Onset Age May Not Increase Kidney Disease Burden among Adult-Onset Type 1 DiabetesPatients—A Multi-Center Cross-Sectional Study

Onset Age May Not Increase Kidney Disease Burden among Adult-Onset Type 1 DiabetesPatients—A Multi-Center Cross-Sectional Study | 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 Onset Age May Not Increase Kidney Disease Burden among Adult-Onset Type 1 DiabetesPatients—A Multi-Center Cross-Sectional Study Ying Qiu, Sihui Luo, Jinhua Yan, Daizhi Yang, Jun Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8787687/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background The incidence of adult-onset type 1 diabetes (T1DM) is rising globally, yet the association between onset age and kidney disease burden—encompassing diabetic kidney disease (DKD), abnormal kidney function, microalbuminuria, and high progression risk of DKD—remains unclear in this population. This multi-center cross-sectional study aimed to investigate this correlation, addressing a critical knowledge gap for clinical practice and public health management. Methods A total of 481 adult-onset T1DM participants from the Guangdong T1DM Translational Medicine Study were stratified by onset age into <30 years (n=269) and ≥30 years (n=212) groups. Demographic, clinical, and laboratory data were collected, and kidney disease indicators were assessed per ADA 2025 standards. Logistic regression analyses were used to evaluate the association between onset age and kidney disease, with adjustments for gender, age of visit, diabetes duration, metabolic factors, and islet autoantibodies. Results The overall DKD prevalence was 22.7%, with no significant differences between the <30 years (23.8%) and ≥30 years (21.2%) groups ( P = 0.505). Similarly, microalbuminuria (23.0% vs. 19.3%, P = 0.325) and high progression risk of DKD (5.2% vs. 5.7%, P = 0.826) showed no intergroup disparities. Although the ≥30 years group had lower median eGFR (105.5 vs. 118.5 ml/min/1.73m², P < 0.001) and higher abnormal kidney function prevalence (23.1% vs. 13.8%, P = 0.008) in unadjusted analysis, these differences disappeared after adjusting for confounding factors. Multivariate logistic regression confirmed onset age ≥30 years was not independently associated with DKD (OR=1.32, 95% CI: 0.53-3.24, P = 0.552), microalbuminuria (OR=1.48, 95% CI: 0.59-3.72, P = 0.399), or high progression risk of DKD (OR=0.43, 95% CI: 0.07-2.78, P = 0.377). Conclusions Onset age may not increase kidney disease burden in adult-onset T1DM patients, differing from type 2 diabetes. Clinical risk assessment should prioritize comprehensive metabolic control (blood glucose, blood pressure, lipids) and regular kidney function monitoring, rather than onset age, providing actionable guidance for optimizing care in this growing population. Clinical trial number: not applicable. Type 1 diabetes mellitus Adult-onset Onset age Kidney disease burden Figures Figure 1 Figure 2 Contributions to the Literature Text box 1. Contributions to the Literature • Fills the gap of limited research on onset age and kidney disease in adult-onset type 1 diabetes, differing from studies focusing on children/adolescents or type 2 diabetes. • Provides population health evidence that onset age need not be a key factor in kidney disease risk assessment for this group. • Guides clinical practice to prioritize metabolic control over onset age, aiding public health management of this growing patient population. Introduction Type 1 diabetes mellitus (T1DM) is characterized by β-cell destruction and absolute insulin deficiency, and was traditionally considered a disease affecting mainly children and adolescents [ 1 ]. However, emerging epidemiological evidence indicates that adult-onset T1DM (diagnosed at ≥ 18 years) accounts for a substantial proportion of new cases globally [ 2 ]. For instance, in high-risk regions like southeastern Sweden, the incidence of T1DM in individuals aged 40–100 years (34.0/100,000/year) is comparable to that in children and adolescents (37.8/100,000/year) [ 3 ]. In China, a low-risk area for T1DM, adults constitute approximately two-thirds of clinically diagnosed new T1DM cases [ 4 – 6 ]. Adult-onset T1DM patients often present with distinct clinical features, including higher body mass index (BMI), milder initial symptoms, and potential diagnostic challenges due to overlap with type 2 diabetes (T2DM) [ 7 – 9 ], which underscores the need for targeted research on this population. Onset age is widely recognized as one of the key determinants of diabetes-related complications. Previous studies have demonstrated that onset age contributes to identifying subtypes of adult T2DM [ 10 , 11 ]. For T1DM, patients diagnosed at an earlier age have shown higher hazard ratios for all-cause mortality and various cardiovascular diseases (CVDs) compared with those diagnosed later in life [ 12 ]. However, these insights are primarily derived from comparative studies between children/adolescents and young adults (typically 30 years of age) not yet included in the analysis. Additionally, some research has indicated that a higher age at onset significantly increases the risk of macular edema in T1DM patients [ 13 ]. Nevertheless, this study mainly analyzed the impact of three age-at-onset groups (0–4 years, 5–14 years, and 15–40 years) on diabetic macular edema, lacking an exploration of the effects of different age-at-onset subgroups within the adult population (e.g., < 30 years vs. ≥ 30 years). Furthermore, compared with adolescent-onset T1DM patients and age-matched T2DM patients, adult-onset T1DM patients face a higher risk of a broad spectrum of complications, such as severe hypoglycemia, end-stage renal disease (ESRD), and CVD) as well as mortality [ 14 ]. However, this study neither conducted comparisons between different age at onset subgroups within the adult population nor excluded the mixed population with T1DM and T2DM-like phenotypes. In contrast, previous study has suggested that for adult-onset T1DM, whether defined from the perspective of clinical diagnosis or autoantibody testing, its clinical characteristics, disease progression, and genetic susceptibility are not affected by the age at diagnosis [ 15 ]. This finding raises the question of whether onset age independently influences kidney disease burden in this population. Diabetic kidney disease (DKD) is a leading cause of ESRD worldwide, imposing significant morbidity, mortality, and healthcare burden in T1DM patients [ 16 , 17 ]. Notably, adult-onset T1DM patients may face unique renal risk factors, including age-related declines in kidney function, comorbid metabolic abnormalities, and delayed diagnosis [ 18 – 20 ], all of which synergistically exacerbate the initiation and progression of kidney disease. Therefore, given the rising prevalence of adult-onset T1DM and the unclear association between onset age and kidney disease (e.g., DKD, microalbuminuria, etc.) in this patient, this study was conducted based on data from the Guangdong Type 1 Diabetes Translational Medicine Study (GTT) to investigate the prevalence of kidney disease among patients with adult-onset T1DM and further analyze the correlation between onset age and kidney disease burden in this patients. Methods Study design and participants selection This cross-sectional study utilized baseline data from the GTT collected between 2010 and 2017. The GTT study is a multi-center observational study that recruited T1DM patients from 16 tertiary hospitals across 12 cities in Guangdong Province, China. Trained and certified medical staff collected clinical data prospectively at enrollment and during annual follow-ups in accordance with standardized operating procedures, and the detailed study design has been described in previous publications [21-23]. This study was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat‐Sen University (ID: [2010]2-36). Written informed consent was obtained from all subjects. As described in our earlier publications [23-25], all participants enrolled in GTT study cohort were required to satisfy three core prerequisites: insulin dependence, a confirmed diagnosis of T1D by a board-certified endocrinologist, and fulfillment of at least one of the following four criteria: (i) overt clinical manifestations of diabetes-associated metabolic disorder; (ii) a history of diabetic ketosis or ketoacidosis; (iii) positive serological results for diabetes-related autoantibodies; and (iv) fasting and stimulated C-peptide concentrations below 200 pmol/L. For participants with an ambiguous initial diagnosis, further definitive verification was performed by specialist endocrinologists at the Guangdong Diabetes Centre. For the purpose of the present analysis, the study sample was further restricted to individuals diagnosed with T1D between 18 and 65 years of age, who had a disease duration exceeding 1 year at the time of recruitment. Exclusion criteria included a documented episode of ketosis or ketoacidosis, acute infection, or acute inflammatory conditions within 3 months prior to enrolment, as well as incomplete data pertaining to diabetic kidney disease (DKD). A comprehensive overview of the study’s inclusion and exclusion criteria is illustrated in Figure 1. Data collection Demographic data (age, gender), anthropometric measurements (height, weight, waist circumference, hip circumference) for calculating body mass index (BMI), waist-to-hip ratio (WHR), and clinical information (diabetes duration, onset characteristics, medication use, chronic complications, and comorbidities) were extracted from electronic medical records. Glycated hemoglobin A1c (HbA1c) was quantified via affinity chromatography using the Affinion AS100 point-of-care analyzer (Axis-Shield Diagnostic Ltd., Dundee, Scotland), with a reference range of 4.3-6.1% and total coefficient of variation <3%. At study enrollment, whole blood or serum samples were collected, and concentrations of C-peptide, diabetes-related autoantibodies (including glutamic acid decarboxylase antibody [GAD-A], zinc transporter 8 antibody [ZnT8-A], and islet cell antigen 512 antibody [ICA512-A]), lipid profiles (triglycerides [TG], total cholesterol [TC], and low-density lipoprotein cholesterol [LDL-C]), serum uric acid (SUA), and serum creatinine (SCr) were quantified using commercially available test kits in accordance with standard procedures (see the previously published protocol [23] for detailed methodology). In addition, the estimated glucose disposal rate (eGDR), a validated index established by our research group in prior work [26] , was utilized to evaluate the degree of insulin resistance in study participants. Grouping The adult-onset T1DM refers to the onset of T1DM at 18 years or older [24,25,27]. According to the onset age, participants were divided into 2 groups: group 1 (onset-age < 30 years) and group 2 (onset-age ≥30 years). Assessment of kidney disease and definitions Indicators of the assessment of kidney disease included DKD, abnormal kidney function, microalbuminuria, and high progression risk of DKD. We adhered to the Standards of Care in Diabetes-2025 by the American Diabetes Association (ADA): DKD was diagnosed based on established criteria [17], eGFR was calculated using the chronic kidney disease epidemiology collaboration (CKD-EPI) equation [28], and microalbuminuria was defined as a urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g [17]. The eGFR < 90 ml/min/1.73 m 2 was defined as abnormal kidney function [29,30]. DKD progression risk stratification was conducted according to the ADA and Kidney Disease: Improving Global Outcomes (KDIGO) guideline [29]. High progression risk of DKD included high and very high progression risk of DKD. Statistical analysis All statistical analyses were performed using IBM SPSS Statistics 27.0 version (IBM Corporation, Armonk, NY, USA). Continuous variables with normal distribution are presented as means ± standard deviation (SD), while skewed variables are presented as median (interquartile range [IQR]). Categorical variables are presented as n (%). Differences between onset age groups were compared using independent samples t -test, Mann-Whitney U test, or chi-square test as appropriate. Logistic regression analyses were used to assess the association between onset age (explanatory variable: ≥30 years vs. <30 years as reference) and kidney disease (dependent variable). Three models were constructed to adjust for potentially confounding factors. Model 1 was unadjusted. Model 2 was adjusted for gender, age of visit, duration of diabetes, BMI, and WHR. Model 3 was further adjusted for SBP, DBP, HbA1c, LDL-C, SUA, glucose disposal rate (GDR), insulin dosage, and islet autoantibodies (GAD-A, ZNT8-A, and ICA512-A) positive rate based on Model 2. In addition, subgroup analyses were performed by gender, diabetes duration (<5 years vs. ≥5 years), and HbA1c (<7% vs. ≥7%) to explore potential effect modifiers. Interaction terms between onset age and each stratification variable were included in the logistic regression models, and P <0.05 was considered statistically significant for interactions. A two-tailed P <0.05 was considered statistically significant for all analyses. Results Patient characteristics and comparison of baseline covariates A total of 481 