Food-derived dihydromyricetin and metabolic dysfunction-associated steatotic liver disease: a preclinical systematic review and meta-analysis

Abstract

Background:

Dihydromyricetin (DHM) is a food-derived flavonoid widely investigated as a nutraceutical candidate for metabolic dysfunction–associated steatotic liver disease (MASLD) in preclinical models; however, its overall efficacy in diet-induced MASLD/NAFLD models has not been systematically quantified.

Methods:

This PRISMA 2020 systematic review and meta-analysis was registered in PROSPERO (CRD420251119087). PubMed, Embase, Web of Science Core Collection, the Cochrane Library, and four major Chinese databases were searched from inception to December 15, 2025. Controlled murine studies comparing DHM monotherapy with high-fat diet controls were included. Random-effects meta-analyses pooled standardized mean differences (SMDs) with 95% confidence intervals; risk of bias was assessed using SYRCLE’s tool.

Results:

Fourteen controlled studies were included. Compared with controls, DHM reduced hepatic triglycerides and total cholesterol, improved liver enzymes (ALT, AST, ALP), and decreased body weight and liver index. DHM improved serum lipid profiles (lower total cholesterol and LDL; higher HDL) and glucose homeostasis (lower fasting glucose and insulin). Antioxidant defenses increased (SOD, CAT, GSH, GSH-Px) with reduced malondialdehyde, while inflammatory markers (TNF-α and IL-6) decreased. At the signaling level, DHM increased the pAMPK/AMPK ratio. Heterogeneity was moderate to high for several outcomes, partly explained by dose and treatment duration.

Conclusion:

In murine diet-induced MASLD/NAFLD models, DHM shows promising multidomain benefits across various physiological outcomes, though some variability remains due to differences in study design. More standardized preclinical designs and well-controlled nutraceutical/clinical studies are needed to define clinically relevant, bioavailable dosing and efficacy.

Systematic review registration:

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251119087, PROSPERO, Identifier CRD420251119087.

1 Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), has become the most prevalent chronic liver condition worldwide, affecting approximately 25–38% of adults (1–11). As the hepatic manifestation of metabolic syndrome, MASLD is closely associated with central obesity, insulin resistance, type 2 diabetes mellitus, and dyslipidemia (12–16). Without timely intervention, the disease may progress to non-alcoholic steatohepatitis (NASH), advanced fibrosis, cirrhosis, or even hepatocellular carcinoma (17–19). Although the therapeutic landscape is rapidly evolving, currently approved pharmacotherapies target MASH with fibrosis in selected patient population, and there remains no widely accepted drug therapy for the broader MASLD spectrum (20–23). Lifestyle intervention and weight management remain foundational, but their efficacy is often limited by poor long-term adherence (24, 25).

Dihydromyricetin (DHM), a natural flavonoid primarily derived from Ampelopsis grossedentata (vine tea), has a long history of consumption in East Asia and has recently attracted increasing attention for its potential metabolic benefits (26–28). Preclinical studies suggest that DHM may alleviate hepatic steatosis, improve metabolic profiles, and attenuate oxidative stress in NAFLD models (29, 30). However, the evidence across individual studies remains inconsistent. For example, the reported effects of DHM on body weight and serum lipid levels vary substantially (30, 31): some studies observed increases in serum triglycerides after DHM administration, whereas others reported marked reductions (32, 33). Such discrepancies highlight the need for a systematic and quantitative synthesis to clarify the overall effects of DHM (34).

To date, no systematic review or meta-analysis has comprehensively evaluated the therapeutic efficacy of DHM in experimental NAFLD mouse models. To address this gap, we conducted a systematic review and preclinical meta-analysis of murine studies to integrate the existing evidence and provide a consolidated assessment of DHM’s hepatoprotective potential across multiple physiological domains relevant to MASLD pathogenesis. Given the growing interest in natural product-based interventions, our findings may also help guide the future development of DHM-derived pharmacological or nutraceutical formulations.

2 Materials and methods

2.1 Protocol and reporting standards

This systematic review and meta-analysis adhered to the PRISMA 2020 guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the SYRCLE (Systematic Review Centre for Laboratory Animal Experimentation) recommendations for animal studies (35, 36). The study protocol was developed a priori and specified predefined eligibility criteria, data extraction procedures, and statistical methodologies. The protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration number: CRD420251119087) and is publicly accessible at https://www.crd.york.ac.uk/PROSPERO/view/CRD420251119087.

2.2 Literature search strategy

A comprehensive literature search was conducted across eight databases from inception to December 15, 2025: PubMed, Embase, Web of Science Core Collection, and the Cochrane Library, as well as four major Chinese databases—China National Knowledge Infrastructure, Wanfang Data, VIP Database for Chinese Technical Periodicals, and Chinese Biomedical Literature Database. The search strategy incorporated both controlled vocabulary and free-text terms related to the intervention (dihydromyricetin) and the disease condition (MASLD, MAFLD, NAFLD, NASH). No language restrictions were applied. Restriction to murine studies was implemented during the screening stage based on predefined eligibility criteria.

Reference lists of eligible articles and relevant reviews were also screened to identify additional studies. Duplicate records were removed before screening. Two reviewers (S.J. And T.J.) independently screened titles and abstracts, followed by full texts, to determine study eligibility. Discrepancies were resolved through discussion or consultation with a third reviewer. The detailed PubMed search strategy is provided in Table 1 as an example with equivalent terms adapted to the indexing systems and syntax of the remaining databases.

Importantly, inclusion of four major Chinese databases ensured that relevant preclinical studies published in the Chinese-language journals were systematically captured. Because dihydromyricetin is derived from traditional Chinese medicine sources and much of the experimental work has been conducted in China, searching both international and Chinese databases strengthened the comprehensiveness of the review and reduced potential language and publication bias.

