Small and large fiber sensory polyneuropathy in type 2 diabetes: Influence of diagnostic criteria on neuropathy subtypes.

Diabetic polyneuropathy (DPN) can be classified based on fiber diameter into three subtypes: small fiber neuropathy (SFN), large fiber neuropathy (LFN), and mixed fiber neuropathy (MFN). We examined the effect of different diagnostic models on the frequency of polyneuropathy subtypes in type 2 diabetes patients with DPN. This study was based on patients from the Danish Center for Strategic Research in Type 2 Diabetes cohort. We defined DPN as probable or definite DPN according to the Toronto Consensus Criteria. DPN was then subtyped according to four distinct diagnostic models. A total of 277 diabetes patients (214 with DPN and 63 with no DPN) were included in the study. We found a considerable variation in polyneuropathy subtypes by applying different diagnostic models independent of the degree of certainty of DPN diagnosis. For probable and definite DPN, the frequency of subtypes across diagnostic models varied from: 1.4% to 13.1% for SFN, 9.3% to 21.5% for LFN, 51.4% to 83.2% for MFN, and 0.5% to 14.5% for non-classifiable neuropathy (NCN). For the definite DPN group, the frequency of subtypes varied from: 1.6% to 13.5% for SFN, 5.6% to 20.6% for LFN, 61.9% to 89.7% for MFN, and 0.0% to 6.3% for NCN. The frequency of polyneuropathy subtypes depends on the type and number of criteria applied in a diagnostic model. Future consensus criteria should clearly define sensory functions to be tested, methods of testing, and how findings should be interpreted for both clinical practice and research purpose.

Imbalanced learning: Improving classification of diabetic neuropathy from magnetic resonance imaging.

One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply oversampling strategies in an attempt to balance the classes and improve classification performance. We evaluated four different classifiers from k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP) and decision trees (DT) with 73 oversampling strategies. In this work, we used imbalanced learning oversampling techniques to improve classification in datasets that are distinctively sparser and clustered. This work reports the best oversampling and classifier combinations and concludes that the usage of oversampling methods always outperforms no oversampling strategies hence improving the classification results.

Associations of Serum S100B and S100P With the Presence and Classification of Diabetic Peripheral Neuropathy in Adults With Type 2 Diabetes: A Case-Cohort Study.

OBJECTIVES: Novel biomarkers of diabetic peripheral neuropathy provide potentially useful information for early identification and treatment of diabetic neuropathy, ultimately serving to reduce the burden of disease. This study was designed to investigate the potential associations of serum S100B and S100P (calcium-modulated proteins) with the presence and classification of diabetic peripheral neuropathy in adults with type 2 diabetes. METHODS: In a case-cohort setting, the data of 44 participants diagnosed with diabetic peripheral neuropathy, 44 control participants with type 2 diabetes but free of peripheral neuropathy and 87 healthy control individuals were collected and analyzed. RESULTS: Serum S100P concentrations were elevated in participants with diabetic peripheral neuropathy compared with their controls with type 2 diabetes (median [IQR]: 2,235 pg/mL [1,497.5 to 2,680] vs. 1,200 pg/mL [975 to 1,350)], respectively; p<0.001). Conversely, serum S100B values were comparable in these 2 groups (p=0.570). Those with the typical diabetic peripheral neuropathy had significantly higher serum S100P levels compared to their counterparts with the atypical group of diabetic peripheral neuropathies (p=0.048). The independent significant association between serum S100P and diabetic peripheral neuropathy persisted into the multivariable adjusted logistic regression model (OR for S100P: 1.004 [95% CI 1.002 to 1.006]; p<0.001). CONCLUSIONS: The present study's findings demonstrated that serum S100P is a more significant indicator of peripheral neuropathy in type 2 diabetes than is serum S100B. Prospective longitudinal studies are required to confirm the prognostic value of baseline serum S100P to predict incident peripheral neuropathy in people with diabetes.

Minimal ensemble based on subset selection using ECG to diagnose categories of CAN.

