The limited role of serum neurofilament light chain in predicting pain severity of patients with diabetic polyneuropathy.

Pain is one of many complaints expressed by patients with diabetic polyneuropathy. However, no objective measure for pain severity has been available. Neurofilament light chains have been widely used for assessing axonal damage in the neuronal system. Hence, we sought to investigate whether neurofilament light chains can serve as a marker reflecting pain severity in diabetic polyneuropathy. We enrolled the patients with diabetic polyneuropathy. Serum concentrations of neurofilament light chain were then measured using a single-molecule array. Pain severity was evaluated using painDETECT and the Brief Pain Inventory. Moreover, laboratory results including, serum creatinine, HbA1c, and glomerular filtration rate. A correlation test was used to analyze each variable. A total of 42 patients were enrolled. Neurofilament light chain levels were unable to reflect current neuropathic pain severity. However, high levels of neurofilament light chain were a significant predictor of poor diabetes control (r = 0.41; p = 0.02) and kidney damage (r = 0.45; p = 0.01). Serum levels of neurofilament light chain could not reflect current pain severity but was strongly associated with kidney dysfunction and poor diabetes control. Other biomarkers that could predict pain severity need to be uncovered.

Expert opinion on screening, diagnosis and management of diabetic peripheral neuropathy: a multidisciplinary approach.

The proposed expert opinion aimed to address the current knowledge on conceptual, clinical, and therapeutic aspects of diabetic peripheral neuropathy (DPN) and to provide a guidance document to assist clinicians for the best practice in DPN care. The participating experts consider the suspicion of the disease by clinicians as a key factor in early recognition and diagnosis, emphasizing an improved awareness of the disease by the first-admission or referring physicians. The proposed "screening and diagnostic" algorithm involves the consideration of DPN in a patient with prediabetes or diabetes who presents with neuropathic symptoms and/or signs of neuropathy in the presence of DPN risk factors, with careful consideration of laboratory testing to rule out other causes of distal symmetric peripheral neuropathy and referral for a detailed neurological work-up for a confirmative test of either small or large nerve fiber dysfunction in atypical cases. Although, the first-line interventions for DPN are currently represented by optimized glycemic control (mainly for type 1 diabetes) and multifactorial intervention (mainly for type 2 diabetes), there is a need for individualized pathogenesis-directed treatment approaches for DPN. Alpha-lipoic acid (ALA) seems to be an important first-line pathogenesis-directed agent, given that it is a direct and indirect antioxidant that works with a strategy targeted directly against reactive oxygen species and indirectly in favor of endogenous antioxidant capacity for improving DPN conditions. There is still a gap in existing research in the field, necessitating well-designed, robust, multicenter clinical trials with sensitive endpoints and standardized protocols to facilitate the diagnosis of DPN via a simple and effective algorithm and to track progression of disease and treatment response. Identification of biomarkers/predictors that would allow an individualized approach from a potentially disease-modifying perspective may provide opportunities for novel treatments that would be efficacious in early stages of DPN, and may modify the natural course of the disease. This expert opinion document is expected to increase awareness among physicians about conceptual, clinical, and therapeutic aspects of DPN and to assist them in timely recognition of DPN and translating this information into their clinical practice for best practice in the management of patients with DPN.

Assessment of neuropathy subtypes in type 1 diabetes.

