Risk Factors of Diabetes Incidence: A Retrospective Cohort Study in Abu Dhabi

Risk Factors of Diabetes Incidence: A Retrospective Cohort Study in Abu Dhabi | 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 Article Risk Factors of Diabetes Incidence: A Retrospective Cohort Study in Abu Dhabi Latifa Baynouna AlKetbi, Rudina AlKetbi, Mariam AlShamsi, Nico Nagelkerke, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6214000/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Diabetes mellitus (DM) is a global health burden. Monitoring its determinants and incidence trends is important for identifying risk factors and projecting future health service needs. Method: The Abu Dhabi Risk Study (ADRS ) is a retrospective cohort study in Abu Dhabi, United Arab Emirates (UAE). Diabetes-free participants were followed for an average of 9.2 years for the development of new diabetes. Cox regression was used to develop a prediction model and identify significant determinants. Results: Over the 12-year follow-up period, 643 individuals developed new diabetes, with an overall incidence of 7.4%. The prevalence of DM increased to 28.5% in males, 25.3% in females, and 31.9% among males. Significant risk factors for developing new diabetes were a higher level of HbA1C, current smoking status at screening, and a higher level of eGFR. The model developed showed good performance in predicting new diabetes with a c-statistics of 0.837 (0.818-0.856), a sensitivity of 75.1%, and a specificity of 78.1%. Determinants of developing pre-DM included higher Diastolic Blood Pressure (DBP), total cholesterol, Random Blood Sugar (RBS), Body Mass Index (BMI), age, and lower High-Density Lipoprotein (HDL) levels. Gender and smoking status were not significant determinants for the diagnosis of prediabetes. The cumulative prevalence of prediabetes and diabetes is increasing steadily, with a plateau reached at 40 in the case of pre-DM and 60 with DM, and a decline with increasing age. Conclusion:The prevalence of diabetes in Abu Dhabi remains high. The Derived model is valuable for informing clinical practice and preventing diabetes. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes Health sciences/Risk factors Incidence Prediction Model Cohost study Risk factors Figures Figure 1 Figure 2 Background The prevalence of diabetes mellitus among United Arab Emirates nationals is among the highest in the world ( 1 ). The latest reported prevalence was 21% among males and 23% among females, with a similarly high prevalence of its main risk factors, such as obesity ( 2 , 3 ). This significant health burden contributes to mortality, quality of life, and healthcare utilization ( 1 , 4 , 5 ). Factors affecting its prevalence in different countries and ethnicities are important to assess( 6 ). Such factors include healthcare system preparedness and responses toward prevention and management, aging, urbanization, culture, and physical inactivity ( 7 ). Therefore, studies on variation in diabetes epidemiology are crucial, especially Cohort studies. Especially strong evidence exists that diabetes mellitus can be prevented or delayed. Healthier Lifestyles and the use of pharmacological interventions such as metformin reduce the rate of progression to type 2 diabetes in people with impaired glucose tolerance, with lifestyle interventions being as effective as pharmacological treatment. Such interventions target multiple risk factors, but once stopped, the effects are not sustained ( 8 ) Therefore, risk assessment is crucial to identify individuals with higher risks of diabetes for the initiation of suitable and effective interventions and to be monitored as well. Risk scores developed from research-based prediction models are key in assessing and identifying such patients( 9 ) (10) . They as well guide policy decisions in diabetes surveillance and prevention to decrease the progression of diabetes to complications, disability, and mortality. Studies on the incidence of diabetes mellitus type 2 in Abu-Dhabi are rare despite diabetes having a relatively good focus of research on diabetes in the country ( 11 , 12 ). The United Arab Emirates is a rapidly developing country with a well-resourced healthcare system ( 13 ). Strategies for the early detection and management of chronic diseases on a large scale have been implemented for many years. For example, the cardiovascular screening program that started in 2008, Weqaya, could have one of the best screening coverages in the world and was designed to assess cardiovascular risk factors among the Abu Dhabi population. This study is part of a large retrospective cohort study, which includes a sample of the Weqaya screening participants from 2011 to 2013. Diabetes-free Abu Dhabi cardiovascular screening program participants were retrospectively evaluated for the incidence of diabetes, prediabetes, and risk factors studied. Methods This is a retrospective cohort study in the Emirate of Abu Dhabi, United Arab Emirates. The participants were from Weqaya, a national screening program in Abu Dhabi (3). They were UAE nationals aged 18 years and older eligible for their government’s free comprehensive health insurance plan. Data collected at baseline included demographic data, self-reported health indicators, including smoking status, physical activity, preexisting CVD (angina, heart attack, transient ischemic attack, stroke, other circulatory disorder), family history of premature cardiovascular disease (a first-degree relative with a heart attack or stroke before the age of 50 years), history of cardiovascular risk factors for diabetes, hypertension, and dyslipidemia and whether participants were taking medication for these conditions. Anthropometric measures included waist and hip circumference, body mass index (BMI in kg/m2), and a single arterial blood pressure reading. A digital automatic blood pressure monitor measured blood pressure from the left arm with the patient relaxed and seated. Hematological parameters included non-fasting glucose (mmol/L), total cholesterol, high-density lipoprotein (HDL) cholesterol (mmol/L), glycosylated hemoglobin (HbA1c), vitamin D, and creatinine. Some subjects' data were missing, so only subjects with completed data were included. The glomerular filtration rate was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. At baseline, 8699 subjects in the national cardiovascular screening program of 2011-2013, with an average follow-up of 9.2 years, were eligible for enrollment if they were non-diabetic. Diabetes was identified as HbA1c of >_6.4%, a documented diabetes diagnosis or ICD code. Participants were assessed retrospectively in 2023 for health outcomes. The pre-diabetic definition was an HbA1c of <5.7%. Age-adjusted prevalence for age-specific comparisons between countries utilized the population percentage in each 5-year age group in the new WHO World Standard population, based on the expected evolution of the world’s population age structure over the first quarter of the 21st century (14) . All participants had their eGFR determined. The method to determine the age-specific percentiles for the estimated glomerular filtration rate was the LMS method (15). The resultant age and sex-specific GFR percentiles were derived from this cohort after excluding subjects with comorbidities such as diabetes, hypertension, coronary heart disease, stroke, and cancer. Subjects were classified in appropriate eGFR percentiles according to age, sex, and eGFR. Statistical Analysis: Prediction models for diabetes were developed using Cox survival regression for progression to diabetes as an endpoint. Patients were censored at the time of death or their last HbA1c measurement if <6.4%. Potential predictors included age, RBS, BMI, Curren smoking, Sex, HBA1C, Mean BP, eGFR, HDL, and HTN. (Table 2). ROC curves and C-statistics were used to evaluate the performance of the Cox-derived risk scores at the end of the follow-up . Results The prevalence of DM at baseline was 22.2%, 18.5% in females and 25.8% in males. The HbA1c was ordered based on routine care according to the physician's decision and the patient's agreement. Among the 6772 diabetes-free subjects, 3537 females and 3235 males, 643 developed new diabetes over the follow-up years, giving an overall incidence of 7.4%, 317 (7.3%) females, and 326 (7.5%) males over the 12-year follow-up period. The incidence of DM fluctuated over the years of follow-up Appendix 1. Over the years of follow-up, the prevalence increased to 28.5%, 25.3% in females, and 31.9% among males. The overall age-standardized prevalence increased from 18% to 23.7%. The participant's demographics at baseline are shown in Table 1. Smoking was less common (0.7%) among females than (20.9%) males. 