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.

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.

Background: Diabetic peripheral neuropathy (DPN) is a multifactorial complication in which metabolic dysregulation, altered body composition, and neuromuscular impairment may contribute to structural foot alterations and ulceration risk. These domains are often studied separately, limiting understanding of their combined relationship with digital deformities and diabetic-foot risk. Objective: To evaluate exploratory associations between digital deformities, diabetic-foot risk, and integrated anthropometric, metabolic, bioimpedance-derived, functional, and neuromuscular markers. Methods: This single-center case–control study included 65 adults: 28 with type 2 diabetes and 37 non-diabetic controls. Assessments included anthropometry, bioimpedance-derived parameters — phase angle (PhA), ECW/TBW ratio, and muscle quality — handgrip dynamometry, HbA1c, and the triglyceride–glucose (TyG) index. Diabetic-foot risk was classified according to IWGDF criteria in participants with type 2 diabetes. Analyses included Shapiro–Wilk tests, Student’s t-test or Mann–Whitney U test, Pearson or Spearman correlations, and one-way ANOVA with Tukey post-hoc tests. Missing data were below 5% and were imputed. Results: Diabetic-foot risk correlated with waist circumference (r = 0.443, p < 0.001), BMI (r = 0.333, p = 0.005), HbA1c (r = 0.560, p < 0.001), and TyG index (r = 0.454, p < 0.001). Digital deformities were associated with fasting glucose (r = 0.573, p < 0.001), HbA1c (r = 0.560, p < 0.001), and TyG index (r = 0.565, p < 0.001). Inverse associations were observed for handgrip strength (r = −0.340, p = 0.004), muscle quality (r = −0.311, p = 0.009), and PhA (r = −0.285, p = 0.018). Exploratory subgroup analyses showed differences across analytical risk strata for PhA (F = 6.99, p = 0.002), muscle quality (F = 6.99, p = 0.002), and handgrip strength (F = 4.04, p = 0.022). Conclusions: Digital deformities and diabetic-foot risk in DPN were associated with combined metabolic, bioimpedance-derived, and systemic neuromuscular alterations. Handgrip strength and bioimpedance-derived parameters showed exploratory associations with risk and deformities; however, their role as complementary assessment measures requires confirmation in larger longitudinal studies with adjusted models.

Purpose: Traditional markers such as HbA1c and BMI are commonly used to assess metabolic risk in adolescents with type 1 diabetes mellitus (T1DM), but novel biomarkers like the triglyceride-glucose (TyG) index and phase angle (PA) may provide a more comprehensive evaluation of metabolic risk. However, their potential role in this population remains unclear. We aimed (1) to evaluate the association of the TyG index and PA with glycemic control, cardiovascular risk and body composition in adolescents with T1DM, and (2) to determine optimal cut-off points for these biomarkers to predict metabolic risk. Methods: Seventy-three adolescents (46.6% female, 10–19 years) with T1DM from two hospitals were included in this cross-sectional study. Sociodemographic, bioelectrical impedance (BIA) and clinical data were collected during routine visits. The TyG index was calculated from fasting triglyceride and glucose levels, and PA was measured via BIA. Correlations were analyzed using Pearson or Spearman coefficients, and ROC curve analyses were used to determine optimal cut-off points. Results: PA correlated positively with skeletal muscle and fat-free mass and inversely with cardiovascular risk indicators. TyG was strongly associated with adiposity, glycemic variability and cardiovascular risk markers. A preliminary TyG threshold of 8.09 was identified, while no PA cut-off could be established. Conclusions: PA and the TyG index are promising, non-invasive biomarkers that complement traditional measures in assessing metabolic risk in adolescents with T1DM. Integrating these biomarkers into routine evaluation may enhance early risk detection and support holistic management in this population. Trial registration number: NCT07021326.

Hematophobia, or fear of blood, needles, and injections, is a specific phobia that impacts procedural adherence and patient outcomes in healthcare settings. This study evaluates the effectiveness of virtual reality (VR) as a tool for anxiety reduction during venipuncture, focusing on patients with varying degrees of hematophobia. This analytical, experimental study involved 185 patients divided into case (VR intervention) and control (standard procedure) groups across two settings: a clinical laboratory and a hematology day hospital. Anxiety was measured pre- and post-procedure using the visual analog scale (VAS) and the blood-injection phobia inventory (BIPI). Statistical analyses, including the Wilcoxon test, assessed the impact of VR across demographic and procedural variables. The prevalence of hematophobia was high, with 32.4% of patients classified as severe, 31.9% as moderate, and 35.7% as mild. VR significantly reduced pre- and post-procedure anxiety, particularly in patients with moderate hematophobia (p < 0.05). Patients under 40 years of age and women exhibited greater anxiety reductions with VR, while the impact was less pronounced in older adults and those with severe hematophobia. The regression analysis identified VR as the strongest predictor of anxiety reduction (β = 1.80, p = 0.0001), underscoring its efficacy in mitigating procedural stress. This study confirms VR's potential as an effective intervention for managing hematophobia in clinical contexts, particularly among patients with moderate anxiety. The technology's effectiveness depends on patient age, familiarity with VR, and the severity of hematophobia. Despite these benefits, challenges such as cost, potential VR sickness, and limited efficacy in severe cases highlight the need for tailored implementation strategies. VR is a valuable tool for reducing anxiety during venipuncture, improving procedural adherence, and enhancing patient experiences. Its integration into hospital protocols should prioritize personalization based on patient profiles to maximize clinical and economic benefits.