CVOT Summit Report 2023: new cardiovascular, kidney, and metabolic outcomes.

The 9th Cardiovascular Outcome Trial (CVOT) Summit: Congress on Cardiovascular, Kidney, and Metabolic Outcomes was held virtually on November 30-December 1, 2023. This reference congress served as a platform for in-depth discussions and exchange on recently completed outcomes trials including dapagliflozin (DAPA-MI), semaglutide (SELECT and STEP-HFpEF) and bempedoic acid (CLEAR Outcomes), and the advances they represent in reducing the risk of major adverse cardiovascular events (MACE), improving metabolic outcomes, and treating obesity-related heart failure with preserved ejection fraction (HFpEF). A broad audience of endocrinologists, diabetologists, cardiologists, nephrologists and primary care physicians participated in online discussions on guideline updates for the management of cardiovascular disease (CVD) in diabetes, heart failure (HF) and chronic kidney disease (CKD); advances in the management of type 1 diabetes (T1D) and its comorbidities; advances in the management of CKD with SGLT2 inhibitors and non-steroidal mineralocorticoid receptor antagonists (nsMRAs); and advances in the treatment of obesity with GLP-1 and dual GIP/GLP-1 receptor agonists. The association of diabetes and obesity with nonalcoholic steatohepatitis (NASH; metabolic dysfunction-associated steatohepatitis, MASH) and cancer and possible treatments for these complications were also explored. It is generally assumed that treatment of chronic diseases is equally effective for all patients. However, as discussed at the Summit, this assumption may not be true. Therefore, it is important to enroll patients from diverse racial and ethnic groups in clinical trials and to analyze patient-reported outcomes to assess treatment efficacy, and to develop innovative approaches to tailor medications to those who benefit most with minimal side effects. Other keys to a successful management of diabetes and comorbidities, including dementia, entail the use of continuous glucose monitoring (CGM) technology and the implementation of appropriate patient-physician communication strategies. The 10th Cardiovascular Outcome Trial Summit will be held virtually on December 5-6, 2024 ( http://www.cvot.org ).

Real-world effectiveness and safety of insulin glargine 100 U/mL plus lixisenatide in adults with type 2 diabetes: An international, multicentre, 12-month, prospective observational study.

AIM: To assess the impact of insulin glargine (100 U/mL) and lixisenatide (iGlarLixi) fixed-ratio combination therapy on the overall management of glycaemia in patients with type 2 diabetes (T2D), previously inadequately controlled with oral antidiabetic drugs ± basal insulin or glucagon-like peptide-1 receptor agonists (GLP-1 RAs). MATERIALS AND METHODS: This 12-month, international, multicentre, prospective, observational study included patients (age ≥ 18 years) with T2D who had initiated iGlarLixi within 1 month prior to study inclusion. Data were collected at study inclusion, month 3, month 6 and month 12 from patient diaries, self-measured plasma glucose, and questionnaires. The primary endpoint was change in HbA1c from baseline to month 6. RESULTS: Of the 737 eligible participants (mean age: 57.8 [standard deviation: 11.2] years; male: 49%), 685 had baseline and post-baseline HbA1c data available. The least squares mean change in HbA1c from baseline to month 6 was -1.4% (standard error [95% confidence interval (CI)]: 0.05 [-1.5, -1.3]). The absolute change from baseline at month 12 was -1.7% ± 1.9% (95% CI: -1.9, -1.5). There were 72 hypoglycaemia events reported during the study period, with a very low incidence of severe hypoglycaemia (two participants [rate: 0.003 events per patient-year]). CONCLUSIONS: This real-world observational study shows that initiation of iGlarLixi in people with T2D inadequately controlled on oral antidiabetic drugs ± basal insulin or GLP-1 RAs improves glycaemic control with a low incidence of hypoglycaemia.

Corneal confocal microscopy detects early nerve regeneration after pharmacological and surgical interventions: Systematic review and meta-analysis.

