The liver is the largest and one of the most metabolically complex organs in the human body, playing a vital role in nutrient metabolism and the detoxification of drugs and harmful substances (1). Among hepatic disorders, fatty liver disease—originally classified into Non-Alcoholic Fatty Liver Disease (NAFLD) and Alcoholic Fatty Liver Disease (AFLD)—has emerged as a major global concern. NAFLD refers to hepatic steatosis accompanied by inflammation and hepatocyte ballooning in individuals who do not consume alcohol in significant quantities (2).

In recent years, a paradigm shift in terminology has taken place, with NAFLD now redefined as Metabolic dysfunction-associated fatty liver disease (MAFLD) to better reflect the underlying metabolic disturbances rather than solely excluding alcohol use. This renaming aims to reduce the stigma associated with terms like “non-alcoholic” and “fatty,” which are often misunderstood as indicative of alcoholism or poor lifestyle choices (3). Global consensus on this nomenclature—led by major associations such as AASLD, EASL, and ALEH—was formalized in 2023, resulting in a new classification system (4).

As part of this change, Steatotic Liver Disease (SLD) has become the umbrella term for hepatic steatosis of any cause. Within this classification, MAFLD has been replaced by Metabolic dysfunction-associated steatotic liver disease (MASLD), and NASH is now referred to as Metabolic dysfunction-associated steatohepatitis (MASH) (5). Unlike NAFLD, MASLD diagnosis does not require the exclusion of other causes but rather the presence of steatosis with at least one of several defined metabolic risk factors. This change provides a more inclusive and pathophysiologically relevant framework (4).

The pathogenesis of MASLD is multifactorial, involving genetic predispositions, metabolic abnormalities, environmental influences, and gut microbiota interactions. Central obesity and insulin resistance are key drivers, though other risk factors such as hypertension, dyslipidemia, obstructive sleep apnea, hypothyroidism, and even abnormal sleep duration also contribute significantly (6). In particular, metabolic syndrome—characterized by central obesity, dyslipidemia, hypertension, and insulin resistance—has been widely recognized as a major contributor to MASLD development. In Indonesia, the prevalence of metabolic syndrome reaches 23% (7).

Moreover, work-related factors such as long years of service, prolonged sitting duration, and chronic occupational stress have also been linked to systemic inflammation and metabolic dysfunction. These conditions may promote hepatic fat accumulation through hormonal pathways, including elevated cortisol levels that contribute to insulin resistance and liver steatosis (8–10).

Globally, the prevalence of MASLD has increased markedly in the past two decades. A recent review showed a rise from 26% (1990–2006) to 38% (2016–2019), with the highest rates observed in Latin America (44.4%), followed by the Middle East and North Africa (11). In Southeast Asia, the prevalence is 33.1%, with Indonesia ranking the highest at 51.04%, ahead of Singapore (40.43%) and Malaysia (38.5%). While the global average is estimated at 25% (12), localized studies in Indonesia reveal even more alarming statistics—for instance, a study in Makassar found that 95.5% of obese patients and 67.2% of hypertriglyceridemic individuals had fatty liver disease (13).

Despite this rising trend, there is a notable absence of epidemiological data on fatty liver disease among employees in academic institutions in Eastern Indonesia. Limited research in this region hampers efforts to formulate targeted health interventions and workplace health policies (14). Most previous studies have primarily focused on the general population or clinical patients in hospital settings. However, academic staff as part of the formal, educated workforce remain underrepresented in current research—particularly in Eastern Indonesia. This is noteworthy, as chronic occupational stress and sedentary work patterns in this group may pose significant risk factors for MASLD development.

Therefore, this study aims to examine the relationship between individual characteristics—including sex, age, exercise habits, coffee and alcohol consumption, smoking habits, obesity, diabetes mellitus, dyslipidemia, and hypertension—and work-related factors such as years of service, employment status, and work unit, with the severity of fatty liver disease among university employees. The findings are expected to contribute valuable insights to the growing body of evidence on MASLD within occupational health contexts, particularly in academic work environments.

