Determinants of Chronic Kidney Disease among Adult Diabetic Patients at Dessie Comprehensive Specialized Hospital,Northeast  Ethiopia,2024: An institution-based case control study

Determinants of Chronic Kidney Disease among Adult Diabetic Patients at Dessie Comprehensive Specialized Hospital,Northeast Ethiopia,2024: An institution-based case control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of Chronic Kidney Disease among Adult Diabetic Patients at Dessie Comprehensive Specialized Hospital,Northeast Ethiopia,2024: An institution-based case control study Ali Mohammed Wolle, Gebiyaw Wudie Tsegaye, Abebaw Gedef Azene This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5318799/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic kidney disease is a condition where high blood glucose or blood pressure damages the blood vessels in the kidneys and reduces their function. It develops slowly; so many people do not realize they have it until it has reached an advanced stage. Chronic kidney disease can be prevented by controlling blood glucose and blood pressure, avoiding harmful medications, and changing lifestyle. Though prior studies were conducted on chronic kidney disease among adult diabetics in the Amhara region, possible determinant such as glycated hemoglobin level was not assessed. Objective To identify the determinants of Chronic Kidney Disease Among Adult Diabetic Patients at Dessie Comprehensive Specialized Hospital, Northeast Ethiopia,2024. Methods We conducted institutional-based unmatched case-control study between January-1 to June-30-2024. Cases were adult diabetic patient with chronic kidney disease and controls were adult diabetic patient without chronic kidney. All cases were included while systematic random sampling was used to select controls. Data from interview, laboratory and clinical records were collected and entered into Epi info version 7.2, then exported to Statistical Package for Social Science version 27 for analysis. Multivariable binary logistic regression was used to identify determinants of chronic kidney disease and a p-value less than 0.05 was considered as statistically significant. Results A total of 95 cases and 190 controls were recruited in the study. The median (interquartile range) age of cases and controls were 62 (67 − 45) and 37.5 (61-26.75) years respectively. The study revealed that poor glycemic control (HbA1c) (AOR: 3.33, 95% CI: 1.63–6.81), age ≥ 60 years (AOR: 2.70, 95% CI: 1.36–5.37), presence of albuminuria (AOR: 4.83, 95% CI: 2.19–10.73), analgesics used (AOR: 2.43, 95% CI: 1.26–4.70), and duration of diabetes greater than or equal to 10 years (AOR: 2.52, 95% CI: 1.30–4.88) had statistically significant association with chronic kidney disease among adult diabetic patients. Conclusion The study finding indicated that poor glycemic control, older age, positive albuminuria, use of analgesics, and long duration of diabetes were significantly associated with chronic kidney disease among adult diabetic patients. We recommend that individualized glycemic target for older age and long duration of diabetic patients. Chronic kidney Disease Diabetes glycated hemoglobin Adult Dessie Ethiopia Background Chronic kidney disease (CKD) is a condition characterized by the gradual loss of kidney function over at least three months( 1 ). Specific criteria, such as evidence of kidney damage and a decreased glomerular filtration rate (GFR), are used to make the diagnosis( 2 ). Diabetes mellitus is the most frequent cause of chronic kidney disease ( 1 – 4 ). It causes when there is uncontrolled blood glucose that damages the blood vessels in the kidneys and due to the buildup of waste products that suppress insulin production then reducing the function of the kidney( 4 ). Both type 1 diabetes, an autoimmune form of diabetes, and type 2 diabetes, which is largely linked to lifestyle factors such as smoking, alcohol use and obesity can cause CKD( 5 ). The prevention of chronic kidney disease in diabetic patients includes controlled blood glucose and blood pressure levels, lifestyle changes, including making healthier choices about what you eat and drink, physical activity, and treatment with medications. These approaches and treatments may keep CKD from worsening and prevent additional health problems in diabetic patients ( 6 ). Chronic kidney disease is extremely common and has emerged as one of the leading non-communicable causes of death worldwide. It is projected to affect an increasing number of individuals over time and to further rise in importance among the various global causes of death( 7 ). According to a study, there were 135 million patients, 2.6 million incident cases, and 0.5 million deaths of chronic kidney disease in diabetics worldwide in 2019( 8 ). The global burden of chronic kidney disease among diabetic patients was 26.2%( 9 ). In a systematic review and meta-analysis study done in Africa, the pooled prevalence of Chronic kidney disease among patients with diabetes was estimated between 24.7%-32.6%( 10 , 11 ). Ethiopia is one of the developing countries with a high burden of chronic kidney disease due to the swift changes in lifestyle. According to a systematic review and meta-analysis study done in Ethiopia, the pooled prevalence of Chronic kidney disease among diabetic patients was 18.22%( 12 ). Whereas the burden of chronic kidney disease among adult diabetic patients in the Amhara region varies from 14.3–26.3% ( 13 – 15 ). Chronic kidney disease among diabetic patients may experience major effects on their personal, family, and national health as well as economic and psychosocial aspects( 16 ). Several prevalence studies have been conducted on chronic kidney disease among diabetic patients in the Amhara region, specifically in Bahir Dar, Dessie, and Gondar ( 13 – 15 ). However, these studies have shown inconsistencies in the variables used. For instance, in the study conducted in Dessie, variables such as habitual alcohol use, smoking cigarettes, Khat chewing, use of traditional medicine, and analgesics were not included( 14 ). However, variables like Khat chewing, Lipid profile and hemoglobin A1c (HbA1c) were not assessed at Bahir Dar, Dessie, and Gondar ( 13 – 15 ). One important factor that needs to be addressed in this study is the glycated hemoglobin A1c (HbA1c) level and its association with CKD in adult DM patients. Those studies used fasting blood glucose test to measure the status of glycemic control which provides a snapshot of blood glucose levels at a moment, and influenced by recent food intake, stress, illness, and medication adherence. While HbA1c reflects long-term glycemic control and treatment effectiveness, less affected by short-term fluctuations, provides an overall measure of average blood glucose levels over time( 17 – 19 ). HbA1c is a widely used biomarker for long-term glucose control in individuals with diabetes. It provides crucial information about average blood glucose levels over a prolonged period, thus serving as a valuable tool for diagnosing and managing diabetes ( 20 – 22 ). While there is a considerable body of literature exploring the clinical utility of HbA1c, there remains a noticeable gap in the research concerning on CKD among adult DM patients in our study area. This study aims to bridge this gap by investigating CKD among adult DM patients in relation to HbA1c levels. By examining this under-explored area, we can gain valuable insights into the management of adult diabetic patients. The findings of this study will not only contribute to the existing body of knowledge about HbA1c but also provide clinicians and researchers with a deeper understanding in the management of CKD among adult DM patients, enabling them to make more informed decisions regarding patient care and future research directions. To overcome those limitations, our study focused on measuring blood glucose levels using HbA1c as a variable, along with other important factors, and employed a case-control study design. By including variables such as habitual alcohol use, smoking cigarettes, Khat chewing, use of traditional medicine, analgesics and lipid profile, to provide a more comprehensive understanding of the potential risk factors contributing to CKD in diabetic patients. The aim of our study was to identify the determinant of chronic kidney disease among adult diabetic patients who receive follow-up care at Dessie Comprehensive Specialized Hospital. Methods and Materials Study Setting The study was carried out at the Dessie Comprehensive Specialized Hospital in Dessie city, Northeast Ethiopia. Dessie Comprehensive Specialized Hospital provides a comprehensive medical service for nearly 10 million populations in the eastern part of Amhara and the neighboring Afar and Tigray regions. The hospital provided nearly 420,000 outpatient and more than 35,000 inpatient services annually. And also, it gives a follow up care for more than 1200 diabetic patients monthly. It delivers diagnostics, therapeutic, monitoring and tertiary care services for all diabetic and CKD patients. Patients with diabetes and chronic kidney disease can receive curative and preventive care at the hospital( 23 ). The hospital used dipstick test for albumin for follow up care( 24 ). Study period The study was conducted from 01-01-2024 to 01-06-2024. Study design An institution-based unmatched case-control study design was conducted. Source population Cases all adults aged 18 years or older who were diagnosed as diabetic patients with CKD at Dessie Comprehensive Specialized Hospital. Controls all adults all adults aged 18 years or older who were diagnosed as diabetic patients without CKD at Dessie Comprehensive Specialized Hospital. Study population Cases all adults aged 18 years or older who were diagnosed as diabetic patients with CKD at least 3 months follow up at Dessie Comprehensive Specialized Hospital during the data collection period. Controls all adults aged 18 years or older who were diagnosed as diabetic patients without CKD at least 3 months follow up at Dessie Comprehensive Specialized Hospital during the data collection period. Eligible criteria Inclusion: Case all adults aged 18 years or older who presented to Dessie Comprehensive Specialized Hospital during the data collection periods, who had diabetes mellitus with chronic kidney disease that had been diagnosed at least three months prior to data collection period. Control all adults aged 18 years or older who presented to Dessie Comprehensive Specialized Hospital during the data collection periods and had diabetes mellitus that had been diagnosed at least three months prior to data collection period without chronic kidney disease. Exclusion Excluded from the study were those who had incomplete information regarding their diagnosis and follow-up history for CKD and DM and were either critically ill or suffering from a psychiatric disorder at the time of data collection period. Study variables Dependent variable: Chronic kidney disease among adult diabetic patients (Yes/No) Independent variable: Socio-demographic factors Age, sex, residence, level of education, occupational type, and marital status. Comorbidity-related factors presence of hypertension, pre-existing hypertension and cardiovascular disease. Clinically related factors types of medications, duration of diabetes, types of diabetes, body mass index and use of analgesics. Behavioral factors smoking, alcohol drinking, chewing chat, physical exercise, use of traditional medicines Biochemical characteristics hemoglobinA1c, level of albuminuria, total cholesterol level, high-density lipoprotein, and low-density lipoprotein. Operational definitions CKD is urine albumin/creatinine ratio at or above 30 mg/g that is present for ≥ 3 months with or without evidence of kidney damage( 1 ). Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both( 4 , 25 ). Good glycemic control a study participant with HbA1c value below 8.0%. Poor glycemic control a study participant with HbA1c value 8.0% and above. High total cholesterol a study participant with > 200 mg/dl High LDL a study participant with ≥ 120 mg/dl Low HDL a study participant with 160 mg/dl Albuminuria Urine dipstick result was used to determine urine albumin level which was reported as negative, or + 1, to + 4 or positive. Smoking Smoking status was assessed by, “How many of the past 7 days did you smoke a cigarette or cigar, even just one puff?” Respondents who reported 0 days were considered a non-smoker. All others were categorized as smokers( 25 ). Alcohol consumption Determined by using the CAGE international screening tool to discuss a patient’s alcohol use/abuse. Each response to the four CAGE questions is scored with points; either 0 points for “no” or 1 point for “yes”. A total score of less than 2 was considered as non-alcoholic and 2 and above was considered as alcoholic( 26 ). Khat chewing Assessed by screening tools that have five questions and a 15 score. If a person scores a total of five and below considered a non-Khat chewer and ≥ 6 will be a Khat chewer( 25 ). Sample size determination The sample size was determined using Epi Info version 7.2, by analyzing published research of predictor variables then used systolic blood pressure as a predictor variable from Bahir Dar, Ethiopia( 15 ). The calculated sample size was 263, then by adding 10% of the non-respondent rates, 10% × 263 = 26.310% × 263 = 26.3, the final sample size was 291. Therefore, 97 cases and 194 controls, were chosen from the source population to conduct an unmatched hospital-based case-control study. Sampling technique and procedures During the follow-up period, all 97 cases were enrolled and controls were selected by using a systematic random sampling technique. Total number of controls (N) = 1200, then calculating the interval (Kth), Kth = N/n = 1200/194 = 7 The first unit which is 3, selected randomly using lottery techniques, from 1 to 7 patients. Subsequently, to reach a sample size of 194 controls, the selected individuals would be the 3rd, 10th, 17th and so on, until we reach the 194th individual. Data collection methods Data was collected by using a structured face-to-face interviewing questionnaire and patient’s chart review. To ensure accuracy and consistency, the questionnaire was initially prepared in English and then translated into the local Amharic language by a proficient bilingual speaker. Subsequently, a different individual translated the questionnaire back into English to validate the accuracy of the translation. To conduct the data collection process, three certified nurses, one lab technologist, and one supervisor were involved. The supervisor ensured that the data collectors submitted their collected data daily. The supervisor then reviewed the data for consistency and completeness to ensure its quality. Measurement Tools Anthropometric measurements The height of study participants was measured at standing position without footwear or headgear by using a stadiometer. Their head, shoulders, buttocks, and heels were aligned with the vertical surface of the audiometer. The weight was measured using a portable weighing scale in kilograms. Participants stood without any footwear that could affect their body weight. Afterward, the body mass index (BMI) was calculated by dividing the weight (in kilograms) by the square of the height (in meters). The BMI values were then used to classify participants into different categories: underweight (BMI < 18.5), normal weight (BMI 18.5–24.9), overweight (BMI 25-29.9), and obese (BMI ≥ 30)( 3 , 27 ). Vital Sign Measurement Blood pressure was measured by using an analog sphygmomanometer and stethoscope. The measurements were taken from the upper arm of the patient, with the arm positioned at heart level after the patient had been seated for at least 5 minutes. Biochemical Measurement The laboratory technologist collects three milliliters of blood from each study participant into a heparinized EDTA tube and separates the serum separate the serum. Then an Automated Chemistry Machine was utilized to perform the HbA1c assay, total cholesterol, triglyceride, high density lipoprotein and low density lipoprotein at Dessie Comprehensive Specialized Hospital( 24 ). A laboratory technologist collected ten milliliters of freshly voided urine from study participants to do urine dipstick test to assess the urine albumin level. The results of the test were reported as negative (absence of albumin) or graded on a scale from + 1 to + 4, indicating increasing levels of albumin presence in the urine( 24 ). Data processing and analysis The data was entered by using Epi Info version `7.2, then it was exported to SPSS version 27 for analysis. Frequencies and cross tabulations were used to summarize descriptive statistics. The results were presented using figures, tables, and words. The enter method was used for multivariable binary logistic regression. In the case of multivariable binary logistic regression, a variable was considered as a candidate if its p-value in the simple binary logistic analysis was less than or equal to 0.25. Variables that had a P-value of ≤ 0.05 at a 95% confidence interval in the multiple binary logistic regressions were considered statistically significant. To evaluate the associations between CKD among adult diabetic patients with the independent variables, the 95% confidence interval and P-value were employed. Adjusted odds ratio was used to assess the strength of association between CKD with the predictor variables. The final multiple binary logistic regression model was fitted using the model Hosmer and Lemeshow test with a p-value of 0.85, Omnibus test p-value < 0.001 and Nagelkerke R Square value is 0.494, which mean the proportion of the total variation in the outcome that can be explained by the independent variables in the model. Data quality control Data quality was assured by training for data collectors and supervisor. To ensure the accuracy of laboratory tests, the clinical chemistry laboratory at DCSH strictly followed standard operating procedures (SOPs). Before the actual data collection period, a pre-test of the questionnaire was conducted on 15 patients (5% of the total sample size) at Boru-Meda Hospital. Before the analysis, the frequency and cross-tabulation of each variable with the outcome variable were performed. Results Socio-demographic Characteristics of the Study Participants From a sample of 291(97 case and 194 controls), 285 (95 cases and 190 controls) were participated in the study giving a response rate of 97.9%. The median age of cases and controls were 62 and 37.5 with interquartile range of (67 − 45) and (61-26.75) years respectively. Fifty-nine (62.1%) cases and 101 (53.2%) controls were male. Regarding the residential areas, 56(58.9%) of cases and 133(70.0%) controls living in urban areas. In terms of marital status, 68(71.6%) of cases and 100 (52.6%) of controls were married. According to education level, 26(27.4%) of cases and 67 (35.5%) of controls were completed college and above. Thirty-three (31.6%) of cases and 60(31.6%) of controls were self-employed (Table 1 ). Table 1 Socio demographic characteristics of diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia, 2024. Variables Category Disease Status Cases (n) = 95 (%) Controls (n) = 190 (%) Age Greater than or equal to 60 years 62(65.3) 52(27.4) Less than 60 years 33(34.7) 138(72.6) Gender Male 59(62.1) 101(53.2) Female 36(37.9) 89(46.8) Residence Rural 39(41.1) 57(30.0) Urban 56(58.9) 133(70.0 Education Unable to read and write 22(23.2) 38(20.0) Read and write 6(6.3) 37(19.5) Primary 17(17.9) 23(12.1) Secondary 24(25.3) 25(13.2) Collage and above 26(27.4) 67(35.3) Marital status Single 10(10.5) 60(31.6) Married 68(71.6) 100(52.6) Divorced 11(11.6) 20(10.5) Widowed 6(6.3) 10(5.3) Occupation Government employee 13(13.7) 20(10.5) Non-government employee 8(8.4) 16(8.4) Self-employee* 33(34.7) 60(31.6) Student 11(11.6) 45(23.7) Housewife 9(9.5) 13(6.8) Retired 11(11.6) 18(9.5) Farmer 10(10.5) 18(9.5) * Such as merchants, contractors and etc. Clinical and comorbidity characteristics of the study participants Among the study participants, 73(76.8%) of cases and 95(50.0%) of controls were had type two diabetic mellitus. Sixty (63.2%) of cases and fifty (26.3%) of controls have ten years and above duration after the diagnosis of diabetic mellitus. Regarding medical confirmed comorbidity, 34(35.8%) of cases and 38(20.0%) of controls had pre-existing hypertension (Table 2 ). Table 2 Clinical characteristics of diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia, 2024. Variables Category Disease Status Cases (n) = 95 (%) Controls (n) = 190 (%) Type of DM Type One 22(23.2) 95(50.0) Type Two 73(76.8) 95(50.0) Duration of Diabetes Less than 10 years 35(36.8) 140(73.7) Greater or equal to 10 years 60(63.2) 50(26.3) Pre-existing Hypertension Present 34(35.8) 38(20.0) Absent 61(64.2) 152(80.0) Pre-existing CVD Present 21(22.1) 24(12.6) Absent 74(77.9) 166(87.4) Use of Analgesics Used 56(58.9) 50(26.3) Not used 39(41.1) 140(73.7) Have Hypertension < 140/90 mmHg 50(52.6) 136(71.6) ≥ 140/90 mmHg 45(47.4) 54(28.4) Medication use Oral hypoglycemic agent 71(74.7) 97(51.1) Insulin 24(25.3) 93(48.9) Among the study participants, 17(17.9%) of cases and 25(13.2%) of controls have over weight (Table 3 ). Table 3 Proportion of body mass index categories among diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia, 2024. Variables Category Disease Status Cases (n) = 95 (%) Controls (n) = 190 (%) Body Mass Index Underweight 5(5.3) 12(6.3) Normal 50(52.6) 123(64.7) Over weight 17(17.9) 25(13.2) Obese 23(24.2) 30(15.8) Behavioral characteristics of the study participants Among the study participants, 15(15.8%) of cases and 64(33.7%) of controls was done physical exercise. According to the study finding 38(40.0%) of cases and 53(27.9%) of controls had past history of smoking cigarette. From the study participants, 9(9.5%) of cases and 9(4.7%) of controls and 10(10.5%) of cases and 11(5.8%) of controls were had alcohol consumption and Khat chewing habit respectively (Table 3 ). Table 3 Behavioural characteristics of diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia, 2024. Variables Category Disease Status Cases (n) = 95 (%) Controls (n) = 190 (%) Current smoking status Smoker 12(12.6) 9(4.7) Non-smoker 83(87.4) 181(95.3) Past history of smoking status Yes 38(40.0) 53(27.9) No 57(60.0) 137(72.1) Alcohol consumption habit Alcoholic 9(9.5) 9(4.7) Non-alcoholic 86(90.5) 181(95.3) Khat chewing Khat chewer 10(10.5) 11(5.8) Non-Khat chewer 85(89.5) 179(94.2) Previously use of Traditional medicines Yes 15(15.8) 33(17.4) No 80(84.2) 157(82.6) Current use of traditional medicines Yes 7(7.4) 6(3.2) No 88(92.6) 184(96.8) Biochemical characteristics of the study participants According to the finding of this study, seventy-five of cases (78.9%) had 8% and above level of HbA1c, while 20 (21.1%) had less than 8%. Among controls, 67(35.3%) and 123 (64.7%) of study participants 8% and above and less than 8% level of HbA1c respectively. More cases 75(78.9%) than controls 67(35.3%) had 8% and above level of HbA1c Fifty-four of cases (56.8%) tested positive for albuminuria, while a smaller proportion (43.2%) tested negative. In contrast, a significantly lower percentage of controls (13.2%) tested positive for albuminuria. From the study participants, 22 (23.2%) of cases had more than 200mg/dl total serum cholesterol, while the majority (76.8%) had below 200 mg/dl. Among controls, a similar pattern was observed, with 21.6% having more than 200mg/dl total serum cholesterol and the majority (78.4%) having below 200 mg/dl. A significant percentage of cases (63.2%) had less than 40 mg/dl of HDL levels, while a smaller portion (36.8%) had more than 40 mg/dl, indicating a more favorable lipid profile. In the control group, a higher percentage (73.7%) had more than 40 mg/dl of HDL levels. The majority of cases (56.8%) had above 120 mg/dl of LDL levels, while a smaller portion (43.2%) had below 120 mg/dl. Among controls, a higher percentage (75.3%) had less or equal to 120 mg/dl of LDL levels. A significant proportion of cases (52.6%) had greater than 160 mg/dl of triglyceride levels, while a slightly smaller portion (47.4%) had below 160 mg/dl. In the control group, a lower percentage (28.9%) had greater than 160 mg/dl of triglyceride levels. Factors associated with CKD among adult diabetic patients All variables were assessed using simple binary logistics regression and variables which has a p-value ≤ 0.25 was eligible for multiple binary logistic regression analysis. Variables