Association of the systemic immuno-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio with diabetic microvascular complications.

Background: The systemic immuno-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are widely used and have been shown to be predictive indicators of various diseases. Diabetic nephropathy (DN), retinopathy (DR), and peripheral neuropathy (DPN) are the most prominent and common microvascular complications, which have seriously negative impacts on patients, families, and society. Exploring the associations with these three indicators and diabetic microvascular complications are the main purpose. Methods: There were 1058 individuals with type 2 diabetes mellitus (T2DM) in this retrospective cross-sectional study. SII, NLR, and PLR were calculated. The diseases were diagnosed by endocrinologists. Logistic regression and subgroup analysis were applied to evaluate the association between SII, NLP, and PLR and diabetic microvascular complications. Results: SII, NLR, and PLR were significantly associated with the risk of DN [odds ratios (ORs): 1.52, 1.71, and 1.60, respectively] and DR [ORs: 1.57, 1.79, and 1.55, respectively] by multivariate logistic regression. When NLR ≥2.66, the OR was significantly higher for the risk of DPN (OR: 1.985, 95% confidence interval: 1.29-3.05). Subgroup analysis showed no significant positive associations across different demographics and comorbidities, including sex, age, hypertension, HbA1c (glycated hemoglobin), and dyslipidemia. Conclusion: This study found a positive relationship between NLR and DN, DR, and DPN. In contrast, SII and PLR were found to be only associated with DN and DR. Therefore, for the diagnosis of diabetic microvascular complications, SII, NLR and PLR are highly valuable.

Wearable Movement Exploration Device with Machine Learning Algorithm for Screening and Tracking Diabetic Neuropathy-A Cross-Sectional, Diagnostic, Comparative Study.

BACKGROUND: Diabetic neuropathy is one of the most common complications of diabetes mellitus. The aim of this study is to evaluate the Moveo device, a novel device that uses a machine learning (ML) algorithm to detect and track diabetic neuropathy. The Moveo device comprises 4 sensors positioned on the back of the hands and feet accompanied by a mobile application that gathers data and ML algorithms that are hosted on a cloud platform. The sensors measure movement signals, which are then transferred to the cloud through the mobile application. The cloud triggers a pipeline for feature extraction and subsequently feeds the ML model with these extracted features. METHODS: The pilot study included 23 participants. Eleven patients with diabetes and suspected diabetic neuropathy were included in the experimental group. In the control group, 8 patients had suspected radiculopathy, and 4 participants were healthy. All participants underwent an electrodiagnostic examination (EDx) and a Moveo examination, which consists of sensors placed on the feet and back of the participant's hands and use of the mobile application. The participant performs six tests that are part of a standard neurological examination, and a ML algorithm calculates the probability of diabetic neuropathy. A user experience questionnaire was used to compare participant experiences with regard to both methods. RESULTS: The total accuracy of the algorithm is 82.1%, with 78% sensitivity and 87% specificity. A high linear correlation up to 0.722 was observed between Moveo and EDx features, which underpins the model's adequacy. The user experience questionnaire revealed that the majority of patients preferred the less painful method. CONCLUSIONS: Moveo represents an accurate, easy-to-use device suitable for home environments, showing promising results and potential for future usage.

Assessment of the severity of diabetic polyneuropathy aids in predicting the risk of developing diabetic complications in patients with untreated diabetes.

This study aimed to determine the efficacy of assessing the severity of diabetic polyneuropathy (DPN) in patients with untreated diabetes. Seventy-two patients with untreated type 2 diabetes who were hospitalized for glycemic control were enrolled and divided into the following two groups: patients who had no prior diagnosis and patients who were unattended or had discontinued treatment. Electrophysiological criteria consistent with Baba's classification were used to diagnose and assess the severity of DPN. The patients were divided into three subgroups: no DPN (stage 0), mild DPN (stage 1), and moderate or more-severe DPN (stages 2-4). Intergroup comparisons were performed for the clinical characteristics and the results of the nerve conduction studies. Twenty-two (30%), 25 (35%), and 25 (35%) patients were categorized into the no DPN, mild DPN, and moderate or more-severe DPN subgroups, respectively. The number of patients who were unattended or had discontinued treatment in the moderate or more-severe DPN subgroup was significantly higher than that in the no DPN subgroup. The patients in the moderate or more-severe DPN subgroup had an increased risk of developing diabetic retinopathy and nephropathy, with odds ratios of 19.5 and 11.0 for advanced stages of retinopathy and nephropathy, respectively. Thus, the assessment of the severity of DPN could aid in the prediction of the risk of developing diabetic complications in patients with untreated diabetes.