2.3 Eligibility criteria

Studies were included if they met the following criteria: (1) Animal model: Studies employing mouse models of NAFLD, regardless of strain or sex, in which the disease was induced by high-fat diet (HFD) or similar metabolic stressors. (2) Intervention: DHM monotherapy, administered at any dose, duration, or route, without co-treatment with other pharmacological or dietary agents. (3) Comparator: HFD-fed mice that did not receive DHM treatment. (4) Outcomes: Studies were eligible if they reported at least one outcome relevant to NAFLD-related pathophysiological processes. These included, but were not limited to indicators of general physiological status (e.g., body weight, liver index), lipid metabolism, liver function, glucose regulation, oxidative stress, inflammation, or hepatic signaling pathways. No restrictions were imposed on the type of outcome during the literature screening stage to ensure comprehensive capture of relevant evidence. Specific outcomes of interest were then determined based on the availability of data and included in the meta-analysis where appropriate. (5) Study design: Controlled animal experiments published as original research articles in peer-reviewed journals.

The exclusion criteria were as follows: (1) Studies based on in vitro experiments, human subjects, reviews, case reports, conference abstracts, or computational models; (2) Animal studies not conducted in mice (e.g., rat models); (3) Studies lacking a valid control HFD-fed control group or those using DHM in combination with other active treatments; (4) Duplicate publications or secondary analyses derived from the same dataset; (5) Full-text articles unavailable or missing essential quantitative data (e.g., standard deviation or sample size); (6) Absence of any prespecified outcome indicators.

2.4 Data extraction

Two independent reviewers screened the included studies and extracted data using a standardized data extraction template. Disagreements were resolved through discussion or, when necessary, adjudicated by a third investigator. The following information was collected from each eligible publication: (1) basic study information, including first author, publication year, and journal; (2) animal characteristics such as mouse strain, sex, age, and sample size; (3) NAFLD modeling methods (e.g., diet composition and induction duration); (4) intervention details, including DHM dosage, route of administration, and treatment duration; (5) control group characteristics (e.g., high-fat diet without DHM); and (6) outcome measures and corresponding numerical data.

When studies involved multiple DHM dose groups, only data from the highest therapeutically effective dose were extracted for the primary meta-analysis, to estimate the upper bound (maximum therapeutic potential) of DHM efficacy under the tested experimental conditions, consistent with prior preclinical meta-analyses (37, 38). The “highest therapeutically effective dose” used in the meta-analysis was based on the original studies included in this review. This dose was determined by the authors of each study based on their experimental design, where the highest dose tested was considered therapeutically effective in the respective models. This approach is commonly used in preclinical meta-analyses to avoid dilution of treatment effects by subtherapeutic doses, but may overestimate efficacy and was therefore interpreted conservatively in the Discussion (39). For studies reporting outcomes at multiple time points, results from the latest time point were preferentially selected. When relevant data were available only in graphical form, numerical values were estimated using GetData Graph Digitizer (version 2.26). When liver index was reported as a percentage, it was converted to a consistent metric prior to pooling (i.e., percentage values were divided by 100). If dispersion was reported as SE or SEM rather than SD, SD was calculated using SD = SE/SEM × √n. When dispersion was reported as mean ± S without specifying whether S represented SD or SEM, we conservatively treated S as SEM and converted it accordingly to avoid potential inflation of SMDs. All extracted data were entered into a pre-designed spreadsheet for subsequent statistical synthesis.

In this review, all extracted outcomes were categorized into eight functional domains to facilitate structured synthesis: (1) hepatic lipid profile; (2) liver enzymes; (3) anthropometric parameters; (4) serum lipid profile; (5) glucose metabolism; (6) oxidative stress; (7) inflammatory cytokines; (8) hepatic signaling proteins. Given the diversity of biochemical and molecular markers reported across animal studies, we did not pre-specify primary or secondary outcomes. Instead, all available endpoints were systematically extracted, and then grouped post hoc into eight functional domains to allow structured synthesis.

2.5 Risk of bias assessment

The risk of bias of all included studies was independently assessed by two reviewers using SYRCLE’s Risk of Bias (RoB) tool, which is specifically designed for preclinical animal research (40). This tool evaluates ten domains of bias, including sequence generation, baseline characteristics, allocation concealment, random housing, blinding of caregivers and investigators, random outcome assessment, blinding of outcome assessors, incomplete outcome data, selective outcome reporting, and other sources of bias. Each domain was rated as having a “low,” “high,” or “unclear” risk of bias based on the methodological details reported in each study.

Any discrepancies between the two reviewers were resolved through discussion, and if consensus could not be achieved, a third senior reviewer was consulted for arbitration. The overall risk-of-bias profile was visually summarized using Review Manager (RevMan, version 5.4).

2.6 Statistical analysis

All statistical analyses were performed using Stata version 15.1 (StataCorp, College Station, TX, USA). For continuous outcomes, standardized mean differences (SMDs) with corresponding 95% confidence intervals (CIs) were calculated to estimate the effects of DHM on NAFLD-related parameters. SMDs were used instead of weighted mean differences (WMDs) because the included studies assessed the same outcomes (e.g., triglyceride (TG), total cholesterol (Tche)) using different scales, units, or laboratory methods. Standardization enabled direct comparison across studies and improved the robustness of pooled estimates.

Given the considerable variability in animal models (e.g., age, strain, induction protocols) and experimental designs (e.g., dosage, intervention duration, outcome assessment methods), all pooled analyses were conducted using a random-effects model (DerSimonian and Laird method), irrespective of the results of heterogeneity tests. Between-study heterogeneity was assessed using the I² statistic and Cochran’s Q test. Sensitivity analyses were performed using a leave-one-out approach for outcomes with at least three studies to evaluate the influence of individual studies on pooled estimates.