such as age, gender, duration of diabetes, pre-existing hypertension and cardiovascular disease, use of analgesics, past smoking cigarette, body mass index, presence of hypertension, presence of albuminuria, level of HbA1c, HDL and triglyceride had a p-value < = 0.25 and included for further analysis. In multiple binary logistic regression analysis, poor glycemic control (HbA1c), older age, positive albuminuria, analgesics drug use, and longer diabetes duration had statistically significance association with CKD in adult diabetic patients at 95% CI (Table 4 ). Table 4 Factors associated with chronic kidney disease among adult diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia,2024. Variables Category CKD status COR (95%CI) AOR (95%CI) Cases (n) = 95 Controls (n) = 190 Age >=60 62 52 4.99(2.94–8.46) 2.70(1.36–5.37) * < 60 33 138 1 1 Gender Male 59 101 1.44(0.87–2.39) 1.10(0.55–2.23) Female 36 89 1 1 Pre-existing Hypertension Present 34 38 2.23(1.29–3.86) 1.32(0.59–2.95) Absent 61 152 1 1 Pre-existing CVD Present 21 24 1.96(1.03–3.75) 1.77(0.75–4.21) Absent 74 166 1 1 Use of Analgesics Used 56 50 4.02(2.39–6.77) 2.43(1.26–4.70) * Not used 39 140 1 1 History of smoking Yes 38 53 1.72(1.03–2.90) 0.92(0.44–1.91) No 57 137 1 1 HbA1c Poor 75 67 6.88(3.87–12.25) 3.33(1.63–6.81 ) * Good 20 123 1 1 Albuminuria Positive 54 25 8.69(4.84–15.60) 4.85(2.19–10.73) * Negative 41 165 1 1 HDL Low 60 50 4.80(2.83–8.13) 1.17(0.52–2.64) Normal 35 140 1 1 Triglyceride High 50 55 2.73(1.64–4.54) 1.31(0.61–2.80) Normal 45 135 1 1 Have Hypertension Yes 45 136 1 0.71(0.32–155) No 50 54 0.44(0.26–0.74) 1 Duration of Diabetic = 10 years 60 50 0.21(0.12–0.35) 2.52(1.30–4.88) * BMI Obese 23 30 1.70(0.93–3.14) 1.22(0.50–2.96) Non-obese 72 160 1 1 COR = Crude Odds Ratio, AOR = Adjusted Odds Ratios, CI = Confidence interval * = P value ≤ 0.05 Discussion This study was conducted to identify the determinant factors of chronic kidney disease among adult diabetic patients at Dessie Comprehensive Specialized Hospital, Amhara Region, Ethiopia, 2024.The finding of the study suggests that poor glycemic control, older age, positive albuminuria, analgesics drug use, and longer diabetes duration are associated with an increased probability of developing chronic kidney disease (CKD) in adult diabetic patients. The study found that the odds of CKD were 3.33 times higher among adult diabetic patients with poor glycemic control compared to those with good glycemic control (AOR: 3.33, 95% CI: 1.63–6.81). This finding was in line with a study done at Tigray and Jimma hospitals ( 28 – 30 ). This suggests that poorly controlled blood sugar level in diabetic patients can significantly increase the risk of chronic kidney disease. When blood sugar levels remain high over time, it can lead to damage to the blood vessels in the kidneys and nephrons. This damage impairs the proper functioning of the kidneys and can contribute to the development and progression of chronic kidney disease( 6 ). Besides, our study revealed that, the odds of CKD were 2.70 times higher among adult diabetic patients with age ≥ 60 years as compared to their counterparts (AOR: 2.70, 95% CI: 1.36–5.37).This finding is congruent with other studies done at United Kingdom, Australia, Palestine, Botswana, Ghana, Uganda, Harar, Gondar and Bahr Dar( 13 , 15 , 31 – 38 ). This implies that the mechanisms underlying this association due to age-related physiological changes in glomerular filtration rate( 2 , 39 ). Moreover, the odds of CKD were 4.83 times higher among adult diabetic patients with positive albuminuria as compared to those patients with negative albuminuria (AOR: 4.83, 95% CI: 2.19–10.73). The result was consistent with studies were done at Harar, Jimma, and Bahir Dar ( 15 , 31 , 40 ). When waste products became building up and damage the small blood vessels in the kidneys. This damage impairs the kidneys' ability to filter waste products properly, leading to the leakage of albumin into the urine ( 5 , 41 – 44 ). As well, the odds of CKD were 2.43 times higher among adult diabetic patients with those who used analgesics drugs as compared to their counterparts (AOR: 2.43, 95% CI: 1.26–4.70). Our study provided that there is a statistically significant association between the use of analgesic drugs and the probability of developing chronic kidney disease in adult diabetic patients. This might be due to the long term and high doses usage of the drugs causes interstitial nephritis, which can impair their ability to filter waste products and failed to maintain proper kidney function then end with CKD( 39 ). Furthermore, the odds of CKD were 2.52 times higher among adult diabetic patients with those who had greater than or equal to 10 years duration of diabetic as compared to those patients had less than 10 years duration (AOR: 2.52, 95% CI: 1.30–4.88). Studies conducted in Botswana, Uganda, Ghana and Ethiopia provides evidenced that supports the association between the duration of DM and the development of chronic kidney disease among adult diabetic patients ( 31 , 32 , 34 , 36 , 45 ). This may be due to the prolonged exposure to high levels of glucose among patients, that damaged the tiny blood vessels of kidney and reduced blood flow and causes CKD( 4 , 39 ). In our studies, we found that factors such as body mass index, type of diabetes, pre-existing hypertension, high-density lipoprotein (HDL), triglyceride levels, and alcohol consumption did not show statistically significant association with the development of chronic kidney disease among adult diabetic patients( 14 , 28 , 40 , 46 – 49 ). However, it's important to note that these results may vary due to differences in study design, sample size, and the population being studied. Strengthen and Limitation of the study The study utilizes multiple data sources, such as interviews, medical records, and laboratory tests, to validate exposure and outcome information. This approach reduces the reliance on self-reporting and enhances the accuracy of data collected. Since we conducted hospital-based case-control study and as such, inpatient cases were not included. Therefore, further studies with similar designs are needed to validate and expand upon these findings in order to obtain a more comprehensive understanding of the factors influencing the outcome in the broader population of adult diabetic patients. Conclusion and Recommendations The study finding indicated that poor glycemic control, older age, positive albuminuria, use of analgesics, and long duration of diabetes were significantly associated with chronic kidney disease among adult diabetic patients. We recommend that individualized glycemic target for older age and long duration of diabetic patients. And also avoid or use the lowest effective dose and for the shortest duration possible of nonsteroidal anti-inflammatory drugs. Declarations Acknowledgement We would like to express our appreciation to the study participants for their willingness to give the required information and staff of diabetes and CKD clinic of Dessie comprehensive specialized hospital for their cooperation during data collection process. We would also like to acknowledge the data collectors. Author contributions Ali Mohammed Wolle: Contributed to designing the study, writing original draft, formal data analysis, data interpretation, manuscript preparation, and finalization. Gebiyaw Wudie: Supervision review, editing and data interpretation. Abebaw Gedef: Supervision review, editing and data interpretation. All authors read and approved the final manuscript. The study is research article Ethics approval The study was approved by Bahir Dar University, College of Medicine and Health Science of Institutional review board with the protocol number of (#868/2023). The study follows the national and International ethical guideline. The name of the participants was not used in collecting the data from the medical files. Confidentiality was maintained by keeping the data collection forms locked in a secure cabinet and the electronic data file was kept securely in a password-protected computer. Data obtained in the course of the study was only handled by the research team. To ensure proper authorization, a letter of authorization was obtained from the Amhara Public Health Institution. Consent to participate As this article had both primary and secondary data source patient informed consent was required. Written informed consent was obtained from each participant in the study after providing them with a comprehensive understanding of the study's objectives. It was made clear that participation was voluntary and that individuals had the right to decline participation at any time. The privacy of the participants was ensured by using codes to keep their information confidential. Participants were also informed that if any abnormal results were identified, they would be notified and appropriately referred to a physician for further care. All the procedures that included human participants adhered to the Declaration of Helsinki. Consent for publication Not applicable. Clinical trial number Not applicable Conflict of interest The authors have indicated that they have no conflicts of interest regarding the content of this article. Funding There is no funding to report. References National institution of diabetes and digestive and kidney diseases. Diagnosis and test of chronic kidney disease. 2022. Kidney Health Australia. Chronic Kidney Disease (CKD) Management in Primary Care (4th edition),. 2020. National Institute of Health. The US National Heart, Lung and Blood Institute criteria,. 2020. National institution of diabetes and digestive and kidney diseases. The Link Between Diabetes and Kidney Disease ,. 2020. James M, Dennis S. Diabetes and Chronic Kidney Disease,. verywellhealth. 2022. Center for Disease prevention and Control. Chronic Kidney Disease Fact Sheet ,. 2023. Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney International Supplements. 2022;12(1):7-11. Deng Y, Li N, Wu Y, Wang M, Yang S, Zheng Y, et al. Global, regional, and national burden of diabetes-related chronic kidney disease from 1990 to 2019. Frontiers in endocrinology. 2021;12:672350. Koye DN, Magliano DJ, Nelson RG, Pavkov ME. The Global Epidemiology of Diabetes and Kidney Disease. Advances in Chronic Kidney Disease. 2018 2018/03/01/;25(2):121-32. Abd ElHafeez S, Bolignano D, D’Arrigo G, Dounousi E, Tripepi G, Zoccali C. Prevalence and burden of chronic kidney disease among the general population and high-risk groups in Africa: a systematic review. BMJ open. 2018;8(1):e015069. Kaze AD, Ilori T, Jaar BG, Echouffo-Tcheugui JB. Burden of chronic kidney disease on the African continent: a systematic review and meta-analysis. BMC Nephrology. 2018 2018/06/01;19(1):125. Tolossa T, Fetensa G, Regassa B, Yilma M, Besho M, Fekadu G, et al. Burden and Determinants of Chronic Kidney Disease Among Diabetic Patients in Ethiopia: A Systematic Review and Meta-Analysis. Public Health Reviews. 2021 2021-April-09;42. English. Damtie S, Biadgo B, Baynes HW, Ambachew S, Melak T, Asmelash D, et al. Chronic kidney disease and associated risk factors assessment among diabetes mellitus patients at a tertiary hospital, Northwest Ethiopia. Ethiopian journal of health sciences. 2018;28(6). Fiseha T, Tamir Z. Prevalence and awareness of chronic kidney disease among adult diabetic outpatients in Northeast Ethiopia. BMC nephrology. 2020;21(1):1-7. Tesfe D, Adugna M, Nigussie ZM, Woldeyohanins AE, Kifle ZD. The proportion of chronic kidney disease and its associated factors among adult diabetic patients at Tibebe Ghion Specialized Hospital, Bahir Dar, Ethiopia. Metabolism Open. 2022;15:100198. Elshahat S, Cockwell P, Maxwell AP, Griffin M, O’Brien T, O’Neill C. The impact of chronic kidney disease on developed countries from a health economics perspective: A systematic scoping review. PLOS ONE. 2020;15(3):e0230512. Ghazanfari Z, Haghdoost AA, Alizadeh SM, Atapour J, Zolala F. A Comparison of HbA1c and Fasting Blood Sugar Tests in General Population. Int J Prev Med. 2010 Summer;1(3):187-94. PubMed PMID: 21566790. Pubmed Central PMCID: PMC3075530. Epub 2011/05/14. eng. Ketema EB, Kibret KT. Correlation of fasting and postprandial plasma glucose with HbA1c in assessing glycemic control; systematic review and meta-analysis. Archives of Public Health. 