Evaluation of cardiac autonomic dysfunctions in children with type 1 diabetes mellitus.

BACKGROUND: Cardiovascular autonomic neuropathy (CAN) is a serious complication of diabetes, impacting the autonomic nerves that regulate the heart and blood vessels. Timely recognition and treatment of CAN are crucial in averting the onset of cardiovascular complications. Both clinically apparent autonomic neuropathy and subclinical autonomic neuropathy, particularly CAN pose a significant risk of morbidity and mortality in children with type 1 diabetes mellitus (T1DM). Notably, CAN can progress silently before manifesting clinically. In our study, we assessed patients with poor metabolic control, without symptoms, following the ISPAD 2022 guideline. The objective is is to determine which parameters we can use to diagnose CAN in the subclinical period. METHODS: Our study is a cross-sectional case-control study that includes 30 children diagnosed with T1DM exhibiting poor metabolic control (average HbA1c > 8.5% for at least 1 year) according to the ISPAD 2022 Consensus Guide. These patients, who are under the care of the pediatric diabetes clinic, underwent evaluation through four noninvasive autonomic tests: echocardiography, 24-h Holter ECG for heart rate variability (HRV), cardiopulmonary exercise test, and tilt table test. RESULTS: The average age of the patients was 13.73 ± 1.96 years, the average diabetes duration was 8 ± 3.66 years, and the 1-year average HbA1c value was 11.34 ± 21%. In our asymptomatic and poorly metabolically controlled patient group, we found a decrease in HRV values, the presence of postural hypotension with the tilt table test, and a decrease in ventricular diastolic functions that are consistent with the presence of CAN. Despite CAN, the systolic functions of the ventricles were preserved, and the dimensions of the cardiac chambers and cardiopulmonary exercise test were normal. CONCLUSIONS: CAN is a common complication of T1DM, often associated with the patient's age and poor glycemic control. HRV, active orthostatic tests, and the evaluation of diastolic dysfunctions play significant roles in the comprehensive assessment of CAN. These diagnostic measures are valuable tools in identifying autonomic dysfunction at an early stage, allowing for timely intervention and management to mitigate the impact of cardiovascular complications associated with T1DM.

Diabetic Peripheral Neuropathy: Prevention and Treatment.

Diabetic peripheral neuropathy occurs in up to 50% of patients with diabetes mellitus and increases the risk of diabetic foot ulcers and infections. Consistent screening and clear communication are essential to decrease disparities in assessment of neuropathic symptoms and diagnosis. Physicians should address underlying risk factors such as poor glycemic control, vitamin B12 deficiency, elevated blood pressure, and obesity to reduce the likelihood of developing neuropathy. First-line drug therapy for painful diabetic peripheral neuropathy includes duloxetine, gabapentin, amitriptyline, and pregabalin; however, these medications do not restore sensation to affected extremities. Evidence for long-term benefit and safety of first-line treatment options is lacking. Second-line drug therapy includes nortriptyline, imipramine, venlafaxine, carbamazepine, oxcarbazepine, topical lidocaine, and topical capsaicin. Periodic, objective monitoring of medication response is critical because patients may not obtain desired pain reduction, adverse effects are common, and serious adverse effects can occur. Opioids should generally be avoided. Nondrug therapies with low- to moderate-quality evidence include exercise and neuromodulation with spinal cord stimulation or transcutaneous electrical nerve stimulation. Peripheral transcutaneous electrical nerve stimulation is well tolerated and inexpensive, but benefits are modest. Other treatments, such as acupuncture, alpha-lipoic acid, acetyl-L-carnitine, cannabidiol, and onabotulinumtoxinA need further study in patients with diabetic peripheral neuropathy.

Predictive Value of Combined HbA1c and Neutrophil-to-Lymphocyte Ratio for Diabetic Peripheral Neuropathy in Type 2 Diabetes.

