Risk Factors For Diabetic Neuropathy Research Articles

A review on comparative study of effectiveness of pregabalin and carbamazepine in painful diabetic peripheral neuropathy using mc gill's pain questionnaire

Diabetic neuropathy is a unique degenerative disorder of the peripheral nervous system that preferentially targets sensory axons and autonomic axons. The loss of sensory function that begins distally in the lower extremities is known as Diabetic neuropathy. It is characterized by burning pain, paresthesias and numbness in stocking glove pattern that progress proximally from the feet and hands. Diabetic neuropathy is a highly prevalent condition that substantially affects patients by increasing falls causing pain and reducing quality of life. The major risk factors for diabetic neuropathy is hyperglycemia. In addition to poor glycemic control, more-severe symptoms of diabetic neuropathy are associated with advanced age, hypertension, the duration of diabetes, dyslipidemia, smoking and heavy alcohol intake. Diabetic neuropathy is a major cause for disability of feet and hands globally. Anti-convulsants like pregabalin and carbamazepine are the medications used in the treatment of painful diabetic neuropathy.

Hyperglycaemia Aggravates Oxidised Low-Density Lipoprotein-Induced Schwann Cell Death via Hyperactivation of Toll-like Receptor 4.

Increased low-density lipoprotein levels are risk factors for diabetic neuropathy. Diabetes mellitus is associated with elevated metabolic stress, leading to oxidised low-density lipoprotein formation. Therefore, it is important to investigate the mechanisms underlying the pathogenesis of diabetic neuropathy in diabetes complicated by dyslipidaemia with increased levels of oxidised low-density lipoprotein. Here, we examined the effects of hyperglycaemia and oxidised low-density lipoprotein treatment on Schwann cell death and its underlying mechanisms. Immortalised mouse Schwann cells were treated with oxidised low-density lipoprotein under normo- or hyperglycaemic conditions. We observed that oxidised low-density lipoprotein-induced cell death increased under hyperglycaemic conditions compared with normoglycaemic conditions. Moreover, hyperglycaemia and oxidised low-density lipoprotein treatment synergistically upregulated the gene and protein expression of toll-like receptor 4. Pre-treatment with TAK-242, a selective toll-like receptor 4 signalling inhibitor, attenuated hyperglycaemia- and oxidised low-density lipoprotein-induced cell death and apoptotic caspase-3 pathway. Our findings suggest that the hyperactivation of toll-like receptor 4 signalling by hyperglycaemia and elevated oxidised low-density lipoprotein levels synergistically exacerbated diabetic neuropathy; thus, it can be a potential therapeutic target for diabetic neuropathy.

Correlation of peripheral neuropathy with serum vitamin D levels and HbA1C variations in type 2 diabetes mellitus patients

Introduction and Aim: The burden of diagnosed and undiagnosed diabetes will increase in the coming decade. Vitamin D deficiency and HbA1C variability are risk factors for diabetic neuropathy, and the association was studied in the present study. Materials and Methods: Data from 346 Type II diabetes patients reporting in a tertiary care Hospital in North India after approval of the ethics committee was analyzed. Michigan Neuropathy Screening Instrument (MNSI) was used to screen the patients for neuropathy. Vitamin D levels were measured. Four HbA1C levels (once every three months) done at least one-year preceding enrolment in the study were analyzed. A nerve conduction study was performed in the Neurophysiology lab. Data were analyzed using SPSS 21.0. Results: Diabetic peripheral neuropathy (DPN) was present in 54.91% of patients. They had a higher BMI, HbA1C %, fasting blood glucose, and low Vitamin D levels. Nerve conduction studies showed more pronounced changes in patients with severe DPN grades. Variability of HbA1C% correlated positively with the duration of diabetes and BMI, and the relation was negative with vitamin D levels. Vitamin D correlated negatively with duration, Hb A1C variability, and BMI. The correlation of HbA1C variability with motor and sensory conduction velocity and amplitude was negative, and with latency, it was positive. Vitamin D correlated positively with amplitude and conduction velocity and negatively with latency. Conclusion: Nerve conduction study, variability in HBA1C, and Vitamin D levels can act as tools to detect DPN. Vitamin D is a modifiable risk factor that needs monitoring in people with diabetes.

