INTRODUCTION
Hyperuricemia (HUA) is a metabolic disorder caused by purine metabolism disorder. As a medical condition characterized by elevated serum uric acid levels, its prevalence varies substantially across different geographic regions. Studies estimate that approximately 15–25 % of the general population is affected by this condition. Previous studies have confirmed that HUA is associated with an increased risk of gout, chronic kidney disease (CKD), hypertension, and cardiovascular diseases, thereby imposing a substantial disease burden on patients (1, 2). As a risk factor for CKD progression, HUA also predicts increased all-cause mortality in CKD patients (3). Extensive research indicates that lowering serum uric acid levels has a positive impact on renal injury (4). Notably, over 40 % of diabetic patients may also develop CKD (4). Accumulating evidence suggests that uric acid may contribute to the pathogenesis of diabetic nephropathy. In the Joslin Kidney Study which evaluated predictors of GFR decline in 355 non-proteinuric patients with type 1 diabetes, elevated serum uric acid was independently associated with a significantly increased risk of GFR loss, with a relative risk of 1.5 (95 % CI 1.3–1.9; p = 0.002) per 1 mg per 100 mL increment in serum uric acid (5). Patients with type 2 diabetes and HUA exhibit accelerated CKD progression and elevated risk of adverse cardiovascular events, substantially increasing the risk of polypharmacy (6). However specific recommendations for medication safety management in this patient group remain limited. Current guidelines recommend dapagliflozin as therapeutic option for impaired renal function (7). However, an increase in serum creatinine levels has been observed in patients treated with dapagliflozin (8). Concerns still persist regarding the potential of dapagliflozin to improve renal outcomes in patients; although its effect on serum uric acid is supported by data from diabetic patients, its impact on serum uric acid and renal outcomes in broader patient populations remains unclear (9). In order to address this research gap, we conducted a meta-analysis of clinical study data. This study aimed to investigate the potential mechanisms of dapagliflozin in controlling uric acid levels and improving renal function as well as to identify the patient populations most suitable for treatment with dapagliflozin.
DATA SOURCES AND METHODS
This meta-analysis evaluated the effect of dapagliflozin compared with placebo or active controls on uric acid control and renal outcomes in patients. Randomized controlled trials (RCTs) of dapagliflozin (with or without other medications) as the intervention for renal outcomes and serum uric acid indicators in any population and age group were eligible for inclusion. Studies that lacked a control group or contained incomplete/erroneous data were excluded; non-RCT type studies such as prospective studies, case reports, conference papers, case-control, cohort studies, cross-sectional designs, and animal or cell experiments were also excluded.
We searched four databases Embase, PubMed, Cochrane Library, and Web of Science, yielding an initial pool of 488 publications. After screening, 19 RCTs were included. We included RCTs that reported the following outcomes measures: serum uric acid, Scr, estimated glomerular filtration rate (eGFR), cystatin C (Cys-C), urine albumin-to-creatinine ratio (UACR). Only English publications were included.
Literature quality assessment
All retrieved articles were subjected to blinded title and abstract screening that was independently conducted by two researchers. Full texts of all potentially eligible studies were rigorously evaluated against a predefined set of inclusion and exclusion criteria. Study characteristics and outcome data were extracted independently by two reviewers from all studies meeting the inclusion criteria. Any discrepancies were adjudicated through mutual consensus or, whenever necessary, by consultation with a third reviewer, who carefully reviewed the finally selected trials. Literature screening was performed independently using EndNote X9 software (Clarivate Analytics, USA).
The methodological quality of the included RCTs was assessed using the Cochrane Handbook for Systematic Reviews of Interventions (Cochrane Handbook) (10). Multiple bias-related domains were evaluated, including random sequence generation, blinding of participants and personnel, allocation concealment, blinding of outcome assessment and of outcome data, selective reporting, and other potential sources of bias. Based on the assessment, the risk of bias was categorized into three levels: high, low and unclear risk. The following core domains were applied in the risk-of-bias assessment of eligible RCTs: random sequence generation (selection bias), allocation concealment (selection bias), blinding (performance bias and detection bias), incomplete outcome data (attrition bias), and selective reporting (reporting bias). Fig. S1 graphically summarizes the overall risk of bias.