BACKGROUND AND OBJECTIVE: Early diagnosis of cardiac autonomic neuropathy (CAN) is critical for reversing or decreasing its progression and prevent complications. Diagnostic accuracy or precision is one of the core requirements of CAN detection. As the standard Ewing battery tests suffer from a number of shortcomings, research in automating and improving the early detection of CAN has recently received serious attention in identifying additional clinical variables and designing advanced ensembles of classifiers to improve the accuracy or precision of CAN diagnostics. Although large ensembles are commonly proposed for the automated diagnosis of CAN, large ensembles are characterized by slow processing speed and computational complexity. This paper applies ECG features and proposes a new ensemble-based approach for diagnosis of CAN progression. METHODS: We introduce a Minimal Ensemble Based On Subset Selection (MEBOSS) for the diagnosis of all categories of CAN including early, definite and atypical CAN. MEBOSS is based on a novel multi-tier architecture applying classifier subset selection as well as the training subset selection during several steps of its operation. Our experiments determined the diagnostic accuracy or precision obtained in 5 × 2 cross-validation for various options employed in MEBOSS and other classification systems. RESULTS: The experiments demonstrate the operation of the MEBOSS procedure invoking the most effective classifiers available in the open source software environment SageMath. The results of our experiments show that for the large DiabHealth database of CAN related parameters MEBOSS outperformed other classification systems available in SageMath and achieved 94% to 97% precision in 5 × 2 cross-validation correctly distinguishing any two CAN categories to a maximum of five categorizations including control, early, definite, severe and atypical CAN. CONCLUSIONS: These results show that MEBOSS architecture is effective and can be recommended for practical implementations in systems for the diagnosis of CAN progression.

[Neurological complications in patients with diabetes mellitus type 1 treated with different methods of insulinotherapy]. / Nevrologicheskie oslozhneniya u patsientov s sakharnym diabetom 1-go tipa pri ispol'zovanii raznykh metodov insulinoterapii.

The study aims to examine the neurological complications of patients with type 1 diabetes treated with different methods of insulin therapy. MATERIAL AND METHODS: Thirty-four patients, aged from 18 to 40 years, with diabetes mellitus type 1 receiving insulinotherapy, with a duration of the disease 14.25±9.25 years, have been examined. By the time of the study the patients of the first group have been treated with continuous subcutaneous insulin infusion therapy (CSII) for 4.5±1.5 years following 11.3±5.4 years of standard basal-bolus therapy; the patients of the second group have been treated with basal-bolus insulin therapy for 12.7±7.7 years. RESULTS AND CONCLUSION: Neurological status and the presence of vegetative symptoms of the patients were assessed, following tests were performed: MMSE, MoCA, HADS, TSS, NSS, NDS. The results demonstrated that the patients from the group of CSII therapy have had less signs and symptoms of neuropathy, cognitive disorders and better emotional condition than the patients from the group of basal-bolus therapy.

Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study.

OBJECTIVES: Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients' demographic characteristics and clinical features. METHODS: In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used. RESULTS: For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy. CONCLUSIONS: The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases.

[Atypical neuropathies associated with diabetes]. / Neuropathies atypiques du diabète.

Diabetes is the leading cause of neuropathy worldwide and, due to the epidemic progression of the affection, prevalence of diabetic neuropathies will increase in the near future. Beside the typical diabetic neuropathy pattern and the common entrapment neuropathies, several unusual clinical forms have been described with either a symmetrical or an asymmetrical pattern. Treatment-induced neuropathy is an acute sensory affection most commonly related to acute glycemic control. Pain is debilitating and associated with vegetative dysfunction. Prevention is important, as resolution is often incomplete. Several patterns or asymmetrical neuropathies of inflammatory and ischemic origin were described long ago in the lower limb. They are debilitating, most often painful and require steroid treatment. Other patterns affecting the thoracolumbar region or the upper limbs or involving a painless motor deficit must be identified as specific treatments are sometimes needed. An association between diabetes and chronic inflammatory demyelinating polyneuropathy has not been demonstrated but diagnosis may be suggested due to the misleading low conduction velocities seen in classical diabetic neuropathy. Like any other patient, the diabetic patient may present a neuropathy unrelated to diabetes. To facilitate patient care, neurologists should be aware of such clinical entities.

Diabetic neuropathy: Part 1.

To conclude, diabetes is associated with a variety of chronic and acute neuropathies, the commonest form being distal symmetric polyneuropathy. Performing an annual screening through a good neurological history and clinical examination and using a sensitive screening tool can facilitate an early diagnosis. More sensitive and quantitative measures of detecting early peripheral nerve injury including skin biopsy for intra-epidermal and dermal nerve fiber density and confocal corneal microscopy, hold promise to identify neuropathy patients early in their disease course.

Abnormalities of plantar pressure distribution in early, intermediate, and late stages of diabetic neuropathy.

Inconsistent findings with regard to plantar pressure while walking in the diabetic population may be due to the heterogeneity of the studied groups resulting from the classification/grouping criteria adopted. The clinical diagnosis and classification of diabetes have inherent uncertainties that compromise the definition of its onset and the differentiation of its severity stages. A fuzzy system could improve the precision of the diagnosis and classification of diabetic neuropathy because it takes those uncertainties into account and combines different assessment methods. Here, we investigated how plantar pressure abnormalities evolve throughout different severity stages of diabetic polyneuropathy (absent, n=38; mild, n=20; moderate, n=47; severe, n=24). Pressure distribution was analysed over five areas while patients walked barefoot. Patients with mild neuropathy displayed an increase in pressure-time integral at the forefoot and a lower peak pressure at the heel. The peak and pressure-time integral under the forefoot and heel were aggravated in later stages of the disease (moderate and severe) compared with early stages of the disease (absent and mild). In the severe group, lower pressures at the lateral forefoot and hallux were observed, which could be related to symptoms that develop with the aggravation of neuropathy: atrophy of the intrinsic foot muscles, reduction of distal muscle activity, and joint stiffness. Although there were clear alterations over the forefoot and in a number of plantar areas with higher pressures within each severity stage, they did not follow the aggravation evolution of neuropathy classified by the fuzzy model. Based on these results, therapeutic interventions should begin in the early stages of this disease to prevent further consequences of the disease.

