Association of cardiovascular risk factors with diabetic kidney disease severity in the Iranian population

Association of cardiovascular risk factors with diabetic kidney disease severity in the Iranian population | 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 Association of cardiovascular risk factors with diabetic kidney disease severity in the Iranian population Rad Ghannadzadeh, Maryam Karimi Ghahfarokhi, Ali Golestani, Shahrzad Mohseni, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7081883/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Diabetic Kidney Disease (DKD), a serious complication of diabetes mellitus (DM), is a leading cause of end-stage renal disease and a significant contributor to cardiovascular morbidity. This study explores the association of cardiovascular disease (CVD) risk factors with severity of diabetic kidney disease in Iran. Utilizing data from the 2021 nationwide STEPwise approach to Noncommunicable Disease Risk Factor Surveillance (STEPS), this cross-sectional study included 3,272 diabetic adults aged ≥ 25 years. DKD was defined based on eGFR and albuminuria levels using KDIGO guideline. Key CVD risk factors were analyzed using multivariable ordinal logistic regression. Among the participants, 7.64% (6.73,8.55) and 20.3% (18.92,21.68) were at high/very high and moderately increased risk of DKD respectively. Older age [OR = 1.81 (1.19,2.74)], longer diabetes duration (> 10 years) [OR = 1.34(1.08,1.66)], uncontrolled glycemia (HbA1c ≥ 7) [OR = 1.57 (1.32,1.88)], dyslipidemia [OR = 1.49 (1.10,2.04)], systolic blood pressure ≥ 140 [OR = 1.42 (1.17,1.73)], and use of insulin [OR = 2.46 (1.60,3.78)] or multiple antihypertensive medications [OR = 2.11 (1.59,2.79)] were significantly associated with higher DKD risk. CVD risk factors, particularly hypertension, dyslipidemia, and poor glycemic control, have a strong association with severity of DKD. These findings underscore the need for early risk stratification and targeted interventions to delay renal deterioration in diabetic patients. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors Diabetic Kidney Disease Cardiovascular Risk Factors Diabetes Mellitus Hypertension Dyslipidemia Glycemic Control Introduction Diabetes mellitus (DM) is a global epidemic reshaping the health landscape of the 21st century. Every 6 seconds, someone succumbs to diabetes-related complications 1 , and an estimated figure of 537 million adults, over 10 percent of the global population live daily with the disease 2 . By 2045, this number is projected to expand to 783 million, due to aging and lifestyle changes 2 , 3 ., it is expected that the number of older diabetic adults (55–64 years) will rise from 10.8–21.7% in 2045 4 . Beyond its direct effect, diabetes is responsible for devastating complications, that diabetic kidney disease (DKD) is the most important among them. DKD, defined by albuminuria or reduced renal filtration, is the leading cause of end stage renal disease (ESRD) worldwide. DKD causes up to 50% of all cases of ESRD in the United States 5 and around 35% of renal replacement therapy in Iran 6 . DKD affects 30–40% of diabetic patients and has two major complications. It can lead to ESRD and it doubles the risks for cardiovascular mortality in diabetic patients 7 – 9 . DKD serves as a very strong accelerator of cardiovascular disease (CVD), considered the leading cause of mortality in diabetes 10 . This dual threat originates from the interaction of metabolic, vascular, and inflammatory pathways that are significantly influenced by the major CVD risk factors—hypertension, obesity, dyslipidemia, and smoking 11 . Research underlines the different mechanisms through which the risk factors for CVD affect DKD. Hypertension, which may affect up to 70% of diabetic patients, accelerates glomerular injury 12 ; dyslipidemia, a prevalent comorbidity present in patients with DKD, promotes renal injury through lipid-dependent mechanisms 13 , 14 . Obesity enhances insulin resistance and renal hyperfiltration, thus establishing a vicious circle of metabolic and renal stress 15 ; and smoking impairs endothelial function, reducing renal perfusion and amplifying inflammatory injury 16 . Although research has identified that these factors contribute to adverse outcomes of DKD, there is a need to understand prevalence and impact in different nations of diabetic with DKD. Leveraging nationally representative data from the 2021 STEPwise approach to Noncommunicable Disease Risk Factor Surveillance (STEPS) in Iran, this study pioneers a comprehensive overview of the prevalence and distribution of CVD risk factors in diabetic patients with DKD as comorbidity. In this study, we aimed to investigate the impact of cardiovascular risk factors on the progression of diabetic kidney disease and identify which specific factors—including obesity, abdominal obesity, hypertension, use of antihypertensive medications, use of glucose-lowering drugs, smoking, high salt intake, insufficient physical activity, and hyperlipidemia—contribute to the worsening severity of diabetic kidney disease. By identifying these risk factors, our goal was to enable the timely detection of high-risk patients and to help prevent further progression of the disease through targeted interventions. Results Ultimately, 3,272 diabetic patients with an average age of 57.76 ± 0.21 years were analyzed. Males to Females ratio was 59%/41% individuals lived in rural areas to those lived in urban areas was 74%/26%. The baseline characteristics of these patients are presented in Table 1 . Table 1 Baseline Characteristics of the total diabetic population (weighted and 95%CI) Variable Category Total DM (n = 3272) Age (years) - 57.76 \(\:\pm\:\) 0.21 (57.35,58.17) Sex (%) Male 59 (0.57,0.61) Female 41 (0.39,0.43) Residency (%) Urban 26 (0.24,0.28) Rural 74 (0.72,0.76) Smoking (Question: ever) (%) Yes 19 (0.17,0.20) BMI (kg/m 2 ) - 29.43 \(\:\pm\:\) 0.10 (29.23,29.63) WC (cm) - 101.07 \(\:\pm\:\) 0.26 (100.56,101.57) SBP (mmHg) - 136.31 \(\:\pm\:\) 0.39 (135.54,137.08) DBP (mmHg) - 81.18 \(\:\pm\:\) 0.22 (80.74,81.61) FBS (mg/dl) - 151.78 \(\:\pm\:\) 1.20 (149.42,154.14) HbA1c (%) - 7.85 \(\:\pm\:\) 0.04 (7.78,7.92) Total cholesterol (mg/dl) - 171.15 \(\:\pm\:\) 0.84 (169.51,172.79) LDL-C (mg/dl) - 94.21 \(\:\pm\:\) 0.71 (92.81,95.59) HDL-C (mg/dl) - 40.89 \(\:\pm\:\) 0.19 (40.50,41.28) TG (mg/dl) - 180.70 \(\:\pm\:\) 1.95 (176.88,184.52) eGFR (mL/min/1.73m²) - 86.39 \(\:\pm\:\) 0.38 (85.65,87.15) Urine Albumin (mg/g) - 58.26 \(\:\pm\:\) 4.32 (49.78,66.73) Hypertension (%) Yes 65 (0.63,0.67) Descriptive statistics for the study population (n = 3,272) with diabetes aged ≥ 25 years in Iran, 2021. Data are presented as weighted means ± standard error (SE) or weighted percentages with 95% confidence intervals (CI), unless otherwise specified. Variables include anthropometric, clinical, and laboratory parameters DM: Diabetes mellitus; BMI: Body mass index; WC: Waist circumference; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FBS: Fasting blood sugar; HbA1C: Hemoglobin A1C; LDL-C: Low density lipoprotein-cholesterol; HDL-C: High density lipoprotein-cholesterol; TG: Triglyceride; eGFR: estimated glomerular filtration rate As shown in Table 2 , 72.1% (70.56,73.64) of patients were categorized as low-risk. The majority of patients (40.13%) had a GFR above 90 and albuminuria below 30. A total of 20.3% (18.92,21.68) diabetic patients fell into the moderately increased risk category. Additionally, 7.64% (6.73,8.55) patients were classified as high/very high-risk. These details are presented in the Table 2 . Table 2: Distribution of Patients According to KDIGO Risk Categories Based on eGFR and Albuminuria [Number, Weighted Prevalence (%)] DKD stage (n, %) A1 A2 A3 G1 1313 (40.13) 259 (7.91) 34 (1.04) G2 1046 (31.97) 229 (7.00) 42 (1.28) G3a 175 (5.35) 45 (1.38) 22 (0.67) G3b 30 (0.92) 34 (1.04) 17 (0.52) Di G4 3 (0.09) 5 (0.15) 9 (0.28) G5 3 (0.09) 0 6 (0.18) Classification of diabetic individuals (n=3,272) into low (green), moderate (yellow), and high/very high-risk (orange) categories of diabetic kidney disease (DKD) based on the KDIGO 2012 guidelines. GFR stages range from G1 to G5 and albuminuria stages from A1 to A3. DKD: Diabetic kidney disease; GFR: Glomerular filtration rate According to this classification, the prevalence of high/very high-risk DKD increases with age. About 2.64% (1.14,6.00) of patients in the under 40 years group are classified in the high and very high-risk category. This increases to 3.59% (2.67,4.81) in the 40–59 years group and 12.25% (10.54,14.20) in the 60 years or older group. Age older than 60 is statistically associated with the severity of DKD (p-value < 0.001). There was no significant difference in the prevalence of different severities of DKD between men and women at various risk levels. the prevalence of severe DKD based on the duration of diabetes increases from 5.48% (4.56,6.57) in patients with less than 10 years of diabetes to 13.62% (11.22,16.43) in those with more than 10 years. According to this analysis, there was no significant association between residence location and the severity of DKD. The prevalence of DKD with high/very high risk in individuals with less than 7 years of education, 7–11 years of education, and 12 or more years of education decreases from 9.54% (8.21,11.05) to 5.21% (3.32,8.09) and 3.46% (2.27,5.24), respectively. In other words, educational level ≥ 12 was associated with decelerated severity of DKD [OR = 0.74 (0.59,0.93)] Table 3 . Table 3 Demographic and clinical characteristics of patients with diabetes according to DKD classification [Weighted prevalence 95% (CI)] Variable category Low risk DKD (ref) Moderate risk DKD Very high/high risk DKD OR crude p-value Age category (years) < 40 86.14 (80.80,90.18) 11.21 (7.63,16.18) 2.64 (1.14,6.00) - - 40–59 81.15 (78.85,83.24) 15.27 (13.36,17.39) 3.59 (2.67,4.81) 1.35 (0.94,1.95) 0.103 ≥ 60 60.87 (58.08,63.59) 26.88 (24.44,29.47) 12.25 (10.54,14.20) 3.77 (2.64,5.39) < 0.001 Sex Female 75.59 (70.31,74.76) 19.8 (17.89,21.87) 7.60 (6.39,9.03) - - Male 71.79 (69.03,74.39) 20.92 (18.60,23.46) 7.29 (5.90,8.97) 1.08 (0.92,1.26) 0.333 DM duration (years) < 10 76.22 (74.30,78.04) 18.30 (16.66,20.06) 5.48 (4.56,6.57) - - ≥ 10 60.08 (56.24,63.81) 26.30 (23.01,29.88) 13.62 (11.22,16.43) 2.25 (1.89,2.66) < 0.001 Residency Rural 73.11 (71.09,75.04) 19.66 (17.94,21.49) 7.24 (6.17,8.47) - - Urban 69.86 (66.35,73.16) 21.98 (19.05,25.22) 8.16 (6.34,10.43) 0.89 (0.76,1.07) 0.221 Educational (years) < 7 67.9 (65.59,70.13) 22.56 (20.59,24.66) 9.54 (8.21,11.05) - - 7–11 78.75 (74.36,82.57) 16.04 (12.74,20) 5.21 (3.32,8.09) 0.61 (0.48,0.77) < 0.001 ≥ 12 79.24 (75.80,82.30) 17.3 (14.47,20.55) 3.46 (2.27,5.24) 0.52 (0.42,0.63) < 0.001 Employment status Unemployed 79.49 (76.16,82.46) 16.77 (14.06,19.88) 3.74 (2.49,5.59) - - Employed 69.97 (67.91,71.95) 21.45 (19.69,23.32) 8.58 (7.44,9.89) 1.76 (1.45,2.14) < 0.001 Family history of diabetes No 72.77 (70.21,75.18) 21.02 (18.83,23.40) 6.21 (5.01,6.21) - - Yes 72.12 (69.64,74.48) 19.53 (17.48,21.76) 8.34 (6.97,9.96) 1.03 (0.88,1.20) 0.724 Ever Smoking No 72.93 (71.01,74.77) 19.73 (18.09,21.47) 7.35 (6.32,8.52) - - Yes 69.54 (65.30,73.49) 22.39 (18.90,26.31) 8.07 (5.95,10.86) 1.19 (0.99,1.44) 0.069 Physical activity (METs) sufficient 70.76 (68.73,72.72) 21.15 (19.41,23) 8.09 (6.98,9.35) - - insufficient 76.99 (73.55,80.11) 17.47 (14.71,20.62) 5.54 (3.99,7.622) 0.76 (0.79,0.97) 0.005 Dietary salt intake (gr/day) (Median cut-point) < 9.79 72.84 (70.38,75.17) 20.18 (18.09,22.44) 6.98 (5.75,8.46) - - ≥ 9.79 71.70 (69.18,74.08) 20.24 (18.15,22.52) 8.06 (6.69,9.67) 1.07 (0.92,1.24) 0.4 BMI ≥ 25 kg/m 2 No 76.34 (72.47,79.81) 17.65 (14.59,21.20) 6.01 (4.29,8.36) - - Yes 71.28 (69.31,73.18) 20.84 (19.15,22.63) 7.88 (6.81,9.11) 1.18 (0.97,1.44) 0.105 WC ≥ 95 cm No 76.98 (73.76,79.91) 17.14 (14.56,20.07) 5.88 (4.39,7.83) - - Yes 70.55 (68.46,72.55) 21.34 (19.55,23.24) 8.11 (6.98,9.42) 1.33 (1.11,1.58) 0.002 SBP ≥ 140 mmHg No 78.73 (76.62,80.69) 16.51 (14.74,18.44) 4.77 (3.82,5.94) - - Yes (Hypertension) 62.86 (59.93,65.70) 25.62 (23.08,28.32) 11.52 (9.75,13.57) 1.99 (1.71,2.33) < 0.001 DBP ≥ 90 mmHg No 74.16 (72.20,76.02) 18.94 (17.29,20.71) 6.90 (5.88,8.09) - - Yes (Hypertension) 65.84 (61.97,69.52) 24.55 (21.27,28.15) 9.61 (7.52,12.20) 1.40 (1.18,1.67) < 0.001 Hypertension No 84.45 (81.95,86.66) 13.21 (11.16,15.57) 2.34 (1.55,3.52) - - Yes 65.72 (63.44,67.93) 24.05 (22.08,26.13) 10.23 (8.89,11.75) 2.73 (2.28,3.27) < 0.001 Glycemic status Controlled (HbA1c < 7%) 79.87 (77.40,82.13) 14.95 (12.96,17.17) 5.19 (4.03,6.65) - - Uncontrolled (HbA1c ≥ 7%) 66.39 (63.90,68.78) 24.67 (22.50,26.97) 8.95 (7.59,10.52) 1.92 (1.64,2.26) < 0.001 Dyslipidemia No 82.07 (76.92,86.28) 16.05 (12.07,21.04) 1.87 (0.77,4.46) - - Yes 71.22 (69.36,73) 20.71 (19.12,22.39) 8.07 (7.05,9.23) 1.84 (1.39,2.45) < 0.001 CVD No 74.68 (72.80,76.47) 19.03 (17.43,20.74) 6.29 (5.34,7.40) - - Yes 61.51 (56.99,65.83) 25.74 (21.94,29.95) 12.76 (10.10,15.99) 1.94 (1.62,2.33) < 0.001 Aspirin use No 78.60 (76.49,80.57) 16.33 (14.58,18.25) 5.07 (4.08,6.28) - - Yes 63.30 (60.38,66.14) 25.81 (23.27,28.53) 10.88 (9.18,12.86) 2.09 (1.79,2.44) < 0.001 Anti-hypertensive medications (ACE-ARB) No hypertensive drug 80.46 (79.37,82.39) 15.27 (13.97,17.66) 3.82 (2.96,4.92) - - ACE-I or ARB, n (%) 62.83 (59,66.5) 26.26 (22.96,29.84) 10.92 (8.76,13.53) 2.38 (1.98,2.85) < 0.001 ACEI or ARB + others 54.22 (47.97,60.34) 30.87 (25.38,36.96) 14.91 (11.08,19.75) 3.52 (2.75,4.51) < 0.001 Only other classes 66.07 (60.04,71.62) 21.17 (16.64,26.55) 12.75 (9.12,17.55) 2.16 (1.66,2.81) < 0.001 Statin use No 76.57 (74.40,78.60) 18.45 (16.60,20.45) 4.99 (4.00,6.19) - - Yes 66.34 (63.47,69.10) 22.74 (20.32,25.35) 10.92 (9.21,12.89) 1.66 (1.43,1.94) < 0.001 Antidiabetic medications No antidiabetic 77.78 (75.53,79.89) 16.53 (14.69,18.56) 5.68 (4.56,7.06) - - Only oral 68.48 (65.56,71.27) 23.91 (21.37,26.65) 7.61 (6.15,9.38) 1.48 (1.26,1.74) < 0.001 Only insulin 48.48 (38.30,57.78) 30.02 (21.39,40.34) 21.50 (14.46,30.74) 4.19 (2.79,6.31) < 0.001 Oral + Insulin, n (%) 62.48 (54.34,69.97) 22.93 (16.69,30.64) 14.59 (9.84,21.09) 2.08 (1.49,2.92) < 0.001 Weighted prevalence of DKD (low, moderate, and high/very high risk) in diabetic patients by demographic, clinical, and treatment-related variables. Data are expressed as percentages with 95% CI. Odds ratios (OR) represent the crude association of each factor with high/very high-risk DKD. P-values derived from logistic regression analysis. DKD: Diabetic kidney disease; OR: Odds ratio; DM: Diabetes mellitus; BMI: Body mass index; WC: Waist circumference; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FBS: Fasting blood sugar; CVD: Cardiovascular disease Table 4 Multivariable Ordinal Logistic Regression for Predictors of DKD Severity outcome Category Odds ratio Robust std. error p-value 95% conf. interval Age (years) < 40 - - - - 40–59 0.84 0.17 0.402 (0.56, 1.26 ≥ 60 1.81 0.38 0.006 (1.19,2.74) Waist ≥ 95 cm No - - - - Yes 1.16 0.12 0.148 (0.95,1.41) Education (years) < 7 - - - - 7–11 0.93 0.12 0.591 (0.72,1.21) ≥ 12 0.74 0.08 0.008 (0.59,0.93) Ever smoker No - - - - Yes 1.30 0.14 0.010 (1.07,1.59) DM duration (years) - - - - - Yes 1.34 0.15 0.007 (1.08,1.66) CVD No - - - - Yes 1.21 0.13 0.076 (0.98,1.51) SBP ≥ 140 mmHg No - - - - Yes 1.42 0.14 < 0.001 (1.17,1.73) DBP ≥ 90 mmHg No - - - - Yes 1.17 0.13 0.153 (0.94,1.46) Dyslipidemia No - - - - Yes 1.49 0.23 0.010 (1.10,2.04) Glycemic status Controlled (HbA1c < 7) - - - - Uncontrolled (HbA1c ≥ 7) 1.57 0.14 < 0.001 (1.32,1.88) Aspirin No - - - - Yes 1.16 0.11 0.104 (0.97,1.39) Anti-hypertensive medication (ACE-ARB) No hypertensive drug - - - - ACE-I or ARB, n (%) 1.55 0.16 < 0.001 (1.26,1.90) ACEI or ARB + others 2.11 0.30 < 0.001 (1.59,2.79) Only other classes 1.02 0.21 0.019 (0.69,1.49) Antidiabetic medications No antidiabetic - - - - Only oral 1.00 0.09 0.96 (0.83,1.22) Only insulin 2.46 0.54 < 0.001 (1.60,3.78) Oral + Insulin, n (%) 1.02 0.19 0.926 (0.69,1.49) Adjusted odds ratios (AOR), robust standard errors, and 95% confidence intervals from multivariable ordinal logistic regression models assessing the association between key cardiovascular and metabolic risk factors and increasing severity of DKD. AORs reflect independent associations after controlling for other variables in the model. A p-value < 0.05 was considered statistically significant. DKD: Diabetic kidney disease; DM: Diabetes mellitus; SBP: Systolic blood pressure; DBP: Diastolic blood pressure Variables such as daily salt intake, smoking, and family history of diabetes did not show a significant association with the severity of DKD. On the other hand, sufficient physical activity is associated with DKD severity at different levels (p-value = 0.005) (Table 3 ). In patients with a BMI of 25 or higher, the prevalence of DKD with high/very high and moderately increased risk increase by 1.87% [from 6.01% (4.29,8.36) to 7.88% (6.81,9.11)] and 3.19% (from 17.65 (14.59,21.20) to 20.84 (19.15,22.63), respectively, compared to patients with a normal BMI, but this increase is not statistically significant. Similar to BMI, abdominal obesity—measured by waist circumference—did not have a significant effect on DKD severity (Table 3 ). A systolic blood pressure of 140 mmHg or higher increased the prevalence of DKD with high/very high risk and moderately increased risk by 6.75% (5.93,7.63) and 9.11% (8.34,9.88), respectively. For diastolic blood pressure of 90 mmHg or higher, the increase is 2.71% (1.64,4.11) and 5.61% (3.98,7.44), respectively. Among three variables—systolic blood pressure of 140 mmHg or higher, diastolic blood pressure of 90 mmHg or higher, and hypertension—only high systolic blood pressure showed a significant association with DKD severity at moderate and high-risk levels [OR = 1.42 (1.17,1.73)]. Additionally, the use of antihypertensive medications, similar to systolic hypertension itself, was significantly associated with the severity of DKD at moderate and high-risk levels [OR = 1.55 (1.26,1.90)]. Among patients using a combination of ACEI or ARB drugs with other medications, the prevalence of DKD with high risk is 14.91% (11.08,19.75). This rate is 10.92% (8.76,13.53) in patients taking only ACEI or ARB drugs and 12.57% (9.12,17.55) in those using other antihypertensive medications except ACEI and ARB. Chance of DKD with higher severity is 3.52 times more in patients taking ACEI or ARB concurrent with other medications Table 3 . Among the medications used, such as hypoglycemic agents, aspirin, and statins only insulin injection is statistically associated with the severity of DKD. Specifically, oral hypoglycemic agents, aspirin, and statins contribute to the increased severity of DKD by approximately 1.93% (1.59%,2.32%), 5.81% (5.10%,6.58%) and 5.93% (5.21,6.70), respectively. Notably, patients using insulin have a high-risk DKD prevalence of approximately 21.50% (14.46,30.74), which is 13.89% higher than that of patients using oral medications. Odds of progression of DKD is 4.19 times higher in patients injecting insulin which is the highest OR crude among all analyzed risk factors. In addition to pharmacological factors, underlying metabolic disorders play a significant role in the prevalence of DKD. Among these, CVD is associated with the highest prevalence of DKD, followed by diabetes and dyslipidemia. The prevalence of high-risk DKD in patients with CVD and those with dyslipidemia is 12.76% (10.10,15.99) and 8.07% (7.05,9.23), respectively. Furthermore, beyond the presence of CVD and dyslipidemia, glycemic control—defined by hemoglobin A1c levels above or below 7%—has a statistically significant impact on the prevalence and progression of DKD [OR = 1.57 (1.32,1.88)] Table 3 . The multivariable ordinal logistic regression analysis identified several significant predictors of increased diabetic kidney disease (DKD) severity. Individuals aged 60 and above had significantly higher odds of more severe DKD compared to those under 40 (AOR: 1.81, p = 0.006). A longer duration of diabetes was also associated with higher DKD severity (AOR: 1.34, p = 0.007). Higher education (≥ 12 years) was linked to reduced odds of DKD severity (AOR: 0.74, p = 0.008). Smoking (AOR: 1.30, p = 0.010), elevated systolic blood pressure (SBP ≥ 140 mmHg; AOR: 1.42, p < 0.001), dyslipidemia (AOR: 1.49, p = 0.010), and uncontrolled glycemic status (HbA1c ≥ 7%; AOR: 1.57, p < 0.001) were significantly associated with increased DKD severity. Use of ACE inhibitors or ARBs, either alone (AOR: 1.55, p < 0.001) or in combination with other antihypertensives (AOR: 2.11, p < 0.001), and insulin-only therapy (AOR: 2.46, p < 0.001) were also significantly linked to more severe DKD. Other factors, such as cardiovascular disease, aspirin use, diastolic blood pressure, and waist circumference, did not show statistically significant associations. Discussion This study indicates that cardiovascular risk factors; hyperlipidemia, uncontrolled diabetes hypertension, and central obesity are implicated in the severity DKD. It is noteworthy that ever smoking, family history of diabetes, overweight/obesity and salt-rich diets do not influence progression of DKD. Ultimately, statins, aspirin, multiple antihypertensive medications and insulin injection are increased probability of DKD compared to others. Moreover, the prevalence of high/very high-risk DKD in this study was estimated 7.73%. DKD develops through several metabolic abnormalities in the renal tissue such as glomerular hyper-filtration, progressive albuminuria, decrease in the estimated GFR and ultimately end stage renal disease. Diabetes leads to some metabolic changes that may alter kidney function leading to glomerular hyperfiltration and albuminuria 36 . Given the increasing life expectancy and the rising incidence of type 2 diabetes, the number of diabetic patients affected by DKD is on the rise 37 – 39 . In diabetic patients, DKD leads to increased mortality and disability and is a major cause of renal replacement therapies, such as dialysis and kidney transplantation 40 – 42 . According to the Global Burden of Disease CKD Study, diabetes is the underlying cause of chronic kidney disease (CKD) in 30.7% of patients. In other words, diabetes is the only etiology of CKD that has shown an increasing trend since 1990, with a 9.5% rise from 1990 to 2017 43 . Globally, the prevalence of CKD stage 3 and higher among diabetic patients is estimated to range from 6–39.3% 44–50 . In this study, the prevalence of CKD stage 3 and above was found to be 10.76%, which aligns with global estimates. A similar study in Thailand reported a CKD prevalence of 24.4% among diabetic patients 51 . This indicates a lower prevalence of CKD stage 3 and above in Iran. This difference may be attributed to variations in diagnostic tools and racial differences, as the decline in GFR progresses more rapidly in Black populations 52 . In Middle East and north Africa (MENA) region the prevalence of CKD is rapidly increasing, 215.7% from 1990 to 2019. Similar finding were reported for incidence rate (302.2% rise from 1990 to 2019) 53 . As of today, several studies have reported the prevalence of DKD in MENA region. According to a study conducted by Al-zahrani et al the prevalence of DKD was reported 18.9% in Saudi-Arabia 54 . similarly, the prevalence of DKD was reported 42.5% in Oman 55 . According to a systematic review and meta-analysis conducted on 9 studies the prevalence of DKD in Middle East was estimated from 10.8–60.78% which is significantly higher than global estimations 56 . Long-term increased blood glucose breeds functional and structural alterations in kidney cells 57 . Previous studies have shown that having diabetes for more than 15 years is associated with a higher risk of developing diabetic nephropathy. A study conducted in Saudi Arabia demonstrated a direct correlation between the duration of diabetes and the prevalence of kidney disease, with the highest incidence observed after 15 years of diabetes 29 . Similarly, another study found that a diabetes duration of more than 10 years increases the risk of nephropathy, whereas a duration of less than 10 years is associated with a lower risk 58 . This raises the question of the precise threshold duration of diabetes that significantly increases the risk of kidney disease. In the present study, it was revealed that patients with a diabetes duration of more than 10 years had a 2.25 times higher likelihood of developing DKD, a statistically significant association ( p -value < 0.001). Therefore, after 10 years of diabetes, patients require more intensive monitoring and early screening for kidney disease to facilitate timely diagnosis and management. 59 . This study also demonstrated that poor glycemic control—reflected by an HbA1c level above 7%—increases the likelihood of renal involvement in diabetic patients by 1.92 times, a statistically significant finding. Therefore, it can be concluded that HbA1c levels above 7% are associated with a substantially higher risk of DKD compared to lower levels, underscoring the importance of strict blood glucose control in diabetic patients. Among the available treatment modalities, the highest risk for the development of kidney disease is observed in patients receiving insulin therapy. This is expected, as insulin is typically prescribed to individuals whose diabetes cannot be adequately controlled with oral antidiabetic agents or who initially present with more severe disease. Therefore, it is reasonable to conclude that insulin-treated patients are at a higher risk of developing renal dysfunction. CVD risk factors such as dyslipidemia, which develop in diabetic patients, predispose them to a decline in GFR and albuminuria, thereby accelerating kidney function deterioration. A study conducted by Gall et al. demonstrated that diabetic patients with an average duration of 5.8 years since diagnosis exhibited a higher prevalence of increased urine albumin-to-creatinine ratio 60 . This finding suggests that, in patients who have had diabetes for less than ten years, factors such as elevated serum lipid levels play a significant role in the progression of kidney dysfunction and act as independent contributors to its onset. The study also indicated that coexisting dyslipidemia increases the likelihood of kidney disease progression by 1.84 times. similarly, in this study patients who take lipid-lowering medications such as statins for primary prevention or treatment of dyslipidemia are 5.93% more likely to experience high/very high-grade kidney dysfunction compared to those who do not use these medications. Moreover, the likelihood of kidney function deterioration in statin users is 1.66 times higher than in non-users. This finding can be interpreted in two ways. First, patients on statins may have had more severe metabolic disorders and poor controlled diabetes, making them more susceptible to kidney involvement. Alternatively, it could suggest that statin use does not improve kidney function. To investigate the effectiveness of statins in enhancing renal function in diabetic patients, a systematic review was conducted 61 . This review analyzed nine studies involving 3,426 patients and ultimately found that statin therapy leads to an improvement in eGFR, a reduction in serum creatinine levels, and a protective effect on kidney function. Therefore, these findings may be attributed to the more severe lipid disorders observed in these patients. Hypertension is a modifiable risk factor of CVD and independent risk factor for developing kidney dysfunction and elevated creatinine levels in diabetic patients 62 – 64 . Similar to our study, a study demonstrated that diabetic patients with hypertension have a 1.67-fold increased risk of developing DKD 65 . Comparable findings were also observed in this study, as well as in another study conducted by Siddiqui et al. 66 , which yielded similar results. The findings of this study align with previous research, further confirming the association between hypertension and kidney dysfunction. This study also showed that hypertension increases the risk of kidney impairment by 2.73 times. Notably, patients who use a combination of multiple antihypertensive medications have a greater likelihood of developing DKD compared to those who take only a single antihypertensive drug. This could be attributed to the fact that patients requiring multiple antihypertensive medications likely had a weaker response to monotherapy, necessitating the use of multiple drugs for adequate blood pressure control. On the other hand, patients who take a single antihypertensive medication have more than twice the risk of developing kidney disease. This finding suggests that merely having hypertension predisposes diabetic patients to DKD, and the use of antihypertensive drugs does not reduce this risk to the same extent as in non-hypertensive patients. Another noteworthy finding is that diabetic patients who take ACEIs or ARBs have a slightly higher risk of developing DKD compared to others. Specifically, the prevalence of high/very high-risk DKD is 1.83 folds higher in patients using ACEIs or ARBs than in those on other medications. However, the trend is reversed for moderately increased DKD, where the prevalence is approximately 5% higher in patients taking ACEIs or ARBs. These findings suggest that while ACEI or ARB therapy does not significantly reduce the risk of developing DKD, it may help prevent its progression. A study previously demonstrated that diabetic patients with a SBP above 140 mmHg had a significantly higher statistical risk of developing DKD compared to those with an SBP below 130 mmHg. Similarly, this study found that SBP above 140 mmHg doubles the risk of kidney dysfunction 67 . On the other hand, the previous study suggested that, unlike systolic blood pressure, DBP had little effect on kidney disease progression 67 . However, the present study indicates that DBP ≥ 90 mmHg is statistically associated with an increased risk of DKD. Nevertheless, elevated SBP poses a greater risk for DKD compared to elevated DBP. Thus, SBP plays a more critical role in the development of diabetic kidney disease and should be given greater attention in clinical management. Previous studies have demonstrated that aspirin use in diabetic patients with concomitant renal impairment does not significantly reduce cardiovascular outcomes or mortality 68 . As a result, the use of aspirin for primary prevention in this population is not recommended. However, other studies have indicated that aspirin