BACKGROUND Diabetic peripheral neuropathy (DPN) is a prevalent complication affecting over 60% of type 2 diabetes patients. Early diagnosis is challenging, leading to irreversible impacts on quality of life. This study explores the predictive value of combining HbA1c and Neutrophil-to-Lymphocyte Ratio (NLR) for early DPN detection. MATERIAL AND METHODS An observational study was conducted at the First People's Hospital of Linping District, Hangzhou spanning from May 2019 to July 2020. Data on sex, age, biochemical measurements were collected from electronic medical records and analyzed. Employing multivariate logistic regression analysis, we sought to comprehend the factors influencing the development of DPN. To assess the predictive value of individual and combined testing for DPN, a receiver operating characteristic (ROC) curve was plotted. The data analysis was executed using R software (Version: 4.1.0). RESULTS The univariate and multivariate logistic regression analysis identified the level of glycated hemoglobin (HbA1C) (OR=1.94, 95% CI: 1.27-3.14) and neutrophil-to-lymphocyte ratio (NLR) (OR=4.60, 95% CI: 1.15-22.62, P=0.04) as significant risk factors for the development of DPN. Receiver operating characteristic (ROC) curve analysis demonstrated that HbA1c, NLR, and their combined detection exhibited high sensitivity in predicting the development of DPN (71.60%, 90.00%, and 97.2%, respectively), with moderate specificity (63.8%, 45.00%, and 50.00%, respectively). The area under the curve (AUC) for these predictors was 0.703, 0.661, and 0.733, respectively. CONCLUSIONS HbA1c and NLR emerge as noteworthy risk indicators associated with the manifestation of DPN in patients with type 2 diabetes. The combined detection of HbA1c and NLR exhibits a heightened predictive value for the development of DPN.

Multi-feature, Chinese-Western medicine-integrated prediction model for diabetic peripheral neuropathy based on machine learning and SHAP.

BACKGROUND: Clinical studies have shown that diabetic peripheral neuropathy (DPN) has been on the rise, with most patients presenting with severe and progressive symptoms. Currently, most of the available prediction models for DPN are derived from general clinical information and laboratory indicators. Several Traditional Chinese medicine (TCM) indicators have been utilised to construct prediction models. In this study, we established a novel machine learning-based multi-featured Chinese-Western medicine-integrated prediction model for DPN using clinical features of TCM. MATERIALS AND METHODS: The clinical data of 1581 patients with Type 2 diabetes mellitus (T2DM) treated at the Department of Endocrinology of the First Affiliated Hospital of Anhui University of Chinese Medicine were collected. The data (including general information, laboratory parameters and TCM features) of 1142 patients with T2DM were selected after data cleaning. After baseline description analysis of the variables, the data were divided into training and validation sets. Four prediction models were established and their performance was evaluated using validation sets. Meanwhile, the accuracy, precision, recall, F1 score and area under the curve (AUC) of ROC were calculated using ten-fold cross-validation to further assess the performance of the models. An explanatory analysis of the results of the DPN prediction model was carried out using the SHAP framework based on machine learning-based prediction models. RESULTS: Of the 1142 patients with T2DM, 681 had a comorbidity of DPN, while 461 did not. There was a significant difference between the two groups in terms of age, cause of disease, systolic pressure, HbA1c, ALT, RBC, Cr, BUN, red blood cells in the urine, glucose in the urine, and protein in the urine (p < 0.05). T2DM patients with a comorbidity of DPN exhibited diverse TCM symptoms, including limb numbness, limb pain, hypodynamia, thirst with desire for drinks, dry mouth and throat, blurred vision, gloomy complexion, and unsmooth pulse, with statistically significant differences (p < 0.05). Our results showed that the proposed multi-featured Chinese-Western medicine-integrated prediction model was superior to conventional models without characteristic TCM indicators. The model showed the best performance (accuracy = 0.8109, precision = 0.8029, recall = 0.9060, F1 score = 0.8511, and AUC = 0.9002). SHAP analysis revealed that the dominant risk factors that caused DPN were TCM symptoms (limb numbness, thirst with desire for drinks, blurred vision), age, cause of disease, and glycosylated haemoglobin. These risk factors were exerted positive effects on the DPN prediction models. CONCLUSIONS: A multi-feature, Chinese-Western medicine-integrated prediction model for DPN was established and validated. The model improves early-stage identification of high-risk groups for DPN in the diagnosis and treatment of T2DM, while also providing informative support for the intelligent management of chronic conditions such as diabetes.