Corneal confocal microscopy (CCM) is an ophthalmic imaging technique that enables the identification of corneal nerve fibre degeneration and regeneration. To undertake a systematic review and meta-analysis of studies utilizing CCM to assess for corneal nerve regeneration after pharmacological and surgical interventions in patients with peripheral neuropathy. Databases (EMBASE [Ovid], PubMed, CENTRAL and Web of Science) were searched to summarize the evidence from randomized and non-randomized studies using CCM to detect corneal nerve regeneration after pharmacological and surgical interventions. Data synthesis was undertaken using RevMan web. Eighteen studies including 958 patients were included. CCM identified an early (1-8 months) and longer term (1-5 years) increase in corneal nerve measures in patients with peripheral neuropathy after pharmacological and surgical interventions. This meta-analysis confirms the utility of CCM to identify nerve regeneration following pharmacological and surgical interventions. It could be utilized to show a benefit in clinical trials of disease modifying therapies for peripheral neuropathy.

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.

Untargeted Metabolomic Profiling Reveals Differentially Expressed Serum Metabolites and Pathways in Type 2 Diabetes Patients with and without Cognitive Decline: A Cross-Sectional Study.

Diabetes is recognized as a risk factor for cognitive decline, but the underlying mechanisms remain elusive. We aimed to identify the metabolic pathways altered in diabetes-associated cognitive decline (DACD) using untargeted metabolomics. We conducted liquid chromatography-mass spectrometry-based untargeted metabolomics to profile serum metabolite levels in 100 patients with type 2 diabetes (T2D) (54 without and 46 with DACD). Multivariate statistical tools were used to identify the differentially expressed metabolites (DEMs), and enrichment and pathways analyses were used to identify the signaling pathways associated with the DEMs. The receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of a set of metabolites. We identified twenty DEMs, seven up- and thirteen downregulated in the DACD vs. DM group. Chemometric analysis revealed distinct clustering between the two groups. Metabolite set enrichment analysis found significant enrichment in various metabolite sets, including galactose metabolism, arginine and unsaturated fatty acid biosynthesis, citrate cycle, fructose and mannose, alanine, aspartate, and glutamate metabolism. Pathway analysis identified six significantly altered pathways, including arginine and unsaturated fatty acid biosynthesis, and the metabolism of the citrate cycle, alanine, aspartate, glutamate, a-linolenic acid, and glycerophospholipids. Classifier models with AUC-ROC > 90% were developed using individual metabolites or a combination of individual metabolites and metabolite ratios. Our study provides evidence of perturbations in multiple metabolic pathways in patients with DACD. The distinct DEMs identified in this study hold promise as diagnostic biomarkers for DACD patients.

SGLT2i and GLP-1 RA therapy in type 1 diabetes and reno-vascular outcomes: a real-world study.

AIMS/HYPOTHESIS: Insulin is the primary treatment for type 1 diabetes. However, alternative glucose-lowering therapies are used adjunctively, but importantly are off-label in type 1 diabetes. Little work has previously been undertaken to evaluate safety with long-term efficacy and cardio-renal benefits of such therapies. We sought to investigate the real-world impact of sodium-glucose cotransporter 2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapy in individuals with type 1 diabetes in relation to effect on blood glucose levels, adverse events and cardio-renal outcomes. METHODS: We performed a retrospective cohort study of all patients aged 18 or over with type 1 diabetes on the TriNetX platform, a global collaborative network providing access to real-time, anonymised medical records. We included patients who had been treated with an SGLT2i or GLP-1 RA for at least 6 months and analysed the efficacy, safety and cardio-renal outcomes 5 years after initiation of therapy. RESULTS: We identified 196,691 individuals with type 1 diabetes, 13% of whom were treated with adjunctive glucose-lowering therapy in addition to insulin. Included in the core analysis were 1822 patients treated with a GLP-1 RA and 992 individuals treated with an SGLT2i. Both agents provided clinically meaningful reductions in HbA1c (-2.6 mmol/mol [-0.2%] with SGLT2i and -5.4 mmol/mol [-0.5%] with GLP-1 RA). The SGLT2i treated cohort showed preservation of eGFR over a 5-year period compared with the GLP-1 RA treated cohort (+3.5 ml/min per 1.73 m2 vs -7.2 ml/min per 1.73 m2, respectively), including patients with established chronic kidney disease (CKD). The SGLT2i treated cohort experienced higher rates of diabetic ketoacidosis (DKA) (RR 2.08 [95% CI 1.05, 4.12] p=0.0309) and urinary tract infection/pyelonephritis (RR 2.27 [95% CI 1.12, 4.55] p=0.019) compared with the GLP-1 RA treated cohort. However, the SGLT2i treated cohort were less likely to develop heart failure (RR 0.44 [95% CI 0.23, 0.83] p=0.0092), CKD (RR 0.49 [95% CI 0.28, 0.86] p=0.0118) and be hospitalised for any cause (RR 0.59 [95% CI 0.46, 0.76] p≤0.0001) when compared with the GLP-1 RA treated cohort. CONCLUSIONS/INTERPRETATION: Both SGLT2is and GLP-1 RAs have potential benefits as adjunctive agents in type 1 diabetes. SGLT2is provide cardio-renal benefits, despite an increase in the risk of DKA and urinary tract infection compared with GLP-1 RA therapy. Long-term evaluation of the efficacy and safety of these adjunctive therapies is required to guide their use in individuals with type 1 diabetes.

