{"paper_id":"3349d4f7-4009-46f5-b894-ffcc7d417eef","body_text":"Integrated Analysis of Neuromuscular Dysfunction and Metabolic Dysregulation in Diabetic Peripheral Neuropathy: Associations with Digital Deformities and Clinical Risk Stratification in a Case-Control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Analysis of Neuromuscular Dysfunction and Metabolic Dysregulation in Diabetic Peripheral Neuropathy: Associations with Digital Deformities and Clinical Risk Stratification in a Case-Control Study Esther Soler-Climent, Erika Melendez-Oliva, Jessica Román-Marroquí, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6812313/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diabetic peripheral neuropathy (DPN), a common diabetes complication, arises from neuromuscular deterioration and metabolic dysregulation. These changes heighten the risk of hammer- and claw-toe deformities, disrupt foot biomechanics, and predispose patients to ulcers and amputations. Because DPN is multifactorial, integrating metabolic and neuromuscular indicators is critical. Objective : Identify predictors of digital deformities and diabetic-foot risk by combining surface electromyography (sEMG), hand dynamometry, bioimpedance, and intermuscular connectivity metrics—transfer entropy (TE) and normalised mutual information (NMI). Methods : In this case-control study, 65 adults (28 with type 2 diabetes, 37 controls) were assessed at a single centre. Outcomes included IWGDF foot-risk grade, bioimpedance-derived body composition, metabolic markers (HbA1c, triglyceride–glucose index), and neuromuscular tests (handgrip, sEMG, IMC/PDC). Correlations, ANOVA with post-hoc contrasts, and multiple imputation handled statistical analysis and missing data. Results : Greater waist circumference, higher BMI, and poorer metabolic profiles (glucose, HbA1c, triglycerides) were linked to elevated foot risk. Claw or hammer toes co-occurred with weaker handgrip, lower muscle quality, and reduced phase angle. Hand dynamometry proved a simple yet sensitive biomarker of neuromuscular decline. Findings suggest that interventions combining strict glycaemic control with strategies to enhance neuromuscular connectivity—such as functional electrical stimulation and targeted muscle strengthening—may attenuate deformity progression. Conclusions : DPN reflects an interplay of metabolic, biomechanical, and neuromuscular factors extending beyond the foot itself. An integrated clinical assessment that merges anthropometric, metabolic, and neuromuscular data can flag high-risk patients earlier. Holistic management—tight glycaemic control plus focused rehabilitation—offers potential to prevent digital deformities and downstream complications. Larger longitudinal studies are warranted to validate these approaches and optimise outcomes. Diabetic Peripheral Neuropathy Digital Deformities Hand Dynamometry Surface Electromyography Bioimpedance Transfer Entropy Highlights Integrated Approach to DPN Assessment: -Anthropometric, metabolic, and neuromuscular indicators (sEMG, IMC/PDC, hand dynamometry, bioimpedance) were combined to more precisely characterize diabetic peripheral neuropathy progression and its association with claw and hammer toe deformities. Muscle Imbalance as a Primary Mechanism of Deformities :The loss of motor units and disrupted synergy between intrinsic and extrinsic foot muscles are the main drivers behind digital deformities, with muscle atrophy and thickening of the plantar aponeurosis further compromising foot biomechanics and increasing the risk of injury. Advances in Neuromuscular Assessment (TE and NMI) : Transfer Entropy (TE) and Normalized Mutual Information (NMI)) quantify intermuscular coupling. In early DPN, TE between lower limbs and feet increases compared to controls due to compensatory mechanisms and decreases in advanced DPN stages, indicating progressive motor disconnection. Link Between Handgrip Strength and Diabetic Foot Risk : Non-dominant hand dynamometry shows an inverse relationship with diabetic foot risk, suggesting its utility as a low-cost biomarker for early detection of neuromuscular dysfunction, particularly where diagnostic resources are limited. Importance of Body Composition and Metabolic Control : Bioimpedance measurements (phase angle, intracellular/extracellular water ratio, muscle quality) highlight connections between nutritional status, body composition, and diabetic foot risk. Chronic hyperglycemia, the triglyceride-glucose index, and dyslipidemia are associated with digital deformities, underscoring the need for strict metabolic control. New Therapeutic Proposals Based on Muscular Connectivity : Observed alterations in TE and NMI in connectivity in the muscles of the lower extremities and feet suggest that interventions such as functional electrical stimulation, targeted neuromuscular rehabilitation, and intrinsic muscle strengthening may slow atrophy and improve motor synchronization, particularly in early and moderate stages. Longitudinal studies with high-resolution electromyographic measurements and extended clinical follow-up are needed to determine their efficacy. INTRODUCTION Diabetic peripheral neuropathy (DPN) is one of the most common and disabling complications of diabetes mellitus (DM), affecting approximately 50% of diabetic patients at some point in the disease ( 1 ). Its progression involves the gradual deterioration of peripheral nerve fibers, impacting sensory, motor, and autonomic nervous system functions, leading to symptoms such as dysesthesias, neuropathic pain, loss of motor control, and postural alterations that increase the risk of falls and functional disability ( 2 , 3 ). Early diagnosis of DPN is crucial, as its progression can lead to severe complications such as diabetic foot ulcers, infections, amputations, and musculoskeletal deformities ( 4 ). Among these, hammer and claw toes are of particular interest due to their impact on foot biomechanics, affecting plantar load distribution, altering gait, and increasing susceptibility to skin lesions and neuropathic ulcers ( 5 ). Pathophysiological Mechanisms of DPN and Its Relationship with Digital Deformities Despite its high prevalence, the exact pathophysiological mechanisms of DPN have not been fully elucidated. It has been suggested that neuromuscular damage results from a combination of sustained hyperglycemia, microvascular dysfunction, oxidative stress, chronic inflammation, and alterations in neuronal excitability ( 6 , 7 ). In particular, motor unit loss and neuromuscular control dysregulation appear to be responsible for alterations in force generation, movement coordination, and postural stability in DPN patients ( 8 ). A key finding in DPN is the imbalance between intrinsic and extrinsic foot muscles, favoring the development of hammer and claw toes. While intrinsic muscles play an essential role in stabilizing foot arches and aligning the toes, their progressive degeneration in DPN leads to compensatory dominance of extrinsic muscles, resulting in digital contractures ( 9 ). Magnetic resonance imaging and computed tomography studies have demonstrated that patients with DPN and digital deformities exhibit significant atrophy of the lumbrical and interosseous muscles, as well as compensatory thickening of the plantar aponeurosis, altering foot biomechanics and increasing forefoot pressure ( 10 ). Furthermore, finite element model analyses have revealed that hammer toes generate increased internal stress on soft and bony tissues, contributing to an abnormal redistribution of forces during gait and accelerating the structural degeneration of the diabetic foot ( 11 ). Muscle Network Connectivity assessment in Diabetic Peripheral Neuropathy Patients In this context, muscular network connectivity, evaluated using surface electromyography (sEMG), has emerged as a promising tool for analyzing motor activity synchronization and detecting neuromuscular alterations in DPN ( 12 – 14 ). Recent studies have shown that in early stages of DPN, TE and NMI between lower limbs and feet mucles increased, which may be due to neuronal hyperexcitality and compensatory reorganization of the central nervous system (CNS). In contrast, in advanced stages, muscular connectivity these muscle pairs significantly decreases, suggesting a progressive disconnection of motor circuits ( 15 ) ( 16 ). On the other hand, transfer entropy allows the analysis of information flow directionality in motor activity. In healthy individuals, neuromuscular signal transmission follows a predominant pattern from distal to proximal muscles, ensuring proper motor coordination. However, in patients with advanced DPN, this directionality is altered, suggesting dysfunction in the central integration of motor control, which may explain the increased postural instability and predisposition to falls observed in these patients ( 12 , 17 ). Clinical Impact and Alternative Biomarkers in DPN Assessment From a clinical perspective, these findings suggest that IMC and PDC could serve as key neuromuscular biomarkers for monitoring DPN progression and personalizing rehabilitation strategies ( 18 ). It has been observed that motor unit loss initially affects distal muscles, such