Diabetic neuropathy: mechanisms, emerging treatments, and subtypes.

Diabetic neuropathies (DNs) differ in clinical course, distribution, fiber involvement (type and size), and pathophysiology, the most typical type being a length-dependent distal symmetric polyneuropathy (DSP) with differing degrees of autonomic involvement. The pathogenesis of diabetic DSP is multifactorial, including increased mitochondrial production of free radicals due to hyperglycemia-induced oxidative stress. Mechanisms that impact neuronal activity, mitochondrial function, membrane permeability, and endothelial function include formation of advanced glycosylation end products, activation of polyol aldose reductase signaling, activation of poly(ADP ribose) polymerase, and altered function of the Na(+)/K(+)-ATPase pump. Hyperglycemia-induced endoplasmic reticulum stress triggers several neuronal apoptotic processes. Additional mechanisms include impaired nerve perfusion, dyslipidemia, altered redox status, low-grade inflammation, and perturbation of calcium balance. Successful therapies require an integrated approach targeting these mechanisms. Intensive glycemic control is essential but is insufficient to prevent onset or progression of DSP, and disease-modifying treatments for DSP have been disappointing. Atypical forms of DN include subacute-onset sensory (symmetric) or motor (asymmetric) predominant conditions that are frequently painful but generally self-limited. DNs are a major cause of disability, associated with reduced quality of life and increased mortality.

Effect of diabetic neuropathy severity classified by a fuzzy model in muscle dynamics during gait.

BACKGROUND: Electromyography (EMG) alterations during gait, supposedly caused by diabetic sensorimotor polyneuropathy, are subtle and still inconsistent, due to difficulties in defining homogeneous experimental groups with a clear definition of disease stages. Since evaluating these patients involve many uncertainties, the use of a fuzzy model could enable a better discrimination among different stages of diabetic polyneuropathy and lead to a clarification of when changes in muscle activation start occurring. The aim of this study was to investigate EMG patterns during gait in diabetic individuals with different stages of DSP severity, classified by a fuzzy system. METHODS: 147 subjects were divided into a control group (n = 30) and four diabetic groups: absent (n = 43), mild (n = 30), moderate (n = 16), and severe (n = 28) neuropathy, classified by a fuzzy model. The EMG activity of the vastus lateralis, tibialis anterior, and gastrocnemius medialis were measured during gait. Temporal and relative magnitude variables were compared among groups using ANOVA tests. RESULTS: Muscle activity changes are present even before an established neural involvement, with delay in vastus lateralis peak and lower tibialis anterior relative magnitude. These alterations suggest an impaired ankle shock absorption mechanism, with compensation at the knee. This condition seems to be more pronounced in higher degrees of neuropathy, as there is an increased vastus lateralis activity in the mild and severe neuropathy groups. Tibialis anterior onset at terminal stance was anticipated in all diabetic groups; at higher degrees of neuropathy, the gastrocnemius medialis exhibited activity reduction and peak delay. CONCLUSION: EMG alterations in the vastus lateralis and tibialis anterior occur even in the absence of diabetic neuropathy and in mild neuropathic subjects, seemingly causing changes in the shock absorption mechanisms at the heel strike. These changes increase with the onset of neural impairments, and the gastrocnemius medialis starts presenting altered activity in the later stages of the disease (moderate and severe neuropathy). The degree of severity of diabetic neuropathy must be taken into account when analyzing diabetic patients' biomechanical patterns of locomotion; we recommend the use of a fuzzy model for classification of disease stages.

An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy.

OBJECTIVE: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN). We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery. This is important as not all five Ewing tests can always be applied in each situation in practice. METHODS AND MATERIAL: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN. We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests. RESULTS: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery. We found the best sequences of tests for cost-function equal to the number of tests. The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93. They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests. The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure. We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained. CONCLUSIONS: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure. The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test. Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence.

