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The nomenclature of fatty liver disease and its impact on obesity traits, insulin resistance, and hepatic fibrosis

Lipids in Health and Disease volume 24, Article number: 339 (2025) Cite this article

Abstract

Background

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Definitions of nonalcoholic fatty liver disease (NAFLD), metabolic dysfunction-associated FLD (MAFLD), and steatotic liver disease (SLD) have been proposed to better guide clinical practice and epidemiological studies. The effects of the nomenclature on the incidence of FLD and its associations with obesity phenotypes, insulin resistance (IR), and liver fibrosis were examined in this study.

Methods

NAFLD, MAFLD, and metabolic dysfunction-associated steatotic liver disease (MASLD) were diagnosed on the basis of ultrasound examination and metabolic disorders among 6,718 community-dwelling individuals from southeast China. Six obesity phenotypes, seven IR surrogates, and the NAFLD Fibrosis Score (NFS) were applied to evaluate their association with FLDs through multivariable logistic regression models, restricted cubic splines, and receiver operating characteristic curves.

Results

The prevalence of FLD, NAFLD, MAFLD, and MASLD was 35.47%, 33.34%, 34.77%, and 32.79%, respectively. The associations of obesity-FLD, IR-FLD, and FLD-NFS were statistically significant across all the FLD definitions. Patients with MAFLD demonstrated slightly higher odds ratios (ORs) than those with FLD, NAFLD, and MASLD. However, alcoholic-FLD (AFLD), which is included in the MAFLD nomenclature, showed lower ORs with obesity and IR and lower NFS, significantly differently from other FLDs. Among all obesity and IR indices, triglyceride and glucose index body mass index (TyG-BMI), the TyG-waist height ratio (TyG-WHtR), and the TyG-waist circumstance (TyG-WC) were the best at predicting FLDs and ORs with respect to NFS.

Conclusion

The nomenclature of MAFLD covers a wider range of FLD than NAFLD and MASLD do, but the heterogeneity of AFLD is nonnegligible. Compared with MASLD, NAFLD remains a practical and efficient definition for large-scale population screening, especially in resource-limited settings. TyG-BMI, TyG-WHtR, and TyG-WC could better predict FLD and associated fibrosis, affirming their potential as simple and cost-effective tools to support health monitoring and early intervention.

Introduction

Nonalcoholic fatty liver disease (NAFLD) is charactered by fat deposits in the liver parenchyma and represents a continuum of disease severity, beginning with mild hepatic steatosis and potentially advancing to nonalcoholic steatohepatitis (NASH) or more severe conditions such as fibrosis and cirrhosis in individuals who do not consume alcohol [1]. With a global prevalence of approximately25%, NAFLD is a chronic liver disease that has emerged as an increasingly significant public health concern worldwide [1,2,3]. However, debates on the nomenclature persist, with questions about how alcohol consumption affects liver condition [4] and the growing influence of obesity and metabolic dysfunction on disease severity and progression is now widely recognized, mirroring the global rise in obesity [5,6,7]. Metabolic dysfunction-associated FLD (MAFLD) was defined in 2020 to emphasize the metabolic components of the disease and eliminate the uncertainties related to alcohol consumption [7]. However, the criteria for defining “metabolic health” remain a point of contention [8]. It is unclear whether more aggressive clinical progression is related to more severe metabolic derangement, excessive alcohol consumption, or both [9]. The 2023 revised nomenclature proposed the term steatotic liver disease (SLD) for excessive fat accumulation in the liver, with metabolic dysfunction-associated SLD (MASLD) specifically referring to cases linked to metabolic issues [10]. The impact of these changes on epidemiology studies, especially among Chinese individuals with lower body mass index (BMI) [11] but comparable or higher rates of metabolic dysfunction than European and American populations [12, 13], remains a topic of interest.

Globally, 75% of obese individuals are estimated to have NAFLD [14]. The pathophysiology of NAFLD is heavily impacted by insulin resistance (IR) [15]. Both obesity and IR are key players in the activation of hepatic stellate cells, fibrogenesis, and liver disease-related mortality [5]. Fibrosis is recognized as a key prognostic factor [16], with the NAFLD Fibrosis Score (NFS) demonstrating superior performance in predicting NAFLD-related advanced fibrosis and mortality in those diagnosed with NAFLD in both meta-analyses and systematic reviews [17, 18]. All of these have been commonly suggested for preventing and treating FLD and associated condition. Following the change in nomenclature from NAFLD to MAFLD and MASLD, whether the clinical importance of obesity and IR phenotypes varies with the shift and which nomenclature of FLD implies more advanced fibrosis should be clarified, which could facilitate precise diagnosis, treatment, and prevention of FLD and associated disease, particularly in a sizable population.

In the current investigation, various markers of obesity and IR were included to investigate their association with the risks of FLD and advanced fibrosis and to estimate their potential heterogeneity under differently defined FLD, enhancing the understanding FLD terminologies.

Methods

Study population

A Fuqing cohort study, aiming to recruit 50,000 participants in southeast China and focusing on native residents in the 35–75 age group, is currently ongoing. The baseline study commenced in February 2019, with its initial phase. The second phase, which included more comprehensive data collection, started in July 2020 [19]. The current analysis included participants from the baseline Survey from July 2020 to June 2021. Participants were excluded for reasons including incomplete questionnaire information (N = 236); insufficient anthropometric measurements (N = 244); missing data from blood measurements (N = 107) or abdominal ultrasonography (N = 324); or the presence of outliers (N = 34) [20]. After exclusions, 6718 participants remained for the final analysis (Fig. 1).

Fig. 1
figure 1

Flowchart of study design

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Data collection

As previously detailed in earlier studies, all participants were required to complete an interview with a structured questionnaire and undergo physical examinations [21,22,23]. In summary, a structured questionnaire (https://cohort.fjmu.edu.cn/), including information on demographics and lifestyle factors (smoking, drinking, and physical activity), personal and familial disease history, was administered through in-person. Fasting blood glucose (FBG), 2-h postload blood glucose (2 h-PG), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), C-reactive protein (CRP), albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT) (DiaSys, Holzheim, Germany), fasting insulin (FINS), glycosylated haemoglobin (HbA1c) (Arkray, Kyoto, Japan), FINS, and platelets (Sysmex, Kobe, Japan) were measured following standard laboratory procedures. Body weight (kg), height (cm), waist and hip circumference (WC and HC, cm), systolic and diastolic blood pressure (SBP and DBP) were measured.

Hypertension was identified as SBP/DBP ≥ 140/90 mmHg, a self-reported history of hypertension, and/or the use of antihypertensive drugs. Type 2 diabetes mellitus (T2DM) was defined as FBG ≥ 7.0 mmol/L, 2 h-PG ≥ 11.1 mmol/L, HbA1c ≥ 6.5%, a self-disclosed history of diabetic, and/or under treatment of antidiabetic drugs. Prediabetes was defined as a FBG concentration of 5.6–6.9 mmol/L, a 2 h-PG concentration of 7.8–11.1 mmol/L, and/or an HbA1c concentration of 5.7–6.4% among participants without DM [24].

