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Research Article

Effects of Traditional Chinese Decoctions on Glycemic and Renal Outcomes in Diabetic Kidney Disease: A Systematic Review and Network Meta-analysis

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DOI:

10.3791/73056

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July 31st, 2026

 ,  ,  ,  ,  ,  ,  ,  , 
1Graduate School, Tianjin University of Traditional Chinese Medicine, 2School of Acupuncture and Tuina, Changchun University of Chinese Medicine, 3Tianjin Academy of Traditional Chinese Medicine, 4The Affiliated Hospital of Tianjin Academy of Traditional Chinese Medicine

In This Article

Summary

This systematic review and network meta-analysis of 97 randomized trials involving 8,956 participants compared traditional Chinese herbal decoctions for diabetic kidney disease. Several decoctions showed potential benefits for glycemic control and renal outcomes, but methodological limitations and heterogeneity warrant cautious interpretation and further high-quality trials.

Abstract

This network meta-analysis evaluated traditional Chinese medicine (TCM) decoctions as adjuncts to conventional treatment for diabetic kidney disease (DKD). The outcomes included glycated hemoglobin A1c (HbA1c), 24-hour urinary protein excretion (24 hUP), serum creatinine (Cr), and blood urea nitrogen (BUN). PubMed, Embase, the Cochrane Library, Web of Science, CNKI, Wanfang, and VIP were searched from inception to January 20, 2026, for randomized controlled trials of traditional Chinese herbal decoctions for DKD. Two reviewers independently screened studies, extracted data, and assessed risk of bias using RoB 2.0. Bayesian network meta-analysis was conducted using R 4.4.1 and Stata 15, with mean differences and 95% credible intervals calculated for continuous outcomes and SUCRA values used to rank interventions. Ninety-seven trials involving 8,956 participants were included. YQYYHXT, FFHQT, and YSTLT were associated with greater reductions in HbA1c than control treatment, with YQYYHXT ranking highest. JWTHCQT ranked highest for reducing 24-hour urinary protein, YSTLT ranked highest for lowering serum creatinine, and ZWT ranked highest for reducing blood urea nitrogen. Several decoctions may improve glycemic and renal outcomes; however, methodological limitations, heterogeneity, and reliance on ranking probabilities warrant cautious interpretation. Further high-quality randomized trials are needed.

Introduction

Diabetes is a common chronic metabolic disorder worldwide and a growing public health challenge1. Long-term hyperglycemia can lead to a wide range of chronic complications, among which diabetic kidney disease (DKD) is one of the most serious microvascular manifestations2. Epidemiological evidence suggests that approximately 30–40% of individuals with diabetes eventually develop DKD3. DKD is not only a major contributor to chronic kidney disease but also a leading cause of end-stage renal disease (ESRD) globally4. As the condition progresses, patients may develop persistent proteinuria, progressive decline in renal function, and a higher risk of cardiovascular complications, which substantially reduces quality of life and increases the overall burden on healthcare systems5,6. Slowing the progression of DKD has therefore become an important goal in current clinical management.

Current therapeutic strategies mainly focus on comprehensive metabolic control, including strict regulation of blood glucose, blood pressure, and lipid levels. Pharmacological interventions such as renin–angiotensin system inhibitors are commonly used to reduce proteinuria and delay renal deterioration2,7. Despite these approaches, disease progression remains difficult to prevent in a considerable proportion of patients8. Despite advances in conventional management, many patients remain at risk of progressive kidney dysfunction. This residual clinical burden supports the evaluation of adjunctive therapeutic options; however, their efficacy and safety require separate and rigorous assessment9.

Traditional Chinese medicine (TCM) has long been used in the management of diabetes and its complications. Within the theoretical framework of TCM, DKD is often associated with syndromes such as “thirst and wasting” and “edema,” and its pathogenesis is frequently described as involving deficiency of qi and yin, spleen–kidney deficiency, and blood stasis obstructing the collaterals10. Chinese herbal decoctions are formulated according to the principles of syndrome differentiation and holistic regulation. Through multi-component and multi-target actions, these formulas are thought to exert therapeutic effects on metabolic and renal dysfunction and are widely used as adjunctive therapy for DKD11. In recent years, a growing number of clinical studies have examined the potential benefits of Chinese herbal decoctions in DKD. Some trials have reported improvements in glycemic control, including reductions in glycated hemoglobin (HbA1c), while others have suggested potential renal protective effects, such as reductions in 24-hour urinary protein (24 hUP) and improvements in serum creatinine (Cr) and blood urea nitrogen (BUN) levels12,13,14. In addition, many herbal formulas consist of multiple medicinal components that may act synergistically through mechanisms such as anti-inflammatory activity, antioxidant effects, improvement of microcirculation, and inhibition of glomerular sclerosis15.