Behavior of neuropathy symptom score and neuropathy disability score in patients with and without peripheral diabetic neuropathy: A retrospective cohort study.

BACKGROUND: This study aimed to stablish cut-off of early diagnosis of diabetic polyneuropathy (PDN) based on neuropathy symptom score (NSS) and neuropathy disability score (NDS); to determine the behavior of NDD and NDS in patients with and without PDN; and to verify the association between clinical and demographic variables with both tests. METHODS: This retrospective cohort included 86 patients with diabetes. The NSS and NDS evaluations were collected in medical records in two moments: initial (entry into service) and final (after three years). Individuals were categorized in three groups: G1- PDN in both evaluations (N = 27); G2- PDN only in the final evaluation (N = 16); G3-individuals without PDN (N = 43). A ROC curve was performed to evaluate the sensitivity and specificity of NSS and NDS for PDN diagnosis. ANOVA was used to compare NSS and NDS between groups and evaluations, and multiple regression was performed to find predictors of PDN. RESULTS: The NSS and NDS showed excellent sensitivity and specificity (NDS ≥1.5 and NSS ≥6.5) for PDN diagnosis. There was a significant difference between groups in initial (p = 0.000) and final (p = 0.000) NDS and NSS evaluations. There was an association between peripheral arterial disease (PAD) and increase in NSS (p = 0.024) in G2; and association between loss of protective sensation (LOPS) and increase in NSS in G3 (p < 0.001). CONCLUSION: NSS and NDS tests showed excellent sensitivity and specificity for early PDN diagnosis. Behavior of both tests can differ patients with and without PDN. Furthermore, PAD and LOPS can be a predictor of PDN evolution.

Identification and validation of NAD+ metabolism-related biomarkers in patients with diabetic peripheral neuropathy.

Background: The mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN. Methods: DPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011. Results: 5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone. Conclusion: ENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN.

Association of serum NF-κB levels with peripheral neuropathy in type 2 diabetes mellitus patients: a pilot study.

OBJECTIVES: Hyperglycaemia-induced inflammation plays a vital role in the development of diabetic peripheral neuropathy (DPN). Recent evidences had reported the involvement of the transcription factor nuclear factor kappa-light-chain-enhancer of activated B cell (NF-κB) in diabetic experimental models. So, this pilot study aimed to evaluate serum NF-κB levels in DPN patients. METHODS: We recruited 50 T2DM patients, of which 25 were T2DM with neuropathy and 25 were T2DM without neuropathy. In all the participants peripheral neuropathy was diagnosed based on Total neuropathy score (TNS). Serum NF-κB levels were measured by ELISA. RESULTS: We observed that the serum NF-κB levels were higher in DPN patients in comparison to T2DM patients without neuropathy. On spearman correlation, a positive correlation was found between serum NF-κB levels and TNS in the DPN group (r=0.741, p<0.001). The regression model shows the TNS to be an independent determinant of serum NF-κB levels after adjustment for potential confounders like age, duration of diabetes, and HbA1C (B=81.34; p<0.001). CONCLUSIONS: NF-κB activation plays a key role in promoting inflammation which is associated with the progression of DPN. In this respect, the study of NF-κB levels in serum may be an additional diagnostic marker for DPN.