The Risk factors of diabetic neuropathy in type 2 diabetic patient in Services Hospital Lahore

Objective: To investigate the risk factors and to study the effect of these risk factors on the diabetic neuropathy (DN). Method: th The study was a retrospective case-control, hospital-based study and was carried out from18th November 2021 to 20 December 2021 through the questionnaire. Respondents were patients of diabetes mellitus (n=277) who visited the Diabetes Management Center of Services Hospital, Lahore. Binary Logistic regression model was fitted using SPSS 20.0 Result: Sample comprised of 277 subjects and among these, 122 and 155 were cases and controls respectively. Female gender (OR=2.225), duration of diabetes (OR=1.066), blood glucose level in fasting (OR=1.006), and age (OR= 1.042) have a significant effect on the prevalence of diabetic neuropathy. Conclusion: The risk of Diabetic Neuropathy has been found higher, due to an increase in age, duration of diabetes and uncontrolled glucose level in the blood. Female patients of diabetes mellitus have higher probability of DN as compared to male patients.

Nomogram analysis of risk factors for diabetic neuropathy in primary type 2 diabetic mellitus patients in Zhejiang Province

Objective: To investigate the risk factors of diabetic nephropathy (DN) in primary type 2 diabetes mellitus (T2DM) patients and to quantitatively analyze the risk of DN by nomogram modeling. Methods: A total of 1 588 primary T2DM patients from 17 townships and streets in Zhejiang Province were enrolled from June 2018 to August 2018 in this cross-sectional study, with an average age of (56.8±10.1) years (50.06% male) and a mean disease duration of 9 years. The clinical data, biochemical test results, and fundus photographs of all T2DM patients were collected, and logistic regression analysis was used to screen the risk factors of DN. Then, a nomogram model was used to quantitatively analyze the risk of DN. Results: DN occurred in 27.71% (440/1 588 cases) primary type 2 diabetes patients. Hemoglobin A1c (HbA1c) (OR=1.159, 95%CI 1.039-1.292), systolic blood pressure (OR=1.041, 95%CI 1.031-1.051), serum creatinine (Scr) (OR=1.011, 95%CI 1.004-1.017), serum globulin (GLOB) (OR=1.072, 95%CI 1.039-1.105), diabetic retinopathy (DR) (OR=1.463, 95%CI 1.073-1.996), education level of more than junior high school (OR=2.018, 95%CI 1.466-2.777), and moderate-intensity exercise (OR=0.751, 95%CI 0.586-0.961) were influencing factors of DN. Nomogram model analysis showed that the total score of each factor of DN ranged from 64-138 points, and the corresponding risk rate ranged from 0.1-0.9. The nomogram model also predicted a C-index value of 0.753 (95%CI 0.726-0.781) and an area under the receiver operating characteristic curve of DN of 0.753. Internal verification of the C-index reached 0.738. The model displayed medium predictive power and could be applied in clinical practice. Conclusions: HbA1c, systolic blood pressure, Scr, GLOB, DR, and more than a junior high school education are independent risk factors of DN. Nomogram modeling can more intuitively evaluate the risk of DN in primary T2DM patients.

Prediction of Diabetic Neuropathy Using Machine Learning Techniques

Peripheral polyneuropathy is one of the most common complications in patients with diabetes mellitus, and it results in neuropathic pain, falling tendency, and foot ulcers as well as sensory and motor impairments. Numerous risk factors for diabetic neuropathy had been revealed through statistical analysis; however, statistics draw population inferences and might not be suitable for providing realtime prediction for each patient in clinical practice. Machine learning techniques were developed to find any predictive patterns based on input data. Such strategies can help predict neuropathy in diabetic patients, enabling prevention or early treatment to increase quality of life in diabetic patients. This article summarizes recent studies concerning the prediction of diabetic neuropathy using machine learning techniques, and suggests approaches for useful translation of these methods in the medical field.