INTRODUCTION: Diabetic polyneuropathy (DPN), a common complication of diabetes, can manifest as small, large, or mixed fiber neuropathy (SFN, LFN, and MFN, respectively), depending on the type of fibers involved. Despite evidence indicating small fiber involvement prior to large fiber involvement in type 1 diabetes mellitus (T1DM)-associated DPN, no evidence has been produced to determine the more prevalent subtype. We aim to determine the more prevalent type of nerve fiber damage-SFN, LFN, and MFN-in T1DM-associated DPN, both with and without pain. RESEARCH DESIGN AND METHODS: In this cross-sectional study, participants (n=216) were divided into controls; T1DM; T1DM with non-painful DPN (NP-DPN); and T1DM with painful DPN (P-DPN). DPN was further subgrouped based on neuropathy severity. The more prevalent type of fiber damage was determined applying small and large fiber-specific tests and three diagnostic models: model 1 (≥1 abnormal test); model 2 (≥2 abnormal tests); and model 3 (≥3 abnormal tests). RESULTS: MFN showed the highest prevalence in T1DM-associated DPN. No differences in neuropathy subtype were found between NP-DPN and P-DPN. DPN, with prevalent SFN plateaus between models 2 and 3. All models showed increased prevalence of MFN according to DPN severity. Model 3 showed increased DPN with prevalent LFN in early neuropathy. DPN with prevalent SFN demonstrated a similar, but non-significant pattern. CONCLUSIONS: DPN primarily manifests as MFN in T1DM, with no differentiation between NP-DPN and P-DPN. Additionally, we propose model 2 as an initial criterion for diagnosing DPN with a more prevalent SFN subtype in T1DM. Lastly, the study suggests that in mild stages of DPN, one type of nerve fiber (either small or large) is more susceptible to damage.

Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors.

BACKGROUND: Diabetic peripheral neuropathy (DPN) and lower extremity arterial disease (LEAD) are significant contributors to diabetic foot ulcers (DFUs), which severely affect patients' quality of life. This study aimed to develop machine learning (ML) predictive models for DPN and LEAD and to identify both shared and distinct risk factors. METHODS: This retrospective study included 479 diabetic inpatients, of whom 215 were diagnosed with DPN and 69 with LEAD. Clinical data and laboratory results were collected for each patient. Feature selection was performed using three methods: mutual information (MI), random forest recursive feature elimination (RF-RFE), and the Boruta algorithm to identify the most important features. Predictive models were developed using logistic regression (LR), random forest (RF), and eXtreme Gradient Boosting (XGBoost), with particle swarm optimization (PSO) used to optimize their hyperparameters. The SHapley Additive exPlanation (SHAP) method was applied to determine the importance of risk factors in the top-performing models. RESULTS: For diagnosing DPN, the XGBoost model was most effective, achieving a recall of 83.7%, specificity of 86.8%, accuracy of 85.4%, and an F1 score of 83.7%. On the other hand, the RF model excelled in diagnosing LEAD, with a recall of 85.7%, specificity of 92.9%, accuracy of 91.9%, and an F1 score of 82.8%. SHAP analysis revealed top five critical risk factors shared by DPN and LEAD, including increased urinary albumin-to-creatinine ratio (UACR), glycosylated hemoglobin (HbA1c), serum creatinine (Scr), older age, and carotid stenosis. Additionally, distinct risk factors were pinpointed: decreased serum albumin and lower lymphocyte count were linked to DPN, while elevated neutrophil-to-lymphocyte ratio (NLR) and higher D-dimer levels were associated with LEAD. CONCLUSIONS: This study demonstrated the effectiveness of ML models in predicting DPN and LEAD in diabetic patients and identified significant risk factors. Focusing on shared risk factors may greatly reduce the prevalence of both conditions, thereby mitigating the risk of developing DFUs.

Enteric neuropathy in diabetes: Implications for gastrointestinal function.

Diabetes, commonly known for its metabolic effects, also critically affects the enteric nervous system (ENS), which is essential in regulating gastrointestinal (GI) motility, secretion, and absorption. The development of diabetes-induced enteric neuropathy can lead to various GI dysfunctions, such as gastroparesis and irregular bowel habits, primarily due to disruptions in the function of neuronal and glial cells within the ENS, as well as oxidative stress and inflammation. This editorial explores the pathophysiological mechanisms underlying the development of enteric neuropathy in diabetic patients. Additionally, it discusses the latest advances in diagnostic approaches, emphasizing the need for early detection and intervention to mitigate GI complications in diabetic individuals. The editorial also reviews current and emerging therapeutic strategies, focusing on pharmacological treatments, dietary management, and potential neuromodulatory interventions. Ultimately, this editorial highlights the necessity of a multidisciplinary approach in managing enteric neuropathy in diabetes, aiming to enhance patient quality of life and address a frequently overlooked complication of this widespread disease.

