Association of general obesity, metabolically healthy obesity, and abdominal adiposity markers with elevated liver enzymes: a population-based study

Abstract

The effects of metabolically healthy obesity and abdominal adiposity on liver enzymes remain unclear. This study was a population-based cross-sectional analysis that was conducted among 10,009 adults aged 35–70 years in southwest Iran between 2016 and 2018. Clinical data, including liver enzyme levels (ALT, AST, ALP, and GGT), anthropometric indices (waist circumference, waist-to-hip ratio, BMI), lifestyle factors (physical activity, smoking), laboratory measures (triglycerides, HDL, cholesterol, FBS), and sociodemographic factors (wealth status, residence type, education level) were collected. Multiple logistic regression analyses were used to assess associations. The findings showed that the prevalence of liver enzyme abnormalities was 35.8% (95% CI: 34.9–36.8) among the participants enrolled in the Hoveyzeh cohort study. Elevated liver enzymes were significantly more common in individuals with obesity, abnormal waist circumference (WC), increased waist-to-hip ratio (WHR), metabolically unhealthy obesity, dyslipidemia, metabolic syndrome, hypertension, diabetes, and physical inactivity. Based on multiple logistic regression analyses, the odds of liver enzyme abnormalities were 36% higher in individuals with obesity (Odds Ratio [OR] = 1.36, 95% CI: 1.25–1.48), 48% higher in those with abnormal WC (OR = 1.48, 95% CI: 1.33–1.65), 75% higher in those with metabolically unhealthy obesity (OR = 1.75, 95% CI: 1.60–1.90), and more than twofold higher in those with elevated WHR (OR = 2.26, 95% CI: 1.92–2.66). Overall, all three forms of obesity (general, abdominal, and phenotypic) were associated with elevated liver enzymes. Strategies targeting obesity and metabolic health may help prevent liver dysfunction in this population.

Introduction

Obesity is a major global public health concern, strongly associated with increased risks of non-communicable diseases (NCDs), such as type 2 diabetes, cardiovascular disease, and metabolic dysfunction-associated steatotic liver disease (MASLD)¹. Obesity rates are rising in Iran, with a 2024 umbrella meta-analysis reporting adult prevalence of overweight at 27.4% and obesity at 17.2%². The economic impact of obesity is considerable; global analyses estimate that overweight and obesity accounted for 2.19% of global GDP in 2019, projected to rise to 3.29% by 2060³. Although national economic data are limited, the increasing prevalence of obesity likely imposes substantial healthcare and economic burdens in Iran.

Although body mass index (BMI) has traditionally been used to estimate obesity-related health risks, it does not fully capture fat distribution or metabolic variability⁴. Central obesity, measured by waist circumference (WC) and waist-to-hip ratio (WHR), more accurately reflects visceral fat accumulation and demonstrates stronger associations with adverse liver outcomes⁵. Obesity phenotype refers to the classification of obesity based on a combination of body composition and metabolic characteristics rather than solely on body mass index (BMI). It helps identify individuals who may have a high BMI but low metabolic risk, as well as those with normal BMI who exhibit metabolic abnormalities. Common obesity phenotypes include metabolically healthy obesity (MHO), characterized by high BMI without metabolic disturbances; metabolically unhealthy obesity (MUO), involving high BMI with metabolic disorders such as hypertension, diabetes, or dyslipidemia; and metabolically unhealthy normal weight (MUNW), which describes individuals with normal BMI but adverse metabolic profiles⁶. Obesity phenotypes, particularly metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO), play a critical role in predicting liver-related risk. Although individuals with MHO lack overt metabolic dysfunction, emerging evidence suggests they may still be at increased risk for liver injury⁷. Those who transition to MUO exhibit higher fatty liver index (FLI) scores and a greater risk of liver fibrosis⁸.

