Emergence, Isolation and Coexistence of nephropathy and retinopathy among diabetic mellitus type 2 patients: A cross-sectional Study from a tertiary hospital-based population of Eritrea.

Emergence, Isolation and Coexistence of nephropathy and retinopathy among diabetic mellitus type 2 patients: A cross-sectional Study from a tertiary hospital-based population of Eritrea. | 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 Emergence, Isolation and Coexistence of nephropathy and retinopathy among diabetic mellitus type 2 patients: A cross-sectional Study from a tertiary hospital-based population of Eritrea. Samuel Tekle Mengistu, Ghirmay Ghebrekidan Ghebremeskel, Oliver Okoth Achila, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7622691/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: DM2 is a growing chronic metabolic disorder affecting the aging populations in LMICs.The current prevalence of diabetic microvascular complications and their associated factors is relatively unknown in Eritrea. We aimed to determine the magnitude of retinopathy and nephropathy in patients who followed up in the study site as well as identify associated demographic and clinical factors . Methodology: Hospital based cross-sectional study was conducted among 302 type 2 diabetic patients attending in Halibet Referral Hospital, Diabetes Follow-up Clinic in Asmara. The presence of microvascular complications was defined as having one of DR or DN upon physician diagnosis. Socio-demographic and clinical information of patients were collected using questionnaires and patients’ clinical records. Relationships between DN and DR and the diagnostic efficacy of DR for DN were explored. Results: Diabetic microvascular complications were documented in 84.1% participants. Diabetic nephropathy showed the highest prevalence (43.3%), followed by coexisting nephropathy and retinopathy (30.1%). Our study demonstrated a clear relationship of age, diabetes duration, systolic blood pressure and Framingham risk score with microvascular complications of diabetes. Furthermore, the diagnostic accuracy of retinopathy in detecting nephropathy has been explored in this study, where retinopathy showed lower sensitivity (40%) and specificity (60%) with accuracy rate of 46%. Patients with diabetic nephropathy and retinopathy had a higher median systolic blood pressure [130 (IQR: 120-146)], as determined by the Kruskal-Wallis test (p-value = 0.06). More notably, participants with coexisting complications had a significantly higher median Framingham risk score of 21.9 (IQR: 14.7-33.2); P-value=0.001. Conclusion: Early recognition and timely intervention of microvascular complications remains central in designing effective preventive strategies in diabetes. The findings underscore the urgent need for targeted interventions focusing on lifestyle modifications, early detection, and effective management of diabetes and its associated complications. The association of diabetic retinopathy with diabetic nephropathy as a viable indicator early screening and timely identification of kidney diseases for diabetic patients in resource limited settings. Internal Medicine Diabetic retinopathy Diabetic nephropathy associated factors Africa Eritrea Figures Figure 1 Figure 2 Introduction Diabetes mellitus (DM) is a chronic metabolic disorder distinguished by persistent hyperglycemia, which continues to pose an imminent public health threat throughout the globe [ 1 , 2 ]. Globally, the burden of diabetes is experiencing an exponential surge, which is being marked as a defining health challenge of the twenty-first century. Thus, the need for dynamic, innovative, and integrated approaches to preventing and managing chronic diseases is paramount [ 3 ]. In 2021, the International Diabetes Federation (IDF) reported that the global prevalence of diabetes had reached 537 million diagnosed adults between the ages of 20 and 79 years, and is estimated to increase to 643 million by 2030, projected to double to 783 million by 2045 [ 4 ]. Africa is one of the disproportionately impacted continents, with a total of 25 million adults living with diabetes, where 73% of diabetic patients remain undiagnosed. The toll is expected to increase by 142%, affecting an additional 45 million healthy adults by 2050 [ 5 , 6 ]. The burden is unevenly distributed in low- and middle-income countries, particularly in resource-limited African settings, such as developing East African nations like Eritrea, due to the emergence of urbanization, rapid lifestyle changes, genetic predispositions, and the growing phenomenon of an aging population [ 1 , 2 , 4 , and 7 ]. Unpublished data from Eritrea's health management system indicate that DM is a growing public health concern, significantly surpassing several infectious diseases such as malaria, tuberculosis, and human immunodeficiency virus (HIV) in terms of all-cause morbidity and mortality [ 8 ][ 9 ]. Almost one-third, or 35%, of T2DM patients in Africa face the risk of developing microvascular complications within the first three years of diagnosis [ 10 ]. The prevalence of microvascular complications ranged from 33% to 61% in Africa [ 11 , 12 ]. Late presentation of disease followed by advanced complications, suboptimal access to care, and persistent gaps in diabetic management remain the persuasive challenge in most clinical settings in the region [ 13 ][ 14 ]. T2DM, accompanied by tiny blood vessel damage, is linked with potential microvascular injury, predisposing patients to long-term nephropathy and retinopathy complications. Progressive vascular impairment is a prominent pathogenic precursor for chronic kidney diseases and vision-challenging eye conditions, including blindness [ 12 ]. The subsequent squeal lead to debilitating and life-threatening health conditions, profoundly affecting the workload of health services and straining health economic costs immensely [ 15 ]. The common risk factors associated with type 2 diabetic micro complications include poor glycemic control, longer duration of diabetes, older male, obesity, smoking, hypertension, and physical inactivity [ 10 , 16 ]. Diabetic retinopathy (DR) and diabetic nephropathy (DN) are two prevalent microvascular complications of DM, contributing a significant burden to the quality of life and cost of healthcare systems (R). Globally, DR affects approximately one-third of people with diabetes, while DN is estimated to occur in 20–40% of patients [ 17 ]. Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus, often characterized by an asymptomatic onset and silent progression of the disease, affecting the retina and overall ocular health. Left undiagnosed and untreated, DR leads to visual impairment and blindness in the adult working age groups and the elderly population [ 18 ]. Fundus photography is the gold standard diagnostic method used for imaging diabetic retinopathy and assessing the microvascular environment of the retina non-invasively. Similarly, diabetic nephropathy (DN) is another significant microvascular complication of diabetes impacting small blood vessels of the kidney and leading cause of end-stage renal disease (ESRD) worldwide [ 19 ]. DN develops in approximately 10–20% of patients with type 2 diabetes mellitus, typically after 10 years of diagnosis, and is associated with high morbidity and mortality [ 20 ][ 21 ]. The clinical landscape of DN is presented with deterioration of renal function, progressive decline in glomerular filtration rate (GFR), subsequently followed by chronic kidney disease (CKD) and eventual manifestation of end-stage renal disease (ESRD). A biopsy of the renal tissue is the most definitive method for diagnosing DN. Still, its application in routine clinical settings is limited due to its invasive nature, associated risk of hemorrhage, and high cost of operation, making it the least feasible option. The widely common practice for screening DN in LMICs relies on urine and blood tests, using the levels of albuminuria and glomerular filtration rate (GFR) as a functional evaluation of the kidneys. However, its clinical success as an effective diagnostic tool in primary care and low-resource settings is hampered due to existing apparent gaps in awareness and insufficient access to laboratory testing, thereby compromising the established clinical consensus on the benefits of early screening and delaying the timely detection and intervention in diabetes disease management [ 22 ]. Several studies consistently point to shared pathological convergence of retinopathy and nephropathy, striking the parallels of ocular and renal damage with an observable commonalities in the structural and physiological pathways [ 19 , 23 ], rather than perceived distinctly as merely isolated phenomenon of disease mechanisms., According to the " common soil hypothesis", eye and kidney diseases originate from mutually shared pathology, encompassing a notable overlap in hyperglycemia induced oxidative stress, inflammation, vascular permeability, and endothelial dysfunction characteristics [ 24 ]. Retinal imaging has been established as a non-invasive and low-cost tool offering valuable insights into microvascular alterations of diabetic complications thereby serving as a surrogate biomarker for systemic kidney damage [ 25 ][ 18 ]. Although various studies were undertaken, diabetic microvascular complications remain a pressing public health challenge, with their associated factors varying significantly according to the population and study setting. Specific data on the prevalence of diabetic microvascular complications and studies investigating the relationships between retinopathy and nephropathy are limited in East Africa, underscoring the need for localized research to address regional epidemiological nuances and optimize healthcare delivery [ 26 , 27 ]. Recent studies from neighboring countries, such as Ethiopia, have highlighted varying prevalence rates and risk factors for DR and DN, emphasizing the need for population-targeted and context-specific research [ 16 , 28 ]. These findings underscore the importance of integrating local insights into global efforts to combat diabetic complications and reduce the socioeconomic burden of these diseases. Early detection and effective management of diabetic microvascular complications are fundamental in improving clinical outcomes and reducing the economic costs linked with diabetes. Interventions initiated at early stages pave beneficial paths towards patient prognosis journey by enhancing quality of life, extending life expectancy and mitigating the cost of hospitalizations. The current understanding of the diabetes burden and patterns of microvascular complications in Eritrea remains inadequate, hindering progress at the national level in chronic disease prevention and limiting the scale-up of clinical management in hospitals. In this cross-sectional study, we analyzed data from tertiary hospital-based diabetic patients evaluating the concordance and discordance of microvascular diabetic complications, with the objective of (1) quantifying the magnitude of diabetic nephropathy and retinopathy complications, (2) characterizing demographic and clinical factors associated with diabetic nephropathy and retinopathy patients. Additionally, the study explores the potential role of retinopathy as a predictive marker and screening tool for nephropathy. Material and methods Study Population A cross-sectional hospital study was conducted on 302 patients in Halibet national Referral Hospital in Asmara, Eritrea, between February 2020 and June 2020. It provides medical services to the approximately 560,000 residents of Asmara and adjoining catchment areas. Several considerations have been undertaken in selecting a hospital and study setting. Eritrea’s healthcare system is highly centralized. Halibet, alongside Haz-Haz Hospital, is one of the primary healthcare facilities in Asmara that offers Diabetes Mellitus (DM) clinics and follow-up services, making it a primary center for diabetic management and attracting the most prominent pool of DM in the country. The hospital receives a comprehensive profile of DM patients nationwide through referrals from other health centers or self-referral patients, managed by a multidisciplinary team of general practitioners, pharmacists, and nurses during their scheduled follow-up visits. The sample size was calculated based on the techniques established in prior work [ 29 ]. Briefly, information from the clinic records served as the sampling frame, and a random selection process was employed, whereby every second patient attending the facility during the study period was selected. Inclusion criteria were: 1) male or female patients (aged ≥ 30 years); 2) diagnosed with Type 2 DM (according to WHO criteria). Exclusion criteria were: 1) patients with Type 1 DM; 2) hospitalized patients at the time of the study; 3) patients not willing to give informed consent for participation; and 3) patients characterized with specific mental or psychiatric disorders (including severe mental retardation and any indication of substance abuse). The study was conducted in adherence to the foundational principles of the Declaration of Helsinki, with ethical approval granted by the Ethics Committee of the Eritrean Institutional Review Boards. All participants provided informed consent before the initiation of the study. Patient stratification Figure 1 shows patients were categorized into four groups based on absence, or presence of one or two microvascular complications: Nephropathy (n = 32) only patients, DN (+) RN (-); Retinopathy (n = 131) only patients DN (-) RN (+), patients presented with both Nephropathy and Retinopathy (n = 91) DN (+) RN (+); and patients without neither (n = 487), DN (-) RN (-). Data collection Patient charts and questionnaires were used to gather information about patients' physical, socio-demographic, clinical, behavioral, and DM care-associated characteristics. As previously described in the work of Achila et al., variables extracted from patient charts included DM status, comorbidities including hypertension, duration of DM, anti-diabetic drug regimen, and CVD [ 29 ]. A standardized questionnaire was employed, incorporating multiple queries on various socio-economic, anthropometric, lifestyle, and clinical factors. A laboratory analysis of sampled blood specimens was performed to collect further information and assess additional clinical parameters. (Interview, rather than self-administered). Demographic and Clinical characteristics assessments Patients' demographic characteristics were collected from medical records, including age, sex, anthropometric status (height, weight, and body mass index (BMI)), level of education, employment status, marital status, physical exercise, systolic blood pressure (SBP), diastolic blood pressure (DBP), blood glucose level, duration of diabetes, smoking status, alcohol consumption, history of hypertension, and risk score of cardiovascular disease (CVD). A range of anthropometric measurements was conducted, including assessing general obesity. Weight was measured with a carefully calibrated balancing scale (Zhongshan Camry Electronic Co. Ltd, China) following a standardized protocol. Additionally, waist circumference (WC), an indicator of abdominal obesity or visceral adiposity (VAT), was measured using standard procedures [ 30 ]. Abnormal waist circumference was defined using gender-specific parameters for women > 80 cm and > 94 cm for men. Body mass index (BMI) was determined using the standard formula: BMI = weight (kg)/height (m 2 ), and its categorization was performed using the WHO established criterion: underweight (≤ 18.4 kg/m 2 ), average weight (18.5–24.9 kg/m 2) , overweight (25.0–29.9 kg/m 2 ) and obese (≥ 30 kg/m 2 ) [ 31 ]. Lifestyle factors such as alcohol intake, dietary selection, and tobacco consumption were defined as previously described [ 29 ]. Duration of diabetes mellitus (DM) was defined as the period between patient enrollment in the study and patient diagnosis by a physician. Computation of the DM duration was made by subtracting the age at diagnosis from the patient’s current age using hospital health records. The risk for developing CVD was defined as low risk 30% based on the Framingham Risk Score (FRS) prediction tool for estimating the likelihood of heart attack or stroke occurrence in 10 years. Laboratory and clinical chemistry measurements All clinical chemistry analyses were conducted at Sembel Hospital, where blood samples were processed per established guidelines. After fasting for at least 8 hours, 5 milliliters (ml) of blood was drawn from the median cubital vein, with samples divided into biochemistry tubes and analyzed within 3 hours. A Beckman Coulter AU480 Chemistry analyzer was employed for analyzing hemoglobin A1C (HbA1c) and serum creatinine. HbA1c categories were classified according to the American Diabetes Association (ADA) guidelines [ 32 ]. According to WHO guidelines, a well-calibrated digital sphygmomanometer (MDF® Lenus Digital Blood Pressure Monitor) was used to measure blood pressure (BP). Patients rested for approximately 15 minutes before the measurement process. Subsequently, three BP readings were taken at 5-minute intervals, with the final BP calculated as the average of the second and third measurements. Hypertension (HTN) was defined as either diastolic BP (DBP) / systolic BP (SBP) ≥ 90/140 mmHg, a previous diagnosis of HTN, or self-reported antihypertensive medication use. Measurement, evaluation, and diagnosis of DN: Estimated glomerular filtration rate (eGFR ): The Modification of Diet in Renal Disease (MDRD) was used to calculate eGFR. According to this formula, eGFR = 186 x [Serum Creatinine (mg/dl)] − 1.154 x (Age) −0.203 x (0.742 if female). Reduced eGFR was defined as a value ≤ 60ml/min/1.73m 2 . Renal function was defined as normal (eGFR > 90 mL/min/1.73 m2), mildly impaired (eGFR 60–89 mL/min/1.73 m2), or indicative of chronic kidney disease (CKD) (GFR < 60 mL/min/1.73 m2). For this study, e|GFR < 90 mL/min/1.73 m2 was used as a cut-off to define diabetic nephropathy. Measurement, evaluation, and diagnosis of DR: Fundoscopic (Ophthalmoscopic) Examination Diabetic retinopathy (DR) was diagnosed based on fundus photography results. Following mydriasis, an ophthalmologist and an optometrist conducted a comprehensive fundus evaluation. Dilated-pupil fundus examination (DFE) was carried out using a slit-lamp ophthalmoscope (Zeiss SL 115 Classic Slit Lamp, Carl