A colour Doppler ultrasound system was employed by experienced sonographers, who were not informed of the clinical and laboratory data, to capture abdominal ultrasound images from all participants.

Definitions of FLD, NAFLD, AFLD, MAFLD, and MASLD

FLD was characterized by the existence of hepatic steatosis on the basis of abdominal ultrasound.

NAFLD was diagnosed by the existence of FLD without heavy drinking absence (alcohol consumption ≥ 30 g/day for men and 20 g/day for women).

Alcoholic fatty liver disease (AFLD) was identified by the occurrence of FLD and significant alcohol consumption.

MAFLD was identified by the existence of FLD with ≥ 1 conditions below: (1) BMI ≥ 23.0 kg/m2 (2), T2DM, and (3) at least two metabolic dysregulations among individuals with BMI < 23.0 kg/m2. Metabolic dysregulations include (a) WC ≥ 90 cm for men and ≥ 80 cm for women; (b) BP ≥ 130/85 mmHg or under drug treatment; (c) TG ≥ 1.70 mmol/L or specific drug treatment; (d) HDL-c < 1.0 mmol/L for men and < 1.3 mmol/L for women; (e) prediabetes; (f) homeostasis model assessment of insulin resistance (HOMA-IR) ≥ 2.5; and (g) CRP > 2 mg/L.

MASLD was diagnosed when FLD combined with any one of the five cardiometabolic adult criteria. The five criteria were (a) a BMI ≥ 23 kg/m2 or a WC ≥ 94/80 cm for Asian men and women; (b) an FBG ≥ 5.6 mmol/L or an HbA1c ≥ 5.7% or T2DM or treatment for T2DM; (c) a BP ≥ 130/85 mmHg or antihypertensive treatment; (d) a TG ≥ 1.70 mmol/L or lipid-lowering treatment; and (e) an HDL-c < 1.0 mmol/L for men and < 1.3 mmol/L for women or lipid-lowering treatment [10].

Obesity phenotype

Seven indices, namely, the BMI, WC, waist-to-hip ratio (WHR) [25], waist-to-height ratio (WHtR) [26], lipid accumulation product (LAP) [27], visceral adiposity index (VAI) [28], and Chinese VAI (CVAI) [29], were used to describe the obesity phenotype. The calculation formulas were as follows:

$$\mathrm{BMI}=\mathrm{Weight}\;\left[\mathrm{kg}\right]/{\mathrm{Height}^2\;\left[\mathrm m^2\right]\;}$$
$$\mathrm{WHR}=\mathrm{WC}\;\left[\mathrm{cm}\right]/\mathrm{HC}\;\left[\mathrm{cm}\right]$$
$$\mathrm{WHtR}=\mathrm{WC}\;\left[\mathrm{cm}\right]/\mathrm{Height}\;\left[\mathrm{cm}\right]$$
$$\mathrm{LAP}=\mathrm{TG}\;\left[\mathrm{mmol}/\mathrm L\right]\times\left(\mathrm{WC}\left[\mathrm{cm}\right]-65\right)$$

Male:

$$\begin{aligned} \mathrm{VAI}=&\mathrm{WC}\left[\mathrm{cm}\right]/\left(\left(39.68+1.88\times\mathrm{BMI}\left[\mathrm{kg}/\mathrm m^2\right]\right)\right. \\& \left.\times\left(1.31/\mathrm{HDL}-\mathrm c\;\left[\mathrm{mmol}/\mathrm L\right]\right)\right. \\& \left.\times\left(\mathrm{TG}\left[\mathrm{mmol}/\mathrm L\right]/1.03\right)\right) \end{aligned}$$
$$\begin{aligned} \mathrm{CVAI}=&-267.93+0.68\times\mathrm{Age}\;\left[\mathrm{years}\right]\;+0.03\times\mathrm{BMI}\;\left[\mathrm{kg}/\mathrm m^2\right]\\&+4.00\times\mathrm{WC}\left[\mathrm{cm}\right]+22.00\times\log_{10}\left(\mathrm{TG}\left[\mathrm{mmol}/\mathrm L\right]\right)\\&-16.32\times\mathrm{HDL}-\mathrm c\left[\mathrm{mmol}/\mathrm L\right] \end{aligned}$$

Female:

$$\mathrm{LAP}=\mathrm{TG}\;\left[\mathrm{mmol}/\mathrm L\right]\times\left(\mathrm{Waist}\;\left[\mathrm{cm}\right]-58\right)$$
$$\begin{aligned} \mathrm{VAI}=&\mathrm{WC}\left[\mathrm{cm}\right]/\left(\left(36.58+1.89\times\mathrm{BMI}\;\left[\mathrm{kg}/\mathrm m^2\right]\right)\right. \\& \left.\times\left(1.52/\mathrm{HDL}-\mathrm c\;\left[\mathrm{mmol}/\mathrm L\right]\right)\right. \\& \left.\times\left(\mathrm{TG}\left[\mathrm{mmol}/\mathrm L\right]/0.81\right)\right) \end{aligned}$$
$$\begin{aligned} \mathrm{CVAI}=&-187.32+1.71\times\mathrm{Age}\;\left[\mathrm{years}\right]\;\\&+4.23\times\mathrm{BMI}\;\left[\mathrm{kg}/\mathrm m^2\right]+1.12\times\mathrm{WC}\;\left[\mathrm{cm}\right]\\&+39.76\times\log_{10}\mathrm{TG}\;\left[\mathrm{mmol}/\mathrm L\right]\\&-11.66\times\mathrm{HDL}-\mathrm c\left[\mathrm{mmol}/\mathrm L\right] \end{aligned}$$

IR surrogates

FINS, homeostatic model assessment of IR (HOMA-IR), metabolic score for insulin resistance (METS-IR) [30], triglyceride and glucose index (TyG) [31], TyG-body mass index (TyG-BMI) [32], TyG-waist height ratio (TyG-WHtR) [33], and TyG-waist circumference (TyG-WC) [34] were utilized as surrogate markers for IR. The calculations for these markers were based on specific formulas below:

$$\mathrm{HOMA}-\mathrm{IR}=(\mathrm{FBG}\;\left[\mathrm{mmol}/\mathrm L\right]\times\mathrm{FINS}\;\left[\mathrm{mU}/\mathrm L\right])/22.5$$
$$\begin{aligned} \mathrm{METS}-\mathrm{IR}=&\text{In}\;\left(2\times\mathrm{FBG}\;\left[\mathrm{mmol}/\mathrm L\right]\;+\mathrm{TG}\;\left[\mathrm{mmol}/\mathrm L\right]\right. \\& \left.\times\mathrm{BMI}\;\left[\mathrm{kg}/\mathrm m^2\right]\right)/\text{In}\;\left(\mathrm{LDL}-\mathrm c\;\left[\mathrm{mmol}/\mathrm L\right]\right) \end{aligned}$$
$$\begin{aligned} \mathrm{TyG}=&\text{In}\;\left((\mathrm{TG}\;\left[\mathrm{mmol}/\mathrm L\right]\times\mathrm{88.545})\times(\mathrm{FBG}\left[\mathrm{mmol}/\mathrm L\right]\times18\right)/2) \end{aligned}$$
$$\mathrm{TyG}-\mathrm{BMI}=\mathrm{TyG}\times\mathrm{BMI}\;\left[\mathrm{kg}/\mathrm m^2\right]$$
$$\mathrm{TyG}-\mathrm{WHtR}=\mathrm{TyG}\times\mathrm{WHtR}$$
$$\mathrm{TyG}-\mathrm{WC}=\mathrm{TyG}\times\mathrm{WC}\;\left[\mathrm{cm}\right]$$

Advanced fibrosis

NFS was employed to evaluate advanced fibrosis, with a high probability defined as NFS >0.675, an intermediate probability as NFS ranging from −1.455 to 0.675, and a low probability as NFS ≤ −1.455 [35]. The NFS was figured out as follows:

  • NFS= −1.675 + (0.037 × age [years]) + (0.094 × BMI [kg/m2])+ (1.13 × impaired fasting glucose (IFG)/DM [yes=1, no=0]) + (0.99 × AST/ALT ratio) - (0.013 × platelet [109/L])- (0.66 × Albumin [g/dL]).

