OBJECTIVETo evaluate mechanisms underlying diabetic neuropathy progression using indexes of sural nerve morphometry obtained from two identical randomized, placebo-controlled clinical trials.RESEARCH DESIGN AND METHODSSural nerve myelinated fiber density (MFD), nerve conduction velocities (NCVs), vibration perception thresholds, clinical symptom scores, and a visual analog scale for pain were analyzed in participants with diabetic neuropathy. A loss of ≥500 fibers/mm2 in sural nerve MFD over 52 weeks was defined as progressing diabetic neuropathy, and a MFD loss of ≤100 fibers/mm2 during the same time interval as nonprogressing diabetic neuropathy. The progressing and nonprogressing cohorts were matched for baseline characteristics using an O'Brien rank-sum and baseline MFD.RESULTSAt 52 weeks, the progressing cohort demonstrated a 25% decrease (P < 0.0001) from baseline in MFD, while the nonprogressing cohort remained unchanged. MFD was not affected by active drug treatment (P = 0.87), diabetes duration (P = 0.48), age (P = 0.11), or BMI (P = 0.30). Among all variables tested, elevated triglycerides and decreased peroneal motor NCV at baseline significantly correlated with loss of MFD at 52 weeks (P = 0.04).CONCLUSIONSIn this cohort of participants with mild to moderate diabetic neuropathy, elevated triglycerides correlated with MFD loss independent of disease duration, age, diabetes control, or other variables. These data support the evolving concept that hyperlipidemia is instrumental in the progression of diabetic neuropathy.

Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes and is associated with significant morbidity and mortality. DPN is characterized by progressive, distal-to-proximal degeneration of peripheral nerves that leads to pain, weakness, and eventual loss of sensation. The mechanisms underlying DPN pathogenesis are uncertain, and other than tight glycemic control in type 1 patients, there is no effective treatment. Mouse models of type 1 (T1DM) and type 2 diabetes (T2DM) are critical to improving our understanding of DPN pathophysiology and developing novel treatment strategies. In this review, we discuss the most widely used T1DM and T2DM mouse models for DPN research, with emphasis on the main neurologic phenotype of each model. We also discuss important considerations for selecting appropriate models for T1DM and T2DM DPN studies and describe the promise of novel emerging diabetic mouse models for DPN research. The development, characterization, and comprehensive neurologic phenotyping of clinically relevant mouse models for T1DM and T2DM will provide valuable resources for future studies examining DPN pathogenesis and novel therapeutic strategies.

OBJECTIVE—Activation of the cyclooxygenase (COX) pathway with secondary neurovascular deficits are implicated in the pathogenesis of experimental diabetic peripheral neuropathy (DPN). The aim of this study was to explore the interrelationships between hyperglycemia, activation of the COX-2 pathway, and oxidative stress and inflammation in mediating peripheral nerve dysfunction and whether COX-2 gene inactivation attenuates nerve fiber loss in long-term experimental diabetes. RESEARCH DESIGN AND METHODS—Motor and sensory digital nerve conduction velocities, sciatic nerve indexes of oxidative stress, prostaglandin content, markers of inflammation, and intraepidermal nerve fiber (IENF) density were measured after 6 months in control and diabetic COX-2–deficient (COX-2−/−) and littermate wild-type (COX-2+/+) mice. The effects of a selective COX-2 inhibitor, celecoxib, on these markers were also investigated in diabetic rats. RESULTS—Under normal conditions, there were no differences in blood glucose, peripheral nerve electrophysiology, markers of oxidative stress, inflammation, and IENF density between COX-2+/+ and COX-2−/− mice. After 6 months, diabetic COX-2+/+ mice experienced significant deterioration in nerve conduction velocities and IENF density and developed important signs of increased oxidative stress and inflammation compared with nondiabetic mice. Diabetic COX-2−/− mice were protected against functional and biochemical deficits of experimental DPN and against nerve fiber loss. In diabetic rats, selective COX-2 inhibition replicated this protection. CONCLUSIONS—These data suggest that selective COX-2 inhibition may be useful for preventing or delaying DPN.

Both the structure and the amount of sleep are important for brain function. Entry into deep, restorative stages of sleep is time dependent; short sleep bouts selectively eliminate these states. Fragmentation-induced cognitive dysfunction is a feature of many common human sleep pathologies. Whether sleep structure is normally regulated independent of the amount of sleep is unknown. Here, we show that in Drosophila melanogaster, activation of a subset of serotonergic neurons fragments sleep without major changes in the total amount of sleep, dramatically reducing long episodes that may correspond to deep sleep states. Disruption of sleep structure results in learning deficits that can be rescued by pharmacologically or genetically consolidating sleep. We identify two reciprocally connected sets of ellipsoid body neurons that form the heart of a serotoninmodulated circuit that controls sleep architecture. Taken together, these findings define a circuit essential for controlling the structure of sleep independent of its amount.

Sleep pressure and sleep depth are key regulators of wake and sleep. Current methods of measuring these parameters in Drosophila melanogaster have low temporal resolution and/or require disrupting sleep. Here we report analysis tools for high-resolution, noninvasive measurement of sleep pressure and depth from movement data. Probability of initiating activity, P(Wake), measures sleep depth while probability of ceasing activity, P(Doze), measures sleep pressure. In vivo and computational analyses show that P(Wake) and P(Doze) are largely independent and control the amount of total sleep. We also develop a Hidden Markov Model that allows visualization of distinct sleep/wake substates. These hidden states have a predictable relationship with P(Doze) and P(Wake), suggesting that the methods capture the same behaviors. Importantly, we demonstrate that both the Doze/Wake probabilities and the sleep/wake substates are tied to specific biological processes. These metrics provide greater mechanistic insight into behavior than measuring the amount of sleep alone.