Glycemic profile variability: An independent risk factor for diabetic neuropathy in patients with type 2 diabetes

BackgroundImpaired glycemic control is a potential predictor for macro- and microvascular complications of diabetes, which could be recognized by glycemic variability. The aim of this 10-year prospective cohort study presented here is to gain a better understanding of the correlation between GV and diabetic peripheral neuropathy (DPN) as one of the most common complications of T2DM. MethodsSince February 2010, 1152 adult patients with T2DM have been followed-up. Baseline features, anthropometric measurements, and laboratory findings were collected and documented during ten years. The association between DPN incidence and glycemic profile variability was evaluated using cox regression analysis. The coefficient of variation of glycemic indices within subjects was calculated and compared using an independent sample t-test. ResultsIndividuals who developed neuropathy had significantly higher mean levels of glycemic indices (HbA1c, FBS, and 2hpp), urinary albumin excretion, mean creatinine levels, and a longer duration of diabetes. A significant positive correlation between incidence of DPN and glycemic profile variability (cv-FBS10 %, cv-FBS20 %, cv-2hpp20 %, cv-HbA1c5 % and cv-HbA1c10 %) was revealed. Results also showed that higher variability of FBS was associated with the higher risk of neuropathy incidence (HR: 12.29, p-value: 0.045), which indicates that glycemic profile variability is an independent risk factor for DPN in patients with T2DM. ConclusionVariability of glycemic profiles from a visit to visit, regardless of sustained hyperglycemia, was indeed a significant risk factor for DPN in diabetic type 2 patients. CV-FBS was the most critical glycemic variability indices for DPN development.

Corneal confocal microscopy identifies small nerve fibre damage in patients with hypertriglyceridemia

Hypertriglyceridemia has been identified as a risk factor for diabetic neuropathy. Patients with hypertriglyceridemia underwent assessment of neuropathy and corneal confocal microscopy. 24 patients with severe hypertriglyceridemia defined as a triglyceride level more than 5.5 mmol/L (485 mg/dL) with no history of diabetes and 19 age-matched controls underwent assessment of HbA1c, blood pressure, fasting lipid profile, neuropathy disability score (NDS) and corneal confocal microscopy (CCM). Patients with hypertriglyceridemia had a significantly higher NDS (P<0.001) and lower CNFD (no./mm2) (27.1 [25.0-29.9] Vs 35.9 [31.2-40.6], p<0.001), CNBD (no./mm2) (55.4±22.3 Vs 91.6±30.8, p<0.001), CNFL (mm/mm2) (19.2±4.3 Vs 26.7±4.4, p<0.001) and IWL (mm/mm2) (24.3±6.9 Vs 36.6±10.0, p<0.001) compared to control subjects. In subjects with hypertriglyceridemia serum triglyceride levels correlated with CNFD (rho= -0.473, p=0.002), CNBD (rho= -0.341, p=0.043), CNFL (rho= -0.446, p=0.006) and IWL (rho= -0.408, p=0.034), no correlation was found between triglycerides and CCM parameters in subjects without hypertriglyceridemia. Subjects with metabolic syndrome had a lower CNFD (32.3 [29.2-37.5] Vs 27.1 [20.8-30.2] no./mm2, p=0.003), CNBD (20.1±6.0 Vs 23.9±5.3 no./mm2, p=0.036), CNFL (57.7±26.9 Vs 79.2±32.6 mm/mm2, p=0.037) and IWL (25.4±7.1 Vs 32.9±11.2 mm/mm2, p=0.036) compared to subjects without metabolic syndrome. Hypertriglyceridemia and metabolic syndrome are associated with small nerve fibre damage and clinical neuropathy. Elevated serum triglycerides may be a potential therapeutic target for the treatment of peripheral neuropathy.

IDF21-0032 Risk factors of diabetic neuropathy in Romanian elderly patients with type 2 diabetes mellitus

Background: Chronic hyperglycemia leads to nerve fiber damage, so most of the patients with diabetes mellitus (DM) with about two decades of disease duration will have at least one manifestation of neuropathy.

