Prevalence and risk factors of metabolic dysfunction-associated fatty liver disease in the adult Georgian population: a nationwide multiregional clinic-based study

Abstract

Background & aim

In Georgia, the prevalence of MAFLD remains poorly defined, as evidence largely stems from small-scale hospital studies. This research used a nationwide multiregional clinic-based sample to estimate MAFLD prevalence and its associated factors.

Methods

A cross-sectional, clinic-based study was conducted on 11,269 adults attending check-up clinics across Georgia from 2021 to 2025. A stratified random sampling approach considering age, sex, BMI, comorbidities and region was used to derive a representative analytic sample (n = 5,602). MAFLD was diagnosed using ultrasound and international consensus positive diagnostic criteria.

Results

MAFLD affected 51.6% of Georgian adults, with higher prevalence in older age, men and individuals with obesity or metabolic comorbidities. Prevalence varied significantly across regions, with the highest in Tbilisi.

Conclusion

MAFLD is highly prevalent in Georgia. Due to the clinic-based nature of the sample, results should be interpreted with consideration of potential selection bias.

Background

Metabolic dysfunction-associated fatty liver disease (MAFLD) represents the hepatic manifestation of systemic metabolic disorders, strongly linked with obesity, type 2 diabetes and other metabolic syndromes (Sangro et al., 2023) [1]. MAFLD includes a wide spectrum of liver injury, including simple steatosis and metabolic dysfunction-associated steatohepatitis (MASH) that may lead to serious complications such as liver cirrhosis and liver cancer (Chai et al., 2023) [2]. Its global burden has risen in parallel with lifestyle-related disease and now affects roughly one in four adults worldwide. Reported prevalence varies across continents, from 13% in Africa to over 30% in South America and the Middle East (Younossi et al., 2016) [3] MAFLD is diagnosed when hepatic steatosis of more than 5% is present together with at least one positive metabolic criterion, without requiring alcohol exclusion (Eslam et al., 2020) [7]. Although liver biopsy is the gold standard, imaging modalities (ultrasound, CT and MRI) are widely used for diagnosis in large-scale studies (Marchesini et al., 2016) [4]. Nowadays, there is no specific treatment and clinical management relies primarily on addressing metabolic comorbidities and preventing progression towards fibrosis, cirrhosis and HCC.

In Georgia, the true prevalence of MAFLD is still poorly characterized. Existing evidence comes mainly from small hospital-based studies, which either examined specific subgroups (e.g. a study from Tbilisi State Medical University on postmenopausal women) (Parkosadze et al., 2012) [5] or reported diagnostic challenges without providing national prevalence rates (Mamatsashvili et al., 2025) [6]. This study aimed to provide a nationwide estimate using a clinic-based cohort while acknowledging limitations in population representativeness.

Table 1 shows comparison of study sample demographic and metabolic characteristics with estimated national population data.

Table 1.

Comparison of analytic sample with national adult population

Variable	Study samplE (%)	National populatioN (%)	Absolute difference (%)
Sex – Male	2,857 (51%)	47.0% (Geostat)	+ 4.0%
Age < 30	468 (8.4%)	19.5% (Geostat)	−11.1%
Age 30–39	1,661 (29.7%)	24.0% (Geostat)	+ 5.7%
Age 40–49	1,341 (23.9%)	18.0% (Geostat)	+ 5.9%
Age ≥ 50	2,132 (38.1%)	38.5% (Geostat)	−0.4%
Region – Tbilisi	2,969 (53.0%)	34.0% (Geostat)	+ 19.0%
BMI ≥ 30 (Obese)	1,409 (25.2%)	17.0% (NatHealthSurvey)	+ 8.2%

Methods

Study population and design

We conducted a cross-sectional multiregional clinic-based epidemiologic study using data from adults attending two clinics in Tbilisi (Raymann and Enmedic) between 2021 and 2025. The initial database included 11,269 individuals aged ≥ 18 years. A representative analytic sample of 5,602 participants was selected for analysis.

Sampling, randomization and data collection

To ensure representativeness of the clinic population, a stratified random sampling strategy was implemented. The initial cohort of 11,269 adults was stratified by factors influencing MAFLD risk: age group (< 30, 30–39, 40–49, ≥ 50), sex, BMI category, presence of metabolic conditions (type 2 diabetes, hypertension, dyslipidemia) and geographic region. Within each stratum, participants were randomly selected using computer-generated random numbers (SPSS v20), ensuring proportional representation of high-risk and low-risk subgroups while reducing selection bias. Although individuals from all major Georgian regions were included, the study remains clinic-based rather than truly population-based, which is acknowledged as a limitation in the Discussion.

