Association of Non-alcoholic Fatty Liver Disease

Open Access Original Article

DOI: 10.7759/cureus.1107

Association of Non-alcoholic Fatty Liver Disease with Conduction Defects on Electrocardiogram Muhammad A. Mangi 1 , Abdul M. Minhas 2 , Hiba Rehman 2 , Furquan Pathan 3 , Hong Liang 4 , Sary Beidas 1 1. GME Internal Medicine, Orange Park Medical Center 2. GME Internal Medicine , Orange Park Medical Center 3. GME Intenral Medicine , Orange Park Medical Center 4. GME Internal Medicine , North Florida Regional Medical Center/CyberKnife at North Florida  Corresponding author: Muhammad A. Mangi, [email protected] Disclosures can be found in Additional Information at the end of the article

Abstract Background: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of liver disease in developed countries. The association of NAFLD with conduction defects is unknown. The aim of our study was to find whether an association exists between conduction defects and NAFLD. Methods: This is a case-control retrospective study of 700 patients admitted to Orange Park Medical Center, Orange Park, Florida from 2009 to 2015. Patients with a history of alcohol use, congenital heart disease, infiltrative malignancy, and myocarditis were excluded from the study. NAFLD was diagnosed by detection of hepatic steatosis on abdominal ultrasound or computerized tomography (CT) scan. Electrocardiograms (EKGs) were performed on all 700 patients and were interpreted by a cardiologist. Univariate logistic regression was used to assess the association between NAFLD and the variables of demographics, clinical characteristics, medicine use, EKG changes, and conduction defects, while multivariate logistic regression with backward elimination method was performed to determine if NAFLD is one of the most important risk factors for conduction defects.

Received 02/15/2017 Review began 03/13/2017 Review ended 03/13/2017 Published 03/21/2017 © Copyright 2017 Mangi et al. This is an open access article distributed under the terms of

Results: The study population included 408 patients with NAFLD and 292 patients with NoNAFLD. A total of 155 conduction defects occurred in 140 patients; conduction defects included 25.7% (36) patients with first degree block, 2.1% (three) patients with Mobitz type 1 block, 41.4% (58) patients with right bundle branch block (RBBB), 17.9% (25) patients with left bundle branch block (LBBB), 11.4% (16) patients with bifascicular block, and 12.1% (17) patients with nonspecific intraventricular block. Multivariate logistic regression with backward elimination method identified six risk factors for conduction defects; these included NAFLD (odds ratio (OR) 2.38; 95% confidence interval (CI) 1.51-3.73, p21 drinks/week for men and >14 drinks/week for women. Other exclusions included acute viral hepatitis, chronic viral hepatitis, congenital heart disease, infiltrative malignancy, myocarditis or cardiac surgeries. Also, patients who did not have an abdominal USG, CT scan abdomen or EKG were excluded from the study.

Predictor variables Baseline demographic characteristics collected included age, gender, race, obese, chronic conditions (asthma/chronic obstructive pulmonary disease (COPD), congestive heart failure

2017 Mangi et al. Cureus 9(3): e1107. DOI 10.7759/cureus.1107

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(CHF), diabetes mellitus, hypertension) ischemic heart disease, NAFLD, medication use (betablockers, calcium channel blockers, digoxin, amiodarone, adenosine), cirrhosis, thyroid disorders, smoking, cocaine use (Table 1). We did not include body mass index (BMI) in our study due to lack of availability of height in the medical record for most patients.

Outcome variables Conduction defect identified on EKG was the primary outcome variable. The EKG changes for conduction defects were determined by reviewing EKG tracings already verified by a cardiologist. Secondary outcomes for this study included the presence of other EKG changes (for example, atrial fibrillation, premature atrial contractions (PACs), premature ventricular contractions (PVCs), axis deviation, low voltage, prolonged QTc interval, hypertrophy, and ST wave changes).

Statistical analysis Univariate logistic regression analysis was used to assess the association between NAFLD, baseline demographics, clinical characteristics, medicine use, EKG changes, and conduction defects (Table 2). Multivariate logistic regression analysis with backward elimination method was performed to determine if NAFLD is a risk factor for conduction defects from 19 selected predictors/factors (age, sex, race, obesity, coronary artery disease (CAD), hypertension, diabetes mellitus, CHF, smoking, COPD, asthma, thyroid disorder, antipsychotic medicine, hyperlipidemia, and NAFLD) (Table 3). The risk estimates were reported as odds ratios (OR) with 95% confidence intervals (CI). A p-value < 0.05 was considered statistically significant. R version 3.3.1 (University of Auckland, New Zealand) was primarily used for statistical analysis and SAS 9.4 (SAS Institute Inc., Cary, NC) was used to validate R output.


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FIGURE 1: Study design flowchart

Results A total of 700 patients were included in the study. NAFLD was identified on abdominal imaging in 408 patients. In the No-NAFLD group, there were 292 patients. The median age was 58 years (standard deviation (SD)=15.3 years). There were 293 males (41.9%), and 407 females (58.1%). Most patients were Caucasian (n=534, 76.3%). Other baseline characteristics are listed below (Table 1). A total of 155 conduction defects were identified in 140 patients demonstrated on EKG (Table 4).


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ALL

NAFLD

No NAFLD

N,% or Median, SD

N,% or Median, SD

N,% or Median, SD

Age (median)(SD)

57.9

(15.3)

59

(13.6)

56.3

(17.5)

Sex (males)

293

(41.8%)

178

(43.6%)

115

(39.4%)

307

(58.2%)

230

(56.4%)

177

(60.8%)

Caucasian

534

(76.4%)

318

(80%)

216

(74.2%)

African American

98

(14%)

45

(11%)

53

(18.3%)

Hispanic

17

(0.02%)

16

(3.9%)

1

(0.3%)

Other

50

(0.07%)

29

(7.1%)

21

(7.2%)

Smokers

237

(33.8%)

142

(34.8%)

96

(32.9%)

Illicit Drug Users

73

(10.4%)

46

(11.3%)

27

(9.3%)

Hypertensive

421

(60.1%)

262

(64.2%)

159

(54.5%)

CAD

94

(13.4%)

45

(11.0%)

49

(16.8%)

Hyperlipidemia

230

(32.8%)

123

(30.2%)

107

(36.6%)

Diabetics

224

(32%)

156

(38.2%)

68

(23.3%)

Obese

339

(48.4%)

214

(52.5%)

125

(42.8%)

CHF

38

(0.05%)

23

(5.6%)

15

(5.1%)

(females) Race

TABLE 1: Demographics and clinical characteristics of the study population

Univariate analysis Univariate analysis of EKG findings, (conduction defect, PACs/PVCs, hypertrophy, axis deviation, ST wave changes, low voltage, and QTc prolongation) identified a positive association with NAFLD (p-value