as the extensor digitorum brevis (ED) and flexor digitorum brevis (FD), before compromising proximal muscles like the tibialis anterior (TA) and gastrocnemius (GM) ( 19 ). Given that access to advanced neuromuscular assessment tools may be limited in resource-constrained settings, hand dynamometry has been proposed as an accessible and useful clinical biomarker for DPN evaluation. Studies have shown that reduced grip strength correlates with lower limb strength loss, postural instability, and the presence of digital deformities in DPN ( 5 ). This correlation suggests that manual dynamometry could serve as a cost-effective and efficient tool for identifying patients with early neuromuscular dysfunction, facilitating diagnosis and preventing falls and functional disability in advanced DPN patients ( 9 ). Since DPN has a multifactorial etiology, it is essential to adopt a comprehensive approach that combines metabolic and neuromuscular indicators to more accurately assess the associated risk factors. In this context, this study aimed to identify predictors of foot deformities and risk in patients with DPN by using a combination of muscular connectivity -assessed with transfer entropy (TE) or normalized mutual information (NMI)-, hand dynamometry, and bioimpedance. METHODS Design This comparative case-control study was conducted at the General Hospital of Elche (Alicante, Spain) between March and June 2023. Participants were divided into two groups: individuals with diabetes (cases) and non-diabetic individuals (controls) to identify predictive biomarkers for DFU risk. Inclusion criteria for cases included a diabetes diagnosis of over five years, age 18–80 years, regular clinical follow-ups, and a diabetic foot risk grade of 0, 1, or 2 per the International Working Group on the Diabetic Foot (IWGDF) classification ( 20 ), alongside recent blood test results. Controls were non-diabetic individuals aged 18–80 years within the hospital’s health department coverage area, with recent blood test results. Exclusion criteria included difficulties walking or standing, prior treatment with plantar orthoses, presence of lower extremity edema, wounds at electrode sites, or a diabetic foot risk grade of 3. Data Collection Participants attended two visits within three days. During the first visit, they received study information, signed informed consent, underwent diabetic foot assessment, and were assigned a risk grade. The second visit included body composition analysis (anthropometry and bioimpedance), hand dynamometry, biochemical parameter collection from recent blood tests, and sEMG recordings of the intrinsic foot muscles. Ethics Approval and Consent to Participate The study followed the principles of the Declaration of Helsinki. Ethical approval was obtained from the Research Ethics Committee HGU Elche-Spain (protocol PI 138/2022) on January 31, 2023. All participants provided signed informed consent. Variables Outcome Variable: Risk Grade of Diabetic Foot The 2019 IWGDF risk stratification system was used to assign a risk grade to each case group participant based on the presence of peripheral arterial disease (PAD), loss of protective sensitivity, or severe complications such as diabetic foot ulcers, lower limb amputation, or end-stage renal disease (20). Additionally, the classification considered the presence of diabetic foot deformities (DFD), including structural abnormalities such as claw toes or prominent metatarsal heads, which can contribute to increased plantar pressure and a higher risk of ulceration. Predictive Variables: Body composition was assessed using the TANITA MC-780MA Segmental Multi-Frequency Analyzer, which provided a comprehensive evaluation of fat mass, muscle quality, total body water, metabolic age, basal metabolic rate, and phase angle (PA)(21). Additionally, the analyzer estimated key bioimpedance-derived metrics, including the extracellular water/total body water (ECW/TBW) ratio and the extracellular mass/body cell mass (ECM/BCM) ratio, which complement PA by offering detailed insights into fluid distribution and tissue composition. PA =(Resistance (R)Reactance (Xc))×( π 180) {1} These parameters, along with measurements of fat-free mass, lean mass, bone mineral content, and visceral fat index, allow for a holistic assessment of cellular health and functional integrity, crucial for understanding the multifactorial risks associated with diabetes. Standardized conditions were followed to ensure measurement reliability: no vigorous exercise within 24 hours, no large meals 2–4 hours prior, no caffeine or alcohol within 8 hours, and an empty bladder before assessment (22). The PA, calculated as an indicator of cellular integrity and functionality, reflects tissue capacitive properties and resistance to electrical current flow, further contributing to the evaluation of muscle quality and metabolic efficiency. sEMG was recorded using the Noraxon Ultium device with software version MR3 3.14. The signals were captured at a sampling rate of 2000 Hz per channel, applying a high-pass filter at 20 Hz and a low-pass filter at 500 Hz. Adhesive electrodes were positioned on the intrinsic foot muscles following SENIAM guidelines to ensure proper skin preparation and optimal electrode placement. The recorded sEMG signals were processed using MATLAB (R2022b, MathWorks, MA, USA), applying band-pass filtering and segmentation algorithms to identify muscle activations. Muscular connectivity metrics (TE and NMI among others) were calculated and the results were shown in (6). Sarcopenia Risk Index. Estimated using the European Working Group on Sarcopenia in Older People (EWGSOP) algorithm (23), which includes assessments of walking speed, muscle strength, and muscle mass: A-Walking Speed: Measured over 4 meters; <0.8 m/s was considered poor performance. B-Muscle Strength: Assessed using the ActivForce 2 mechanical dynamometer. Measurements were performed separately for the dominant and non-dominant hand, with three trials conducted for each. The average of the two highest readings was used for analysis. Low muscle strength was defined as <30 kg per EWGSOP guidelines. Additional Variables Metabolic Age Difference. The difference between metabolic and chronological age offers further insights into aging-related metabolic risks. Biochemical Parameters. Biochemical variables included total cholesterol, LDL, HDL, albumin, total lymphocyte count, glucose, triglycerides, and glycosylated hemoglobin (HbA1c), all obtained from recent clinical blood tests. TyG Index = ln ⁡ (fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2) {2} The Controlling Nutritional Status (CONUT) score was calculated using serum albumin, total lymphocyte count, and total cholesterol levels (24). Statistical Analysis Sample size estimation was performed using Epi Info™, based on 13,182 diabetic individuals in the study area and a 6% expected ulcer frequency. With a 95% confidence level and a 9% confidence limit, the minimum sample size was 26 per group. Data analysis was conducted using Jamovi software. Normality was assessed with the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots. For normally distributed data, Pearson correlations, Student’s t-tests, and one-way ANOVA were used. For non-normal data, Spearman correlations, Mann-Whitney U, or Kruskal-Wallis tests were applied. Statistical significance was set at p < 0.05, with high significance at p < 0.001. Multiple comparison corrections and imputation for missing data were included. Group comparisons involved one-way ANOVA with Tukey post-hoc tests for normal variables and Kruskal-Wallis tests with Dwass-Steel-Critchlow-Fligner post-hoc adjustments for non-normal variables. A two-phase approach assessed correlations and biomarker relevance: initial analysis of all participants followed by stratified analysis by case-control status for validation. Handling Missing Data Missing data (<5%) were identified as \"Missing Completely at Random\" (MCAR). Multiple Imputation by Chained Equations (MICE) in R generated five imputed datasets, with predictor variables encompassing all relevant demographic and clinical data. Post-imputation checks ensured consistency between imputed and original data distributions. Analyses were performed on each imputed dataset, and Rubin’s rules were applied to combine results, ensuring robust estimates. Results Sample Characteristics In the final sample of 65 participants, 28 (43.1%) had type 2 diabetes and 37 (56.9%) were part of the control group. Of the participants, 38.6% were male, and 61.4% were female. Regarding age distribution, 42.0% were between 45 and 64 years old, 39.1% were over 65, and 18.8% were 44 years or younger. Concerning educational background, 7.2% were uneducated, 40.6% had completed primary education, 26.1% had attended secondary education, and the remaining 26.1% had attained a university degree. Regarding income levels, 79.7% reported a medium income, while 20.3% had a low income. Among the 28 participants with type 2 diabetes, all had undergone a complete diabetic foot risk assessment according to the guidelines of the International Working Group on the Diabetic Foot (IWGDF)(20). The distribution was as follows: 10 subjects (35.7%) were classified as low risk, 6 (21.4%) as moderate risk, and 12 (42.9%) as high risk. This demographic composition, with a significant proportion of individuals of middle or advanced age, aligns with the profile typically observed in individuals with type 2 diabetes in healthcare systems within the region. None of the participants in the low-risk group (LW) showed clinically apparent signs of diabetic peripheral neuropathy (DPN), while all individuals in the moderate/high-risk group (MH) exhibited visible signs of DPN. Biomarker Analysis Table 1. Correlations between the studied variables and the diabetic Foot Risk for the whole sample (N=65). Predictive variable Correlation P value a R 2 P value b Relaxed arm circumference 0.064 0.604 0.0040 0.604 Waist circumference 0.443 <0.001 ** 0.1960 <0.001** Non-Dominant Hand DYN -0.340 0.004 ** 0.0786 0.019* Glucose 0.532 <0.001 ** 0.2620 <0.001** HbA1c 0.560 <0.001** 0.3400 <0.001** Triglycerides 0.345 0.004 ** 0.0674 0.034* TYG Index 0.454 <0.001** 0.1910 <0.001** BMI 0.333 0.005 ** 0.0968 0.009** Visceral Fat Index 0.293 0.015* 0.0756 0.022* Sarcopenia Risk Index 0.178 0.143 0.0159 0.302 CONUT 0.430 0.008 ** 0.2810 <0.001** PA -0.332 0.005 ** 0.0969 0.009** Basal Metabolism (Kcal) 0.068 0.580 0.0045 0.581 Metabolic Age 0.370 0.002 ** 0.1370 0.002** Age 0.299 0.012* 0.0833 0.015* Muscular Quality -0.311 0.009** 0.0969 0.009** Tot. Body Water (%) -0.264 0.028* 0.0697 0.028* Abbreviations: BMI, body mass index; CONUT, controlling nutritional status; DYN, dynamometry; HbA1c, glycated hemoglobin; PA, phase angel; TYG index: triglyceride glucose index. a P values by Pearson's coefficient for normal variables and Spearman's for non-normal variables. b P values Linear Regression In terms of anthropometric measurements, waist circumference showed a moderate positive correlation with diabetic foot risk (r = 0.443, p < 0.001; R² = 0.196, p < 0.001), similar to BMI (r = 0.333, p = 0.005; R² = 0.0968, p = 0.009) and visceral fat index (r = 0.293, p = 0.015; R² = 0.0756, p = 0.022) ( Table 1 ) . Likewise, both metabolic age (r = 0.370, p = 0.002; R² = 0.137, p = 0.002) and chronological age (r = 0.299, p = 0.012; R² = 0.0833, p = 0.015) were significantly associated with increased diabetic foot risk. Regarding metabolic parameters, glucose (r = 0.532, p < 0.001; R² = 0.262, p < 0.001) and HbA1c (r = 0.560, p < 0.001; R² = 0.340, p < 0.001) exhibited strong positive correlations, while triglycerides (r = 0.345, p = 0.004; R² = 0.0674, p = 0.034) were also significantly correlated. Likewise, the triglyceride-glucose (TYG) index was positively associated with risk (r = 0.454, p < 0.001; R² = 0.191, p < 0.001). From a nutritional standpoint, the Controlling Nutritional Status (CONUT) score showed a positive association (r = 0.430, p = 0.008; R² = 0.281, p < 0.001). With respect to muscle function and bioimpedance-derived variables, muscle quality (r = -0.311, p = 0.009; R² = 0.0969, p = 0.009), and total body water percentage (r = -0.264, p = 0.028; R² = 0.0697, p = 0.028). Other variables, such as relaxed arm circumference (r = 0.064, p = 0.604; R² = 0.0040, p = 0.604), the sarcopenia risk index (r = 0.178, p = 0.143; R² = 0.0159, p = 0.302), and basal metabolic rate (r = 0.068, p = 0.580; R² = 0.0045, p = 0.581), did not display statistically significant associations with diabetic foot risk. Table 2. Correlations between the studied variables and Non-Dominant Hand Dynamometry ( DYN) for the whole sample (N=65). Predictive variable Correlation P value a R 2 P value b Risk Grade of Diabetic Foot -0,340 0,004 ** 0,078 0,019 * Age -0,344 0,004 ** 0,091 0,011 * Arm Circumference 0,065 0,594 0,033 0,138 Calf Circumference 0,23 0,058 * 0,101 0,008 * 4-Meter Walking Speed -0,338 0,005 ** 0,093 0,013 * HbA1c -0,21 0,097 0,031 0,166 BMI -0,002 0,989 0,024 0,207 Fat-Free Mass 0,381 0,001 ** 0,126 0,003 ** Lean Mass 0,395 0,001 ** 0,293 <0,001 ** Muscle Quality 0,395 <0,001 ** 0,126 0,003 ** Sarcopenia Risk Index 0,381 <0,001 ** 0,251 <0,001 ** Total Body Water 0,471 <0,001 ** 0,23 <0,001 ** Extracellular Water 0,333 0,005 ** 0,098 0,009 * Intracellular Water 0,542 <0,001 ** 0,37 <0,001 ** ECW/TBW Ratio -0,548 <0,001 ** 0,296 <0,001 ** ECM/BCM Ratio -0,351 0,003 ** 0,083 0,017 * Bone Mineral Conten 0,455 <0,001 ** 0,287 <0,001 ** Visceral Fat Index 0,126 0,301 0,094 0,011 * Basal Metabolic Rate 0,447 <0,001 ** 0,284 <0,001 ** PA 0,331 0,003 ** 0,109 0,003 ** Abbreviations: BMI, body mass index; ECW/TBW ratio, extracellular water/total body water ratio; ECM/BCM ratio, extracellular mass/body cell mass ratio; HbA1c, glycated hemoglobin; PA, phase angel. a P values by Pearson's coefficient for normal variables and Spearman's for non-normal variables. b P values Linear Regression The correlation analysis between potential predictors and non-dominant hand dynamometry revealed several significant relationships. Diabetic foot risk grade showed an inverse association (r = -0.340, p = 0.004; R² = 0.078, p = 0.019), indicating lower grip strength in individuals with higher diabetic foot risk. Similarly, older age correlated negatively with DYN (r = -0.344, p = 0.004; R² = 0.091, p = 0.011). Functional performance, measured via 4-meter walking speed, was also inversely linked to handgrip strength (r = -0.338, p = 0.005; R² = 0.093, p = 0.013) (Table 2) . Among anthropometric measures, calf circumference showed a borderline correlation (r = 0.230, p = 0.058), yet reached significance in linear regression (R² = 0.101, p = 0.008). In contrast, arm circumference (r = 0.065, p = 0.594; R² = 0.033, p = 0.138) and BMI (r = -0.002, p = 0.989; R² = 0.024, p = 0.207) did not exhibit statistically significant associations. Body composition variables were generally strong predictors of grip strength. Fat-free mass (r = 0.381, p = 0.001; R² = 0.126, p = 0.003), lean mass (r = 0.395, p = 0.001; R² = 0.293, p < 0.001), and muscle quality (r = 0.395, p < 0.001; R² = 0.126, p = 0.003) all showed positive correlations. Likewise, the sarcopenia risk index (r = 0.381, p < 0.001; R² = 0.251, p < 0.001) emerged as a notable predictor, suggesting that higher muscle mass or quality aligns with improved handgrip performance. Bioimpedance-derived parameters corroborated these findings. Total body water (r = 0.471, p < 0.001; R² = 0.230, p < 0.001) and intracellular water (r = 0.542, p < 0.001; R² = 0.370, p < 0.001) were positively associated with handgrip strength, whereas the ECW/TBW ratio (r = -0.548, p < 0.001; R² = 0.296, p < 0.001) and ECM/BCM ratio (r = -0.351, p = 0.003; R² = 0.083, p = 0.017) showed inverse relationships. These results suggest that a higher proportion of intracellular fluid and lower extracellular fluid accumulation support better muscle function. Bone mineral content (r = 0.455, p < 0.001; R² = 0.287, p < 0.001) and basal metabolic rate (r = 0.447, p < 0.001; R² = 0.284, p < 0.001) were also positively correlated with handgrip strength, pointing to the relevance of both skeletal health and metabolic expenditure. Table 3. Correlations between the studied variables and PA for the whole sample (N=65). Predictive variable Correlation P valuea R2 P valueb Relaxed arm circumference 0.336 0.005* * 0.113 0.005* * Non-Dominant Hand DYN 0.331 0.003** 0.109 0.003** HbA1c -0.251 0.047* 0.0682 0.039* Triglycerides 0.075 0.547 0.0009 0.811 TYG Index 0.054 0.666 0.0006 0.837 BMI 0.091 0.457 0.2333 0.211 Visceral Fat Index 0.049 0.688 0.0004 0.871 Sarcopenia Risk Index 0.330 0.006** 0.0926 0.011* CONUT -0.457 0.005** 0.244 0.002** Basal Metabolism (Kcal) 0.350 0.003** 0.102 0.007** Metabolic Age -0.477 <0.001** 0.160 <0.001** Age -0.547 <0.001** 0.258 <0.001** Muscular Quality 1.000 <0.001** 0.999 <0.001** Tot. Body Water (%) 0.391 <0.001** 0.123 0.003** Abbreviations: BMI, body mass index; CONUT, controlling nutritional status; DYN, dynamometry; HbA1c, glycated hemoglobin; TYG index: triglyceride glucose index. a P values by Pearson's coefficient for normal variables and Spearman's for non-normal variables. b P values Linear Regression The correlation analysis between the examined variables and phase angle (PA) revealed several significant associations. Among anthropometric measures, relaxed arm circumference showed a positive correlation with PA (r = 0.336, p = 0.005; R² = 0.113, p = 0.005), indicating that greater upper-arm muscle mass may support better cellular integrity. In contrast, age exhibited a strong negative correlation (r = -0.547, p < 0.001; R² = 0.258, p < 0.001), suggesting a decline in muscle cell function with advancing age. A similar inverse association emerged for metabolic age (r = -0.477, p < 0.001; R² = 0.160, p < 0.001) were inversely related, indicating a decline in cellular integrity with advancing age (Table 3). Metabolic and nutritional variables also played an important role. Hemoglobin A1c (r = -0.251, p = 0.047; R² = 0.0682, p = 0.039) was inversely related to PA, implying that suboptimal glycemic control may compromise cellular integrity. In addition, the Controlling Nutritional Status (CONUT) score (r = -0.457, p = 0.005; R² = 0.244, p = 0.002) was negatively associated, reinforcing the importance of adequate nutritional status for maintaining muscle cell health. Regarding