Risk factors associated with Indian Type 2 diabetes patients with chronic kidney disease: CITE study, A Cross-sectional, Real-world, Observational Study

Risk factors associated with Indian Type 2 diabetes patients with chronic kidney disease: CITE study, A Cross-sectional, Real-world, Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk factors associated with Indian Type 2 diabetes patients with chronic kidney disease: CITE study, A Cross-sectional, Real-world, Observational Study A Kumar, A Mazumdar, AK Bhattacharjee, A Gupta, A Dasgupta, B Sinha, and 23 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5919300/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 May, 2025 Read the published version in BMC Nephrology → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Type 2 diabetes (T2D) is the leading cause of chronic kidney disease (CKD) worldwide. Identifying clinical and laboratory associations with CKD in T2D can assist physicians in targeting modifiable risk factors. In light of limited data from India, the CITE (CKD in Indian T2D Evaluation) study was conducted. Methods: The multicenter, cross-sectional CITE study included 3,325 patients from 28 centres across India over three months. CKD was defined as a persistent decline in kidney function (eGFR < 60 ml/min for ≥ 3 months) or an elevated urine albumin-creatinine ratio (UACR) in at least two samples. Descriptive statistics summarised patient characteristics, while logistic regression analyses identified significant risk factors for CKD. Results: The prevalence of CKD in T2D was 34%, with a median patient age of 59.9 years and 60.72% having a T2D duration of more than 10 years. Reduced eGFR (< 60 ml/min) was significantly associated with age ≥ 60 years (OR: 2.46, 95% CI 2.11–2.87, P < 0.001), T2D duration of more than 10 years (OR: 2.27, 95% CI 1.76–2.92, P < 0.001), HbA1c (OR: 1.04, 95% CI 1.00–1.08, P = 0.03), and SBP (OR: 1.005, 95% CI 1.002–1.009, P 300 mg/g) was linked to a non-vegetarian diet (OR: 1.95, 95% CI 1.59–2.39, P < 0.001) and tobacco use (OR: 1.38, 95% CI 1.14–1.65, P = 0.001). CKD also increased the odds of comorbidities. Conclusion: The CITE study highlights the prevalence and risk factors of CKD in Indian T2D patients. Longitudinal studies are needed to evaluate these associations further. Figures Figure 1 Figure 2 Figure 3 1.0 Introduction Type 2 diabetes (T2D) mellitus is one of the most common chronic diseases, accounting for significant global morbidity and mortality. The global prevalence of T2D in 2021 was estimated at 6.1% (529 million patients), with an additional 9.1% having impaired glucose tolerance and 5.8% having impaired fasting glucose. This equates to a total of 762 million individuals with pre-diabetes who are likely to contribute to the existing disease burden. (1,2) Chronic kidney disease (CKD) is the most common comorbidity associated with T2D, with a prevalence ranging between 20% and 30%. (3) The development of CKD in the context of T2D can lead to end-stage kidney disease (ESKD), renal failure, and cardiovascular death in the long term. (4) In Asia, the prevalence of CKD is estimated to be higher (34%) due to the greater prevalence of metabolic disorders. (5) The START-INDIA multi-centric study estimates CKD prevalence to exceed 40%, whereas a single-centre cross-sectional study from New Delhi, India, reports a prevalence of 34.4%. (6,7) Increasing age, advanced duration of diabetes, smoking, obesity, and hypertension have been identified as associated risk factors in a meta-analysis of global data. (8) However, limited and heterogeneous data from India suggest advanced age, high body mass index (BMI), and duration of diabetes as significant predictors, with no association observed with glycemic control. (9) The heterogeneity in prevalence data globally and within India could be attributed to the diagnostic criteria used for CKD, which often involved an estimated glomerular filtration rate (eGFR) of less than 60 ml/min without accounting for the urine albumin-to-creatinine ratio (UACR). In light of these discrepancies, the CITE study was initiated in India to identify CKD in T2D patients using the American Diabetes Association (ADA) and Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines. These guidelines define CKD as a UACR > 30 mg/g in at least two out of three readings and/or an eGFR < 60 ml/min persistent for three months. (10) This cross-sectional analysis is the first in India to identify significant clinical and laboratory risk associations related to CKD in patients with T2D. 2.0 Materials and Methods 2.1 Study Population T2D patients aged over 18 years with established CKD were included in the analysis. The study aimed to assess the prevalence of CKD in T2D patients and to identify clinically relevant attributes associated with CKD. Data were collected prospectively from 28 diabetes and endocrinology clinics across India over three months (15th December to 15th March). Patients with missing data (those lacking two out of three abnormal UACR values or an eGFR < 60 ml/min persistent for ≥ 3 months), patients on renal replacement therapy, those with non-diabetic kidney disease (NDKD), and those with acute renal insufficiency were excluded. The Park Clinic Ethics Committee approved the study protocol on 23rd December 2023 and was registered with the Open Science Framework (OSF) Registries ( https://doi.org/10.17605/OSF.IO/BRF6U ). 2.2 Data Collection and Evaluation T2D patients visiting the clinics were screened for CKD using UACR (spot sample) and eGFR (calculated using the CKD-EPI method), which aligns with the ADA and KDQOI recommendations. Persistently elevated UACR and eGFR values and relevant baseline history, demographic parameters, laboratory parameters, and medication history were recorded and entered into a pre-approved Excel sheet. A steering committee defined the data entry parameters, considering global recommendations and regional sensitivities. Relevant recorded history included hypertension, dyslipidaemia, diabetes duration, dietary habits, tobacco usage, current glycaemic status, and associated comorbidities such as glycated haemoglobin (HbA1c), atherosclerotic cardiovascular disease (ASCVD), heart failure, peripheral vascular disease, retinopathy, and diabetic neuropathy. CKD in T2D was confirmed using a UACR > 30 mg/g in at least two out of three readings and/or an eGFR 300 mg/g). Urine samples with significant pus cells or red blood cells, indicative of urinary tract infections or haematuria, respectively, were excluded. 2.3 Statistical Analysis The analysis used DATAtab (Online Statistics Calculator, DATAtab e.U., Graz, Austria) and R Studio (R Core Team, 2024, R version 4.2.3). Descriptive statistics were expressed as percentages for categorical variables and mean or median with standard deviation or interquartile range (IQR) for continuous variables. The decision to report the mean or median was based on the data distribution, which was assessed using the Kolmogorov-Smirnov test. Analytical statistics were conducted using UACR, eGFR, and a combination of both as dependent variables. Multinomial logistic regression analysis was performed for different eGFR categories and CKD status (UACR > 30 mg/g + eGFR < 60 ml/min), while binomial logistic regression was conducted for eGFR. The regression model significance was assessed using the chi-squared statistic, with a significance level set at p < 0.05 (95% confidence interval). 