Multimodal testing reveals subclinical neurovascular dysfunction in prediabetes, challenging the diagnostic threshold of diabetes.

AIM: To explore if novel non-invasive diagnostic technologies identify early small nerve fibre and retinal neurovascular pathology in prediabetes. METHODS: Participants with normoglycaemia, prediabetes or type 2 diabetes underwent an exploratory cross-sectional analysis with optical coherence tomography angiography (OCT-A), handheld electroretinography (ERG), corneal confocal microscopy (CCM) and evaluation of electrochemical skin conductance (ESC). RESULTS: Seventy-five participants with normoglycaemia (n = 20), prediabetes (n = 29) and type 2 diabetes (n = 26) were studied. Compared with normoglycaemia, mean peak ERG amplitudes of retinal responses at low (16-Td·s: 4.05 µV, 95% confidence interval [95% CI] 0.96-7.13) and high (32-Td·s: 5·20 µV, 95% CI 1.54-8.86) retinal illuminance were lower in prediabetes, as were OCT-A parafoveal vessel densities in superficial (0.051 pixels/mm2 , 95% CI 0.005-0.095) and deep (0.048 pixels/mm2 , 95% CI 0.003-0.093) retinal layers. There were no differences in CCM or ESC measurements between these two groups. Correlations between HbA1c and peak ERG amplitude at 32-Td·s (r = -0.256, p = 0.028), implicit time at 32-Td·s (r = 0.422, p < 0.001) and 16-Td·s (r = 0.327, p = 0.005), OCT parafoveal vessel density in the superficial (r = -0.238, p = 0.049) and deep (r = -0.3, p = 0.017) retinal layers, corneal nerve fibre length (CNFL) (r = -0.293, p = 0.017), and ESC-hands (r = -0.244, p = 0.035) were observed. HOMA-IR was a predictor of CNFD (ß = -0.94, 95% CI -1.66 to -0.21, p = 0.012) and CNBD (ß = -5.02, 95% CI -10.01 to -0.05, p = 0.048). CONCLUSIONS: The glucose threshold for the diagnosis of diabetes is based on emergent retinopathy on fundus examination. We show that both abnormal retinal neurovascular structure (OCT-A) and function (ERG) may precede retinopathy in prediabetes, which require confirmation in larger, adequately powered studies.

Corneal confocal microscopy identifies corneal nerve fiber loss in patients with migraine.

BACKGROUND/HYPOTHESIS: Migraine affects >1 billion people but its pathophysiology remains poorly understood. Alterations in the trigeminovascular system play an important role. We have compared corneal nerve morphology in patients with migraine to healthy controls. METHODS: Sixty patients with episodic (n = 32) or chronic (n = 28) migraine and 20 age-matched healthy control subjects were studied cross-sectionally. Their migraine characteristics and signs and symptoms of dry eyes were assessed. Manual and automated quantification of corneal nerves was undertaken by corneal confocal microscopy. RESULTS: In patients with migraine compared to controls, manual corneal nerve fiber density (P < 0.001), branch density (P = 0.015) and length (P < 0.001); and automated corneal nerve fiber density (P < 0.001), branch density (P < 0.001), length (P < 0.001), total branch density (P < 0.001), nerve fiber area (P < 0.001), nerve fiber width (P = 0.045) and fractal dimension (P < 0.001) were lower. Automated corneal nerve fiber density was higher in patients with episodic migraine and aura (P = 0.010); and fractal dimension (P = 0.029) was lower in patients with more headache days in the last three months. Automated corneal nerve fiber density predicted a significant amount of the observed variance in pain intensity (adjusted r2 = 0.14, partial r = -0.37, P = 0.004) in patients with migraine. CONCLUSIONS: Corneal confocal microscopy reveals corneal nerve loss in patients with migraine. It may serve as an objective imaging biomarker of neurodegeneration in migraine.

