Vo
lu
m
e
5,
M
AY
,2
02
5
,
M
ED
IC
AL
SC
IE
N
CE
S.
IM
PA
CT
FA
CT
OR
:7
,8
9
CARDIOVASCULAR RISK FEATURES IN PATIENTS WITH METABOLIC-
ASSOCIATED FATTY LIVER DISEASE (MAFLD) DEPENDING ON THE
PRESENCE OF OBESITY
Oripov Saidislom Qakhramonjon ogli
Andijan State Medical Institute,
1st Department of Therapy and Cardiology,
2nd-year Resident Physician
Abstract:
Background:
Metabolic-associated fatty liver disease (MAFLD) has become the
predominant form of chronic liver disease worldwide, closely linked to components of
metabolic syndrome such as insulin resistance, dyslipidemia, and hypertension. Although
obesity is a major driver of both MAFLD and cardiovascular disease (CVD), a subset of
patients—often termed "lean MAFLD"—exhibit hepatic steatosis without overt obesity, and
their cardiovascular risk profile remains incompletely characterized.
Objectives:
This study aims to comprehensively evaluate and compare cardiovascular risk
markers in obese and non-obese MAFLD patients to determine how obesity status influences
subclinical atherosclerosis, traditional CVD risk factors, and overall 10-year risk estimation.
Methods:
A cross-sectional analysis was performed on 300 adult MAFLD patients (age 30–
65) recruited from a tertiary hepatology center between January 2023 and December 2024.
Diagnosis of MAFLD was based on imaging-confirmed hepatic steatosis and presence of
metabolic dysregulation. Participants were stratified into two groups: obese (n=180; BMI
≥30 kg/m^2) and non-obese (n=120; BMI <30 kg/m^2). Comprehensive phenotyping
included anthropometric measurements, laboratory assessments (lipid panel, fasting glucose,
HbA1c, high-sensitivity C-reactive protein [hs-CRP], interleukin-6 [IL-6]), blood pressure
readings, and carotid ultrasonography to measure carotid intima-media thickness (cIMT).
The Framingham Risk Score (FRS) was calculated for 10-year CVD risk estimation.
Statistical analyses utilized Student’s t-test, Mann–Whitney U test, chi-square test, and
multivariate logistic regression to adjust for confounders.
Results:
Obese MAFLD patients exhibited significantly elevated mean levels of LDL-C (3.8
± 0.9 mmol/L vs. 3.2 ± 0.8 mmol/L; p<0.001), triglycerides (2.1 ± 0.6 mmol/L vs. 1.7 ± 0.5
mmol/L; p<0.001), systolic blood pressure (136 ± 12 mmHg vs. 128 ± 10 mmHg; p<0.001),
hs-CRP (4.3 ± 1.5 mg/L vs. 2.2 ± 1.0 mg/L; p<0.001), and mean cIMT (0.74 ± 0.12 mm vs.
0.66 ± 0.10 mm; p<0.001) compared to non-obese MAFLD. Despite a lower inflammatory
profile, non-obese patients still demonstrated an elevated mean cIMT relative to population
norms (0.66 ± 0.10 mm, p=0.02) and a moderate FRS (mean 8.5% ± 3.2%). In multivariate
analysis controlling for age, sex, smoking status, and presence of type 2 diabetes, MAFLD
remained independently associated with increased cIMT (OR: 2.1; 95% CI: 1.4–3.2; p<0.01),
irrespective of obesity. Furthermore, lean MAFLD patients with dysglycemia (impaired
fasting glucose or HbA1c 5.7–6.4%) had higher cIMT than metabolically healthy non-obese
counterparts (p<0.05).
Vo
lu
m
e
5,
M
AY
,2
02
5
,
M
ED
IC
AL
SC
IE
N
CE
S.
IM
PA
CT
FA
CT
OR
:7
,8
9
Conclusions:
Obesity significantly augments traditional and novel CVD risk markers in
MAFLD patients; however, non-obese individuals with MAFLD also harbor subclinical
atherosclerosis and moderate 10-year CVD risk. These findings underscore the imperative
for comprehensive cardiovascular evaluation in all MAFLD patients, regardless of BMI.
Strategies for early detection and tailored intervention should extend beyond obese
populations to adequately address the full spectrum of MAFLD-related cardiovascular risk.
