Anxiety and depression symptoms correlate with metabolic indicators in metabolic dysfunction-associated steatotic liver disease patients

Abstract

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 32.4% of the global population and is closely associated with metabolic syndrome. Emerging evidence suggests a bidirectional relationship between MASLD and mental health disorders, with depression and anxiety prevalence significantly elevated in MASLD patients. Anxiety and depression may exacerbate metabolic dysfunction through mechanisms including hypothalamic-pituitary-adrenal axis dysregulation, chronic inflammation, and behavioral changes. However, comprehensive data on the correlation between psychological symptoms and metabolic indicators in MASLD patients remain limited, particularly in Asian populations.

AIM

To investigate the occurrence of anxiety and depression symptoms in patients with MASLD and their correlation with metabolic indicators, providing theoretical basis for comprehensive management of MASLD patients.

METHODS

A cross-sectional study design was adopted, enrolling 328 MASLD patients who visited the Department of Gastroenterology and Endocrinology at a tertiary hospital from January 2023 to June 2024. The Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS) were used to assess patients’ anxiety and depression symptoms. Metabolic indicators were collected simultaneously, including body mass index (BMI), waist circumference, fasting blood glucose (FBG), glycated hemoglobin (HbA1c), total cholesterol, triglycerides (TG), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), alanine aminotransferase (ALT), aspartate aminotransferase, and hepatic fat content. Pearson correlation analysis and multiple linear regression analysis were used to explore the correlation between anxiety and depression symptoms and metabolic indicators.

RESULTS

Among 328 MASLD patients, the detection rate of anxiety symptoms was 41.5% (136/328), depression symptoms was 38.7% (127/328), and anxiety-depression comorbidity was 28.4% (93/328). Correlation analysis showed that SAS scores were positively correlated with BMI (r = 0.423, P < 0.001), waist circumference (r = 0.387, P < 0.001), TG (r = 0.356, P < 0.001), FBG (r = 0.312, P < 0.001), HbA1c (r = 0.298, P < 0.01), and hepatic fat content (r = 0.341, P < 0.001), and negatively correlated with HDL-C (r = -0.267, P < 0.01). SDS scores were positively correlated with BMI (r = 0.401, P < 0.001), waist circumference (r = 0.369, P < 0.001), TG (r = 0.334, P < 0.001), ALT (r = 0.289, P < 0.01), and hepatic fat content (r = 0.318, P < 0.001). Multiple linear regression analysis showed that BMI (β = 0.312, P < 0.001), TG (β = 0.245, P < 0.01), and hepatic fat content (β = 0.198, P < 0.05) were independent correlates of anxiety symptoms; BMI (β = 0.289, P < 0.001), waist circumference (β = 0.231, P < 0.01), and ALT (β = 0.187, P < 0.05) were independent correlates of depression symptoms.

CONCLUSION

MASLD patients have a high detection rate of anxiety and depression symptoms, which are significantly correlated with multiple metabolic indicators. Obesity, lipid metabolism disorders, and hepatic fat deposition are significantly associated with anxiety and depression symptoms. Clinical practice should emphasize psychological health assessment in MASLD patients and adopt comprehensive intervention measures to improve patients' metabolic status and psychological state.

Key Words: Metabolic dysfunction-associated steatotic liver disease; Anxiety; Depression; Metabolic indicators; Cross-sectional study; Body mass index; Lipid metabolism

Core Tip: This cross-sectional study systematically assesses anxiety and depression symptoms alongside comprehensive metabolic indicators in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). We found high rates of psychological symptoms that are significantly associated with key metabolic parameters including body mass index, triglycerides, liver fat content, and liver enzymes. These findings support a multidimensional relationship between mental health and metabolic dysfunction in MASLD and highlight the importance of integrating psychological screening and metabolic management in clinical care to improve overall outcomes. This work provides a basis for more holistic strategies in MASLD patient management.

INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a clinical-pathological syndrome characterized primarily by excessive fat accumulation in hepatocytes, excluding alcohol and other clear causes of liver damage, and has become one of the most common chronic liver diseases globally[1]. Epidemiological surveys show that the global MASLD prevalence is approximately 32.4%, showing a continuous growth trend, rising from 25.5% before 2005 to 37.8% after 2016[2]. In Asia, the MASLD prevalence is approximately 30%, with mainland China having an annual incidence rate as high as 63/1000 person-years, ranking first in Asia[3]. The adult MASLD prevalence in China has reached 29.2%, with more than 240 million MASLD patients currently, accounting for more than one-fifth of the global MASLD population[4], showing rapid growth and younger trends, making it China’s leading chronic liver disease. MASLD is not only a liver disease but also a hepatic manifestation of metabolic syndrome, closely related to metabolic diseases such as obesity, type 2 diabetes, dyslipidemia, and hypertension[5]. Studies show that approximately 55.5% of type 2 diabetes patients have comorbid MASLD, while MASLD patients have significantly increased risk of cardiovascular disease[6]. This disease can progress to metabolic dysfunction-associated steatohepatitis (MASH), liver fibrosis, cirrhosis, and even hepatocellular carcinoma, with approximately 20%-30% of MASLD patients progressing to MASH, seriously threatening patients’ life and health and imposing heavy economic burdens on families and society[7].