Comparative efficacy and safety of Gla-300 versus IDegAsp in insulin-naïve people with type 2 diabetes mellitus uncontrolled on oral anti-diabetics.

AIM: To compare the efficacy and safety of insulin glargine-300 once daily (Gla-300) with insulin degludec/aspart (IDegAsp) once daily in patients with type 2 diabetes (T2D) inadequately controlled on oral anti-diabetic drugs (OADs). MATERIALS AND METHODS: A systematic literature review of randomized controlled trials was followed by an indirect treatment comparison of studies involving insulin naïve adults, inadequately controlled [glycated haemoglobin (HbA1c) ≥7.0%] on OADs, who received Gla-300 or IDegAsp once daily. Outcomes of interest were change in HbA1c, blood glucose, weight and insulin dose, as well as incidence and event rate of hypoglycaemia and other adverse events. RESULTS: Four trials with broadly similar baseline patient characteristics were included in the meta-analyses and indirect treatment comparison. At 24-28 weeks, the indirect comparison of Gla-300 to IDegAsp once daily estimated no statistically significant difference for change in HbA1c (%) from baseline [mean difference of 0.10% (95% CI: -0.20, 0.39; p = .52)]; a statistically significant mean difference of -1.31 kg (95% CI: -1.97, -0.65; p < .05) for change in body weight from baseline; statistically significant odds ratios of 0.62 (95% CI: 0.41, 0.93; p < .05) for incidence of any hypoglycaemia; and 0.47 (95% CI: 0.25, 0.87; p < .05) for incidence of anytime confirmed hypoglycaemia (plasma glucose <3.0-3.1 mmol/L). No significant differences were observed for insulin dose and adverse events. CONCLUSION: In insulin-naïve patients with T2D inadequately controlled on OADs, commencing Gla-300 shows a comparable HbA1c reduction, but with significantly less weight gain and a lower incidence of any and confirmed hypoglycaemia compared with commencing IDegAsp.

A cross-sectional study of the prevalence and clinical management of atherosclerotic cardiovascular diseases in patients with type 2 diabetes across the Middle East and Africa (PACT-MEA): Study design and rationale.

AIM: To investigate the epidemiology and clinical management of patients with type 2 diabetes (T2D) and established atherosclerotic cardiovascular disease (eASCVD) or high/very high ASCVD risk, defined by the 2021 European Society of Cardiology Guidelines, in seven countries in the Middle East and Africa (PACT-MEA; NCT05317845), and to assess physicians' attitudes and the basis for their decision-making in the management of these patients. MATERIALS AND METHODS: PACT-MEA is a cross-sectional, observational study undertaken in Bahrain, Egypt, Jordan, Kuwait, Qatar, South Africa and the United Arab Emirates based on a medical chart review of approximately 3700 patients with T2D in primary and secondary care settings, and a survey of approximately 400 physicians treating patients with T2D. RESULTS: The primary and secondary objectives are to determine the prevalence of eASCVD and high/very high ASCVD risk in patients with T2D. Current treatment with cardioprotective antidiabetic medication, the proportion of patients meeting the treatment criteria for reimbursement in the study countries where there is an applicable reimbursement guideline, and physician-reported factors in clinical decision-making in T2D management, will also be assessed. CONCLUSIONS: This large cross-sectional study will establish the estimated prevalence and management of eASCVD and high/very high ASCVD risk in patients with type 2 diabetes across the Middle East and Africa.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions.

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia.

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ -0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.

Genetic Profiling of Sodium Channels in Diabetic Painful and Painless and Idiopathic Painful and Painless Neuropathies.