muscle function and composition, non-dominant hand dynamometry (r = 0.331, p = 0.003; R² = 0.109, p = 0.003) correlated positively with PA, as did the sarcopenia risk index (r = 0.330, p = 0.006; R² = 0.0926, p = 0.011), suggesting that stronger muscle performance and lower sarcopenia risk correspond to enhanced cellular function. Furthermore, muscle quality stood out with a near-perfect association (r = 1.000, p < 0.001; R² = 0.999, p < 0.001), underscoring the essential role of muscle integrity in PA. Basal metabolic rate (r = 0.350, p = 0.003; R² = 0.102, p = 0.007) showed a positive correlation, indicating that higher metabolic activity aligns with better cellular health, and total body water percentage (r = 0.391, p < 0.001; R² = 0.123, p = 0.003) also emerged as a key factor, highlighting the relevance of appropriate fluid distribution. Conversely, several variables did not show significant correlations with PA, including triglycerides (r = 0.075, p = 0.547), triglyceride-glucose index (r = 0.054, p = 0.666), BMI (r = 0.091, p = 0.457), and visceral fat index (r = 0.049, p = 0.688). Table 4. Correlations between the studied variables and the claw toes or prominent metatarsal heads for the whole sample (N=65). Predictive variable Correlation P value a R 2 P value b Relaxed arm circumference 0,336 0,005 ** 0,113 0,005 ** Non-Dominant Hand DYN -0,34 0,004 ** 0,0786 0,019 * Glucose 0,573 <0.001 ** 0,295 <0.001 ** HbA1c 0,56 <0.001 ** 0,34 <0.001 ** Triglycerides 0,434 <0.001 ** 0,137 0,002 * TYG Index 0,565 <0.001 ** 0,314 <0.001 ** BMI 0,239 0,048 * 0,084 0,016 * Visceral Fat Index 0,304 0,011 * 0,019 0,254 Sarcopenia Risk Index 0,187 0,124 0,065 0,035 * CONUT 0,345 0,037 * 0,19 0,007 * PA -0,285 0,018 * 0,075 0,023 * Basal Metabolism (Kcal) 0,35 0,003 ** 0,102 0,007 * Metabolic Age -0,477 <0.001 ** 0,16 <0.001 ** Age -0,547 <0.001 ** 0,258 <0.001 ** Muscular Quality -0,311 0,009 * 0,0969 0,009 * Tot. Body Water (%) 0,391 <0.001 ** 0,123 0,003 ** Abbreviations: BMI, body mass index; CONUT, controlling nutritional status; DYN, dynamometry; HbA1c, glycated hemoglobin; PA, phase angel; TYG index: triglyceride glucose index. a P values by Pearson's coefficient for normal variables and Spearman's for non-normal variables. b P values Linear Regression The correlation analysis between the predictive variables and the presence of claw toes or prominent metatarsal heads (N = 65) revealed several significant associations (Table 4). Among anthropometric measures, relaxed arm circumference showed a moderate positive correlation (r = 0.336, p = 0.005; R² = 0.113, p = 0.005), whereas non-dominant hand dynamometry was inversely associated with these foot deformities (r = -0.340, p = 0.004; R² = 0.0786, p = 0.019). Regarding metabolic parameters, both glucose (r = 0.573, p < 0.001; R² = 0.295, p < 0.001) and HbA1c (r = 0.560, p < 0.001; R² = 0.340, p < 0.001) exhibited strong positive correlations, underscoring the role of poor glycemic control in the development of structural foot abnormalities. Similarly, dyslipidemia and insulin resistance appear to contribute to these complications, as evidenced by the significant associations observed for triglycerides (r = 0.434, p < 0.001; R² = 0.137, p = 0.002) and the triglyceride-glucose (TYG) index (r = 0.565, p < 0.001; R² = 0.314, p < 0.001). Body composition indices also played a role; BMI was positively correlated with foot deformities (r = 0.239, p = 0.048; R² = 0.084, p = 0.016), and visceral fat index showed a significant positive correlation by Pearson’s analysis (r = 0.304, p = 0.011), although its linear regression did not reach statistical significance (R² = 0.019, p = 0.254). The sarcopenia risk index, while showing a modest positive correlation (r = 0.187, p = 0.124), was significantly associated with foot deformities in the regression model (R² = 0.065, p = 0.035). Nutritional status also appears to be an important determinant, as reflected by the moderate positive correlation observed for the CONUT score (r = 0.345, p = 0.037; R² = 0.190, p = 0.007). Conversely, phase angle (PA), a marker of cellular integrity, was negatively associated with the presence of claw toes or prominent metatarsal heads (r = -0.285, p = 0.018; R² = 0.075, p = 0.023), indicating that diminished cellular health may contribute to foot structural alterations. Additional metabolic and musculoskeletal parameters further elucidated these relationships. Basal metabolic rate (r = 0.350, p = 0.003; R² = 0.102, p = 0.007) was positively correlated with foot deformities, suggesting that metabolic inefficiencies may be implicated. Moreover, both age (r = -0.547, p < 0.001; R² = 0.258, p < 0.001) and metabolic age (r = -0.477, p < 0.001; R² = 0.160, p < 0.001) were strongly and negatively correlated with these structural abnormalities, highlighting the impact of advanced biological aging. Finally, muscular quality was inversely associated (r = -0.311, p = 0.009; R² = 0.0969, p = 0.009), while total body water percentage was positively correlated (r = 0.391, p < 0.001; R² = 0.123, p = 0.003) with the presence of claw toes or prominent metatarsal heads. Table 5. One-Way ANOVA (Fisher’s) and Tukey Post-Hoc Test. anova Post Hoc v ariables F P value a Comparisons P value b PA 6.99 0.002 ** C vs 0 0.999 C vs 1-2 0.002 ** 0 vs 1-2 0.029 * Fat Mass (%) 3.42 0.039 * C vs 0 0.985 C vs 1-2 0.036 * 0 vs 1-2 0.210 Fat-Free Mass (%) 3.24 0.045 * C vs 0 0.996 C vs 1-2 0.044 * 0 vs 1-2 0.205 Muscle Quality 6.99 0.002 ** C vs 0 0.999 C vs 1-2 0.002 ** 0 vs 1-2 0.029 * Total Body Water (%) 3.79 0.028 * C vs 0 0.996 C vs 1-2 0.027 * 0 vs 1-2 0.156 Metabolic Age 5.53 0.006 ** C vs 0 0.956 C vs 1-2 0.008 ** 0 vs 1-2 0.037 * Non-Dominant Hand DYN 4,04 0.022* C vs 0 0.758 C vs 1-2 0.0089 ** 0 vs 1-2 0.031 * Abbreviations: DYN, dynamometry; PA, phase angel. a P value ANOVA Test b P value Tukey post-hoc test The one-way ANOVA (Fisher’s test) and subsequent Tukey post-hoc analysis revealed significant differences among control participants (C) and individuals classified into diabetic foot risk groups (0 and 1–2) according to the IWGDF criteria (Table 5). Phase angle (PA) differed significantly between groups (F = 6.99, p = 0.002), with controls exhibiting significantly higher PA values than the risk grade 1–2 group (p = 0.002), and a notable difference also observed between risk grade 0 and risk grade 1–2 (p = 0.029), indicating a progressive decline in cellular integrity with increasing diabetic foot risk. Body composition parameters also varied significantly across groups. Fat mass (%) showed significant overall differences (F = 3.42, p = 0.039), with controls having lower fat mass compared to the risk grade 1–2 group (p = 0.036). Similarly, fat-free mass (%) differed significantly (F = 3.24, p = 0.045), with controls displaying higher values than those in risk grade 1–2 (p = 0.044). Muscle quality, a critical indicator of neuromuscular function, also showed significant group differences (F = 6.99, p = 0.002); controls exhibited markedly better muscle quality than risk grade 1–2 (p = 0.002), and a significant decline was evident when comparing risk grade 0 with risk grade 1–2 (p = 0.029). Total body water percentage varied significantly as well (F = 3.79, p = 0.028), with the control group demonstrating higher hydration status than the risk grade 1–2 group (p = 0.027). Metabolic age was significantly different across groups (F = 5.53, p = 0.006). Controls had a significantly lower metabolic age compared to individuals in risk grade 1–2 (p = 0.008), and a significant increase in metabolic age was observed between risk grade 0 and risk grade 1–2 (p = 0.037), suggesting accelerated metabolic deterioration with increased diabetic foot risk. Finally, non-dominant hand dynamometry exhibited significant differences (F = 4.04, p = 0.022), with risk grade 1–2 participants demonstrating significantly lower grip strength than controls (p = 0.0089), and subjects in risk grade 0 showing greater strength compared to those in risk grade 1–2 (p = 0.031). Discussion This discussion builds on the findings previously published by Junquera-Godoy et al. (2024)(6), who analyzed sEMG activity in 60 of the 65 participants included in this sample. In that earlier study, they reported a reduction in sEMG amplitude in the tibialis anterior and extensor digitorum brevis muscles, accompanied by an increase in the mean frequency of the signal, which was interpreted as indicative of a progressive denervation process and loss of motor units. Furthermore, significant alterations in intermuscular muscular network connectivity mechanisms due DPN were observed with the TE parameter showing the best performance in discriminating DPN patients, even at early stages: TE from medial gastrocnemius-flexor digitorum brevis and medial gastrocnemius-extensor digitorum brevis muscle pairs differentiated could be potential biomarkers for early DPN detection. The data revealed a significant increase in information transfer and muscle connectivity in the LW group with respect to the CT group, while the MH group obtained significantly lower values for these metrics than the other two groups. These findings could uncover essential neuromuscular mechanisms for