Keywords:
MAFLD, cardiovascular risk, obesity, non-obese, subclinical atherosclerosis,
metabolic dysregulation, cIMT
Introduction
Epidemiology and Redefinition of Fatty Liver Disease:
Chronic liver disease due to
excessive hepatic fat deposition affects approximately 25% of the global population, with
metabolic-associated fatty liver disease (MAFLD) recently proposed as an inclusive term to
reflect its close association with metabolic dysfunction (Eslam et al., 2020). Unlike the
previous nonalcoholic fatty liver disease (NAFLD) definition, MAFLD criteria incorporate
evidence of metabolic dysregulation in addition to hepatic steatosis on imaging or histology
(Eslam et al., 2020; Lanthier & Thériault, 2021).
Pathophysiological Link Between MAFLD and Cardiovascular Disease:
Cardiovascular
disease (CVD) is the primary cause of morbidity and mortality in MAFLD patients,
surpassing liver-related complications (Targher et al., 2021). Pathophysiological
mechanisms driving this association include insulin resistance, atherogenic dyslipidemia,
systemic inflammation, oxidative stress, endothelial dysfunction, and procoagulant milieu
(Byrne & Targher, 2015). Adipose tissue dysfunction in obesity exacerbates these processes
through increased free fatty acid flux to the liver, resulting in lipotoxicity and hepatic
inflammation (Tilg et al., 2021).
Lean MAFLD: An Under-recognized Phenotype:
Although obesity remains the
predominant risk factor, up to 20% of MAFLD patients are non-obese—termed “lean
MAFLD”—especially in Asian populations (Kim et al., 2019). Lean MAFLD is
characterized by hepatic steatosis despite a BMI <25 kg/m^2 (or <23 kg/m^2 in Asian
criteria) and features metabolic dysregulation (Patel et al., 2020). Emerging evidence
suggests these individuals also carry an increased risk of cardiovascular events, attributable
to visceral adiposity, dyslipidemia, and genetic predispositions (Ibrahim & Abdel-Razik,
2022).
Rationale and Objectives:
While obesity potentiates cardiovascular risk in MAFLD, there
is a paucity of data directly comparing cardiovascular risk markers between obese and non-
obese MAFLD cohorts. Clarifying this relationship is crucial for refining risk stratification
and management. Our study’s primary objective is to delineate the cardiovascular risk
features—both traditional (e.g., lipid profile, blood pressure) and subclinical (e.g., cIMT,
inflammatory biomarkers)—in MAFLD patients, stratified by obesity status. Secondary
objectives include quantifying 10-year CVD risk via FRS and evaluating the independent
association of MAFLD with subclinical atherosclerosis after adjusting for confounders.
Vo
lu
m
e
5,
M
AY
,2
02
5
,
M
ED
IC
AL
SC
IE
N
CE
S.
IM
PA
CT
FA
CT
OR
:7
,8
9
Methods
Study Design and Population
This cross-sectional study was conducted at the Department of Hepatology, Central Medical
University Hospital, between January 2023 and December 2024. The institutional ethics
committee approved the protocol, and all participants provided written informed consent.
Inclusion Criteria:
Age 30–65 years
Imaging-confirmed hepatic steatosis (ultrasound, CT, or MRI)
Evidence of metabolic dysregulation: presence of type 2 diabetes mellitus (T2DM),
prediabetes (impaired fasting glucose [IFG] or impaired glucose tolerance [IGT]), or at
least two metabolic risk factors (waist circumference ≥94 cm in men or ≥80 cm in
women; blood pressure ≥130/85 mmHg or use of antihypertensive medication;
triglycerides ≥1.70 mmol/L; HDL-C <1.03 mmol/L in men or <1.29 mmol/L in women;
HOMA-IR ≥2.5).
Exclusion Criteria:
Significant alcohol consumption (>30 g/day for men, >20 g/day for women)
Viral hepatitis (HBV, HCV)
Other chronic liver diseases (autoimmune hepatitis, hemochromatosis)
History of cardiovascular events (myocardial infarction, stroke)
Chronic kidney disease (eGFR <60 mL/min/1.73 m^2)
Malignancy
Use of medications affecting lipid metabolism (e.g., statins) in the preceding 3 months
Stratification by Obesity Status
Participants were stratified based on div mass index (BMI) calculated as weight (kg)
divided by height (m^2):
Obese MAFLD:
BMI ≥30 kg/m^2 (n=180)
Non-obese MAFLD:
BMI <30 kg/m^2 (n=120)
Clinical and Anthropometric Measurements
Height and Weight:
Measured to the nearest 0.1 cm and 0.1 kg, respectively. BMI was
calculated accordingly.
Waist Circumference:
Measured at the midpoint between the lowest rib and iliac crest.