Statistical analysis

Before data analysis, the normality of continuous data was evaluated using Kolmogorov‒Smirnov test. And mean ± standard deviation (SD) or median with interquartile range (IQR) were used to describe the distribution of normally or nonnormally data. Frequencies or percentages were listed for categorical data. The differences of continuous and categorical variables between men and women were tested using Student’s t or the Mann‒Whitney test and chi-square test.

Multivariable logistic regression models were used to analyse the effects of the obesity phenotype and IR Surrogates on FLD, NAFLD, MAFLD, and MASLD, with adjustments for potential confounders. Model 1 was crude; Model 2 was adjusted for age and sex; Model 3 was additionally modified to account for educational attainment, current alcohol consumption status, current smoking status, and the degree of physical activity, which was classified as low, moderate, or high. When the relationships between IR and FLD, obesity phenotypes and NFS, and IR and NFS were specifically evaluated, BMI was further adjusted in addition to the adjustments made in Model 3. A restricted cubic spline (RCS) was employed to investigate the potential nonlinear relationship between the obesity phenotype and IR surrogates with different types of FLD. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive power of the obesity phenotype and IR surrogates for various FLDs.

Furthermore, the associations of obesity phenotype with IR surrogates, obesity phenotype with NFS, and IR surrogates with NFS in the context of FLD, NAFLD, MAFLD, and MASLD, respectively, were assessed with linear and logistic regression models. To account for the possible confounding effects of pharmacotherapy, medication use was additionally adjusted (including antidiabetic drugs, antihypertensive drugs, or lipid-lowering therapies) when the associations between obesity and IR indicators and various FLD classifications were analysed.

AFLD, characterized by alcohol consumption as the primary driver of hepatic steatosis, was also studied. Associations of obesity with AFLD, IR with AFLD, and AFLD with NFS were investigated. Additionally, the associations of obesity with IR, obesity with NFS, and IR with NFS were examined among the AFLD population.

Two-sided P < 0.05 was considered to indicate statistical significance. All the statistical analyses were carried out with SAS, version 9.4 (SAS Institute Inc., Cary, NC, USA) and R, version 4.3.0.

Results

The prevalence of FLD, NAFLD, MAFLD, and MASLD

Among the 6718 participants, 2321 were males and 4397 were females, with more than 60% of participants being older than 55 years. Current drinking was more prevalent among males (17.19%) than among females (2.50%). Compared with females, male participants tended to be older, more educated, more frequently employed, and have higher rates of alcohol and cigarette use (Table 1). The prevalence for each condition was as follows: FLD (35.47%), NAFLD (33.34%), MAFLD (34.77%), and MASLD (32.79%) (Table 2). Among the 2383 FLD participants, 2190 (91.27%) met the standards for NAFLD, MAFLD, and MASLD simultaneously, whereas 138 (6.55%) and 29 (1.22%) were diagnosed with MAFLD and AFLD versus NAFLD only, respectively. Thirteen (0.63%) FLD participants fulfilled the standards for NAFLD and MASLD, but not for MAFLD. Conversely, 8 patients overlapped between the NAFLD and MAFLD groups, yet were excluded from the MASLD group. Additionally, only 5 (0.1%) out of 2383 FLD patients had AFLD (Fig. 2).

Table 1 The clinico-demographic characteristics of the study population
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Table 2 The metabolic indicators in non-FLD, FLD and differently-defined FLD groups
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Fig. 2
figure 2

Venn diagram illustrating the overlap between different nomenclatures of FLD

Note: The numbers in the diagram represent the count of individuals in each subset 

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Compared with the non-FLD population, all FLD types exhibited higher levels of SBP, DBP, obesity indices, IR surrogates, FBG, HbA1c, AST, ALT, NFS, TC, TG, and LDL-c but decreased HDL-c levels (Table 2). In this study, a heterogeneous pattern was identified within the AFLD population on the basis of a Venn diagram (Fig. 2): 138 participants who were diagnosed with both AFLD and MAFLD and only 5 participants who were diagnosed with AFLD alone. Notably, compared with the other FLD categories, the group with combined AFLD and MAFLD had higher levels of obesity and IR markers, including WC, WHR, LAP, CVAI, TyG-BMI, and TyG-WC. In contrast, patients with only AFLD had considerably lower levels of metabolic indicators, including BMI, WC, WHR, FINS, and HOMA-IR (Supplemental Table S1).

Both continuous and categorical variables of obesity phenotypes and IR were analysed to determine their associations with FLD, NAFLD, MAFLD, and MASLD. Z-transformed continuous variables were used to standardize the OR calculations for disease risk. Quartiles of all the indices were then calculated to categorize the participants into Q1 (lowest), Q2, Q3, and Q4 (highest) groups. Using logistic models, the odds ratios (ORs) for FLD in Q2, Q3, and Q4 were calculated with Q1 acting as the reference group.

After adjusting for covariates (with additional BMI adjustment for analysis involving IR and FLDs), all seven obesity indices and seven IR surrogates were found to be connected with a greater risk of all defined FLD types (Supplemental Table S2-S3). According to the RCS analysis, there is a nonlinear connection between all obesity phenotypes and FLD (Supplemental Figure S1-S2). Although the ORs for all the indices were similar across the four FLD types, the ORs for MAFLD were slightly greater than those for MASLD and comparable to those for FLD and NAFLD (Fig. 3A and B).

Fig. 3
figure 3

Forest plots of the associations of obesity phenotypes (A) and IR surrogates (B) with categories of FLD

Note: adjusted for sex and age (categorized as 35-44, 45-54, 55-64, and 65-75 years),educational level (0, 1-6, 7-9, >9 years), current alcohol consumption (yes/no), current smoking status (yes/no), and physical activity level (low, moderate, high) (with additional BMI adjustment for analysis involving IR and FLDs). The highest quartiles (Q4) of each obesity phenotypes and IR surrogates is regarded as case group

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The results of the ROC analysis for obesity phenotypes and IR surrogates in predicting various FLDs are summarized in Fig. 4 and Supplemental Table S4-S5. Among the seven obesity phenotypes, BMI and WC (except for NAFLD), LAP, and CVAI demonstrated strong performance with the area under the curve (AUC) values over 0.80. A similar strong performance was observed for the IR Surrogates, TyG-BMI, TyG-WHtR, and TyG-WC, in all four FLD categories. The differences in the AUCs among the four FLD categories for each index were less than 0.03, which was deemed negligible.

