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2. Predictors and outcomes of heart failure with mid‐range ejection fraction

Author

Bhambhani, Vijeta, Kizer, Jorge R, Lima, Joao AC, van der Harst, Pim, Bahrami, Hossein, Nayor, Matthew, de Filippi, Christopher R, Enserro, Danielle, Blaha, Michael J, Cushman, Mary, Wang, Thomas J, Gansevoort, Ron T, Fox, Caroline S, Gaggin, Hanna K, Kop, Willem J, Liu, Kiang, Vasan, Ramachandran S, Psaty, Bruce M, Lee, Douglas S, Brouwers, Frank P, Hillege, Hans L, Bartz, Traci M, Benjamin, Emelia J, Chan, Cheeling, Allison, Matthew, Gardin, Julius M, Januzzi, James L, Levy, Daniel, Herrington, David M, van Gilst, Wiek H, Bertoni, Alain G, Larson, Martin G, de Boer, Rudolf A, Gottdiener, John S, Shah, Sanjiv J, and Ho, Jennifer E

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Cardiovascular, Clinical Research, Heart Disease, Good Health and Well Being, Aged, Cause of Death, Echocardiography, Female, Follow-Up Studies, Heart Failure, Heart Ventricles, Humans, Incidence, Male, Middle Aged, Prognosis, Prospective Studies, Risk Assessment, Stroke Volume, Survival Rate, United States, Heart failure, Risk factor, Ejection fraction, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

AimsWhile heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF) are well described, determinants and outcomes of heart failure with mid-range ejection fraction (HFmrEF) remain unclear. We sought to examine clinical and biochemical predictors of incident HFmrEF in the community.Methods and resultsWe pooled data from four community-based longitudinal cohorts, with ascertainment of new heart failure (HF) classified into HFmrEF [ejection fraction (EF) 41-49%], HFpEF (EF ≥50%), and HFrEF (EF ≤40%). Predictors of incident HF subtypes were assessed using multivariable Cox models. Among 28 820 participants free of HF followed for a median of 12 years, there were 200 new HFmrEF cases, compared with 811 HFpEF and 1048 HFrEF. Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, and prior myocardial infarction (multivariable adjusted P ≤ 0.003 for all). Biomarkers that predicted HFmrEF included natriuretic peptides, cystatin-C, and high-sensitivity troponin (P ≤ 0.0004 for all). Natriuretic peptides were stronger predictors of HFrEF [hazard ratio (HR) 2.00 per 1 standard deviation increase, 95% confidence interval (CI) 1.81-2.20] than of HFmrEF (HR 1.51, 95% CI 1.20-1.90, P = 0.01 for difference), and did not differ in their association with incident HFmrEF and HFpEF (HR 1.56, 95% CI 1.41-1.73, P = 0.68 for difference). All-cause mortality following the onset of HFmrEF was worse than that of HFpEF (50 vs. 39 events per 1000 person-years, P = 0.02), but comparable to that of HFrEF (46 events per 1000 person-years, P = 0.78).ConclusionsWe found overlap in predictors of incident HFmrEF with other HF subtypes. In contrast, mortality risk after HFmrEF was worse than HFpEF, and similar to HFrEF.

Published
2018

3. Causal Effect of Plasminogen Activator Inhibitor Type 1 on Coronary Heart Disease

Author

Song, Ci, Burgess, Stephen, Eicher, John D, O'Donnell, Christopher J, Johnson, Andrew D, Huang, Jie, Sabater‐Lleal, Maria, Asselbergs, Folkert W, Tregouet, David, Shin, So‐Youn, Ding, Jingzhong, Baumert, Jens, Oudot‐Mellakh, Tiphaine, Folkersen, Lasse, Smith, Nicholas L, Williams, Scott M, Ikram, Mohammad A, Kleber, Marcus E, Becker, Diane M, Truong, Vinh, Mychaleckyj, Josyf C, Tang, Weihong, Yang, Qiong, Sennblad, Bengt, Moore, Jason H, Williams, Frances MK, Dehghan, Abbas, Silbernagel, Günther, Schrijvers, Elisabeth MC, Smith, Shelly, Karakas, Mahir, Tofler, Geoffrey H, Silveira, Angela, Navis, Gerjan J, Lohman, Kurt, Chen, Ming‐Huei, Peters, Annette, Goel, Anuj, Hopewell, Jemma C, Chambers, John C, Saleheen, Danish, Lundmark, Per, Psaty, Bruce M, Strawbridge, Rona J, Boehm, Bernhard O, Carter, Angela M, Meisinger, Christa, Peden, John F, Bis, Joshua C, McKnight, Barbara, Öhrvik, John, Taylor, Kent, Franzosi, Maria Grazia, Seedorf, Udo, Collins, Rory, Franco‐Cereceda, Anders, Syvänen, Ann‐Christine, Goodall, Alison H, Yanek, Lisa R, Cushman, Mary, Müller‐Nurasyid, Martina, Folsom, Aaron R, Basu, Saonli, Matijevic, Nena, van Gilst, Wiek H, Kooner, Jaspal S, Hofman, Albert, Danesh, John, Clarke, Robert, Meigs, James B, Kathiresan, Sekar, Reilly, Muredach P, Klopp, Norman, Harris, Tamara B, Winkelmann, Bernhard R, Grant, Peter J, Hillege, Hans L, Watkins, Hugh, Spector, Timothy D, Becker, Lewis C, Tracy, Russell P, März, Winfried, Uitterlinden, Andre G, Eriksson, Per, Cambien, Francois, Morange, Pierre‐Emmanuel, Koenig, Wolfgang, Soranzo, Nicole, van der Harst, Pim, Liu, Yongmei, Hamsten, Anders, Ehret, Georg B, Munroe, Patricia B, Rice, Kenneth M, Bochud, Murielle, Chasman, Daniel I, Smith, Albert V, Tobin, Martin D, and Verwoert, Germaine C

Subjects
Clinical Research, Heart Disease - Coronary Heart Disease, Cardiovascular, Heart Disease, Aging, Biomarkers, Blood Glucose, Coronary Disease, Fibrinolysis, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Incidence, Lipoproteins, HDL, Mendelian Randomization Analysis, Multivariate Analysis, Observational Studies as Topic, Odds Ratio, Plasminogen Activator Inhibitor 1, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, coronary heart disease, genome-wide association study, Mendelian randomization, plasminogen activator inhibitor type 1, single nucleotide polymorphism, genome‐wide association study, Cardiorespiratory Medicine and Haematology
Abstract

