{"paper_id":"e0d53c02-c9cb-446b-96a8-e1285f3f6f7f","body_text":"Young-onset diabetes poses a greater risk of end-stage renal disease (ESRD) than cardiovascular disease and stroke: a retrospective cohort study of UK Biobank | 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 Young-onset diabetes poses a greater risk of end-stage renal disease (ESRD) than cardiovascular disease and stroke: a retrospective cohort study of UK Biobank Debasish Kar, Richard Byng, Stuart Spicer, Nicolas Farina, Jonathan Pinkney, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5951257/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In the last three decades, in people with diabetes, while the mortality and morbidity due to cardiovascular disease (CVD) and stroke have declined, the incidence of end-stage renal disease (ESRD) has crept up. The precise cause for this shift is unknown. Intriguingly, during the same period, the incidence of diabetes in younger people has almost tripled worldwide. This study examines whether young-onset diabetes is a greater risk for ESRD compared to CVD and stroke. Methods We conducted this retrospective cohort study using data from volunteers (n = 502,408) aged 40–69 years recruited in UK Biobank between 2006 and 2010. The exposure variable was the age of diabetes diagnosis, while the outcomes of interest were ESRD, myocardial infarction, angina, and stroke. The cumulative follow-up period from the mean age of diabetes to the mean age of the outcomes of interest was 838,592 person-years. Univariate and multivariate logistic regression models were fitted to assess odds ratios (ORs) and 95% confidence intervals (CIs). Model performance was evaluated using receiver operating characteristics (ROC) analysis and calibration plots. Findings: Out of 26,206 people with diabetes, 1.16% (n = 303) had ESRD, 8.58% (n = 2250) had a myocardial infarction, 7.47% (n = 1958) had angina, and 2.94% (n = 771) had a stroke. Compared to later-onset, young-onset diabetes is linked with an earlier onset of ESRD. Based on the age of diabetes diagnosis, the univariate logistic regression showed that compared to those diagnosed after the age of 60, the odds of ESRD for those diagnosed at ages < 20, 20–40, and 41–60 years were OR [2.33 (95% CI 1.50–3.84), 7.78 (95% CI 4.81–13.16), and 5.26 (95% CI 3.00–9.40)], respectively. Myocardial infarction and stroke did not have a statistically significant relationship with diabetes diagnosis age. Multivariate models adjusted for sex and albuminuria confirmed the increased odds of ESRD in people with younger onset and longer duration of diabetes. Interpretation: People with younger onset and longer duration of diabetes are at a higher risk of ESRD than CVD and stroke. Endocrinology & Metabolism young-onset diabetes end-stage renal disease cardiovascular disease myocardial infarction and stroke Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction End-stage renal disease (ESRD) is one of the most expensive and debilitating complications of diabetes. Over the last three decades, in people with diabetes, the global burden of ESRD has been rising steadily, while all-cause and cardiovascular disease (CVD) mortality is declining. 1–3 Data from the Renal Registry UK indicates that diabetic kidney disease (DKD) accounted for nearly 30% of patients requiring renal replacement therapy (RRT) in 2022. 4 Between 2013 and 2022, the proportion of RRT initiated due to DKD rose by 4.2%, while other causes of ESRD either declined or remained steady. 5 In 2022, in the UK, almost a quarter of individuals with DKD requiring RRT were below the age of 54, while the median age for RRT was 64, highlighting a significant disparity in RRT initiation age between people with and without diabetes. 6 This epidemiological shift has far-reaching implications, as younger ESRD patients are likely to require prolonged RRT or renal transplant, imposing substantial financial burdens on healthcare systems, productivity losses, and societal costs. The epidemiology of type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) has changed drastically over the last 3 decades. While T1DM remains a disease that begins in childhood and adolescence, T2DM is no longer an exclusive disease of middle-aged and older people. Since 2000, the standardised incidence rate of T2DM in people aged < 40 has almost tripled worldwide. 7–9 Based on the incidence of T1DM and T2DM in young adults between 2002 and 2017, the US Centre for Disease Control and Prevention (CDC) developed a prediction model to estimate the prevalence of diabetes in children and adolescents under the age of 20. The model showed that if the rate of diagnoses stays the same, by 2060, T1DM cases would remain about the same, but the T2DM cases would increase by about 70%. However, if the rate of new diagnoses continues to rise, T1DM cases would increase by 65% while T2DM cases would increase by 700%. 10 A similar trend is predicted for the UK and other parts of the world. 11,12 This drift in diabetes epidemiology has extensive clinical and public health implications which require careful evaluation and diabetes management policy realignment. Traditionally, people with DKD were more likely to die of CVD before progressing to ESRD. For instance, a nationwide cohort study in Finland showed that the cumulative risk of death within 10 and 20 years from the diagnosis of T2DM was 34% and 64%, compared to a much lower risk of ESRD at 0.29% and 0.74%, respectively. 13 Multiple studies have shown that people with DKD are 10 to 40 times more likely to die of CVD rather than progressing to ESRD. 14–16 However, evolving global trends of CVD events, mortality and ESRD in people with diabetes indicate a reversal. A Swedish national cohort examined the changes in all-cause mortality and CVD events in people with T1DM and T2DM from 1998 to 2012. People with T1DM had 40%, and T2DM had a 20% reduction in CVD outcomes compared to the control group. 3 While all-cause and CVD mortality among people with diabetes is decreasing 17 , an increasing number of younger people are developing DKD and progressing to ESRD. 18 This study investigates how the age of diabetes onset relates to vascular outcomes such as ESRD, myocardial infarction, angina, and stroke, as well as its connection to the age at which ESRD begins. It also examines how factors like diabetes diagnosis age, smoking behaviours such as starting and stopping smoking age, and gender affect urinary albumin concentrations (UAC), a key marker for ESRD, CVD and stroke. The results aim to support further research for validation and guide policies to address the global rise in ESRD cases. Method Study design and population We conducted this retrospective cohort analysis using data from the individuals who filled out the UK Biobank study questionnaire. It did not differentiate between the types of diabetes. For this study, we used instance 0, which was the assessment visit. For descriptive analyses, we used the entire cohort, and for exploratory analyses, we used participants with diabetes. The inclusion criteria were self-reported diabetes (UK Biobank data field – 2443, “Diabetes diagnosed by a doctor”), and exclusion criteria were those who did not answer this question or responded as “No”. The study selection process is available in the flow chart. (Supplementary material 1 – Fig. 1 ). The complete coding schedule is available online (Supplementary material 4). The study was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 19 (Supplementary material 5) The UK Biobank is a large, population-based cohort comprising over 500,000 volunteers aged 40–69 years recruited between 2006 and 2010 from 22 assessment centres across England, Wales and Scotland. During their assessment visits, participants provided electronic consent and completed a touchscreen-based questionnaire. The questionnaire collected extensive demographic and lifestyle information, including current and past medical history, medication use, ethnic and socioeconomic profiles, occupation, educational background, smoking status, diet, exercise and alcohol consumption habits. It also included the ages of diabetes, hypertension and ESRD diagnoses and the ages of starting and stopping smoking. 20 Exposure and outcome variables The p opulation for our study was people with diabetes; the e xposure was the age of diabetes diagnosis; the c omparison was amongst four groups based on the age of diabetes diagnosis, i.e., < 20, 20–40, 41–60 and > 60 and the primary outcomes were ESRD, myocardial infarction, angina and stroke and the secondary outcome was UAC. We used sex, age of starting and stopping smoking, duration of diabetes and hypertension as confounding variables. Information about myocardial infarction, angina, stroke and ESRD was obtained from the questionnaire. UK Biobank subsequently cross-checked the information with electronic health records and confirmed the accuracy. 21 Following the Kidney Disease Improving Global Outcome (KDIGO) guideline, we defined normoalbuminuria as a UAC value of < 20 mg/dl in a spot sample of urine, microalbuminuria UAC value 20–200 mg/dl, and macroalbuminuria UAC value > 200 mg/dl. 22 The Townsend deprivation index was used to define deprivation, and based on the score, the cohort was divided into five quintiles from the least to the most deprived. As almost 94% of the study participants were from white backgrounds, we divided ethnicity into two groups – white and non-white. Statistical analyses By identifying individuals with diabetes and hypertension from the questionnaire, we calculated the duration of diabetes and hypertension by subtracting the age of diagnosis from the participant’s age on the assessment date. For ex-smokers, the duration of smoking was calculated by subtracting the age they stopped smoking from the age they started. For current smokers, the duration was determined by subtracting the starting age from the participant’s current age. Baseline characteristics of participants were stratified into two groups – those with and without diabetes. Descriptive statistics were reported as means ± standard deviation for normally distributed numerical variables and medians with interquartile range (IQR) for non-normally distributed numerical variables. Categorical variables were presented as numbers and percentages. Statistical significance between the two groups, with and without diabetes, was assessed using the Wilcoxon Rank-Sum test for non-parametric numerical variables, an independent t-test for parametric numerical variables, and a Chi-squared test for categorical variables. We selected the longitudinal cohort of eligible UK Biobank study participants with diabetes and followed them until the outcomes of interest, i.e., myocardial infarction, angina, stroke and ESRD. We also analysed the UAC value at the time of assessment visits as a composite secondary outcome marker for ESRD, CVD and stroke. Follow-up time was from the mean age of diabetes diagnosis to the mean age of the occurrence of the events of interest. We also used the age of hypertension diagnosis, the duration of hypertension and diabetes, smoking status, and age of starting and stopping smoking as explanatory variables for stratified analyses. In addition, we did further analyses to explore the relationship between the age of diabetes and hypertension diagnoses and the age of ESRD onset. A loess method was applied to explore the UAC trend based on the age of diabetes and hypertension