concentric remodeling 0.70 (0.66 to 0.74) 0.0083 (11.3%) 0.022 0.23 (0.067 to 0.39) 0.0058 LV hypertrophy 0.70 (0.66 to 0.74) 0.017 (20.7%) 0.0033 0.27 (0.11 to 0.43) 0.0011 Abnormal LV LS 0.56 (0.53 to 0.62) 0.022 (202.0%) <0.0001 0.33 (0.18 to 0.49) <0.0001 LV diastolic dysfunction 0.82 (0.78 to 0.86) 0.0007 (0.45%) 0.84 0.093 (−0.11 to 0.30) 0.38 ≥2 LV abnormalities 0.76 (0.72 to 0.80) 0.0087 (7.3%) 0.071 0.22 (0.042 to 0.40) 0.016 The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) reflect the improvements in classification by adding insulin resistance (by HOMA-IR) to the 2013 ACC/AHA risk score. HOMA-IR, Homeostatic Model for Assessment of Insulin Resistance; LS, longitudinal strain; LV, left ventricular.

Risk enhancers of LV maladaptation

Conclusions

The 2013 ACC/AHA risk score adequately captured the risk for echocardiographic phenotypes of early-stage HF. As risk enhancer, insulin resistance might improve risk stratification of subclinical HF in subjects at intermediate risk.

Acknowledgement/Funding

The European Union, European Research Council and the Flanders Scientific Research Fund supported this study.

P1923Deep and machine learning models to improve risk prediction of cardiovascular disease using data extraction from electronic health records

Background

Advances in precision medicine will require an increasingly individualized prognostic evaluation of patients in order to provide the patient with appropriate therapy. The traditional statistical methods of predictive modeling, such as SCORE, PROCAM, and Framingham, according to the European guidelines for the prevention of cardiovascular disease, not adapted for all patients and require significant human involvement in the selection of predictive variables, transformation and imputation of variables. In ROC-analysis for prediction of significant cardiovascular disease (CVD), the areas under the curve for Framingham: 0.62–0.72, for SCORE: 0.66–0.73 and for PROCAM: 0.60–0.69. To improve it, we apply for approaches to predict a CVD event rely on conventional risk factors by machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR).

Methods

For machine learning, we applied logistic regression (LR) and recurrent neural networks with long short-term memory (LSTM) units as a deep learning algorithm. We extract from longitudinal EHR the following features: demographic, vital signs, diagnoses (ICD-10-cm: I21-I22.9: I61-I63.9) and medication. The problem in this step, that near 80 percent of clinical information in EHR is “unstructured” and contains errors and typos. Missing data are important for the correct training process using by deep learning & machine learning algorithm. The study cohort included patients between the ages of 21 to 75 with a dynamic observation window. In total, we got 31517 individuals in the dataset, but only 3652 individuals have all features or missing features values can be easy to impute. Among these 3652 individuals, 29.4% has a CVD, mean age 49.4 years, 68,2% female.

Evaluation

We randomly divided the dataset into a training and a test set with an 80/20 split. The LR was implemented with Python Scikit-Learn and the LSTM model was implemented with Keras using Tensorflow as the backend.

Results

We applied machine learning and deep learning models using the same features as traditional risk scale and longitudinal EHR features for CVD prediction, respectively. Machine learning model (LR) achieved an AUROC of 0.74–0.76 and deep learning (LSTM) 0.75–0.76. By using features from EHR logistic regression and deep learning models improved the AUROC to 0.78–0.79.

Conclusion

The machine learning models outperformed a traditional clinically-used predictive model for CVD risk prediction (i.e. SCORE, PROCAM, and Framingham equations). This approach was used to create a clinical decision support system (CDSS). It uses both traditional risk scales and models based on neural networks. Especially important is the fact that the system can calculate the risks of cardiovascular disease automatically and recalculate immediately after adding new information to the EHR. The results are delivered to the user's personal account.

P1576Association of colorectal cancer with genetic and epigenetic variation in PEAR1 - a population-based cohort study

Background

Platelet Endothelial Aggregation Receptor 1 (PEAR1) modulates angiogenesis and platelet contact-induced activation, which play a role in the pathogenesis of colorectal cancer.

Purpose

To study the association of colorectal cancer with genetic and epigenetic variation in PEAR1.

