Masatake Kobayashi1, Olivier Huttin1, Martin Magnusson2, João Pedro Ferreira1, Erwan Bozec1, Anne-Cecile Huby1, Gregoire Preud'homme1, Kevin Duarte1, Zohra Lamiral1, Kevin Dalleau3, Emmanuel Bresso3, Malika Smaïl-Tabbone4, Marie-Dominique Devignes4, Peter M Nilsson5, Margret Leosdottir6, Jean-Marc Boivin1, Faiez Zannad1, Patrick Rossignol1, Nicolas Girerd7. 1. Centre d'Investigations Cliniques Plurithématique 1433, Université de Lorraine, Institut national de la santé et de la recherche médicale 1116, Centre Hospitalier Universitaire Régional de Nancy, France; French Clinical Research Infrastructure Network "Investigation" Network Initiative-Cardiovascular and Renal Clinical Trialists" Cardiovascular and Renal Clinical Trialists Network, France. 2. Department of Clinical Sciences, Lund University, Malmö, Sweden; Department of Cardiology, Skåne University Hospital, Malmö, Sweden; Wallenberg Centre for Molecular Medicine, Lund University, Sweden. 3. Laboratoire lorrain de Recherche en Informatique et ses Applications, Unité Mixte de Recherche 7503, Université de Lorraine, Vandoeuvre-lès-Nancy, France. 4. French Clinical Research Infrastructure Network "Investigation" Network Initiative-Cardiovascular and Renal Clinical Trialists" Cardiovascular and Renal Clinical Trialists Network, France; Laboratoire lorrain de Recherche en Informatique et ses Applications, Unité Mixte de Recherche 7503, Université de Lorraine, Vandoeuvre-lès-Nancy, France. 5. Department of Clinical Sciences, Lund University, Malmö, Sweden; Department of Internal Medicine, Lund University, Skåne University Hospital, Malmö, Sweden. 6. Department of Clinical Sciences, Lund University, Malmö, Sweden; Department of Cardiology, Skåne University Hospital, Malmö, Sweden. 7. Centre d'Investigations Cliniques Plurithématique 1433, Université de Lorraine, Institut national de la santé et de la recherche médicale 1116, Centre Hospitalier Universitaire Régional de Nancy, France; French Clinical Research Infrastructure Network "Investigation" Network Initiative-Cardiovascular and Renal Clinical Trialists" Cardiovascular and Renal Clinical Trialists Network, France. Electronic address: n.girerd@chru-nancy.fr.
Abstract
OBJECTIVES: This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes. BACKGROUND: Asymptomatic cardiac dysfunction leads to a high risk of long-term cardiovascular morbidity and mortality; however, better echocardiographic classification of asymptomatic individuals remains a challenge. METHODS: Echocardiographic phenotypes were identified using K-means clustering in the first generation of the STANISLAS (Yearly non-invasive follow-up of Health status of Lorraine insured inhabitants) cohort (N = 827; mean age: 60 ± 5 years; men: 48%), and their associations with vascular function and circulating biomarkers were also assessed. These phenotypes were externally validated in the Malmö Preventive Project cohort (N = 1,394; mean age: 67 ± 6 years; men: 70%), and their associations with the composite of cardiovascular mortality (CVM) or heart failure hospitalization (HFH) were assessed as well. RESULTS: Three echocardiographic phenotypes were identified as "mostly normal (MN)" (n = 334), "diastolic changes (D)" (n = 323), and "diastolic changes with structural remodeling (D/S)" (n = 170). The D and D/S phenotypes had similar ages, body mass indices, cardiovascular risk factors, vascular impairments, and diastolic function changes. The D phenotype consisted mainly of women and featured increased levels of inflammatory biomarkers, whereas the D/S phenotype, consisted predominantly of men, displayed the highest values of left ventricular mass, volume, and remodeling biomarkers. The phenotypes were predicted based on a simple algorithm including e', left ventricular mass and volume (e'VM algorithm). In the Malmö cohort, subgroups derived from e'VM algorithm were significantly associated with a higher risk of CVM and HFH (adjusted HR in the D phenotype = 1.87; 95% CI: 1.04 to 3.37; adjusted HR in the D/S phenotype = 3.02; 95% CI: 1.71 to 5.34). CONCLUSIONS: Among asymptomatic, middle-aged individuals, echocardiographic data-driven classification based on the simple e'VM algorithm identified profiles with different long-term HF risk. (4th Visit at 17 Years of Cohort STANISLAS-Stanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442).
OBJECTIVES: This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes. BACKGROUND: Asymptomatic cardiac dysfunction leads to a high risk of long-term cardiovascular morbidity and mortality; however, better echocardiographic classification of asymptomatic individuals remains a challenge. METHODS: Echocardiographic phenotypes were identified using K-means clustering in the first generation of the STANISLAS (Yearly non-invasive follow-up of Health status of Lorraine insured inhabitants) cohort (N = 827; mean age: 60 ± 5 years; men: 48%), and their associations with vascular function and circulating biomarkers were also assessed. These phenotypes were externally validated in the Malmö Preventive Project cohort (N = 1,394; mean age: 67 ± 6 years; men: 70%), and their associations with the composite of cardiovascular mortality (CVM) or heart failure hospitalization (HFH) were assessed as well. RESULTS: Three echocardiographic phenotypes were identified as "mostly normal (MN)" (n = 334), "diastolic changes (D)" (n = 323), and "diastolic changes with structural remodeling (D/S)" (n = 170). The D and D/S phenotypes had similar ages, body mass indices, cardiovascular risk factors, vascular impairments, and diastolic function changes. The D phenotype consisted mainly of women and featured increased levels of inflammatory biomarkers, whereas the D/S phenotype, consisted predominantly of men, displayed the highest values of left ventricular mass, volume, and remodeling biomarkers. The phenotypes were predicted based on a simple algorithm including e', left ventricular mass and volume (e'VM algorithm). In the Malmö cohort, subgroups derived from e'VM algorithm were significantly associated with a higher risk of CVM and HFH (adjusted HR in the D phenotype = 1.87; 95% CI: 1.04 to 3.37; adjusted HR in the D/S phenotype = 3.02; 95% CI: 1.71 to 5.34). CONCLUSIONS: Among asymptomatic, middle-aged individuals, echocardiographic data-driven classification based on the simple e'VM algorithm identified profiles with different long-term HF risk. (4th Visit at 17 Years of Cohort STANISLAS-Stanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442).