2015 2015/09/25;73(1):43. Theresa Vuskovich D. Compare HbA1c to Fasting Blood Glucose. Everlywell. 2022. Sandler CN, McDonnell ME. The role of hemoglobin A1c in the assessment of diabetes and cardiovascular risk. Cleveland Clinic Journal of Medicine. 2016;83(5 Suppl 1):S4-S10. verywellhealth. Blood Glucose Test, Preparation and Procedure. 2022. Wright L, Hirsch I. Metrics Beyond Hemoglobin A1C in Diabetes Management: Time in Range, Hypoglycemia, and Other Parameters. Diabetes Technology & Therapeutics. 2017 05/01;19:S-16. Dessie Comprhensive Specialized Hospital. Annual Performance. 2024. Dessie comprehensive specialized hospital Laboratory department. Standard operationg Procedures for Biochemical tests. 2023. Federal Ministry Health of Ethiopia. Guidelines on Clinical and Programmatic Management of Major Non Communicable Diseases. 2021. verywellhealth. Addressing Alcohol Abuse With the Cut down, Annoyed, Guilty, Eye-opener Questionnaire. 2022. World Health Organization. Physical status: The use of and interpretation of anthropometry, Report of a WHO Expert Committee: World Health Organization; 1995. Hintsa S, Dube L, Abay M, Angesom T, Workicho A. Determinants of diabetic nephropathy in Ayder Referral Hospital, Northern Ethiopia: A case-control study. PLOS ONE. 2017;12(4):e0173566. Mariye Zemicheal T, Bahrey Tadesse D, Tasew Atalay H, Teklay Weldesamuel G, Gebremichael GB, Tesfay HN, et al. Determinants of Diabetic Nephropathy among Diabetic Patients in General Public Hospitals of Tigray, Ethiopia, 2018/19. International Journal of Endocrinology. 2020 2020/09/21;2020:6396483. Tilahun A, Waqtola C, Tewodros G, Amare D, Yohannis M. Major micro vascular complications and associated risk factors among diabetic outpatients in Southwest Ethiopia. Endocrinol Metab Syndr. 2017;6(4):272. Cheru A, Edessa D, Regassa LD, Gobena T. Incidence and predictors of chronic kidney disease among patients with diabetes treated at governmental hospitals of Harari Region, eastern Ethiopia, 2022. Frontiers in Public Health. 2024;11:1290554. Ephraim RKD, Arthur E, Owiredu WKBA, Adoba P, Agbodzakey H, Eghan BA. Chronic Kidney Disease Stages Among Diabetes Patients in the Cape Coast Metropolis. Saudi Journal of Kidney Diseases and Transplantation. 2016;27(6):1231-8. PubMed PMID: 00936703-201627060-00018. González-Pérez A, Saéz ME, Vizcaya D, Lind M, Rodríguez LAG. Impact of chronic kidney disease definition on assessment of its incidence and risk factors in patients with newly diagnosed type 1 and type 2 diabetes in the UK: A cohort study using primary care data from the United Kingdom. Primary care diabetes. 2020;14(4):381-7. Kirya M, Bwayo D, Otim ME, Mutoo PB, Masaba JPM, Okibure A, et al. Prevalence of Biomarkers and Associated Factors for Chronic Kidney Disease in Adult Diabetic Out-patients in a Tertiary Hospital in Eastern Uganda-a Cross-sectional Study. 2024. Nazzal Z, Hamdan Z, Masri D, Abu-Kaf O, Hamad M. Prevalence and risk factors of chronic kidney disease among Palestinian type 2 diabetic patients: a cross-sectional study. BMC nephrology. 2020;21:1-8. Rwegerera GM, Molefe-Baikai OJ, Masaka A, Shimwela M, Rivera YP, Oyewo TA, et al. Prevalence of chronic kidney disease using estimated glomerular filtration rate among diabetes patients attending a tertiary clinic in Botswana. Hospital Practice. 2018 2018/08/08;46(4):214-20. Sukkar L, Kang A, Hockham C, Young T, Jun M, Foote C, et al. Incidence and associations of chronic kidney disease in community participants with diabetes: a 5-year prospective analysis of the EXTEND45 study. Diabetes care. 2020;43(5):982-90. Tannor EK, Sarfo FS, Mobula LM, Sarfo‐Kantanka O, Adu‐Gyamfi R, Plange‐Rhule J. Prevalence and predictors of chronic kidney disease among Ghanaian patients with hypertension and diabetes mellitus: A multicenter cross‐sectional study. The journal of clinical Hypertension. 2019;21(10):1542-50. National Kideny Foundation. Estimate glomerulare filtration rate. 2021. Debele GR, Hajure M, Wolde HF, Yenit MK. Incidence and Predictors of Chronic Kidney Disease among Diabetes Mellitus Patients: A Retrospective Follow-Up Study at a Tertiary Health-Care Setting of Ethiopia. Diabetes, metabolic syndrome and obesity : targets and therapy [Internet]. 2021 2021; 14:[4381-90 pp.]. Afkarian M, Zelnick LR, Hall YN, Heagerty PJ, Tuttle K, Weiss NS, et al. Clinical manifestations of kidney disease among US adults with diabetes, 1988-2014. Jama. 2016;316(6):602-10. de Boer IH, Rue TC, Hall YN, Heagerty PJ, Weiss NS, Himmelfarb J. Temporal trends in the prevalence of diabetic kidney disease in the United States. Jama. 2011;305(24):2532-9. He F, Xia X, Wu X, Yu X, Huang F. Diabetic retinopathy in predicting diabetic nephropathy in patients with type 2 diabetes and renal disease: a meta-analysis. Diabetologia. 2013;56:457-66. Molitch ME, Steffes M, Sun W, Rutledge B, Cleary P, De Boer IH, et al. Development and progression of renal insufficiency with and without albuminuria in adults with type 1 diabetes in the diabetes control and complications trial and the epidemiology of diabetes interventions and complications study. Diabetes care. 2010;33(7):1536-43. Shiferaw W, Yirga T, Aynalem Y. Chronic Kidney Disease among Diabetes Patients in Ethiopia: A Systematic Review and Meta-Analysis. International Journal of Nephrology. 2020 10/10;2020. Abdulkadr M, Merga H, Mizana BA, Terefe G, Dube L. Chronic Kidney Disease and Associated Factors among Diabetic Patients at the Diabetic Clinic in a Police Hospital, Addis Ababa. Ethiopian Journal of Health Sciences. 2022;32(2). Aberra T, Feleke Y, Tarekegn G, Bikila D, Melesse M. Prevalence and associated factors of diabetic nephropathy at Tikur Anbessa comprehensive specialized University hospital, Addis Ababa, Ethiopia. African Journal of Nephrology. 2022;25(1):35-45. Bekele MM. Prevalence and associated factors of chronic kidney disease among diabetic patients that attend public hospitals of Addis Ababa: Addis Ababa University; 2016. Fiseha T, Kassim M, Yemane T. Chronic kidney disease and underdiagnosis of renal insufficiency among diabetic patients attending a hospital in Southern Ethiopia. BMC nephrology. 2014;15:1-5. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5318799","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375635975,"identity":"57919077-3dc5-4f40-bef9-a3e6ee769fc4","order_by":0,"name":"Ali Mohammed Wolle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACdsYGBiACAcMHCQY2QJqx8QBeLcwILcYGHwrSQFoaCGgBqwEzzSRnfDgMZuHVwt/M3Lrh5w67aN325g3SPAbn7da2HwbaUmMTjUuLxGHGtpu9Z5Jzt505VmDMY3A7eduZRKCWY2m5Dbj0ALXc4G1jzt12I8cgGaTF7ABQC2PDYZxa5EG2/G2rz912/43BYR6Dc8lm5x/i12IA1HKbt+0w0BYew8YZBgfszG4QsMUQpEW27TjQL2nFDB8MkhPMbgBtScDjF7nj7c9uvm2rzt12/PD2Hwl/7OzNzqc/fPChxga399FBIlhlArHKQcCeFMWjYBSMglEwMgAAqYVurjkHJj0AAAAASUVORK5CYII=","orcid":"","institution":"Bahir Dar University","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"Mohammed","lastName":"Wolle","suffix":""},{"id":375635976,"identity":"ac5ebd87-147d-4c71-8890-5111c06f674a","order_by":1,"name":"Gebiyaw Wudie Tsegaye","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Gebiyaw","middleName":"Wudie","lastName":"Tsegaye","suffix":""},{"id":375635977,"identity":"d2258955-0663-45d8-874d-7c770e7901d7","order_by":2,"name":"Abebaw Gedef Azene","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Abebaw","middleName":"Gedef","lastName":"Azene","suffix":""}],"badges":[],"createdAt":"2024-10-23 12:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5318799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5318799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79376743,"identity":"bff8035b-7c9f-4265-8d08-c6afe340e228","added_by":"auto","created_at":"2025-03-27 15:23:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1279616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5318799/v1/18731d14-5d50-4687-997e-7f8f7ba7738b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of Chronic Kidney Disease among Adult Diabetic Patients at Dessie Comprehensive Specialized Hospital,Northeast Ethiopia,2024: An institution-based case control study","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic kidney disease (CKD) is a condition characterized by the gradual loss of kidney function over at least three months(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Specific criteria, such as evidence of kidney damage and a decreased glomerular filtration rate (GFR), are used to make the diagnosis(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiabetes mellitus is the most frequent cause of chronic kidney disease (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). It causes when there is uncontrolled blood glucose that damages the blood vessels in the kidneys and due to the buildup of waste products that suppress insulin production then reducing the function of the kidney(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Both type 1 diabetes, an autoimmune form of diabetes, and type 2 diabetes, which is largely linked to lifestyle factors such as smoking, alcohol use and obesity can cause CKD(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevention of chronic kidney disease in diabetic patients includes controlled blood glucose and blood pressure levels, lifestyle changes, including making healthier choices about what you eat and drink, physical activity, and treatment with medications. These approaches and treatments may keep CKD from worsening and prevent additional health problems in diabetic patients (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChronic kidney disease is extremely common and has emerged as one of the leading non-communicable causes of death worldwide. It is projected to affect an increasing number of individuals over time and to further rise in importance among the various global causes of death(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). According to a study, there were 135\u0026nbsp;million patients, 2.6\u0026nbsp;million incident cases, and 0.5\u0026nbsp;million deaths of chronic kidney disease in diabetics worldwide in 2019(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The global burden of chronic kidney disease among diabetic patients was 26.2%(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In a systematic review and meta-analysis study done in Africa, the pooled prevalence of Chronic kidney disease among patients with diabetes was estimated between 24.7%-32.6%(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Ethiopia is one of the developing countries with a high burden of chronic kidney disease due to the swift changes in lifestyle. According to a systematic review and meta-analysis study done in Ethiopia, the pooled prevalence of Chronic kidney disease among diabetic patients was 18.22%(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Whereas the burden of chronic kidney disease among adult diabetic patients in the Amhara region varies from 14.3\u0026ndash;26.3% (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChronic kidney disease among diabetic patients may experience major effects on their personal, family, and national health as well as economic and psychosocial aspects(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral prevalence studies have been conducted on chronic kidney disease among diabetic patients in the Amhara region, specifically in Bahir Dar, Dessie, and Gondar (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, these studies have shown inconsistencies in the variables used. For instance, in the study conducted in Dessie, variables such as habitual alcohol use, smoking cigarettes, Khat chewing, use of traditional medicine, and analgesics were not included(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, variables like Khat chewing, Lipid profile and hemoglobin A1c (HbA1c) were not assessed at Bahir Dar, Dessie, and Gondar (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne important factor that needs to be addressed in this study is the glycated hemoglobin A1c (HbA1c) level and its association with CKD in adult DM patients. Those studies used fasting blood glucose test to measure the status of glycemic control which provides a snapshot of blood glucose levels at a moment, and influenced by recent food intake, stress, illness, and medication adherence. While HbA1c reflects long-term glycemic control and treatment effectiveness, less affected by short-term fluctuations, provides an overall measure of average blood glucose levels over time(\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHbA1c is a widely used biomarker for long-term glucose control in individuals with diabetes. It provides crucial information about average blood glucose levels over a prolonged period, thus serving as a valuable tool for diagnosing and managing diabetes (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). While there is a considerable body of literature exploring the clinical utility of HbA1c, there remains a noticeable gap in the research concerning on CKD among adult DM patients in our study area.\u003c/p\u003e \u003cp\u003eThis study aims to bridge this gap by investigating CKD among adult DM patients in relation to HbA1c levels. By examining this under-explored area, we can gain valuable insights into the management of adult diabetic patients. The findings of this study will not only contribute to the existing body of knowledge about HbA1c but also provide clinicians and researchers with a deeper understanding in the management of CKD among adult DM patients, enabling them to make more informed decisions regarding patient care and future research directions.\u003c/p\u003e \u003cp\u003eTo overcome those limitations, our study focused on measuring blood glucose levels using HbA1c as a variable, along with other important factors, and employed a case-control study design. By including variables such as habitual alcohol use, smoking cigarettes, Khat chewing, use of traditional medicine, analgesics and lipid profile, to provide a more comprehensive understanding of the potential risk factors contributing to CKD in diabetic patients. The aim of our study was to identify the determinant of chronic kidney disease among adult diabetic patients who receive follow-up care at Dessie Comprehensive Specialized Hospital.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003eThe study was carried out at the Dessie Comprehensive Specialized Hospital in Dessie city, Northeast Ethiopia. Dessie Comprehensive Specialized Hospital provides a comprehensive medical service for nearly 10\u0026nbsp;million populations in the eastern part of Amhara and the neighboring Afar and Tigray regions. The hospital provided nearly 420,000 outpatient and more than 35,000 inpatient services annually. And also, it gives a follow up care for more than 1200 diabetic patients monthly. It delivers diagnostics, therapeutic, monitoring and tertiary care services for all diabetic and CKD patients. Patients with diabetes and chronic kidney disease can receive curative and preventive care at the hospital(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The hospital used dipstick test for albumin for follow up care(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy period\u003c/h3\u003e\n\u003cp\u003eThe study was conducted from 01-01-2024 to 01-06-2024.\u003c/p\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eAn institution-based unmatched case-control study design was conducted.\u003c/p\u003e\n\u003ch3\u003eSource population\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eCases\u003c/strong\u003e \u003cp\u003eall adults aged 18 years or older who were diagnosed as diabetic patients with CKD at Dessie Comprehensive Specialized Hospital.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eControls\u003c/strong\u003e \u003cp\u003eall adults all adults aged 18 years or older who were diagnosed as diabetic patients without CKD at Dessie Comprehensive Specialized Hospital.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eCases\u003c/strong\u003e \u003cp\u003eall adults aged 18 years or older who were diagnosed as diabetic patients with CKD at least 3 months follow up at Dessie Comprehensive Specialized Hospital during the data collection period.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eControls\u003c/strong\u003e \u003cp\u003eall adults aged 18 years or older who were diagnosed as diabetic patients without CKD at least 3 months follow up at Dessie Comprehensive Specialized Hospital during the data collection period.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEligible criteria\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eInclusion:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCase\u003c/strong\u003e \u003cp\u003eall adults aged 18 years or older who presented to Dessie Comprehensive Specialized Hospital during the data collection periods, who had diabetes mellitus with chronic kidney disease that had been diagnosed at least three months prior to data collection period.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eControl\u003c/strong\u003e \u003cp\u003eall adults aged 18 years or older who presented to Dessie Comprehensive Specialized Hospital during the data collection periods and had diabetes mellitus that had been diagnosed at least three months prior to data collection period without chronic kidney disease.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eExclusion\u003c/h3\u003e\n\u003cp\u003eExcluded from the study were those who had incomplete information regarding their diagnosis and follow-up history for CKD and DM and were either critically ill or suffering from a psychiatric disorder at the time of data collection period.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eDependent variable:\u003c/h2\u003e \u003cp\u003eChronic kidney disease among adult diabetic patients (Yes/No)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIndependent variable:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eSocio-demographic factors\u003c/strong\u003e \u003cp\u003eAge, sex, residence, level of education, occupational type, and marital status.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eComorbidity-related factors\u003c/strong\u003e \u003cp\u003epresence of hypertension, pre-existing hypertension and cardiovascular disease.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinically related factors\u003c/strong\u003e \u003cp\u003etypes of medications, duration of diabetes, types of diabetes, body mass index and use of analgesics.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBehavioral factors\u003c/strong\u003e \u003cp\u003esmoking, alcohol drinking, chewing chat, physical exercise, use of traditional medicines\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBiochemical characteristics\u003c/strong\u003e \u003cp\u003ehemoglobinA1c, level of albuminuria, total cholesterol level, high-density lipoprotein, and low-density lipoprotein.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOperational definitions\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCKD\u003c/strong\u003e \u003cp\u003eis urine albumin/creatinine ratio at or above 30 mg/g that is present for \u0026ge;\u0026thinsp;3 months with or without evidence of kidney damage(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDiabetes\u003c/strong\u003e \u003cp\u003eis a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGood glycemic control\u003c/strong\u003e \u003cp\u003ea study participant with HbA1c value below 8.0%.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePoor glycemic control\u003c/strong\u003e \u003cp\u003ea study participant with HbA1c value 8.0% and above.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHigh total cholesterol\u003c/strong\u003e \u003cp\u003ea study participant with \u0026gt;\u0026thinsp;200 mg/dl\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHigh LDL\u003c/strong\u003e \u003cp\u003ea study participant with \u0026ge;\u0026thinsp;120 mg/dl\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLow HDL\u003c/strong\u003e \u003cp\u003ea study participant with \u0026lt;\u0026thinsp;40 mg/dl\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHigh triglyceride level\u003c/strong\u003e \u003cp\u003ea study participant with \u003cb\u003e\u0026gt;\u003c/b\u003e\u0026thinsp;160 mg/dl\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAlbuminuria\u003c/strong\u003e \u003cp\u003eUrine dipstick result was used to determine urine albumin level which was reported as negative, or +\u0026thinsp;1, to +\u0026thinsp;4 or positive.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSmoking\u003c/strong\u003e \u003cp\u003eSmoking status was assessed by, \u0026ldquo;How many of the past 7 days did you smoke a cigarette or cigar, even just one puff?\u0026rdquo; Respondents who reported 0 days were considered a non-smoker. All others were categorized as smokers(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAlcohol consumption\u003c/strong\u003e \u003cp\u003eDetermined by using the CAGE international screening tool to discuss a patient\u0026rsquo;s alcohol use/abuse. Each response to the four CAGE questions is scored with points; either 0 points for \u0026ldquo;no\u0026rdquo; or 1 point for \u0026ldquo;yes\u0026rdquo;. A total score of less than 2 was considered as non-alcoholic and 2 and above was considered as alcoholic(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKhat chewing\u003c/strong\u003e \u003cp\u003eAssessed by screening tools that have five questions and a 15 score. If a person scores a total of five and below considered a non-Khat chewer and \u0026ge;\u0026thinsp;6 will be a Khat chewer(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSample size determination\u003c/h2\u003e \u003cp\u003eThe sample size was determined using Epi Info version 7.2, by analyzing published research of predictor variables then used systolic blood pressure as a predictor variable from Bahir Dar, Ethiopia(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The calculated sample size was 263, then by adding 10% of the non-respondent rates, 10% \u0026times; 263\u0026thinsp;=\u0026thinsp;26.310% \u0026times; 263\u0026thinsp;=\u0026thinsp;26.3, the final sample size was 291. Therefore, 97 cases and 194 controls, were chosen from the source population to conduct an unmatched hospital-based case-control study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSampling technique and procedures\u003c/h2\u003e \u003cp\u003eDuring the follow-up period, all 97 cases were enrolled and controls were selected by using a systematic random sampling technique.\u003c/p\u003e \u003cp\u003eTotal number of controls (N)\u0026thinsp;=\u0026thinsp;1200, then calculating the interval (Kth),\u003c/p\u003e \u003cp\u003eKth\u0026thinsp;=\u0026thinsp;N/n\u0026thinsp;=\u0026thinsp;1200/194\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e \u003cp\u003eThe first unit which is 3, selected randomly using lottery techniques, from 1 to 7 patients. Subsequently, to reach a sample size of 194 controls, the selected individuals would be the 3rd, 10th, 17th and so on, until we reach the 194th individual.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData collection methods\u003c/h2\u003e \u003cp\u003eData was collected by using a structured face-to-face interviewing questionnaire and patient\u0026rsquo;s chart review. To ensure accuracy and consistency, the questionnaire was initially prepared in English and then translated into the local Amharic language by a proficient bilingual speaker. Subsequently, a different individual translated the questionnaire back into English to validate the accuracy of the translation. To conduct the data collection process, three certified nurses, one lab technologist, and one supervisor were involved. The supervisor ensured that the data collectors submitted their collected data daily. The supervisor then reviewed the data for consistency and completeness to ensure its quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement Tools\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eAnthropometric measurements\u003c/h2\u003e \u003cp\u003eThe height of study participants was measured at standing position without footwear or headgear by using a stadiometer. Their head, shoulders, buttocks, and heels were aligned with the vertical surface of the audiometer. The weight was measured using a portable weighing scale in kilograms. Participants stood without any footwear that could affect their body weight. Afterward, the body mass index (BMI) was calculated by dividing the weight (in kilograms) by the square of the height (in meters). The BMI values were then used to classify participants into different categories: underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), normal weight (BMI 18.5\u0026ndash;24.9), overweight (BMI 25-29.9), and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eVital Sign Measurement\u003c/h2\u003e \u003cp\u003eBlood pressure was measured by using an analog sphygmomanometer and stethoscope. The measurements were taken from the upper arm of the patient, with the arm positioned at heart level after the patient had been seated for at least 5 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical Measurement\u003c/h2\u003e \u003cp\u003eThe laboratory technologist collects three milliliters of blood from each study participant into a heparinized EDTA tube and separates the serum separate the serum. Then an Automated Chemistry Machine was utilized to perform the HbA1c assay, total cholesterol, triglyceride, high density lipoprotein and low density lipoprotein at Dessie Comprehensive Specialized Hospital(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e A laboratory technologist collected ten milliliters of freshly voided urine from study participants to do urine dipstick test to assess the urine albumin level. The results of the test were reported as negative (absence of albumin) or graded on a scale from +\u0026thinsp;1 to +\u0026thinsp;4, indicating increasing levels of albumin presence in the urine(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eData processing and analysis\u003c/h2\u003e \u003cp\u003eThe data was entered by using Epi Info version `7.2, then it was exported to SPSS version 27 for analysis. Frequencies and cross tabulations were used to summarize descriptive statistics. The results were presented using figures, tables, and words. The enter method was used for multivariable binary logistic regression. In the case of multivariable binary logistic regression, a variable was considered as a candidate if its p-value in the simple binary logistic analysis was less than or equal to 0.25. Variables that had a P-value of \u0026le;\u0026thinsp;0.05 at a 95% confidence interval in the multiple binary logistic regressions were considered statistically significant. To evaluate the associations between CKD among adult diabetic patients with the independent variables, the 95% confidence interval and P-value were employed. Adjusted odds ratio was used to assess the strength of association between CKD with the predictor variables.\u003c/p\u003e \u003cp\u003eThe final multiple binary logistic regression model was fitted using the model Hosmer and Lemeshow test with a p-value of 0.85, Omnibus test p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and Nagelkerke R Square value is 0.494, which mean the proportion of the total variation in the outcome that can be explained by the independent variables in the model.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eData quality control\u003c/h2\u003e \u003cp\u003eData quality was assured by training for data collectors and supervisor. To ensure the accuracy of laboratory tests, the clinical chemistry laboratory at DCSH strictly followed standard operating procedures (SOPs). Before the actual data collection period, a pre-test of the questionnaire was conducted on 15 patients (5% of the total sample size) at Boru-Meda Hospital. Before the analysis, the frequency and cross-tabulation of each variable with the outcome variable were performed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic Characteristics of the Study Participants\u003c/h2\u003e \u003cp\u003e From a sample of 291(97 case and 194 controls), 285 (95 cases and 190 controls) were participated in the study giving a response rate of 97.9%. The median age of cases and controls were 62 and 37.5 with interquartile range of (67\u0026thinsp;\u0026minus;\u0026thinsp;45) and (61-26.75) years respectively. Fifty-nine (62.1%) cases and 101 (53.2%) controls were male.\u003c/p\u003e \u003cp\u003eRegarding the residential areas, 56(58.9%) of cases and 133(70.0%) controls living in urban areas. In terms of marital status, 68(71.6%) of cases and 100 (52.6%) of controls were married. According to education level, 26(27.4%) of cases and 67 (35.5%) of controls were completed college and above. Thirty-three (31.6%) of cases and 60(31.6%) of controls were self-employed (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio demographic characteristics of diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia, 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDisease Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (n)\u0026thinsp;=\u0026thinsp;95 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls (n)\u0026thinsp;=\u0026thinsp;190 (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreater than or equal to 60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(65.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(27.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138(72.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101(53.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89(46.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(30.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133(70.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnable to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRead and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(12.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(13.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollage and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(35.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(31.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100(52.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-government employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(8.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employee*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60(31.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(23.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e* Such as merchants, contractors and etc.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eClinical and comorbidity characteristics of the study participants\u003c/h2\u003e \u003cp\u003eAmong the study participants, 73(76.8%) of cases and 95(50.0%) of controls were had type two diabetic mellitus. Sixty (63.2%) of cases and fifty (26.3%) of controls have ten years and above duration after the diagnosis of diabetic mellitus. Regarding medical confirmed comorbidity, 34(35.8%) of cases and 38(20.0%) of controls had pre-existing hypertension (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia, 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDisease Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (n)\u0026thinsp;=\u0026thinsp;95 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls (n)\u0026thinsp;=\u0026thinsp;190 (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType One\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType Two\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDuration of Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(73.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreater or equal to 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(26.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre-existing Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152(80.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre-existing CVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(12.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166(87.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUse of Analgesics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(26.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(73.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHave Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;140/90 mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136(71.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;140/90 mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(28.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedication use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOral hypoglycemic agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97(51.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93(48.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the study participants, 17(17.9%) of cases and 25(13.2%) of controls have over weight (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProportion of body mass index categories among diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia, 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDisease Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (n)\u0026thinsp;=\u0026thinsp;95 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls (n)\u0026thinsp;=\u0026thinsp;190 (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123(64.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(13.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(15.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eBehavioral characteristics of the study participants\u003c/h2\u003e \u003cp\u003eAmong the study participants, 15(15.8%) of cases and 64(33.7%) of controls was done physical exercise.\u003c/p\u003e \u003cp\u003eAccording to the study finding 38(40.0%) of cases and 53(27.9%) of controls had past history of smoking cigarette. From the study participants, 9(9.5%) of cases and 9(4.7%) of controls and 10(10.5%) of cases and 11(5.8%) of controls were had alcohol consumption and Khat chewing habit respectively (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBehavioural characteristics of diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia, 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDisease Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (n)\u0026thinsp;=\u0026thinsp;95 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls (n)\u0026thinsp;=\u0026thinsp;190 (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCurrent smoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(87.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181(95.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePast history of smoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(27.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137(72.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlcohol consumption habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcoholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-alcoholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181(95.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKhat chewing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKhat chewer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Khat chewer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179(94.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePreviously use of Traditional medicines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157(82.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCurrent use of traditional medicines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184(96.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical characteristics of the study participants\u003c/h2\u003e \u003cp\u003eAccording to the finding of this study, seventy-five of cases (78.9%) had 8% and above level of HbA1c, while 20 (21.1%) had less than 8%. Among controls, 67(35.3%) and 123 (64.7%) of study participants 8% and above and less than 8% level of HbA1c respectively. More cases 75(78.9%) than controls 67(35.3%) had 8% and above level of HbA1c\u003c/p\u003e \u003cp\u003eFifty-four of cases (56.8%) tested positive for albuminuria, while a smaller proportion (43.2%) tested negative. In contrast, a significantly lower percentage of controls (13.2%) tested positive for albuminuria.\u003c/p\u003e \u003cp\u003eFrom the study participants, 22 (23.2%) of cases had more than 200mg/dl total serum cholesterol, while the majority (76.8%) had below 200 mg/dl. Among controls, a similar pattern was observed, with 21.6% having more than 200mg/dl total serum cholesterol and the majority (78.4%) having below 200 mg/dl. A significant percentage of cases (63.2%) had less than 40 mg/dl of HDL levels, while a smaller portion (36.8%) had more than 40 mg/dl, indicating a more favorable lipid profile. In the control group, a higher percentage (73.7%) had more than 40 mg/dl of HDL levels. The majority of cases (56.8%) had above 120 mg/dl of LDL levels, while a smaller portion (43.2%) had below 120 mg/dl. Among controls, a higher percentage (75.3%) had less or equal to 120 mg/dl of LDL levels. A significant proportion of cases (52.6%) had greater than 160 mg/dl of triglyceride levels, while a slightly smaller portion (47.4%) had below 160 mg/dl. In the control group, a lower percentage (28.9%) had greater than 160 mg/dl of triglyceride levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with CKD among adult diabetic patients\u003c/h2\u003e \u003cp\u003eAll variables were assessed using simple binary logistics regression and variables which has a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.25 was eligible for multiple binary logistic regression analysis. Variables such as age, gender, duration of diabetes, pre-existing hypertension and cardiovascular disease, use of analgesics, past smoking cigarette, body mass index, presence of hypertension, presence of albuminuria, level of HbA1c, HDL and triglyceride had a p-value\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.25 and included for further analysis.\u003c/p\u003e \u003cp\u003eIn multiple binary logistic regression analysis, poor glycemic control (HbA1c), older age, positive albuminuria, analgesics drug use, and longer diabetes duration had statistically significance association with CKD in adult diabetic patients at 95% CI (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with chronic kidney disease among adult diabetic patients attending at Dessie comprehensive specialized hospital, Northeast Ethiopia,2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCKD status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (n)\u0026thinsp;=\u0026thinsp;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls (n)\u0026thinsp;=\u0026thinsp;190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.99(2.94\u0026ndash;8.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.70(1.36\u0026ndash;5.37) *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44(0.87\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10(0.55\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre-existing Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23(1.29\u0026ndash;3.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32(0.59\u0026ndash;2.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre-existing CVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96(1.03\u0026ndash;3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.77(0.75\u0026ndash;4.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUse of Analgesics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.02(2.39\u0026ndash;6.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.43(1.26\u0026ndash;4.70) *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHistory of smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.72(1.03\u0026ndash;2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92(0.44\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.88(3.87\u0026ndash;12.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.33(1.63\u0026ndash;6.81\u003c/b\u003e) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlbuminuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.69(4.84\u0026ndash;15.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.85(2.19\u0026ndash;10.73) *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.80(2.83\u0026ndash;8.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17(0.52\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73(1.64\u0026ndash;4.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31(0.61\u0026ndash;2.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHave Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71(0.32\u0026ndash;155)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44(0.26\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDuration of Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;= 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21(0.12\u0026ndash;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.52(1.30\u0026ndash;4.88) *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70(0.93\u0026ndash;3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22(0.50\u0026ndash;2.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-obese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCOR\u0026thinsp;=\u0026thinsp;Crude Odds Ratio, AOR\u0026thinsp;=\u0026thinsp;Adjusted Odds Ratios, CI\u0026thinsp;=\u0026thinsp;Confidence interval * = P value\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study was conducted to identify the determinant factors of chronic kidney disease among adult diabetic patients at Dessie Comprehensive Specialized Hospital, Amhara Region, Ethiopia, 2024.The finding of the study suggests that poor glycemic control, older age, positive albuminuria, analgesics drug use, and longer diabetes duration are associated with an increased probability of developing chronic kidney disease (CKD) in adult diabetic patients.\u003c/p\u003e \u003cp\u003eThe study found that the odds of CKD were 3.33 times higher among adult diabetic patients with poor glycemic control compared to those with good glycemic control (AOR: 3.33, 95% CI: 1.63–6.81). This finding was in line with a study done at Tigray and Jimma hospitals (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This suggests that poorly controlled blood sugar level in diabetic patients can significantly increase the risk of chronic kidney disease. When blood sugar levels remain high over time, it can lead to damage to the blood vessels in the kidneys and nephrons. This damage impairs the proper functioning of the kidneys and can contribute to the development and progression of chronic kidney disease(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBesides, our study revealed that, the odds of CKD were 2.70 times higher among adult diabetic patients with age ≥ 60 years as compared to their counterparts (AOR: 2.70, 95% CI: 1.36–5.37).This finding is congruent with other studies done at United Kingdom, Australia, Palestine, Botswana, Ghana, Uganda, Harar, Gondar and Bahr Dar(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e–\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This implies that the mechanisms underlying this association due to age-related physiological changes in glomerular filtration rate(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, the odds of CKD were 4.83 times higher among adult diabetic patients with positive albuminuria as compared to those patients with negative albuminuria (AOR: 4.83, 95% CI: 2.19–10.73). The result was consistent with studies were done at Harar, Jimma, and Bahir Dar (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). When waste products became building up and damage the small blood vessels in the kidneys. This damage impairs the kidneys' ability to filter waste products properly, leading to the leakage of albumin into the urine (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs well, the odds of CKD were 2.43 times higher among adult diabetic patients with those who used analgesics drugs as compared to their counterparts (AOR: 2.43, 95% CI: 1.26–4.70). Our study provided that there is a statistically significant association between the use of analgesic drugs and the probability of developing chronic kidney disease in adult diabetic patients. This might be due to the long term and high doses usage of the drugs causes interstitial nephritis, which can impair their ability to filter waste products and failed to maintain proper kidney function then end with CKD(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the odds of CKD were 2.52 times higher among adult diabetic patients with those who had greater than or equal to 10 years duration of diabetic as compared to those patients had less than 10 years duration (AOR: 2.52, 95% CI: 1.30–4.88). Studies conducted in Botswana, Uganda, Ghana and Ethiopia provides evidenced that supports the association between the duration of DM and the development of chronic kidney disease among adult diabetic patients (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). This may be due to the prolonged exposure to high levels of glucose among patients, that damaged the tiny blood vessels of kidney and reduced blood flow and causes CKD(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our studies, we found that factors such as body mass index, type of diabetes, pre-existing hypertension, high-density lipoprotein (HDL), triglyceride levels, and alcohol consumption did not show statistically significant association with the development of chronic kidney disease among adult diabetic patients(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). However, it's important to note that these results may vary due to differences in study design, sample size, and the population being studied.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eStrengthen and Limitation of the study\u003c/h2\u003e \u003cp\u003eThe study utilizes multiple data sources, such as interviews, medical records, and laboratory tests, to validate exposure and outcome information. This approach reduces the reliance on self-reporting and enhances the accuracy of data collected.\u003c/p\u003e \u003cp\u003eSince we conducted hospital-based case-control study and as such, inpatient cases were not included. Therefore, further studies with similar designs are needed to validate and expand upon these findings in order to obtain a more comprehensive understanding of the factors influencing the outcome in the broader population of adult diabetic patients.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eThe study finding indicated that poor glycemic control, older age, positive albuminuria, use of analgesics, and long duration of diabetes were significantly associated with chronic kidney disease among adult diabetic patients. We recommend that individualized glycemic target for older age and long duration of diabetic patients. And also avoid or use the lowest effective dose and for the shortest duration possible of nonsteroidal anti-inflammatory drugs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our appreciation to the study participants for their willingness to give the required information and staff of diabetes and CKD clinic of Dessie comprehensive specialized hospital for their cooperation during data collection process. We would also like to acknowledge the data collectors.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAli Mohammed Wolle:\u003c/strong\u003e Contributed to designing the study, writing original draft, formal data analysis, data interpretation, manuscript preparation, and finalization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGebiyaw Wudie:\u003c/strong\u003e Supervision review, editing and data interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbebaw Gedef:\u003c/strong\u003e Supervision review, editing and data interpretation.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eThe study is research article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by Bahir Dar University, College of Medicine and Health Science of Institutional review board with the protocol number of (#868/2023). The study follows the national and International ethical guideline. The name of the participants was not used in collecting the data from the medical files. Confidentiality was maintained by keeping the data collection forms locked in a secure cabinet and the electronic data file was kept securely in a password-protected computer. Data obtained in the course of the study was only handled by the research team. To ensure proper authorization, a letter of authorization was obtained from the Amhara Public Health Institution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this article had both primary and secondary data source patient informed consent was required.\u0026nbsp;Written informed consent was obtained from each participant in the study after providing them with a comprehensive understanding of the study\u0026apos;s objectives. It was made clear that participation was voluntary and that individuals had the right to decline participation at any time. The privacy of the participants was ensured by using codes to keep their information confidential. Participants were also informed that if any abnormal results were identified, they would be notified and appropriately referred to a physician for further care.\u0026nbsp;All the procedures that included human participants adhered to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have indicated that they have no conflicts of interest regarding the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding to report.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNational institution of diabetes and digestive and kidney diseases. 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Metabolism Open. 2022;15:100198.\u003c/li\u003e\n\u003cli\u003eElshahat S, Cockwell P, Maxwell AP, Griffin M, O\u0026rsquo;Brien T, O\u0026rsquo;Neill C. The impact of chronic kidney disease on developed countries from a health economics perspective: A systematic scoping review. PLOS ONE. 2020;15(3):e0230512.\u003c/li\u003e\n\u003cli\u003eGhazanfari Z, Haghdoost AA, Alizadeh SM, Atapour J, Zolala F. A Comparison of HbA1c and Fasting Blood Sugar Tests in General Population. Int J Prev Med. 2010 Summer;1(3):187-94. PubMed PMID: 21566790. Pubmed Central PMCID: PMC3075530. Epub 2011/05/14. eng.\u003c/li\u003e\n\u003cli\u003eKetema EB, Kibret KT. Correlation of fasting and postprandial plasma glucose with HbA1c in assessing glycemic control; systematic review and meta-analysis. Archives of Public Health. 2015 2015/09/25;73(1):43.\u003c/li\u003e\n\u003cli\u003eTheresa Vuskovich D. Compare HbA1c to Fasting Blood Glucose. Everlywell. 2022.\u003c/li\u003e\n\u003cli\u003eSandler CN, McDonnell ME. The role of hemoglobin A1c in the assessment of diabetes and cardiovascular risk. Cleveland Clinic Journal of Medicine. 2016;83(5 Suppl 1):S4-S10.\u003c/li\u003e\n\u003cli\u003everywellhealth. Blood Glucose Test, Preparation and Procedure. 2022.\u003c/li\u003e\n\u003cli\u003eWright L, Hirsch I. Metrics Beyond Hemoglobin A1C in Diabetes Management: Time in Range, Hypoglycemia, and Other Parameters. Diabetes Technology \u0026amp; Therapeutics. 2017 05/01;19:S-16.\u003c/li\u003e\n\u003cli\u003eDessie Comprhensive Specialized Hospital. Annual Performance. 2024.\u003c/li\u003e\n\u003cli\u003eDessie comprehensive specialized hospital Laboratory department. Standard operationg Procedures for Biochemical tests. 2023.\u003c/li\u003e\n\u003cli\u003eFederal Ministry Health of Ethiopia. Guidelines on Clinical and Programmatic Management of Major Non Communicable Diseases. 2021.\u003c/li\u003e\n\u003cli\u003everywellhealth. Addressing Alcohol Abuse With the Cut down, Annoyed, Guilty, Eye-opener Questionnaire. 2022.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Physical status: The use of and interpretation of anthropometry, Report of a WHO Expert Committee: World Health Organization; 1995.\u003c/li\u003e\n\u003cli\u003eHintsa S, Dube L, Abay M, Angesom T, Workicho A. Determinants of diabetic nephropathy in Ayder Referral Hospital, Northern Ethiopia: A case-control study. PLOS ONE. 2017;12(4):e0173566.\u003c/li\u003e\n\u003cli\u003eMariye Zemicheal T, Bahrey Tadesse D, Tasew Atalay H, Teklay Weldesamuel G, Gebremichael GB, Tesfay HN, et al. Determinants of Diabetic Nephropathy among Diabetic Patients in General Public Hospitals of Tigray, Ethiopia, 2018/19. International Journal of Endocrinology. 2020 2020/09/21;2020:6396483.\u003c/li\u003e\n\u003cli\u003eTilahun A, Waqtola C, Tewodros G, Amare D, Yohannis M. Major micro vascular complications and associated risk factors among diabetic outpatients in Southwest Ethiopia. Endocrinol Metab Syndr. 2017;6(4):272.\u003c/li\u003e\n\u003cli\u003eCheru A, Edessa D, Regassa LD, Gobena T. Incidence and predictors of chronic kidney disease among patients with diabetes treated at governmental hospitals of Harari Region, eastern Ethiopia, 2022. Frontiers in Public Health. 2024;11:1290554.\u003c/li\u003e\n\u003cli\u003eEphraim RKD, Arthur E, Owiredu WKBA, Adoba P, Agbodzakey H, Eghan BA. Chronic Kidney Disease Stages Among Diabetes Patients in the Cape Coast Metropolis. Saudi Journal of Kidney Diseases and Transplantation. 2016;27(6):1231-8. PubMed PMID: 00936703-201627060-00018.\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez-P\u0026eacute;rez A, Sa\u0026eacute;z ME, Vizcaya D, Lind M, Rodr\u0026iacute;guez LAG. Impact of chronic kidney disease definition on assessment of its incidence and risk factors in patients with newly diagnosed type 1 and type 2 diabetes in the UK: A cohort study using primary care data from the United Kingdom. Primary care diabetes. 2020;14(4):381-7.\u003c/li\u003e\n\u003cli\u003eKirya M, Bwayo D, Otim ME, Mutoo PB, Masaba JPM, Okibure A, et al. Prevalence of Biomarkers and Associated Factors for Chronic Kidney Disease in Adult Diabetic Out-patients in a Tertiary Hospital in Eastern Uganda-a Cross-sectional Study. 2024.\u003c/li\u003e\n\u003cli\u003eNazzal Z, Hamdan Z, Masri D, Abu-Kaf O, Hamad M. Prevalence and risk factors of chronic kidney disease among Palestinian type 2 diabetic patients: a cross-sectional study. BMC nephrology. 2020;21:1-8.\u003c/li\u003e\n\u003cli\u003eRwegerera GM, Molefe-Baikai OJ, Masaka A, Shimwela M, Rivera YP, Oyewo TA, et al. Prevalence of chronic kidney disease using estimated glomerular filtration rate among diabetes patients attending a tertiary clinic in Botswana. Hospital Practice. 2018 2018/08/08;46(4):214-20.\u003c/li\u003e\n\u003cli\u003eSukkar L, Kang A, Hockham C, Young T, Jun M, Foote C, et al. Incidence and associations of chronic kidney disease in community participants with diabetes: a 5-year prospective analysis of the EXTEND45 study. Diabetes care. 2020;43(5):982-90.\u003c/li\u003e\n\u003cli\u003eTannor EK, Sarfo FS, Mobula LM, Sarfo‐Kantanka O, Adu‐Gyamfi R, Plange‐Rhule J. Prevalence and predictors of chronic kidney disease among Ghanaian patients with hypertension and diabetes mellitus: A multicenter cross‐sectional study. The journal of clinical Hypertension. 2019;21(10):1542-50.\u003c/li\u003e\n\u003cli\u003eNational Kideny Foundation. Estimate glomerulare filtration rate. 2021.\u003c/li\u003e\n\u003cli\u003eDebele GR, Hajure M, Wolde HF, Yenit MK. Incidence and Predictors of Chronic Kidney Disease among Diabetes Mellitus Patients: A Retrospective Follow-Up Study at a Tertiary Health-Care Setting of Ethiopia. Diabetes, metabolic syndrome and obesity : targets and therapy [Internet]. 2021 2021; 14:[4381-90 pp.].\u003c/li\u003e\n\u003cli\u003eAfkarian M, Zelnick LR, Hall YN, Heagerty PJ, Tuttle K, Weiss NS, et al. Clinical manifestations of kidney disease among US adults with diabetes, 1988-2014. Jama. 2016;316(6):602-10.\u003c/li\u003e\n\u003cli\u003ede Boer IH, Rue TC, Hall YN, Heagerty PJ, Weiss NS, Himmelfarb J. Temporal trends in the prevalence of diabetic kidney disease in the United States. Jama. 2011;305(24):2532-9.\u003c/li\u003e\n\u003cli\u003eHe F, Xia X, Wu X, Yu X, Huang F. Diabetic retinopathy in predicting diabetic nephropathy in patients with type 2 diabetes and renal disease: a meta-analysis. Diabetologia. 2013;56:457-66.\u003c/li\u003e\n\u003cli\u003eMolitch ME, Steffes M, Sun W, Rutledge B, Cleary P, De Boer IH, et al. Development and progression of renal insufficiency with and without albuminuria in adults with type 1 diabetes in the diabetes control and complications trial and the epidemiology of diabetes interventions and complications study. Diabetes care. 2010;33(7):1536-43.\u003c/li\u003e\n\u003cli\u003eShiferaw W, Yirga T, Aynalem Y. Chronic Kidney Disease among Diabetes Patients in Ethiopia: A Systematic Review and Meta-Analysis. International Journal of Nephrology. 2020 10/10;2020.\u003c/li\u003e\n\u003cli\u003eAbdulkadr M, Merga H, Mizana BA, Terefe G, Dube L. Chronic Kidney Disease and Associated Factors among Diabetic Patients at the Diabetic Clinic in a Police Hospital, Addis Ababa. Ethiopian Journal of Health Sciences. 2022;32(2).\u003c/li\u003e\n\u003cli\u003eAberra T, Feleke Y, Tarekegn G, Bikila D, Melesse M. Prevalence and associated factors of diabetic nephropathy at Tikur Anbessa comprehensive specialized University hospital, Addis Ababa, Ethiopia. African Journal of Nephrology. 2022;25(1):35-45.\u003c/li\u003e\n\u003cli\u003eBekele MM. Prevalence and associated factors of chronic kidney disease among diabetic patients that attend public hospitals of Addis Ababa: Addis Ababa University; 2016.\u003c/li\u003e\n\u003cli\u003eFiseha T, Kassim M, Yemane T. Chronic kidney disease and underdiagnosis of renal insufficiency among diabetic patients attending a hospital in Southern Ethiopia. BMC nephrology. 2014;15:1-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic kidney Disease, Diabetes, glycated hemoglobin, Adult, Dessie, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-5318799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5318799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic kidney disease is a condition where high blood glucose or blood pressure damages the blood vessels in the kidneys and reduces their function. It develops slowly; so many people do not realize they have it until it has reached an advanced stage. Chronic kidney disease can be prevented by controlling blood glucose and blood pressure, avoiding harmful medications, and changing lifestyle. Though prior studies were conducted on chronic kidney disease among adult diabetics in the Amhara region, possible determinant such as glycated hemoglobin level was not assessed.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo identify the determinants of Chronic Kidney Disease Among Adult Diabetic Patients at Dessie Comprehensive Specialized Hospital, Northeast Ethiopia,2024.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted institutional-based unmatched case-control study between January-1 to June-30-2024. Cases were adult diabetic patient with chronic kidney disease and controls were adult diabetic patient without chronic kidney. All cases were included while systematic random sampling was used to select controls. Data from interview, laboratory and clinical records were collected and entered into Epi info version 7.2, then exported to Statistical Package for Social Science version 27 for analysis. Multivariable binary logistic regression was used to identify determinants of chronic kidney disease and a p-value less than 0.05 was considered as statistically significant.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 95 cases and 190 controls were recruited in the study. The median (interquartile range) age of cases and controls were 62 (67\u0026thinsp;\u0026minus;\u0026thinsp;45) and 37.5 (61-26.75) years respectively. The study revealed that poor glycemic control (HbA1c) (AOR: 3.33, 95% CI: 1.63\u0026ndash;6.81), age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (AOR: 2.70, 95% CI: 1.36\u0026ndash;5.37), presence of albuminuria (AOR: 4.83, 95% CI: 2.19\u0026ndash;10.73), analgesics used (AOR: 2.43, 95% CI: 1.26\u0026ndash;4.70), and duration of diabetes greater than or equal to 10 years (AOR: 2.52, 95% CI: 1.30\u0026ndash;4.88) had statistically significant association with chronic kidney disease among adult diabetic patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study finding indicated that poor glycemic control, older age, positive albuminuria, use of analgesics, and long duration of diabetes were significantly associated with chronic kidney disease among adult diabetic patients. We recommend that individualized glycemic target for older age and long duration of diabetic patients.\u003c/p\u003e","manuscriptTitle":"Determinants of Chronic Kidney Disease among Adult Diabetic Patients at Dessie Comprehensive Specialized Hospital,Northeast Ethiopia,2024: An institution-based case control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 08:14:19","doi":"10.21203/rs.3.rs-5318799/v1","editorialEvents":[{"type":"communityComments","content":2}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8ff84cc-edf0-4be7-8f40-875c5f2f85a1","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-27T15:23:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-15 08:14:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5318799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5318799","identity":"rs-5318799","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}