adult-onset T1DM participants were included in this analysis. Characteristics are shown in Table 1. Of all participants, 227 (47.2%) were male, diabetic duration of 5.1 (2.8, 8.4) years and HbA1c level of 8.4 (7.1, 10.1) %. There were a total of 109 (22.7%) participants diagnosed with DKD, 86 (17.9%) diagnosed with abnormal kidney function, 103 (21.4%) diagnosed with microalbuminuria and 26 (5.4%) diagnosed with high progression risk of DKD at study enrolment. The median onset age of 481 participants was 28.5 (23.5, 35.3) years. Accordingly, participants were divided into 2 groups, with 269 (55.9%) in the <30 years onset group and 212 (44.1%) in the ≥30 years group (Table 1). The median onset age was 23.9 (21.2, 26.9) years in the <30 years group and 36.2 (32.5, 41.7) years in the ≥30 years group ( P < 0.001). There were no significant differences in gender distribution ( P = 0.720), diabetes duration ( P = 0.818), HbA1c ( P = 0.880), or fasting/2h postprandial C-peptide levels ( P = 0.315 and P = 0.211, respectively) between the two groups. The ≥30 years group had significantly higher WHR (0.87 vs. 0.84, P = 0.001), SBP (120.0 vs. 110.0 mmHg, P = 0.001), DBP (73.0 vs. 70.0 mmHg, P = 0.041), and lower insulin requirement (0.64 vs. 0.71 IU/kg/day, P = 0.020) compared to the <30 years group. BMI was slightly higher in the ≥30 years group (20.8 vs. 20.6 kg/m², P = 0.017). The positive rate of ICA512 antibody was significantly lower in the ≥30 years group (9.0% vs. 15.6%, P = 0.030), while there were no significant differences in LDL-C, SUA, or other autoantibody positivity (GAD, ZnT8) between the two groups (all P > 0.05). Kidney diseases by Onset Age Group As shown in Table 1 and Figure 2, the prevalence of DKD was similar between the <30 years and ≥30 years groups (23.8% vs. 21.2%, P = 0.505). There were no significant differences in the prevalence of microalbuminuria (23.0% vs. 19.3%, P = 0.325) or high progression risk of DKD (5.2% vs. 5.7%, P = 0.826) between the two groups. In contrast, the ≥30 years group had a significantly lower median eGFR (105.5 vs. 118.5 ml/min/1.73m², P < 0.001) and higher prevalence of abnormal kidney function (23.1% vs. 13.8%, P = 0.008) compared to the <30 years group. There were no significant differences in UACR ( P = 0.441) or SCr ( P = 0.257) between the two groups (Table 1). Correlations between onset age and kidney disease Logistic regression analyses (Table 2) showed that in the unadjusted model (Model 1), onset age ≥30 years was significantly associated with lower risk of abnormal kidney function (OR=0.53, 95% CI: 0.33-0.85, P = 0.008) but not with DKD (OR=1.16, 95% CI: 0.75-1.79, P = 0.505), microalbuminuria (OR=1.25, 95% CI: 0.80-1.95, P = 0.325), or high progression risk of DKD (OR=0.92, 95% CI: 0.41-2.02, P = 0.826). After adjusting for gender, age of visit, diabetes duration, BMI, and WHR (Model 2), the association between onset age ≥30 years and abnormal kidney function was attenuated and no longer significant (OR=1.87, 95% CI: 0.80-4.36, P = 0.148). Further adjustment for metabolic factors (SBP, DBP, HbA1c, LDL-C, SUA, GDR, insulin dosage) and islet autoantibodies (GAD-A, ZNT8-A, and ICA512-A) positive rate in Model 3 did not restore significance for kidney disease: DKD (OR=1.32, 95% CI: 0.53-3.24, P = 0.552), abnormal kidney function (OR=1.67, 95% CI: 0.61-4.61, P = 0.322), microalbuminuria (OR=1.48, 95% CI: 0.59-3.72, P = 0.399), and high progression risk of DKD (OR=0.43, 95% CI: 0.07-2.78, P = 0.377). Subgroup Analyses Subgroup analyses (Table 3) revealed that gender was a significant effect modifier for the association between onset age and high progression risk of DKD ( P for interaction = 0.046). Advanced age (onset age ≥30 years) may attenuate the high progression risk of DKD in male patients (OR=0.002, 95% CI: 0.00-1.22, P = 0.058), but may exert no impact on such risk in female patients (OR=1.33, 95% CI: 0.05-35.68, P > 0.05). For other kidney disease (including DKD, abnormal kidney function, and microalbuminuria), no significant interactions were observed between onset age and gender (all P for interaction >0.05). Additionally, in the subgroup of diabetes duration ≥5 years and HbA1c ≥7%, onset age ≥30 years was associated with a significantly higher risk of abnormal kidney function (OR=3.35, 95% CI: 1.08-10.41, P =0.037; OR=3.60, 95% CI: 1.02-12.68, P =0.046, respectively). Nevertheless, for any kidney disease (including DKD, abnormal kidney function, microalbuminuria, or high progression risk of DKD), the interaction terms between onset age and diabetes duration, or HbA1c level were all P >0.05, indicating no significant effect modification. Discussion This multi-center cross-sectional study focused on the correlation between onset age and kidney disease burden in adult-onset T1DM patients, with additional exploration of effect modifiers. The key findings were: (1) onset age (stratified as < 30 years vs. ≥30 years) was not associated with the prevalence of DKD, microalbuminuria, or high progression risk of DKD; (2) older onset age was linked to lower baseline eGFR and higher prevalence of abnormal kidney function in unadjusted analysis, but this association was eliminated after adjusting for confounding factors; (3) gender acted as an effect modifier in the association between onset age and high progression risk of DKD; by contrast, diabetes duration and HbA1c level did not modify the association, but onset age ≥ 30 years was associated with a significantly higher risk of abnormal kidney function in participants with diabetes duration ≥ 5 years and HbA1c ≥ 7%. These results support the conclusion that onset age may not increase kidney disease burden in adult-onset T1DM, with gender-specific differences observed only in DKD progression risk. To the best of our knowledge, this is the first study to investigate the association between onset age and kidney disease in patients with adult-onset T1D. Thomas et al. [ 15 ] reported that age at diagnosis (before vs. after 35 years) did not alter the progression of beta-cell dysfunction or metabolic complications in robustly defined adult-onset T1DM patients, suggesting that autoimmune mechanisms and disease progression are similar across adult onset age. However, this study primarily investigated whether the age at diagnosis alters the clinical manifestations of adult-onset T1D — including weight loss, the incidence of diabetic ketoacidosis, and blood glucose levels — as well as the rate of C-peptide decline and genetic susceptibility. It did not focus on chronic complications in patients, especially renal complications, as our study does. Furthermore, the study by Casu et al. [ 7 ] have reported that T1DM diagnosed in childhood and adulthood exhibits negligible differences in clinical characteristics and outcomes, which resonates with the findings of the present study that onset age is not associated with the prevalence of DKD. Nevertheless, this study mainly analyzed the impact of onset age in children/adolescents and adults, but did not explore the role of subgroups stratified by different onset age within the adult population, nor did it investigate the effect of onset age on kidney disease. In addition, this study confirmed that onset age (stratified as < 30 years vs. ≥30 years) was not associated with the prevalence of DKD, microalbuminuria, or high progression risk of DKD, which was different from those of prior investigations that evaluated long-term kidney disease stratified by onset age of T1DM. Previous study showed that pre-adolescent-onset patients exhibit superior renal prognoses relative to their adolescent- or adult-onset counterparts. A population-based study conducted in Hong Kong, China, revealed that adult-onset T1DM patients are at a significantly elevated risk of ESRD compared with adolescent-onset cases, with this association being more pronounced among patients with late-onset adult T1DM (≥ 40 years of age) [ 14 ]. Two large-scale population-based cohort studies from Finland and Sweden, which recruited newly diagnosed T1DM patients aged ≤ 29 years and ≤ 34 years, respectively, demonstrated that the lowest risk of ESKD was observed in those diagnosed at < 5–10 years of age [ 31 , 32 ]. Additionally, national surveillance data from Norway indicated that the 30-year cumulative incidence of ESRD among T1DM patients was 2.9% in those diagnosed under 15 years of age, as opposed to 4.8% in those diagnosed between 15 and 29 years of age [ 33 , 34 ]. However, these investigations were predominantly predicated on comparative analyses between childhood/adolescent-onset and adult-onset T1DM cohorts. They neither performed stratified comparisons among subgroups stratified by distinct onset age within the adult population, nor accounted for the confounding influence of chronological age on study outcomes. More importantly, these prior studies incorporated extended follow-up durations and targeted ESRD—a severe late-stage renal complication—as their primary endpoint. In stark contrast, the present study employed a cross-sectional study design and evaluated relatively early-stage renal complications as indicators. In this study, we also found that, in unadjusted analyses, the reduced eGFR in older-onset age T1DM patients was likely attributable to the physiological age-related decline in kidney function. In the general population, eGFR exhibits a natural downward trajectory with advancing age after the fourth decade of life [ 19 ], a degenerative process that is further accelerated by DM [ 17 ]. Notably, participants in the ≥ 30 years age-at-onset group had a significantly higher age at recruitment (43.2 vs. 29.9 years, P <0.001), a variable that confounded the putative association between onset age and eGFR in the unadjusted model. Following adjustment for age at recruitment and other confounding covariates, this association ceased to be statistically significant, confirming that chronological age—not onset age—represents the primary determinant of intergroup eGFR disparities. Additionally, the present study detected no significant association between onset age and microalbuminuria, a finding consistent with that of prior investigations involving patients with latent autoimmune diabetes in adults (LADA) [ 35 ]. Meanwhile, all age-at-onset subgroups included in this study had a relatively short median diabetes duration (approximately 5 years), long-term prospective longitudinal studies with extended follow-up periods are warranted to further validate these preliminary findings. A key novel finding of this study was that sex modifies the association between onset age and high progression risk of DKD. This study demonstrated that advanced age (onset age ≥ 30 years) may attenuate the high progression risk of DKD in male patients, whereas it appeared to exert no impact on such risk in female patients. This phenomenon may be attributed to the fact that the influence of age on the effect of sex difference on the progression of chronic kidney disease, or females benefit from estrogen-mediated renal protective effects [ 36 ]. Although no significant interaction was observed, in the subgroups of diabetes duration ≥ 5 years or HbA1c ≥ 7%, onset age ≥ 30 years significantly increased the risk of abnormal kidney function. This finding suggests that long T1DM duration and poor glycemic control may amplify the potential adverse effects of late-onset on kidney disease. Previous studies have clearly established that prolonged diabetes duration was associated with cumulative damage to renal microvasculature caused by persistent hyperglycemia [ 37 , 38 ]. In addition, long-term poor glycemic control exacerbates oxidative stress, inflammatory responses, and endothelial dysfunction in renal tissues [ 37 , 39 ]. Therefore, the synergistic effect of these unfavorable factors and late-onset diabetes (coupled with the natural physiological decline of kidney function in adults [ 19 ]) further accelerates the deterioration of renal filtration capacity, ultimately resulting in a marked elevation in the risk of renal dysfunction. In this study, the positive rate of ICA512 antibody was significantly higher in the onset age <30 years group than in the ≥ 30 years group (15.6% vs.9.0%, P = 0.030), reflecting stronger islet autoimmune attack in onset age <30 years subgroup—previous study has shown that T1DM patients with a younger age at diagnosis and high titers of islet cell antibodies (ICA) are more prone to rapid β-cell function loss [ 40 ]. However, there was no significant difference in C peptide levels (fasting and 2h after meals) between the two groups in this study (all P > 0.05), which may be related to the short duration (median disease duration of 5 years). Long-term follow-up may capture β-cell function decline difference mediated by autoantibody profile variation. The intergroup difference in ICA512 antibody may indicate a unique immune phenotype in young adult-onset T1DM. However, although there is heterogeneity in autoimmune phenotypes, this difference did not affect short-term kidney disease, indicating that renal risk is mainly dominated by traditional factors such as metabolic control. One of the strengths of the present study is the strict eligibility criteria for selecting T1DM patients, which integrated assessments of highly specific biomarkers (autoantibodies and C-peptide) and clinical characteristics. This is crucial for reducing potential misclassification of diabetes types, as diagnosing T1DM in the elderly population poses certain challenges [ 9 ]. Furthermore, this study is the first to explore the impact of different onset ages on kidney disease in adult-onset T1DM, thereby filling the knowledge gap regarding kidney disease burden in patients with adult-onset T1DM.