3.0 Results A total of 10,915 T2D patients were screened, of whom 3,493 were diagnosed with CKD based on the pre-specified diagnostic criteria. After excluding patients with missing data, 3,325 data points (4.8% dropout) were included in the final analysis. The overall prevalence of CKD in patients with T2D was 32%. The median age of the patients was 59.9 (12.1) years, with 64.6% being male. Among the cohort, 60.72% had a T2D duration exceeding 10 years, 77.62% were non-vegetarians, and 79.79% used tobacco. The median BMI was 25.78 (4.98) kg/m², median HbA1c was 7.7% (2.3), median systolic blood pressure (SBP) was 138.4 (26) mmHg, and median diastolic blood pressure (DBP) was 80 (14) mmHg. Obesity, defined as a BMI ≥ 25 kg/m², was observed in 57.74% of patients, with only 31.46% achieving an HbA1c < 7.0%. Additionally, 73.86% of CKD patients with T2D had a history of hypertension (HT), although only 41.08% achieved the target blood pressure of < 130/80 mmHg (Table 1 ). Table 1 Baseline characteristics of T2D patients with CKD. Categorical Variable n(%) Continuous variable Median IQR Age ≥60 years 1906(57.32) UACR (mg/g) 115.7 269.81 Male 2148(64.6) eGFR 65 47 T2D duration > 10 years 2019(60.72) HBA1c (%) 7.7 2.3 Non-vegetarian diet 2581(77.62) BMI (kg/m 2 ) 25.78 4.98 HBA1c < 7% 1046(31.46) SBP (mm of Hg) 138.4 26 BMI≥25 kg/m 2 1920(57.74) DBP (mm of Hg) 80 14 BMI 23.0-24.9 kg/m 2 668(20.09) Tobacco use (Yes) 672(20.21) Hypertension 2456(73.86) ASCVD 653(19.64) HF 167(5.02) Retinopathy 384(11.54) Neuropathy 649(19.52) Within CKD categories, 62.71% had microalbuminuria, 28.75% had macroalbuminuria, and 45.35% had an eGFR < 60 ml/min. Among patients with microalbuminuria, 45.02% had an eGFR ≥ 60 ml/min, whereas only 9.62% had an eGFR ≥ 60 ml/min. A minority (8.54%) of CKD patients had normoalbuminuria (Fig. 1 ). Furthermore, 36.81% of patients had eGFR 30 mg/g. 3.1 Binary Logistic Regression Analysis: eGFR Categories and Associated Clinical Attributes The continuous variables systolic blood pressure (SBP), diastolic blood pressure (DBP), and glycated haemoglobin (HbA1c), along with the categorical variables hypertension status and HbA1c categories ( 5). As a result, these confounding variables were excluded where applicable. The analysis revealed significantly increased odds of having an eGFR < 60 ml/min in patients aged ≥ 60 years (odds ratio [OR]: 2.46, 95% confidence interval [CI] 2.11–2.87, P < 0.001) and in those with a T2D duration of more than 10 years (OR: 2.27, 95% CI 1.76–2.92, P < 0.001). (Appendix supplementary table 1) Additionally, each 1% increase in HbA1c was associated with a 4% higher odds of eGFR < 60 ml/min (95% CI 1.00–1.08, P = 0.03), and each mmHg increase in SBP was linked to a 0.5% increased odds of worsening renal function (95% CI 1.002–1.009, P < 0.001). (Fig. 2 ) The regression model was statistically significant (chi-squared statistic = 310.5, degrees of freedom = 5, P 300 mg/g) associated with age ≥ 60 years (odds ratio [OR]: 1.44, 95% confidence interval [CI] 1.23–1.68, P < 0.001), a non-vegetarian diet (OR: 1.95, 95% CI 1.59–2.39, P < 0.001), tobacco use (OR: 1.38, 95% CI 1.14–1.65, P = 0.001), and HbA1c ≥ 7% (OR: 1.31, 95% CI 1.10–1.55, P = 0.002). (Appendix supplementary table 2) Additionally, for each mmHg increase in SBP, there was a 1% increase in the odds of macroalbuminuria (95% CI 1.006–1.014, P < 0.001). The regression model was statistically significant (chi-squared statistic = 113.96, degrees of freedom [df] = 5, P 5 years was significantly associated (OR: 1.42, 95% CI 1.09–1.84, P = 0.009). Clinical and laboratory variables strongly correlating with CKD, defined as a combination of eGFR 30 mg/g, included age ≥ 60 years, female gender, a T2D duration of > 10 years, tobacco use, HbA1c, and SBP. This regression model was also significant (chi-squared statistic = 203.6, df = 7, P < 0.001). 3.3 Multinomial and Binary Logistic Regression Analysis: Comorbidities and Associated Clinical Attributes Binary logistic regression was conducted using eGFR as the dependent variable. For every 1 ml/min reduction in eGFR below 60 ml/min, there was a significant increase in the odds of retinopathy (OR: 2.40, 95% CI 1.92–2.99, P < 0.001), neuropathy (OR: 1.54, 95% CI 1.30–1.83, P < 0.001), atherosclerotic cardiovascular disease (ASCVD) (OR: 1.95, 95% CI 1.64–2.32, P < 0.001), and heart failure (HF) (OR: 5.07, 95% CI 3.45–7.44, P < 0.001). The regression model was statistically significant (chi-squared statistic = 86.82, df = 4, P < 0.001). (Fig. 3 ) Multinomial logistic regression was conducted using UACR as the dependent variable. In the presence of macroalbuminuria, significant associations were found with ASCVD (OR: 1.27, 95% CI 1.05–1.54, P = 0.014), HF (OR: 1.85, 95% CI 1.34–2.61, P < 0.001), and retinopathy (OR: 2.14, 95% CI 1.71–2.68, P < 0.001). No significant association was observed with neuropathy. The regression model was statistically significant (chi-squared statistic = 85.53, df = 4, P < 0.001). (Fig. 3 ) No significant associations were identified between comorbidities and microalbuminuria (UACR 30–300 mg/g). 4.0 Discussion 4.1 Global Prevalence Data and Risk Associations. Type 2 diabetes (T2D) is the most common metabolic disorder, responsible for significant health morbidity and mortality. According to global estimates, the prevalence of macrovascular complications is approximately 32.2%, with atherosclerotic cardiovascular disease (ASCVD) accounting for 21.2%. (11) Heart failure (HF) prevalence ranges between 19% and 26%, while retinopathy and neuropathy prevalence stand at 27% and 26.71%, respectively. (12,13,14) The global prevalence of chronic kidney disease (CKD) in T2D is around 25%, further amplifying the burden of T2D-associated comorbidities. (15) Key factors associated with CKD in T2D include advanced age, smoking, hypertension, obesity, and ASCVD. (16) A cross-sectional study from Thailand identified advanced age, uncontrolled diabetes, retinopathy, and elevated uric acid levels as significant risk factors. (17) Similarly, data from China and Palestine reported significant associations with advanced age, hypertension, T2D duration, comorbidities, and poor glycemic control. (18,19) 4.2 Indian Prevalence Data and Risk Associations. The START-INDIA study primarily aimed to determine the prevalence of CKD in T2D patients, but risk attributes were not assessed. (6) A single-centre, cross-sectional study identified advanced age, T2D duration, and body mass index (BMI) as significant risk factors. (9) An observational study conducted in Delhi and Bhubaneswar found obesity to contribute significantly to CKD development and progression in T2D patients. At the same time, a retrospective analysis from South India concluded that all T2D patients with proteinuria had retinopathy, with contributing factors being HbA1c and systolic blood pressure (SBP). (20,21) 4.3 CITE Study Findings. This is the first pan-Indian study to identify significant clinical and laboratory findings and CKD-related comorbidities in T2D patients. The overall prevalence of CKD in T2D patients was 32%. Most patients (60.72%) had a T2D duration exceeding 10 years, with a male preponderance. Median HbA1c and blood pressure values were 7.7% and 138/80 mmHg, respectively. Most patients were obese, reported a history of tobacco use, and followed a non-vegetarian diet. The predominant type of CKD identified was the albuminuric variety, with 62.71% diagnosed with microalbuminuria and 28.75% with macroalbuminuria. Compromised kidney function, defined as an eGFR < 60 ml/min, was observed in 45.35% of patients, while a small minority (8.54%) had non-albuminuric CKD. Significant variables associated with an eGFR 10 years. For every 1% increase in HbA1c and one mmHg increase in SBP, there was a 4% and 1% increase in the odds of worsening renal function. Each 1 ml/min reduction in eGFR was associated with significantly increased odds of retinopathy, neuropathy, ASCVD, and HF. In