Original papers
DOI: 10.11152/mu-1015Non-alcoholic fatty liver disease, bulb carotid intima-media thickness and obesity phenotypes: results of a prospective observational study
Anca D. Farcaş
1, Camelia Larisa Vonica
2, Adela C. Golea
31
1
stMedical Clinic – Internal Medicine, Cardiology, and Gastroenterology,
2Diabetes, Nutrition, and Metabolic Diseases Clinic,
3Emergency Medicine Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj‑Napoca, Romania
Received 19.02.2017 Accepted 18.05.2017 Med Ultrason
2017, Vol. 19, No 3, 265‑271
Corresponding author: Camelia Larisa Vonica
Medical Specialties Department ‑ Diabetes, Nutrition and Metabolic Diseases Clinic 8 Victor Babes Street
400012, Cluj‑Napoca, Romania,
Phone: +40747642020, Fax: 0040264594455 E‑mail: [email protected]
Introduction
Obesity represents a public health problem due to its increasing prevalence despite public awareness programs [1]. Based on cardiovascular (CV) risk factors, clinical studies have identified 2 types of obesity – metabolically healthy obesity (MHO) and metabolically unhealthy obe‑
sity (MUHO). MHO are characterized by the presence of obesity as defined by a body mass index (BMI) equal
or over 30 kg/m
2without metabolic CV risk factors.
MUHO associates obesity with the presence of meta‑
bolic CV risk factors and an increased risk of diabetes and CV diseases [2‑4]. Studies assessing the health risks associated with MHO have shown conflicting results, some showing similar or lower risk of CV disease and diabetes when compared to MUHO. Despite numerous clinical cross‑sectional and prospective epidemiological studies evaluating the CV risk associated with this obe‑
sity phenotype and its clinical implications, controversies surrounding the health risks associated with MHO re‑
main [5‑7]. Therefore, it still under debate whether MHO represents a distinct phenotype compared with MUHO (lower health associated risks during lifetime or just a MUHO precursor) [8].
In parallel with the increasing prevalence of obesity an increased prevalence of nonalcoholic fatty liver dis‑
ease (NAFLD) has been reported [9]. Obesity and ab‑
Abstract
Aims: The objective of this prospective study was to assess the correlation between carotid intima‑media thickness at the common carotid (CIMTc) and carotid bifurcation (CIMTb) level, hepatic fat accumulation, and obesity phenotypes. Material and methods: Two hundred obese adults, in which CIMTc and CIMTb thickness was determined, were included. According to body mass index (BMI) and presence of metabolic syndrome (MetS), patients were classified as metabolically healthy obese (MHO, obesity without MetS) and metabolically unhealthy obese (MUHO, obesity with MetS). MHO patients were further classified as MHO1 (obese with increased waist circumference) and MHO2 (obese with increased waist circumference plus one of the 4 criteria for MetS). Non‑alcoholic fatty liver disease (NAFLD) presence was assessed by fatty liver index (FLI).
Results: CIMTc and CIMTb increased with obesity phenotypes from 0.74 mm and 1.04 mm in MHO1 to 0.84 mm and 1.23 mm in MHO2 and 0.88 mm and 1.74 mm in MUHO. Obesity phenotypes were significantly correlated with CIMTb. NAFLD frequency increased from 66.0% in the MHO1 to 73.0% in the MHO2 and 84.2% in the MUHO (p<0.05). Independent of age, BMI, total cholesterol, HbA1c, and HOMA-IR, the CIMTc was significantly associated with FLI in all obesity phenotypes and CIMTb only in MHO2 and MUHO. Conclusions: Our results suggest that subclinical atherosclerosis varies according to obesity phenotypes and is correlated with the hepatic fat accumulation.
Keywords: carotid intima‑media thickness; non‑alcoholic fatty liver disease; obesity phenotypes
dominal obesity, metabolic syndrome, and type 2 diabe‑
tes have been identified among risk factors for NAFLD [10], which in turn is associated with an increased risk of fatal and non‑fatal CV events and increased risk of total and CV diseases mortality [11]. The gold standard for NAFLD diagnosis is the liver biopsy but its invasive nature limits its use. Fatty liver index (FLI) score [12]
is a noninvasive method widely used in epidemiological studies for NAFLD screening, showing good sensitivity compared with magnetic resonance spectroscopy for de‑
tecting fatty liver [13].
