Inflammatory Gene Expression,Insulin Resistance,and Circulating Markers Vary by Visceral Adiposity in Patients with Metabolic Dysfunction

Abstract

Background:

Metabolic dysfunction–associated steatotic liver disease (MASLD) is driven by complex immune and inflammatory mechanisms. Visceral adiposity, a key contributor, worsens inflammation, immune dysregulation, and insulin resistance.

Objective:

This study examines correlations between inflammatory genes, insulin resistance markers, and inflammatory markers across visceral adiposity levels in patients with MASLD.

Methods:

This cross-sectional study included 102 patients with MASLD. Assessments included body mass index, visceral adiposity index (VAI), a body shape index (ABSI), inflammatory markers, gene expression from peripheral white blood cells, and serologic inflammatory proteins. We calculated insulin resistance markers, such as homeostasis model assessment–insulin resistance index (HOMA-IR), triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio, triglyceride-glucose (TyG) index, and neutrophil-to-HDL ratio (NHR). Pearson correlation coefficients evaluated parameter associations between low and high VAI and ABSI groups.

Results:

The higher VAI group presented with some elevated markers, such as HOMA-IR (5.21 ± 3.42 vs. 4.34 ± 4.62), TG/HDL-C (4.08 ± 1.97 vs. 2.20 ± 1.07), TyG (9.03 ± 0.48 vs. 8.70 ± 0.51), and NHR (1.86 ± 0.75 vs. 1.45 ± 0.64) compared with the low VAI group, indicating potentially greater insulin resistance and systemic inflammation. Monocyte chemoattractant protein-1 and interleukin-6 genes were strongly correlated in the low VAI group (R = 0.94, P < 0.001) but more weakly correlated in the high VAI group (R = 0.63, P < 0.001).

Conclusion:

These findings highlight differential immune changes across visceral adiposity levels in MASLD, supporting the need for tailored interventions based on adiposity profiles.

Keywords

metabolic dysfunction–associated steatotic liver disease inflammatory gene insulin resistance visceral adiposity index a body shape index

Introduction

Metabolic dysfunction–associated steatotic liver disease (MASLD) is currently the most common chronic liver disease, with a global prevalence of one-fourth.¹ The pathogenesis of MASLD is complex and involves various immune and inflammatory responses. The interplay between innate and adaptive immunity plays a crucial role in the progression of MASLD, particularly as it transitions from simple hepatic steatosis to more severe forms like steatohepatitis and cirrhosis. The innate immune response involves pro-inflammatory cytokines such as tumor necrosis factor (TNF) α, IL-1β, and IL-6, which play a pivotal role in driving inflammation in MASLD.² The adaptive immune response includes the activation of T cells, and the associated activators, Tumor Necrosis Factor Superfamily Member (TNFSF) 14 and TNFSF15, are linked to the progression of MASLD.^3–5 The TNFSF14 level is associated with the disease development of MASLD.⁶ Adipose tissue is also important in the pathogenesis and progression of MASLD. Dysfunctional white adipose tissue in obesity contributes to MASLD by promoting ectopic fat accumulation in the liver, releasing pro-inflammatory cytokines and free fatty acids, and exacerbating insulin resistance, inflammation, and fibrosis.⁷

Body mass index (BMI) is a traditional index for evaluating obesity; however, it may not be sensitive enough in detecting visceral adipose tissue (VAT) accumulation. The visceral adiposity index (VAI) is a validated, noninvasive tool for classifying patients with MASLD and correlates strongly with hepatic steatosis and fibrosis severity.^8,9 VAI outperforms traditional measures (e.g., BMI) in stratifying visceral fat-related risk.⁸ A body shape index (ABSI) is an anthropometric measure used to assess body composition, with higher ABSI indicating more severe visceral fat accumulation. Previous studies have demonstrated that the combination of ABSI and BMI is highly effective in identifying nonalcoholic fatty liver disease (NAFLD).¹⁰

In MASLD, inflammatory genes are influenced by VAT. In VAT, adipocyte progenitor cells are identified as significant sources of MCP-1, which is crucial for the accumulation of M1 macrophages, further promoting inflammation.¹¹ Past study also reported which individuals with obese MASLD had higher levels of VAT, IL-6, insulin resistance, and TG, as well as lower HDL-C, than those with normal weight and no MASLD, indicating that these factors are important in the pathogenesis of obese MASLD.¹² Abnormal expression of immune-related genes, including IL-1β and TNF-α, is crucial for MASLD development.¹³ However, the expression patterns of immune-related genes among patients with MASLD with varying levels of visceral adiposity accumulation remain unclear. Similarly, studies on gene-to-gene interactions, gene-to-immunological marker relationships, and insulin resistance markers across different visceral adiposity accumulation groups in patients with MASLD are still limited.

This study aims to enhance the understanding of MASLD pathogenesis by examining the associations between inflammatory gene expression and both circulating and calculated inflammatory markers under different levels of visceral adiposity. Additionally, we examine correlations between genes, gene-to-calculated inflammatory markers, gene-to-insulin resistance markers, circulating inflammatory marker-to-circulating inflammatory marker, and circulating inflammatory markers-to-calculated insulin resistance markers across different visceral adiposity groups in patients with MASLD. Through the above analyses, we aim to explore the correlations among these parameters under different visceral adiposity conditions in patients with MASLD, thereby enhancing our understanding of MASLD pathogenesis.

Materials and Methods

Study population

This cross-sectional study was approved by the Research Ethics Committee of Taipei Veterans General Hospital, Taipei, Taiwan on August 24, 2023 (Protocol Code: 2023-08-018BC). After a detailed explanation of the study, all participants signed a written informed consent. All research methods were conducted in accordance with relevant guidelines and regulations. This study is the second wave of the MAFLD research project at Taipei Veterans General Hospital.¹⁴ The study population consisted of adults over 20 years old who visited the internal medicine clinic between February 2023 and May 2024. The eligibility criteria included: (1) adults aged 20 years or older; (2) individuals with autonomous decision-making ability; (3) individuals who are independent in activities of daily living; and (4) patients were diagnosed with MASLD during clinic visits. The exclusion criteria included: (1) cognitive impairment; (2) concurrent unstable cardiovascular diseases, including coronary artery disease, valvular heart disease, and acute stroke; and (3) inability to cooperate with the examinations in this study.