Plasminogen activator inhibitor type 1 (PAI-1) plays an essential role in the fibrinolysis system and thrombosis. Population studies have reported that blood PAI-1 levels are associated with increased risk of coronary heart disease (CHD). However, it is unclear whether the association reflects a causal influence of PAI-1 on CHD risk. To evaluate the association between PAI-1 and CHD, we applied a 3-step strategy. First, we investigated the observational association between PAI-1 and CHD incidence using a systematic review based on a literature search for PAI-1 and CHD studies. Second, we explored the causal association between PAI-1 and CHD using a Mendelian randomization approach using summary statistics from large genome-wide association studies. Finally, we explored the causal effect of PAI-1 on cardiovascular risk factors including metabolic and subclinical atherosclerosis measures. In the systematic meta-analysis, the highest quantile of blood PAI-1 level was associated with higher CHD risk comparing with the lowest quantile (odds ratio=2.17; 95% CI: 1.53, 3.07) in an age- and sex-adjusted model. The effect size was reduced in studies using a multivariable-adjusted model (odds ratio=1.46; 95% CI: 1.13, 1.88). The Mendelian randomization analyses suggested a causal effect of increased PAI-1 level on CHD risk (odds ratio=1.22 per unit increase of log-transformed PAI-1; 95% CI: 1.01, 1.47). In addition, we also detected a causal effect of PAI-1 on elevating blood glucose and high-density lipoprotein cholesterol. Our study indicates a causal effect of elevated PAI-1 level on CHD risk, which may be mediated by glucose dysfunction.

Published
2017

4. Identifying Subpopulations with Distinct Response to Treatment Using Plasma Biomarkers in Acute Heart Failure: Results from the PROTECT Trial

Author

Liu, Licette CY, Valente, Mattia AE, Postmus, Douwe, O’Connor, Christopher M, Metra, Marco, Dittrich, Howard C, Ponikowski, Piotr, Teerlink, John R, Cotter, Gad, Davison, Beth, Cleland, John GF, Givertz, Michael M, Bloomfield, Daniel M, van Veldhuisen, Dirk J, Hillege, Hans L, van der Meer, Peter, and Voors, Adriaan A

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Cardiovascular, Clinical Research, Heart Disease, Acute Disease, Aged, Biomarkers, Diuretics, Female, Heart Failure, Humans, Male, Xanthines, Acute heart failure, Treatment heterogeneity, Subpopulation treatment effect pattern plot, Rolofylline, Pharmacology and Pharmaceutical Sciences, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Pharmacology and pharmaceutical sciences
Abstract

BackgroundOver the last 50 years, clinical trials of novel interventions for acute heart failure (AHF) have, with few exceptions, been neutral or shown harm. We hypothesize that this might be related to a differential response to pharmacological therapy.MethodsWe studied the magnitude of treatment effect of rolofylline across clinical characteristics and plasma biomarkers in 2033 AHF patients and derived a biomarker-based responder sum score model. Treatment response was survival from all-cause mortality through day 180.ResultsIn the overall study population, rolofylline had no effect on mortality (HR 1.03, 95% CI 0.82-1.28, p = 0.808). We found no treatment interaction across clinical characteristics, but we found interactions between several biomarkers and rolofylline. The biomarker-based sum score model included TNF-R1α, ST2, WAP four-disulfide core domain protein HE4 (WAP-4C), and total cholesterol, and the score ranged between 0 and 4. In patients with score 4 (those with increased TNF-R1α, ST2, WAP-4C, and low total cholesterol), treatment with rolofylline was beneficial (HR 0.61, 95% CI 0.40-0.92, p = 0.019). In patients with score 0, treatment with rolofylline was harmful (HR 5.52, 95% CI 1.68-18.13, p = 0.005; treatment by score interaction p

Published
2017

6. 52 Genetic Loci Influencing Myocardial Mass

Author

van der Harst, Pim, van Setten, Jessica, Verweij, Niek, Vogler, Georg, Franke, Lude, Maurano, Matthew T, Wang, Xinchen, Leach, Irene Mateo, Eijgelsheim, Mark, Sotoodehnia, Nona, Hayward, Caroline, Sorice, Rossella, Meirelles, Osorio, Lyytikäinen, Leo-Pekka, Polašek, Ozren, Tanaka, Toshiko, Arking, Dan E, Ulivi, Sheila, Trompet, Stella, Müller-Nurasyid, Martina, Smith, Albert V, Dörr, Marcus, Kerr, Kathleen F, Magnani, Jared W, Del Greco M., Fabiola, Zhang, Weihua, Nolte, Ilja M, Silva, Claudia T, Padmanabhan, Sandosh, Tragante, Vinicius, Esko, Tõnu, Abecasis, Gonçalo R, Adriaens, Michiel E, Andersen, Karl, Barnett, Phil, Bis, Joshua C, Bodmer, Rolf, Buckley, Brendan M, Campbell, Harry, Cannon, Megan V, Chakravarti, Aravinda, Chen, Lin Y, Delitala, Alessandro, Devereux, Richard B, Doevendans, Pieter A, Dominiczak, Anna F, Ferrucci, Luigi, Ford, Ian, Gieger, Christian, Harris, Tamara B, Haugen, Eric, Heinig, Matthias, Hernandez, Dena G, Hillege, Hans L, Hirschhorn, Joel N, Hofman, Albert, Hubner, Norbert, Hwang, Shih-Jen, Iorio, Annamaria, Kähönen, Mika, Kellis, Manolis, Kolcic, Ivana, Kooner, Ishminder K, Kooner, Jaspal S, Kors, Jan A, Lakatta, Edward G, Lage, Kasper, Launer, Lenore J, Levy, Daniel, Lundby, Alicia, Macfarlane, Peter W, May, Dalit, Meitinger, Thomas, Metspalu, Andres, Nappo, Stefania, Naitza, Silvia, Neph, Shane, Nord, Alex S, Nutile, Teresa, Okin, Peter M, Olsen, Jesper V, Oostra, Ben A, Penninger, Josef M, Pennacchio, Len A, Pers, Tune H, Perz, Siegfried, Peters, Annette, Pinto, Yigal M, Pfeufer, Arne, Pilia, Maria Grazia, Pramstaller, Peter P, Prins, Bram P, Raitakari, Olli T, Raychaudhuri, Soumya, Rice, Ken M, Rossin, Elizabeth J, Rotter, Jerome I, Schafer, Sebastian, Schlessinger, David, and Schmidt, Carsten O

Subjects
Medical Physiology, Biomedical and Clinical Sciences, Genetics, Heart Disease - Coronary Heart Disease, Cardiovascular, Heart Disease, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Animals, Cardiomegaly, Genetic Loci, Genome-Wide Association Study, Humans, electrocardiogram, genetic association study, heart failure, left ventricular hypertrophy, QRS, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundMyocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death.ObjectivesThis meta-analysis sought to gain insights into the genetic determinants of myocardial mass.MethodsWe carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment.ResultsWe identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10(-8). These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo.ConclusionsTaken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets.