Elevated liver enzymes, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT), are well-established biomarkers of liver injury and early indicators of MASLD⁹. These enzymes are commonly assessed through liver function tests (LFTs), which aid in diagnosing and monitoring liver conditions¹⁰. The prevalence of MASLD in Iran is substantial. The Guilan Cohort Study reported MASLD prevalence of 33.7%¹¹. A Tehran-based cohort of 5,058 adults followed for a median of 5 years identified 562 incident MASLD cases, highlighting the burden of this disease in Iranian populations¹².

Evidence indicates that both general and abdominal obesity are independently associated with elevated liver enzyme levels, even in the absence of metabolic syndrome¹³. Furthermore, WC and WHR exhibit stronger correlations with liver enzyme elevations compared to BMI¹⁴. Individuals with type 2 diabetes, and obesity have significantly higher odds of MASLD¹⁵. Previous studies in Iran have demonstrated an association between metabolic obesity phenotypes and fatty liver disease. Individuals with MHO have an increased prevalence of MASLD and a higher risk of hepatic fibrosis⁷.

Despite the growing evidence linking obesity to liver dysfunction, several aspects remain unclear. One such aspect is whether MHO is truly benign and whether central adiposity markers such as WC and WHR better predict liver enzyme abnormalities than BMI. In studies conducted on the Hoveyzeh cohort population^16,17, it was shown that the mean liver enzyme levels are significantly higher in individuals with diabetes¹⁷ and those with unhealthy metabolic obesity¹⁶. However, evidence from large, population-based studies in Iran that specifically examine the combined associations of general obesity, abdominal adiposity, and MHO with elevated liver enzymes remains scarce, even as obesity rates continue to rise. To address this gap, the present study examines associations between different obesity measures and phenotypes and elevated liver enzymes in a large adult cohort from Southwest Iran.

Results

Among a total number of 10,009 assessed individuals, the mean age was 48.76 years (SD = 9.21), ranging from 35 to 70 years. The study population was predominantly female (59.8%). The overall prevalence of liver enzyme abnormalities was 35.80% (95% CI: 34.86–36.75).

Table 1 presents the relationship between liver enzyme abnormalities and sociodemographic, metabolic, and lifestyle factors. Among all factors examined, metabolic risk factors demonstrated the strongest correlations with elevated liver enzymes. Specifically, individuals with obesity (40.49% vs. 32.80%), dyslipidemia (41.89% vs. 31.13%), metabolic syndrome (42.83% vs. 31.28%), hypertension (60.69% vs. 34.54%), and diabetes (43.98% vs. 33.46%) had significantly higher prevalence of liver enzyme disorders (p < 0.001 for all comparisons).

Table 1.

Demographic characteristics, socioeconomic status, life style factors and health status of the participant by liver enzyme disorder.