Eleven neurology-related proteins measured in serum are positively correlated to the severity of diabetic neuropathy.

About 20% of patients with diabetes suffer from chronic pain with neuropathic characteristics. We investigated the multivariate associations between 92 neurology-related proteins measured in serum from 190 patients with painful and painless diabetic neuropathy. Participants were recruited from the Pain in Neuropathy Study, an observational cross-sectional multicentre study in which participants underwent deep phenotyping. In the exploration cohort, two groups were defined by hierarchical cluster analyses of protein data. The proportion of painless vs painful neuropathy did not differ between the two groups, but one group had a significantly higher grade of neuropathy as measured by the Toronto Clinical Scoring System (TCSS). This finding was replicated in the replication cohort. Analyzing both groups together, we found that a group of 11 inter-correlated proteins (TNFRSF12A, SCARB2, N2DL-2, SKR3, EFNA4, LAYN, CLM-1, CD38, UNC5C, GFR-alpha-1, and JAM-B) were positively associated with TCSS values. Notably, EFNA4 and UNC5C are known to be part of axon guidance pathways. To conclude, although cluster analysis of 92 neurology-related proteins did not distinguish painful from painless diabetic neuropathy, we identified 11 proteins which positively correlated to neuropathy severity and warrant further investigation as potential biomarkers.

Utility of Visual Evoked Potentials (VEPs) study in the evaluation of visual pathway dysfunction in diabetics without retinopathy: correlations with diabetic peripheral neuropathy and other clinical findings.

Aim and objectives: Visual dysfunction in diabetes mellitus (DM) is multifactorial and can be due to vascular disease, and metabolic abnormalities that can affect the retina, optic nerve, and visual pathways. Visual evoked potential (VEP) is an electrophysiological test that can quantify the functional integrity of the visual pathways from the retina via the optic nerves, and optic tracts to the visual cortices. In this study, we aimed to investigate the visual pathway dysfunction among diabetics without retinopathy compared with healthy controls and to look for any correlation with diabetic neuropathy, duration of diabetes, or HbA1c level. Methods: The study included 75 diabetic patients and 75 age and sex-matched controls. VEPs were recorded using the pattern reversal stimulation method on the Medtronic EMG EP machine, and P100 latency and N75-P100 amplitude were recorded in both diabetic patients and healthy controls. Results: Mean P100 latency was significantly prolonged and N75-P100 amplitude significantly reduced among diabetic cases compared to healthy controls (p < 0.001). Among diabetics with peripheral neuropathy, P100 latency was significantly prolonged and N75-P100 amplitude was significantly reduced compared to diabetics without peripheral neuropathy. A significant positive correlation of VEP P100 latency (p < 0.001) and a negative correlation with N75-P100 amplitude (p < 0.001) with duration of disease were also found. Conclusion: VEP changes are observed in diabetics before the development of retinopathy or peripheral neuropathy indicating optic pathway dysfunction, which precedes the development of these complications. Early preclinical visual pathway dysfunction can warrant taking the necessary measures to reduce diabetic complications. Abbreviations: DM = Diabetes Mellitus, VEP = Visual Evoked Potential, HbA1c = Hemoglobin A1 c, MRI = Magnetic Resonance Imaging, EEG = Electroencephalography, P100 = Positive wave peak at latency 100 ms (millisecond), N75 = Negative wave peak at latency 75 ms (millisecond), N145 = Negative wave peak at latency 145 ms (millisecond), OCT = Optical coherence tomography, PRVEP = Pattern Reversal Visual Evoked Potential, NCS = Nerve Conduction Study, SSR = Sympathetic Skin Response, IL1 = Interleukin-1, LIF = Leukemia inhibitory factor, CNTF = Ciliary neurotrophic factor, TNF alpha = Tumor necrosis factor-alpha, TGF-beta = Transforming growth factor-beta.