Physical measurement

Participants completed a questionnaire that collected information on demographic data, past medical history, and health behaviors such as smoking, alcohol consumption, and physical activity. Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ)-short form.¹⁵ The IPAQ-short form employs both continuous and categorical scoring approaches. For continuous scoring, physical activity is quantified using metabolic equivalent task (MET)-minutes per week, with walking assigned 3.3 METs, moderate-intensity activities 4.0 METs, and vigorous-intensity activities 8.0 METs. The total score represents the sum of all activity categories. The categorical system classifies participants into three levels: low (insufficient activity to meet higher categories), moderate (meeting criteria such as ≥3 days of vigorous activity for ≥20 min daily, or ≥5 days of moderate activity for ≥30 min daily, or ≥600 MET-minutes weekly), and high (≥3 days of vigorous activity achieving ≥1500 MET-minutes weekly, or ≥7 days of any activity achieving ≥3000 MET-minutes weekly).¹⁶

Participants then completed physical assessments, including seated blood pressure, body height, weight, waist circumference (WC), hip, arm, and calf circumferences, maximal grip strength, a 6-min walk test, a five-times sit-to-stand test, the Short Physical Performance Battery (SPPB), and the Mini Nutritional Assessment (MNA). Blood pressure was measured twice, and the average value was used for analysis. Maximal grip strength was measured using the Jamar® Plus + Digital Hand Dynamometer. Participants were asked to exert their maximal grip strength, with both hands being measured three times. The highest value was used for analysis.

For the 6-min walk test, participants walked for 6 min, and the walking distance was recorded. For the five-times sit-to-stand test, participants were asked to stand up from a standard-height chair (approximately 43–45 cm from the ground) with a straight back and no armrests, and perform five consecutive sit-to-stand repetitions as quickly as possible. The time taken by participants was recorded. The SPPB includes assessments of balance, gait speed, and the five-times sit-to-stand test. The total score reflects the participant’s overall physical performance, with a total score of ≥10 indicating normal physical function.¹⁷ The MNA evaluates nutritional status, including weight changes, dietary habits, and body measurements. The total score categorizes individuals as having normal nutrition (24–30 points), at risk of malnutrition (17–23.5 points), or being malnourished (<17 points).¹⁸

Blood test

Blood test was performed under a fasting status for more than 8 hr. The items of blood tests included peripheral blood cell counts and differential counts, platelet count (10⁹/L), fasting plasma glucose (mg/dL), hemoglobin A1c (HbA1c, %), triglycerides (TG, mg/dL), high-density lipoprotein cholesterol (HDL-C, mg/dL), aspartate aminotransferase (AST, U/L), alanine aminotransferase (ALT, U/L), albumin (g/dL), total insulin level (μU/mL), C-reactive protein (CRP, mg/dL), hepatitis B surface antigen (HBsAg, COI), hepatitis C antibody (Anti-HCV, COI), and gene analysis of peripheral white blood cells. Additional biochemical tests were carried out using automated analyzers at the central laboratory of Taipei Veterans General Hospital.

Gene expression levels of peripheral white blood cells

Blood samples of 2.5–10 mL were collected according to standard procedures in tubes containing anticoagulants. Buffy coat layers were obtained by centrifugation (1500–2000g, 10–15 min, room temperature), mixed with RNA stabilizer at a 1:2 ratio, and frozen at −80°C until RNA extraction. Total RNA was extracted from buffy coat samples using TRIzol™ reagent (catalog #15596026, Invitrogen, ThermoFisher Scientific, Waltham, MA, USA) and reverse transcribed with the PrimeScript RT reagent kit (Takara, Tokyo, Japan) according to the manufacturer’s protocol. Quantitative real-time RNA concentration was measured using a Nanodrop system (ThermoFisher Scientific), and the 260/280 ratio calculated from Nanodrop software was used to assess protein contamination. Polymerase chain reaction was performed on a Rotor-Gene Q real-time PCR system (Qiagen, Hilden, Germany) with SYBR Green fluorescent dye methodology and analyzed with the manufacturer’s software. This study analyzes the expression of various genes in white blood cells, primarily focusing on their roles in regulating inflammatory responses, encompassing key mechanisms in both pro-inflammatory and anti-inflammatory processes, including cluster of differentiation (CD)14, toll-like receptor (TLR4), CD163, TNFα, nuclear factor kappa B (NFkB), IL-1β, IL-6, MCP-1, nitric oxide synthase (iNOS), IL-4, IL-10, peroxisome proliferator-activated receptor gamma 2 (PPAR-γ2), and CD206. All primers in this study were designed using Primer-BLAST software and synthesized by Integrated DNA Technologies (Singapore) (see Supplementary Table S1). Data were calculated by the 2^−ΔΔCT method and normalized to glyceraldehyde-3-phosphate dehydrogenase.

Visceral fat accumulation and insulin resistance variables

Various metabolic variables related to insulin resistance, lipid metabolism, and visceral fat accumulation were analyzed. The VAI was calculated by using the following formula specific to the Chinese population¹⁹:

VAI (men) = −267.93 + 0.68 × age + 0.03 × BMI + 4.00 × WC + 22.00 × log₁₀ (TG) − 16.32 × HDL-C; VAI (women) = −187.32 + 1.71 × age + 4.23 × BMI + 1.12 × WC + 39.76 × log₁₀ (TG) − 11.66 × HDL-C.

ABSI was calculated as: WC (m)/[BMI^(2/3) × height^1/2 (m)].²⁰ Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), calculated as insulin (µU/mL) × glucose (mg/dL)/405, was used to estimate insulin resistance.²¹ The TG/HDL-C ratio served as a marker of dyslipidemia, providing insight into the balance between TG and HDL-C levels.²² Additionally, the TyG, calculated as ln[TG (mg/dL) × fasting plasma glucose (mg/dL)/2], was employed as a reliable marker of insulin resistance.²² To further explore the relationship between insulin resistance risk and body composition, we calculated TyGWC by multiplying TyG by WC, as a composite indicator correlated to cardiovascular and diabetes mortality.²³

Inflammatory variables

Inflammatory ratios are calculated from routine blood parameters and provide insight into systemic inflammation, including neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-HDL ratio (NHR), lymphocyte-to-monocyte ratio (LMR), monocyte-to-HDL cholesterol ratio (MHR), platelet-to-lymphocyte ratio (PLR), AST/ALT ratio, and C-reactive protein-to-albumin ratio (CAR). In addition, the AST/ALT ratio is used to assess liver health. Elevated NLR, NHR, PLR, and CAR are associated with higher inflammatory response, while a lower LMR may indicate weakened immune surveillance.²⁴ Furthermore, NLR, PLR, and LMR were associated with NAFLD risk.²⁵

Serology levels of inflammatory and anti-inflammatory proteins

Serology proteins were analyzed by commercial ELISA. The ELISA kits for Syndecan-1 (SDC1) and soluble CD163 (sCD163) were obtained from Elabscience® Technology Co., Ltd. (Houston, TX, USA). The kits for MCP-1, IL-1β, IL-6, and TNFα were sourced from ABclonal® Technology Co., Ltd. (Woburn, MA, USA), while those for TNFSF14 and TNFSF15 were purchased from Wuhan Fine Biotech Co., Ltd. (Hubei, China).