​ Meanwhile, the study has several limitations: (1) the cross-sectional design precludes inference of causal relationships, and longitudinal studies were required to evaluate the long-term impact of onset age on the progression of kidney disease. (2) the median duration of diabetes was relatively short (5.1 years), which may lead to the omission of advanced renal complications. (3) lifestyle factors (e.g., smoking, physical activity, diet) and medication use (e.g., renin-angiotensin-aldosterone system inhibitors) were not fully adjusted for, and these factors may influence the onset and progression of kidney disease. (4) the study population was restricted to Chinese adult-onset T1DM patients, so the results may not directly generalize to other ethnic groups. Conclusion Onset age may not increase the kidney disease burden in patients with adult-onset T1DM, which was different from Type 2 diabetes. When assessing kidney disease risk in adult-onset T1DM patients, clinicians should focus on the comprehensive management of metabolic risk factors (e.g., blood glucose, blood pressure, blood lipids) and regular kidney function monitoring, with no need to consider the age of onset as required for type 2 diabetes patients and adolescent-onset T1DM patients. Future longitudinal studies with longer follow-up duration are warranted to further validate the aforementioned findings and explore the long-term impact of onset age on DKD progression. Abbreviations T1DM: Type 1 Diabetes Mellitus T2DM: Type 2 Diabetes Mellitus DKD: Diabetic Kidney Disease BMI: Body Mass Index CVD: Cardiovascular Disease ESRD: End-Stage Renal Disease GTT: Guangdong Type 1 Diabetes Translational Medicine Study WHR: Waist-to-Hip Ratio HbA1c: Glycated Hemoglobin A1c GAD-A: Glutamic Acid Decarboxylase Antibody ZnT8-A: Zinc Transporter 8 Antibody ICA512-A: Islet Cell Antigen 512 Antibody TG: Triglycerides TC: Total Cholesterol LDL-C: Low-Density Lipoprotein Cholesterol SUA: Serum Uric Acid SCr: Serum Creatinine eGDR: Estimated Glucose Disposal Rate ADA: American Diabetes Association eGFR: Estimated Glomerular Filtration Rate CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration UACR: Urinary Albumin-to-Creatinine Ratio CKD: Chronic Kidney Disease KDIGO: Kidney Disease: Improving Global Outcomes SBP: Systolic Blood Pressure DBP: Diastolic Blood Pressure SD: Standard Deviation IQR: Interquartile Range OR: Odds Ratio CI: Confidence Interval ICD: International Classification of Diseases LADA: Latent Autoimmune Diabetes in Adults Declarations Ethics approval and consent to participate Ethical approval for this study was conducted by the Ethics Committee of the Third Affiliated Hospital of Sun Yat‐Sen University (ID: [2010]2-36). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. All participants were informed about the purpose of the study, assured of confidentiality, and provided written consent prior to participation. Participation was voluntary, and respondents could withdraw at any time without consequence. Consent for publication Consent for publication was obtained from all participants involved in the study. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests There are no conflicts of interest or competing interests. Funding This study was not supported by any funds. Authors’ contributions YQ analyzed the data, conducted the literature search and led drafting of the manuscript. SHL and JHY contributed to study screening and data extraction. DZY provided data management and overall supervision. JJ conceived and designed the study. All authors provided edits to the manuscript, approved the final manuscript, and had final responsibility for the decision to submit for publication. Acknowledgments The authors thank all the doctors, nurses, technicians, and patients for their dedication to this study in the 16 participating hospitals. References Quattrin T, Mastrandrea LD, Walker LSK. Type 1 diabetes. Lancet. 2023;401(10394):2149-2162. Gregory GA, Robinson TIG, Linklater SE, Wang F, Colagiuri S, de Beaufort C, Donaghue KC, Magliano DJ, Maniam J, Orchard TJ, Rai P, Ogle GD. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol. 2022;10(10):741-760. Thunander M, Petersson C, Jonzon K, Fornander J, Ossiansson B, Torn C, Edvardsson S, Landin-Olsson M. Incidence of type 1 and type 2 diabetes in adults and children in Kronoberg, Sweden. Diabetes Res Clin Pract. 2008;82(2):247-255. 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Genetic, autoimmune, and clinical characteristics of childhood- and adult-onset type 1 diabetes. Diabetes Care. 2000;23(9):1326-1332. Leslie RD, Evans-Molina C, Freund-Brown J, Buzzetti R, Dabelea D, Gillespie KM, Goland R, Jones AG, Kacher M, Phillips LS, Rolandsson O, Wardian JL, Dunne JL. Adult-Onset Type 1 Diabetes: Current Understanding and Challenges. Diabetes Care. 2021;44(11):2449-2456. Steinarsson AO, Rawshani A, Gudbjörnsdottir S, Franzén S, Svensson A-M, Sattar N. Short-term progression of cardiometabolic risk factors in relation to age at type 2 diabetes diagnosis: a longitudinal observational study of 100,606 individuals from the Swedish National Diabetes Register. Diabetologia. 2018;61(3):599-606. Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, Vikman P, Prasad RB, Aly DM, Almgren P, Wessman Y, Shaat N, Spégel P, Mulder H, Lindholm E, Melander O, Hansson O, Malmqvist U, Lernmark Å, Lahti K, Forsén T, Tuomi T, Rosengren AH, Groop L. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361-369. Rawshani A, Sattar N, Franzén S, Rawshani A, Hattersley AT, Svensson A-M, Eliasson B, Gudbjörnsdottir S. Excess mortality and cardiovascular disease in young adults with type 1 diabetes in relation to age at onset: a nationwide, register-based cohort study. Lancet. 2018;392(10146):477-486. Hietala K, Forsblom C, Summanen P, Groop P-H. Higher age at onset of type 1 diabetes increases risk of macular oedema. Acta Ophthalmol. 2012;91(8):709-715. Fan Y, Lau ESH, Wu H, Yang A, Chow E, Kong APS, Ma RCW, Chan JCN, Luk AOY. Incident cardiovascular-kidney disease, diabetic ketoacidosis, hypoglycaemia and mortality in adult-onset type 1 diabetes: a population-based retrospective cohort study in Hong Kong. Lancet Reg Health West Pac. 2023;34:100730. Thomas NJ, Hill AV, Dayan CM, Oram RA, McDonald TJ, Shields BM, Jones AG. Age of Diagnosis Does Not Alter the Presentation or Progression of Robustly Defined Adult-Onset Type 1 Diabetes. Diabetes Care. 2023;46(6):1156-1163. Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, Saran R, Wang AY-M, Yang C-W. Chronic kidney disease: global dimension and perspectives. Lancet. 2013;382(9888):260-272. Chronic Kidney Disease and Risk Management: Standards of Care in Diabetes-2025. Diabetes Care. 2025;48(1 Suppl 1):S239-S251. Dybiec J, Szlagor M, Młynarska E, Rysz J, Franczyk B. Structural and Functional Changes in Aging Kidneys. Int J Mol Sci. 2022;23(23):15435. Liu P, Quinn RR, Lam NN, Elliott MJ, Xu Y, James MT, Manns B, Ravani P. Accounting for Age in the Definition of Chronic Kidney Disease. JAMA Intern Med. 2021;181(10):1359-1366. Cardiovascular Disease and Risk Management: Standards of Care in Diabetes-2025. Diabetes Care. 2025;48(1 Suppl 1):S207-S238. Li J, Yang D, Yan J, Huang B, Zhang Y, Weng J. Secondary diabetic ketoacidosis and severe hypoglycaemia in patients with established type 1 diabetes mellitus in China: a multicentre registration study. Diabetes Metab Res Rev. 2014;30(6):497-504. Liu L, Yang D, Zhang Y, Lin S, Zheng X, Lin S, Chen L, Zhang X, Li L, Liang G, Yao B, Yan J, Weng J. Glycaemic control and its associated factors in Chinese adults with type 1 diabetes mellitus. Diabetes Metab Res Rev. 2015;31(8):803-810. Yang D, Deng H, Luo G, Wu G, Lin S, Yuan L, Xv M, Li S, Zhang X, Wu J, Lang J, Liang G, Lin J, Chen D, Li L, Fang Y, Wu Y, Ou W, Li J, Weng J, Yan J. Demographic and clinical characteristics of patients with type 1 diabetes mellitus: A multicenter registry study in Guangdong, China. J Diabetes. 2016;8(6):847-853. Jiang J, Huang W, Lan L, Zheng X, Luo S, Ding Y, Yan J, Ren W, Tang K, Yang D. Related factors for kidney disease and high chronic kidney disease progression risk in adult-onset type 1 diabetes mellitus patients from China: a multi-center cross-sectional study. Ren Fail. 2025;47(1):2483389. Jiang J, Wang P, Jiang J, Zhang T, Ding Y, Zheng X, Luo S, Yan J, Tang K, Yang D. Association between metabolic phenotype and diabetic kidney disease in adult-onset type 1 diabetes patients from China: A multi-center cross-sectional study. Diabetes Res Clin Pract. 2025;226:112347. Zheng X, Huang B, Luo S, Yang D, Bao W, Li J, Yao B, Weng J, Yan J. A new model to estimate insulin resistance via clinical parameters in adults with type 1 diabetes. Diabetes Metab Res Rev. 2017;33(4). Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, Carty C, Chaput J-P, Chastin S, Chou R, Dempsey PC, DiPietro L, Ekelund U, Firth J, Friedenreich CM, Garcia L, Gichu M, Jago R, Katzmarzyk PT, Lambert E, Leitzmann M, Milton K, Ortega FB, Ranasinghe C, Stamatakis E, Tiedemann A, Troiano RP, van der Ploeg HP, Wari V, Willumsen JF. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-1462. Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, Crews DC, Doria A, Estrella MM, Froissart M, Grams ME, Greene T, Grubb A, Gudnason V, Gutiérrez OM, Kalil R, Karger AB, Mauer M, Navis G, Nelson RG, Poggio ED, Rodby R, Rossing P, Rule AD, Selvin E, Seegmiller JC, Shlipak MG, Torres VE, Yang W, Ballew SH, Couture SJ, Powe NR, Levey AS. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. N Engl J Med. 2021;385(19):1737-1749. KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int. 2022;102(5S):S1-S127. Levey AS, Eckardt K-U, Dorman NM, Christiansen SL, Hoorn EJ, Ingelfinger JR, Inker LA, Levin A, Mehrotra R, Palevsky PM, Perazella MA, Tong A, Allison SJ, Bockenhauer D, Briggs JP, Bromberg JS, Davenport A, Feldman HI, Fouque D, Gansevoort RT, Gill JS, Greene EL, Hemmelgarn BR, Kretzler M, Lambie M, Lane PH, Laycock J, Leventhal SE, Mittelman M, Morrissey P, Ostermann M, Rees L, Ronco P, Schaefer F, St Clair Russell J, Vinck C, Walsh SB, Weiner DE, Cheung M, Jadoul M, Winkelmayer WC. Nomenclature for kidney function and disease: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference. Kidney Int. 2020;97(6):1117-1129. Möllsten A, Svensson M, Waernbaum I, Berhan Y, Schön S, Nyström L, Arnqvist HJ, Dahlquist G. Cumulative risk, age at onset, and sex-specific differences for developing end-stage renal disease in young patients with type 1 diabetes: a nationwide population-based cohort study. Diabetes. 2010;59(7):1803-1808. Helve J, Sund R, Arffman M, Harjutsalo V, Groop P-H, Grönhagen-Riska C, Finne P. Incidence of End-Stage Renal Disease in Patients With Type 1 Diabetes. Diabetes Care. 2017;41(3):434-439. Gagnum V, Stene LC, Leivestad T, Joner G, Skrivarhaug T. Long-term Mortality and End-Stage Renal Disease in a Type 1 Diabetes Population Diagnosed at Age 15-29 Years in Norway. Diabetes Care. 2016;40(1):38-45. Gagnum V, Saeed M, Stene LC, Leivestad T, Joner G, Skrivarhaug T. Low Incidence of End-Stage Renal Disease in Childhood-Onset Type 1 Diabetes Followed for Up to 42 Years. Diabetes Care. 2017;41(3):420-425. Lu J, Hou X, Zhang L, Hu C, Zhou J, Pang C, Pan X, Bao Y, Jia W. Associations between clinical characteristics and chronic complications in latent autoimmune diabetes in adults and type 2 diabetes. Diabetes Metab Res Rev. 2015;31(4):411-420. Kasimay O, Sener G, Cakir B, Yüksel M, Cetinel S, Contuk G, Yeğen BC. Estrogen protects against oxidative multiorgan damage in rats with chronic renal failure. Ren Fail. 2009;31(8):711-725. Kim CS, Suh SH, Choi HS, Bae EH, Ma SK, Kim B, Han K-D, Kim SW. Impact of diabetes duration and hyperglycemia on the progression of diabetic kidney disease: Insights from the KNHANES 2019-2021. World J Diabetes. 2025;16(5):102094. Wu Z, Gao Y, Zuo C-Y, Wang X-R, Chen X-H, Zhou X-H, Gao W-J. The status of studies on the mechanism of microcirculatory dysfunction in the process of diabetic kidney injury. Diabetol Metab Syndr. 2025;17(1):154. Yang J, Liu Z. Mechanistic Pathogenesis of Endothelial Dysfunction in Diabetic Nephropathy and Retinopathy. Front Endocrinol (Lausanne). 