Karakteristik faktor- faktor risiko terjadinya neuropati diabetik pada pasien diabetes melitus tipe 2 di RSI Siti Rahmah Padang Tahun 2019-2020

Introduction: Diabetic neuropathy is the most common complication in patients with type 2 diabetes melitus. Type II diabetes mellitus is a disease characterized by high blood sugar levels. Increased blood sugar levels for a long time will damage the peripheral nerve vessels and cause the process of sending signals between the central nervous system and the peripheral nervous system is disrupted. This disorder is known as diabetic neuropathy. Identification of risk factors can decrease the incidence of diabetic neuropathy Aims: to determine characteristic risk factors for diabetic neuropathy in type 2 diabetes mellitus patients at RSI Siti Rahmah Padang in 2019-2020. Method: This research is an observational descriptive study with etrospective data collection methods.. The population is diabetes melitus type 2 patients with neuropathic complications at Siti Rahmah Padang Hospital in 2019-2020 who have complete medical records and no history of head trauma and stroke with a sample of 52 patients. Sampling from the medical record data of the Siti Rahmah Padang Hospital in 2019-2020. Results: Univariate data analysis with SPSS version 16.0 program. The highest age was in the 56-65 year category ( late elderly group) with 24 patients (46.2%), the highest gender was in the female with 36 patients (69.2%), Based on long suffering from diabetes most common is ≥ 5 years, namely 28 patients ( 53,8%) and based on hypertension state most common is with hypertension, namely 27 patients (51,9%) .Conclusion: Identification of risk factors can decrease the incidence of diabetic neuropathy

Identification of independent risk factors for diabetic neuropathy progression in patients with type 2 diabetes mellitus.

ObjectiveTo identify independent risk factors for diabetic neuropathy (DN) in patients with type 2 diabetes mellitus (T2DM).MethodsWe retrospectively analyzed 376 patients with T2DM at the First Affiliated Hospital of Fujian Medical University, China between January 2013 and October 2016. Multivariate logistic regression was used to explore potential risk factors for progression of DN in patients with T2DM. Effect sizes were estimated using odds ratios (ORs) and 95% confidence intervals (CIs).ResultsThe prevalence of DN in patients with T2DM was 43.1%. Multivariate logistic regression indicated that retinopathy (OR: 2.755, 95% CI: 1.599–4.746); diabetic nephropathy (OR: 2.196, 95% CI: 1.279–3.772); longer duration of T2DM (OR: 1.081, 95% CI: 1.045–1.120); use of insulin (OR: 1.091, 95% CI: 1.018–1.170); longer history of alcohol consumption (OR: 1.034, 95% CI: 1.010–1.059); and higher blood urea nitrogen (OR: 1.081, 95% CI: 1.009–1.159) were associated with increased risk of DN in patients with T2DM.ConclusionsRetinopathy, diabetic nephropathy, longer duration of T2DM, use of insulin, longer history of alcohol consumption, and higher blood urea nitrogen were independent risk factors for DN. These findings should be verified in large-scale prospective studies.

Designing a Logistic Regression Model for a Dataset to Predict Diabetic Foot Ulcer in Diabetic Patients: High-Density Lipoprotein (HDL) Cholesterol Was the Negative Predictor.

Objectives Although the risk factors for diabetic neuropathy and diabetic foot ulcer have been detected, there was no practical modeling for their prediction. We aimed to design a logistic regression model on an Iranian dataset to predict the probability of experiencing diabetic foot ulcers up to a considered age in diabetic patients. Methods The present study was a statistical modeling on a previously published dataset. The covariates were sex, age, body mass index (BMI), fasting blood sugar (FBS), hemoglobin A1C (HbA1C), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), insulin dependency, and statin use. The final model of logistic regression was designed through a manual stepwise method. To study the performance of the model, an area under receiver operating characteristic (AUC) curve was reported. A scoring system was defined according to the beta coefficients to be used in logistic function for calculation of the probability. Results The pretest probability for the outcome was 30.83%. The final model consisted of age (β1 = 0.133), BMI (β2 = 0.194), FBS (β3 = 0.011), HDL (β4 = −0.118), and insulin dependency (β5 = 0.986) (P < 0.1). The performance of the model was definitely acceptable (AUC = 0.914). Conclusion This model can be used clinically for consulting the patients. The only negative predictor of the risk is HDL cholesterol. Keeping the HDL level more than 50 (mg/dl) is strongly suggested. Logistic regression modeling is a simple and practical method to be used in the clinic.