clinical practice, aid in developing suitable rehabilitation strategies, and act as biomarkers for tracking muscle synergy evolution (7). Building on these observations, the present study broadens the scope by evaluating metabolic, anthropometric, and systemic neuromuscular functionality markers, focusing especially on claw toe deformity and its relationship to motor dysfunction.The results reinforce the premise that claw toe deformity cannot be explained solely by unrecognized fractures or microtrauma associated with loss of sensation. Rather, its primary origin lies in neuromuscular deterioration (2,25). Although the combination of muscle weakness and biomechanical instability could predispose to microfractures that may hasten or exacerbate the deformity (26), evidence points to the loss of motor units, and the alteration of muscle synergy as the predominant pathophysiological mechanism (27). In particular, the imbalance between extensor and flexor muscles in the foot, documented through sEMG (7), translates into abnormal activation patterns, compensatory overactivation, and ultimately global desynchronization in advanced neuropathy (28). This progressive motor disorganization is reflected in the digitiform posture characteristic of claw toe. Intrinsic muscle atrophy of the foot emerges as a key factor in the development of claw toe deformity. Evidence shows a progressive reduction in the cross-sectional area of the interosseous and lumbrical muscles (29-31), leading to partial collapse of the plantar arch and impairing normal toe alignment (32, 33). Our findings reinforce this association, demonstrating that claw toe deformity coexists with markers of poor metabolic control, including HbA1c (r = 0.560, R² = 0.340, p < 0.001), blood glucose (r = 0.573, R² = 0.295, p < 0.001), and the triglyceride-glucose index (TYG) (r = 0.565, R² = 0.314, p < 0.001). These findings are consistent with previous studies linking chronic hyperglycemia to an acceleration of the denervation process (28, 30). Consequently, dysregulated glycemia further impairs foot biomechanics, increasing the likelihood of both digital deformities and ulcerations. From a systemic perspective, hand dynamometry in the non-dominant hand was inversely correlated with diabetic foot risk (r = -0.340, R² = 0.0786, p = 0.019), suggesting a global pattern of neuromuscular dysfunction. This aligns with observations in the distal musculature of the lower extremities, where lower electromyographic amplitude, reduced motor synchronization, and decreased strength have been documented (7, 25). Moreover, the reduction in phase angle (PA) (r = -0.332, R² = 0.0969, p = 0.009) and poorer muscle quality (r = -0.311, R² = 0.0969, p = 0.009) further corroborate that cellular deterioration and fatty infiltration extend beyond the lower limbs, affecting other body segments. The comparative group analysis (ANOVA) revealed significant differences in handgrip strength (F = 4.04, p = 0.022), particularly between participants at high diabetic peripheral neuropathy (DPN) risk and controls (p = 0.0089). Additionally, subjects in risk grade 0 exhibited greater strength than those in risk grade 1–2 (p = 0.031), supporting the notion of progressive—rather than merely localized—muscle impairment. These results highlight the need for early detection and targeted interventions to mitigate neuromuscular decline and preserve functional capacity in individuals at risk for DPN. This scenario calls for a multifaceted therapeutic approach: optimal glycemic control, targeted muscle strengthening, and neuromotor training. Proposed methods include functional electrical stimulation, vibration therapy, and specific exercise programs (33), which could slow atrophy and preserve motor connectivity (35, 36). Recent clinical trials have also explored novel therapeutic options for diabetic peripheral neuropathy, such as PDA-002, highlighting the ongoing search for effective treatments (36). This approach is consistent with studies demonstrating improvements in muscular connectivity through the reinforcement of distal musculature (28, 32). Nonetheless, the variability in treatment protocols and the lack of consensus on frequency and duration limit the generalization of results (29). Future longitudinal investigations should incorporate high-resolution electromyographic measurements and indicators of strength and body composition to evaluate the temporal progression of claw toe deformity and validate the efficacy of various interventions (6). In conclusion, this study—complementary to the previous sEMG-based analysis of the same cohort (7)—supports the view that claw toe deformity arises from a multifactorial process led by neuromuscular disorganization and worsened by poor metabolic control. Although microfractures (25) may secondarily contribute, the determining factor is the loss of synchrony and atrophy of the intrinsic foot musculature, ultimately constraining foot biomechanics and confirming recent findings regarding the differential impact of diabetic neuropathy on distal muscle weakness (38). Reduced intermuscular connectivity in dorsiflexor and plantar flexor muscle pairs and metabolic imbalance highlight the need for comprehensive intervention programs. Optimizing neural connectivity and preserving muscle mass could delay or mitigate contractures and severe complications, underscoring the imperative to design rehabilitation protocols coupled with rigorous clinical follow-up. In this way, integrating accessible tools (dynamometry, bioimpedance) with advanced methods (electromyography, IMC analysis) offers a holistic and promising perspective for improving quality of life in patients with DPN (39). Study Limitations and Future Research Although the results of this study provide valuable insight into the relationship between neuromuscular dysfunction and the onset of digital deformities in diabetic peripheral neuropathy (DPN), several limitations must be taken into account when interpreting the findings. First, this is a case-control design conducted at a single hospital center with a moderately small sample size. Such a setup limits the generalizability of the data to other populations and clinical settings, and reduces the statistical power to detect more nuanced or subtle differences. Second, although the diabetic foot risk classification outlined by the International Working Group on the Diabetic Foot (IWGDF) was applied, patients with risk grade 3 were not included. This exclusion was based on concerns about skin integrity during the application and removal of sEMG electrodes, given the lesions and dermal fragility characteristic of advanced disease stages. As a result, the range of severity examined does not encompass the most severe form of diabetic foot, which may lead to an underestimation of the clinical complexity associated with more advanced DPN. Likewise, the neuromuscular assessment was performed through sEMG and included measuring non-dominant hand strength. Although manual dynamometry has been proposed as a highly useful complementary biomarker, this approach may not capture all the dimensions of motor dysfunction in the lower extremities, the main focus of diabetic foot pathology. Moreover, factors such as potential musculoskeletal issues in the wrist or elbow and interindividual variability can affect grip strength values, making it difficult to directly extrapolate these findings to the muscular status of the foot and leg. Regarding body composition, although bioimpedance analysis is validated, it remains subject to fluctuations related to hydration status, fat distribution, and the patient’s nutritional condition. These variables can result in changes to phase angle, the ratio of intracellular to extracellular water, and other markers of cellular health, thus limiting the precision of the estimates. Finally, the cross-sectional nature of the study prevents establishing causality between neuromuscular changes, metabolic control, and the appearance or progression of digital deformities. Future longitudinal studies with larger sample sizes and extended follow-up periods will be needed to confirm the progression of DPN and to assess the efficacy of therapeutic interventions aimed at preventing severe complications in the diabetic foot. Conclusion This study demonstrates that diabetic peripheral neuropathy (DPN) is a systemic condition involving neuromuscular deterioration and metabolic dysfunction, with consequences that extend beyond the local foot structure. First, the observed correlation between digital deformity (claw toe), glycemic imbalance (HbA1c, fasting glucose, and the triglyceride-glucose index), and intrinsic muscle atrophy underscores the critical role of chronic hyperglycemia in the progressive loss of motor function. The disruption of muscle synergy—driven by the reduction in motor units, decreased transfer entropy in the advanced stages, but increased in early stages, and altered recruitment patterns—directly affects foot biomechanics, facilitating contractures and deformities. Moreover, these findings highlight the influence of factors such as chronological and “metabolic” age, a reduced phase angle, and diminished muscle quality, which collectively