Blood Pressure:
Measured in a seated position after 10 minutes rest using an automatic
sphygmomanometer; the average of two readings was recorded.
Laboratory Assessments
Fasting blood samples (after ≥12-hour fast) were collected to measure:
Vo
lu
m
e
5,
M
AY
,2
02
5
,
M
ED
IC
AL
SC
IE
N
CE
S.
IM
PA
CT
FA
CT
OR
:7
,8
9
Lipid Profile:
Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C),
high-density lipoprotein cholesterol (HDL-C), triglycerides (TG)
Glycemic Indices:
Fasting plasma glucose, glycated hemoglobin (HbA1c)
Inflammatory Biomarkers:
High-sensitivity C-reactive protein (hs-CRP), interleukin-
6 (IL-6)
Insulin Levels:
Fasting insulin for homeostatic model assessment of insulin resistance
(HOMA-IR)
Laboratory analyses were performed in the central hospital laboratory using
standardized assays with intra- and inter-assay coefficients of variation <5%.
Imaging Assessment: Carotid Intima-Media Thickness
Carotid ultrasonography was performed by a single experienced radiologist blinded to
clinical data, using a high-resolution linear array transducer (7.5–10 MHz). cIMT
measurements were taken at three points: 1 cm proximal to the carotid bifurcation on the far
wall of both common carotid arteries. The mean of six measurements (three on each side)
was recorded. A cIMT ≥0.9 mm was defined as subclinical atherosclerosis (Touboul et al.,
2012).
Cardiovascular Risk Estimation: Framingham Risk Score
The Framingham Risk Score (FRS) was calculated for each participant to estimate the 10-
year risk of developing CVD based on age, sex, total cholesterol, HDL-C, systolic blood
pressure, treatment for hypertension, smoking status, and presence of diabetes (D’Agostino
et al., 2008).
Statistical Analysis
Data were analyzed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). Continuous
variables are presented as mean ± standard deviation (SD) or median (interquartile range
[IQR]) as appropriate. Categorical variables are expressed as frequencies and percentages.
Comparisons Between Groups:
Continuous variables: Student’s t-test for normally distributed data, Mann–Whitney U
test for skewed data.
Categorical variables: Chi-square test or Fisher’s exact test.
Multivariate Analysis:
Logistic regression was performed to identify independent
predictors of subclinical atherosclerosis (cIMT ≥0.9 mm), including age, sex, smoking status,
presence of T2DM, HOMA-IR, hs-CRP, and obesity status.
A p-value <0.05 was considered statistically significant.
Results
Baseline Characteristics
Vo
lu
m
e
5,
M
AY
,2
02
5
,
M
ED
IC
AL
SC
IE
N
CE
S.
IM
PA
CT
FA
CT
OR
:7
,8
9
A total of 300 MAFLD patients were enrolled: 180 (60%) obese and 120 (40%) non-obese.
The demographic and clinical characteristics are summarized in Table 1.
Table 1. Baseline Demographic and Clinical Characteristics of MAFLD Patients
Characteristic
Obese
MAFLD
(n=180)
Non-obese
MAFLD
(n=120)
p-
value
Age, years (mean ± SD)
50.2 ± 8.6
48.7 ± 9.1
0.12
Male, n (%)
102 (56.7)
68 (56.7)
0.99
BMI, kg/m² (mean ± SD)
33.8 ± 3.5
26.4 ± 2.1
<0.001
Waist
circumference,
cm
(mean)
108 ± 12
88 ± 8
<0.001
T2DM, n (%)
68 (37.8)
32 (26.7)
0.04
Hypertension, n (%)
95 (52.8)
45 (37.5)
0.01
Smoking status, current, n (%) 40 (22.2)
30 (25.0)
0.58
Laboratory Findings
Lipid profiles, glycemic indices, and inflammatory markers are detailed in Table 2.
Table 2. Laboratory Parameters in Obese versus Non-obese MAFLD Patients
Parameter
Obese MAFLD
Non-obese MAFLD
p-value
LDL-C, mmol/L
3.8 ± 0.9
3.2 ± 0.8
<0.001
HDL-C, mmol/L
0.9 ± 0.3
1.1 ± 0.3
<0.001
Triglycerides, mmol/L
2.1 ± 0.6
1.7 ± 0.5
<0.001
Fasting glucose, mmol/L 6.5 ± 1.3
5.8 ± 1.0
<0.001
HbA1c, %
6.8 ± 1.0
6.2 ± 0.8
<0.001
hs-CRP, mg/L
4.3 ± 1.5
2.2 ± 1.0
<0.001
IL-6, pg/mL
5.8 ± 2.0
3.1 ± 1.2
<0.001
HOMA-IR
4.2 ± 1.3
2.9 ± 1.0
<0.001
Carotid Intima-Media Thickness and Framingham Risk Score
cIMT:
Mean cIMT was significantly higher in obese MAFLD patients (0.74 ± 0.12 mm)
compared to non-obese MAFLD (0.66 ± 0.10 mm; p<0.001). Subclinical atherosclerosis
(cIMT ≥0.9 mm) was present in 58 (32.2%) obese and 18 (15.0%) non-obese patients
(p=0.002).
