Authors: AlaaAldeen Makki Mohamed Ali, Mazin Yousif Babiker, Habab Khalid Elkheir, Ali Awadallah Saeed, Nadia Al Mazrouei, Sami Fatehi Abdalla, Anas Ali Alhur, Abdulaziz Alqadi, Abdullah S. Alghamdi, Safaa Badi, Mohamed H. Ahmed
Categories: Research, Prevalence, Risk factor, Diabetic neuropathy, Type 2 diabetes mellitus, Cross-sectional study
Source: BMC Endocrine Disorders
Authors: AlaaAldeen Makki Mohamed Ali, Mazin Yousif Babiker, Habab Khalid Elkheir, Ali Awadallah Saeed, Nadia Al Mazrouei, Sami Fatehi Abdalla, Anas Ali Alhur, Abdulaziz Alqadi, Abdullah S. Alghamdi, Safaa Badi, Mohamed H. Ahmed
Diabetic peripheral neuropathy (DPN) is a common and costly complication that occurs in both Type 1 and Type 2 diabetes mellitus. The prevalence of neuropathy is estimated to be around 8% in newly diagnosed patients and exceeds 50% in those with long-standing disease. The aim of this study was to determine the prevalence of DPN among individuals with Type 2 diabetes mellitus in Sudan and to identify the factors associated with DPN.
This cross-sectional study was conducted in the outpatient setting of an endocrinology clinic at a tertiary care centre (Diabetes Specialized Centres) in Khartoum State between January 2020 and January 2021.
Out of the 236 patients included in the study, the mean age was 55.7 ± 11.5 years, and 54.7% were female. The prevalence of DPN was found to be 43%. Logistic regression analysis revealed that the following factors were significantly associated with DPN: body mass index (BMI) (P = 0.022, OR = 3.733), male gender (P = 0.003, OR = 7.165), educational level (P = 0.034, OR = 20.577), duration of diabetes (P = 0.014, OR = 13.121), age (P = 0.001, OR = 2.735), HbA1c level (P = 0.002, OR = 1.782), alcohol consumption (P = 0.000, OR = 212.158), smoking status (P = 0.005, OR = 0.063), and the presence of hypertension (P = 0.001, OR = 11.493).
There a relatively high prevalence of DPN in the diabetic population of Sudan, and significantly associated with the duration of diabetes, BMI, age, HbA1c levels, male gender, alcohol consumption, smoking status, and hypertension. Regular screening and patient education are needed to reduce DPN in Sudanese patients with diabetes.
NA.
The online version contains supplementary material available at 10.1186/s12902-025-02124-7.
Diabetes mellitus (DM) describes a group of metabolic disorders characterized by abnormalities in the metabolism of carbohydrates, fats, and proteins. It is associated with chronic complications, including neuropathic, microvascular, and macrovascular disorders [1–4], and is estimated to affect 5% to 10% of adults worldwide [5]. The development of Type 2 diabetes mellitus (T2DM) is attributed to insulin resistance in peripheral tissues and inadequate insulin secretion by pancreatic β-cells [6, 7]. T2DM has become a major health burden in Africa, with an estimated 14 million people affected in 2011 and projections suggesting this number will rise to 28 million by 2030 [8, 9].
Neuropathy is a frequent complication of both T1DM and T2DM, with a prevalence of 8% in newly diagnosed patients and over 50% in those with long-standing disease [10, 11]. Among individuals with T2DM, the incidence of neuropathy is approximately 45%, while it ranges from 54% to 59% in those with T1DM. Diabetes mellitus can lead to various forms of neuropathy, including distal symmetric sensorimotor polyneuropathy, small fibre neuropathy, acute severe distal sensory polyneuropathy, autonomic neuropathy, diabetic neuropathic cachexia, hypoglycemic neuropathy, treatment-induced neuropathy (insulin neuritis), polyradiculopathy, diabetic radiculoplexopathy, mononeuropathies, and cranial neuropathies (particularly oculomotor neuropathy) [11, 12]. After excluding other possible causes, DPN—a well-known microvascular complication of T2DM—is defined as the presence of peripheral nerve dysfunction in individuals with diabetes affecting 30% to 50% of diabetic patients [13–15].