Zeiss Meditec AG, Jena, Germany). The examination included a detailed assessment of all four retinal quadrants, with grading based on the International Clinical Diabetic Retinopathy Severity Scales [ 33 ]. Each participant’s DR was defined by the grade of the worst degradable eye [ 34 ]. Data analysis Data entry was performed by a single personnel and several investigators conducted independent accuracy checks. All statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 26.0 (IBM Corporation, Chicago, IL, U.S.A.). Descriptive statistics of continuous variables with normal data distribution were expressed as mean and standard deviation (SD), and median with Interquartile range (IQR) were employed for non-parametrically distributed variables. Categorical demographic and clinical variables were presented as absolute values and percentages. Chi-square (χ²) test, Fisher’s exact test, and Kendall tau-b or tau-c tests were used for comparison of groups and evaluation of the hypothesis of independence. Depending on the distribution nature of the data, a one-way analysis of variance (ANOVA) or its non-parametric counterpart, Kruskal-Wallis test, was applied to compare the means or medians differences within multiple groups, and post-hoc comparisons were conducted using the application of Tukey’s HSD test. Diagnostic efficacy of DR for DN was estimated using sensitivity, and specificity analyses. All P-value tests were two-sided, and values < 0.05 were considered statistically significant. Results Baseline characteristics of study participants A total of 302 patients with type 2 diabetes mellitus participated in the study. The demographic and clinical characteristics of patients are presented in Table 1. The median age of diabetic patients was 58 (IQR: 43–73), and the study comprised 53% male participants. Almost half of the study participants had the late onset of diabetes within the age category of 51–60. In contrast, the elderly population above 65 years old constitutes one-fourth of the diabetic cohorts. The majority of patients included in the study were employed (52.9%) and married (89.3%), whereas 45% of the study population lacked formal education. Concerning behavioral and lifestyle aspects of participants, cigarette smoking and alcohol consumption were prevalent in 5% and 21.9% of patients, respectively. Sedentary lifestyle choices, without regular physical exercise, were observed in 45.4% of the study patients. The clinical profile of patients revealed the median duration of diabetes type 2, since the time of diagnosis was 10 years with IQR of (6–17), and a higher proportion of patients (50.3%) had experienced diabetic illness within the time frame of 6–16 years. The median BMI was 24.5 kg/m² (21.9–26.9). A small proportion of patients, 14.6% (n = 14), were underweight, while the majority, 50.7% (n = 153) of the patients had a normal BMI. Almost one third of study participants, 35.4% (n = 107), were overweight, and an additional 9.3% (n = 21) fell into the obese BMI category. Among 302 subjects diagnosed with type 2 diabetes mellitus, a higher systolic blood pressure (> 130 mmHg) was observed in 30.9% (n = 93) of patients, whereas an elevated diastolic blood pressure (> 95 mmHg) was evident in 15.3% (n = 46) of participants. Systolic blood pressure 85 in 15.3%. The median HbA1C was 8.8 (8.1–9.5) g/dL, with the majority (85.4%) of participants having HbA1C > 7.5 g/dL. Further, the median Framingham score was 19 (IQR: 11.8–30.1), with a score of < 3 observed in only 4.7%. Addressing the anthropometric profile of participants, the median BMI was 24.5 (IQR: 21.9–26.9) kg/m 2, with the majority (34.5%) of participants in the overweight category. The median waist circumference and waist-to-hip ratio were 94 (IQR: 88–100) cm and 100 (IQR: 0.8–0.9) cm, while the majority (59.3%) had abnormal waist circumference. See Table 1 for details. Table 1: Clinical and Demographic Characteristics of Study Population Characteristics Total N (%) Total 302 Gender Female 141 (46.7) Male 161 (53.3) Age, in years 58 (51-66) 65 76 (25.2) Education level None 45 (14.9) Primary 108 (35.8) Secondary 98 (32.5) Tertiary 51 (16.9) Occupation Unemployed 143 (47.4) Employed 159 (52.9) Marital status Married 266 (89.3) Single 32 (10.7) smoking No 287 (95) Yes 15 (5) Alcohol consumption No 236 (78.1) Yes 66 (21.9) Regular exercise Yes 165 (54.6) No 137 (45.4) Diabetes duration in years, median (IQR) 10 (6-17) 20 34 (11.3) BMI in Kg/m2, median (IQR) 24.5 (21.9-26.9) Underweight 14 (4.6) Normal 153 (50.7) Overweight 107 (35.4) Obese 28 (9.3) Waist circumference in cm, median (IQR) 94 (88-100) Abnormal 179 (59.3) Normal 123 (40.7) Hip circumference in cm, Median (IQR) 100 (95-107) Waist to Hip ratio (WHR), Median (IQR) 0.93 (0.897-0.97) Abnormal 47 (15.6) Normal 255 (84.4) Systolic BP 130 (115.5-140) 130 93 (30.9) Diastolic BP 80 (80-80) 85 46 (15.3) Hgb A1C (g/dL) 8.8 (8.1-9.5) ≤ 7.5 44 (14.6) > 7.5 258 (85.4) Framingham Score, median (IQR) 19 (11.8-30.1) 30 76 (25.2) Abbreviations: BP- Blood pressure, BMI- Body mass index, Hgb- hemoglobin, IQR- Interquartile range, N- number, WHR- Waist to hip ratio Prevalence of Diabetes mellitus microvascular complications in study participants Figure 1 displays the proportion of participants with diabetic retinopathy, diabetic nephropathy, and patients with evidence of co-existing nephropathy and retinopathy microvascular complications. According to this study, nearly half of the participants displayed evidence of diabetic nephropathy (131, 43.3%), followed by a high proportion were participants who had both retinopathy and nephropathy (91, 30.1%). Meanwhile, Retinopathy alone was observed in 32 (10.5%) participants. In contrast, only 48 (15.8%) had presented none of the complications of DM2. Sociodemographic factors associated with diabetic retinopathy and diabetic nephropathy Table 2 displays participants' demographic characteristics stratified by type 2 diabetes mellitus complications. In this analysis, retinopathy was observed in a significantly higher proportion in males compared to females [24 (75%) in males vs 8 (25%) in females, p-value = 0.04]. Notably, participants with no abnormality had significantly lower median age [52.5 (IQR: 42.25-61.75)] years compared to higher age categories, Kruskal Wallis test p-value = 0.006. Furthermore, participants in the 51-65 age group had a significantly higher proportion of both complications [54 (59.3%), p-value = 0.002]. Table 2: Sociodemographic factors associated with diabetic retinopathy and nephropathy Characteristics DN (+) RN (+) N (%) DN (+) RN (_) N (%) DN (_) RN (+) N (%) DN (_) RN (_) N (%) p-value Total N (%) Total 91 (30.1) 131 (43.3) 32 (10.5) 48 (15.8) 302 Gender Female 46 (50.5) 67 (51.1) 8 (25) 20 (41.7) 0.044 a (8.12) 141 (46.7) Male 45 (49.5) 64 (48.9) 24 (75) 28 (58.3) 161 (53.3) Age, in years 60 (52-64) 59 (54-68) 56 (50-67) 52.5 (42.25-61.75) 0.006 c 58 (51-66) 65 19 (20.9) 37 (28.2) 11 (34.4) 9 (18.8) 76 (25.2) Educational Level None 18 (19.8) 22 (16.8) 3 (9.4) 2 (4.2) 0.056 b (16.45) 45 (14.9) Primary 32 (35.2) 52 (39.7) 7 (21.9) 17 (35.4) 108 (35.8) Secondary 25 (27.5) 40 (30.5) 12 (37.5) 21 (43.8) 98 (32.5) Tertiary 16 (17.6) 17 (13) 10 (31.1) 8 (16.7) 51 (16.9) Occupation Unemployed 47 (51.6) 66 (50.4) 10 (31.3) 20 (41.7) 0.16 a (5.12) 143 (47.4) Employed 44 (48.4) 65 (49.6) 22 (68.8) 28 (58.3) 159 (52.9) Marital status Married 85 (93.4) 112 (86.8) 28 (90.3) 41 (87.2) 0.45 b (2.6) 266 (89.3) Single 6 (6.6) 17 (13.2) 3 (9.7) 6 (12.8) 32 (10.7) Alcohol consumption No 71 (78) 105 (80.2) 26 (81.3) 34 (70.8) 0.57 a (1.99) 236 (78.1) Yes 20 (22) 26 (19.8) 6 (18.8) 14 (29.2) 66 (21.9) Regular Exercise Yes 51 (56) 63 (48.1) 20 (62.5) 31 (64.6) 0.2 a (5) 165 (54.6) No 40 (44) 68 (51.9) 12 (37.5) 17 (35.4) 137 (45.4) Diabetes duration in years, median (IQR) 15 (8-20) 10 (6-16) 14.5 (8.5-19.5) 8 (6-12.7) <0.001 c 10 (6-17) < 5 10 (11) 32 (24.4) 3 (9.4) 11 (22.9) 0.002 b (31.2) 56 (18.5) 6-10 27 (29.7) 47 (35.9) 9 (28.1) 21 (43.8) 104 (34.4) 11-15 13 (14.3) 17 (13) 6 (18.8) 12 (25) 48 (15.9) 16-20 27 (29.7) 24 (18.3) 7 (21.9) 2 (4.2) 60 (19.9) > 20 14 (15.4) 11 (8.4) 7 (21.9) 2 (4.2) 34 (11.3) DN (+) RN (+)- Diabetic Nephropathy with retinopathy, DN (+) RN (_)- Diabetic nephropathy without retinopathy, DN (_) RN (+)- Diabetic retinopathy and DN (_) RN (_)- No diabetic retinopathy nor nephropathy. Superscripts: a- Chi-square test, b- Fischer’s exact test, c- Kruskal-Wallis test Clinical and anthropometric factors associated with diabetic retinopathy and diabetic nephropathy In this analysis, participants' clinical and anthropometric profiles were stratified by diabetes mellitus complications. Diabetes mellitus patients with diabetic nephropathy and retinopathy had higher median systolic blood pressure [130 (IQR: 120-146), Kruskal Wallis test p-value = 0.06]. More notably, participants with both complications had significantly higher median Framingham risk score of 21.9 (IQR: 14.7-33.2), Kruskal Wallis test p-value=0.001. Further, a significantly higher proportion of both complications were observed in participants with Framingham scores of 15-30 and > 30, 39 (42.9%) and 29 (31.9%), respectively chi-square p-value =0.01. However, no significant differences were observed among four groups regarding BMI (P=0.8), diastolic blood pressure (p=0.1), waist circumference (P=0.36), hip circumference (P=0.6), waist to hip ratio (P=0.8), HgbA1C (P=0.2). Table 3: Clinical and anthropometric factors associated with diabetic retinopathy and diabetic nephropathy Characteristics DN (+) RN (+) N (%) DN (+) RN (_) N (%) DN (_) RN (+) N (%) DN (_) RN (_) N (%) p-value Total N (%) BMI in Kg/m2, median (IQR) 24.8 (21.6-27.5) 24.5 (21.9-27.1) 24.7 (22.9-26.6) 24.5 (21.9-25.9) 0.8 c 24.5 (21.9-26.9) Underweight 5 (5.5) 6 (4.6) 1 (3.1) 2 (4.2) 0.96 b (3) 14 (4.6) Normal 43 (47.3) 65 (49.6) 17 (53.1) 28 (58.3) 153 (50.7) Overweight 36 (39.6) 47 (35.9) 10 (31.3) 14 (29.2) 107 (35.4) Obese 7 (7.7) 13 (9.9) 4 (12.5) 4 (8.3) 28 (9.3) Waist circumference in cm, median (IQR) 95 (90-101) 94 (87-100) 94.5 (86.3-101) 95 (87.2-5-99.8) 0.36 94 (88-100) Abnormal 39 (42.9) 58 (44.3) 10 (31.3) 16 (33.3) 0.37 a (3.1) 179 (59.3) Normal 52 (57.1) 73 (55.7) 22 (68.8) 32 (66.7) 123 (40.7) Hip circumference in cm, Median (IQR) 102 (96-109) 100 (95-107) 99 (95.3-107) 99 (95-105.7) 0.6 c 100 (95-107) Waist to Hip ratio (WHR), Median (IQR) 0.94 (0.9-0.97) 0.93 (0.9-0.97) 0.95 (0.91-0.97) 0.94 (0.89-0.97) 0.8 0.93 (0.897-0.97) Abnormal 12 (13.2) 22 (16.8) 5 (15.6) 8 (16.7) 0.99 a (0.58) 47 (15.6) Normal 79 (86.8) 109 (83.2) 27 (84.4) 40 (83.3) 255 (84.4) Systolic BP 130 (120-146) 130 (110-140) 120 (120-130) 120 (110-140) 0.06a 130 (115.5-140) 130 36 (38.6) 36 (27.5) 7 (22.5) 14 (29.2) 93 (30.9) Diastolic BP 80 (80-80) 80 (70-80) 80 (80-80) 80 (80-80) 0.1 c 80 (80-80) 85 17 (18.7) 16 (12.2) 6 (19.4) 7 (14.6) 46 (15.3) Hgb A1C (g/dL) 8.9 (8.3-9.6) 8.8 (8-9.5) 8.7 (7.8-9.3) 8.7 (7.9-9.3) 0.2 c 8.8 (8.1-9.5) ≤ 7.5 8 (8.8) 24 (18.3) 4 (12.5) 8 (16.7) 0.24 b (4.2) 44 (14.6) > 7.5 83 (91.2) 107 (81.7) 28 (87.5) 40 (83.3) 258 (85.4) Framingham Score, median (IQR) 21.9 (14.7-33.2) 18.7 (11.7-27.1) 20.7 (14.7-40.8) 14.1 (6.5-23.6) 0.001 c 19 (11.8-30.1) 30 29 (31.9) 29 (22.1) 11 (35.5) 7 (14.6) 76 (25.2) Abbreviations: BP- Blood pressure, BMI- Body mass index, Hgb- hemoglobin, IQR- Interquartile range, N- number, WHR- Waist to hip ratio. DN (+) RN (+)- Diabetic Nephropathy with retinopathy, DN (+) RN (_)- Diabetic nephropathy without retinopathy, DN (_) RN (+)- Diabetic retinopathy and DN (_) RN (_)- No diabetic retinopathy nor nephropathy. Superscripts: a- Chi-square test, b- Fischer’s exact test, c- Kruskal-Wallis test Diagnostic efficacy of DR for DN The sensitivity and specificity of DR in detecting DN in patients with type 2 diabetes mellitus were 40% and 60% respectively, with an overall diagnostic accuracy of 46% (Table 4). Table 4: Sensitivity of DR for diagnosing DN Sensitivity Specificity Accuracy DR 40% 60% 46% Discussion The global burden of diabetes mellitus (DM) continues to rise in low and middle-income countries, with East Africa witnessing an unprecedented rate of increase in prevalence. Diabetic retinopathy (DR) and diabetic nephropathy (DN) are among the most prominent microvascular complications, contributing substantially to adverse health outcomes and putting enormous strain on healthcare systems. However, there is a dearth of population specific data on its prevalence, and the paucity of country relevant statistics has been evident in most African nations. The present study aimed to explore the prevalence, correlated factors, and the shared interrelationships between diabetic nephropathy and retinopathy. In this study we observed highest prevalence of nephropathy (43.3%), followed by moderate prevalence in diabetic patients with coexisting nephropathy and retinopathy (30.1%), while a lowest prevalence of retinopathy (10.5%) has been reported in diabetic patients participated in the study. Our study also reveals a clear relationship of age, gender, level of education, diabetes duration, systolic blood pressure and Framingham risk score with microvascular complications of diabetes. Furthermore, the diagnostic accuracy of retinopathy in detecting nephropathy was explored in this study. Retinopathy showed relatively low sensitivity (40%) and specificity (60%), with an overall accuracy rate of 46%. These findings suggest that retinopathy alone is not a reliable screening tool for nephropathy. However, in resource-limited settings where access to advanced diagnostic methods is restricted, retinopathy assessment may still provide some value as an adjunctive indicator for identifying patients at higher risk who require further evaluation. These findings provide a comprehensive overview of the clinical and demographic features, prevalence of complications, and associated factors among patients with type 2 diabetes mellitus (T2DM) in Eritrea. Further, underscoring valuable insights into T2DM patients and emphasizing on the importance of tailored interventions for designing effective disease management and enhancing efficient prevention strategies. In this tertiary hospital study, our findings showed that the combined magnitude of DR and DN was 84.1%. The findings of this study is higher than the previous microvascular studies conducted in Ethiopia (21.8) [16], china (34.5%)[35], Saudi Arabia (34.3%)[36], Ghana (35.3%)[37] Southern India (52.1%)[38], Tanzania (57.6%) [10], USA (77%)[39]. Higher prevalence of diabetic microvascular indicates that diabetic complications were not adequately controlled, placing a heavy burden on the existing chronic disease management and healthcare system. The regional variation and global heterogeneity might be related to the variation attributed from differences in sample size, study setting, diagnostic criteria, clinical practices, accessibility health care services, glycemic control practices of patients and quality of diabetic management. On the other hand, hospital based findings, longer duration of diabetes and older diabetic participants might explain the prevalence difference observed in comparison to previous studies. Some of the other major reasons one could speculate is that in most African nations, including Eritrea, there is a profound gap in screening microvascular complications, where the majority of people are not receiving regular assessments, and a significant proportion of patients remain undiagnosed until the later stages of presentation of nephropathy and retinopathy morbidities. The primary factors contributing to the burden in LMICs include limited access to healthcare, inadequate screening infrastructure, and low public awareness [40] [41]. The Sociodemographic characteristics observed among 302 diabetic patients had shown similarities with other studies. Almost half of the participants were females (46.7%) and with T2DM, predominantly middle-aged, with a median age of 58 (IQR: 51-65). In line with the trends seen from the cohort of DISCOVERY study in the Middle East and Africa (MEA), females account for (47.5%) and the mean age of participants was 54.3 years. This demographic profile aligns with previous studies in similar regions, emphasizing the increasing prevalence of T2DM among older adults in developing countries [42]. The majority of participants were employed (60.6%, 95% CI: 55.0% - 66.0%) and married (77.5%, 95% CI: 72.8% - 82.0%), reflecting the socio-economic status and familial support, which can influence disease management [43]. Regarding lifestyle factors, a significant proportion of patients did not exercise regularly (68.2%, 95% CI: 63.0%-73.5%) and had a high prevalence of alcohol consumption (40.4%, 95% CI: 35.2%-45.8%). These behaviors contribute to poor glycemic control and increased cardiovascular risk, highlighting the need for lifestyle interventions tailored to local cultural norms [44]. The median duration of diabetes was 10 years (IQR: 5-15 years), with a substantial number of participants diagnosed for more than a decade, which was comparatively higher than the DISCOVER cohort 6.2 years [45]. This prolonged duration correlates with an increased risk of complications such as diabetic nephropathy and retinopathy, as observed in this study [10]. Anthropometric measurements revealed a median BMI (BMI: 27.5 kg/m², IQR: 24.6-30.1 kg/m²), which is consistent with the global trend of rising obesity rates among T2DM patients [46]. The majority of participants had abnormal waist circumference (59.3%), indicating central obesity, which is strongly associated with insulin resistance and cardiovascular disease risk [47]. Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus and the leading cause of visual loss in the elderly. Hyperglycemia and altered metabolic pathways lead to oxidative stress, contributing to neurodegeneration in the early stages of diabetic retinopathy [19,48]. Clinic-based surveys in diabetes management have reported a DR prevalence ranging from 7.0% to 62.4% globally and (13%-86%) in East Africa [49]. Our study showed that 32% of diabetic patients were presented with retinopathy microvascular complications. Similarly, population-based studies have also identified high DR prevalence rates of 35.9% in Kenya [50], 20.5% in Nigeria [51], and 17.9% in Egypt [52] and an overall prevalence of 28% in East Africa [49] . DN is one of most frequently occurring microvascular complications with around 30-40% of diabetic patients developing chronic kidney diseases [19]. The developmental stages of nephropathy is marked with the onset of hyperglycaemia-induced glomerular hyperfiltration and endothelial dysfunction, accompanied by membrane thickening, albuminuria, progression to CKD and ultimate renal function failure[21] [20]. Diabetic nephropathy was the most prevalent complication in our study participants, affecting 43.3% of participants (95% CI: 37.9%-48.8%), followed by combined retinopathy and nephropathy in 30.1% (95% CI: 25.4%-35.2%). The prevalence of nephropathy is higher in contrast to the findings reported from systematic review and meta-analysis from North America countries among diabetic patients in USA, Canada and Mexico with pooled prevalence of 24.2%, 31.2% and 31.2%, respectively [53]. DN is suggested to be more frequent among patients with diabetes in Africa compared to those in developed countries due to delayed diagnosis, limited screening and diagnostic resources, poor control of blood sugar and other risk factors, and inadequate early-stage treatment [7]. The results of our correlation analysis revealed an established relationship between diabetic patients' characteristics and microvascular complications where age, duration of diabetes, systolic blood pressure and Framingham score were significant factors. Additionally, we found gender, level of education, employment history, BMI, physical exercise and alcohol consumption were not related to microvascular microvascular complications. These findings highlight the significant burden of microvascular complications among T2DM patients in Eritrea, consistent with findings in other African countries [54]. The association between longer diabetes duration and higher prevalence of complications highlights the importance of early diagnosis and effective management strategies to mitigate disease progression [55]. Gender differences were noted in the prevalence of complications, with males showing a higher prevalence of retinopathy alone. This aligns with studies suggesting gender-specific differences in the manifestation and progression of diabetic complications [56]. Age was significantly associated with both retinopathy and nephropathy, with older participants at greater risk, consistent with findings from other regional studies [57]. Educational level and occupation did not show significant associations with complications, highlighting the complex interplay of socioeconomic factors in disease outcomes [58]. Participants with both retinopathy and nephropathy exhibited higher systolic blood pressure (median: 140 mmHg, IQR: 130-150 mmHg) and Framingham risk scores (median: 15, IQR: 10-20), indicating a higher cardiovascular risk profile. Elevated blood pressure is a well-established risk factor for diabetic nephropathy and retinopathy, necessitating aggressive blood pressure control to prevent progression to end-stage renal disease and vision loss [59]. Landmark epidemiological studies and diverse clinical studies have confirmed that the age of the patient, male gender, duration of diabetes, poor glycemic control, hypertension, and obesity are among the prominent risk factors contributing to the development of microvascular complications in the course of a diabetic patient's journey [10–12]. The relationship between DN and DR is characterized by reciprocal pathogenic pathways in which hemodynamic load, oxidative stress, and inflammatory cytokine mediators contribute to the progression of both conditions [60–63]. Chronic hyperglycemia is the most prevalent hallmark of diabetic microangiopathy, linked with deleterious effects on ophthalmic and renal function. High blood glucose, inflammation, oxidative stress and vascular permeability are the common shared pathogeneses between nephropathy and retinopathy marked with the expression of IL-1β, IL-6, and TNF-α, and subsequent activation of nuclear-factor κB (NF-κB) and signal transducer-activated activator of transcription factor 3 (STAT3) signal transduction pathways, thereby leading to the development of microvascular complications in the respective organs; kidneys and eyes [18,19]. DR is a prevalent microvascular manifestation in diabetes mellitus patients that serves as an established indicator for microvascular health. Fundoscopic evidence of diabetic lesions is associated with a heightened likelihood of DN development and progression [64]. In our study, we further explored to assess the predictive value of diabetic retinopathy on nephropathy risk in type 2 diabetic patients using sensitivity and specificity diagnostic accuracy tests. Our findings reported the sensitivity and specificity of DR for predicted DN were 40% and 60% respectively. Conversely, evidence synthesized from meta-analysis demonstrated that DR could distinguish DN from non-diabetic renal diseases (NDRD) with pooled sensitivity 0.65% and 0.75% [65]. The morphological resemblances and functional similarities of the retina and glomerulus offer a window of opportunity to utilize the retina as an accessible surrogate to “detect and predict “the microvascularity changes in the kidney of diabetic patients. Several studies have demonstrated a clinical correlation between diabetic retinopathy and nephropathy, owing to the notion that retinopathy serves as a viable screening tool and a potential non-invasive biomarker for predicting the onset and early stages of microangiopathy in the kidneys [62, 66, and 67]. The potential of using eye screening images, the presence of PDR, and clinically significant macular edema (CSME) has been established in several studies as a good indicator of kidney health and a promising predictor of the risk of developing kidney disease [64]. Utilizing the retinopathy finding alongside eGFR renal parameters as an early screening tool and an indicative biomarker offers significant value for timely referral to nephropathic evaluation, thereby addressing the shortcomings of the existing standard kidney function tests in resource-limited settings. Strengths and Limitations To our knowledge, this is the first national cross-sectional study on the prevalence of diabetic microcomplications at a tertiary level of healthcare in Eritrea, correlating distinct and shared factors associated with patients with nephropathy and retinopathy. However, the study falls short in assessing the type and severity of diabetic microvascular complications due to resource restrictions. Most of the patients were diagnosed late, and there is an existing knowledge gap on early detection and screening practices for diabetic patients. The findings are drawn from a single-center study conducted in a small clinical setting, where the results are preliminary and should be interpreted with caution when extrapolated to a larger diabetic community and generalized to broader populations in Eritrea. Conclusion The research findings provide actionable insights into the epidemiology of type 2 diabetes and its complications in Eritrea. The evidence generated reinforces clinical practices and policy recommendations, with relevance to benefiting patient care and resource-limited settings globally, where the need for evidence-based interventions is paramount. The findings highlight the pressing need for early interventions and comprehensive diabetes management strategies, underscoring the need for targeted interventions directed at lifestyle modification interventions, strengthening early detection modalities and optimization of diabetes management care. Several targeted interventions can be recommended including as a strategic response to alleviate the existing knowledge gap in managing diabetic microvascular complications. Comprehensive approaches are imperative to mitigate the challenges of suboptimal practices in diabetic screening, diagnosis, and management in resource-limited settings . Future research should investigate longitudinal outcomes and rigorously evaluate the impact of tailored interventions on enhancing health outcomes among patients with type 2 diabetes. Declarations Author Contributions All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Funding The study didn’t receive any funding. Disclosure The authors have declared that no competing interests exist. References Lovic D, Piperidou A, Zografou I, Grassos H, Pittaras A, Manolis A (2020) The growing epidemic of diabetes mellitus. Curr Vasc Pharmacol 18:104–109 Hossain MJ, Al-Mamun M, Islam MR (2024) Diabetes mellitus, the fastest growing global public health concern: Early detection should be focused. Health Sci Rep 7. 10.1002/hsr2.2004 Ong KL, Stafford LK, Mclaughlin SA (2023) Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global burden of disease study 2021. Lancet 402:203–234 Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB et al (2022) IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 183:109119 International Diabetes Federation (2021) IDF Diabetes Atlas. Brussels, Belgium IDF Diabetes Atlas In: Diabetes Atlas [Internet]. 18 Jul 2024 [cited 8 Sep 2025]. Available: https://diabetesatlas.org/ Pastakia S, Pekny C, Manyara S, Fischer L (2017) Diabetes in sub-Saharan Africa – from policy to practice to progress: targeting the existing gaps for future care for diabetes. Diabetes Metab Syndr Obes 10:247–263 Dunachie S, Chamnan P (2019) The double burden of diabetes and global infection in low and middle-income countries. Trans R Soc Trop Med Hyg 113:56–64 Abdu A, Bakrey H, Hamed M, Idris T (2025) Non-communicable diseases in Eritrea: a review of challenges, risk factors and public health strategies. Discov Public Health 22. 10.1186/s12982-025-00449-1 Shillah WB, Yahaya JJ, Morgan ED, Bintabara D (2024) Predictors of microvascular complications in patients with type 2 diabetes mellitus at regional referral hospitals in the central zone, Tanzania: a cross-sectional study. Sci Rep 14. 10.1038/s41598-024-55556-x Solela G, Gessesse HA, Zegeye H, Worku A, Leulseged B (2024) Prevalence, patterns, and determinants of vascular complications of type 2 diabetes in a teaching hospital in Addis Ababa, Ethiopia: a retrospective study. BMC Endocr Disord 24:190 Merid F, Getahun F, Esubalew H, Gezahegn T (2024) Diabetic microvascular complications and associated factors in patients with type 2 diabetes in Southern Ethiopia. Front Endocrinol (Lausanne) 15. 10.3389/fendo.2024.1342680 Abdul-Samed A-B, Peprah EB, Jahan Y, Reichenberger V, Balabanova D, Mirzoev T et al (2024) Exploring the path to optimal diabetes care by unravelling the contextual factors affecting access, utilisation, and quality of primary health care in West Africa: A scoping review protocol. PLoS ONE 19:e0294917 1 Use Data To Transform Diabetes Care And Lives With Diabetes 2 The Lancet Commission on Diabetes Einarson TR, Acs A, Ludwig C, Panton UH (2018) Economic burden of cardiovascular disease in type 2 diabetes: A systematic review. Value Health 21:881–890 Negussie YM, Sento M, Fati NM (2024) Diabetic microvascular complications among adults with type 2 diabetes in Adama, central Ethiopia. Sci Rep 14. 10.1038/s41598-024-77183-2 Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045 Teo, Zhen Ling. Zhen Ling Ophthalmol 128: 1580–1591 Pearce I, Simó R, Lövestam-Adrian M, Wong DT, Evans M (2019) Association between diabetic eye disease and other complications of diabetes: Implications for care. A systematic review. Diabetes Obes Metab 21:467–478 Li Y, Liu Y, Liu S, Gao M, Wang W, Chen K et al (2023) Diabetic vascular diseases: molecular mechanisms and therapeutic strategies. Signal Transduct Target Ther 8:152 Marijn M, Speeckaert P (2025) Trends in nephrology: from diabetic kidney disease to CKD with risk factor diabetes. Nephrol Dialysis Transplantation Adebayo-Gege GI, Adegbola PI, Adedayo LD, Oyefabi AM, Oyeyemi IT, Olubukola O et al (2025) Prevalence of nephropathy among patients with diabetes mellitus in Africa: a systematic review and meta-analysis. Front Clin Diabetes Healthc 6. 10.3389/fcdhc.2025.1551088 Smirčić Duvnjak L, Božičević S, Vučić Lovrenčić M (2023) Diagnostic challenges of diabetic kidney disease. Biochem Med (Zagreb) 33:227–241 Farrah TE, Dhillon B, Keane PA, Webb DJ, Dhaun N (2020) The eye, the kidney, and cardiovascular disease: old concepts, better tools, and new horizons. Kidney Int 98:323–342 Fernandes Silva L, Vangipurapu J, Laakso M (2021) The common soil hypothesis revisited—risk factors for type 2 diabetes and cardiovascular disease. Metabolites 11:691 Li Y, Su X, Ye Q, Guo X, Xu B, Guan T et al (2021) The predictive value of diabetic retinopathy on subsequent diabetic nephropathy in patients with type 2 diabetes: a systematic review and meta-analysis of prospective studies. Ren Fail 43:231–240 Berhe T, Yimer M, Lema D, Molla MD (2021) Prevalence of diabetic nephropathy in Ethiopia: A systematic review and meta-analysis of hospital-based observational studies. BMC Nephrol 22:1–11 Ghebremeskel GG, Embaye A, Tsegay G, Tsegay T, Ghebreyesus T, Kahsay H (2022) Magnitude of diabetic retinopathy and associated factors among diabetic patients attending eye clinics at public hospitals in Mekelle town. BMC Ophthalmol 22:1–10 Bekele M, Tadesse S (2022) Retinopathy and its associated factors among diabetic patients attending diabetic retinopathy unit of Debre Tabor General Hospital, Northwest Ethiopia, 2021: A cross-sectional study. J Ophthalmol East Afr 6:10–16 Achila OO, Ghebretinsae M, Kidane A, Simon M, Makonen S, Rezene Y (2020) Factors Associated with Poor Glycemic and Lipid Levels in Ambulatory Diabetes Mellitus Type 2 Patients in Asmara, Eritrea: A Cross-Sectional Study. J Diabetes Res 2020:5901569 Fiseha T, Alemayehu E, Kassahun W, Adamu A, Gebreweld A (2018) Factors associated with glycemic control among diabetic adult out-patients in Northeast Ethiopia. BMC Res Notes 11:316 Nuttall FQ (2015) Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today 50:117–128 American Diabetes Association (2018) Standards of medical care in diabetes-2018 abridged for primary care providers. Clin Diabetes 36:14–37 Wilkinson CP, Ferris FL 3rd, Klein RE, Lee PP, Agardh CD, Davis M et al (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110:1677–1682 Burgess PI, Allain TJ, García-Fiñana M, Beare NAV, Msukwa G, Harding SP (2014) High prevalence in Malawi of sight-threatening retinopathy and visual impairment caused by diabetes: identification of population-specific targets for intervention. Diabet Med 31:1643–1650 Bui HDT, Jing X, Lu R, Chen J, Ngo V, Cui Z et al (2019) Prevalence of and factors related to microvascular complications in patients with type 2 diabetes mellitus in Tianjin, China: a cross-sectional study. Ann Transl Med 7:325–325 Saiyed NS, Yagoub U, Al Qahtani B, Al Zahrani AM, Al Hariri I, Syed MJ et al (2022) Risk factors of microvascular complications among type 2 diabetic patients using cox proportional hazards models: A cohort study in tabuk Saudi Arabia. J Multidiscip Healthc 15:1619–1632 Annani-Akollor ME, Addai-Mensah O, Fondjo LA, Sallah L, Owiredu E-W, Acheampong E et al (2019) Predominant complications of type 2 diabetes in Kumasi: A 4-year retrospective cross-sectional study at a teaching hospital in Ghana. Med (Kaunas) 55:125 Govindarajan Venguidesvarane A, Jasmine A, Varadarajan S, Shriraam V, Muthuthandavan AR, Durai V et al (2020) Prevalence of vascular complications among type 2 diabetic patients in a Rural Health center in south India. J Prim Care Community Health 11. 10.1177/2150132720959962 Ramanathan AS (2017) Correlation of duration, hypertension and glycemic control with microvascular complications of diabetes mellitus at a tertiary care hospital. Integr Mol Med 4. 10.15761/imm.1000272 Pastakia SD, Pekny CR, Manyara SM, Fischer L (2017) Diabetes in sub-Saharan Africa - from policy to practice to progress: targeting the existing gaps for future care for diabetes. Diabetes Metab Syndr Obes 10:247–263 Diabetes and Microvascular Complications in African Patients: Rural vs. Urban Cases Shaw JE, Sicree RA, Zimmet PZ (2010) Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 87:4–14 Limiri DM (2024) The Impact of Socioeconomic Status on the Prevalence and Management of Diabetes. Premier J Public Health Bovet P, Viswanathan B, Faeh D (2017) The impact of social and cultural factors on diabetes care in the World Health Organization African region. Curr Diab Rep. ;17 Hafidh K, Malek R, Al-Rubeaan K, Kok A, Bayram F, Echtay A et al (2022) Prevalence and risk factors of vascular complications in type 2 diabetes mellitus: Results from discover Middle East and Africa cohort. Front Endocrinol (Lausanne) 13. 10.3389/fendo.2022.940309 Unadike BC, Eregie A, Ohwovoriole AE (2020) Prevalence of overweight and obesity among diabetics and non-diabetics in Benin City, Nigeria. Nigeria Niger J Clin Pract 23:696–702 Papaetis GS, Papakyriakou P, Panagiotou TN (2015) State of the art paper Central obesity, type 2 diabetes and insulin: exploring a pathway full of thorns. Arch Med Sci 3:463–482 He W, Tang P, Lv H (2025) Targeting oxidative stress in diabetic retinopathy: mechanisms, pathology, and novel treatment approaches. Front Immunol 16. 10.3389/fimmu.2025.1571576 Abuhay HW, Tesfie TK, Alemayehu MA, Agimas MC, Yismaw GA, Alemu GG et al (2025) Prevalence of diabetic retinopathy and its associated factors among adults in East African countries: A systematic review and meta-analysis. PLoS ONE 20:e0316160 Mathenge W, Bastawrous A, Peto T, Leung I, Yorston D, Foster A et al (2014) Prevalence and correlates of diabetic retinopathy in a population-based survey of older people in Nakuru, Kenya. Ophthalmic Epidemiol 21:169–177 Kyari F, Tafida A, Sivasubramaniam S, Murthy GVS, Peto T, Gilbert CE et al (2014) Prevalence and risk factors for diabetes and diabetic retinopathy: results from the Nigeria national blindness and visual impairment survey. BMC Public Health 14:1299 AlSawahli H, Mpyet CD, Ezzelarab G, Hassanin I, Shalaby M, Safa O et al (2021) Population-based cross-sectional prevalence survey of diabetes and diabetic retinopathy in Sohag-Egypt, 2019. BMJ Open 11:e047757 Zahra S, Saleem MK, Ejaz KF, Akbar A, Jadoon SK, Hussain S et al (2024) Prevalence of nephropathy among diabetic patients in North American region: A systematic review and meta-analysis. Med (Baltim) 103:e39759 Mbanya JCN, Motala AA, Sobngwi E, Assah FK, Enoru ST (2010) Diabetes in sub-Saharan Africa. Lancet 375:2254–2266 Tikellis G, Thomas MC (2012) Early detection of diabetic kidney disease. Curr Diab Rep 12:323–332 Chou CF, Sherrod CE, Zhang X Association between diabetic eye disease and other complications of diabetes: implications for care. A population-based study in the U.S. Diabetes Care Fotso JC, Fogham MN The socio-economic determinants of diabetes mellitus: empirical evidence from Cameroon Agyemang C, Meeks K, Beune E Obesity and type 2 diabetes in sub-Saharan Africans-is the burden in today’s Africa similar to African migrants in Europe? Yuan C, Wang J, Zou MX (2018) Diabetic retinopathy and the risk of mortality in type 2 diabetes: a systematic review and meta-analysis. BMJ Open Diabetes Res Care Yue T, Shi Y, Luo S, Weng J, Wu Y, Zheng X (2022) The role of inflammation in immune system of diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Front Immunol 13. 