Adrenal insufficiency is the inadequate secretion of glucocorticoid and/or mineralocorticoid secretion from the adrenal cortex. Primary adrenal insufficiency is the result of failure of the adrenal gland and secondary adrenal insufficiency is due to a lack of stimulation via pituitary adrenocorticotropic hormone or hypothalamic corticotropin-releasing hormone. Adrenal insufficiency may cause non-specific symptoms. Early detection and testing based on clinical suspicion may prevent subsequent presentation with adrenal crisis. Once identified, a low baseline cortisol (often <100 nmol/L) alongside raised adrenocorticotropic hormone (ACTH) can be enough to diagnose primary adrenal insufficiency. However, confirmatory testing can be done using the cosyntopin (Synacthen®) stimulation test or the insulin tolerance test, which is the gold standard for secondary adrenal insufficiency. The underlying cause of adrenal insufficiency can often be identified via a strategic approach to investigation. Adrenal crisis is a life-threatening medical emergency which must be treated immediately if there is strong clinical suspicion with fluids and corticosteroids otherwise can be fatal. Patients must be educated and empowered to take control of their own medical management.

Diabetic peripheral neuropathy (DPN) is the leading cause of neuropathy worldwide resulting in excess morbidity and mortality. We aimed to develop an artificial intelligence deep learning algorithm to classify the presence or absence of peripheral neuropathy (PN) in participants with diabetes or pre-diabetes using corneal confocal microscopy (CCM) images of the sub-basal nerve plexus. A modified ResNet-50 model was trained to perform the binary classification of PN (PN+) versus no PN (PN−) based on the Toronto consensus criteria. A dataset of 279 participants (149 PN−, 130 PN+) was used to train (n = 200), validate (n = 18), and test (n = 61) the algorithm, utilizing one image per participant. The dataset consisted of participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141), and pre-diabetes (n = 50). The algorithm was evaluated using diagnostic performance metrics and attribution-based methods (gradient-weighted class activation mapping (Grad-CAM) and Guided Grad-CAM). In detecting PN+, the AI-based DLA achieved a sensitivity of 0.91 (95%CI: 0.79–1.0), a specificity of 0.93 (95%CI: 0.83–1.0), and an area under the curve (AUC) of 0.95 (95%CI: 0.83–0.99). Our deep learning algorithm demonstrates excellent results for the diagnosis of PN using CCM. A large-scale prospective real-world study is required to validate its diagnostic efficacy prior to implementation in screening and diagnostic programmes.

Impaired rate-dependent depression of the Hoffman reflex (HRDD) is a potential biomarker of impaired spinal inhibition in patients with painful diabetic neuropathy. However, the optimum stimulus-response parameters that identify patients with spinal disinhibition are currently unknown. We systematically compared HRDD, performed using trains of 10 stimuli at five stimulation frequencies (0.3, 0.5, 1, 2 and 3 Hz), in 42 subjects with painful and 62 subjects with painless diabetic neuropathy with comparable neuropathy severity, and 34 healthy controls. HRDD was calculated using individual and mean responses compared to the initial response. At stimulation frequencies of 1, 2 and 3 Hz, HRDD was significantly impaired in patients with painful diabetic neuropathy compared to patients with painless diabetic neuropathy for all parameters and for most parameters when compared to healthy controls. HRDD was significantly enhanced in patients with painless diabetic neuropathy compared to controls for responses towards the end of the 1 Hz stimulation train. Receiver operating characteristic curve analysis in patients with and without pain showed that the area under the curve was greatest for response averages of stimuli 2–4 and 2–5 at 1 Hz, AUC = 0.84 (95%CI 0.76–0.92). Trains of 5 stimuli delivered at 1 Hz can segregate patients with painful diabetic neuropathy and spinal disinhibition, whereas longer stimulus trains are required to segregate patients with painless diabetic neuropathy and enhanced spinal inhibition.

Objective: In this study, we evaluate small and large nerve fibre pathology in relation to diabetic foot ulceration (DFU) and incident cardiovascular and cerebrovascular events in type 1 diabetes (T1D). Methods: A prospective observational study was conducted on people with T1D without diabetic peripheral neuropathy (DPN) (n = 25), T1D with DPN (n = 28), T1D with DFU (n = 25) and 32 healthy volunteers. ROC analysis of parameters was conducted to diagnose DPN and DFU, and multivariate Cox regression analysis was performed to evaluate the predictive ability of corneal nerves for cardiac and cerebrovascular events over 3 years. Results: Corneal nerve fibre length (CNFL), fibre density (CNFD) and branch density (CNBD) were lower in T1D-DPN and T1D-DFU vs. T1D (all p < 0.001). In ROC analysis, CNFD (sensitivity 88%, specificity 87%; AUC 0.93; p < 0.001; optimal cut-off 7.35 no/mm²) and CNFL (sensitivity 76%, specificity 77%; AUC 0.90; p < 0.001; optimal cut-off 7.01 mm/mm²) had good ability to differentiate T1D with and without DFU. Incident cardiovascular events (p < 0.001) and cerebrovascular events (p < 0.001) were significantly higher in T1D-DPN and T1D-DFU. Corneal nerve loss, specifically CNFD predicted incident cardiovascular (HR 1.67, 95% CI 1.12 to 2.50, p = 0.01) and cerebrovascular (HR 1.55, 95% CI 1.06 to 2.26, p = 0.02) events. Conclusions: Our study provides threshold values for corneal nerve fibre metrics for neuropathic foot at risk of DFU and further demonstrates that lower CNFD predicts incident cardiovascular and cerebrovascular events in T1D.

There is a need to accurately identify patients with diabetes at higher risk of developing and progressing diabetic peripheral neuropathy (DPN). Fifty subjects with Type 1 Diabetes Mellitus (T1DM) and sixteen age matched healthy controls underwent detailed neuropathy assessments including symptoms, signs, quantitative sensory testing (QST), nerve conduction studies (NCS), intra epidermal nerve fiber density (IENFD) and corneal confocal microscopy (CCM) at baseline and after 2 years of follow-up. Overall, people with type 1 diabetes mellitus showed no significant change in HbA1c, blood pressure, lipids or neuropathic symptoms, signs, QST, neurophysiology, IENFD and CCM over 2 years. However, a sub-group (n = 11, 22%) referred to as progressors, demonstrated rapid corneal nerve fiber loss (RCNFL) with a reduction in corneal nerve fiber density (CNFD) (p = 0.0006), branch density (CNBD) (p = 0.0002), fiber length (CNFL) (p = 0.0002) and sural (p = 0.04) and peroneal (p = 0.05) nerve conduction velocities, which was not related to a change in HbA1c or cardiovascular risk factors. The majority of people with T1DM and good risk factor control do not show worsening of neuropathy over 2 years. However, CCM identifies a sub-group of people with T1DM who show a more rapid decline in corneal nerve fibers and nerve conduction velocity.