Clinical characteristics and the risk factors of patients with chronic kidney disease in young-onset versus late-onset newly diagnosed type 2 diabetes

Clinical characteristics and the risk factors of patients with chronic kidney disease in young-onset versus late-onset newly diagnosed type 2 diabetes | 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 Clinical characteristics and the risk factors of patients with chronic kidney disease in young-onset versus late-onset newly diagnosed type 2 diabetes Yuliang Cui, Ying Li, Shan Yue, Pei Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7121363/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 The association between young-onset diabetes and chronic kidney disease has been well studied, however, few studies have described the features of chronic kidney disease (CKD) specifically in young-onset diabetes. We aimed to compare the clinical characteristics and risk factors for CKD between young-onset and late-onset newly diagnosed type 2 diabetes (T2DM). Methods In this retrospective study, 1194 newly diagnosed T2DM patients were categorized into young-onset (diagnostic age < 40 years) and late-onset (≥ 40 years) groups, further stratified by CKD status. Anthropometric and laboratory data were collected retrospectively. Clinical differences were analyzed using t-tests, Mann-Whitney U tests, and chi-square tests. Logistic regression was used to identify the risk factors for CKD. Results In newly diagnosed T2DM patients, CKD prevalence was similar between young-onset and late-onset groups (24.1% vs. 21.5%, p = 0.163), with no significant association between young-onset diabetes and CKD (OR: 1.16, 95%CI: 0.88–1.53, P = 0.292). Compared to CKD with late-onset diabetes, young-onset patients had higher urinary albumin-to-creatinine ratio, eGFR, BMI, diastolic blood pressure, lipid levels, fasting blood glucose, liver enzymes, and higher metabolic-associated steatotic liver disease prevalence. In both young and late onset groups, diastolic blood pressure and fasting blood glucose were independently associated with CKD, however, gamma-glutamyl transferase was independently associated with CKD only in the young-onset group. Conclusion Young-onset diabetes may not be an independent risk factor for CKD in newly diagnosed T2DM, but CKD patients with young-onset diabetes exhibit distinct clinical characteristics compared to those with late-onset diabetes. Elevated gamma-glutamyl transferase specifically predicted CKD in young-onset T2DM. young-onset type 2 diabetes diabetic kidney disease clinical characteristics risk factors 1. Background Chronic kidney disease (CKD) has emerged as a significant global public health challenge, with its rising prevalence and associated mortality imposing a growing burden on healthcare systems worldwide [ 1 ]. Type 2 diabetes (T2DM), which is the most serious public health problem with an increasing global prevalence [ 2 ], is the leading cause of CKD in most countries [ 3 ]. The rapid spread of T2DM has markedly intensified the epidemiology of T2DM associated CKD [ 4 ]. In the United States, over 40% of individuals with T2DM are affected by CKD [ 5 ]. This situation is even more alarming in Asia, where the prevalence of CKD among adults with type 2 diabetes has been reported to reach a strikingly high level of 58.6%[ 6 ]. CKD is a progressive condition that often leads to end-stage kidney disease (ESKD), with its high costs and resource demands largely attributable to renal replacement therapy (RRT), for which T2DM is the leading indication [ 7 ]. Beyond renal complications, CKD significantly heightens the likelihood of various complications associated with T2DM, such as cardiovascular disease (CVD), stroke, heart failure, infections, diminished quality of life, adverse drug reactions, and premature death [ 8 , 9 ]. Given these severe consequences, early detection and intervention for CKD in patients with T2DM are critical to prevent ESKD and reduce related complications, thereby improving patient outcomes and alleviating the economic and societal burden of CKD. With the change of modern lifestyle and diet structure, increasing numbers of young-onset type 2 diabetes (YOD), defined as diagnosis aged younger than 40 years, have been observed [ 10 , 11 ]. On a global level, prevalence estimates of diabetes among people aged 20–39 years increased from 2.9% (63 million people) in 2013 to 3·8% (260 million) in 2021 [ 12 ]. Patients with YOD are suggested to have distinctive clinical phenotype than patients with late-onset type 2 diabetes (LOD) defined as the age of diagnosis of diabetes ≥ 40 years, including poorer glycemic control, higher levels of BMI and LDL cholesterol, heavier β-cell dysfunction, a stronger family history, and a higher proportion of insulin therapy[ 13 – 16 ]. These characteristics indicate that patients with YOD may have distinct pathophysiology and follow divergent clinical paths compared to patients with LOD. YOD is associated with a higher prevalence of comorbidities and follows a more aggressive natural history, due to longer diabetes duration and worse self-management [ 17 ]. Indeed, previous studies have demonstrated that patients with YOD have higher risks of macrovascular complications and mortality compared to those with LOD[ 18 , 19 ]. However, the association between YOD and CKD risk remains inconclusive. Some studies have shown that YOD patients are more prone to developing CKD and display more severe renal clinicopathological manifestations[ 20 – 22 ]. Alternatively, other studies have proposed that the higher prevalence of CKD in YOD group may lose statistical significance after adjustment for diabetes duration[ 23 , 24 ]. Nevertheless, research on the YOD-CKD relationship is relatively limited, particularly in newly diagnosed diabetes cohorts, where YOD and LOD patients share comparable diabetes durations. Moreover, few studies have comprehensively characterized the clinical features and risk factors associated with CKD in young-onset versus late-onset T2DM. Therefore, we conducted this study to examine the association between YOD and CKD in newly diagnosed patients and to compare CKD-related clinical characteristics and risk factors between YOD and LOD. 2. Methods 2.1 Research design and participants The study population selection followed our previously published methodology with minor modifications [25]. Briefly, we conducted a retrospective analysis of newly diagnosed, drug-naïve T2DM patients hospitalized at our institution between 2013-2025. Diagnostic criteria for T2DM included: (1) meeting standard glycemic thresholds (FPG ≥7.0 mmol/L, random PG ≥11.1 mmol/L, or 2-h PG ≥11.1 mmol/L after OGTT); (2) absence of autoimmune diabetes markers (GADA, IAA or ICA); and (3) clinical exclusion of other diabetes subtypes based on phenotypic characteristics. We excluded patients with acute diabetic complications, pregnancy-related diabetes, or severe comorbid conditions, as detailed in our prior work [25]. Informed consent was obtained from each patient included in the study. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki (6th revision, 2008) as reflected in a priori approval by the ethics committees of Qilu Hospital of Shandong University Dezhou Hospital. 2.2 Physical and laboratory examinations The data including age, sex and disease duration were carefully recorded. Blood pressure, height, and weight were collected following standardized protocols. Fasting venous blood (≥8 h) was collected for laboratory analysis. The measurements of serology parameters including fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutaryl transferase (γ-GGT), serum uric acid (SUA), creatinine (Cr), and blood urea nitrogen (BUN) were performed using a Hitachi 7600 automated analyzer (Tokyo, Japan). Glycosylated hemoglobin (HbA1c) was measured by high-performance liquid chromatography (HPLC). The fasting insulin (FINS) was detected by radioimmunoassay. Urine examination was performed using first-morning void samples to measure microalbumin and creatinine concentrations. The urinary albumin to creatinine ratio (UACR) was calculated. All participants underwent a liver ultrasonography after fasting for over eight hours. The examinations were performed by a trained technician in the ultrasound department. Body Mass Index (BMI) was calculated by dividing an individual's weight (in kilograms) by the square of their height (in meters). The estimated glomerular filtration rate (eGFR) was calculated using CKD-EPI formula [26]. The insulin resistance level was estimated using homeostatic model assessment insulin resistance index (HOMA-IR): FINS (Mu/ml)*FBG (mmol/l)/22.5 [27]. 2.3 Definitions of chronic kidney disease Chronic kidney disease is diagnosed by persistent albuminuria (UACR≥30 mg/g two out of three times) or a sustained eGFR<60 mL/min/1.73 m² over 3 months [28]. 2.4 Definitions of metabolic dysfunction-associated steatotic liver disease (MASLD) MASLD was diagnosed in the presence of hepatic steatosis accompanied by at least one of the following factors: (1) BMI≥25 kg/m 2 (≥ 23 kg/m 2 in Asian) or waist circumference > 94 cm in men, > 80 cm in women, or ethnicity adjusted; (2) Fasting serum glucose ≥ 100 mg/dL (≥ 5.6 mmol/L) or 2-hour post-load glucose level ≥ 140 mg/dL (≥ 7.8 mmol/L) or HbA1c ≥ 5.7% or on specific drug treatment; (3) Blood pressure ≥ 130/85 mmHg or specific drug treatment; (4) Plasma triglycerides ≥ 150 mg/dL (≥ 1.70 mmol/L) or specific drug treatment; (5) Plasma HDL cholesterol < 40 mg/dL (< 1.0 mmol/L) for men and < 50 mg/dL (< 1.3 mmol/L) for women or specific drug treatment [29]. 2.5 Statistical analysis The study participants were stratified based on the age of onset into two groups: a young-onset group (YOD, age of onset <40 years) and a late-onset group (LOD, age of onset ≥40 years). Subsequently, within each age-stratified group, subjects were further categorized according to CKD status into two subgroups: the CKD subgroup and the non-CKD subgroup. Statistical processing was performed using SPSS software (version 21.0; IBM Corp., USA). Kolmogorov-Smirnov test was conducted to evaluate the normality of the continuous variables before analysis. The quantitative data were reported as means ± standard deviations for normally distributed data or medians (interquartile ranges) for non-normally distributed data. Qualitative data were reported as percentages (%). Group comparisons were performed using independent samples t-tests for normally distributed quantitative data, Mann-Whitney U tests for non-normally distributed quantitative data, and chi-square tests for categorical variables.The logistic regression analyses were used to determine the association between YOD and CKD, as well as to identify the risk factors for CKD. P <0.05 (Two-sided) was recognized to indicate statistical significance. 