heighten the vulnerability of the diabetic foot to injury and ulceration. Additionally, our evidence underscores the value of non-dominant hand dynamometry as a cost-effective and complementary biomarker for the early detection of neuromuscular dysfunction associated with DPN. The inverse relationship between grip strength and diabetic foot risk—along with the higher prevalence of digital deformities in individuals presenting elevated HbA1c or triglyceride-glucose index—emphasizes the need for a holistic approach that integrates both muscular/nutritional evaluation and stringent glycemic control. In this regard, bioimpedance analysis emerges as a relevant tool for assessing cellular integrity and fluid distribution, given that reduced intracellular water and expanded extracellular compartments appear to indicate greater tissue fragility and lower functional capacity. Finally, the comparative analysis across risk groups supports a progressive and multifactorial pattern of deterioration. Integrating personalized therapeutic strategies—ranging from metabolic interventions (to optimize glycemic control) to rehabilitation protocols focused on restoring muscle strength and neuromuscular connectivity—may help contain or even delay the onset of digital deformities and severe diabetic foot complications. This underlines the importance of early intervention programs and close monitoring, drawing on accessible tools (dynamometry, bioimpedance measurements) and advanced methods (electromyography, muscular connectivity analysis by transfer entropy computed on surface electromyographic recordings) to enhance clinical surveillance. Future longitudinal studies with larger samples will be essential for validating these recommendations and refining prevention and treatment protocols aimed at minimizing the functional impact of DPN. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. Ethical approvals were obtained from the Research Ethics Committee. Participants were informed of the study's objective and provided signed written informed consent. Consent for publication “Not applicable” Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors report no conflict of interest. Funding This study was supported by grants from the Agència Valenciana de la Innovació (INNEST/2021/365) and the promoter POLISABIO (POLISABIO22_AP05). CrediT statement E. Soler-Climent: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. E. Melendez Oliva: review & editing, Validation, Formal analysis. J. Roman-Marroqui: review & editing, Validation, Methodology, Formal analysis. C. Martinez-Corbalan: Writing – review & editing, Writing – original draft, Supervision, Methodology, Formal analysis. G. Prats-Boluda: Writing – review & editing, Validation, Supervision, Funding acquisition, Project administration. I. Junquera-Godoy: Writing – review & editing, Validation, Supervision, Funding acquisition, Project administration G. Gonzalez-Lorente: Writing – review & editing, Validation, Formal analysis. J. L. Martinez-de-Juan: Writing – review & editing, Validation, Methodology, Formal analysis. R.M. Cuadrado-Zaplana: Writing – review & editing, Writing – original draft, Validation, Supervision, Investigation. Acknowledgements “Not applicable” References Kimura, T., Thorhauer, E. D., Kindig, M. W., Shofer, J. B., Sangeorzan, B. J., & Ledoux, W. R. (2020). Neuropathy, claw toes, intrinsic muscle volume, and plantar aponeurosis thickness in diabetic feet. 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Journal of the peripheral nervous system : JPNS , 26 (3), 276–289. https://doi.org/10.1111/jns.12457 Van Eetvelde BLM, Lapauw B, Proot P, Vanden Wyngaert K, Helleputte S, Stautemas J, Cambier DC, Calders P. The impact of diabetic neuropathy on the distal versus proximal comparison of weakness in lower and upper limb muscles of patients with type 2 diabetes mellitus: a cross-sectional study. J Musculoskelet Neuronal Interact. 2021 Dec 1;21(4):464-474. PMID: 34854385; PMCID: PMC8672402. Salmen, T., Pietrosel, V. A., Hernest, G., Chiper, G. V., Florea, D. E., Popa, L. M., ... & Radulian, G. (2020). Early Diagnosis of Peripheral Diabetic Neuropathy–Something Old that Should Always Be Considered Something New. Romanian Journal of Diabetes Nutrition and Metabolic Diseases , 27 (2), 99-103. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6812313\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":506044315,\"identity\":\"2acd1109-b813-4840-aa59-97ce7d47656b\",\"order_by\":0,\"name\":\"Esther Soler-Climent\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie2QMQrCMBSGXxDskgPEqVdoR6GaqxiETro7vlJolqhrj2PI4NIDOAoOrrpVKWqqKLi0ugnmm/7hffAlAA7HT9LB11gdPlPIQ6F26PxrpUM/EXyZJLsZRJx7OjHR2fjgyW2jEhQ6DQuIhaICzXRhQqRF0KwwkfUQzIgyYhUVE2STlrBcyBPCld+Vvoo5+vvmMNiIzH7AiqhagTISyKAlzL7Fho2FKgTqOUbjjE6aFV+m+ogw5J405lBWbLD01i1hNeTyHBlAt/3+jerLe4fD4fgLbjtfSCfc+njwAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Conselleria de Sanitat Universal i Salut Pública\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Esther\",\"middleName\":\"\",\"lastName\":\"Soler-Climent\",\"suffix\":\"\"},{\"id\":506044316,\"identity\":\"071b3779-db44-45a5-a4ae-58b84ad42f95\",\"order_by\":1,\"name\":\"Erika Melendez-Oliva\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Alicante\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Erika\",\"middleName\":\"\",\"lastName\":\"Melendez-Oliva\",\"suffix\":\"\"},{\"id\":506044319,\"identity\":\"3085aae3-4081-4591-ae52-0cc722e0cfee\",\"order_by\":2,\"name\":\"Jessica Román-Marroquí\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Conselleria de Sanitat Universal i Salut Pública\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jessica\",\"middleName\":\"\",\"lastName\":\"Román-Marroquí\",\"suffix\":\"\"},{\"id\":506044321,\"identity\":\"10923139-6b23-43b4-b1e9-14a3290e7c8c\",\"order_by\":3,\"name\":\"Carmen Martinez-Corbalán\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Conselleria de Sanitat Universal i Salut Pública\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Carmen\",\"middleName\":\"\",\"lastName\":\"Martinez-Corbalán\",\"suffix\":\"\"},{\"id\":506044323,\"identity\":\"2ca6da9a-6419-4972-9cf0-be7835c77a92\",\"order_by\":4,\"name\":\"Gema Prats-Boluda\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universitat Poltècnica de València\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gema\",\"middleName\":\"\",\"lastName\":\"Prats-Boluda\",\"suffix\":\"\"},{\"id\":506044325,\"identity\":\"ec937969-ea99-4d9f-ad56-6058993f5ab3\",\"order_by\":5,\"name\":\"Isabel Junquera-Godoy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universitat Poltècnica de València\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Isabel\",\"middleName\":\"\",\"lastName\":\"Junquera-Godoy\",\"suffix\":\"\"},{\"id\":506044326,\"identity\":\"c0340a56-531e-448b-99bb-a6650afbeb96\",\"order_by\":6,\"name\":\"Gemma Gonzalez-Lorente\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universitat Poltècnica de València\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gemma\",\"middleName\":\"\",\"lastName\":\"Gonzalez-Lorente\",\"suffix\":\"\"},{\"id\":506044327,\"identity\":\"7e802770-1ee0-44da-99c2-b092340b9b07\",\"order_by\":7,\"name\":\"Jose Luis Martinez-de-Juan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universitat Poltècnica de València\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jose\",\"middleName\":\"Luis\",\"lastName\":\"Martinez-de-Juan\",\"suffix\":\"\"},{\"id\":506044328,\"identity\":\"769ae58e-d175-4575-a440-0311b0ab432a\",\"order_by\":8,\"name\":\"Rosa María Cuadrado-Zaplana\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Conselleria de Sanitat Universal i Salut Pública\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rosa\",\"middleName\":\"María\",\"lastName\":\"Cuadrado-Zaplana\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-03 14:23:24\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6812313/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6812313/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":91166078,\"identity\":\"0dc80312-f2eb-4650-a816-a2439cf05c35\",\"added_by\":\"auto\",\"created_at\":\"2025-09-12 10:31:40\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1891395,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6812313/v1/ae87b7a1-7def-40b0-a434-479639a3688c.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Integrated Analysis of Neuromuscular Dysfunction and Metabolic Dysregulation in Diabetic Peripheral Neuropathy: Associations with Digital Deformities and Clinical Risk Stratification in a Case-Control Study\",\"fulltext\":[{\"header\":\"Highlights\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eIntegrated Approach to DPN Assessment:\\u003c/strong\\u003e-Anthropometric, metabolic, and neuromuscular indicators (sEMG, IMC/PDC, hand dynamometry, bioimpedance) were combined to more precisely characterize diabetic peripheral neuropathy progression and its association with claw and hammer toe deformities.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMuscle Imbalance as a Primary Mechanism of Deformities\\u003c/strong\\u003e:The loss of motor units and disrupted synergy between intrinsic and extrinsic foot muscles are the main drivers behind digital deformities, with muscle atrophy and thickening of the plantar aponeurosis further compromising foot biomechanics and increasing the risk of injury.