Distal symmetric polyneuropathy (DSPN) is the most prevalent form of DPN, affecting over 90% of patients [16]. Symptoms of DSPN may include reduced sensation, numbness, tingling, burning, or painful sensations. Different types of nerve fibres are responsible for transmitting sensations such as light touch, pain, temperature, and vibration. Although weakness is uncommon in early diabetic neuropathy, weakness in the toe flexor and extensor muscles can occur [17, 18].
Up to 25% of patients may develop painful diabetic neuropathy [11, 19]. Both autonomic and somatic divisions of the nervous system experience progressive nerve fibre loss [20, 21]. Foot ulceration and painful neuropathy are frequent DSPN complications that significantly impact morbidity and mortality [22].
Hyperglycemia is the primary risk factor for the development of DPN. Additional independent risk factors include increasing age, longer disease duration, hypertension, elevated triglycerides, higher body mass index (BMI), alcohol consumption, cigarette smoking, and greater height [12, 23, 24]. Population-based studies have demonstrated a gradient in the prevalence of highest in patients with diabetes mellitus, followed by those with impaired glucose tolerance or impaired fasting glucose, and lowest in individuals with normal glucose metabolism [12, 25]. The pathogenesis of diabetic peripheral neuropathy is multifactorial, involving metabolic and vascular mechanisms [12, 26].
This study’s main objective was to determine the prevalence of DPN in Sudanese people with T2DM and aimed to determine and examine potential associated factors, such as clinical traits, lifestyle factors, metabolic indicators, and demographic variables that may be linked to the development of DPN. Unlike previous studies in Sudan that evaluated mixed cohorts of Type 1 and Type 2 diabetes or relied on single neuropathy assessment tools, this study specifically investigates DPN in adults with T2DM using a combined validated screening method (NDS + NSS). Furthermore, it explores socio-education determinants and cardiovascular comorbidities as potential predictors of DPN, which have not been previously addressed in Sudanese populations.
The current study is a cross-sectional epidemiological investigation that was conducted in an outpatient setting at a secondary and tertiary care endocrinology clinic in Khartoum State.
This survey comprised five Khartoum State secondary and higher institutions,
Two are from the Khartoum area, namely the Ibrahim Malik Teaching Hospital’s diabetic clinic and Zenam Diabetic Center.Two are from the Omdurman area, namely the Military Hospital’s Diabetic Clinic and the Khalil Diabetic Center at Omdurman Teaching Hospital.One was a diabetic clinic at Bahri Teaching Hospital, located in the Bahri locality.
The study was conducted from January 2020 to January 2021.
Sudanese patients with T2DM attending previously selected centers and willing to participate were considered eligible.
Patients of either sex who were diagnosed with type 2 DM of any duration, established as per American Diabetes Association (ADA) guidelines and willing to participate were included in the study.
The presence of foot ulceration.