3. Results 3.1 General characteristics of the patients The physical and biochemical indicators of the patients are presented in Table 1 . There were 1194 patients newly diagnosed with T2DM, including 473 YOD and 721 LOD. The average age of patients with YOD was 31.48 ± 6.24 years; the LOD group averaged 54.88 ± 8.73 years. Patients with YOD had significant clinical characteristics distinct from those of patients with LOD. Specifically, patients with YOD had a higher proportion of males (YOD 69.13% vs. LOD 56.17%, p < 0.001) than patients with LOD. Additionally, patients with YOD had higher average levels of BMI, DBP, TC, TG, FBG, SUA, HOMA-IR, and eGFR, and lower average levels of HDL-C, BUN, and Cr compared to patients with LOD. Moreover, the YOD group had a higher proportion of MASLD (YOD 79.7% vs. LOD 70.18%, p < 0.001), and higher average levels of ALT, AST, and γ-GGT than patients with LOD. However, the levels of SBP, HbA1c, LDL-C, and UACR in the YOD group were comparable to those in the LOD group. Importantly, there was no difference in the course (YOD 0.33 years vs. LOD 0.25 years, p = 0.826) between the YOD and LOD groups (Table 1 ). Table 1 Basic/clinical characteristics of study participants and the comparisons of parameters between subjects with CKD and those without. General Indexes All patients (n = 1194 ) YOD (n = 473 ) LOD (n = 721 ) P value Gender, male (%) 732 (61.31%) 327 (69.13%) 405 (56.17%) <0.001* Age(yr) 45.61 ± 13.87 31.48 ± 6.24 54.88 ± 8.73 <0.001* Course(yr) 0.25 (0.08-1) 0.33 (0.06-1) 0.25 (0.08-1) 0.826 BMI(kg/m2) 26.96 ± 4.25 28.27 ± 4.75 26.11 ± 3.63 <0.001* SBP(mmHg) 135.44 ± 16.83 134.73 ± 15.66 135.91 ± 17.55 0.236 DBP(mmHg) 84.65 ± 11.89 85.68 ± 11.91 83.97 ± 11.84 0.015* HbA1c(%) 10.12 ± 2.38 10.25 ± 2.28 10.04 ± 2.43 0.16 TC(mmol/L) 5.34 ± 1.34 5.5 ± 1.37 5.23 ± 1.31 0.001* TG(mmol/L) 1.77 (1.18–2.95) 2.24 (1.45–3.84) 1.53 (1.06–2.4) <0.001* HDL-C(mmol/L) 1.16 ± 0.34 1.08 ± 0.31 1.21 ± 0.34 <0.001* LDL-C(mmol/L) 3.33 ± 0.88 3.38 ± 0.86 3.29 ± 0.89 0.128 FBG(mmol/L) 10.54 ± 3.75 11.17 ± 3.48 10.13 ± 3.87 <0.001* BUN(mmol/L) 5.34 ± 1.61 4.94 ± 1.42 5.61 ± 1.68 <0.001* Cr(umol/L) 60.91 ± 18.19 58.44 ± 15.76 62.52 ± 19.46 <0.001* SUA(umol/L) 336.48 ± 107.11 374.91 ± 111.34 310.77 ± 96.02 <0.001* HOMA-IR 2.35 (2.20–3.56) 3.56 (2.59–4.49) 2.34 (1.97–2.69) <0.001* eGFR(mL/min/1.73 m2) 111.13 ± 18.76 125.53 ± 14.33 101.79 ± 15.02 <0.001* UACR(mg/g) 11.37 (6.55–26.07) 11.97 (6.68–30.47) 11 (6.41-25) 0.112 ALT(IU/L) 26 (17–44) 37.25 (21-60.85) 22.5 (16-33.4) <0.001* AST(IU/L) 24 (18–33) 27 (19–41) 22 (18–28) <0.001* γ-GGT(IU/L) 34 (22-53.2) 39 (25.95-60) 29.8 (20.9-48.15) <0.001* MASLD(%) 883 (73.95%) 377 (79.7%) 506 (70.18%) <0.001* CKD(%) 269 (22.53%) 114 (24.1%) 155 (21.5%) 0.163 BMI: body mass index, SBP: systolic pressure, DBP: diastolic pressure, TC: total cholesterol, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBG: fasting blood glucose, BUN: blood urea nitrogen, Cr: creatinine, SUA: serum uric acid, HOMA-IR: insulin resistance index, eGFR: estimated glomerular filtration rate, UACR: urinary albumin to creatinine ratio, ALT: alanine aminotransferase, AST: aspartate aminotransferase, γ-GGT: γ-glutaryl transferase, MASLD: metabolic dysfunction-associated steatotic liver disease, YOD: young onset diabetes, LOD: late onset diabetes. CKD: chronic kidney disease. The t test and chi-square test with different samples were adopted for comparisons between groups. * P < 0.05 was considered as statistically significant difference. 3.2 Association between YOD and the risk of CKD The prevalence of CKD in the YOD group was slightly higher than that in the LOD group (YOD 24.1% vs. LOD 21.5%); however, this difference was not statistically significant (p = 0.163) (Table 1 ). We further assessed the relationship between YOD and CKD using logistic regression analysis. The results showed that there was no significant association between YOD and the presence of CKD (OR: 1.16, 95%CI: 0.88–1.528, P = 0.292). 3.3 Differences of parameters between YOD and LOD individuals in the CKD subgroup. Among CKD patients, those with YOD showed a significantly higher proportion of males (69.42% vs. 45.16%, p < 0.001), elevated levels of BMI, DBP, TC, TG, FBG, SUA, HOMA-IR, eGFR and UACR, along with lower BUN and Cr levels (all p < 0.05) compared to LOD patients. Additionally, MASLD prevalence was markedly higher in YOD patients (89.47% vs. 74.84%, p < 0.001), accompanied by increased ALT, AST and γ-GGT levels. No significant differences were observed in disease course, SBP, HbA1c, HDL-C or LDL-C between the YOD and LOD groups (Table 2 ). Table 2 Differences of parameters between YOD and LOD individuals in the CKD subgroup General Indexes YOD with CKD (n = 114 ) LOD with CKD (n = 155 ) P value Gender, male (%) 78 (68.42%) 70 (45.16%) <0.001* Age(yr) 31.44 ± 6.37 54.95 ± 8.96 <0.001* Course(yr) 0.25 (0.05-1) 0.17 (0.04–1.63) 0.934 BMI(kg/m2) 29.99 ± 5.29 26.69 ± 3.97 <0.001* SBP(mmHg) 142.91 ± 18.74 140.94 ± 20.72 0.422 DBP(mmHg) 90.93 ± 13.79 85.83 ± 13.99 0.003* HbA1c(%) 10.29 ± 2.09 10.36 ± 2.35 0.815 TC(mmol/L) 5.88 ± 1.66 5.28 ± 1.37 0.001* TG(mmol/L) 3.24 (1.66–6.82) 1.72 (1.26–2.76) <0.001* HDL-C(mmol/L) 1.11 ± 0.41 1.16 ± 0.29 0.201 LDL-C(mmol/L) 3.33 ± 0.89 3.36 ± 0.96 0.77 FBG(mmol/L) 12.18 ± 3.49 11.09 ± 4.22 0.025* BUN(mmol/L) 4.92 ± 1.31 5.88 ± 2.1 <0.001* Cr(umol/L) 59.34 ± 20.45 65.99 ± 31.18 0.039* SUA(umol/L) 403.16 ± 113.19 323.34 ± 104.32 <0.001* HOMA-IR 3.56 (2.95–5.81) 2.34 (2.24–3.46) <0.001* eGFR(mL/min/1.73 m2) 124.75 ± 18.92 98.46 ± 22.04 <0.001* UACR(mg/g) 104.21 (56.56-266.41) 77.02 (40-168.67) 0.011* ALT(IU/L) 39.2 (26.78–74.75) 23 (15–33) <0.001* AST(IU/L) 28 (22-50.08) 22 (17.7–27) <0.001* γ-GGT(IU/L) 48.3 (36-71.5) 31 (20-53.8) <0.001* MASLD(%) 102 (89.47%) 116 (74.84%) 0.002* BMI: body mass index, SBP: systolic pressure, DBP: diastolic pressure, TC: total cholesterol, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBG: fasting blood glucose, BUN: blood urea nitrogen, Cr: creatinine, SUA: serum uric acid, HOMA-IR: insulin resistance index, eGFR: estimated glomerular filtration rate, UACR: urinary albumin to creatinine ratio, ALT: alanine aminotransferase, AST: aspartate aminotransferase, γ-GGT: γ-glutaryl transferase, MASLD: metabolic dysfunction-associated steatotic liver disease, YOD: young onset diabetes, LOD: late onset diabetes. CKD: chronic kidney disease. The t test and chi-square test with different samples were adopted for comparisons between groups. * P < 0.05 was considered as statistically significant difference. 3.4 Association between clinical characteristics and CKD in YOD and LOD groups In the YOD group, patients with CKD exhibited a higher prevalence of MASLD and significantly higher levels of BMI, SBP, DBP, TC, TG, FBG, SUA, HOMA-IR, ALT, AST, and γ-GGT than non-CKD patients (Table 3 ). Univariate logistic regression analysis revealed that BMI, SBP, DBP, TC, TG, FBG, SUA, ALT, AST, γ-GGT, and MASLD were positively associated with CKD (Table 4 ). Multivariate logistic regression analysis showed that SBP, FBG, and γ-GGT remained independently related to an increased prevalence of CKD after adjustment for multiple variables (Table 4 ). Table 3 Differences of parameters between CKD and non-CKD individuals in the YOD and LOD subgroups. YOD (n = 473) LOD(n = 721) General Indexes CKD (n = 114 ) Non-CKD (n = 359 ) P value CKD (n = 155 ) Non-CKD (n = 566) P value Gender, male (%) 78 (68.42% ) 249 (69.36%) 0.468 70 (45.15%) 335 (59.18%) 0.001* Age(yr) 31.44 ± 6.37 31.49 ± 6.2 0.935 54.95 ± 8.96 54.86 ± 8.68 0.913 Course(yr) 0.25 (0.05-1) 0.42 (0.06-1) 0.787 0.17 (0.04–1.63) 0.25 (0.08-1) 0.516 BMI(kg/m 2 ) 29.99 ± 5.29 27.73 ± 4.44 <0.001* 26.69 ± 3.97 25.94 ± 3.52 0.024* SBP(mmHg) 142.91 ± 18.74 132.13 ± 13.58 <0.001* 140.03 ± 20.38 134.66 ± 16.38 0.001* DBP(mmHg) 90.93 ± 13.79 84.02 ± 10.74 <0.001* 85.7 ± 13.95 83.46 ± 11.15 0.037* HbA1c(%) 10.29 ± 2.09 10.23 ± 2.34 0.82 10.36 ± 2.35 9.96 ± 2.45 0.077 TC(mmol/L) 5.88 ± 1.66 5.38 ± 1.24 0.001* 5.28 ± 1.37 5.22 ± 1.29 0.644 TG(mmol/L) 3.24 (1.66–6.82) 2.11 (1.39–3.19) <0.001* 1.72 (1.26–2.76) 1.47 (1.02–2.31) 0.003* HDL(mmol/L) 1.11 ± 0.41 1.08 ± 0.27 0.375 1.16 ± 0.29 1.21 ± 0.35 0.095 LDL(mmol/L) 3.33 ± 0.89 3.39 ± 0.84 0.451 3.36 ± 0.96 3.28 ± 0.87 0.342 FBG(mmol/L) 12.18 ± 3.49 10.84 ± 3.41 <0.001* 11.07 ± 4.22 9.86 ± 3.73 0.001* BUN(mmol/L) 4.92 ± 1.31 4.94 ± 1.45 0.834 5.88 ± 2.1 5.53 ± 1.53 0.065 Cr(umol/L) 59.34 ± 20.45 58.16 ± 13.98 0.496 65.99 ± 31.18 61.57 ± 14.66 0.093 SUA(umol/L) 403.16 ± 113.19 365.94 ± 109.38 0.002* 323.34 ± 104.32 307.32 ± 93.42 0.068 HOMA-IR 4.56 ± 2.74 3.78 ± 2.11 0.004* 3.09 ± 2.1 2.51 ± 1.33 <0.001* eGFR(mL/min/1.73 m2) 124.75 ± 18.92 125.77 ± 12.57 0.516 98.46 ± 22.04 102.69 ± 12.32 0.025* UACR(mg/g) 104.21 (56.56-266.41) 8.92 (5.79–14.64) <0.001* 77.02 (40-168.67) 8.81 (5.69–13.87) <0.001* ALT(IU/L) 39.2 (26.78–74.75) 35 (19–58) 0.004* 23 (15–33) 22.3 (16-33.4) 0.42 AST(IU/L) 28 (22-50.08) 27 (18–39) 0.007* 22 (17.7–27) 21.9 (18-28.55) 0.646 γ-GGT(IU/L) 48.3 (36-71.5) 38 (24–57) <0.001* 31 (20-53.8) 29 (21-47.5) 0.569 MASLD(%) 102 (89.47%) 275 (76.6%) 0.001* 116 (74.83%) 390 (68.9%) 0.166 BMI: body mass index, SBP: systolic pressure, DBP: diastolic pressure, TC: total cholesterol, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBG: fasting blood glucose, BUN: blood urea nitrogen, Cr: creatinine, SUA: serum uric acid, HOMA-IR: insulin resistance index, eGFR: estimated glomerular filtration rate, UACR: urinary albumin to creatinine ratio, ALT: alanine aminotransferase, AST: aspartate aminotransferase, γ-GGT: γ-glutaryl transferase, MASLD: metabolic dysfunction-associated steatotic liver disease, YOD: young onset diabetes, LOD: late onset diabetes. CKD: chronic kidney disease. The t test and chi-square test with different samples were adopted for comparisons between groups. * P < 0.05 was considered as statistically significant difference. Table 4 Association between clinical characteristics and CKD in YOD and LOD subgroups Variables YOD LOD Univariate logistic regression analysis Multivariate logistic regression analysis Univariate logistic regression analysis Multivariate logistic regression analysis OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value Gender (female/male) 1.405(0.663–1.645) 0.85 1.761(1.231–2.519) 0.002* Age(yr) 0.999(0.965–1.033) 0.935 1.002(0.981–1.022) 0.913 Course(yr) 0.992(0.882–1.117) 0.899 1.038(0.936–1.15) 0.48 BMI(kg/m2) 1.104(1.055–1.156) <0.001* 1.056(1.007–1.108) 0.025* SBP(mmHg) 1.046(1.031–1.061) <0.001* 1.027 (1.005–1.05) 0.016* 1.018(1.007–1.028) <0.001* 1.021 (1.01–1.031) 0.016* DBP(mmHg) 1.051(1.031–1.071) <0.001* 1.017(1.002–1.032) 0.028* HbA1c(%) 1.011(0.92–1.112) 0.819 1.07(0.993–1.152) 0.077 TC(mmol/L) 1.279(1.098–1.49) 0.002* 1.032(0.902–1.181) 0.644 TG(mmol/L) 1.107(1.056–1.162) <0.001* 1.006(0.958–1.057) 0.805 HDL(mmol/L) 1.338(0.699–2.562) 0.38 0.583(0.31–1.095) 0.583 LDL(mmol/L) 0.908(0.706–1.167) 0.45 1.102(0.903–1.344) 0.341 FBG(mmol/L) 1.114(1.049–1.183) <0.001* 1.074 (1.004–1.149) 0.037* 1.078(1.032–1.126) 0.001* 1.062 (1.007–1.121) 0.028* BUN(mmol/L) 0.984(0.844–1.147) 0.834 1.122(1.012–1.245) 0.029* Cr(umol/L) 1.005(0.991–1.018) 0.496 1.01(1.002–1.019) 0.017* SUA(umol/L) 1.003(1.001–1.005) 0.002* 1.002(1.000-1.003) 0.069 HOMA-IR 1.145(1.05–1.247) 0.002* 1.229(1.107–1.365) <0.001* ALT(IU/L) 1.005(1.001–1.009) 0.023* 0.998(0.988–1.007) 0.602 AST(IU/L) 1.01(1.003–1.017) 0.007* 0.997(0.986–1.008) 0.595 γ-GGT(IU/L) 1.011(1.006–1.015) <0.001* 1.008 (1.002–1.014) 0.011* 1.003(0.999–1.007) 0.1 MASLD 2.596(1.361–4.954) 0.004* 1.342(0.896–2.011) 0.153 BMI: body mass index, SBP: systolic pressure, DBP: diastolic pressure, TC: total cholesterol, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBG: fasting blood glucose, BUN: blood urea nitrogen, Cr: creatinine, SUA: serum uric acid, HOMA-IR: insulin resistance index, eGFR: estimated glomerular filtration rate, UACR: urinary albumin to creatinine ratio, ALT: alanine aminotransferase, AST: aspartate aminotransferase, γ-GGT: γ-glutaryl transferase, MASLD: metabolic dysfunction-associated steatotic liver disease, YOD: young onset diabetes, LOD: late onset diabetes. CKD: chronic kidney disease. * P < 0.05 was considered as statistically significant difference. 