Dataset Information

Examining the Relationship and Prognostic Implication of Diabetic Status and Extracellular Matrix Expansion by Cardiac Magnetic Resonance.

ABSTRACT:

Background

Although not fully understood, diabetes mellitus is thought to be associated with cardiac fibrosis and stiffness due to alteration of myocardial extracellular matrix. Newer cardiac magnetic resonance techniques may be able to identify extracellular matrix expansion by measuring extracellular volume fraction (ECV). We used cardiac magnetic resonance to evaluate the association of alteration in the extracellular matrix with diabetic status and its implications on incident heart failure events and all-cause mortality.

Methods

We studied 442 patients who underwent comprehensive contrast cardiac magnetic resonance to assess cardiac morphology and function, left ventricular replacement fibrosis, and pre-post contrast T1 mapping to quantify ECV. The cohort did not have coexisting pathologies associated with ECV alteration. We categorized our final cohort based on diabetic status using criteria from the American Diabetic Association. Subsequent heart failure hospitalization and all-cause death were ascertained.

Results

Our patients were predominantly white with a median age of 57 with 48% being men. Compared with nondiabetes mellitus, diabetes mellitus was significantly associated with elevated ECV after adjusting for clinical and imaging covariates: β coefficient 1.33 (95% CI, 0.22-2.44); P=0.02. Over a median follow-up of 24.5 (interquartile range, 14.8-33.4) months, 52 deaths and 24 heart failure events occurred. Patients with diabetes mellitus and elevated ECV had the worst outcomes compared with patients with diabetes mellitus and normal ECV or nondiabetics. Elevated ECV remained an independent predictor of outcomes (hazard ratio, 3.31 [95% CI, 1.93-5.67]; P<0.001) after adjusting for covariates.

Conclusions

Elevated ECV is an independent predictor of mortality among patients with diabetes mellitus and may have an additive effect with diabetes mellitus on outcomes. ECV may represent a novel noninvasive biomarker to evaluate severity of diabetic heart disease.

SUBMITTER: Khan MA

PROVIDER: S-EPMC7384549 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.