The Impact of the serum level of trace elements on the severity of neuropathy in type 2 Diabetic patients

Background and aim: This study was designed in order to estimate the alterations in the levels of serum zinc, magnesium and copper in patients with type 2 diabetes mellitus with diabetic peripheral neuropathy in comparison to those without neuropathy and their relation to duration of diabetes and glycemic control.Method: 71 patients with Type 2 diabetes recruited and divided into 2 groups: Group 1: 47 patients with diabetic neuropathy and Group 2: 24 patients with type 2 diabetes without diabetic neuropathy. The presence and absence of neuropathy was assessed using neuropathy disability score and neuropathy symptom score. Full history and examination were taken from all participants. Neuropathy symptom score and neuropathy disability score was assessed. A blood sample was taken for measuring A1c, serum zinc, magnesium, and copper Results: There was a significant difference between both groups as regard duration of diabetes and serum copper level (P value< 0.05). After performing linear regression analysis, the duration of Diabetes was the significant risk factor for the diabetic neuropathy symptoms. On the other hand, there was no significant difference between the 2 groups as regard serum zinc, serum magnesium, glycated hemoglobin, age, sex, and hypertension.Conclusion: High serum CU and low serum Mg and Zn are not independent risk factors for diabetic neuropathy as previously thought, and its alterations in diabetic patients may be due to the hyperglycemia itself

Risk factors associated with peripheral neuropathy among diabetic individuals: a case control study

Background: Diabetic neuropathy is one among the most common complication in diabetes mellitus. Diabetic peripheral neuropathy hinders the quality of life causing morbidity and mortality. The purpose of this study was to find the risk factors associated with diabetic neuropathy.Methods: This case control study involved 100 diabetic patients attending the Dohnavur fellowship hospital, Dohnavur from October 2019 to March 2020. Sociodemographic profile and diabetic characteristics of the study group were obtained and analysed. Diagnosis of Diabetic Neuropathy was done by using the diagnostic method proposed by American Diabetic Association.Results: Of the total study population with mean age 59.43 years, 63% had family history of diabetes. Almost 70% had poor diabetic control. Statistically significant relationships were found between neuropathy and duration of diabetes, glycaemic control, history of hypertension, monofilament test and pinprick sensation.Conclusions: In this study, glycemic control, dyslipidemia and hypertension were modifiable risk factors for diabetic neuropathy. Early interventional programs to sensitize diabetics on these factors could improve the quality of life of Diabetic patients.

A study of magnesium supplementation on sensory nerve conduction velocity (N.C.V) in patients of diabetic neuropathy

Introduction: Globally, diabetes is the leading known cause of neuropathy. In type 2 diabetes mellitus, neuropathy being the most common complication contributes to majority of morbidity and mortality in such patients. Hyperglycemia is the main risk factor for diabetic neuropathy. A positive association exists between low serum magnesium levels and symptoms characteristics of peripheral neuropathy. Materials and Methods: The study was done in 60 diabetic neuropathy patients attending the neurology O.P.D of S.N Medical College & hospital, between February 2009 to August 2010. These patients were divided into two groups. Group I (n=30) receiving magnesium and metformin therapy for a period of 16 weeks. Group II (n=30) receiving only metformin therapy. The blood samples were collected and analyzed for serum magnesium at 0, 4, 8, 16 weeks respectively. Nerve conduction velocity for common peroneal nerve was done at 0, 4, 8, 16 weeks respectively. Average of males in group 1 was 54.36±1.18yrs and of females was 53.09±1.47yrs and in group II was 55.94±1.62yrs and of females was 52.83±1.58yrs respectively. Results: In Group I mean baseline value for sensory N.C.V of sural nerve is 42.23±0.76 and after treatment values at 4 weeks is 46.85±0.99, 8 weeks 49.57±1.08, at 16 weeks 50.62+0.85 respectively. The difference between before and after treatment was extremely significant in Group I. In Group II mean Baseline value for sensory N.C.V of Sural N. is 42.89±0.63 and after treatment values at 4 weeks is 43.74+0.63, 8 weeks 44.1310.61, and at 16 weeks 44.53±0.62 respectively. Statistically insignificant difference was observed in group II patients for sensory nerve conduction velocity before and after treatment values at 16 weeks (p>0.05). Conclusion: Magnesium supplementation along with regular anti diabetic therapy improves sensory nerve conduction velocity. Keywords: Diabetic neuropathy, Magnesium supplementation, Sensory nerve conduction velocity.

Predicting Diabetic Neuropathy Risk Level Using Artificial Neural Network and Clinical Parameters of Subjects With Diabetes.

Background:A risk assessment tool has been developed for automated estimation of level of neuropathy based on the clinical characteristics of patients. The smart tool is based on risk factors for diabetic neuropathy, which utilizes vibration perception threshold (VPT) and a set of clinical variables as potential predictors.Methods:Significant risk factors included age, height, weight, urine albumin-to-creatinine ratio, glycated hemoglobin, total cholesterol, and duration of diabetes. The continuous-scale VPT was recorded using a neurothesiometer and classified into three categories based on the clinical thresholds in volts (V): low risk (0-20.99 V), medium risk (21-30.99 V), and high risk (≥31 V).Results:The initial study had shown that by just using patient data (n = 5088) an accuracy of 54% was achievable. Having established the effectiveness of the “classical” method, a special Neural Network based on a Proportional Odds Model was developed, which provided the highest level of prediction accuracy (>70%) using the simulated patient data (n = 4158).Conclusion:In the absence of any assessment devices or trained personnel, it is possible to establish with reasonable accuracy a diagnosis of diabetic neuropathy by means of the clinical parameters of the patient alone.

Glycemic Variability and Diabetic Neuropathy in Young Adults With Type 1 Diabetes.

Background: Glycemic variability (GV) may attribute to the pathogenesis of diabetic neuropathy. The aim of this cross-sectional study was to investigate the association between GV and distal symmetric polyneuropathy (DSPN) and cardiovascular autonomic neuropathy (CAN) in a Danish population of young adults with type 1 diabetes.Methods: Young adults between 18 and 24 years with type 1 diabetes were included in this cross-sectional study. CAN was assessed by cardiovascular autonomic reflex tests (CARTs) and heart rate variability (HRV). DSPN was assessed by light pressure, pain and vibration perception, electrochemical skin conductance, sural nerve conduction velocity (SNCV), and amplitude potential (SNAP). GV were obtained by continuous glucose monitoring including coefficient of variation (CV), SD, continuous overall net glycemic action (CONGA), and mean amplitude of glucose excursions (MAGE).Results: The study comprised 133 young adults (43.6% males), mean age of 22 years (SD 1.6). Unadjusted, higher CV was associated with a decreased risk of sural nerve conduction (P = 0.03), abnormal SNAP (P = 0.04) and incidents of definite CAN (P = 0.04). Likewise, higher CONGA was associated with increasing incidents of subclinical DSPN (P = 0.03), abnormal SNAP (P = 0.01), and SNCV (P = 0.02). However, both associations were not statistically significant in the fully adjusted model. Higher MAGE was associated with slightly increasing measures of HRV (P = 0.03) but only when fully adjusted. When correcting for multiple tests significance was lost. A significant association was found between HbA1c and measures of both DSPN (P < 0.02) and HRV (P < 0.03) in fully adjusted models.Conclusions: No significant associations between GV and diabetic neuropathy were found after adjusting for risk factors and multiple tests. This suggests that GV may not be a risk factor for diabetic neuropathy in young adults with type 1 diabetes. However, long-term effects of GV excursions may still play a role in the pathogenic mechanisms leading to neuropathy in later life.

Prevalence and risk factors for diabetic neuropathy and painful diabetic neuropathy in primary and secondary healthcare in Qatar.