Methods

Among 2532 randomly recruited participants enrolled in the family-based Flemish Study on Environment, Genes and Health Outcomes (51.2% women; mean age 44.8 years), we recorded the incidence of colorectal cancer and genotyped SNP rs12566888 located in intron 1 of the PEAR1 gene. In 929 participants, we also measured the methylation at 16 CpG sites in the PEAR1 promoter. In multivariable-adjusted analyses, we contrasted the risk of colorectal cancer in minor-allele (T) carriers vs. major allele (GG) homozygotes. We applied partial least square discriminant analysis (PLS-DA) to identify methylation sites associated with colorectal cancer.

Results

Over a median follow-up of 18.1 years, 49 patients developed colorectal cancer. While accounting for clustering within families and adjusting for sex, age, body mass index, the total-to-HDL cholesterol ratio, serum creatinine, plasma glucose, smoking and drinking, use of antiplatelet and nonsteroidal anti-inflammatory drug, the hazard ratio contrasting minor allele carriers vs. major allele homozygotes was 2.17 (95% confidence interval, 1.18–3.99; P=0.013). Bootstrapped analyses, from which we randomly excluded from two to nine cancer cases, provided confirmatory results. PLS-DA identified two methylation sites in the PEAR1 promoter associated with higher colorectal cancer risk and two with lower risk. In-silico analysis suggested that methylation of the PEAR1 promoter at these four sites affects binding of the transcription factors p53, PAX5, and E2F-1, thereby modulating gene expression.

Potential pathways

Conclusions

Our findings suggest that genetic and epigenetic variation in PEAR1 modulates the risk of colorectal cancer in white Flemish.

Acknowledgement/Funding

The European Union, the European Research Council, the European Research Area Net for Cardiovascular Diseases, and the Research Foundation Flanders.

P3819Machine learning for predicting early left ventricular abnormalities in the general population

Background

Current heart failure guidelines emphasize the importance of timely detection of subclinical left ventricular (LV) remodelling and dysfunction for more precise risk stratification of asymptomatic subjects. Both LV diastolic dysfunction (LVDD) and LV hypertrophy (LVH) as assessed by echocardiography are known independent prognostic markers of future cardiovascular events in the community. However, selective screening strategies of individuals at risk who would benefit most from in-depth cardiac phenotyping are lacking.

Purpose

We assess the utility of several Machine Learning (ML) classifiers built on clinical and biochemical features for detecting subclinical LV abnormalities.

Methods

We included 1407 participants (mean age, 51 years, 51% women) randomly recruited from the general population. We used echocardiographic parameters reflecting LV diastolic function and structure to define LV abnormalities (LVDD, n=239; LVH, n=135). After that four supervised ML algorithms (Random Forest (RF), Gradient Boosting (GD), Stochastic Gradient Descent (SGD) and Support Vector Machines (SV)) were built based on routine clinical, hemodynamic and laboratory data (features; n=61) to categorize LVDD and LVH (two prediction tasks). We applied a 10-fold stratified cross-validation set-up.

Results

ML classifiers exhibited a high area under the ROC (AUC) for predicting LVDD with values between 88.5% and 93.1% (Figure, left panel). Age, BMI, different components of blood pressure, antihypertensive treatment, routine biomarkers such as serum electrolytes, creatinine, blood sugar, leptin, uric acid, lipid profile, as well as blood cell counts were the top selected features for predicting LVDD. Prediction AUC of ML algorithms for detection of LVH was somewhat lower than for LVDD and ranged from 72.5% to 78.7% (Figure, right panel). The top selected features for LVH classifier were similar to those of LVDD, but also included social class, serum gamma-glutamyl transferase, fasting insulin, plasma renin activity and cortisol.

ROC curves (sensitivity-1-specificity)

Conclusions

ML algorithms combining routinely measured clinical and laboratory data have shown high accuracy of LVDD and LVH prediction. These ML classifiers might be useful to preselect individuals at risk for further in depth echocardiographic examination, monitoring and implementation of preventive strategies in order to delay transition to disease symptoms.

6075Workload adjusted blood pressure response rather than peak systolic blood pressure is associated with increased all-cause mortality in males; results from 7097 treadmill exercise tests

Background

Systolic blood pressure (SBP) is routinely measured during exercise testing (ET) and is in part determined by cardiac output and peripheral vascular resistance. A frequently used threshold for defining hypertensive response to exercise is ≥210 mmHg but this does not account for the fact that SBP is related to workload, via cardiac output.

Purpose

To examine the prognostic implications of considering external workload (METs) adjusted SBP response to exercise.