diagnoses and the age of starting smoking in current and ex-smokers, who reported ESRD, myocardial infarction, angina and stroke. We fitted a univariate logistic regression model to assess if the age of diabetes diagnosis can predict ESRD, myocardial infarction, angina and stroke outcomes. Finally, in model 1, we used the multivariable logistic regression method to explore how the age of diagnosis of diabetes and in model 2, the duration of diabetes predicts ESRD. In these models, we adjusted for sex and albuminuria categories as confounding variables. Model discrimination was evaluated using receiver operating characteristics (ROC) and the area under the curve (AUC). The goodness of fit (GOF) was assessed by analysing the calibration plot and Hosmer and Lemeshow GOF test. Concordance with the assumptions of the logistic regression model was assessed using the Durbin-Watson test for the independence of observation and the Variance Inflation Factor (VIF) for multicollinearity. Missing data were analysed using Multiple Imputations of Chained Equation (MICE). All analyses were conducted using software R version 4.4.3. Results Out of a total of 497,895 eligible study participants who consented to share their data, 5.26% (n = 26,206) had diabetes. We did not have precise data about the types of diabetes available from the questionnaire. However, 8.26% (n = 2247) of study participants reported being on insulin, suggesting that the number of T1DM is unlikely to be more than 2247. (Supplementary material 4 – UKB data field 6143). The selection process is in the flow chart. (Supplementary material 1 - Fig. 1 ). The mean follow-up period for people with diabetes who had ESRD and stroke was 9 years and 11 years, respectively. The follow-up period for myocardial infarction and angina was 12 years. Thus, the total person-years of follow-up for those who had ESRD was 235,854 person-years, for stroke 288,266 person-years, and myocardial infarction and angina was 314,472 person-years which equates to 838,592 person-years follow-up. (Supplementary materials 1 – Fig. 2 ). Apart from the age, BMI and waist circumference, all other numerical variables had non-parametric distribution. The distribution of numerical variables is in the histograms with normality lines. (Supplementary materials 1 - Figs. 3 to 9). 44.7% (n = 210,892) of study participants without diabetes were male, compared to 60.6% (n = 15,876) in the group with diabetes. In terms of diabetes diagnosis, 14.9% (n = 3735) were diagnosed before the age of 40, 66.2% (n = 16,609) between the ages of 40 to 60, and 18.9% (n = 4752) were diagnosed after the age of 60. The prevalence of hypertension in those without diabetes was 22.5% (n = 106,143), and in those with diabetes was 49.5% (n = 12,975). Amongst those with ESRD, the median age of diabetes diagnosis was 54 (IQR 47–61) years, and the median age of ESRD diagnosis was 57 (IQR 47–67) years. Compared to people without, those with diabetes had a higher number of current and ex-smokers, started smoking at younger ages and smoked for longer duration. Likewise, compared to those without, those with diabetes had higher BMI and waist circumference and were more likely to be on lipid-lowering medications. Compared to 22.5% of people without, 46.3% of people with diabetes had albuminuria. ESRD was reported by 0.2% (n = 875) of those without and 1.2% (n = 303) with diabetes, which corresponds to a prevalence rate of ESRD of 200 per 100,000 population without diabetes and 1,200 per 100,000 population in those with diabetes. Macrovascular complications were more prevalent in people with diabetes than without. Angina was reported in 7.5% (n = 1958) of individuals with diabetes compared to 2% (n = 9220) in those without. Myocardial infarction was reported by 8.6% (n = 2250) of individuals with diabetes compared to 2% (n = 9198) in those without. Likewise, in the diabetes group, 2.9% (n = 771) had a stroke compared to 1.1% (n = 5367) in the group without diabetes. Those who had myocardial infarction, angina and stroke were older than those who had ESRD. The mean age of people with diabetes who had myocardial infarction, angina, stroke and ESRD was 62.55 ± 5.84, 62.68 ± 5.71, 61.99 ± 6.37, and 59 ± 7.61 years. Baseline characteristics are summarised in Table 1 . Table 1 Baseline characteristics of the UK Biobank cohort Variables No diabetes group (n = 471,689) Diabetes Group (n = 26,206) p-value Age - mean (SD) 56.87 (8.11) 60.05 (7.23) < 0.001 Sex (Male %) 210892 (44.7) 15876 (60.6) < 0.001 Ethnicity (White %) 443818 (94.3) 24909 (94.1) 0.312 Deprivation quintile (%) Least deprived 94332 (20) 5145 (20) 0.635 Less deprived 94173 (20) 5275 (20) Moderately deprived 94229 (20) 5232 (20) More deprived 94199 (20) 5235 (20) Most deprived 94168 (20) 5287 (20) Diabetes diagnosis age category Diabetes diagnosis age < 40 N/A 3735 (14.9) N/A Diabetes diagnosis age 40–60 N/A 16609 (66.2) Diabetes diagnosis age > 60 N/A 4752 (18.9) Hypertension (%) 106143 (22.5) 12975 (49.5) Hypertension diagnosis age category Hypertension diagnosis age < 40 23193 (19.8) 3268 (20) < 0.001 Hypertension diagnosis age 40–60 78893 (67.4) 11400 (69.8) Hypertension diagnosis age > 60 15008 (12.8) 1655 (10.1) ESRD (%) 875 (0.2) 303 (1.2) ESRD diagnosis age ESRD diagnosis age < 40 145 (16.6) 25 (8.3) < 0.001 ESRD diagnosis age 40–60 379 (43.3) 117 (38.6) ESRD diagnosis age > 60 351 (40.1) 161 (53.1) Angina (%) 9220 (2) 1958 (7.5) < 0.001 Myocardial infarction (%) 9198 (2) 2250 (8.6) < 0.001 Stroke (%) 5367 (1.1) 771 (2.9) < 0.001 Smoking status (%) Non-smoker 260571 (55.2) 12074 (46.1) < 0.001 Ex-smoker 161349 (34.2) 11212 (42.8) Current smoker 49769 (10.6) 2920 (11.1) Ex-smokers smoking starting age (median, IQR) 17 (15 − 18) 16 (15–18) < 0.001 Age stopped smoking (median, IQR) 39 (30–48) 43 (34–52) < 0.001 Ex-smokers smoking duration (median, IQR) 21 (13–31) 26 (17–35) < 0.001 Current smokers’ smoking starting age (median, IQR) 16 (15–19) 16 (14–19) < 0.001 Current smokers’ smoking duration (median, IQR) 38 (31–45) 42 (35–47) < 0.001 Body mass index - mean (SD) 27.21 (4.63) 31.33 (5.92) < 0.001 Waist circumference (mean ± SD) 89.60 (13.08) 102.55 (14.58) < 0.001 Lipid-lowering medication (%) 27095 (5.7) 7190 (27.4) < 0.001 Anti-hypertensive medication (%) 26569 (5.6) 1110 (4.2) < 0.001 Albuminuria category (%) Normoalbuminuria 107005 (77.6) 7709 (57.7) < 0.001 Microalbuminuria 28933 (21) 4852 (36.3) Macroalbuminuria 2019 (1.5) 807 (6) Diabetes and hypertension diagnoses age, duration of diabetes and hypertension and UAC. The relationship between diabetes and hypertension diagnoses ages and smoking starting ages in current and ex-smokers, with the trend in UAC values who developed ESRD, myocardial infarction, angina and stroke, were evaluated using the loess method. Those with ESRD showed that UAC value peaked when diabetes was diagnosed at an age under 30 years and declined for later diagnosis ages. (Fig. 1 ) A similar trend was observed for the duration of diabetes and UAC values. (Supplementary material 1- Fig. 1 0). On the contrary, the relationship between the age of hypertension diagnosis and duration of hypertension with UAC value was steady. (Fig. 1 and supplementary material 1- Fig. 1 0). There was no relationship between the age of diabetes and hypertension diagnoses with UAC values in those who had a myocardial infarction, angina and stroke. (Supplementary material 1 - Figs. 11–13) Age of starting smoking in current and ex-smokers and UAC values In those with ESRD, current smokers had a monophasic rise of UAC and ex-smokers had a biphasic rise of UAC based on the age of starting smoking. (Fig. 2 ) In contrast, the UAC values did not show any relationship between the age of starting smoking in current and ex-smokers in those who had myocardial infarction, angina and stroke. (Supplementary materials 1 – Figs. 14–16). ESRD diagnosis age based on diabetes and hypertension diagnosis age In people with diabetes and ESRD, the mean age of ESRD onset was 57.11 ± 6.62 years. There was a linear relationship between the age of diabetes diagnosis and the age of ESRD diagnosis, showing that the younger the onset of diabetes, the earlier the onset of ESRD. (Fig. 3 ) There was a significant difference in the ESRD onset age between sexes based on the age of diabetes and hypertension diagnosis. Females who developed diabetes at an age < 20 years had ESRD at an earlier age than males. On the contrary, males who had hypertension below the age of 20 had ESRD earlier than females. (Fig. 4 ) Univariate logistic regression: ESRD, myocardial infarction, angina and stroke risk The univariate unadjusted logistic regression model showed that the risk of ESRD is significantly higher in people with diabetes who were diagnosed before the age of 60 years. Compared to those who developed above the age of 60, those who developed diabetes at an age < 20, 20 to 40 and 41 to 60, the odds of ESRD were 5.26 (95% CI 3.00–9.40), 7.78 (95% CI 4.81–13.16) and 2.33 (95% CI 1.50–3.84), respectively. Likewise, in people with diabetes and hypertension, compared to those who developed hypertension above the age of 60, the age of diagnosis < 20, 20 to 40 and 41 to 60 was associated with the odds of ESRD 2.20 (1.58–3.11), 5.03 (3.79–6.81), and 1.53 (1.16–2.06), respectively. There was no statistically significant association between the younger age of diabetes onset with myocardial infarction and stroke. (Table 2 ). Adjusted multivariate regression models – ESRD and age of diabetes diagnosis The logistic regression model was fitted to explore how the univariate model performed when the age of diabetes diagnosis, gender and albuminuria were adjusted in the model. We did not fit the age of diabetes diagnosis and the duration of diabetes in the same model because of interdependence and a violation of the assumption of logistic regression. In model 1, we adjusted for the age of diabetes diagnosis, and in model 2, we adjusted for diabetes duration to understand how these risk factors influence ESRD outcomes. Model 1 In model 1, when age, sex and albuminuria were adjusted, the odds of ESRD compared to those who were diagnosed with diabetes above the age of 60, those who were diagnosed < 20, 20 to 40, and 41 to 60 were 4.71 (95% CI 2.47–9.28), 4.67 (95% CI 2.63–8.78), 1.94 (95% CI 1.16–3.49), respectively. Compared to people with normoalbuminuria, the odds of ESRD in those with microalbuminuria and proteinuria were 3.15 (95% CI 2.09–4.86) and 25.03 (95% CI 16.73–38.41), respectively. There was no statistically significant difference in the odds between the male and female gender. Model 2 Model 2 showed that diabetes duration is also associated with an increased risk of ESRD. The odds of ESRD were increased with a longer duration of diabetes 1.02 (95% CI 1.01–1.03). Similarly, compared to those with normoalbuminuria, the odds of ESRD in those with microalbuminuria were 3.23 (95% CI 2.14–4.98), and those with proteinuria were 27.22 (95% CI 18.22–41.74). There was no statistically significant relationship between gender and ESRD risk. ESRD, diabetes diagnosis age and diabetes duration - model performance and validation Discrimination Both models demonstrated excellent model performance, with the area under the curve (AUC) above 80%. The concordance statistics for model 1 was 81%, and model 2 was 82%, suggesting the model’s performance in predicting true positive cases of ESRD. (Fig. 5 ) Calibration The calibration plots show the reliability of