may improve renal function in diabetic patients 69 , 70 . In the present study, it was shown that, in addition to the presence of cardiovascular disease—which independently increases the risk of DKD—aspirin use further elevates this risk. Specifically, aspirin use was associated with a 2.09-fold increase in the likelihood of developing DKD. Moreover, the prevalence of high or very high-risk DKD among patients who used Aspirin was approximately 5% greater compared to those who did not. Since the majority of these patients are prescribed aspirin for secondary prevention, it is likely that they have a history of prior cardiovascular events. Therefore, the observed increase in DKD risk may, at least in part, be attributable to the more advanced cardiovascular disease burden in this subgroup. Additionally, this study concluded that obesity which was defined with BMI ≥ 25, was not associated with progression of kidney dysfunction in diabetic patients. In contrast, abdominal obesity which was measured by WC was related to diabetic kidney disease progression. According to a systematic review and meta-analysis in which 14 cross-sectional studies were explored, it was concluded that abdominal obesity parameter like WC were associated with increased likelihood of DKD 71 . Several studies have demonstrated that smoking is an independent risk factor for the development of diabetic kidney disease due to hyperlipidemia, ,oxidative stress, deposition of advanced end glycation products, and glomerulosclerosis 72 , 73 . In contrast, this study demonstrated that smoking is not associated with the onset or progression of diabetic nephropathy (DN). This association was also found to be statistically non-significant. A systematic review previously indicated that smoking has a minimal effect on the development and progression of DN 16 . Specifically, current and total smoking were not linked to diabetic kidney disease, whereas former smoking was associated with an increased risk of DN in diabetic patients. These findings align with the results of the present study, as total smoking—defined as smoking even once in a lifetime—was considered as smoking in this analysis. Therefore, it can be concluded that total smoking is not associated with the development of DN, and smokers should undergo DKD screening in the same manner as non-smokers. Policy Implications Given the significant economic and healthcare burden imposed by diabetes, it is essential to establish a system for early identification of patients at higher risk for developing DKD. Early screening and timely diagnosis in these individuals can play a crucial role in preventing disease progression and reducing associated complications. Based on the findings of this study, diabetic patients receiving multiple medications—such as aspirin, statins, more than one antihypertensive agent, and insulin—those with poor glycemic status, a diabetes duration exceeding 10 years, and individuals with coexisting CVD risk factors; dyslipidemia, hypertension, and central obesity should be prioritized for more frequent and earlier renal assessments. To facilitate this, a risk assessment model could be developed to stratify patients based on key predictive factors. According to this study, the variables with the highest odds ratios—namely age over 60, presence of hypertension, use of multiple antihypertensive medications, and insulin therapy—should be assigned the greatest weight in such a model. Furthermore, clinicians should be encouraged to monitor renal function at shorter intervals in patients presenting with these high-risk factors. Strengths and limitations of the current study This study used data from the 2021 STEPS survey, a national, population-based cross-sectional study, suggesting a strong representative sample of the adult Iranian population. In addition, the sample size is relatively large (3,272 diabetic patients), which increases the statistical power of the study. And a wide range of CVD risk factors have been evaluated in the present study. Both points are the strengths of our study. Conversely, the cross-sectional design of the study limits the ability to determine causal relationships between CVD risk factors and DKD. Moreover, some data are self-reported that might be increase the information bias. Conclusion In conclusion, some CVD risk factors including hyperlipidemia, uncontrolled diabetes hypertension, and central obesity are associated with the development of DKD. So, it is crucial to determine these risk factors in diabetic patients and design a specific protocol for screening and early diagnosis of DKD in patients with those risk factors. By recognized high-risk/very high-risk patients in early stages some interventions might be beneficial for decelerating the progression of DKD. In fact, policy makers should establish some policies and scoring systems to recognize high-risk patients to prevent long-term renal replacement therapy in future. Materials and Methods Research design and participants In the current study, we used data of the STEPS 2021 survey which is a national, population-based cross-sectional study conducted on a representative sample of adult Iranian population to determine the prevalence of risk factors for non-communicable diseases. The STEPS includes three phases. The first phase is a questionnaire. The second and third phases are anthropometric and biochemical measures, respectively. Detailed information including study design and study objectives were provided for all participants 17 . In next step, an informed consent was attained from all participants. To obtain a representative sample at both the national and provincial levels, a systematic cluster sampling approach was used, based on provincial population sizes and appropriate weighting in the following phases, 27,745 participants underwent physical assessments, and 18,119 individuals submitted laboratory samples. The second phase encompassed all adults aged 18 and above, whereas the third phase was limited to participants aged 25 and older 17 . The inclusion criteria comprised all individuals with diabetes aged 25 years and older, for whom biochemical data, particularly albuminuria and estimated glomerular filtration rate (eGFR), were available. Exclusion criteria were patients with incomplete data for any of the variables, younger than 25 years old, or have past history of CVD. Based on these criteria, 3,322 diabetic patients were included in the study, among whom 50 individuals were excluded due to missing albuminuria and eGFR data. This study was conducted according to the Declaration of Helsinki guidelines, and all methods were performed in accordance with institutional and national ethical standards. The Research Ethics Committee of the Endocrine & Metabolism Research Institute at Tehran University of Medical Sciences approved the study (Reference Code: IR.TUMS.EMRI.REC.1403.100). Data collection In the first stage of the STEPS survey, information was collected using a structured questionnaire that covered various topics such as demographics, diet, medical history, physical activity, quality of life, lifestyle advice, cancer screenings, injuries, tobacco and alcohol use, and household possessions. The second stage consisted of physical assessments, including measurements of weight, height, waist and hip circumference, blood pressure, and heart rate. Height was measured using a standard stadiometer with participants standing upright against a wall, ensuring proper alignment of the heels, hips, and head. Weight was recorded with a calibrated digital scale (Inofit), with participants wearing light clothing and no shoes. Before taking blood pressure readings, participants rested for 15 minutes. Blood pressure was then measured three times at three-minute intervals using a Beurer sphygmomanometer, with the final value being the average of the second and third readings. The third stage involved lab tests analyzing total serum cholesterol, HDL cholesterol, triglycerides, fasting plasma glucose, whole blood HbA1C, and urine samples for albumin and creatinine. All samples were transported and stored under a cold chain system and analyzed at a central lab. Definitions of Variables Diabetic kidney disease (DKD) was defined as an estimated glomerular filtration rate (eGFR) 30 mg/g in individuals with diabetes 18 . The occurrence of DKD was evaluated according to the Kidney Disease Improving Global Outcomes (KDIGO) classification, with a low-risk category comprising G1 (GFR > 90 mL/min/1.73m²) and A1 (albuminuria < 30 mg/g), or G2 (GFR 60–89 mL/min/1.73m²) with A1. Six eGFR categories were included namely; G1, G2, G3a, G3b, G4 and G5 (G1 represents ≥ 90, G2 represents 60–89, G3a represents 45–59, G3b represents 30–44, G4 represents 15–29 and G5 represents below 15 of eGFR). Three Albuminuria includes: A1 300 mg/g. Low-risk category comprises of G1A1 and G2A1; moderately increased risk category includes G1A2, G2A2 and G3aA1; and high/very high-risk category comprises of G1A3, G2A3, G3aA2, G3bA1, G3aA3, G3bA2, G3bA3, G4A3, and G5A3 19–22 . The demographic variables assessed included age (categorized as under 40, 40 to under 65, and 65 or older), sex, place of residence (urban or rural), marital status (never married, married, or divorced/widowed), province, and health insurance coverage (basic or supplementary). Education level was measured as the number of years of schooling completed (0, 1–6, 7–11, or 12 or more years) and categorized into three groups: <7 years, 7–11 years, and ≥ 12 years of formal education. Employment status was grouped into employed, unemployed, retired, or engaged in unpaid work. Smoking status was considered positive if the individual had ever smoked any tobacco products 23 . Physical activity was classified as adequate if it exceeded 600 MET-minutes per week and inadequate if below this threshold, following WHO guidelines 24 , 25 . High salt intake was defined as daily consumption exceeding the median intake of 9.79 g/day in this dataset. Diabetes was defined by fasting blood sugar (FBS) ≥ 126 mg/dL, HbA1C ≥ 6.5%, or the use of glucose-lowering medications 26 , 27 . Control of diabetes was based on HbA1C values, with < 7% considered controlled and ≥ 7% uncontrolled. Duration of diabetes was classified into two groups: under 10 years and over 10 years. Treatment for diabetes was categorized into three groups: oral medications only, insulin only, or a combination of both, based on participants’ reports of insulin and/or oral antidiabetic use. Family history of diabetes was recorded if participants reported a diagnosis in any first-degree relatives (parents, siblings, or children). Cardiovascular disease (CVD) was defined by a self-reported history of heart attack, angina, coronary procedures (such as bypass surgery, angioplasty, or stent insertion), or stroke diagnosed by a healthcare professional. Hypertension was defined as an average systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medications 28 , 29 .. Obesity/overweight was defined by a body mass index (BMI) ≥ 25 kg/m² 30,31 . Central obesity was identified as a waist circumference ≥ 95 cm, regardless of sex 32 . Dyslipidemia was present if any of the following criteria were met: triglycerides ≥ 150 mg/dL, total cholesterol ≥ 200 mg/dL, LDL-C ≥ 160 mg/dL, HDL-C < 40 mg/dL in men or < 50 mg/dL in women, or current use of lipid-lowering medications 33 – 35 . Medication use (including statins, aspirin, angiotensin-converting enzyme inhibitors [ACEIs], and angiotensin receptor blockers [ARBs]) was considered positive if any of these drugs were prescribed. Statistical Analysis Demographic variables were summarized using weighted proportions (± SE) for categorical variables and weighted means (± SE) for continuous variables. The outcome variable was risk category for DKD, categorized as: Low risk, Moderate risk, and High/Very high risk. Multivariable ordinal logistic regression (OLR) was employed to examine independent associations between risk factors and DKD severity. In this model, each coefficient represents the change in the logarithm odds of being in a higher versus lower DKD risk category per unit increase in the corresponding predictor. To ensure the validity of the OLR model, the proportional odds assumption was tested using Brant’s test. Given the clustered nature of the data, robust standard errors were used to account for potential heteroscedasticity and within-cluster correlation. A backward stepwise elimination procedure was used for variable selection. The initial model included all candidate explanatory variables. Variables with p-values > 0.2 were removed sequentially to derive the final model. A p-value threshold of 0.2 was selected to minimize the exclusion of potentially meaningful predictors with borderline significance. All statistical analyses were performed using R software (version 4.4.1; release 2023.06.0). Associations were reported as odds ratios (OR) and adjusted odds ratios (AOR) with corresponding 95% confidence intervals (CI). A p-value < 0.05 was considered statistically significant. Abbreviations ACEI: Angiotensin-Converting Enzyme Inhibitor AOR: Adjusted Odds Ratio ARB: Angiotensin Receptor Blocker BMI: Body Mass Index CVD: Cardiovascular Disease DBP: Diastolic Blood Pressure DKD: Diabetic Kidney Disease DM: Diabetes Mellitus eGFR: Estimated Glomerular Filtration Rate FBS: Fasting Blood Sugar HbA1c: Hemoglobin A1C HDL-C: High-Density Lipoprotein Cholesterol KDIGO: Kidney Disease: Improving Global Outcomes LDL-C: Low-Density Lipoprotein Cholesterol MET: Metabolic Equivalent of Task OLR: Ordinal Logistic Regression OR: Odds Ratio SBP: Systolic Blood Pressure STEPS: STEPwise Approach to NCD Risk Factor Surveillance TG: Triglycerides WC: Waist Circumference Declarations Acknowledgement None. Author contribution RG: data curation, original draft preparation, reviewing, and editing; MKG: methodology, formal analysis, validation, reviewing, and editing;AG: methodology, validation, reviewing, and editing;SHM: reviewed the literature, reviewing, and editing; SKH: data curation, reviewing, and editing; AZ: data curation, reviewing, and editing; MPS; supervision, data curation, validation, reviewing, and editing; OTM: supervision, data curation, validation, reviewing, and editing; MRM: reviewing and editing; SA supervised the project; All authors revised the manuscript carefully and approved the final draft. RG and MKG: contributed equally as first authors. MPS and OTM: contributed equally as correspondence authors. Ethics approval and consent to participate This study was conducted according to the Declaration of Helsinki guidelines, and all methods were performed in accordance with institutional and national ethical standards. The Research Ethics Committee of the Endocrine & Metabolism Research Institute at Tehran University of Medical Sciences approved the study (Reference Code: IR.TUMS.EMRI.REC.1403.100). Participants provided informed consent in person after receiving written information detailing the study's objectives and procedures. Consent for publication Not required. Availability of data and materials The datasets can be obtained from the corresponding author upon reasonable request. All inquiries regarding data access should be directed to the corresponding author. Funding There was no funding. Conflict of Interest The authors had no conflict of interest. AI declaration AI was not applied for any part of this study. References Kidanie, B. B. et al. Determinants of Diabetic Complication Among Adult Diabetic Patients in Debre Markos Referral Hospital, Northwest Ethiopia, 2018: Unmatched Case Control Study. Diabetes Metab. Syndr. Obes. 13 , 237–245. 10.2147/dmso.S237250 (2020). Sun, H. et al. 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Bmj 314 , 783 (1997). Lv, J., Ren, C. & Hu, Q. Effect of statins on the treatment of early diabetic nephropathy: a systematic review and meta-analysis of nine randomized controlled trials. Annals Palliat. Med. 10 , 115481557–115411557 (2021). Stamler, J., Vaccaro, O., Neaton, J. D., Wentworth, D. & Group, M. R. F. I. T. R. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes care . 16 , 434–444 (1993). Tapp, R. J. et al. Albuminuria is evident in the early stages of diabetes onset: results from the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab). Am. J. Kidney Dis. 44 , 792–798 (2004). Hovind, P. et al. Predictors for the development of microalbuminuria and macroalbuminuria in patients with type 1 diabetes: inception cohort study. Bmj 328 , 1105 (2004). Wagnew, F. et al. Diabetic nephropathy and hypertension in diabetes patients of sub-Saharan countries: a systematic review and meta-analysis. BMC Res. Notes . 11 , 1–7 (2018). Siddiqui, K., George, T. P., Joy, S. S. & Alfadda, A. A. Risk factors of chronic kidney disease among type 2 diabetic patients with longer duration of diabetes. Front. Endocrinol. 13 , 1079725 (2022). Bakris, G. L. et al. Effects of blood pressure level on progression of diabetic nephropathy: results from the RENAAL study. Arch. Intern. Med. 163 , 1555–1565 (2003). Lin, Y. C., Chen, B. L., Chen, W. T., Chien, L. N. & Huang, C. Y. Low-dose aspirin for prevention of cardiovascular mortality in patients with type 2 diabetes and chronic kidney disease: A real‐world nationwide cohort study. J. Diabetes Invest. 15 , 459–467 (2024). Sacco, M. et al. Primary prevention of cardiovascular events with low-dose aspirin and vitamin E in type 2 diabetic patients: results of the Primary Prevention Project (PPP) trial. Diabetes care . 26 , 3264–3272 (2003). Leung, W. Y. et al. Lack of benefits for prevention of cardiovascular disease with aspirin therapy in type 2 diabetic patients-a longitudinal observational study. Cardiovasc. Diabetol. 8 , 1–10 (2009). Zhao, Q., Yi, X. & Wang, Z. Meta-Analysis of the Relationship between Abdominal Obesity and Diabetic Kidney Disease in Type 2 Diabetic Patients. Obes. Facts . 14 , 338–345. 10.1159/000516391 (2021). Chakkarwar, V. A. Smoking in diabetic nephropathy: sparks in the fuel tank? World J. diabetes . 3 , 186 (2012). Orth, S. R. & Hallan, S. I. Smoking: a risk factor for progression of chronic kidney disease and for cardiovascular morbidity and mortality in renal patients—absence of evidence or evidence of absence? Clin. J. Am. Soc. Nephrol. 3 , 226–236 (2008). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7081883","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488023797,"identity":"fdb12474-1bb7-4f50-a599-c3166fa90eb5","order_by":0,"name":"Rad Ghannadzadeh","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rad","middleName":"","lastName":"Ghannadzadeh","suffix":""},{"id":488023798,"identity":"fe0d3916-4f2a-4f26-93d8-4ddca1fe9f95","order_by":1,"name":"Maryam Karimi Ghahfarokhi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"Karimi","lastName":"Ghahfarokhi","suffix":""},{"id":488023799,"identity":"ebbfd1cf-7a03-4f71-8116-9ad452274b94","order_by":2,"name":"Ali Golestani","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Golestani","suffix":""},{"id":488023800,"identity":"37acee0f-9d8f-4056-98dc-9da9bb08e0b2","order_by":3,"name":"Shahrzad Mohseni","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shahrzad","middleName":"","lastName":"Mohseni","suffix":""},{"id":488023801,"identity":"6422556f-9bdf-4f5c-991b-92249d291f27","order_by":4,"name":"Sepehr Khosravi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sepehr","middleName":"","lastName":"Khosravi","suffix":""},{"id":488023802,"identity":"604c3a1e-2c91-4a96-965f-f90aed20b1b2","order_by":5,"name":"Alireza Azarboo","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Azarboo","suffix":""},{"id":488023803,"identity":"8e3c8fc6-b928-4e28-a311-35106d327cd2","order_by":6,"name":"Mohammadreza Mohajeri-Tehrani","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammadreza","middleName":"","lastName":"Mohajeri-Tehrani","suffix":""},{"id":488023804,"identity":"1f94e0eb-4fe1-4034-827d-69931ed4c791","order_by":7,"name":"Mahnaz Pejman Sani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYNACAxsGNhSBBMJa0iBaDhCvheEwhDqAVxEU6M5uf/i5oOC8PZ908wPmj23b7BnYDz9geLgHtxazO2eMpWcY3E5skzlmwHCw7XZiA0+aAUPCMzxabuQwSPMY3E5gk0gAawH6IgfoFzxONLuR/vg3j8E5ezaJ9A8gLfYM/G8IaUkwA9pygLFNIgdsC2ODBCFb7pwxs+YxSE4Eaik4cOYc0FMSzwwO4NVyu/3xbZ4/dvbyM9I3Pqgou23Pz5/88OEPPFoYJJDYYHVsDITiRwKv7CgYBaNgFIwCIAAAVbFQ4LttJZIAAAAASUVORK5CYII=","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mahnaz","middleName":"Pejman","lastName":"Sani","suffix":""},{"id":488023805,"identity":"337b8fb2-bd40-49c0-ba83-2f7fb0c83ea9","order_by":8,"name":"Ozra Tabatabaei-Malazy","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ozra","middleName":"","lastName":"Tabatabaei-Malazy","suffix":""}],"badges":[],"createdAt":"2025-07-09 08:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7081883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7081883/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-34742-5","type":"published","date":"2026-01-09T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100069619,"identity":"dfd312f4-9e39-439f-88c2-a4f350ea42ba","added_by":"auto","created_at":"2026-01-12 16:15:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1513163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7081883/v1/3d348e3e-d1a8-44dd-8f1b-63c273707443.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of cardiovascular risk factors with diabetic kidney disease severity in the Iranian population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus (DM) is a global epidemic reshaping the health landscape of the 21st century. Every 6 seconds, someone succumbs to diabetes-related complications \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and an estimated figure of 537\u0026nbsp;million adults, over 10 percent of the global population live daily with the disease \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. By 2045, this number is projected to expand to 783\u0026nbsp;million, due to aging and lifestyle changes \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e., it is expected that the number of older diabetic adults (55\u0026ndash;64 years) will rise from 10.8\u0026ndash;21.7% in 2045 \u003csup\u003e4\u003c/sup\u003e. Beyond its direct effect, diabetes is responsible for devastating complications, that diabetic kidney disease (DKD) is the most important among them. DKD, defined by albuminuria or reduced renal filtration, is the leading cause of end stage renal disease (ESRD) worldwide. DKD causes up to 50% of all cases of ESRD in the United States \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and around 35% of renal replacement therapy in Iran \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. DKD affects 30\u0026ndash;40% of diabetic patients and has two major complications. It can lead to ESRD and it doubles the risks for cardiovascular mortality in diabetic patients \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. DKD serves as a very strong accelerator of cardiovascular disease (CVD), considered the leading cause of mortality in diabetes \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This dual threat originates from the interaction of metabolic, vascular, and inflammatory pathways that are significantly influenced by the major CVD risk factors\u0026mdash;hypertension, obesity, dyslipidemia, and smoking \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eResearch underlines the different mechanisms through which the risk factors for CVD affect DKD. Hypertension, which may affect up to 70% of diabetic patients, accelerates glomerular injury \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e; dyslipidemia, a prevalent comorbidity present in patients with DKD, promotes renal injury through lipid-dependent mechanisms \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Obesity enhances insulin resistance and renal hyperfiltration, thus establishing a vicious circle of metabolic and renal stress \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e; and smoking impairs endothelial function, reducing renal perfusion and amplifying inflammatory injury \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough research has identified that these factors contribute to adverse outcomes of DKD, there is a need to understand prevalence and impact in different nations of diabetic with DKD. Leveraging nationally representative data from the 2021 STEPwise approach to Noncommunicable Disease Risk Factor Surveillance (STEPS) in Iran, this study pioneers a comprehensive overview of the prevalence and distribution of CVD risk factors in diabetic patients with DKD as comorbidity. In this study, we aimed to investigate the impact of cardiovascular risk factors on the progression of diabetic kidney disease and identify which specific factors\u0026mdash;including obesity, abdominal obesity, hypertension, use of antihypertensive medications, use of glucose-lowering drugs, smoking, high salt intake, insufficient physical activity, and hyperlipidemia\u0026mdash;contribute to the worsening severity of diabetic kidney disease. By identifying these risk factors, our goal was to enable the timely detection of high-risk patients and to help prevent further progression of the disease through targeted interventions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eUltimately, 3,272 diabetic patients with an average age of 57.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 years were analyzed. Males to Females ratio was 59%/41% individuals lived in rural areas to those lived in urban areas was 74%/26%. The baseline characteristics of these patients are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics of the total diabetic population (weighted and 95%CI)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal DM (n\u0026thinsp;=\u0026thinsp;3272)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.76 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.21 (57.35,58.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (0.57,0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (0.39,0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eResidency (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (0.24,0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (0.72,0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking (Question: ever) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (0.17,0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.43 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.10 (29.23,29.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.07 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.26 (100.56,101.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.31 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.39 (135.54,137.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.18 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.22 (80.74,81.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBS (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151.78 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 1.20 (149.42,154.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.85 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.04 (7.78,7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.15 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.84 (169.51,172.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.21 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.71 (92.81,95.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.89 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.19 (40.50,41.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180.70 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 1.95 (176.88,184.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.39 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 0.38 (85.65,87.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine Albumin (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.26 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 4.32 (49.78,66.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (0.63,0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDescriptive statistics for the study population (n\u0026thinsp;=\u0026thinsp;3,272) with diabetes aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years in Iran, 2021. Data are presented as weighted means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE) or weighted percentages with 95% confidence intervals (CI), unless otherwise specified. Variables include anthropometric, clinical, and laboratory parameters\u003c/p\u003e\n \u003cp\u003eDM: Diabetes mellitus; BMI: Body mass index; WC: Waist circumference; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FBS: Fasting blood sugar; HbA1C: Hemoglobin A1C; LDL-C: Low density lipoprotein-cholesterol; HDL-C: High density lipoprotein-cholesterol; TG: Triglyceride; eGFR: estimated glomerular filtration rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, 72.1% (70.56,73.64) of patients were categorized as low-risk. The majority of patients (40.13%) had a GFR above 90 and albuminuria below 30. A total of 20.3% (18.92,21.68) diabetic patients fell into the moderately increased risk category. Additionally, 7.64% (6.73,8.55) patients were classified as high/very high-risk. These details are presented in the Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eDistribution of Patients According to KDIGO Risk Categories Based on eGFR and Albuminuria [Number, Weighted Prevalence (%)]\u003c/div\u003e\n \u003ctable style=\"border-collapse: collapse;border: none;width: 564px;\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113.15pt;border: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eDKD stage (n, %)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eA1\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94.5pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eA2\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58.5pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eA3\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113.15pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113.15pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eG1\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(0, 176, 80);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e1313 (40.13)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: yellow;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e259 (7.91)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e34 (1.04)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113.15pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eG2\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(0, 176, 80);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e1046 (31.97)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: yellow;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e229 (7.00)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e42 (1.28)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113.15pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eG3a\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: yellow;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e175 (5.35)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e45 (1.38)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e22 (0.67)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113.15pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eG3b\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e30 (0.92)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e34 (1.04)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e17 (0.52)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113.15pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eDi G4\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e3 (0.09)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e5 (0.15)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e9 (0.28)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113.15pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eG5\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:16px;line-height:115%;font-family:\"Aptos\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e3 (0.09)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;background: rgb(255, 102, 0);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:0cm;line-height:normal;font-size:16px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",serif;color:black;'\u003e6 (0.18)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 422.75pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;line-height:150%;font-size:16px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-size:15px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eClassification of diabetic individuals (n=3,272) into low (green), moderate (yellow), and high/very high-risk (orange) categories of diabetic kidney disease (DKD) based on the KDIGO 2012 guidelines. GFR stages range from G1 to G5 and albuminuria stages from A1 to A3.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;line-height:150%;font-size:16px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style='font-family:\"Times New Roman\",serif;'\u003eDKD: Diabetic kidney disease; GFR: Glomerular filtration rate\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to this classification, the prevalence of high/very high-risk DKD increases with age. About 2.64% (1.14,6.00) of patients in the under 40 years group are classified in the high and very high-risk category. This increases to 3.59% (2.67,4.81) in the 40\u0026ndash;59 years group and 12.25% (10.54,14.20) in the 60 years or older group. Age older than 60 is statistically associated with the severity of DKD (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was no significant difference in the prevalence of different severities of DKD between men and women at various risk levels. the prevalence of severe DKD based on the duration of diabetes increases from 5.48% (4.56,6.57) in patients with less than 10 years of diabetes to 13.62% (11.22,16.43) in those with more than 10 years. According to this analysis, there was no significant association between residence location and the severity of DKD. The prevalence of DKD with high/very high risk in individuals with less than 7 years of education, 7\u0026ndash;11 years of education, and 12 or more years of education decreases from 9.54% (8.21,11.05) to 5.21% (3.32,8.09) and 3.46% (2.27,5.24), respectively. In other words, educational level\u0026thinsp;\u0026ge;\u0026thinsp;12 was associated with decelerated severity of DKD [OR\u0026thinsp;=\u0026thinsp;0.74 (0.59,0.93)] Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and clinical characteristics of patients with diabetes according to DKD classification [Weighted prevalence 95% (CI)]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow risk DKD (ref)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerate risk DKD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVery high/high risk DKD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR crude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge category (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.14 (80.80,90.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.21 (7.63,16.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64 (1.14,6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.15 (78.85,83.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.27 (13.36,17.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59 (2.67,4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (0.94,1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.87 (58.08,63.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.88 (24.44,29.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.25 (10.54,14.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77 (2.64,5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.59 (70.31,74.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.8 (17.89,21.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.60 (6.39,9.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.79 (69.03,74.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.92 (18.60,23.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.29 (5.90,8.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.92,1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM duration (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.22 (74.30,78.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.30 (16.66,20.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.48 (4.56,6.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.08 (56.24,63.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.30 (23.01,29.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.62 (11.22,16.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25 (1.89,2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.11 (71.09,75.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.66 (17.94,21.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.24 (6.17,8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.86 (66.35,73.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.98 (19.05,25.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.16 (6.34,10.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.76,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.9 (65.59,70.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.56 (20.59,24.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.54 (8.21,11.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.75 (74.36,82.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.04 (12.74,20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.21 (3.32,8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 (0.48,0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.24 (75.80,82.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.3 (14.47,20.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.46 (2.27,5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52 (0.42,0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.49 (76.16,82.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.77 (14.06,19.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74 (2.49,5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.97 (67.91,71.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.45 (19.69,23.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.58 (7.44,9.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76 (1.45,2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.77 (70.21,75.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.02 (18.83,23.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.21 (5.01,6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.12 (69.64,74.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.53 (17.48,21.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.34 (6.97,9.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.88,1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver Smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.93 (71.01,74.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.73 (18.09,21.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.35 (6.32,8.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.54 (65.30,73.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.39 (18.90,26.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.07 (5.95,10.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19 (0.99,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity (METs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.76 (68.73,72.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.15 (19.41,23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.09 (6.98,9.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.99 (73.55,80.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.47 (14.71,20.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.54 (3.99,7.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76 (0.79,0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDietary salt intake (gr/day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Median cut-point)\u0026thinsp;\u0026lt;\u0026thinsp;9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.84 (70.38,75.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.18 (18.09,22.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.98 (5.75,8.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.70 (69.18,74.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.24 (18.15,22.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.06 (6.69,9.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (0.92,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.34 (72.47,79.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.65 (14.59,21.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.01 (4.29,8.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.28 (69.31,73.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.84 (19.15,22.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.88 (6.81,9.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.97,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC\u0026thinsp;\u0026ge;\u0026thinsp;95 cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.98 (73.76,79.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.14 (14.56,20.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.88 (4.39,7.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.55 (68.46,72.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.34 (19.55,23.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.11 (6.98,9.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33 (1.11,1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.73 (76.62,80.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.51 (14.74,18.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.77 (3.82,5.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (Hypertension)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.86 (59.93,65.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.62 (23.08,28.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.52 (9.75,13.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99 (1.71,2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.16 (72.20,76.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.94 (17.29,20.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.90 (5.88,8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (Hypertension)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.84 (61.97,69.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.55 (21.27,28.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.61 (7.52,12.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (1.18,1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.45 (81.95,86.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.21 (11.16,15.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34 (1.55,3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.72 (63.44,67.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.05 (22.08,26.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.23 (8.89,11.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.73 (2.28,3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlycemic status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled (HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.87 (77.40,82.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.95 (12.96,17.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.19 (4.03,6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUncontrolled (HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.39 (63.90,68.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.67 (22.50,26.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.95 (7.59,10.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92 (1.64,2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyslipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.07 (76.92,86.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.05 (12.07,21.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87 (0.77,4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.22 (69.36,73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.71 (19.12,22.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.07 (7.05,9.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84 (1.39,2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.68 (72.80,76.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.03 (17.43,20.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.29 (5.34,7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.51 (56.99,65.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.74 (21.94,29.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.76 (10.10,15.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94 (1.62,2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAspirin use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.60 (76.49,80.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.33 (14.58,18.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07 (4.08,6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.30 (60.38,66.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.81 (23.27,28.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.88 (9.18,12.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09 (1.79,2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnti-hypertensive medications (ACE-ARB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo hypertensive drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.46 (79.37,82.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.27 (13.97,17.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82 (2.96,4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACE-I or ARB, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.83 (59,66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.26 (22.96,29.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.92 (8.76,13.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38 (1.98,2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEI or ARB\u0026thinsp;+\u0026thinsp;others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.22 (47.97,60.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.87 (25.38,36.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.91 (11.08,19.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.52 (2.75,4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly other classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.07 (60.04,71.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.17 (16.64,26.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.75 (9.12,17.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16 (1.66,2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatin use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.57 (74.40,78.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.45 (16.60,20.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.99 (4.00,6.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.34 (63.47,69.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.74 (20.32,25.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.92 (9.21,12.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (1.43,1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntidiabetic medications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo antidiabetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.78 (75.53,79.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.53 (14.69,18.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.68 (4.56,7.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly oral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.48 (65.56,71.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.91 (21.37,26.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.61 (6.15,9.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48 (1.26,1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.48 (38.30,57.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.02 (21.39,40.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.50 (14.46,30.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.19 (2.79,6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOral\u0026thinsp;+\u0026thinsp;Insulin, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.48 (54.34,69.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.93 (16.69,30.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.59 (9.84,21.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08 (1.49,2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eWeighted prevalence of DKD (low, moderate, and high/very high risk) in diabetic patients by demographic, clinical, and treatment-related variables. Data are expressed as percentages with 95% CI. Odds ratios (OR) represent the crude association of each factor with high/very high-risk DKD. P-values derived from logistic regression analysis.\u003c/p\u003e\n \u003cp\u003eDKD: Diabetic kidney disease; OR: Odds ratio; DM: Diabetes mellitus; BMI: Body mass index; WC: Waist circumference; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FBS: Fasting blood sugar; CVD: Cardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable Ordinal Logistic Regression for Predictors of DKD Severity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eoutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRobust std. error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% conf. interval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.56, 1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.19,2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist\u0026thinsp;\u0026ge;\u0026thinsp;95 cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.95,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.72,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.59,0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver smoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.07,1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM duration (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.08,1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.98,1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.17,1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.94,1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyslipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.10,2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlycemic status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled (HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUncontrolled (HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.32,1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAspirin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.97,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnti-hypertensive medication (ACE-ARB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo hypertensive drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACE-I or ARB, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.26,1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEI or ARB\u0026thinsp;+\u0026thinsp;others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.59,2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly other classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.69,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntidiabetic medications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo antidiabetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly oral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.83,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnly insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.60,3.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOral\u0026thinsp;+\u0026thinsp;Insulin, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.69,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAdjusted odds ratios (AOR), robust standard errors, and 95% confidence intervals from multivariable ordinal logistic regression models assessing the association between key cardiovascular and metabolic risk factors and increasing severity of DKD. AORs reflect independent associations after controlling for other variables in the model. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n \u003cp\u003eDKD: Diabetic kidney disease; DM: Diabetes mellitus; SBP: Systolic blood pressure; DBP: Diastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eVariables such as daily salt intake, smoking, and family history of diabetes did not show a significant association with the severity of DKD. On the other hand, sufficient physical activity is associated with DKD severity at different levels (p-value\u0026thinsp;=\u0026thinsp;0.005) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn patients with a BMI of 25 or higher, the prevalence of DKD with high/very high and moderately increased risk increase by 1.87% [from 6.01% (4.29,8.36) to 7.88% (6.81,9.11)] and 3.19% (from 17.65 (14.59,21.20) to 20.84 (19.15,22.63), respectively, compared to patients with a normal BMI, but this increase is not statistically significant. Similar to BMI, abdominal obesity\u0026mdash;measured by waist circumference\u0026mdash;did not have a significant effect on DKD severity (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA systolic blood pressure of 140 mmHg or higher increased the prevalence of DKD with high/very high risk and moderately increased risk by 6.75% (5.93,7.63) and 9.11% (8.34,9.88), respectively. For diastolic blood pressure of 90 mmHg or higher, the increase is 2.71% (1.64,4.11) and 5.61% (3.98,7.44), respectively. Among three variables\u0026mdash;systolic blood pressure of 140 mmHg or higher, diastolic blood pressure of 90 mmHg or higher, and hypertension\u0026mdash;only high systolic blood pressure showed a significant association with DKD severity at moderate and high-risk levels [OR\u0026thinsp;=\u0026thinsp;1.42 (1.17,1.73)]. Additionally, the use of antihypertensive medications, similar to systolic hypertension itself, was significantly associated with the severity of DKD at moderate and high-risk levels [OR\u0026thinsp;=\u0026thinsp;1.55 (1.26,1.90)]. Among patients using a combination of ACEI or ARB drugs with other medications, the prevalence of DKD with high risk is 14.91% (11.08,19.75). This rate is 10.92% (8.76,13.53) in patients taking only ACEI or ARB drugs and 12.57% (9.12,17.55) in those using other antihypertensive medications except ACEI and ARB. Chance of DKD with higher severity is 3.52 times more in patients taking ACEI or ARB concurrent with other medications Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eAmong the medications used, such as hypoglycemic agents, aspirin, and statins only insulin injection is statistically associated with the severity of DKD. Specifically, oral hypoglycemic agents, aspirin, and statins contribute to the increased severity of DKD by approximately 1.93% (1.59%,2.32%), 5.81% (5.10%,6.58%) and 5.93% (5.21,6.70), respectively. Notably, patients using insulin have a high-risk DKD prevalence of approximately 21.50% (14.46,30.74), which is 13.89% higher than that of patients using oral medications. Odds of progression of DKD is 4.19 times higher in patients injecting insulin which is the highest OR crude among all analyzed risk factors. In addition to pharmacological factors, underlying metabolic disorders play a significant role in the prevalence of DKD. Among these, CVD is associated with the highest prevalence of DKD, followed by diabetes and dyslipidemia. The prevalence of high-risk DKD in patients with CVD and those with dyslipidemia is 12.76% (10.10,15.99) and 8.07% (7.05,9.23), respectively. Furthermore, beyond the presence of CVD and dyslipidemia, glycemic control\u0026mdash;defined by hemoglobin A1c levels above or below 7%\u0026mdash;has a statistically significant impact on the prevalence and progression of DKD [OR\u0026thinsp;=\u0026thinsp;1.57 (1.32,1.88)] Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe multivariable ordinal logistic regression analysis identified several significant predictors of increased diabetic kidney disease (DKD) severity. Individuals aged 60 and above had significantly higher odds of more severe DKD compared to those under 40 (AOR: 1.81, p\u0026thinsp;=\u0026thinsp;0.006). A longer duration of diabetes was also associated with higher DKD severity (AOR: 1.34, p\u0026thinsp;=\u0026thinsp;0.007). Higher education (\u0026ge;\u0026thinsp;12 years) was linked to reduced odds of DKD severity (AOR: 0.74, p\u0026thinsp;=\u0026thinsp;0.008). Smoking (AOR: 1.30, p\u0026thinsp;=\u0026thinsp;0.010), elevated systolic blood pressure (SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg; AOR: 1.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), dyslipidemia (AOR: 1.49, p\u0026thinsp;=\u0026thinsp;0.010), and uncontrolled glycemic status (HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7%; AOR: 1.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with increased DKD severity. Use of ACE inhibitors or ARBs, either alone (AOR: 1.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or in combination with other antihypertensives (AOR: 2.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and insulin-only therapy (AOR: 2.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significantly linked to more severe DKD. Other factors, such as cardiovascular disease, aspirin use, diastolic blood pressure, and waist circumference, did not show statistically significant associations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study indicates that cardiovascular risk factors; hyperlipidemia, uncontrolled diabetes hypertension, and central obesity are implicated in the severity DKD. It is noteworthy that ever smoking, family history of diabetes, overweight/obesity and salt-rich diets do not influence progression of DKD. Ultimately, statins, aspirin, multiple antihypertensive medications and insulin injection are increased probability of DKD compared to others. Moreover, the prevalence of high/very high-risk DKD in this study was estimated 7.73%.\u003c/p\u003e\u003cp\u003eDKD develops through several metabolic abnormalities in the renal tissue such as glomerular hyper-filtration, progressive albuminuria, decrease in the estimated GFR and ultimately end stage renal disease. Diabetes leads to some metabolic changes that may alter kidney function leading to glomerular hyperfiltration and albuminuria\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the increasing life expectancy and the rising incidence of type 2 diabetes, the number of diabetic patients affected by DKD is on the rise\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In diabetic patients, DKD leads to increased mortality and disability and is a major cause of renal replacement therapies, such as dialysis and kidney transplantation\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. According to the Global Burden of Disease CKD Study, diabetes is the underlying cause of chronic kidney disease (CKD) in 30.7% of patients. In other words, diabetes is the only etiology of CKD that has shown an increasing trend since 1990, with a 9.5% rise from 1990 to 2017\u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGlobally, the prevalence of CKD stage 3 and higher among diabetic patients is estimated to range from 6\u0026ndash;39.3%\u003csup\u003e44\u0026ndash;50\u003c/sup\u003e. In this study, the prevalence of CKD stage 3 and above was found to be 10.76%, which aligns with global estimates. A similar study in Thailand reported a CKD prevalence of 24.4% among diabetic patients\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. This indicates a lower prevalence of CKD stage 3 and above in Iran. This difference may be attributed to variations in diagnostic tools and racial differences, as the decline in GFR progresses more rapidly in Black populations\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In Middle East and north Africa (MENA) region the prevalence of CKD is rapidly increasing, 215.7% from 1990 to 2019. Similar finding were reported for incidence rate (302.2% rise from 1990 to 2019)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. As of today, several studies have reported the prevalence of DKD in MENA region. According to a study conducted by Al-zahrani et al the prevalence of DKD was reported 18.9% in Saudi-Arabia\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. similarly, the prevalence of DKD was reported 42.5% in Oman\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. According to a systematic review and meta-analysis conducted on 9 studies the prevalence of DKD in Middle East was estimated from 10.8\u0026ndash;60.78% which is significantly higher than global estimations\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLong-term increased blood glucose breeds functional and structural alterations in kidney cells\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that having diabetes for more than 15 years is associated with a higher risk of developing diabetic nephropathy. A study conducted in Saudi Arabia demonstrated a direct correlation between the duration of diabetes and the prevalence of kidney disease, with the highest incidence observed after 15 years of diabetes\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Similarly, another study found that a diabetes duration of more than 10 years increases the risk of nephropathy, whereas a duration of less than 10 years is associated with a lower risk\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. This raises the question of the precise threshold duration of diabetes that significantly increases the risk of kidney disease. In the present study, it was revealed that patients with a diabetes duration of more than 10 years had a 2.25 times higher likelihood of developing DKD, a statistically significant association (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, after 10 years of diabetes, patients require more intensive monitoring and early screening for kidney disease to facilitate timely diagnosis and management. \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. This study also demonstrated that poor glycemic control\u0026mdash;reflected by an HbA1c level above 7%\u0026mdash;increases the likelihood of renal involvement in diabetic patients by 1.92 times, a statistically significant finding. Therefore, it can be concluded that HbA1c levels above 7% are associated with a substantially higher risk of DKD compared to lower levels, underscoring the importance of strict blood glucose control in diabetic patients. Among the available treatment modalities, the highest risk for the development of kidney disease is observed in patients receiving insulin therapy. This is expected, as insulin is typically prescribed to individuals whose diabetes cannot be adequately controlled with oral antidiabetic agents or who initially present with more severe disease. Therefore, it is reasonable to conclude that insulin-treated patients are at a higher risk of developing renal dysfunction.\u003c/p\u003e\u003cp\u003eCVD risk factors such as dyslipidemia, which develop in diabetic patients, predispose them to a decline in GFR and albuminuria, thereby accelerating kidney function deterioration. A study conducted by Gall et al. demonstrated that diabetic patients with an average duration of 5.8 years since diagnosis exhibited a higher prevalence of increased urine albumin-to-creatinine ratio\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. This finding suggests that, in patients who have had diabetes for less than ten years, factors such as elevated serum lipid levels play a significant role in the progression of kidney dysfunction and act as independent contributors to its onset. The study also indicated that coexisting dyslipidemia increases the likelihood of kidney disease progression by 1.84 times. similarly, in this study patients who take lipid-lowering medications such as statins for primary prevention or treatment of dyslipidemia are 5.93% more likely to experience high/very high-grade kidney dysfunction compared to those who do not use these medications. Moreover, the likelihood of kidney function deterioration in statin users is 1.66 times higher than in non-users. This finding can be interpreted in two ways. First, patients on statins may have had more severe metabolic disorders and poor controlled diabetes, making them more susceptible to kidney involvement. Alternatively, it could suggest that statin use does not improve kidney function. To investigate the effectiveness of statins in enhancing renal function in diabetic patients, a systematic review was conducted\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. This review analyzed nine studies involving 3,426 patients and ultimately found that statin therapy leads to an improvement in eGFR, a reduction in serum creatinine levels, and a protective effect on kidney function. Therefore, these findings may be attributed to the more severe lipid disorders observed in these patients.\u003c/p\u003e\u003cp\u003eHypertension is a modifiable risk factor of CVD and independent risk factor for developing kidney dysfunction and elevated creatinine levels in diabetic patients\u003csup\u003e\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Similar to our study, a study demonstrated that diabetic patients with hypertension have a 1.67-fold increased risk of developing DKD \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Comparable findings were also observed in this study, as well as in another study conducted by Siddiqui et al.\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, which yielded similar results. The findings of this study align with previous research, further confirming the association between hypertension and kidney dysfunction. This study also showed that hypertension increases the risk of kidney impairment by 2.73 times. Notably, patients who use a combination of multiple antihypertensive medications have a greater likelihood of developing DKD compared to those who take only a single antihypertensive drug. This could be attributed to the fact that patients requiring multiple antihypertensive medications likely had a weaker response to monotherapy, necessitating the use of multiple drugs for adequate blood pressure control.\u003c/p\u003e\u003cp\u003eOn the other hand, patients who take a single antihypertensive medication have more than twice the risk of developing kidney disease. This finding suggests that merely having hypertension predisposes diabetic patients to DKD, and the use of antihypertensive drugs does not reduce this risk to the same extent as in non-hypertensive patients. Another noteworthy finding is that diabetic patients who take ACEIs or ARBs have a slightly higher risk of developing DKD compared to others. Specifically, the prevalence of high/very high-risk DKD is 1.83 folds higher in patients using ACEIs or ARBs than in those on other medications. However, the trend is reversed for moderately increased DKD, where the prevalence is approximately 5% higher in patients taking ACEIs or ARBs. These findings suggest that while ACEI or ARB therapy does not significantly reduce the risk of developing DKD, it may help prevent its progression. A study previously demonstrated that diabetic patients with a SBP above 140 mmHg had a significantly higher statistical risk of developing DKD compared to those with an SBP below 130 mmHg. Similarly, this study found that SBP above 140 mmHg doubles the risk of kidney dysfunction\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. On the other hand, the previous study suggested that, unlike systolic blood pressure, DBP had little effect on kidney disease progression\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. However, the present study indicates that DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg is statistically associated with an increased risk of DKD. Nevertheless, elevated SBP poses a greater risk for DKD compared to elevated DBP. Thus, SBP plays a more critical role in the development of diabetic kidney disease and should be given greater attention in clinical management.\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated that aspirin use in diabetic patients with concomitant renal impairment does not significantly reduce cardiovascular outcomes or mortality\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. As a result, the use of aspirin for primary prevention in this population is not recommended. However, other studies have indicated that aspirin may improve renal function in diabetic patients\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. In the present study, it was shown that, in addition to the presence of cardiovascular disease\u0026mdash;which independently increases the risk of DKD\u0026mdash;aspirin use further elevates this risk. Specifically, aspirin use was associated with a 2.09-fold increase in the likelihood of developing DKD. Moreover, the prevalence of high or very high-risk DKD among patients who used Aspirin was approximately 5% greater compared to those who did not. Since the majority of these patients are prescribed aspirin for secondary prevention, it is likely that they have a history of prior cardiovascular events. Therefore, the observed increase in DKD risk may, at least in part, be attributable to the more advanced cardiovascular disease burden in this subgroup.\u003c/p\u003e\u003cp\u003eAdditionally, this study concluded that obesity which was defined with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25, was not associated with progression of kidney dysfunction in diabetic patients. In contrast, abdominal obesity which was measured by WC was related to diabetic kidney disease progression. According to a systematic review and meta-analysis in which 14 cross-sectional studies were explored, it was concluded that abdominal obesity parameter like WC were associated with increased likelihood of DKD\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSeveral studies have demonstrated that smoking is an independent risk factor for the development of diabetic kidney disease due to hyperlipidemia, ,oxidative stress, deposition of advanced end glycation products, and glomerulosclerosis\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. In contrast, this study demonstrated that smoking is not associated with the onset or progression of diabetic nephropathy (DN). This association was also found to be statistically non-significant. A systematic review previously indicated that smoking has a minimal effect on the development and progression of DN\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Specifically, current and total smoking were not linked to diabetic kidney disease, whereas former smoking was associated with an increased risk of DN in diabetic patients. These findings align with the results of the present study, as total smoking\u0026mdash;defined as smoking even once in a lifetime\u0026mdash;was considered as smoking in this analysis. Therefore, it can be concluded that total smoking is not associated with the development of DN, and smokers should undergo DKD screening in the same manner as non-smokers.\u003c/p\u003e\u003cp\u003ePolicy Implications\u003c/p\u003e\u003cp\u003eGiven the significant economic and healthcare burden imposed by diabetes, it is essential to establish a system for early identification of patients at higher risk for developing DKD. Early screening and timely diagnosis in these individuals can play a crucial role in preventing disease progression and reducing associated complications.\u003c/p\u003e\u003cp\u003eBased on the findings of this study, diabetic patients receiving multiple medications\u0026mdash;such as aspirin, statins, more than one antihypertensive agent, and insulin\u0026mdash;those with poor glycemic status, a diabetes duration exceeding 10 years, and individuals with coexisting CVD risk factors; dyslipidemia, hypertension, and central obesity should be prioritized for more frequent and earlier renal assessments. To facilitate this, a risk assessment model could be developed to stratify patients based on key predictive factors. According to this study, the variables with the highest odds ratios\u0026mdash;namely age over 60, presence of hypertension, use of multiple antihypertensive medications, and insulin therapy\u0026mdash;should be assigned the greatest weight in such a model. Furthermore, clinicians should be encouraged to monitor renal function at shorter intervals in patients presenting with these high-risk factors.\u003c/p\u003e\u003cp\u003eStrengths and limitations of the current study\u003c/p\u003e\u003cp\u003eThis study used data from the 2021 STEPS survey, a national, population-based cross-sectional study, suggesting a strong representative sample of the adult Iranian population. In addition, the sample size is relatively large (3,272 diabetic patients), which increases the statistical power of the study. And a wide range of CVD risk factors have been evaluated in the present study. Both points are the strengths of our study.\u003c/p\u003e\u003cp\u003eConversely, the cross-sectional design of the study limits the ability to determine causal relationships between CVD risk factors and DKD. Moreover, some data are self-reported that might be increase the information bias.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, some CVD risk factors including hyperlipidemia, uncontrolled diabetes hypertension, and central obesity are associated with the development of DKD. So, it is crucial to determine these risk factors in diabetic patients and design a specific protocol for screening and early diagnosis of DKD in patients with those risk factors. By recognized high-risk/very high-risk patients in early stages some interventions might be beneficial for decelerating the progression of DKD. In fact, policy makers should establish some policies and scoring systems to recognize high-risk patients to prevent long-term renal replacement therapy in future.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eResearch design and participants\u003c/p\u003e\u003cp\u003eIn the current study, we used data of the STEPS 2021 survey which is a national, population-based cross-sectional study conducted on a representative sample of adult Iranian population to determine the prevalence of risk factors for non-communicable diseases. The STEPS includes three phases. The first phase is a questionnaire. The second and third phases are anthropometric and biochemical measures, respectively. Detailed information including study design and study objectives were provided for all participants\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In next step, an informed consent was attained from all participants. To obtain a representative sample at both the national and provincial levels, a systematic cluster sampling approach was used, based on provincial population sizes and appropriate weighting in the following phases, 27,745 participants underwent physical assessments, and 18,119 individuals submitted laboratory samples. The second phase encompassed all adults aged 18 and above, whereas the third phase was limited to participants aged 25 and older\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe inclusion criteria comprised all individuals with diabetes aged 25 years and older, for whom biochemical data, particularly albuminuria and estimated glomerular filtration rate (eGFR), were available. Exclusion criteria were patients with incomplete data for any of the variables, younger than 25 years old, or have past history of CVD. Based on these criteria, 3,322 diabetic patients were included in the study, among whom 50 individuals were excluded due to missing albuminuria and eGFR data.\u003c/p\u003e\u003cp\u003e This study was conducted according to the Declaration of Helsinki guidelines, and all methods were performed in accordance with institutional and national ethical standards. The Research Ethics Committee of the Endocrine \u0026amp; Metabolism Research Institute at Tehran University of Medical Sciences approved the study (Reference Code: IR.TUMS.EMRI.REC.1403.100).\u003c/p\u003e\u003cp\u003eData collection\u003c/p\u003e\u003cp\u003eIn the first stage of the STEPS survey, information was collected using a structured questionnaire that covered various topics such as demographics, diet, medical history, physical activity, quality of life, lifestyle advice, cancer screenings, injuries, tobacco and alcohol use, and household possessions. The second stage consisted of physical assessments, including measurements of weight, height, waist and hip circumference, blood pressure, and heart rate. Height was measured using a standard stadiometer with participants standing upright against a wall, ensuring proper alignment of the heels, hips, and head. Weight was recorded with a calibrated digital scale (Inofit), with participants wearing light clothing and no shoes. Before taking blood pressure readings, participants rested for 15 minutes. Blood pressure was then measured three times at three-minute intervals using a Beurer sphygmomanometer, with the final value being the average of the second and third readings. The third stage involved lab tests analyzing total serum cholesterol, HDL cholesterol, triglycerides, fasting plasma glucose, whole blood HbA1C, and urine samples for albumin and creatinine. All samples were transported and stored under a cold chain system and analyzed at a central lab.\u003c/p\u003e\u003cp\u003eDefinitions of Variables\u003c/p\u003e\u003cp\u003eDiabetic kidney disease (DKD) was defined as an estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min or microalbuminuria\u0026thinsp;\u0026gt;\u0026thinsp;30 mg/g in individuals with diabetes\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The occurrence of DKD was evaluated according to the Kidney Disease Improving Global Outcomes (KDIGO) classification, with a low-risk category comprising G1 (GFR\u0026thinsp;\u0026gt;\u0026thinsp;90 mL/min/1.73m\u0026sup2;) and A1 (albuminuria\u0026thinsp;\u0026lt;\u0026thinsp;30 mg/g), or G2 (GFR 60\u0026ndash;89 mL/min/1.73m\u0026sup2;) with A1. Six eGFR categories were included namely; G1, G2, G3a, G3b, G4 and G5 (G1 represents\u0026thinsp;\u0026ge;\u0026thinsp;90, G2 represents 60\u0026ndash;89, G3a represents 45\u0026ndash;59, G3b represents 30\u0026ndash;44, G4 represents 15\u0026ndash;29 and G5 represents below 15 of eGFR). Three Albuminuria includes: A1\u0026thinsp;\u0026lt;\u0026thinsp;30 mg/g, A2 30\u0026ndash;300 mg/g, A3\u0026thinsp;\u0026gt;\u0026thinsp;300 mg/g. Low-risk category comprises of G1A1 and G2A1; moderately increased risk category includes G1A2, G2A2 and G3aA1; and high/very high-risk category comprises of G1A3, G2A3, G3aA2, G3bA1, G3aA3, G3bA2, G3bA3, G4A3, and G5A3\u003csup\u003e19\u0026ndash;22\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe demographic variables assessed included age (categorized as under 40, 40 to under 65, and 65 or older), sex, place of residence (urban or rural), marital status (never married, married, or divorced/widowed), province, and health insurance coverage (basic or supplementary). Education level was measured as the number of years of schooling completed (0, 1\u0026ndash;6, 7\u0026ndash;11, or 12 or more years) and categorized into three groups: \u0026lt;7 years, 7\u0026ndash;11 years, and \u0026ge;\u0026thinsp;12 years of formal education. Employment status was grouped into employed, unemployed, retired, or engaged in unpaid work. Smoking status was considered positive if the individual had ever smoked any tobacco products\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Physical activity was classified as adequate if it exceeded 600 MET-minutes per week and inadequate if below this threshold, following WHO guidelines\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. High salt intake was defined as daily consumption exceeding the median intake of 9.79 g/day in this dataset.\u003c/p\u003e\u003cp\u003eDiabetes was defined by fasting blood sugar (FBS)\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, HbA1C\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or the use of glucose-lowering medications\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Control of diabetes was based on HbA1C values, with \u0026lt;\u0026thinsp;7% considered controlled and \u0026ge;\u0026thinsp;7% uncontrolled. Duration of diabetes was classified into two groups: under 10 years and over 10 years. Treatment for diabetes was categorized into three groups: oral medications only, insulin only, or a combination of both, based on participants\u0026rsquo; reports of insulin and/or oral antidiabetic use. Family history of diabetes was recorded if participants reported a diagnosis in any first-degree relatives (parents, siblings, or children).\u003c/p\u003e\u003cp\u003eCardiovascular disease (CVD) was defined by a self-reported history of heart attack, angina, coronary procedures (such as bypass surgery, angioplasty, or stent insertion), or stroke diagnosed by a healthcare professional. Hypertension was defined as an average systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or current use of antihypertensive medications\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.. Obesity/overweight was defined by a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;\u003csup\u003e30,31\u003c/sup\u003e. Central obesity was identified as a waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;95 cm, regardless of sex\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Dyslipidemia was present if any of the following criteria were met: triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL, total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL, LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;160 mg/dL, HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL in men or \u0026lt;\u0026thinsp;50 mg/dL in women, or current use of lipid-lowering medications\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMedication use (including statins, aspirin, angiotensin-converting enzyme inhibitors [ACEIs], and angiotensin receptor blockers [ARBs]) was considered positive if any of these drugs were prescribed.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDemographic variables were summarized using weighted proportions (\u0026plusmn;\u0026thinsp;SE) for categorical variables and weighted means (\u0026plusmn;\u0026thinsp;SE) for continuous variables. The outcome variable was risk category for DKD, categorized as: Low risk, Moderate risk, and High/Very high risk. Multivariable ordinal logistic regression (OLR) was employed to examine independent associations between risk factors and DKD severity. In this model, each coefficient represents the change in the logarithm odds of being in a higher versus lower DKD risk category per unit increase in the corresponding predictor. To ensure the validity of the OLR model, the proportional odds assumption was tested using Brant\u0026rsquo;s test. Given the clustered nature of the data, robust standard errors were used to account for potential heteroscedasticity and within-cluster correlation. A backward stepwise elimination procedure was used for variable selection. The initial model included all candidate explanatory variables. Variables with p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.2 were removed sequentially to derive the final model. A p-value threshold of 0.2 was selected to minimize the exclusion of potentially meaningful predictors with borderline significance. All statistical analyses were performed using R software (version 4.4.1; release 2023.06.0). Associations were reported as odds ratios (OR) and adjusted odds ratios (AOR) with corresponding 95% confidence intervals (CI). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACEI: Angiotensin-Converting Enzyme Inhibitor\u003c/p\u003e\n\u003cp\u003eAOR: Adjusted Odds Ratio\u003c/p\u003e\n\u003cp\u003eARB: Angiotensin Receptor Blocker\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eCVD: Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003eDBP: Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eDKD: Diabetic Kidney Disease\u003c/p\u003e\n\u003cp\u003eDM: Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eeGFR: Estimated Glomerular Filtration Rate\u003c/p\u003e\n\u003cp\u003eFBS: Fasting Blood Sugar\u003c/p\u003e\n\u003cp\u003eHbA1c: Hemoglobin A1C\u003c/p\u003e\n\u003cp\u003eHDL-C: High-Density Lipoprotein Cholesterol\u003c/p\u003e\n\u003cp\u003eKDIGO: Kidney Disease: Improving Global Outcomes\u003c/p\u003e\n\u003cp\u003eLDL-C: Low-Density Lipoprotein Cholesterol\u003c/p\u003e\n\u003cp\u003eMET: Metabolic Equivalent of Task\u003c/p\u003e\n\u003cp\u003eOLR: Ordinal Logistic Regression\u003c/p\u003e\n\u003cp\u003eOR: Odds Ratio\u003c/p\u003e\n\u003cp\u003eSBP: Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eSTEPS: STEPwise Approach to NCD Risk Factor Surveillance\u003c/p\u003e\n\u003cp\u003eTG: Triglycerides\u003c/p\u003e\n\u003cp\u003eWC: Waist Circumference\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRG: data curation, original draft preparation, reviewing, and editing; MKG: methodology, formal analysis, validation, reviewing, and editing;AG: methodology, validation, reviewing, and editing;SHM: reviewed the literature, reviewing, and editing; SKH: data curation, reviewing, and editing; AZ: data curation, reviewing, and editing; MPS; supervision, data curation, validation, reviewing, and editing; OTM: supervision, data curation, validation, reviewing, and editing; MRM: reviewing and editing; SA supervised the project; All authors revised the manuscript carefully and approved the final draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRG and MKG: contributed equally as first authors.\u003c/p\u003e\n\u003cp\u003eMPS and OTM: contributed equally as correspondence authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the Declaration of Helsinki guidelines, and all methods were performed in accordance with institutional and national ethical standards.\u0026nbsp;The Research Ethics Committee of the Endocrine \u0026amp; Metabolism Research Institute at Tehran University of Medical Sciences approved the study (Reference Code: IR.TUMS.EMRI.REC.1403.100). Participants provided informed consent in person after receiving written information detailing the study's objectives and procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets can be obtained from the corresponding author upon reasonable request. All inquiries regarding data access should be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors had no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI was not applied for any part of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKidanie, B. B. et al. Determinants of Diabetic Complication Among Adult Diabetic Patients in Debre Markos Referral Hospital, Northwest Ethiopia, 2018: Unmatched Case Control Study. \u003cem\u003eDiabetes Metab. Syndr. 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Smoking: a risk factor for progression of chronic kidney disease and for cardiovascular morbidity and mortality in renal patients\u0026mdash;absence of evidence or evidence of absence? \u003cem\u003eClin. J. Am. Soc. Nephrol.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 226\u0026ndash;236 (2008).\u003c/span\u003e\u003c/li\u003e\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":"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":"Diabetic Kidney Disease, Cardiovascular Risk Factors, Diabetes Mellitus, Hypertension, Dyslipidemia, Glycemic Control","lastPublishedDoi":"10.21203/rs.3.rs-7081883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7081883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiabetic Kidney Disease (DKD), a serious complication of diabetes mellitus (DM), is a leading cause of end-stage renal disease and a significant contributor to cardiovascular morbidity. This study explores the association of cardiovascular disease (CVD) risk factors with severity of diabetic kidney disease in Iran. Utilizing data from the 2021 nationwide STEPwise approach to Noncommunicable Disease Risk Factor Surveillance (STEPS), this cross-sectional study included 3,272 diabetic adults aged\u0026thinsp;\u0026ge;\u0026thinsp;25 years. DKD was defined based on eGFR and albuminuria levels using KDIGO guideline. Key CVD risk factors were analyzed using multivariable ordinal logistic regression. Among the participants, 7.64% (6.73,8.55) and 20.3% (18.92,21.68) were at high/very high and moderately increased risk of DKD respectively. Older age [OR\u0026thinsp;=\u0026thinsp;1.81 (1.19,2.74)], longer diabetes duration (\u0026gt;\u0026thinsp;10 years) [OR\u0026thinsp;=\u0026thinsp;1.34(1.08,1.66)], uncontrolled glycemia (HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7) [OR\u0026thinsp;=\u0026thinsp;1.57 (1.32,1.88)], dyslipidemia [OR\u0026thinsp;=\u0026thinsp;1.49 (1.10,2.04)], systolic blood pressure\u0026thinsp;\u003cb\u003e\u0026ge;\u003c/b\u003e\u0026thinsp;140 [OR\u0026thinsp;=\u0026thinsp;1.42 (1.17,1.73)], and use of insulin [OR\u0026thinsp;=\u0026thinsp;2.46 (1.60,3.78)] or multiple antihypertensive medications [OR\u0026thinsp;=\u0026thinsp;2.11 (1.59,2.79)] were significantly associated with higher DKD risk. CVD risk factors, particularly hypertension, dyslipidemia, and poor glycemic control, have a strong association with severity of DKD. These findings underscore the need for early risk stratification and targeted interventions to delay renal deterioration in diabetic patients.\u003c/p\u003e","manuscriptTitle":"Association of cardiovascular risk factors with diabetic kidney disease severity in the Iranian population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 10:04:01","doi":"10.21203/rs.3.rs-7081883/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T03:13:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-26T08:40:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T17:00:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T22:05:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"624189486832878125313655559371268585","date":"2025-07-22T02:11:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239705299686141042916968255665500737619","date":"2025-07-19T10:30:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212204820263792278497385018230772553707","date":"2025-07-18T13:41:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61252678598991485859154865281303291256","date":"2025-07-17T06:08:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4312533645313850909637023428267119796","date":"2025-07-16T21:38:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269984776383602788118310432173752602511","date":"2025-07-16T19:45:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T17:25:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T17:21:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-14T16:37:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-12T04:33:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-12T04:29:39+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":"3f398556-2020-49f4-8ee2-2700da87dcff","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51806309,"name":"Health sciences/Diseases"},{"id":51806310,"name":"Health sciences/Endocrinology"},{"id":51806311,"name":"Health sciences/Medical research"},{"id":51806312,"name":"Health sciences/Nephrology"},{"id":51806313,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-12T16:08:18+00:00","versionOfRecord":{"articleIdentity":"rs-7081883","link":"https://doi.org/10.1038/s41598-025-34742-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-09 15:58:10","publishedOnDateReadable":"January 9th, 2026"},"versionCreatedAt":"2025-07-21 10:04:01","video":"","vorDoi":"10.1038/s41598-025-34742-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-34742-5","workflowStages":[]},"version":"v1","identity":"rs-7081883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7081883","identity":"rs-7081883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}