The sample size was calculated using the following equation\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ :n=\left({z}^{2}\times:p\right)\times:\left(1-p\right)\div{e}^{2}
Where n is the sample size, z denotes the confidence level score, p denotes the sample proportion stated in decimals, and e denotes the margin of error expressed in decimals. After computation in accordance with the prevalence determined by a prior study by Elmadhoun et al., which revealed that the prevalence of diabetes mellitus was 19%, the sample size in this study is equal to 237 [27].\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \:n=\left({1.96}^{2}\times\:0.19\right)\times\:\left(1-0.19\right)\div{0.05}^{2}=237.48 $$\end{document} Patients were recruited using convenience sampling from hospital during the study period. The sample size of 236 patients is comparable to similar cross-sectional neuropathy studies in low- and middle-income settings. The final sample reflects realistic recruitment capacity across five centers during the COVID-19 period. Although larger samples may provide greater statistical power, the present sample size is adequate for logistic regression analysis according to standard epidemiological recommendations risk. ### Data collection Data were collected using a structured questionnaire designed in English and filled by the principal investigator, which was pre-tested and validated to ensure clarity and reliability. The questionnaire used in this study was adapted from previously published tools in related studies [28, 29]. By interviewing the participant, a pre-tested, self-designed questionnaire was utilized to gather information about demographics (age, sex, and place of residence), educational attainment, socioeconomic status, lifestyle traits (smoking, snuffing, and alcohol intake), and medication profiles. Physical examinations were conducted, and data were also recorded using a pre-tested, self-designed data collection sheet that was borrowed from prior research. ### Clinical and biochemical measurements An electronic weighing scale (Seca, Birmingham, United Kingdom) was used to measure the participant’s body weight to the nearest 0.1 kg. Other anthropometric measurements included weight, height, and body mass index (BMI; kg/m2). Without wearing shoes, height was measured with a stadiometer (Seca, Birmingham, United Kingdom) to the nearest 0.5 cm. Weight (kg) divided by height (m) squared (kg/m2) was used to compute the body mass index (BMI). Obesity was defined as having a BMI greater than 30 kg/m2 using the WHO’s suggested cut-off values [30]. Socioeconomic status (SES) was classified into low, middle, and high categories based on self-reported monthly household income and employment status. Low SES was defined as insufficient income to meet basic needs; middle SES as income sufficient for basic needs with minimal surplus; and high SES as the ability to cover basic needs and maintain savings [27]. An electronic vital sign monitor (Microlife BP W90, Microlife AG, 9443 Widnau, Switzerland) was used to test blood pressure. Participants were seated for 5 min, then had their blood pressure measured three times in a row in the right arm. The three measurements’ means were reported. The Eighth Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC8) guidelines defined hypertension as systolic blood pressure (SBP) > 140 mmHg and/or diastolic blood pressure (DBP) > 90 mmHg [16] and/or using blood-pressure-lowering drugs. Fasting blood glucose, HbA1c, lipid profile (HDL, LDL, TG), serum creatinine, and serum urea nitrogen were reported from the most recent lab results and/or from the available clinical records on the patient monitoring card. The ADA defines normal levels of HDL (>50 mg/dL), LDL (100 mg/dL), and TG (150 mg/dL) [31]. Diabetes mellitus type 2 was confirmed according to ADA diagnostic criteria (HbA1c > 6.5%, FBS > 126 mg/dL, or RBS > 200 mg/dL). Glycemic control was categorized based on HbA1c levels, where HbA1c ≤ 7% was considered controlled and >7% uncontrolled.” [32]. ### Assessment of neuropathy Biochemical parameters were collected from the most recent laboratory investigation reports recorded in the clinical records [33]. Neuropathy assessments, including the Neuropathy Disability Score (NDS) and Neuropathy Symptom Score (NSS), were performed by trained clinicians following standardized procedures [34]. Inter-rater reliability was assessed