Published
2016

7. A combined clinical and biomarker approach to predict diuretic response in acute heart failure

Author

ter Maaten, Jozine M, Valente, Mattia AE, Metra, Marco, Bruno, Noemi, O’Connor, Christopher M, Ponikowski, Piotr, Teerlink, John R, Cotter, Gad, Davison, Beth, Cleland, John G, Givertz, Michael M, Bloomfield, Daniel M, Dittrich, Howard C, van Veldhuisen, Dirk J, Hillege, Hans L, Damman, Kevin, and Voors, Adriaan A

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Kidney Disease, Prevention, Cardiovascular, Clinical Research, Acute Disease, Atherosclerosis, Biomarkers, Clinical Trials, Phase III as Topic, Diuretics, Electrolytes, Furosemide, Heart Failure, Humans, Kidney Diseases, Randomized Controlled Trials as Topic, Retrospective Studies, Time Factors, Treatment Outcome, Diuretic response, Heart failure, Prediction, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundPoor diuretic response in acute heart failure is related to poor clinical outcome. The underlying mechanisms and pathophysiology behind diuretic resistance are incompletely understood. We evaluated a combined approach using clinical characteristics and biomarkers to predict diuretic response in acute heart failure (AHF).Methods and resultsWe investigated explanatory and predictive models for diuretic response--weight loss at day 4 per 40 mg of furosemide--in 974 patients with AHF included in the PROTECT trial. Biomarkers, addressing multiple pathophysiological pathways, were determined at baseline and after 24 h. An explanatory baseline biomarker model of a poor diuretic response included low potassium, chloride, hemoglobin, myeloperoxidase, and high blood urea nitrogen, albumin, triglycerides, ST2 and neutrophil gelatinase-associated lipocalin (r(2) = 0.086). Diuretic response after 24 h (early diuretic response) was a strong predictor of diuretic response (β = 0.467, P < 0.001; r(2) = 0.523). Addition of diuretic response after 24 h to biomarkers and clinical characteristics significantly improved the predictive model (r(2) = 0.586, P < 0.001).ConclusionsBiomarkers indicate that diuretic unresponsiveness is associated with an atherosclerotic profile with abnormal renal function and electrolytes. However, predicting diuretic response is difficult and biomarkers have limited additive value. Patients at risk of poor diuretic response can be identified by measuring early diuretic response after 24 h.

Published
2016

10. Gene-Age Interactions in Blood Pressure Regulation: A Large-Scale Investigation with the CHARGE, Global BPgen, and ICBP Consortia

Author

Simino, Jeannette, Shi, Gang, Bis, Joshua C, Chasman, Daniel I, Ehret, Georg B, Gu, Xiangjun, Guo, Xiuqing, Hwang, Shih-Jen, Sijbrands, Eric, Smith, Albert V, Verwoert, Germaine C, Bragg-Gresham, Jennifer L, Cadby, Gemma, Chen, Peng, Cheng, Ching-Yu, Corre, Tanguy, de Boer, Rudolf A, Goel, Anuj, Johnson, Toby, Khor, Chiea-Chuen, Study, LifeLines Cohort, Alizadeh, Behrooz Z, Boezen, H Marike, Bruinenberg, Marcel, Franke, Lude, van der Harst, Pim, Hillege, Hans L, van der Klauw, Melanie M, Navis, Gerjan, Ormel, Johan, Postma, Dirkje S, Rosmalen, Judith GM, Slaets, Joris P, Snieder, Harold, Stolk, Ronald P, Wolffenbuttel, Bruce HR, Wijmenga, Cisca, Lluís-Ganella, Carla, Luan, Jian’an, Lyytikäinen, Leo-Pekka, Nolte, Ilja M, Sim, Xueling, Sõber, Siim, van der Most, Peter J, Verweij, Niek, Zhao, Jing Hua, Amin, Najaf, Boerwinkle, Eric, Bouchard, Claude, Dehghan, Abbas, Eiriksdottir, Gudny, Elosua, Roberto, Franco, Oscar H, Gieger, Christian, Harris, Tamara B, Hercberg, Serge, Hofman, Albert, James, Alan L, Johnson, Andrew D, Kähönen, Mika, Khaw, Kay-Tee, Kutalik, Zoltan, Larson, Martin G, Launer, Lenore J, Li, Guo, Liu, Jianjun, Liu, Kiang, Morrison, Alanna C, Ong, Rick Twee-Hee, Papanicolau, George J, Penninx, Brenda W, Psaty, Bruce M, Raffel, Leslie J, Raitakari, Olli T, Rice, Kenneth, Rivadeneira, Fernando, Rose, Lynda M, Sanna, Serena, Scott, Robert A, Siscovick, David S, Uitterlinden, Andre G, Vaidya, Dhananjay, Vasan, Ramachandran S, Vithana, Eranga Nishanthie, Völker, Uwe, Völzke, Henry, Watkins, Hugh, Young, Terri L, Aung, Tin, Bochud, Murielle, Farrall, Martin, Hartman, Catharina A, Laan, Maris, Lakatta, Edward G, Lehtimäki, Terho, and Loos, Ruth JF

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Clinical Research, Human Genome, Aging, 2.1 Biological and endogenous factors, Aetiology, Adolescent, Adult, Age Factors, Aged, Blood Pressure, Cohort Studies, Humans, Middle Aged, Young Adult, LifeLines Cohort Study, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Although age-dependent effects on blood pressure (BP) have been reported, they have not been systematically investigated in large-scale genome-wide association studies (GWASs). We leveraged the infrastructure of three well-established consortia (CHARGE, GBPgen, and ICBP) and a nonstandard approach (age stratification and metaregression) to conduct a genome-wide search of common variants with age-dependent effects on systolic (SBP), diastolic (DBP), mean arterial (MAP), and pulse (PP) pressure. In a two-staged design using 99,241 individuals of European ancestry, we identified 20 genome-wide significant (p ≤ 5 × 10(-8)) loci by using joint tests of the SNP main effect and SNP-age interaction. Nine of the significant loci demonstrated nominal evidence of age-dependent effects on BP by tests of the interactions alone. Index SNPs in the EHBP1L1 (DBP and MAP), CASZ1 (SBP and MAP), and GOSR2 (PP) loci exhibited the largest age interactions, with opposite directions of effect in the young versus the old. The changes in the genetic effects over time were small but nonnegligible (up to 1.58 mm Hg over 60 years). The EHBP1L1 locus was discovered through gene-age interactions only in whites but had DBP main effects replicated (p = 8.3 × 10(-4)) in 8,682 Asians from Singapore, indicating potential interethnic heterogeneity. A secondary analysis revealed 22 loci with evidence of age-specific effects (e.g., only in 20 to 29-year-olds). Age can be used to select samples with larger genetic effect sizes and more homogenous phenotypes, which may increase statistical power. Age-dependent effects identified through novel statistical approaches can provide insight into the biology and temporal regulation underlying BP associations.

Published
2014

11. Identifying Pathophysiological Mechanisms in Heart Failure With Reduced Versus Preserved Ejection Fraction

Author

Tromp, Jasper, Westenbrink, B. Daan, Ouwerkerk, Wouter, van Veldhuisen, Dirk J., Samani, Nilesh J., Ponikowski, Piotr, Metra, Marco, Anker, Stefan D., Cleland, John G., Dickstein, Kenneth, Filippatos, Gerasimos, van der Harst, Pim, Lang, Chim C., Ng, Leong L., Zannad, Faiez, Zwinderman, Aelko H., Hillege, Hans L., van der Meer, Peter, and Voors, Adriaan A.