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAdvances in Neuromuscular Assessment (TE and NMI)\\u003c/strong\\u003e: Transfer Entropy (TE) and Normalized Mutual Information (NMI)) quantify intermuscular coupling. In early DPN, TE between lower limbs and feet increases compared to controls due to compensatory mechanisms and decreases in advanced DPN stages, indicating progressive motor disconnection.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLink Between Handgrip Strength and Diabetic Foot Risk\\u003c/strong\\u003e : Non-dominant hand dynamometry shows an inverse relationship with diabetic foot risk, suggesting its utility as a low-cost biomarker for early detection of neuromuscular dysfunction, particularly where diagnostic resources are limited.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eImportance of Body Composition and Metabolic Control\\u003c/strong\\u003e: Bioimpedance measurements (phase angle, intracellular/extracellular water ratio, muscle quality) highlight connections between nutritional status, body composition, and diabetic foot risk. Chronic hyperglycemia, the triglyceride-glucose index, and dyslipidemia are associated with digital deformities, underscoring the need for strict metabolic control.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNew Therapeutic Proposals Based on Muscular Connectivity\\u003c/strong\\u003e: Observed alterations in TE and NMI in connectivity in the muscles of the lower extremities and feet suggest that interventions such as functional electrical stimulation, targeted neuromuscular rehabilitation, and intrinsic muscle strengthening may slow atrophy and improve motor synchronization, particularly in early and moderate stages. Longitudinal studies with high-resolution electromyographic measurements and extended clinical follow-up are needed to determine their efficacy.\\u003c/p\\u003e\"},{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eDiabetic peripheral neuropathy (DPN) is one of the most common and disabling complications of diabetes mellitus (DM), affecting approximately 50% of diabetic patients at some point in the disease (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). Its progression involves the gradual deterioration of peripheral nerve fibers, impacting sensory, motor, and autonomic nervous system functions, leading to symptoms such as dysesthesias, neuropathic pain, loss of motor control, and postural alterations that increase the risk of falls and functional disability (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eEarly diagnosis of DPN is crucial, as its progression can lead to severe complications such as diabetic foot ulcers, infections, amputations, and musculoskeletal deformities (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). Among these, hammer and claw toes are of particular interest due to their impact on foot biomechanics, affecting plantar load distribution, altering gait, and increasing susceptibility to skin lesions and neuropathic ulcers (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003ePathophysiological Mechanisms of DPN and Its Relationship with Digital Deformities\\u003c/h3\\u003e\\n\\u003cp\\u003eDespite its high prevalence, the exact pathophysiological mechanisms of DPN have not been fully elucidated. It has been suggested that neuromuscular damage results from a combination of sustained hyperglycemia, microvascular dysfunction, oxidative stress, chronic inflammation, and alterations in neuronal excitability (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). In particular, motor unit loss and neuromuscular control dysregulation appear to be responsible for alterations in force generation, movement coordination, and postural stability in DPN patients (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eA key finding in DPN is the imbalance between intrinsic and extrinsic foot muscles, favoring the development of hammer and claw toes. While intrinsic muscles play an essential role in stabilizing foot arches and aligning the toes, their progressive degeneration in DPN leads to compensatory dominance of extrinsic muscles, resulting in digital contractures (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eMagnetic resonance imaging and computed tomography studies have demonstrated that patients with DPN and digital deformities exhibit significant atrophy of the lumbrical and interosseous muscles, as well as compensatory thickening of the plantar aponeurosis, altering foot biomechanics and increasing forefoot pressure (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e). Furthermore, finite element model analyses have revealed that hammer toes generate increased internal stress on soft and bony tissues, contributing to an abnormal redistribution of forces during gait and accelerating the structural degeneration of the diabetic foot (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eMuscle Network Connectivity assessment in Diabetic Peripheral Neuropathy Patients\\u003c/h2\\u003e\\u003cp\\u003eIn this context, muscular network connectivity, evaluated using surface electromyography (sEMG), has emerged as a promising tool for analyzing motor activity synchronization and detecting neuromuscular alterations in DPN (\\u003cspan additionalcitationids=\\\"CR13\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). Recent studies have shown that in early stages of DPN, TE and NMI between lower limbs and feet mucles increased, which may be due to neuronal hyperexcitality and compensatory reorganization of the central nervous system (CNS). In contrast, in advanced stages, muscular connectivity these muscle pairs significantly decreases, suggesting a progressive disconnection of motor circuits (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e) (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eOn the other hand, transfer entropy allows the analysis of information flow directionality in motor activity. In healthy individuals, neuromuscular signal transmission follows a predominant pattern from distal to proximal muscles, ensuring proper motor coordination. However, in patients with advanced DPN, this directionality is altered, suggesting dysfunction in the central integration of motor control, which may explain the increased postural instability and predisposition to falls observed in these patients (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eClinical Impact and Alternative Biomarkers in DPN Assessment\\u003c/h3\\u003e\\n\\u003cp\\u003eFrom a clinical perspective, these findings suggest that IMC and PDC could serve as key neuromuscular biomarkers for monitoring DPN progression and personalizing rehabilitation strategies (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). It has been observed that motor unit loss initially affects distal muscles, such as the extensor digitorum brevis (ED) and flexor digitorum brevis (FD), before compromising proximal muscles like the tibialis anterior (TA) and gastrocnemius (GM) (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eGiven that access to advanced neuromuscular assessment tools may be limited in resource-constrained settings, hand dynamometry has been proposed as an accessible and useful clinical biomarker for DPN evaluation. Studies have shown that reduced grip strength correlates with lower limb strength loss, postural instability, and the presence of digital deformities in DPN (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThis correlation suggests that manual dynamometry could serve as a cost-effective and efficient tool for identifying patients with early neuromuscular dysfunction, facilitating diagnosis and preventing falls and functional disability in advanced DPN patients (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eSince DPN has a multifactorial etiology, it is essential to adopt a comprehensive approach that combines metabolic and neuromuscular indicators to more accurately assess the associated risk factors. In this context, this study aimed to identify predictors of foot deformities and risk in patients with DPN by using a combination of muscular connectivity -assessed with transfer entropy (TE) or normalized mutual information (NMI)-, hand dynamometry, and bioimpedance.\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eDesign\\u003c/h2\\u003e\\u003cp\\u003eThis comparative case-control study was conducted at the General Hospital of Elche (Alicante, Spain) between March and June 2023. Participants were divided into two groups: individuals with diabetes (cases) and non-diabetic individuals (controls) to identify predictive biomarkers for DFU risk. Inclusion criteria for cases included a diabetes diagnosis of over five years, age 18\\u0026ndash;80 years, regular clinical follow-ups, and a diabetic foot risk grade of 0, 1, or 2 per the International Working Group on the Diabetic Foot (IWGDF) classification (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e), alongside recent blood test results. Controls were non-diabetic individuals aged 18\\u0026ndash;80 years within the hospital\\u0026rsquo;s health department coverage area, with recent blood test results. Exclusion criteria included difficulties walking or standing, prior treatment with plantar orthoses, presence of lower extremity edema, wounds at electrode sites, or a diabetic foot risk grade of 3.