Growth Differentiation Factor 15 and Matrix Metalloproteinase 3 in Plasma as Biomarkers for Neuropathy and Nephropathy in Type 1 Diabetes.

Diabetic neuropathy and nephropathy are common complications of type 1 diabetes (T1D). The symptoms are often elusive in the early stages, and available diagnostic methods can be improved using biomarkers. Matrix metalloproteinase 3 (MMP-3) has been identified in the kidneys and is thought to be involved in diabetic nephropathy. Growth differentiation factor 15 (GDF-15) has been suggested to have positive effects in diabetes, but is otherwise associated with adverse effects such as cardiovascular risk, declined kidney function, and neurodegeneration. This study aims to investigate plasma MMP-3 and GDF-15 as systemic biomarkers for diabetic neuropathy and nephropathy in T1D. The study involves patients with childhood-onset T1D (n = 48, age 38 ± 4 years) and a healthy control group (n = 30, age 38 ± 5 years). Neurophysiology tests, evaluations of albuminuria, and measurements of routine biochemical markers were conducted. The neuropathy impairment assessment (NIA) scoring system, where factors such as loss of sensation and weakened reflexes are evaluated, was used to screen for symptoms of neuropathy. MMP-3 and GDF-15 concentrations were determined in heparinized plasma using ELISA kits. In total, 9 patients (19%) had albuminuria, and 25 (52%) had diabetic neuropathy. No significant differences were found in MMP-3 concentrations between the groups. GDF-15 levels were higher in T1D, with median and interquartile range (IQR) of 358 (242) pg/mL in T1D and 295 (59) in controls (p < 0.001). In the merged patient group, a positive correlation was found between MMP-3 and plasma creatinine, a negative correlation was found between MMP-3 and estimated glomerular filtration rate (eGFR; rho = -0.358, p = 0.012), and there was a positive correlation between GDF-15 and NIA (rho = 0.723, p < 0.001) and high-sensitive C-reactive protein (rho = 0.395, p = 0.005). MMP-3 was increased in macroalbuminuria and correlated positively with NIA only in the nine T1D patients with albuminuria (rho = 0.836, p = 0.005). The present study indicates that high MMP-3 is associated with low eGFR, high plasma creatinine, and macroalbuminuria, and that GDF-15 can be a biomarker for diabetic neuropathy in T1D. MMP-3 may be useful as biomarker for neuropathy in T1D with albuminuria.

Diabetic peripheral neuropathy and glycemic variability assessed by continuous glucose monitoring: A systematic review and meta-analysis.

Continuous glucose monitoring (CGM)-derived metrics have been used to accurately assess glycemic variability (GV) to facilitate management of diabetes mellitus, yet their relationship with diabetic peripheral neuropathy (DPN) is not fully understood. We performed a systematic review and meta-analysis to evaluate the association between GV metrics and the risk of developing DPN. Nine studies totaling 3,649 patients with type 1 and type 2 diabetes mellitus were included. A significant association was found between increased GV, as indicated by metrics including standard deviation (SD) with OR and 95% CI of 2.58 (1.45-4.57), mean amplitude of glycemic excursions (MAGE) with OR and 95% CI of 1.90 (1.01-3.58), mean of daily difference (MODD) with OR and 95% CI of 2.88 (2.17-3.81) and the incidence of DPN. Our findings support a link between higher GV and an increased risk of DPN in patients with diabetes. These findings highlight the potential of GV metrics as indicators for the development of DPN, advocating for their inclusion in diabetes management strategies to potentially mitigate neuropathy risk. Longitudinal studies with longer observation periods and larger sample sizes are necessary to validate these associations across diverse populations.