Publication bias was assessed using Egger’s test and visual inspection of funnel plots when ten or more studies were available for a given outcome (41). When publication bias was indicated, the trim-and-fill method was applied to examine the robustness of the pooled results (42). A two-tailed p-value < 0.05 was considered statistically significant. All analyses were reported in accordance with PRISMA 2020 guidelines to ensure methodological transparency and reproducibility. All analytical steps were independently cross-checked to ensure data integrity.

3 Results

3.1 Study selection

The systematic search retrieved 213 records from electronic databases and 1 additional record from other sources, yielding a total of 214 records. After removing 95 duplicates, 119 records (databases n = 118; other sources n = 1) underwent title and abstract screening, resulting in the exclusion of 41 records that were irrelevant to the research topic. The full texts of the remaining 78 articles (77 from databases and 1 from other sources) were assessed for eligibility.

Of the 77 database-derived full-text articles, 63 were excluded for the following reasons: conference abstract (n = 1), non-murine animal studies (n = 3), in vitro studies (n = 4), reviews (n = 20), and studies irrelevant to the research question (n = 35). The single article identified through other sources was excluded after full-text review due to irrelevant outcome measures. Ultimately, fourteen murine NAFLD studies evaluating DHM interventions were included in the systematic review and meta-analysis (Figure 1).

Figure 1

All included studies were conducted in China between 2017 and 2024, employed high-fat diet–induced NAFLD models in male mice, and administered DHM orally either by gavage (25–1,000 mg/kg/day) or via drinking water at a concentration of 1.28 mg/mL for 4–16 weeks of DHM treatment. Key study characteristics are summarized in Table 1.

3.2 Characteristics of included studies

The fourteen included studies were published between 2017 and 2024, all conducted in China, and exclusively used HFD–induced NAFLD mouse models (29–33, 43–51). Most studies used wild-type C57BL/6 J mice, whereas two studies employed C57BL/6 J LDLR⁻/⁻ mice (44, 46) and another used C57BL/6 J ApoE⁻/⁻ mice (47) to model severe dyslipidemia. All studies were conducted in male animals. Detailed group sizes and total sample numbers for each study are provided in Supplementary Table S1.

DHM was administered as monotherapy in all included studies. Thirteen studies (29–32, 43–51) delivered DHM via oral gavage at doses ranging from 25 to 1,000 mg/kg/day, while one study (33) delivered DHM in drinking water at a concentration of 1.28 mg/mL. Because daily intake relative to body weight was not reported, the approximate daily dose was estimated at ~300 mg/kg/day using the formula Dose = concentration × daily water intake ÷ body weight, assuming a daily water intake of 4–6 mL and 18–20 g body weight for 7-week-old male C57BL/6 J mice (52–55). Treatment durations ranged from 4 to 16 weeks. All studies used vehicle-treated HFD-fed mice as controls (see Supplementary Table S1 for details).

The outcome measures reported across the included studies were diverse and were categorized into eight functional domains: (1) Hepatic lipid profile—triglycerides (TG) and total cholesterol (Tche). (2) Liver injury biomarkers—alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP). (3) Anthropometric parameters—body weight, liver index. (4) Serum lipid profile—TG, Tche, low-density lipoprotein cholesterol (LDL), and high-density lipoprotein cholesterol (HDL). (5) Glucose metabolism—fasting blood glucose and serum insulin. (6) Oxidative stress biomarkers—superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), glutathione peroxidase (GSH-Px), and malondialdehyde (MDA). (7) Inflammatory cytokines—hepatic interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α), as well as serum TNF-α. (8) Hepatic signaling proteins—peroxisome proliferator–activated receptor-α (PPAR-α), carnitine palmitoyltransferase-1 (CPT-1), and the ratio of phosphorylated to total AMP-activated protein kinase (pAMPK/AMPK).

A summary of the study characteristics—including mouse strain, sample size, DHM dose, administration method, treatment duration, and outcome measures—is provided in Supplementary Table S1. As a whole, these studies contributed to a growing body of preclinical evidence supporting the hepatoprotective, anti-inflammatory, and antioxidative effects of DHM in diet-induced NAFLD. The consistent use of HFD-based murine models and the relatively uniform dosing regimens enhance comparability, while the inclusion of both wild-type and genetically susceptible strains permits broader exploration of DHM’s therapeutic relevance across varying degrees of metabolic dysfunction and disease severity.

3.3 Risk of bias, publication bias, and sensitivity analysis

The methodological quality of the 14 included studies was evaluated using SYRCLE’s risk-of-bias tool. None of the studies was rated as having a high risk of bias in any domain. For random sequence generation (selection bias), one study was judged as low risk, whereas the remaining studies were assessed as unclear due to insufficient methodological reporting. Regarding baseline characteristics, seven studies adequately reported comparable groups and were rated as low risk, while the others were judged as unclear. Allocation concealment was not described in any study and therefore all were classified as unclear risk. For random housing (performance bias), eight studies were considered low risk, and the remainder were unclear. Only one study explicitly reported blinding of investigators, and one reported random outcome assessment; all others were judged as unclear. Similarly, only one study reported blinding of outcome assessors, with the rest assessed as unclear. All studies were rated as low risk for incomplete outcome data, and selective reporting was generally well controlled, with only one study judged as unclear. No other sources of bias were identified. Overall, the methodological quality of the included animal studies was moderate. The primary concerns stemmed from inadequate reporting of randomization procedures, allocation concealment, and blinding, whereas outcome completeness and selective reporting were largely at low risk (Supplementary Figures S1, S2). These patterns indicate that incomplete reporting of key design safeguards remains a common limitation across the current preclinical evidence base.