2022;13:816400. Decochez K, Keymeulen B, Somers G, Dorchy H, De Leeuw IH, Mathieu C, Rottiers R, Winnock F, ver Elst K, Weets I, Kaufman L, Pipeleers DG, Gorus FK. Use of an islet cell antibody assay to identify type 1 diabetic patients with rapid decrease in C-peptide levels after clinical onset. Belgian Diabetes Registry. Diabetes Care. 2000;23(8):1072-1078. Tables Table 1. Characteristics among all participants and in groups stratified by onset age Characteristics Total patients (n=481) Onset age ＜30 years (n=269) Onset age ≥30 years (n=212) P -value Male, n(%) 227 (47.2) 125 (46.5） 102 (48.1) 0.720 Onset age (years) 28.5 (23.5, 35.3) 23.9 (21.2, 26.9) 36.2 (32.5, 41.7) ＜0.001 Age of visit (years) 35.6 (28.8, 42.9) 29.9 (25.9, 33.9) 43.2 (39.3, 48.3) ＜0.001 Duration of DM (years) 5.1 (2.8,8.4 ) 5.0 (2.9, 8.4) 5.0 (2.7, 8.1) 0.818 HbA1c (%) 8.4 (7.1, 10.1) 8.8 (7.3, 10.8) 8.2 (7.4, 10.0) 0.880 BMI (kg/m 2 ) 20.7 (19.1, 22.6) 20.6 (18.7, 22.5) 20.8 (19.6, 22.5) 0.017 WHR 0.85 (0.81, 0.90 ) 0.84 (0.80, 0.88) 0.87 (0.82, 0.90) 0.001 SBP (mmHg) 113.0 (106.0, 124.0) 110.0 (102.0, 120.0) 120.0 (109.0, 128) 0.001 DBP (mmHg) 71.0 (66.0, 80.0) 70.0 (65.0, 78.0) 73.0 (68.0, 80.0) 0.041 Rest heart rate 80.0 (74.0, 86.0) 80.0 (75.0, 86.0) 79.0 (72.0, 85.0) 0.270 LDL-C (mmol/L) 2.6 (2.1, 3.3) 2.6 (2.1, 3.3) 2.6 (2.1, 3.3) 0.290 SUA (umol/L) 273.0 (224.0, 335.0) 261.0 (221.0, 331.0) 272.0 (233.0, 330.0) 0.724 UACR (mg/g) 11.5 (5.7, 23.0) 12.0 (5.6, 23.0) 11.1 (5.3, 20.0) 0.441 SCr (umol/L) 66.0 (55.0, 79.0) 65.3 (54.0, 78.0) 68.0 (56.0, 79.0) 0.257 eGFR (ml/min/1.73m 2 ) 111.9 (98.4, 122.6) 118.5 (106.9, 127.3) 105.5 (92.1, 113.8) ＜0.001 In GDR (mg/min/kg) 1.6 ( 1.4, 1.8) 1.6 (1.4, 1.8) 1.6 (1.4, 1.8) 0.788 Insulin requirement(IU/kg/d) 0.67(0.53, 0.81) 0.71 (0.54, 0.88) 0.64 (0.52, 0.77) 0.020 GAD-A (positive, n(%)) 189 (39.3) 109 (40.5) 80 (37.7) 0.535 ZNT8-A (positive, n(%)) 32 (6.7) 21 (7.8) 11 (5.2) 0.253 ICA512-A (positive, n(%)) 61 (12.7) 42 (15.6) 19 (9.0) 0.030 Fasting C-peptide (ng/ml) 0.13 (0.05, 0.25) 0.13 (0.06, 0.28) 0.12 (0.04, 0.24) 0.315 2h postprandial C-peptide (ng/ml) 0.13 (0.06, 0.33) 0.17 (0.05, 0.39) 0.13 (0.05, 0.26) 0.211 DKD, n(%) 109 (22.7) 64 (23.8) 45 (21.2) 0.505 Abnormal kidney function, n(%) 86 (17.9) 37 (13.8) 49 (23.1) 0.008 Microalbuminuria, n(%) 103 (21.4) 62 (23.0) 41 (19.3) 0.325 High progression risk of DKD, n(%) 26 (5.4) 14 (5.2) 12 (5.7) 0.826 NOTE: DM, diabetes mellitus; HbA1c, glycated hemoglobin A1c; BMI, body mass index; WHR, waist hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid; UACR, urinary albumin–creatinine ratio; SCr, serum creatinine; eGFR, estimated glomerular filtration rate; GDR, glucose disposal rate; GAD-A, Glutamic acid decarboxylase antibody; ZnT8-A, Zinc Transporter 8 antibody; ICA512-A, islet cell antigen 512 antibody; DKD, diabetic kidney disease. Table 2. Correlations between onset age and kidney diseases among adult-onset T1DM patients Models DKD Abnormal kidney function Microalbuminuria High progression risk of DKD OR (95% CI ) P -value OR (95% CI ) P -value OR (95% CI ) P -value OR (95% CI ) P -value Model 1 Onset age <30 years 1(Ref.) 0.505 1(Ref.) 0.008 1(Ref.) 0.325 1(Ref.) 0.826 Onset age ≥30 years 1.16 (0.75-1.79) 0.53 (0.33-0.85) 1.25 (0.80-1.95) 0.92 (0.41-2.02) Model 2 Onset age <30 years 1(Ref.) 0.994 1(Ref.) 0.148 1(Ref.) 0.971 1(Ref.) 0.142 Onset age ≥30 years 1.00 (0.46-2.17) 1.87 (0.80-4.36) 1.02 (0.46-2.26) 0.32 (0.07-1.46) Model 3 Onset age <30 years 1(Ref.) 0.552 1(Ref.) 0.322 1(Ref.) 0.399 1(Ref.) 0.377 Onset age ≥30 years 1.32 (0.53-3.24) 1.67 (0.61-4.61) 1.48 (0.59-3.72) 0.43 (0.07-2.78) NOTE: Model 1: unadjusted, Model 2: adjusted for gender, age of visit, duration of T1DM, BMI, and WHR, Model 3: Model 2+SBP, DBP, HbA1c, LDL-C, SUA, GDR, insulin dosage, GAD-A positive rate, ZNT8-A positive rate, and ICA512-A positive rate. Abbreviations: T1DM, type 1 diabetes mellitus; DKD, diabetic kidney disease; OR, odds ratio; CI, confidence interval; Ref, reference; BMI, body mass index; WHR, waist-to-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid; GDR, glucose disposal rate; GAD-A, Glutamic acid decarboxylase antibody; ZnT8-A, Zinc Transporter 8 antibody; ICA512-A, islet cell antigen 512 antibody. Table 3. Stratified analyses of the associations between onset age and kidney disease among adult-onset T1DM patients Characteristics DKD Abnormal kidney function Microalbuminuria High progression risk of DKD OR (95% CI ) P for interation OR (95% CI ) P for interation OR (95% CI ) P for interation OR (95% CI ) P for interation Gender 0.791 0.283 0.670 0.046 Male 1.13 (0.25-5.09) 3.66 (0.49-27.62) 1.32 (0.29-6.09) 0.002 (0.00-1.22) 0.058 Female 1.50 (0.45-5.05) 0.93 (0.22-3.87) 1.61 (0.47-5.50) 1.33 (0.05-35.68) Duration of T1DM(years) 0.352 0.978 0.379 0.310 <5 1.41 (0.32-6.25) 2.12 (0.28-15.93) 1.41 (0.29-6.85) 801.16 (0.12-5394406.62) ≥5 2.12 (0.73-6.18) 3.35 (1.08-10.41) 0.037 2.34 (0.80-6.79) 1.19 (0.18-7.99) HbA1c (%) 0.889 0.500 0.879 0.473 <7 0.93 (0.06-13.54) 0.09 (0.01-1.50) 0.77 (0.05-11.55) - ≥7 1.79 (0.62-5.12) 3.60 (1.02-12.68) 0.046 2.13 (0.73-6.23) 0.38 (0.04-3.71) NOTE: Logistic regression model was used to estimate odds ratio (OR) and 95% confidence interval (CI) after adjusting for gender, age of visit, duration of T1DM, BMI, WHR, SBP, DBP, HbA1c, LDL-C, SUA, GDR, insulin dosage, GAD-A positive rate, ZNT8-A positive rate, and ICA512-A positive rate. but the model was not adjusted for the stratification variable itself. Given the small number of patients with high progression risk of DKD in the subgroup with HbA1c < 7.0% (n = 7), the results of the correlation analysis between onset age and high progression risk of DKD (presented as OR and 95% CI) could not be visualized and were therefore denoted as “-”. Reference group: Onset age <30 years Abbreviations: T1DM, type 1 diabetes mellitus; DKD, diabetic kidney disease; OR, odds ratio; CI, confidence interval; BMI, body mass index; WHR, waist-to-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid; GDR, glucose disposal rate; GAD-A, Glutamic acid decarboxylase antibody; ZnT8-A, Zinc Transporter 8 antibody; ICA512-A, islet cell antigen 512 antibody. Additional Declarations No competing interests reported. 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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-8787687","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593001254,"identity":"3bd4a889-3d61-4045-b263-e9f0ac89c520","order_by":0,"name":"Ying Qiu","email":"","orcid":"","institution":"The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Qiu","suffix":""},{"id":593001255,"identity":"25e1c1be-ce6f-4326-8727-7f753763fd30","order_by":1,"name":"Sihui Luo","email":"","orcid":"","institution":"The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Sihui","middleName":"","lastName":"Luo","suffix":""},{"id":593001256,"identity":"bf9486bc-b023-4ad6-9453-43afb5b5b6c5","order_by":2,"name":"Jinhua Yan","email":"","orcid":"","institution":"the Third Affiliated Hospital of Sun Yat‐Sen University","correspondingAuthor":false,"prefix":"","firstName":"Jinhua","middleName":"","lastName":"Yan","suffix":""},{"id":593001257,"identity":"44600054-1447-45ef-96f2-cbced2bd064d","order_by":3,"name":"Daizhi Yang","email":"","orcid":"","institution":"the Third Affiliated Hospital of Sun Yat‐Sen University","correspondingAuthor":false,"prefix":"","firstName":"Daizhi","middleName":"","lastName":"Yang","suffix":""},{"id":593001258,"identity":"0e72ab76-eb5e-420f-ab6f-83235a929e67","order_by":4,"name":"Jun Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACxmYQycPAwMbe2P7hA1RUgigtfDyH2xhnQFXj1QIHchLpbcw8xGhhbmd+9vCLzOE8NonEtse2bXV1/A3MB2/zMNjl4XYYm7mxDM/hYjaeh+3GuW2HJSQOsCVb8zAkF+Pxi5m0BM/hxDb2xAbp3LYDEgYMPGbSPAwHEhtwamH/BtHCANRi2VYH1ML/jYAWHjPJDyAtHIlt0oxtzCBb2AhpKZNm4ElPbOM52GzYc+6w5IzDbMaWcwyScWox7D++TfJnj3Xi/Pb2hw9+lNXx87c3P7zxpsIOtxagBDNvD7IQM4gwwKEeCORBjvvxA7eCUTAKRsEoGAUMACM0T3uQOshbAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-02-04 14:24:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8787687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8787687/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102939829,"identity":"67f1dc45-3d3c-421e-85de-8e4ddb504ba7","added_by":"auto","created_at":"2026-02-18 16:58:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160184,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for inclusion and exclusion of the study participants.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GTT, Guangdong Type 1 Diabetes Translational Medicine Study; T1DM, Type 1 Diabetes mellitus; UACR, urinary albumin-to-creatinine ratio\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8787687/v1/502b94bc03b8112ce5170c2f.jpeg"},{"id":102939828,"identity":"d9c6100c-deed-4e54-8566-ae28764e2775","added_by":"auto","created_at":"2026-02-18 16:58:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":373797,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of kidney complications between two groups stratified by onset age among 481 adult-onset T1DM (2A. DKD; 2B. Abnormal kidney function; 2C. Microalbuminuria; 2D. High progression risk of DKD) .\u003c/p\u003e\n\u003cp\u003eAbbreviations: T1DM, type 1 diabetes mellitus; DKD, diabetic kidney disease\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8787687/v1/3e088400dbd54ac099ea3bbd.png"},{"id":102964429,"identity":"efd03e88-6c5f-41cb-b6b0-b6f80abce6e1","added_by":"auto","created_at":"2026-02-19 04:22:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1085967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8787687/v1/50d3ecf7-34d4-439c-a5eb-e61b95939632.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOnset Age May Not Increase Kidney Disease Burden among Adult-Onset Type 1 DiabetesPatients—A Multi-Center Cross-Sectional Study\u003c/p\u003e","fulltext":[{"header":"Contributions to the Literature","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 546px;\"\u003e\n \u003cp\u003eText box 1. Contributions to the Literature\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 546px;\"\u003e\n \u003cp\u003e\u0026bull; Fills the gap of limited research on onset age and kidney disease in adult-onset type 1 diabetes, differing from studies focusing on children/adolescents or type 2 diabetes.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 546px;\"\u003e\n \u003cp\u003e\u0026bull; Provides population health evidence that onset age need not be a key factor in kidney disease risk assessment for this group.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 546px;\"\u003e\n \u003cp\u003e\u0026bull; Guides clinical practice to prioritize metabolic control over onset age, aiding public health management of this growing patient population.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Introduction","content":"\u003cp\u003eType 1 diabetes mellitus (T1DM) is characterized by β-cell destruction and absolute insulin deficiency, and was traditionally considered a disease affecting mainly children and adolescents [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, emerging epidemiological evidence indicates that adult-onset T1DM (diagnosed at \u0026ge;\u0026thinsp;18 years) accounts for a substantial proportion of new cases globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For instance, in high-risk regions like southeastern Sweden, the incidence of T1DM in individuals aged 40\u0026ndash;100 years (34.0/100,000/year) is comparable to that in children and adolescents (37.8/100,000/year) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In China, a low-risk area for T1DM, adults constitute approximately two-thirds of clinically diagnosed new T1DM cases [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Adult-onset T1DM patients often present with distinct clinical features, including higher body mass index (BMI), milder initial symptoms, and potential diagnostic challenges due to overlap with type 2 diabetes (T2DM) [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which underscores the need for targeted research on this population.\u003c/p\u003e \u003cp\u003eOnset age is widely recognized as one of the key determinants of diabetes-related complications. Previous studies have demonstrated that onset age contributes to identifying subtypes of adult T2DM [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For T1DM, patients diagnosed at an earlier age have shown higher hazard ratios for all-cause mortality and various cardiovascular diseases (CVDs) compared with those diagnosed later in life [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, these insights are primarily derived from comparative studies between children/adolescents and young adults (typically\u0026thinsp;\u0026lt;\u0026thinsp;30 years of age), with the middle-aged and elderly population (\u0026gt;\u0026thinsp;30 years of age) not yet included in the analysis. Additionally, some research has indicated that a higher age at onset significantly increases the risk of macular edema in T1DM patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, this study mainly analyzed the impact of three age-at-onset groups (0\u0026ndash;4 years, 5\u0026ndash;14 years, and 15\u0026ndash;40 years) on diabetic macular edema, lacking an exploration of the effects of different age-at-onset subgroups within the adult population (e.g., \u0026lt; 30 years vs. \u0026ge; 30 years). Furthermore, compared with adolescent-onset T1DM patients and age-matched T2DM patients, adult-onset T1DM patients face a higher risk of a broad spectrum of complications, such as severe hypoglycemia, end-stage renal disease (ESRD), and CVD) as well as mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, this study neither conducted comparisons between different age at onset subgroups within the adult population nor excluded the mixed population with T1DM and T2DM-like phenotypes.