Assessment of subclinical LV myocardial dysfunction in T2DM patients with diabetic peripheral neuropathy: a cardiovascular magnetic resonance study.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is the most prevalent complication of diabetes, and has been demonstrated to be independently associated with cardiovascular events and mortality. This aim of this study was to investigate the subclinical left ventricular (LV) myocardial dysfunction in type 2 diabetes mellitus (T2DM) patients with and without DPN. METHODS: One hundred and thirty T2DM patients without DPN, 61 patients with DPN and 65 age and sex-matched controls who underwent cardiovascular magnetic resonance (CMR) imaging were included, all subjects had no symptoms of heart failure and LV ejection fraction ≥ 50%. LV myocardial non-infarct late gadolinium enhancement (LGE) was determined. LV global strains, including radial, circumferential and longitudinal peak strain (PS) and peak systolic and diastolic strain rates (PSSR and PDSR, respectively), were evaluated using CMR feature tracking and compared among the three groups. Multivariable linear regression analyses were performed to determine the independent factors of reduced LV global myocardial strains in T2DM patients. RESULTS: The prevalence of non-infarct LGE was higher in patients with DPN than those without DPN (37.7% vs. 19.2%, p = 0.008). The LV radial and longitudinal PS (radial: 36.60 ± 7.24% vs. 33.57 ± 7.30% vs. 30.72 ± 8.68%; longitudinal: - 15.03 ± 2.52% vs. - 13.39 ± 2.48% vs. - 11.89 ± 3.02%), as well as longitudinal PDSR [0.89 (0.76, 1.05) 1/s vs. 0.80 (0.71, 0.93) 1/s vs. 0.77 (0.63, 0.87) 1/s] were decreased significantly from controls through T2DM patients without DPN to patients with DPN (all p < 0.001). LV radial and circumferential PDSR, as well as circumferential PS were reduced in both patient groups (all p < 0.05), but were not different between the two groups (all p > 0.05). Radial and longitudinal PSSR were decreased in patients with DPN (p = 0.006 and 0.003, respectively) but preserved in those without DPN (all p > 0.05). Multivariable linear regression analyses adjusting for confounders demonstrated that DPN was independently associated with LV radial and longitudinal PS (ß = - 3.025 and 1.187, p = 0.014 and 0.003, respectively) and PDSR (ß = 0.283 and - 0.086, p = 0.016 and 0.001, respectively), as well as radial PSSR (ß = - 0.266, p = 0.007). CONCLUSIONS: There was more severe subclinical LV dysfunction in T2DM patients complicated with DPN than those without DPN, suggesting further prospective study with more active intervention in this cohort of patients.

Lipid Levels and Risk of Diabetic Polyneuropathy in 2 Danish Type 2 Diabetes Cohorts.