Methods

We reviewed all symptom-limited treadmill ET in males between 1987 and 2007 at a single centre (inclusion/exclusion criteria detailed in figure 1A). SBP was measured standing at rest and at peak exercise. Workload adjusted BP response with exercise (SBP/MET slope) was calculated as ΔSBP/ΔMET. METs were calculated from peak speed and grade according to the standard American College of Sports Medicine (ACSM) formula. Age-predicted peak METs was calculated as: 18 - 0.15 × age. Ten-year Cox proportional hazard ratios (HR) with 95% confidence intervals were calculated and adjusted as outlined in figure 1B.

Results

7097 subjects were included, of which 1559 (22%) died within 10 years. Survivors were younger (57.2±10.6 y vs. 64.5±10.3 y, p<0.001) and reached higher % of age-predicted METs (97±33% vs. 82±33%, p<0.001). Survivors had higher peak SBP (181±26 vs. 176±27 mmHg, p<0.001) as well as greater ΔSBP (49±22 vs. 42±22 mmHg, p<0.001), while they had lower SBP/MET slope (7.0±4.4 vs. 8.9±6.5 mmHg/MET, p<0.001). A peak SBP ≥210 mmHg was associated with better survival; 10-yr adjusted HR: 0.76 (0.64–0.88, p<0.001). In contrast, a higher SBP/MET slope was associated with increased mortality (table 1).

Table 1. Ten year adjusted hazard ratios Variable HR (95% CI) P Variable HR (95% CI) P Variable HR (95% CI) P Peak SBP, Q1: 100–159 mmHg REF REF Delta SBP, Q1: 1–29 mmHg REF REF SBP/MET slope, Q1: 0.2–4.2 REF REF Peak SBP, Q2: 160–179 mmHg 0.81 (0.71–0.94) 0.006 Delta SBP, Q2: 30–46 mmHg 0.80 (0.70–0.91) 0.001 SBP/MET slope, Q2: 4.3–6.2 0.95 (0.81–1.12) 0.562 Peak SBP, Q3: 180–199 mmHg 0.68 (0.58–0.78) <0.001 Delta SBP, Q3: 47–61 mmHg 0.76 (0.66–0.88) <0.001 SBP/MET slope, Q3: 6.2–9.1 1.18 (1.01–1.37) 0.032 Peak SBP, Q4: ≥200 mmHg 0.60 (0.51–0.69) <0.001 Delta SBP, Q4: ≥62 mmHg 0.59 (0.50–0.69) <0.001 SBP/MET slope, Q4: ≥9.1 1.40 (1.22– 1.62) <0.001 HR, hazard ratio (adjusted according to figure 1B); SBP, systolic blood pressure; MET, metabolic equivalent of task; Q1–Q4, quartiles (Q1 as reference).

Figure 1

Conclusion

Workload adjusted blood pressure response to exercise in contrast to peak BP response was associated with increased mortality in male patients referred for ET. Of note, reaching a BP of at least 210 mmHg (suggested to define a hypertensive response to exercise) was associated with a 24% reduction in all-cause mortality.

Acknowledgement/Funding

K Hedman was supported by post-doc. grants from the Fulbright Commission, the Swedish Society of Medicine, County Council of Östergötland, Sweden

P5334A 15-year follow-up of 315 patients with familial hypercholesterolemia from the North-West region of Russia

Aim of the study

to ascertain genetic and environmental factors influencing the time course of FH during a 15-year follow-up in a large FH cohort from the North-West region of Russia.

We screened more than 1ehz746.0302 high risk patients in order to reveal FH in Saint-Petersburg and Petrozavodsk using DLCN criteria. In most patients DNA analysis was also performed.