In recent years, increasing research has focused on the mental health issues of MASLD patients, recognizing that this disease not only affects patients’ physical health but also has important impacts on their psychological state. MASLD shares common pathophysiological mechanisms with various mental disorders, including metabolic disorders, genetic factors, and lifestyle[8]. Anxiety, depression, and other psychological problems may in turn exacerbate metabolic abnormalities, leading to poor dietary control, reduced exercise compliance, and decreased medication adherence, forming a vicious cycle. A large cohort study showed that during a 10-year follow-up period, 21.2% of MASLD patients were diagnosed with depression and 7.9% with anxiety disorders, with risks of depression and anxiety increasing by 21% and 23% respectively, and this association was more significant in women[9]. These gender disparities likely reflect complex interactions among biological, hormonal, and psychosocial factors. Biological mechanisms include sex hormone fluctuations, particularly estrogen’s modulatory effects on serotonergic neurotransmission and hypothalamic-pituitary-adrenal (HPA) axis reactivity, as well as inherent sex differences in neuroendocrine stress responsivity. Systematic reviews and meta-analyses indicate that the risk of depression in MASLD patients is 1.13-1.46 times higher than in the general population[10,11], seriously affecting patients’ quality of life and disease prognosis.

Anxiety and depression may affect metabolic function through multiple biological mechanisms. Studies show that anxiety and depression can lead to HPA axis dysfunction, increasing cortisol secretion and promoting visceral fat accumulation and the development of insulin resistance[12]. MASLD patients have chronic mild HPA axis hyperactivation, manifested by elevated 24-hour urinary free cortisol levels, closely related to liver pathology severity[13]. Additionally, pro-inflammatory cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) are elevated in patients with anxiety and depression. These inflammatory factors not only participate in the pathogenesis of depression but also play important roles in the occurrence and development of MASLD, possibly serving as important bridges connecting psychological state and metabolic abnormalities[8]. Obesity, as an important risk factor for MASLD, has a bidirectional association with anxiety and depression, with meta-analysis showing that obesity increases depression risk by approximately 55%, while depression also increases obesity occurrence risk[14]. Therefore, in-depth exploration of the relationship between anxiety and depression symptoms and metabolic indicators in MASLD patients is of great significance for understanding disease pathogenesis and formulating comprehensive prevention and treatment strategies.

However, current research on the relationship between anxiety and depression symptoms and metabolic indicators in MASLD patients remains relatively limited, with inconsistent results. Some studies have found that anxiety and depression symptoms in MASLD patients are significantly correlated with metabolic indicators such as body mass index (BMI), triglycerides (TG), and fasting blood glucose (FBG)[15]. However, most studies have small sample sizes, lack comprehensive analysis of multiple metabolic indicators, and domestic research data are relatively scarce. Notably, regular exercise has been proven to significantly improve hepatic fat content in MASLD patients, with systematic reviews showing that exercise can increase the treatment response rate for achieving ≥ 30% relative reduction in magnetic resonance imaging (MRI)-measured hepatic fat content by 3.5 times, and this effect is independent of weight loss[16].

In-depth exploration of the correlation between anxiety and depression symptoms and metabolic indicators in MASLD patients, clarifying the relationship strength and influencing factors between the two, helps comprehensively understand MASLD pathogenesis, identify high-risk populations, and provide scientific basis for formulating comprehensive prevention and treatment strategies. Based on this, this study adopts a cross-sectional design, evaluates anxiety and depression symptoms in MASLD patients through standardized scales, systematically collects multiple metabolic indicators including obesity indicators, glucose metabolism indicators, lipid metabolism indicators, liver function indicators, and hepatic fat content, and conducts in-depth analysis using various statistical methods, aiming to comprehensively explore the correlation and influencing factors between them. The research results will provide theoretical basis for comprehensive management and individualized intervention of MASLD patients, provide reference for clinicians to simultaneously focus on mental health issues when diagnosing and treating MASLD patients, with a view to improving patients' metabolic status and mental health levels through comprehensive intervention, reducing disease progression risk, and improving overall quality of life.

MATERIALS AND METHODS

Research design and study subjects

This study adopted a cross-sectional research design, consecutively enrolling MASLD patients who visited the outpatient and inpatient departments of Gastroenterology and Endocrinology at our hospital from January 2023 to June 2024. MASLD diagnosis criteria referred to the “Guidelines for the Prevention and Treatment of MASLD (2018 Update)”, meeting the following conditions: (1) Imaging examination [liver ultrasound, computed tomography (CT), or MRI] indicating hepatic fat content ≥ 5%; (2) Excluding excessive alcohol consumption history (weekly alcohol consumption equivalent to ethanol < 140 g for men, < 70 g for women); and (3) Excluding other specific diseases that can cause fatty liver, such as viral hepatitis, drug-induced liver disease, autoimmune liver disease, Wilson’s disease, and hereditary hemochromatosis. Specifically, patients were diagnosed with MASLD according to criteria consistent with the contemporary definition: Diagnosis required evidence of hepatic steatosis (≥ 5% hepatic fat content on ultrasound or MRI-PDFF) plus at least one of the following five cardiometabolic risk factors: (1) BMI ≥ 25 kg/m² (or ≥ 23 kg/m² for Asian populations) or waist circumference ≥ 90 cm for males/≥ 80 cm for females; (2) Fasting glucose ≥ 5.6 mmol/L, or 2-hour post-load glucose ≥ 7.8 mmol/L, or glycated hemoglobin (HbA1c) ≥ 5.7%, or type 2 diabetes, or treatment for hyperglycemia; (3) Blood pressure ≥ 130/85 mmHg or specific antihypertensive treatment; (4) TG ≥ 1.70 mmol/L or lipid-lowering treatment; and (5) High-density lipoprotein cholesterol (HDL-C) < 1.0 mmol/L for males and < 1.3 mmol/L for females or lipid-lowering treatment.

Inclusion criteria included: (1) Age 18-75 years; (2) Meeting MASLD diagnostic criteria; (3) Voluntarily participating in research and signing informed consent; and (4) Able to cooperate in completing questionnaire surveys and related examinations. Exclusion criteria included: (1) Patients with severe heart, brain, kidney, or other organ dysfunction; (2) Patients with malignant tumors or other serious diseases; (3) Previously diagnosed with mental illness or long-term use of anti-anxiety and depression medications; (4) Pregnant or lactating women; and (5) Incomplete questionnaire completion or missing data. This study was approved by the hospital ethics committee (Ethics approval No. 2023-KY-125), and all subjects signed informed consent.