Exploring the role of mitochondrial-associated and peripheral neuropathy genes in the pathogenesis of diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a prevalent and serious complication of diabetes mellitus, impacting the nerves in the limbs and leading to symptoms like pain, numbness, and diminished function. While the exact molecular and immune mechanisms underlying DPN remain incompletely understood, recent findings indicate that mitochondrial dysfunction may play a role in the advancement of this diabetic condition. METHODS: Two RNA transcriptome datasets (codes: GSE185011 and GSE95849), comprising samples from diabetic peripheral neuropathy (DPN) patients and healthy controls (HC), were retrieved from the Gene Expression Omnibus (GEO) database hosted by the National Center for Biotechnology Information (NCBI). Subsequently, differential expression analysis and gene set enrichment analysis were performed. Protein-protein interaction (PPI) networks were constructed to pinpoint key hub genes associated with DPN, with a specific emphasis on genes related to mitochondria and peripheral neuropathy disease (PND) that displayed differential expression. Additionally, the study estimated the levels of immune cell infiltration in both the HC and DPN samples. To validate the findings, quantitative polymerase chain reaction (qPCR) was employed to confirm the differential expression of selected genes in the DPN samples. RESULTS: This research identifies four hub genes associated mitochondria or PN. Furthermore, the analysis revealed increased immune cell infiltration in DPN tissues, particularly notable for macrophages and T cells. Additionally, our investigation identified potential drug candidates capable of regulating the expression of the four hub genes. These findings were corroborated by qPCR results, reinforcing the credibility of our bioinformatics analysis. CONCLUSIONS: This study provides a comprehensive overview of the molecular and immunological characteristics of DPN, based on both bioinformatics and experimental methods.

Genetic Factors Associated with the Development of Neuropathy in Type 2 Diabetes.

Neuropathy is a serious and frequent complication of type 2 diabetes (T2DM). This study was carried out to search for genetic factors associated with the development of diabetic neuropathy by whole exome sequencing. For this study, 24 patients with long-term type 2 diabetes with neuropathy and 24 without underwent detailed neurological assessment and whole exome sequencing. Cardiovascular autonomic function was evaluated by cardiovascular reflex tests. Heart rate variability was measured by the triangle index. Sensory nerve function was estimated by Neurometer and Medoc devices. Neuropathic symptoms were characterized by the neuropathy total symptom score (NTSS). Whole exome sequencing (WES) was performed on a Thermo Ion GeneStudio S5 system determining the coding sequences of approximately 32,000 genes comprising 50 million base pairs. Variants were detected by Ion Reporter software and annotated using ANNOVAR, integrating database information from dbSNP, ClinVar, gnomAD, and OMIM. Integrative genomics viewer (IGV) was used for visualization of the mapped reads. We have identified genetic variants that were significantly associated with increased (22-49-fold) risk of neuropathy (rs2032930 and rs2032931 of recQ-mediated genome instability protein 2 (RMI2) gene), rs604349 of myosin binding protein H like (MYBPHL) gene and with reduced (0.07-0.08-fold) risk (rs917778 of multivesicular body subunit 12B (MVB12B) and rs2234753 of retinoic acid X receptor alpha (RXRA) genes). The rs2032930 showed a significant correlation with current perception thresholds measured at 5 Hz and 250 Hz for n. medianus (p = 0.042 and p = 0.003, respectively) and at 5 Hz for n. peroneus (p = 0.037), as well as the deep breath test (p = 0.022) and the NTSS (p = 0.023). The rs2032931 was associated with current perception thresholds (p = 0.003 and p = 0.037, respectively), deep breath test (p = 0.022), and NTSS (p = 0.023). The rs604349 correlated with values measured at 2000 (p = 0.049), 250 (p = 0.018), and 5 Hz (p = 0.005) for n. medianus, as well as warm perception threshold measured by Medoc device (p = 0.042). The rs2234753 showed correlations with a current perception threshold measured at 2000 Hz for n. medianus (p = 0.020), deep breath test (p = 0.040), and NTSS (p = 0.003). There was a significant relationship between rs91778 and cold perception threshold (p = 0.013). In our study, genetic variants have been identified that may have an impact on the risk of neuropathy developing in type 2 diabetic patients. These results could open up new opportunities for early preventive measures and might provide targets for new drug developments in the future.

Values and diagnostic accuracy of sensory nerve action potentials in control participants and participants with diabetes with and without clinical diabetic neuropathy, based on neuropathy scale measurements.