Publication bias was assessed for six outcomes with ten or more studies: ALT, body weight, serum TG, serum Tche, serum LDL, and serum HDL (Supplementary Figure S3). Egger’s test indicated potential publication bias for serum Tche (p = 0.01), while no significant bias was detected for the remaining outcomes (all p > 0.05) (Supplementary Table S2). To further explore robustness, a trim-and-fill analysis was conducted for serum Tche; however, no trimming was performed, and the pooled effect size remained unchanged. Although Egger’s test did not indicate significant bias for serum TG (p > 0.05), the funnel plot appeared asymmetric; trim-and-fill analysis was therefore performed, which again showed no trimming and no change in the pooled estimate. This pattern may reflect the limited power of Egger’s test with a moderate number of studies and suggests that the observed asymmetry is more likely attributable to between-study heterogeneity rather than true publication bias.

Sensitivity analyses were conducted for 19 outcomes with at least three studies included. Leave-one-out analyses supported that sequential exclusion of any single study did not substantially alter the pooled estimates, supporting the robustness of the findings (Supplementary Figures S4–S11; inflammatory cytokines are shown in Supplementary Figure S10). Outcomes with fewer than three studies were not subjected to sensitivity analysis, as exclusion of one study would cause the results to be determined entirely by a single dataset, rendering such analyses uninformative.

Of all the above-mentioned outcomes assessed, 12 outcomes were associated with the study where the DHM dose was estimated based on assumptions about daily water intake (~300 mg/kg/day). These outcomes included body weight, serum TG, ALT, AST, hepatic MDA, SOD, GSH, GSH-Px, CAT, TNF-α, IL-6, and IL-1β. The sensitivity analysis showed that the estimated dose did not significantly affect the overall findings for these outcomes, indicating that the uncertainty from the estimated dose had minimal impact on the pooled results. While such uncertainties exist, their effect on the meta-analysis was negligible. Future studies would benefit from using more precise dosing methods to reduce such uncertainties.

3.4 Effects of intervention

To better reflect MASLD disease progression, the pooled outcomes are presented in a pathophysiologically ordered framework, moving from metabolic and lipid disturbances to organ injury, oxidative stress, inflammation, and upstream signaling pathways.

3.4.1 Lipid profile

Seven studies (29, 31, 33, 44, 46, 48, 51) (n = 106 mice) evaluated hepatic TG, and five studies (31, 33, 44, 46, 48) (n = 66 mice) assessed hepatic total cholesterol following DHM treatment in high-fat diet-induced NAFLD mice. Meta-analysis using a random-effects model supported a significant reduction in hepatic TG levels (SMD = −3.32, 95% CI: −4.77, −1.87, p < 0.001; I² = 80.3%, p < 0.001) and in hepatic Tche (SMD = −1.98, 95% CI: −3.58, −0.39, p = 0.015; I² = 82.4%, p < 0.001) in DHM-treated groups compared with controls (Figure 2).

Figure 2

Thirteen studies (29–33, 43–47, 49–51) (n = 216 mice) evaluated serum triglycerides, twelve (29–31, 33, 43–47, 49–51) (n = 204) assessed Tche, eleven (29–31, 33, 43–47, 49, 50) (n = 174) examined LDL, and ten (29–31, 43–47, 49, 50) (n = 154) measured HDL in HFD-induced NAFLD mice treated with DHM. Meta-analyses showed that DHM had no significant effect on serum TG levels (SMD = −0.69, 95% CI: −1.65 to 0.26; I² = 86.3%, p < 0.001). In contrast, significant reductions were observed in serum Tche (SMD = −2.01, 95% CI: −2.92 to −1.10, p < 0.001; I² = 83.8%, p < 0.001) and LDL (SMD = −1.73, 95% CI: −2.60 to −0.78, p < 0.001; I² = 82.9%, p < 0.001), accompanied by a significant increase in HDL (SMD = 1.38, 95% CI: 0.32 to 2.44, p = 0.011; I² = 85.9%, p < 0.001). Collectively, these findings indicate that DHM favorably modulates circulating lipid profiles by lowering Tche and LDL while elevating HDL levels (Figure 3).

Figure 3

3.4.2 Anthropometric parameters

Thirteen studies (29–33, 43–46, 48–51) (n = 212 mice) examined the effects of DHM on body weight, and six studies (29, 30, 44, 48–50) (n = 102 mice) evaluated the liver index, defined as the ratio of liver weight to body weight, in HFD-induced NAFLD mice. The pooled analyses revealed significant reductions in both body weight (SMD = −2.61, 95% CI: −3.87 to −1.36, p < 0.001; I² = 90.4%, p < 0.001) and liver index (SMD = −2.80, 95% CI: −4.52 to −1.08, p = 0.001; I² = 87.8%, p < 0.001) in DHM-treated groups compared with controls (Figure 4).

Figure 4

3.4.3 Glucose metabolism

Five studies (46, 49–51, 56) (n = 108 mice) evaluated FBG, and three studies (44, 46, 49) (n = 58) assessed fasting insulin concentrations in HFD-induced NAFLD mice. Meta-analysis supported a significant reduction in FBG following DHM administration compared with controls (SMD = −1.90, 95% CI: −3.09, −0.72, p < 0.001; I² = 83.0%, p < 0.001). Similarly, DHM supplementation significantly reduced circulating insulin levels (SMD = −1.73, 95% CI: −3.27 to −0.19, p = 0.027; I² = 79.9%, p = 0.007). These results suggest that DHM ameliorates glucose dysregulation and improves insulin sensitivity in murine NAFLD models (Figure 5).

Figure 5

3.4.4 Liver injury

Ten studies (29, 30, 33, 43, 44, 46–48, 50, 51) (n = 166 mice) reported serum ALT, nine studies (30, 33, 43, 44, 46–48, 50, 51) (n = 156 mice) assessed AST, and two studies (30, 43) (n = 32 mice) evaluated ALP levels in HFD-induced NAFLD mice treated with DHM. Meta-analyses demonstrated significant reductions in ALT (SMD = −3.79, 95% CI: −4.71, −2.86, p < 0.001; I² = 64.2%, p = 0.003), AST(SMD = −2.97, 95% CI: −3.89 to −2.06, p < 0.001; I² = 71.2%, p = 0.001), and ALP (SMD = −5.77, 95% CI: −7.41 to −4.14, p < 0.001; I² = 7.3%, p = 0.299) in DHM-treated groups compared with controls (Figure 6).