Demographic variables collected from all participants included age, sex and region of origin. Clinical data comprised BMI, serum ALT, lipid profile and patient history of diabetes, hypertension, dyslipidemia and alcohol consumption. BMI categories followed World Health Organization criteria: underweight (BMI < 18.5 kg/m²), normal weight (BMI 18.5–24.9 kg/m²), overweight (BMI 25–29.9 kg/m²) and obese (BMI ≥ 30 kg/m²) (Chalasani et al., 2012) [8]. For prevalence analysis, participants were categorized into two time frames (2021–2023 and 2023–2025). Geographic origin was grouped into seven administrative regions: Tbilisi, Kakheti, Imereti, Samegrelo-Zemo Svaneti, Adjara, Samtskhe-Javakheti and Shida Kartli. All measurements and variable definitions followed standardized protocols to ensure accuracy, consistency and reproducibility across participants.

Diagnostic criteria

The diagnostic criteria for MAFLD followed the internationally accepted consensus (Eslam et al., 2020) [7]. MAFLD is diagnosed when hepatic steatosis is present on imaging or laboratory assessment and at least one of the following conditions is met:

• Overweight/Obesity (BMI ≥ 25 kg/m²).

• Type 2 diabetes mellitus.

• Evidence of metabolic dysregulation.

MAFLD diagnosis does not require exclusion of alcohol use or other chronic liver disease, as MAFLD is based on positive criteria rather than exclusion criteria. Importantly, because these variables form part of the diagnostic criteria, they aren’t treated as independent comorbidities in regression analysis.

Standardized abdominal ultrasound was used to detect hepatic steatosis and grade severity (I-III) based on echogenicity and visualization of vascular structures.

Statistical analysis

Categorical variables were presented as percentages and assessed with chi-square or Fisher’s exact test where appropriate. Factors significantly associated with MAFLD on univariate analysis (P < 0.01) were entered into a multivariate logistic regression model to identify independent predictors. Analyses were performed using IBM SPSS Statistics, version 20.0 (Chicago, IL).

Results

Study cohort

Of the 11,269 individuals initially screened, 5,602 adults fulfilled all eligibility criteria and were included in the final analysis. The cohort was nearly evenly distributed by sex (51% male) with a mean age of 41.7 ± 12.8 years. Baseline demographic, anthropometric and metabolic characteristics differed substantially between individuals with or without MAFLD (Tables 2 and 3).

Table 2.

Sociodemographic characteristics

Variable	MAFLD (+)	MAFLD (-)	Total	P Value
No. of patients	2,890 (51.6%)	2,712 (48.4%)	5,602 (100%)
Age, years (mean ± SD)	44.61 ± 12.75	38.69 ± 12.78	41.7 ± 12.8	< 0.001
< 30	179 (6.2%)	289 (10.65%)	468 (8.35%)
30–39	676 (23.4%)	985 (36.32%)	1661(29.65%)
40–49	948 (32.8%)	393 (14.49)	1341 (23.93%)
> 50	1086 (37.6%)	1046 (38.54%)	2132 (38.07%)
Sex, male	2037 (71.3%)	820 (28.7%)	2857 (51%)	< 0.001

Table 3.

Clinical and metabolic characteristics

Variable	MAFLD (+)	MAFLD (-)	Total	P Value
Dyslipidemia	503 (17.4%)	206 (7.6%)	709 (12.65%)	< 0.001
Hypertension	714 (24.7%)	226 (8.3%)	940 (16.77%)	< 0.001
Serum ALT, U/L	35.6 ± 24.67	23.6 ± 18.5	29.62 ± 21.58	< 0.001
BMI, Kg/m2	29.38 ± 4.63	24.58 ± 3.85	26.91 ± 4.88	< 0.001
< 18	9 (0.3%)	74 (2.7%)	83 (1.5%)
19–25	355 (12.3%)	1239 (45.7%)	1594 (28.46%)
25–30	1396 (48.3%)	1066 (39.3%)	2462 (43.94)
30–35	908 (31.4%)	244 (9%)	1152 (20.58%)
35–45	193 (6.7%)	79 (2.9%)	218 (3.89%)
> 45	29 (1%)	10 (0.4%)	39 (0.7%)

T2D and metabolic dysregulation are diagnostic criteria and therefore not analyzed as independent comorbidities

Participants diagnosed with MAFLD were significantly older (44.6 ± 12.8 vs. 38.7 ± 12.7 years, P < 0.001), predominantly male (71.3% vs. 28.7%, P < 0.001) and had considerably higher BMI (29.4 ± 4.6 vs. 24.6 ± 3.8 kg/m², P < 0.001). Compared with non-MAFLD individuals, they also exhibited a markedly higher prevalence of type 2 diabetes, hypertension, dyslipidemia and elevated ALT levels (all P < 0.001).