macroalbuminuric CKD, significant associations were observed with age ≥ 60, a non-vegetarian diet, tobacco use, and poor glycaemic control. Additionally, for each mmHg increase in SBP, the odds of macroalbuminuria increased by 1%. Except for neuropathy, all comorbidities were significantly associated with macroalbuminuria. Microalbuminuria was significantly associated only with T2D duration and showed no correlation with comorbidities. In patients with advanced CKD (eGFR 30 mg/g), significant associations were identified with age ≥ 60 years, female gender, T2D duration > 10 years, tobacco use, HbA1c, and SBP. In contrast to earlier studies from India, the CITE 2 study demonstrated a strong association between glycaemic control and CKD. 4.4 Study Limitations. The primary limitation of this study is its cross-sectional design. As data were captured at a single point in time, the impact of clinical and laboratory variables and comorbidities on CKD progression could not be assessed. A longitudinal design would better address this issue and provide insights into moderate associations and causal relationships. Additionally, while data were collected across the country, the zone-wise distribution was underpowered to explore regional outcome variations. Lastly, the impact of T2D and CKD varies with disease progression. Earlier onset of T2D, compared to longer duration, could yield differential results. The study population predominantly included patients with a T2D duration > 10 years, so the findings are limited to this group. 4.5 Strengths of the Study. This is the first well-designed, multi-centric, cross-sectional study from India. Data were collected prospectively following ADA/KDOQI guidelines for CKD definition. Unlike previous studies, which often relied on eGFR alone or a single creatinine and UACR sample, the CITE study adhered to the recommended protocols for CKD diagnosis. Data were collected from multiple regions across the country, enabling the generalisability of the findings. This is the largest cohort of T2D and CKD patients analysed for prevalence and risk attributes, with a statistical power 0.90. 5.0 Conclusion The nationwide CITE 2 study found a 34% prevalence of CKD in T2D patients. Key risk factors associated with CKD included age ≥ over 60, duration of T2D, SBP, HbA1c, and comorbidities (HF, ASCVD, retinopathy, and neuropathy). Tobacco use and a non-vegetarian diet were additional factors associated with proteinuric CKD (UACR > 300 mg/g). Macroalbuminuria, but not microalbuminuria, was associated with risk factors and comorbidities. Prospective longitudinal studies are required to explore these findings further and gain a more robust understanding of CKD evolution with increasing T2D duration. Declarations Author Contribution S.G. and B.S. conceptualised the study. S.T. and S.G. performed the statistical analysis. All authors reviewed the manuscript. Disclosures: The authors do not have any conflict to disclose for this manuscript. Financial Support and Sponsorship section: No external financial support was received. Conflict of interest: None. Data availability statement : Upon reasonable request, the authors can provide all data related to the preparation of this manuscript. Ethical Aspects: The Park Ethics Committee approved the C hronic Kidney Disease in I ndian T ype 2 Diabetes Patients E valuation (CITE) study protocol. Study registration: The CITE study protocol was registered at Open Science Framework (OSF) Registries. DOI https://doi.org/10.17605/OSF.IO/BRF6U) Acknowledgement: To all the physicians and centres who agreed to share the data essential to conducting this analysis. References Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. GBD 2021 Diabetes Collaborators. The Lancet 2023; 402(10397): 203-234. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(23)01301-6/fulltext Rooney MR, Fang M, Ogurtsova K, Ozkan B, Echouffo-Tcheugui JB, Boyko EJ, Magliano DJ, Selvin E. Global Prevalence of Prediabetes. Diabetes Care. 2023; 1;46(7):1388-1394. doi: 10.2337/dc22-2376.https://pubmed.ncbi.nlm.nih.gov/37196350/ Koye DN, Magliano DJ, Nelson RG, Pavkov ME. The global epidemiology of diabetes and kidney disease. Adv Chronic Kidney Dis. 2018;25(2):121–132. 10.1053/j.ackd.2017.10.011https://pubmed.ncbi.nlm.nih.gov/29580576/ Afkarian M, Sachs MC, Kestenbaum B, Hirsch IB, Tuttle KR, Himmelfarb J, de Boer IH. Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol. 2013;24(2):302-8. doi: 10.1681/ASN.2012070718.https://journals.lww.com/jasn/abstract/2013/02000/kidney_disease_and_increased_mortality_risk_in.18.aspx Xie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, Maddukuri G, Tsai CY, Floyd T, Al-Aly Z. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94(3):567-581. doi: 10.1016/j.kint.2018.04.011.https://pubmed.ncbi.nlm.nih.gov/30078514/ Prasannakumar M, Rajput R, Seshadri K, Talwalkar P, Agarwal P, Gokulnath G, Kotak B, Raza A, Vasnawala H, Teli C. An observational, cross-sectional study to assess the prevalence of chronic kidney disease in type 2 diabetes patients in India (START -India). Indian J Endocrinol Metab. 2015;19(4):520-3. doi: 10.4103/2230-8210.157857.https://pubmed.ncbi.nlm.nih.gov/26180769/ Hussain S, Habib A, Najmi AK. Limited Knowledge of Chronic Kidney Disease among Type 2 Diabetes Mellitus Patients in India. Int J Environ Res Public Health. 2019;16(8):1443. doi: 10.3390/ijerph16081443.https://pmc.ncbi.nlm.nih.gov/articles/PMC6518175/ Fenta, E.T., Eshetu, H.B., Kebede, N. et al. Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol Metab Syndr; 2023: 15, 245. https://doi.org/10.1186/s13098-023-01202-x Tewari A, Tewari V, Tewari J. A Cross-Sectional Study for Prevalence and Association of Risk Factors of Chronic Kidney Disease Among People With Type 2 Diabetes in the Indian Setting. Cureus 2021; 13(9): e18371. doi:10.7759/cureus.18371.https://www.cureus.com/articles/69632-a-cross-sectional-study-for-prevalence-and-association-of-risk-factors-of-chronic-kidney-disease-among-people-with-type-2-diabetes-in-the-indian-setting#!/ de Boer IH, Khunti K, Sadusky T, Tuttle KR, Neumiller JJ, Rhee CM, Rosas SE, Rossing P, Bakris G. Diabetes Management in Chronic Kidney Disease: A Consensus Report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care. 2022;45(12):3075-3090. doi: 10.2337/dci22-0027.https://diabetesjournals.org/care/article/45/12/3075/147614/Diabetes-Management-in-Chronic-Kidney-Disease-A Elisa Dal Canto, Antonio Ceriello, Lars Rydén, Marc Ferrini, Tina B Hansen, Oliver Schnell, Eberhard Standl, Joline WJ Beulens, Diabetes as a cardiovascular risk factor: An overview of global trends of macro and microvascular complications, European Journal of Preventive Cardiology , 2019;26(2):25–32. https://doi.org/10.1177/2047487319878371 Jia G, Hill MA, Sowers JR. Diabetic Cardiomyopathy: An Update of Mechanisms Contributing to This Clinical Entity. Circ Res. 2018;122(4):624-638. doi: 10.1161/CIRCRESAHA.117.311586.https://pubmed.ncbi.nlm.nih.gov/29449364/ Thomas R, Halim S, Gurudas S, Sivaprasad S, Owens D. IDF Diabetes Atlas: a review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Pract. 2019;157:107840.https://pubmed.ncbi.nlm.nih.gov/31733978/ Lu Y, Xing P, Cai X, Luo D, Li R, Lloyd C, Sartorius N, Li M. Prevalence and Risk Factors for Diabetic Peripheral Neuropathy in Type 2 Diabetic Patients From 14 Countries: Estimates of the INTERPRET-DD Study. Front Public Health. 2020;8:534372. doi: 10.3389/fpubh.2020.534372.https://pubmed.ncbi.nlm.nih.gov/33194943/ Afkarian M, Sachs MC, Kestenbaum B, Hirsch IB, Tuttle KR, Himmelfarb J, de Boer IH. Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol. 2013;24(2):302-8. doi: 10.1681/ASN.2012070718.https://pubmed.ncbi.nlm.nih.gov/23362314/ Fenta ET, Eshetu HB, Kebede N, Bogale EK, Zewdie A, Kassie TD, Anagaw TF, Mazengia EM, Gelaw SS. Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol Metab Syndr. 2023;28;15(1):245. doi: 10.1186/s13098-023-01202-x.https://pubmed.ncbi.nlm.nih.gov/38012781/ Jitraknatee, J., Ruengorn, C. & Nochaiwong, S. Prevalence and Risk Factors of Chronic Kidney Disease among Type 2 Diabetes Patients: A Cross-Sectional Study in Primary Care Practice. Sci Rep 2020; 10:6205. https://doi.org/10.1038/s41598-020-63443-4 Shi L, Xue Y, Yu X, Wang Y, Hong T, Li X, Ma J, Zhu D, Mu Y. Prevalence and Risk Factors of Chronic Kidney Disease in Patients With Type 2 Diabetes in China: Cross-Sectional Study. JMIR Public Health Surveill. 2024;10:e54429. doi: 10.2196/54429.https://pubmed.ncbi.nlm.nih.gov/39213031/ Nazzal, Z., Hamdan, Z., Masri, D . , Abu-Kaf, O., Hamad, M. Prevalence and risk factors of chronic kidney disease among Palestinian type 2 diabetic patients: a cross-sectional study. BMC Nephrol 21 , 484 (2020). https://doi.org/10.1186/s12882-020-02138-4 Dash SC, Agarwal SK, Panigrahi A, Mishra J, Dash D. Diabetes, Hypertension and Kidney Disease Combination "DHKD Syndrome" is common in India. J Assoc Physicians India. 2018;66(3):30-3. PMID: 30341865.https://pubmed.ncbi.nlm.nih.gov/30341865/ Viswanathan VV, Snehalatha C, Ramachandran A, Viswanathan M. Proteinuria in NIDDM in south India: analysis of predictive factors. Diabetes Res Clin Pract. 1995;28(1):41-6. doi: 10.1016/0168-8227(95)01057-k. https://pubmed.ncbi.nlm.nih.gov/7587911/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 May, 2025 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 03 Feb, 2025 Editor assigned by journal 30 Jan, 2025 Submission checks completed at journal 30 Jan, 2025 First submitted to journal 28 Jan, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5919300","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409048343,"identity":"29825e77-71b1-4811-87dc-1d831fc568fa","order_by":0,"name":"A Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"","lastName":"Kumar","suffix":""},{"id":409048344,"identity":"22845649-f485-4224-9fa6-dc7dafe3e90b","order_by":1,"name":"A Mazumdar","email":"","orcid":"","institution":"KPC Medical College and Hospital","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"","lastName":"Mazumdar","suffix":""},{"id":409048345,"identity":"f7a7038a-d12a-400d-836e-04cd59fbe935","order_by":2,"name":"AK Bhattacharjee","email":"","orcid":"","institution":"ILS Hospital, Agartala","correspondingAuthor":false,"prefix":"","firstName":"AK","middleName":"","lastName":"Bhattacharjee","suffix":""},{"id":409048346,"identity":"2c3930c4-2b8d-4ef5-899d-86d433325d87","order_by":3,"name":"A Gupta","email":"","orcid":"","institution":"Rajasthan Dental College and Hospital","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"","lastName":"Gupta","suffix":""},{"id":409048347,"identity":"2327cdeb-f48d-46be-9d91-ef5ce0fe203f","order_by":4,"name":"A Dasgupta","email":"","orcid":"","institution":"Rudraksh Superspeciality Care","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"","lastName":"Dasgupta","suffix":""},{"id":409048348,"identity":"40f615f5-2007-4cda-b6bb-1cf662b3e124","order_by":5,"name":"B Sinha","email":"","orcid":"","institution":"Manipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"B","middleName":"","lastName":"Sinha","suffix":""},{"id":409048349,"identity":"884b0817-f27c-473f-93e6-157f18a7984e","order_by":6,"name":"B Saboo","email":"","orcid":"","institution":"Diabetes Care \u0026 Hormone Clinic","correspondingAuthor":false,"prefix":"","firstName":"B","middleName":"","lastName":"Saboo","suffix":""},{"id":409048350,"identity":"d8f3ea2f-7acf-4408-9bca-8b7414bd711d","order_by":7,"name":"C Selvan","email":"","orcid":"","institution":"M.S. 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14:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5919300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5919300/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-025-04164-6","type":"published","date":"2025-05-16T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75406091,"identity":"4429461d-f07e-497f-a753-392ee55bb14b","added_by":"auto","created_at":"2025-02-04 08:50:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145173,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent categories of CKD. (a). Albuminuria categories, (b). eGFR categories, (c). eGFR categories based on different categories of albuminuria.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5919300/v1/8d47b008beb2a7eeee6f0343.png"},{"id":75406093,"identity":"6b8af796-d9f2-4fb8-87c0-f7db67d49f37","added_by":"auto","created_at":"2025-02-04 08:50:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":308489,"visible":true,"origin":"","legend":"\u003cp\u003eOdds of eGFR\u0026lt;60 ml/min with continuous variables (HBA1c and SBP).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5919300/v1/400f23ee8939cc6903f91a84.png"},{"id":75406096,"identity":"3ec3128c-aa30-47d7-b383-28e7301fd604","added_by":"auto","created_at":"2025-02-04 08:50:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":231390,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant clinical, laboratory and variables associated with eGFR \u0026lt;60 ml/min or UACR\u0026gt;300 mg/g.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5919300/v1/fdef0123b42b554244c66cae.png"},{"id":83067778,"identity":"07b27e8e-aa7e-4597-afee-50613cd33ea4","added_by":"auto","created_at":"2025-05-19 16:05:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1502089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5919300/v1/fb27ef17-60fa-41be-90f8-2997ef4e3459.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk factors associated with Indian Type 2 diabetes patients with chronic kidney disease: CITE study, A Cross-sectional, Real-world, Observational Study","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eType 2 diabetes (T2D) mellitus is one of the most common chronic diseases, accounting for significant global morbidity and mortality. The global prevalence of T2D in 2021 was estimated at 6.1% (529\u0026nbsp;million patients), with an additional 9.1% having impaired glucose tolerance and 5.8% having impaired fasting glucose. This equates to a total of 762\u0026nbsp;million individuals with pre-diabetes who are likely to contribute to the existing disease burden. (1,2) Chronic kidney disease (CKD) is the most common comorbidity associated with T2D, with a prevalence ranging between 20% and 30%. (3) The development of CKD in the context of T2D can lead to end-stage kidney disease (ESKD), renal failure, and cardiovascular death in the long term. (4) In Asia, the prevalence of CKD is estimated to be higher (34%) due to the greater prevalence of metabolic disorders. (5)\u003c/p\u003e \u003cp\u003eThe START-INDIA multi-centric study estimates CKD prevalence to exceed 40%, whereas a single-centre cross-sectional study from New Delhi, India, reports a prevalence of 34.4%. (6,7) Increasing age, advanced duration of diabetes, smoking, obesity, and hypertension have been identified as associated risk factors in a meta-analysis of global data. (8) However, limited and heterogeneous data from India suggest advanced age, high body mass index (BMI), and duration of diabetes as significant predictors, with no association observed with glycemic control. (9)\u003c/p\u003e \u003cp\u003eThe heterogeneity in prevalence data globally and within India could be attributed to the diagnostic criteria used for CKD, which often involved an estimated glomerular filtration rate (eGFR) of less than 60 ml/min without accounting for the urine albumin-to-creatinine ratio (UACR).