Neuropathic pain is a frequent feature of diabetic peripheral neuropathy (DPN) and small fiber neuropathy (SFN). Resolving the genetic architecture of these painful neuropathies will lead to better disease management strategies, counselling and intervention. Our aims were to profile ten sodium channel genes (SCG) expressed in a nociceptive pathway in painful and painless DPN and painful and painless SFN patients, and to provide a perspective for clinicians who assess patients with painful peripheral neuropathy. Between June 2014 and September 2016, 1125 patients with painful-DPN (n = 237), painless-DPN (n = 309), painful-SFN (n = 547) and painless-SFN (n = 32), recruited in four different centers, were analyzed for SCN3A, SCN7A-SCN11A and SCN1B-SCN4B variants by single molecule Molecular inversion probes-Next Generation Sequence. Patients were grouped based on phenotype and the presence of SCG variants. Screening of SCN3A, SCN7A-SCN11A, and SCN1B-SCN4B revealed 125 different (potential) pathogenic variants in 194 patients (17.2%, n = 194/1125). A potential pathogenic variant was present in 18.1% (n = 142/784) of painful neuropathy patients vs. 15.2% (n = 52/341) of painless neuropathy patients (17.3% (n = 41/237) for painful-DPN patients, 14.9% (n = 46/309) for painless-DPN patients, 18.5% (n = 101/547) for painful-SFN patients, and 18.8% (n = 6/32) for painless-SFN patients). Of the variants detected, 70% were in SCN7A, SCN9A, SCN10A and SCN11A. The frequency of SCN9A and SCN11A variants was the highest in painful-SFN patients, SCN7A variants in painful-DPN patients, and SCN10A variants in painless-DPN patients. Our findings suggest that rare SCG genetic variants may contribute to the development of painful neuropathy. Genetic profiling and SCG variant identification should aid in a better understanding of the genetic variability in patients with painful and painless neuropathy, and may lead to better risk stratification and the development of more targeted and personalized pain treatments.

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes.

AIMS/HYPOTHESIS: We aimed to develop an artificial intelligence (AI)-based deep learning algorithm (DLA) applying attribution methods without image segmentation to corneal confocal microscopy images and to accurately classify peripheral neuropathy (or lack of). METHODS: The AI-based DLA utilised convolutional neural networks with data augmentation to increase the algorithm's generalisability. The algorithm was trained using a high-end graphics processor for 300 epochs on 329 corneal nerve images and tested on 40 images (1 image/participant). Participants consisted of healthy volunteer (HV) participants (n = 90) and participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141) and prediabetes (n = 50) (defined as impaired fasting glucose, impaired glucose tolerance or a combination of both), and were classified into HV, those without neuropathy (PN-) (n = 149) and those with neuropathy (PN+) (n = 130). For the AI-based DLA, a modified residual neural network called ResNet-50 was developed and used to extract features from images and perform classification. The algorithm was tested on 40 participants (15 HV, 13 PN-, 12 PN+). Attribution methods gradient-weighted class activation mapping (Grad-CAM), Guided Grad-CAM and occlusion sensitivity displayed the areas within the image that had the greatest impact on the decision of the algorithm. RESULTS: The results were as follows: HV: recall of 1.0 (95% CI 1.0, 1.0), precision of 0.83 (95% CI 0.65, 1.0), F1-score of 0.91 (95% CI 0.79, 1.0); PN-: recall of 0.85 (95% CI 0.62, 1.0), precision of 0.92 (95% CI 0.73, 1.0), F1-score of 0.88 (95% CI 0.71, 1.0); PN+: recall of 0.83 (95% CI 0.58, 1.0), precision of 1.0 (95% CI 1.0, 1.0), F1-score of 0.91 (95% CI 0.74, 1.0). The features displayed by the attribution methods demonstrated more corneal nerves in HV, a reduction in corneal nerves for PN- and an absence of corneal nerves for PN+ images. CONCLUSIONS/INTERPRETATION: We demonstrate promising results in the rapid classification of peripheral neuropathy using a single corneal image. A large-scale multicentre validation study is required to assess the utility of AI-based DLA in screening and diagnostic programmes for diabetic neuropathy.

Corneal nerve loss is related to the severity of painful diabetic neuropathy.