BACKGROUND AND OBJECTIVES: Reduction of blood lipids may aid in preventing diabetic polyneuropathy (DPN), but evidence remains conflicting. We investigated the association between lipid parameters and DPN risk in individuals with type 2 diabetes mellitus (T2DM). METHODS: We conducted a population-based cohort study of individuals with newly diagnosed T2DM and a cross-sectional study using a clinically recruited T2DM cohort. Triglycerides, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and non-HDL cholesterol were measured in routine diabetes care. Each lipid parameter was categorized according to the latest cutoffs in clinical guidelines on dyslipidemia. DPN was assessed with validated hospital diagnosis codes in the population-based cohort and with the Michigan Neuropathy Screening Instrument questionnaire in the clinical cohort. We calculated hazard ratios (HRs) using Cox regression and prevalence ratios (PRs) using Poisson regression. RESULTS: We included 61,853 individuals in the population-based cohort (median age 63 [quartiles 54-72] years) and 4,823 in the clinical cohort (median age 65 [quartiles 57-72] years). The incidence rate of hospital-diagnosed DPN in the population-based cohort was 3.6 per 1000 person-years during a median follow-up of 7.3 years. Achieving guideline targets for HDL, LDL, and non-HDL cholesterol showed no association with DPN risk. By contrast, adjusted HRs (95% CI) for DPN were 1.02 (0.89-1.18) for triglyceride levels between 150 and 204 mg/dL (1.7-2.3 mmol/L) and 1.28 (1.13-1.45) for levels >204 mg/dL (2.3 mmol/L). In the clinical cohort with a DPN prevalence of 18%, DPN associated strongly with triglycerides >204 mg/dL (2.3 mmol/L) with an adjusted PR (95% CI) of 1.40 (1.21-1.62). The prevalence of DPN was modestly elevated for individuals with HDL cholesterol <39 mg/dL (1.0/1.3 mmol/L) in men and <50 mg/dL (1.3 mmol/L) in women (PR 1.13 [0.99-1.28]) and for individuals with non-HDL cholesterol >131 mg/dL (3.4 mmol/L) (PR 1.27 [1.05-1.52]). In both cohorts, spline models showed an increasing risk of DPN starting from triglyceride levels >124 mg/dL (1.4 mmol/L). All results were similar among statin users. DISCUSSION: High triglyceride levels are a strong DPN risk factor. Future intervention studies shall determine whether triglyceride reduction is more important for DPN prevention than reduction of other lipids.

Correlation of serum Meteorin-like (Metrnl) level with type 2 diabetic peripheral neuropathy.

OBJECTIVE: Meteorin-like (Metrnl), a secreted myokine, is a newly discovered neurotrophic factor. The aim of this study was to determine if there is a correlation between the Metrnl level and diabetic peripheral neuropathy (DPN). METHODS: The investigation was conducted on a sample of 80 patients with type 2 diabetes mellitus (T2DM) and 60 healthy controls. The T2DM patients were categorized into two subgroups based on skin biopsy: the DPN subgroup (n = 20) and the diabetes without neuropathy subgroup (n = 60). RESULTS: The T2DM groups had higher serum Metrnl concentrations compared with the controls. The serum Metrnl concentration was significantly lower in the DPN group than in T2DM patients without neuropathy. Logistic regression analysis demonstrated a notable correlation between serum Metrnl and DPN (OR: 0.997, 95% CI: 0.995-1.000, P < 0.05). Serum Metrnl level was negatively correlated with age and SBP after a simple logistic regression analysis. CONCLUSION: Serum Metrnl concentration is independently correlated with DPN.

Can the 128-Hz tuning fork be an alternative to the biothesiometer for diabetic peripheral neuropathy screening? A cross-sectional study in a tertiary hospital in East India.

INTRODUCTION: Diabetic neuropathy is frequently underdiagnosed and undertreated. Logistic problems accompany the routine use of the biothesiometer. Hence, we attempted to find a more easily available alternative. RESEARCH DESIGN AND METHODS: 149 patients with diabetes visiting the outpatient endocrinology clinic were assessed for vibration sense using a 128-Hz tuning fork (absolute timing method) and a biothesiometer. A reading of >25 V on the biothesiometer (known as vibration perception threshold or VPT) was taken as the diagnostic criterion for severe neuropathy while >15 V was used as an indicator of the mild form. The sensitivity and specificity were calculated by constructing the receiver operating characteristic curve (ROC). A p value of <0.05 was considered as statistically significant. RESULTS: The timed tuning fork (TTF) test showed a statistically significant correlation with the VPT measurements (r=-0.5, p=0.000). Using the VPT findings as a reference, a timed tuning fork cut-off of 4.8 s was 76% sensitive and 77% specific in diagnosing mild neuropathy while absent tuning fork sensation demonstrated 70% sensitivity and 90% specificity in detecting severe neuropathy. CONCLUSIONS: The tuning fork test demonstrated significant sensitivity and specificity in diagnosing diabetic peripheral neuropathy when compared against the biothesiometer. A cut-off of 4.8 s can be a useful indicator of the early stages of onset of the condition.