Variable		Liver enzyme disorder: Yes n (%)	Liver enzyme disorder: No n (%)	P-value
Age group	35–44	11,413 (35.89)	2524 (64.11)	0.033
	45–54	1213 (36.99)	2066 (63.01)
	55–64	734(35.31)	1345 (64.69)
	≥ 65	223 (31.23)	491 (68.77)
Sex	Male	1417 (35.20)	2609 (64.80)	0.303
	Female	2166 (36.20)	3817 (63.80)
Marital status	Single	113 (32.94)	230 (67.06)	0.164
	Married	3122 (35.64)	5638 (64.36)
	Widow	289 (39.21)	448 (60.79)
	Divorced	59 (34.91)	110 (65.09)
Residence Type	Urban	2186 (35.40)	3990 (64.60)	0.286
	Rural	2397 (36.45)	2436 (63.55)
Education level	Illiteracy	2236 (36.01)	3973 (63.99)	0.350
	Primary school	601 (36.10)	1064 (63.90)
	Secondary school	245 (36.40)	428 (63.60)
	High school Diploma	239 (32.25)	502 (67.75)
	University	262 (36.34)	459 (63.66)
Obesity	Yes	1581 (40.49)	2324 (59.51)	< 0.001
	No	2002 (32.80)	4102 (67.20)
Physical activity	Q1	986 (39.39)	1517 (60.61)	< 0.001
	Q2	902 (36.01)	1603 (63.99)
	Q3	864 (34.45)	1644 65.55)
	Q4	831(33.33)	1662 (66.67)
Wealth status	Poorest	719 (35.95)	1281 (64.05)	0.066
	Poor	746 (36.69)	1287 (63.31)
	Moderate	655 (33.05)	1327 (66.95)
	Rich	750 (37.07)	1273 (62.93)
	Richest	713 (36.17)	1258 (63.83)
Smoking	Smoker	707 (33.84)	1382 (66.16)	0.036
	Nonsmoker	2876 (36.31)	5044 (63.69)
Dyslipidemia	Normal	1765 (31.13)	3904 (68.87)	< 0.001
	High	1818 (41.89)	2522 (58.11)
Metabolic syndrome	Yes	1676 (42.83)	2237 (57.17)	< 0.001
	No	1907 (31.28)	4189 (68.72)
Hypertension	Yes	1604 (60.69)	1039 (39.31)	< 0.001
	No	4822 (65.46)	2544 (34.54)
Diabetes	Yes	979 (43.98)	1247 (56.02)	< 0.001
	No	2604 (33.46)	5179 (66.54)
MHO	Yes	1982 (31.08)	4396 (68.92)	< 0.001
	No	1601 (44.09)	2030 (55.91)
WHR	Normal	208 (21.22)	772 (78.78)	< 0.001
	Abnormal	3375 (37.38)	5654 (62.62)
WC	Normal	1073 (31.16)	2371 (68.84)	< 0.001
	Abnormal	2510 (38.23)	4055 (61.77)

*P < 0.05 significant for chi-square test.

Physical inactivity was inversely associated with liver health, with the least active quartile (Q1) exhibiting a higher prevalence of liver enzyme abnormalities than the most active quartile (Q4) (39.39% vs. 33.33%, p < 0.001). Anthropometric markers also showed significant associations: abnormal waist circumference (38.23% vs. 31.16%) and waist-to-hip ratio (37.38% vs. 21.22%) were linked to higher prevalence of liver enzyme disorders (p < 0.001).

Sociodemographic factors were generally not associated with liver enzyme abnormalities. The highest prevalence liver enzyme abnormalities showed in the 45–54 age group. In contrast, sex, marital status, residence type, educational level, and wealth status were not significantly associated with liver enzyme abnormalities (p > 0.05) (Table 1).

Multiple logistic regression analysis demonstrates significant associations between obesity measures and elevated liver enzymes in Southwest Iran (Table 2). General obesity showed a positive association with (OR = 1.57, 95% CI: 1.43–1.72, p < 0.001) and GGT (OR = 1.44, 95% CI: 1.25–1.66, p < 0.001), but not with ALP.

Table 2.

Multiple logistic regression analysis to assess the relationship of liver enzymes with general, abdominal obesity, WHR and metabolic healthy obesity controlled for the potential confounders in Southwest Iran.