4. Discussion In this cross-sectional study, we examined the clinical characteristics of patients with newly diagnosed T2DM and CKD. We found that young-onset diabetes (YOD) was not associated with a higher prevalence of CKD in this population. However, among CKD patients, those with YOD exhibited significantly distinct clinical features compared to those with LOD, including more severe metabolic abnormalities, along with higher eGFR and UACR levels. Moreover, YOD patients showed a strong association between CKD and MASLD, with elevated γ-GGT emerging as an independent predictor of CKD, a finding not observed in LOD patients. The incidence of T2DM is increasingly affecting younger populations. Recent studies show a significant rise in young-onset T2DM (diagnosed before age 40), indicating the disease is no longer primarily associated with older adults but is becoming more common among adolescents and young adults [ 30 ]. Compared with late-onset T2DM, individuals diagnosed at a younger age face a higher risk of developing severe chronic complications, including both macrovascular and microvascular diseases [ 31 , 32 ]. Previous studies have reported YOD is associated with an increased risk of CKD after adjustment for multiple clinical characteristics [ 20 ]. In a prospective analysis of 84,384 Korea patients with T2DM, the risk of developing CKD in patients with YOD was 1.7 times higher than those with LOD [ 21 ]. Moreover, patients with young-onset T2DM and CKD are at an increased risk of CKD progression including ESRD [ 33 ]. However, the association between YOD and CKD in newly diagnosed T2DM remains uncertain, as most prior studies have been conducted in existing diabetes cases, where disease duration may be an important confounding factor. Our study found that in newly diagnosed T2DM, the prevalence of CKD did not significantly differ between the YOD and LOD groups. Logistic regression analysis further confirmed the absence of a significant association between YOD and CKD, suggesting that YOD may not be a risk factor for CKD in this population. This finding aligns with several previous studies, which indicated that the observed association between YOD and CKD might be largely attenuated after accounting for disease duration. After adjusting for diabetes duration, no significant association remained between YOD and CKD [ 23 , 24 ]. Nevertheless, our study showed that CKD patients with YOD had significantly distinct characteristics including more severe metabolic disorders and renal injury compared to those with LOD. Previous studies comparing YOD and LOD have found poorer metabolic control in YOD, likely due to lower adherence to a healthy diet and insufficient self-management [ 34 , 35 ]. Multiple metabolic abnormalities including obesity, hypertension, hyperglycemia, dyslipidemia, and insulin resistance have been shown to contribute to glomerular lesions, leading to microalbuminuria and glomerular hyperfiltration in early stages, and progressing to loss of renal function in later stages [ 36 – 38 ]. Moreover, an increasing number of metabolic disorders were associated with a higher risk of CKD [ 39 ]. Herein, in CKD patients, those with YOD exhibited significantly higher BMI, blood pressure, blood glucose, blood lipids, serum uric acid, and insulin resistance levels, indicating more severe metabolic dysregulation. Additionally, the UACR levels were significantly higher in CKD patients with YOD than in those with LOD. These findings suggest that YOD may exacerbate glomerular lesions in CKD patients, while the elevated eGFR observed in YOD could result from glomerular hyperfiltration—potentially driven by higher BMI levels. Looker et al [ 40 ] demonstrated that YOD was strongly associated with more severe glomerular pathological manifestations including greater glomerular basement membrane (GBM) width and mesangial fractional volume than late-onset participants in DKD patients. In addition, glomerular sclerosis percentage, glomerular volume, mesangial fractional volume, and GBM width were inversely associated with age at diabetes onset. Our study highlights the importance of early detection and systematic management of metabolic abnormalities in patients with YOD to prevent glomerular lesion progression. More importantly, our study identified a significant association between CKD and MASLD in YOD patients. In this population, subjects with CKD had significantly higher MASLD prevalence and elevated liver enzymes (ALT, AST, γ-GGT) than those without CKD. Furthermore, MASLD, ALT, AST, γ-GGT were positively associated with CKD and after multivariable adjustment, γ-GGT remained independently associated with increased CKD prevalence. Notably, these associations were absent in the LOD group. MASLD, which evolved from the term nonalcoholic fatty liver disease (NAFLD), is defined by the presence of hepatic steatosis in combination with one or more metabolic risk factors [ 41 ]. MASLD is strongly associated with T2DM, occurring in 58.84%-72.65% of individuals with T2DM [ 42 ]. Additionally, MASLD significantly exacerbates diabetes related microvascular complications [ 43 ]. The link between CKD and NAFLD/MASLD has been highlighted by several previous studies [ 41 , 44 , 45 ], but little is known about how this association differs by age of diabetes onset. Our results indicated that, in patients with newly diagnosed T2DM, MASLD appeared to be positively associated with CKD only in young-onset T2DM, whereas no significant relationship was observed in late-onset T2DM patients. The stronger association between CKD and MASLD in YOD patients may be attributable to the more prolonged exposure to metabolic disturbances, including worse insulin resistance, hyperglycemia and dyslipidemia. These factors likely accelerate both hepatic and renal injury through mechanisms such as oxidative stress, advanced glycation end-product accumulation, and systemic inflammation [ 41 ]. These findings reveal a potential hepatorenal link in young T2DM, highlighting the need for combined screening for MASLD and CKD. Aggressive management of metabolic risk factors (e.g., glycemic control, statin therapy, and weight loss) in critical to prevent multiorgan complications. Furthermore, some agents (e.g., GLP-1 RAs, SGLT2 inhibitors) may warrant prioritization in this high-risk population, given their dual benefits on hepatic and renal outcomes [ 41 ]. γ-GGT is a key enzyme in glutathione metabolism and a biomarker for hepatobiliary diseases [ 46 ]. While its association with CKD remains controversial in the general populations [ 47 – 50 ], our study revealed that elevated γ-GGT independently predicts CKD risk in YOD patients, even after adjusting for BMI, blood pressure, glucose, lipids, SUA, insulin resistance and other liver enzymes, whereas no such association was observed in LOD patients. This suggests γ-GGT may serve as a young onset-specific biomarker for CKD in T2DM. In this population, γ-GGT assessment could facilitate earlier identification of individuals at high risk of CKD. The mechanisms underlying the association between γ-GGT and CKD remain poorly understood. MASLD may play a key role in mediating the γ-GGT-CKD association [ 48 ]. Increased serum γ-GGT levels are considered a reliable marker of MASLD and are associated with higher liver fat content [ 51 ]. MASLD itself may promote CKD and accelerated atherosclerosis by releasing various hepatokines(e.g., fetuin-A, angiopoietin-like proteins, insulin-like growth factor binding protein 1 and glycoprotein nonmetastatic melanoma protein B) from the fatty/inflamed liver, which are elevated in MASLD patients and likely contribute directly to CKD development [ 52 ]. This could further explain the lack of association between γ-GGT and CKD in the late-onset group, given that CKD was not linked to MASLD in these patients. Another possible mechanism linking serum γ-GGT and CKD could be oxidative stress. Beyond predicting liver dysfunction, γ-GGT catalyzes glutathione breakdown, generating reactive oxygen species and serving as a systemic oxidative stress marker [ 53 ]. Since oxidative stress promotes renal injury via inflammation [ 54 ], this pathway may also explain the γ-GGT-CKD association. This study has several limitations. First, its cross-sectional design can only identify associations but not establish causality. Second, CKD was diagnosed clinically (via eGFR and albuminuria) without kidney biopsy, potentially misclassifying early CKD or non-diabetic kidney disease. Third, our cohort comprised newly diagnosed T2DM patients, limiting generalizability to advanced disease stages. Fourth, eGFR-based CKD definition may be less accurate in subgroups with extreme body weights or ethnic variability, though it remains a recommended tool for large studies. Additionally, unmeasured confounders (e.g., dietary habits or genetic factors) and variability in liver enzyme measurements (e.g., γ-GGT fluctuations due to alcohol use) could influence the results. Future longitudinal studies with kidney biopsy validation and more comprehensive features are warranted to establish causality and explore age-specific mechanisms linking MASLD, γ-GGT, and CKD in T2DM. 5. Conclusions In this study of newly diagnosed T2DM patients, YOD was not independently associated with a higher prevalence of CKD compared to LOD. However, YOD patients with CKD exhibited distinct clinical features, including more severe metabolic disturbances (e.g., higher BMI, SBP, DBP, TC, TG, FBG, SUA, and HOMA-IR) and elevated markers of renal injury (higher eGFR and UACR). Additionally, YOD patients showed a stronger association between CKD and MASLD compared to LOD patients. Elevated γ-GGT emerged as an independent predictor of CKD in the YOD group, suggesting its potential role as a YOD-specific biomarker for renal risk assessment. These findings highlight the need for early detection and management of metabolic abnormalities and MASLD in YOD patients to prevent CKD progression. Abbreviations CKD: chronic kidney disease T2DM: type 2 diabetes mellitus YOD: young-onset diabetes LOD: late-onset diabetes BMI: body mass index SBP: systolic blood pressure DBP: diastolic blood pressure ALT: alanine aminotransferase AST: aspartate aminotransferase γ-GGT: γ-glutaryl transferase TC: total cholesterol TG: triglycerides HDL: high-density lipoprotein cholesterol LDL: low-density lipoprotein cholesterol FBG: fasting blood glucose SUA: serum uric acid Cr: creatinine BUN: blood urea nitrogen UACR: urinary albumin to creatinine ratio eGFR: estimated glomerular filtration rate HbA1c: glycosylated hemoglobin FINS: fasting insulin HOMA-IR: insulin resistance index MASLD: metabolic dysfunction-associated steatotic liver disease Declarations Ethics approval and consent to participate The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki (6th revision, 2008) as reflected in a priori approval by the ethics committees of Qilu Hospital of Shandong University Dezhou Hospital. Written informed consent was provided by each participant. Consent for publication: Informed consent for publication was obtained from each participant included in the study. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests: The authors declare that there are no competing interests associated with this manuscript. Funding: This article was founded by the National Key Research and Development Program of China [grant numbers 2024YFC3607400]; the Tianjin Institute of Urology talent funding program [grant numbers MYSRC2024]; the First Level Leading Talent Project of "123 Climbing Plan"for Clinical Talents of Tianjin Medical University; the "Tian jin Medical Talents"oroject, the second batch of high-level talents selection project in health industry in Tianjin [grant numbers TJSJMYXYC-D2-014]; the Key Project of Natural Science Foundation of Tian jin, [grant numbers 22JCZDJCO0590] and the Tian jin Key Medical Discipline (Specialtv) Construct Project [grant numbers TJYXZDXK-032A] Authors' contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ying Li, and Shan Yue. The Data analysis and first draft preparation of the manuscript were performed by Yuliang Cui. The conceptualization and writing- reviewing were performed by Pei Yu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements: The authors would be grateful for language services by Duoease Scientific Service Center. References GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709-733. Magliano DJ, Islam RM, Barr ELM, et al. Trends in incidence of total or type 2 diabetes: systematic review. BMJ. 2019;366:l5003. Li H, Lu W, Wang A, et al. Changing epidemiology of chronic kidney disease as a result of type 2 diabetes mellitus from 1990 to 2017: Estimates from Global Burden of Disease 2017. J Diabetes Investig. 2021;12(3):346-356. Anders HJ, Huber TB, Isermann B, Schiffer M. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol. 2018;14(6):361-377. Shen Z, Zhang Z, Zhao W. Relationship between plasma homocysteine and chronic kidney disease in US patients with type 2 diabetes mellitus: a cross-sectional study. BMC Nephrol. 2022;23(1):419. Wu AY, Kong NC, de Leon FA, et al. An alarmingly high prevalence of diabetic nephropathy in Asian type 2 diabetic patients: the MicroAlbuminuria Prevalence (MAP) Study. Diabetologia. 2005;48(1):17-26. Thomas MC, Cooper ME, Zimmet P. Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease. Nat Rev Nephrol. 2016;12(2):73-81. McCullough PA, Li S, Jurkovitz CT, et al. Chronic kidney disease, prevalence of premature cardiovascular disease, and relationship to short-term mortality. Am Heart J. 2008;156(6):1132. Shubrook JH, Neumiller JJ, Wright E. Management of chronic kidney disease in type 2 diabetes: screening, diagnosis and treatment goals, and recommendations. Postgrad Med. 2022;134(4):376-387. Magliano DJ, Sacre JW, Harding JL,et al. Young-onset type 2 diabetes mellitus - implications for morbidity and mortality. Nat Rev Endocrinol. 2020;16(6):321-331. Strati M, Moustaki M, Psaltopoulou T, et al. Early onset type 2 diabetes mellitus: an update. Endocrine. 2024;85(3):965-978. The Lancet Diabetes Endocrinology. Alarming rise in young-onset type 2 diabetes. Lancet Diabetes Endocrinol. 2024;12(7):433. Baek HS, Park JY, Yu J, et al. Characteristics of Glycemic Control and Long-Term Complications in Patients with Young-Onset Type 2 Diabetes. Endocrinol Metab (Seoul). 2022;37(4):641-651. Koye DN, Ling J, Dibato J, et al. Temporal Trend in Young-Onset Type 2 Diabetes-Macrovascular and Mortality Risk: Study of U.K. Primary Care Electronic Medical Records. Diabetes Care. 2020;43(9):2208-2216. Fan Y, Fan B, Lau ESH, et al. Comparison of beta-cell function between Hong Kong Chinese with young-onset type 2 diabetes and late-onset type 2 diabetes. Diabetes Res Clin Pract. 2023;205:110954. Yeung RO, Zhang Y, Luk A, et al. Metabolic profiles and treatment gaps in young-onset type 2 diabetes in Asia (the JADE programme): a cross-sectional study of a prospective cohort [published correction appears in Lancet Diabetes Endocrinol. Lancet Diabetes Endocrinol. 2014;2(12):935-943. Misra S, Ke C, Srinivasan S, et al. Current insights and emerging trends in early-onset type 2 diabetes. Lancet Diabetes Endocrinol. 2023;11(10):768-782. Huo X, Gao L, Guo L, et al. Risk of non-fatal cardiovascular diseases in early-onset versus late-onset type 2 diabetes in China: a cross-sectional study. Lancet Diabetes Endocrinol. 2016;4(2):115-124. Al-Saeed AH, Constantino MI, Molyneaux L, et al. An Inverse Relationship Between Age of Type 2 Diabetes Onset and Complication Risk and Mortality: The Impact of Youth-Onset Type 2 Diabetes. Diabetes Care. 2016;39(5):823-829. Liu JJ, Liu S, Gurung RL, et al. Risk of progressive chronic kidney disease in individuals with early-onset type 2 diabetes: a prospective cohort study. Nephrol Dial Transplant. 2020;35(1):115-121. Lee J, Lee SH, Yoon KH, et al. Risk of developing chronic kidney disease in young-onset Type 2 diabetes in Korea. Sci Rep. 2023;13(1):10100. Published 2023 Jun 21. Wu L, Zhao YY, Li MR, et al. Clinical and pathological characteristics of DKD patients with early-onset type 2 diabetes. J Diabetes Complications. 2023;37(8):108520. Cho Y, Park HS, Huh BW, et al. Prevalence and risk of diabetic complications in young-onset versus late-onset type 2 diabetes mellitus. Diabetes Metab. 2022;48(6):101389. Chan JC, Lau ES, Luk AO, et al. Premature mortality and comorbidities in young-onset diabetes: a 7-year prospective analysis. Am J Med. 2014;127(7):616-624. Cui Y, Qu Z, Li L, Hu W. Gender difference in the association between serum uric acid and metabolic dysfunction-associated steatotic liver disease in patients with newly diagnosed type 2 diabetes. BMC Gastroenterol . 2025;25(1):322. Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20-29. Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294(1):E15-E26. Webster AC, Nagler EV, Morton RL, et al. Chronic Kidney Disease. Lancet. 2017;389(10075):1238-1252. Rinella ME, Lazarus JV, Ratziu V, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J Hepatol. 2023;79(6):1542-1556. Zhang FS, Li HJ, Yu X, et al. Global trends and hotspots of type 2 diabetes in children and adolescents: A bibliometric study and visualization analysis. World J Diabetes 2025; 15;16(1):96032. Lascar N, Brown J, Pattison H, et al. Type 2 diabetes in adolescents and young adults. Lancet Diabetes Endocrinol. 2018 Jan;6(1):69-80. Lin B, Coleman RL, Bragg F, et al. Younger-onset compared with later-onset type 2 diabetes: an analysis of the UK Prospective Diabetes Study (UKPDS) with up to 30 years of follow-up (UKPDS 92). Lancet Diabetes Endocrinol. 2024;12(12):904-914. Zheng L, Chen X, Luo T, et al. Early-Onset Type 2 Diabetes as a Risk Factor for End-Stage Renal Disease in Patients With Diabetic Kidney Disease. Prev Chronic Dis. 2020;17:E50. Jiang Y, Lai X. Clinical features of early-onset type 2 diabetes and its association with triglyceride glucose-body mass index: a cross-sectional study. Front Endocrinol (Lausanne). 2024;15:1356942. Wong SKW, Soon W, Griva K, et al. Diabetes knowledge, self-efficacy and dietary, psychological and physical health barriers: Comparing young and usual-onset type 2 diabetes. Diabet Med. 2024;41(3):e15207. Chen J, Gu D, Chen CS, et al. Association between the metabolic syndrome and chronic kidney disease in Chinese adults. Nephrol Dial Transplant 2007;22(4):1100-1106. Ryoo JH, Chun H, Lee HS, et al. Clinical associations between metabolic syndrome and the development of microalbuminuria in Korean men. Diabetes Res Clin Pract. 2015;107(3):407-414. Lin L, Tan W, Pan X, et al. Metabolic Syndrome-Related Kidney Injury: A Review and Update. Front Endocrinol (Lausanne). 2022;13:904001. Kurella M, Lo JC, Chertow GM. Metabolic syndrome and the risk for chronic kidney disease among nondiabetic adults. J Am Soc Nephrol. 2005;16(7):2134-2140. Looker HC, Pyle L, Vigers T, et al. Structural Lesions on Kidney Biopsy in Youth-Onset and Adult-Onset Type 2 Diabetes. Diabetes Care. 2022;45(2):436-443. Bilson J, Mantovani A, Byrne CD, et al. Steatotic liver disease, MASLD and risk of chronic kidney disease. Diabetes Metab. 2024;50(1):101506. En Li Cho E, Ang CZ, Quek J, et al. Global prevalence of non-alcoholic fatty liver disease in type 2 diabetes mellitus: an updated systematic review and meta-analysis. Gut 2023;72(11):2138-48. Mantovani A, Dalbeni A, Beatrice G, et al. Non-Alcoholic Fatty Liver Disease and Risk of Macro- and Microvascular Complications in Patients with Type 2 Diabetes. J Clin Med 2022;11(4):968. Targher G, Chonchol M, Bertolini L, et al. Increased risk of CKD among type 2 diabetics with nonalcoholic fatty liver disease. J Am Soc Nephrol. 2008;19(8):1564-1570. Chang Y, Ryu S, Sung E, et al. Nonalcoholic fatty liver disease predicts chronic kidney disease in nonhypertensive and nondiabetic Korean men. Metabolism. 2008;57(4):569-576. Whitfield JB. Gamma glutamyl transferase. Crit Rev Clin Lab Sci. 2001;38(4):263-355. Cain LR, Ducatman AM, Shankar A. The relationship between gamma-glutamyl transferase levels and chronic kidney disease among Appalachian adults. W V Med J. 2012;108(1):8-13. Targher G, Kendrick J, Smits G, et al. Relationship between serum gamma-glutamyltransferase and chronic kidney disease in the United States adult population. Findings from the National Health and Nutrition Examination Survey 2001-2006. Nutr Metab Cardiovasc Dis. 2010;20(8):583-590. Fan Y, Jin X, Man C, Gong D. Association of serum gamma-glutamyltransferase with chronic kidney disease risk: a meta-analysis. Free Radic Res. 2018;52(8):819-825. Kunutsor SK, Laukkanen JA. Gamma-glutamyltransferase and risk of chronic kidney disease: A prospective cohort study. Clin Chim Acta. 2017;473:39-44. Xing Y, Chen J, Liu J, Ma H. Associations Between GGT/HDL and MAFLD: A Cross-Sectional Study. Diabetes Metab Syndr Obes. 2022;15:383-394. Yang M, Luo S, Yang J, et al. Crosstalk between the liver and kidney in diabetic nephropathy. Eur J Pharmacol. 2022;931:175219. Lee DH, Blomhoff R, Jacobs Jr DR. Is serum gamma glutamyltransferase a marker of oxidative stress? Free Radic Res 2004;38:535e9. Modaresi A, Nafar M, Sahraei Z. Oxidative stress in chronic kidney disease. Iran J Kidney Dis 2015;9(3):165–79. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7121363","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496093839,"identity":"aa547ca5-e012-4b58-b4cd-6afa5fed908e","order_by":0,"name":"Yuliang Cui","email":"","orcid":"","institution":"Tianjin Medical University Chu Hsien-I Memorial Hospital \u0026 Institute of Endocrinology","correspondingAuthor":false,"prefix":"","firstName":"Yuliang","middleName":"","lastName":"Cui","suffix":""},{"id":496093840,"identity":"86918658-b000-4b02-82ad-e977c635a8e3","order_by":1,"name":"Ying Li","email":"","orcid":"","institution":"Qilu Hospital of Shandong University Dezhou Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":496093841,"identity":"cee981c3-a10c-48be-a5c7-ce3aeb6de595","order_by":2,"name":"Shan Yue","email":"","orcid":"","institution":"Qilu Hospital of Shandong University Dezhou Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Yue","suffix":""},{"id":496093843,"identity":"b4a8e16e-44b1-4b33-bef1-09baf6121d82","order_by":3,"name":"Pei Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYLCChAoILUGCljMGpGphbCNFi8GN3IMPHs77E21wgPngbR4GuzwitOQlGyRuM8jdcIAt2ZqHIbmYCC05ZhIQLTxm0jwMBxIbiNBi/iNxDkgL/zeitZgxJDaAbWEjTovkmXfJEgnHjHNnHmYztpxjkExYC9/x3IMff9TI5fYdb354402FHWEtCgd4oCxmsDsJqQcC+QYewopGwSgYBaNghAMACBs9ufXGnkYAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin Medical University Chu Hsien-I Memorial Hospital \u0026 Institute of Endocrinology","correspondingAuthor":true,"prefix":"","firstName":"Pei","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-07-14 12:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7121363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7121363/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90147485,"identity":"04dbfe47-6a9c-46be-84f6-e976157dd2db","added_by":"auto","created_at":"2025-08-29 06:10:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1182707,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7121363/v1/37f98bd3-4381-4444-be37-f8afe861be0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical characteristics and the risk factors of patients with chronic kidney disease in young-onset versus late-onset newly diagnosed type 2 diabetes","fulltext":[{"header":"1. Background","content":"\u003cp\u003eChronic kidney disease (CKD) has emerged as a significant global public health challenge, with its rising prevalence and associated mortality imposing a growing burden on healthcare systems worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Type 2 diabetes (T2DM), which is the most serious public health problem with an increasing global prevalence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], is the leading cause of CKD in most countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The rapid spread of T2DM has markedly intensified the epidemiology of T2DM associated CKD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the United States, over 40% of individuals with T2DM are affected by CKD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This situation is even more alarming in Asia, where the prevalence of CKD among adults with type 2 diabetes has been reported to reach a strikingly high level of 58.6%[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CKD is a progressive condition that often leads to end-stage kidney disease (ESKD), with its high costs and resource demands largely attributable to renal replacement therapy (RRT), for which T2DM is the leading indication [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Beyond renal complications, CKD significantly heightens the likelihood of various complications associated with T2DM, such as cardiovascular disease (CVD), stroke, heart failure, infections, diminished quality of life, adverse drug reactions, and premature death [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Given these severe consequences, early detection and intervention for CKD in patients with T2DM are critical to prevent ESKD and reduce related complications, thereby improving patient outcomes and alleviating the economic and societal burden of CKD.\u003c/p\u003e\u003cp\u003eWith the change of modern lifestyle and diet structure, increasing numbers of young-onset type 2 diabetes (YOD), defined as diagnosis aged younger than 40 years, have been observed [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. On a global level, prevalence estimates of diabetes among people aged 20\u0026ndash;39 years increased from 2.9% (63\u0026nbsp;million people) in 2013 to 3\u0026middot;8% (260\u0026nbsp;million) in 2021 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Patients with YOD are suggested to have distinctive clinical phenotype than patients with late-onset type 2 diabetes (LOD) defined as the age of diagnosis of diabetes\u0026thinsp;\u0026ge;\u0026thinsp;40 years, including poorer glycemic control, higher levels of BMI and LDL cholesterol, heavier β-cell dysfunction, a stronger family history, and a higher proportion of insulin therapy[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These characteristics indicate that patients with YOD may have distinct pathophysiology and follow divergent clinical paths compared to patients with LOD.\u003c/p\u003e\u003cp\u003eYOD is associated with a higher prevalence of comorbidities and follows a more aggressive natural history, due to longer diabetes duration and worse self-management [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Indeed, previous studies have demonstrated that patients with YOD have higher risks of macrovascular complications and mortality compared to those with LOD[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the association between YOD and CKD risk remains inconclusive. Some studies have shown that YOD patients are more prone to developing CKD and display more severe renal clinicopathological manifestations[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Alternatively, other studies have proposed that the higher prevalence of CKD in YOD group may lose statistical significance after adjustment for diabetes duration[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Nevertheless, research on the YOD-CKD relationship is relatively limited, particularly in newly diagnosed diabetes cohorts, where YOD and LOD patients share comparable diabetes durations. Moreover, few studies have comprehensively characterized the clinical features and risk factors associated with CKD in young-onset versus late-onset T2DM. Therefore, we conducted this study to examine the association between YOD and CKD in newly diagnosed patients and to compare CKD-related clinical characteristics and risk factors between YOD and LOD.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Research design and participants\u003c/p\u003e\n\u003cp\u003eThe study population selection followed our previously published methodology with minor modifications [25]. Briefly, we conducted a retrospective analysis of newly diagnosed, drug-naïve T2DM patients hospitalized at our institution between 2013-2025. Diagnostic criteria for T2DM included: (1) meeting standard glycemic thresholds (FPG ≥7.0 mmol/L, random PG ≥11.1 mmol/L, or 2-h PG ≥11.1 mmol/L after OGTT); (2) absence of autoimmune diabetes markers (GADA, IAA or ICA); and (3) clinical exclusion of other diabetes subtypes based on phenotypic characteristics. We excluded patients with acute diabetic complications, pregnancy-related diabetes, or severe comorbid conditions, as detailed in our prior work [25].\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from each patient included in the study. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki (6th revision, 2008) as reflected in a priori approval by the ethics committees of Qilu Hospital of Shandong University Dezhou Hospital. \u003c/p\u003e\n\u003cp\u003e2.2 Physical and laboratory examinations\u003c/p\u003e\n\u003cp\u003eThe data including age, sex and disease duration were carefully recorded. Blood pressure, height, and weight were collected following standardized protocols. Fasting venous blood (≥8 h) was collected for laboratory analysis. The measurements of serology parameters including fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutaryl transferase (γ-GGT), serum uric acid (SUA), creatinine (Cr), and blood urea nitrogen (BUN) were performed using a Hitachi 7600 automated analyzer (Tokyo, Japan). Glycosylated hemoglobin (HbA1c) was measured by high-performance liquid chromatography (HPLC). The fasting insulin (FINS) was detected by radioimmunoassay. Urine examination was performed using first-morning void samples to measure microalbumin and creatinine concentrations. The urinary albumin to creatinine ratio (UACR) was calculated. All participants underwent a liver ultrasonography after fasting for over eight hours. The examinations were performed by a trained technician in the ultrasound department. Body Mass Index (BMI) was calculated by dividing an individual's weight (in kilograms) by the square of their height (in meters). The estimated glomerular filtration rate (eGFR) was calculated using CKD-EPI formula [26]. The insulin resistance level was estimated using homeostatic model assessment insulin resistance index (HOMA-IR): FINS (Mu/ml)*FBG (mmol/l)/22.5 [27].\u003c/p\u003e\n\u003cp\u003e2.3 Definitions of chronic kidney disease\u003c/p\u003e\n\u003cp\u003eChronic kidney disease is diagnosed by persistent albuminuria (UACR≥30 mg/g two out of three times) or a sustained eGFR\u0026lt;60 mL/min/1.73 m² over 3 months [28].\u003c/p\u003e\n\u003cp\u003e2.4 Definitions of metabolic dysfunction-associated steatotic liver disease (MASLD)\u003c/p\u003e\n\u003cp\u003eMASLD was diagnosed in the presence of hepatic steatosis accompanied by at least one of the following factors: (1) BMI≥25 kg/m\u003csup\u003e2\u003c/sup\u003e (≥ 23 kg/m\u003csup\u003e2\u003c/sup\u003e in Asian) or waist circumference \u0026gt; 94 cm in men, \u0026gt; 80 cm in women, or ethnicity adjusted; (2) Fasting serum glucose ≥ 100 mg/dL (≥ 5.6 mmol/L) or 2-hour post-load glucose level ≥ 140 mg/dL (≥ 7.8 mmol/L) or HbA1c ≥ 5.7% or on specific drug treatment; (3) Blood pressure ≥ 130/85 mmHg or specific drug treatment; (4) Plasma triglycerides ≥ 150 mg/dL (≥ 1.70 mmol/L) or specific drug treatment; (5) Plasma HDL cholesterol \u0026lt; 40 mg/dL (\u0026lt; 1.0 mmol/L) for men and \u0026lt; 50 mg/dL (\u0026lt; 1.3 mmol/L) for women or specific drug treatment [29]. \u003c/p\u003e\n\u003cp\u003e2.5 Statistical analysis\u003c/p\u003e\n\u003cp\u003eThe study participants were stratified based on the age of onset into two groups: a young-onset group (YOD, age of onset \u0026lt;40 years) and a late-onset group (LOD, age of onset ≥40 years). Subsequently, within each age-stratified group, subjects were further categorized according to CKD status into two subgroups: the CKD subgroup and the non-CKD subgroup. Statistical processing was performed using SPSS software (version 21.0; IBM Corp., USA). Kolmogorov-Smirnov test was conducted to evaluate the normality of the continuous variables before analysis. The quantitative data were reported as means ± standard deviations for normally distributed data or medians (interquartile ranges) for non-normally distributed data. Qualitative data were reported as percentages (%). Group comparisons were performed using independent samples t-tests for normally distributed quantitative data, Mann-Whitney U tests for non-normally distributed quantitative data, and chi-square tests for categorical variables.The logistic regression analyses were used to determine the association between YOD and CKD, as well as to identify the risk factors for CKD. \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 (Two-sided) was recognized to indicate statistical significance.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 General characteristics of the patients\u003c/h2\u003e\u003cp\u003eThe physical and biochemical indicators of the patients are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were 1194 patients newly diagnosed with T2DM, including 473 YOD and 721 LOD. The average age of patients with YOD was 31.48\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24 years; the LOD group averaged 54.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.73 years. Patients with YOD had significant clinical characteristics distinct from those of patients with LOD. Specifically, patients with YOD had a higher proportion of males (YOD 69.13% vs. LOD 56.17%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than patients with LOD. Additionally, patients with YOD had higher average levels of BMI, DBP, TC, TG, FBG, SUA, HOMA-IR, and eGFR, and lower average levels of HDL-C, BUN, and Cr compared to patients with LOD. Moreover, the YOD group had a higher proportion of MASLD (YOD 79.7% vs. LOD 70.18%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher average levels of ALT, AST, and γ-GGT than patients with LOD. However, the levels of SBP, HbA1c, LDL-C, and UACR in the YOD group were comparable to those in the LOD group. Importantly, there was no difference in the course (YOD 0.33 years vs. LOD 0.25 years, p\u0026thinsp;=\u0026thinsp;0.826) between the YOD and LOD groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic/clinical characteristics of study participants and the comparisons of parameters between subjects with CKD and those without.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral Indexes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll patients (n\u0026thinsp;=\u0026thinsp;1194 )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYOD (n\u0026thinsp;=\u0026thinsp;473 )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLOD (n\u0026thinsp;=\u0026thinsp;721 )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, male (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e732 (61.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e327 (69.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e405 (56.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.61\u0026thinsp;\u0026plusmn;\u0026thinsp;13.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.48\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCourse(yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25 (0.08-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33 (0.06-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25 (0.08-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.27\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135.44\u0026thinsp;\u0026plusmn;\u0026thinsp;16.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.73\u0026thinsp;\u0026plusmn;\u0026thinsp;15.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135.91\u0026thinsp;\u0026plusmn;\u0026thinsp;17.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.97\u0026thinsp;\u0026plusmn;\u0026thinsp;11.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.77 (1.18\u0026ndash;2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.24 (1.45\u0026ndash;3.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.53 (1.06\u0026ndash;2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.91\u0026thinsp;\u0026plusmn;\u0026thinsp;18.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.44\u0026thinsp;\u0026plusmn;\u0026thinsp;15.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.52\u0026thinsp;\u0026plusmn;\u0026thinsp;19.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUA(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e336.48\u0026thinsp;\u0026plusmn;\u0026thinsp;107.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e374.91\u0026thinsp;\u0026plusmn;\u0026thinsp;111.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e310.77\u0026thinsp;\u0026plusmn;\u0026thinsp;96.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.35 (2.20\u0026ndash;3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.56 (2.59\u0026ndash;4.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.34 (1.97\u0026ndash;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR(mL/min/1.73 m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111.13\u0026thinsp;\u0026plusmn;\u0026thinsp;18.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125.53\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.79\u0026thinsp;\u0026plusmn;\u0026thinsp;15.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUACR(mg/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.37 (6.55\u0026ndash;26.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.97 (6.68\u0026ndash;30.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (6.41-25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (17\u0026ndash;44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.25 (21-60.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.5 (16-33.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (18\u0026ndash;33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (19\u0026ndash;41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (18\u0026ndash;28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eγ-GGT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (22-53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (25.95-60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.8 (20.9-48.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMASLD(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e883 (73.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e377 (79.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e506 (70.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269 (22.