Carotid intima‑media thickness (CIMT) is a simple and non‑invasive method of the assessment of subclini‑
cal atherosclerosis and has been shown to be an inde‑
pendent predictor of CV disease risk [14‑16]. Evaluation of CIMT includes evaluation of common carotid artery (CIMTc), bifurcation (bulb; CIMTb), and internal carotid artery and it has been shown that the association of the CIMT with CV risk factors varies according to the seg‑
ment assessed [17]. The association between CIMT and NAFLD has been reported in the past years and this asso‑
ciation was independent of other CV risk factors [18,19].
Currently, limited data are available on the relation‑
ship between hepatic fat content and atherosclerosis ac‑
cording to obesity phenotypes. A recent study showed that MHO participants had significantly lower levels of CIMT and intrahepatic triglycerides content compared with the MUHO participants and intrahepatic triglycer‑
ides content was independently associated with meta‑
bolic syndrome (MetS) components and increased CIMT [20].
In this context, we aimed to investigate the correla‑
tion between subclinical carotid atherosclerosis assessed by CIMT at common carotid and carotid bifurcation lev‑
el, hepatic fat, and obesity phenotypes.
Material and methods
This was a prospective study performed in the Emer‑
gency County Clinical Hospital Cluj‑Napoca, Romania.
We included 200 obese patients as defined by a BMI ≥30 kg/m
2, who presented, between February 2014 and No‑
vember 2015 for nutritional and metabolic status evalu‑
ation in the Diabetes, Nutrition and Metabolic Diseases Clinic. We excluded from the study patients under 18 years of age, with prior diagnosis of autoimmune, vi‑
ral (hepatitis virus B, C, D), toxic or uncertain etiology hepatitis, with high alcohol consumption (>140 g/week), diabetes mellitus, pregnancy, with hypolipemic and/or weight loss medication. The patients signed an informed consent prior to enrollment and Institutional Ethics Com‑
mittee approval was obtained.
Clinical assessments
We recorded for all patients demographic and clinical variables such age, weight, height, waist circumference, BMI [calculated as weight (kg)/height (m
2)], associated disease (hypertension, dyslipidemia, etc), and their medi‑
cation.
Waist circumference (WC) was measured with the patients standing, with a measuring tape halfway be‑
tween the ribcage and the iliac crest, horizontally, at the end of a complete expiration.
Blood pressure (BP) was measured according to the guidelines [21] after a 5 minute resting, in sitting posi‑
tion. Two measurements were performed for each arm at 2 minutes interval. The arm with the highest BP was chosen and the average value of the measurements was computed.
Metabolic syndrome definition
Classifying patients as MHO or MUHO was per‑
formed after a series of explorations to identify the MetS components according to the International Diabetes Fed‑
eration criteria [22]: 1) abdominal obesity: WC >94 cm (men) and >80 cm (women); 2) hypertriglyceridemia:
≥150 mg/dl; 3) low levels of HDL-C: <40 mg/dl (men) and <50 mg/ dl (women) or specific treatment; 4) hyper‑
tension: ≥130/85 mmHg or specific treatment; 5) high fasting glucose: ≥100 mg/dl.
Assay and indices assessment
Blood samples were drawn from the cubital vein after a 12‑h fasting. Fasting plasma glucose (FPG), triglycer‑
ides, total cholesterol, HDL‑cholesterol, LDL‑cholester‑
ol, gamma‑glutamyl transferase (GGT), transaminases, uric acid, fasting insulinemia (for insulinoresistance), glycated hemoglobin (HbA1c), apolipoprotein A1 and B were determined using a Beckman Coulter UniCel DxI 600. Insulin resistance was estimated using homeostasis model assessment (HOMA‑IR) as [fasting glucose (mg/
dL) × fasting insulin (μUI/mL)]/405 [23].
Based on FPG values, patients were classified as hav‑
ing dysglicemia (prediabetes) if they had an FPG level from 110 to less than 126 mg/dl [24]. NAFLD was diag‑
nosed using the fatty liver index (FLI) score. FLI requires for calculation BMI, WC, triglycerides, and GGT [13].
A FLI score > 60 is considered to be suggestive for the presence of NAFLD [13].