Fig. 4
figure 4

ROC curves for obesity phenotypes (A) and IR surrogates (B) to predict different categories of FLD

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The association of FLD with hepatic fibrosis

In logistic regression model 4, an OR of 1.43 (95% confidence interval (CI): 1.25 to 1.65) was found for the association between FLD and intermediate-to-high NFS (Table 3). For NAFLD, MAFLD, and MASLD, the ORs were found to be 1.41 (95%CI: 1.22–1.62), 1.45 (95%CI: 1.26–1.67), and 1.42 (95%CI: 1.23–1.64).

Table 3 Association between FLD, FLD subtypes and liver fibrosis indicator (NFS) by logistic regression analysis
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Associations between obesity phenotypes, IR, and fibrosis according to different FLD definitions

The associations of obesity-to-IR, obesity-to-NFS, and IR-to-NFS were examined across FLD, NAFLD, MAFLD, and MASLD. In populations with different FLD classifications, a notable positive connection was detected between any obesity phenotype and any IR surrogate in both multivariable-adjusted linear (Supplemental Table S6) and logistic regression models, with the highest quantile of the IR surrogate as the case group (Supplemental Table S7). Regarding the link between obesity and NFS, BMI, WC, WHR, WHtR, LAP, and CVAI were found to be positively linked with NFS (Supplemental Table S8), as well as with intermediate-to-high NFS (Supplemental Table S9). With respect to the IR surrogates, HOMA-IR, METS-IR, TyG, TyG-BMI, TyG-WHtR, and TyG-WC showed a significant association with elevated NFS in both linear and logistic regression analyses (Fig. 5 and Supplemental Table S10-11). As shown in Supplemental Figure S3, medication use, including antidiabetic drugs, antihypertensive drugs, or lipid-lowering therapies, was further adjusted. After this adjustment, the associations with IR traits and FLDs slightly shifted. FINS shifted from a nonsignificant to a weak negative association with all types of FLD. Other IR indicators tended to decrease in association with FLD and NAFLD. Notably, in both MAFLD and MASLD, these associations changed from statistically significant to nonsignificant.

Fig. 5
figure 5

Forest plots of the associations between obesity indicators (A) and IR traits (B) with NFS among different categories of FLD

Note: adjusted for sex and age (categorized as 35-44, 45-54, 55-64, and 65-75 years), educational level (0, 1-6, 7-9, >9 years), current alcohol consumption (yes/no), current smoking status (yes/no), and physical activity level (low, moderate, high) (with additional BMI adjustment for analysis involving IR and FLDs). All obesity indicators and NFS are Z-transformed

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AFLD and its association with obesity phenotypes, IR, and fibrosis

Considering the impact of alcohol consumption on FLD definitions, AFLD was defined. A Venn diagram revealed that 138 (98.11%) out of 143 patients with AFLD were also diagnosed with MAFLD (Fig. 2). All obesity phenotypes and 6 IR surrogates, with the exception of METS-IR, were linked to a higher risk of AFLD according to linear regression, logistic regression, and RCS analyses (Supplemental Table S12-S13 and Supplemental Figure S4). However, the ORs and AUCs of obesity phenotypes (expected VAI and CVAI) and IR surrogates (TyG-BMI and TyG-WHtR) were lower for AFLD than for other types of FLD (Supplemental Figure S5, Supplemental Table S14). A multivariable-adjusted regression model showed an OR of 1.50 (95%CI: 0.90–2.50) for AFLD, highlighting its link to intermediate-to-high NFS (Supplemental Table S15).

Additionally, the positive correlation between obesity phenotypes and IR surrogates was stronger in patients with AFLD than in those with other FLD subtypes (Supplemental Table S16). Linear regression models showed positive association between the NFS and only BMI, WC, WHR, WHtR, CVAI, TyG-BMI, TyG-WHtR, and TyG-WC. Nonetheless, these correlations were not observed in the logistic regression analysis (Supplemental Table S17-18). A similar pattern was observed for AFLD after medication adjustment: while the associations with obesity indicators remained stable, the associations with IR traits were attenuated and became nonsignificant after adjusting for medication use (Supplemental Table S19).

Discussion

In this study, the epidemiology of FLD, defined as NAFLD, MAFLD, and MASLD, was assessed according to their respective definitions, and their associations with obesity phenotypes, IR, and fibrosis among community-dwelling individuals in Southeast China were examined. While there is high concordance among these definitions, MAFLD tends to be more common among FLD participants and is slightly more strongly linked to obesity and IR than NAFLD and MASLD. However, caution is needed when considering the overlap between AFLD and MAFLD, which underscores the complicated interaction between alcohol intake and metabolic disorders.

Obesity and IR are the fundamental pathogenic mechanisms [14, 15]. The existence of fibrosis indicated a significant risk of long-term adverse outcomes in NAFLD, with its reversal and improvement being primary goals in clinical treatment [16]. Therefore, changes in obesity, IR, and fibrosis were explored across differently-defined FLD. This endeavour revealed that all obesity phenotypes and IR traits were correlated with a greater risk of NAFLD, which in turn was positively linked to fibrosis. These associations were also observed for MAFLD and MASLD. Among obesity and IR traits examined for NAFLD, MAFLD, and MASLD, TyG-BMI, TyG-WHtR, and TyG-WC were found to be strong predictors of various FLD types. These markers are recommended for cost-effective and convenient early screening of NAFLD/MAFLD and liver fibrosis [36]. Notably, compared with NAFLD and MASLD, obesity and IR were slightly more strongly associated with MAFLD.

Unlike the exclusionary nature of NAFLD, MAFLD was proposed as an inclusive diagnostic criterion to describe obesity, T2DM, and other metabolic dysfunctions in FLD [7]. The shift from NAFLD to MAFLD highlights the fundamental role of “metabolic dysfunction” in the pathogenesis of FLD while avoiding an excessive focus on “alcohol” [37]. Previous studies, including this study, have reported a higher incidence and increased likelihood of severe liver disease in people with MAFLD than in patients with NAFLD [23, 38]. However, a new study reported similar clinical characteristics as well as all-cause and cause-specific mortality in comparison of MAFLD and NAFLD patients. The key difference was found in the drivers of liver-related mortality. IR is the primary driver in NAFLD, suggesting that NAFLD may better identify metabolic abnormalities associated with IR, a key target for drug development [39].

