Diabetic kidney disease (DKD), one of the most severe microvascular complications of diabetes, affects approximately 40% of diabetic patients during disease progression^[1]. While traditional risk factors such as hyperglycemia and hypertension are well-recognized, the mechanistic complexity of hyperuricemia as an emerging risk marker remains incompletely elucidated.

Recent studies have confirmed an independent association between serum uric acid levels and renal function decline^[2–3]. However, existing evidence has important limitations: most studies rely on cross-sectional designs, making causal inference difficult; there is a lack of comprehensive analytical frameworks integrating population epidemiology, clinical longitudinal data, and molecular mechanisms; and the relationship between dynamic changes in uric acid and renal function progression in real-world settings is understudied.

HUA activates the renin‒angiotensin system (RAS), impairs endothelial nitric oxide release, and induces renal vasoconstriction and hypertension, ultimately exacerbating kidney injury^[4–5]. Clinical studies have confirmed that elevated SUA levels correlate with mild reductions in the estimated glomerular filtration rate (eGFR), even in individuals with normal baseline renal function^[6]. Notably, polymorphisms in urate transporter genes (SLC2A9 and ABCG2) may modulate renal outcomes in HUA^[7–9] .

This study innovatively constructs a triple evidence chain: first, confirming the cross-sectional association between HUA and DKD using nationally representative data; second, validating the long-term impact of HUA on renal function decline through a real-world longitudinal cohort; and finally, revealing potential biological mechanisms via transcriptomic analysis. This "macro-to-micro" integrated research strategy provides a comprehensive perspective for understanding the role of HUA in DKD.

Diabetes Mellitus: Participants were classified as having diabetes if they met ≥ 1 of the following criteria:

Diabetic Kidney Disease (DKD) was diagnosed by either a reduced eGFR < 60 mL/min/1.73 m² (calculated via the CKD-EPI equation) or albuminuria (urine albumin-to-creatinine ratio [UACR] ≥ 30 mg/g) ^[12].

Hyperuricemia (HUA) was defined as SUA ≥ 7.0 mg/dL (416.0 µmol/L) for males or ≥ 6.0 mg/dL (357.0 µmol/L) for females^[4].

Demographic and clinical variables were extracted from NHANES questionnaires, including age was categorized into ≥ 20, ≥40, and ≥ 60 years; And race/ethnicity was classified as Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other^[13]. Anthropometric measures included body mass index (BMI), which was calculated as weight (kg)/height (m²) and was stratified into normal (< 25 kg/m²), overweight (25–29.9 kg/m²), and obese (≥ 30 kg/m²) groups. Health-related variables included smoking status (defined as lifetime consumption of ≥ 100 cigarettes^[14]) and hypertension status, identified through self-reported diagnosis, antihypertensive medication use, or elevated blood pressure (average systolic ≥ 140 mmHg and/or diastolic ≥ 90 mmHg)^[15]. Laboratory analyses included Scr, HbA1c, UACR, and SUA, with the eGFR calculated via the CKD-EPI formula^[16]. In subsequent weighted logistic regression and machine learning analyses, HbA1c was dichotomized using a cut-off of ≥ 6.5%^[17], whereas UACR was categorized as normoalbuminuria (UACR < 30 mg/g), microalbuminuria (30–300 mg/g), or macroalbuminuria (> 300 mg/g)^[18]. Socioeconomic factors included household income categorized by the ratio of family income to poverty (PIR) as low (≤ 1.3), middle (> 1.3–3.5), or high (> 3.5)^[19]; education level (less than high school, high school/equivalent, or college/above); and marital status (married/cohabiting, widowed/divorced/separated, or never married). This comprehensive data framework enabled multi-aspect analysis of population health determinants.

Differential expression analysis was conducted via the "limma" package in R, with thresholds set at |log2-fold change (FC)| >1 and adjusted P value < 0.05. Differentially expressed genes (DEGs) between DKD patients and healthy controls were subjected to pathway enrichment analysis via the "clusterProfiler" package. A curated set of 159 uric acid metabolism-related genes was retrieved from the Molecular Signatures Database (MSigDB). Intersection analysis was conducted to identify overlapping DEGs associated with uric acid metabolism. External validation of the candidate DEGs was conducted via the Nephroseq V5 platform (Nephroseq Login).

All data processing and statistical analyses were performed in R (version 4.4.3; https://www.r-project.org/), incorporating sampling weights and complex survey design adjustments. Continuous variables are summarized as weighted medians with interquartile ranges (IQRs), whereas categorical variables are reported as weighted proportions.

For group comparisons, Student’s t tests or Mann‒Whitney U tests were used for continuous variables, and chi‒square tests were used for categorical data. The annual eGFR change rate for each patient was calculated using linear mixed models, fitting the model: eGFR = β₀ + β₁ × Time, where β₁ represents the individual slope^[20]. The progression of DKD was defined as a ≥ 25% decline in eGFR from baseline, creating a dichotomous variable where patients meeting this threshold were coded as 1 (progressors) and others as 0 (non-progressors)^[21].The machine learning models were evaluated via the "pROC" package. A two-tailed P value < 0.05 indicated statistical significance.

Multivariable logistic regression models showed that HUA was significantly associated with DKD before and after adjusting for age, race, education level, PIR, hypertension status, and HbA1c(Table 2).

Model 3 = Model 2 covariates + PIR + hypertension status + HbA1c status.

By integrating cross-sectional survey, real-world longitudinal cohort, and transcriptomic analysis, this study provides multi-level evidence for the role of HUA in DKD. Three main findings warrant emphasis:

First, cross-sectional analysis confirmed a strong association between HUA and DKD, consistent with previous studies. More importantly, real-world longitudinal data indicate that this association has a temporal cumulative effect—patients with HUA not only have worse baseline renal function but also experience a faster rate of renal function decline. This "double-hit" pattern suggests that HUA may act as an accelerator for DKD progression.

Second, real-world data revealed a dose-response relationship between uric acid levels and renal function decline. We found that even uric acid levels within the traditional normal range were associated with renal function decline, supporting the notion of "no safe threshold" for uric acid^[22]. This finding provides evidence for revising current uric acid control targets.

Third, mechanistic studies elucidated that HUA likely affects the kidney through multiple pathways. Beyond known inflammatory and oxidative stress pathways, our transcriptomic analysis uncovered evidence of purine metabolic reprogramming. Specifically, the upregulation of RRM2 and IMPDH2 suggests that uric acid might directly participate in renal remodeling by influencing cell cycle and proliferation.

From a clinical practice perspective, our findings support: incorporating uric acid monitoring into routine DKD management; adopting more proactive renal protection strategies for diabetic patients with HUA; and establishing uric acid control targets based on individualized risk assessment.

Study strengths include the integration of multiple evidence chains and long-term follow-up real-world data. Limitations encompass potential confounding in real-world data and the single-center design. Future prospective intervention studies are needed to validate the impact of urate-lowering therapy on DKD progression.

This study, integrating epidemiological, real-world longitudinal, and transcriptomic evidence, confirms that hyperuricemia is a significant risk factor for the development and progression of diabetic kidney disease. Real-world evidence demonstrates that patients with hyperuricemia experience a faster rate of renal function decline, with a clear dose-response relationship. Mechanistically, hyperuricemia may accelerate renal injury through multiple pathways, including purine metabolic reprogramming, oxidative stress, and epigenetic regulation. These findings support the incorporation of uric acid control into comprehensive DKD management strategies and provide new insights for personalized treatment.