General data collection

Uniformly designed questionnaires were used to collect patients' general demographic and clinical data. Demographic data included gender, age, education level (elementary school and below, junior high school, high school or secondary vocational school, junior college and above), marital status (married, unmarried, divorced or widowed), occupation type (mental labor, physical labor, retired or unemployed), family per capita monthly income, etc. Lifestyle data included smoking history (defined as smoking ≥ 1 cigarette daily for ≥ 6 months continuously or cumulatively), alcohol consumption history, exercise habits (defined as regular exercise ≥ 3 times per week, ≥ 30 minutes each time), dietary habits (high-fat diet, high-sugar diet, irregular diet, etc.). Disease-related data included MASLD disease duration, comorbidity status (hypertension, diabetes, dyslipidemia, etc.), family history (liver disease family history, diabetes family history, cardiovascular disease family history), etc. All questionnaires were administered through face-to-face interviews by research personnel who underwent unified training to ensure data accuracy and completeness.

Psychological state assessment

The Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS) were used to assess patients’ anxiety and depression symptoms. The SAS scale was compiled by Zung in 1971, containing 20 items with 4-level scoring (1-4 points). The total score is the sum of all item scores multiplied by 1.25, with the integer part taken, ranging from 25-100 points. According to Chinese norms, SAS standard score ≥ 50 points indicates anxiety symptoms, with 50-59 points as mild anxiety, 60-69 points as moderate anxiety, and ≥ 70 points as severe anxiety. The SDS scale was also compiled by Zung, containing 20 items with the same scoring method as SAS. Standard score ≥ 53 points indicates depression symptoms, with 53-62 points as mild depression, 63-72 points as moderate depression, and ≥ 73 points as severe depression. Both scales have good reliability and validity and are widely used in Chinese populations. In the current study sample, both instruments demonstrated excellent internal consistency, with Cronbach’s alpha of 0.89 for the SAS and 0.91 for the SDS. Questionnaires were completed independently by patients, with research personnel providing item-by-item explanations for those with difficulty understanding, without any suggestion or guidance. Each questionnaire took approximately 10-15 minutes to complete, and research personnel checked questionnaire completeness on-site after completion.

Physical examination and body composition measurement

All subjects underwent standardized physical examinations. Height was measured using a standing height scale, accurate to 0.1 cm, with subjects barefoot, chest out, looking forward, heels, buttocks, and scapulae close to the column. Weight was measured using an electronic scale, accurate to 0.1 kg, with subjects wearing light clothing after emptying bladder and bowel. BMI = weight (kg)/height² (m²). According to Chinese standards, BMI < 18.5 kg/m² indicates underweight, 18.5-23.9 kg/m² indicates normal weight, 24.0-27.9 kg/m² indicates overweight, and ≥ 28.0 kg/m² indicates obesity. Waist circumference was measured at the midpoint between the lower edge of the rib arch and the iliac crest, at end-expiration using a soft tape, accurate to 0.1 cm. Male waist circumference ≥ 90 cm and female waist circumference ≥ 85 cm were defined as central obesity. Blood pressure was measured using a standard mercury sphygmomanometer, with subjects resting quietly for 10 minutes, sitting position, measuring right upper arm blood pressure, three consecutive measurements at 2-minute intervals, taking the average. Systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, or currently taking antihypertensive medications, indicated hypertension diagnosis.

Laboratory indicator testing

All subjects had 5-10 mL elbow venous blood collected in the early morning on an empty stomach (fasting 8-12 hours), with serum separated and testing completed on the same day. Biochemical indicator testing used an automatic biochemical analyzer (Hitachi 7600), with indicators including: FBG, HbA1c, total cholesterol (TC), TG, low-density lipoprotein cholesterol (LDL-C), HDL-C, alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transferase, alkaline phosphatase, total bilirubin, albumin, creatinine, uric acid, etc.

Diagnostic criteria for metabolic abnormalities were as follows: FBG ≥ 6.1 mmol/L or HbA1c ≥ 6.5% or diagnosed diabetes defined glucose metabolism abnormality; TC ≥ 5.2 mmol/L or TG ≥ 1.7 mmol/L or LDL-C ≥ 3.4 mmol/L or HDL-C < 1.0 mmol/L (male) or < 1.3 mmol/L (female) defined dyslipidemia. All testing was performed by qualified laboratory technicians with internal quality control and external quality assessment to ensure accuracy and reliability of test results.

Imaging examination

Liver ultrasound examination used a color Doppler ultrasound diagnostic instrument (GE LOGIQ E9), probe frequency 3.5-5.0 MHz, performed by ultrasound physicians with more than 10 years of experience. Subjects fasted for more than 8 hours before examination, in supine position, with full exposure of the upper abdomen, multi-sectional scanning of the liver. Ultrasound diagnostic criteria for fatty liver referred to the “Guidelines for the Prevention and Treatment of MASLD”, with fatty liver diagnosed when meeting 2 or more of the following 3 criteria: (1) Diffuse enhancement of near-field liver echo (stronger than kidney and spleen), with gradual attenuation of far-field echo; (2) Poor visualization of intrahepatic vessel structures; and (3) Blunt liver edge angles.

Fatty liver severity grading: Mild grade showed slight enhancement of liver echo with intrahepatic vessel structures still visible; Moderate grade showed moderate enhancement of liver echo with poor visualization of intrahepatic vessel structures; Severe grade showed significant enhancement of liver echo with difficult visualization of intrahepatic vessel structures, poor or incomplete visualization of right liver lobe capsule and diaphragm echo. Some patients (n = 156) underwent liver CT or MRI-PDFF (proton density fat fraction) examination for quantitative assessment of hepatic fat content, with hepatic fat content ≥ 5% diagnosed as fatty liver.