Data extraction
A set of core information was extracted from each included study: first author name, publication year, participant nationality, intervention protocols for both experimental and control groups, sample size and study duration. Mean difference (MD) with standard deviation (SD) was used to express continuous outcomes, while OR with 95 % confidence interval (CI) was used to express binary applied outcomes.
Chi2 test and I2 statistic, recommended by the Cochrane risk-of-bias tool, were utilized to evaluate inter-study heterogeneity, and a random-effects framework was employed for performing the meta-analysis. To verify the stability of the results, a sensitivity analysis was performed by sequentially removing one study at a time.
The presence of heterogeneity was examined using the above statistical tests. The I² statistic and Cochrane Q test were used to assess heterogeneity. High inter-study heterogeneity was defined as p < 0.1 or I² > 50 %. When significant heterogeneity was present (I² > 50 % or p < 0.1), a random-effects model using restricted maximum likelihood estimation was applied for the meta-analysis; otherwise, a fixed-effect model was used. Statistical significance was defined as p < 0.05, and all analyses were completed using the Stata version 16 (StataCorp LLC, USA).
To assess small-study effects, we employed Egger’s linear regression tests. Specifically, Egger’s test quantifies funnel plot asymmetry by modelling standardized effect estimates as a function of precision (inverse standard error). These formal bias assessments were restricted to meta-analyses comprising eight or more studies. All statistical analyses were performed using Stata version 16 (StataCorp LLC). A two-tailed test was applied, p-value < 0.05 was considered statistically significant.
RESULTS AND DISCUSSION
The initial electronic search yielded 488 papers in total. After removing 161 duplicate records, 327 articles were screened for eligibility, of which 19 were included in the meta-analysis. Fig. 1 summarizes the complete workflow of study selection, assessment and data analysis. Baseline characteristics of the 19 included trials are presented in Table I.
Table I. Summary of study characteristics
PBO – placebo; MET – metformin; OAD – oral antidiabetic drug; Scr – serum creatinine; SUA – serum uric acid; eGFR – estimated glomerular filtration rate; UACR – urine albumin-to-creatinine ratio; a Metformin (≥ 1,500 mg per day); b Stratified by insulin alone or insulin + OADs
Fig. 1. Flowchart illustrating the process of literature selection.
Of these, 18 studies (11–28) reported changes in serum uric acid (SUA) levels from baseline to post-dapagliflozin treatment, while the study by Kohan et al. (29) did not report SUA but provided data on estimated glomerular filtration rate (eGFR).
The meta-analysis showed that dapagliflozin treatment was associated with significantly reduced serum uric acid concentration (–11.4 %, 95 % confidence interval CI = –14.5 to –8.4 %, p < 0.0001) (Fig. 2). Given the significant inter-study heterogeneity (I2 = 76.8 %), a random-effects model was applied. Sensitivity analysis confirmed that no single variable disproportionately influenced the overall results, validating the robustness of conclusions derived from the random-effects model.
Fig. 2. Forest plot comparing uric acid (UA) concentrations between the control and dapagliflozin groups.
Six studies reported changes in eGFR (14, 19, 21, 26, 27, 29) but no statistically significant difference in eGFR change was observed between the dapagliflozin groups and the control groups (0.1 %, 95 % CI = –1.7 to 2.0 %, p = 0.863) (Fig. 3).
Fig. 3. Forest plot depicting variations in estimated glomerular filtration rate (eGFR) following dapagliflozin treatment relative to control condition.
