Ekaterina von Rauchhaupt

Context Due to the heterogenous clinical symptoms and deficits, the diagnosis of diabetic polyneuropathy (DPN) is still difficult in clinical routines, leading to increased morbidity and mortality. Objective We studied the correlation of phase angle (PhA) of bioelectrical impedance analysis (BIA) with clinical, laboratory, and physical markers of DPN to evaluate PhA as a possible diagnostic method for DPN. Materials and methods In this cross-sectional observational study as part of the Heidelberg Study on Diabetes and Complications, we examined 104 healthy individuals and 205 patients with type 2 diabetes mellitus (T2D), among which 63 had DPN. The PhA was calculated from multifrequency BIA. Nerve conduction studies, quantitative sensory testing (QST) and diffusion-weighted magnetic resonance neurography to determine fractional anisotropy (FA) reflecting peripheral nerve integrity were performed. Results T2D patients with DPN had lower PhA values (5.71 ± 0.10) compared to T2D patients without DPN (6.07 ± 0.08, P = .007, + 6.1%) and healthy controls (6.18 ± 0.08, P < .001, + 7.9%). Confounder-adjusted analyses showed correlations of the PhA with conduction velocities and amplitudes of the peroneal (β=.28; β=.31, P < .001) and tibial nerves (β=.28; β=.32, P < .001), Z-scores of QST (thermal detection β=.30, P < .05) and the FA (β=.60, P < .001). Receiver-operating characteristic analysis showed similar performance of PhA in comparison to the mentioned diagnostic methods. Conclusion The study shows that PhA is, in comparison to other test systems used, at least an equally good and much easier to handle investigator-independent marker for detection of DPN.

Aim To investigate the association of early peripheral sensory dysfunction (EPSD) identified through quantitative sensory testing (QST) with factors related to a dysmetabolic status in individuals with and without type 2 diabetes (T2DM) without peripheral neuropathy (PN), and the impact of those factors on PN development. Methods A total of 225 individuals (117 and 108 without and with T2DM, respectively) without PN based on clinical and electrophysiological criteria were analyzed. Comparative analysis was conducted between those identified as “healthy” and those with EPSD based on a standardized QST protocol. A total of 196 were followed-up over a mean of 2.64 years for PN occurrence. Results Among those without T2DM, apart from male sex, height, and higher fat and lower lean mass, only higher insulin resistance (IR; homeostatic model assessment for IR: odds ratio [OR], 1.70; P = .009; McAuley index OR, 0.62, P = .008), was independently associated with EPSD. In T2DM, metabolic syndrome (OR, 18.32; P < .001) and skin advanced glycation end-products (AGEs; OR, 5.66; P = .003) were independent predictors of EPSD. In longitudinal analysis, T2DM (hazard ratio [HR], 3.32 vs no diabetes mellitus; P < .001), EPSD (adjusted HR, 1.88 vs healthy; P = .049 adjusted for diabetes mellitus and sex), higher IR and AGEs predicted PN development. Among the 3 EPSD-associated sensory phenotypes, “sensory loss” was most strongly associated with PN development (adjusted HR, 4.35; P = .011). Conclusion We demonstrate for the first time the utility of a standardized QST-based approach in identifying early sensory deficits in individuals with and without T2DM. These are associated with a dysmetabolic status signified by IR markers, metabolic syndrome, and higher AGEs, which in turn are shown to influence PN development.