10.3389/fimmu.2022.1055087 Fang J, Luo C, Zhang D, He Q, Liu L (2023) Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study. Front Endocrinol (Lausanne) 14. 10.3389/fendo.2023.1265711 Wang Q, Cheng H, Jiang S, Zhang L, Liu X, Chen P et al (2024) The relationship between diabetic retinopathy and diabetic nephropathy in type 2 diabetes. Front Endocrinol (Lausanne) 15. 10.3389/fendo.2024.1292412 Li J, Cao Y, Liu W, Wang Q, Qian Y, Lu P (2019) Correlations among diabetic microvascular complications: A systematic review and meta-analysis. Sci Rep 9:3137 Ou S-H, Chang W-C, Wu L-Y, Wang S-I, Wei JC-C, Lee P-T (2024) Diabetic macular edema is predictive of renal failure in patients with diabetes mellitus and chronic kidney disease. J Clin Endocrinol Metab 109:761–770 He F, Xia X, Wu XF, Yu XQ, Huang FX (2013) Diabetic retinopathy in predicting diabetic nephropathy in patients with type 2 diabetes and renal disease: a meta-analysis. Diabetologia 56:457–466 Saini DC, Kochar A, Poonia R (2021) Clinical correlation of diabetic retinopathy with nephropathy and neuropathy. Indian J Ophthalmol 69:3364–3368 Liu Z, Li X, Wang Y, Song Y, Liu Q, Gong J et al (2023) The concordance and discordance of diabetic kidney disease and retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study of 26,809 patients from 5 primary hospitals in China. Front Endocrinol (Lausanne) 14:1133290 Additional Declarations The authors declare no competing interests. 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-7622691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515465523,"identity":"589cf0b9-dada-444b-8d85-42dc3786c77a","order_by":0,"name":"Samuel Tekle Mengistu","email":"","orcid":"https://orcid.org/0000-0002-2817-1421","institution":"Department of Internal Medicine, Nakfa Hospital, Ministry of Health, Northern Red Sea Branch, Nakfa, Eritrea","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"Tekle","lastName":"Mengistu","suffix":""},{"id":515465524,"identity":"96c6d4a7-9e23-441e-8e05-a883c60d865f","order_by":1,"name":"Ghirmay Ghebrekidan Ghebremeskel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDACCcYGKIv5AIgrQ4oWtgQQl4cILXAWjwGYJKhDPrq57dONmsN28/3XfH51o8aCh4H98NEN+LQY3jnYPDvn2OHkjTfebrPOOQZ0GE9a2g28WmYkNjPnsB1ONpxxdptxDhtQiwSPGRFa/oG0nHlmnPOPCC3yEkAtuW2H7eT5e5gf57YRocUArKUvPcFAgs0MyJDgYSPkF/kZ6Y+Zc75Z28v3H378OedbnRw/++Fj+G05AKaaEzfcSGADxxEbPuVgWxrAVB3QlgPMHwipHgWjYBSMgpEJAJRfTJshVtuQAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0275-2279","institution":"Department of Internal Medicine, Nakfa Hospital, Ministry of Health, Northern Red Sea Branch, Nakfa, Eritrea","correspondingAuthor":true,"prefix":"","firstName":"Ghirmay","middleName":"Ghebrekidan","lastName":"Ghebremeskel","suffix":""},{"id":515465525,"identity":"f62caebf-3d74-4fa9-969e-e6eb53b34c95","order_by":2,"name":"Oliver Okoth Achila","email":"","orcid":"https://orcid.org/0000-0001-8013-0785","institution":"Unit of Clinical Laboratory Science, Orotta College of Medicine and Health Sciences, Asmara, Eritrea","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"Okoth","lastName":"Achila","suffix":""},{"id":515465526,"identity":"bb6ac4b1-f42a-4e77-93f3-3855c7c487a5","order_by":3,"name":"Misgana Teklehaimanot Tsegai","email":"","orcid":"","institution":"Department of Internal Medicine, Barentu Hospital, Ministry of Health, Gash Barka Branch, Barentu, Eritrea","correspondingAuthor":false,"prefix":"","firstName":"Misgana","middleName":"Teklehaimanot","lastName":"Tsegai","suffix":""},{"id":515465527,"identity":"5a26fe5e-2ada-4cf0-932f-27cdfefd95e2","order_by":4,"name":"Henok Afewerki kidane","email":"","orcid":"","institution":"Department of Internal Medicine, Barentu Hospital, Ministry of Health, Gash Barka Branch, Barentu, Eritrea","correspondingAuthor":false,"prefix":"","firstName":"Henok","middleName":"Afewerki","lastName":"kidane","suffix":""},{"id":515465528,"identity":"bb941c54-c48b-4b4f-b3fd-ef39f5eb786a","order_by":5,"name":"Yonas Tesfagabr Abraham","email":"","orcid":"https://orcid.org/0009-0000-6788-5788","institution":"Department of Internal Medicine, Orotta School of Medicine and Dentistry, Asmara, Eritrea, Department of Neurology, Alzheimer ’s disease Research Center, University of California Davis, Sacramento, CA, USA, Department of Population and Public Health Research, African Community Health Institute, San Jose, CA, USA.","correspondingAuthor":false,"prefix":"","firstName":"Yonas","middleName":"Tesfagabr","lastName":"Abraham","suffix":""},{"id":515465529,"identity":"91ce9604-64fa-4b0b-ac97-7d54d798e40d","order_by":6,"name":"Robel Afeworki Habte","email":"","orcid":"https://orcid.org/0009-0002-0081-8999","institution":"Department of Population and Public Health Research, African Community Health Institute, San Jose, CA, USA. Department of Community Medicine, Orotta School of Medicine and Dentistry, Asmara, Eritrea.","correspondingAuthor":false,"prefix":"","firstName":"Robel","middleName":"Afeworki","lastName":"Habte","suffix":""}],"badges":[],"createdAt":"2025-09-15 16:21:23","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7622691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7622691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91515691,"identity":"a00fe289-b256-4b8f-bbcf-8a752a315596","added_by":"auto","created_at":"2025-09-17 09:15:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of study participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7622691/v1/b141c1449d02d84cc00b1f45.png"},{"id":91517232,"identity":"85e55285-b556-41d3-b719-4d9637b7a2e4","added_by":"auto","created_at":"2025-09-17 09:31:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54063,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of diabetes mellitus type 2 complication in study participants.\u003c/p\u003e\n\u003cp\u003eAbbreviations: DN (+) RN (+)- Diabetic Nephropathy with retinopathy, DN (+) RN (_)- Diabetic nephropathy without retinopathy, DN (_) RN (+)- Diabetic retinopathy, and DN (_) RN (_)- No diabetic retinopathy nor nephropathy.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7622691/v1/021b35d24e46490d31d89d1f.png"},{"id":91518154,"identity":"b796da58-661c-441b-bd7c-f6ecec2784ee","added_by":"auto","created_at":"2025-09-17 09:39:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1272706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7622691/v1/ceae7077-2a49-4dfb-9208-83c035545a97.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEmergence, Isolation and Coexistence of nephropathy and retinopathy among diabetic mellitus type 2 patients: A cross-sectional Study from a tertiary hospital-based population of Eritrea.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus (DM) is a chronic metabolic disorder distinguished by persistent hyperglycemia, which continues to pose an imminent public health threat throughout the globe [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Globally, the burden of diabetes is experiencing an exponential surge, which is being marked as a defining health challenge of the twenty-first century. Thus, the need for dynamic, innovative, and integrated approaches to preventing and managing chronic diseases is paramount [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In 2021, the International Diabetes Federation (IDF) reported that the global prevalence of diabetes had reached 537\u0026nbsp;million diagnosed adults between the ages of 20 and 79 years, and is estimated to increase to 643\u0026nbsp;million by 2030, projected to double to 783\u0026nbsp;million by 2045 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Africa is one of the disproportionately impacted continents, with a total of 25\u0026nbsp;million adults living with diabetes, where 73% of diabetic patients remain undiagnosed. The toll is expected to increase by 142%, affecting an additional 45\u0026nbsp;million healthy adults by 2050 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The burden is unevenly distributed in low- and middle-income countries, particularly in resource-limited African settings, such as developing East African nations like Eritrea, due to the emergence of urbanization, rapid lifestyle changes, genetic predispositions, and the growing phenomenon of an aging population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, and \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Unpublished data from Eritrea's health management system indicate that DM is a growing public health concern, significantly surpassing several infectious diseases such as malaria, tuberculosis, and human immunodeficiency virus (HIV) in terms of all-cause morbidity and mortality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlmost one-third, or 35%, of T2DM patients in Africa face the risk of developing microvascular complications within the first three years of diagnosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The prevalence of microvascular complications ranged from 33% to 61% in Africa [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Late presentation of disease followed by advanced complications, suboptimal access to care, and persistent gaps in diabetic management remain the persuasive challenge in most clinical settings in the region [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. T2DM, accompanied by tiny blood vessel damage, is linked with potential microvascular injury, predisposing patients to long-term nephropathy and retinopathy complications. Progressive vascular impairment is a prominent pathogenic precursor for chronic kidney diseases and vision-challenging eye conditions, including blindness [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The subsequent squeal lead to debilitating and life-threatening health conditions, profoundly affecting the workload of health services and straining health economic costs immensely [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The common risk factors associated with type 2 diabetic micro complications include poor glycemic control, longer duration of diabetes, older male, obesity, smoking, hypertension, and physical inactivity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDiabetic retinopathy (DR) and diabetic nephropathy (DN) are two prevalent microvascular complications of DM, contributing a significant burden to the quality of life and cost of healthcare systems (R). Globally, DR affects approximately one-third of people with diabetes, while DN is estimated to occur in 20\u0026ndash;40% of patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus, often characterized by an asymptomatic onset and silent progression of the disease, affecting the retina and overall ocular health. Left undiagnosed and untreated, DR leads to visual impairment and blindness in the adult working age groups and the elderly population [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Fundus photography is the gold standard diagnostic method used for imaging diabetic retinopathy and assessing the microvascular environment of the retina non-invasively. Similarly, diabetic nephropathy (DN) is another significant microvascular complication of diabetes impacting small blood vessels of the kidney and leading cause of end-stage renal disease (ESRD) worldwide [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. DN develops in approximately 10\u0026ndash;20% of patients with type 2 diabetes mellitus, typically after 10 years of diagnosis, and is associated with high morbidity and mortality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The clinical landscape of DN is presented with deterioration of renal function, progressive decline in glomerular filtration rate (GFR), subsequently followed by chronic kidney disease (CKD) and eventual manifestation of end-stage renal disease (ESRD). A biopsy of the renal tissue is the most definitive method for diagnosing DN. Still, its application in routine clinical settings is limited due to its invasive nature, associated risk of hemorrhage, and high cost of operation, making it the least feasible option. The widely common practice for screening DN in LMICs relies on urine and blood tests, using the levels of albuminuria and glomerular filtration rate (GFR) as a functional evaluation of the kidneys. However, its clinical success as an effective diagnostic tool in primary care and low-resource settings is hampered due to existing apparent gaps in awareness and insufficient access to laboratory testing, thereby compromising the established clinical consensus on the benefits of early screening and delaying the timely detection and intervention in diabetes disease management [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies consistently point to shared pathological convergence of retinopathy and nephropathy, striking the parallels of ocular and renal damage with an observable commonalities in the structural and physiological pathways [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], rather than perceived distinctly as merely isolated phenomenon of disease mechanisms., According to the \" common soil hypothesis\", eye and kidney diseases originate from mutually shared pathology, encompassing a notable overlap in hyperglycemia induced oxidative stress, inflammation, vascular permeability, and endothelial dysfunction characteristics [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Retinal imaging has been established as a non-invasive and low-cost tool offering valuable insights into microvascular alterations of diabetic complications thereby serving as a surrogate biomarker for systemic kidney damage [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough various studies were undertaken, diabetic microvascular complications remain a pressing public health challenge, with their associated factors varying significantly according to the population and study setting. Specific data on the prevalence of diabetic microvascular complications and studies investigating the relationships between retinopathy and nephropathy are limited in East Africa, underscoring the need for localized research to address regional epidemiological nuances and optimize healthcare delivery [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Recent studies from neighboring countries, such as Ethiopia, have highlighted varying prevalence rates and risk factors for DR and DN, emphasizing the need for population-targeted and context-specific research [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings underscore the importance of integrating local insights into global efforts to combat diabetic complications and reduce the socioeconomic burden of these diseases.\u003c/p\u003e\u003cp\u003eEarly detection and effective management of diabetic microvascular complications are fundamental in improving clinical outcomes and reducing the economic costs linked with diabetes. Interventions initiated at early stages pave beneficial paths towards patient prognosis journey by enhancing quality of life, extending life expectancy and mitigating the cost of hospitalizations. The current understanding of the diabetes burden and patterns of microvascular complications in Eritrea remains inadequate, hindering progress at the national level in chronic disease prevention and limiting the scale-up of clinical management in hospitals. In this cross-sectional study, we analyzed data from tertiary hospital-based diabetic patients evaluating the concordance and discordance of microvascular diabetic complications, with the objective of (1) quantifying the magnitude of diabetic nephropathy and retinopathy complications, (2) characterizing demographic and clinical factors associated with diabetic nephropathy and retinopathy patients. Additionally, the study explores the potential role of retinopathy as a predictive marker and screening tool for nephropathy.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eA cross-sectional hospital study was conducted on 302 patients in Halibet national Referral Hospital in Asmara, Eritrea, between February 2020 and June 2020. It provides medical services to the approximately 560,000 residents of Asmara and adjoining catchment areas. Several considerations have been undertaken in selecting a hospital and study setting. Eritrea\u0026rsquo;s healthcare system is highly centralized. Halibet, alongside Haz-Haz Hospital, is one of the primary healthcare facilities in Asmara that offers Diabetes Mellitus (DM) clinics and follow-up services, making it a primary center for diabetic management and attracting the most prominent pool of DM in the country. The hospital receives a comprehensive profile of DM patients nationwide through referrals from other health centers or self-referral patients, managed by a multidisciplinary team of general practitioners, pharmacists, and nurses during their scheduled follow-up visits.\u003c/p\u003e\u003cp\u003eThe sample size was calculated based on the techniques established in prior work [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Briefly, information from the clinic records served as the sampling frame, and a random selection process was employed, whereby every second patient attending the facility during the study period was selected. Inclusion criteria were: 1) male or female patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;30 years); 2) diagnosed with Type 2 DM (according to WHO criteria). Exclusion criteria were: 1) patients with Type 1 DM; 2) hospitalized patients at the time of the study; 3) patients not willing to give informed consent for participation; and 3) patients characterized with specific mental or psychiatric disorders (including severe mental retardation and any indication of substance abuse). The study was conducted in adherence to the foundational principles of the Declaration of Helsinki, with ethical approval granted by the Ethics Committee of the Eritrean Institutional Review Boards. All participants provided informed consent before the initiation of the study.