Quality control

To ensure research data quality and reliability, this study implemented strict quality control measures. First, all research staff (including questionnaire surveyors, physical examination personnel, testing personnel, etc.) underwent unified training, familiarizing themselves with research protocols, operating standards, and precautions. Second, questionnaire surveys used a dual-person verification system, with one research personnel conducting the survey and another conducting on-site review, promptly correcting any issues found. Third, all testing equipment underwent regular calibration and maintenance, with laboratory testing implementing full-process quality control including internal quality control and external quality assessment. Fourth, a unified data entry system was established, using dual-person dual-entry method, with two data entry personnel independently entering data, then conducting consistency checks, verifying and correcting inconsistent data. Fifth, regular interim checks and data verification were conducted during the research process to promptly identify and resolve issues. Sixth, all original materials were properly preserved, establishing complete research archives for traceability and verification.

Statistical analysis

SPSS 26.0 statistical software was used for data analysis. First, all data underwent normality tests (Kolmogorov-Smirnov test) and homogeneity of variance tests. Measurement data were expressed as mean ± SD or median (interquartile range) [M (P25, P75)]. For data conforming to normal distribution, independent sample t-tests were used for comparisons between two groups, and one-way analysis of variance (One-way ANOVA) was used for comparisons among multiple groups, with LSD-t test for further pairwise comparisons. Mann-Whitney U test or Kruskal-Wallis H test were used for data not conforming to normal distribution. Count data were expressed as n (%), with χ² test or Fisher’s exact test for group comparisons. Correlation analysis used Pearson correlation analysis (normally distributed data) or Spearman rank correlation analysis (non-normally distributed data), with correlation coefficients |r| < 0.3 indicating weak correlation, 0.3 ≤ |r| < 0.5 indicating moderate correlation, and |r| ≥ 0.5 indicating strong correlation. Multiple linear regression analysis was used to explore influencing factors of anxiety and depression symptoms, with variables having P < 0.10 in univariate analysis included, using stepwise regression method (entry criterion α = 0.05, removal criterion α = 0.10) to screen independent variables. Logistic regression analysis was used to explore risk factors for anxiety and depression symptoms, calculating odds ratios (OR) and 95% confidence intervals (95%CI). All statistical tests were two-sided, with P < 0.05 considered statistically significant.

RESULTS

General characteristics of study subjects

This study enrolled 328 MASLD patients meeting criteria, including 198 males (60.4%) and 130 females (39.6%), with a male-to-female ratio of approximately 3:2. Age ranged from 22-73 years, with mean age (48.6 ± 11.3) years, including 78 patients < 40 years (23.8%), 186 patients 40-59 years (56.7%), and 64 patients ≥ 60 years (19.5%). For education level, 38 patients had elementary school or below (11.6%), 92 had junior high school (28.0%), 118 had high school or secondary vocational school (36.0%), and 80 had junior college or above (24.4%). For marital status, 284 were married (86.6%), 26 unmarried (7.9%), and 18 divorced or widowed (5.5%). For occupation type, 142 were mental laborers (43.3%), 116 physical laborers (35.4%), and 70 retired or unemployed (21.3%). MASLD disease duration was (3.8 ± 2.6) years. There were 118 smokers (36.0%), 94 drinkers (28.7%), and only 86 with regular exercise habits (26.2%). For comorbidities, 156 had hypertension (47.6%), 128 had type 2 diabetes (39.0%), 246 had dyslipidemia (75.0%), and 198 had metabolic syndrome (60.4%). Imaging grading showed 142 cases of mild fatty liver (43.3%), 126 cases of moderate fatty liver (38.4%), and 60 cases of severe fatty liver (18.3%). These baseline characteristics indicate that MASLD patients enrolled in this study were mainly middle-aged males with high proportions of metabolic abnormalities, and most patients lacked regular exercise habits (Table 1).

Table 1 General characteristics of study subjects.

Item	Classification	n (%)
Gender
	Male	198 (60.4)
	Female	130 (39.6)
Age (years)
	< 40	78 (23.8)
	40-59	186 (56.7)
	≥ 60	64 (19.5)
Education level
	Elementary school and below	38 (11.6)
	Junior high school	92 (28.0)
	High school or secondary vocational	118 (36.0)
	Junior college and above	80 (24.4)
Marital status
	Married	284 (86.6)
	Unmarried	26 (7.9)
	Divorced or widowed	18 (5.5)
Occupation type
	Mental labor	142 (43.3)
	Physical labor	116 (35.4)
	Retired or unemployed	70 (21.3)
MASLD disease duration (years)	mean ± SD	3.8 ± 2.6
Smoking
	Yes	118 (36.0)
	No	210 (64.0)
Alcohol consumption
	Yes	94 (28.7)
	No	234 (71.3)
Regular exercise habits
	Yes	86 (26.2)
	No	242 (73.8)
Comorbid hypertension
	Yes	156 (47.6)
	No	172 (52.4)
Comorbid type 2 diabetes
	Yes	128 (39.0)
	No	200 (61.0)
Comorbid dyslipidemia
	Yes	246 (75.0)
	No	82 (25.0)
Comorbid metabolic syndrome
	Yes	198 (60.4)
	No	130 (39.6)
Imaging grading
	Mild fatty liver	142 (43.3)
	Moderate fatty liver	126 (38.4)
	Severe fatty liver	60 (18.3)

MASLD: Metabolic dysfunction-associated steatotic liver disease.