In contrast, AFLD highlights the hepatotoxicity of excessive alcohol consumption [5], which is a distinct secondary cause of liver fat accumulation [40]. Alcohol intake and metabolic disorders often coexist as contributing factors and are intertwined in the pathophysiology of FLD in clinical practice, distinguishing AFLD as a separate entity from NAFLD [6]. These findings support this interplay, as in contrast to NAFLD, AFLD is generally less strongly linked to obesity, IR, and fibrosis but exhibits notable heterogeneity. Excessive alcohol consumption is a primary driver of AFLD, this condition is also driven by metabolic syndrome and other metabolic irregularities. Research has demonstrated that obesity, high triglyceride levels, and low HDL-c may serve as independent risk factors for AFLD [41]. Furthermore, multiple elements of metabolic syndrome independently enhance the likelihood of serious liver disease, even when alcohol consumption remains within the threshold for NAFLD [42]. Moderate alcohol intake is connected to the development of liver fibrosis and has a synergistic effect on T2DM in individuals with metabolic-associated steatohepatitis, especially in the clinical management of the main drivers of disease [43]. Alcohol consumption remains a significant contributing factor to liver damage, highlighting the joint contribution of alcohol use and metabolic dysfunction on FLD progression.

Specifically, two distinct AFLD phenotypes were identified: only AFLD and combined AFLD and MAFLD. Compared with the other categories, the latter subgroup demonstrated significantly higher levels of obesity and IR indicators, suggesting that alcohol-related liver disease can coexist with metabolically driven liver injury. Clinically, AFLD patients with current metabolic dysfunction may benefit from targeted metabolic evaluation and lifestyle intervention strategies, similar to those with NAFLD. However, NAFLD emphasizes the importance of metabolic factors. Although the MAFLD framework attempts to capture these mixed phenotypes and includes almost all FLD, its broader and more complex criteria may reduce the feasibility of disease monitoring and clinical management, especially in large-scale public health screening. Nevertheless, MAFLD is more likely to represent a more diverse phenotype and serves as an affirmative term to encompass FLD with recognized metabolic dysfunction beyond just NAFLD [7].

In NAFLD, the terms “alcoholic” and “fatty” could carry stigmatizing connotations. To address this, MASLD was proposed by a multisociety committee of experts worldwide, and “fatty” was replaced with the medical term “steatotic”. FLD is a condition associated with at least one cardiometabolic risk factor [10]. Recent studies have shown significant overlap in screening, diagnostic algorithms, and biomarker studies between NAFLD and MAFLD [44,45,46,47]. In this study, MASLD was present in 32.79% of the Subjects, accounting for 91.90% of the FLD patients. Those who fulfilled the NAFLD diagnosis also met MASLD, and only 37 out of 2240 NAFLD patients were not identified as MASLD. The associations with obesity, IR, and fibrosis were similar between NAFLD and MASLD. An investigation conducted in the United States indicated that NAFLD and MASLD can be used synonymously, although MASLD may pose a somewhat higher mortality risk [48]. Compared with MAFLD, MASLD simplifies the diagnostic process by incorporating five easily accessible metabolic dysfunctions and excluding CRP and HOMA-IR, which are rarely measured in screening and routine clinical settings [46]. However, only a minimal discrepancy between MASLD and MAFLD was observed in the current and previous studies [49,50,51]. In addition, given the coexistence of metabolic dysfunctions and excessive drinking, metabolic dysfunction-associated liver disease (MetALD) is also intended to capture a broader spectrum of FLD patients, aiming to differentiate them from patients with alcohol-related and other mixed aetiologies when MASLD is defined [10].

The evolution of a terminology is often accompanied by advancements in understanding pathological mechanisms or insights into clinical diagnosis, risk stratification, and/or clinical outcomes [37, 52]. Although there is minimal variation in the association with obesity, IR, and fibrosis of liver disease, these nomenclatures should be used cautiously, irrespective of the criteria employed. Various factors can drive fatty degeneration of the liver, such as obesity, IR, alcohol, and drugs [53,54,55]. MAFLD aims to capture all FLD with metabolic dysfunctions; however, with consideration of the primary driver in patient classification, its universal application to all FLD patients may be limited, especially in the clinical management of the main drivers of disease [39]. Furthermore, more metabolic disorders but fewer possible pathogenic factors should be considered, increasing the difficulty of routine clinical management. Similarly, MASLD also highlights the importance of defining metabolic disorders without considering disease drivers, which may limit the effect of disease management. In contrast, NAFLD calls for the exclusion of FLD with recognized factors and includes only FLD with possible metabolic drivers, providing clearer guidelines for clinical management strategies of FLD.

The use of the NAFLD definition in the present large-population study and other public screening programs is still suggested, as its diagnostic criteria are simpler and require fewer metabolic measurements. Since the associations between obesity, IR, and liver fibrosis are consistent across NAFLD, MAFLD, and MASLD definitions, adopting a more complex diagnostic framework may not yield significant clinical benefit in risk stratification or treatment prioritization. Therefore, despite the pathophysiological diversity revealed by the MAFLD model, the definition of NAFLD remains a practical and efficient approach in population-level screening and research contexts. Therefore, while terminology refinement may improve pathophysiological specificity, its practical application is limited by complexity and resource demands. It is necessary to reassess the application scenarios of these definitions in clinical practice to find more effective diagnostic strategies for various medical settings [56].

The study population consisted of rural residents aged 35–75 years. This specific demographic and lifestyle profile, which differs significantly from that of urban populations, is crucial for the generalizability of the findings. Recent research consistently shows notable disparities in FLD prevalence and severity when comparing rural and urban residents. An Indian-based epidemiology study reported NAFLD incidence of 23.2% (95%CI: 19.8–26.6) in urban participants versus 22.5% (95%CI: 19.0–26.0) in rural individuals. However, hepatic fibrosis was significantly more common in urban residents (16.5% [95%CI: 13.8–19.8]) than in rural participants (5.2% [95%CI: 3.8–6.7]) [57]. This disparity likely stems from distinct differences in dietary habits, health care access, and levels of physical activity. In urban areas, dietary patterns increasingly align with Western dietary habits. This shift is associated with an aberrant gut microbiome composition, which contributes to the increase in chronic diseases accompanying urbanization [58]. Conversely, rural residents often face critical challenges in terms of health care access, potentially leading to delayed or missed disease diagnoses and lower disease awareness, all of which critically influence FLD outcomes [59]. Furthermore, while physical activity is consistently linked to a reduced incidence of NAFLD in both settings, specific types and intensities of physical activity, as well as opportunities for engagement, may differ [60, 61]. The associations between obesity, IR, and FLD are also complex and multifaceted, exhibiting notable variations across different age groups. In young NAFLD patients, while obesity and IR are central to its development, the pathogenesis may involve other factors, including genetic predispositions, epigenetic factors, and specific environmental exposures (such as high-sugar diets) [62]. With ageing, the liver’s morphology and function change, leading to decreased proliferative and metabolic functions and a heighted risk of developing FLDs [63]. These findings highlight that while obesity and IR are fundamental to FLD across all ages, specific pathogenic pathways are distinctly influenced by age. Given the above reasons, the results of this study, obtained from a specific rural adult community, should be interpreted with caution in terms of their applicability to urban settings or broader age demographics.

Strengths and limitations

This investigation has two prominent strengths. First, by comparing their prevalence and associations with obesity, IR, and liver fibrosis, this research addressed the impacts of the shift in FLD terminology on important risk factors and disease progression, which could aid in decision-making in disease treatment and prevention. Second, a wide array of obesity phenotypes and IR surrogates were introduced to comprehensively describe the obesity and IR characteristics of differently defined FLD definitions.