Definite FH (≥8 according to DLCN) was found in 315 patients (221 - in Saint-Petersburg, 94 - in Petrozavodsk). These patients were followed-up for 15 years and more. CHD was more frequent in male patients, patients older than 60 years of age and was associated with higher levels of total cholesterol, LDL cholesterol, lower levels of HDL cholesterol, arterial hypertension, smoking and higher DLCN scoring. Patients without CHD had lowest LDL/HDL ratio (5.2±0.45) whereas patients with stable and progressive CHD had LDL/HDL ratio - 7.7±0.89 and 10.4±0.78, respectively (p≤0.05). Genetic study revealed only 1 homozygous patient and 1 patient with apoB-100 (FDP) gene mutation. Only 1 case of FH-North Karelia mutation that is typical for Finland was found in Petrozavodsk. Most of revealed mutations in LDL-receptor gene were unique i.e. found only in 1 family. This suggest the absence of a strong founder effect associated with FH in the North-West Region of Russia. Due to high heterogeneity of FH-causing mutations we failed to establish interrelations between type of LDL-receptor gene mutations and severity of atherosclerosis and CHD time course. 14% of FH patients didn't take any hypolipidemic medications at the onset of the follow-up, whereas 61% took statins and 25% statin + ezetimibe. Homozygous patient was treated both with statin, ezetimibe, evolocumab and LDL-apheresis. Nevertheless, 26% of the treated group didn't achieve the target LDL-C levels. It is interesting that 40% of patients who didn't reach LDL-goals were current smokers (compared with 5% of patients who reached LDL-goals). 33 patients (10.5%) died during the follow-up mostly due to cardiovascular complications. Death rates in FH patients were strongly associated with age, male sex, LDL/HDL ratio, smoking and effectiveness of hypolipidemic treatment. FH is strongly associated with high CHD risk; the time course of FH is much more favourable in females, in patients with low LDL/HDL ratio and free of other modifiable risk factors. Type of LDL-receptor gene mutation doesn't influence lipid levels or clinical manifestations of FH.

Fungal Planet description sheets: 868-950

Novel species of fungi described in this study include those from various countries as follows: Australia, Chaetomella pseudocircinoseta and Coniella pseudodiospyri on Eucalyptus microcorys leaves, Cladophialophora eucalypti, Teratosphaeria dunnii and Vermiculariopsiella dunnii on Eucalyptus dunnii leaves, Cylindrium grande and Hypsotheca eucalyptorum on Eucalyptus grandis leaves, Elsinoe salignae on Eucalyptus saligna leaves, Marasmius lebeliae on litter of regenerating subtropical rainforest, Phialoseptomonium eucalypti (incl. Phialoseptomonium gen. nov.) on Eucalyptus grandis × camaldulensis leaves, Phlogicylindrium pawpawense on Eucalyptus tereticornis leaves, Phyllosticta longicauda as an endophyte from healthy Eustrephus latifolius leaves, Pseudosydowia eucalyptorum on Eucalyptus sp. leaves, Saitozyma wallum on Banksia aemula leaves, Teratosphaeria henryi on Corymbia henryi leaves. Brazil, Aspergillus bezerrae, Backusella azygospora, Mariannaea terricola and Talaromyces pernambucoensis from soil, Calonectria matogrossensis on Eucalyptus urophylla leaves, Calvatia brasiliensis on soil, Carcinomyces nordestinensis on Bromelia antiacantha leaves, Dendryphiella stromaticola on small branches of an unidentified plant, Nigrospora brasiliensis on Nopalea cochenillifera leaves, Penicillium alagoense as a leaf endophyte on a Miconia sp., Podosordaria nigrobrunnea on dung, Spegazzinia bromeliacearum as a leaf endophyte on Tilandsia catimbauensis, Xylobolus brasiliensis on decaying wood. Bulgaria, Kazachstania molopis from the gut of the beetle Molops piceus. Croatia, Mollisia endocrystallina from a fallen decorticated Picea abies tree trunk. Ecuador, Hygrocybe rodomaculata on soil. Hungary, Alfoldia vorosii (incl. Alfoldia gen. nov.) from Juniperus communis roots, Kiskunsagia ubrizsyi (incl. Kiskunsagia gen. nov.) from Fumana procumbens roots. India, Aureobasidium tremulum as laboratory contaminant, Leucosporidium himalayensis and Naganishia indica from windblown dust on glaciers. Italy, Neodevriesia cycadicola on Cycas sp. leaves, Pseudocercospora pseudomyrticola on Myrtus communis leaves, Ramularia pistaciae on Pistacia lentiscus leaves, Neognomoniopsis quercina (incl. Neognomoniopsis gen. nov.) on Quercus ilex leaves. Japan, Diaporthe fructicola on Passiflora edulis × P. edulis f. flavicarpa fruit, Entoloma nipponicum on leaf litter in a mixed Cryptomeria japonica and Acer spp. forest. Macedonia, Astraeus macedonicus on soil. Malaysia, Fusicladium eucalyptigenum on Eucalyptus sp. twigs, Neoacrodontiella eucalypti (incl. Neoacrodontiella gen. nov.) on Eucalyptus urophylla leaves. Mozambique, Meliola gorongosensis on dead Philenoptera violacea leaflets. Nepal, Coniochaeta dendrobiicola from Dendriobium lognicornu roots. New Zealand, Neodevriesia sexualis and Thozetella neonivea on Archontophoenix cunninghamiana leaves. Norway, Calophoma sandfjordenica from a piece of board on a rocky shoreline, Clavaria parvispora on soil, Didymella finnmarkica from a piece of Pinus sylvestris driftwood. Poland, Sugiyamaella trypani from soil. Portugal, Colletotrichum feijoicola from Acca sellowiana. Russia, Crepidotus tobolensis on Populus tremula debris, Entoloma ekaterinae, Entoloma erhardii and Suillus gastroflavus on soil, Nakazawaea ambrosiae from the galleries of Ips typographus under the bark of Picea abies. Slovenia, Pluteus ludwigii on twigs of broadleaved trees. South Africa, Anungitiomyces stellenboschiensis (incl. Anungitiomyces gen. nov.) and Niesslia stellenboschiana on Eucalyptus sp. leaves, Beltraniella pseudoportoricensis on Podocarpus falcatus leaf litter, Corynespora encephalarti on Encephalartos sp. leaves, Cytospora pavettae on Pavetta revoluta leaves, Helminthosporium erythrinicola on Erythrina humeana leaves, Helminthosporium syzygii on a Syzygium sp. bark canker, Libertasomyces aloeticus on Aloe sp. leaves, Penicillium lunae from Musa sp. fruit, Phyllosticta lauridiae on Lauridia tetragona leaves, Pseudotruncatella bolusanthi (incl. Pseudotruncatellaceae fam. nov.) and Dactylella bolusanthi on Bolusanthus speciosus leaves. Spain, Apenidiella foetida on submerged plant debris, Inocybe grammatoides on Quercus ilex subsp. ilex forest humus, Ossicaulis salomii on soil, Phialemonium guarroi from soil. Thailand, Pantospora chromolaenae on Chromolaena odorata leaves. Ukraine, Cadophora helianthi from Helianthus annuus stems. USA, Boletus pseudopinophilus on soil under slash pine, Botryotrichum foricae, Penicillium americanum and Penicillium minnesotense from air. Vietnam, Lycoperdon vietnamense on soil. Morphological and culture characteristics are supported by DNA barcodes.