Published
2018
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12. The Association of Obesity and Cardiometabolic Traits With Incident HFpEF and HFrEF

Author

Savji, Nazir, Meijers, Wouter C., Bartz, Traci M., Bhambhani, Vijeta, Cushman, Mary, Nayor, Matthew, Kizer, Jorge R., Sarma, Amy, Blaha, Michael J., Gansevoort, Ron T., Gardin, Julius M., Hillege, Hans L., Ji, Fei, Kop, Willem J., Lau, Emily S., Lee, Douglas S., Sadreyev, Ruslan, van Gilst, Wiek H., Wang, Thomas J., Zanni, Markella V., Vasan, Ramachandran S., Allen, Norrina B., Psaty, Bruce M., van der Harst, Pim, Levy, Daniel, Larson, Martin, Shah, Sanjiv J., de Boer, Rudolf A., Gottdiener, John S., and Ho, Jennifer E.

Published
2018
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13. Biomarker-Guided Versus Guideline-Based Treatment of Patients With Heart Failure: Results From BIOSTAT-CHF

Author

Ouwerkerk, Wouter, Zwinderman, Aeilko H., Ng, Leong L., Demissei, Biniyam, Hillege, Hans L., Zannad, Faiez, van Veldhuisen, Dirk J., Samani, Nilesh J., Ponikowski, Piotr, Metra, Marco, ter Maaten, Jozine M., Lang, Chim C., van der Harst, Pim, Filippatos, Gerasimos, Dickstein, Kenneth, Cleland, John G., Anker, Stefan D., and Voors, Adriaan A.

Published
2018
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15. Trajectories of renal biomarkers and new‐onset heart failure in the general population: Findings from the PREVEND study

Author

Sakaniwa, Ryoto, primary, Tromp, Jasper, additional, Streng, Koen W., additional, Suthahar, Navin, additional, Kieneker, Lyanne M., additional, Postmus, Douwe, additional, Iso, Hiroyasu, additional, Gansevoort, Ron T., additional, Bakker, Stephan J.L., additional, Hillege, Hans L., additional, de Boer, Rudolf A., additional, and Demissei, Biniyam G., additional

Published
2023
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17. Multimarker Analysis of Serially Measured GDF-15, NT-proBNP, ST2, GAL-3, cTnI, Creatinine, and Prognosis in Acute Heart Failure

Author

Cardiologie, Team Medisch, Gürgöze, Muhammed T., Van Vark, Laura C., Baart, Sara J., Kardys, Isabella, Akkerhuis, K. Martijn, Manintveld, Olivier C., Postmus, Douwe, Hillege, Hans L., Lesman-Leegte, Ivonne, Asselbergs, Folkert W., Brunner-La-Rocca, Hans Peter, Van Den Bos, Ewout J., Orsel, Joke G., De Ridder, Stijn P.J., Pinto, Yigal M., Boersma, Eric, Cardiologie, Team Medisch, Gürgöze, Muhammed T., Van Vark, Laura C., Baart, Sara J., Kardys, Isabella, Akkerhuis, K. Martijn, Manintveld, Olivier C., Postmus, Douwe, Hillege, Hans L., Lesman-Leegte, Ivonne, Asselbergs, Folkert W., Brunner-La-Rocca, Hans Peter, Van Den Bos, Ewout J., Orsel, Joke G., De Ridder, Stijn P.J., Pinto, Yigal M., and Boersma, Eric

Published
2023

18. Multimarker Analysis of Serially Measured GDF-15, NT-proBNP, ST2, GAL-3, cTnI, Creatinine, and Prognosis in Acute Heart Failure

Author

Gürgöze, Muhammed T., Van Vark, Laura C., Baart, Sara J., Kardys, Isabella, Akkerhuis, K. Martijn, Manintveld, Olivier C., Postmus, Douwe, Hillege, Hans L., Lesman-Leegte, Ivonne, Asselbergs, Folkert W., Brunner-La-Rocca, Hans Peter, Van Den Bos, Ewout J., Orsel, Joke G., De Ridder, Stijn P.J., Pinto, Yigal M., Boersma, Eric, Gürgöze, Muhammed T., Van Vark, Laura C., Baart, Sara J., Kardys, Isabella, Akkerhuis, K. Martijn, Manintveld, Olivier C., Postmus, Douwe, Hillege, Hans L., Lesman-Leegte, Ivonne, Asselbergs, Folkert W., Brunner-La-Rocca, Hans Peter, Van Den Bos, Ewout J., Orsel, Joke G., De Ridder, Stijn P.J., Pinto, Yigal M., and Boersma, Eric

Abstract

Background: Studies on serially measured GDF-15 (growth differentiation factor 15) in acute heart failure (HF) are limited. Moreover, several pathophysiological pathways contribute to HF. Therefore, we aimed to explore the (additional) prognostic value of serially measured GDF-15 using a multi-marker approach to more accurately predict HF risk. Methods: TRIUMPH (Translational Initiative on Unique and Novel Strategies for Management of Patients With Heart Failure) is a prospective cohort of 496 patients with acute HF who were enrolled in 14 hospitals in the Netherlands between 2009 and 2014. Blood sampling was scheduled at 7 moments during 1-year follow-up. GDF-15, NT-proBNP (N-terminal pro-B-type natriuretic peptide), ST2 (suppression of tumorigenicity 2), galectin-3, troponin I, and creatinine were measured in a central laboratory. We associated repeated measurements of these biomarkers with the composite primary end point of all-cause mortality and HF rehospitalization, using multivariable joint modeling. Results: Median age was 74 years, and 37% were women. Median baseline GDF-15 was 4632 pg/mL. The primary end point was reached in 188 (40%) patients. The average estimated GDF-15 level increased weeks before the primary end point was reached. The hazard ratio per 1 SD difference in log-GDF-15 was 2.14 (95% CI, 1.78-2.57) unadjusted, 1.96 (1.49-2.53) after adjustment for clinical confounders and 1.44 (1.05-1.91) when jointly modeled with all biomarkers. The adjusted HRs for NT-proBNP were 2.38 (1.78-3.33) and 1.52 (1.15-2.08), respectively. The multimarker model combining GDF-15, NT-proBNP, and troponin I provided a favorable risk discrimination (area under the curve=0.785). Conclusions: Sequentially measured GDF-15 independently and dynamically predicts risk of adverse outcomes during 1-year follow-up after index admission for acute HF. NT-proBNP remains a robust predictor among potential candidates.