Figure 6

3.4.5 Oxidative stress markers

Six studies (33, 43, 45–48) (n = 96) evaluated SOD, three studies (33, 46, 47) (n = 52) assessed CAT, three studies (33, 43, 48) (n = 48) examined GSH, two studies (33, 46) (n = 32) measured GSH-Px, and five studies (33, 43, 45–47) (n = 80) determined MDA levels in hepatic tissue. Meta-analyses demonstrated significant increases in SOD (SMD = 3.39, 95% CI 1.55 to 5.23, p < 0.001; I² = 87.2%, p < 0.001), CAT (SMD = 2.42, 95% CI 1.38 to 3.46, p < 0.001; I² = 47.9%, p = 0.147), GSH (SMD = 3.86, 95% CI 1.90 to 5.83, p < 0.001; I² = 70.4%, p = 0.034), and GSH-Px (SMD = 1.79, 95% CI 0.48 to 3.10, p = 0.007; I² = 50.9%, p = 0.153), accompanied by a significant reduction in MDA (SMD = −2.72, 95% CI –4.18 to −1.26, p < 0.001; I² = 78.6%, p = 0.001). These findings indicate that DHM markedly attenuates hepatic oxidative stress by enhancing antioxidant enzyme activities and reducing lipid peroxidation (Figure 7).

Figure 7

3.4.6 Inflammatory cytokines

Two studies (33, 48) (n = 36) reported hepatic IL-1β, three studies (45, 46, 50) (n = 44) assessed hepatic IL-6, four studies (45, 46, 48, 50) (n = 76) measured hepatic TNF-α, and two studies (30, 46) (n = 32) determined serum TNF-α levels. Meta-analyses revealed no significant change in IL-1β (SMD = −2.61, 95% CI –5.61 to 0.40, p = 0.089; I² = 88.0%, p = 0.004), whereas IL-6 (SMD = −3.52, 95% CI –4.91 to −2.13, p < 0.001; I² = 45.0%, p = 0.162), hepatic TNF-α (SMD = −5.79, 95% CI –8.22 to −3.36, p = 0.011; I² = 88.9%, p < 0.001), and serum TNF-α (SMD = −5.09, 95% CI –7.08 to −3.09, p < 0.001; I² = 73.3%, p = 0.011) were significantly reduced in DHM-treated mice. In sum, these findings suggest that DHM exerts potent anti-inflammatory effects both systemically and within hepatic tissue (Figure 8).

Figure 8

3.4.7 Hepatic signaling proteins

Three studies (45, 48, 51) (n = 58) measured hepatic PPAR-α, two studies (45, 51) (n = 42) assessed CPT-1, and two studies (29, 46) (n = 22) examined pAMPK/AMPK ratio. Meta-analyses revealed no significant differences in PPAR-α (SMD = 2.91, 95% CI –1.20 to 7.02, p = 0.164; I² = 94.7%, p < 0.001) or CPT-1 (SMD = 1.72, 95% CI –3.84 to 7.27, p = 0.545; I² = 95.1%, p < 0.001), whereas the pAMPK/AMPK ratio was significantly increased in DHM-treated mice (SMD = 6.07, 95% CI 0.69 to 11.45, p = 0.033; I² = 78.0%, p = 0.033). These molecular findings suggest that DHM may activate AMPK signaling, potentially contributing to its observed lipid-lowering and antioxidant effects (Supplementary Figure S12).

3.5 Subgroup analysis by intervention duration

To explore potential sources of heterogeneity, subgroup analyses were conducted according to intervention dose (> 200 mg/kg/day vs. ≤ 200 mg/kg/day) (Table 2) and treatment duration (> 8 weeks vs. ≤ 8 weeks) (Table 3). In dose-based analyses, high doses (> 200 mg/kg/day) were associated with greater improvements in hepatic TG, hepatic Tche, PPAR-α, MDA, TNF-α, body weight, liver index, serum lipid parameters, and insulin, whereas lower dose DHM (≤ 200 mg/kg/day) showed more pronounced effects on ALT, AST, FBG, and several oxidative stress markers, including SOD, CAT, and GSH. Heterogeneity decreased to varying degrees for most outcomes after dose stratification, with five outcomes (hepatic Tche, FBG, insulin, CAT, and GSH) exhibiting no observed heterogeneity (I² = 0%).

When stratified by intervention duration, long-term treatment (> 8 weeks) generally produced more stable and consistent benefits compared with short-term interventions (≤ 8 weeks). For example, heterogeneity for hepatic Tche decreased from I² = 82.4% overall to 0% within the long-term subgroup. Additionally, significant improvements in hepatic TG, TNF-α, serum TG, serum Tche, and HDL were observed only in the long-term group. In contrast, several outcomes—such as liver index and FBG— remained heterogeneous despite duration-based stratification (Table 3).

4 Discussion

4.1 Principal findings and possible mechanisms

This preclinical meta-analysis synthesized data from 14 murine studies and provides the first consolidated evidence that DHM exerts multidimensional hepatoprotective effects in diet-induced NAFLD models. Overall, DHM significantly attenuated hepatic lipid accumulation, lowered circulating atherogenic lipids, improved liver injury biomarkers (ALT, AST, ALP), reduced body weight and liver index, ameliorated glucose dysregulation, and enhanced antioxidant and anti-inflammatory responses. Taken together, these findings suggest that DHM does not act through a single pathway but rather through a coordinated modulation of metabolic, redox, and inflammatory networks, supporting its candidacy as a multi-target natural compound for MASLD. The following sections discuss these findings along the MASLD progression cascade, from metabolic overload and steatosis to hepatocellular injury and upstream signaling regulation.