A total of 78 employees with ultrasound-confirmed fatty liver disease were included. Women represented 56.4%, and most participants were aged ≥45 years (92.3%), reflecting a predominantly middle-aged to older workforce. Regarding lifestyle factors, 74.4% reported engaging in regular exercise, while all respondents denied smoking and alcohol use. Coffee consumption was reported by 6.4% of participants. For metabolic conditions, 26.9% were obese, 20.5% had diabetes mellitus, 70.5% had dyslipidemia, and 44.9% had hypertension. In terms of occupational characteristics, 51.3% had worked for more than 26 years, and 78.2% were academic staff. Most respondents (84.6%) worked in academic units (Table 1). Based on ultrasound grading, 73.1% had mild fatty liver (Grade 1), while 26.9% had moderate-to-severe disease (Grades 2–3) (Table 2).

The bivariate analysis identified sex, obesity, dyslipidemia, and years of service as variables significantly associated with fatty liver severity (p < 0.05). Male sex was associated with higher odds of moderate-to-severe disease (OR = 7.154, 95% CI 1.894–27.015; p = 0.002). Obesity increased the likelihood nearly fivefold (OR = 4.756, 95% CI 1.001–22.551; p = 0.035). Dyslipidemia showed the strongest crude association (OR = 18.295, 95% CI 1.993–167.909; p = 0.010). Participants with ≥26 years of service showed a significant bivariate association (OR = 3.696, 95% CI 1.249–10.933; p = 0.015). Other individual factors—including age, diabetes, hypertension, coffee intake, and work unit—showed no significant associations in bivariate testing (Table 3).

Binary logistic regression was performed to identify dominant factors associated with fatty liver severity. The results showed that sex, obesity, and dyslipidemia remained significantly associated in the final model.

The variable years of service were not significantly associated with Fatty Liver severity in the multivariate model (Table 4). The adjusted associations identified in the multivariate logistic regression model are illustrated in Figure 1.

This study explored the relationship between individual and occupational factors and the severity of fatty liver disease among university employees. The findings revealed that male sex, obesity, and dyslipidemia were significantly associated with moderate-to-severe fatty liver, as demonstrated in both bivariate and multivariate analyses. In contrast, variables such as physical activity, years of service, and employment status did not show statistically significant associations in the final model.

Sex was a consistent predictor of fatty liver severity (p = 0.002; OR = 10.167; 95% CI = 2.283–45.285), indicating that male respondents were over 10 times more likely to experience moderate-to-severe fatty liver compared to females. This finding is consistent with previous studies (15, 16), which attribute the disparity to hormonal factors and fat distribution—particularly the higher accumulation of visceral fat in males, which contributes to hepatic inflammation and insulin resistance.

Obesity was also significantly associated with disease severity (p = 0.049; OR = 6.358; 95% CI = 1.010–40.028), reinforcing its role as a central component in the pathogenesis of MASLD. Previous studies (16–18) describe obesity as a key driver in the “multiple-hit” hypothesis of fatty liver disease, where it exacerbates other metabolic disturbances. These studies further demonstrated that BMI positively correlates with hepatic fat accumulation and that even modest weight loss (5–10%) can reduce hepatic steatosis.

Dyslipidemia emerged as the strongest independent risk factor [p = 0.010; OR = 18.295; 95% CI = (1.993–167.909)], with affected individuals being over 15 times more likely to develop moderate-to-severe fatty liver. Previous findings (19) noted that lipid abnormalities—especially elevated triglycerides and low HDL—strongly contribute to hepatic fibrosis in MASLD patients. These findings highlight dyslipidemia's central role not only in the onset but also in the progression of liver disease.

Years of service >26 years showed a potential association in the bivariate analysis (p = 0.015; OR = 3.696), but this was not statistically significant in the multivariate model (p = 0.079). Nevertheless, the elevated OR suggests a clinically meaningful trend. Prolonged employment may reflect cumulative exposure to sedentary behavior, work-related stress, and unhealthy lifestyle patterns. A previous study by Wang et al. (20) found that working more than 50 h per week increased MASLD risk by 57%, while Huang et al. (21) showed that night shift workers with >10 years of service had significantly higher MASLD risk. Although this study did not account for shift work, the evidence supports the impact of long-term occupational exposure on metabolic liver dysfunction.