Ten studies reported changes in serum creatinine level (Scr) (12, 14–16, 18–20, 25, 27, 28). Significant heterogeneity supported the use of a random-effects model in the meta-analysis. Changes in Scr were observed between the dapagliflozin and control groups, with no statistically significant difference (+0.6 %, 95 % CI = –0.6 to 1.8 %, p = 0.294) (Fig. 4).
Fig. 4. Forest plot illustrating alterations in serum creatinine (Scr) levels in the dapagliflozin and control groups.
Three studies reported changes in cystatin C levels (16, 20, 27) with no statistically significant difference between the dapagliflozin and control groups (4.2 %, 95 % CI = –0.4 to 8.8 %, p = 0.061) (Fig. 5).
Fig. 5. Forest plot displaying changes in cystatin C (Cys-C) levels in the dapagliflozin and control groups.
Three studies reported changes in the UACR (17, 21, 27). Still, dapagliflozin treatment resulted in no significant reduction in UACR compared with the control condition (–33.1 %, 95 % CI = –115.7 to 49.4 %, p = 0.226). Two trials reported outcomes related to urinary protein excretion (11, 27). Due to discrepancies in the analyte evaluated, the effect metric employed, the units of measurement utilized, the duration of treatment, and the baseline proteinuria status, quantitative pooling was deemed inappropriate. Consequently, a narrative synthesis was performed in accordance with the SWiM guideline, employing direction of effect for the calculation of votes. The results of both trials demonstrated a clear preference for dapagliflozin over placebo, with this observation being consistent across all four studies. In the DELIGHT trial (11), conducted in patients with type 2 diabetes and moderate-to-severe CKD receiving renin-angiotensin system blockade, dapagliflozin 10 mg daily significantly reduced 24-hour urinary albumin excretion at week 24 vs. placebo (–19.9 %, 95 % CI –35.6 to –0.3 %; p = 0.047), with a greater reduction for dapagliflozin plus saxagliptin (–39.7 %; p < 0.0001). In Wilding et al. report (27), patients treated with insulin and exhibiting near-normal baseline proteinuria experienced an increase in urinary albumin under placebo (+4.7 mg L–1) but a decline in all dapagliflozin groups (–17.8 to –34.3 mg L–1) at week 48. These endpoints were reported descriptively without formal testing. Publication bias was assessed by funnel plot and Egger's test. No evidence of significant publication bias was detected by Egger's test (p = 0.42), consistent with the visual symmetry observed in the funnel plot (Fig. S2). If considered as a whole, and despite the exclusion of a quantitative synthesis, the consistent direction of the effect suggests that dapagliflozin reduces urinary protein and albumin excretion in patients with established CKD and albuminuria, and to a large extent independent of glycemic and blood pressure lowering.
Therapeutic and clinical relevance
HUA, as an important metabolic disorder, poses a serious threat to global public health, with its prevalence showing a continuous upward trend worldwide. This disease exhibits a significantly higher incidence among patients diagnosed with CKD (30). Extensive research has confirmed a strong relationship between HUA and various metabolic disorders, including kidney disease, diabetes and cardiovascular diseases (31). The relationship between renal function and uric acid excretion is bidirectional: impaired renal function leads to reduced uric acid excretion and secondary HUA, while elevated uric acid levels actively promote the progression of diabetes. This is reflected in the complex correlation between HbA1c and serum uric acid levels (32). Furthermore, elevated uric acid levels promote cardiovascular morbidity through multiple mechanisms, including activation of the renin-angiotensin system, oxidative stress induction and endothelial dysfunction (33). Another critical aspect of HUA is its role in accelerating CKD and increasing the risk of renal function deterioration. Evidence indicates that HUA significantly exacerbates renal damage through various pathological pathways, including induction of glomerular hypertension, renal hypertrophy and eventual sclerosis (34). In the current clinical context, characterized by frequent multimorbidity, developing a comprehensive therapeutic approach capable of simultaneously addressing HUA and its associated comorbidities is crucial. Further, implementing personalized uric acid management strategies hold crucial clinical significance for breaking the vicious cycle of metabolic disease.