Assessment of hepatic steatosis and fibrosis

This study uses vibration-controlled transient elastography (VCTE, FibroScan® 630 Expert) to diagnose fatty liver. The VCTE was operated by an experienced hepatogastroenterology specialist. VCTE generates shock waves using physical impact to induce compression of liver tissue, producing shear waves in the vertical direction. The controlled attenuation parameter (CAP) is then measured to quantify the fat content in the liver.²⁶ Aligned with other Chinese studies, fatty liver was diagnosed with a CAP value ≥238 dB/m.²⁷ Liver fibrosis was determined by the fibrosis-4 (FIB-4) index.²⁸ FIB-4 = (age [year] × AST [U/L])/((platelet [10⁹/L]) × (ALT [U/L]^1/2)). FIB-4 <1.3 was regarded as low risk of advanced fibrosis.²⁹

Definition of MASLD

Once fatty liver was confirmed via VCTE, patients who did not have significant alcohol use (weekly intake of more than 140 g for women and 210 g for men) were classified as having MASLD if they met at least one of the following criteria³⁰: 1.

A BMI of 25 kg/m² or greater, or a waist circumference of 90 cm or more for men and 80 cm or more for women (for Asian populations).

Fasting blood glucose level of 100 mg/dL or higher, HbA1c of 5.7% or higher, use of antidiabetic medication, or a history of type 2 diabetes.

Blood pressure of at least 130/85 mmHg or use of antihypertensive medication.

TG of 150 mg/dL or higher, or the use of lipid-lowering agents.

HDL-C levels less than 40 mg/dL for men and 50 mg/dL for women, or the use of lipid-lowering agents.

Statistics

Participants with MASLD were dichotomized based on VAI and ABSI values to compare the effects of different levels of visceral fat accumulation in patients with MASLD. The medians of VAI (=134) and ABSI (=0.08) were used as cutoff points. Chi-square tests and Mann–Whitney U tests were used to compare between high and low VAI and ABSI groups for categorical and continuous variables, respectively. To test whether sex modified the associations between VAI/ABSI and inflammatory markers, we conducted stratified analyses. Based on the classifications of VAI and ABSI, we calculated the Pearson correlation coefficients for the following: (1) gene-to-gene, (2) gene-to-serologic proteins, (3) serologic proteins-to-serologic proteins, and (4) gene-to-inflammatory markers and insulin resistance markers. The Pearson correlation coefficients were visualized with correlation maps using GraphPad Prism version 3.0 (GraphPad Software, San Diego, CA, USA) and ggplot2 package in R version 4.5.2 (R Foundation for Statistical Computing, Vienna, Austria). Statistical analyses were conducted with SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA). The significance level was set at P < 0.05.

Results

Basic characteristics, blood tests, and physical measurements

We enrolled 102 patients with MASLD for the analysis. Patients with MASLD with a higher VAI were more likely to be current smokers (27.45% vs. 7.84%, P = 0.002, Table 1) and had higher mean levels of TG (163.93 vs. 114.41 mg/dL, P < 0.001, Table 2), arm circumference (32.18 vs. 29.24 cm, P < 0.001), calf circumference (39.98 vs. 37.50 cm, P = 0.003), BMI (31.14 vs. 26.64 kg/m², P < 0.001), waist circumference (102.79 vs. 90.49 cm, P < 0.001), and waist-to-hip ratio (0.95 vs. 0.90, P = 0.001), and lower mean levels of HDL-C (42.94 vs. 51.88 mg/dL, P < 0.001) and distance in the 6-min walk test (428.09 vs. 480.10 m, P < 0.001) than those with a lower VAI.

Table 1.

Basic Characteristics of Participants with MASLD Stratified by VAI and ABSI

	MASLD, VAI <134n = 51 (50.00%)	MASLD, VAI ≥134n = 51 (50.00%)	P value	MASLD, ABSI <0.08n = 51 (50.00%)	MASLD, ABSI ≥0.08n = 51 (50.00%)	P value	Total
Age (years), mean (SD)	60.18 (10.01)	55.75 (14.69)	0.053	54.78 (11.67)	61.14 (13.00)	0.011**	57.96 (12.70)
Male, n (%)	38 (74.51)	30 (58.82)	0.093	38 (74.51)	30 (58.82)	0.093	68 (66.67)
Female, n (%)	13 (25.49)	21 (41.18%)	0.093	13 (25.49)	21 (41.18%)	0.093	34 (33.33)
Married, n (%)	38 (74.51)	34 (66.67)	0.385	34 (66.67)	38 (74.51)	0.385	72 (70.59)
Education level, n (%)						0.114
Below high school	15 (29.41)	19 (37.25)	0.401	18 (35.29)	16 (31.37)	0.674	34 (33.33)
College and above	36 (70.59)	32 (62.75)	0.401	33 (64.75)	35 (68.63)	0.674	68 (66.67)
Metabolic equivalent of task-min/week, mean (SD)	2035.42 (1802.97)	1550.00 (1982.53)	0.073	2131.569 (2004.13)	1453.855 (1746.58)	0.072	1792.71 (1901.19)
Physical activity, n (%)
Low	11 (21.57)	21 (41.18)	0.073	16 (31.37)	16 (31.37)		32 (31.17)
Moderate	30 (58.82)	25 (49.02)		25 (49.02)	30 (58.82)	0.346	55 (53.92)
High	10 (19.61)	5 (9.80)		10 (19.60)	5 (9.80)		15 (14.71)
Smoking, n (%)
No	28 (54.90)	31 (60.78)	0.002**	28 (54.90)	31 (60.78)	0.582	59 (57.84)
Current smoker	4 (7.84)	14 (27.45)		11 (21.57)	7 (13.73)		18 (17.65)
Ex-smoker	19 (37.25)	6 (11.76)		12 (23.53)	13 (25.49)		25 (24.51)
Past history, n (%)
Hypertension	25 (49.02)	24 (47.06)	0.843	26 (50.98)	23 (45.10)	0.552	49 (48.04)
Hyperlipidemia	25 (49.02)	30 (58.82)	0.406	30 (58.82)	25 (49.02)	0.406	55 (53.92)
Type 2 diabetes mellitus	18 (35.29)	17 (33.33)	0.835	16 (31.37)	19 (37.25)	0.532	35 (34.31)
Cardiovascular disease	7 (13.73)	6 (11.76)	0.767	8 (15.69)	5 (9.80)	0.373	13 (12.75)
Hepatitis B carrier	12 (23.53)	5 (9.80)	0.094	8 (15.69)	9 (17.64)	0.642	17 (16.67)
Hepatitis C carrier	2 (3.92)	1 (1.96)	0.611	1 (1.96)	2 (3.92)	1	3 (2.94)