Detection and distribution of anxiety and depression symptoms

Among 328 MASLD patients, 136 cases (41.5%) had anxiety symptoms, including 86 cases of mild anxiety (26.2%), 38 cases of moderate anxiety (11.6%), and 12 cases of severe anxiety (3.7%). Depression symptoms were detected in 127 cases (38.7%), including 78 cases of mild depression (23.8%), 35 cases of moderate depression (10.7%), and 14 cases of severe depression (4.3%). The comorbidity rate of anxiety and depression was 28.4% (93/328). By gender, females had significantly higher detection rates of anxiety symptoms (51.5% vs 35.4%, χ² = 8.34, P < 0.01) and depression symptoms (48.5% vs 32.8%, χ² = 8.14, P < 0.01) than males. By age group, patients < 40 years had the lowest detection rates of anxiety symptoms (32.1%) and depression symptoms (29.5%), while patients 40-59 years had the highest detection rates (45.2% and 42.5% respectively), with significant differences among age groups (P < 0.05). By fatty liver severity, the detection rates of both anxiety and depression symptoms increased with fatty liver severity, with severe fatty liver patients having significantly higher detection rates than mild and moderate patients (P < 0.01) (Table 2).

Table 2 Detection rates of anxiety and depression symptoms in different subgroups, n (%).

Subgroup	Number	Anxiety	Depression
Gender
Male	198	70 (35.4)	65 (32.8)
Female	130	66 (51.5)	62 (48.5)
Age group
< 40 years	78	25 (32.1)	23 (29.5)
40-59 years	186	84 (45.2)	79 (42.5)
≥ 60 years	64	27 (42.2)	25 (39.1)
Fatty liver severity
Mild	142	46 (32.4)	42 (29.6)
Moderate	126	54 (42.9)	50 (39.7)
Severe	60	36 (60.0)	35 (58.3)

Metabolic indicators in study subjects

The metabolic indicators of all study subjects showed that obesity and metabolic disorders were common. Average BMI was (28.4 ± 3.7) kg/m², with 256 patients (78.0%) being overweight or obese. Average waist circumference was (96.8 ± 10.2) cm, with 218 patients (66.5%) meeting central obesity criteria. Average FBG was (6.8 ± 2.1) mmol/L, and average HbA1c was (6.5% ± 1.4%). Average TC was (5.4 ± 1.1) mmol/L, average TG was (2.6 ± 1.5) mmol/L, average LDL-C was (3.3 ± 0.9) mmol/L, and average HDL-C was (1.1 ± 0.3) mmol/L. For liver function indicators, average ALT was (52.3 ± 28.6) U/L, and average AST was (38.7 ± 19.4) U/L. These results indicate widespread obesity and metabolic disorders in MASLD patients (Table 3).

Table 3 Metabolic indicators of study subjects (mean ± SD).

Indicator	Value (n = 328)
BMI (kg/m2)	28.4 ± 3.7
Waist circumference (cm)	96.8 ± 10.2
FBG (mmol/L)	6.8 ± 2.1
HbA1c (%)	6.5 ± 1.4
TC (mmol/L)	5.4 ± 1.1
TG (mmol/L)	2.6 ± 1.5
LDL-C (mmol/L)	3.3 ± 0.9
HDL-C (mmol/L)	1.1 ± 0.3
ALT (U/L)	52.3 ± 28.6
AST (U/L)	38.7 ± 19.4

BMI: Body mass index; FBG: Fasting blood glucose; HbA1c: Glycated hemoglobin; TC: Triglycerides; TG: Triglycerides; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase.

Correlation analysis between anxiety symptoms and metabolic indicators

Pearson correlation analysis showed that SAS scores were significantly correlated with multiple metabolic indicators (Figure 1). For obesity-related indicators, SAS scores were significantly positively correlated with BMI (r = 0.423, P < 0.001) and waist circumference (r = 0.387, P < 0.001), indicating that the greater the degree of obesity, the more obvious the anxiety symptoms. Among lipid metabolism indicators, SAS scores were significantly positively correlated with TG (r = 0.356, P < 0.001), positively correlated with TC (r = 0.245, P < 0.01) and LDL-C (r = 0.228, P < 0.01), and significantly negatively correlated with HDL-C (r = -0.267, P < 0.01), suggesting that lipid metabolism disorders are closely related to anxiety symptoms. In terms of glucose metabolism, SAS scores were positively correlated with FBG (r = 0.312, P < 0.001) and HbA1c (r = 0.298, P < 0.01), reflecting that glucose metabolism abnormalities may interact with anxiety states. Liver function indicators showed that SAS scores were positively correlated with ALT (r = 0.334, P < 0.001) and AST (r = 0.287, P < 0.01), and significantly positively correlated with hepatic fat content (r = 0.341, P < 0.001), indicating that liver function damage and the degree of hepatic fat deposition are associated with anxiety symptoms. Overall, the correlation coefficients between BMI, waist circumference, TG, and hepatic fat content with SAS scores all exceeded 0.3, showing moderate correlation strength, and are the main metabolic factors affecting anxiety symptoms in MASLD patients. These results suggest that anxiety symptoms in MASLD patients are closely related to obesity, lipid metabolism disorders, glucose metabolism abnormalities, liver function damage, and hepatic fat deposition, showing a multi-factor synergistic effect (Figure 1).

Figure 1 Comparison of metabolic indicators between metabolic dysfunction-associated steatotic liver disease patients with and without anxiety/depression symptoms. Bar chart displays correlation coefficients (r values) between Self-rating Anxiety Scale scores and 11 metabolic parameters. Blue bars indicate positive correlations; orange bar indicates negative correlation. Body mass index showed the strongest positive correlation (r = 0.423), while high-density lipoprotein cholesterol exhibited negative correlation (r = -0.267). ^aP < 0.01, ^bP < 0.001. SAS: Self-rating Anxiety Scale; BMI: Body mass index; TG: Triglycerides; FBG: Fasting blood glucose; HbA1c: Glycated hemoglobin; TC: Total cholesterol; LDL-C: Low-density lipoprotein cholesterol; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; HDL-C: High-density lipoprotein cholesterol.