Associations between multiple neurological biomarkers and distal sensorimotor polyneuropathy: KORA F4/FF4 study.

AIMS: The aim of this study was to assess associations between neurological biomarkers and distal sensorimotor polyneuropathy (DSPN). MATERIALS AND METHODS: Cross-sectional analyses were based on 1032 participants aged 61-82 years from the population-based KORA F4 survey, 177 of whom had DSPN at baseline. The prevalence of type 2 diabetes was 20%. Prospective analyses used data from 505 participants without DSPN at baseline, of whom 125 had developed DSPN until the KORA FF4 survey. DSPN was defined based on the examination part of the Michigan Neuropathy Screening Instrument. Serum levels of neurological biomarkers were measured using proximity extension assay technology. Associations between 88 biomarkers and prevalent or incident DSPN were estimated using Poisson regression with robust error variance and are expressed as risk ratios (RR) and 95% CI per 1-SD increase. Results were adjusted for multiple confounders and multiple testing using the Benjamini-Hochberg procedure. RESULTS: Higher serum levels of CTSC (cathepsin C; RR [95% CI] 1.23 (1.08; 1.39), pB-H = 0.044) and PDGFRα (platelet-derived growth factor receptor A; RR [95% CI] 1.21 (1.08; 1.35), pB-H = 0.044) were associated with prevalent DSPN in the total study sample. CDH3, JAM-B, LAYN, RGMA and SCARA5 were positively associated with DSPN in the diabetes subgroup, whereas GCP5 was positively associated with DSPN in people without diabetes (all pB-H for interaction <0.05). None of the biomarkers showed an association with incident DSPN (all pB-H>0.05). CONCLUSIONS: This study identified multiple novel associations between neurological biomarkers and prevalent DSPN, which may be attributable to functions of these proteins in neuroinflammation, neural development and myelination.

[To determine the value of iMAX, a new electrodiagnostic method, and its application in the evaluation of peripheral motor nerve excitability].

Objective: To test the new method of iMAX (the minimum stimulus current that elicits the maximum compound muscle action potential amplitude) electrodiagnosis, verify the feasibility of this method in evaluating the excitability of peripheral motor axons, and preliminarily explore the clinical application value. Methods: This study was a cross-sectional study. A total of 50 healthy subjects were recruited from the outpatient department of Peking University Third Hospital from June 2022 to March 2023, including 25 males and 25 females, aged 25-68 (48±8) years. Eleven patients with Charcot-Marie-Pain-1A (CMT1A), 7 males and 4 females, aged 19-55 (41±13) years and 21 patients with diabetic peripheral neuropathy (DPN), 10 males and 11 females, aged 28-79 (53±16) years were enrolled in this study. iMAX of bilateral median nerves, ulnar nerves and peroneal nerves were detected in all patients. Repeatable motor responses with minimum motor threshold and amplitude of at least 0.1 mV and the minimum stimulus current intensity, at which the maximum compound muscle action potential amplitude is elicited, were measured respectively [1 mA increment is called (iUP) and, 0.1 mA adjustment is called (iMAX)].Comparison of the parameters: the parameters of threshold, iUP and iMAX were compared among different age groups, genders and sides, body mass index(BMI) values and detection time , as well as between CMT1A patients, DPN patients and healthy subjects. Results: In healthy subjects, the threshold, iUP value and iMAX value were (1.8±0.7) mA, (4.4±1.2) mA, and (4.2±1.3) mA respectively; ulnar nerve (3.1±1.6) mA, (6.8±3.2) mA, (6.4±3.2) mA; peroneal nerve (3.7±2.0) mA, (7.8±2.8) mA, (7.4±2.9) mA. There were statistically significant differences in threshold, iUP value and iMAX value among different age groups (all P<0.001).With the increase of age, there was a trend of increasing threshold, iUP, and iMAX values in different nerves, and the differences are statistically significant (all P<0.001). There were no significant differences in gender, side and detection time threshold, iUP value and iMAX value (all P>0.05). The parameters of healthy subjects with high BMI value were higher than those of healthy subjects with low BMI value(all P<0.05). Compared with the healthy subjects, the parameters of 11 CMT1A patients were significantly increased (all P<0.05), and the parameters of 21 DPN patients were slightly increased (P<0.05). Conclusion: The new iMAX method reflects the excitability of motor axons and early axonal dysfunction, which is an important supplement to the traditional nerve conduction, and can be used to monitor motor axon excitability disorders.