Bases fisiopatológicas para una clasificación de la neuropatía diabética / A new physiopathological classification of diabetic neuropathy

Nowadays, Diabetic Neuropathy (DN) is considered the most common cause of peripheral neuropathy in clinical practice. It can affect sensitive, motor or autonomic nerve fibers, with symmetric, asymmetric, acute or chronic presentations. Due to this variability, with multiple physiopathologic mechanisms involved, a complex clinical classification has been used until recently. The aim of this review is to present a new classification of diabetic neuropathy, based on its physiopathology. It is divided in metabolic microvascular and hypoxic, autoimmune and inflammatory, compressive, secondary to complications ofdiabetes and related to treatment. It must be understood that DN is notjust a functional disease, but a complication of diabetes with molecular and pathological substrates caused by hyperglycemia. Therefore, normalization of blood glucose is a fundamental step towards the successful prevention and treatment of DN.

[Diabetic neuropathies: clinical sub-types, early detection, and asking help from neurologist]. / Diabetische Neuropathien: Klinische Manifestationsformen, Früherkennung und Zuweisung zu einem Neurologen.

In diabetes mellitus, it is expected to see a common, mainly sensitive, distal symmetrical polyneuropathy (DPN) involving a large proportion of diabetic patients according to known risk factors. Several other diabetic peripheral neuropathies are recognized, such as dysautonomia and multifocal neuropathies including lumbosacral radiculoplexus and oculomotor palsies. In this review, general aspects of DPN and other diabetic neuropathies are examined, and it is discussed why and how the general practitioner has to perform a yearly examination. At the present time, some consensus emerge to ask help from neurologist when faced to other forms of peripheral neuropathies than distal symmetrical DPN.

Diabetic cervical radiculoplexus neuropathy: a distinct syndrome expanding the spectrum of diabetic radiculoplexus neuropathies.

Diabetic lumbosacral radiculoplexus neuropathy is a subacute painful, asymmetrical lower limb neuropathy due to ischaemic injury and microvasculitis. The occurrence of a cervical diabetic radiculoplexus neuropathy has been postulated. Our objective was to characterize the clinical features and pathological alterations of diabetic cervical radiculoplexus neuropathy, to see if they are similar to diabetic lumbosacral radiculoplexus neuropathy and due to ischaemic injury and microvasculitis. We identified patients with diabetic cervical radiculoplexus neuropathy by review of the Mayo Clinic database from 1996 to 2008. We systematically reviewed the clinical features, laboratory studies, neurophysiological findings, neuroimaging and pathological features and compared the findings with a previously published diabetic lumbosacral radiculoplexus neuropathy cohort. Eighty-five patients (56 males, 67 with Type 2 diabetes mellitus) were identified. The median age was 62 years (range 32-83). The main presenting symptom was pain (53/85). At evaluation, weakness was the most common symptom (84/85), followed by pain (69/85) and numbness (56/85). Neuropathic deficits were moderate (median motor neuropathy impairment score 10.0 points) and improved at follow-up. Upper, middle and lower brachial plexus segments were involved equally and pan-plexopathy was not unusual (25/85). Over half of patients (44/85) had at least one additional body region affected (30 contralateral cervical, 20 lumbosacral and 16 thoracic) as is found in diabetic lumbosacral radiculoplexus neuropathy. Recurrent disease occurred in 18/85. Neurophysiology showed axonal neuropathy (80/80) with paraspinal denervation (21/65), and abnormal autonomic (23/24) and sensory testing (10/13). Cerebrospinal fluid protein was elevated (median 70 mg/dl). Magnetic resonance imaging showed brachial plexus abnormality in all (38/38). Nerve biopsies (11 upper and 11 lower limbs) showed ischaemic injury (axonal degeneration, multifocal fibre loss 15/22, focal perineurial thickening 16/22, injury neuroma 5/22) and increased inflammation (epineural perivascular inflammation 22/22, haemosiderin deposition 6/22, vessel wall inflammation 14/22 and microvasculitis 5/22). We therefore conclude that (i) diabetic cervical radiculoplexus neuropathy is a predominantly monophasic, upper limb diabetic neuropathy with pain followed by weakness and involves motor, sensory and autonomic fibres; (ii) the neuropathy begins focally and often evolves into a multifocal or bilateral condition; (iii) the pathology of diabetic cervical radiculoplexus neuropathy demonstrates ischaemic injury often from microvasculitis; and (iv) diabetic cervical radiculoplexus neuropathy shares many of the clinical and pathological features of diabetic lumbosacral radiculoplexus neuropathy, providing evidence that these conditions are best categorized together within the spectrum of diabetic radiculoplexus neuropathies.

Guidelines in practice: treatment of painful diabetic neuropathy.

This article describes a patient with a painful diabetic peripheral neuropathy. Features of his history, examination, and diagnostic workup are presented. His treatment course is described as guided by the AAN's evidence-based guideline on the treatment of painful diabetic neuropathy. Lastly, features of coding for diabetic peripheral neuropathy are reviewed.

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic.

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment.