Subclinical cardiac dysfunction in obesity patients is linked to autonomic dysfunction: findings from the CARDIOBESE study

Abstract

Aims: Obesity doubles the lifetime risk of developing heart failure. Current knowledge on the role of obesity in causing cardiac dysfunction is insufficient for optimal risk stratification. The aim of this study was first to estimate the prevalence of subclinical cardiac dysfunction in obesity patients and second to investigate the underlying pathophysiology.

Methods and results: The CARDIOBESE study is a cross-sectional multicentre study of 100 obesity patients [body mass index (BMI) ≥ 35 kg/m² ] without known cardiovascular disease and 50 age-matched and gender-matched non-obese controls (BMI ≤ 30 kg/m² ). Echocardiography was performed, blood samples were collected, and a Holter monitor was affixed. Fifty-nine obesity patients [48 (42-50) years, 70% female] showed subclinical cardiac dysfunction: 57 patients had decreased global longitudinal strain (GLS), and two patients with normal GLS had either diastolic dysfunction or increased brain natriuretic peptide (BNP). Only one non-obese control had diastolic dysfunction, and none had another sign of cardiac dysfunction. Multivariable logistic analysis identified male gender and standard deviation of all NN intervals (SDNN) index, which is a measure of autonomic dysfunction, as independent significant risk factors for subclinical cardiac dysfunction in obesity patients.

Conclusions: There was a high prevalence (61%) of subclinical cardiac dysfunction in obesity patients without known cardiovascular disease, which appeared to be best identified by GLS. Subclinical cardiac dysfunction in obesity was linked to autonomic dysfunction and male gender, and not to the presence of traditional cardiac risk factors, increased C-reactive protein, increased BNP, increased high-sensitivity troponin I, or increased left ventricular mass.

Keywords: Cardiac dysfunction; Global longitudinal strain; Heart rate variability; Obesity/obese; Speckle-tracking echocardiography.

Figure 1

Measurement of global longitudinal strain (GLS) by speckle‐tracking analysis in an obesity patient [45‐year‐old woman, body mass index (BMI) 38.4 kg/m²]. (A) Apical four‐chamber view with measurement of longitudinal strain. (B) Apical two‐chamber view with measurement of longitudinal strain. (C) Apical three‐chamber view with measurement of longitudinal strain. (D) Bull's eye graph showing longitudinal strain for all myocardial segments, of which a weighted mean was used to derive GLS.

Figure 2

Difference in clinical characteristics and cardiac dysfunction parameters in (A) obesity patients vs. non‐obese controls. (B) Obesity patients with vs. obesity patients without cardiac dysfunction. Arrows indicate whether parameters were increased or decreased in obesity patients (A) or in obesity patients with cardiac dysfunction (B). Bold and underlined parameters are identified as significant risk factors for cardiac dysfunction in obesity patients by multivariate analysis. ACE, angiotensin‐converting enzyme; ALAT, alanine aminotransferase; ARBs, angiotensin II receptor blockers; BMI, body mass index; CRP, C‐reactive protein; e′, early diastolic mitral annular velocity; E‐wave, early diastolic transmitral flow velocity; GLS, global longitudinal strain; HbA1c, glycated haemoglobin; HDL, high‐density lipoprotein cholesterol; LDL, low‐density lipoprotein cholesterol; LV, left ventricular; OSAS, obstructive sleep apnoea syndrome; SDNN, standard deviation of NN intervals.

Figure 3

ROC curve for the prediction model for cardiac dysfunction in obesity patients. Model; combination of SDNN, SDNN index, gender, ALAT, glucose, and triglycerides. Area under the curve = 0.72 (95% CI: 0.61–0.82, P < 0.001). ALAT, alanine aminotransferase; ROC, receiver operating characteristic; SDNN, standard deviation of all NN intervals.