**P < 0.05.

ABSI, a body shape index; MASLD, metabolic dysfunction–associated steatotic liver disease; SD, standard deviation; VAI, visceral adipose index.

Table 2.

Blood Tests and Physical Measurements of Participants, Stratified by VAI and ABSI

	MASLD, VAI <134n = 51 (50.00%)	MASLD, VAI ≥134n = 51 (50.00%)	P value	MASLD, ABSI <0.08n = 51 (50.00%)	MASLD, ABSI ≥0.08n = 51 (50.00%)	P value	Total
Blood tests, mean (SD)
Creatinine (mg/dL)	0.89 (0.18)	0.89 (0.19)	0.714	0.92 (0.18)	0.87 (0.19)	0.082	0.89 (0.18)
eGFR	83.27 (15.61)	85.02 (22.38)	0.892	83.81 (17.71)	84.47 (20.76)	0.828	84.14 (19.19)
Fasting plasma glucose (mg/dL)	119.20 (29.34)	112.94 (29.20)	0.109	112.98 (24.88)	119.28 (33.09)	0.400	116.13 (29.29)
HbA1c (%)	6.36 (0.89)	6.40 (1.03)	0.759	6.25 (0.87)	6.52 (1.02)	0.172	6.38 (0.96)
TG (mg/dL)	114.41 (70.43)	163.93 (68.29)	<0.001**	136.27 (65.75)	142.08 (80.83)	0.763	139.17 (73.37)
HDL-C (mg/dL)	51.88 (14.64)	42.94 (10.03)	<0.001**	46.86 (13.88)	47.80 (12.63)	0.446	47.32 (13.22)
AST (U/L)	34.18 (25.41)	35.96 (29.79)	0.520	36.27 (30.46)	33.86 (24.58)	0.872	35.07 (27.56)
ALT (U/L)	35.78 (22.12)	46.04 (45.21)	0.098	45.84 (41.14)	35.98 (29.06)	0.024**	40.91 (35.79)
ALT >40 (n, %)	15 (29.41)	18 (35.29)	0.53	18 (35.29)	15 (29.41)	0.526	33 (32.35)
Albumin (g/dL)	4.57 (0.26)	4.53 (0.24)	0.266	4.54 (0.28)	4.57 (0.22)	0.748	4.55 (0.25)
CRP (mg/dL)	0.32 (0.62)	0.23 (0.20)	0.562	0.24 (0.26)	0.31 (0.60)	0.872	0.28 (0.46)
Physical measurement, mean (SD)
Systolic blood pressure (mmHg)	131.59 (13.75)	131.74 (16.19)	0.867	129.79 (14.85)	133.53 (14.96)	0.196	131.66 (14.95)
Diastolic blood pressure (mmHg)	76.37 (9.68)	75.03 (10.85)	0.199	77.45 (9.80)	73.94 (10.45)	0.051	75.70 (10.25)
Body mass index (BMI, kg/m²)	26.64 (2.51)	31.14 (5.14)	<0.001**	31.43 (4.76)	26.35 (2.70)	<0.001**	28.89 (4.62)
Waist circumferences (cm)	90.49 (13.18)	102.79 (12.50)	<0.001**	98.79 (18.3085)	94.49 (7.90)	0.017**	96.64 (14.20)
Waist-to-hip ratio	0.90 (0.13)	0.95 (0.09)	0.001**	0.90 (0.14)	0.95 (0.06)	0.020**	0.92 (0.11)
Muscle parameters, mean (SD)
Arm circumference (cm)	29.24 (2.68)	32.18 (4.30)	<0.001**	32.73 (3.60)	28.68 (2.96)	<0.001**	30.71 (3.86)
Calf circumference (cm)	37.50 (3.27)	39.98 (4.36)	0.003**	41.27 (3.40)	36.21 (2.88)	<0.001**	38.74 (4.03)
Maximal grip strength (kg)	38.69 (10.47)	34.55 (10.32)	0.059	39.99 (10.80)	33.25 (9.22)	0.002**	36.62 (10.55)
Six-min walk test (m)	480.10 (69.82)	428.09 (70.62)	<0.001**	459.44 (77.13)	449.19 (72.34)	0.567	454.37 (74.58)
Five-times sit-to-stand test (sec)	10.03 (3.43)	11.33 (3.80)	0.076	10.14 (3.79)	11.22 (3.48)	0.100	10.68 (3.66)
Short Physical Performance Battery protocol	11.44 (0.82)	10.85 (1.46)	0.058	11.23 (1.31)	11.06 (1.11)	0.180	11.15 (1.21)
Mini Nutritional Assessment (MNA) score	26.89 (2.01)	26.04 (2.30)	0.033**	25.90 (2.41)	27.03 (1.80)	0.017**	26.47 (2.19)

**P < 0.05.

ABSI, a body shape index; ALT, alanine aminotransferase; AST, aspartate transaminase; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; MASLD, metabolic dysfunction–associated steatotic liver disease; SD, standard deviation; TG, triglyceride; VAI, visceral adipose index.

Patients with MASLD with a higher ABSI were older (mean age 61.14 vs. 54.78 years, P = 0.011, Table 1), had lower mean levels of arm circumference (28.68 vs. 32.73 cm, P < 0.001, Table 2), calf circumference (36.21 vs. 41.27 cm, P < 0.001), maximal grip strength (33.25 vs. 39.99 kg, P = 0.002), and BMI (26.35 vs. 31.43 kg/m², P < 0.001), and higher mean levels of waist-to-hip ratio (0.95 vs. 0.90, P = 0.020) and MNA score (27.03 vs. 25.90, P = 0.017) than those with a lower ABSI.