There are several limitations. First, while liver biopsy is most accurate tool for diagnosing FLD and fibrosis, its invasive process and high cost restrict its application in large population-based studies. Therefore, ultrasonography was used because of its good sensitivity and specificity in FLD diagnosis, especially in primary care settings. The NFS, developed among NAFLD patients to identify advanced fibrosis [35], is calculated using a simple scoring system. However, the misclassification of FLD and fibrosis is possible. Second, the IR diagnosis was made by easily accessible blood measurements because of the challenges associated with using gold standard measurements such as hyperinsulinaemic-euglycaemic clamps. In this study, all reported indicators of IR were summarized, and their associations with various FLDs were examined to minimize the bias from one or two indicators as much as possible. Third, the study’s observational and cross-sectional design may lead to residual or unmeasured confounding and reverse causation. Ultimately, the study only included rural residents who were between 35 and 75 years old, whose demographic and lifestyle characteristics are distinct from those of urban populations, which may restrict the generalizability of the observations [22]. Further studies involving urban populations and broader age ranges are warranted to validate these findings and enhance their external applicability.

Conclusion

In conclusion, these findings underscore the importance of balancing scientific precision with clinical practicality in the evolving nomenclature of FLD. Although MAFLD/MASLD terminology represents conceptual advantages because it emphasizes metabolic dysfunction, its diagnostic complexity may hinder its large-scale implementation, especially in rural or resource-limited settings. NAFLD remains pragmatically and scientifically justified for early identification, population-level surveillance, and maintenance of continuity in research and clinical care. For primary care physicians in community settings, the use of well-established NAFLD criteria can lead to simplified patient assessment. Furthermore, TyG and related indices showed high discriminative performance across all FLD definitions, supporting their utility as simple, noninvasive, and cost-effective tools for screening and early risk identification. Clinicians should consider incorporating these indices into routine practice to efficiently triage at-risk individuals. The minimal discrepancy between MASLD and NAFLD suggests that these terms may be used interchangeably in research and clinical contexts, although the added diagnostic burden of MASLD should be considered. In addition, the coexistence of metabolic dysfunction among AFLD patients suggests that rigid diagnostic boundaries may fail to capture mixed disease aetiologies. Clinicians should maintain suspicion for metabolic disease patients with significant alcohol consumption. Therefore, transitioning from NAFLD to MAFLD or MASLD in terms of nomenclature is not advocated. Continued researches are required to verify these findings and support updates in terminology and diagnostic strategies.

FLD, fatty liver disease; NAFLD, non-alcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; MAFLD, metabolic dysfunction-associated fatty liver disease; SLD, steatotic liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; BMI, body mass index; IR, insulin resistance; NFS, NAFLD Fibrosis Score; FBG, fasting blood glucose; 2 h-PG, 2-h post-load blood glucose; TG, triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; CRP, C-reactive protein; AST, aspartate aminotransferase; ALT, alanine aminotransferase; HbA1c, glycosylated hemoglobin; FINS, fasting insulin; WC, waist circumference; HC, hip circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; T2DM, Type 2 diabetes mellitus; DM, diabetes mellitus; AFLD, alcoholic fatty liver disease; HOMA-IR, homeostatic model assessment of insulin resistance; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; LAP, lipid accumulation product; VAI, visceral adiposity index; CVAI, Chinese visceral adiposity index; FINS, fasting insulin; HOMA-IR, homeostatic model assessment of insulin resistance; METS-IR, metabolic score for insulin resistance; TyG, triglyceride and glucose index; TyG-WHtR, triglyceride and glucose index waist-to-hip ratio; TyG-WC, TyG-waist circumstance; IFG, impaired fasting glucose; SD, standardized deviation; IQR, median with interquartile range; RCS, Restricted cubic spline; ROC, Receiver operating characteristic; OR, odds ratio; CI, confidence interval; MetALD, metabolic dysfunction-associated liver disease.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Cotter TG, Rinella M. Nonalcoholic fatty liver disease 2020: the state of the disease. Gastroenterology. 2020;158(7):1851–64.

    Article  PubMed  CAS  Google Scholar 

  2. Buzzetti E, Pinzani M, Tsochatzis EA. The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). Metabolism. 2016;65(8):1038–48.

    Article  PubMed  CAS  Google Scholar 

  3. Huang DQ, El-Serag HB, Loomba R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2021;18(4):223–38.

    Article  PubMed  Google Scholar 

  4. Staufer K, Huber-Schonauer U, Strebinger G, Pimingstorfer P, Suesse S, Scherzer TM, et al. Ethyl glucuronide in hair detects a high rate of harmful alcohol consumption in presumed non-alcoholic fatty liver disease. J Hepatol. 2022;77(4):918–30.

    Article  PubMed  CAS  Google Scholar 

  5. Diaz LA, Arab JP, Louvet A, Bataller R, Arrese M. The intersection between alcohol-related liver disease and nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol. 2023;20(12):764–83.

    Article  PubMed  Google Scholar 

  6. Idalsoaga F, Kulkarni AV, Mousa OY, Arrese M, Arab JP. Non-alcoholic fatty liver disease and alcohol-related liver disease: two intertwined entities. Front Med. 2020;7:448.

    Article  Google Scholar 

  7. Eslam M, Sanyal AJ, George J, International Consensus P. MAFLD: A Consensus-Driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology. 2020;158(7):1999–2014. e1.

    Article  PubMed  CAS  Google Scholar 

  8. Younossi ZM, Rinella ME, Sanyal AJ, Harrison SA, Brunt EM, Goodman Z, et al. From NAFLD to MAFLD: implications of a premature change in terminology. Hepatology. 2021;73(3):1194–8.

    Article  PubMed  Google Scholar 

  9. van Kleef LA, de Knegt RJ, Brouwer WP. Metabolic dysfunction-associated fatty liver disease and excessive alcohol consumption are both independent risk factors for mortality. Hepatology. 2023;77(3):942–8.

    Article  PubMed  Google Scholar 

  10. Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78(6):1966–86.

    Article  PubMed  Google Scholar 

  11. Agbim U, Carr RM, Pickett-Blakely O, Dagogo-Jack S. Ethnic disparities in adiposity: focus on non-alcoholic fatty liver disease, visceral, and generalized obesity. Curr Obes Rep. 2019;8(3):243–54.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Goh VH, Tain CF, Tong TY, Mok HP, Wong MT. Are BMI and other anthropometric measures appropriate as indices for obesity? A study in an Asian population. J Lipid Res. 2004;45(10):1892–8.

    Article  PubMed  CAS  Google Scholar 

  13. Zhang X, Zhang M, Zhao Z, Huang Z, Deng Q, Li Y, et al. Geographic variation in prevalence of adult obesity in China: results from the 2013–2014 national chronic disease and risk factor surveillance. Ann Intern Med. 2020;172(4):291–3.

    Article  PubMed  Google Scholar 

  14. Quek J, Chan KE, Wong ZY, Tan C, Tan B, Lim WH, et al. Global prevalence of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in the overweight and obese population: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2023;8(1):20–30.