BACKGROUND AND PURPOSE: Previously it has been shown that patients with painful diabetic neuropathy (PDN) have greater corneal nerve loss compared to patients with painless diabetic neuropathy. This study investigated if the severity of corneal nerve loss was related to the severity of PDN. METHODS: Participants with diabetic neuropathy (n = 118) and healthy controls (n = 38) underwent clinical and neurological evaluation, quantitative sensory testing, nerve conduction testing and corneal confocal microscopy and were categorized into those with no (n = 43), mild (n = 34) and moderate-to-severe (n = 41) neuropathic pain. RESULTS: Corneal nerve fibre density (p = 0.003), corneal nerve fibre length (p < 0.0001) and cold perception threshold (p < 0.0001) were lower and warm perception threshold was higher (p = 0.002) in patients with more severe pain, but there was no significant difference in the neuropathy disability score (p = 0.5), vibration perception threshold (p = 0.5), sural nerve conduction velocity (p = 0.3) and amplitude (p = 0.7), corneal nerve branch density (p = 0.06) and deep breathing heart rate variability (p = 0.08) between patients with differing severity of PDN. The visual analogue scale correlated significantly with corneal nerve fibre density (r = -0.3, p = 0.0002), corneal nerve branch density (r = -0.3, p = 0.001) and corneal nerve fibre length (r = -0.4, p < 0.0001). Receiver operating curve analysis showed that corneal nerve fibre density had an area under the curve of 0.78 with a sensitivity of 0.73 and specificity of 0.72 for the diagnosis of PDN. CONCLUSIONS: Corneal confocal microscopy reveals increasing corneal nerve fibre loss with increasing severity of neuropathic pain and a good diagnostic outcome for identifying patients with PDN.

Corneal nerve and nerve conduction abnormalities in children with type 1 diabetes.

OBJECTIVE: In vivo corneal confocal microscopy (CCM) is a novel, rapid, and non-invasive technique that identifies early small fiber damage and can predict the progression and development of clinical neuropathy in adults with type 1 diabetes. However, its usefulness in children is not well established. This study compared corneal confocal microscopy with neuropathic symptoms, signs, and objective measures of neuropathy for the diagnosis of diabetic neuropathy in children with type 1 diabetes. RESEARCH DESIGN AND METHODS: A total of 83 children with type 1 diabetes and 83 healthy participants of similar age underwent assessment of neuropathy symptoms, signs, nerve conduction studies, quantitative sensory and autonomic function testing, and in vivo CCM. RESULTS: Only of 3/83 (4%) children with type 1 diabetes had subclinical neuropathy. However, corneal nerve fiber density (p = 0.001), branch density (p = 0.006), fiber length (p = 0.002), tibial motor nerve amplitude and conduction velocity, and sural sensory nerve amplitude and conduction velocity (all p < 0.004) were lower in participants with type 1 diabetes than in the controls. Vibration, cooling, and warm perception thresholds and deep breathing heart rate variability were not found to be different (all p > 0.05) between children with type 1 diabetes and healthy controls. Multivariate regression analysis identified a possible association between body mass index and decreased corneal nerves. CONCLUSIONS: Decreased corneal nerves and abnormal nerve conduction were found in children with type 1 diabetes. CCM may allow rapid objective detection of subclinical diabetic neuropathy in children and adolescents with type 1 diabetes.

Zone-wise examination of optical coherence tomography features and their correspondence to multifocal electroretinography in eyes with nonproliferative diabetic retinopathy.