No differences in gene expression levels of peripheral white blood cells and serology proteins

Figure 1 shows the expression of various genes in white blood cells relevant to inflammation. There are no differences in gene expression between high and low VAI and ABSI groups among MASLD participants. Table 3 reveals that in the comparison between high and low VAI groups, no statistically significant differences were observed in the mean serology protein expression levels of SDC1, sCD163, MCP-1, IL-1β, IL-6, TNFα, TNFSF14, and TNFSF15. Similarly, when stratified by ABSI, no significant differences were noted in these protein expressions between participants with low and high ABSI values.

FIG. 1.

Gene expression levels of peripheral white blood cells from participants (mean ± standard error of the mean), stratified by VAI and ABSI. All P values between VAI and ABSI groups >0.05 to No statistically significant differences in gene expression were observed between VAI and ABSI groups (all P > 0.05). The sample number in each VAI and ABSI group = 51. ABSI, a body shape index; CD14, cluster of differentiation 14; CD163, cluster of differentiation 163; CD206, cluster of differentiation 206; IL-1β, interleukin 1 beta; IL-4, interleukin 4; IL-10, interleukin 10; IL6, interleukin 6; iNOS, inducible nitric oxide synthase; MASLD, metabolic dysfunction–associated steatotic liver disease; MCP-1, monocyte chemoattractant protein 1 (also known as CCL2); NFkB, nuclear factor kappa-light-chain-enhancer of activated B cells; PPAR-γ2, peroxisome proliferator-activated receptor gamma 2; TLR4, toll-like receptor 4; TNFα, tumor necrosis factor alpha; VAI, visceral adipose index.

Table 3.

The Level of Circulating Inflammatory Markers (Mean, SD) and the Pearson Correlation Coefficients for Gene-to-Gene Expression in Peripheral White Blood Cells Among MASLD Participants, Stratified by VAI and ABSI

Circulating inflammatory markers	MASLD, VAI <134n = 51 (50.00%)	MASLD, VAI ≥134n = 51 (50.00%)	P value	MASLD, ABSI <0.08n = 51 (50.00%)	MASLD, ABSI ≥0.08n = 51 (50.00%)	P value	Total
[SDC1], ng/mL	64.33 (55.44)	53.97 (28.84)	0.317	55.39 (26.87)	62.91 (57.17)	0.947	59.15 (44.60)
[sCD163], ng/mL	188.28 (63.00)	194.71 (74.50)	0.859	181.21 (70.11)	203.95 (66.66)	0.210	191.73 (68.97)
[MCP-1], ng/mL	2290.28 (1864.33)	2099.54 (2078.97)	0.686	2245.74 (2294.73)	2144.07 (1595.43)	0.804	2194.91 (1967.11)
[IL-1β], pg/mL	947.22 (934.66)	860.55 (742.05)	0.992	948.66 (701.28)	859.11 (965.49)	0.115	903.89 (840.81)
[IL-6], pg/mL	841.06 (613.41)	865.62 (563.52)	0.671	902.05 (552.77)	804.64 (619.47)	0.213	853.34 (586.20)
[TNFα], pg/mL	963.32 (417.12)	1133.61 (530.28)	0.128	1063.22 (491.80)	1033.71 (477.15)	0.746	1048.46 (482.35)
[TNFSF14], pg/mL	142.05 (48.75)	167.36 (71.16)	0.050	151.17 (61.77)	158.24 (62.66)	0.476	154.7 (62.01)
[TNFSF15], ng/mL	178.82 (342.97)	171.38 (179.47)	0.072	183.01 (318.20)	167.19 (220.20)	0.784	175.1 (272.38)
Pearson correlation coefficients
MCP-1 and IL-6	R = 0.94; P < 0.001	R = 0.63; P < 0.001**		R = 0.77; P < 0.001	R = 0.45; P = 0.002**
MCP-1 and TNFα	R = 0.94; P < 0.001	R = 0.48; P = 0.001**		R = 0.57; P < 0.001	R = 0.80; P < 0.001**
PPARγ2 and IL-1β	R = 0.60; P < 0.001	R = 0.94; P < 0.001**		R = 0.92; P < 0.001	R = 0.74; P < 0.001**
NFkB and IL-10	R = −1.00; P < 0.001	R = −1.00; P < 0.001**		R = −1.00; P < 0.001	R = −1.00; P < 0.001**

**P < 0.05.

ABSI, a body shape index; IL, interleukin; MASLD, metabolic dysfunction–associated steatotic liver disease; MCP-1, monocyte chemoattractant protein-1; NFkB, nuclear factor kappa-light-chain-enhancer of activated B cells; PPAR-γ2, peroxisome proliferator-activated receptor gamma 2; SDC, syndecan-1; sCD163, soluble CD163; TNFα, tumor necrosis factor alpha; TNFSF, tumor necrosis factor receptor superfamily; VAI, visceral adipose index.

Gene expression of MCP-1 and IL-6/IL-1β, PPARγ2 and IL-1β showed a moderate positive correlation

The Pearson correlation analysis further revealed several significant gene-to-gene expression correlations within the MASLD participants (Table 3). MCP-1 and IL-6 showed strong positive correlations in the low VAI (R = 0.94) and low ABSI (R = 0.77) groups, with moderate correlations in the high VAI (R = 0.63) and high ABSI (R = 0.45) groups. MCP-1 and TNFα were also significantly correlated in all subgroups, strongest in low VAI (R = 0.94) and high ABSI (R = 0.80). PPARγ2 and IL-1β showed strong correlations across all groups, especially in high VAI (R = 0.94) and low ABSI (R = 0.92). NFkB and IL-10 consistently showed perfect negative correlations (R = −1.00, P < 0.001).

Figure 2 shows the correlation map of gene and gene expression between VAI and ABSI groups. The gene expression of IL-4 and IL-10 showed negative correlations, while IL-6 and IL-1β exhibited positive correlations in all groups. We also noted that the positive correlations of NFkB with TLR4, CD163 with TNFα, and TLR4 with IL-4, as well as the negative correlation of TLR4 with IL-10, were stronger in the low ABSI group than in the high ABSI group.

FIG. 2.

Pearson correlation coefficients of gene-to-gene in low (A) and high (B) visceral adiposity index (VAI) groups and low (C) and high (D) a body shape index (ABSI) groups. *P < 0.05. CD14, cluster of differentiation 14; CD163, cluster of differentiation 163; CD206, cluster of differentiation 206; IL, interleukin; iNOS, inducible nitric oxide synthase; MCP-1, monocyte chemoattractant protein 1; NFkB, nuclear factor kappa-light-chain-enhancer of activated B cells; PPAR-γ2, peroxisome proliferator-activated receptor gamma 2; TLR4, toll-like receptor 4; TNFα, tumor necrosis factor alpha.

Patients with MASLD with a higher VAI showed more severe insulin resistance and systemic inflammation

Patients with MASLD with a higher VAI had higher values of CAP, HOMA-IR, TG/HDL-C ratio, TyG, TyGWC, and NHR (315.24 vs. 295.49; 5.21 vs. 4.34; 4.08 vs. 2.20; 9.03 vs. 8.70; 926.00 vs. 787.10; 1.86 vs. 1.45, respectively; all P < 0.05, Table 4). Patients with MASLD with a higher ABSI exhibited a higher level of the AST/ALT ratio (1.26 vs. 0.86, P = 0.020). Otherwise, no significant differences were observed between high and low VAI and ABSI groups in FIB-4, LMR, CAR, and PLR.

Table 4.

Hepatic Steatosis/Fibrotic, Calculated Insulin Resistance and Inflammatory Markers Among Patients with MASLD, Stratified by VAI and ABSI

	MASLD, VAI <134n = 51	MASLD, VAI ≥134n = 51	P value	MASLD, ABSI <0.08n = 51	MASLD, ABSI ≥0.08n = 51	P value	Total
Steatosis and fibrotic markers
FIB-4, mean (SD)	1.51 (0.92)	1.39 (0.91)	0.389	1.35 (0.88)	1.56 (0.94)	0.119	1.45 (0.92)
FIB-4 >1.3, n (%)	21 (41.18)	25 (49.02)	0.426	21 (41.18)	25 (49.02)	0.426	46 (45.10)
CAP, mean (SD)	295.49 (36.54)	315.24 (38.53)	0.006**	305.45 (39.6)	305.27 (38.53)	0.944	305.36 (38.65)
CAP >292, n (%)	26 (50.98)	37 (74.00)	0.017**	35 (68.63)	28 (56.00)	0.190	63 (62.38)
Calculated insulin resistance markers, mean (SD)
HOMA-IR	4.34 (4.62)	5.21 (3.42)	0.038**	4.89 (3.80)	4.65 (4.39)	0.767	4.77 (4.07)
TG/HDL-C ratio	2.20 (1.07)	4.08 (1.97)	<0.001**	3.23 (1.89)	3.08 (1.81)	0.629	3.16 (1.84)
TyG	8.70 (0.51)	9.03 (0.48)	<0.001**	8.84 (0.46)	8.88 (0.58)	0.940	8.86 (0.52)
TyGWC	787.10 (127.16)	926.00 (109.67)	<0.001**	869.80 (169.77)	840.52 (94.13)	0.099	855.16 (137.36)
Calculated inflammatory markers, mean (SD)
NLR (neutrophil lymphocyte ratio)	2.22 (1.89)	2.34 (2.28)	0.752	1.85 (0.73)	2.70 (2.80)	0.038**	2.28 (2.08)
AST/ALT ratio	1.23 (1.25)	0.90 (0.36)	0.418	0.86 (0.37)	1.26 (1.23)	0.020**	1.06 (0.93)
NHR (neutrophil HDL ratio)	1.45 (0.64)	1.86 (0.75)	0.002**	1.66 (0.74)	1.65 (0.72)	0.997	1.65 (0.72)
LMR (lymphocyte-to-monocyte ratio)	5.57 (2.14)	5.51 (1.95)	1.000	5.73 (2.24)	5.35 (1.82)	0.712	5.54 (2.04)
CAR (creatinine-to-albumin ratio)	0.07 (0.13)	0.05 (0.04)	0.676	0.06 (0.06)	0.07 (0.12)	1.000	0.06 (0.10)
PLR (platelet-to-lymphocyte ratio)	8.52 (5.39)	9.12 (6.37)	0.369	7.75 (2.83)	9.90 (7.74)	0.381	8.81 (5.87)

**P < 0.05.

ABSI, a body shape index; ALT, alanine aminotransferase; AST, aspartate transaminase; CAP, controlled attenuation parameter; FIB-4, fibrosis-4 index; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment–insulin resistance index; MASLD, metabolic dysfunction–associated steatotic liver disease; TG, triglyceride; TyG, triglyceride-glucose index; VAI, visceral adipose index; TyGWC, triglyceride-glucose index × waist circumference

The association of inflammatory genes and inflammatory proteins was obvious in low VAI patients with MASLD

Figure 3 demonstrates a significant positive correlation between the IL-1β gene and [TNFSF15] among patients with MASLD with low VAI, a relationship not observed in the high VAI group. Additionally, CD14 gene and [MCP-1] exhibit a negative association in the low VAI group, which is absent in the high VAI group. There are positive correlations between [TNFα] and NFkB gene, [TNFα] and IL-4 gene, and negative correlation between [TNFα] and IL-10 gene in low ABSI group but not in high ABSI group.

FIG. 3.

Pearson correlation coefficients of gene-to-circulating inflammatory protein in low (A) and high (B) visceral adiposity index (VAI) groups and low (C) and high (D) a body shape index (ABSI) groups. *P < 0.05. CD14, cluster of differentiation 14; CD163, cluster of differentiation 163; CD206, cluster of differentiation 206; IL, interleukin; iNOS, inducible nitric oxide synthase; MCP-1, monocyte chemoattractant protein 1; NFkB, nuclear factor kappa-light-chain-enhancer of activated B cells; PPAR-γ2, peroxisome proliferator-activated receptor gamma 2; SDC1, syndecan-1; sCD163, soluble CD163; TLR4, toll-like receptor 4; TNFα, tumor necrosis factor alpha; TNFSF, tumor necrosis factor superfamily.

Figure 4 reveals consistent positive correlations between [MCP-1] and [IL-6], [MCP-1] and [TNFα] in both low and high VAI groups. The positive correlation between [IL-1β] and [SDC1] was obvious in low VAI group but absent in high VAI group. The positive correlation between [IL-1β] and [SDC1] was observed in high ABSI group but not low ABSI group.

FIG. 4.