both models in predicting ESRD. The alignment of observed proportions with predicted probabilities indicates robust model performance, supporting their use in clinical decision-making processes. (Fig. 6 ) Missing data and adherence to the assumptions of logistic regression Missing data were analysed using the Multiple Imputation of Chained Equation (MICE) plotted. (Supplementary material 2 - Fig. 17). This analysis showed that the missing data were likely to be randomly missing. Logistic regression assumptions were examined for the independence of observation using Durbin-Watson test) and multicollinearity by Variance Inflation Factor (VIF). Both tests showed the models satisfied the assumptions (Supplementary Materials 3). Discussion In this large prospective cohort of UK Biobank study participants, we found that people with younger onset diabetes are more likely to develop ESRD than myocardial infarction, angina or stroke within the follow-up time. Moreover, they are more likely to develop ESRD at a younger age than those who had myocardial infarction, angina and stroke. In addition, concurrent diagnosis of hypertension may exacerbate the risk. People who develop diabetes at a younger age are more likely to be exposed to prolonged periods of hyperglycaemia, and this study showed that the longer the duration of diabetes, the higher the risk of ESRD. The adjusted model showed that there was no statistically significant relationship between the age of onset of diabetes with myocardial infarction and stroke, suggesting that the recent surge in the ESRD cases in people with diabetes might have been caused by the evolving epidemiology of T2DM in young and adolescents. This study also showed that there was an upward trend in the UAC values in current and ex-smokers who started smoking in their teenage years. Interestingly, for ex-smokers, there was a trend of the second rise, perhaps mediated by post-cessation weight gain and early post-cessation adaptation of the cardiometabolic milieu. These findings suggest that teenage smoking should be actively discouraged, and those who have already started smoking should be supported to quit and remain abstinent long-term. Although both T1DM and T2DM present with hyperglycaemia, the pathophysiology of the conditions is disparate. T1DM is an autoimmune disease that usually presents in childhood and adolescence and requires insulin initiation for survival. 23 T2DM, on the other hand, is a complex metabolic disease with multiple biochemical and haemodynamic aberrations, posing a higher risk of vascular complications, mortality and morbidity. 24 The earlier the onset of T2DM, the higher the risk of vascular complications and mortality. A recently published 30-year follow-up analysis of the UK Prospective Diabetes Study (UKPDS) showed that the standardised mortality rates (SMR) in younger-onset diabetes (< 40 years) was almost 4-fold higher than later-onset (≥ 40 years) [3.72 (95% CI 2.98–4.64)]. The incidence rate of macrovascular complications (myocardial infarction, peripheral vascular disease and stroke) was significantly higher in later-onset diabetes than in younger-onset. However, the incidence rate of microvascular complications was higher in the younger-onset, compared to the later-onset group [14.5 (95% CI 11.9–17.7) vs 12.1 (95% CI 11.3–13.0)] per 1000-person years. 25 Therefore, the finding of this study for microvascular complications of ESRD is in keeping with the existing knowledge. This is the first study to demonstrate that a younger age of diabetes diagnosis predisposes to an earlier onset of ESRD. This finding has significant clinical and policy implications. Young-onset T2DM is a distinct phenotype in metabolic deregulation, and it does not follow the usual adult-onset T2DM long quiescent phase of steady progression over many years. In contrast, young-onset T2DM is a rapidly progressive condition where the annual decline in pancreatic β-cell function is 20–35%, compared to almost 7% in adult-onset T2DM. 26 Some disparate characteristics of young-onset T2DM compared to the adult-onset are – higher prevalence of obesity in young-onset T2DM than adult-onset (95% vs 50%), 27 more pronounced visceral adiposity and hepatic ectopic fat deposition, 28 impaired insulin signalling in skeletal muscles interfering with glucose disposal pathway, 29 systemic inflammation, evidenced by a high level of high-sensitive C-reactive protein (CRP), Tumour Necrosis Factor alpha (TNF-α) and interleukin 1 beta, 30,31 maternal gestational diabetes, 32 and pubertal surges in the circulating insulin-antagonistic hormones such as growth hormones, corticosteroid and sex hormones. 33 Accounting for all the above pathophysiological processes, this study shows that renovascular complications may predate cardiovascular and stroke. Effective management of factors such as childhood obesity and smoking linked with insulin resistance and T2DM may reduce the growing burden of ESRD worldwide. The strength of this study is that it has a large dataset with a diverse range of study participants and a novel stratified analysis. The areas under the curve above 80% for the age of diabetes diagnosis and duration of diabetes indicate excellent discriminatory power between those who developed ESRD compared to those who did not. Similarly, the calibration plot confirmed good fitness. However, this study has several limitations. We used the questionnaire filled out by the study participants to identify cases of diabetes. It did not specify the type of diabetes. Therefore, we cannot comment on the number of type 1 and type 2 DM included in the study. However, as the number of people reported being on insulin was 2247, the number of T1DM cases is unlikely to be more than 8.26%, which is in keeping with the estimated prevalence of 8% published by Diabetes UK in 2022. 34 The study was a retrospective cohort design with limited predictive value. The age of diagnosis of diabetes and ESRD was obtained from the questionnaire and was not verified, which is open to recall bias. UK Biobank is a voluntary dataset and is not representative of real-world data; therefore, the findings may not be generalisable. Despite the above limitations, the findings of this study indicate that the changing T2DM epidemiology may be affecting ESRD outcomes more disparately than CVD and stroke outcomes, which is of great clinical and public health importance. The financial and societal costs of ESRD are exponential, but with timely identification and risk management, they can be potentially averted. In the UK alone, approximately £14 billion is spent annually on managing diabetes complications 35 , with hospital-based RRT for each ESRD patient costing an estimated £32,678 each year. 36 For those who need RRT, the UK economy loses an estimated £372 million annually due to abstinence from work, which is likely to increase to £2 billion by 2030. 37 People with ESRD lose 25–56% in lifetime employment duration and 32–66% in lifetime productivity. 38 With almost 40% of individuals with diabetes expected to develop DKD in their lifetimes, often within 10 years of diagnosis 39,40 and around 45% of ESRD in developed countries linked to diabetes 41 , the financial burden is expected to rise further. Globally, over 10 million people require RRT due to ESRD, although this is likely to represent less than 10% of those in need, as many low- and middle-income countries (LMICs) lack reliable data. 42 A recent health economic study projected that between 2022 and 2027, the annual global cost of RRT will increase by 9.3%, from $ 372 billion to $ 406.7 billion. 43 In LMICs, where healthcare systems are often privately funded, the cost of RRT is frequently unaffordable, leading to premature mortality. 44 It is estimated that diabetes and hypertension account for up to 70% of ESRD cases, which are potentially preventable by pharmacological and lifestyle intervention. 45 Similarly, childhood smoking is a strong predictor for ESRD and is a modifiable risk factor. 46,47 The Majority of long-term smokers start smoking in their early to late teenage years, and discouraging them from taking up the habit of smoking can make a big difference. 48,49 Conclusion Younger onset diabetes and hypertension may be the driver behind the recent surge in ESRD cases. They are more likely to develop it at a younger age, requiring RRT for a longer duration. It can also put enormous pressure on the healthcare budget and loss of productivity. Factors that predispose to young-onset diabetes should be identified and managed as a global priority. Declarations Ethical approval - The UK Biobank received ethical approval from the Northwest Multicentre Research Ethics Committee (REC reference 11/NW/03820), and all participants provided written informed consent. PPI involvement - There was no formal or informal involvement of patients or the public in this research. Consent for publication – All UKB study participants consented to their data being used for medical research and publication. Competing interests –SdL is the Director of the Oxford Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), which is included in his academic role at the University of Oxford. He has received research funding through his University from AstraZeneca, GlaxoSmithKline (GSK), Lily, Moderna, Medical Science Division (MSD), Sanofi, Seqirus, and Takada. He has also served as an advisory board member for AstraZeneca, GSK, Sanofi, Seqirus, and Pfizer. RB is the Deputy Director of the NIHR Peninsula Applied Research Collaboration (PenARC). UK is the Chair of the NIHR Advanced Fellowship Committee. All other co-authors declare no competing interest. Funding – This project is funded by the NIHR through a personal clinical lecturer award to the lead author (CL-2023-2024-001). Authors’ contributions – DK was responsible for conceptualisation, data access, data cleaning, statistical analyses, and drafting the manuscript. RB, SdeL, and AS contributed to the study conceptualisation, data analyses, and supervision and reviewed the manuscript. MN contributed to the statistical analyses. SS and JP provided clinical insights to the paper. All other co-authors read the draft and commented on it. Availability of data and materials – UK Biobank data can be obtained by application (www.ukbiobank.ac.uk). This is an open-access article distributed with the Creative Commons Attribution (CC BY) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the license is given, and an indication of whether changes were made. https://creativecommons.org/licenses/by/4.0/ Acknowledgements – The research was conducted using the UK Biobank resource under application no 61894. For the purposes of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any arising Author Accepted Manuscript version. When appropriate, AI software Grammarly and ChatGPT were used to improve readability without any impact on the study findings and their interpretation. References Yu Y, Zhang M, Tang Y, et al. Global disease burden attributable to kidney dysfunction, 1990–2019: A health inequality and trend analysis based on the global burden of disease study. Diabetes Research and Clinical Practice 2024: 111801. Thomas B. The Global Burden of Diabetic Kidney Disease: Time Trends and Gender Gaps. Current Diabetes Reports 2019; 19 (4): 1-7. Rawshani A, Rawshani A, Franzén S, et al. Mortality and Cardiovascular Disease in Type 1 and Type 2 Diabetes. New England Journal of Medicine 2017; 376 (15): 1407-18. th Renal Registry Report. 