Hyperuricemia and Diabetic Kidney Disease: A Mechanistic Exploration and Clinical Translation Study Based on Multi-Omics Integration and Real-World Evidence

Hyperuricemia and Diabetic Kidney Disease: A Mechanistic Exploration and Clinical Translation Study Based on Multi-Omics Integration and Real-World Evidence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hyperuricemia and Diabetic Kidney Disease: A Mechanistic Exploration and Clinical Translation Study Based on Multi-Omics Integration and Real-World Evidence YuXia Zi, JiaMin He, WenXing Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8127837/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Diabetic kidney disease (DKD), a severe microvascular complication of diabetes. Emerging evidence implicates hyperuricemia (HUA) as a critical yet underexplored contributor to DKD pathogenesis. Methods This study integrates cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), transcriptomic analysis from the Gene Expression Omnibus (GEO) database, and single-center real-world longitudinal cohort data. Using multivariate regression models, machine learning algorithms, differential gene expression analysis, and the individual slope method, we systematically investigated the association between HUA and DKD and its underlying mechanisms. Results Among 5,766 diabetic patients from NHANES, the prevalence of HUA was 38.7% in the DKD group. After multivariate adjustment, HUA independently increased the risk of DKD. Real-world data analysis of diabetic patients revealed the prevalence of HUA was 37.6% ,and a significant negative correlation between baseline serum uric acid levels and the annual estimated glomerular filtration rate (eGFR) decline rate. Patients with HUA had an increased risk of rapid renal function decline. Transcriptomic analysis identified eight uric acid metabolism-related differentially expressed genes (DEGs). To assess clinical relevance, we analysed correlations between urate-related genes and DKD traits via Nephroseq v5. Our findings suggest that HUA may accelerate DKD progression via multi-aspect. Conclusion Hyperuricemia accelerates DKD progression through multiple molecular mechanisms. Personalized uric acid management strategies based on real-world evidence hold significant clinical importance. Diabetic Kidney Disease Hyperuricemia Real-World Study Machine Learning Gene expression analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction 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. Methods Data Sources and Study Population NHANES Data : Data from seven cycles (2005- March 2020) were extracted ( https://wwwn.cdc.gov/nchs/nhanes/ ) : 2005–2006 (10,348 participants), 2007–2008 (10,149 participants), 2009–2010 (10,537 participants), 2011–2012 (9,756 participants), 2013–2014 (10,175 participants), 2015–2016 (9,971 participants), and 2017–March 2020 (15,560 participants), totalling 76,496 eligible participants. Inclusion criteria: 1) Diagnosis of diabetes; 2) Complete measurement data on uric acid, serum creatinine (Scr), and albuminuria. Participants < 20 years, participants with malignancy or pregnancy status, or incomplete data were excluded. Real-World Cohort : Data from patients attending a single center between 2020–2025 were collected. Inclusion criteria: 1) Diagnosis of diabetes; 2) At least three creatinine measurements ≥ 0.5 years apart; 3) Complete uric acid measurement data. Patients with acute kidney injury, Participants < 18 years, or incomplete data were excluded. Transcriptomic Data : Two transcriptomic datasets (GSE30528 and GSE30529) from the NCBI Gene Expression Omnibus ( GEO ; https://www.ncbi.nlm.nih.gov/geo/ ) were selected for microarray analysis [ 10 ] . These datasets, generated via the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), encompass gene expression profiles from the glomerular and tubular compartments of renal tissues derived from 13 healthy controls and 9 DKD patients. Definitions of Diabetes, DKD, and Hyperuricemia Diabetes Mellitus: Participants were classified as having diabetes if they met ≥ 1 of the following criteria: Fasting plasma glucose ≥ 126 mg/dL; Glycated haemoglobin (HbA1c) ≥ 6.5%; Current use of glucose-lowering agents or insulin; Self-reported physician-diagnosed diabetes [ 11 ] . 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 ] . Covariates 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 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. Identification of Potential Key Uric Acid Metabolism-Related Genes Involved in DKD Pathogenesis 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). Statistical Methods 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. Results Characteristics of the NHANES Study Populations The cohort included 5,766 diabetic patients, representing 23,225,972 US adults after weighting. Patients in the DKD group were older, less educated, and more likely to have hypertension and HUA. (38.7% vs. 21.6%) (Table 1). Table 1 Baseline characteristics according to DKD status DKD p 0 1 n 15044682.1 8181290.4 Gender, n(%) Male 7951767.3 (52.9) 4302797.8 (52.6) 0.906 Female 7092914.8 (47.1) 3878492.5 (47.4) Age, years, n(%) ≥ 20 1884087.4 (12.5) 610560.0 (7.5) < 0.001 ≥ 40 7383115.6 (49.1) 2542433.1 (31.1) ≥ 60 5777479.1 (38.4) 5028297.3 (61.5) Race , n(%) Mexican American 1671859.2 (11.1) 920430.4 (11.3) 0.293 Non-Hispanic White 1099603.9 (7.3) 502929.0 (6.1) Non-Hispanic Black 8547360.8 (56.8) 4568731.1 (55.8) Non-Hispanic Asian 2250123.9 (15.0) 1367476.4 (16.7) Other Race 1475734.3 (9.8) 821723.4 (10.0) Education level , n(%) Less than high school 3243175.4 (21.6) 2278714.3 (27.9) < 0.001 High school grade or equivalent 3795087.8 (25.2) 2380191.4 (29.1) Some college or above 8006418.9 (53.2) 3522384.6 (43.1) Marital status , n(%) Married/Living with Partner 10060541.7 (66.9) 4763990.3 (58.2) < 0.001 Widowed/Divorced/Separated 3322535.0 (22.1) 2704437.8 (33.1) Never married 1661605.4 (11.0) 712862.2 (8.7) BMI, kg/m2, n(%) < 25 1601215.2 (10.6) 948196.5 (11.6) 0.027 ≥ 25 3961433.6 (26.3) 1824316.9 (22.3) ≥ 30 9482033.2 (63.0) 5408777.0 (66.1) PIR, n(%) ≤ 1.3 3294487.6 (21.9) 2406448.9 (29.4) 3.5 5459770.6 (36.3) 2065155.0 (25.2) Hypertension status , n(%) No 5244231.6 (34.9) 1492458.8 (18.2) < 0.001 Yes 9800450.5 (65.1) 6688831.5 (81.8) hyperuricemia , n(%) No 