\u003c/p\u003e \u003cp\u003e In light of these discrepancies, the CITE study was initiated in India to identify CKD in T2D patients using the American Diabetes Association (ADA) and Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines. These guidelines define CKD as a UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g in at least two out of three readings and/or an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min persistent for three months. (10) This cross-sectional analysis is the first in India to identify significant clinical and laboratory risk associations related to CKD in patients with T2D.\u003c/p\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eT2D patients aged over 18 years with established CKD were included in the analysis. The study aimed to assess the prevalence of CKD in T2D patients and to identify clinically relevant attributes associated with CKD. Data were collected prospectively from 28 diabetes and endocrinology clinics across India over three months (15th December to 15th March). Patients with missing data (those lacking two out of three abnormal UACR values or an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min persistent for \u0026ge;\u0026thinsp;3 months), patients on renal replacement therapy, those with non-diabetic kidney disease (NDKD), and those with acute renal insufficiency were excluded. The Park Clinic Ethics Committee approved the study protocol on 23rd December 2023 and was registered with the Open Science Framework (OSF) Registries (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/BRF6U\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/BRF6U\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection and Evaluation\u003c/h2\u003e \u003cp\u003eT2D patients visiting the clinics were screened for CKD using UACR (spot sample) and eGFR (calculated using the CKD-EPI method), which aligns with the ADA and KDQOI recommendations. Persistently elevated UACR and eGFR values and relevant baseline history, demographic parameters, laboratory parameters, and medication history were recorded and entered into a pre-approved Excel sheet. A steering committee defined the data entry parameters, considering global recommendations and regional sensitivities.\u003c/p\u003e \u003cp\u003eRelevant recorded history included hypertension, dyslipidaemia, diabetes duration, dietary habits, tobacco usage, current glycaemic status, and associated comorbidities such as glycated haemoglobin (HbA1c), atherosclerotic cardiovascular disease (ASCVD), heart failure, peripheral vascular disease, retinopathy, and diabetic neuropathy. CKD in T2D was confirmed using a UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g in at least two out of three readings and/or an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min persistent for three months. Significant albuminuria was further classified into microalbuminuria (UACR 30\u0026ndash;300 mg/g) and macroalbuminuria (UACR\u0026thinsp;\u0026gt;\u0026thinsp;300 mg/g). Urine samples with significant pus cells or red blood cells, indicative of urinary tract infections or haematuria, respectively, were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe analysis used DATAtab (Online Statistics Calculator, DATAtab e.U., Graz, Austria) and R Studio (R Core Team, 2024, R version 4.2.3). Descriptive statistics were expressed as percentages for categorical variables and mean or median with standard deviation or interquartile range (IQR) for continuous variables. The decision to report the mean or median was based on the data distribution, which was assessed using the Kolmogorov-Smirnov test. Analytical statistics were conducted using UACR, eGFR, and a combination of both as dependent variables. Multinomial logistic regression analysis was performed for different eGFR categories and CKD status (UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g\u0026thinsp;+\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min), while binomial logistic regression was conducted for eGFR. The regression model significance was assessed using the chi-squared statistic, with a significance level set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (95% confidence interval).\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cp\u003eA total of 10,915 T2D patients were screened, of whom 3,493 were diagnosed with CKD based on the pre-specified diagnostic criteria. After excluding patients with missing data, 3,325 data points (4.8% dropout) were included in the final analysis.\u003c/p\u003e \u003cp\u003eThe overall prevalence of CKD in patients with T2D was 32%. The median age of the patients was 59.9 (12.1) years, with 64.6% being male. Among the cohort, 60.72% had a T2D duration exceeding 10 years, 77.62% were non-vegetarians, and 79.79% used tobacco. The median BMI was 25.78 (4.98) kg/m\u0026sup2;, median HbA1c was 7.7% (2.3), median systolic blood pressure (SBP) was 138.4 (26) mmHg, and median diastolic blood pressure (DBP) was 80 (14) mmHg. Obesity, defined as a BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;, was observed in 57.74% of patients, with only 31.46% achieving an HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7.0%. Additionally, 73.86% of CKD patients with T2D had a history of hypertension (HT), although only 41.08% achieved the target blood pressure of \u0026lt;\u0026thinsp;130/80 mmHg (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of T2D patients with CKD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategorical Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge \u0026ge;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1906(57.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUACR (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2148(64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D duration\u0026thinsp;\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019(60.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHBA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-vegetarian diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2581(77.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBA1c\u0026thinsp;\u0026lt;\u0026thinsp;7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1046(31.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBP (mm of Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026ge;25 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1920(57.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDBP (mm of Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI 23.0-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e668(20.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e672(20.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2456(73.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e653(19.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167(5.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e384(11.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e649(19.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWithin CKD categories, 62.71% had microalbuminuria, 28.75% had macroalbuminuria, and 45.35% had an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min. Among patients with microalbuminuria, 45.02% had an eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 ml/min, whereas only 9.62% had an eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 ml/min. A minority (8.54%) of CKD patients had normoalbuminuria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, 36.81% of patients had eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min and UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Binary Logistic Regression Analysis: eGFR Categories and Associated Clinical Attributes\u003c/h2\u003e \u003cp\u003eThe continuous variables systolic blood pressure (SBP), diastolic blood pressure (DBP), and glycated haemoglobin (HbA1c), along with the categorical variables hypertension status and HbA1c categories (\u0026lt;\u0026thinsp;7% and \u0026ge;\u0026thinsp;7%), demonstrated significant multicollinearity issues (variance inflation factor, VIF\u0026thinsp;\u0026gt;\u0026thinsp;5). As a result, these confounding variables were excluded where applicable.\u003c/p\u003e \u003cp\u003eThe analysis revealed significantly increased odds of having an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (odds ratio [OR]: 2.46, 95% confidence interval [CI] 2.11\u0026ndash;2.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in those with a T2D duration of more than 10 years (OR: 2.27, 95% CI 1.76\u0026ndash;2.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Appendix supplementary table 1) Additionally, each 1% increase in HbA1c was associated with a 4% higher odds of eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min (95% CI 1.00\u0026ndash;1.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), and each mmHg increase in SBP was linked to a 0.5% increased odds of worsening renal function (95% CI 1.002\u0026ndash;1.009, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe regression model was statistically significant (chi-squared statistic\u0026thinsp;=\u0026thinsp;310.5, degrees of freedom\u0026thinsp;=\u0026thinsp;5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multinomial Logistic Regression Analysis: UACR Categories and Associated Clinical Attributes\u003c/h2\u003e \u003cp\u003eThere were significant odds of macroalbuminuria (UACR\u0026thinsp;\u0026gt;\u0026thinsp;300 mg/g) associated with age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (odds ratio [OR]: 1.44, 95% confidence interval [CI] 1.23\u0026ndash;1.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a non-vegetarian diet (OR: 1.95, 95% CI 1.59\u0026ndash;2.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tobacco use (OR: 1.38, 95% CI 1.14\u0026ndash;1.65, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7% (OR: 1.31, 95% CI 1.10\u0026ndash;1.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). (Appendix supplementary table 2) Additionally, for each mmHg increase in SBP, there was a 1% increase in the odds of macroalbuminuria (95% CI 1.006\u0026ndash;1.014, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The regression model was statistically significant (chi-squared statistic\u0026thinsp;=\u0026thinsp;113.96, degrees of freedom [df]\u0026thinsp;=\u0026thinsp;5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor microalbuminuria, only a T2D duration of \u0026gt;\u0026thinsp;5 years was significantly associated (OR: 1.42, 95% CI 1.09\u0026ndash;1.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003eClinical and laboratory variables strongly correlating with CKD, defined as a combination of eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min and UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g, included age\u0026thinsp;\u0026ge;\u0026thinsp;60 years, female gender, a T2D duration of \u0026gt;\u0026thinsp;10 years, tobacco use, HbA1c, and SBP. This regression model was also significant (chi-squared statistic\u0026thinsp;=\u0026thinsp;203.6, df\u0026thinsp;=\u0026thinsp;7, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Multinomial and Binary Logistic Regression Analysis: Comorbidities and Associated Clinical Attributes\u003c/h2\u003e \u003cp\u003eBinary logistic regression was conducted using eGFR as the dependent variable. For every 1 ml/min reduction in eGFR below 60 ml/min, there was a significant increase in the odds of retinopathy (OR: 2.40, 95% CI 1.92\u0026ndash;2.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), neuropathy (OR: 1.54, 95% CI 1.30\u0026ndash;1.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), atherosclerotic cardiovascular disease (ASCVD) (OR: 1.95, 95% CI 1.64\u0026ndash;2.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and heart failure (HF) (OR: 5.07, 95% CI 3.45\u0026ndash;7.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The regression model was statistically significant (chi-squared statistic\u0026thinsp;=\u0026thinsp;86.82, df\u0026thinsp;=\u0026thinsp;4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMultinomial logistic regression was conducted using UACR as the dependent variable. In the presence of macroalbuminuria, significant associations were found with ASCVD (OR: 1.27, 95% CI 1.05\u0026ndash;1.54, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), HF (OR: 1.85, 95% CI 1.34\u0026ndash;2.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and retinopathy (OR: 2.14, 95% CI 1.71\u0026ndash;2.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant association was observed with neuropathy. The regression model was statistically significant (chi-squared statistic\u0026thinsp;=\u0026thinsp;85.53, df\u0026thinsp;=\u0026thinsp;4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eNo significant associations were identified between comorbidities and microalbuminuria (UACR 30\u0026ndash;300 mg/g).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Global Prevalence Data and Risk Associations.\u003c/h2\u003e \u003cp\u003eType 2 diabetes (T2D) is the most common metabolic disorder, responsible for significant health morbidity and mortality. According to global estimates, the prevalence of macrovascular complications is approximately 32.2%, with atherosclerotic cardiovascular disease (ASCVD) accounting for 21.2%. (11) Heart failure (HF) prevalence ranges between 19% and 26%, while retinopathy and neuropathy prevalence stand at 27% and 26.71%, respectively. (12,13,14) The global prevalence of chronic kidney disease (CKD) in T2D is around 25%, further amplifying the burden of T2D-associated comorbidities. (15) Key factors associated with CKD in T2D include advanced age, smoking, hypertension, obesity, and ASCVD. (16) A cross-sectional study from Thailand identified advanced age, uncontrolled diabetes, retinopathy, and elevated uric acid levels as significant risk factors. (17) Similarly, data from China and Palestine reported significant associations with advanced age, hypertension, T2D duration, comorbidities, and poor glycemic control. (18,19)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Indian Prevalence Data and Risk Associations.\u003c/h2\u003e \u003cp\u003eThe START-INDIA study primarily aimed to determine the prevalence of CKD in T2D patients, but risk attributes were not assessed. (6) A single-centre, cross-sectional study identified advanced age, T2D duration, and body mass index (BMI) as significant risk factors. (9) An observational study conducted in Delhi and Bhubaneswar found obesity to contribute significantly to CKD development and progression in T2D patients. At the same time, a retrospective analysis from South India concluded that all T2D patients with proteinuria had retinopathy, with contributing factors being HbA1c and systolic blood pressure (SBP). (20,21)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 CITE Study Findings.\u003c/h2\u003e \u003cp\u003eThis is the first pan-Indian study to identify significant clinical and laboratory findings and CKD-related comorbidities in T2D patients. The overall prevalence of CKD in T2D patients was 32%. Most patients (60.72%) had a T2D duration exceeding 10 years, with a male preponderance. Median HbA1c and blood pressure values were 7.7% and 138/80 mmHg, respectively. Most patients were obese, reported a history of tobacco use, and followed a non-vegetarian diet.\u003c/p\u003e \u003cp\u003eThe predominant type of CKD identified was the albuminuric variety, with 62.71% diagnosed with microalbuminuria and 28.75% with macroalbuminuria. Compromised kidney function, defined as an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min, was observed in 45.35% of patients, while a small minority (8.54%) had non-albuminuric CKD.\u003c/p\u003e \u003cp\u003eSignificant variables associated with an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min included HbA1c, SBP, age\u0026thinsp;\u0026ge;\u0026thinsp;60 years, and T2D duration\u0026thinsp;\u0026gt;\u0026thinsp;10 years. For every 1% increase in HbA1c and one mmHg increase in SBP, there was a 4% and 1% increase in the odds of worsening renal function. Each 1 ml/min reduction in eGFR was associated with significantly increased odds of retinopathy, neuropathy, ASCVD, and HF.\u003c/p\u003e \u003cp\u003eIn macroalbuminuric CKD, significant associations were observed with age\u0026thinsp;\u0026ge;\u0026thinsp;60, a non-vegetarian diet, tobacco use, and poor glycaemic control. Additionally, for each mmHg increase in SBP, the odds of macroalbuminuria increased by 1%. Except for neuropathy, all comorbidities were significantly associated with macroalbuminuria. Microalbuminuria was significantly associated only with T2D duration and showed no correlation with comorbidities.\u003c/p\u003e \u003cp\u003eIn patients with advanced CKD (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min and UACR\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g), significant associations were identified with age\u0026thinsp;\u0026ge;\u0026thinsp;60 years, female gender, T2D duration\u0026thinsp;\u0026gt;\u0026thinsp;10 years, tobacco use, HbA1c, and SBP. In contrast to earlier studies from India, the CITE 2 study demonstrated a strong association between glycaemic control and CKD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Study Limitations.\u003c/h2\u003e \u003cp\u003eThe primary limitation of this study is its cross-sectional design. As data were captured at a single point in time, the impact of clinical and laboratory variables and comorbidities on CKD progression could not be assessed. A longitudinal design would better address this issue and provide insights into moderate associations and causal relationships. Additionally, while data were collected across the country, the zone-wise distribution was underpowered to explore regional outcome variations. Lastly, the impact of T2D and CKD varies with disease progression. Earlier onset of T2D, compared to longer duration, could yield differential results. The study population predominantly included patients with a T2D duration\u0026thinsp;\u0026gt;\u0026thinsp;10 years, so the findings are limited to this group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Strengths of the Study.\u003c/h2\u003e \u003cp\u003eThis is the first well-designed, multi-centric, cross-sectional study from India. Data were collected prospectively following ADA/KDOQI guidelines for CKD definition. Unlike previous studies, which often relied on eGFR alone or a single creatinine and UACR sample, the CITE study adhered to the recommended protocols for CKD diagnosis. Data were collected from multiple regions across the country, enabling the generalisability of the findings. This is the largest cohort of T2D and CKD patients analysed for prevalence and risk attributes, with a statistical power 0.90.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThe nationwide CITE 2 study found a 34% prevalence of CKD in T2D patients. Key risk factors associated with CKD included age\u0026thinsp;\u0026ge;\u0026thinsp;over 60, duration of T2D, SBP, HbA1c, and comorbidities (HF, ASCVD, retinopathy, and neuropathy). Tobacco use and a non-vegetarian diet were additional factors associated with proteinuric CKD (UACR\u0026thinsp;\u0026gt;\u0026thinsp;300 mg/g).\u003c/p\u003e \u003cp\u003eMacroalbuminuria, but not microalbuminuria, was associated with risk factors and comorbidities. Prospective longitudinal studies are required to explore these findings further and gain a more robust understanding of CKD evolution with increasing T2D duration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.G. and B.S. conceptualised the study. S.T. and S.G. performed the statistical analysis. All authors reviewed the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDisclosures:\u0026nbsp;\u003c/strong\u003eThe authors do not have any conflict to disclose for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Support and Sponsorship section:\u0026nbsp;\u003c/strong\u003eNo external financial support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: Upon reasonable request, the authors can provide all data related to the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Aspects: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Park Ethics Committee approved the \u003cstrong\u003eC\u003c/strong\u003ehronic Kidney Disease in \u003cstrong\u003eI\u003c/strong\u003endian \u003cstrong\u003eT\u003c/strong\u003eype 2 Diabetes Patients \u003cstrong\u003eE\u003c/strong\u003evaluation (CITE) study protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy registration:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CITE study protocol was registered at Open Science Framework (OSF) Registries. DOI https://doi.org/10.17605/OSF.IO/BRF6U)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eTo all the physicians and centres who agreed to share the data essential to conducting this analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. GBD 2021 Diabetes Collaborators. The Lancet 2023; 402(10397): 203-234. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(23)01301-6/fulltext\u003c/li\u003e\n\u003cli\u003eRooney MR, Fang M, Ogurtsova K, Ozkan B, Echouffo-Tcheugui JB, Boyko EJ, Magliano DJ, Selvin E. Global Prevalence of Prediabetes. Diabetes Care. 2023; 1;46(7):1388-1394. doi: 10.2337/dc22-2376.https://pubmed.ncbi.nlm.nih.gov/37196350/\u003c/li\u003e\n\u003cli\u003eKoye DN, Magliano DJ, Nelson RG, Pavkov ME. The global epidemiology of diabetes and kidney disease. Adv Chronic Kidney Dis. 2018;25(2):121\u0026ndash;132. 10.1053/j.ackd.2017.10.011https://pubmed.ncbi.nlm.nih.gov/29580576/\u003c/li\u003e\n\u003cli\u003eAfkarian M, Sachs MC, Kestenbaum B, Hirsch IB, Tuttle KR, Himmelfarb J, de Boer IH. Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol. 2013;24(2):302-8. doi: 10.1681/ASN.2012070718.https://journals.lww.com/jasn/abstract/2013/02000/kidney_disease_and_increased_mortality_risk_in.18.aspx\u003c/li\u003e\n\u003cli\u003eXie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, Maddukuri G, Tsai CY, Floyd T, Al-Aly Z. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94(3):567-581. doi: 10.1016/j.kint.2018.04.011.https://pubmed.ncbi.nlm.nih.gov/30078514/\u003c/li\u003e\n\u003cli\u003ePrasannakumar M, Rajput R, Seshadri K, Talwalkar P, Agarwal P, Gokulnath G, Kotak B, Raza A, Vasnawala H, Teli C. An observational, cross-sectional study to assess the prevalence of chronic kidney disease in type 2 diabetes patients in India (START -India). Indian J Endocrinol Metab. 2015;19(4):520-3. doi: 10.4103/2230-8210.157857.https://pubmed.ncbi.nlm.nih.gov/26180769/\u003c/li\u003e\n\u003cli\u003eHussain S, Habib A, Najmi AK. Limited Knowledge of Chronic Kidney Disease among Type 2 Diabetes Mellitus Patients in India. Int J Environ Res Public Health. 2019;16(8):1443. doi: 10.3390/ijerph16081443.https://pmc.ncbi.nlm.nih.gov/articles/PMC6518175/\u003c/li\u003e\n\u003cli\u003eFenta, E.T., Eshetu, H.B., Kebede, N. \u003cem\u003eet al.\u003c/em\u003e Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol Metab Syndr; 2023: 15, 245. https://doi.org/10.1186/s13098-023-01202-x\u003c/li\u003e\n\u003cli\u003eTewari A, Tewari V, Tewari J. A Cross-Sectional Study for Prevalence and Association of Risk Factors of Chronic Kidney Disease Among People With Type 2 Diabetes in the Indian Setting. Cureus 2021; 13(9): e18371. doi:10.7759/cureus.18371.https://www.cureus.com/articles/69632-a-cross-sectional-study-for-prevalence-and-association-of-risk-factors-of-chronic-kidney-disease-among-people-with-type-2-diabetes-in-the-indian-setting#!/\u003c/li\u003e\n\u003cli\u003ede Boer IH, Khunti K, Sadusky T, Tuttle KR, Neumiller JJ, Rhee CM, Rosas SE, Rossing P, Bakris G. Diabetes Management in Chronic Kidney Disease: A Consensus Report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care. 2022;45(12):3075-3090. doi: 10.2337/dci22-0027.https://diabetesjournals.org/care/article/45/12/3075/147614/Diabetes-Management-in-Chronic-Kidney-Disease-A\u003c/li\u003e\n\u003cli\u003eElisa Dal Canto, Antonio Ceriello, Lars Ryd\u0026eacute;n, Marc Ferrini, Tina B Hansen, Oliver Schnell, Eberhard Standl, Joline WJ Beulens, Diabetes as a cardiovascular risk factor: An overview of global trends of macro and microvascular complications, \u003cem\u003eEuropean Journal of Preventive Cardiology\u003c/em\u003e, 2019;26(2):25\u0026ndash;32. https://doi.org/10.1177/2047487319878371\u003c/li\u003e\n\u003cli\u003eJia G, Hill MA, Sowers JR. Diabetic Cardiomyopathy: An Update of Mechanisms Contributing to This Clinical Entity. Circ Res. 2018;122(4):624-638. doi: 10.1161/CIRCRESAHA.117.311586.https://pubmed.ncbi.nlm.nih.gov/29449364/\u003c/li\u003e\n\u003cli\u003eThomas R, Halim S, Gurudas S, Sivaprasad S, Owens D. IDF Diabetes Atlas: a review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Pract. 2019;157:107840.https://pubmed.ncbi.nlm.nih.gov/31733978/\u003c/li\u003e\n\u003cli\u003eLu Y, Xing P, Cai X, Luo D, Li R, Lloyd C, Sartorius N, Li M. Prevalence and Risk Factors for Diabetic Peripheral Neuropathy in Type 2 Diabetic Patients From 14 Countries: Estimates of the INTERPRET-DD Study. Front Public Health. 2020;8:534372. doi: 10.3389/fpubh.2020.534372.https://pubmed.ncbi.nlm.nih.gov/33194943/\u003c/li\u003e\n\u003cli\u003eAfkarian M, Sachs MC, Kestenbaum B, Hirsch IB, Tuttle KR, Himmelfarb J, de Boer IH. Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol. 2013;24(2):302-8. doi: 10.1681/ASN.2012070718.https://pubmed.ncbi.nlm.nih.gov/23362314/\u003c/li\u003e\n\u003cli\u003eFenta ET, Eshetu HB, Kebede N, Bogale EK, Zewdie A, Kassie TD, Anagaw TF, Mazengia EM, Gelaw SS. Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol Metab Syndr. 2023;28;15(1):245. doi: 10.1186/s13098-023-01202-x.https://pubmed.ncbi.nlm.nih.gov/38012781/\u003c/li\u003e\n\u003cli\u003eJitraknatee, J., Ruengorn, C. \u0026amp; Nochaiwong, S. Prevalence and Risk Factors of Chronic Kidney Disease among Type 2 Diabetes Patients: A Cross-Sectional Study in Primary Care Practice. Sci Rep 2020; 10:6205. https://doi.org/10.1038/s41598-020-63443-4\u003c/li\u003e\n\u003cli\u003eShi L, Xue Y, Yu X, Wang Y, Hong T, Li X, Ma J, Zhu D, Mu Y. Prevalence and Risk Factors of Chronic Kidney Disease in Patients With Type 2 Diabetes in China: Cross-Sectional Study. JMIR Public Health Surveill. 2024;10:e54429. doi: 10.2196/54429.https://pubmed.ncbi.nlm.nih.gov/39213031/\u003c/li\u003e\n\u003cli\u003eNazzal, Z., Hamdan, Z., Masri, D\u003cem\u003e.\u003c/em\u003e, Abu-Kaf, O., Hamad, M. Prevalence and risk factors of chronic kidney disease among Palestinian type 2 diabetic patients: a cross-sectional study. \u003cem\u003eBMC Nephrol\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 484 (2020). https://doi.org/10.1186/s12882-020-02138-4\u003c/li\u003e\n\u003cli\u003eDash SC, Agarwal SK, Panigrahi A, Mishra J, Dash D. Diabetes, Hypertension and Kidney Disease Combination \u0026quot;DHKD Syndrome\u0026quot; is common in India. J Assoc Physicians India. 2018;66(3):30-3. PMID: 30341865.https://pubmed.ncbi.nlm.nih.gov/30341865/\u003c/li\u003e\n\u003cli\u003eViswanathan VV, Snehalatha C, Ramachandran A, Viswanathan M. Proteinuria in NIDDM in south India: analysis of predictive factors. Diabetes Res Clin Pract. 1995;28(1):41-6. doi: 10.1016/0168-8227(95)01057-k. https://pubmed.ncbi.nlm.nih.gov/7587911/\u003c/li\u003e\n\u003c/ol\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":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5919300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5919300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eType 2 diabetes (T2D) is the leading cause of chronic kidney disease (CKD) worldwide. Identifying clinical and laboratory associations with CKD in T2D can assist physicians in targeting modifiable risk factors. In light of limited data from India, the CITE (CKD in Indian T2D Evaluation) study was conducted.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThe multicenter, cross-sectional CITE study included 3,325 patients from 28 centres across India over three months. CKD was defined as a persistent decline in kidney function (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min for \u0026ge;\u0026thinsp;3 months) or an elevated urine albumin-creatinine ratio (UACR) in at least two samples. Descriptive statistics summarised patient characteristics, while logistic regression analyses identified significant risk factors for CKD.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe prevalence of CKD in T2D was 34%, with a median patient age of 59.9 years and 60.72% having a T2D duration of more than 10 years. Reduced eGFR (\u0026lt;\u0026thinsp;60 ml/min) was significantly associated with age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (OR: 2.46, 95% CI 2.11\u0026ndash;2.87, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), T2D duration of more than 10 years (OR: 2.27, 95% CI 1.76\u0026ndash;2.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HbA1c (OR: 1.04, 95% CI 1.00\u0026ndash;1.08, P\u0026thinsp;=\u0026thinsp;0.03), and SBP (OR: 1.005, 95% CI 1.002\u0026ndash;1.009, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Macroalbuminuria (UACR\u0026thinsp;\u0026gt;\u0026thinsp;300 mg/g) was linked to a non-vegetarian diet (OR: 1.95, 95% CI 1.59\u0026ndash;2.39, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and tobacco use (OR: 1.38, 95% CI 1.14\u0026ndash;1.65, P\u0026thinsp;=\u0026thinsp;0.001). CKD also increased the odds of comorbidities.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThe CITE study highlights the prevalence and risk factors of CKD in Indian T2D patients. Longitudinal studies are needed to evaluate these associations further.\u003c/p\u003e","manuscriptTitle":"Risk factors associated with Indian Type 2 diabetes patients with chronic kidney disease: CITE study, A Cross-sectional, Real-world, Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-04 08:50:04","doi":"10.21203/rs.3.rs-5919300/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-03T09:42:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-30T10:43:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-30T10:43:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-01-28T14:44:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f24bd6f-b710-4659-b32b-887036058aca","owner":[],"postedDate":"February 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:00:15+00:00","versionOfRecord":{"articleIdentity":"rs-5919300","link":"https://doi.org/10.1186/s12882-025-04164-6","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-05-16 15:57:00","publishedOnDateReadable":"May 16th, 2025"},"versionCreatedAt":"2025-02-04 08:50:04","video":{"identity":"c68bb689bcaf9893337071c3f3ccc8f2"},"vorDoi":"10.1186/s12882-025-04164-6","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04164-6","workflowStages":[]},"version":"v1","identity":"rs-5919300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5919300","identity":"rs-5919300","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}