    Article  PubMed  CAS  Google Scholar 

  15. Ahmed B, Sultana R, Greene MW. Adipose tissue and insulin resistance in obese. Biomed Pharmacother. 2021;137:111315.

    Article  PubMed  CAS  Google Scholar 

  16. Brunt EM, Kleiner DE, Wilson LA, Sanyal AJ, Neuschwander-Tetri BA. Nonalcoholic steatohepatitis clinical research N. improvements in histologic features and diagnosis associated with improvement in fibrosis in nonalcoholic steatohepatitis: results from the nonalcoholic steatohepatitis clinical research network treatment trials. Hepatology. 2019;70(2):522–31.

    Article  PubMed  CAS  Google Scholar 

  17. Lee J, Vali Y, Boursier J, Spijker R, Anstee QM, Bossuyt PM, et al. Prognostic accuracy of FIB-4, NAFLD fibrosis score and APRI for NAFLD-related events: a systematic review. Liver Int. 2021;41(2):261–70.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Mozes FE, Lee JA, Selvaraj EA, Jayaswal ANA, Trauner M, Boursier J, et al. Diagnostic accuracy of non-invasive tests for advanced fibrosis in patients with NAFLD: an individual patient data meta-analysis. Gut. 2022;71(5):1006–19.

    Article  PubMed  Google Scholar 

  19. Huang W, Feng R, Xu X, Ma M, Chen J, Wang J, et al. Loss of Anthropometry-Lipids relationship in obese adults: A Cross-Sectional study in Southern China. Clin Epidemiol. 2023;15:191–201.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Shah ND, Ventura-Cots M, Abraldes JG, Alboraie M, Alfadhli A, Argemi J, et al. Alcohol-related liver disease is rarely detected at early stages compared with liver diseases of other etiologies worldwide. Clin Gastroenterol Hepatol. 2019. https://doi.org/10.1016/j.cgh.2019.01.026.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Su W, Chen M, Xiao L, Du S, Xue L, Feng R, et al. Association of metabolic dysfunction-associated fatty liver disease, type 2 diabetes mellitus, and metabolic goal achievement with risk of chronic kidney disease. Front Public Health. 2022;10:1047794.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Basnet TB, Du S, Feng R, Gao J, Gong J, Ye W. Fatty liver mediates the association of hyperuricemia with prediabetes and diabetes: a weighting-based mediation analysis. Front Endocrinol (Lausanne). 2023;14:1133515.

    Article  PubMed  Google Scholar 

  23. Liu Q, Han M, Li M, Huang X, Feng R, Li W, et al. Shift in prevalence and systemic inflammation levels from NAFLD to MAFLD: a population-based cross-sectional study. Lipids Health Dis. 2023;22(1):185.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. American Diabetes Association Professional Practice C. 2. Classification and diagnosis of diabetes: standards of medical care in Diabetes-2022. Diabetes Care. 2022;45(Suppl 1):S17–38.

    Article  Google Scholar 

  25. Bell JA, Richardson TG, Wang Q, Sanderson E, Palmer T, Walker V, et al. Effects of general and central adiposity on circulating lipoprotein, lipid, and metabolite levels in UK biobank: a multivariable Mendelian randomization study. The Lancet Regional Health. 2022;21:100457.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86.

    Article  PubMed  CAS  Google Scholar 

  27. Kahn HS. The lipid accumulation product is better than BMI for identifying diabetes: a population-based comparison. Diabetes Care. 2006;29(1):151–3.

    Article  PubMed  Google Scholar 

  28. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral adiposity index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33(4):920–2.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Wan H, Wang Y, Xiang Q, Fang S, Chen Y, Chen C, et al. Associations between abdominal obesity indices and diabetic complications: Chinese visceral adiposity index and neck circumference. Cardiovasc Diabetol. 2020;19(1):118.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wang Z, He H, Xie Y, Li J, Luo F, Sun Z, et al. Non-insulin-based insulin resistance indexes in predicting atrial fibrillation recurrence following ablation: a retrospective study. Cardiovasc Diabetol. 2024;23(1):87.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304.

    Article  PubMed  Google Scholar 

  32. Er LK, Wu S, Chou HH, Hsu LA, Teng MS, Sun YC, et al. Triglyceride glucose-body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals. PLoS ONE. 2016;11(3):e0149731.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Al Akl NS, Haoudi EN, Bensmail H, Arredouani A. The triglyceride glucose-waist-to-height ratio outperforms obesity and other triglyceride-related parameters in detecting prediabetes in normal-weight Qatari adults: a cross-sectional study. Front Public Health. 2023;11:1086771.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Dang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018. Cardiovasc Diabetol. 2024;23(1):8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, et al. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology. 2007;45(4):846–54.

    Article  PubMed  CAS  Google Scholar 

  36. Xue Y, Xu J, Li M, Gao Y. Potential screening indicators for early diagnosis of NAFLD/MAFLD and liver fibrosis: triglyceride glucose index-related parameters. Front Endocrinol (Lausanne). 2022;13:951689.

    Article  PubMed  Google Scholar 

  37. Ratziu V, Rinella M, Beuers U, Loomba R, Anstee QM, Harrison S, et al. The times they are a-changin’ (for NAFLD as well). J Hepatol. 2020;73(6):1307–9.

    Article  PubMed  Google Scholar 

  38. Ayada I, van Kleef LA, Alferink LJM, Li P, de Knegt RJ, Pan Q. Systematically comparing epidemiological and clinical features of MAFLD and NAFLD by meta-analysis: focusing on the non-overlap groups. Liver Int. 2022;42(2):277–87.

    Article  PubMed  Google Scholar 

  39. Younossi ZM, Paik JM, Al Shabeeb R, Golabi P, Younossi I, Henry L. Are there outcome differences between NAFLD and metabolic-associated fatty liver disease? Hepatology. 2022;76(5):1423–37.

    Article  PubMed  Google Scholar 

  40. Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, et al. The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American association for the study of liver diseases. Hepatology. 2018;67(1):328–57.

    Article  PubMed  Google Scholar 

  41. Yoshimura Y, Hamaguchi M, Hashimoto Y, Okamura T, Nakanishi N, Obora A, et al. Obesity and metabolic abnormalities as risks of alcoholic fatty liver in men: NAGALA study. BMC Gastroenterol. 2021;21(1):321.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Åberg F, Helenius-Hietala J, Puukka P, Färkkilä M, Jula A. Interaction between alcohol consumption and metabolic syndrome in predicting severe liver disease in the general population. Hepatology. 2018;67(6):2141–9.

    Article  PubMed  Google Scholar 

  43. Marti-Aguado D, Calleja JL, Vilar-Gomez E, Iruzubieta P, Rodríguez-Duque JC, Del Barrio M, et al. Low-to-moderate alcohol consumption is associated with increased fibrosis in individuals with metabolic dysfunction-associated steatotic liver disease. J Hepatol. 2024;81(6):930–40.

    Article  PubMed  CAS  Google Scholar 

  44. Song SJ, Lai JC, Wong GL, Wong VW, Yip TC. Can we use old NAFLD data under the new MASLD definition? J Hepatol. 2024;80(2):e54–6.