65.5% of females had an HDL of more than 1.29 compared to 28.5% of males. Concerning cardiovascular health, 6.5% of males and 4.9% of females were under lipid-lowering therapy, whereas 38.4% of males and 33.6% of females had cholesterol levels more than 5.2. There were (36.0% and 28.8%) of males with optimum and normal blood pressure, respectively, compared to (66.8% and 18.0%) of females. Hypertension had been diagnosed in 11.3% of males compared to 6.7% of females. Obesity was slightly more prevalent among females, with 20.8% having class I (30-34.9) (16)obesity, 9.5% having class II (35-39.9), and 5.3% being class III (>40), compared to 20.2%, 6.6%, and 3.7%, respectively, among males. HbA1C is the main determinant of new DM, with a nearly seven-fold increase in risk for each one-unit rise in HbA1C. A one-unit increase in Random glucose RBS had a 1.2% increase in the risk of DM, while the hazard of developing DM decreased by 0.403 for each one-unit rise in HDL. From multivariate Cox regression, for each one-year increase in age, the risk of DM increases by 1.03%, adjusted for other risk factors. Appendix 2 Different percentiles of eGFR have varying effects, with lower percentiles showing a risk of developing DM comparable with the highest percentile, the 97 th percentile. Smokers have a 1.425% increased risk, and obesity is a significant risk factor, with a 1.017% increase in risk with each one-unit increase in BMI. Mean BP is another significant risk factor, with a 1.009% increase in risk with each one-unit mmHg increase in Mean BP. However, the hypertension was not statistically significant. The DM hazard ratio steadily increases with increasing HbA1C. At the pre-DM cutoff level of 5.7, the HR is 10, doubles at 6, and triples at 6.5, the chosen cutoff point for DM, as shown in Appendix 3. Finally, although higher BMI was associated with a higher risk of progression to DM, as per Appendix 4, subjects with normal BMI and overweight also have high levels of HBA1C. The model developed showed good performance with a c-statistics of 0.837 (0.818-0.856). The ROC is shown in Figure 1 with a cutoff point identified for the hazard ratio of 2.06, it has a sensitivity of 75.1%, and specificity of 78.1%. HA1C alone as a predictor of DM performance was lower than the model but close to it, with a c statistic of 0.784 (0.761-0.807). The hazard ratio's cutoff for HBA1C identified by the model is 5.650, with a sensitivity of 68.6% and specificity of 73.2%. In comparison, the c-statistics for using only random blood sugar as a predictor was 0.698 (0.672-0.723). With regard to pre-DM, among non-diabetic patients, 804 did not do the HA1C after screening, or it is not documented (17.7%), 803 in the non-pre-DM group. Based on the latest test, the mean HBA1C in the pre-Dm group was 5.9, SD 0.2 (min 4.9 max 6.4), and in the non-pre-DM range group, was 5.2, SD =0.21 (min 2.8 max 5.7) among those with no pre-DM at screening, 14.45%. Interestingly, almost half, 44.1%, of those with pre-DM at screening reverted to normal values. Determinants of developing pre-dm were higher screening DBP, total cholesterol, RBS, BMI, and age, as well as lower levels of HDL Table 3. Gender and smoking status were not significant determinants for the diagnosis of prediabetes during follow-up. Figure 2 is a scatter plot showing the cumulative prevalence of prediabetes and diabetes with age. It is increasing steadily, with a plateau reached at 40 years of age in the case of pre-DM and 60 with DM. After the plateau, there is a decline with increasing age. Discussion This study is the first longitudinal population-representative examination of diabetes incidence in an area considered among the highest in prevalence. Our estimate of prevalence for 2011 is close to the International Diabetes Federation's (IDF) estimate of 18.8% in 2011 for the UAE (17). With an increase in age-standardized prevalence from 18.8% to 23.7 %, the problem of DM is worsening, underscoring the urgent need for comprehensive public health interventions in the region. The incidence is close to previously reported study in Abu Dhabi (11). By taking proactive steps to address the underlying determinants of diabetes, it will be possible to mitigate its impact on population health and improve outcomes for individuals living with or at risk of developing the disease. The model of prediction of progression from non-DM to DM derived from this study was excellent. The high c-statistic value suggests that our model has excellent discriminatory power in distinguishing between individuals who will develop diabetes mellitus (DM) and those who will not. This level of accuracy is particularly significant in the context of diabetes, where early identification of at-risk individuals is crucial for targeting preventive measures. This study model's ability to identify a broader range of significant risk factors compared to previous studies may enhance its utility in clinical practice (18, 19). Notably, it should facilitate a holistic approach to tailor interventions and preventive strategies based on each patient's risk factors. Such a multidimensional approach not only enhances the accuracy of diabetes prediction but also provides valuable insights into the complex interplay of factors contributing to disease development. Not surprisingly, advancing age emerged as a significant predictor of diabetes progression, with a 5.1% increase in risk for each one-year increase. Among potentially modifiable risk factors, higher levels of HbA1c, lower levels of HDL, higher BMI, higher DBP, and higher RBS were also associated with increased diabetes risk. These identified risk factors could be targeted through early intervention, focusing on lifestyle choices, including physical activity and dietary choices (20, 21). Recent studies have suggested that high HDL levels decrease insulin resistance 16 . In addition, HDL is known to have anti-inflammatory properties, which may reduce chronic inflammation, which is often thought to lead to insulin resistance (22). The high predictive value of HbA1c alone highlights the extent to which DM constitutes a chronic progressive disorder. It also highlights how to monitor this progression, as measuring HBA1c is a valid, simple test with minimal day-to-day fluctuation. It obviates a requirement for prior fasting and has superior predictive value compared to random blood glucose (RBG) (23) (24). Another risk factor is random blood glucose (RBG). A single RBG ≥ 100 mg/dL was suggested as more strongly associated with undiagnosed diabetes, similar to this study. In fact, the cutoff value for the best sensitivity and specificity to detect DM in this study was 98, similar to their result supporting the use of abnormal RBG values as a risk factor for diabetes that should be considered in screening guidelines (25). With regard to blood pressure, in this study, we found that high DBP carries a much higher risk of developing diabetes than SBP. In other studies, both SBP and DBP were linked to DM progression (26). Smoking increases the risk of DM by altering the body