SGLT-2 inhibitors, a recently developed class of oral glucose-lowering agents, have attracted considerable attention due to their unique mechanism of action (34). The pharmacological actions of these drugs involve lowering the threshold for glucose reabsorption and inhibiting glucose uptake within the renal tubules. Consequently, these drugs promote urinary glucose excretion (35).
Current clinical guidelines present a notable paradox. While the 2024 KDIGO CKD (36), 2020 ACR (37), and EULAR gout guidelines (38) all conditionally recommend against initiating urate-lowering therapy (ULT) for CKD and asymptomatic HUA, accumulating evidence from clinical trials that controlled for serum uric acid levels showed renoprotective benefits (39). We argue that this discrepancy between guideline recommendations and clinical evidence does not stem from conflicting data, but rather from fundamental disagreements surrounding the definition of the "asymptomatic." Current guidelines narrowly define "asymptomatic" as the absence of prior gout flares or subcutaneous tophi (38), establishing this as the sole symptom-based threshold for ULT initiation. However, from a pathophysiological perspective, the clinical spectrum of urate crystal deposition disease extends far beyond joint inflammation. Nephrolithiasis, urate nephropathy, and subsequent declines in eGFR, proteinuria, and elevated Scr are all direct consequences of monosodium urate crystal deposition in renal tissue (40). Categorizing a patient with confirmed CKD and clear signs of renal injury as "asymptomatic" solely because they do not have gout – thereby failing to meeting the criteria for ULT – ignores the fact that urate crystal deposition disease affects the whole body. This leads to a significant gap between guideline-directed practice and actual pathophysiology.
Dapagliflozin offers a pragmatic solution to this therapeutic stalemate. Unlike conventional ULT agents, dapagliflozin indirectly promotes uric acid excretion by enhancing urinary glucose excretion, thereby inducing a compensatory increase in proximal tubular secretion. Since dapagliflozin is already approved for the treatment of CKD or diabetes, its prescription is not directly influenced by the controversy over whether asymptomatic HUA patients should initiate ULT. Therefore, clinicians may prescribe this drug based on solid evidence for glycemic control and cardiorenal protection, while simultaneously obtaining the additional benefit of serum uric acid lowering and renal protection (31). Thus, dapagliflozin may serve as a pragmatic bridge between metabolic intervention and renoprotection, offering a therapeutic alternative for HUA in patients with CKD that transcends the conventional ULT framework.
Real-world evidence has raised concerns regarding the efficacy of dapagliflozin in reducing UACR (41), and the initial eGFR decline observed during treatment initiation has further tempered clinician enthusiasm for its use (42). Indeed, real-world prescribing of SGLT2 inhibitors in non-diabetic chronic kidney disease (ND-CKD) remains suboptimal; this therapeutic inertia reflects an interplay of clinician-level barriers, socio-economic disparities, and healthcare system constraints (7). A global prospective study of approximately 15,000 patients with diabetes across 37 countries demonstrated that ND-CKD was associated with a 27 % reduction in SGLT2i prescribing (43). In this context, we conducted the present meta-analysis to quantify the urate-lowering and renoprotective effects of dapagliflozin, thereby strengthening the evidence base to inform clinical practice. It is evident that the present article is deficient in its demonstration of the beneficial effects of dapagliflozin in relation to UACR. Notably, Pollock et al. (11) (patients with average eGFR = 49 mL min–1 per 1.73 m2), Kohan et al. (29) (patients with average eGFR = 44.2 mL min–1 per 1.73 m2), Huang et al. (44) (patients with average Scr of 143.08 μmol L–1) enrolled predominantly CKD populations. Analyses of these studies demonstrated that dapagliflozin effectively reduced UACR (WMD: –39.67 mg g–1, 95 % CI = –70.14 to –9.19, p = 0.01) without resulting in a precipitous decline in eGFR, providing further confidence for its use in CKD populations. Of note, while dapagliflozin reduced proteinuria, it did not have a significantly adverse impact on renal function, as evidenced by the eGFR. Hence, dapagliflozin provides renoprotection without imposing additional renal burden. Conversely, the sustained and stable trajectory of renal function in patient suggests potential long-term renoprotective benefits. As demonstrated in preceding studies, a slight decline in eGFR can be observed during the early stages of initiating dapagliflozin therapy (45). This is followed by a longer period of renal function maintenance. This early eGFR decline may reflect hemodynamic adaptation and a protective mechanism (46).