2022. https://ukkidney.org/audit-research/annual-report/24th-annual-report-data-31122020 (accessed March 19, 2023. UK Renal Registry summary fof annual report: Analyses of adult data to the end of 2021. 2023. https://ukkidney.org/sites/renal.org/files/UK%20Renal%20Registry%20Report%202021%20-%20Patient%20Summary_0.pdf. Renal registry UK 25th Annual report. 2022. https://ukkidney.org/audit-research/annual-report (accessed September 20, 2023. Perng W, Conway R, Mayer-Davis E, Dabelea D. Youth-Onset Type 2 Diabetes: The Epidemiology of an Awakening Epidemic. Diabetes Care 2023; 46 (3): 490-9. Pinhas-Hamiel O, Zeitler P. The global spread of type 2 diabetes mellitus in children and adolescents. The Journal of pediatrics 2005; 146 (5): 693-700. Lascar N, Brown J, Pattison H, Barnett AH, Bailey CJ, Bellary S. Type 2 diabetes in adolescents and young adults. The Lancet Diabetes & Endocrinology 2018; 6 (1): 69-80. Diabetes in Young People Is on the rise. https://www.cdc.gov/diabetes/data-research/research/young-people-diabetes-on-rise.html. Candler TP, Mahmoud O, Lynn RM, Majbar AA, Barrett TG, Shield JPH. Continuing rise of type 2 diabetes incidence in children and young people in the UK. Diabetic Medicine 2018; 35 (6): 737-44. Xie J, Wang M, Long Z, et al. Global burden of type 2 diabetes in adolescents and young adults, 1990-2019: systematic analysis of the Global Burden of Disease Study 2019. BMJ 2022: e072385. Finne P, Groop P-H, Arffman M, et al. Cumulative Risk of End-Stage Renal Disease Among Patients With Type 2 Diabetes: A Nationwide Inception Cohort Study. Diabetes Care 2019; 42 (4): 539-44. Pálsson R, Patel UD. Cardiovascular Complications of Diabetic Kidney Disease. Advances in Chronic Kidney Disease 2014; 21 (3): 273-80. Collins AJ, Li S, Gilbertson DT, Liu J, Chen SC, Herzog CA. Chronic kidney disease and cardiovascular disease in the Medicare population. Kidney Int Suppl 2003; (87): S24-31. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalisation. N Engl J Med 2004; 351 (13): 1296-305. Raghavan S, Vassy JL, Ho YL, et al. Diabetes Mellitus–Related All‐Cause and Cardiovascular Mortality in a National Cohort of Adults. Journal of the American Heart Association 2019; 8 (4): e011295. Nguyen NTQ, Cockwell P, Maxwell AP, Griffin M, O'Brien T, O'Neill C. Chronic kidney disease, health-related quality of life and their associated economic burden among a nationally representative sample of community dwelling adults in England. PLoS One 2018; 13 (11): e0207960. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE) statement: guidelines for reporting observational studies. Lancet (London, England) 2007; 370 (9596): 1453. Sudlow C, Gallacher J, Allen N, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Medicine 2015; 12 (3): e1001779. Definitions of End Stage Renal Disease - Algorithmically-defined outcomes (ADOs) - version 2.0, 2022. Levey AS, Eckardt K-U, Tsukamoto Y, et al. Definition and classification of chronic kidney disease: A position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney International 2005; 67 (6): 2089-100. Alberti KGMM, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation. Diabetic Medicine 1998; 15 (7): 539-53. Dabelea D, Department of Epidemiology CSoPHA, Stafford JM, et al. Association of Type 1 Diabetes vs Type 2 Diabetes Diagnosed During Childhood and Adolescence With Complications During Teenage Years and Young Adulthood. JAMA 2017; 317 (8): 825-35. Lin B, Coleman RL, Bragg F, Maddaloni E, Holman RR, Adler AI. Younger-onset compared with later-onset type 2 diabetes: an analysis of the UK Prospective Diabetes Study (UKPDS) with up to 30 years of follow-up (UKPDS 92). The Lancet Diabetes & Endocrinology 2024. Group TS. Effects of metformin, metformin plus rosiglitazone, and metformin plus lifestyle on insulin sensitivity and β-cell function in TODAY. Diabetes care 2013; 36 (6): 1749-57. Magliano DJ, Sacre JW, Harding JL, Gregg EW, Zimmet PZ, Shaw JE. Young-onset type 2 diabetes mellitus—Implications for morbidity and mortality. Nature Reviews Endocrinology 2020; 16 (6): 321-31. Barrett T, Jalaludin MY, Turan S, Hafez M, Shehadeh N, on behalf of the Novo Nordisk Pediatric Type 2 Diabetes Global Expert P. Rapid progression of type 2 diabetes and related complications in children and young people—A literature review. Pediatric Diabetes 2020; 21 (2): 158-72. Samuel VT, Shulman GI. Integrating Mechanisms for Insulin Resistance: Common Threads and Missing Links. Cell 2012; 148 (5): 852-71. Reinehr T, Karges B, Meissner T, et al. Fibroblast Growth Factor 21 and Fetuin-A in Obese Adolescents With and Without Type 2 Diabetes. The Journal of Clinical Endocrinology & Metabolism 2015; 100 (8): 3004-10. Reinehr T, Karges B, Meissner T, et al. Inflammatory Markers in Obese Adolescents with Type 2 Diabetes and Their Relationship to Hepatokines and Adipokines. The Journal of Pediatrics 2016; 173 : 131-5. Scholtens DM, Kuang A, Lowe LP, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): maternal glycemia and childhood glucose metabolism. Diabetes care 2019; 42 (3): 381-92. Valaiyapathi B, Gower B, Ashraf AP. Pathophysiology of type 2 diabetes in children and adolescents. Current diabetes reviews 2020; 16 (3): 220-9. How many people in the UK have diabetes? https://www.diabetes.org.uk/about-us/about-the-charity/our-strategy/statistics (accessed January 15 2025). Cost of diabetes to UK estimated at £14 billion, research shows.: University of York. Roberts G, Holmes J, Williams G, et al. Current costs of dialysis modalities: A comprehensive analysis within the United Kingdom. Peritoneal Dialysis International 2022; 42 (6): 578-84. Kidney disease: A UK public health emergency. 2023. https://www.kidneyresearchuk.org/wp-content/uploads/2023/06/Economics-of-Kidney-Disease-full-report_accessible.pdf (accessed July 14, 2023. Wang F, Hwang J-S, Huang W-Y, Chang Y-T, Wang J-D. Estimation of lifetime productivity loss from patients with chronic diseases: methods and empirical evidence of end-stage kidney disease from Taiwan. Health Economics Review 2024; 14 (1): 10. Parving H-H, Hommel E, Mathiesen E, et al. Prevalence of microalbuminuria, arterial hypertension, retinopathy, and neuropathy in patients with insulin dependent diabetes. Br Med J (Clin Res Ed) 1988; 296 (6616): 156-60. Huang ES, Basu A, O’Grady M, Capretta JC. Projecting the future diabetes population size and related costs for the US Diabetes Care 2009; 32 (12): 2225-9. Zimmet P, Alberti K, Shaw J. Global and societal implications of the diabetes epidemic. Nature 2001; 414 (6865): 782-7. Eggers PW. Has the incidence of end-stage renal disease in the USA and other countries stabilised? Current opinion in nephrology and hypertension 2011; 20 (3): 241-5. Chadban S, Arıcı M, Power A, et al. Projecting the economic burden of chronic kidney disease at the patient level (Inside CKD): a microsimulation modelling study. eClinicalMedicine 2024; 72 : 102615. Van Biesen W, Jha V, Abu-Alfa AK, et al. Considerations on equity in management of end-stage kidney disease in low-and middle-income countries. Kidney international supplements 2020; 10 (1): e63-e71. Lea JP, Nicholas SB. Diabetes mellitus and hypertension: key risk factors for kidney disease. Journal of the National Medical Association 2002; 94 (8 Suppl): 7S. Patel DR. Smoking and children. The Indian Journal of Pediatrics 1999; 66 : 817-24. Omoloja A, Chand D, Greenbaum L, et al. Cigarette smoking and second-hand smoking exposure in adolescents with chronic kidney disease: a study from the Midwest Pediatric Nephrology Consortium. Nephrology Dialysis Transplantation 2011; 26 (3): 908-13. Swan AV, Creeser R, Murray M. When and why children first start to smoke. International Journal of Epidemiology 1990; 19 (2): 323-30. García-Esquinas E, Loeffler LF, Weaver VM, Fadrowski JJ, Navas-Acien A. Kidney function and tobacco smoke exposure in US adolescents. Pediatrics 2013; 131 (5): e1415-23. Tables Tables 2 and 3 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Tables2and3.docx v1scirepsupplmatESRDDMAge20250122.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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03:23:49\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":25815,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Tables2and3.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5951257/v1/f363058376a03d2269edd6d7.docx\"},{\"id\":76250462,\"identity\":\"306065eb-a16b-487d-b1de-f3934daeb19a\",\"added_by\":\"auto\",\"created_at\":\"2025-02-14 03:23:46\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":193104,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"v1scirepsupplmatESRDDMAge20250122.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5951257/v1/692bd25d5ec3991a01ae39ff.docx\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eYoung-onset diabetes poses a greater risk of end-stage renal disease (ESRD) than cardiovascular disease and stroke: a retrospective cohort study of UK Biobank\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eEnd-stage renal disease (ESRD) is one of the most expensive and debilitating complications of diabetes. Over the last three decades, in people with diabetes, the global burden of ESRD has been rising steadily, while all-cause and cardiovascular disease (CVD) mortality is declining. \\u003csup\\u003e1\\u0026ndash;3\\u003c/sup\\u003e Data from the Renal Registry UK indicates that diabetic kidney disease (DKD) accounted for nearly 30% of patients requiring renal replacement therapy (RRT) in 2022. \\u003csup\\u003e4\\u003c/sup\\u003e Between 2013 and 2022, the proportion of RRT initiated due to DKD rose by 4.2%, while other causes of ESRD either declined or remained steady. \\u003csup\\u003e5\\u003c/sup\\u003e In 2022, in the UK, almost a quarter of individuals with DKD requiring RRT were below the age of 54, while the median age for RRT was 64, highlighting a significant disparity in RRT initiation age between people with and without diabetes. \\u003csup\\u003e6\\u003c/sup\\u003e This epidemiological shift has far-reaching implications, as younger ESRD patients are likely to require prolonged RRT or renal transplant, imposing substantial financial burdens on healthcare systems, productivity losses, and societal costs.\\u003c/p\\u003e \\u003cp\\u003eThe epidemiology of type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) has changed drastically over the last 3 decades. While T1DM remains a disease that begins in childhood and adolescence, T2DM is no longer an exclusive disease of middle-aged and older people. Since 2000, the standardised incidence rate of T2DM in people aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;40 has almost tripled worldwide. \\u003csup\\u003e7\\u0026ndash;9\\u003c/sup\\u003e Based on the incidence of T1DM and T2DM in young adults between 2002 and 2017, the US Centre for Disease Control and Prevention (CDC) developed a prediction model to estimate the prevalence of diabetes in children and adolescents under the age of 20. The model showed that if the rate of diagnoses stays the same, by 2060, T1DM cases would remain about the same, but the T2DM cases would increase by about 70%. However, if the rate of new diagnoses continues to rise, T1DM cases would increase by 65% while T2DM cases would increase by 700%. \\u003csup\\u003e10\\u003c/sup\\u003e A similar trend is predicted for the UK and other parts of the world. \\u003csup\\u003e11,12\\u003c/sup\\u003e This drift in diabetes epidemiology has extensive clinical and public health implications which require careful evaluation and diabetes management policy realignment.\\u003c/p\\u003e \\u003cp\\u003eTraditionally, people with DKD were more likely to die of CVD before progressing to ESRD. For instance, a nationwide cohort study in Finland showed that the cumulative risk of death within 10 and 20 years from the diagnosis of T2DM was 34% and 64%, compared to a much lower risk of ESRD at 0.29% and 0.74%, respectively. \\u003csup\\u003e13\\u003c/sup\\u003e Multiple studies have shown that people with DKD are 10 to 40 times more likely to die of CVD rather than progressing to ESRD. \\u003csup\\u003e14\\u0026ndash;16\\u003c/sup\\u003e However, evolving global trends of CVD events, mortality and ESRD in people with diabetes indicate a reversal. A Swedish national cohort examined the changes in all-cause mortality and CVD events in people with T1DM and T2DM from 1998 to 2012. People with T1DM had 40%, and T2DM had a 20% reduction in CVD outcomes compared to the control group. \\u003csup\\u003e3\\u003c/sup\\u003e While all-cause and CVD mortality among people with diabetes is decreasing \\u003csup\\u003e17\\u003c/sup\\u003e, an increasing number of younger people are developing DKD and progressing to ESRD. \\u003csup\\u003e18\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eThis study investigates how the age of diabetes onset relates to vascular outcomes such as ESRD, myocardial infarction, angina, and stroke, as well as its connection to the age at which ESRD begins. It also examines how factors like diabetes diagnosis age, smoking behaviours such as starting and stopping smoking age, and gender affect urinary albumin concentrations (UAC), a key marker for ESRD, CVD and stroke. The results aim to support further research for validation and guide policies to address the global rise in ESRD cases.\\u003c/p\\u003e\"},{\"header\":\"Method\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design and population\\u003c/h2\\u003e \\u003cp\\u003eWe conducted this retrospective cohort analysis using data from the individuals who filled out the UK Biobank study questionnaire. It did not differentiate between the types of diabetes. For this study, we used instance 0, which was the assessment visit. For descriptive analyses, we used the entire cohort, and for exploratory analyses, we used participants with diabetes. The inclusion criteria were self-reported diabetes (UK Biobank data field \\u0026ndash; 2443, \\u0026ldquo;Diabetes diagnosed by a doctor\\u0026rdquo;), and exclusion criteria were those who did not answer this question or responded as \\u0026ldquo;No\\u0026rdquo;. The study selection process is available in the flow chart. (Supplementary material 1 \\u0026ndash; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The complete coding schedule is available online (Supplementary material 4). The study was conducted following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. \\u003csup\\u003e19\\u003c/sup\\u003e (Supplementary material 5)\\u003c/p\\u003e \\u003cp\\u003eThe UK Biobank is a large, population-based cohort comprising over 500,000 volunteers aged 40\\u0026ndash;69 years recruited between 2006 and 2010 from 22 assessment centres across England, Wales and Scotland. During their assessment visits, participants provided electronic consent and completed a touchscreen-based questionnaire. The questionnaire collected extensive demographic and lifestyle information, including current and past medical history, medication use, ethnic and socioeconomic profiles, occupation, educational background, smoking status, diet, exercise and alcohol consumption habits. It also included the ages of diabetes, hypertension and ESRD diagnoses and the ages of starting and stopping smoking. \\u003csup\\u003e20\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eExposure and outcome variables\\u003c/h3\\u003e\\n\\u003cp\\u003eThe \\u003cb\\u003ep\\u003c/b\\u003eopulation for our study was people with diabetes; the \\u003cb\\u003ee\\u003c/b\\u003exposure was the age of diabetes diagnosis; the \\u003cb\\u003ec\\u003c/b\\u003eomparison was amongst four groups based on the age of diabetes diagnosis, i.e., \\u0026lt;\\u0026thinsp;20, 20\\u0026ndash;40, 41\\u0026ndash;60 and \\u0026gt;\\u0026thinsp;60 and the primary outcomes were ESRD, myocardial infarction, angina and stroke and the secondary outcome was UAC. We used sex, age of starting and stopping smoking, duration of diabetes and hypertension as confounding variables. Information about myocardial infarction, angina, stroke and ESRD was obtained from the questionnaire. UK Biobank subsequently cross-checked the information with electronic health records and confirmed the accuracy. \\u003csup\\u003e21\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eFollowing the Kidney Disease Improving Global Outcome (KDIGO) guideline, we defined normoalbuminuria as a UAC value of \\u0026lt;\\u0026thinsp;20 mg/dl in a spot sample of urine, microalbuminuria UAC value 20\\u0026ndash;200 mg/dl, and macroalbuminuria UAC value\\u0026thinsp;\\u0026gt;\\u0026thinsp;200 mg/dl. \\u003csup\\u003e22\\u003c/sup\\u003e The Townsend deprivation index was used to define deprivation, and based on the score, the cohort was divided into five quintiles from the least to the most deprived. As almost 94% of the study participants were from white backgrounds, we divided ethnicity into two groups \\u0026ndash; white and non-white.\\u003c/p\\u003e\\n\\u003ch3\\u003eStatistical analyses\\u003c/h3\\u003e\\n\\u003cp\\u003eBy identifying individuals with diabetes and hypertension from the questionnaire, we calculated the duration of diabetes and hypertension by subtracting the age of diagnosis from the participant\\u0026rsquo;s age on the assessment date. For ex-smokers, the duration of smoking was calculated by subtracting the age they stopped smoking from the age they started. For current smokers, the duration was determined by subtracting the starting age from the participant\\u0026rsquo;s current age.\\u003c/p\\u003e \\u003cp\\u003eBaseline characteristics of participants were stratified into two groups \\u0026ndash; those with and without diabetes. Descriptive statistics were reported as means\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation for normally distributed numerical variables and medians with interquartile range (IQR) for non-normally distributed numerical variables. Categorical variables were presented as numbers and percentages. Statistical significance between the two groups, with and without diabetes, was assessed using the Wilcoxon Rank-Sum test for non-parametric numerical variables, an independent t-test for parametric numerical variables, and a Chi-squared test for categorical variables.\\u003c/p\\u003e \\u003cp\\u003eWe selected the longitudinal cohort of eligible UK Biobank study participants with diabetes and followed them until the outcomes of interest, i.e., myocardial infarction, angina, stroke and ESRD. We also analysed the UAC value at the time of assessment visits as a composite secondary outcome marker for ESRD, CVD and stroke. Follow-up time was from the mean age of diabetes diagnosis to the mean age of the occurrence of the events of interest. We also used the age of hypertension diagnosis, the duration of hypertension and diabetes, smoking status, and age of starting and stopping smoking as explanatory variables for stratified analyses. In addition, we did further analyses to explore the relationship between the age of diabetes and hypertension diagnoses and the age of ESRD onset. A loess method was applied to explore the UAC trend based on the age of diabetes and hypertension diagnoses and the age of starting smoking in current and ex-smokers, who reported ESRD, myocardial infarction, angina and stroke.\\u003c/p\\u003e \\u003cp\\u003eWe fitted a univariate logistic regression model to assess if the age of diabetes diagnosis can predict ESRD, myocardial infarction, angina and stroke outcomes. Finally, in model 1, we used the multivariable logistic regression method to explore how the age of diagnosis of diabetes and in model 2, the duration of diabetes predicts ESRD. In these models, we adjusted for sex and albuminuria categories as confounding variables.\\u003c/p\\u003e \\u003cp\\u003eModel discrimination was evaluated using receiver operating characteristics (ROC) and the area under the curve (AUC). The goodness of fit (GOF) was assessed by analysing the calibration plot and Hosmer and Lemeshow GOF test. Concordance with the assumptions of the logistic regression model was assessed using the Durbin-Watson test for the independence of observation and the Variance Inflation Factor (VIF) for multicollinearity. Missing data were analysed using Multiple Imputations of Chained Equation (MICE). All analyses were conducted using software R version 4.4.3.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eOut of a total of 497,895 eligible study participants who consented to share their data, 5.26% (n\\u0026thinsp;=\\u0026thinsp;26,206) had diabetes. We did not have precise data about the types of diabetes available from the questionnaire. However, 8.26% (n\\u0026thinsp;=\\u0026thinsp;2247) of study participants reported being on insulin, suggesting that the number of T1DM is unlikely to be more than 2247. (Supplementary material 4 \\u0026ndash; UKB data field 6143). The selection process is in the flow chart. (Supplementary material 1 - Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The mean follow-up period for people with diabetes who had ESRD and stroke was 9 years and 11 years, respectively. The follow-up period for myocardial infarction and angina was 12 years. Thus, the total person-years of follow-up for those who had ESRD was 235,854 person-years, for stroke 288,266 person-years, and myocardial infarction and angina was 314,472 person-years which equates to 838,592 person-years follow-up. (Supplementary materials 1 \\u0026ndash; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Apart from the age, BMI and waist circumference, all other numerical variables had non-parametric distribution. The distribution of numerical variables is in the histograms with normality lines. (Supplementary materials 1 - Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e to 9).\\u003c/p\\u003e \\u003cp\\u003e44.7% (n\\u0026thinsp;=\\u0026thinsp;210,892) of study participants without diabetes were male, compared to 60.6% (n\\u0026thinsp;=\\u0026thinsp;15,876) in the group with diabetes. In terms of diabetes diagnosis, 14.9% (n\\u0026thinsp;=\\u0026thinsp;3735) were diagnosed before the age of 40, 66.2% (n\\u0026thinsp;=\\u0026thinsp;16,609) between the ages of 40 to 60, and 18.9% (n\\u0026thinsp;=\\u0026thinsp;4752) were diagnosed after the age of 60. The prevalence of hypertension in those without diabetes was 22.5% (n\\u0026thinsp;=\\u0026thinsp;106,143), and in those with diabetes was 49.5% (n\\u0026thinsp;=\\u0026thinsp;12,975). Amongst those with ESRD, the median age of diabetes diagnosis was 54 (IQR 47\\u0026ndash;61) years, and the median age of ESRD diagnosis was 57 (IQR 47\\u0026ndash;67) years. Compared to people without, those with diabetes had a higher number of current and ex-smokers, started smoking at younger ages and smoked for longer duration. Likewise, compared to those without, those with diabetes had higher BMI and waist circumference and were more likely to be on lipid-lowering medications. Compared to 22.5% of people without, 46.3% of people with diabetes had albuminuria.\\u003c/p\\u003e \\u003cp\\u003eESRD was reported by 0.2% (n\\u0026thinsp;=\\u0026thinsp;875) of those without and 1.2% (n\\u0026thinsp;=\\u0026thinsp;303) with diabetes, which corresponds to a prevalence rate of ESRD of 200 per 100,000 population without diabetes and 1,200 per 100,000 population in those with diabetes. Macrovascular complications were more prevalent in people with diabetes than without. Angina was reported in 7.5% (n\\u0026thinsp;=\\u0026thinsp;1958) of individuals with diabetes compared to 2% (n\\u0026thinsp;=\\u0026thinsp;9220) in those without. Myocardial infarction was reported by 8.6% (n\\u0026thinsp;=\\u0026thinsp;2250) of individuals with diabetes compared to 2% (n\\u0026thinsp;=\\u0026thinsp;9198) in those without. Likewise, in the diabetes group, 2.9% (n\\u0026thinsp;=\\u0026thinsp;771) had a stroke compared to 1.1% (n\\u0026thinsp;=\\u0026thinsp;5367) in the group without diabetes. Those who had myocardial infarction, angina and stroke were older than those who had ESRD. The mean age of people with diabetes who had myocardial infarction, angina, stroke and ESRD was 62.55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.84, 62.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.71, 61.99\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.37, and 59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.61 years. Baseline characteristics are summarised in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline characteristics of the UK Biobank cohort\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo diabetes group\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;471,689)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDiabetes Group\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;26,206)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAge - mean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e56.87 (8.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60.05 (7.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSex (Male %)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e210892 (44.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15876 (60.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eEthnicity (White %)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e443818 (94.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24909 (94.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.312\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eDeprivation quintile (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLeast deprived\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94332 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5145 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e0.635\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLess deprived\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94173 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5275 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModerately deprived\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94229 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5232 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMore deprived\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94199 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5235 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMost deprived\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94168 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5287 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eDiabetes diagnosis age category\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDiabetes diagnosis age\\u0026thinsp;\\u0026lt;\\u0026thinsp;40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN/A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3735 (14.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eN/A\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDiabetes diagnosis age 40\\u0026ndash;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN/A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16609 (66.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDiabetes diagnosis age\\u0026thinsp;\\u0026gt;\\u0026thinsp;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN/A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4752 (18.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e106143 (22.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12975 (49.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eHypertension diagnosis age category\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHypertension diagnosis age\\u0026thinsp;\\u0026lt;\\u0026thinsp;40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23193 (19.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3268 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHypertension diagnosis age 40\\u0026ndash;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e78893 (67.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11400 (69.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHypertension diagnosis age\\u0026thinsp;\\u0026gt;\\u0026thinsp;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15008 (12.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1655 (10.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eESRD (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e875 (0.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e303 (1.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eESRD diagnosis age\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eESRD diagnosis age\\u0026thinsp;\\u0026lt;\\u0026thinsp;40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e145 (16.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25 (8.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eESRD diagnosis age 40\\u0026ndash;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e379 (43.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e117 (38.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eESRD diagnosis age\\u0026thinsp;\\u0026gt;\\u0026thinsp;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e351 (40.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e161 (53.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAngina (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9220 (2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1958 (7.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMyocardial infarction (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9198 (2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2250 (8.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eStroke (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5367 (1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e771 (2.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eSmoking status (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNon-smoker\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e260571 (55.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12074 (46.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEx-smoker\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e161349 (34.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11212 (42.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCurrent smoker\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49769 (10.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2920 (11.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eEx-smokers smoking starting age (median, IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17 (15 \\u0026minus;\\u0026thinsp;18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16 (15\\u0026ndash;18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAge stopped smoking (median, IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39 (30\\u0026ndash;48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e43 (34\\u0026ndash;52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eEx-smokers smoking duration (median, IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21 (13\\u0026ndash;31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26 (17\\u0026ndash;35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCurrent smokers\\u0026rsquo; smoking starting age (median, IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (15\\u0026ndash;19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16 (14\\u0026ndash;19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCurrent smokers\\u0026rsquo; smoking duration (median, IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38 (31\\u0026ndash;45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e42 (35\\u0026ndash;47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBody mass index - mean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.21 (4.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31.33 (5.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eWaist circumference (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e89.60 (13.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e102.55 (14.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eLipid-lowering medication (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27095 (5.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7190 (27.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAnti-hypertensive medication (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26569 (5.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1110 (4.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eAlbuminuria category (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNormoalbuminuria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e107005 (77.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7709 (57.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMicroalbuminuria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28933 (21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4852 (36.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMacroalbuminuria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2019 (1.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e807 (6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDiabetes and hypertension diagnoses age, duration of diabetes and hypertension and UAC.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe relationship between diabetes and hypertension diagnoses ages and smoking starting ages in current and ex-smokers, with the trend in UAC values who developed ESRD, myocardial infarction, angina and stroke, were evaluated using the loess method. Those with ESRD showed that UAC value peaked when diabetes was diagnosed at an age under 30 years and declined for later diagnosis ages. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) A similar trend was observed for the duration of diabetes and UAC values. (Supplementary material 1- Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e0). On the contrary, the relationship between the age of hypertension diagnosis and duration of hypertension with UAC value was steady. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and supplementary material 1- Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e0). There was no relationship between the age of diabetes and hypertension diagnoses with UAC values in those who had a myocardial infarction, angina and stroke. (Supplementary material 1 - Figs.