PURPOSE: To examine (1) the retinal structure by optical coherence tomography (OCT) and function by means of multifocal electroretinography (mfERG) in eyes with and without nonproliferative diabetic retinopathy (NPDR) (2) for correspondence between local retinal function and OCT zones with retinal lesions. METHODS: One hundred and thirty-two eligible participants (30 with nonproliferative DR (NPDR) and 102 with diabetes with no DR) underwent comprehensive ophthalmic examination, optical coherence tomography for retinal thickness measures, mfERG, and ultra-wide field fundus photography. OCT Early Treatment Diabetic Retinopathy Study (ETDRS) grid was overlaid on to mfERG plots. RESULTS: Those with NPDR had significantly thicker full retinal measures in the nine (ETDRS) zones compared to no DR. mfERG P1 latencies in rings 1-6 were significantly delayed, while the response densities in rings 4-6 were lower in the NPDR group. Significant negative correlation was noted between OCT thickness and mfERG P1 response densities in many ETDRS zones. Significant positive correlation was noted between P1 latencies and OCT thickness in a few zones. The combination of cystic spaces, microaneurysms, and hard exudates were present in all zones and were associated with a decrease in P1 response densities compared to no lesions. Reduced P1 response densities were associated with a sporadic delay in the mfERG latencies and vice versa. The number of lesions did not show correspondence to the mfERG measures. CONCLUSIONS: In eyes with NPDR, retinal function is differentially correlated with the DR lesions on OCT and can be assessed using multimodal imaging modalities.

A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images.

Diabetes mellitus (DM) is one of the most prevalent diseases in the world, and is correlated to a high index of mortality. One of its major complications is diabetic foot, leading to plantar ulcers, amputation, and death. Several studies report that a thermogram helps to detect changes in the plantar temperature of the foot, which may lead to a higher risk of ulceration. However, in diabetic patients, the distribution of plantar temperature does not follow a standard pattern, thereby making it difficult to quantify the changes. The abnormal temperature distribution in infrared (IR) foot thermogram images can be used for the early detection of diabetic foot before ulceration to avoid complications. There is no machine learning-based technique reported in the literature to classify these thermograms based on the severity of diabetic foot complications. This paper uses an available labeled diabetic thermogram dataset and uses the k-mean clustering technique to cluster the severity risk of diabetic foot ulcers using an unsupervised approach. Using the plantar foot temperature, the new clustered dataset is verified by expert medical doctors in terms of risk for the development of foot ulcers. The newly labeled dataset is then investigated in terms of robustness to be classified by any machine learning network. Classical machine learning algorithms with feature engineering and a convolutional neural network (CNN) with image-enhancement techniques are investigated to provide the best-performing network in classifying thermograms based on severity. It is found that the popular VGG 19 CNN model shows an accuracy, precision, sensitivity, F1-score, and specificity of 95.08%, 95.08%, 95.09%, 95.08%, and 97.2%, respectively, in the stratification of severity. A stacking classifier is proposed using extracted features of the thermogram, which is created using the trained gradient boost classifier, XGBoost classifier, and random forest classifier. This provides a comparable performance of 94.47%, 94.45%, 94.47%, 94.43%, and 93.25% for accuracy, precision, sensitivity, F1-score, and specificity, respectively.

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques.

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter-the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.

Peripheral Ion Channel Gene Screening in Painful- and Painless-Diabetic Neuropathy.

Neuropathic pain is common in diabetic peripheral neuropathy (DN), probably caused by pathogenic ion channel gene variants. Therefore, we performed molecular inversion probes-next generation sequencing of 5 transient receptor potential cation channels, 8 potassium channels and 2 calcium-activated chloride channel genes in 222 painful- and 304 painless-DN patients. Twelve painful-DN (5.4%) patients showed potentially pathogenic variants (five nonsense/frameshift, seven missense, one out-of-frame deletion) in ANO3 (n = 3), HCN1 (n = 1), KCNK18 (n = 2), TRPA1 (n = 3), TRPM8 (n = 3) and TRPV4 (n = 1) and fourteen painless-DN patients (4.6%-three nonsense/frameshift, nine missense, one out-of-frame deletion) in ANO1 (n = 1), KCNK18 (n = 3), KCNQ3 (n = 1), TRPA1 (n = 2), TRPM8 (n = 1), TRPV1 (n = 3) and TRPV4 (n = 3). Missense variants were present in both conditions, presumably with loss- or gain-of-functions. KCNK18 nonsense/frameshift variants were found in painless/painful-DN, making a causal role in pain less likely. Surprisingly, premature stop-codons with likely nonsense-mediated RNA-decay were more frequent in painful-DN. Although limited in number, painful-DN patients with ion channel gene variants reported higher maximal pain during the night and day. Moreover, painful-DN patients with TRP variants had abnormal thermal thresholds and more severe pain during the night and day. Our results suggest a role of ion channel gene variants in neuropathic pain, but functional validation is required.