Altered intracellular localization and valosin-containing protein (p97 VCP) interaction underlie ATP7A-related distal motor neuropathy

ATP7A is a P-type ATPase that regulates cellular copper homeostasis by activity at the trans-Golgi network (TGN) and plasma membrane (PM), with the location normally governed by intracellular copper concentration. Defects in ATP7A lead to Menkes disease or its milder variant, occipital horn syndrome or to a newly discovered condition, ATP7A-related distal motor neuropathy (DMN), for which the precise pathophysiology has been obscure. We investigated two ATP7A motor neuropathy mutations (T994I, P1386S) previously associated with abnormal intracellular trafficking. In the patients' fibroblasts, total internal reflection fluorescence microscopy indicated a shift in steady-state equilibrium of ATP7AT994I and ATP7AP1386S, with exaggerated PM localization. Transfection of Hek293T cells and NSC-34 motor neurons with the mutant alleles tagged with the Venus fluorescent protein also revealed excess PM localization. Endocytic retrieval of the mutant alleles from the PM to the TGN was impaired. Immunoprecipitation assays revealed an abnormal interaction between ATP7AT994I and p97/VCP, an ubiquitin-selective chaperone which is mutated in two autosomal dominant forms of motor neuron disease: amyotrophic lateral sclerosis and inclusion body myopathy with early-onset Paget disease and fronto-temporal dementia. Small-interfering RNA (SiRNA) knockdown of p97/VCP corrected ATP7AT994I mislocalization. Flow cytometry documented that non-permeabilized ATP7AP1386S fibroblasts bound a carboxyl-terminal ATP7A antibody, consistent with relocation of the ATP7A di-leucine endocytic retrieval signal to the extracellular surface and partially destabilized insertion of the eighth transmembrane helix. Our findings illuminate the mechanisms underlying ATP7A-related DMN and establish a link between p97/VCP and genetically distinct forms of motor neuron degeneration.

Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features

This article is devoted to an empirical investigation of per- formance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using subsets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.

Retinal nerve fibre layer thinning associated with diabetic peripheral neuropathy

Aims
To investigate the relationship between retinal nerve fibre layer thickness and peripheral neuropathy in patients with Type 2 diabetes, particularly in those who are at higher risk of foot ulceration.

Methods
Global and sectoral retinal nerve fibre layer thicknesses were measured at 3.45 mm diameter around the optic nerve head using optical coherence tomography (OCT). The level of neuropathy was assessed in 106 participants (82 with Type 2 diabetes and 24 healthy controls) using the 0–10 neuropathy disability score. Participants were stratified into four neuropathy groups: none (0–2), mild (3–5), moderate (6–8), and severe (9–10). A neuropathy disability score ‡ 6 was used to define those at higher risk of foot ulceration. Multivariable regression analysis was performed to assess the effect of neuropathy disability scores, age, disease duration and retinopathy on RNFL thickness.

Results
Inferior (but not global or other sectoral) retinal nerve fibre layer thinning was associated with higher neuropathy disability scores (P = 0.03). The retinal nerve fibre layer was significantly thinner for the group with neuropathy disability scores ‡ 6 in the inferior quadrant (P < 0.005). Age, duration of disease and retinopathy levels did not significantly influence retinal nerve fibre layer thickness. Control participants did not show any significant differences in thickness measurements from the group with diabetes and no neuropathy (P > 0.24 for global and all sectors).

Conclusions
Inferior quadrant retinal nerve fibre layer thinning is associated with peripheral neuropathy in patients with Type 2 diabetes, and is more pronounced in those at higher risk of foot ulceration.

Visual sensitivity loss in the central 30° of visual field is associated with diabetic peripheral neuropathy

Aims/hypothesis
Impaired central vision has been shown to predict diabetic peripheral neuropathy (DPN). Several studies have demonstrated diffuse retinal neurodegenerative changes in diabetic patients prior to retinopathy development, raising the prospect that non-central vision may also be compromised by primary neural damage. We hypothesise that type 2 diabetic patients with DPN exhibit visual sensitivity loss in a distinctive pattern across the visual field, compared with a control group of type 2 diabetic patients without DPN.

Methods
Increment light sensitivity was measured by standard perimetry in the central 30° of visual field for two age-matched groups of type 2 diabetic patients, with and without neuropathy (n = 40/30). Neuropathy status was assigned using the neuropathy disability score. Mean visual sensitivity values were calculated globally, for each quadrant and for three eccentricities (0–10°, 11–20° and 21–30°). Data were analysed using a generalised additive mixed model (GAMM).