\u003c/p\u003e\u003cp\u003ePatient stratification\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows patients were categorized into four groups based on absence, or presence of one or two microvascular complications: Nephropathy (n\u0026thinsp;=\u0026thinsp;32) only patients, DN (+) RN (-); Retinopathy (n\u0026thinsp;=\u0026thinsp;131) only patients DN (-) RN (+), patients presented with both Nephropathy and Retinopathy (n\u0026thinsp;=\u0026thinsp;91) DN (+) RN (+); and patients without neither (n\u0026thinsp;=\u0026thinsp;487), DN (-) RN (-).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003ePatient charts and questionnaires were used to gather information about patients' physical, socio-demographic, clinical, behavioral, and DM care-associated characteristics. As previously described in the work of Achila et al., variables extracted from patient charts included DM status, comorbidities including hypertension, duration of DM, anti-diabetic drug regimen, and CVD [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A standardized questionnaire was employed, incorporating multiple queries on various socio-economic, anthropometric, lifestyle, and clinical factors. A laboratory analysis of sampled blood specimens was performed to collect further information and assess additional clinical parameters. (Interview, rather than self-administered).\u003c/p\u003e\n\u003ch3\u003eDemographic and Clinical characteristics assessments\u003c/h3\u003e\n\u003cp\u003ePatients' demographic characteristics were collected from medical records, including age, sex, anthropometric status (height, weight, and body mass index (BMI)), level of education, employment status, marital status, physical exercise, systolic blood pressure (SBP), diastolic blood pressure (DBP), blood glucose level, duration of diabetes, smoking status, alcohol consumption, history of hypertension, and risk score of cardiovascular disease (CVD). A range of anthropometric measurements was conducted, including assessing general obesity. Weight was measured with a carefully calibrated balancing scale (Zhongshan Camry Electronic Co. Ltd, China) following a standardized protocol. Additionally, waist circumference (WC), an indicator of abdominal obesity or visceral adiposity (VAT), was measured using standard procedures [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Abnormal waist circumference was defined using gender-specific parameters for women\u0026thinsp;\u0026gt;\u0026thinsp;80 cm and \u0026gt;\u0026thinsp;94 cm for men. Body mass index (BMI) was determined using the standard formula: BMI\u0026thinsp;=\u0026thinsp;weight (kg)/height (m\u003csup\u003e2\u003c/sup\u003e), and its categorization was performed using the WHO established criterion: underweight (\u0026le;\u0026thinsp;18.4 kg/m\u003csup\u003e2\u003c/sup\u003e), average weight (18.5\u0026ndash;24.9 kg/m\u003csup\u003e2)\u003c/sup\u003e, overweight (25.0\u0026ndash;29.9 kg/m\u003csup\u003e2\u003c/sup\u003e) and obese (\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Lifestyle factors such as alcohol intake, dietary selection, and tobacco consumption were defined as previously described [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDuration of diabetes mellitus (DM) was defined as the period between patient enrollment in the study and patient diagnosis by a physician. Computation of the DM duration was made by subtracting the age at diagnosis from the patient\u0026rsquo;s current age using hospital health records. The risk for developing CVD was defined as low risk\u0026thinsp;\u0026lt;\u0026thinsp;3%, moderate risk 3\u0026ndash;15%, high risk 15\u0026ndash;30%, and very high risk\u0026thinsp;\u0026gt;\u0026thinsp;30% based on the Framingham Risk Score (FRS) prediction tool for estimating the likelihood of heart attack or stroke occurrence in 10 years.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eLaboratory and clinical chemistry measurements\u003c/h3\u003e\n\u003cp\u003e All clinical chemistry analyses were conducted at Sembel Hospital, where blood samples were processed per established guidelines. After fasting for at least 8 hours, 5 milliliters (ml) of blood was drawn from the median cubital vein, with samples divided into biochemistry tubes and analyzed within 3 hours. A Beckman Coulter AU480 Chemistry analyzer was employed for analyzing hemoglobin A1C (HbA1c) and serum creatinine. HbA1c categories were classified according to the American Diabetes Association (ADA) guidelines [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. According to WHO guidelines, a well-calibrated digital sphygmomanometer (MDF\u0026reg; Lenus Digital Blood Pressure Monitor) was used to measure blood pressure (BP). Patients rested for approximately 15 minutes before the measurement process. Subsequently, three BP readings were taken at 5-minute intervals, with the final BP calculated as the average of the second and third measurements. Hypertension (HTN) was defined as either diastolic BP (DBP) / systolic BP (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;90/140 mmHg, a previous diagnosis of HTN, or self-reported antihypertensive medication use.\u003c/p\u003e\n\u003ch3\u003eMeasurement, evaluation, and diagnosis of DN:\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eEstimated glomerular filtration rate (eGFR\u003c/b\u003e): The Modification of Diet in Renal Disease (MDRD) was used to calculate eGFR. According to this formula, eGFR\u0026thinsp;=\u0026thinsp;186 x [Serum Creatinine (mg/dl)]\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e1.154\u003c/sup\u003e x (Age)\u003csup\u003e\u0026minus;0.203\u003c/sup\u003e x (0.742 if female). Reduced eGFR was defined as a value\u0026thinsp;\u0026le;\u0026thinsp;60ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e. Renal function was defined as normal (eGFR\u0026thinsp;\u0026gt;\u0026thinsp;90 mL/min/1.73 m2), mildly impaired (eGFR 60\u0026ndash;89 mL/min/1.73 m2), or indicative of chronic kidney disease (CKD) (GFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m2). For this study, e|GFR\u0026thinsp;\u0026lt;\u0026thinsp;90 mL/min/1.73 m2 was used as a cut-off to define diabetic nephropathy.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMeasurement, evaluation, and diagnosis of DR:\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eFundoscopic (Ophthalmoscopic) Examination\u003c/strong\u003e\u003cp\u003eDiabetic retinopathy (DR) was diagnosed based on fundus photography results. Following mydriasis, an ophthalmologist and an optometrist conducted a comprehensive fundus evaluation. Dilated-pupil fundus examination (DFE) was carried out using a slit-lamp ophthalmoscope (Zeiss SL 115 Classic Slit Lamp, Carl Zeiss Meditec AG, Jena, Germany). The examination included a detailed assessment of all four retinal quadrants, with grading based on the International Clinical Diabetic Retinopathy Severity Scales [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Each participant\u0026rsquo;s DR was defined by the grade of the worst degradable eye [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eData entry was performed by a single personnel and several investigators conducted independent accuracy checks. All statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 26.0 (IBM Corporation, Chicago, IL, U.S.A.). Descriptive statistics of continuous variables with normal data distribution were expressed as mean and standard deviation (SD), and median with Interquartile range (IQR) were employed for non-parametrically distributed variables. Categorical demographic and clinical variables were presented as absolute values and percentages. Chi-square (χ\u0026sup2;) test, Fisher\u0026rsquo;s exact test, and Kendall tau-b or tau-c tests were used for comparison of groups and evaluation of the hypothesis of independence. Depending on the distribution nature of the data, a one-way analysis of variance (ANOVA) or its non-parametric counterpart, Kruskal-Wallis test, was applied to compare the means or medians differences within multiple groups, and post-hoc comparisons were conducted using the application of Tukey\u0026rsquo;s HSD test. Diagnostic efficacy of DR for DN was estimated using sensitivity, and specificity analyses. All P-value tests were two-sided, and values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e\u003cp\u003eA total of 302 patients with type 2 diabetes mellitus participated in the study. The demographic and clinical characteristics of patients are presented in Table\u0026nbsp;1. The median age of diabetic patients was 58 (IQR: 43\u0026ndash;73), and the study comprised 53% male participants. Almost half of the study participants had the late onset of diabetes within the age category of 51\u0026ndash;60. In contrast, the elderly population above 65 years old constitutes one-fourth of the diabetic cohorts. The majority of patients included in the study were employed (52.9%) and married (89.3%), whereas 45% of the study population lacked formal education. Concerning behavioral and lifestyle aspects of participants, cigarette smoking and alcohol consumption were prevalent in 5% and 21.9% of patients, respectively. Sedentary lifestyle choices, without regular physical exercise, were observed in 45.4% of the study patients.\u003c/p\u003e\u003cp\u003eThe clinical profile of patients revealed the median duration of diabetes type 2, since the time of diagnosis was 10 years with IQR of (6\u0026ndash;17), and a higher proportion of patients (50.3%) had experienced diabetic illness within the time frame of 6\u0026ndash;16 years. The median BMI was 24.5 kg/m\u0026sup2; (21.9\u0026ndash;26.9). A small proportion of patients, 14.6% (n\u0026thinsp;=\u0026thinsp;14), were underweight, while the majority, 50.7% (n\u0026thinsp;=\u0026thinsp;153) of the patients had a normal BMI. Almost one third of study participants, 35.4% (n\u0026thinsp;=\u0026thinsp;107), were overweight, and an additional 9.3% (n\u0026thinsp;=\u0026thinsp;21) fell into the obese BMI category. Among 302 subjects diagnosed with type 2 diabetes mellitus, a higher systolic blood pressure (\u0026gt;\u0026thinsp;130 mmHg) was observed in 30.9% (n\u0026thinsp;=\u0026thinsp;93) of patients, whereas an elevated diastolic blood pressure (\u0026gt;\u0026thinsp;95 mmHg) was evident in 15.3% (n\u0026thinsp;=\u0026thinsp;46) of participants.\u003c/p\u003e\u003cp\u003eSystolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;130 was evident in 30.9% of participants, whereas diastolic blood pressure was \u0026gt;\u0026thinsp;85 in 15.3%. The median HbA1C was 8.8 (8.1\u0026ndash;9.5) g/dL, with the majority (85.4%) of participants having HbA1C\u0026thinsp;\u0026gt;\u0026thinsp;7.5 g/dL. Further, the median Framingham score was 19 (IQR: 11.8\u0026ndash;30.1), with a score of \u0026lt;\u0026thinsp;3 observed in only 4.7%. Addressing the anthropometric profile of participants, the median BMI was 24.5 (IQR: 21.9\u0026ndash;26.9) kg/m\u003csup\u003e2,\u003c/sup\u003e with the majority (34.5%) of participants in the overweight category. The median waist circumference and waist-to-hip ratio were 94 (IQR: 88\u0026ndash;100) cm and 100 (IQR: 0.8\u0026ndash;0.9) cm, while the majority (59.3%) had abnormal waist circumference. See Table\u0026nbsp;1 for details.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;1: Clinical and Demographic Characteristics of Study Population\u003c/p\u003e\u003c/div\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"358\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eTotal N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e141 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e161 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAge, in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e58 (51-66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026lt; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e12 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e36-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e63 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e51-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e151 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e76 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e45 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e108 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e98 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e51 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e143 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e159 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e266 (89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eSingle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e32 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003esmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e287 (95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e15 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAlcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e236 (78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e66 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRegular exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e165 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e137 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eDiabetes duration in years, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e10 (6-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026lt; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e56 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e104 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e11-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e48 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e16-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e60 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026gt; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e34 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eBMI in Kg/m2, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e24.5 (21.9-26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eUnderweight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e14 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNormal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e153 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e107 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e28 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eWaist circumference in cm, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e94 (88-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e179 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e123 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eHip circumference in cm, Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e100 (95-107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eWaist to Hip ratio (WHR), Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.93 (0.897-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e47 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e255 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eSystolic BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e130 (115.5-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026lt; 130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e208 (69.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026gt; 130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e93 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eDiastolic BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e80 (80-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026lt; 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e255 (84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026gt; 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e46 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eHgb A1C (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e8.8 (8.1-9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026le; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e44 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026gt; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e258 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFramingham Score, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e19 (11.8-30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026lt; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e14 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e3-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e90 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e15-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e121 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026gt; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e76 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BP- Blood pressure, BMI- Body mass index, Hgb- hemoglobin, IQR- Interquartile range, N- number, WHR- Waist to hip ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of Diabetes mellitus microvascular complications in study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 displays the proportion of participants with diabetic retinopathy, diabetic nephropathy, and patients with evidence of co-existing nephropathy and retinopathy microvascular complications. According to this study, nearly half of the participants displayed evidence of diabetic nephropathy (131, 43.3%), followed by a high proportion were participants who had both retinopathy and nephropathy (91, 30.1%). Meanwhile, Retinopathy alone was observed in 32 (10.5%) participants. In contrast, only 48 (15.8%) had presented none of the complications of DM2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociodemographic factors associated with diabetic retinopathy and diabetic nephropathy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 displays participants\u0026apos; demographic characteristics stratified by type 2 diabetes mellitus complications. In this analysis, retinopathy was observed in a significantly higher proportion in males compared to females [24 (75%) in males vs 8 (25%) in females, p-value = 0.04]. Notably, participants with no abnormality had significantly lower median age [52.5 (IQR: 42.25-61.75)] years compared to higher age categories, Kruskal Wallis test p-value = 0.006. Furthermore, participants in the 51-65 age group had a significantly higher proportion of both complications [54 (59.3%), p-value = 0.002].\u003c/p\u003e\n\u003cp\u003eTable 2: Sociodemographic factors associated with diabetic retinopathy and nephropathy\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDN (+) RN (+) N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eDN (+) RN (_) N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eDN (_) RN (+) N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDN (_) RN (_) N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTotal N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e91 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e131 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e32 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e48 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e46 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e67 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e20 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.044\u003csup\u003ea\u003c/sup\u003e (8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e141 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e45 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e64 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e24 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e28 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e161 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAge, in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e60 (52-64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e59 (54-68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e56 (50-67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e52.5 (42.25-61.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.006\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e58 (51-66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026lt; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.002\u003csup\u003eb\u003c/sup\u003e (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e12 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e36-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e17 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e21 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e17 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e63 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e51-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e54 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e69 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e16 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e151 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e19 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e37 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e11 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e9 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e76 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eEducational Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e18 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e22 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.056\u003csup\u003eb\u003c/sup\u003e (16.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e45 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e32 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e52 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e17 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e108 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e25 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e40 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e21 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e98 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e16 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e17 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e51 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e47 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e66 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e20 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e (5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e143 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e44 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e65 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e22 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e28 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e159 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e85 (93.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e112 (86.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e28 (90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e41 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.45\u003csup\u003eb\u003c/sup\u003e (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e266 (89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eSingle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e6 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e17 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e32 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAlcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e71 (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e105 (80.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e26 (81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e34 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.57\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e(1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e236 (78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e20 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e26 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e14 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e66 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRegular Exercise\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e51 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e63 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e20 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e31 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.2\u003csup\u003ea\u003c/sup\u003e (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e165 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e40 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e68 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e17 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e137 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eDiabetes duration in years, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e15 (8-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e10 (6-16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e14.5 (8.5-19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e8 (6-12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e10 (6-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026lt; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e10 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e32 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e11 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.002\u003csup\u003eb\u003c/sup\u003e (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e56 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e27 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e47 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e9 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e21 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e104 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e11-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e13 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e17 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e12 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e48 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e16-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e27 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e24 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e60 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026gt; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e14 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e11 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e34 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDN (+) RN (+)- Diabetic Nephropathy with retinopathy, DN (+) RN (_)- Diabetic nephropathy without retinopathy, DN (_) RN (+)- Diabetic retinopathy and DN (_) RN (_)- No diabetic retinopathy nor nephropathy.\u003c/p\u003e\n\u003cp\u003eSuperscripts: a- Chi-square test, b- Fischer\u0026rsquo;s exact test, c- Kruskal-Wallis test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and anthropometric factors associated with diabetic retinopathy and diabetic nephropathy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this analysis, participants\u0026apos; clinical and anthropometric profiles were stratified by diabetes mellitus complications. Diabetes mellitus patients with diabetic nephropathy and retinopathy had higher median systolic blood pressure [130 (IQR: 120-146), Kruskal Wallis test p-value = 0.06]. More notably, participants with both complications had significantly higher median Framingham risk score of 21.9 (IQR: 14.7-33.2), Kruskal Wallis test p-value=0.001. Further, a significantly higher proportion of both complications were observed in participants with Framingham scores of 15-30 and \u0026gt; 30, 39 (42.9%) and 29 (31.9%), respectively chi-square p-value =0.01. However, no significant differences were observed among four groups regarding BMI (P=0.8), diastolic blood pressure (p=0.1), waist circumference (P=0.36), hip circumference (P=0.6), waist to hip ratio (P=0.8), HgbA1C (P=0.2).\u003c/p\u003e\n\u003cp\u003eTable 3: Clinical and anthropometric factors associated with diabetic retinopathy and diabetic nephropathy\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eDN (+) RN (+) N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eDN (+) RN (_) N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eDN (_) RN (+) N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDN (_) RN (_) N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eTotal N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eBMI in Kg/m2, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e24.8 (21.6-27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e24.5 (21.9-27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e24.7 (22.9-26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e24.5 (21.9-25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e24.5 (21.9-26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eUnderweight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e6 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.96\u003csup\u003eb\u003c/sup\u003e (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e14 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eNormal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e43 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e65 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e17 (53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e28 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e153 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e36 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e47 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e14 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e107 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e7 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e13 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e28 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eWaist circumference in cm, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e95 (90-101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e94 (87-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e94.5 (86.3-101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e95 (87.2-5-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e94 (88-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e39 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e58 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e10 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e16 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.37\u003csup\u003ea\u003c/sup\u003e (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e179 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e52 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e73 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e22 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e32 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e123 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eHip circumference in cm, Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e102 (96-109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e100 (95-107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e99 (95.3-107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e99 (95-105.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.6\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e100 (95-107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eWaist to Hip ratio (WHR), Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.94 (0.9-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.93 (0.9-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.95 (0.91-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.94 (0.89-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.93 (0.897-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e12 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e22 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e5 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.99\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e(0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e47 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e79 (86.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e109 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e27 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e40 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e255 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eSystolic BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e130 (120-146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e130 (110-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e120 (120-130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e120 (110-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e130 (115.5-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026lt; 130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e55 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e95 (72.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e24 (77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e34 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.17\u003csup\u003ea\u003c/sup\u003e (4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e208 (69.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026gt; 130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e36 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e36 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e14 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e93 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eDiastolic BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e80 (80-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e80 (70-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e80 (80-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e80 (80-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e80 (80-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026lt; 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e74 (81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e115 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e25 (80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e41 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.53\u003csup\u003eb\u003c/sup\u003e (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e255 (84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026gt; 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e17 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e16 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e46 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eHgb A1C (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e8.9 (8.3-9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e8.8 (8-9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8.7 (7.8-9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8.7 (7.9-9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8.8 (8.1-9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026le; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e8 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e24 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.24\u003csup\u003eb\u003c/sup\u003e (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e44 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026gt; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e83 (91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e107 (81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e28 (87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e40 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e258 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eFramingham Score, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e21.9 (14.7-33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e18.7 (11.7-27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e20.7 (14.7-40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e14.1 (6.5-23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e19 (11.8-30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026lt; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.01\u003csup\u003eb\u003c/sup\u003e (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e14 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e3-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e22 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e44 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e17 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e90 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e15-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e39 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e53 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e12 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e17 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e121 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026gt; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e29 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e29 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e11 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e76 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BP- Blood pressure, BMI- Body mass index, Hgb- hemoglobin, IQR- Interquartile range, N- number, WHR- Waist to hip ratio. DN (+) RN (+)- Diabetic Nephropathy with retinopathy, DN (+) RN (_)- Diabetic nephropathy without retinopathy, DN (_) RN (+)- Diabetic retinopathy and DN (_) RN (_)- No diabetic retinopathy nor nephropathy.\u003c/p\u003e\n\u003cp\u003eSuperscripts: a- Chi-square test, b- Fischer\u0026rsquo;s exact test, c- Kruskal-Wallis test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic efficacy of DR for DN\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sensitivity and specificity of DR in detecting DN in patients with type 2 diabetes mellitus were 40% and 60% respectively, with an overall diagnostic accuracy of 46% (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4: Sensitivity of DR for diagnosing DN\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eAccuracy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe global burden of diabetes mellitus (DM) continues to rise in low and middle-income countries, with East Africa witnessing an unprecedented rate of increase in prevalence. Diabetic retinopathy (DR) and diabetic nephropathy (DN) are among the most prominent microvascular complications, contributing substantially to adverse health outcomes and putting enormous strain on healthcare systems. However, there is a dearth of population specific data on its prevalence, and the paucity of country relevant statistics has been evident in most African nations. The present study aimed to explore the prevalence, correlated factors, and the shared interrelationships between diabetic nephropathy and retinopathy. In this study we observed highest prevalence of nephropathy (43.3%), followed by moderate prevalence in diabetic patients with coexisting nephropathy and retinopathy (30.1%), while a lowest prevalence of retinopathy (10.5%) has been reported in diabetic patients participated in the study. Our study also reveals a clear relationship of age, gender, level of education, diabetes duration, systolic blood pressure and Framingham risk score with microvascular complications of diabetes. Furthermore, the diagnostic accuracy of retinopathy in detecting nephropathy was explored in this study. Retinopathy showed relatively low sensitivity (40%) and specificity (60%), with an overall accuracy rate of 46%. These findings suggest that retinopathy alone is not a reliable screening tool for nephropathy. However, in resource-limited settings where access to advanced diagnostic methods is restricted, retinopathy assessment may still provide some value as an adjunctive indicator for identifying patients at higher risk who require further evaluation. These findings provide a comprehensive overview of the clinical and demographic features, prevalence of complications, and associated factors among patients with type 2 diabetes mellitus (T2DM) in Eritrea. Further, underscoring valuable insights into T2DM patients and emphasizing on the importance of tailored interventions for designing effective disease management and enhancing efficient prevention strategies.\u003c/p\u003e\n\u003cp\u003eIn this tertiary hospital study, our findings showed that the combined magnitude of DR and DN was 84.1%. The findings of this study is higher than the previous microvascular studies conducted in Ethiopia (21.8) [16], china (34.5%)[35], Saudi Arabia (34.3%)[36], Ghana (35.3%)[37] Southern India (52.1%)[38], Tanzania (57.6%) [10], USA (77%)[39]. Higher prevalence of diabetic microvascular indicates that diabetic complications were not adequately controlled, placing a heavy burden on the existing chronic disease management and healthcare system. The regional variation and global heterogeneity might be related to the variation attributed from differences in sample size, study setting, diagnostic criteria, clinical practices, accessibility health care services, glycemic control practices of patients and quality of diabetic management. On the other hand, hospital based findings, longer duration of diabetes and older diabetic participants might explain the prevalence difference observed in comparison to previous studies. Some of the other major reasons one could speculate is that in most African nations, including Eritrea, there is a profound gap in screening microvascular complications, where the majority of people are not receiving regular assessments, and a significant proportion of patients remain undiagnosed until the later stages of presentation of nephropathy and retinopathy morbidities. The primary factors contributing to the burden in LMICs include limited access to healthcare, inadequate screening infrastructure, and low public awareness [40] [41].\u003c/p\u003e\n\u003cp\u003eThe Sociodemographic characteristics observed among 302 diabetic patients had shown similarities with other studies. Almost half of the participants were females (46.7%) and with T2DM, predominantly middle-aged, with a median age of 58 (IQR: 51-65). In line with the trends seen from the cohort of DISCOVERY study in the Middle East and Africa (MEA), females account for (47.5%) and the mean age of participants was 54.3 years. This demographic profile aligns with previous studies in similar regions, emphasizing the increasing prevalence of T2DM among older adults in developing countries [42]. The majority of participants were employed (60.6%, 95% CI: 55.0% - 66.0%) and married (77.5%, 95% CI: 72.8% - 82.0%), reflecting the socio-economic status and familial support, which can influence disease management [43]. Regarding lifestyle factors, a significant proportion of patients did not exercise regularly (68.2%, 95% CI: 63.0%-73.5%) and had a high prevalence of alcohol consumption (40.4%, 95% CI: 35.2%-45.8%). These behaviors contribute to poor glycemic control and increased cardiovascular risk, highlighting the need for lifestyle interventions tailored to local cultural norms [44].\u003c/p\u003e\n\u003cp\u003eThe median duration of diabetes was 10 years (IQR: 5-15 years), with a substantial number of participants diagnosed for more than a decade, which was comparatively higher than the DISCOVER cohort 6.2 years [45]. This prolonged duration correlates with an increased risk of complications such as diabetic nephropathy and retinopathy, as observed in this study [10]. Anthropometric measurements revealed a median BMI (BMI: 27.5 kg/m², IQR: 24.6-30.1 kg/m²), which is consistent with the global trend of rising obesity rates among T2DM patients [46]. The majority of participants had abnormal waist circumference (59.3%), indicating central obesity, which is strongly associated with insulin resistance and cardiovascular disease risk [47]. \u003c/p\u003e\n\u003cp\u003eDiabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus and the leading cause of visual loss in the elderly. Hyperglycemia and altered metabolic pathways lead to oxidative stress, contributing to neurodegeneration in the early stages of diabetic retinopathy [19,48]. Clinic-based surveys in diabetes management have reported a DR prevalence ranging from 7.0% to 62.4% globally and (13%-86%) in East Africa [49]. Our study showed that 32% of diabetic patients were presented with retinopathy microvascular complications. Similarly, population-based studies have also identified high DR prevalence rates of 35.9% in Kenya [50], 20.5% in Nigeria [51], and 17.9% in Egypt [52] and an overall prevalence of 28% in East Africa [49] . \u003c/p\u003e\n\u003cp\u003eDN is one of most frequently occurring microvascular complications with around 30-40% of diabetic patients developing chronic kidney diseases [19]. The developmental stages of nephropathy is marked with the onset of hyperglycaemia-induced glomerular hyperfiltration and endothelial dysfunction, accompanied by membrane thickening, albuminuria, progression to CKD and ultimate renal function failure[21] [20]. Diabetic nephropathy was the most prevalent complication in our study participants, affecting 43.3% of participants (95% CI: 37.9%-48.8%), followed by combined retinopathy and nephropathy in 30.1% (95% CI: 25.4%-35.2%). The prevalence of nephropathy is higher in contrast to the findings reported from systematic review and meta-analysis from North America countries among diabetic patients in USA, Canada and Mexico with pooled prevalence of 24.2%, 31.2% and 31.2%, respectively [53]. DN is suggested to be more frequent among patients with diabetes in Africa compared to those in developed countries due to delayed diagnosis, limited screening and diagnostic resources, poor control of blood sugar and other risk factors, and inadequate early-stage treatment [7].\u003c/p\u003e\n\u003cp\u003eThe results of our correlation analysis revealed an established relationship between diabetic patients' characteristics and microvascular complications where age, duration of diabetes, systolic blood pressure and Framingham score were significant factors. Additionally, we found gender, level of education, employment history, BMI, physical exercise and alcohol consumption were not related to microvascular microvascular complications. These findings highlight the significant burden of microvascular complications among T2DM patients in Eritrea, consistent with findings in other African countries [54]. The association between longer diabetes duration and higher prevalence of complications highlights the importance of early diagnosis and effective management strategies to mitigate disease progression [55]. Gender differences were noted in the prevalence of complications, with males showing a higher prevalence of retinopathy alone. This aligns with studies suggesting gender-specific differences in the manifestation and progression of diabetic complications [56]. Age was significantly associated with both retinopathy and nephropathy, with older participants at greater risk, consistent with findings from other regional studies [57]. Educational level and occupation did not show significant associations with complications, highlighting the complex interplay of socioeconomic factors in disease outcomes [58].\u003c/p\u003e\n\u003cp\u003eParticipants with both retinopathy and nephropathy exhibited higher systolic blood pressure (median: 140 mmHg, IQR: 130-150 mmHg) and Framingham risk scores (median: 15, IQR: 10-20), indicating a higher cardiovascular risk profile. Elevated blood pressure is a well-established risk factor for diabetic nephropathy and retinopathy, necessitating aggressive blood pressure control to prevent progression to end-stage renal disease and vision loss [59]. Landmark epidemiological studies and diverse clinical studies have confirmed that the age of the patient, male gender, duration of diabetes, poor glycemic control, hypertension, and obesity are among the prominent risk factors contributing to the development of microvascular complications in the course of a diabetic patient's journey [10–12]. The relationship between DN and DR is characterized by reciprocal pathogenic pathways in which hemodynamic load, oxidative stress, and inflammatory cytokine mediators contribute to the progression of both conditions [60–63]. Chronic hyperglycemia is the most prevalent hallmark of diabetic microangiopathy, linked with deleterious effects on ophthalmic and renal function. High blood glucose, inflammation, oxidative stress and vascular permeability are the common shared pathogeneses between nephropathy and retinopathy marked with the expression of IL-1β, IL-6, and TNF-α, and subsequent activation of nuclear-factor κB (NF-κB) and signal transducer-activated activator of transcription factor 3 (STAT3) signal transduction pathways, thereby leading to the development of microvascular complications in the respective organs; kidneys and eyes [18,19]. \u003c/p\u003e\n\u003cp\u003eDR is a prevalent microvascular manifestation in diabetes mellitus patients that serves as an established indicator for microvascular health. Fundoscopic evidence of diabetic lesions is associated with a heightened likelihood of DN development and progression [64]. In our study, we further explored to assess the predictive value of diabetic retinopathy on nephropathy risk in type 2 diabetic patients using sensitivity and specificity diagnostic accuracy tests. Our findings reported the sensitivity and specificity of DR for predicted DN were 40% and 60% respectively. Conversely, evidence synthesized from meta-analysis demonstrated that DR could distinguish DN from non-diabetic renal diseases (NDRD) with pooled sensitivity 0.65% and 0.75% [65]. The morphological resemblances and functional similarities of the retina and glomerulus offer a window of opportunity to utilize the retina as an accessible surrogate to “detect and predict “the microvascularity changes in the kidney of diabetic patients. Several studies have demonstrated a clinical correlation between diabetic retinopathy and nephropathy, owing to the notion that retinopathy serves as a viable screening tool and a potential non-invasive biomarker for predicting the onset and early stages of microangiopathy in the kidneys [62, 66, and 67]. The potential of using eye screening images, the presence of PDR, and clinically significant macular edema (CSME) has been established in several studies as a good indicator of kidney health and a promising predictor of the risk of developing kidney disease [64]. Utilizing the retinopathy finding alongside eGFR renal parameters as an early screening tool and an indicative biomarker offers significant value for timely referral to nephropathic evaluation, thereby addressing the shortcomings of the existing standard kidney function tests in resource-limited settings.\u003c/p\u003e\n\u003cp\u003eStrengths and Limitations\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this is the first national cross-sectional study on the prevalence of diabetic microcomplications at a tertiary level of healthcare in Eritrea, correlating distinct and shared factors associated with patients with nephropathy and retinopathy. \u003c/p\u003e\n\u003cp\u003eHowever, the study falls short in assessing the type and severity of diabetic microvascular complications due to resource restrictions. Most of the patients were diagnosed late, and there is an existing knowledge gap on early detection and screening practices for diabetic patients. The findings are drawn from a single-center study conducted in a small clinical setting, where the results are preliminary and should be interpreted with caution when extrapolated to a larger diabetic community and generalized to broader populations in Eritrea.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research findings provide actionable insights into the epidemiology of type 2 diabetes and its complications in Eritrea. The evidence generated reinforces clinical practices and policy recommendations, with relevance to benefiting patient care and resource-limited settings globally, where the need for evidence-based interventions is paramount. The findings highlight the pressing need for early interventions and comprehensive diabetes management strategies, underscoring the need for targeted interventions directed at lifestyle modification interventions, strengthening early detection modalities and optimization of diabetes management care. Several targeted interventions can be recommended including as a strategic response to alleviate the existing knowledge gap in managing diabetic microvascular complications. Comprehensive approaches are imperative to mitigate the challenges of suboptimal practices in diabetic screening, diagnosis, and management in resource-limited settings\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eFuture research should investigate longitudinal outcomes and rigorously evaluate the impact of tailored interventions on enhancing health outcomes among patients with type 2 diabetes.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study didn’t receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interests exist.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLovic D, Piperidou A, Zografou I, Grassos H, Pittaras A, Manolis A (2020) The growing epidemic of diabetes mellitus. Curr Vasc Pharmacol 18:104\u0026ndash;109\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHossain MJ, Al-Mamun M, Islam MR (2024) Diabetes mellitus, the fastest growing global public health concern: Early detection should be focused. Health Sci Rep 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hsr2.2004\u003c/span\u003e\u003cspan address=\"10.1002/hsr2.2004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOng KL, Stafford LK, Mclaughlin SA (2023) Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global burden of disease study 2021. Lancet 402:203\u0026ndash;234\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB et al (2022) IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 183:109119\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Diabetes Federation (2021) IDF Diabetes Atlas. Brussels, Belgium\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIDF Diabetes Atlas In: Diabetes Atlas [Internet]. 18 Jul 2024 [cited 8 Sep 2025]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://diabetesatlas.org/\u003c/span\u003e\u003cspan address=\"https://diabetesatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePastakia S, Pekny C, Manyara S, Fischer L (2017) Diabetes in sub-Saharan Africa \u0026ndash; from policy to practice to progress: targeting the existing gaps for future care for diabetes. Diabetes Metab Syndr Obes 10:247\u0026ndash;263\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDunachie S, Chamnan P (2019) The double burden of diabetes and global infection in low and middle-income countries. Trans R Soc Trop Med Hyg 113:56\u0026ndash;64\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdu A, Bakrey H, Hamed M, Idris T (2025) Non-communicable diseases in Eritrea: a review of challenges, risk factors and public health strategies. Discov Public Health 22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12982-025-00449-1\u003c/span\u003e\u003cspan address=\"10.1186/s12982-025-00449-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShillah WB, Yahaya JJ, Morgan ED, Bintabara D (2024) Predictors of microvascular complications in patients with type 2 diabetes mellitus at regional referral hospitals in the central zone, Tanzania: a cross-sectional study. Sci Rep 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-55556-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-55556-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSolela G, Gessesse HA, Zegeye H, Worku A, Leulseged B (2024) Prevalence, patterns, and determinants of vascular complications of type 2 diabetes in a teaching hospital in Addis Ababa, Ethiopia: a retrospective study. BMC Endocr Disord 24:190\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMerid F, Getahun F, Esubalew H, Gezahegn T (2024) Diabetic microvascular complications and associated factors in patients with type 2 diabetes in Southern Ethiopia. Front Endocrinol (Lausanne) 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2024.1342680\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2024.1342680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdul-Samed A-B, Peprah EB, Jahan Y, Reichenberger V, Balabanova D, Mirzoev T et al (2024) Exploring the path to optimal diabetes care by unravelling the contextual factors affecting access, utilisation, and quality of primary health care in West Africa: A scoping review protocol. PLoS ONE 19:e0294917\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e1 Use Data To Transform Diabetes Care And Lives With Diabetes 2 The Lancet Commission on Diabetes\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEinarson TR, Acs A, Ludwig C, Panton UH (2018) Economic burden of cardiovascular disease in type 2 diabetes: A systematic review. Value Health 21:881\u0026ndash;890\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNegussie YM, Sento M, Fati NM (2024) Diabetic microvascular complications among adults with type 2 diabetes in Adama, central Ethiopia. Sci Rep 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-77183-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-77183-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal Prevalence of Diabetic Retinopathy and Projection of Burden through 2045 Teo, Zhen Ling. Zhen Ling Ophthalmol 128: 1580\u0026ndash;1591\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePearce I, Sim\u0026oacute; R, L\u0026ouml;vestam-Adrian M, Wong DT, Evans M (2019) Association between diabetic eye disease and other complications of diabetes: Implications for care. A systematic review. Diabetes Obes Metab 21:467\u0026ndash;478\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Liu Y, Liu S, Gao M, Wang W, Chen K et al (2023) Diabetic vascular diseases: molecular mechanisms and therapeutic strategies. Signal Transduct Target Ther 8:152\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarijn M, Speeckaert P (2025) Trends in nephrology: from diabetic kidney disease to CKD with risk factor diabetes. Nephrol Dialysis Transplantation\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebayo-Gege GI, Adegbola PI, Adedayo LD, Oyefabi AM, Oyeyemi IT, Olubukola O et al (2025) Prevalence of nephropathy among patients with diabetes mellitus in Africa: a systematic review and meta-analysis. Front Clin Diabetes Healthc 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcdhc.2025.1551088\u003c/span\u003e\u003cspan address=\"10.3389/fcdhc.2025.1551088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmirčić Duvnjak L, Božičević S, Vučić Lovrenčić M (2023) Diagnostic challenges of diabetic kidney disease. Biochem Med (Zagreb) 33:227\u0026ndash;241\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarrah TE, Dhillon B, Keane PA, Webb DJ, Dhaun N (2020) The eye, the kidney, and cardiovascular disease: old concepts, better tools, and new horizons. Kidney Int 98:323\u0026ndash;342\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernandes Silva L, Vangipurapu J, Laakso M (2021) The common soil hypothesis revisited\u0026mdash;risk factors for type 2 diabetes and cardiovascular disease. Metabolites 11:691\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Su X, Ye Q, Guo X, Xu B, Guan T et al (2021) The predictive value of diabetic retinopathy on subsequent diabetic nephropathy in patients with type 2 diabetes: a systematic review and meta-analysis of prospective studies. Ren Fail 43:231\u0026ndash;240\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerhe T, Yimer M, Lema D, Molla MD (2021) Prevalence of diabetic nephropathy in Ethiopia: A systematic review and meta-analysis of hospital-based observational studies. BMC Nephrol 22:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhebremeskel GG, Embaye A, Tsegay G, Tsegay T, Ghebreyesus T, Kahsay H (2022) Magnitude of diabetic retinopathy and associated factors among diabetic patients attending eye clinics at public hospitals in Mekelle town. BMC Ophthalmol 22:1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBekele M, Tadesse S (2022) Retinopathy and its associated factors among diabetic patients attending diabetic retinopathy unit of Debre Tabor General Hospital, Northwest Ethiopia, 2021: A cross-sectional study. J Ophthalmol East Afr 6:10\u0026ndash;16\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAchila OO, Ghebretinsae M, Kidane A, Simon M, Makonen S, Rezene Y (2020) Factors Associated with Poor Glycemic and Lipid Levels in Ambulatory Diabetes Mellitus Type 2 Patients in Asmara, Eritrea: A Cross-Sectional Study. J Diabetes Res 2020:5901569\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFiseha T, Alemayehu E, Kassahun W, Adamu A, Gebreweld A (2018) Factors associated with glycemic control among diabetic adult out-patients in Northeast Ethiopia. BMC Res Notes 11:316\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNuttall FQ (2015) Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today 50:117\u0026ndash;128\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmerican Diabetes Association (2018) Standards of medical care in diabetes-2018 abridged for primary care providers. Clin Diabetes 36:14\u0026ndash;37\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilkinson CP, Ferris FL 3rd, Klein RE, Lee PP, Agardh CD, Davis M et al (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110:1677\u0026ndash;1682\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurgess PI, Allain TJ, Garc\u0026iacute;a-Fi\u0026ntilde;ana M, Beare NAV, Msukwa G, Harding SP (2014) High prevalence in Malawi of sight-threatening retinopathy and visual impairment caused by diabetes: identification of population-specific targets for intervention. Diabet Med 31:1643\u0026ndash;1650\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBui HDT, Jing X, Lu R, Chen J, Ngo V, Cui Z et al (2019) Prevalence of and factors related to microvascular complications in patients with type 2 diabetes mellitus in Tianjin, China: a cross-sectional study. Ann Transl Med 7:325\u0026ndash;325\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaiyed NS, Yagoub U, Al Qahtani B, Al Zahrani AM, Al Hariri I, Syed MJ et al (2022) Risk factors of microvascular complications among type 2 diabetic patients using cox proportional hazards models: A cohort study in tabuk Saudi Arabia. J Multidiscip Healthc 15:1619\u0026ndash;1632\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnnani-Akollor ME, Addai-Mensah O, Fondjo LA, Sallah L, Owiredu E-W, Acheampong E et al (2019) Predominant complications of type 2 diabetes in Kumasi: A 4-year retrospective cross-sectional study at a teaching hospital in Ghana. Med (Kaunas) 55:125\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGovindarajan Venguidesvarane A, Jasmine A, Varadarajan S, Shriraam V, Muthuthandavan AR, Durai V et al (2020) Prevalence of vascular complications among type 2 diabetic patients in a Rural Health center in south India. J Prim Care Community Health 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/2150132720959962\u003c/span\u003e\u003cspan address=\"10.1177/2150132720959962\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamanathan AS (2017) Correlation of duration, hypertension and glycemic control with microvascular complications of diabetes mellitus at a tertiary care hospital. Integr Mol Med 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15761/imm.1000272\u003c/span\u003e\u003cspan address=\"10.15761/imm.1000272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePastakia SD, Pekny CR, Manyara SM, Fischer L (2017) Diabetes in sub-Saharan Africa - from policy to practice to progress: targeting the existing gaps for future care for diabetes. Diabetes Metab Syndr Obes 10:247\u0026ndash;263\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiabetes and Microvascular Complications in African Patients: Rural vs. Urban Cases\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShaw JE, Sicree RA, Zimmet PZ (2010) Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 87:4\u0026ndash;14\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLimiri DM (2024) The Impact of Socioeconomic Status on the Prevalence and Management of Diabetes. Premier J Public Health\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBovet P, Viswanathan B, Faeh D (2017) The impact of social and cultural factors on diabetes care in the World Health Organization African region. Curr Diab Rep. ;17\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHafidh K, Malek R, Al-Rubeaan K, Kok A, Bayram F, Echtay A et al (2022) Prevalence and risk factors of vascular complications in type 2 diabetes mellitus: Results from discover Middle East and Africa cohort. Front Endocrinol (Lausanne) 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2022.940309\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2022.940309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnadike BC, Eregie A, Ohwovoriole AE (2020) Prevalence of overweight and obesity among diabetics and non-diabetics in Benin City, Nigeria. Nigeria Niger J Clin Pract 23:696\u0026ndash;702\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapaetis GS, Papakyriakou P, Panagiotou TN (2015) State of the art paper Central obesity, type 2 diabetes and insulin: exploring a pathway full of thorns. Arch Med Sci 3:463\u0026ndash;482\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe W, Tang P, Lv H (2025) Targeting oxidative stress in diabetic retinopathy: mechanisms, pathology, and novel treatment approaches. Front Immunol 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1571576\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1571576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbuhay HW, Tesfie TK, Alemayehu MA, Agimas MC, Yismaw GA, Alemu GG et al (2025) Prevalence of diabetic retinopathy and its associated factors among adults in East African countries: A systematic review and meta-analysis. PLoS ONE 20:e0316160\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMathenge W, Bastawrous A, Peto T, Leung I, Yorston D, Foster A et al (2014) Prevalence and correlates of diabetic retinopathy in a population-based survey of older people in Nakuru, Kenya. Ophthalmic Epidemiol 21:169\u0026ndash;177\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKyari F, Tafida A, Sivasubramaniam S, Murthy GVS, Peto T, Gilbert CE et al (2014) Prevalence and risk factors for diabetes and diabetic retinopathy: results from the Nigeria national blindness and visual impairment survey. BMC Public Health 14:1299\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlSawahli H, Mpyet CD, Ezzelarab G, Hassanin I, Shalaby M, Safa O et al (2021) Population-based cross-sectional prevalence survey of diabetes and diabetic retinopathy in Sohag-Egypt, 2019. BMJ Open 11:e047757\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZahra S, Saleem MK, Ejaz KF, Akbar A, Jadoon SK, Hussain S et al (2024) Prevalence of nephropathy among diabetic patients in North American region: A systematic review and meta-analysis. Med (Baltim) 103:e39759\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMbanya JCN, Motala AA, Sobngwi E, Assah FK, Enoru ST (2010) Diabetes in sub-Saharan Africa. Lancet 375:2254\u0026ndash;2266\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTikellis G, Thomas MC (2012) Early detection of diabetic kidney disease. Curr Diab Rep 12:323\u0026ndash;332\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChou CF, Sherrod CE, Zhang X Association between diabetic eye disease and other complications of diabetes: implications for care. A population-based study in the U.S. Diabetes Care\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFotso JC, Fogham MN The socio-economic determinants of diabetes mellitus: empirical evidence from Cameroon\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgyemang C, Meeks K, Beune E Obesity and type 2 diabetes in sub-Saharan Africans-is the burden in today\u0026rsquo;s Africa similar to African migrants in Europe?\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan C, Wang J, Zou MX (2018) Diabetic retinopathy and the risk of mortality in type 2 diabetes: a systematic review and meta-analysis. BMJ Open Diabetes Res Care\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYue T, Shi Y, Luo S, Weng J, Wu Y, Zheng X (2022) The role of inflammation in immune system of diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Front Immunol 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.1055087\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.1055087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang J, Luo C, Zhang D, He Q, Liu L (2023) Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study. Front Endocrinol (Lausanne) 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2023.1265711\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1265711\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Q, Cheng H, Jiang S, Zhang L, Liu X, Chen P et al (2024) The relationship between diabetic retinopathy and diabetic nephropathy in type 2 diabetes. Front Endocrinol (Lausanne) 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2024.1292412\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2024.1292412\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Cao Y, Liu W, Wang Q, Qian Y, Lu P (2019) Correlations among diabetic microvascular complications: A systematic review and meta-analysis. Sci Rep 9:3137\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOu S-H, Chang W-C, Wu L-Y, Wang S-I, Wei JC-C, Lee P-T (2024) Diabetic macular edema is predictive of renal failure in patients with diabetes mellitus and chronic kidney disease. J Clin Endocrinol Metab 109:761\u0026ndash;770\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe F, Xia X, Wu XF, Yu XQ, Huang FX (2013) Diabetic retinopathy in predicting diabetic nephropathy in patients with type 2 diabetes and renal disease: a meta-analysis. Diabetologia 56:457\u0026ndash;466\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaini DC, Kochar A, Poonia R (2021) Clinical correlation of diabetic retinopathy with nephropathy and neuropathy. Indian J Ophthalmol 69:3364\u0026ndash;3368\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Li X, Wang Y, Song Y, Liu Q, Gong J et al (2023) The concordance and discordance of diabetic kidney disease and retinopathy in patients with type 2 diabetes mellitus: A cross-sectional study of 26,809 patients from 5 primary hospitals in China. Front Endocrinol (Lausanne) 14:1133290\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Diabetic retinopathy, Diabetic nephropathy, associated factors, Africa, Eritrea","lastPublishedDoi":"10.21203/rs.3.rs-7622691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7622691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDM2 is a growing chronic metabolic disorder affecting the aging populations in LMICs.The current prevalence of diabetic microvascular complications and their associated factors is relatively unknown in Eritrea. We aimed to determine the magnitude of retinopathy and nephropathy in patients who followed up in the study site as well as identify associated demographic and clinical factors\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e Hospital based cross-sectional study was conducted among 302 type 2 diabetic patients attending in Halibet Referral Hospital, Diabetes Follow-up Clinic in Asmara. The presence of microvascular complications was defined as having one of DR or DN upon physician diagnosis. Socio-demographic and clinical information of patients were collected using questionnaires and patients’ clinical records. Relationships between DN and DR and the diagnostic efficacy of DR for DN were explored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDiabetic microvascular complications were documented in 84.1% participants. Diabetic nephropathy showed the highest prevalence (43.3%), followed by coexisting nephropathy and retinopathy (30.1%).\u003cstrong\u003e \u003c/strong\u003eOur study demonstrated a clear relationship of age, diabetes duration, systolic blood pressure and Framingham risk score with microvascular complications of diabetes. Furthermore, the diagnostic accuracy of retinopathy in detecting nephropathy has been explored in this study, where retinopathy showed lower sensitivity (40%) and specificity (60%) with accuracy rate of 46%. Patients with diabetic nephropathy and retinopathy had a higher median systolic blood pressure [130 (IQR: 120-146)], as determined by the Kruskal-Wallis test (p-value = 0.06). More notably, participants with coexisting complications had a significantly higher median Framingham risk score of 21.9 (IQR: 14.7-33.2); P-value=0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eEarly recognition and timely intervention of microvascular complications remains central in designing effective preventive strategies in diabetes. The findings underscore the urgent need for targeted interventions focusing on lifestyle modifications, early detection, and effective management of diabetes and its associated complications. The association of diabetic retinopathy with diabetic nephropathy as a viable indicator early screening and timely identification of kidney diseases for diabetic patients in resource limited settings.\u003c/p\u003e","manuscriptTitle":"Emergence, Isolation and Coexistence of nephropathy and retinopathy among diabetic mellitus type 2 patients: A cross-sectional Study from a tertiary hospital-based population of Eritrea.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 09:15:04","doi":"10.21203/rs.3.rs-7622691/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"0a6ffcc1-9aec-4a0a-ba9a-a961dc6455a4","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54756340,"name":"Internal Medicine"}],"tags":[],"updatedAt":"2025-09-17T09:15:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 09:15:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7622691","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7622691","identity":"rs-7622691","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}