Approximately 25 % of diabetic patients develop diabetic foot ulcers (DFUs), significantly increasing morbidity, mortality, and healthcare costs. Effective control and prevention are crucial. This study aims to identify easily measurable parameters for predicting DFU risk by assessing the correlation between Phase Angle (PA) and the Triglyceride-Glucose (TyG) index with DFU risk. A comparative case-control study was conducted at the General Hospital of Elche from March to June 2023 with 70 participants (33 with diabetes, 37 without). Cases had diabetes for over five years and a diabetic foot risk grade of 0, 1, or 2 (IWGDF 2019). Exclusion criteria included inability to walk, prior use of orthoses, and severe complications like edema or wounds. Predictive variables were PA, TyG index, body composition, and biochemical markers. Statistical analyses included Pearson/Spearman tests for correlations, Student's t-test/Mann-Whitney test for group comparisons, and ANOVA/Kruskal-Wallis tests for normally and non-normally distributed variables. PAand TyG index were strongly linked to diabetic foot risk, supporting their potential as biomarkers. Significant relationships with other relevant biomarkers were also confirmed. PA and TyG index are valuable, easily measurable biomarkers for assessing diabetic foot risk, and can be monitored in primary care settings. Implementing these biomarkers in routine practice could enhance the management of diabetic complications, particularly in resource-limited settings, by enabling early detection and intervention, thus improving patient outcomes and reducing the burden of advanced complications.

Diabetic peripheral neuropathy (DPN) is a prevalent complication of chronic diabetes mellitus and has a significant impact on quality of life. DPN typically manifests itself as a symmetrical, length-dependent sensorimotor polyneuropathy with severe effects on gait. Surface electromyography (sEMG) is a valuable low-cost tool for assessing muscle activation patterns and precise identification of abnormalities. For the present study, we used information theory methods, such as cross-correlation (CC), normalized mutual information (NMI), conditional granger causality (CG-Causality), and transfer entropy (TE), to evaluate muscle network connectivity in three population groups: 33 controls (healthy volunteers, CT), 10 diabetic patients with a low risk of DPN (LW), and 17 moderate/high risk patients (MH). The results obtained indicated significant alterations in the intermuscular coupling mechanisms due to diabetes and DPN, with the TE group showing the best performance in detecting differences. The data revealed a significant increase in information transfer and muscle connectivity in the LW group over the CT group, while the MH group obtained significantly lower values for these metrics than the other two groups. These findings highlight the sEMG coupling metrics’ potential to reveal neuromuscular mechanisms that could aid the development of targeted rehabilitation strategies and help monitor DPN patients.

Distal symmetric polyneuropathy (DSP) is the most common diabetic neuropathy (75% of all diabetic neuropathies), potentially leading to plantar ulcers and lower limb amputation. Early detection of at-risk individuals is essential for timely intervention. This study aimed to evaluate different surface EMG (sEMG) parameters from isometric and dynamic exercises as biomarkers for early DSP detection and monitoring. The study involved 61 participants (34 controls and 27 cases with low, moderate and high levels of severity). sEMG signals were recorded from four lower-limb muscles (tibialis anterior, medial gastrocnemius, extensor digitorum brevis, and flexor digitorum brevis) and characterized by their amplitude, frequency, complexity and shape. Significant sEMG differences were found between controls and diabetic patients at low and moderate/high DSP risk, especially in the extensor digitorum and flexor digitorum muscles during isometric and dynamic exercises. Diabetic patients showed lower amplitude and complexity, with higher frequency, peakedness, and asymmetry. Parameters like root mean square, sample entropy, and central shape distance effectively distinguished between groups, highlighting changes in motor unit recruitment and muscle quality. Dynamic and isometric exercises emphasized their complementary roles in assessing muscle function, supporting sEMG's potential as a non-invasive tool for monitoring neuromuscular changes in diabetes.

Diabetic foot ulcers (DFUs) are a severe complication of type 2 diabetes mellitus (T2DM), linked to high morbidity and mortality. This study explores biomarkers related to DFU risk and progression to improve management strategies. This study was conducted to identify early biomarkers that could predict DFU risk before ulceration occurs. A case-control study was conducted with 33 T2DM patients and 37 non-diabetic controls. Bioimpedance-derived metrics—phase angle (PA), extracellular water-to-total body water ratio (AEC/ACT), and extracellular mass-to-body cell mass ratio (ECMe/BCMe)—were analyzed alongside lipid profiles (total cholesterol, LDL, HDL), the Controlling Nutritional Status (CONUT) index, and the triglyceride-glucose index. DFU risk was classified using International Working Group on Diabetic Foot criteria. Low LDL levels (< 50th percentile) correlated with reduced PA in cases (p < 0.001), indicating impaired cellular function and tissue repair. PA negatively correlated with DFU risk (p = 0.005), supporting its utility as a health marker. HDL positively correlated with AEC/ACT in cases (p = 0.004), suggesting its role in fluid regulation. The CONUT index correlated negatively with total cholesterol (p = 0.007) and LDL (p = 0.004), highlighting malnutrition’s impact on lipid imbalances. Hydric indices increased across DFU risk groups, with AEC/ACT significantly higher in Risk Group 1 versus controls (p = 0.048). ECMe/BCMe correlated positively with AEC/ACT in low LDL cases (p = 0.013), reflecting metabolic stress. LDL and PA are critical biomarkers for DFU risk and progression, offering insights into cellular integrity, tissue repair, and fluid balance. Integrating these biomarkers into clinical practice could optimize DFU management, reduce complications, and improve patient outcomes. The online version contains supplementary material available at 10.1186/s13098-025-01806-5.