\u003c/p\u003e \u003cp\u003eIn contrast, previous study has suggested that for adult-onset T1DM, whether defined from the perspective of clinical diagnosis or autoantibody testing, its clinical characteristics, disease progression, and genetic susceptibility are not affected by the age at diagnosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This finding raises the question of whether onset age independently influences kidney disease burden in this population.\u003c/p\u003e \u003cp\u003eDiabetic kidney disease (DKD) is a leading cause of ESRD worldwide, imposing significant morbidity, mortality, and healthcare burden in T1DM patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, adult-onset T1DM patients may face unique renal risk factors, including age-related declines in kidney function, comorbid metabolic abnormalities, and delayed diagnosis [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], all of which synergistically exacerbate the initiation and progression of kidney disease.\u003c/p\u003e \u003cp\u003eTherefore, given the rising prevalence of adult-onset T1DM and the unclear association between onset age and kidney disease (e.g., DKD, microalbuminuria, etc.) in this patient, this study was conducted based on data from the Guangdong Type 1 Diabetes Translational Medicine Study (GTT) to investigate the prevalence of kidney disease among patients with adult-onset T1DM and further analyze the correlation between onset age and kidney disease burden in this patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and participants selection\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study utilized baseline data from the GTT collected between 2010 and 2017. The GTT study is a multi-center observational study that recruited T1DM patients from 16 tertiary hospitals across 12 cities in Guangdong Province, China. Trained and certified medical staff collected clinical data prospectively at enrollment and during annual follow-ups in accordance with standardized operating procedures, and the detailed study design has been described in previous publications\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[21-23].\u0026nbsp;This study was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat‐Sen University (ID:\u0026nbsp;[2010]2-36). Written informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003eAs described in our earlier publications [23-25], all participants enrolled in GTT study cohort were required to satisfy three core prerequisites: insulin dependence, a confirmed diagnosis of T1D by a board-certified endocrinologist, and fulfillment of at least one of the following four criteria: (i) overt clinical manifestations of diabetes-associated metabolic disorder; (ii) a history of diabetic ketosis or ketoacidosis; (iii) positive serological results for diabetes-related autoantibodies; and (iv) fasting and stimulated C-peptide concentrations below 200 pmol/L.\u0026nbsp;For participants with an ambiguous initial diagnosis, further definitive verification was performed by specialist endocrinologists at the Guangdong Diabetes Centre.\u0026nbsp;For the purpose of the present analysis, the study sample was further restricted to individuals diagnosed with T1D between 18 and 65 years of age, who had a disease duration exceeding 1 year at the time of recruitment. Exclusion criteria included a documented episode of ketosis or ketoacidosis, acute infection, or acute inflammatory conditions within 3 months prior to enrolment, as well as incomplete data pertaining to diabetic kidney disease (DKD). A comprehensive overview of the study\u0026rsquo;s inclusion and exclusion criteria is illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003eData collection\u003c/p\u003e\n\u003cp\u003eDemographic data (age, gender), anthropometric measurements (height, weight, waist circumference, hip circumference) for calculating body mass index (BMI), waist-to-hip ratio (WHR), and clinical information (diabetes duration, onset characteristics, medication use, chronic complications, and comorbidities) were extracted from electronic medical records. Glycated hemoglobin A1c (HbA1c) was quantified via affinity chromatography using the Affinion AS100 point-of-care analyzer (Axis-Shield Diagnostic Ltd., Dundee, Scotland), with a reference range of 4.3-6.1% and total coefficient of variation \u0026lt;3%.\u0026nbsp;At study enrollment, whole blood or serum samples were collected, and concentrations of C-peptide, diabetes-related autoantibodies (including glutamic acid decarboxylase antibody [GAD-A], zinc transporter 8 antibody [ZnT8-A], and islet cell antigen 512 antibody [ICA512-A]), lipid profiles (triglycerides [TG], total cholesterol [TC], and low-density lipoprotein cholesterol [LDL-C]), serum uric acid (SUA), and serum creatinine (SCr) were quantified using commercially available test kits in accordance with standard procedures (see the previously published protocol [23] for detailed methodology). In addition, the estimated glucose disposal rate (eGDR), a validated index established by our research group in prior work [26]\u0026nbsp;, was utilized to evaluate the degree of insulin resistance in study participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGrouping \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe adult-onset T1DM refers to the onset of T1DM at 18 years or older [24,25,27]. According to the onset age, participants were divided into 2 groups: group 1 (onset-age \u0026lt; 30 years) and group 2 (onset-age \u0026ge;30 years).\u003c/p\u003e\n\u003cp\u003eAssessment of kidney disease and definitions \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndicators of the assessment of kidney disease included DKD, abnormal kidney function, microalbuminuria, and high progression risk of DKD. We adhered to the Standards of Care in Diabetes-2025 by the American Diabetes Association (ADA): DKD was diagnosed based on established criteria [17], eGFR was calculated using the\u0026nbsp;chronic kidney disease epidemiology collaboration (CKD-EPI) equation\u0026nbsp;[28], and microalbuminuria was defined as a urinary albumin-to-creatinine ratio (UACR) \u0026ge;30 mg/g\u0026nbsp;[17].\u0026nbsp;The eGFR\u0026nbsp;\u0026lt;\u0026nbsp;90 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e was defined as abnormal kidney function\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[29,30]. DKD progression risk stratification was conducted according to the ADA and Kidney Disease: Improving Global Outcomes (KDIGO) guideline\u0026nbsp;[29]. High progression risk of DKD included high and very high progression risk of DKD.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics 27.0 version (IBM Corporation, Armonk, NY, USA). Continuous variables with normal distribution are presented as means \u0026plusmn; standard deviation (SD), while skewed variables are presented as median (interquartile range [IQR]). Categorical variables are presented as n (%). Differences between onset age groups were compared using independent samples \u003cem\u003et\u003c/em\u003e-test, \u003cem\u003eMann-Whitney U\u003c/em\u003e test, or \u003cem\u003echi-square\u003c/em\u003e test as appropriate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLogistic regression analyses were used to assess the association between onset age (explanatory variable: \u0026ge;30 years vs. \u0026lt;30 years as reference) and kidney disease (dependent variable). Three models were constructed to adjust for\u0026nbsp;potentially confounding factors.\u0026nbsp;Model 1 was unadjusted.\u0026nbsp;Model 2\u0026nbsp;was adjusted for gender, age of visit, duration of diabetes, BMI, and WHR. Model 3 was\u0026nbsp;further\u0026nbsp;adjusted for SBP, DBP, HbA1c, LDL-C, SUA, glucose disposal rate (GDR), insulin dosage, and islet autoantibodies (GAD-A, ZNT8-A,\u0026nbsp;and\u0026nbsp;ICA512-A) positive rate\u0026nbsp;based on Model 2.\u0026nbsp;In addition,\u0026nbsp;subgroup analyses were performed by gender, diabetes duration (\u0026lt;5 years vs. \u0026ge;5 years), and HbA1c (\u0026lt;7% vs. \u0026ge;7%) to explore potential effect modifiers. Interaction terms between onset age and each stratification variable were included in the logistic regression models, and \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.05 was considered statistically significant for interactions. A two-tailed \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.05 was considered statistically significant for all analyses.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics and comparison of baseline covariates\u003c/p\u003e\n\u003cp\u003eA total of 481 adult-onset T1DM participants were included in this analysis. Characteristics are shown in Table 1. Of all participants, 227 (47.2%) were male, diabetic duration of 5.1 (2.8, 8.4) years and HbA1c level of 8.4 (7.1, 10.1) %. There were a total of 109 (22.7%) participants diagnosed with DKD, 86 (17.9%) diagnosed with\u0026nbsp;abnormal kidney function, 103 (21.4%) diagnosed with microalbuminuria and 26 (5.4%) diagnosed with high progression\u0026nbsp;risk of DKD\u0026nbsp;at study enrolment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe median onset age of 481 participants was 28.5 (23.5, 35.3) years. Accordingly, participants were divided into 2 groups, with 269 (55.9%) in the \u0026lt;30 years onset group and 212 (44.1%) in the \u0026ge;30 years group (Table 1). The median onset age was 23.9 (21.2, 26.9) years in the \u0026lt;30 years group and 36.2 (32.5, 41.7) years in the \u0026ge;30 years group (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). There were no significant differences in gender distribution (\u003cem\u003eP\u003c/em\u003e = 0.720), diabetes duration (\u003cem\u003eP\u003c/em\u003e = 0.818), HbA1c (\u003cem\u003eP\u003c/em\u003e = 0.880), or fasting/2h postprandial C-peptide levels (\u003cem\u003eP\u003c/em\u003e = 0.315 and \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.211, respectively) between the two groups. The \u0026ge;30 years group had significantly higher WHR (0.87 vs. 0.84, \u003cem\u003eP\u003c/em\u003e = 0.001), SBP (120.0 vs. 110.0 mmHg, \u003cem\u003eP\u003c/em\u003e = 0.001), DBP (73.0 vs. 70.0 mmHg, \u003cem\u003eP\u003c/em\u003e = 0.041), and lower insulin requirement (0.64 vs. 0.71 IU/kg/day, \u003cem\u003eP\u003c/em\u003e = 0.020) compared to the \u0026lt;30 years group. BMI was slightly higher in the \u0026ge;30 years group (20.8 vs. 20.6 kg/m\u0026sup2;, \u003cem\u003eP\u003c/em\u003e = 0.017). The positive rate of ICA512 antibody was significantly lower in the \u0026ge;30 years group (9.0% vs. 15.6%, \u003cem\u003eP\u003c/em\u003e = 0.030), while there were no significant differences in LDL-C, SUA, or other autoantibody positivity (GAD, ZnT8) between the two groups (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eKidney diseases by Onset Age Group\u003c/p\u003e\n\u003cp\u003eAs shown in Table 1 and Figure 2, the prevalence of DKD was similar between the \u0026lt;30 years and \u0026ge;30 years groups (23.8% vs. 21.2%, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.505). There were no significant differences in the prevalence of microalbuminuria (23.0% vs. 19.3%, \u003cem\u003eP\u003c/em\u003e = 0.325) or high progression risk of DKD (5.2% vs. 5.7%, \u003cem\u003eP\u003c/em\u003e = 0.826) between the two groups. In contrast, the \u0026ge;30 years group had a significantly lower median eGFR (105.5 vs. 118.5 ml/min/1.73m\u0026sup2;, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) and higher prevalence of\u0026nbsp;abnormal kidney function\u0026nbsp;(23.1% vs. 13.8%, \u003cem\u003eP\u003c/em\u003e = 0.008) compared to the \u0026lt;30 years group. There were no significant differences in UACR (\u003cem\u003eP\u003c/em\u003e = 0.441) or SCr (\u003cem\u003eP\u003c/em\u003e = 0.257) between the two groups (Table 1).\u003c/p\u003e\n\u003cp\u003eCorrelations between onset age and kidney disease\u003c/p\u003e\n\u003cp\u003eLogistic regression analyses (Table 2) showed that in the unadjusted model (Model 1), onset age \u0026ge;30 years was significantly associated with lower risk of\u0026nbsp;abnormal kidney function\u0026nbsp;(OR=0.53, 95% CI: 0.33-0.85, \u003cem\u003eP\u003c/em\u003e = 0.008) but not with DKD (OR=1.16, 95% CI: 0.75-1.79, \u003cem\u003eP\u003c/em\u003e = 0.505), microalbuminuria (OR=1.25, 95% CI: 0.80-1.95, \u003cem\u003eP\u003c/em\u003e = 0.325), or high progression risk of DKD (OR=0.92, 95% CI: 0.41-2.02, \u003cem\u003eP\u003c/em\u003e = 0.826). After adjusting for gender, age of visit, diabetes duration, BMI, and WHR (Model 2), the association between onset age \u0026ge;30 years and\u0026nbsp;abnormal kidney function\u0026nbsp;was attenuated and no longer significant (OR=1.87, 95% CI: 0.80-4.36, \u003cem\u003eP\u003c/em\u003e = 0.148). Further adjustment for metabolic factors (SBP, DBP, HbA1c, LDL-C, SUA, GDR, insulin dosage) and islet autoantibodies (GAD-A, ZNT8-A,\u0026nbsp;and\u0026nbsp;ICA512-A) positive rate in Model 3 did not restore significance for kidney disease: DKD (OR=1.32, 95% CI: 0.53-3.24, \u003cem\u003eP\u003c/em\u003e = 0.552),\u0026nbsp;abnormal kidney function\u0026nbsp;(OR=1.67, 95% CI: 0.61-4.61, \u003cem\u003eP\u003c/em\u003e = 0.322), microalbuminuria (OR=1.48, 95% CI: 0.59-3.72, \u003cem\u003eP\u003c/em\u003e = 0.399), and high progression risk of DKD (OR=0.43, 95% CI: 0.07-2.78, \u003cem\u003eP\u003c/em\u003e = 0.377).