on a subset of patients, showing good agreement (Cohen’s kappa = 0.85), ensuring consistency in measurements. #### The following components make up the neuropathy disability score (NDS) Using a 128 Hz tuning fork, vibration perception is evaluated over bony prominences on the ventral side of the feet, including the 1st, 3rd, and 5th metatarsal heads.Light touch perception using four locations on the foot (the dorsal aspect of the first, third, and fifth metatarsal heads) and a 10-g Semmes-Weinstein monofilament. When tested, the participant should close his or her eyes to detect the impression of pressure at the proper place.Use a disposable pin with just enough pressure applied to it to cause the skin on the dorsal surface of the hallux to distort as a pinprick sensation test. Failure to detect a pinprick across either hallux was used to describe loss of pinprick sensation. Each of the tests have a passing score of 0 and a failing score of 2, respectively. #### The neuropathy symptoms score (NSS) includes posing the following queries to the participants Does the participant experience dizziness when walking? The need for visual contact, increasing in the dark, walking erratically in the dark, and not touching the floor?Does the participant’s legs or feet hurt, itch, or feel tender in any other way? Occurs either at rest or at night and has nothing to do with activity?Does the individual experience prickle in his or her legs or feet? Occurs at night or throughout the day?Does the individual have any numb spots on his or her legs or feet? Each of the questions listed above has a score of 1 for a yes response and 0 for a no response. The patient is diagnosed with DPN if their combined NDS + NSS score is 7 or above. ### Data analysis The IBM^®^, Statistical Package for Social Sciences (SPSS version 26) was used to code, enter, and analyze the data. Continuous variables were expressed as means ± SD, while categorical variables were represented as frequencies and percentages. We first used chi-square or Fisher’s exact tests to look at the links between DPN and categorical variables. Variables exhibiting *P* < 0.20 in univariate analyses were incorporated into a multivariable logistic regression model to ascertain independent correlates of DPN. Variance Inflation Factors (VIF) were used to check for multicollinearity. Variables with VIF > 5 were checked for redundancy and removed if they were not needed. The Hosmer–Lemeshow test was used to see how well the model fit. After removing any further relevant confounders, binary logistic regression was performed to find independent predictors of peripheral neuropathy. In the analysis, a p-value of 0.05 or lower was regarded as statistically significant. Single (Median) imputation was used to fill in missing data for variables with less than 10% missingness. We used SPSS v26 for all the analyses, and *P* < 0.05 was the level of statistical significance. ## Result ### Socio-demographic characteristics of the participants Out of 236 participating patients included in this study, 54.7% were females. The average age was 55.7 (± 11.5) years among them 36% aged from 53 to 60 years old. Nearly one-third of them completed their education till secondary school level. Of them, 48.7% had middle economic status, while 75.8% were lived in rural areas, and near to half of them had normal BMI. The duration of diabetes was 11–22 years for more than one third of the participants (37.3%), while only 9.3% of them reported that they are suffering from diabetes for more than 20 years. Regarding the social history of the participating patients; 14% of the participants were smokers, and 7.2% were tobacco snuffer. On the other hands, as few as 3.4% of the participants consume alcohol. With respect to the history of hypertension, one out of five of the study participants has hypertensions (Table 1). Table 1Socio-demographic characteristics and clinical data for the participating patientsVariablesNumber (%) **Gender:** Male107 (45.3)Female129 (54.7)**Age**:18–252 (0.8)26–3512 (5.1)36–4427 (11.4)45–5233 (14)53–6085 (36)61–6852 (22)> 6825 (10.6) **Age** Mean (± SD)**Educational level**:Uneducated27 (11.4)Primary53 (22.5)High school59 (25)College77 (32.6)Post-graduation20 (8.5)**Economic status**:Low80 (33.9)Middle115 (48.7)High41 (17.4)**Residence**:Rural179 (75.8)Urban57 (24.2)**BMI**:Normal106 (44.9)Overweight87 (36.9)Obese43 (18.2) **DM duration** < 2 