Aims/IntroductionThis study determined the prevalence and risk factors for diabetic peripheral neuropathy (DPN) and painful DPN (pDPN) in patients with type 2 diabetes in primary healthcare (PHC) and secondary healthcare (SHC) in Qatar.Materials and MethodsThis was a cross‐sectional multicenter study. Adults with type 2 diabetes were randomly enrolled from four PHC centers and two diabetes centers in SHC in Qatar. Participants underwent assessment of clinical and metabolic parameters, DPN and pDPN.ResultsA total of 1,386 individuals with type 2 diabetes (297 from PHC and 1,089 from SHC) were recruited. The prevalence of DPN (14.8% vs 23.9%, P = 0.001) and pDPN (18.1% vs 37.5%, P < 0.0001) was significantly lower in PHC compared with SHC, whereas those with DPN at high risk for diabetic foot ulceration (31.8% vs 40.0%, P = 0.3) was comparable. The prevalence of undiagnosed DPN (79.5% vs 82.3%, P = 0.66) was comparably high, but undiagnosed pDPN (24.1% vs 71.5%, P < 0.0001) was lower in PHC compared with SHC. The odds of DPN and pDPN increased with age and diabetes duration, and DPN increased with poor glycemic control, hyperlipidemia and hypertension, whereas pDPN increased with obesity and reduced physical activity.ConclusionsThe prevalence of DPN and pDPN in type 2 diabetes is lower in PHC compared with SHC, and is attributed to overall better control of risk factors and referral bias due to patients with poorly managed complications being referred to SHC. However, approximately 80% of patients had not been previously diagnosed with DPN in PHC and SHC. Furthermore, we identified a number of modifiable risk factors for PDN and pDPN.

Diabetic neuropathy in the elderly

The increase in the diabetes mellitus (DM) prevalence and incidence has taken on the features of the epidemic. Particularly often, DM occurs in the elderly and senile patients, while at least half of the cases of diabetes in this population remain undiagnosed. DM contributes to the deterioration of the quality of life, causes formation of dependence and progression of various geriatric syndromes leading to increased mortality. Elderly age is an independent risk factor for diabetic neuropathy. The main clinical forms of which are represented by chronic sensorimotor and autonomic impairments, complicated by impaired cardiovascular, gastrointestinal and urogenital functions, as well as chronic neuropathic pain syndrome. Progression of diabetic neuropathy aggravates walking and balance disorders, increases the risk of falls and loss of mobility, and significantly increases the risk of adverse diabetes outcomes. The article discusses in detail risk factors, pathogenesis, differential diagnosis and the possibilities of clinical and non-clinical evaluation of patients with suspected diabetic neuropathy. Possibilities for pain assessment in elderly people with communicative disorders or concomitant cognitive impairment are demonstrated. The article presents an algorithm for management of diabetic neuropathy in elderly ones, ways for optimization of risk and benefit balance, as well as the possibility of etiopathogenetic and symptomatic treatment of pain, autonomic disorders and prevention of late complications of diabetes with use of alpha-lipouc acid, taking into account key characteristics of discussed population.

Prevalence and risk factors of diabetic neuropathy in Qassim, Saudi Arabia

Background and Aim: Diabetic neuropathy is the commonest early and long-term complication of diabetes. Saudi Arabia has a high prevalence of diabetic neuropathy reaching (65%), which necessitate the establishment of a preventive program. The aim of the study was to measure the prevalence of diabetic neuropathy and its associated risk factors in Qassim, Saudi Arabia. Methods: A cross-sectional study that utilized an adopted a translated version of Michigan neuropathy screening instrument. An electronic questionnaire was filled by each participant after obtaining inform consent. The questionnaire included questions about neuropathy in addition to added questions about demography of participants. After data collection, SPSS version 21.00 was used for data analysis. Results: A total of 374 patients has been included in the analysis. Of which (54%) were females, and (97.1%) were Saudis with a neuropathy was (38.2%). Significant associations with abnormal total scores included; low education (p= 0.007), hypertension (p = 0.017), high body mass index (p < 0.001), use of insulin and/or multidrug for diabetes (p < 0.001). Further analysis of sex, age, and smoking did not show any significance with diabetic neuropathy. Conclusions: Our study concluded that more than one third of diabetics in Qassim region are having diabetic neuropathy. Paying attention to risk factors is an important factor for early detection of the condition.