Published
2023

19. Multimarker Analysis of Serially Measured GDF-15, NT-proBNP, ST2, GAL-3, cTnI, Creatinine, and Prognosis in Acute Heart Failure

Author

Gürgöze, Muhammed T., primary, van Vark, Laura C., additional, Baart, Sara J., additional, Kardys, Isabella, additional, Akkerhuis, K. Martijn, additional, Manintveld, Olivier C., additional, Postmus, Douwe, additional, Hillege, Hans L., additional, Lesman-Leegte, Ivonne, additional, Asselbergs, Folkert W., additional, Brunner-la-Rocca, Hans-Peter, additional, van den Bos, Ewout J., additional, Orsel, Joke G., additional, de Ridder, Stijn P.J., additional, Pinto, Yigal M., additional, and Boersma, Eric, additional

Published
2023
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21. Antiarrhythmic drugs in patients with early persistent atrial fibrillation and heart failure: results of the RACE 3 study

Author

Al-Jazairi, Meelad I H, Nguyen, Bao-Oanh, De With, Ruben R, Smit, Marcelle D, Weijs, Bob, Hobbelt, Anne H, Alings, Marco, Tijssen, Jan G P, Geelhoed, Bastiaan, Hillege, Hans L, Tieleman, Robert G, Van Veldhuisen, Dirk J, Crijns, Harry J G M, Van Gelder, Isabelle C, Blaauw, Yuri, Rienstra, Michiel, MUMC+: MA Med Staf Spec Cardiologie (9), RS: Carim - H01 Clinical atrial fibrillation, Cardiologie, MUMC+: MA Cardiologie (9), Groningen Kidney Center (GKC), Life Course Epidemiology (LCE), Cardiovascular Centre (CVC), Cardiology, and ACS - Heart failure & arrhythmias

Subjects
Male, medicine.medical_specialty, medicine.medical_treatment, Heart failure, Cardioversion, Amiodarone, TERM, Ventricular Function, Left, Early persistent atrial fibrillation, AFFIRM, SOTALOL, Clinical Research, Physiology (medical), Internal medicine, medicine, Humans, AcademicSubjects/MED00200, Sinus rhythm, Flecainide, Aged, business.industry, Sotalol, Stroke Volume, Atrial fibrillation, Middle Aged, medicine.disease, EFFICACY, PREVENTION, Antiarrhythmic drugs, Dronedarone, AMIODARONE, CARDIOVERSION, MAINTENANCE, SAFETY, Cardiology, Female, Rhythm control, SINUS RHYTHM, Cardiology and Cardiovascular Medicine, business, Anti-Arrhythmia Agents, medicine.drug
Abstract

Aims Maintaining sinus rhythm in patients with persistent atrial fibrillation (AF) is challenging. We explored the efficacy of class I and III antiarrhythmic drugs (AADs) in patients with persistent AF and mild to moderate heart failure (HF). Methods and results In the RACE 3 trial, patients with early persistent symptomatic AF and short history of mild to moderate HF with preserved or reduced left ventricular ejection fraction (LVEF) were randomized to targeted or conventional therapy. Both groups received AF and HF guideline-driven treatment. Additionally, the targeted-group received mineralocorticoid receptor antagonists, statins, angiotensin-converting enzyme inhibitors and/or receptor blockers, and cardiac rehabilitation. Class I and III AADs could be instituted in case of symptomatic recurrent AF. Eventually, pulmonary vein isolation could be performed. Primary endpoint was sinus rhythm on 7-day Holter after 1-year. Included were 245 patients, age 65 ± 9 years, 193 (79%) men, AF history was 3 (2–6) months, HF history 2 (1–4) months, 72 (29.4%) had HF with reduced LVEF. After baseline electrical cardioversion (ECV), 190 (77.6%) had AF recurrences; 108 (56.8%) received class I/III AADs; 19 (17.6%) flecainide, 36 (33.3%) sotalol, 3 (2.8%) dronedarone, 50 (46.3%) amiodarone. At 1-year 73 of 108 (68.0%) patients were in sinus rhythm, 44 (40.7%) without new AF recurrences. Maintenance of sinus rhythm was significantly better with amiodarone [n = 29/50 (58%)] compared with flecainide [n = 6/19 (32%)] and sotalol/dronedarone [n = 9/39 (23%)], P = 0.0064. Adverse events occurred in 27 (25.0%) patients, were all minor and reversible. Conclusion In stable HF patients with early persistent AF, AAD treatment was effective in nearly half of patients, with no serious adverse effects reported.

Published
2021

22. Proteomic diversity of high‐density lipoprotein explains its association with clinical outcome in patients with heart failure

Author

Emmens, Johanna Elisabeth, Jones, Donald J.L., Cao, Thong H., Chan, Daniel C.S., Romaine, Simon P.R., Quinn, Paulene A., Anker, Stefan D., Cleland, John G., Dickstein, Kenneth, Filippatos, Gerasimos, Hillege, Hans L., Lang, Chim C., Ponikowski, Piotr, Samani, Nilesh J., van Veldhuisen, Dirk J., Zannad, Faiz, Zwinderman, Aeilko H., Metra, Marco, de Boer, Rudolf A., Voors, Adriaan A., and Ng, Leong L.

Published
2018
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25. Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Author

Cao, Thong Huy, Jones, Donald J. L., Quinn, Paulene A., Chan, Daniel Chu Siong, Hafid, Narayan, Parry, Helen M., Mohan, Mohapradeep, Sandhu, Jatinderpal K., Anker, Stefan D., Cleland, John G., Dickstein, Kenneth, Filippatos, Gerasimos, Hillege, Hans L., Metra, Marco, Ponikowski, Piotr, Samani, Nilesh J., Van Veldhuisen, Dirk J., Zannad, Faiez, Zwinderman, Aeilko H., Voors, Adriaan A., Lang, Chim C., and Ng, Leong L.

Published
2018
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28. Determinants of quality of life in acute heart failure patients with and without comorbidities:a prospective, observational study

Author

van den Berge, Jan C., van Vark, Laura C., Postmus, Douwe, Utens, Elisabeth M. W. J., Hillege, Hans L., Boersma, Eric, Lesman-Leegte, Ivonne, Akkerhuis, K. Martijn, van den Berge, Jan C., van Vark, Laura C., Postmus, Douwe, Utens, Elisabeth M. W. J., Hillege, Hans L., Boersma, Eric, Lesman-Leegte, Ivonne, and Akkerhuis, K. Martijn

Abstract

Aims The relation between non-cardiac comorbidities and health-related quality of life (HRQoL) in patients with heart failure (HF) has been studied to a limited extent. To investigate the HRQoL and their determinants among HF patients with and without comorbidities. Methods and results TRIUMPH (TRanslational Initiative on Unique and novel strategies for Management of Patients with Heart failure) is a Dutch prospective, multicentre study enrolling 496 acute HF patients between 2009 and 2014. We included 334 patients who had completed the HRQoL questionnaires at baseline. The HRQoL was measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) en EuroQuality-of-life five Dimensions (EQ-5D). Comorbidity was defined as having a history of at least one of the following comorbidities: chronic kidney disease, diabetes mellitus, chronic obstructive pulmonary disease (COPD), and/or cerebrovascular accident. Patients with comorbidity (n = 205, 61%) had lower scores on the physical limitation scale and clinical summary score of the KCCQ (P = 0.03 and P = 0.01, respectively). Female sex, COPD, previous HF, increasing body mass index (BMI), elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP), high systolic blood pressure, and the presence of anxiety and/or depression negatively influenced the HRQoL among HF patients with comorbidity. Besides anxiety and depression, we hardly found any other determinant of HRQoL in patients without comorbidity. Conclusion Heart failure patients without comorbidity had better HRQoL than patients with comorbidity. Sex, previous HF, BMI, COPD, systolic blood pressure, NT-proBNP levels, and also anxiety and depression were determinants of HRQoL in patients with comorbidity. In those without comorbidity, apart from anxiety and depression, no further determinants of HRQoL were found.

Published
2022

29. A network analysis to compare biomarker profiles in patients with and without diabetes mellitus in acute heart failure

Author

Sharma, Abhinav, Demissei, Biniyam G., Tromp, Jasper, Hillege, Hans L., Cleland, John G., OʼConnor, Christopher M., Metra, Marco, Ponikowski, Piotr, Teerlink, John R., Davison, Beth A., Givertz, Michael M., Bloomfield, Daniel M., Dittrich, Howard, van Veldhuisen, Dirk J., Cotter, Gad, Ezekowitz, Justin A., Khan, Mohsin A.F., and Voors, Adriaan A.

Published
2017
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30. Mineralocorticoid receptor antagonist pattern of use in heart failure with reduced ejection fraction: findings from BIOSTAT‐CHF

Author

Ferreira, João Pedro, Rossignol, Patrick, Machu, Jean‐Loup, Sharma, Abhinav, Girerd, Nicolas, Anker, Stefan D., Cleland, John G., Dickstein, Kenneth, Filippatos, Gerasimos, Hillege, Hans L., Lang, Chim C., ter Maaten, Jozine M., Metra, Marco, Ng, Leong, Ponikowski, Piotr, Samani, Nilesh J., van Veldhuisen, Dirk J., Zwinderman, Aeilko H., Voors, Adriaan, and Zannad, Faiez

Published
2017
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34. Genome-wide association study for circulating levels of PAI-1 provides novel insights into its regulation

Author

Huang, Jie, Sabater-Lleal, Maria, Asselbergs, Folkert W., Tregouet, David, Shin, So-Youn, Ding, Jingzhong, Baumert, Jens, Oudot-Mellakh, Tiphaine, Folkersen, Lasse, Johnson, Andrew D., Smith, Nicholas L., Williams, Scott M., Ikram, Mohammad A., Kleber, Marcus E., Becker, Diane M., Truong, Vinh, Mychaleckyj, Josyf C., Tang, Weihong, Yang, Qiong, Sennblad, Bengt, Moore, Jason H., Williams, Frances M.K., Dehghan, Abbas, Silbernagel, Günther, Schrijvers, Elisabeth M.C., Smith, Shelly, Karakas, Mahir, Tofler, Geoffrey H., Silveira, Angela, Navis, Gerjan J., Lohman, Kurt, Chen, Ming-Huei, Peters, Annette, Goel, Anuj, Hopewell, Jemma C., Chambers, John C., Saleheen, Danish, Lundmark, Per, Psaty, Bruce M., Strawbridge, Rona J., Boehm, Bernhard O., Carter, Angela M., Meisinger, Christa, Peden, John F., Bis, Joshua C., McKnight, Barbara, Öhrvik, John, Taylor, Kent, Franzosi, Maria Grazia, Seedorf, Udo, Collins, Rory, Franco-Cereceda, Anders, Syvänen, Ann-Christine, Goodall, Alison H., Yanek, Lisa R., Cushman, Mary, Müller-Nurasyid, Martina, Folsom, Aaron R., Basu, Saonli, Matijevic, Nena, van Gilst, Wiek H., Kooner, Jaspal S., Hofman, Albert, Danesh, John, Clarke, Robert, Meigs, James B., Kathiresan, Sekar, Reilly, Muredach P., Klopp, Norman, Harris, Tamara B., Winkelmann, Bernhard R., Grant, Peter J., Hillege, Hans L., Watkins, Hugh, Spector, Timothy D., Becker, Lewis C., Tracy, Russell P., März, Winfried, Uitterlinden, Andre G., Eriksson, Per, Cambien, Francois, Morange, Pierre-Emmanuel, Koenig, Wolfgang, Soranzo, Nicole, van der Harst, Pim, Liu, Yongmei, O'Donnell, Christopher J., and Hamsten, Anders

Published
2012
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35. Cardiac Magnetic Resonance Derived Left Ventricular Eccentricity Index and Right Ventricular Mass Measurements Predict Outcome in Children with Pulmonary Arterial Hypertension.

Author

Haarman, Meindina G., Coenraad, Iris, Hagdorn, Quint A. J., Hillege, Hans L., Willems, Tineke P., Berger, Rolf M. F., and Douwes, Johannes M.

Subjects
HEART radiography, PULMONARY arterial hypertension, LEFT heart ventricle, EVALUATION of medical care, SCIENTIFIC observation, RIGHT heart ventricle, MAGNETIC resonance imaging, RETROSPECTIVE studies, MANN Whitney U Test, RESEARCH funding, DESCRIPTIVE statistics, KAPLAN-Meier estimator, CHI-squared test, DATA analysis software, LONGITUDINAL method, CHILDREN
Abstract

Pulmonary arterial hypertension (PAH) is associated with increased right ventricular (RV) afterload, affecting RV remodeling and RV performance, a major determinant of outcome in PAH-patients. In children with PAH, treatment strategy is guided by risk stratification where noninvasive prognosticators are highly needed. The prognostic value of RV characteristics derived by cardiac magnetic resonance (CMR) has been scarcely studied in pediatric PAH. We aimed to identify CMR-derived morphometric and functional RV characteristics prognostic for outcome in children with PAH. From the Dutch National cohort, thirty-eight children with either idiopathic/heritable PAH (IPAH/HPAH) or PAH associated with congenital heart disease (PAH-CHD), who underwent CMR, were included (median (interquartile range) [IQR] age 13.0 years (10.8–15.0), 66% females). Patients had severe PAH, characterized by their World Health Organization Functional Class, increased N-terminal pro-B-type natriuretic peptide and high pulmonary arterial pressure and pulmonary vascular resistance index at time of CMR. RV-ejection fraction (RVEF), indexed RV-mass (RVMi), the ratio between RV and LV mass (RVM/LVM-ratio) and left ventricular eccentricity index (LVEI) all correlated with transplant-free survival from time of CMR. These correlations could not be confirmed in the PAH-CHD group. This study shows that CMR-derived measures reflecting RV function and remodeling (LVEI, RVMi, RVM/LVM-ratio, RVEF) predict transplant-free survival in children with IPAH/HPAH and may be included in risk stratification scores in pediatric PAH. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Clinical implications of low estimated protein intake in patients with heart failure