Overall prevalence

MAFLD was diagnosed in 2,890 individuals, representing 51.6% of the study population. Among MAFLD cases, the severity of hepatic steatosis was categorized as: mild (Grade I) 57%, moderate (Grade II) 29% and severe (Grade III) 14%.

This distribution suggests that the majority of affected individuals presented in early stages, although a substantial proportion already demonstrated moderate to severe hepatic involvement.

Age-specific prevalence

Prevalence rose steadily with age: 22% in those < 30 years and 37% in the 30–39 group. 53% in the 40–49 group and nearly 65% in those ≥ 50 years (Fig. 1). Older age was strongly associated with higher rates of hepatic steatosis and metabolic comorbidities. This age-related pattern remained consistent across both sexes, indicating that age is an independent determinant of MAFLD burden.

Fig. 1.

Prevalence of MAFLD by Age Group

Sex differences

Across the entire cohort, men had more than double the prevalence of MAFLD compared to women (61% vs. 27%, P < 0.001) (Fig. 2). This trend persisted within every age category, with male sex emerging as a strong independent predictor even after adjustment for BMI and metabolic risk factors. These findings align with global epidemiological patterns indicating increased vulnerability to steatosis among men.

Fig. 2.

Prevalence of MAFLD by sex and age

BMI and adiposity related differences

BMI demonstrated one of the strongest associations with MAFLD. Prevalence increased sharply with rising BMI category. Obesity (BMI ≥ 30 kg/m²) was among the strongest predictors of MAFLD, consistent with its status as a defining metabolic abnormality in the MAFLD diagnostic criteria. Differences in BMI accounted for a substantial proportion of the variance observed across age and sex groups.

Metabolic comorbidities and liver enzymes

Metabolic risk factors were significantly more frequent among MAFLD patients: T2D (14.1% vs. 4.2%), hypertension (24.7% vs. 8.3%), dyslipidemia (17.4% vs. 7.6%) and ALT (35.6 ± 24.7 vs. 23.6 ± 18.5 U/L).

All differences were statistically significant (P < 0.001). These findings highlight the systemic nature of MAFLD as an expression of metabolic dysfunction rather than an isolated hepatic condition.

The results of the univariate and multivariate logistic regression analyses identifying predictors of MAFLD are shown in Table 4.

Table 4.

Univariate/multivariate logistic regression

Variable	Univariate OR (95% CI)	P value	Multivariate OR (95% CI)	P value
Age (per 10-year increase)	1.44 (1.35–1.54)	< 0.001	1.37 (1.28–1.46)	< 0.001
Male sex	4.1 (3.57–4.71)	< 0.001	3.88 (3.37–4.47)	< 0.001
BMI (kg/m2)
25–29.9	4.00 (3.38–4.74)	< 0.001	3.72 (3.16–4.38)	< 0.001
30–34.9	12.5 (10.2–15.4)	< 0.001	11.5 (9.4–14.0)	< 0.001
≥ 35	21.8 (16.6–28.6)	< 0.001	20.1 (15.5–26.0)	< 0.001
Hypertension	3.70 (3.16–4.33)	< 0.001	2.95 (2.49–3.50)	< 0.001
Dyslipidemia	2.58 (2.12–3.15)	< 0.001	2.10 (1.71–2.58)	< 0.001
ALT (per 10 U/L increase)	1.17 (1.13–1.21)	< 0.001	1.11 (1.07–1.16)	< 0.001

Regional variation

Substantial geographic differences were observed across the seven administrative regions: Tbilisi (44% of all MAFLD cases), Samegrelo-Zemo Svaneti – 19% and Adjara – 11% (Fig. 3).

Fig. 3.

Regional distribution of MAFLD in Georgia

Higher prevalence in urbanized regions (e.g., Tbilisi) may reflect differences in lifestyle, dietary patterns and socioeconomic status. These regional disparities underscore the potential need for targeted public health strategies.

Temporal trends

Between 2021 and 2023 and 2023–2025, overall prevalence rose from 42.7% to 57.3% (P < 0.001) (Fig. 4). This increase occurred consistently across all BMI and age categories. The proportion of individuals with moderate or severe steatosis remained relatively stable, indicating that the rise likely reflects increasing incidence rather than disease progression within individuals.