Variable	Enzyme liver elevated	ALT	AST	ALP	GGT
	Odds Ratio (95% CI)	Odds Ratio (95% CI)	Odds Ratio (95% CI)	Odds Ratio (95% CI)	Odds Ratio (95% CI)
General obesity
Model 1	1.39 (1.28–1.51)	1.57 (1.44–1.73)	1.32 (1.18–1.47)	0.99 (0.84–1.15)	1.40 (1.22–1.60)
Model 2	1.39 (1.27–1.51)	1.59 (1.45–1.75)	1.10 (0.99–1.24)	1.03 (0.87–1.21)	1.46 (1.27–1.68)
Model 3	1.36 (1.25–1.48)	1.57 (1.43–1.72)	1.09 (0.98–1.22)	1.01 (0.85–1.18)	1.44 (1.25–1.66)
Abdominal obesity (WC)
Model 1	1.37 (1.25–1.49)	1.34 (1.21–1.47)	1.69 (1.49–1.92)	1.29 (1.09–1.52)	1.34 (1.16–1.56)
Model 2	1.51(1.37–1.68)	1.76 (1.57–1.98)	1.01 (0.87–1.16)	1.14 (0.93–1.40)	1.56 (1.30–1.86)
Model 3	1.48 (1.33–1.65)	1.73 (1.54–1.94)	0.99 (0.85–1.15)	1.11 (0.90–1.36)	1.54 (1.29–1.83)
Abdominal obesity (WHR)
Model 1	2.22 (1.89–2.60)	2.20 (1.83–2.65)	1.82 (1.46–2.28)	2.08 (1.48–2.93)	2.40 (1.75–3.30)
Model 2	2.29 (1.95–2.69)	2.65 (2.20–3.20)	1.55 (1.23–1.94)	1.49 (1.04–2.11)	2.36 (1.71–3.25)
Model 3	2.26 (1.92–2.66)	2.62 (2.17–3.16)	1.54 (1.22–1.93)	1.45 (1.02–2.05)	2.33 (1.69–3.21)
MHO
Model 1	1.75 (1.61–1.90)	1.69 (1.55–1.86)	1.31 (1.17–1.46)	1.97 (1.69–2.29)	2.32 (2.02–2.66)
Model 2	1.78 (1.63–1.93)	1.85 (1.68–2.03)	1.31 (1.17–1.47)	1.70 (1.45–1.99)	2.29 (1.99–2.63)
Model 3	1.75 (1.60–1.90)	1.82 (1.65–2.00.65.00)	1.30 (1.16–1.45)	1.65 (1.41–1.94)	2.25(1.95–2.58)

P < 0.05 was considered a statistically significant level in the logistic regression model; Model 1: crude, Model 2: adjusted for age (years) and sex (male and female). Model 3: model 2 + smoking status, physical activity, and energy consumption. BMI Body Mass Index, WC Waist Circumference, WHR Waist to Hip Ratio, OR odds ratio, CI confidence interval.

Abdominal obesity, as measured by WC, was linked to higher ALT (OR = 1.73, 95% CI: 1.54–1.94, p < 0.001) and GGT (OR = 1.54, 95% CI: 1.29–1.83, p < 0.001). WHR showed the strongest associations across all enzymes, particularly ALT (OR = 2.62, 95% CI: 2.17–3.16, p < 0.001) and GGT (OR = 2.33, 95% CI: 1.69–3.21, p < 0.001).

Metabolically unhealthy obesity (MUO) consistently predicted elevated enzymes (OR = 1.75, 95% CI: 1.60–1.90), with the highest risk observed for GGT (OR = 2.25, 95% CI: 1.95–2.58, p < 0.001).

Discussion

In this large population-based study of adults from Southwest Iran. Our findings indicate that while general obesity was associated with elevated ALT and GGT, abdominal obesity and obesity phenotypes demonstrated stronger predictive value. Waist-hip ratio showed the most robust associations across all liver enzymes, and metabolically unhealthy obesity consistently conferred the highest risk, especially for GGT. These results suggest that central obesity and metabolically unhealthy obesity are more powerful predictors of liver enzyme abnormalities than general obesity alone, emphasizing the importance of assessing both metabolic health and fat distribution in clinical practice.

Elevated liver enzyme levels were most strongly associated with metabolic risk factors, including obesity, dyslipidemia, metabolic syndrome, hypertension, and diabetes. Among lifestyle and anthropometric measures, physical inactivity, abnormal waist circumference, and waist-hip ratio were also significantly linked to liver enzyme abnormalities, whereas sociodemographic characteristics such as sex, education, wealth index, and alcohol use showed no meaningful associations.