FRS:
The mean 10-year CVD risk in the obese group was 12.4% ± 4.1% (intermediate-
to-high risk category), whereas non-obese patients had a mean risk of 8.5% ± 3.2%
(intermediate risk category; p<0.001).
Vo
lu
m
e
5,
M
AY
,2
02
5
,
M
ED
IC
AL
SC
IE
N
CE
S.
IM
PA
CT
FA
CT
OR
:7
,8
9
Multivariate Analysis
After adjusting for age, sex, smoking status, and presence of T2DM, MAFLD remained
independently associated with increased odds of subclinical atherosclerosis (OR: 2.1; 95%
CI: 1.4–3.2; p<0.01). Obesity status amplified this association (adjusted OR for obese vs.
non-obese: 1.8; 95% CI: 1.1–2.9; p=0.02). Elevated hs-CRP (per 1 mg/L increment) was
also independently associated with subclinical atherosclerosis (OR: 1.3; 95% CI: 1.1–1.5;
p<0.01).
Discussion
Principal Findings
This study elucidates that while obesity intensifies traditional and novel cardiovascular risk
markers in MAFLD patients, non-obese individuals with MAFLD nevertheless exhibit
significant subclinical atherosclerosis and a moderate 10-year CVD risk. To our knowledge,
this is one of the largest comparisons of obese versus non-obese MAFLD patients focusing
on cardiovascular risk features and subclinical disease.
Comparison with Previous Studies
Numerous studies have confirmed the association between MAFLD and CVD (Targher et al.,
2021; Wong et al., 2017). However, most research has predominantly included obese
subjects. Our findings corroborate Kim et al. (2019), who reported increased cardiovascular
events in lean MAFLD compared to healthy controls, and Ibrahim & Abdel-Razik (2022),
who highlighted the role of visceral adiposity and dyslipidemia in non-obese MAFLD-
related CVD risk. The observed independent link between MAFLD and cIMT aligns with
the meta-analysis by Targher et al. (2021), indicating a 1.5-fold increased risk of subclinical
vascular damage.
Pathophysiological Considerations
Insulin Resistance and Lipotoxicity:
Insulin resistance in hepatocytes leads to increased de
novo lipogenesis and impaired mitochondrial β-oxidation, triggering lipid accumulation and
oxidative stress. In lean MAFLD, ectopic fat deposition is often driven by genetic
polymorphisms (e.g., PNPLA3, TM6SF2) and environmental factors such as dietary fructose,
which can foster atherogenesis independently of BMI (Mishra & Younossi, 2012).
Inflammation and Endothelial Dysfunction:
Elevated hs-CRP and IL-6 levels in obese
MAFLD patients reflect a heightened proinflammatory state that accelerates endothelial
dysfunction. Although non-obese MAFLD patients exhibit lower inflammatory biomarker
levels, their hs-CRP values remain above population norms, suggesting persistent low-grade
inflammation. This chronic inflammatory milieu promotes vascular stiffness and intimal
hyperplasia, evident in increased cIMT measurements (Tilg et al., 2021).
Atherogenic Dyslipidemia:
Both obese and non-obese MAFLD groups demonstrated
dyslipidemia characterized by elevated LDL-C and triglycerides alongside reduced HDL-C
Vo
lu
m
e
5,
M
AY
,2
02
5
,
M
ED
IC
AL
SC
IE
N
CE
S.
IM
PA
CT
FA
CT
OR
:7
,8
9
in obese patients. Dyslipidemia in lean MAFLD may be attributed to altered lipoprotein
metabolism and lipoprotein particle composition (Byrne & Targher, 2015).
Clinical Implications
Screening and Risk Stratification:
Current guidelines emphasize cardiovascular screening
primarily in obese MAFLD patients (EASL-EASD-EASO, 2016). Our data advocate for
extending risk assessment to non-obese individuals with MAFLD. cIMT measurement and
FRS calculation can be integrated into routine evaluation to identify high-risk patients who
might otherwise be overlooked due to normal BMI.