Fig. 4.

Temporal trend of MAFLD prevalence (2021–2025)

Discussion

This multiregional clinic-based study provides the most comprehensive estimate of MAFLD prevalence in Georgia to date. Overall, 51.6% of adults in our cohort met diagnostic criteria for MAFLD, with prevalence particularly high among older adults, men and individuals with obesity or metabolic abnormalities. These patterns closely align with global epidemiology, reinforcing the central role of systemic metabolic dysfunction in the pathogenesis of MAFLD.

Importantly, several factors identified as “associations,” such as overweight/obesity and type 2 diabetes, are components of the MAFLD diagnostic criteria. Their high prevalence among MAFLD cases therefore reflects the positive case definition rather than independent comorbidities. When diagnostic components were excluded, hypertension, dyslipidemia and elevated ALT remained significant independent predictors, consistent with MAFLD as a multisystem metabolic disorder.

We also observed an increase in prevalence between 2021 and 2023 and 2023–2025. This increase likely reflects a combination of factors, including rising national rates of obesity and metabolic syndrome and lifestyle changes during and following the COVID-19 pandemic. Because steatosis severity distribution remained stable, these trends may indicate increasing incidence rather than progression among existing cases.

Regional differences were another noteworthy observation. Prevalence was highest in Tbilisi and Samegrelo-Zemo Svaneti, while Imereti and Samtskhe-Javakheti showed comparatively lower rates. Such variations may reflect differences in dietary habits, levels of urbanization and socioeconomic status. These findings suggest that tailored regional health strategies may be more effective than uniform nationwide approaches.

Compared with reports from neighboring countries, our results suggest that Georgia faces an equally high, if not higher, burden of metabolic liver disease. For example, studies from Turkey reported increasing NAFLD prevalence over the last decade (Degertekin et al., 2021) [9], but our data based on MAFLD criteria reveal a steeper recent rise. This distinction highlights both the importance of updated diagnostic definitions and the urgent need for country-specific epidemiological data.

Limitations

This study has several limitations. First, it was conducted in a clinic-based cohort and even with stratified random sampling, the sample may not fully represent the general Georgian population. Individuals attending check-up clinics may differ in metabolic risk profile, health awareness or healthcare access, potentially introducing selection bias. Second, alcohol intake was self-reported and may have been underestimated. Third, some metabolic parameters, including CRP and HOMA-IR, were incomplete and therefore not consistently included in analyses and finally, the cross-sectional design limits the ability to draw causal inferences.

Conclusion

In this nationwide multiregional clinic-based cohort, more than half of adults met diagnostic criteria for MAFLD, with higher prevalence among older adults, men and individuals with elevated BMI or metabolic abnormalities. The substantial regional variability and the rise in prevalence over recent years point to an increasing and unevenly distributed metabolic health burden in Georgia. These findings underscore the urgency of developing effective national strategies for obesity prevention, metabolic risk management and early identification of high-risk individuals. Future population-based studies are needed to determine true national prevalence and to guide precision public health interventions.

Abbreviations

MAFLD

Metabolic dysfunction-associated fatty liver disease

BMI

Body mass index

ALT

Alanine aminotransferase

MASH

Metabolic dysfunction-associated steatohepatitis

T2D

Type 2 Diabetes

NAFLD

Non-alcoholic fatty liver disease

Authors’ contributions

Tatia Khachidze conceived and designed the study, supervised data collection and interpretation, drafted the initial manuscript and served as the corresponding author. Gela Sulaberidze provided supervision throughout the study, guided the statistical approach and critically revised the manuscript. Gocha Barbakadze supervised the clinical aspects of the study, provided expert input on methodology and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

Funding

Not applicable.

Data availability

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

Declarations

Ethics approval and consent to participate

The research has been designed and conducted according to standards of human research subject protection as outlined in the Georgian “Law on Health Care” (Chapter XIX “Biomedical Research”) and the following legal instruments of the Council of Europe: the “Convention on Human Rights and Biomedicine” (the Oviedo Convention) and its Additional Protocol “Concerning Biomedical Research”. Both documents have been signed and ratified by Georgia and are a part of the National Legal Framework on the protection of biomedical research subjects. These instruments are consistent with the provisions of the Declaration of Helsinki and serve as the national legal standard for the protection of research subjects.

The current article is based on the research project which has been submitted for ethical review to the Tbilisi State Medical University (TSMU) Biomedical Research Ethics Committee. The research project with consent forms have been approved by the TSMU Biomedical Research Ethics Committee on 29th July of 2021 - the Meeting Protocol # N6-2021/90.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.