composition, insulin sensitivity, and pancreatic β cell function (27). This should add another item to the long list of arguments for healthcare professionals to address smoking prevention and cessation in their patients. Furthermore, onsite at younger age was found to be associated with increased risk of worse cardiovascular outcome (28). This should urge healthcare professionals to focus on the younger age group for prevention and management options, as effective strategies already exist. A higher level of eGFR is surprisingly a predictor of DM. This may suggest early renal complications of hyperglycemia causing hyperfiltration (29). Several findings point to the pathogenic and prognostic significance of glomerular hyperfiltration in the development and course of diabetic kidney diseases (29). No study identified a higher eGFR percentile as a risk factor for diabetes, and this study is the first to show such a relation. The high prevalence of pre-diabetes, particularly among younger age groups, is a significant concern, with 1 in 5 individuals aged 18 exhibiting pre-diabetic status. This is higher than the prevalence in the United States which was 11.1% among adolescents and 15.8% among young adults (24, 30, 31). This underscores the importance of targeted preventive strategies, as early intervention during the pre-diabetic stage can significantly mitigate the risk of progression to diabetes. Over 12 years, approximately half of pre-diabetic individuals maintained their status, similar to Paprott et al, study (32). Only 14.1% of this study subjects transitioned to pre-diabetes from a normal glycemic state. These findings highlight the dynamic nature of pre-diabetes and the potential for intervention to influence disease trajectories(33, 34). Additionally, Olson et al, compared HA1C and oral glucose tolerance tests (OGTT) and the prevalence was underestimated with HBA1C, suggesting a limitation justifying future research (24). The drop in the prevalence of pre-DM later in life is probably due to progression to DM or reverting to normal glycemia. The former is more likely, as DM prevalence in relation to age has a similar trend. Also, the drop in the prevalence of DM later in life may be due to a higher-risk population getting older, replacing the older population. As older adults live more healthy lives, more physical activity, and healthier diets. Another possibility is increased mortality among diabetic but this is not confirmed by any study(34–36). Several limitations of our study must be acknowledged. Firstly, the study's retrospective design may introduce bias and might limit the generalizability of the findings. Additionally, the reliance on electronic health records for data collection may lead to incomplete or unstandardized information. Moreover, the study's focus on the Emirate of Abu Dhabi, UAE may limit its relevance for other populations with different demographic and socio-economic profiles. In conclusion, this study provides critical insights into the incidence and prediction of diabetes in the Emirate of Abu Dhabi, UAE, highlighting the urgent need for comprehensive public health interventions. By addressing the identified risk factors and leveraging predictive modeling, clinicians and policymakers can work towards stemming the tide of the diabetes epidemic and improving health outcomes in the region. However, further research is needed to validate our findings and explore additional factors contributing to diabetes risk and progression. Declarations Ethical approval and consent to participate: The study was approved by the AlAin Human Ethics Committee, approval number 13/58, and Ambulatory Healthcare Services IRB 19-2022. All methods were carried out under relevant guidelines and regulations. The authors confirm that the study was conducted in accordance with the Helsinki Declaration. Consent statement in the Ethics approval and consent to participate: Informed consent was waived by the IRBs as the study was designed for retrospective data gathered as part of patient care and anonymized at analysis. Competing interests: None. Funding: None. Authors' contributions: LBK and NN conceptualized and analyzed data. LBK, MS, RK, MD, NA wrote the manuscript; all other co-authors collected data and reviewed the manuscript. All authors have read and approved the final manuscript. Acknowledgments: None. Consent to publish: Not Applicable. Availability of data and materials: The data that support the findings of this study cannot be shared openly due to restrictions from the institution, Seha Clinics. Data availability is restricted due to institution policies. Latifa Baynouna Alketbi ( [email protected] ) to be contacted regarding this request. References Organization, W. H. The top 10 causes of death. (2020). 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Tables Table 1 Subjects characteristics distributed by sex Characteristic Male Female No. % No. % Age =80 12 0.3% 16 0.4% Total 4360 50.1% 4337 49.9% Education Level Illiterate 477 11.1% 816 18.9% Primary 449 10.4% 411 9.5% Intermediate 1650 38.3% 1290 29.9% Secondary 557 12.9% 354 8.2% University 1070 24.8% 1382 32.1% Post Grad 106 2.5% 58 1.3% Total 4309 49.99% 4311 50.01% Smoking Yes 945 21.7% 16 0.4% No HDL =1.29 1174 26.9% 2690 62% Total 4361 50.1% 4338 49.9% High Cholesterol level =7.2 99 2.3% 85 2% Total 4359 50.1% 4335 49.9% High cholesterol on treatment Yes 744 17.7% 588 13.7% CKD (eGFR) Yes 84 1.9% 61 1.4% No 4277 98.1% 4277 98.6% Hypertension Yes 743 17.2% 543 12.5% No 3565 82.8% 3795 87.5% Blood pressure No 4277 98.1% 4277 98.6% Normal 1196 27.4% 819 18.9% High normal 974 22.3% 465 10.7% Stage 1 649 14.9% 365 8.4% Stage 2 132 3% 52 1.2% Hypertension crisis 16 0.4% 7 0.2% Total 4361 50.1% 4338 49.9% Obesity (BMI ) Underweight 94 2.2% 166 3.8% Normal 1094 25.1% 1115 25.7% Overweight 1696 39% 1275 29.5% Class I obesity 980 22.5% 1039 24% Class II obesity 309 7.1% 469 10.8% Class III obesity 180 4.1% 271 6.3% Total 4353 50.1% 4335 49.9% Table 2: Determinants of progression to DM using Cox regression. B SE Wald df Sig. Exp(B) 95.0% CI for Exp(B Lower Upper Whole age 0.050 0.005 98.934 1 <0.001 1.051 1.041 1.062 HBA1C 1.938 0.146 177.339 1 <0.001 6.945 5.222 9.238 hdl -0.817 0.158 26.703 1 <0.001 0.442 0.324 0.602 GFR 0.015 0.004 13.368 1 <0.001 1.015 1.007 1.024 bmi1 0.019 0.007 6.579 1 0.010 1.019 1.005 1.034 DBP 0.014 0.004 9.573 1 0.002 1.014 1.005 1.022 Current Smok 0.346 0.139 6.180 1 0.013 1.413 1.076 1.855 RANDOM GLU 0.177 0.032 31.200 1 <0.001 1.194 1.122 1.271 Table 3 Baseline characteristics with significant association with changing the status of participants to pre-diabetes. Participants with no diabetes or pre-diabetes are included. B P value OR 95% C.I. DBP 0.01 0.002 1.01 1.004 1.016 Total Cholesterol 0.203 <.001 1.225 1.149 1.305 Random Blood Sugar 0.246 <.001 1.278 1.186 1.377 HDL -0.203 0.03 0.816 0.68 0.981 BMI 0.02 <.001 1.02 1.01 1.031 Age 0.036 <.001 1.037 1.032 1.043 Logistic regression Pre-DM at the end of follow-up is the dependent variable. Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Appendix2.png Appendix3.jpg Appendix4.png Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 May, 2025 Reviews received at journal 06 May, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Editor assigned by journal 03 Apr, 2025 Editor invited by journal 14 Mar, 2025 Submission checks completed at journal 13 Mar, 2025 First submitted to journal 12 Mar, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6214000","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441611513,"identity":"ea5f7296-b319-459d-b534-57a68f2eff17","order_by":0,"name":"Latifa Baynouna AlKetbi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Latifa","middleName":"Baynouna","lastName":"AlKetbi","suffix":""},{"id":441611514,"identity":"a0900b43-8721-46d0-ac1a-289caaa50d5c","order_by":1,"name":"Rudina AlKetbi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Rudina","middleName":"","lastName":"AlKetbi","suffix":""},{"id":441611515,"identity":"f7096b7e-c761-49a7-8d48-3676e01e9586","order_by":2,"name":"Mariam AlShamsi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLCCBwwHgCRj4wMgKcPAwEaElgSIlmYDBgYDHlK0MLBJEKVFt/34ww8Jf+7I6/Yfbqv42PaHh5+9LYHhR8U2nFrMzuQYSyS2PTPcdiOx7ebMNgMeyZ5jBxh7ztzGreVADoNEYsNhxm03GNtu8wK1GNxIb2AGsnFrOf/88Y+EP4ftt50/2FZMnJYbCWYSCWyHE7cdSGxjhmhJO0BAyxszi8S2w8lAvzRLzjhnDPJLwkG8fjmf/vjGhz+HbbedBwbdhzI5OWCIGT74UYFbC3ZwgET1o2AUjIJRMArQAABZHGDqUTsELwAAAABJRU5ErkJggg==","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":true,"prefix":"","firstName":"Mariam","middleName":"","lastName":"AlShamsi","suffix":""},{"id":441611516,"identity":"9f549add-2dac-486d-ad70-5eff2e370645","order_by":3,"name":"Nico Nagelkerke","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Nico","middleName":"","lastName":"Nagelkerke","suffix":""},{"id":441611517,"identity":"0d03e680-d33f-4351-8f95-4ca0152ded2e","order_by":4,"name":"Bachar Afandi","email":"","orcid":"","institution":"Tawam Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bachar","middleName":"","lastName":"Afandi","suffix":""},{"id":441611518,"identity":"eca630e4-a0b0-438a-9527-e41c29bf07ff","order_by":5,"name":"Muna AlDobaee","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Muna","middleName":"","lastName":"AlDobaee","suffix":""},{"id":441611519,"identity":"316f74b1-213b-4dda-a6e8-6476dfb7f79b","order_by":6,"name":"Mariam AlKuwaiti","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"","lastName":"AlKuwaiti","suffix":""},{"id":441611520,"identity":"110caf84-db35-4493-968e-184c0d7465e1","order_by":7,"name":"Mariam AlNeyadi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"","lastName":"AlNeyadi","suffix":""},{"id":441611521,"identity":"bcfa03e8-5c06-4b20-baa8-c136d255c3ba","order_by":8,"name":"Ahmed Humaid","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Humaid","suffix":""},{"id":441611522,"identity":"05e14ac0-49dc-42de-9002-6517a4da894a","order_by":9,"name":"Noura AlAlawi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Noura","middleName":"","lastName":"AlAlawi","suffix":""},{"id":441611523,"identity":"df2cf97f-f9b9-472e-b82e-ad2db2a0d554","order_by":10,"name":"Hamda Aleissaee","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Hamda","middleName":"","lastName":"Aleissaee","suffix":""},{"id":441611524,"identity":"4da32202-f1dd-4fae-8c8d-5d7286396790","order_by":11,"name":"Hanan Abdulbaqi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Hanan","middleName":"","lastName":"Abdulbaqi","suffix":""},{"id":441611525,"identity":"60e6566b-e403-4fee-8d4a-9eb66d296429","order_by":12,"name":"Toqa Fahmawee","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Toqa","middleName":"","lastName":"Fahmawee","suffix":""},{"id":441611526,"identity":"8864df36-6c00-40f8-b3c9-9ea9b4cf515f","order_by":13,"name":"Basil AlHashaikeh","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Basil","middleName":"","lastName":"AlHashaikeh","suffix":""},{"id":441611527,"identity":"9e9916f5-c521-4329-a042-977254331455","order_by":14,"name":"AlYazia AlAzeezi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"AlYazia","middleName":"","lastName":"AlAzeezi","suffix":""},{"id":441611528,"identity":"d3fa14cf-4a02-4571-a3e5-ff63e232917e","order_by":15,"name":"Fatima Shuaib","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Fatima","middleName":"","lastName":"Shuaib","suffix":""},{"id":441611529,"identity":"8d22a06e-7838-4dc4-a45c-3de8a25d1910","order_by":16,"name":"Esraa Mahmoud","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Esraa","middleName":"","lastName":"Mahmoud","suffix":""},{"id":441611530,"identity":"7d293591-4094-44f7-b17e-fd0138bc5712","order_by":17,"name":"Mohammed AlMansoori","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"AlMansoori","suffix":""},{"id":441611531,"identity":"2ffa6d28-1708-41ed-9605-cf8a94f20525","order_by":18,"name":"Ekram Saeed","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Ekram","middleName":"","lastName":"Saeed","suffix":""},{"id":441611532,"identity":"a22211a8-c22e-4516-b825-9cb73f83bf69","order_by":19,"name":"Ahmed AlHassani","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"AlHassani","suffix":""},{"id":441611533,"identity":"561bd989-a343-4416-beb1-6e757c9b846a","order_by":20,"name":"Farah AlFahmawi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Farah","middleName":"","lastName":"AlFahmawi","suffix":""},{"id":441611534,"identity":"46972f90-d40f-4053-b222-3d2951405dce","order_by":21,"name":"Alreem AlDhaheri","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Alreem","middleName":"","lastName":"AlDhaheri","suffix":""},{"id":441611535,"identity":"ee78bd96-98e7-4a8e-b086-7104a00dca65","order_by":22,"name":"Amira AlAhmadi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Amira","middleName":"","lastName":"AlAhmadi","suffix":""},{"id":441611536,"identity":"9741a27c-edda-4615-ac4a-b8493dd92e48","order_by":23,"name":"Nayla AlAhbabi","email":"","orcid":"","institution":"Abu Dhabi Healthcare Services, Seha Clinics","correspondingAuthor":false,"prefix":"","firstName":"Nayla","middleName":"","lastName":"AlAhbabi","suffix":""}],"badges":[],"createdAt":"2025-03-12 17:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6214000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6214000/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-07631-0","type":"published","date":"2025-07-02T15:58:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82077385,"identity":"9b6ff6f3-2e58-424d-9a32-55cb94b97c6c","added_by":"auto","created_at":"2025-05-06 14:01:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104710,"visible":true,"origin":"","legend":"\u003cp\u003eArea Under the Curve of the model developed to predenct development of new diabeted compared to using HA1C only or RBS only.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6214000/v1/143c8983cb0403fce8af9e3e.png"},{"id":82077392,"identity":"485d3224-3b3d-460c-93a5-2e559961f4aa","added_by":"auto","created_at":"2025-05-06 14:01:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54182,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot showing the cumulative prevalence of prediabetes and diabetes with age.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6214000/v1/9a007f527c9f9435a2095ea4.jpg"},{"id":86179885,"identity":"0282ca25-dbe7-4ff1-ad1d-4bc7c107c868","added_by":"auto","created_at":"2025-07-07 16:20:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":972380,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6214000/v1/81c2f670-2ba0-49b7-a3ea-370f5da31cb5.pdf"},{"id":82078879,"identity":"b0577532-e902-43a0-b0ad-eac40e3ece87","added_by":"auto","created_at":"2025-05-06 14:09:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16465,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6214000/v1/0f7f96e2498b953b31269e56.docx"},{"id":82077383,"identity":"477b8f1d-8f97-4dfb-ba3e-066bdadbd4f0","added_by":"auto","created_at":"2025-05-06 14:01:52","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":62255,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2.png","url":"https://assets-eu.researchsquare.com/files/rs-6214000/v1/426b1495c28d260002c66d35.png"},{"id":82077387,"identity":"f55dc771-8ce0-4221-87ca-60bc8d9516f4","added_by":"auto","created_at":"2025-05-06 14:01:52","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":54182,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6214000/v1/9211bccc1131ccb28a321ede.jpg"},{"id":82077386,"identity":"fb6baacd-ce30-4946-afd4-3f6c2648cea6","added_by":"auto","created_at":"2025-05-06 14:01:52","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":54566,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix4.png","url":"https://assets-eu.researchsquare.com/files/rs-6214000/v1/fddb4b4cb3a3d58b4d9e23dd.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factors of Diabetes Incidence: A Retrospective Cohort Study in Abu Dhabi","fulltext":[{"header":"Background","content":"\u003cp\u003eThe prevalence of diabetes mellitus among United Arab Emirates nationals is among the highest in the world (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The latest reported prevalence was 21% among males and 23% among females, with a similarly high prevalence of its main risk factors, such as obesity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This significant health burden contributes to mortality, quality of life, and healthcare utilization (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Factors affecting its prevalence in different countries and ethnicities are important to assess(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Such factors include healthcare system preparedness and responses toward prevention and management, aging, urbanization, culture, and physical inactivity (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, studies on variation in diabetes epidemiology are crucial, especially Cohort studies. Especially strong evidence exists that diabetes mellitus can be prevented or delayed. Healthier Lifestyles and the use of pharmacological interventions such as metformin reduce the rate of progression to type 2 diabetes in people with impaired glucose tolerance, with lifestyle interventions being as effective as pharmacological treatment. Such interventions target multiple risk factors, but once stopped, the effects are not sustained (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Therefore, risk assessment is crucial to identify individuals with higher risks of diabetes for the initiation of suitable and effective interventions and to be monitored as well. Risk scores developed from research-based prediction models are key in assessing and identifying such patients(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) \u003csup\u003e(10)\u003c/sup\u003e. They as well guide policy decisions in diabetes surveillance and prevention to decrease the progression of diabetes to complications, disability, and mortality.\u003c/p\u003e \u003cp\u003eStudies on the incidence of diabetes mellitus type 2 in Abu-Dhabi are rare despite diabetes having a relatively good focus of research on diabetes in the country (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The United Arab Emirates is a rapidly developing country with a well-resourced healthcare system (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Strategies for the early detection and management of chronic diseases on a large scale have been implemented for many years. For example, the cardiovascular screening program that started in 2008, Weqaya, could have one of the best screening coverages in the world and was designed to assess cardiovascular risk factors among the Abu Dhabi population. This study is part of a large retrospective cohort study, which includes a sample of the Weqaya screening participants from 2011 to 2013. Diabetes-free Abu Dhabi cardiovascular screening program participants were retrospectively evaluated for the incidence of diabetes, prediabetes, and risk factors studied.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a retrospective cohort study in the Emirate of Abu Dhabi, United Arab Emirates. The participants were from Weqaya, a national screening program in Abu Dhabi\u0026nbsp;(3). They were UAE nationals aged 18 years and older eligible for their government\u0026rsquo;s free comprehensive health insurance plan.\u003c/p\u003e\n\u003cp\u003eData collected at baseline included demographic data, self-reported health indicators, including smoking status, physical activity, preexisting CVD (angina, heart attack, transient ischemic attack, stroke, other circulatory disorder), family history of premature cardiovascular disease (a first-degree relative with a heart attack or stroke before the age of 50 years), history of cardiovascular risk factors for diabetes, hypertension, and dyslipidemia and whether participants were taking medication for these conditions. Anthropometric measures included waist and hip circumference, body mass index (BMI in kg/m2), and a single arterial blood pressure reading. A digital automatic blood pressure monitor measured blood pressure from the left arm with the patient relaxed and seated. Hematological parameters included non-fasting glucose (mmol/L), total cholesterol, high-density lipoprotein (HDL) cholesterol (mmol/L), glycosylated hemoglobin (HbA1c), vitamin D, and creatinine. Some subjects\u0026apos; data were missing, so only subjects with completed data were included. The glomerular filtration rate was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt baseline, 8699 subjects in the national cardiovascular screening program of 2011-2013, with an average follow-up of 9.2 years, were eligible for enrollment if they were non-diabetic. Diabetes was identified as HbA1c of \u0026gt;_6.4%, a documented diabetes diagnosis or ICD code. Participants were assessed retrospectively in 2023 for health outcomes. \u0026nbsp; The pre-diabetic definition was an HbA1c of \u0026lt;5.7%. \u0026nbsp;Age-adjusted prevalence for age-specific comparisons between countries utilized the population percentage in each 5-year age group in the new WHO World Standard population, based on the expected evolution of the world\u0026rsquo;s population age structure over the first quarter of the 21st century\u0026nbsp;(14)\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eAll participants had their eGFR determined. The method to determine the age-specific percentiles for the estimated glomerular filtration rate was the LMS method (15).\u0026nbsp;The resultant age and sex-specific GFR percentiles were derived from this cohort after excluding subjects with comorbidities such as diabetes, hypertension, coronary heart disease,\u0026nbsp;stroke, and cancer. Subjects were classified in appropriate eGFR percentiles according to age, sex, and eGFR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrediction models for diabetes were developed using Cox survival regression for progression to diabetes as an endpoint. Patients were censored at the time of death or their last HbA1c measurement if \u0026lt;6.4%. Potential predictors included age, RBS, BMI, Curren smoking, Sex, HBA1C, Mean BP, eGFR, HDL, and HTN. \u0026nbsp;(Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC curves and C-statistics were used to evaluate the performance of the Cox-derived risk scores at the end of the follow-up\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe prevalence of DM at baseline was 22.2%, 18.5% in females and 25.8% in males. The HbA1c was ordered based on routine care according to the physician\u0026apos;s decision and the patient\u0026apos;s agreement. Among the 6772 diabetes-free subjects, 3537 females and 3235 males, 643 developed new diabetes over the follow-up years, giving an overall incidence of \u0026nbsp;7.4%, 317 (7.3%) females, and 326 (7.5%) males over the 12-year follow-up period. The incidence of DM fluctuated over the years of follow-up Appendix 1. \u0026nbsp;Over the years of follow-up, the prevalence increased to 28.5%, 25.3% in females, and 31.9% among males. The overall age-standardized prevalence increased from 18% to 23.7%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe participant\u0026apos;s demographics at baseline are shown in Table 1. Smoking was less common (0.7%) among females than (20.9%) males. \u0026nbsp;65.5% of females had an HDL of more than 1.29 compared to 28.5% of males. Concerning cardiovascular health, 6.5% of males and 4.9% of females were under lipid-lowering therapy, whereas 38.4% of males and 33.6% of females had cholesterol levels more than 5.2. There were (36.0% and 28.8%) of males with optimum and normal blood pressure, respectively, compared to (66.8% and 18.0%) of females. Hypertension had been diagnosed in 11.3% of males compared to 6.7% of females. Obesity was slightly more prevalent among females, with 20.8% having class I (30-34.9)\u0026nbsp;(16)obesity, 9.5% having class II (35-39.9), and 5.3% being class III (\u0026gt;40), compared to 20.2%, 6.6%, and 3.7%, respectively, among males.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHbA1C is the main determinant of new DM, with a nearly seven-fold increase in risk for each one-unit rise in HbA1C. A one-unit increase in Random glucose RBS had a 1.2% increase in the risk of DM, while the hazard of developing DM decreased by 0.403 for each one-unit rise in HDL. \u0026nbsp; From multivariate Cox regression, for each one-year increase in age, the risk of DM increases by 1.03%, adjusted for other risk factors. Appendix 2\u003c/p\u003e\n\u003cp\u003eDifferent percentiles of eGFR have varying effects, with lower percentiles showing a risk of developing DM comparable with the highest percentile, the 97\u003csup\u003eth\u003c/sup\u003e percentile. Smokers have a 1.425% increased risk, and obesity is a significant risk factor, with a 1.017% increase in risk with each one-unit increase in BMI. Mean BP is another significant risk factor, with a 1.009% increase in risk with each one-unit mmHg increase in Mean BP. However, the hypertension was not statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DM hazard ratio steadily increases with increasing HbA1C. At the pre-DM cutoff level of 5.7, the HR is 10, doubles at 6, and triples at 6.5, the chosen cutoff point for DM, as shown in Appendix 3. Finally, although higher BMI was associated with a higher risk of progression to DM, as per Appendix 4, subjects with normal BMI and overweight also have high levels of HBA1C.\u003c/p\u003e\n\u003cp\u003eThe model developed showed good performance with a c-statistics of 0.837 (0.818-0.856). The ROC is shown in Figure 1 with a cutoff point identified for the hazard ratio of 2.06, it has a sensitivity of 75.1%, and specificity of 78.1%. HA1C alone as a predictor of DM performance was lower than the model but close to it, with a c statistic of 0.784 (0.761-0.807). The hazard ratio\u0026apos;s cutoff for HBA1C identified by the model is 5.650, with a sensitivity of 68.6% and specificity of 73.2%. In comparison, the c-statistics for using only random blood sugar as a predictor was 0.698 (0.672-0.723).\u003c/p\u003e\n\u003cp\u003eWith regard to pre-DM, among non-diabetic patients, 804 did not do the HA1C after screening, or it is not documented (17.7%), 803 in the non-pre-DM group. Based on the latest test, the mean HBA1C in the pre-Dm group was 5.9, SD 0.2 (min 4.9 max 6.4), and in the non-pre-DM range group, was 5.2, SD =0.21 (min 2.8 max 5.7) among those with no pre-DM at screening, 14.45%. Interestingly, almost half, 44.1%, of those with pre-DM at screening reverted to normal values. Determinants of developing pre-dm were higher screening DBP, total cholesterol, RBS, BMI, and age, as well as lower levels of HDL Table 3. Gender and smoking status were not significant determinants for the diagnosis of prediabetes during follow-up.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 is a scatter plot showing the cumulative prevalence of prediabetes and diabetes with age. It is increasing steadily, with a plateau reached at 40 years of age in the case of pre-DM and 60 with DM. After the plateau, there is a decline with increasing age.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first longitudinal population-representative examination of diabetes incidence in an area considered among the highest in prevalence. Our estimate of prevalence for 2011 is close to the International Diabetes Federation\u0026apos;s (IDF) estimate of 18.8% in 2011 for the UAE\u0026nbsp;(17). With an increase in age-standardized prevalence from 18.8% to 23.7 %, the problem of DM is worsening, underscoring the urgent need for comprehensive public health interventions in the region. The incidence is close to previously reported study in Abu Dhabi\u0026nbsp;(11). By taking proactive steps to address the underlying determinants of diabetes, it will be possible to mitigate its impact on population health and improve outcomes for individuals living with or at risk of developing the disease.\u003c/p\u003e\n\u003cp\u003eThe model of prediction of progression from non-DM to DM derived from this study was excellent. The high c-statistic value suggests that our model has excellent discriminatory power in distinguishing between individuals who will develop diabetes mellitus (DM) and those who will not. This level of accuracy is particularly significant in the context of diabetes, where early identification of at-risk individuals is crucial for targeting preventive measures.\u003c/p\u003e\n\u003cp\u003eThis study model\u0026apos;s ability to identify a broader range of significant risk factors compared to previous studies may enhance its utility in clinical practice\u0026nbsp;(18, 19). Notably, it should facilitate a holistic approach to tailor interventions and preventive strategies based on each patient\u0026apos;s risk factors. Such a multidimensional approach not only enhances the accuracy of diabetes prediction but also provides valuable insights into the complex interplay of factors contributing to disease development.\u003c/p\u003e\n\u003cp\u003eNot surprisingly, advancing age emerged as a significant predictor of diabetes progression, with a 5.1% increase in risk for each one-year increase. Among potentially modifiable risk factors, higher levels of HbA1c, lower levels of HDL, higher BMI, higher DBP, and higher RBS were also associated with increased diabetes risk. These identified risk factors could be targeted through early intervention, focusing on lifestyle choices, including physical activity and dietary choices (20, 21). Recent studies have suggested that high HDL levels decrease insulin resistance\u003csup\u003e16\u003c/sup\u003e. In addition, HDL is known to have anti-inflammatory properties, which may reduce chronic inflammation, which is often thought to lead to insulin resistance (22).\u003c/p\u003e\n\u003cp\u003eThe high predictive value of HbA1c alone highlights the extent to which DM constitutes a chronic progressive disorder. It also highlights how to monitor this progression, as measuring HBA1c is a valid, simple test with minimal day-to-day fluctuation. It obviates a requirement for prior fasting and has superior predictive value compared to random blood glucose (RBG)\u0026nbsp;(23) (24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother risk factor is random blood glucose (RBG). A single RBG \u0026ge; 100 mg/dL was suggested as more strongly associated with undiagnosed diabetes, similar to this study. In fact, the cutoff value for the best sensitivity and specificity to detect DM in this study was 98, similar to their result supporting the use of abnormal RBG values as a risk factor for diabetes that should be considered in screening guidelines (25).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith regard to blood pressure, in this study, we found that high DBP carries a much higher risk of developing diabetes than SBP. \u0026nbsp;In other studies, both SBP and DBP were linked to DM progression (26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSmoking increases the risk of DM by altering the body composition, insulin sensitivity, and pancreatic \u0026beta; cell function (27). This should add another item to the long list of arguments for healthcare professionals to address smoking prevention and cessation in their patients. Furthermore, onsite at younger age was found to be associated with increased \u0026nbsp;risk of worse cardiovascular outcome\u0026nbsp;(28). This should urge healthcare professionals to focus on the younger age group for prevention and management options, as effective strategies already exist. A higher level of\u0026nbsp;eGFR is surprisingly a predictor of DM. This may suggest early renal complications of hyperglycemia causing hyperfiltration (29). Several findings point to the pathogenic and prognostic significance of glomerular hyperfiltration in the development and course of diabetic kidney diseases (29). No study identified a higher eGFR percentile as a risk factor for diabetes, and this study is the first to show such a relation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe high prevalence of pre-diabetes, particularly among younger age groups, is a significant concern, with 1 in 5 individuals aged 18 exhibiting pre-diabetic status. This is higher than the prevalence in the United States which was 11.1% among adolescents and 15.8% among young adults\u0026nbsp;(24, 30, 31). This underscores the importance of targeted preventive strategies, as early intervention during the pre-diabetic stage can significantly mitigate the risk of progression to diabetes.\u0026nbsp;Over 12 years, approximately half of pre-diabetic individuals maintained their status, similar to Paprott et al, study\u0026nbsp;(32). Only 14.1% of this study subjects transitioned to pre-diabetes from a normal glycemic state. These findings highlight the dynamic nature of pre-diabetes and the potential for intervention to influence disease trajectories(33, 34).\u0026nbsp;Additionally, Olson et al, compared HA1C and\u0026nbsp;oral glucose tolerance tests (OGTT) and the prevalence was underestimated with HBA1C, suggesting a limitation justifying future research (24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe drop in the prevalence of pre-DM later in life is probably due to progression to DM or reverting to normal glycemia. The former is more likely, as DM prevalence in relation to age has a similar trend. Also, the drop in the prevalence of DM later in life may be due to a higher-risk population getting older, replacing the older population. As older adults live more healthy lives, more physical activity, and healthier diets. Another possibility is increased mortality among diabetic but this is not confirmed by any study(34\u0026ndash;36). Several limitations of our study must be acknowledged. Firstly, the study\u0026apos;s retrospective design may introduce bias and might limit the generalizability of the findings. Additionally, the reliance on electronic health records for data collection may lead to incomplete or unstandardized information. Moreover, the study\u0026apos;s focus on the Emirate of Abu Dhabi, UAE may limit its relevance for other populations with different demographic and socio-economic profiles.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study provides critical insights into the incidence and prediction of diabetes in the Emirate of Abu Dhabi, UAE, highlighting the urgent need for comprehensive public health interventions. By addressing the identified risk factors and leveraging predictive modeling, clinicians and policymakers can work towards stemming the tide of the diabetes epidemic and improving health outcomes in the region. However, further research is needed to validate our findings and explore additional factors contributing to diabetes risk and progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the AlAin Human Ethics Committee, approval number 13/58, and Ambulatory Healthcare Services IRB 19-2022. All methods were carried out under relevant guidelines and regulations. The authors confirm that the study was conducted in accordance with the Helsinki Declaration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent statement in the Ethics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was waived by the IRBs as the study was designed for retrospective data gathered as part of patient care and anonymized at analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone.\u003cbr\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNone.\u003cbr\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eLBK and NN conceptualized and analyzed data. LBK, MS, RK, MD, NA wrote the manuscript; all other co-authors collected data and reviewed the manuscript. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eNone.\u003cbr\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eNot Applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study cannot be shared openly due to restrictions from the institution, Seha Clinics. Data availability is restricted due to institution policies. Latifa Baynouna Alketbi ([email protected]) to be contacted regarding this request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOrganization, W. H. The top 10 causes of death. (2020). 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Lifestyle and Progression to Type 2 Diabetes in a Cohort of Workers with Prediabetes. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1538 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRooney, M. R. et al. Risk of Progression to Diabetes Among Older Adults With Prediabetes. \u003cem\u003eJAMA Intern. Med.\u003c/em\u003e \u003cb\u003e181\u003c/b\u003e, 511\u0026ndash;519 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoyama, A. K. et al. Progression to Diabetes Among Older Adults With Hemoglobin A1c-Defined Prediabetes in the US. \u003cem\u003eJAMA Netw. Open.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, e228158 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeronese, N. et al. Risk of progression to diabetes and mortality in older people with prediabetes: The English longitudinal study on ageing. \u003cem\u003eAge Ageing\u003c/em\u003e. \u003cb\u003e51\u003c/b\u003e, afab222 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Subjects characteristics distributed by sex\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"LTR\"\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp dir=\"LTR\"\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp dir=\"LTR\"\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e30.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e34.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e23.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e26.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e16.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e18.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e14.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026gt;=80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e50.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e49.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e11.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e18.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e10.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e9.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e38.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e29.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e12.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e8.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e24.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e32.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003ePost Grad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e49.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e50.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e21.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 398px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.91 \u0026ndash; 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e45.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e21.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.16 \u0026ndash; 1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e12.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026gt;=1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e26.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e50.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e49.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eHigh Cholesterol level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e29.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e25.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4.14-5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e35.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e41.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.2-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e23.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e24.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.2-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e7.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026gt;=7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e50.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e49.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eHigh cholesterol on treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e17.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eCKD \u0026nbsp;(eGFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e98.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e98.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e17.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e82.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eBlood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e98.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e98.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e27.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e18.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eHigh normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e22.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eStage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e14.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e8.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eHypertension crisis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e50.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e49.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003eObesity (BMI )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e25.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e25.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e29.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eClass I obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e22.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eClass II obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e10.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eClass III obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp dir=\"LTR\"\u003e50.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp dir=\"LTR\"\u003e49.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Determinants of progression to DM using Cox regression.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003eExp(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp dir=\"LTR\"\u003e95.0% CI for Exp(B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003eWhole age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e98.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003eHBA1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e177.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e9.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003ehdl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e26.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e13.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003ebmi1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e9.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003eCurrent Smok\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp dir=\"LTR\"\u003eRANDOM GLU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e31.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 Baseline characteristics with significant association with changing the status of participants to pre-diabetes. Participants with no diabetes or pre-diabetes are included.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e95% C.I.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003eTotal Cholesterol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003eRandom Blood Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLogistic regression Pre-DM at the end of follow-up is the dependent variable.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Incidence, Prediction Model, Cohost study, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-6214000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6214000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiabetes mellitus (DM) is a global health burden. Monitoring its determinants and incidence trends is important for identifying risk factors and projecting future health service needs.\u003c/p\u003e\n\u003cp\u003eMethod: The Abu Dhabi Risk Study (ADRS ) is a retrospective cohort study in Abu Dhabi, United Arab Emirates (UAE). Diabetes-free participants were followed for an average of 9.2 years for the development of new diabetes. Cox regression was used to develop a prediction model and identify significant determinants.\u003c/p\u003e\n\u003cp\u003eResults: Over the 12-year follow-up period, 643 individuals developed new diabetes, with an overall incidence of 7.4%. The prevalence of DM increased to 28.5% in males, 25.3% in females, and 31.9% among males. Significant risk factors for developing new diabetes were a higher level of HbA1C, current smoking status at screening, and a higher level of eGFR. The model developed showed good performance in predicting new diabetes with a c-statistics of 0.837 (0.818-0.856), a sensitivity of 75.1%, and a specificity of 78.1%. Determinants of developing pre-DM included higher Diastolic Blood Pressure (DBP), total cholesterol, Random Blood Sugar (RBS), Body Mass Index (BMI), age, and lower High-Density Lipoprotein (HDL) levels. Gender and smoking status were not significant determinants for the diagnosis of prediabetes. The cumulative prevalence of prediabetes and diabetes is increasing steadily, with a plateau reached at 40 in the case of pre-DM and 60 with DM, and a decline with increasing age.\u003c/p\u003e\n\u003cp\u003eConclusion:The prevalence of diabetes in Abu Dhabi remains high. The Derived model is valuable for informing clinical practice and preventing diabetes.\u003c/p\u003e","manuscriptTitle":"Risk Factors of Diabetes Incidence: A Retrospective Cohort Study in Abu Dhabi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 14:01:47","doi":"10.21203/rs.3.rs-6214000/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-07T07:00:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-06T11:33:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105226888913458905244119331486706253252","date":"2025-04-11T09:55:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T09:54:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273806288936437035602302710763875595321","date":"2025-04-03T19:24:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T13:56:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T09:25:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-14T11:38:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-13T15:33:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-12T16:59:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e94bcdc3-1c0f-4f1c-9add-a569c78d7328","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47023053,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes"},{"id":47023054,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-07-07T16:12:47+00:00","versionOfRecord":{"articleIdentity":"rs-6214000","link":"https://doi.org/10.1038/s41598-025-07631-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-02 15:58:23","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-05-06 14:01:47","video":"","vorDoi":"10.1038/s41598-025-07631-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-07631-0","workflowStages":[]},"version":"v1","identity":"rs-6214000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6214000","identity":"rs-6214000","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}