years40 (16.9)2–5 years50 (21.2)6–10 years36 (15.3)11–20 years88 (37.3)> 20 years22 (9.3)**Social history**:Smoking33 (14)Snuffing17 (7.2)Alcoholics8 (3.4) **Hypertension** 46 (19.5)SD = Standard Deviation; DM = Diabetes Mellitus; BMI = Body Mass IndexPercentages are based on the total study population (*n* = 236) ### Medications used by the participants 24.6% of the participants were using glimepiride + metformin, while 21.6% were using insulin and 25% of them were using insulin + metformin. Of them, 40.7% were using aspirin as shown in supplementary 1. ### Investigations As higher as 89.8% and 84.7% of the participants were had uncontrolled fasting blood glucose and HbA1c levels respectively. On the other hand, 82.2%, 77.2%, and 84.7% of the participants had their HDL, LDL, and TG levels controlled. While 99.2% of the study participants had normal renal function tests as shown in Table 2. Table 2Different investigations carried out by the participating patientsInvestigationsNormal *N* (%) **FBS:** Uncontrolled212 (89.8)Controlled24 (10.2)**FBS** Mean (± SD)208 (± 77.6)**HbA1c**:Uncontrolled200 (84.7)Controlled36 (15.3)**HbA1c** Mean (± SD)8.75 (± 1.7)**HDL**:Normal194 (82.2)Low42 (17.8)**LDL**:Normal183 (77.5)High53 (22.5)**Triglyceride**:Normal200 (84.7)High34 (14.4)Low2 (0.8)**Serum creatinine**:Normal234 (99.2)High2 (0.8)**BUN**:Normal234 (99.2)High2 (0.8)FBS = Fasting Blood Sugar; HbA1c = Glycated Hemoglobin; HDL = High-Density Lipoprotein; LDL = Low-Density Lipoprotein; BUN = Blood Urea Nitrogen; SD = Standard Deviation ### Prevalence and associated factors for developing diabetic peripheral neuropathy The prevalence of DPN among Type 2 diabetic patients in Khartoum State was found to be 43%, chi-square and fisher exact test were performed to determine if there is an association between the presence of DPN and other variables; we found that, DPN had statistically significant associations with BMI, sex, educational level, DM duration, Age, HDL, LDL, TG, HbA1c levels, alcohol consumption, smoking stratus, and the presence of hypertension as shown in Table 3. Table 3The associations of the presence of DPN with other factors (*n* = 236)VariableLevelTotal (*n*)DPN Yes*n* (%)DPN No*n* (%)*P*-valueBMINormal10650 (47.2%)56 (52.8%)< 0.01Overweight7840 (51.7%)38 (48.3%)Obese437 (16.3%)36 (83.7%)SexMale10765 (60.7%)42 (39.3%)< 0.01Female12937 (28.7%)92 (71.3%)Age (years)18–2520 (0%)2 (100%)< 0.0126–35120 (0%)12 (100%)36–44271 (3.7%)26 (96.3%)45–52334 (12.1%)29 (87.9%)53–608547 (55.3%)38 (44.7%)61–685232 (61.5%)20 (38.5%)> 682518 (72.0%)7 (28.0%)HDLNormal19477 (39.7%)117 (60.3%)0.025Low4225 (59.5%)17 (40.5%)HbA1cUncontrolled200100 (50.0%)100 (50.0%)< 0.01Controlled362 (5.6%)34 (94.4%)Smoking statusNo20380 (39.4%)123 (60.6%)0.004Yes3322 (66.7%)11 (33.3%)Alcohol consumptionNo22895 (41.7%)133 (58.3%)0.023Yes87 (87.5%)1 (12.5%)DM duration< 2 years4013 (32.5%)27 (67.5%)< 0.0012–5 years503 (6.0%)47 (94.0%)6–10 years3613 (36.1%)23 (63.9%)11–20 years8851 (58.0%)37 (42.0%)> 20 years2222 (100%)0 (0%)Education levelUneducated2713 (48.1%)14 (51.9%)< 0.001Primary5341 (77.4%)12 (22.6%)High school5918 (30.5%)41 (69.5%)College7727 (35.1%)50 (64.9%)Postgraduate203 (15.0%)17 (85.0%)LDLNormal18369 (37.7%)114 (62.3%)0.002High5333 (62.3%)20 (37.7%)TriglycerideNormal20080 (40.0%)120 (60.0%)0.033High3420 (58.8%)14 (41.2%)Low22 (100%)0 (0%)HypertensionNo19073 (38.4%)117 (61.6%)0.003Yes4629 (63.0%)17 (37.0%)BMI = Body Mass Index; HDL = High-Density Lipoprotein; LDL = Low-Density Lipoprotein; HbA1c = Glycated Hemoglobin; DM = Diabetes Mellitus. DPN: Diabetes peripheral neuropathy ### Binary logistic regression of the possible predicators’ for DPN A binary logistic regression test was performed to determine the predictors and associated factors for the presence of DPN and we found that after adjusting for multiple variables, only age, male gender, low level of education, diabetes duration, elevated BMI, hypertension, and increased HbA1c levels remained statistically significant independent correlates of diabetic peripheral neuropathy. Other variables that were significant in chi-square analyses like smoking, and dyslipidemia lost their statistical significance once we considered confounding and multicollinearity. In other words, males were more likely to develop DPN than females by 2.95 times (P = 0.002, OR = 2.95, CI = 1.48–5.86). Furthermore, increase in one year of patient’ age will increase the