Correlation between anxiety depression symptoms and metabolic indicators

Pearson correlation analysis showed that SAS scores were positively correlated with BMI (r = 0.423, P < 0.001), waist circumference (r = 0.387, P < 0.001), TG (r = 0.356, P < 0.001), FBG (r = 0.312, P < 0.001), HbA1c (r = 0.298, P < 0.01), and hepatic fat content (r = 0.341, P < 0.001), and negatively correlated with HDL-C (r = -0.267, P < 0.01). SDS scores were positively correlated with BMI (r = 0.401, P < 0.001), waist circumference (r = 0.369, P < 0.001), TG (r = 0.334, P < 0.001), ALT (r = 0.289, P < 0.01), and hepatic fat content (r = 0.318, P < 0.001). These results suggest that obesity indicators, lipid metabolism disorders, glucose metabolism abnormalities, and liver fat deposition are significantly correlated with anxiety and depression symptoms in MASLD patients (Table 4).

Table 4 Correlation between Self-rating Anxiety Scale/Self-rating Depression Scale scores and metabolic indicators.

Metabolic indicator	SAS score (r)	SDS score (r)
BMI	0.423c	0.401c
Waist circumference	0.387c	0.369c
TG	0.356c	0.334c
FBG	0.312c	0.256b
HbA1c	0.298b	0.243b
HDL-C	-0.267b	-0.198a
ALT	0.234b	0.289b
Hepatic fat content	0.341c	0.318c

^aP < 0.05.

^bP < 0.01.

^cP < 0.001.

SAS: Self-rating Anxiety Scale; SDS: Self-rating Depression Scale; BMI: Body mass index; FBG: Fasting blood glucose; HbA1c: Glycated hemoglobin; TG: Triglycerides; HDL-C: High-density lipoprotein cholesterol; ALT: Alanine aminotransferase.

Multiple linear regression analysis of influencing factors for anxiety and depression symptoms

Taking SAS score as the dependent variable, with gender, age, BMI, waist circumference, TG, FBG, HbA1c, HDL-C, and hepatic fat content as independent variables, multiple linear regression analysis showed that BMI (β = 0.312, t = 5.67, P < 0.001), TG (β = 0.245, t = 4.23, P < 0.01), and hepatic fat content (β = 0.198, t = 3.45, P < 0.05) were independent correlates for anxiety symptoms, with the model explaining 38.2% of the variance (R² = 0.382, F = 32.45, P < 0.001). Taking SDS score as the dependent variable, similarly conducting multiple linear regression analysis showed that BMI (β = 0.289, t = 5.12, P < 0.001), waist circumference (β = 0.231, t = 3.89, P < 0.01), and ALT (β = 0.187, t = 3.21, P < 0.05) were independent correlates for depression symptoms, with the model explaining 34.5% of the variance (R² = 0.345, F = 28.67, P < 0.001). These results suggest that obesity, lipid metabolism disorders, and liver fat deposition are significantly associated for anxiety and depression symptoms in MASLD patients (Table 5).

Table 5 Multiple linear regression analysis of anxiety and depression symptoms.

Dependent variable	Independent variable	β	P value
SAS score	BMI	0.312	< 0.001
	TG	0.245	< 0.01
	Hepatic fat content	0.198	< 0.05
SDS score	BMI	0.289	< 0.001
	Waist circumference	0.231	< 0.01
	ALT	0.187	< 0.05

SAS: Self-rating Anxiety Scale; SDS: Self-rating Depression Scale; BMI: Body mass index; TG: Triglycerides; ALT: Alanine aminotransferase.

Multivariate logistic regression analysis of influencing factors for depression symptoms

Using the presence of depression symptoms (SDS score ≥ 53 points) as the dependent variable, multivariate logistic regression analysis was conducted using the same method. Finally, 7 variables entered the regression model (χ² = 108.34, P < 0.001), and the model fit was good (Hosmer-Lemeshow test: χ² = 5.92, P = 0.656). The results showed that BMI remained the primary risk factor for depression symptoms (OR = 1.16, 95%CI: 1.09-1.24, P < 0.001). As an indicator of central obesity, waist circumference increased the risk of depression by 4% for every 1 cm increase (OR = 1.04, 95%CI: 1.02-1.06, P < 0.001), suggesting that abdominal obesity is closely related to depression symptoms. For every 10 U/L increase in ALT level, the risk of depression increased by 15% (OR = 1.15, 95%CI: 1.06-1.25, P < 0.001), indicating that the degree of liver function damage is significantly associated with depression symptoms. Female patients had a 1.98 times higher risk of depression than males (OR = 1.98, 95%CI: 1.24-3.16, P = 0.004). Patients aged ≥ 60 years had a 2.24 times higher risk of depression than younger patients (OR = 2.24, 95%CI: 1.32-3.80, P = 0.003). Elevated TG levels increased the risk of depression (OR = 1.36, 95%CI: 1.14-1.62, P = 0.001). For every 1% increase in hepatic fat content, the risk of depression increased by 6% (OR = 1.06, 95%CI: 1.02-1.10, P = 0.003). In summary, obesity, especially central obesity, liver function damage, female gender, advanced age, lipid metabolism disorders, and hepatic fat deposition are independent risk factors for depression symptoms in MASLD patients (Table 6).

Table 6 Multivariate logistic regression analysis of influencing factors for depression symptoms.

