Dataset Information

Left ventricular subclinical myocardial dysfunction in uncomplicated type 2 diabetes mellitus is associated with impaired myocardial perfusion: a contrast-enhanced cardiovascular magnetic resonance study.

ABSTRACT: BACKGROUND:Early detection of subclinical myocardial dysfunction in patients with diabetes mellitus (DM) is essential for recommending therapeutic interventions that can prevent or reverse heart failure, thereby improving the prognosis in such patients. This study aims to quantitatively evaluate left ventricular (LV) myocardial deformation and perfusion using cardiovascular magnetic resonance (CMR) imaging in patients with type 2 diabetes mellitus (T2DM), and to investigate the association between LV subclinical myocardial dysfunction and coronary microvascular perfusion. METHODS:We recruited 71 T2DM patients and 30 healthy individuals as controls who underwent CMR examination. The T2DM patients were subdivided into two groups, namely the newly diagnosed DM group (n?=?31, patients with diabetes for???5 years) and longer-term DM group (n?=?40, patients with diabetes?>?5 years). LV deformation parameters, including global peak strain (PS), peak systolic strain rate, and peak diastolic strain rate (PSDR), and myocardial perfusion parameters such as upslope, time to maximum signal intensity (TTM), and max signal intensity (Max SI, were measured and compared among the three groups. Pearson's correlation was used to evaluate the correlation between LV deformation and perfusion parameters. RESULTS:Pooled data from T2DM patients showed a decrease in global longitudinal, circumferential, and radial PDSR compared to healthy individuals, apart from lower upslope. In addition, increased TTM and reduced Max SI were found in the longer-term diabetics compared to the normal subjects (p?

SUBMITTER: Liu X

PROVIDER: S-EPMC6206833 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Left ventricular subclinical myocardial dysfunction in uncomplicated type 2 diabetes mellitus is associated with impaired myocardial perfusion: a contrast-enhanced cardiovascular magnetic resonance study.

Liu Xi X Yang Zhi-Gang ZG Gao Yue Y Xie Lin-Jun LJ Jiang Li L Hu Bi-Yue BY Diao Kai-Yue KY Shi Ke K Xu Hua-Yan HY Shen Meng-Ting MT Ren Yan Y Guo Ying-Kun YK

Cardiovascular diabetology 20181030 1

<h4>Background</h4>Early detection of subclinical myocardial dysfunction in patients with diabetes mellitus (DM) is essential for recommending therapeutic interventions that can prevent or reverse heart failure, thereby improving the prognosis in such patients. This study aims to quantitatively evaluate left ventricular (LV) myocardial deformation and perfusion using cardiovascular magnetic resonance (CMR) imaging in patients with type 2 diabetes mellitus (T2DM), and to investigate the associati ...[more]

PMID: 30373588

Similar Datasets

Project description:BackgroundSubclinical LV dysfunction (LVD) identifies heart failure (HF) risk in type 2 diabetes mellitus (T2DM). We sought the extent to which clinical scores (ARIC-HF, WATCH-DM), natriuretic peptides (NTpBNP) and troponin (hs-TnT) were associated with subclinical LV dysfunction (LVD). These associations could inform the ability of these tests to identify which patients should undergo echocardiography.MethodsParticipants with T2DM were prospectively recruited from three community-based populations. ARIC-HF risk at 4 years and WATCH-DM scores were calculated from clinical data. NTpBNP and hs-TnT were measured using an electro-chemiluminescence assay. All underwent a comprehensive echocardiogram. We calculated the sensitivity and specificity of clinical scores and biomarkers to identify abnormal global longitudinal strain (GLS ≥ -16%)), diastolic function (E/e' ≥ 14 or e' < 8 cm/s), left atrial volume index (LAV > 34 ml/m2) and LV hypertrophy (LV mass index > 88 g/m2 (F) > 102 g/m2(M)).ResultsOf 804 participants (median age 69 years [inter-quartile range (IQR) 65-73], 36% female), clinical scores suggested significant HF risk (median ARIC-HF 8% [IQR 4-12]; WATCH-DM 10 points [IQR 8-12]), and the median NTpBNP was 50 pg/mL [IQR 25-101] and hs-TnT 9.6 pg/mL [IQR 6.8-13.6]. Abnormal GLS was present in 126 (17%), elevated E/e' in 114 (15%), impaired e' in 629 (78%), increased LAV in 351 (44%) and LV hypertrophy in 113 (14%). After adjustments for age, body-mass index, and renal function, each standard deviation increase in NTpBNP was associated with a GLS increase of 0.32 (p < 0.001) and hs-TnT increase by 0.26 (p < 0.001). Similar trends were observed with ARIC-HF (standardised β = 0.22, p < 0.001) and WATCH-DM (standardised β = 0.22, p < 0.001) in univariable analyses. However, none of the risk assessment tools provided satisfactory discrimination for abnormal GLS (AUC 63%), diastolic indices (e' AUC 54-61%) or LV mass (AUC 59-67%). At a sensitivity of 90%, there was an unacceptably low (< 50%) specificity.ConclusionAlthough risk assessment based on clinical scores or biomarkers would be desirable to stratify HF risk in people with T2DM, they show a weak relationship with subclinical LVD.

Project description:BackgroundRecent guidelines propose N-terminal pro-B-type natriuretic peptide (NT-proBNP) for recognition of asymptomatic left ventricular (LV) dysfunction (Stage B Heart Failure, SBHF) in type 2 diabetes mellitus (T2DM). Wavelet Transform based signal-processing transforms electrocardiogram (ECG) waveforms into an energy distribution waveform (ew)ECG, providing frequency and energy features that machine learning can use as additional inputs to improve the identification of SBHF. Accordingly, we sought whether machine learning model based on ewECG features was superior to NT-proBNP, as well as a conventional screening tool-the Atherosclerosis Risk in Communities (ARIC) HF risk score, in SBHF screening among patients with T2DM.MethodsParticipants in two clinical trials of SBHF (defined as diastolic dysfunction [DD], reduced global longitudinal strain [GLS ≤ 18%] or LV hypertrophy [LVH]) in T2DM underwent 12-lead ECG with additional ewECG feature and echocardiography. Supervised machine learning was adopted to identify the optimal combination of ewECG extracted features for SBHF screening in 178 participants in one trial and tested in 97 participants in the other trial. The accuracy of the ewECG model in SBHF screening was compared with NT-proBNP and ARIC HF.ResultsSBHF was identified in 128 (72%) participants in the training dataset (median 72 years, 41% female) and 64 (66%) in the validation dataset (median 70 years, 43% female). Fifteen ewECG features showed an area under the curve (AUC) of 0.81 (95% CI 0.787-0.794) in identifying SBHF, significantly better than both NT-proBNP (AUC 0.56, 95% CI 0.44-0.68, p < 0.001) and ARIC HF (AUC 0.67, 95%CI 0.56-0.79, p = 0.002). ewECG features were also led to robust models screening for DD (AUC 0.74, 95% CI 0.73-0.74), reduced GLS (AUC 0.76, 95% CI 0.73-0.74) and LVH (AUC 0.90, 95% CI 0.88-0.89).ConclusionsMachine learning based modelling using additional ewECG extracted features are superior to NT-proBNP and ARIC HF in SBHF screening among patients with T2DM, providing an alternative HF screening strategy for asymptomatic patients and potentially act as a guidance tool to determine those who required echocardiogram to confirm diagnosis. Trial registration LEAVE-DM, ACTRN 12619001393145 and Vic-ELF, ACTRN 12617000116325.

Dataset Information

Left ventricular subclinical myocardial dysfunction in uncomplicated type 2 diabetes mellitus is associated with impaired myocardial perfusion: a contrast-enhanced cardiovascular magnetic resonance study.

Publications

Left ventricular subclinical myocardial dysfunction in uncomplicated type 2 diabetes mellitus is associated with impaired myocardial perfusion: a contrast-enhanced cardiovascular magnetic resonance study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets