ورقة علمية / Research Paper

Background and rationale Albuminuria/proteinuria—even at low levels (microalbuminuria)—is increasingly recognized not only as an early sign of diabetic nephropathy but also as an accessible marker of systemic endothelial dysfunction and predictor of cardiorenal and other target-organ outcomes in people with diabetes. Several contemporary reviews and cohort studies report that urinary albumin excretion correlates with future renal decline and cardiovascular events, and that reducing albuminuria is associated with improved cardiorenal outcomes. Microalbuminuria integrates vascular and renal risk and has been proposed as an early marker of subclinical target-organ damage in diabetes; it identifies patients who would benefit from intensified cardiovascular and renal risk management. A focused review on microalbuminuria in diabetes concluded that microalbuminuria is a risk marker for cardiovascular events and for progression to clinically significant kidney disease, particularly when considered alongside other risk factors (hypertension, dysglycaemia, dyslipidaemia). Recent population studies continue to show strong interactions between diabetes, the presence of proteinuria, and elevated risk of cardiovascular outcomes — suggesting that proteinuria measurement retains prognostic value across diverse settings and can refine risk stratification. Furthermore, several studies have documented associations between microalbuminuria and other microvascular target-organ complications such as diabetic retinopathy and neuropathy, supporting the idea that proteinuria may be an early systemic marker of microvascular injury. However, most existing studies are either longitudinal cohorts in Western populations or focus primarily on renal outcomes. There is limited data from low-resource settings—especially in African and Middle Eastern populations—quantifying the relationship between baseline proteinuria and other forms of organ damage (e.g., retinopathy, neuropathy, cardiovascular disease). Given that diabetic complications account for the majority of morbidity and mortality among diabetic patients in Sudan, this study seeks to utilize existing hospital and laboratory records to evaluate whether proteinuria can serve as an early predictor of multi-organ damage in local diabetic populations. Research Problem Despite evidence linking proteinuria with systemic vascular injury, routine screening and clinical interpretation of proteinuria in local diabetic care remain underutilized. This study aims to determine whether proteinuria recorded in diabetic patient files is associated with existing or future target-organ damage, thereby supporting its use as a low-cost risk stratification tool. General Objective To assess whether proteinuria can serve as an early marker for predicting target-organ damage among diabetic patients, using retrospective hospital data. Specific Objectives 1. To determine the prevalence of proteinuria among diabetic patients in the study population. 2. To assess the association between proteinuria levels and: - Diabetic nephropathy (based on serum creatinine/eGFR changes). - Diabetic neuropathy (based on documented neurological findings). - Cardiovascular complications (e.g., ischemic heart disease, stroke). 3. To identify clinical and biochemical predictors (HbA1c, duration of diabetes, blood pressure, lipids) associated with proteinuria. 4. To develop a prediction model for the risk of target-organ damage using proteinuria and other clinical factors. Hypotheses H1: Proteinuria is significantly associated with increased risk of target-organ damage in diabetic patients. H2: Proteinuria independently predicts organ damage even after adjusting for confounding factors such as age, hypertension, and HbA1c. Methodology Study Design Type: Retrospective record-based cross-sectional and predictive study. Setting: Diabetes outpatient clinic and internal medicine wards, Northern Sudan. Study Population All diabetic patients (type 1 or type 2) who attended the hospital during the specified period and have complete records on proteinuria testing and at least one assessment for target-organ damage. Inclusion Criteria • - Diagnosed diabetes mellitus (Type 1 or Type 2). • - At least one recorded urine protein test (dipstick or albumin-to-creatinine ratio). • - Documented follow-up or evaluation for target-organ damage (renal, retinal, neuropathic, or cardiovascular). Exclusion Criteria • - Known non-diabetic kidney disease (e.g., lupus nephritis). • - Active urinary tract infection at the time of urine testing. • - Pregnancy. • - Incomplete or missing essential data. Data Collection Data will be extracted from hospital records and patient files using a structured data extraction form. Variables: 1. Sociodemographic • Age • Sex • Duration of diabetes • BMI 2. Clinical parameters • Blood pressure • Type of diabetes • Smoking status • Medication (insulin, ACE inhibitors, SGLT2 inhibitors, statins) 3. Laboratory data • Urine protein (dipstick result or UACR category: negative, trace, 1+, 2+, 3+) • Serum creatinine and eGFR • HbA1c • Lipid profile 4. Target-organ damage indicators • Renal: eGFR <60 mL/min/1.73m² or rising creatinine ≥30% from baseline • Neuropathy: documentation of sensory loss or neuropathic pain diagnosis • Cardiovascular: documented myocardial infarction, angina, stroke, or heart failure Data Management and Statistical Analysis Data will be analyzed using R. Descriptive analysis: • Mean ± SD for continuous variables; frequencies and percentages for categorical variables. • Prevalence of proteinuria across the sample. Inferential analysis: 1. Bivariate analysis: • Chi-square or Fisher’s exact test for categorical variables. • Independent t-test or Mann–Whitney for continuous variables. 2. Multivariable analysis: Binary logistic regression to determine independent predictors of organ damage. Model 1: Unadjusted association between proteinuria and organ damage. Model 2: Adjusted for age, sex, duration of diabetes, HbA1c, and blood pressure. 3. Prediction model: • Stepwise logistic regression to build a predictive model for target-organ damage. • Model performance assessed by ROC curve (AUC), sensitivity, and specificity. 4. Significance level: p-value < 0.05 will be considered statistically significant. Sample Size Estimation Using the formula for proportion comparison in retrospective data, assuming: • Prevalence of proteinuria among diabetics ≈ 30% (per Kim et al., 2022). • Expected prevalence of organ damage in those with proteinuria ≈ 50% vs 25% in those without. • 95% confidence and 80% power → minimum sample ≈ 260 patients.

The study will be conducted and reported in accordance with ethical standards, ensuring privacy, data protection, and scientific integrity.

Early detection of patients at risk for target-organ damage can lead to earlier intervention, potentially reducing the incidence and severity of complications such as kidney failure, cardiovascular events, and neuropathy. This may ultimately decrease hospitalizations, long-term disability, and healthcare expenditures.