Carotid Ultrasound Measurements
Ultrasound evaluations were performed by a single examiner (with a 15-year expertise, certified for carotid ultrasonography) using a 3‑10 MHz VF8‑3 linear Trans‑
ducer (ACUSON X300 Ultrasound System). The exami‑
nation was performed with the patient lying in supine position, with a lateral probe position using a standard‑
ized protocol [25]. Wall thickness was measured in lon‑
gitudinal view, at the far wall level, with the transducer positioned strictly perpendicular to this wall (the lumen‑
intima and media‑adventitia interfaces were clearly de‑
fined), including the carotid bifurcation in the image plane. CIMT was measured at 10, 15, and 20 mm below the end of the common carotid artery (CCA), at a plaque- free point on the far wall and the average was considered the CIMTc on that side. Any atherosclerotic thickening
≥1.5 mm was considered a plaque. CIMTc was calcu‑
lated as the average of the CIMT for left and right CCA.
CIMTb was calculated as CIMT average in the thickest point (including plaque) of the left and right internal ca‑
rotid bulb. Variation coefficients of the measurements for the examiner were <5%.
Establishing the groups of patients according to obesity phenotype
According to BMI and presence of MetS [22], par‑
ticipants were classified as MHO (obese without MetS) or MUHO (obese with MetS). MHO patients were fur‑
ther classified as MHO1 (obese with increased WC) and MHO2 (obese with increased WC plus one of the crite‑
rions for MetS).
Statistical analysis
Statistical analysis was performed with SPSS ver‑
sion 20. Kolmogorov‑Smirnov tests were used to evalu‑
ate the distribution of investigated variables. Data was presented as proportions for qualitative variables, mean and standard deviation (SD) or median for continuous variables. The chi-square-test was used to compare cat‑
egorical variables and t‑test for continuous variables.
Mann‑Whitney test or Kruskal‑Wallis test was used for non‑normal distribution variables. Correlations were as‑
sessed with Spearman or Pearson coefficients, according to the variables distribution.
Univariate linear regression was performed for the re‑
lation between CIMT and FLI score in the whole sample and by obesity phenotype, multiple regression analysis was applied for the relation between CIMT and FLI adjusted for age, BMI, total cholesterol, HbA1c, and HOMA-IR. A two-sided p value ≤0.05 was considered statistically significant.
Results
Baseline characteristics for the 200 patients who met the inclusion criteria and were included in the study are shown in Table I. MHO patients were younger, had sig‑
nificantly lower BMI, WC, systolic and diastolic BP com‑
paring with MUHO. Comparing with MHO, the MUHO patients had significantly lower HDL-cholesterol levels, higher triglycerides, uric acid, and CRP levels (p <0.05 for all). The frequency of dysglycemia was 11.5% in the
MHO2 group and 74.0% in the MUHO group. In the MHO2 group 21 patients (40.4%) had increased WC plus hypertension, 18 patients (34.6%) plus low HDL-choles‑
terol, 7 patients (13.5%) plus high triglycerides, and 6 patients (11.5%) plus dysglycemia. CIMTc, CIMTb and FLI score were significantly higher in MUHO patients. .
Comparing MHO1 and MHO2 patients, those with MHO2 had significantly higher WC, glycemia, HbA1c, insulinemia, C‑peptide, HOMA‑IR, total and LDL‑cho‑
lesterol, triglycerides, CRP and uric acid. CIMTc and CIMTb were significantly higher in MHO2 patients than in MHO1 patients. NAFLD frequency was 66.0% in the MHO1 and 73.0% in the MHO2 (p<0.05). FLI score in‑
creased from 63.44 in the MHO1 to 68.01 in the MHO2.
In all groups both CIMTc and CIMTb were directly and significantly correlated with FLI score. Correlation coefficients between CIMTc and FLI score were 0.343, 0.425, and 0.343 (MHO1, MHO2, and MUHO, respec‑
tively); 0.763, 0.443, and 0.754 for CIMTb and FLI score in the three groups (Table II).
Obesity phenotypes were statistically significant cor‑
related only with CIMTb (p <0.05 for all; Table III).
In the univariate regression model, CIMTc was as‑
sociated with the FLI score in the whole sample and CIMTb was associated with the FLI score only in the MHO1 group. After adjusting for age, BMI, total choles‑
terol, HbA1c, and HOMA-IR, a statistically significant association was observed between the CIMTb and FLI score in the MHO2 and MUHO, and CIMTc with the FLI score in all obesity phenotype groups (Table IV).
Discussions
MHO prevalence varies largely, up to 40%, accord‑
ing to the clinical study and the definition criteria, being more common in younger persons [8,26].