    Article  PubMed  Google Scholar 

  45. Hagstrom H, Vessby J, Ekstedt M, Shang Y. 99% of patients with NAFLD meet MASLD criteria and natural history is therefore identical. J Hepatol. 2024;80(2):e76-7.

    Article  PubMed  Google Scholar 

  46. Arora U, Biswas S, Aggarwal S, Duseja A, Shalimar. MASLD screening and diagnostic algorithms are interchangeable with existing NAFLD literature. J Hepatol. 2024;80(2):e89–91.

    Article  PubMed  Google Scholar 

  47. Ratziu V, Boursier J, Fibrosis AGSL. Confirmatory biomarker diagnostic studies are not needed when transitioning from NAFLD to MASLD. J Hepatol. 2024;80(2):e51–2.

    Article  PubMed  Google Scholar 

  48. Younossi ZM, Paik JM, Stepanova M, Ong J, Alqahtani S, Henry L. Clinical profiles and mortality rates are similar for metabolic dysfunction-associated steatotic liver disease and non-alcoholic fatty liver disease. J Hepatol. 2024;80(5):694–701.

    Article  PubMed  CAS  Google Scholar 

  49. Yang T, Yin J, Li J, Wang Q. The influence of different combinations of cardiometabolic risk factors on the prevalence of MASLD and risk of advanced fibrosis deserves attention. J Hepatol. 2024;80(2):e82–5.

    Article  PubMed  CAS  Google Scholar 

  50. Perazzo H, Pacheco AG, Griep RH. Collaborators. Changing from NAFLD through MAFLD to MASLD: similar prevalence and risk factors in a large Brazilian cohort. J Hepatol. 2024;80(2):e72–4.

    Article  PubMed  Google Scholar 

  51. Yang A, Zhu X, Zhang L, Ding Y. Transitioning from NAFLD to MAFLD and MASLD: consistent prevalence and risk factors in a Chinese cohort. J Hepatol. 2024;80(4):e154-5.

    Article  PubMed  Google Scholar 

  52. Flegal KM, Shepherd JA, Looker AC, Graubard BI, Borrud LG, Ogden CL, et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults. Am J Clin Nutr. 2009;89(2):500–8.

    Article  PubMed  CAS  Google Scholar 

  53. Segovia SA, Vickers MH, Harrison CJ, Patel R, Gray C, Reynolds CM. Maternal High-Fat and High-Salt diets have differential programming effects on metabolism in adult male rat offspring. Front Nutr. 2018;5:1.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Baldini F, Khalil M, Bartolozzi A, Vassalli M, Di Ciaula A, Portincasa P, et al. Relationship between liver stiffness and steatosis in obesity conditions: in vivo and in vitro studies. Biomolecules. 2022. https://doi.org/10.3390/biom12050733.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Gao H, Yang J, Pan W, Yang M. Iron overload and the risk of diabetes in the general population: results of the Chinese health and nutrition survey cohort study. Diabetes Metab J. 2022;46(2):307–18.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Xian YX, Weng JP, Xu F. MAFLD vs. NAFLD: shared features and potential changes in epidemiology, pathophysiology, diagnosis, and pharmacotherapy. Chin Med J (Engl). 2020;134(1):8–19.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Asadullah M, Shivashankar R, Shalimar, Kandasamy D, Kondal D, Rautela G, et al. Rural-urban differentials in prevalence, spectrum and determinants of non-alcoholic fatty liver disease in North Indian population. PLoS ONE. 2022;17(2):e0263768.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Du Y, Ding L, Na L, Sun T, Sun X, Wang L, et al. Prevalence of chronic diseases and alterations of gut microbiome in people of Ningxia China during urbanization: an epidemiological survey. Front Cell Infect Microbiol. 2021;11:707402.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Huynh J, Hoque AR, Reddy SSK. Diagnosis frequency and associated factors of nonalcoholic fatty liver disease among United States hospitalized adults in urban vs rural populations from 2007 to 2019: an emerging public health crisis. Endocr Pract. 2025. https://doi.org/10.1016/j.eprac.2025.05.740.

    Article  PubMed  Google Scholar 

  60. Chen Y, Chen Y, Geng B, Zhang Y, Qin R, Cai Y, et al. Physical activity and liver health among urban and rural Chinese adults: results from two independent surveys. J Exerc Sci Fit. 2021;19(1):8–12.

    Article  PubMed  Google Scholar 

  61. Chen W, Cao L, Wu Z. Association between physical activity and prevalence/mortality of non-alcoholic fatty liver disease in different socioeconomic settings. Int J Public Health. 2023;68:1605031.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Pixner T, Stummer N, Schneider AM, Lukas A, Gramlinger K, Julian V, et al. The role of macronutrients in the pathogenesis, prevention and treatment of Non-Alcoholic fatty liver disease (NAFLD) in the paediatric population-a review. Life. 2022. https://doi.org/10.3390/life12060839.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Georgieva M, Xenodochidis C, Krasteva N. Old age as a risk factor for liver diseases: modern therapeutic approaches. Exp Gerontol. 2023;184:112334.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

The authors thank all staff and participants who contributed to the Fuqing cohort.

Funding

This study was jointly supported by the National Natural Science Foundation of the People’s Republic of China [grant number: 82103923], General Program of the Natural Science Foundation of Fujian Province [grant number: 2022J01711], Government of Fuqing city [grant number: 2019B003], and High-level Talents Research Start-up Project of Fujian Medical University (No. XRCZX2020037, XRCZX2022001, XRCZX2023030, and XRCZX2023005). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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Author notes

  1. Liangguang Xiang, Xiaoyun Li and Jiamin Gong contributed equally to this work.

Authors and Affiliations

  1. General Surgery, Affiliated Fuqing Hospital, Fujian Medical University, Fuqing, China

    Liangguang Xiang

  2. Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, Fujian, China

    Xiaoyun Li, Jiamin Gong, Long He & Weimin Ye

  3. Institute of Population Medicine, Fujian Medical University, No 1, Xue Yuan Road, Fuzhou, Fujian, 350122, China

    Wanxin Li, Jun Chen, Shanshan Du & Weimin Ye

  4. Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China

    Weimin Ye

  5. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Ruimei Feng & Weimin Ye

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Contributions

All authors were responsible for the study concept and design and contributed to field investigation and data collection. WY, SD, WL and JC obtained funding. WY and SD designed the study. JG, LH and SD were responsible for data curation. LX and XL did the statistical analysis. JG and RF completed data verification. LX, XL, and JG drafted the manuscript, and all authors revised it for important intellectual content.

Corresponding authors

Correspondence to Shanshan Du or Weimin Ye.

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The Ethics Review Committee of Fujian Medical University approved the study protocol (approval number, [2017-07] and [2020-58]). All participants provided written informed consent prior to data collection.

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The authors declare no competing interests.

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Xiang, L., Li, X., Gong, J. et al. The nomenclature of fatty liver disease and its impact on obesity traits, insulin resistance, and hepatic fibrosis. Lipids Health Dis 24, 339 (2025). https://doi.org/10.1186/s12944-025-02736-x

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X