Previous research by Siswadi et al. (22) among industrial workers showed that prolonged years of service significantly impact employee wellbeing, with high work stress mediating job satisfaction (p = 0.002) and linking to mental and physical workload effects on performance (p < 0.05). Similarly, Saleh et al. (23) found that among air-traffic controllers, longer work duration notably increased stress and psychological fatigue (p = 0.033 and p = 0.001). These findings align with our results, where >26 years of service was significantly associated with moderate-to-severe fatty liver (OR = 3.696; p = 0.015). However, this association was attenuated in the multivariate model. This suggests that the effect of long employment duration may be mediated through metabolic factors—particularly obesity and dyslipidemia—which are strongly correlated with prolonged sedentary work patterns and chronic occupational stress. In other words, employees with longer years of service may accumulate metabolic risks over time, but these risks are more directly captured by obesity and lipid abnormalities in the adjusted model. Thus, the lack of significance in multivariate analysis does not negate its relevance; rather, it indicates an indirect pathway where years of service contribute to MASLD severity through intermediate metabolic mechanisms.

Other individual factors such as age, physical activity, coffee consumption, diabetes mellitus, and hypertension did not show significant associations with fatty liver severity in this sample. While age is commonly considered a risk factor, our findings (p = 0.556) suggest that its effect may be mediated through other variables like obesity and lipid status. Similarly, despite the well-known protective effects of exercise, no significant relationship was found (p = 0.163). This may be due to limitations in measurement, as exercise was reported dichotomously (yes/no), without accounting for intensity, frequency, or duration—factors emphasized by as essential for hepatic improvement (24). Coffee consumption was also recorded in binary form, and only five respondents reported habitual intake, resulting in limited variability. Smoking and alcohol consumption were reported as “No” by all participants; this may reflect social desirability bias or underreporting in a professional university setting. Due to the absence of variation, these variables were not included in further inferential analysis.

Alcohol and smoking were excluded from inferential analysis due to zero responses. While this may reflect healthy habits among university employees, underreporting or social desirability bias cannot be ruled out, especially in professional or culturally conservative settings.

Coffee consumption also showed no association with fatty liver severity (p = 0.718). Although studies suggest hepatoprotective effects of coffee, the limited number of coffee consumers (n = 5) in our sample likely prevented meaningful analysis (25).

Contrary to expectations, diabetes mellitus (p = 0.285) and hypertension (p = 0.952) did not show significant relationships with disease severity. This may result from effective disease management among respondents or limitations in sample size. Two previous studies (17, 26) emphasized the roles of blood glucose and pressure control in slowing MASLD progression, indicating that clinical management may modify the impact of these comorbidities.

Employment status (academic vs. administrative) was also not significantly associated with fatty liver severity (p = 0.656). Despite differing job roles, similar exposure to sedentary behavior and occupational stress might contribute to shared risk profiles. Additionally, the small number of administrative staff (n = 17) could limit statistical power.

From a workplace perspective, most respondents were concentrated in five major units, each representing both academic and administrative functions. Overall, obesity and dyslipidemia were common across these units, while male respondents predominated in most groups. However, years of service was the only factor that showed a statistically significant difference across work units (p = 0.001), with employees in certain units having shorter employment durations. This finding may reflect differences in occupational exposure, work routines, and cumulative metabolic risks among university staff.

Overall, the final multivariate model confirmed sex, obesity, and dyslipidemia as independent predictors of moderate-to-severe fatty liver disease. These findings underscore the potential role of individual and workplace-related factors in MASLD progression. As depicted in Figure 1, the forest plot reinforces these results by summarizing the adjusted odds ratios and confidence intervals in a single visual framework.

This study has several limitations. First, the sample size was relatively small and drawn from a single university setting, which may limit the generalizability of the findings. These associations should be interpreted with caution, as the cross-sectional design limits the ability to infer causal relationships. The identified predictors represent correlations at a single time point rather than temporal sequences of risk factor accumulation. Second, the sampling approach relied on a fixed clinical database of employees with USG-confirmed fatty liver; while appropriate for analytic cross-sectional studies based on secondary data, selection bias and residual confounding cannot be completely excluded. Third, although ultrasonography is widely used in institutional health evaluations in Indonesia, it is not the gold-standard imaging modality for steatosis diagnosis, and the use of USG may reduce diagnostic precision compared to FibroScan. Therefore, the results should not be overgeneralized beyond this specific university population, and further studies with larger, more diverse samples are needed to validate these findings.

Fourth, several lifestyle variables were limited by the structure of the MCU database. Exercise was measured only as a Yes/No response without information on frequency, duration, or intensity, which may lead to misclassification and reduce the ability to detect associations. Coffee consumption was extremely low (n = 5), limiting the interpretability of its effect. Smoking and alcohol use were reported as “No” by all participants, possibly reflecting social desirability bias, which prevented meaningful analysis of these variables.