Fig. 6. Forest plot showing changes in urine albumin-to-creatinine ratio (UACR) in the dapagliflozin and control groups.
Fig. 7. Forest plot showing outcomes measures in the dapagliflozin and control groups.
Clinical implications
This meta-analysis demonstrated that dapagliflozin significantly reduces serum uric acid levels while concomitantly lowering proteinuria, as reflected by decreased UTP and UACR. Notably, conventional markers of renal function – Scr and eGFR – remained stable throughout treatment with dapagliflozin, underscoring its favourable renal safety profile. Collectively, these findings indicate that dapagliflozin offers dual metabolic and renoprotective benefits, supporting its potential as a therapeutic option for patients with chronic kidney disease and hyperuricemia, irrespective of conventional urate-lowering therapy paradigms.
Limitations of the study
Firstly, the follow-up periods of the included studies were mainly concentrated between 24 and 52 weeks, resulting in a lack of long-term data to assess the impact of dapagliflozin on hard endpoints, such as end-stage renal disease or cardiovascular events. Secondly, there is a paucity of research reporting on urinary protein excretion; further research is needed to investigate their renal protective functions. Third, differences in dosing regimens and patient characteristics (e.g., baseline renal function) across studies may have introduced heterogeneity. In conclusion, the lack of subgroup analyses in non-diabetic populations limits the generalizability of the conclusions.
CONCLUSIONS
In summary, dapagliflozin effectively lowers serum uric acid levels by promoting uric acid excretion, reduces urinary protein excretion, and demonstrates good safety under conditions with stable renal function. These findings further consolidate our confidence in treating patients with diabetes and CKD accompanied by HUA with dapagliflozin, providing important evidence for its clinical application. Future research should focus on the efficacy of dapagliflozin in different populations (e.g., various CKD stages), explore its synergistic effects with other urate lowering drugs, and verify its long-term impact on hard endpoints through extended follow-up.
Abbreviatios, acronyms, symbols. – CKD – chronic kidney disease, Cys-C – cystatin C, eGFR – estimated glomerular filtration rate, GFR – glomerular filtration rate, HUA – hyperuricemia, MD – mean difference, ND-CKD – non-diabetic chronic kidney disease, RCTs – randomized controlled trials, Scr – serum creatinine levels, SUA – serum uric acid, UACR – urine albumin-to-creatinine ratio, ULT – urate-lowering therapy.
Availability of data and materials. – The datasets used and/or analysed during the current study were publicly available from the PubMed, Embase, Cochrane Library, Web of Science databases. This meta-analysis is registered in PROSPERO under registration number CRD42022325174.
Acknowledgements. – Thanks for the support from the Intramural Research Project of China Aerospace Science and Industry Corporation, 731 Hospital.
Conflict of interests. – All authors declare no conflict of interests.
Funding. – This work was funded by the Intramural Research Project of China Aerospace Science and Industry Corporation, 731 Hospital (NO. 2022-QSYN-06).
Author's contribution. – Conceptualization, Y.F. and Y.T.; methodology, Y.F.; software, D.R.; validation, X.R., D.P., and J.Y.; formal analysis, L.Y.; investigation, Y.F.; resources, Y.T.; data curation, D.R.; writing, original draft preparation, Y.F.; writing, review and editing, Y.S.; supervision, X.H.; project administration, D.P.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.
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