Results
Global and quadrant between-group visual sensitivity mean differences were marginally but consistently lower (by about 1 dB) in the neuropathy cohort compared with controls. Between-group mean differences increased from 0.36 to 1.81 dB with increasing eccentricity. GAMM analysis, after adjustment for age, showed these differences to be significant beyond 15° eccentricity and monotonically increasing. Retinopathy levels and disease duration were not significant factors within the model (p = 0.90).

Conclusions/interpretation
Visual sensitivity reduces disproportionately with increasing eccentricity in type 2 diabetic patients with peripheral neuropathy. This sensitivity reduction within the central 30° of visual field may be indicative of more consequential loss in the far periphery.

Exploring retinal and functional markers of diabetic neuropathy

Diabetic peripheral neuropathy (DPN) is one of the most debilitating complications of diabetes. DPN is a major cause of foot ulceration and lower limb amputation. Early diagnosis and management are key factors in reducing morbidity and mortality. Current techniques for clinical assessment of DPN are relatively insensitive for detecting early disease or involve invasive procedures such as skin biopsies. There is a need for less painful, non-invasive, safe evaluation methods. Eye-care professionals already play an important role in the management of diabetic retinopathy but recent studies have indicated that the eye may also be an important site for the diagnosis and monitoring of neuropathy. Corneal nerve morphology is a promising marker of diabetic neuropathy occurring elsewhere in the body. Emerging evidence tentatively suggests that retinal anatomical markers and a range of functional visual indicators could similarly provide useful information regarding neural damage in diabetes, although this line of research is less well established. This review outlines the growing body of evidence supporting a potential diagnostic role for retinal structure and visual functional markers in the diagnosis and monitoring of peripheral neuropathy in diabetes.

Corneal markers of diabetic neuropathy

Diabetic neuropathy is a significant clinical problem that currently has no effective therapy, and in advanced cases, leads to foot ulceration and lower limb amputation. The accurate detection, characterization and quantification of this condition are important in order to define at-risk patients, anticipate deterioration, monitor progression, and assess new therapies. This review evaluates novel corneal methods of assessing diabetic neuropathy. Two new noninvasive corneal markers have emerged, and in cross-sectional studies have demonstrated their ability to stratify the severity of this disease. Corneal confocal microscopy allows quantification of corneal nerve parameters and noncontact corneal esthesiometry, the functional correlate of corneal structure, assesses the sensitivity of the cornea. Both these techniques are quick to perform, produce little or no discomfort for the patient, and are suitable for clinical settings. Each has advantages and disadvantages over traditional techniques for assessing diabetic neuropathy. Application of these new corneal markers for longitudinal evaluation of diabetic neuropathy has the potential to reduce dependence on more invasive, costly, and time-consuming assessments, such as skin biopsy.

Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

Analyzing systolic-diastolic interval interaction characteristics in diabetic cardiac autonomic neuropathy progression

Cardiac autonomic neuropathy (CAN), one of the major complications in diabetes, if detected at the subclinical stage allows for effective treatment and avoiding further complication including cardiovascular pathology. Surface ECG (Electrocardiogram)-based diagnosis of CAN is useful to overcome the limitation of existing cardiovascular autonomic reflex tests traditionally used for CAN identification in clinical settings. The aim of this paper is to analyze the changes in the mechanical function of the ventricles in terms of systolic-diastolic interval interaction (SDI) from a surface ECG to assess the severity of CAN progression [no CAN, early CAN (ECAN) or subclinical CAN, and definite CAN (DCAN) or clinical CAN]. ECG signals recorded in supine resting condition from 72 diabetic subjects without CAN (CAN-) and 70 diabetic subjects with CAN were analyzed in this paper. The severity of CAN was determined by Ewing's Cardiovascular autonomic reflex tests. Fifty-five subjects of the CAN group had ECAN and 15 subjects had DCAN. In this paper, we propose an improved version of the SDI parameter (i.e., TQ/RR interval ratio) measured from the electrical diastolic interval (i.e., TQ interval) and the heart rate interval (i.e., RR interval). The performance of the proposed SDI measure was compared with the performance of the existing SDI measure (i.e., QT/TQ interval ratio). The proposed SDI parameter showed significant differences among three groups (no CAN, ECAN, and DCAN). In addition, the proposed SDI parameter was found to be more sensitive in detecting CAN progression than other ECG interval-based features traditionally used for CAN diagnosis. The modified SDI parameter might be used as an alternative measure for the Ewing autonomic reflex tests to identify CAN progression for those subjects who are unable to perform the traditional tests. These findings could also complement the echocardiographic findings of the left ventricular diastolic dysfunction by providing additional information about alteration in systolic and diastolic intervals in heart failure.