probability of getting DPN by 1.42 times (*P* = 0.001, CI OR = 1.18–1.78). Patients with low level of education were more likely to develop DPN by 3.84 times than those who had post graduate degree (*P* = 0.028, CI = 1.15–12.79), and those who had diabetes from 11 to 20 years were more likely to develop DPN by 4.67 times than those who had DM for less than 2 years (*P* = 0.004, CI = 1.65–13.19). Overweight patients were more likely to develop DPN by 1.96 times than those with normal BMI (*P* = 0.038, CI = 1.04–3.67). Hypertensive patients were more likely to had DPN by 3.25 times than non-hypertensive one (*P* = 0.005, CI = 1.42–7.45). Increasing one unit of HbA1c is more likely to increase the probability of getting DNP by 1.31 times (*p* = 0.006, CI = 1.08–1.59), as shown in Table 4. Table 4Binary logistic regression of the possible predicators’ for DPNPredictorAdjusted OR95% CI*P*-valueAge (per year increase)1.421.18–1.780.001Male sex2.951.48–5.860.002Education (low vs. high)3.841.15–12.790.028DM duration (11–20 yrs vs. < 2 yrs)4.671.65–13.190.004DM duration (> 20 yrs vs. < 2 yrs)8.122.11–31.350.002BMI (overweight vs. normal)1.961.04–3.670.038Hypertension3.251.42–7.450.005HbA1c (per 1% increase)1.311.08–1.590.006Low HDL1.440.73–2.830.278High LDL1.560.82–2.980.171High Triglycerides1.320.64–2.740.442Smoking (Yes)1.410.68–2.950.358BMI = Body Mass Index; HDL = High-Density Lipoprotein; LDL = Low-Density Lipoprotein; HbA1c = Glycated Hemoglobin; DM = Diabetes Mellitus ## Discussion The overall prevalence of DPN was found to be 43%, among 236 Sudanese patients with type 2 diabetes mellitus, with a mean age 55.7 years with a slightly higher percentage of females (55%). These findings align with a previous study conducted in Sudan which reported a lower mean age (39.5 years), which may be attributed to the current study’s exclusive focus on patients with type 2 diabetes mellitus, whereas the earlier study included both type 1 and type 2 diabetic patients [35]. Our observed DPN prevalence (43%) is like studies in Jordan (39.5%), Qatar (34%) and slightly lower than that observed in Ethiopia (52%), and Nigeria (55%) but higher than the UK (7%), and Romania (28%) [36–41]. These variations may be explained by differences in study populations, risk factor profiles, diagnostic methodologies, and the type of diabetes examined across studies. For instance, a similar prevalence was found in Iran (45%) [28], whereas Saudi Arabia reported lower (19%) [29–45], these differences may stem from the methods used to assess DPN. The present study and the Iranian study both utilized a combination of the Neuropathy Disability Score (NDS) and Neuropathy Symptom Score (NSS), whereas the Saudi Arabian study relied solely on NDS. This highlights the importance of using both symptom evaluation (NSS) and neurological deficit assessment (NDS) for a more comprehensive diagnosis of DPN. In contrast, a higher prevalence of DPN was recently reported in Tanzania (72%) [46]. This difference may be due to the inclusion of both type 1 and type 2 diabetic patients in the Tanzanian study, while the current study included only type 2 diabetic patients. Regarding associated factors, this study revealed a significant association between uncontrolled HbA1c levels and DPN (*P* = 0.006). Binary logistic regression analysis indicated that for each additional unit of HbA1c, the likelihood of developing DPN increased by 1.31 times. This finding is consistent with previous studies that demonstrated a strong relationship between HbA1c and DPN, emphasizing the importance of strict glycemic control for both management and prevention of DPN [29]. Consistent with previous research, this study found a significant association between the duration of diabetes and the development of DPN (*P* = 0.004, OR = 4.67). Specifically, the likelihood of developing DPN increased 4.67-fold after 11–20 years of disease duration. This underscores the need for continuous evaluation of DPN and foot complications during routine follow-up visits, along with consideration of preventive pharmacological interventions. Although the association of DPN with hyperglycemia, high BMI, hypertension, and smoking has been demonstrated in multiple international studies, the extent to which these factors contribute to neuropathy risk varies across populations due to differences in metabolic control, healthcare access, and socio-cultural habits. Evidence from Sudan has been extremely limited. Therefore, confirming these factors within a Sudanese context provides important epidemiological insights and highlights target groups for preventive interventions at both clinical and community levels. Regarding obesity, this study’s findings are somewhat controversial. The logistic regression analysis revealed a significant association between overweight status and elevated odds of diabetic peripheral neuropathy (DPN), whereas the crude prevalence table indicated a reduced proportion of DPN among obese participants relative to those with a normal body mass index (BMI). This difference is probably due to the small size of the obese group (*n* = 43) and the possibility of selection bias. Even with this uncommon case, obesity is still widely known to be a risk factor for type 2 diabetes and its complications, such as neuropathy as being reported by some studies [29, 36, 37]. Hence, subsequent research utilizing larger and more representative samples is essential for clarifying the real relationship between obesity and diabetic peripheral neuropathy (DPN). An interesting finding in this study is the strong association between educational level and DPN (*P* = 0.028). Binary logistic regression showed that patients with low level of education were 3.84 times more likely to develop DPN compared to those with post-graduate education (OR = 3.84). The high odds ratio may be influenced by the relatively small number of postgraduate participants in the sample. Nevertheless, the impact of education should not be underestimated. Higher educational levels contribute to early diagnosis, consistent follow-up, better understanding of diabetes complications, proper medication use, and adherence to treatment regimens. These findings highlight the critical need for patient education regarding diabetes, its complications, and proper disease management strategies. Regarding gender, our study found that males are at significantly higher risk for developing DPN (*P* = 0.002). Logistic regression analysis revealed that males were 2.95 times more likely to develop DPN compared to females, this is consistent with findings from previous studies [28]. Hypertension was also identified as a significant risk factor for DPN in this study (*P* = 0.005). Logistic regression analysis confirmed that patients with hypertension were 3.25 times more likely to have DPN compared to non-hypertensive individuals. These findings are consistent with other studies [28, 37], and reinforce the role of both hyperglycemia and hypertension in the pathogenesis of DPN [47]. This underscores the importance of blood pressure management as a key preventive measure against DPN. Dyslipidemia, smoking, and snuffing showed weak associations with the development of DPN, which is consistent with previous findings [48]. However, according to chi-square results alcohol consumption was associated with DPN (*P* = 0.023). This aligns with studies indicating the clinical and experimental neurotoxicity of ethanol [49] and highlights the critical role of alcohol reduction or cessation in the prevention and management of DPN. The observed high prevalence of diabetic complications may be attributed to several factors. Poor glycemic control is a major contributor, as prolonged hyperglycemia increases the risk of nerve damage. Delayed diagnosis of diabetes allows sustained periods of elevated blood glucose, further exacerbating complications. Additionally, limited health education and low awareness about disease management can lead to poor adherence to medications, diet, and lifestyle modifications. The higher prevalence observed in men may be influenced by both lifestyle and biological factors. Men often have higher rates of smoking, alcohol consumption, and sedentary behavior, which can worsen complications. Biological differences, such as hormonal influences and variations in fat distribution, may also play a role. Furthermore, men may be less likely to seek timely medical care, resulting in more advanced disease at the time of diagnosis. Collectively, these factors likely contribute to the observed patterns in disease prevalence. ### Implications of the study The results of this study have significant clinical and public health implications. The high number of people with T2DM who also have DPN in Sudan shows how important it is to have routine screening and early detection programs in primary and