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI: body mass index, SBP: systolic pressure, DBP: diastolic pressure, TC: total cholesterol, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBG: fasting blood glucose, BUN: blood urea nitrogen, Cr: creatinine, SUA: serum uric acid, HOMA-IR: insulin resistance index, eGFR: estimated glomerular filtration rate, UACR: urinary albumin to creatinine ratio, ALT: alanine aminotransferase, AST: aspartate aminotransferase, γ-GGT: γ-glutaryl transferase, MASLD: metabolic dysfunction-associated steatotic liver disease, YOD: young onset diabetes, LOD: late onset diabetes. CKD: chronic kidney disease. The t test and chi-square test with different samples were adopted for comparisons between groups. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered as statistically significant difference.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Association between YOD and the risk of CKD\u003c/h2\u003e\u003cp\u003eThe prevalence of CKD in the YOD group was slightly higher than that in the LOD group (YOD 24.1% vs. LOD 21.5%); however, this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.163) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We further assessed the relationship between YOD and CKD using logistic regression analysis. The results showed that there was no significant association between YOD and the presence of CKD (OR: 1.16, 95%CI: 0.88\u0026ndash;1.528, P\u0026thinsp;=\u0026thinsp;0.292).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Differences of parameters between YOD and LOD individuals in the CKD subgroup.\u003c/h2\u003e\u003cp\u003eAmong CKD patients, those with YOD showed a significantly higher proportion of males (69.42% vs. 45.16%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), elevated levels of BMI, DBP, TC, TG, FBG, SUA, HOMA-IR, eGFR and UACR, along with lower BUN and Cr levels (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to LOD patients. Additionally, MASLD prevalence was markedly higher in YOD patients (89.47% vs. 74.84%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accompanied by increased ALT, AST and γ-GGT levels. No significant differences were observed in disease course, SBP, HbA1c, HDL-C or LDL-C between the YOD and LOD groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences of parameters between YOD and LOD individuals in the CKD subgroup\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral Indexes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYOD with CKD (n\u0026thinsp;=\u0026thinsp;114 )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLOD with CKD (n\u0026thinsp;=\u0026thinsp;155 )\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, male (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78 (68.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (45.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.95\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCourse(yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25 (0.05-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17 (0.04\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142.91\u0026thinsp;\u0026plusmn;\u0026thinsp;18.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140.94\u0026thinsp;\u0026plusmn;\u0026thinsp;20.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90.93\u0026thinsp;\u0026plusmn;\u0026thinsp;13.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.24 (1.66\u0026ndash;6.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.72 (1.26\u0026ndash;2.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.34\u0026thinsp;\u0026plusmn;\u0026thinsp;20.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.99\u0026thinsp;\u0026plusmn;\u0026thinsp;31.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUA(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e403.16\u0026thinsp;\u0026plusmn;\u0026thinsp;113.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e323.34\u0026thinsp;\u0026plusmn;\u0026thinsp;104.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.56 (2.95\u0026ndash;5.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.34 (2.24\u0026ndash;3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR(mL/min/1.73 m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e124.75\u0026thinsp;\u0026plusmn;\u0026thinsp;18.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.46\u0026thinsp;\u0026plusmn;\u0026thinsp;22.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUACR(mg/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104.21 (56.56-266.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.02 (40-168.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.2 (26.78\u0026ndash;74.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (15\u0026ndash;33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (22-50.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (17.7\u0026ndash;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eγ-GGT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.3 (36-71.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (20-53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMASLD(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102 (89.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (74.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI: body mass index, SBP: systolic pressure, DBP: diastolic pressure, TC: total cholesterol, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBG: fasting blood glucose, BUN: blood urea nitrogen, Cr: creatinine, SUA: serum uric acid, HOMA-IR: insulin resistance index, eGFR: estimated glomerular filtration rate, UACR: urinary albumin to creatinine ratio, ALT: alanine aminotransferase, AST: aspartate aminotransferase, γ-GGT: γ-glutaryl transferase, MASLD: metabolic dysfunction-associated steatotic liver disease, YOD: young onset diabetes, LOD: late onset diabetes. CKD: chronic kidney disease. The t test and chi-square test with different samples were adopted for comparisons between groups. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered as statistically significant difference.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Association between clinical characteristics and CKD in YOD and LOD groups\u003c/h2\u003e\u003cp\u003eIn the YOD group, patients with CKD exhibited a higher prevalence of MASLD and significantly higher levels of BMI, SBP, DBP, TC, TG, FBG, SUA, HOMA-IR, ALT, AST, and γ-GGT than non-CKD patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Univariate logistic regression analysis revealed that BMI, SBP, DBP, TC, TG, FBG, SUA, ALT, AST, γ-GGT, and MASLD were positively associated with CKD (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Multivariate logistic regression analysis showed that SBP, FBG, and γ-GGT remained independently related to an increased prevalence of CKD after adjustment for multiple variables (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences of parameters between CKD and non-CKD individuals in the YOD and LOD subgroups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eYOD (n\u0026thinsp;=\u0026thinsp;473)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eLOD(n\u0026thinsp;=\u0026thinsp;721)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral Indexes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCKD (n\u0026thinsp;=\u0026thinsp;114 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-CKD (n\u0026thinsp;=\u0026thinsp;359 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCKD (n\u0026thinsp;=\u0026thinsp;155 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-CKD (n\u0026thinsp;=\u0026thinsp;566)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, male (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (68.42% )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e249 (69.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70 (45.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e335 (59.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.49\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.95\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCourse(yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25 (0.05-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42 (0.06-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17 (0.04\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25 (0.08-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142.91\u0026thinsp;\u0026plusmn;\u0026thinsp;18.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132.13\u0026thinsp;\u0026plusmn;\u0026thinsp;13.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e140.03\u0026thinsp;\u0026plusmn;\u0026thinsp;20.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e134.66\u0026thinsp;\u0026plusmn;\u0026thinsp;16.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.93\u0026thinsp;\u0026plusmn;\u0026thinsp;13.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.02\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83.46\u0026thinsp;\u0026plusmn;\u0026thinsp;11.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.037*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.24 (1.66\u0026ndash;6.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.11 (1.39\u0026ndash;3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.72 (1.26\u0026ndash;2.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.47 (1.02\u0026ndash;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.34\u0026thinsp;\u0026plusmn;\u0026thinsp;20.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.16\u0026thinsp;\u0026plusmn;\u0026thinsp;13.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.99\u0026thinsp;\u0026plusmn;\u0026thinsp;31.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61.57\u0026thinsp;\u0026plusmn;\u0026thinsp;14.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUA(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403.16\u0026thinsp;\u0026plusmn;\u0026thinsp;113.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e365.94\u0026thinsp;\u0026plusmn;\u0026thinsp;109.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e323.34\u0026thinsp;\u0026plusmn;\u0026thinsp;104.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e307.32\u0026thinsp;\u0026plusmn;\u0026thinsp;93.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR(mL/min/1.73 m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124.75\u0026thinsp;\u0026plusmn;\u0026thinsp;18.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125.77\u0026thinsp;\u0026plusmn;\u0026thinsp;12.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98.46\u0026thinsp;\u0026plusmn;\u0026thinsp;22.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102.69\u0026thinsp;\u0026plusmn;\u0026thinsp;12.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUACR(mg/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104.21 (56.56-266.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.92\u003c/p\u003e\u003cp\u003e(5.79\u0026ndash;14.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.02 (40-168.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.81\u003c/p\u003e\u003cp\u003e(5.69\u0026ndash;13.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.2 (26.78\u0026ndash;74.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (19\u0026ndash;58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (15\u0026ndash;33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.3 (16-33.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (22-50.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (18\u0026ndash;39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (17.7\u0026ndash;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.9 (18-28.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eγ-GGT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.3 (36-71.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (24\u0026ndash;57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (20-53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29 (21-47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMASLD(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (89.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e275 (76.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e116 (74.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e390 (68.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI: body mass index, SBP: systolic pressure, DBP: diastolic pressure, TC: total cholesterol, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBG: fasting blood glucose, BUN: blood urea nitrogen, Cr: creatinine, SUA: serum uric acid, HOMA-IR: insulin resistance index, eGFR: estimated glomerular filtration rate, UACR: urinary albumin to creatinine ratio, ALT: alanine aminotransferase, AST: aspartate aminotransferase, γ-GGT: γ-glutaryl transferase, MASLD: metabolic dysfunction-associated steatotic liver disease, YOD: young onset diabetes, LOD: late onset diabetes. CKD: chronic kidney disease. The t test and chi-square test with different samples were adopted for comparisons between groups. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered as statistically significant difference.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between clinical characteristics and CKD in YOD and LOD subgroups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eYOD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate logistic regression analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate logistic regression analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eUnivariate logistic regression analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eMultivariate logistic regression analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003cp\u003e(female/male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.405(0.663\u0026ndash;1.645)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.761(1.231\u0026ndash;2.519)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.999(0.965\u0026ndash;1.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.002(0.981\u0026ndash;1.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCourse(yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.992(0.882\u0026ndash;1.117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.038(0.936\u0026ndash;1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.104(1.055\u0026ndash;1.156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.056(1.007\u0026ndash;1.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.046(1.031\u0026ndash;1.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.027\u003c/p\u003e\u003cp\u003e(1.005\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.018(1.007\u0026ndash;1.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.021\u003c/p\u003e\u003cp\u003e(1.01\u0026ndash;1.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.016*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.051(1.031\u0026ndash;1.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.017(1.002\u0026ndash;1.032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.028*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.011(0.92\u0026ndash;1.112)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07(0.993\u0026ndash;1.152)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.279(1.098\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.032(0.902\u0026ndash;1.181)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.107(1.056\u0026ndash;1.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.006(0.958\u0026ndash;1.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.338(0.699\u0026ndash;2.562)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.583(0.31\u0026ndash;1.095)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.908(0.706\u0026ndash;1.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.102(0.903\u0026ndash;1.344)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.114(1.049\u0026ndash;1.183)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.074\u003c/p\u003e\u003cp\u003e(1.004\u0026ndash;1.149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.037*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.078(1.032\u0026ndash;1.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.062\u003c/p\u003e\u003cp\u003e(1.007\u0026ndash;1.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.028*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.984(0.844\u0026ndash;1.147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.122(1.012\u0026ndash;1.245)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.029*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.005(0.991\u0026ndash;1.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.01(1.002\u0026ndash;1.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.017*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUA(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.003(1.001\u0026ndash;1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.002(1.000-1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.145(1.05\u0026ndash;1.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.229(1.107\u0026ndash;1.365)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.005(1.001\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.023*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.998(0.988\u0026ndash;1.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01(1.003\u0026ndash;1.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.997(0.986\u0026ndash;1.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eγ-GGT(IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.011(1.006\u0026ndash;1.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.008\u003c/p\u003e\u003cp\u003e(1.002\u0026ndash;1.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.003(0.999\u0026ndash;1.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMASLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.596(1.361\u0026ndash;4.954)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.342(0.896\u0026ndash;2.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eBMI: body mass index, SBP: systolic pressure, DBP: diastolic pressure, TC: total cholesterol, TG: triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBG: fasting blood glucose, BUN: blood urea nitrogen, Cr: creatinine, SUA: serum uric acid, HOMA-IR: insulin resistance index, eGFR: estimated glomerular filtration rate, UACR: urinary albumin to creatinine ratio, ALT: alanine aminotransferase, AST: aspartate aminotransferase, γ-GGT: γ-glutaryl transferase, MASLD: metabolic dysfunction-associated steatotic liver disease, YOD: young onset diabetes, LOD: late onset diabetes. CKD: chronic kidney disease. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered as statistically significant difference.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this cross-sectional study, we examined the clinical characteristics of patients with newly diagnosed T2DM and CKD. We found that young-onset diabetes (YOD) was not associated with a higher prevalence of CKD in this population. However, among CKD patients, those with YOD exhibited significantly distinct clinical features compared to those with LOD, including more severe metabolic abnormalities, along with higher eGFR and UACR levels. Moreover, YOD patients showed a strong association between CKD and MASLD, with elevated γ-GGT emerging as an independent predictor of CKD, a finding not observed in LOD patients.\u003c/p\u003e\u003cp\u003eThe incidence of T2DM is increasingly affecting younger populations. Recent studies show a significant rise in young-onset T2DM (diagnosed before age 40), indicating the disease is no longer primarily associated with older adults but is becoming more common among adolescents and young adults [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Compared with late-onset T2DM, individuals diagnosed at a younger age face a higher risk of developing severe chronic complications, including both macrovascular and microvascular diseases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Previous studies have reported YOD is associated with an increased risk of CKD after adjustment for multiple clinical characteristics [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In a prospective analysis of 84,384 Korea patients with T2DM, the risk of developing CKD in patients with YOD was 1.7 times higher than those with LOD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, patients with young-onset T2DM and CKD are at an increased risk of CKD progression including ESRD [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the association between YOD and CKD in newly diagnosed T2DM remains uncertain, as most prior studies have been conducted in existing diabetes cases, where disease duration may be an important confounding factor. Our study found that in newly diagnosed T2DM, the prevalence of CKD did not significantly differ between the YOD and LOD groups. Logistic regression analysis further confirmed the absence of a significant association between YOD and CKD, suggesting that YOD may not be a risk factor for CKD in this population. This finding aligns with several previous studies, which indicated that the observed association between YOD and CKD might be largely attenuated after accounting for disease duration. After adjusting for diabetes duration, no significant association remained between YOD and CKD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNevertheless, our study showed that CKD patients with YOD had significantly distinct characteristics including more severe metabolic disorders and renal injury compared to those with LOD. Previous studies comparing YOD and LOD have found poorer metabolic control in YOD, likely due to lower adherence to a healthy diet and insufficient self-management [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Multiple metabolic abnormalities including obesity, hypertension, hyperglycemia, dyslipidemia, and insulin resistance have been shown to contribute to glomerular lesions, leading to microalbuminuria and glomerular hyperfiltration in early stages, and progressing to loss of renal function in later stages [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, an increasing number of metabolic disorders were associated with a higher risk of CKD [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Herein, in CKD patients, those with YOD exhibited significantly higher BMI, blood pressure, blood glucose, blood lipids, serum uric acid, and insulin resistance levels, indicating more severe metabolic dysregulation. Additionally, the UACR levels were significantly higher in CKD patients with YOD than in those with LOD. These findings suggest that YOD may exacerbate glomerular lesions in CKD patients, while the elevated eGFR observed in YOD could result from glomerular hyperfiltration\u0026mdash;potentially driven by higher BMI levels. Looker et al [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] demonstrated that YOD was strongly associated with more severe glomerular pathological manifestations including greater glomerular basement membrane (GBM) width and mesangial fractional volume than late-onset participants in DKD patients. In addition, glomerular sclerosis percentage, glomerular volume, mesangial fractional volume, and GBM width were inversely associated with age at diabetes onset. Our study highlights the importance of early detection and systematic management of metabolic abnormalities in patients with YOD to prevent glomerular lesion progression.\u003c/p\u003e\u003cp\u003eMore importantly, our study identified a significant association between CKD and MASLD in YOD patients. In this population, subjects with CKD had significantly higher MASLD prevalence and elevated liver enzymes (ALT, AST, γ-GGT) than those without CKD. Furthermore, MASLD, ALT, AST, γ-GGT were positively associated with CKD and after multivariable adjustment, γ-GGT remained independently associated with increased CKD prevalence. Notably, these associations were absent in the LOD group. MASLD, which evolved from the term nonalcoholic fatty liver disease (NAFLD), is defined by the presence of hepatic steatosis in combination with one or more metabolic risk factors [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. MASLD is strongly associated with T2DM, occurring in 58.84%-72.65% of individuals with T2DM [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Additionally, MASLD significantly exacerbates diabetes related microvascular complications [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The link between CKD and NAFLD/MASLD has been highlighted by several previous studies [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], but little is known about how this association differs by age of diabetes onset. Our results indicated that, in patients with newly diagnosed T2DM, MASLD appeared to be positively associated with CKD only in young-onset T2DM, whereas no significant relationship was observed in late-onset T2DM patients. The stronger association between CKD and MASLD in YOD patients may be attributable to the more prolonged exposure to metabolic disturbances, including worse insulin resistance, hyperglycemia and dyslipidemia. These factors likely accelerate both hepatic and renal injury through mechanisms such as oxidative stress, advanced glycation end-product accumulation, and systemic inflammation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These findings reveal a potential hepatorenal link in young T2DM, highlighting the need for combined screening for MASLD and CKD. Aggressive management of metabolic risk factors (e.g., glycemic control, statin therapy, and weight loss) in critical to prevent multiorgan complications. Furthermore, some agents (e.g., GLP-1 RAs, SGLT2 inhibitors) may warrant prioritization in this high-risk population, given their dual benefits on hepatic and renal outcomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eγ-GGT is a key enzyme in glutathione metabolism and a biomarker for hepatobiliary diseases [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. While its association with CKD remains controversial in the general populations [\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], our study revealed that elevated γ-GGT independently predicts CKD risk in YOD patients, even after adjusting for BMI, blood pressure, glucose, lipids, SUA, insulin resistance and other liver enzymes, whereas no such association was observed in LOD patients. This suggests γ-GGT may serve as a young onset-specific biomarker for CKD in T2DM. In this population, γ-GGT assessment could facilitate earlier identification of individuals at high risk of CKD. The mechanisms underlying the association between γ-GGT and CKD remain poorly understood. MASLD may play a key role in mediating the γ-GGT-CKD association [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Increased serum γ-GGT levels are considered a reliable marker of MASLD and are associated with higher liver fat content [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. MASLD itself may promote CKD and accelerated atherosclerosis by releasing various hepatokines(e.g., fetuin-A, angiopoietin-like proteins, insulin-like growth factor binding protein 1 and glycoprotein nonmetastatic melanoma protein B) from the fatty/inflamed liver, which are elevated in MASLD patients and likely contribute directly to CKD development [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This could further explain the lack of association between γ-GGT and CKD in the late-onset group, given that CKD was not linked to MASLD in these patients. Another possible mechanism linking serum γ-GGT and CKD could be oxidative stress. Beyond predicting liver dysfunction, γ-GGT catalyzes glutathione breakdown, generating reactive oxygen species and serving as a systemic oxidative stress marker [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Since oxidative stress promotes renal injury via inflammation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], this pathway may also explain the γ-GGT-CKD association.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, its cross-sectional design can only identify associations but not establish causality. Second, CKD was diagnosed clinically (via eGFR and albuminuria) without kidney biopsy, potentially misclassifying early CKD or non-diabetic kidney disease. Third, our cohort comprised newly diagnosed T2DM patients, limiting generalizability to advanced disease stages. Fourth, eGFR-based CKD definition may be less accurate in subgroups with extreme body weights or ethnic variability, though it remains a recommended tool for large studies. Additionally, unmeasured confounders (e.g., dietary habits or genetic factors) and variability in liver enzyme measurements (e.g., γ-GGT fluctuations due to alcohol use) could influence the results. Future longitudinal studies with kidney biopsy validation and more comprehensive features are warranted to establish causality and explore age-specific mechanisms linking MASLD, γ-GGT, and CKD in T2DM.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study of newly diagnosed T2DM patients, YOD was not independently associated with a higher prevalence of CKD compared to LOD. However, YOD patients with CKD exhibited distinct clinical features, including more severe metabolic disturbances (e.g., higher BMI, SBP, DBP, TC, TG, FBG, SUA, and HOMA-IR) and elevated markers of renal injury (higher eGFR and UACR). Additionally, YOD patients showed a stronger association between CKD and MASLD compared to LOD patients. Elevated γ-GGT emerged as an independent predictor of CKD in the YOD group, suggesting its potential role as a YOD-specific biomarker for renal risk assessment. These findings highlight the need for early detection and management of metabolic abnormalities and MASLD in YOD patients to prevent CKD progression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCKD: chronic kidney disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM: type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eYOD: young-onset diabetes\u003c/p\u003e\n\u003cp\u003eLOD: late-onset diabetes\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBP: systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP: diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eALT: alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eAST: aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003e\u0026gamma;-GGT: \u0026gamma;-glutaryl transferase\u003c/p\u003e\n\u003cp\u003eTC: total cholesterol\u003c/p\u003e\n\u003cp\u003eTG: triglycerides\u003c/p\u003e\n\u003cp\u003eHDL: high-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDL: low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eFBG: fasting blood glucose\u003c/p\u003e\n\u003cp\u003eSUA: serum uric acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCr: creatinine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBUN: blood urea nitrogen\u003c/p\u003e\n\u003cp\u003eUACR: urinary albumin to creatinine ratio\u003c/p\u003e\n\u003cp\u003eeGFR: estimated glomerular filtration rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHbA1c: glycosylated hemoglobin\u003c/p\u003e\n\u003cp\u003eFINS: fasting insulin\u003c/p\u003e\n\u003cp\u003eHOMA-IR: insulin resistance index\u003c/p\u003e\n\u003cp\u003eMASLD: metabolic dysfunction-associated steatotic liver disease\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki (6th revision, 2008) as reflected in a priori approval by the ethics committees of Qilu Hospital of Shandong University Dezhou Hospital. Written informed consent was provided by each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for publication was obtained from each participant included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests associated with this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article was founded by the National Key Research and Development Program of China [grant numbers 2024YFC3607400]; the Tianjin Institute of Urology talent funding program [grant numbers MYSRC2024]; the First Level Leading Talent Project of \u0026quot;123 Climbing Plan\u0026quot;for Clinical Talents of Tianjin Medical University; the \u0026quot;Tian jin Medical Talents\u0026quot;oroject, the second batch of high-level talents selection project in health industry in Tianjin [grant numbers TJSJMYXYC-D2-014]; the Key Project of Natural Science Foundation of Tian jin, \u0026nbsp;[grant numbers 22JCZDJCO0590] and the Tian jin Key Medical Discipline (Specialtv) Construct Project [grant numbers TJYXZDXK-032A]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ying Li, and Shan Yue. The Data analysis and first draft preparation of the manuscript were performed by Yuliang Cui. The conceptualization and writing- reviewing were performed by Pei Yu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would be grateful for language services by Duoease Scientific Service Center.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709-733.\u003c/li\u003e\n\u003cli\u003eMagliano DJ, Islam RM, Barr ELM, et al. 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Non-Alcoholic Fatty Liver Disease and Risk of Macro- and Microvascular Complications in Patients with Type 2 Diabetes. J Clin Med 2022;11(4):968.\u003c/li\u003e\n\u003cli\u003eTargher G, Chonchol M, Bertolini L, et al. Increased risk of CKD among type 2 diabetics with nonalcoholic fatty liver disease. J Am Soc Nephrol. 2008;19(8):1564-1570.\u003c/li\u003e\n\u003cli\u003eChang Y, Ryu S, Sung E, et al. Nonalcoholic fatty liver disease predicts chronic kidney disease in nonhypertensive and nondiabetic Korean men. Metabolism. 2008;57(4):569-576. \u003c/li\u003e\n\u003cli\u003eWhitfield JB. Gamma glutamyl transferase. Crit Rev Clin Lab Sci. 2001;38(4):263-355. \u003c/li\u003e\n\u003cli\u003eCain LR, Ducatman AM, Shankar A. The relationship between gamma-glutamyl transferase levels and chronic kidney disease among Appalachian adults. W V Med J. 2012;108(1):8-13.\u003c/li\u003e\n\u003cli\u003eTargher G, Kendrick J, Smits G, et al. 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Eur J Pharmacol. 2022;931:175219.\u003c/li\u003e\n\u003cli\u003eLee DH, Blomhoff R, Jacobs Jr DR. Is serum gamma glutamyltransferase a marker of oxidative stress? Free Radic Res 2004;38:535e9.\u003c/li\u003e\n\u003cli\u003eModaresi A, Nafar M, Sahraei Z. Oxidative stress in chronic kidney disease. Iran J Kidney Dis 2015;9(3):165\u0026ndash;79.\u003c/li\u003e\n\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":"young-onset type 2 diabetes, diabetic kidney disease, clinical characteristics, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7121363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7121363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe association between young-onset diabetes and chronic kidney disease has been well studied, however, few studies have described the features of chronic kidney disease (CKD) specifically in young-onset diabetes. We aimed to compare the clinical characteristics and risk factors for CKD between young-onset and late-onset newly diagnosed type 2 diabetes (T2DM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective study, 1194 newly diagnosed T2DM patients were categorized into young-onset (diagnostic age\u0026thinsp;\u0026lt;\u0026thinsp;40 years) and late-onset (\u0026ge;\u0026thinsp;40 years) groups, further stratified by CKD status. Anthropometric and laboratory data were collected retrospectively. Clinical differences were analyzed using t-tests, Mann-Whitney U tests, and chi-square tests. Logistic regression was used to identify the risk factors for CKD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn newly diagnosed T2DM patients, CKD prevalence was similar between young-onset and late-onset groups (24.1% vs. 21.5%, p\u0026thinsp;=\u0026thinsp;0.163), with no significant association between young-onset diabetes and CKD (OR: 1.16, 95%CI: 0.88\u0026ndash;1.53, P\u0026thinsp;=\u0026thinsp;0.292). Compared to CKD with late-onset diabetes, young-onset patients had higher urinary albumin-to-creatinine ratio, eGFR, BMI, diastolic blood pressure, lipid levels, fasting blood glucose, liver enzymes, and higher metabolic-associated steatotic liver disease prevalence. In both young and late onset groups, diastolic blood pressure and fasting blood glucose were independently associated with CKD, however, gamma-glutamyl transferase was independently associated with CKD only in the young-onset group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eYoung-onset diabetes may not be an independent risk factor for CKD in newly diagnosed T2DM, but CKD patients with young-onset diabetes exhibit distinct clinical characteristics compared to those with late-onset diabetes. Elevated gamma-glutamyl transferase specifically predicted CKD in young-onset T2DM.\u003c/p\u003e","manuscriptTitle":"Clinical characteristics and the risk factors of patients with chronic kidney disease in young-onset versus late-onset newly diagnosed type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 17:27:44","doi":"10.21203/rs.3.rs-7121363/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":"d0242b8b-3813-4579-b981-e12fffe1f83e","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-29T06:09:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 17:27:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7121363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7121363","identity":"rs-7121363","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}