11789467.1 (78.4) 5015608.9 (61.3) < 0.001 Yes 3255215.0 (21.6) 3165681.4 (38.7) Smoking status, , n(%) No 7824153.9 (52.0) 3931650.6 (48.1) 0.052 Yes 7220528.2 (48.0) 4249639.8 (51.9) SUA(median [IQR]) 5.300 [4.500, 6.300] 6.000 [4.900, 7.100] < 0.001 UACR(median [IQR]) 7.997 [5.320, 12.971] 60.740 [31.030, 170.571] < 0.001 Scr (median [IQR]) 0.810 [0.680, 0.940] 1.020 [0.771, 1.320] < 0.001 HbA1c (median [IQR]) 6.700 [6.100, 7.600] 7.000 [6.300, 8.400] < 0.001 eGFR (median [IQR]) 94.832 [81.611, 105.843] 68.490 [50.430, 96.826] < 0.001 Cross-Sectional Association between Hyperuricemia and DKD 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). Table 2 Association between HUA and DKD in patients with diabetes mellitus. hyperuricemia OR 95% CI P Model 1 (Intercept) Reference Yes 2.286 (1.950, 2.680) < 0.001 Model 2 (Intercept) Reference Yes 2.170 (1.843, 2.555) < 0.001 Model 3 (Intercept) Reference Yes 2.138 (1.822, 2.510) < 0.001 95% CI = 95% confidence interval; OR = odds ratio Model 1 = No adjusted Model 2 = Adjusted for age, race and education level Model 3 = Model 2 covariates + PIR + hypertension status + HbA1c status. Machine Learning Prediction Models A cohort of 5,766 diabetic patients was randomly divided into a training set (n = 4,037, 70%) and a test set (n = 1,729, 30%) at a ratio of 7:3. To evaluate the predictive performance of HUA for the development of DKD, five distinct machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression, random forest, support vector machine-recursive feature elimination (SVM-RFE), neural network, and extreme gradient boosting (XGBoost) models, were employed. In Table 3, among the five algorithms applied to the training set, the random forest model exhibited the highest predictive performance, achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.892 (Fig. 1a). In the test set, the LASSO regression model achieved the highest AUC-ROC (0.674, Fig. 1b). Table 3 The AUC-ROC curve, accuracy, sensitivity, specificity, PPV, and NPV from different models. LASSO regression Random forest Support vector machine Neural network XGBoost Train AUC of ROC 0.682 0.892 0.689 0.700 0.758 Accuracy 0.695 0.805 0.653 0.697 0.733 Sensitivity 0.414 0.866 0.604 0.653 0.699 Specificity 0.846 0.772 0.680 0.721 0.751 PPV 0.590 0.671 0.503 0.556 0.600 NPV 0.729 0.915 0.762 0.795 0.823 Test AUC of ROC 0.674 0.632 0.661 0.645 0.659 Accuracy 0.661 0.607 0.633 0.646 0.674 Sensitivity 0.632 0.655 0.638 0.489 0.470 Specificity 0.677 0.580 0.630 0.735 0.790 PPV 0.525 0.468 0.493 0.510 0.558 NPV 0.765 0.749 0.755 0.718 0.725 The relative importance of the 11 selected features was evaluated in both the LASSO regression and random forest predictive models. For random forest, Variable importance was calculated using the Gini Importance method (Fig. 2a). For LASSO regression, Variable importance was ranked by the absolute magnitude of coefficients (Fig. 2b). Variable importance analysis identified age, HUA, and hypertension as the top three predictors for DKD. The relative importance of the 11 selected features. (a) features importance in random forest model. (b) features importance in LASSO regression model. Real-World Longitudinal Cohort Analysis The real-world cohort ultimately included 6,626 patients for analysis, the median follow-up years was 2.21 years. (Table 4). Table 4 Baseline characteristics according to HUA status hyperuricemia p 0 1 n 4136 2493 age (mean (SD)) 60.99 (13.48) 61.51 (13.95) 0.135 gender = M (%) 2582 (62.4) 1449 (58.1) 0.001 baseline_eGFR (median [IQR]) 82.32 [54.77, 97.95] 55.18 [19.12, 82.73] < 0.001 baseline_uric_acid (median [IQR]) 5.20 [4.35, 5.89] 7.77 [7.13, 8.75] < 0.001 eGFR_slope (median [IQR]) -0.64 [-5.06, 6.97] -0.25 [-4.57, 5.89] 0.400 eGFR_follow_up_years (median [IQR]) 2.30 [1.21, 3.55] 2.04 [1.05, 3.35] < 0.001 Association between Uric Acid and Renal Function Decline Linear regression analysis showed a significant negative correlation between baseline uric acid levels and the eGFR slope after adjusting for age, sex, and baseline eGFR. For every 1 mg/dL increase in uric acid, the annual eGFR decline rate increased by 0.506 mL/min/1.73m²/year (Table 5). Table 5 Association between Uric Acid and eGFR slope Estimate Std. Error t value Pr(>|t|) (Intercept) 23.419 1.495 15.67 < 0.001 baseline_uric_acid -0.506 0.114 -4.426 < 0.001 age -0.154 0.016 -9.545 < 0.001 gender = M -1.048 0.443 -2.365 0.018 baseline_eGFR -0.136 0.006 -22.121 < 0.001 Hyperuricemia and Rapid Renal Function Decline Multivariable logistic regression indicated that patients with HUA had a 13.4% increased risk of rapid renal function decline. Other independent risk factors included age, male sex, and higher baseline eGFR levels (Table 6). Table 6 Association between HUA and Progression of DKD OR 95% CI p (Intercept) 0.141 (0.096, 0.207) < 0.001 HUA 1.327 (1.160, 1.518) < 0.001 age 1.01 (1.005, 1.015) < 0.001 gender = M 1.272 (1.110, 1.461) < 0.001 baseline_eGFR 0.992 (0.990, 0.994) < 0.001 Identification of Uric acid metabolism-associated DEGs Following standardization of the raw data and removal of outliers, our analysis identified 413 DEGs in the GSE30528 dataset, consisting of 273 downregulated and 139 upregulated genes. The subsequent intersection of these DEGs with uric acid metabolism-related genes yielded 2 significant urate-associated DEGs, including NME7 (NME/NM23 Family Member 7) and RRM2 (Ribonucleotide Reductase Regulatory Subunit M2). In parallel, the GSE30529 dataset included 529 DEGs (73 downregulated and 456 upregulated), from which we identified 7 DEGs significantly associated with uric acid metabolism, including RRM2, GUCY1A1 (Guanylate Cyclase 1 Soluble Subunit Alpha 1), ADCY7 (Adenylate Cyclase 7), DCK (Deoxycytidine Kinase), PAPSS1 (Phosphoadenosine Phosphosulfate Synthase 1), IMPDH2(Inosine Monophosphate Dehydrogenase 2), and ADA (Adenosine Deaminase).Notably, the RRM2 gene was consistently upregulated in both datasets, suggesting its potential as a key regulator in urate metabolic pathways (Table 7). Table 7 The detailed characteristics of these uric acid metabolism-associated DEGs Datasets Gene logFC AveExpr t P.Value adj.P.Val B Change GSE30528 NME7 -1.237 -0.181 -5.450 < 0.001 0.001 3.092 Down RRM2 1.361 0.388 3.906 0.001 0.011 -0.621 Up GSE30529 IMPDH2 1.116 0.215 4.975 < 0.001 0.003 2.020 Up PAPSS1 1.030 0.087 4.973 < 0.001 0.003 2.014 Up GUCY1A1 1.249 -0.008 4.576 < 0.001 0.004 1.069 Up ADCY7 1.224 0.271 4.366 < 0.001 0.006 0.569 Up RRM2 1.050 0.341 4.206 < 0.001 0.007 0.190 Up DCK 1.257 0.080 4.052 < 0.001 0.009 -0.174 Up ADA 1.005 0.337 3.221 0.004 0.028 -2.094 Up Clinical Correlation Analysis of Identified Uric Acid Metabolism-Related Genes in DKD To evaluate the clinical relevance of these potential urate-regulating transcription factors, we performed correlation analyses between target genes and DKD clinical characteristics via the Nephroseq v5 online tool. The Nephroseq v5 data incorporated in this study were derived from multiple studies employing different GFR estimation equations (MDRD, CG, and CKD-EPI). Notably, compared with non-DKD controls, DKD patients presented significantly increased mRNA expression of RRM2, IMPDH2, PAPSS1, GUCY1A1, ADCY7, DCK, and ADA, but decreased NME7 expression (Fig. 3). Furthermore, in renal tissues, the mRNA expression levels of RRM2, IMPDH2, PAPSS1, GUCY1A1, and DCK were significantly negatively correlated with the GFR in DKD patients Intriguingly, ADCY7 and ADA expression correlated negatively with GFR in both DKD patients and controls, indicating their broader association with renal functional impairment. In contrast, NME7 expression was positively correlated with the GFR in both the DKD and control groups (Fig. 4). Discussion 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. Conclusion 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. Declarations Funding This work was supported by a grant from Reserve Talents Project for Young and Middle-aged Academic and Technical Leaders of Yunnan Province (Project Number: 202205AC160062), Yunnan Key Laboratory of Organ Transplantation (202449CE340016), Medical and Health Talents Project for Xingdian Talent Support Program of Yunnan Province (Project Number: XDYC-YLWS-2023-0030), and First-Class Discipline Team of Kunming Medical University (Project Number: 2024XKTDPY03). Author information Authors and Affiliations Department of Nephrology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China. YuXia Zi, JiaMin He & WenXing Fan Yunnan Key Laboratory of Organ Transplantation, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China. WenXing Fan Author Contributions Statement Y.X.Z. and J.M.H. wrote the manuscript and analyzed data. W.X.F. conceptualized the study, critically reviewed the manuscript, and supervised revisions. All authors approved the final version of the manuscript. Acknowledgments Statement "We acknowledge the Gene Expression Omnibus (GEO) database, Nephroseq.V5 platform contributors, and the National Health and Nutrition Examination Survey (NHANES) research team for providing open-access datasets that were indispensable to this study." Corresponding authors Correspondence to WenXing Fan. Address: Department of Nephrology, First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Kunming, Yunnan Province 650032, China. Telephone number and fax: +86-15987165447; +86-087165324888 E-mail: [email protected] Ethics declarations Ethics approval and consent to participate The procedures followed were in accordance with the ethical standards of the Helsinki Declaration, which were developed by the World Medical Association. This cohort study was approved by the Ethics Committee of First Affiliated Hospital of Kunming Medical University with a waiver for informed consent. Other data used in this study were extracted from public databases. Data availability: Publicly available datasets were analyzed in this study. Data can be found below: www.cdc.gov/nchs/nhanes/ ; https://www.ncbi.nlm.nih.gov/geo/ ; Nephroseq Login. Anonymized data can be obtained from the corresponding author upon reasonable request for legitimate research purposes. Consent for publication: Not applicable. Clinical trial number: Not applicable. Competing interests The authors declare no competing interests. References Alicic RZ, Rooney MT, Tuttle KR. Diabetic Kidney Disease: Challenges, Progress, and Possibilities[J]. Clin J Am Soc Nephrol. 2017;12(12):2032–45. Zhu L, Wang X, Sun J, et al. Hyperuricemia Predicts the Progression of Type 2 Diabetic Kidney Disease in Chinese Patients[J]. Diabetes Ther. 2023;14(3):581–91. 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Ruan Z, Lu T, Chen Y, et al. Association Between Psoriasis and Nonalcoholic Fatty Liver Disease Among Outpatient US Adults[J]. JAMA Dermatol. 2022;158(7):745–53. Nishiwaki H, Missikpode C, Ricardo AC, et al. Time-Updated Estimated GFR Variability Is Associated With Mortality, Cardiovascular Disease, and End-Stage Kidney Disease in Patients With CKD: Findings From the CRIC Study[J]. Am J Kidney Dis. 2025;85(6):695–e7031. Park HC, Lee YK, Cho A, et al. Diabetic retinopathy is a prognostic factor for progression of chronic kidney disease in the patients with type 2 diabetes mellitus[J]. PLoS ONE. 2019;14(7):e0220506. Barnini C, Russo E. Asymptomatic Hyperuricemia and the Kidney: Lessons from the URRAH Study[J], 2025, 15(1). Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"JiaMin","middleName":"","lastName":"He","suffix":""},{"id":553979909,"identity":"d09a5e48-fea9-4654-ba85-d877d16684f4","order_by":2,"name":"WenXing Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBAC9gYQWSEhx8be2PjwAzFaeA6AyDM2xvw8h5uNJYjWwtiWljhzRnqbAA9RWtjPHpPmYTvMuOHmwzYGCQY7Od0GQlp48tIkZ/AcZja4ndj2oIAh2djsAAEt9gw5Zjc+SBxmA2ppN5BgOJC4jZAWHv43ZjcSDA7zGNw82CbBQ5QWCZAtCWkSkjMYidbyxvznjAM2Bvw8icBANiDCLzz8OcbGvP8k6tvYjz98+KHCTo6gFjRgQJryUTAKRsEoGAU4AAC/TUHVFLrloAAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"WenXing","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2025-11-16 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1","display":"","copyAsset":false,"role":"figure","size":87318,"visible":true,"origin":"","legend":"\u003cp\u003eFigure legend not provided with this version\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8127837/v1/46334085223fa4c203d7957e.png"},{"id":97488484,"identity":"1f3bd430-f338-4b5a-80e9-a1150923d903","added_by":"auto","created_at":"2025-12-05 01:53:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73412,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative importance of the 11 selected features. (a) features importance in random forest model. (b) features importance in LASSO regression model.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8127837/v1/b2b6d8a8cbec09451546633f.png"},{"id":97488460,"identity":"09c90a2c-7c97-4b55-b7b4-d290b94d2a68","added_by":"auto","created_at":"2025-12-05 01:53:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63745,"visible":true,"origin":"","legend":"\u003cp\u003e*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, P \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8127837/v1/e236115d51a0c4e423586651.png"},{"id":97488446,"identity":"e75ee860-5f3e-4287-94f2-6b9aad7580c2","added_by":"auto","created_at":"2025-12-05 01:53:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85125,"visible":true,"origin":"","legend":"\u003cp\u003ep \u0026lt; 0.05 was considered statistically significant. GFR: glomerular filtration rate.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8127837/v1/a31ea8c0f158ff8a51c8f847.png"},{"id":101655460,"identity":"ea2a9025-43ea-426e-bc09-f9d0e0fc16ac","added_by":"auto","created_at":"2026-02-02 09:57:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1303713,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8127837/v1/baa0be5d-d88c-4f0e-9fc9-c87c0a03c68c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hyperuricemia and Diabetic Kidney Disease: A Mechanistic Exploration and Clinical Translation Study Based on Multi-Omics Integration and Real-World Evidence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic kidney disease (DKD), one of the most severe microvascular complications of diabetes, affects approximately 40% of diabetic patients during disease progression\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. 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.\u003c/p\u003e\u003cp\u003eRecent studies have confirmed an independent association between serum uric acid levels and renal function decline\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. 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.\u003c/p\u003e\u003cp\u003eHUA activates the renin‒angiotensin system (RAS), impairs endothelial nitric oxide release, and induces renal vasoconstriction and hypertension, ultimately exacerbating kidney injury\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. 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\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Notably, polymorphisms in urate transporter genes (SLC2A9 and ABCG2) may modulate renal outcomes in HUA\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eThis 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.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Sources and Study Population\u003c/h2\u003e\u003cp\u003e\u003cb\u003eNHANES Data\u003c/b\u003e: Data from seven cycles (2005- March 2020) were extracted (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) : 2005\u0026ndash;2006 (10,348 participants), 2007\u0026ndash;2008 (10,149 participants), 2009\u0026ndash;2010 (10,537 participants), 2011\u0026ndash;2012 (9,756 participants), 2013\u0026ndash;2014 (10,175 participants), 2015\u0026ndash;2016 (9,971 participants), and 2017\u0026ndash;March 2020 (15,560 participants), totalling 76,496 eligible participants. Inclusion criteria: 1) Diagnosis of diabetes; 2) Complete measurement data on uric acid, serum creatinine (Scr), and albuminuria. Participants\u0026thinsp;\u0026lt;\u0026thinsp;20 years, participants with malignancy or pregnancy status, or incomplete data were excluded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReal-World Cohort\u003c/b\u003e: Data from patients attending a single center between 2020\u0026ndash;2025 were collected. Inclusion criteria: 1) Diagnosis of diabetes; 2) At least three creatinine measurements\u0026thinsp;\u0026ge;\u0026thinsp;0.5 years apart; 3) Complete uric acid measurement data. Patients with acute kidney injury, Participants\u0026thinsp;\u0026lt;\u0026thinsp;18 years, or incomplete data were excluded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTranscriptomic Data\u003c/b\u003e: Two transcriptomic datasets (GSE30528 and GSE30529) from the NCBI Gene Expression Omnibus (\u003cem\u003eGEO\u003c/em\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were selected for microarray analysis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. These datasets, generated via the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), encompass gene expression profiles from the glomerular and tubular compartments of renal tissues derived from 13 healthy controls and 9 DKD patients.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinitions of Diabetes, DKD, and Hyperuricemia\u003c/h3\u003e\n\u003cp\u003eDiabetes Mellitus: Participants were classified as having diabetes if they met\u0026thinsp;\u0026ge;\u0026thinsp;1 of the following criteria:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGlycated haemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCurrent use of glucose-lowering agents or insulin;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSelf-reported physician-diagnosed diabetes\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eDiabetic Kidney Disease (DKD) was diagnosed by either a reduced eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2; (calculated via the CKD-EPI equation) or albuminuria (urine albumin-to-creatinine ratio [UACR]\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g) \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHyperuricemia (HUA) was defined as SUA\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mg/dL (416.0 \u0026micro;mol/L) for males or \u0026ge;\u0026thinsp;6.0 mg/dL (357.0 \u0026micro;mol/L) for females\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical variables were extracted from NHANES questionnaires, including age was categorized into \u0026ge;\u0026thinsp;20, \u0026ge;40, and \u0026ge;\u0026thinsp;60 years; And race/ethnicity was classified as Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Anthropometric measures included body mass index (BMI), which was calculated as weight (kg)/height (m\u0026sup2;) and was stratified into normal (\u0026lt;\u0026thinsp;25 kg/m\u0026sup2;), overweight (25\u0026ndash;29.9 kg/m\u0026sup2;), and obese (\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) groups. Health-related variables included smoking status (defined as lifetime consumption of \u0026ge;\u0026thinsp;100 cigarettes\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e) and hypertension status, identified through self-reported diagnosis, antihypertensive medication use, or elevated blood pressure (average systolic\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Laboratory analyses included Scr, HbA1c, UACR, and SUA, with the eGFR calculated via the CKD-EPI formula\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In subsequent weighted logistic regression and machine learning analyses, HbA1c was dichotomized using a cut-off of \u0026ge;\u0026thinsp;6.5%\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, whereas UACR was categorized as normoalbuminuria (UACR\u0026thinsp;\u0026lt;\u0026thinsp;30 mg/g), microalbuminuria (30\u0026ndash;300 mg/g), or macroalbuminuria (\u0026gt;\u0026thinsp;300 mg/g)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Socioeconomic factors included household income categorized by the ratio of family income to poverty (PIR) as low (\u0026le;\u0026thinsp;1.3), middle (\u0026gt;\u0026thinsp;1.3\u0026ndash;3.5), or high (\u0026gt;\u0026thinsp;3.5)\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e; 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.\u003c/p\u003e\n\u003ch3\u003eIdentification of Potential Key Uric Acid Metabolism-Related Genes Involved in DKD Pathogenesis\u003c/h3\u003e\n\u003cp\u003eDifferential expression analysis was conducted via the \"limma\" package in R, with thresholds set at |log2-fold change (FC)| \u0026gt;1 and adjusted \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;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).\u003c/p\u003e\n\u003ch3\u003eStatistical Methods\u003c/h3\u003e\n\u003cp\u003eAll data processing and statistical analyses were performed in R (version 4.4.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), 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.\u003c/p\u003e\u003cp\u003eFor group comparisons, Student\u0026rsquo;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\u0026thinsp;=\u0026thinsp;β₀ + β₁ \u0026times; Time, where β₁ represents the individual slope\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The progression of DKD was defined as a\u0026thinsp;\u0026ge;\u0026thinsp;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)\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.The machine learning models were evaluated via the \"pROC\" package. A two-tailed P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv\u003e\n \u003ch2\u003eCharacteristics of the NHANES Study Populations\u003c/h2\u003e\n \u003cp\u003eThe cohort included 5,766 diabetic patients, representing 23,225,972 US adults after weighting. Patients in the DKD group were older, less educated, and more likely to have hypertension and HUA. (38.7% vs. 21.6%) (Table 1).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics according to DKD status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDKD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15044682.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8181290.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7951767.3 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4302797.8 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7092914.8 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3878492.5 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1884087.4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e610560.0 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7383115.6 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2542433.1 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5777479.1 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5028297.3 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace ,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1671859.2 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e920430.4 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1099603.9 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e502929.0 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8547360.8 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4568731.1 (55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2250123.9 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1367476.4 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1475734.3 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e821723.4 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level ,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3243175.4 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2278714.3 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school grade or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3795087.8 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2380191.4 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome college or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8006418.9 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3522384.6 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status ,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/Living with Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10060541.7 (66.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4763990.3 (58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3322535.0 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2704437.8 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1661605.4 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e712862.2 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m2,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1601215.2 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e948196.5 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3961433.6 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1824316.9 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9482033.2 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5408777.0 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3294487.6 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2406448.9 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6290423.9 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3709686.5 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5459770.6 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2065155.0 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension status ,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5244231.6 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1492458.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9800450.5 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6688831.5 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehyperuricemia ,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11789467.1 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5015608.9 (61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3255215.0 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3165681.4 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status, ,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7824153.9 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3931650.6 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7220528.2 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4249639.8 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUA(median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.300 [4.500, 6.300]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.000 [4.900, 7.100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUACR(median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.997 [5.320, 12.971]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.740 [31.030, 170.571]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScr (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.810 [0.680, 0.940]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.020 [0.771, 1.320]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.700 [6.100, 7.600]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.000 [6.300, 8.400]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.832 [81.611, 105.843]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.490 [50.430, 96.826]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eCross-Sectional Association between Hyperuricemia and DKD\u003c/h3\u003e\n\u003cp\u003eMultivariable 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).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAssociation between HUA and DKD in patients with diabetes mellitus.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehyperuricemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.950, 2.680)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.843, 2.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.822, 2.510)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e95% CI\u0026thinsp;=\u0026thinsp;95% confidence interval; OR\u0026thinsp;=\u0026thinsp;odds ratio\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel 1\u0026thinsp;=\u0026thinsp;No adjusted\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel 2\u0026thinsp;=\u0026thinsp;Adjusted for age, race and education level\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eModel 3\u0026thinsp;=\u0026thinsp;Model 2 covariates\u0026thinsp;+\u0026thinsp;PIR\u0026thinsp;+\u0026thinsp;hypertension status\u0026thinsp;+\u0026thinsp;HbA1c status.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ch2\u003eMachine Learning Prediction Models\u003c/h2\u003e\n \u003cp\u003eA cohort of 5,766 diabetic patients was randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;4,037, 70%) and a test set (n\u0026thinsp;=\u0026thinsp;1,729, 30%) at a ratio of 7:3. To evaluate the predictive performance of HUA for the development of DKD, five distinct machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression, random forest, support vector machine-recursive feature elimination (SVM-RFE), neural network, and extreme gradient boosting (XGBoost) models, were employed.\u003c/p\u003e\n \u003cp\u003eIn Table 3, among the five algorithms applied to the training set, the random forest model exhibited the highest predictive performance, achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.892 (Fig. 1a). In the test set, the LASSO regression model achieved the highest AUC-ROC (0.674, Fig. 1b).\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"char\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe AUC-ROC curve, accuracy, sensitivity, specificity, PPV, and NPV from different models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLASSO regression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRandom forest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSupport vector machine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNeural network\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eXGBoost\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC of ROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC of ROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe relative importance of the 11 selected features was evaluated in both the LASSO regression and random forest predictive models. For random forest, Variable importance was calculated using the Gini Importance method (Fig. 2a). For LASSO regression, Variable importance was ranked by the absolute magnitude of coefficients (Fig. 2b). Variable importance analysis identified age, HUA, and hypertension as the top three predictors for DKD.\u003c/p\u003e\n \u003cp\u003eThe relative importance of the 11 selected features. (a) features importance in random forest model. (b) features importance in LASSO regression model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003eReal-World Longitudinal Cohort Analysis\u003c/h2\u003e\n \u003cp\u003eThe real-world cohort ultimately included 6,626 patients for analysis, the median follow-up years was 2.21 years. (Table 4).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics according to HUA status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehyperuricemia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.99 (13.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.51 (13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egender\u0026thinsp;=\u0026thinsp;M (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2582 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1449 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebaseline_eGFR (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.32 [54.77, 97.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.18 [19.12, 82.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebaseline_uric_acid (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.20 [4.35, 5.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.77 [7.13, 8.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR_slope (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.64 [-5.06, 6.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25 [-4.57, 5.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR_follow_up_years (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30 [1.21, 3.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04 [1.05, 3.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAssociation between Uric Acid and Renal Function Decline\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLinear regression analysis showed a significant negative correlation between baseline uric acid levels and the eGFR slope after adjusting for age, sex, and baseline eGFR. For every 1 mg/dL increase in uric acid, the annual eGFR decline rate increased by 0.506 mL/min/1.73m\u0026sup2;/year (Table 5).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation between Uric Acid and eGFR slope\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebaseline_uric_acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egender\u0026thinsp;=\u0026thinsp;M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebaseline_eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-22.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003eHyperuricemia and Rapid Renal Function Decline\u003c/h2\u003e\n \u003cp\u003eMultivariable logistic regression indicated that patients with HUA had a 13.4% increased risk of rapid renal function decline. Other independent risk factors included age, male sex, and higher baseline eGFR levels (Table 6).\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"char\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation between HUA and Progression of DKD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.096, 0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.160, 1.518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.005, 1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egender\u0026thinsp;=\u0026thinsp;M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.110, 1.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebaseline_eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.990, 0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003eIdentification of Uric acid metabolism-associated DEGs\u003c/h2\u003e\n \u003cp\u003eFollowing standardization of the raw data and removal of outliers, our analysis identified 413 DEGs in the GSE30528 dataset, consisting of 273 downregulated and 139 upregulated genes. The subsequent intersection of these DEGs with uric acid metabolism-related genes yielded 2 significant urate-associated DEGs, including NME7 (NME/NM23 Family Member 7) and RRM2 (Ribonucleotide Reductase Regulatory Subunit M2).\u003c/p\u003e\n \u003cp\u003eIn parallel, the GSE30529 dataset included 529 DEGs (73 downregulated and 456 upregulated), from which we identified 7 DEGs significantly associated with uric acid metabolism, including RRM2, GUCY1A1 (Guanylate Cyclase 1 Soluble Subunit Alpha 1), ADCY7 (Adenylate Cyclase 7), DCK (Deoxycytidine Kinase), PAPSS1 (Phosphoadenosine Phosphosulfate Synthase 1), IMPDH2(Inosine Monophosphate Dehydrogenase 2), and ADA (Adenosine Deaminase).Notably, the RRM2 gene was consistently upregulated in both datasets, suggesting its potential as a key regulator in urate metabolic pathways (Table 7).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eThe detailed characteristics of these uric acid metabolism-associated DEGs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDatasets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elogFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAveExpr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP.Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadj.P.Val\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChange\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE30528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNME7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRRM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE30529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMPDH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAPSS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGUCY1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADCY7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRRM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003eClinical Correlation Analysis of Identified Uric Acid Metabolism-Related Genes in DKD\u003c/h2\u003e\n \u003cp\u003eTo evaluate the clinical relevance of these potential urate-regulating transcription factors, we performed correlation analyses between target genes and DKD clinical characteristics via the Nephroseq v5 online tool. The Nephroseq v5 data incorporated in this study were derived from multiple studies employing different GFR estimation equations (MDRD, CG, and CKD-EPI).\u003c/p\u003e\n \u003cp\u003eNotably, compared with non-DKD controls, DKD patients presented significantly increased mRNA expression of RRM2, IMPDH2, PAPSS1, GUCY1A1, ADCY7, DCK, and ADA, but decreased NME7 expression (Fig. 3).\u003c/p\u003e\n \u003cp\u003eFurthermore, in renal tissues, the mRNA expression levels of RRM2, IMPDH2, PAPSS1, GUCY1A1, and DCK were significantly negatively correlated with the GFR in DKD patients Intriguingly, ADCY7 and ADA expression correlated negatively with GFR in both DKD patients and controls, indicating their broader association with renal functional impairment. In contrast, NME7 expression was positively correlated with the GFR in both the DKD and control groups (Fig. 4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy 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:\u003c/p\u003e\u003cp\u003eFirst, 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\u0026mdash;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.\u003c/p\u003e\u003cp\u003eSecond, 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\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. This finding provides evidence for revising current uric acid control targets.\u003c/p\u003e\u003cp\u003eThird, 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 \u003cem\u003eRRM2\u003c/em\u003e and \u003cem\u003eIMPDH2\u003c/em\u003e suggests that uric acid might directly participate in renal remodeling by influencing cell cycle and proliferation.\u003c/p\u003e\u003cp\u003eFrom 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.\u003c/p\u003e\u003cp\u003eStudy 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.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from Reserve Talents Project for Young and Middle-aged Academic and Technical Leaders of Yunnan Province (Project Number: 202205AC160062), Yunnan Key Laboratory of Organ Transplantation (202449CE340016), Medical and Health Talents Project for Xingdian Talent Support Program of Yunnan Province (Project Number: XDYC-YLWS-2023-0030), and First-Class Discipline Team of Kunming Medical University (Project Number: 2024XKTDPY03).\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eDepartment of Nephrology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China.\u003c/p\u003e\n\u003cp\u003eYuXia Zi, JiaMin He \u0026amp; WenXing Fan\u003c/p\u003e\n\u003cp\u003eYunnan Key Laboratory of Organ Transplantation, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China.\u003c/p\u003e\n\u003cp\u003eWenXing Fan\u003c/p\u003e\n\u003cp\u003eAuthor Contributions Statement\u003c/p\u003e\n\u003cp\u003eY.X.Z. and J.M.H. wrote the manuscript and analyzed data. W.X.F. conceptualized the study, critically reviewed the manuscript, and supervised revisions. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments Statement\u003cbr\u003e \"We acknowledge the Gene Expression Omnibus (GEO) database, Nephroseq.V5 platform contributors, and the National Health and Nutrition Examination Survey (NHANES) research team for providing open-access datasets that were indispensable to this study.\"\u003c/p\u003e\n\u003cp\u003eCorresponding authors\u003c/p\u003e\n\u003cp\u003eCorrespondence to WenXing Fan.\u003c/p\u003e\n\u003cp\u003eAddress: Department of Nephrology, First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Kunming, Yunnan Province 650032, China.\u003c/p\u003e\n\u003cp\u003eTelephone number and fax: +86-15987165447; +86-087165324888\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe procedures followed were in accordance with the ethical standards of the Helsinki Declaration, which were developed by the World Medical Association. This cohort study was approved by the Ethics Committee of First Affiliated Hospital of Kunming Medical University with a waiver for informed consent. Other data used in this study were extracted from public databases. \u003c/p\u003e\n\u003cp\u003eData availability:\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. Data can be found below: www.cdc.gov/nchs/nhanes/ ; https://www.ncbi.nlm.nih.gov/geo/ ;\u003c/p\u003e\n\u003cp\u003eNephroseq Login. \u003c/p\u003e\n\u003cp\u003eAnonymized data can be obtained from the corresponding author upon reasonable request for legitimate research purposes.\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number:\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlicic RZ, Rooney MT, Tuttle KR. Diabetic Kidney Disease: Challenges, Progress, and Possibilities[J]. Clin J Am Soc Nephrol. 2017;12(12):2032\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu L, Wang X, Sun J, et al. 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Diabetic retinopathy is a prognostic factor for progression of chronic kidney disease in the patients with type 2 diabetes mellitus[J]. PLoS ONE. 2019;14(7):e0220506.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarnini C, Russo E. Asymptomatic Hyperuricemia and the Kidney: Lessons from the URRAH Study[J], 2025, 15(1).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetic Kidney Disease, Hyperuricemia, Real-World Study, Machine Learning, Gene expression analysis","lastPublishedDoi":"10.21203/rs.3.rs-8127837/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8127837/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDiabetic kidney disease (DKD), a severe microvascular complication of diabetes. Emerging evidence implicates hyperuricemia (HUA) as a critical yet underexplored contributor to DKD pathogenesis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study integrates cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), transcriptomic analysis from the Gene Expression Omnibus (GEO) database, and single-center real-world longitudinal cohort data. Using multivariate regression models, machine learning algorithms, differential gene expression analysis, and the individual slope method, we systematically investigated the association between HUA and DKD and its underlying mechanisms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 5,766 diabetic patients from NHANES, the prevalence of HUA was 38.7% in the DKD group. After multivariate adjustment, HUA independently increased the risk of DKD. Real-world data analysis of diabetic patients revealed the prevalence of HUA was 37.6% ,and a significant negative correlation between baseline serum uric acid levels and the annual estimated glomerular filtration rate (eGFR) decline rate. Patients with HUA had an increased risk of rapid renal function decline. Transcriptomic analysis identified eight uric acid metabolism-related differentially expressed genes (DEGs). To assess clinical relevance, we analysed correlations between urate-related genes and DKD traits via Nephroseq v5. Our findings suggest that HUA may accelerate DKD progression via multi-aspect.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHyperuricemia accelerates DKD progression through multiple molecular mechanisms. Personalized uric acid management strategies based on real-world evidence hold significant clinical importance.\u003c/p\u003e","manuscriptTitle":"Hyperuricemia and Diabetic Kidney Disease: A Mechanistic Exploration and Clinical Translation Study Based on Multi-Omics Integration and Real-World Evidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 01:53:17","doi":"10.21203/rs.3.rs-8127837/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88c79587-25d8-4cd0-a7ea-2b11834481dc","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T09:56:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 01:53:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8127837","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8127837","identity":"rs-8127837","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}