A multibiomarker approach to the assessment of pollution impacts in two Baltic Sea coastal areas in Sweden using caged mussels (Mytilus trossulus)

Blue mussels (Mytilus trossulus) were transplanted in cages for three months in two Swedish coastal areas in the Bothnian Sea (northern Baltic Sea) to investigate the interactions between analysed environmental chemicals and biological responses. A wide array of biological parameters (biomarkers) including antioxidant and biotransformation activity, geno-, cyto- and neurotoxic effects, phagocytosis, bioenergetic status and heart rate were measured to detect the possible effects of contaminants. Integrated Biomarker Response index and Principal Component Analysis performed on the individual biological response data were able to discriminate between the two study areas as well as the contaminated sites from their respective local reference sites. The two contaminated sites outside the cities of Sundsvall (station S1) and Gävle (station G1) were characterised by different biomarker response patterns. Mussels at station S1 showed a low condition index, increased heart rate recovery time and phagocytosis activity coinciding with the highest tissue concentrations of some trace metals, polycyclic aromatic hydrocarbons and organotins. At station G1 the highest organochlorine pesticide concentration was recorded as well as elevations in glutathione S-transferase activity, thiamine content and low lysosomal membrane stability. Significant variability in the geno- and cytotoxic responses and bioenergetic status was also observed at the different caging stations. The results obtained suggest that different chemical mixtures present in the study areas cause variable biological response patterns in organisms.

Blood pressure, cardiovascular outcomes and sodium intake, a critical review of the evidence

Consideration of the role of NaCl (salt) in the pathogenesis and treatment of essential hypertension is one of the overriding research themes both in experimental and clinical medicine. The evidence relating blood pressure to salt intake in humans originates from population studies and randomized clinical trials of interventions on dietary salt intake. Estimates from meta-analyses of trials in normotensive subjects generally are similar to estimates derived from prospective population studies (+ 1.7-mmHg increase in systolic blood pressure per 100 mmol increment in 24‑hour urinary sodium). This estimate, however, does not translate into an increased risk of incident hypertension in subjects consuming a high-salt diet. Prospective studies relating health outcomes to 24‑h urinary sodium excretion produced inconsistent results. Taken together, available evidence does not support the current recommendations of a generalized and indiscriminate reduction of salt intake at the population level. The public should be properly educated about the pros and cons of a decrease in sodium intake, in particular if they are healthy.