Author

Streng, Koen W., primary, Hillege, Hans L., additional, ter Maaten, Jozine M., additional, van Veldhuisen, Dirk J., additional, Dickstein, Kenneth, additional, Ng, Leong L., additional, Samani, Nilesh J., additional, Metra, Marco, additional, Ponikowski, Piotr, additional, Cleland, John G., additional, Anker, Stefan D., additional, Romaine, Simon P.R., additional, Damman, Kevin, additional, van der Meer, Peter, additional, Lang, Chim C., additional, and Voors, Adriaan A., additional

Published
2022
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46. Albuminuria Testing in Hypertension and Diabetes: An Individual-Participant Data Meta-Analysis in a Global Consortium

Author

Shin, Jung-Im, primary, Chang, Alex R., additional, Grams, Morgan E., additional, Coresh, Josef, additional, Ballew, Shoshana H., additional, Surapaneni, Aditya, additional, Matsushita, Kunihiro, additional, Bilo, Henk J.G., additional, Carrero, Juan J., additional, Chodick, Gabriel, additional, Daratha, Kenn B., additional, Jassal, Simerjot K., additional, Nadkarni, Girish N., additional, Nelson, Robert G., additional, Nowak, Christoph, additional, Stempniewicz, Nikita, additional, Sumida, Keiichi, additional, Traynor, Jamie P., additional, Woodward, Mark, additional, Sang, Yingying, additional, Gansevoort, Ron T., additional, Chalmers, John, additional, Tuttle, Katherine, additional, Alicic, Radica, additional, McPherson, Sterling, additional, Jones, Cami, additional, Singh, Gurmukteshwar, additional, Green, Jamie, additional, Kirchner, H. Lester, additional, Shalev, Varda, additional, Bottinger, Erwin P., additional, Loos, Ruth J.F., additional, Ellis, Stephen B., additional, Cuddeback, John, additional, Ciemins, Elizabeth, additional, Carbonara, Emily, additional, Dunning, Stephan, additional, Knowler, William C., additional, Looker, Helen C., additional, Kieneker, Lyane M., additional, Bakker, Stephan J.L., additional, Hillege, Hans L., additional, van der Harst, Pim, additional, Bergstrom, Jacklyn, additional, Ix, Joachim, additional, Kovesdy, Csaba P., additional, Potukuchi, Praveen, additional, Trevisan, Marco, additional, Elinder, Carl Gustaf, additional, Wettermark, Björn, additional, Ärnlöv, Johan, additional, Mark, Patrick B., additional, Thomson, Peter C., additional, Geddes, Colin C., additional, Landman, Gijs W.D., additional, van Hateren, Kornelis J.J., additional, Kleefstra, Nanne, additional, Gutierrez, Orlando, additional, Konta, Tsuneo, additional, Levey, Andrew S., additional, Polkinghorne, Kevan, additional, Schäffner, Elke, additional, Chen, Jingsha, additional, and Surapeneni, Aditya, additional

Published
2021
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48. New genetic loci link adipose and insulin biology to body fat distribution