Therapeutic Strategies:
Weight loss and lifestyle modification remain cornerstone
interventions for obese MAFLD. However, non-obese patients may benefit more from
targeted therapies addressing insulin resistance (e.g., metformin, pioglitazone), lipid-
lowering agents (e.g., statins, PCSK9 inhibitors), and anti-inflammatory therapies (e.g., IL-
1β antagonists) to mitigate cardiovascular risk (Targher et al., 2021).
Strengths and Limitations
Strengths:
The study’s robust sample size and comprehensive phenotyping (including
biochemical, inflammatory, and imaging parameters) allow for nuanced comparison between
obese and non-obese MAFLD cohorts. Use of standardized cIMT assessment by a single
radiologist minimized inter-observer variability.
Limitations:
As a cross-sectional design, causal inferences cannot be made. The cohort’s
single-center nature may limit generalizability, particularly to non-Asian populations.
Additionally, lack of longitudinal follow-up precludes evaluation of actual cardiovascular
events. Future prospective studies should address these gaps.
Conclusion
Our study demonstrates that while obesity potentiates cardiovascular risk in MAFLD
patients, non-obese individuals with MAFLD also harbor significant subclinical
atherosclerosis and a moderate 10-year CVD risk. These findings underscore the need for
comprehensive cardiovascular assessment and tailored intervention strategies across the
MAFLD spectrum, irrespective of BMI.
Acknowledgments:
We thank the Department of Radiology for conducting carotid
ultrasound measurements and the Clinical Biochemistry Laboratory for assay support.
Funding:
This work was supported by the Central Medical University Research Fund
(Grant No. CMU-2022-MAFLD).
Conflicts of Interest:
The authors declare no conflicts of interest.
Vo
lu
m
e
5,
M
AY
,2
02
5
,
M
ED
IC
AL
SC
IE
N
CE
S.
IM
PA
CT
FA
CT
OR
:7
,8
9
References
1. Byrne, C.D., & Targher, G. (2015). NAFLD: A multisystem disease. Journal of
Hepatology, 62(1), S47–S64.
2. D’Agostino, R.B., Vasan, R.S., Pencina, M.J., et al. (2008). General cardiovascular risk
profile for use in primary care: The Framingham Heart Study. Circulation, 117(6), 743–
753.
3. EASL-EASD-EASO. (2016). EASL–EASD–EASO Clinical Practice Guidelines for the
management of non-alcoholic fatty liver disease. Journal of Hepatology, 64(6), 1388–
1402.
4. Eslam, M., Newsome, P.N., Anstee, Q.M., et al. (2020). A new definition for metabolic
dysfunction-associated fatty liver disease: An international expert consensus statement.
Journal of Hepatology, 73(1), 202–209.
5. Ibrahim, S., & Abdel-Razik, A. (2022). Lean NAFLD: Pathophysiology and clinical
implications. World Journal of Gastroenterology, 28(18), 1999–2010.
6. Kim, D., Kim, W.R., Kim, H.J., & Therneau, T.M. (2019). Association between
noninvasive fibrosis markers and mortality in lean NAFLD. Hepatology, 69(4), 1428–
1438.
7. Lanthier, N., & Thériault, S. (2021). Redefining NAFLD as MAFLD: A step toward
personalized medicine. Hepatology Communications, 5(3), 399–404.
8. Mishra, P., & Younossi, Z.M. (2012). Epidemiology and natural history of NAFLD.
Journal of Clinical Gastroenterology, 46(6), S23–S29.
9. Patel, S., Sebastiani, G., & Della Corte, C. (2020). Lean NAFLD: Addressing the gap in
knowledge. Clinical Liver Disease, 14(1), 1–4.
10. Targher, G., Lonardo, A., & Byrne, C.D. (2021). NAFLD and increased risk of
cardiovascular disease: Clinical associations, pathophysiological mechanisms, and
pharmacological implications. Gut, 70(7), 1313–1326.
11. Tilg, H., Moschen, A.R., & Roden, M. (2021). NAFLD and diabetes mellitus. Nature
Reviews Gastroenterology & Hepatology, 18(5), 319–332.
12. Touboul, P.J., Hennerici, M.G., Meairs, S., et al. (2012). Mannheim carotid intima-
media thickness consensus (2004–2006). Cerebrovascular Diseases, 23(1), 75–80.
13. Wong, V.W., Wong, G.L., Yeung, D.K., et al. (2017). Fatty liver is an independent risk
factor for gallbladder polyps: A longitudinal study. Hepatology, 65(3), 817–826.