\\u0026nbsp;11\\u0026ndash;13)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eAge of starting smoking in current and ex-smokers and UAC values\\u003c/h3\\u003e\\n\\u003cp\\u003eIn those with ESRD, current smokers had a monophasic rise of UAC and ex-smokers had a biphasic rise of UAC based on the age of starting smoking. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) In contrast, the UAC values did not show any relationship between the age of starting smoking in current and ex-smokers in those who had myocardial infarction, angina and stroke. (Supplementary materials 1 \\u0026ndash; Figs.\\u0026nbsp;14\\u0026ndash;16).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eESRD diagnosis age based on diabetes and hypertension diagnosis age\\u003c/h2\\u003e \\u003cp\\u003eIn people with diabetes and ESRD, the mean age of ESRD onset was 57.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.62 years. There was a linear relationship between the age of diabetes diagnosis and the age of ESRD diagnosis, showing that the younger the onset of diabetes, the earlier the onset of ESRD. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eThere was a significant difference in the ESRD onset age between sexes based on the age of diabetes and hypertension diagnosis. Females who developed diabetes at an age\\u0026thinsp;\\u0026lt;\\u0026thinsp;20 years had ESRD at an earlier age than males. On the contrary, males who had hypertension below the age of 20 had ESRD earlier than females. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eUnivariate logistic regression: ESRD, myocardial infarction, angina and stroke risk\\u003c/h3\\u003e\\n\\u003cp\\u003eThe univariate unadjusted logistic regression model showed that the risk of ESRD is significantly higher in people with diabetes who were diagnosed before the age of 60 years. Compared to those who developed above the age of 60, those who developed diabetes at an age\\u0026thinsp;\\u0026lt;\\u0026thinsp;20, 20 to 40 and 41 to 60, the odds of ESRD were 5.26 (95% CI 3.00\\u0026ndash;9.40), 7.78 (95% CI 4.81\\u0026ndash;13.16) and 2.33 (95% CI 1.50\\u0026ndash;3.84), respectively. Likewise, in people with diabetes and hypertension, compared to those who developed hypertension above the age of 60, the age of diagnosis\\u0026thinsp;\\u0026lt;\\u0026thinsp;20, 20 to 40 and 41 to 60 was associated with the odds of ESRD 2.20 (1.58\\u0026ndash;3.11), 5.03 (3.79\\u0026ndash;6.81), and 1.53 (1.16\\u0026ndash;2.06), respectively. There was no statistically significant association between the younger age of diabetes onset with myocardial infarction and stroke. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\n\\u003ch3\\u003eAdjusted multivariate regression models – ESRD and age of diabetes diagnosis\\u003c/h3\\u003e\\n\\u003cp\\u003eThe logistic regression model was fitted to explore how the univariate model performed when the age of diabetes diagnosis, gender and albuminuria were adjusted in the model. We did not fit the age of diabetes diagnosis and the duration of diabetes in the same model because of interdependence and a violation of the assumption of logistic regression. In model 1, we adjusted for the age of diabetes diagnosis, and in model 2, we adjusted for diabetes duration to understand how these risk factors influence ESRD outcomes.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eModel 1\\u003c/h2\\u003e \\u003cp\\u003eIn model 1, when age, sex and albuminuria were adjusted, the odds of ESRD compared to those who were diagnosed with diabetes above the age of 60, those who were diagnosed\\u0026thinsp;\\u0026lt;\\u0026thinsp;20, 20 to 40, and 41 to 60 were 4.71 (95% CI 2.47\\u0026ndash;9.28), 4.67 (95% CI 2.63\\u0026ndash;8.78), 1.94 (95% CI 1.16\\u0026ndash;3.49), respectively. Compared to people with normoalbuminuria, the odds of ESRD in those with microalbuminuria and proteinuria were 3.15 (95% CI 2.09\\u0026ndash;4.86) and 25.03 (95% CI 16.73\\u0026ndash;38.41), respectively. There was no statistically significant difference in the odds between the male and female gender.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eModel 2\\u003c/h2\\u003e \\u003cp\\u003eModel 2 showed that diabetes duration is also associated with an increased risk of ESRD. The odds of ESRD were increased with a longer duration of diabetes 1.02 (95% CI 1.01\\u0026ndash;1.03). Similarly, compared to those with normoalbuminuria, the odds of ESRD in those with microalbuminuria were 3.23 (95% CI 2.14\\u0026ndash;4.98), and those with proteinuria were 27.22 (95% CI 18.22\\u0026ndash;41.74). There was no statistically significant relationship between gender and ESRD risk.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eESRD, diabetes diagnosis age and diabetes duration - model performance and validation\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eDiscrimination\\u003c/h2\\u003e \\u003cp\\u003eBoth models demonstrated excellent model performance, with the area under the curve (AUC) above 80%. The concordance statistics for model 1 was 81%, and model 2 was 82%, suggesting the model\\u0026rsquo;s performance in predicting true positive cases of ESRD. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCalibration\\u003c/h2\\u003e \\u003cp\\u003eThe calibration plots show the reliability of both models in predicting ESRD. The alignment of observed proportions with predicted probabilities indicates robust model performance, supporting their use in clinical decision-making processes. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMissing data and adherence to the assumptions of logistic regression\\u003c/h2\\u003e \\u003cp\\u003eMissing data were analysed using the Multiple Imputation of Chained Equation (MICE) plotted. (Supplementary material 2 - Fig.\\u0026nbsp;17). This analysis showed that the missing data were likely to be randomly missing.\\u003c/p\\u003e \\u003cp\\u003eLogistic regression assumptions were examined for the independence of observation using Durbin-Watson test) and multicollinearity by Variance Inflation Factor (VIF). Both tests showed the models satisfied the assumptions (Supplementary Materials 3).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this large prospective cohort of UK Biobank study participants, we found that people with younger onset diabetes are more likely to develop ESRD than myocardial infarction, angina or stroke within the follow-up time. Moreover, they are more likely to develop ESRD at a younger age than those who had myocardial infarction, angina and stroke. In addition, concurrent diagnosis of hypertension may exacerbate the risk. People who develop diabetes at a younger age are more likely to be exposed to prolonged periods of hyperglycaemia, and this study showed that the longer the duration of diabetes, the higher the risk of ESRD.\\u003c/p\\u003e \\u003cp\\u003eThe adjusted model showed that there was no statistically significant relationship between the age of onset of diabetes with myocardial infarction and stroke, suggesting that the recent surge in the ESRD cases in people with diabetes might have been caused by the evolving epidemiology of T2DM in young and adolescents. This study also showed that there was an upward trend in the UAC values in current and ex-smokers who started smoking in their teenage years. Interestingly, for ex-smokers, there was a trend of the second rise, perhaps mediated by post-cessation weight gain and early post-cessation adaptation of the cardiometabolic milieu. These findings suggest that teenage smoking should be actively discouraged, and those who have already started smoking should be supported to quit and remain abstinent long-term.\\u003c/p\\u003e \\u003cp\\u003eAlthough both T1DM and T2DM present with hyperglycaemia, the pathophysiology of the conditions is disparate. T1DM is an autoimmune disease that usually presents in childhood and adolescence and requires insulin initiation for survival. \\u003csup\\u003e23\\u003c/sup\\u003e T2DM, on the other hand, is a complex metabolic disease with multiple biochemical and haemodynamic aberrations, posing a higher risk of vascular complications, mortality and morbidity. \\u003csup\\u003e24\\u003c/sup\\u003e The earlier the onset of T2DM, the higher the risk of vascular complications and mortality. A recently published 30-year follow-up analysis of the UK Prospective Diabetes Study (UKPDS) showed that the standardised mortality rates (SMR) in younger-onset diabetes (\\u0026lt;\\u0026thinsp;40 years) was almost 4-fold higher than later-onset (\\u0026ge;\\u0026thinsp;40 years) [3.72 (95% CI 2.98\\u0026ndash;4.64)]. The incidence rate of macrovascular complications (myocardial infarction, peripheral vascular disease and stroke) was significantly higher in later-onset diabetes than in younger-onset. However, the incidence rate of microvascular complications was higher in the younger-onset, compared to the later-onset group [14.5 (95% CI 11.9\\u0026ndash;17.7) vs 12.1 (95% CI 11.3\\u0026ndash;13.0)] per 1000-person years. \\u003csup\\u003e25\\u003c/sup\\u003e Therefore, the finding of this study for microvascular complications of ESRD is in keeping with the existing knowledge. This is the first study to demonstrate that a younger age of diabetes diagnosis predisposes to an earlier onset of ESRD. This finding has significant clinical and policy implications.\\u003c/p\\u003e \\u003cp\\u003eYoung-onset T2DM is a distinct phenotype in metabolic deregulation, and it does not follow the usual adult-onset T2DM long quiescent phase of steady progression over many years. In contrast, young-onset T2DM is a rapidly progressive condition where the annual decline in pancreatic β-cell function is 20\\u0026ndash;35%, compared to almost 7% in adult-onset T2DM. \\u003csup\\u003e26\\u003c/sup\\u003e Some disparate characteristics of young-onset T2DM compared to the adult-onset are \\u0026ndash; higher prevalence of obesity in young-onset T2DM than adult-onset (95% vs 50%), \\u003csup\\u003e27\\u003c/sup\\u003e more pronounced visceral adiposity and hepatic ectopic fat deposition, \\u003csup\\u003e28\\u003c/sup\\u003e impaired insulin signalling in skeletal muscles interfering with glucose disposal pathway, \\u003csup\\u003e29\\u003c/sup\\u003e systemic inflammation, evidenced by a high level of high-sensitive C-reactive protein (CRP), Tumour Necrosis Factor alpha (TNF-α) and interleukin 1 beta, \\u003csup\\u003e30,31\\u003c/sup\\u003e maternal gestational diabetes, \\u003csup\\u003e32\\u003c/sup\\u003e and pubertal surges in the circulating insulin-antagonistic hormones such as growth hormones, corticosteroid and sex hormones. \\u003csup\\u003e33\\u003c/sup\\u003e Accounting for all the above pathophysiological processes, this study shows that renovascular complications may predate cardiovascular and stroke. Effective management of factors such as childhood obesity and smoking linked with insulin resistance and T2DM may reduce the growing burden of ESRD worldwide.\\u003c/p\\u003e \\u003cp\\u003eThe strength of this study is that it has a large dataset with a diverse range of study participants and a novel stratified analysis. The areas under the curve above 80% for the age of diabetes diagnosis and duration of diabetes indicate excellent discriminatory power between those who developed ESRD compared to those who did not. Similarly, the calibration plot confirmed good fitness. However, this study has several limitations. We used the questionnaire filled out by the study participants to identify cases of diabetes. It did not specify the type of diabetes. Therefore, we cannot comment on the number of type 1 and type 2 DM included in the study. However, as the number of people reported being on insulin was 2247, the number of T1DM cases is unlikely to be more than 8.26%, which is in keeping with the estimated prevalence of 8% published by Diabetes UK in 2022. \\u003csup\\u003e34\\u003c/sup\\u003e The study was a retrospective cohort design with limited predictive value. The age of diagnosis of diabetes and ESRD was obtained from the questionnaire and was not verified, which is open to recall bias. UK Biobank is a voluntary dataset and is not representative of real-world data; therefore, the findings may not be generalisable. Despite the above limitations, the findings of this study indicate that the changing T2DM epidemiology may be affecting ESRD outcomes more disparately than CVD and stroke outcomes, which is of great clinical and public health importance.\\u003c/p\\u003e \\u003cp\\u003eThe financial and societal costs of ESRD are exponential, but with timely identification and risk management, they can be potentially averted. In the UK alone, approximately \\u0026pound;14\\u0026nbsp;billion is spent annually on managing diabetes complications \\u003csup\\u003e35\\u003c/sup\\u003e, with hospital-based RRT for each ESRD patient costing an estimated \\u0026pound;32,678 each year. \\u003csup\\u003e36\\u003c/sup\\u003e For those who need RRT, the UK economy loses an estimated \\u0026pound;372\\u0026nbsp;million annually due to abstinence from work, which is likely to increase to \\u0026pound;2\\u0026nbsp;billion by 2030. \\u003csup\\u003e37\\u003c/sup\\u003e People with ESRD lose 25\\u0026ndash;56% in lifetime employment duration and 32\\u0026ndash;66% in lifetime productivity. \\u003csup\\u003e38\\u003c/sup\\u003e With almost 40% of individuals with diabetes expected to develop DKD in their lifetimes, often within 10 years of diagnosis \\u003csup\\u003e39,40\\u003c/sup\\u003e and around 45% of ESRD in developed countries linked to diabetes \\u003csup\\u003e41\\u003c/sup\\u003e, the financial burden is expected to rise further. Globally, over 10\\u0026nbsp;million people require RRT due to ESRD, although this is likely to represent less than 10% of those in need, as many low- and middle-income countries (LMICs) lack reliable data. \\u003csup\\u003e42\\u003c/sup\\u003e A recent health economic study projected that between 2022 and 2027, the annual global cost of RRT will increase by 9.3%, from \\u003cspan\\u003e$\\u003c/span\\u003e372\\u0026nbsp;billion to \\u003cspan\\u003e$\\u003c/span\\u003e406.7\\u0026nbsp;billion. \\u003csup\\u003e43\\u003c/sup\\u003e In LMICs, where healthcare systems are often privately funded, the cost of RRT is frequently unaffordable, leading to premature mortality. \\u003csup\\u003e44\\u003c/sup\\u003e It is estimated that diabetes and hypertension account for up to 70% of ESRD cases, which are potentially preventable by pharmacological and lifestyle intervention. \\u003csup\\u003e45\\u003c/sup\\u003e Similarly, childhood smoking is a strong predictor for ESRD and is a modifiable risk factor. \\u003csup\\u003e46,47\\u003c/sup\\u003e The Majority of long-term smokers start smoking in their early to late teenage years, and discouraging them from taking up the habit of smoking can make a big difference. \\u003csup\\u003e48,49\\u003c/sup\\u003e \\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eYounger onset diabetes and hypertension may be the driver behind the recent surge in ESRD cases. They are more likely to develop it at a younger age, requiring RRT for a longer duration. It can also put enormous pressure on the healthcare budget and loss of productivity. Factors that predispose to young-onset diabetes should be identified and managed as a global priority.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthical approval -\\u0026nbsp;\\u003c/strong\\u003eThe UK Biobank received ethical approval from the Northwest Multicentre Research Ethics Committee (REC reference 11/NW/03820), and all participants provided written informed consent. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePPI involvement\\u003c/strong\\u003e - There was no formal or informal involvement of patients or the public in this research.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication \\u0026ndash;\\u0026nbsp;\\u003c/strong\\u003eAll UKB study participants consented to their data being used for medical research and publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u0026nbsp;\\u003c/strong\\u003e\\u0026ndash;SdL is the Director of the Oxford Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), which is included in his academic role at the University of Oxford. He has received research funding through his University from AstraZeneca, GlaxoSmithKline (GSK), Lily, Moderna, Medical Science Division (MSD), Sanofi, Seqirus, and Takada. He has also served as an advisory board member for AstraZeneca, GSK, Sanofi, Seqirus, and Pfizer. RB is the Deputy Director of the NIHR Peninsula Applied Research Collaboration (PenARC). UK is the Chair of the NIHR Advanced Fellowship Committee. All other co-authors declare no competing interest.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u0026ndash;\\u0026nbsp;\\u003c/strong\\u003eThis project is funded by the NIHR through a personal clinical lecturer award to the lead author (CL-2023-2024-001).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions \\u0026ndash;\\u0026nbsp;\\u003c/strong\\u003eDK \\u0026nbsp;was responsible for conceptualisation, data access, data cleaning, statistical analyses, and drafting the manuscript. RB, SdeL, and AS contributed to the study conceptualisation, data analyses, and supervision and reviewed the manuscript. MN \\u0026nbsp;contributed to the statistical analyses. SS and JP provided clinical insights to the paper. All other co-authors read the draft and commented on it.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials \\u0026ndash;\\u0026nbsp;\\u003c/strong\\u003eUK Biobank data can be obtained by application (www.ukbiobank.ac.uk). This is an open-access article distributed with the Creative Commons Attribution (CC BY) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the license is given, and an indication of whether changes were made. https://creativecommons.org/licenses/by/4.0/\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements \\u0026ndash;\\u0026nbsp;\\u003c/strong\\u003eThe research was conducted using the UK Biobank resource under application no 61894. For the purposes of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any arising Author Accepted Manuscript version. When appropriate, AI software Grammarly and ChatGPT were used to improve readability without any impact on the study findings and their interpretation.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eYu Y, Zhang M, Tang Y, et al. Global disease burden attributable to kidney dysfunction, 1990\\u0026ndash;2019: A health inequality and trend analysis based on the global burden of disease study. \\u003cem\\u003eDiabetes Research and Clinical Practice\\u003c/em\\u003e 2024: 111801.\\u003c/li\\u003e\\n \\u003cli\\u003eThomas B. The Global Burden of Diabetic Kidney Disease: Time Trends and Gender Gaps. \\u003cem\\u003eCurrent Diabetes Reports\\u003c/em\\u003e 2019; \\u003cstrong\\u003e19\\u003c/strong\\u003e(4): 1-7.\\u003c/li\\u003e\\n \\u003cli\\u003eRawshani A, Rawshani A, Franz\\u0026eacute;n S, et al. 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Kidney function and tobacco smoke exposure in US adolescents. \\u003cem\\u003ePediatrics\\u003c/em\\u003e 2013; \\u003cstrong\\u003e131\\u003c/strong\\u003e(5): e1415-23.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eTables 2 and 3 are available in the Supplementary Files section.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"University of Plymouth\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"young-onset diabetes, end-stage renal disease, cardiovascular disease, myocardial infarction and stroke\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5951257/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5951257/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eIn the last three decades, in people with diabetes, while the mortality and morbidity due to cardiovascular disease (CVD) and stroke have declined, the incidence of end-stage renal disease (ESRD) has crept up. The precise cause for this shift is unknown. Intriguingly, during the same period, the incidence of diabetes in younger people has almost tripled worldwide. This study examines whether young-onset diabetes is a greater risk for ESRD compared to CVD and stroke.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe conducted this retrospective cohort study using data from volunteers (n\\u0026thinsp;=\\u0026thinsp;502,408) aged 40\\u0026ndash;69 years recruited in UK Biobank between 2006 and 2010. The exposure variable was the age of diabetes diagnosis, while the outcomes of interest were ESRD, myocardial infarction, angina, and stroke. The cumulative follow-up period from the mean age of diabetes to the mean age of the outcomes of interest was 838,592 person-years. Univariate and multivariate logistic regression models were fitted to assess odds ratios (ORs) and 95% confidence intervals (CIs). Model performance was evaluated using receiver operating characteristics (ROC) analysis and calibration plots.\\u003c/p\\u003e\\u003ch2\\u003eFindings:\\u003c/h2\\u003e \\u003cp\\u003eOut of 26,206 people with diabetes, 1.16% (n\\u0026thinsp;=\\u0026thinsp;303) had ESRD, 8.58% (n\\u0026thinsp;=\\u0026thinsp;2250) had a myocardial infarction, 7.47% (n\\u0026thinsp;=\\u0026thinsp;1958) had angina, and 2.94% (n\\u0026thinsp;=\\u0026thinsp;771) had a stroke. Compared to later-onset, young-onset diabetes is linked with an earlier onset of ESRD. Based on the age of diabetes diagnosis, the univariate logistic regression showed that compared to those diagnosed after the age of 60, the odds of ESRD for those diagnosed at ages\\u0026thinsp;\\u0026lt;\\u0026thinsp;20, 20\\u0026ndash;40, and 41\\u0026ndash;60 years were OR [2.33 (95% CI 1.50\\u0026ndash;3.84), 7.78 (95% CI 4.81\\u0026ndash;13.16), and 5.26 (95% CI 3.00\\u0026ndash;9.40)], respectively. Myocardial infarction and stroke did not have a statistically significant relationship with diabetes diagnosis age. Multivariate models adjusted for sex and albuminuria confirmed the increased odds of ESRD in people with younger onset and longer duration of diabetes.\\u003c/p\\u003e\\u003ch2\\u003eInterpretation:\\u003c/h2\\u003e \\u003cp\\u003ePeople with younger onset and longer duration of diabetes are at a higher risk of ESRD than CVD and stroke.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Young-onset diabetes poses a greater risk of end-stage renal disease (ESRD) than cardiovascular disease and stroke: a retrospective cohort study of UK Biobank\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-02-14 03:23:30\",\"doi\":\"10.21203/rs.3.rs-5951257/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"7b0e2f0a-1565-41ea-baa8-683929200c9b\",\"owner\":[],\"postedDate\":\"February 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":43770838,\"name\":\"Endocrinology \\u0026 Metabolism\"}],\"tags\":[],\"updatedAt\":\"2025-02-14T03:23:30+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-02-14 03:23:30\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5951257\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5951257\",\"identity\":\"rs-5951257\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}