Author

Shungin, Dmitry, Winkler, Thomas W., Croteau-Chonka, Damien C., Ferreira, Teresa, Locke, Adam E., Mägi, Reedik, Strawbridge, Rona J., Pers, Tune H., Fischer, Krista, Justice, Anne E., Workalemahu, Tsegaselassie, Wu, Joseph M. W., Buchkovich, Martin L., Heard-Costa, Nancy L., Roman, Tamara S., Drong, Alexander W., Song, Ci, Gustafsson, Stefan, Day, Felix R., Esko, Tonu, Fall, Tove, Kutalik, Zoltán, Luan, Jianʼan, Randall, Joshua C., Scherag, André, Vedantam, Sailaja, Wood, Andrew R., Chen, Jin, Fehrmann, Rudolf, Karjalainen, Juha, Kahali, Bratati, Liu, Ching-Ti, Schmidt, Ellen M., Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bragg-Gresham, Jennifer L., Buyske, Steven, Demirkan, Ayse, Ehret, Georg B., Feitosa, Mary F., Goel, Anuj, Jackson, Anne U., Johnson, Toby, Kleber, Marcus E., Kristiansson, Kati, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Palmer, Cameron D., Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J., Prokopenko, Inga, Stančáková, Alena, Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Van Vliet-Ostaptchouk, Jana V., Yengo, Loïc, Zhang, Weihua, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M., Bandinelli, Stefania, Barrett, Amy, Bellis, Claire, Bennett, Amanda J., Berne, Christian, Blüher, Matthias, Böhringer, Stefan, Bonnet, Fabrice, Böttcher, Yvonne, Bruinenberg, Marcel, Carba, Delia B., Caspersen, Ida H., Clarke, Robert, Warwick Daw, E., Deelen, Joris, Deelman, Ewa, Delgado, Graciela, Doney, Alex S. F., Eklund, Niina, Erdos, Michael R., Estrada, Karol, Eury, Elodie, Friedrich, Nele, Garcia, Melissa E., Giedraitis, Vilmantas, Gigante, Bruna, Go, Alan S., Golay, Alain, Grallert, Harald, Grammer, Tanja B., Gräler, Jürgen, Grewal, Jagvir, Groves, Christopher J., Haller, Toomas, Hallmans, Goran, Hartman, Catharina A., Hassinen, Maija, Hayward, Caroline, Heikkilä, Kauko, Herzig, Karl-Heinz, Helmer, Quinta, Hillege, Hans L., Holmen, Oddgeir, Hunt, Steven C., Isaacs, Aaron, Ittermann, Till, James, Alan L., Johansson, Ingegerd, Juliusdottir, Thorhildur, Kalafati, Ioanna-Panagiota, Kinnunen, Leena, Koenig, Wolfgang, Kooner, Ishminder K., Kratzer, Wolfgang, Lamina, Claudia, Leander, Karin, Lee, Nanette R., Lichtner, Peter, Lind, Lars, Lindström, Jaana, Lobbens, Stéphane, Lorentzon, Mattias, Mach, François, Magnusson, Patrik K. E., Mahajan, Anubha, McArdle, Wendy L., Menni, Cristina, Merger, Sigrun, Mihailov, Evelin, Milani, Lili, Mills, Rebecca, Moayyeri, Alireza, Monda, Keri L., Mooijaart, Simon P., Mühleisen, Thomas W., Mulas, Antonella, Müller, Gabriele, Müller-Nurasyid, Martina, Nagaraja, Ramaiah, Nalls, Michael A., Narisu, Narisu, Glorioso, Nicola, Nolte, Ilja M., Olden, Matthias, Rayner, Nigel W., Renstrom, Frida, Ried, Janina S., Robertson, Neil R., Rose, Lynda M., Sanna, Serena, Scharnagl, Hubert, Scholtens, Salome, Sennblad, Bengt, Seufferlein, Thomas, Sitlani, Colleen M., Vernon Smith, Albert, Stirrups, Kathleen, Stringham, Heather M., Sundström, Johan, Swertz, Morris A., Swift, Amy J., Syvänen, Ann-Christine, Tayo, Bamidele O., Thorand, Barbara, Thorleifsson, Gudmar, Tomaschitz, Andreas, Troffa, Chiara, van Oort, Floor V. A., Verweij, Niek, Vonk, Judith M., Waite, Lindsay L., Wennauer, Roman, Wilsgaard, Tom, Wojczynski, Mary K., Wong, Andrew, Zhang, Qunyuan, Hua Zhao, Jing, Brennan, Eoin P., Choi, Murim, Eriksson, Per, Folkersen, Lasse, Franco-Cereceda, Anders, Gharavi, Ali G., Hedman, Åsa K., Hivert, Marie-France, Huang, Jinyan, Kanoni, Stavroula, Karpe, Fredrik, Keildson, Sarah, Kiryluk, Krzysztof, Liang, Liming, Lifton, Richard P., Ma, Baoshan, McKnight, Amy J., McPherson, Ruth, Metspalu, Andres, Min, Josine L., Moffatt, Miriam F., Montgomery, Grant W., Murabito, Joanne M., Nicholson, George, Nyholt, Dale R., Olsson, Christian, Perry, John R. B., Reinmaa, Eva, Salem, Rany M., Sandholm, Niina, Schadt, Eric E., Scott, Robert A., Stolk, Lisette, Vallejo, Edgar E., Westra, Harm-Jan, Zondervan, Krina T., Amouyel, Philippe, Arveiler, Dominique, Bakker, Stephan J. L., Beilby, John, Bergman, Richard N., Blangero, John, Brown, Morris J., Burnier, Michel, Campbell, Harry, Chakravarti, Aravinda, Chines, Peter S., Claudi-Boehm, Simone, Collins, Francis S., Crawford, Dana C., Danesh, John, de Faire, Ulf, de Geus, Eco J. C., Dörr, Marcus, Erbel, Raimund, Eriksson, Johan G., Farrall, Martin, Ferrannini, Ele, Ferrières, Jean, Forouhi, Nita G., Forrester, Terrence, Franco, Oscar H., Gansevoort, Ron T., Gieger, Christian, Gudnason, Vilmundur, Haiman, Christopher A., Harris, Tamara B., Hattersley, Andrew T., Heliövaara, Markku, Hicks, Andrew A., Hingorani, Aroon D., Hoffmann, Wolfgang, Hofman, Albert, Homuth, Georg, Humphries, Steve E., Hyppönen, Elina, Illig, Thomas, Jarvelin, Marjo-Riitta, Johansen, Berit, Jousilahti, Pekka, Jula, Antti M., Kaprio, Jaakko, Kee, Frank, Keinanen-Kiukaanniemi, Sirkka M., Kooner, Jaspal S., Kooperberg, Charles, Kovacs, Peter, Kraja, Aldi T., Kumari, Meena, Kuulasmaa, Kari, Kuusisto, Johanna, Lakka, Timo A., Langenberg, Claudia, Le Marchand, Loic, Lehtimäki, Terho, Lyssenko, Valeriya, Männistö, Satu, Marette, André, Matise, Tara C., McKenzie, Colin A., McKnight, Barbara, Musk, Arthur W., Möhlenkamp, Stefan, Morris, Andrew D., Nelis, Mari, Ohlsson, Claes, Oldehinkel, Albertine J., Ong, Ken K., Palmer, Lyle J., Penninx, Brenda W., Peters, Annette, Pramstaller, Peter P., Raitakari, Olli T., Rankinen, Tuomo, Rao, D. C., Rice, Treva K., Ridker, Paul M., Ritchie, Marylyn D., Rudan, Igor, Salomaa, Veikko, Samani, Nilesh J., Saramies, Jouko, Sarzynski, Mark A., Schwarz, Peter E. H., Shuldiner, Alan R., Staessen, Jan A., Steinthorsdottir, Valgerdur, Stolk, Ronald P., Strauch, Konstantin, Tönjes, Anke, Tremblay, Angelo, Tremoli, Elena, Vohl, Marie-Claude, Völker, Uwe, Vollenweider, Peter, Wilson, James F., Witteman, Jacqueline C., Adair, Linda S., Bochud, Murielle, Boehm, Bernhard O., Bornstein, Stefan R., Bouchard, Claude, Cauchi, Stéphane, Caulfield, Mark J., Chambers, John C., Chasman, Daniel I., Cooper, Richard S., Dedoussis, George, Ferrucci, Luigi, Froguel, Philippe, Grabe, Hans-Jörgen, Hamsten, Anders, Hui, Jennie, Hveem, Kristian, Jöckel, Karl-Heinz, Kivimaki, Mika, Kuh, Diana, Laakso, Markku, Liu, Yongmei, März, Winfried, Munroe, Patricia B., Njølstad, Inger, Oostra, Ben A., Palmer, Colin N. A., Pedersen, Nancy L., Perola, Markus, Pérusse, Louis, Peters, Ulrike, Power, Chris, Quertermous, Thomas, Rauramaa, Rainer, Rivadeneira, Fernando, Saaristo, Timo E., Saleheen, Danish, Sinisalo, Juha, Eline Slagboom, P., Snieder, Harold, Spector, Tim D., Thorsteinsdottir, Unnur, Stumvoll, Michael, Tuomilehto, Jaakko, Uitterlinden, André G., Uusitupa, Matti, van der Harst, Pim, Veronesi, Giovanni, Walker, Mark, Wareham, Nicholas J., Watkins, Hugh, Wichmann, H-Erich, Abecasis, Goncalo R., Assimes, Themistocles L., Berndt, Sonja I., Boehnke, Michael, Borecki, Ingrid B., Deloukas, Panos, Franke, Lude, Frayling, Timothy M., Groop, Leif C., Hunter, David J., Kaplan, Robert C., OʼConnell, Jeffrey R., Qi, Lu, Schlessinger, David, Strachan, David P., Stefansson, Kari, van Duijn, Cornelia M., Willer, Cristen J., Visscher, Peter M., Yang, Jian, Hirschhorn, Joel N., Carola Zillikens, M., McCarthy, Mark I., Speliotes, Elizabeth K., North, Kari E., Fox, Caroline S., Barroso, Inês, Franks, Paul W., Ingelsson, Erik, Heid, Iris M., Loos, Ruth J. F., Cupples, Adrienne L., Morris, Andrew P., Lindgren, Cecilia M., and Mohlke, Karen L

Published
2015
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50. Additional file 3 of A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: results from an individual participant data meta-analysis of four prospective European cohorts

Author

Chen, Yuntao, Voors, Adriaan A., Jaarsma, Tiny, Chim C. Lang, Iziah E. Sama, K. Martijn Akkerhuis, Boersma, Eric, Hillege, Hans L., and Postmus, Douwe

Subjects
Physics::Medical Physics, Computer Science::Operating Systems
Abstract

Additional file 3. Mathematical formulas for the prediction model and a patient example for the illustration of the calculation.

Published
2021
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