Influencing factor	β	SE	Wald χ2	P value	OR	95%CI
BMI (kg/m2)	0.148	0.033	20.12	< 0.001	1.16	1.09-1.24
Waist circumference (cm)	0.039	0.010	15.21	< 0.001	1.04	1.02-1.06
ALT (per 10 U/L)	0.140	0.042	11.11	< 0.001	1.15	1.06-1.25
Female (vs male)	0.683	0.239	8.17	0.004	1.98	1.24-3.16
Age ≥ 60 years (vs < 60 years)	0.806	0.270	8.91	0.003	2.24	1.32-3.80
TG (mmol/L)	0.307	0.089	11.90	0.001	1.36	1.14-1.62
Hepatic fat content (%)	0.058	0.019	9.32	0.003	1.06	1.02-1.10

Model statistics: χ² = 108.34, P < 0.001; Hosmer-Lemeshow test: χ² = 5.92, P = 0.656; Nagelkerke R² = 0.386. BMI: Body mass index; TG: Triglycerides; ALT: Alanine aminotransferase; OR: Odds ratio; CI: Confidence interval.

DISCUSSION

MASLD, as the most common chronic liver disease globally, has seen a significant increase in prevalence over the past two decades, becoming an important public health issue[17]. MASLD is not merely a liver disease but rather a hepatic manifestation of metabolic syndrome, closely associated with metabolic diseases such as obesity, diabetes, dyslipidemia, and hypertension. As understanding of MASLD has deepened, researchers have increasingly focused on patients’ mental health issues. Long-term management of chronic disease, concerns about prognosis, decline in quality of life, and impairment of social functioning may all negatively impact patients’ psychological state. Multiple domestic and international studies in recent years have shown that the prevalence of anxiety and depression symptoms in MASLD patients is significantly higher than in the general population. The latest systematic review indicates that the prevalence of depression among adult MASLD patients is 26.3%, while anxiety prevalence reaches 37.2%[18]. However, research on the relationship between these conditions and metabolic indicators remains relatively limited. In-depth exploration of this issue holds significant importance for developing more comprehensive and effective prevention and treatment strategies. This study provides new evidence for clinical practice through systematic analysis of the relationship between anxiety and depression symptoms and metabolic indicators in MASLD patients.

A complex bidirectional relationship exists between anxiety/depression and metabolic disorders, a theory supported by extensive basic and clinical research. From a neuroendocrine perspective, HPA axis dysfunction serves as an important bridge connecting psychological state and metabolic abnormalities[19]. Chronic stress and depressive states can lead to HPA axis overactivation, resulting in persistently elevated cortisol levels, which in turn promotes visceral fat accumulation, exacerbates insulin resistance, and elevates blood glucose and lipids. From an inflammatory-immune perspective, patients with anxiety and depression exhibit chronic low-grade inflammation, with elevated levels of pro-inflammatory cytokines such as IL-6, TNF-α, and IL-1β[20]. These inflammatory mediators not only participate in the neurobiological mechanisms of depression but are also important drivers of MASLD development and progression, capable of inducing hepatocyte steatosis, inflammatory responses, and fibrosis. A meta-analysis confirmed that elevated levels of inflammatory factors including C-reactive protein, IL-1β, IL-6, and TNF-α are significantly associated with MASLD risk[21]. From an autonomic nervous system perspective, anxiety and depression lead to sympathetic-parasympathetic imbalance, increased catecholamine release, promotion of lipolysis and free fatty acid release, with the latter depositing ectopically in the liver to form fatty liver. Conversely, metabolic disorders may also affect mental health through multiple pathways, including affecting neurotransmitter metabolism, altering brain structure and function, and inducing vascular lesions. This bidirectional relationship forms a vicious cycle, continuously increasing disease burden.

Obesity is a core component of metabolic syndrome and the most important risk factor for MASLD. In the field of mental health, obesity also plays an important role. Studies have shown that obesity can increase the risk of depression and anxiety through multiple mechanisms including metabolic dysfunction, vascular dysfunction, inflammation, insulin and leptin resistance, and hypertension[22]. The relationship between obesity and mental health is multidimensional: From a biological perspective, adipose tissue has been recognized as an important endocrine organ, capable of secreting various adipokines including leptin, adiponectin, and resistin. These adipokines not only regulate energy metabolism and inflammatory responses but may also participate in emotion regulation by affecting the limbic system and prefrontal cortex of the brain. Research has found that peripheral adiponectin levels are negatively correlated with anxiety, depression, and stress-related disorders[23]. Excessive accumulation of visceral fat leads to dysregulated adipokine secretion, and leptin resistance and decreased adiponectin levels may be related to the development of depression. A review indicated that the multiple effects of adipokines and lipid factors and their secretory dysregulation may be important mechanisms underlying the comorbidity of obesity and depression[24]. From a psychosocial perspective, obesity often leads to body dissatisfaction, decreased self-esteem, and social avoidance; obese individuals may also face social discrimination and prejudice, all of which are important sources of psychological problems. Central obesity deserves particular attention; studies have shown a significant bidirectional relationship between visceral adipose tissue and major depressive disorder, as visceral fat has greater metabolic activity, secretes more pro-inflammatory factors, and may have more significant effects on mental health[25]. Weight management therefore becomes a key intervention point for simultaneously improving metabolic status and mental health.

The relationship between lipid metabolism disorders and mental health has received widespread attention in recent years. The brain is one of the organs with the highest lipid content in the body; lipids are not only important components of cell membranes but also participate in neurotransmitter synthesis and signal transduction. Dyslipidemia may affect brain function and emotional state through multiple pathways. Hypertriglyceridemia is often accompanied by increased small dense low-density lipoprotein particles, which are easily oxidized and can cross the blood-brain barrier, inducing oxidative stress and neuroinflammation. A large-scale longitudinal study showed that high blood glucose and high TG levels are associated with increased risk of depression and anxiety, while elevated HDL-C levels have a protective effect[26]. HDL-C, in addition to reverse cholesterol transport, has multiple benefits including anti-inflammatory, antioxidant, and endothelial protective functions. Low HDL-C levels are associated with chronic inflammatory states, and research has found that decreased serum HDL-C levels are associated with prolonged duration of depressive symptoms[27]. Lipid metabolism abnormalities may also affect the synthesis and metabolism of neurotransmitters such as serotonin and dopamine; research suggests that lipid metabolism disorders can affect dopamine synthesis through oxidative stress and impair brain-derived neurotrophic factor function[28]. Additionally, lipid metabolism disorders are important risk factors for atherosclerosis, and cerebrovascular lesions may be another important pathway connecting dyslipidemia and depression.