4.2 Lipid metabolism and hepatic steatosis

A major observation of this study is that DHM improved both hepatic and systemic lipid homeostasis. On the hepatic side, pooled analyses supported large standardized effects on liver triglyceride and total cholesterol, indicating a genuine anti-steatotic action at the organ level. On the systemic level, DHM consistently lowered serum total cholesterol and LDL while increasing HDL; however, serum triglycerides did not change significantly—a pattern that has also been reported for other polyphenolic and flavonoid interventions in metabolic liver disease. This lipid-modulating profile is biologically plausible. DHM has been shown to activate AMPK, which in turn suppresses de novo lipogenesis by inhibiting ACC/FAS and, in parallel, may downregulate hepatic cholesterol synthesis through HMGCR inhibition and promote LDL clearance via LDLR/PCSK9-related pathways (57–66). The lack of significantly pooled increases in PPAR-α or CPT-1 expression in our analysis likely reflects the small number of available studies and methodological variability rather than a true absence of DHM action on fatty acid oxidation. In other words, the phenotypic outcomes—lower hepatic TG, lower serum Tche and LDL, and higher HDL—are more consistent than the molecular readouts—a phenomenon commonly observed in preclinical NAFLD meta-analyses due to variable assay sensitivity and reporting practices.

4.3 Anthropometric parameters

In addition to the biochemical markers, the observed reductions in body weight and liver index after DHM treatment are significant. These anthropometric parameters are widely recognized as early indicators of metabolic dysfunction and liver pathologies in high-fat diet-induced NAFLD models (67–69). Body weight increases and liver enlargement (reflected by liver index) are commonly associated with excessive lipid accumulation in the liver, a key feature of the early to middle stages of NAFLD. The reduction in body weight and liver index following DHM administration suggests that DHM may intervene early in the disease process by reducing metabolic overload and preventing further liver enlargement. Mechanistically, these changes are likely linked to the modulation of lipid metabolism pathways, particularly through the activation of AMPK. AMPK activation inhibits lipogenesis by suppressing ACC/FAS activity and enhances fatty acid oxidation, leading to a reduction in hepatic triglyceride accumulation (70, 71). Additionally, AMPK activation may improve mitochondrial function, which further contributes to a reduction in lipid deposition and liver enlargement (72–74).

4.4 Glucose metabolism

DHM also improved weight-related and glycometabolic parameters, which are pathophysiologically relevant to MASLD. In high-fat diet–induced mouse models, reductions in body weight and liver index are typically parallel to decreases in hepatic fat deposition and low-grade inflammation (75, 76). Consistent with this, our pooled effects on fasting glucose and insulin suggest that DHM may alleviate insulin resistance at both hepatic and peripheral levels. Mechanistically, these findings align with prior evidence showing DHM-induced activation of AMPK and insulin receptor substrate-1/protein kinase B (IRS-1/AKT) signaling, suppression of gluconeogenic enzymes such as PEPCK and G6Pase, and, in some reports, improvements in mitochondrial function and the promotion of adipose tissue browning (57, 77–85). Taken together, these converging actions position DHM among plant-derived compounds that target the “metabolic core” of MASLD rather than acting solely through antioxidant pathways.

4.5 Liver injury

DHM treatment significantly reduced liver injury biomarkers (ALT, AST, ALP), suggesting its potential hepatoprotective effects. These findings align with the known role of oxidative stress and inflammation in the progression of NAFLD (86–89). DHM’s ability to reduce liver injury is likely mediated through its antioxidant and anti-inflammatory properties. Mechanistically, DHM has been shown to enhance antioxidant defenses by increasing SOD, CAT, GSH, and GSH-Px, and by reducing MDA levels, which mitigates lipid peroxidation and oxidative damage to hepatocytes (90–93). This action likely prevents hepatocellular injury and liver dysfunction. In parallel, DHM downregulated key inflammatory mediators, including TNF-α and IL-6, both of which play a central role in liver damage and fibrosis in NAFLD (94–96). By suppressing these pro-inflammatory cytokines, DHM may help reduce liver inflammation, a critical component in the progression of the disease (97, 98). These findings suggest that DHM’s hepatoprotective effects are mediated by a combination of oxidative stress attenuation and inflammatory cytokine suppression, which are key to alleviating hepatocellular injury and potentially preventing further disease progression in NAFLD. Future studies should explore the role of other signaling pathways, such as Nrf2, to fully elucidate the molecular mechanisms underlying these protective effects (99–101).

4.6 Oxidative stress and inflammatory cytokines

Another important finding is the concurrent reinforcement of antioxidant defenses and suppression of inflammatory mediators. DHM markedly increased SOD, CAT, GSH, and GSH-Px and reduced MDA, indicating mitigation of lipid peroxidation and restoration of redox balance. This antioxidant pattern is consistent with activation of the canonical Keap1–Nrf2–heme oxygenase-1 (HO-1) axis, a mechanism frequently reported for DHM in extrahepatic models (102–110). In parallel, DHM significantly downregulated hepatic and circulating TNF-α and hepatic IL-6, both of which are key drivers of hepatocellular injury, insulin resistance, and fibrogenic signaling in MASLD. The absence of a significant pooled effect on IL-1β likely reflects the small number of available studies and wide confidence intervals, rather than a true lack of biological response. Overall, these data support a mechanistic model in which DHM interrupts the oxidative-stress–inflammation vicious cycle: activation of Nrf2 and improved mitochondrial function reduce ROS generation, which subsequently dampens NF-κB/NLRP3 activity and cytokine release (103, 104, 111–113). This coordinated antioxidant and anti-inflammatory signature may represent a comparative advantage of DHM over other flavonoids that target predominantly either lipid metabolism or oxidative stress alone (114–116).