\u003c/p\u003e\n\u003cp\u003eSubgroup Analyses\u003c/p\u003e\n\u003cp\u003eSubgroup analyses (Table 3) revealed that gender was a significant effect modifier for the association between onset age and high progression risk of DKD (\u003cem\u003eP\u003c/em\u003e for interaction = 0.046). Advanced age (onset age \u0026ge;30 years) may attenuate the high progression risk of DKD in male patients (OR=0.002, 95% CI: 0.00-1.22, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.058), but may exert no impact on such risk in female patients (OR=1.33, 95% CI: 0.05-35.68, \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). For other kidney disease (including DKD,\u0026nbsp;abnormal kidney function, and\u0026nbsp;microalbuminuria), no significant interactions were observed between onset age and gender (all \u003cem\u003eP\u003c/em\u003e for interaction \u0026gt;0.05). Additionally, in the subgroup of diabetes duration \u0026ge;5 years and HbA1c \u0026ge;7%, onset age \u0026ge;30 years was associated with a significantly higher risk of\u0026nbsp;abnormal kidney function\u0026nbsp;(OR=3.35, 95% CI: 1.08-10.41, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e=0.037; OR=3.60, 95% CI: 1.02-12.68, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e=0.046, respectively). Nevertheless, for any kidney disease (including DKD,\u0026nbsp;abnormal kidney function, microalbuminuria, or high progression risk of DKD), the interaction terms between onset age and diabetes duration, or HbA1c level were all \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt;0.05, indicating no significant effect modification.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis multi-center cross-sectional study focused on the correlation between onset age and kidney disease burden in adult-onset T1DM patients, with additional exploration of effect modifiers. The key findings were: (1) onset age (stratified as \u0026lt;\u0026thinsp;30 years vs. \u0026ge;30 years) was not associated with the prevalence of DKD, microalbuminuria, or high progression risk of DKD; (2) older onset age was linked to lower baseline eGFR and higher prevalence of abnormal kidney function in unadjusted analysis, but this association was eliminated after adjusting for confounding factors; (3) gender acted as an effect modifier in the association between onset age and high progression risk of DKD; by contrast, diabetes duration and HbA1c level did not modify the association, but onset age\u0026thinsp;\u0026ge;\u0026thinsp;30 years was associated with a significantly higher risk of abnormal kidney function in participants with diabetes duration\u0026thinsp;\u0026ge;\u0026thinsp;5 years and HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7%. These results support the conclusion that onset age may not increase kidney disease burden in adult-onset T1DM, with gender-specific differences observed only in DKD progression risk.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to investigate the association between onset age and kidney disease in patients with adult-onset T1D. Thomas et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported that age at diagnosis (before vs. after 35 years) did not alter the progression of beta-cell dysfunction or metabolic complications in robustly defined adult-onset T1DM patients, suggesting that autoimmune mechanisms and disease progression are similar across adult onset age. However, this study primarily investigated whether the age at diagnosis alters the clinical manifestations of adult-onset T1D \u0026mdash; including weight loss, the incidence of diabetic ketoacidosis, and blood glucose levels \u0026mdash; as well as the rate of C-peptide decline and genetic susceptibility. It did not focus on chronic complications in patients, especially renal complications, as our study does. Furthermore, the study by Casu et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] have reported that T1DM diagnosed in childhood and adulthood exhibits negligible differences in clinical characteristics and outcomes, which resonates with the findings of the present study that onset age is not associated with the prevalence of DKD. Nevertheless, this study mainly analyzed the impact of onset age in children/adolescents and adults, but did not explore the role of subgroups stratified by different onset age within the adult population, nor did it investigate the effect of onset age on kidney disease.\u003c/p\u003e \u003cp\u003eIn addition, this study confirmed that onset age (stratified as \u0026lt;\u0026thinsp;30 years vs. \u0026ge;30 years) was not associated with the prevalence of DKD, microalbuminuria, or high progression risk of DKD, which was different from those of prior investigations that evaluated long-term kidney disease stratified by onset age of T1DM. Previous study showed that pre-adolescent-onset patients exhibit superior renal prognoses relative to their adolescent- or adult-onset counterparts. A population-based study conducted in Hong Kong, China, revealed that adult-onset T1DM patients are at a significantly elevated risk of ESRD compared with adolescent-onset cases, with this association being more pronounced among patients with late-onset adult T1DM (\u0026ge;\u0026thinsp;40 years of age) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Two large-scale population-based cohort studies from Finland and Sweden, which recruited newly diagnosed T1DM patients aged\u0026thinsp;\u0026le;\u0026thinsp;29 years and \u0026le;\u0026thinsp;34 years, respectively, demonstrated that the lowest risk of ESKD was observed in those diagnosed at \u0026lt;\u0026thinsp;5\u0026ndash;10 years of age [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, national surveillance data from Norway indicated that the 30-year cumulative incidence of ESRD among T1DM patients was 2.9% in those diagnosed under 15 years of age, as opposed to 4.8% in those diagnosed between 15 and 29 years of age [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, these investigations were predominantly predicated on comparative analyses between childhood/adolescent-onset and adult-onset T1DM cohorts. They neither performed stratified comparisons among subgroups stratified by distinct onset age within the adult population, nor accounted for the confounding influence of chronological age on study outcomes. More importantly, these prior studies incorporated extended follow-up durations and targeted ESRD\u0026mdash;a severe late-stage renal complication\u0026mdash;as their primary endpoint. In stark contrast, the present study employed a cross-sectional study design and evaluated relatively early-stage renal complications as indicators.\u003c/p\u003e \u003cp\u003eIn this study, we also found that, in unadjusted analyses, the reduced eGFR in older-onset age T1DM patients was likely attributable to the physiological age-related decline in kidney function. In the general population, eGFR exhibits a natural downward trajectory with advancing age after the fourth decade of life [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], a degenerative process that is further accelerated by DM [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, participants in the \u0026ge;\u0026thinsp;30 years age-at-onset group had a significantly higher age at recruitment (43.2 vs. 29.9 years, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), a variable that confounded the putative association between onset age and eGFR in the unadjusted model. Following adjustment for age at recruitment and other confounding covariates, this association ceased to be statistically significant, confirming that chronological age\u0026mdash;not onset age\u0026mdash;represents the primary determinant of intergroup eGFR disparities. Additionally, the present study detected no significant association between onset age and microalbuminuria, a finding consistent with that of prior investigations involving patients with latent autoimmune diabetes in adults (LADA) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Meanwhile, all age-at-onset subgroups included in this study had a relatively short median diabetes duration (approximately 5 years), long-term prospective longitudinal studies with extended follow-up periods are warranted to further validate these preliminary findings.\u003c/p\u003e \u003cp\u003eA key novel finding of this study was that sex modifies the association between onset age and high progression risk of DKD. This study demonstrated that advanced age (onset age\u0026thinsp;\u0026ge;\u0026thinsp;30 years) may attenuate the high progression risk of DKD in male patients, whereas it appeared to exert no impact on such risk in female patients. This phenomenon may be attributed to the fact that the influence of age on the effect of sex difference on the progression of chronic kidney disease, or females benefit from estrogen-mediated renal protective effects [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Although no significant interaction was observed, in the subgroups of diabetes duration\u0026thinsp;\u0026ge;\u0026thinsp;5 years or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7%, onset age\u0026thinsp;\u0026ge;\u0026thinsp;30 years significantly increased the risk of abnormal kidney function. This finding suggests that long T1DM duration and poor glycemic control may amplify the potential adverse effects of late-onset on kidney disease. Previous studies have clearly established that prolonged diabetes duration was associated with cumulative damage to renal microvasculature caused by persistent hyperglycemia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, long-term poor glycemic control exacerbates oxidative stress, inflammatory responses, and endothelial dysfunction in renal tissues [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, the synergistic effect of these unfavorable factors and late-onset diabetes (coupled with the natural physiological decline of kidney function in adults [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) further accelerates the deterioration of renal filtration capacity, ultimately resulting in a marked elevation in the risk of renal dysfunction.\u003c/p\u003e \u003cp\u003eIn this study, the positive rate of ICA512 antibody was significantly higher in the onset age \u0026lt;30 years group than in the \u0026ge;\u0026thinsp;30 years group (15.6% vs.9.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), reflecting stronger islet autoimmune attack in onset age \u0026lt;30 years subgroup\u0026mdash;previous study has shown that T1DM patients with a younger age at diagnosis and high titers of islet cell antibodies (ICA) are more prone to rapid β-cell function loss [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, there was no significant difference in C peptide levels (fasting and 2h after meals) between the two groups in this study (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05), which may be related to the short duration (median disease duration of 5 years). Long-term follow-up may capture β-cell function decline difference mediated by autoantibody profile variation. The intergroup difference in ICA512 antibody may indicate a unique immune phenotype in young adult-onset T1DM. However, although there is heterogeneity in autoimmune phenotypes, this difference did not affect short-term kidney disease, indicating that renal risk is mainly dominated by traditional factors such as metabolic control.\u003c/p\u003e \u003cp\u003eOne of the strengths of the present study is the strict eligibility criteria for selecting T1DM patients, which integrated assessments of highly specific biomarkers (autoantibodies and C-peptide) and clinical characteristics. This is crucial for reducing potential misclassification of diabetes types, as diagnosing T1DM in the elderly population poses certain challenges [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, this study is the first to explore the impact of different onset ages on kidney disease in adult-onset T1DM, thereby filling the knowledge gap regarding kidney disease burden in patients with adult-onset T1DM.​\u003c/p\u003e \u003cp\u003eMeanwhile, the study has several limitations: (1) the cross-sectional design precludes inference of causal relationships, and longitudinal studies were required to evaluate the long-term impact of onset age on the progression of kidney disease. (2) the median duration of diabetes was relatively short (5.1 years), which may lead to the omission of advanced renal complications. (3) lifestyle factors (e.g., smoking, physical activity, diet) and medication use (e.g., renin-angiotensin-aldosterone system inhibitors) were not fully adjusted for, and these factors may influence the onset and progression of kidney disease. (4) the study population was restricted to Chinese adult-onset T1DM patients, so the results may not directly generalize to other ethnic groups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOnset age may not increase the kidney disease burden in patients with adult-onset T1DM, which was different from Type 2 diabetes. When assessing kidney disease risk in adult-onset T1DM patients, clinicians should focus on the comprehensive management of metabolic risk factors (e.g., blood glucose, blood pressure, blood lipids) and regular kidney function monitoring, with no need to consider the age of onset as required for type 2 diabetes patients and adolescent-onset T1DM patients. Future longitudinal studies with longer follow-up duration are warranted to further validate the aforementioned findings and explore the long-term impact of onset age on DKD progression.