Review and Evaluation of European National Clinical Practice Guidelines for the Treatment and Management of Active Charcot Neuro-Osteoarthropathy in Diabetes Using the AGREE-II Tool Identifies an Absence of Evidence-Based Recommendations.

Background: Charcot neuro-osteoarthropathy (CNO) is a rare but devastating complication of diabetes associated with high rates of morbidity; yet, many nonfoot specialists are unaware of it, resulting in missed and delayed diagnosis. Clinical practice guidelines (CPGs) have proven useful in improving quality of care and standardizing practice in diabetes and diabetic foot care. However, little is known about the consistency in recommendations for identification and management of active CNO. Aim: The aim of this study is to review European national diabetes CPGs for the diagnosis and management of active CNO and to assess their methodological rigor and transparency. Methods: A systematic search was performed to identify diabetes national CPGs across Europe. Guidelines in any language were reviewed to explore whether they provided a definition for active CNO and recommendations for diagnosis, monitoring, and management. Methodological rigor and transparency were assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE-II) tool, which comprises 23 key items organized within six domains with an overall guideline assessment score of ≥ 60% considered to be of adequate quality to recommend use. Each guideline was assessed by two reviewers, and inter-rater agreement (Kendall's W) was calculated for AGREE-II scores. Results: Seventeen CPGs met the inclusion criteria. Breadth of CNO content varied across guidelines (median (IQR) word count: 327; Q1 = 151; Q3 = 790), and 53% provided a definition for active CNO. Recommendations for diagnosis and monitoring were provided by 82% and 53%, respectively, with offloading being the most common management recommendation (88%). Four guidelines (24%) reached threshold for recommendation for use in clinical practice (≥ 60%) with the scope and purpose domain scoring highest (mean (SD): 67%, ± 23%). The remaining domains had average scores ranging between 19% and 53%. Inter-rater agreement was strong (W = 0.882; p < 0.001). Conclusions: European national CPGs for diabetes provide limited recommendations on active CNO. All guidelines showcased deficits in their methodology, suggesting that more rigorous methods should be employed for diabetes CPG development across Europe.

A Computer-Aided Screening Solution for the Identification of Diabetic Neuropathy From Standing Balance by Leveraging Multi-Domain Features.

The early diagnosis of diabetic neuropathy (DN) is fundamental in order to enact timely therapeutic strategies for limiting disease progression. In this work, we explored the suitability of standing balance task for identifying the presence of DN. Further, we proposed two diagnosis pathways in order to succeed in distinguishing between different stages of the disease. We considered a cohort of non-neuropathic (NN), asymptomatic neuropathic (AN), and symptomatic neuropathic (SN) diabetic patients. From the center of pressure (COP), a series of features belonging to different description domains were extracted. In order to exploit the whole information retrievable from COP, a majority voting ensemble was applied to the output of classifiers trained separately on different COP components. The ensemble of kNN classifiers provided over 86% accuracy for the first diagnosis pathway, made by a 3-class classification task for distinguishing between NN, AN, and SN patients. The second pathway offered higher performances, with over 97% accuracy in identifying patients with symptomatic and asymptomatic neuropathy. Notably, in the last case, no asymptomatic patient went undetected. This work showed that properly leveraging all the information that can be mined from COP trajectory recorded during standing balance is effective for achieving reliable DN identification. This work is a step toward a clinical tool for neuropathy diagnosis, also in the early stages of the disease.