Our observations are similar to previous reports – MHO has a lower CV risk profile compared to MUHO [26,27]. Elevated CIMTc and CIMTb, found in several MHO1 patients were correlated with ApoB/ApoA1 ratio, but not with LDL levels. Therefore, our results support the recommendations of the guidelines, to assess apoB even when LDL levels are normal because apoB corre‑
lates with atherosclerosis and predicts CV events [28].
Marini et al showed that CIMT increased from 0.68
mm in non‑obese to 0.79 in MHO and 0.89 in obese with
insulin resistance [27]. From our findings, increasing
values were observed from MHO1 to MHO2, reaching
the highest value in MUHO. Supposedly both CIMTc
and CIMTb could increase with the number of MetS
components. Previous studies have shown that CIMT
and carotid plaque prevalence are associated with the
presence of MetS [23, 29] and the number of its compo‑
nents [29‑33]. Each additional component of the MetS is associated with a 0.02 mm increase in the CIMTc, in‑
dependent of age, gender, family history of CVD, and smoking [34].
We found that only CIMTb was correlated with the obesity phenotype and systolic and diastolic BP in all groups, suggesting that BP‑induced shear stress could explain the higher yearly growth rate of CIMTb com‑
pared to CIMTc [35]. Polak et al [17] showed that FPG and diastolic BP had a stronger association with CIMTc while hypertension, diabetes, and smoking with CIMTb.
We showed that FLI score increased in parallel with obesity phenotype groups. The prevalence of NAFLD increased from MHO1 to MUHO. Similarly, Zhang et al showed that the intrahepatic triglyceride content was significantly lower in MHO compared to MUHO and this
content is a better predictor for MUHO than BMI, WC or percentage of body fat [20].
Intrahepatic fat accumulation and NAFLD are as‑
sociated with a more adverse CV risk profile [36-40], while NAFLD is associated with insulin resistance, MetS and an atherogenic lipid profile [37, 41]. Furthermore, NAFLD patients have a higher prevalence of coronary artery lesions [36], higher CIMT, and atherosclerotic plaques [38], as well as a higher incidence of CVD, and increased CV mortality [39, 40]. We found significant correlations between the FLI score, presence of NAFLD, CIMTc, and CIMTb in all obesity phenotypes. Independ‑
ent of age, BMI, total cholesterol, HbA1c, and HOMA‑
IR, the CIMTc was significantly associated with FLI in all obesity phenotypes, unlike CIMTb (only in MHO2 and MUHO), suggesting that hepatic fat accumulation plays a role in the determination of the obesity phenotype asso‑
Table I. Clinical, anthropometric, and metabolic characteristics of patients grouped according to the presence MetS.
Assessed parameters MHO MUHO p#
AllN=100 MHO1
N=48 MHO2
M=52 p* N=100
Age (years) 41.02±11.33 39.23±10.13 42.33±12.12 0.001 46.31±14.82 0.001
Weight (kg) 88.06±11.81 88.06±11.81 103.87±26.89 0.001 103.87±26.89 0.001
Waist (cm) 106.90±16.77 100.00±10.27 111.50±18.79 0.01 118.47±12.51 0.010
BMI (kg/m2) 31.72±5.42 29.98±4.45 32.8±15.77 0.23 36.40±5.75 0.006
SBP (mmHg) 127.13±22.64 113.24± 8.75 134.21±10.13 0.021 148.21±19.34 0.002
DBP (mmHg) 78.56±16.12 62.44 ±6.21 89.24±12.13 0.024 95.21±19.43 0.008
Total cholesterol (mg/dL) 186.39±43.21 179.60±43.37 196.80±42.28 0.023 206.63±50.23 0.120 LDL‑cholesterol (mg/dL) 123.61±39.09 105.14±24.81 125.26±51.68 0.015 130.26±51.68 0.600 HDL‑cholesterol (mg/dL) 49.18±10.70 52.13±8.02 45.26±11.91 0.01 41.20±11.84 0.019 Triglycerides (mg/dL) 106 (83.23; 120.4) 88 (75.5; 111.5) 112 (91; 122.5) 0.12 135 (77;191.5) 0.002
ApoB/ApoA1 1.43±0.550 0.85±0.33 0.71±0.14 0.23 1.44±.230 0.929
Glycemia (mg/dL) 76.83±37.30 72.75±26.94 79.56±38.08 0.01 97.28±36.13 0.052
HbA1c (%) 5.55 (5.3;5.9) 5.3 (5.20; 5.45) 5.6 (5.45; 5.95) 0.01 6.1 (5.85; 6.25) 0.001 Insulinemia (μUI/mL) 9 (7.5;11.3) 8.7 (7.60; 9.30) 10 (8.15; 16.65) 0.001 15.45 (13.3;23.85) 0.027 C‑peptide (ng/mL) 2.25 (2.1;4.0) 2.1 (1.8; 2.2) 2.7 (2.2; 4.4) 0.009 3.25 (2.45; 5.65) 0.038 HOMA‑IR 1.91 (1.02; 5.45) 2.43 (1.02; 3.25) 4.16 (2.25; 6.22) 0.001 6.12 (5.44; 7.51) 0.001
ASAT (IU/mL) 23.02±11.45 20.13±10.34 26.47±14.03 0.13 43.37±13.02 0.035
ALAT (IU/mL) 25.12±13.42 21.03±11.02 27.01±13.49 0.19 45.23±15.27 0.031
GGT (IU/mL) 34.25±10.89 24.38±11.02 35.67±14.89 0.21 48.38±16.72 0.037
Uric acid (mg/dL) 5.63±1.53 4.96±1.20 6.11±1.59 0.001 6.70±1.05 0.013
CRP (mg/dL) 1.90 (1; 3.3) 2.56 (0.95; 3.10) 1.40 (1.05; 3.75) 0.000 4.8 (2.3; 7.5) 0.035
CIMTc (mm) 0.81±0.19 0.74±0.17 0.84 ± 0.19 0.001 0.88±0.17 0.036
CIMTb (mm) 1.2 (1; 1.3) 1.2 (0.95; 1.3) 1.2 (1.1; 1.3) 0.01 1.5 (1.3; 2) 0.008
FLI score 66.65±26.52 63.44±25.32 68.01±26.82 0.009 79.89±26.97 0.002
NAFLD 70.0 66.7 73 0.005 84.2 0.013
The results are expressed as number (%), mean±SD or median (Q1;Q3). *p values are provided for the comparison between MHO1 and MHO2.
#p values are provided for the comparison between MHO and MUHO.N = number of participants; BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; HbA1c – A1c glycated hemoglobin; HOMA‑IR = homeostasis model assessment; ASAT
= aspartate transaminase; ALAT = alanine transaminase; GGT = gamma‑glutamyl transferase; CRP = C‑reactive protein; CIMTc = carotid intima‑media thickness measured at common carotid artery level; CIMTb = carotid intima‑media thickness measured at carotid bulb level;
FLI = fat liver index; NAFLD = non‑alcoholic fatty liver disease; MHO = metabolically healthy obese; MHO1 = obese with increased WC;
MHO2 = obese with increased WC plus one of the criterions for MetS; MUHO = metabolically unhealthy obese.
ciated with subclinical atherosclerosis, probably through increased cytokines production [42]. Currently, there is scarce data on the association of CIMT and hepatic fat accumulation in obesity phenotypes. The only available
study we could identify showed that irrespective of obe‑
sity phenotype and independent of percentage of body fat, an increase in the intrahepatic triglyceride content was associated with a higher risk of increased CIMT [20].
Table II. Correlations of carotid‑intima media thickness with measured parameters according to the obesity phenotype.
MHO1 MHO2 MUHO
Assessed parameters CIMTc CIMTb CIMTc CIMTb CIMTc CIMTb
Age (years) 0.569* 0.523* 0.289 0.083 0.678* 0.550
Waist (cm) 0.523 0.110 0.503* 0.336* ‑0.165 0.218
BMI (kg/m2) 0.368 0.667 0.137 ‑0.065 ‑0.337 ‑0.600
SBP (mmHg) 0.505 0.514* 0.523 0.544* 0.505 0.603*
DBP (mmHg) 0.489 0.563* 0.512 0.570* 0.489 0.563*
LDL cholesterol (mg/dL) ‑0.413 ‑0.306 0.001 0.355 0.297 0.218
HDL cholesterol (mg/dL) 0.042 0.518 0.212 ‑0.315 0.338 0.327
Triglycerides (mg/dL) 0.200 ‑0.409 ‑0.056 0.222 0.463 0.491
ApoB/ApoA1 -0.629* ‑0.691 ‑0.015 0.462 ‑0.036 ‑0.522
Glycemia (mg/dL) 0.750* 0.710* 0.083 ‑0.442 ‑0.493 0.327
HbA1c (%) ‑0.209 ‑0.662 ‑0.110 ‑0.050 ‑0.021 0.806
HOMA‑IR ‑0.267 ‑0.564 0.332 ‑0.500 0.327 0.866
Uric acid (mg/dL) 0.407 0.218 0.231 0.077 0.528 0.794
CRP (mg/dL) ‑0.360 0.051 0.389 0.780* ‑0.118 0.500
FLI score# 0.343* 0.763* 0.425* 0.443* 0.343* 0.754*
NAFLD 0.368* 0.792* 0.502* 0.434* 0.368* 0.783*
*p values <0.05 showing a statistically significant association; #FLI score included in the correlation analysis as a continuous variable; N = number of participants; BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; HbA1c – A1c glycated hemoglobin; HOMA‑IR = homeostasis model assessment; CRP = C‑reactive protein; CIMTc = carotid intima‑media thickness measured at common carotid artery level; CIMTb = carotid intima‑media thickness measured at carotid bulb level; FLI = fat liver index; NAFLD = non‑
alcoholic fatty liver disease; MHO = metabolically healthy obese; MHO1 =obese with increased WC; MHO2 = obese with increased WC plus one of the criterions for MetS; MUHO = metabolically unhealthy obese.
Table III. Correlations of the carotid‑intima media thickness with the obesity phenotypes.
Assessed parameters MHO/MUHO MHO1/MHO2/MUHO
CIMTc 0.286 (p=0.060) 0.204 (p=0.184)
CIMTb 0.483 (p=0.015) 0.518 (p=0.008)
CIMTc = carotid intima‑media thickness measured at common carotid artery level; CIMTb = carotid intima‑media thickness measured at carotid bulb level; MHO = metabolically healthy obese; MUHO = metabolically unhealthy obese.
Table IV. Association of carotid‑intima media thickness and with fatty liver index score in the whole sample and by obesity pheno‑
types
Assessed parameters CIMTc CIMTb
β p β p
All sample 0.283 0.005 0.072 0.416
MHO1 0.160 0.351 0.763 <0.001
MHO2 0.030 0.891 0.126 0.449
MUHO ‑0.151 0.378 0.052 0.671
All sample# 0.116 0.226 0.058 0.763
MHO1# -0.577 0.001 -* -*
MHO2# -1.199 <0.001 1.586 <0.001
MUHO# -7.713 <0.001 -0.980 0.004
#Models adjusted for age, BMI, total cholesterol, CRP, HbA1c, HOMA‑IR. *Could not be calculated due to high number of covariates compared to sample size; β – coefficient of correlation; CIMTc = carotid intima-media thickness measured at common carotid artery level;
CIMTb = carotid intima‑media thickness measured at carotid bulb level; MHO = metabolically healthy obese; MHO1 = obese with increased WC; MHO2 = obese with increased WC plus one of the criterions for MetS; MUHO = metabolically unhealthy obese.
Our study has some limitations that we must ac‑
knowledge. The sample size was relatively small and therefore, future prospective evaluation in a larger scale study is required. We could not collect accurate informa‑
tion on the smoking status for all patients (years of smok‑
ing, passive smoking, etc) and therefore we could not assess its impact on CIMT. NAFLD was assessed by the FLI score and not by liver biopsy nor by an ultrasound exam. However, FLI score was shown to have a good performance in NAFLD identification [43,44]. The study subjects were selected from ambulatory patients and not from the general population. Also we did not take into ac‑
count the different effects of associated therapy on MetS components (anti-inflammatory drugs, contraceptives, alternative therapies etc). Because of the small number of patients, dividing them into groups according to the association of MetS component would not have allowed an accurate statistical analysis. Therefore we do not have results on the impact of each MetS component – WC pair on NAFLD and CIMT.
Conclusions
Our results support previous findings suggesting the degree of subclinical atherosclerosis varies according to obesity phenotypes and is associated with hepatic fat ac‑
cumulation. Hepatic fat accumulation increased accord‑
ing to the obesity phenotype and may represent a pre‑
dictor of metabolic changes in obesity. Further studies investigating the association between NAFLD and CIMT progression in all obesity phenotypes are required.
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