Although numerous randomized controlled trials (RCTs) have evaluated different Chinese herbal decoctions for DKD, their results remain inconsistent. Variations in intervention protocols, study design, and sample size may partly explain these discrepancies16,17. Conventional pairwise meta-analysis generally compares only two interventions at a time, which limits its ability to evaluate multiple treatment options simultaneously. Network meta-analysis (NMA) provides an alternative approach by combining both direct and indirect evidence within a single analytical framework. This method allows comparisons across multiple interventions and enables estimation of relative treatment rankings, thereby offering more comprehensive evidence to support clinical decision-making.

To better clarify the comparative effects of different herbal decoctions, randomized controlled trials investigating Chinese herbal interventions for DKD were systematically collected and analyzed using a network meta-analysis approach. The analysis focused on key metabolic and renal outcomes, including HbA1c, 24 hUP, serum creatinine, and blood urea nitrogen. By integrating available evidence and comparing multiple treatment strategies, this study aims to provide updated evidence regarding the potential role of traditional Chinese herbal decoctions in the management of DKD.

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Protocol

Evidence acquisition

The study was designed and reported in accordance with the PRISMA guidelines18. The protocol was registered in the PROSPERO database (registration number: CRD420261307956). The completed PRISMA 2020 checklist has been uploaded as a separate reporting checklist (Supplementary File 1). All the materials used in this study are listed in the Table of Materials.

Literature search strategy

Seven databases—PubMed, Embase, the Cochrane Library, Web of Science, CNKI, Wanfang Data, and VIP—were searched from their respective inception dates through January 20, 2026. Database-specific controlled vocabulary (MeSH or Emtree) was combined with free-text terms for diabetic kidney disease, traditional Chinese medicine, and randomized controlled trials. The reference lists of pertinent reports were also checked to identify additional eligible records. Title and abstract screening began on February 14, 2026; full-text assessment began on February 25, 2026; and data extraction began on March 5, 2026. Two reviewers worked independently at each stage, resolving disagreements by discussion and, when needed, adjudication by a third reviewer.

Inclusion and exclusion criteria

Inclusion criteria

Eligible studies were randomized controlled trials involving participants diagnosed with diabetic kidney disease, without restrictions on age, sex, or disease duration. Interventions consisted of traditional Chinese herbal decoctions administered alone or in combination with conventional Western medical therapy. Eligible comparators included conventional treatment, Western medical therapy, or placebo. The primary outcomes were glycated hemoglobin (HbA1c) and 24-hour urinary protein excretion (24 hUP), while the secondary outcomes were serum creatinine (Cr) and blood urea nitrogen (BUN).

Exclusion criteria

Case reports, retrospective studies, reviews, animal experiments, and duplicate publications were excluded. Studies evaluating interventions other than decoction-based Chinese herbal medicine, including single-herb preparations and proprietary Chinese medicine formulations, were also excluded. Additional reasons for exclusion included unavailable or non-extractable outcome data, unclear identification of primary or secondary outcomes, failure of the study population to meet the diagnostic criteria for diabetic kidney disease, and insufficient distinction between the intervention and control groups.

Literature screening and data extraction

Records retrieved from the databases were imported into EndNote 21 (Clarivate Analytics) and de-duplicated. Two reviewers then assessed eligibility independently against the prespecified criteria. Screening proceeded in two passes: records that were clearly irrelevant were removed after title and abstract review, and reports retained at that stage underwent full-text evaluation. Reviewer disagreements were settled through discussion; a third reviewer adjudicated unresolved cases. Study selection and reasons for exclusion were documented in a PRISMA flow diagram. Using a prespecified form, two reviewers independently recorded the first author, year of publication, participant and arm sample sizes, participant characteristics, intervention and comparator details, treatment duration, and, for each reported outcome, its definition, unit, assessment time, mean, and standard deviation. Differences between extracted entries were reconciled by consensus. When essential numerical information was missing or ambiguous, the corresponding author was contacted. An outcome was omitted from the relevant synthesis if usable data could not be obtained.