secondary healthcare settings. Public health interventions in Sudan e.g. regular foot checks, community screening for recognizing individuals at elevated risk—specifically those with chronic diabetes, inadequate glycemic control, and advanced age—can enable prompt interventions to avert disease progression and associated complications, including foot ulcers, infections, and amputations. From a clinical standpoint, incorporating straightforward and economical instruments such as the Neuropathy Disability Score (NDS) and Neuropathy Symptoms Score (NSS) into routine diabetes management can improve diagnostic capabilities, particularly in resource-limited environments. Nevertheless, investment in more objective diagnostic modalities, such as nerve conduction studies, would enhance accuracy and case detection. These results underscore the necessity of fortifying national diabetes management programs, emphasizing comprehensive care that incorporates neuropathy evaluation. Health education campaigns aimed at modifiable risk factors—such as glycemic control, smoking cessation, and alcohol reduction—can also help lessen the burden of DPN. Additionally, the study facilitates future longitudinal and interventional research to enhance the understanding of causal relationships and assess the efficacy of preventive and therapeutic strategies for DPN in the Sudanese population. A key contribution of this study is the identification of lower educational level and hypertension as strong independent predictors of DPN in Sudanese individuals with T2DM, providing novel insights into the social and cardiovascular determinants of neuropathy risk in the region. ### Limitation and strength of the study This study has several limitations that should be acknowledged. Firstly, its cross-sectional design and use of the convenient sampling technique method, these will prevent the establishment of causality between identified associated factors and DPN; only associations could be inferred. Secondly, the study was limited to five centers in Khartoum, so the results may not represent all diabetic patients in Khartoum. Thirdly, use of clinical tests (NDS/NSS) instead of nerve conduction studies may have missed subclinical cases. Additionally, convenience sampling may result in selection bias and limit the generalizability of our results. We also note that self-reported behaviors like cigarette and alcohol consumption may not be reported accurately because of social desirability bias in conservative cultures. These factors may affect the observed associations and must be considered when interpreting the findings. Many patients in this sample had uncontrolled diabetes, which reflects the true situation in Sudanese clinical settings where access to optimal diabetes management is limited. While a larger number of controlled subjects could allow more balanced group comparisons, selective recruitment would have introduced bias. To mitigate this limitation, multivariable logistic regression was applied to adjust for differences across glycemic control status and other covariates. The study has some strengths despite these limitations. One of the only systematic studies to investigate DPN prevalence and factors among Sudanese diabetics, it fills a significant role in regional literature. Standardsized, established clinical tools (NDS and NSS) improve internal consistency and comparability with comparable low- and middle-income studies. The huge sample size and wide variety of demographic, metabolic, and lifestyle characteristics support the analysis. Finally, the baseline data can enrich clinical practice and guide longitudinal and interventional research to improve diabetic neuropathy screening and care in Sudan. ## Conclusion The study found a high prevalence of DPN among Sudanese patients with type 2 diabetes mellitus, compared to global estimates. The duration of diabetes, poor glycemic control, increasing age, male gender, and alcohol consumption were identified as significant associated factors for the development of DPN. Improved glycemic control, regular foot examinations, and patient education are essential preventive measures. ### Recommendations Future longitudinal and interventional studies are recommended to confirm these findings and to better understand causal relationships between the development or progression of DPN and associated factors. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1