General obesity, as defined by BMI, was associated with elevated levels of ALT and GGT. Consistent with previous studies^{13,14,18–20}, our findings indicate that higher BMI correlates with elevations in these enzymes, reflecting MASLD-related liver stress prevalent among obese individuals. These elevated levels are indicative of MASLD-related liver stress, which is highly prevalent in obese individuals. Obesity independently contributes to abnormal liver function through mechanisms involving insulin resistance, oxidative stress, and chronic inflammation. ALT serves as a marker of hepatocyte injury, whereas GGT is sensitive to liver fat accumulation and oxidative damage. Both enzymes show positive correlation with higher BMI and metabolic risk factors. These findings indicate that obesity influences liver enzyme profiles not only through mechanisms related to hepatic steatosis but also through broader metabolic disturbances, including insulin resistance, systemic inflammation, and oxidative stress^19,20.

Metabolically unhealthy obesity (MUO) was associated with significantly elevated odds of abnormal liver enzymes, with the strongest associations observed for GGT. These results align with prior studies^13,21. For example, a large cross-sectional study in China reported strong association between GGT and ALT with MUO, while AST was not significantly associated. This study also suggested that GGT may be a better diagnostic marker for MUO than ALT or AST²¹. Another large study in the US supported our finding, showing that general and abdominal obesity combined with metabolic dysfunction had additive adverse effects on liver injury indicators (ALT, AST, ALP, GGT). This study confirmed the elevated odds rations across these enzymes in MUO¹³. These findings underscore that MUO represents a high-risk phenotype for hepatic injury beyond the effect of BMI alone.

Gamma-glutamyl transferase (GGT) has been widely recognized as a sensitive biomarker of liver injury and oxidative stress, particularly in the context of metabolically unhealthy obesity (MUO), although alanine aminotransferase (ALT) and alkaline phosphatase (ALP) also contribute to the assessment of hepatic dysfunction, while aspartate aminotransferase (AST) tends to show weaker associations^22,23. The elevation of liver enzymes observed in MUO is primarily attributed to increased hepatic fat accumulation (hepatic steatosis) and more pronounced hepatic insulin resistance. These metabolic disturbances promote hepatocellular injury, chronic low-grade inflammation, and oxidative stress, ultimately leading to the release of liver enzymes, particularly GGT, into the circulation^21,24. These pathophysiological mechanisms may explain the stronger association observed between MUO and elevated GGT levels compared to other liver enzymes.

Abdominal obesity, measured by waist circumference and waist-hip ratio, was strongly linked to elevated ALT and GGT, as supported by other studies. For instance, a large Finnish cohort study found that liver enzymes and liver disease risk increased with higher WC, particularly in individuals with high WHR. WHR captures body fat distribution and is a better marker of visceral fat, which is closely linked to liver injury²⁵. Similar findings come from other population studies and genetic analyses supporting a causal role of WHR in metabolic dysfunction-associated steatotic liver disease (MASLD) risk^5,26. The stronger associations of WHR and WC with liver enzyme elevation are explained by the role of abdominal fat in the pathogenesis of liver injury. Excess visceral fat increases fat deposition in the liver (hepatic steatosis), leading to insulin resistance, hepatic inflammation, oxidative stress, and subsequent liver cell damage. This damage results in the release of liver enzymes such as ALT and GGT into the bloodstream. WHR better reflects harmful visceral fat accumulation relative to hip circumference, thus showing stronger correlations with liver enzyme elevations and liver disease risk^25,26.

Strengths of our study include a large and representative sample size, enhancing the generalizability of the findings. Additionally, controlling for demographic, and lifestyle risk factors, which reduces confounding and clarifies the direct association between obesity phenotypes (general, abdominal, metabolically healthy or unhealthy) and liver enzyme elevations. The use of multiple measures such as BMI, WC, and WHR enabled a detailed assessment of overall and abdominal obesity influences on liver enzymes, enhancing the study’s comprehensiveness. Furthermore, analyzing of several liver enzymes (ALT, AST, GGT, ALP) provided a nuanced view of liver function abnormalities related to obesity. Limitations of the study include the cross-sectional design, which limits causal inference between obesity types and changes in liver enzymes. Despite controlling for lifestyle factors, residual confounding by unmeasured factors (e.g., dietary quality) may exist.