Pearson correlation coefficients of circulating inflammatory protein-to-circulating inflammatory protein in low (A) and high (B) visceral adiposity index (VAI) groups and low (C) and high (D) a body shape index (ABSI) groups. *P < 0.05. IL, interleukin; MCP-1, monocyte chemoattractant protein 1; sCD163, soluble CD163; SDC1, syndecan-1; TNFα, tumor necrosis factor alpha; TNFSF, tumor necrosis factor superfamily.

Figure 5 shows a significant positive association between TLR4 gene and NHR, as well as between TLR4 gene and MHR, in the low VAI group. In the high VAI group, it demonstrates a positive association between CD14 gene and NLR, and a negative association between CD14 gene and TyGWC. We also noted a significant positive association between NLR and NFkB gene, NLR and IL4 gene, and negative association between NLR and IL-10 gene in the low ABSI group. But these associations were absent in high ABSI group. A negative association between serum level of [SDC1] and TyG was noted in low VAI group but not high VAI group (Fig. 6). Weak correlations were also noted between serum level of [IL-1β] and HOMA-IR, and [TNFα] and TyG, [TNFα] and TG/HDL ratio, [TNFSF14] and TG/HDL ratio in high ABSI group but not in low ABSI group.

FIG. 5.

Pearson correlation coefficients of gene-to-calculated inflammatory and insulin resistance markers in low (A) and high (B) visceral adiposity index (VAI) groups and low (C) and high (D) a body shape index (ABSI) groups. *P < 0.05. AST/ALT, aspartate aminotransferase/alanine aminotransferase ratio; CAR, C-reactive protein-to-albumin ratio; CD14, cluster of differentiation 14; CD163, cluster of differentiation 163; CD206, cluster of differentiation 206; HOMA-IR, homeostasis model assessment–insulin resistance; IL, interleukin; iNOS, inducible nitric oxide synthase; LMR, lymphocyte-to-monocyte ratio; MCP-1, monocyte chemoattractant protein 1; MHR, monocyte-to-HDL ratio; NFkB, nuclear factor kappa-light-chain-enhancer of activated B cells; NHR, neutrophil-to-HDL ratio; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PPAR-γ2, peroxisome proliferator-activated receptor gamma 2; TG/HDL, triglyceride/high-density lipoprotein cholesterol ratio; TLR4, toll-like receptor 4; TNFα, tumor necrosis factor alpha; TyG, triglyceride-glucose index; TyGWC, triglyceride-glucose index × waist circumference.

FIG. 6.

Pearson correlation coefficients of circulating inflammatory markers-to-calculated insulin resistance markers in low (A) and high (B) visceral adiposity index (VAI) groups and low (C) and high (D) a body shape index (ABSI) groups. *P < 0.05. HOMA-IR, homeostasis model assessment–insulin resistance; IL, interleukin; MCP-1, monocyte chemoattractant protein 1; sCD163, soluble CD163; SDC1, syndecan-1; TG/HDL, triglyceride/high-density lipoprotein cholesterol ratio; TNFα, tumor necrosis factor alpha; TNFSF, tumor necrosis factor superfamily; TyG, triglyceride-glucose index; TyGWC, triglyceride-glucose index × waist circumference.

Sex-specific analysis

Sex modified the association between VAI/ABSI and some inflammatory markers. The expression of the CD14 gene was higher in the higher VAI group in females but not males (P for interaction = 0.016; Supplementary Table S2). No significant interactions with sex were observed for circulating inflammatory marker expression in the VAI and ABSI groups (Supplementary Table S3). In addition, FIB-4 levels were higher in the high ABSI group in females (P for interaction = 0.017; Supplementary Table S4).

Discussion

We found positive correlations between inflammatory genes in patients with MASLD, including MCP-1 and IL-6, MCP-1 and TNFα, and PPARγ2 and IL-1β. Positive correlations between certain inflammatory genes and markers were more prominent in the low VAI group than in the high VAI group, including IL-1β and [TNFSF15], TLR4 and NHR, and TLR4 and MHR. Additionally, significant correlations were more evident in the low ABSI group than in the high ABSI group, including [TNFα] and NFkB, [TNFα] and IL-4, NFkB and NLR, and IL-4 and NLR.

In obese individuals, increased macrophage infiltration in adipose tissue secretes pro-inflammatory cytokines and leads to an inflammatory environment.³¹ Macrophages further interact with other immune cells, enhancing the inflammatory response and contributing to systemic inflammation associated with obesity.³² The roles of inflammatory cytokines such as MCP-1, IL-6, TNFα, and IL-1β are critical in the pathogenesis of MASLD. MCP-1 is involved in recruiting monocytes to sites of inflammation, promoting hepatic inflammation in NAFLD.^33,34 IL-6 and IL-1β are pro-inflammatory cytokines that play a significant role in the inflammatory response and are associated with insulin resistance.^35,36 IL-1β mediates macrophage-adipocyte crosstalk, stimulating adipocytes to produce pro-inflammatory cytokines (i.e., IL-6, MCP-1) and exacerbated pro-inflammatory reaction and insulin signaling.^37,38 Obesity is associated with systemic inflammation and adipose tissue inflammation. The release of pro-inflammatory adipokines such as TNF-α, IL-6, and MCP-1 from inflamed adipose tissue mediates insulin resistance at two levels: locally through autocrine effects on adipocyte insulin signaling and metabolism, and systemically through endocrine effects on insulin-sensitive tissues such as skeletal muscle and liver.³⁹ Adipose tissue browning is a process by which white adipose tissue transforms into beige fat with thermogenic capacity, which can be inhibited by TNF-α and IL-1β. Under conditions of excessive inflammation, macrophages secrete cytokines such as TNF-α, which promote adipose tissue fibrosis and suppress angiogenesis, thereby dysregulating adipose tissue remodeling.³⁹

The VAI and ABSI are both metrics used to assess body fat distribution and associated health risks, but they differ in their methodologies and predictive capabilities. The calculation of VAI, which uses WC, BMI, TG, and HDL-C levels, targets visceral fat and its link to metabolic disorders.¹⁹ In contrast, ABSI evaluates body shape to assess obesity-related health risks, offering insights beyond those provided by BMI.⁴⁰ Therefore, this study observed certain discrepancies in the results between the high and low VAI and ABSI groups. For instance, the positive correlations between the MCP-1 gene and the TNFα gene, as well as between the IL-1β gene and the SDC1 gene, were evident in the low VAI group and the high ABSI group. These discrepancies may reflect the different aspects of body fat and health risks captured by VAI and ABSI. VAI focuses on visceral fat, which is closely linked to inflammation and metabolic issues, possibly influencing genes like MCP-1 and TNFα. In contrast, ABSI emphasizes body shape and fat distribution, which may align with different gene expression patterns, such as IL-1β and SDC1. These findings suggest that VAI and ABSI capture complementary but distinct features of adiposity, which may explain the inconsistencies. Further research is needed to better understand these differences.