4.7 Hepatic signaling proteins

The activation of hepatic signaling molecules plays a key role in DHM’s hepatoprotective effects. Our meta-analysis revealed that DHM significantly increased the pAMPK/AMPK ratio (SMD = 6.07, 95% CI 0.69–11.45), supporting activation of AMPK signaling, which is crucial for regulating lipid metabolism, inflammation, and oxidative stress (117–119). AMPK activation suppresses de novo lipogenesis by inhibiting ACC and FAS and promotes fatty acid oxidation, which contributes to the reduction of hepatic triglycerides and serum cholesterol levels (120, 121). This finding aligns with previous studies indicating that AMPK activation improves mitochondrial function and increases oxidative phosphorylation, thereby contributing to enhanced antioxidant defenses (122, 123). However, no significant changes were observed in PPAR-α and CPT-1 expression, which could reflect the relatively small number of studies and methodological variability in assessing these markers. PPAR-α is a critical regulator of fatty acid oxidation, and its activation is commonly associated with improved lipid profiles (124–126). The lack of significant changes in PPAR-α expression may be due to variations in experimental conditions or the insufficient number of studies available for this marker. Similarly, the lack of significant effects on CPT-1, a key enzyme in mitochondrial fatty acid oxidation, may indicate that DHM’s effects on fatty acid metabolism could be mediated through other pathways, such as AMPK signaling (127–129).

In addition to AMPK-centered signaling, mTORC1 represents a complementary nutrient-sensing hub that is functionally linked to several outcome domains observed in our analysis. Notably, AMPK is a well-established upstream negative regulator of mTORC1, and the increased pAMPK/AMPK ratio (i.e., AMPK activation) observed across included studies is conceptually consistent with restrained mTORC1 activity (e.g., via TSC2 and/or Raptor phosphorylation) (130–132). However, because most included studies assessed pathway markers without pharmacological inhibition or genetic perturbation, this AMPK–mTORC1 linkage should be interpreted as mechanistic plausibility rather than confirmed causality. Such AMPK–mTOR antagonism is pathophysiologically relevant to MASLD, as mTORC1 overactivation promotes anabolic lipid programs and suppresses autophagy, whereas its inhibition favors metabolic stress adaptation (133, 134). This AMPK–mTOR nutrient-sensing axis has been increasingly emphasized in recent MASLD-related mechanistic studies, where mTORC1 signaling is described as a central regulator of lipid synthesis, autophagy suppression, and metabolic transcriptional programs (133, 134). In addition, persistent mTORC1 activation has been linked to hyperinsulinemia-driven steatotic progression through PDX1-dependent endocrine mechanisms (135), while coordinated AMPK/SIRT1–mTOR balance is considered critical for mitochondrial quality control and metabolic stress resistance (130). This framework may also be indirectly consistent with our pooled findings showing improved oxidative stress markers (increased SOD, CAT, and GSH with reduced MDA), since mTORC1 suppression is associated with restored autophagic flux and mitochondrial quality control (130, 133). In addition, mTOR signaling has been implicated in inflammatory metabolic programming (133, 136, 137), providing a plausible upstream context for the observed reductions in TNF-α and IL-6, although causal linkage cannot be established from the current data.

These molecular findings suggest that DHM’s beneficial effects in NAFLD may be at least in part mediated by AMPK signaling, contributing to its lipid-lowering and antioxidant actions. While further studies are needed to fully elucidate the role of PPAR-α and CPT-1, the significant increase in pAMPK/AMPK ratio supports the idea that DHM appears to target key metabolic pathways involved in lipid metabolism and oxidative stress.

Importantly, the mechanistic signals identified in the included studies appear to follow a hierarchical pattern rather than representing isolated pathways. AMPK activation is likely positioned upstream, given its central role in regulating lipid metabolism, mitochondrial function, and oxidative stress responses (117, 118, 138). Downstream changes in antioxidant defense systems (e.g., SOD, CAT, GSH-related enzymes) and inflammatory mediators (TNF-α, IL-6) may therefore represent secondary effects of metabolic reprogramming rather than independent primary targets. However, because most included studies measured pathway markers without functional blockade experiments, these relationships should be interpreted as mechanistic associations rather than confirmed causal chains.

4.8 Heterogeneity

Notably, several pooled effects expressed as SMDs were large in magnitude. In preclinical animal studies, SMDs can be inflated when within-study variability is small (e.g., homogeneous strains, standardized housing and assays) or when methodological and measurement differences across laboratories affect the dispersion of outcomes (139). Therefore, the absolute magnitude of SMDs should be interpreted cautiously; we primarily emphasize the consistency in the direction of effects across studies and the concordant improvements observed across multiple biological domains, while acknowledging substantial between-study heterogeneity.

Nevertheless, the present meta-analysis revealed moderate-to-high heterogeneity across several outcomes. Our subgroup analyses indicated that both dose (>200 mg/kg/day vs. ≤ 200 mg/kg/day) and treatment duration (>8 weeks vs. ≤8 weeks) contributed meaningfully to this variability. Higher doses tended to yield stronger improvements in hepatic steatosis, circulating lipids, body weight, and inflammatory markers, whereas lower doses sometimes elicited more pronounced improvements in liver enzymes and certain antioxidant indices. Similarly, longer intervention periods yielded more consistent and homogeneous benefits; for several outcomes (e.g., hepatic Tche, FBG, insulin, CAT, and GSH), heterogeneity dropped to 0% once stratified by duration. This pattern suggests that DHM may require sufficient exposure time to fully engage metabolic and inflammatory pathways, while some redox-related responses may occur earlier during treatment. Residual heterogeneity after stratification is likely attributable to inherent variations in study design, including differences in mouse strain (C57BL/6 J vs. LDLR⁻/⁻ vs. ApoE⁻/⁻), diet composition, DHM formulation and purity, housing conditions, and the generally incomplete reporting of randomization and blinding. Such methodological inconsistencies are well recognized in preclinical MASLD research and have been repeatedly emphasized in ARRIVE and SYRCLE guidance (140, 141).