BACKGROUND: The assessment of the normative values of sensory nerve action potentials (SNAP) and their diagnostic accuracies using validated neuropathy-assessment tools to classify participants into groups with and without neuropathy was not previously described in the literature. METHODS: The Utah Early Neuropathy Scale (UENS), Michigan neuropathy-screening instrument, and nerve conduction data were collected prospectively. We described and compared the values of the sural, superficial peroneal sensory (SPS), and superficial radial SNAP amplitude in different age groups for three groups. Group 1 (G1)-control participants (UENS <5), group 2 (G2)-participants with diabetes without clinical diabetic neuropathy (UENS <5), and group 3 (G3)-participants with clinical diabetic neuropathy (UENS ≥5). We also described the diagnostic accuracy of single-nerve amplitude and a combined sensory polyneuropathy index (CSPNI) that consists of four total points (one point for each of the following nerves if their amplitude was <25% lower limit of normal: right sural, left sural, right SPS, and left SPS potentials). RESULTS: We assessed 135 participants, including 41, 37, and 57 participants in G1, G2, and G3, respectively, with age median (interquartile ranges) of 51 (45-56), 47 (38-56), and 54 (51-61) years, respectively, whereas 19 (46.3%), 18 (48.7%), and 32 (56.14%) of them were males, respectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores were 68.4%, 92.3%, 86.7%, and 80% for the sural amplitude; 86%, 58.3%, 62%, and 84% for the SPS amplitude; 66.7%, 94.4%, 90.5%, and 78.2% for the CSPNI of 3; and 54.4%, 98.6%, 96.9%, and 73.2% for the CSPNI of 4, respectively. CONCLUSION: Sural nerve had a high specificity for neuropathy; however, the CSPNI had the highest specificity and PPV, whereas the SPS had the highest sensitivity and NPV.

Bruns Garland Syndrome as the first presentation of type 2 diabetes: two case reports and a practical approach to diagnosis.

BACKGROUND: Diabetes is a global health problem causing a significant burden on the healthcare systems both due to the disease itself and associated complications. Diabetic radiculoplexus neuropathies or Bruns-Garland syndrome constitutes a rare form of microvascular complications, more commonly affecting the lumbosacral plexus and, very rarely, the cervical plexus. We describe two Sri Lankan males who presented with diabetic lumbosacral radiculoplexus neuropathy and diabetic cervical radiculoplexus neuropathy as the initial manifestation of diabetes. CASE DESCRIPTION: Case 1: a 49-year-old Sri Lankan hotel chef presented with subacute painful weakness and wasting of the left upper arm for 3 months and weight loss. Left upper limb proximal muscles were wasted with diminished power and reflexes. A nerve conduction study showed comparative amplitude reduction. An electromyogram revealed positive sharp waves, frequent fibrillations, and high amplitude polyphasic motor unit potentials with reduced recruitment in proximal muscles of left upper limb. Case-2: a 47-year-old Sri Lankan carpenter presented with subacute progressive asymmetrical painful weakness and wasting of bilateral thighs for 5 months and weight loss. Lower limb proximal muscles were wasted with reduced power and knee jerks. The nerve conduction study was normal. The electromyogram was similar to case 1 involving both quadratus femoris muscles, which was more prominent on the left side. The work up for an underlying etiology revealed only elevated fasting blood glucose and HbA1c, suggesting a new diagnosis of diabetes associated with neurological symptoms. Patient 1 was diagnosed with diabetic cervical radiculoplexus neuropathy and patient 2 with diabetic lumbosacral radiculoplexus neuropathy. Both showed significant improvement following optimization of glycemic control together with symptomatic treatment and physiotherapy. CONCLUSION: Diagnosis of diabetic radiculoplexus neuropathy requires a comprehensive workup to rule out other sinister pathologies. This case report has a dual importance; it describes diabetic radiculoplexus neuropathy as the very first manifestation of two previously healthy people, giving rise to a new diagnosis of diabetes and, at the same time, reporting on diabetic cervical radiculoplexus neuropathy, which is extremely rare and has never been previously reported in Sri Lanka.

Prognostic value of longitudinal HbA1c variability in predicting the development of diabetic sensorimotor polyneuropathy among patients with type 2 diabetes mellitus: A prospective cohort observational study.