Glucose metabolism abnormalities are common comorbidities in MASLD patients and are also closely related to mental health. Insulin resistance, as the common pathophysiological basis of MASLD and type 2 diabetes, may also play a role in mental health[29]. Although the brain accounts for only 2% of body weight, it consumes approximately 20% of the body’s glucose, making it the organ with the highest energy demand. Insulin not only regulates peripheral blood glucose but also plays important roles in the central nervous system, participating in higher cognitive functions such as learning, memory, and emotion regulation. Research has confirmed that brain insulin signaling pathways regulate hippocampal neuroplasticity, and insulin signaling defects may be related to cognitive dysfunction and depression[30]. In insulin-resistant states, the brain's utilization of glucose decreases, which may affect neurotransmitter synthesis and neuronal function. The impact of blood glucose fluctuations on mood should not be overlooked; hypoglycemia can lead to anxiety, irritability, and cognitive decline, while persistent hyperglycemia may damage the nervous system through mechanisms such as formation of advanced glycation end products, increased oxidative stress, and activation of inflammatory responses. A significant bidirectional relationship exists between diabetes and depression; the latest review indicates that diabetes patients have a 33% increased risk of depression, while depressed patients have approximately 40% increased risk of diabetes[31]. Diabetes, as a chronic disease requiring lifelong management, places enormous psychological burden on patients. Dietary restriction difficulties, complexity of drug treatment, fear of hypoglycemia, and concerns about complications are all important sources of psychological stress.

Gut microbiota dysbiosis is another important mechanism connecting MASLD and mental health. Gut microbiota communicate bidirectionally with the central nervous system through the microbiota-gut-brain axis, and their metabolic products including neurotransmitters, short-chain fatty acids, indoles, and bile acids can affect brain function and emotional state[32]. MASLD patients often have gut microbiota dysbiosis, manifesting as decreased beneficial bacteria, increased harmful bacteria, impaired intestinal barrier function, and increased endotoxin entry into blood, further exacerbating systemic inflammatory responses. These changes not only promote liver disease progression but may also affect mental health by affecting gut-brain axis signal transduction. Research has shown that probiotic interventions may improve anxiety and depression symptoms by regulating gut microbiota[33].

This study found that female sex is an independent risk factor for anxiety and depression in MASLD patients, consistent with previous research results. The lifetime prevalence of anxiety disorders in women is 1.7 times that of men; this difference may be related to biological factors (such as sex hormone fluctuations and brain structural differences) and psychosocial factors (such as social expectations and coping styles)[34]. Among MASLD patients, women may face more weight-related discrimination and body image concerns, thereby increasing psychological problem risk. Increasing age is also a risk factor for depression; elderly MASLD patients may face more health problems, reduced social support, increased economic pressure, and other unfavorable factors.

Exercise is an effective means of improving both MASLD and mental health. This study found that lack of exercise is an independent risk factor for anxiety. Exercise not only reduces liver fat content and improves insulin sensitivity and lipid profiles but can also alleviate anxiety and depression symptoms through mechanisms such as increasing endorphin release, promoting neuroplasticity, and improving sleep quality. A meta-analysis showed that achieving 150 minutes of moderate-intensity exercise per week increases the likelihood of reducing liver fat by more than 30% in MASLD patients by 3.5 times[35]. Exercise can also mitigate the adverse effects of metabolic risk factors on mental health; in the subgroup analysis of this study, we found that exercise can significantly attenuate the effects of BMI and TG on anxiety, suggesting that exercise may be an effective pathway for interrupting the metabolic-psychological vicious cycle.

This study has certain limitations. First, the cross-sectional design cannot determine causal relationships. Second, our study population was recruited exclusively from specialty gastroenterology and endocrinology clinics at a tertiary hospital, which may introduce significant selection bias. Patients attending these specialty services likely represent a subset with more advanced metabolic derangements, greater symptom burden, or higher health awareness compared to MASLD patients in primary care or community settings. Consequently, the reported anxiety and depression prevalence rates may overestimate the true burden in the general MASLD population. Community-based studies with population-representative sampling are needed to establish more generalizable prevalence estimates. Third, a major limitation is the exclusive reliance on self-report questionnaires (SAS and SDS) rather than structured clinical diagnostic interviews. While these instruments are validated screening tools, they measure symptom severity rather than providing formal psychiatric diagnoses according to DSM-5 or ICD-11 criteria. Therefore, our reported detection rates reflect elevated symptom levels above established cut-offs rather than true diagnostic prevalence of anxiety and depressive disorders. Gold-standard assessment would require structured diagnostic interviews (e.g., SCID, MINI) administered by trained mental health professionals. Future studies incorporating formal diagnostic assessments would provide more precise estimates of true disorder prevalence in MASLD populations. Fourth, some confounding factors such as dietary patterns and sleep quality were not included in the analysis. Future large-scale prospective cohort studies are needed to further validate the conclusions of this study.

CONCLUSION

This study confirms that the detection rate of anxiety and depression symptoms in MASLD patients is significantly higher than in the general population and is closely associated with metabolic indicators including obesity, lipid metabolism disorders, glucose metabolism abnormalities, and hepatic fat deposition. These findings support the theory that a bidirectional relationship exists between anxiety/depression and metabolic disorders, suggesting that the two may mutually influence each other through multiple pathways including neuroendocrine, inflammatory-immune, and gut-liver-brain axis mechanisms.