Although VAI and ABSI reflect visceral adiposity and metabolic dysfunction, our study found no significant differences in inflammatory gene expression in peripheral white blood cells between groups. This may be due to insufficient statistical power from the limited sample size. Moreover, peripheral white blood cells may not be the optimal tissue for assessing these inflammatory genes. Previous studies have shown macrophage infiltration is significantly elevated in adipose tissue but not in circulating monocytes in high-fat diet-fed mice, indicating that peripheral white blood cells may inadequately reflect the tissue-specific inflammatory state.⁴¹ Furthermore, gene expression and protein levels do not always correlate. Inflammatory gene transcription can be transient and responsive to acute metabolic states, whereas VAI and ABSI reflect chronic adiposity patterns.

While the current consensus suggests that higher visceral adiposity in patients with MASLD strongly correlates with elevated inflammatory markers,¹² our study reveals a more nuanced picture. This study observed that the correlations between certain genes or gene-serologic proteins were stronger in the low VAI or ABSI groups compared to the high groups. For instance, the associations between IL-4 gene and TLR4 gene, as well as IL-10 gene and TLR4 gene, were more pronounced in the low ABSI group than in the high ABSI group. Similarly, IL-1β and [TNFSF15] showed significant correlations in the low VAI group but not in the high VAI group. This phenomenon may be attributed to immune exhaustion. Immune exhaustion is a significant consequence of obesity, impairing T cell activation and inflammatory potential, thereby contributing to immune status change.⁴² Sim et al. also reported a higher prevalence of senescent CD28−CD57+ T cells (CD4⁺ and CD8+) in individuals with type 2 diabetes mellitus and greater insulin resistance. Furthermore, individuals with advanced liver conditions, such as nonalcoholic steatohepatitis or cirrhosis, exhibited high expression of exhaustion-related genes in T cells.⁴³ Previous studies have demonstrated that TNFSF15 is associated with T cell activation.⁴ In line with this, our findings of a reduced correlation between IL-1β and [TNFSF15] further indirectly support the presence of immune exhaustion in individuals with high visceral adiposity.

This study employed novel indicators, including the VAI and ABSI, to classify the degree of visceral adipose accumulation among individuals in the MASLD group. We also analyzed numerous inflammatory genes, serologic proteins, as well as insulin resistance and inflammatory variables to conduct a comprehensive analysis. This study also has several limitations. First, the relatively small sample size may have increased the risk of Type II error, potentially obscuring some gene-related associations. Second, this study only analyzed the Pearson correlation coefficients between variables without adjusting for possible confounders, which may limit the interpretation of the findings. The cross-sectional design allowed for the assessment of associations rather than causation. Larger studies with increased sample sizes and well-constructed study designs are warranted in the future to further investigate the roles of inflammatory genes in visceral adiposity among patients with MASLD. Third, the study did not consider the influence of medication use, which may alter the expression of inflammatory markers. However, medication use of antidiabetic, lipid-lowering, or anti-inflammatory agents was similar between high and low VAI and ABSI groups (data not shown). The influence may be limited. Finally, the formula of VAI was specific to the Chinese population, which can limit the generalizability of our results. Despite these limitations, this study contributes to precision medicine in MASLD. We found differential expression patterns of inflammatory markers across different visceral adiposity groups, which facilitates risk stratification, targeted therapies, and biomarker selection in patients with low and high VAI/ABSI.

Conclusions

We observed that the correlations between inflammatory gene-to-gene interactions and inflammatory gene-to-immunological marker levels differed between the low and high VAI and ABSI groups, providing evidence of immune exhaustion associated with varying degrees of visceral adipose accumulation. Future research should further explore the underlying mechanisms to inform potential interventions and develop tailored strategies based on the extent of abdominal fat accumulation for patients with MASLD.

Authors’ Contributions

Y.-H.L. contributed to patient recruitment, statistical analysis, data interpretation, and article writing. S.-Y.T. and C.-C.M. were in charge of statistical analysis and figure creation. S.-Y.H. assisted with genetic analysis techniques. Y.-Y.Y. contributed to patient recruitment, study conception, and data interpretation. C.-W.S. and H.-C.S. supported patient recruitment and data interpretation. M.-C.H. provided assistance with data interpretation and article revision.

Footnotes

Author Disclosure Statement

There are no potential conflicts of interest to disclose.

Funding Information

This research was supported by the Taipei Veterans General Hospital, Taipei, Taiwan (grant nos. VTA115-V1-6-1& V115C-023, V115EA-004), National Yang Ming Chiao Tung University (114Q159502), and the Ministry of Science and Technology in Taiwan (grant nos. NSTC 112-2314-B-A049-043-MY3, NSTC 114-2410-H-A49-029-MY2).

Supplemental Material

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Inflammatory Gene Expression,Insulin Resistance,and Circulating Markers Vary by Visceral Adiposity in Patients with Metabolic Dysfunction–Associated Steatotic Liver Disease

Abstract

Background:

Objective:

Methods:

Results:

Conclusion:

Keywords

Introduction

Materials and Methods

Study population

Physical measurement

Blood test

Gene expression levels of peripheral white blood cells

Visceral fat accumulation and insulin resistance variables

Inflammatory variables

Serology levels of inflammatory and anti-inflammatory proteins

Assessment of hepatic steatosis and fibrosis

Definition of MASLD

Statistics

Results

Basic characteristics, blood tests, and physical measurements

No differences in gene expression levels of peripheral white blood cells and serology proteins

Gene expression of MCP-1 and IL-6/IL-1β, PPARγ2 and IL-1β showed a moderate positive correlation

Patients with MASLD with a higher VAI showed more severe insulin resistance and systemic inflammation

The association of inflammatory genes and inflammatory proteins was obvious in low VAI patients with MASLD

Sex-specific analysis

Discussion

Conclusions

Authors’ Contributions

Footnotes

Author Disclosure Statement

Funding Information

Supplemental Material

References

Supplementary Material