AIMS/INTRODUCTION: This prospective cohort study aims to identify the optimal measure of glycated hemoglobin (HbA1c) variability and to explore its relationship with the development of new diabetic sensorimotor polyneuropathy (DSPN) in individuals with type 2 diabetes mellitus, building upon previous cross-sectional studies that highlighted a significant association between HbA1c visit-to-visit variability and DSPN. MATERIALS AND METHODS: In a prospective study, 321 participants diagnosed with type 2 diabetes mellitus underwent comprehensive clinical assessments, neurophysiologic studies, and laboratory evaluations at enrollment and follow-up. Various indices, including HbA1c standard deviation (HbA1c SD), coefficient of variation (HbA1c CV), HbA1c change score (HbA1c HVS), and average real variability (HbA1c ARV), were employed to calculate the visit-to-visit variability HbA1c based on 3 month intervals. The investigation focused on examining the associations between these indices and the development of new DSPN. RESULTS: The average follow-up duration was 16.9 ± 6.9 months. The Cox proportional hazards model identified age (P = 0.001), diabetes duration (P = 0.024), and HbA1C ARV (P = 0.031) as the sole factors associated with the development of new DSPN. Furthermore, the cumulative risk of developing DSPN over 1 year demonstrated a significant association with HbA1C ARV (P = 0.03, log-rank test). CONCLUSIONS: Apart from age and diabetes duration, HbA1c variability emerged as a robust predictor for the occurrence of new DSPN. Among the various measures of HbA1c variability evaluated, HbA1c ARV demonstrated the highest potential as a reliable indicator for anticipating the onset of new DSPN.

Earlier diagnosis of peripheral neuropathy in primary care: A call to action.

Peripheral neuropathy (PN) often remains undiagnosed (~80%). Earlier diagnosis of PN may reduce morbidity and enable earlier risk factor reduction to limit disease progression. Diabetic peripheral neuropathy (DPN) is the most common PN and the 10 g monofilament is endorsed as an inexpensive and easily performed test for DPN. However, it only detects patients with advanced neuropathy at high risk of foot ulceration. There are many validated questionnaires to diagnose PN, but they can be time-consuming and have complex scoring systems. Primary care physicians (PCPs) have busy clinics and lack access to a readily available screening method to diagnose PN. They would prefer a short, simple, and accurate tool to screen for PN. Involving the patient in the screening process would not only reduce the time a physician requires to make a diagnosis but would also empower the patient. Following an expert meeting of diabetologists and neurologists from the Middle East, South East Asia and Latin America, a consensus was formulated to help improve the diagnosis of PN in primary care using a simple tool for patients to screen themselves for PN followed by a consultation with the physician to confirm the diagnosis.

Estimated glucose disposal rate predicts the risk of diabetic peripheral neuropathy in type 2 diabetes: A 5-year follow-up study.

BACKGROUND: Insulin resistance is associated with chronic complications of diabetes, including diabetic peripheral neuropathy (DPN). Estimated glucose disposal rate (eGDR), calculated by the common available clinical factors, was proved to be an excellent tool to measure insulin resistance in large patient population. Few studies have explored the association between eGDR and DPN longitudinally. Therefore, we performed the current study to analyze whether eGDR could predict the risk of DPN. METHODS: In this prospective study, 366 type 2 diabetes (T2DM) subjects without DPN were enrolled from six communities in Shanghai in 2011-2014 and followed up until 2019-2020. Neuropathy was assessed by Michigan Neuropathy Screening Instrument (MSNI) at baseline and at the end of follow-up. FINDINGS: After 5.91 years, 198 of 366 participants progressed to DPN according to MNSI examination scores. The incidence of DPN in the low baseline eGDR (eGDR < 9.15) group was significantly higher than in the high baseline eGDR (eGDR ≥ 9.15) group (62.37% vs. 45.56%, p = .0013). The incidence of DPN was significantly higher in patients with sustained lower eGDR level (63.69%) compared with those with sustained higher eGDR level (35.80%). Subjects with low baseline eGDR (eGDR < 9.15) had significantly higher risk of DPN at the end of follow-up (odds ratio = 1.75), even after adjusting for other known DPN risk factors. CONCLUSIONS: The 5-year follow-up study highlights the importance of insulin resistance represented by eGDR in the development of DPN in T2DM. Diabetic patients with low eGDR are more prone to DPN and, therefore, require more intensive screening and more attention.

Machine Learning Models for Prediction of Diabetic Microvascular Complications.

IMPORTANCE AND AIMS: Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN). METHODS: A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics. RESULTS: Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance. CONCLUSIONS AND RELEVANCE: There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.