Conclusion

In this large population-based study of adults from Southwest Iran, we found that central and metabolically unhealthy obesity are stronger predictors of liver enzyme abnormalities than general obesity. Elevated liver enzymes were also strongly associated with key metabolic risk factors including dyslipidemia, metabolic syndrome, hypertension, and diabetes highlighting the close interplay between obesity, metabolic health, and liver function. These findings emphasize that clinicians should prioritize screening for central and metabolically unhealthy obesity when evaluating liver function, rather than relying solely on general obesity measures such as BMI. For policymakers, the results underscore the urgent need to strengthen public health strategies targeting obesity prevention, promotion of physical activity, and early detection of metabolic disorders to reduce the growing burden of liver-related diseases.

Methods

Study design and participants

This population-based cross-sectional study used data from the enrollment phase of the Hoveyzeh Cohort Study (HCS)²⁷. The HCS enrolled 10,009 adults aged 35 to 70 years from May 2016 to August 2018 in southwestern Iran. Inclusion criteria consisted of the age of 35–70 years old, resident of Hoveyzeh, without severe mental disorders, ability to answer the questionnaires without help, and not being deaf or hard of hearing.

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.REC.1403.145).

Anthropometric indexes measurement

The anthropometric measurements were performed by trained personnel. height (cm) was measured using a ruler (Seca 206 precision of 0.1 cm) in a standing position without shoes, shoulders relaxed, facing forward with the head facing the wall. Weigh (kg) was measured with minimal clothing on a standing scale (Seca 755 precision 0.05 kg). Additionally, a locking tape measure (Seca) was used to measure waist, wrist, and hip circumference (cm). Three anthropometric indexes were used in this analysis, including BMI, WC, WHR. These anthropometric indexes were calculated using the following equations (Eqs. 1–2):

1

2

A body mass index of 30 and above was defined as obese (yes/no). WHR (normal/Abnormal), WC (normal/Abnormal). Metabolically Healthy Obesity (MHO) is defined as a phenotype characterized by obesity (BMI ≥ 30 kg/m²) in individuals who exhibit absence of type 2 diabetes, normal blood pressure (< 130/85 mmHg without medication), favorable lipid profile (triglycerides < 150 mg/dL and HDL cholesterol ≥ 40 mg/dL in men ≥ 50 mg/dL in women), and normal inflammatory markers, despite their elevated adiposity²⁸.

Liver enzyme assessment

Serum liver enzymes, including alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), and alanine aminotransferase (ALT), were measured using standard laboratory methods. For the purpose of this study, elevated liver enzyme levels were defined as ALP > 305 IU/L²⁹, and AST, ALT, exceeding 30 U/L in men and 20 U/L in women³⁰ and GGT levels > 55men,>38 women³¹. These cut-offs were used to classify participants with abnormal liver function for subsequent analyses.

Covariates

The other variables used in this analysis were age groups (35–44, 45–54,55–64, and ≥ 65 years), sex (male, female), marital State (single, married, widow, divorced), Education level (Illiteracy, Primary school, Secondary school, High school Diploma, University), and area of residence (urban and rural). The Wealth Index in this study measures household assets and categorizes households into five quintiles from poorest to richest based on household’s assets such as TVs, bicycles, cars, and computers. To evaluate the physical activity of the participants, the international physical activity questionnaire (IPAQ) was utilized. Its validity and reliability had been previously assessed in a study conducted by Moghadam et al.³². The physical activity and metabolic equivalent (MET) scores were reported for a 24-h task³³. The physical activity score was categorized into quartiles in our analysis. The diet intake was evaluated by a quantitative 130-item food frequency questionnaire (FFQ) and for analyzing dietary intake data such as energy consumption, N4 software for nutrition was used. The validity and reliability of the FFQ have already been stablished for Iranian population³⁴. The trained interviewers asked participants to report how often, on average, they have consumed each food item daily, weekly, monthly, or yearly scale over the last year. In this study, some potential sources of bias, including social acceptability bias, incorrect response bias, and recall biases can affect the results especially in assessing the FFQ and socioeconomic indexes. To minimize recall bias, the participants were asked the participants about food consumption for relatively short time durations and current status of the socioeconomic position. To reduce the probability of social acceptability bias, interviewers from other neighborhoods were assigned for each participant. To control incorrect responses bias, food photo albums and various depicting food sizes, such as spoons, plates, boxes, matchboxes, and glasses were utilized. A smoker is someone who has consumed at least 100 cigarettes over the course of their lifetime²⁷. Dyslipidemia is defined as meeting the criteria for at least one of the disorders mentioned above or using lipid-lowering medications, according to the ATP III classification. This can be assessed in adults by measuring a full lipoprotein profile following a 10–12 h fast³⁵. Metabolic syndrome (MetS) was diagnosed based on the presence of at least three of the following five criteria: (1) abdominal obesity, defined as a waist circumference ≥ 102 cm in men or ≥ 88 cm in women; (2) elevated serum triglycerides (≥ 150 mg/dL) or use of medications for hypertriglyceridemia; (3) abnormal high-density lipoprotein (HDL) cholesterol levels (≤ 40 mg/dL in men and ≤ 50 mg/dL in women) or use of drugs for low HDL cholesterol; (4) elevated blood pressure (≥ 130/85 mmHg) or use of antihypertensive medications; and (5) elevated fasting plasma glucose (FPG ≥ 100 mg/dL) or use of medications for hyperglycemia³⁶. Diabetes was defined as a fasting blood glucose (FBG) level of 126 mg/dL or higher, the use of antidiabetic medications, or a self-reported diagnosis of diabetes^37,38. Hypertension is identified in individuals who have a systolic blood pressure greater than 140 mmHg, a diastolic blood pressure greater than 90 mmHg, are taking antihypertensive medications, or report a previous diagnosis of hypertension²⁷.

Statistical analysis

All statistical analyses were conducted using Stata 14 (Stata Corp). Continuous variables were assessed for normality using Shapiro-Wilk tests and reported as mean ± standard deviation, while categorical variables were presented as frequencies (%). Differences between groups with and without liver enzyme disorders were analyzed using chi-square tests for categorical variables. Multiple logistic regression models were used to evaluate associations between obesity measures (BMI, WC, WHR, and MHO) and elevated liver enzymes. Adjusted odds ratios (AORs) with 95% confidence intervals were reported across three models: model 1(unadjusted), model 2 (age/sex-adjusted), and fully adjusted (including age, sex, smoking, physical activity and energy consumption). All p-values were two-tailed with statistical significance set at p < 0.05.

Abbreviations

WC

Waist circumference

WHR

Waist-to-hip ratio

OR

Odds Ratio

NCDs

Non-communicable diseases

MASLD

Metabolic dysfunction-associated steatotic liver disease

BMI

Body mass index

MHO

Metabolically healthy obesity

MUO

Metabolically unhealthy obesity

ALT

Alanine aminotransferase

AST

Aspartate aminotransferase

ALP

Alkaline phosphatase

GGT

Gamma-glutamyl transferase

LFTs

Liver function tests

HCS

Hoveyzeh Cohort Study

IPAQ

International physical activity questionnaire

MET

Metabolic equivalent task

FFQ

Food frequency questionnaire

AORs

Adjusted odds ratios

CI

Confidence interval

Author contributions

BCh and ZR: conceptualization, project administration, formal analysis, methodology, writing- review&editing and final approval of the version to be submitted. ASh: supervision and writing- review&editing. LM: data curation, project administration, writing-original draft, writing- review&editing manuscript. All authors reviewed the manuscript.

Funding

This work was supported by the Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences (Grant number HCS-0302). The funder had no role in the design, data collection, data analysis, and reporting of this study.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The ethics committee approved the study protocol of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.REC.1403.145). This study was conducted based on the Helsinki Declaration and its later amendments. On the registration day, informed written consent was obtained from the study participants.