Standardization of decoction composition and preparation

For each intervention, two reviewers extracted the base formula, individual herbal components, reported dose, formula modifications, preparation method, administration frequency, and treatment duration. Herbal doses were converted to grams when direct conversion was possible; unclear doses or preparation procedures were recorded as not reported and were not imputed. Modified prescriptions were assigned to the same network node only when the original study explicitly identified the base formula and retained its principal components. The extracted preparations were considered members of the same formula family rather than pharmacologically identical products. Differences in dosage, processing, and modified compatibility were treated as potential sources of clinical heterogeneity.

Risk of bias

Two reviewers independently evaluated each included trial with Cochrane's revised risk-of-bias tool for randomized trials (RoB 2)19. The assessment covered bias arising from randomization, deviations from intended interventions, missing outcome data, outcome measurement, and selection of the reported result. RoB 2 decision rules were used to assign each domain—and the overall result—to low risk, some concerns, or high risk. Differences in judgment were reconciled through discussion.

GRADE assessment

For each outcome, certainty in the network estimates was rated under the grading of recommendations assessment, development, and evaluation (GRADE) framework20 and its extension for network meta-analysis32. Evidence from randomized trials started at high certainty and was downgraded when warranted for risk of bias, inconsistency, indirectness, imprecision, or publication bias. Final ratings were reported as high, moderate, low, or very low.

Statistical analysis

Analyses used post-treatment means and standard deviations; endpoint and change-from-baseline values were not pooled together. Effects were summarized as mean differences (MDs) with 95% credible intervals (CrIs). Units were harmonized to percentage points for HbA1c, g/24 h for 24-hour urinary protein, µmol/L for serum creatinine, and mmol/L for blood urea nitrogen, with other reported units converted by standard factors. When an SD was absent, it was derived from a standard error or 95% confidence interval if sufficient information was available; values were not borrowed from other studies. An outcome contribution was excluded when no usable dispersion measure could be recovered. An arm-level Bayesian network meta-analysis was fitted separately for each outcome. Continuous measurements were modeled with a normal likelihood and identity link within a random-effects consistency model. Basic treatment effects received diffuse normal priors with mean 0 and standard deviation 15 × om.scale, whereas the between-study heterogeneity standard deviation received a Uniform (0, om.scale) prior. All arms of multi-arm trials entered the model jointly; shared comparators were represented once so that correlations between contrasts were retained.

Four Markov chain Monte Carlo (MCMC) chains were run for each model, with 5,000 adaptation iterations and 20,000 subsequent draws per chain; the thinning interval was 1. Chain mixing and convergence were examined using trace plots, density plots, and the Brooks–Gelman–Rubin potential scale reduction factor (PSRF), for which a value below 1.05 was considered acceptable. Model adequacy was judged by comparing posterior residual deviance with the number of unconstrained data points and by contrasting deviance information criterion (DIC) values from consistency and inconsistency models. Bayesian models were fitted with the gemtc (version 1.1-1) package in R version 4.4.133, and Stata 15 was used for supplementary analyses. Ranking distributions were summarized as surface under the cumulative ranking curve (SUCRA) values. Exploratory univariable meta-regression analyses were performed separately for each outcome to assess country, treatment duration, total sample size, and publication year as potential sources of heterogeneity. Regression coefficients (β) and two-sided p values were reported. A p value of <0.05 was considered statistically significant. Because these analyses were exploratory, the findings were interpreted cautiously.

Subgroup and sensitivity analyses
Because substantial heterogeneity was observed in several direct comparisons, post hoc exploratory subgroup analyses were conducted using random-effects pairwise meta-analysis. Studies were stratified according to treatment duration (≤12 weeks vs >12 weeks), DKD stage (early-stage/stage III vs advanced-stage/stage IV or later), concomitant Western medical treatment (ACE inhibitor or angiotensin receptor blocker-based treatment vs other conventional treatment), and total sample size (<100 vs ≥100 participants). Subgroup differences were evaluated using an interaction test rather than by comparing statistical significance within individual subgroups. Analyses were performed only when at least two studies were available in each subgroup. Studies with mixed or insufficiently reported DKD stages or treatment regimens were classified as unclear and excluded from the corresponding interaction test.

Between-study heterogeneity was assessed using the I2 statistic. Potential sources of heterogeneity were explored using univariable meta-regression for country, treatment duration, total sample size, and publication year. Subgroup analyses according to treatment duration, diabetic kidney disease stage, baseline conventional treatment, and patient characteristics were considered when the relevant study-level information was sufficiently reported, and the resulting network remained connected. Sensitivity analysis excluding trials with a high overall risk of bias was prespecified. When an analysis could not be performed because of incomplete reporting, sparse strata, or network disconnection, the reason was reported, and the remaining uncertainty was considered in the interpretation.