In addition, differences in administration route may also have contributed to heterogeneity. Most studies administered DHM by oral gavage, whereas one study delivered DHM through drinking water with an estimated dose conversion. Variations in administration route can influence absorption kinetics, effective exposure, and dose precision, thereby potentially affecting outcome variability. Although sensitivity analyses suggested that the estimated-dose study did not materially alter pooled results, route-related pharmacokinetic differences remain a plausible contributor to between-study heterogeneity. Taken together, these factors indicate that heterogeneity in the present meta-analysis is largely explainable by biological and design-related variability rather than inconsistent treatment direction. In addition, because data from the highest therapeutically tested dose per study were selected according to a prespecified rule, the pooled effect sizes may reflect upper-bound efficacy estimates rather than average-dose effects, and should therefore be interpreted conservatively. Although subgroup analyses were performed, substantial residual heterogeneity persisted for some outcomes. Therefore, these pooled estimates—particularly for outcomes with substantial residual heterogeneity—should be interpreted with caution, as unresolved between-study variability may reduce the robustness and precision of the estimates.

4.9 Limitations and future directions

Despite these encouraging findings, several limitations should be acknowledged. First, although 14 studies were included, some endpoints (insulin, IL-6, CAT, signaling proteins) were reported by only two or three experiments, which reduces precision and makes these outcomes more susceptible to small-study effects.

Second, while all studies included in this meta-analysis were conducted in China using male high-fat diet mouse models, this homogeneity in animal models enhances comparability and reduces experimental variability. However, it may limit the generalizability of the results, particularly when extrapolating findings to female animals, different MASLD models (e.g., Western-diet, MCD, or metabolic/hypertensive comorbid models), or diverse racial/regional populations. Previous research has shown that metabolic responses, including lipid accumulation and inflammation, may vary not only due to sex and genetic background, but also based on the disease model used. For instance, different models of diet-induced steatosis may result in distinct disease progression patterns, affecting how treatments like DHM are evaluated (56, 142–144). Given these considerations, future studies should aim to include female animal models, models of advanced disease stages, and models from diverse geographical backgrounds to better reflect the full spectrum of human disease progression and treatment responses. This restricted biological diversity may also contribute to variability in effect size estimates and treatment responsiveness, since both sex differences and model severity are known modifiers of metabolic and inflammatory outcomes in MASLD.

Third, substantial between-study heterogeneity was observed for several outcomes, which is a common feature of preclinical meta-analyses and may limit the precision of some pooled estimates. Despite this, effect directions were generally consistent across outcomes, and leave-one-out analyses indicated that no single study unduly drove the overall findings, supporting the overall robustness of the findings.

DHM is a food-derived flavonoid predominantly found in vine tea (Ampelopsis grossedentata), with reported high abundance in dried plant material, which supports its nutraceutical appeal (145). However, DHM exhibits limited oral bioavailability due to solubility/instability and rapid metabolism, and formulation or delivery strategies may be required to achieve sustained systemic exposure (146). Notably, human studies in metabolic liver disease have used capsule-based supplementation regimens (e.g., 150 mg capsules administered twice daily for 3 months), and early-phase safety/pharmacokinetic dose-escalation studies are ongoing, suggesting practical feasibility of supplement-level dosing while highlighting the need for further optimization of formulation and long-term safety evaluation (82).

Another important limitation relates to mechanistic inference. Most included studies evaluated pathway activity using expression or phosphorylation markers without employing pathway inhibition, pharmacological blockade, or genetic manipulation models (e.g., knockout or knockdown designs). Therefore, although AMPK-, Nrf2-, and inflammation-related pathways were consistently associated with DHM treatment effects, direct causal relationships cannot be firmly established. Future mechanistic studies using pathway-specific inhibition or genetic models are needed to validate mechanistic hierarchy and causality.

In view of these findings, DHM may be regarded as a promising, mechanistically plausible, and biologically active candidate compound for MASLD; however, it remains at the preclinical stage of the translational pathway. Future research should prioritize (i) standardized animal studies employing predefined DHM doses, at least two treatment durations (≤8 weeks and ≥12 weeks), and harmonized MASLD outcome panels; (ii) mechanistic investigations that directly link AMPK/Nrf2/SIRT1 activation to histological and metabolic improvement; and (iii) early-phase clinical trials or rigorously controlled nutraceutical studies in patients with MASLD to determine whether the biochemical and inflammatory benefits observed in mice translate to humans. Well-designed translational studies and clinical trials are ultimately required to establish whether the preclinical advantages of DHM can be reproduced in MASLD patients.

5 Conclusion

This systematic review and preclinical meta-analysis suggests that DHM confers promising benefits across steatosis-related, metabolic, oxidative stress, and inflammatory endpoints in murine models of NAFLD. These findings support DHM as a multifaceted hepatoprotective candidate with mechanistic plausibility and potential translational value. While additional work is needed to establish its translational relevance, the current evidence base strengthens the rationale for continued investigation of DHM in metabolic liver disease. However, the high variability observed in some outcomes suggests that further research with more standardized protocols is necessary to confirm these findings. The translational relevance to humans remains uncertain due to species differences and methodological variability in the current preclinical evidence. In conclusion, while the evidence from this meta-analysis supports the potential benefits of DHM in the treatment of NAFLD/MASLD, future studies should address the limitations regarding sex bias and geographical variability (142, 143). Expanding research to include female animal models and more diverse populations will be essential for improving the generalizability and clinical applicability of DHM treatment (56, 144).

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