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Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes. As a new detection method for DPN, corneal confocal microscopy (CCM) is characterised by rapid, non-invasive, sensitive, and quantitative characteristics, as well as good repeatability. By detecting changes in the corneal nerves, DPN can be diagnosed early, and the severity of neuropathy evaluated. It is currently an ideal DPN evaluation method and has good clinical application prospects. This paper reviews the application and progress of CCM in the evaluation of DPN and summarises the evaluation methods of CCM, corneal nerve, and DPN to provide new ideas for the clinical diagnosis and treatment of DPN.

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BACKGROUND: Symptoms of autonomic neuropathy (AN) are common in patients with diabetes and advanced renal disease. As yet different domains of autonomic neuropathy cannot be detected by a singular laboratory or invasive test. COMPASS 31, a new self-assessment test, has shown reliable results not only in cardiac autonomic neuropathy but also in different sub-domains when judging manifestation of AN by scores. METHODS: One hundred eighty-three patients with or without diabetes were enrolled, one hundred nineteen of them were treated with permanent dialysis therapy (HD), sixty-four patients served as controls (eGFR > 60 ml/min.) Using COMPASS 31 different symptoms of AN were assessed (orthostatic intolerance, vasomotor, secretomotor, gastrointestinal, bladder, pupillomotor changes) and transferred into AN-scores. RESULTS: AN was more pronounced in dialysis patients compared with controls (AN-score 27,5 vs. 10,0; p < 0,01). These differences were present also in every sub-domain of AN (orthostatic intolerance, vasomotor, secretomotor, gastrointestinal, bladder, pupillomotor changes; p < 0,05 for all sub-domains). In diabetic patients there was a strong correlation between symptoms of AN and diabetes duration (correlation coefficient r = 0,45, p < 0,001). Current glycemic control (HbA1c), body mass index (BMI), sex, and height had no influence on AN when comparing dialysis patients and controls. C-reactive protein (CRP) showed a positive linear correlation with AN-scores (correlation coefficient r = 0,21; p < 0,05). CONCLUSION: Symptoms of AN are more pronounced in dialysis patients not only in total but also in all different domains of neuropathic changes. Longlasting diabetic disease promotes development of AN, as duration of diabetes was positively correlated with AN. Future longitudinal studies might help to identify the high cardiovascular and mortality risk in dialysis patients by the easy-to-use COMPASS 31 without need of invasive and time-spending methods for diagnosing AN.

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BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown. METHODS: This was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set. RESULTS: In an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74-0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86-1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73-1.00). CONCLUSIONS: AI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk. TRIAL REGISTRATION: This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).

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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.

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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.

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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.

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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.

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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.

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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.

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This systematic review aimed to explore the relationship between diabetic peripheral neuropathy (DPN) and cardiac autonomic neuropathy (CAN) in individuals with type 1 and 2 diabetes mellitus (DM). METHODS: The systematic review follow the protocol registered in Prospero (CRD42020182899). Two authors independently searched the PubMed, Scopus, Embase, Cochrane, and Web of Science databases. Discrepancies were resolved by a third author. The review included observational studies investigating the relationship between CAN and DPN in individuals with DM. RESULTS: Initially, out of 1165 studies, only 16 were selected, with 42.8 % involving volunteers with one type of diabetes, 14.3 % with both types of diabetes and 14.3 % not specify the type. The total number of volunteers was 2582, mostly with type 2 DM. It was analyzed that there is a relationship between CAN and DPN. It was observed that more severe levels of DPN are associated with worse outcomes in autonomic tests. Some studies suggested that the techniques for evaluating DPN might serve as risk factors for CAN. CONCLUSION: The review presents a possible relationship between DPN and CAN, such as in their severity.

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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.

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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.

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AIMS: Podiatrists constitute a key member of a multidisciplinary foot care team, but their services remain underutilized. We sought to gain insights into the daily practice of podiatrists focusing on screening for and monitoring of diabetic sensorimotor polyneuropathy (DSPN) as well as foot management. METHODS: This cross-sectional survey included 125 podiatrists from 12 federal states across Germany who responded to an online questionnaire. RESULTS: The majority of patients treated in podiatry practices were referred by general practitioners and diabetologists. Screening for or follow-up of DSPN was performed by 36% of the respondents at least once a year, by 28% only at initial examination, by 21% only at suspicion, and by 10% basically at each treatment visit. Instruments to assess vibration, touch/pressure, and temperature sensation were used by 81% to 94% of the podiatrists. Previously undiagnosed DSPN and foot ulcers were detected frequently/very frequently (≥6 cases/mo) by 24.0 and 18.4% of the podiatrists, respectively. Almost all podiatrists advised daily self-monitoring of feet and appropriate foot care and >50% gave advice on medical treatment. CONCLUSIONS: Podiatrists play an important role in the detection, monitoring, and management of both DSPN and diabetic foot ulcers, suggesting that the utilization of their services should be fostered.

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OBJECTIVES: To evaluate the determinants of orthostatic hypotension (OH) in type 2 diabetes (T2D) and the usefulness of Δheart rate (HR)/Δsystolic blood pressure (SBP), index of cardiac baroreflex function, in identifying neurogenic OH. METHODS: In 208 participants with T2D, we diagnosed early cardiovascular autonomic neuropathy (CAN) and confirmed CAN according to 1 and 2 HR-based cardiovascular reflex tests (HR-CARTs). Through OH test we defined OH as SBP falls of ≥20 and ≥30 mm Hg with supine SBPs of <140 and ≥140 mm Hg, respectively. In participants with OH, we used the lying-to-standing and OH test and its diagnostic accuracy for neurogenic OH (as OH plus confirmed HR-CAN). RESULTS: OH was present in 25 participants and associated with lower HR-CART scores, higher glycosylated hemoglobin level, the presence of CAN, retinopathy, and peripheral vascular disease, the absence of hypertension, and physical activity (all, P < .05) but not with interfering drugs and ß-blockers. In a multiple logistic regression, HR-CAN was the main determinant of OH (odds ratio, 4.74) with physical activity and hypertension (odds ratios, 0.16 and 0.23; R2 = 0.22). ΔHR/ΔSBP had a good diagnostic accuracy for neurogenic OH (area under the receiver operating characteristic curve, 0.816 ± 0.087) and, at the cutoff of 0.5 bpm/mm Hg, a sensitivity of 100% and specificity of 63.2%. CONCLUSION: CAN remains the primary determinant of OH in T2D but does not explain all its variance. The index ΔHR/ΔSBP may represent a useful clinical tool to identify neurogenic OH.

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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.

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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.

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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.

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