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eT1DM: Type 1 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eT2DM: Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eDKD: Diabetic Kidney Disease\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eCVD: Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003eESRD: End-Stage Renal Disease\u003c/p\u003e\n\u003cp\u003eGTT: Guangdong Type 1 Diabetes Translational Medicine Study\u003c/p\u003e\n\u003cp\u003eWHR: Waist-to-Hip Ratio\u003c/p\u003e\n\u003cp\u003eHbA1c: Glycated Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eGAD-A: Glutamic Acid Decarboxylase Antibody\u003c/p\u003e\n\u003cp\u003eZnT8-A: Zinc Transporter 8 Antibody\u003c/p\u003e\n\u003cp\u003eICA512-A: Islet Cell Antigen 512 Antibody\u003c/p\u003e\n\u003cp\u003eTG: Triglycerides\u003c/p\u003e\n\u003cp\u003eTC: Total Cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C: Low-Density Lipoprotein Cholesterol\u003c/p\u003e\n\u003cp\u003eSUA: Serum Uric Acid\u003c/p\u003e\n\u003cp\u003eSCr: Serum Creatinine\u003c/p\u003e\n\u003cp\u003eeGDR: Estimated Glucose Disposal Rate\u003c/p\u003e\n\u003cp\u003eADA: American Diabetes Association\u003c/p\u003e\n\u003cp\u003eeGFR: Estimated Glomerular Filtration Rate\u003c/p\u003e\n\u003cp\u003eCKD-EPI: Chronic Kidney Disease Epidemiology Collaboration\u003c/p\u003e\n\u003cp\u003eUACR: Urinary Albumin-to-Creatinine Ratio\u003c/p\u003e\n\u003cp\u003eCKD: Chronic Kidney Disease\u003c/p\u003e\n\u003cp\u003eKDIGO: Kidney Disease: Improving Global Outcomes\u003c/p\u003e\n\u003cp\u003eSBP: Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eDBP: Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eSD: Standard Deviation\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile Range\u003c/p\u003e\n\u003cp\u003eOR: Odds Ratio\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n\u003cp\u003eICD: International Classification of Diseases\u003c/p\u003e\n\u003cp\u003eLADA: Latent Autoimmune Diabetes in Adults\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was conducted by the Ethics Committee of the Third Affiliated Hospital of Sun Yat‐Sen University (ID: [2010]2-36). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. All participants were informed about the purpose of the study, assured of confidentiality, and provided written consent prior to participation. Participation was voluntary, and respondents could withdraw at any time without consequence.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication was obtained from all participants involved in the study.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest or competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was not supported by any funds.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYQ\u0026nbsp;analyzed the data, conducted the literature search and led drafting of the manuscript. SHL and JHY\u0026nbsp;contributed to study screening and data extraction. DZY provided data management and overall supervision. JJ conceived and designed the study. All authors provided edits to the manuscript, approved the final manuscript, and had final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thank all the doctors, nurses, technicians, and patients for their dedication to this study in the 16 participating hospitals.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQuattrin T, Mastrandrea LD, Walker LSK. Type 1 diabetes. Lancet.\u003cem\u003e \u003c/em\u003e2023;401(10394):2149-2162.\u003c/li\u003e\n\u003cli\u003eGregory GA, Robinson TIG, Linklater SE, Wang F, Colagiuri S, de Beaufort C, Donaghue KC, Magliano DJ, Maniam J, Orchard TJ, Rai P, Ogle GD. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. 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Kidney Int.\u003cem\u003e \u003c/em\u003e2022;102(5S):S1-S127.\u003c/li\u003e\n\u003cli\u003eLevey AS, Eckardt K-U, Dorman NM, Christiansen SL, Hoorn EJ, Ingelfinger JR, Inker LA, Levin A, Mehrotra R, Palevsky PM, Perazella MA, Tong A, Allison SJ, Bockenhauer D, Briggs JP, Bromberg JS, Davenport A, Feldman HI, Fouque D, Gansevoort RT, Gill JS, Greene EL, Hemmelgarn BR, Kretzler M, Lambie M, Lane PH, Laycock J, Leventhal SE, Mittelman M, Morrissey P, Ostermann M, Rees L, Ronco P, Schaefer F, St Clair Russell J, Vinck C, Walsh SB, Weiner DE, Cheung M, Jadoul M, Winkelmayer WC. Nomenclature for kidney function and disease: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference. Kidney Int.\u003cem\u003e \u003c/em\u003e2020;97(6):1117-1129.\u003c/li\u003e\n\u003cli\u003eM\u0026ouml;llsten A, Svensson M, Waernbaum I, Berhan Y, Sch\u0026ouml;n S, Nystr\u0026ouml;m L, Arnqvist HJ, Dahlquist G. Cumulative risk, age at onset, and sex-specific differences for developing end-stage renal disease in young patients with type 1 diabetes: a nationwide population-based cohort study. Diabetes.\u003cem\u003e \u003c/em\u003e2010;59(7):1803-1808.\u003c/li\u003e\n\u003cli\u003eHelve J, Sund R, Arffman M, Harjutsalo V, Groop P-H, Gr\u0026ouml;nhagen-Riska C, Finne P. Incidence of End-Stage Renal Disease in Patients With Type 1 Diabetes. Diabetes Care.\u003cem\u003e \u003c/em\u003e2017;41(3):434-439.\u003c/li\u003e\n\u003cli\u003eGagnum V, Stene LC, Leivestad T, Joner G, Skrivarhaug T. Long-term Mortality and End-Stage Renal Disease in a Type 1 Diabetes Population Diagnosed at Age 15-29 Years in Norway. Diabetes Care.\u003cem\u003e \u003c/em\u003e2016;40(1):38-45.\u003c/li\u003e\n\u003cli\u003eGagnum V, Saeed M, Stene LC, Leivestad T, Joner G, Skrivarhaug T. Low Incidence of End-Stage Renal Disease in Childhood-Onset Type 1 Diabetes Followed for Up to 42 Years. Diabetes Care.\u003cem\u003e \u003c/em\u003e2017;41(3):420-425.\u003c/li\u003e\n\u003cli\u003eLu J, Hou X, Zhang L, Hu C, Zhou J, Pang C, Pan X, Bao Y, Jia W. Associations between clinical characteristics and chronic complications in latent autoimmune diabetes in adults and type 2 diabetes. Diabetes Metab Res Rev.\u003cem\u003e \u003c/em\u003e2015;31(4):411-420.\u003c/li\u003e\n\u003cli\u003eKasimay O, Sener G, Cakir B, Y\u0026uuml;ksel M, Cetinel S, Contuk G, Yeğen BC. Estrogen protects against oxidative multiorgan damage in rats with chronic renal failure. Ren Fail.\u003cem\u003e \u003c/em\u003e2009;31(8):711-725.\u003c/li\u003e\n\u003cli\u003eKim CS, Suh SH, Choi HS, Bae EH, Ma SK, Kim B, Han K-D, Kim SW. Impact of diabetes duration and hyperglycemia on the progression of diabetic kidney disease: Insights from the KNHANES 2019-2021. World J Diabetes.\u003cem\u003e \u003c/em\u003e2025;16(5):102094.\u003c/li\u003e\n\u003cli\u003eWu Z, Gao Y, Zuo C-Y, Wang X-R, Chen X-H, Zhou X-H, Gao W-J. The status of studies on the mechanism of microcirculatory dysfunction in the process of diabetic kidney injury. Diabetol Metab Syndr.\u003cem\u003e \u003c/em\u003e2025;17(1):154.\u003c/li\u003e\n\u003cli\u003eYang J, Liu Z. Mechanistic Pathogenesis of Endothelial Dysfunction in Diabetic Nephropathy and Retinopathy. Front Endocrinol (Lausanne).\u003cem\u003e \u003c/em\u003e2022;13:816400.\u003c/li\u003e\n\u003cli\u003eDecochez K, Keymeulen B, Somers G, Dorchy H, De Leeuw IH, Mathieu C, Rottiers R, Winnock F, ver Elst K, Weets I, Kaufman L, Pipeleers DG, Gorus FK. Use of an islet cell antibody assay to identify type 1 diabetic patients with rapid decrease in C-peptide levels after clinical onset. Belgian Diabetes Registry. Diabetes Care.\u003cem\u003e \u003c/em\u003e2000;23(8):1072-1078.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Characteristics among all participants and in groups stratified by onset age\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eTotal patients\u003c/p\u003e\n \u003cp\u003e(n=481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eOnset age\u0026nbsp;＜30 years (n=269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eOnset age \u0026ge;30 years (n=212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eMale, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e227 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e125 (46.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e102 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eOnset age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e28.5 (23.5, 35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e23.9 (21.2, 26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e36.2 (32.5, 41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eAge of visit (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e35.6 (28.8, 42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e29.9 (25.9, 33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e43.2 (39.3, 48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eDuration of DM (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e5.1 (2.8,8.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e5.0 (2.9, 8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e5.0 (2.7, 8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e8.4 (7.1, 10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e8.8 (7.3, 10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e8.2 (7.4, 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e20.7 (19.1, 22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e20.6 (18.7, 22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e20.8 (19.6, 22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.85 (0.81, 0.90 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e0.84 (0.80, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.87 (0.82, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e113.0 (106.0, 124.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e110.0 (102.0, 120.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e120.0 (109.0, 128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e71.0 (66.0, 80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e70.0 (65.0, 78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e73.0 (68.0, 80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eRest heart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e80.0 (74.0, 86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e80.0 (75.0, 86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e79.0 (72.0, 85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2.6 (2.1, 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e2.6 (2.1, 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e2.6 (2.1, 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eSUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e273.0 (224.0, 335.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e261.0 (221.0, 331.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e272.0 (233.0, 330.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eUACR (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e11.5 (5.7, 23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e12.0 (5.6, 23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e11.1 (5.3, 20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eSCr (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e66.0 (55.0, 79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e65.3 (54.0, 78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e68.0 (56.0, 79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eeGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e111.9 (98.4, 122.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e118.5 (106.9, 127.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e105.5 (92.1, 113.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eIn GDR (mg/min/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1.6 ( 1.4, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e1.6 (1.4, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.6 (1.4, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eInsulin requirement(IU/kg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.67(0.53, 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e0.71 (0.54, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.64 (0.52, 0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eGAD-A (positive, n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e189 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e109 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e80 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eZNT8-A (positive, n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e32 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e21 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e11 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eICA512-A (positive, n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e61 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e42 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e19 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eFasting C-peptide (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.13 (0.05, 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e0.13 (0.06, 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.12 (0.04, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e2h postprandial C-peptide (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e0.13 (0.06, 0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e0.17 (0.05, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.13 (0.05, 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eDKD, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e109 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e64 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e45 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eAbnormal kidney function,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e86 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e37 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e49 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eMicroalbuminuria,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e103 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e62 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e41 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eHigh progression\u0026nbsp;risk of DKD,\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e26 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003e14 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e12 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNOTE: DM, diabetes mellitus; HbA1c, glycated hemoglobin A1c; BMI, body mass index; WHR, waist hip ratio;\u0026nbsp;SBP, systolic blood pressure; DBP, diastolic blood pressure;\u0026nbsp;LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid;\u0026nbsp;UACR, urinary albumin\u0026ndash;creatinine ratio; SCr, serum creatinine; eGFR, estimated glomerular filtration rate; GDR,\u0026nbsp;glucose disposal rate;\u0026nbsp;GAD-A, Glutamic acid decarboxylase\u0026nbsp;antibody;\u0026nbsp;ZnT8-A, Zinc Transporter 8 antibody; ICA512-A, islet cell antigen 512 antibody; DKD, diabetic kidney disease.\u003c/p\u003e\n\u003cp\u003eTable 2. Correlations between onset age and kidney diseases among adult-onset T1DM patients\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003eModels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003eAbnormal kidney function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMicroalbuminuria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003eHigh progression\u0026nbsp;risk of DKD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eOR (95%\u0026nbsp;\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eOR (95%\u0026nbsp;\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eOR (95%\u0026nbsp;\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eOR (95%\u0026nbsp;\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOnset age \u0026lt;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOnset age \u0026ge;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.16 (0.75-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.53 (0.33-0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.25 (0.80-1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.92 (0.41-2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOnset age \u0026lt;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOnset age \u0026ge;30 \u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00 (0.46-2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.87 (0.80-4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.02 (0.46-2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.32 (0.07-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOnset age \u0026lt;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1(Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOnset age \u0026ge;30 \u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.32 (0.53-3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.67 (0.61-4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.48 (0.59-3.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.43 (0.07-2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNOTE: Model 1: unadjusted,\u0026nbsp;Model 2: adjusted for gender, age of visit, duration of T1DM, BMI, and WHR,\u0026nbsp;Model 3: Model 2+SBP, DBP, HbA1c, LDL-C, SUA,\u0026nbsp;GDR,\u0026nbsp;insulin dosage,\u0026nbsp;GAD-A\u0026nbsp;positive\u0026nbsp;rate,\u0026nbsp;ZNT8-A\u0026nbsp;positive\u0026nbsp;rate,\u0026nbsp;and\u0026nbsp;ICA512-A\u0026nbsp;positive\u0026nbsp;rate.\u003c/p\u003e\n\u003cp\u003eAbbreviations:\u0026nbsp;T1DM, type 1 diabetes\u0026nbsp;mellitus;\u0026nbsp;DKD, diabetic kidney disease; OR, odds ratio; CI, confidence interval;\u0026nbsp;\u0026nbsp;Ref, reference;\u0026nbsp;BMI, body mass index; WHR, waist-to-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin A1c;\u0026nbsp;LDL-C, low-density lipoprotein cholesterol;\u0026nbsp;SUA, serum uric acid;\u0026nbsp;GDR,\u0026nbsp;glucose disposal rate;\u0026nbsp;GAD-A, Glutamic acid decarboxylase\u0026nbsp;antibody;\u0026nbsp;ZnT8-A, Zinc Transporter 8 antibody; ICA512-A, islet cell antigen 512 antibody.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3.\u0026nbsp;Stratified analyses of the associations\u0026nbsp;between onset age and kidney disease among adult-onset T1DM patients\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 143px;\"\u003e\n \u003cp\u003eDKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 154px;\"\u003e\n \u003cp\u003eAbnormal kidney function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 155px;\"\u003e\n \u003cp\u003eMicroalbuminuria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eHigh \u0026nbsp;progression risk of DKD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eOR (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor interation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eOR (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor interation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eOR (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor interation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eOR (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor interation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.13 (0.25-5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3.66 (0.49-27.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.32 (0.29-6.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.002 (0.00-1.22) 0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.50 (0.45-5.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.93 \u0026nbsp;(0.22-3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.61 (0.47-5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.33 (0.05-35.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eDuration of T1DM(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.41 (0.32-6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2.12 (0.28-15.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.41 (0.29-6.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e801.16 (0.12-5394406.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.12 (0.73-6.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3.35 (1.08-10.41)\u003c/p\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2.34 (0.80-6.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.19 (0.18-7.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.93 (0.06-13.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.09 (0.01-1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.77 (0.05-11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026ge;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.79 (0.62-5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3.60 (1.02-12.68)\u003c/p\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2.13 (0.73-6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.38 (0.04-3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNOTE:\u0026nbsp;Logistic regression\u0026nbsp;model was\u0026nbsp;used to estimate\u0026nbsp;odds ratio (OR) and 95% confidence interval (CI)\u0026nbsp;after adjusting for\u0026nbsp;gender, age of visit, duration of T1DM, BMI, WHR, SBP, DBP, HbA1c, LDL-C, SUA,\u0026nbsp;GDR,\u0026nbsp;insulin dosage,\u0026nbsp;GAD-A positive rate, ZNT8-A positive rate,\u0026nbsp;and\u0026nbsp;ICA512-A positive rate.\u0026nbsp;but\u0026nbsp;the model was not adjusted for the\u0026nbsp;stratification variable itself.\u0026nbsp;Given the small number of patients with high progression risk of DKD in the subgroup with HbA1c \u0026lt; 7.0% (n = 7), the results of the correlation analysis between onset age and high progression risk of DKD (presented as OR and 95% CI) could not be visualized and were therefore denoted as \u0026ldquo;-\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eReference\u0026nbsp;group:\u0026nbsp;Onset age \u0026lt;30 years\u003c/p\u003e\n\u003cp\u003eAbbreviations: T1DM, type 1 diabetes mellitus; DKD, diabetic kidney disease; OR, odds ratio; CI, confidence interval; \u0026nbsp;BMI, body mass index; WHR, waist-to-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid; \u0026nbsp;GDR, glucose disposal rate; GAD-A, Glutamic acid decarboxylase antibody; ZnT8-A, Zinc Transporter 8 antibody; ICA512-A, islet cell antigen 512 antibody.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 1 diabetes mellitus, Adult-onset, Onset age, Kidney disease burden","lastPublishedDoi":"10.21203/rs.3.rs-8787687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8787687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe incidence of adult-onset type 1 diabetes (T1DM) is rising globally, yet the association between onset age and kidney disease burden—encompassing diabetic kidney disease (DKD), abnormal kidney function, microalbuminuria, and high progression risk of DKD—remains unclear in this population. This multi-center cross-sectional study aimed to investigate this correlation, addressing a critical knowledge gap for clinical practice and public health management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 481 adult-onset T1DM participants from the Guangdong T1DM Translational Medicine Study were stratified by onset age into \u0026lt;30 years (n=269) and ≥30 years (n=212) groups. Demographic, clinical, and laboratory data were collected, and kidney disease indicators were assessed per ADA 2025 standards. Logistic regression analyses were used to evaluate the association between onset age and kidney disease, with adjustments for gender, age of visit, diabetes duration, metabolic factors, and islet autoantibodies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall DKD prevalence was 22.7%, with no significant differences between the \u0026lt;30 years (23.8%) and ≥30 years (21.2%) groups (\u003cem\u003eP\u003c/em\u003e = 0.505). Similarly, microalbuminuria (23.0% vs. 19.3%, \u003cem\u003eP\u003c/em\u003e = 0.325) and high progression risk of DKD (5.2% vs. 5.7%, \u003cem\u003eP\u003c/em\u003e = 0.826) showed no intergroup disparities. Although the ≥30 years group had lower median eGFR (105.5 vs. 118.5 ml/min/1.73m², \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and higher abnormal kidney function prevalence (23.1% vs. 13.8%, \u003cem\u003eP\u003c/em\u003e= 0.008) in unadjusted analysis, these differences disappeared after adjusting for confounding factors. Multivariate logistic regression confirmed onset age ≥30 years was not independently associated with DKD (OR=1.32, 95% CI: 0.53-3.24, \u003cem\u003eP \u003c/em\u003e= 0.552), microalbuminuria (OR=1.48, 95% CI: 0.59-3.72, \u003cem\u003eP \u003c/em\u003e= 0.399), or high progression risk of DKD (OR=0.43, 95% CI: 0.07-2.78, \u003cem\u003eP \u003c/em\u003e= 0.377).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnset age may not increase kidney disease burden in adult-onset T1DM patients, differing from type 2 diabetes. Clinical risk assessment should prioritize comprehensive metabolic control (blood glucose, blood pressure, lipids) and regular kidney function monitoring, rather than onset age, providing actionable guidance for optimizing care in this growing population.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e","manuscriptTitle":"Onset Age May Not Increase Kidney Disease Burden among Adult-Onset Type 1 DiabetesPatients—A Multi-Center Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 16:57:55","doi":"10.21203/rs.3.rs-8787687/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-19T05:29:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T01:34:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179347177571538859376510271964343601027","date":"2026-03-10T16:28:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T09:21:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218845575899045607277084125360814474266","date":"2026-03-10T05:47:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T11:45:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201811105220470200160279753967308290317","date":"2026-02-13T19:27:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T14:53:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T17:06:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T17:04:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2026-02-04T14:15:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"52fdcfdb-a3c0-4eef-b9d0-23deacb4bd46","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T05:39:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 16:57:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8787687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8787687","identity":"rs-8787687","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}