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Results

Literature screening results

Through database searches, 4,795 relevant articles were identified. Specifically, PubMed yielded 158 articles, Embase 413, Cochrane Library 231, Web of Science 198, CNKI 954, Wan fang 1,071, and VIP Database 1,770. After removal of 1,265 duplicates, 3,530 records remained for title and abstract screening. Of these, 3,410 were excluded, leaving 120 reports for full-text assessment. After excluding 23 reports that did not meet eligibility criteria, 9...

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Discussion

This network meta-analysis identified outcome-specific adjunctive associations rather than a single herbal formula that was consistently superior across all outcomes. Compared with conventional treatment, Yiqi Yangyin Huoxue Decoction (YQYYHXT) was associated with a reduction in HbA1c (MD, −1.09 percentage points; 95% CrI, −1.20 to −0.97), although heterogeneity was substantial (I2 = 87%). Jiawei Taohe Chengqi Decoction (JWTHCQT) showed the largest estimated reduction in 24 hUP (MD, −1....

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Disclosures

OpenAI ChatGPT was used to assist with English translation and language editing. All AI-assisted content was reviewed and verified by the authors, who take full responsibility for the accuracy and integrity of the submitted work. The authors declare that they have no competing interests.

Acknowledgements

PROSPERO registration: CRD420261307956; registered February 13, 2026 (version 1.0). This review received no specific external or commercial funding. Institutional support was provided by the Scientific Research Project of Hebei Provincial Administration of Traditional Chinese Medicine (Grant No. T2025052) and the "High-Caliber Talents Cultivation Program" of The Affiliated Hospital of Tianjin Academy of Traditional Chinese Medicine (Document No. Jinzhongyanfuren [2024]55). The supporting institutions had no role in the design of the review, study selection, data extraction, analysis, or interpretation, preparation of the manuscript, or the decision to submit the manuscript for publication.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
China National Knowledge Infrastructure (CNKI) (database)China National Knowledge InfrastructureNot applicableSearched from inception to 20 Jan 2026. URL: https://www.cnki.net/
Cochrane Library—CENTRAL (database)CochraneNot applicableSearched from inception to 20 Jan 2026. URL: https://www.cochranelibrary.com/central
Cochrane Risk of Bias 2 (RoB 2) toolCochraneRoB 2.0; version date not recordedUsed to assess study-level risk of bias in randomized trials. URL: https://www.riskofbias.info/welcome/rob-2-0-tool
Embase (database)Elsevier B.V.Not applicableSearched from inception to 20 Jan 2026. URL: https://www.elsevier.com/products/embase
EndNote (review-management software)Clarivate21Used for importing records and deduplication. URL: https://endnote.com/
gemtc (R package)Comprehensive R Archive Network (CRAN)1.1-1Used for Bayesian network meta-analysis, model fitting, and treatment rankings. URL: https://cran.r-project.org/package=gemtc
GRADE approach (assessment tool)GRADE Working GroupNot applicableMethodological framework used for certainty-of-evidence assessment; assessments were performed manually without dedicated software. URL: https://www.gradeworkinggroup.org/
JAGS (statistical software/backend)Martyn Plummer / SourceForge4.3.1Bayesian MCMC engine called through gemtc. URL: https://mcmc-jags.sourceforge.io/
PubMed (database)U.S. National Library of Medicine, NCBINot applicableSearched from inception to 20 Jan 2026. URL: https://pubmed.ncbi.nlm.nih.gov/
R (statistical software)R Foundation for Statistical Computing4.4.1Used for Bayesian network meta-analysis and statistical graphics. URL: https://www.r-project.org/
Stata (statistical software)StataCorp LLCStata/SE 15.1Used for supplementary statistical analyses and graphical summaries. URL: https://www.stata.com/stata15/
VIP Chinese Science and Technology Journal Database (database)Chongqing VIP Information Co., Ltd.Not applicableSearched from inception to 20 Jan 2026. URL: https://www.cqvip.com/
Wanfang Data (database)Beijing Wanfang Data Co., Ltd.Not applicableSearched from inception to 20 Jan 2026. URL: https://www.wanfangdata.com.cn/
Web of Science Core Collection (database)ClarivateNot applicableSearched from inception to 20 Jan 2026. URL: https://www.webofscience.com/

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