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eData Collection\\u003c/h3\\u003e\\n\\u003cp\\u003eParticipants attended two visits within three days. During the first visit, they received study information, signed informed consent, underwent diabetic foot assessment, and were assigned a risk grade. The second visit included body composition analysis (anthropometry and bioimpedance), hand dynamometry, biochemical parameter collection from recent blood tests, and sEMG recordings of the intrinsic foot muscles.\\u003c/p\\u003e\\u003ch3\\u003eEthics Approval and Consent to Participate\\u003c/h3\\u003e\\n\\u003cp\\u003eThe study followed the principles of the Declaration of Helsinki. Ethical approval was obtained from the Research Ethics Committee HGU Elche-Spain (protocol PI 138/2022) on January 31, 2023. All participants provided signed informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eVariables\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eOutcome Variable: \\u0026nbsp;Risk Grade of Diabetic Foot\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe 2019 IWGDF risk stratification system was used to assign a risk grade to each case group participant based on the presence of peripheral arterial disease (PAD), loss of protective sensitivity, or severe complications such as diabetic foot ulcers, lower limb amputation, or end-stage renal disease (20). Additionally, the classification considered the presence of diabetic foot deformities (DFD), including structural abnormalities such as claw toes or prominent metatarsal heads, which can contribute to increased plantar pressure and a higher risk of ulceration.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ePredictive Variables:\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBody composition was assessed using the TANITA MC-780MA Segmental Multi-Frequency Analyzer, which provided a comprehensive evaluation of fat mass, muscle quality, total body water, metabolic age, basal metabolic rate, and phase angle (PA)(21). Additionally, the analyzer estimated key bioimpedance-derived metrics, including the extracellular water/total body water (ECW/TBW) ratio and the extracellular mass/body cell mass (ECM/BCM) ratio, which complement PA by offering detailed insights into fluid distribution and tissue composition.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePA\\u0026nbsp;=(Resistance\\u0026nbsp;(R)Reactance\\u0026nbsp;(Xc))\\u0026times;(\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026pi;\\u003c/strong\\u003e\\u003cstrong\\u003e180) {1}\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThese parameters, along with measurements of fat-free mass, lean mass, bone mineral content, and visceral fat index, allow for a holistic assessment of cellular health and functional integrity, crucial for understanding the multifactorial risks associated with diabetes.\\u003c/p\\u003e\\n\\u003cp\\u003eStandardized conditions were followed to ensure measurement reliability: no vigorous exercise within 24 hours, no large meals 2\\u0026ndash;4 hours prior, no caffeine or alcohol within 8 hours, and an empty bladder before assessment (22). \\u0026nbsp;The PA, calculated as an indicator of cellular integrity and functionality, reflects tissue capacitive properties and resistance to electrical current flow, further contributing to the evaluation of muscle quality and metabolic efficiency.\\u003c/p\\u003e\\n\\u003cp\\u003esEMG was recorded using the Noraxon Ultium device with software version MR3 3.14. The signals were captured at a sampling rate of 2000 Hz per channel, applying a high-pass filter at 20 Hz and a low-pass filter at 500 Hz. Adhesive electrodes were positioned on the intrinsic foot muscles following SENIAM guidelines to ensure proper skin preparation and optimal electrode placement. The recorded sEMG signals were processed using MATLAB (R2022b, MathWorks, MA, USA), applying band-pass filtering and segmentation algorithms to identify muscle activations. Muscular connectivity metrics (TE and NMI among others) were calculated and the results were shown in (6).\\u003c/p\\u003e\\n\\u003cp\\u003eSarcopenia Risk Index. Estimated using the European Working Group on Sarcopenia in Older People (EWGSOP) algorithm (23), which includes assessments of walking speed, muscle strength, and muscle mass: \\u0026nbsp;A-Walking Speed: Measured over 4 meters; \\u0026lt;0.8 m/s was considered poor performance. B-Muscle Strength: Assessed using the ActivForce 2 mechanical dynamometer. Measurements were performed separately for the dominant and non-dominant hand, with three trials conducted for each. The average of the two highest readings was used for analysis. Low muscle strength was defined as \\u0026lt;30 kg per EWGSOP guidelines.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAdditional Variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMetabolic Age Difference.\\u0026nbsp;The difference between metabolic and chronological age offers further insights into aging-related metabolic risks.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBiochemical Parameters.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBiochemical variables included total cholesterol, LDL, HDL, albumin, total lymphocyte count, glucose, triglycerides, and glycosylated hemoglobin (HbA1c), all obtained from recent clinical blood tests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTyG Index = ln\\u003c/strong\\u003e\\u003cstrong\\u003e⁡\\u003c/strong\\u003e\\u003cstrong\\u003e(fasting triglycerides (mg/dL) \\u0026times; fasting glucose (mg/dL) / 2) {2}\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Controlling Nutritional Status (CONUT) score was calculated using serum albumin, total lymphocyte count, and total cholesterol levels (24).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSample size estimation was performed using Epi Info\\u0026trade;, based on 13,182 diabetic individuals in the study area and a 6% expected ulcer frequency. With a 95% confidence level and a 9% confidence limit, the minimum sample size was 26 per group. \\u0026nbsp; Data analysis was conducted using Jamovi software. Normality was assessed with the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots. For normally distributed data, Pearson correlations, Student\\u0026rsquo;s t-tests, and one-way ANOVA were used. For non-normal data, Spearman correlations, Mann-Whitney U, or Kruskal-Wallis tests were applied. Statistical significance was set at p \\u0026lt; 0.05, with high significance at p \\u0026lt; 0.001. Multiple comparison corrections and imputation for missing data were included.\\u003c/p\\u003e\\n\\u003cp\\u003eGroup comparisons involved one-way ANOVA with Tukey post-hoc tests for normal variables and Kruskal-Wallis tests with Dwass-Steel-Critchlow-Fligner post-hoc adjustments for non-normal variables. A two-phase approach assessed correlations and biomarker relevance: initial analysis of all participants followed by stratified analysis by case-control status for validation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHandling Missing Data\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMissing data (\\u0026lt;5%) were identified as \\u0026quot;Missing Completely at Random\\u0026quot; (MCAR). Multiple Imputation by Chained Equations (MICE) in R generated five imputed datasets, with predictor variables encompassing all relevant demographic and clinical data. Post-imputation checks ensured consistency between imputed and original data distributions. Analyses were performed on each imputed dataset, and Rubin\\u0026rsquo;s rules were applied to combine results, ensuring robust estimates.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSample Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;In the final sample of 65 participants, 28 (43.1%) had type 2 diabetes and 37 (56.9%) were part of the control group. Of the participants, 38.6% were male, and 61.4% were female. Regarding age distribution, 42.0% were between 45 and 64 years old, 39.1% were over 65, and 18.8% were 44 years or younger. Concerning educational background, 7.2% were uneducated, 40.6% had completed primary education, 26.1% had attended secondary education, and the remaining 26.1% had attained a university degree. Regarding income levels, 79.7% reported a medium income, while 20.3% had a low income. Among the 28 participants with type 2 diabetes, all had undergone a complete diabetic foot risk assessment according to the guidelines of the International Working Group on the Diabetic Foot (IWGDF)(20). The distribution was as follows: 10 subjects (35.7%) were classified as low risk, 6 (21.4%) as moderate risk, and 12 (42.9%) as high risk. This demographic composition, with a significant proportion of individuals of middle or advanced age, aligns with the profile typically observed in individuals with type 2 diabetes in healthcare systems within the region. None of the participants in the low-risk group (LW) showed clinically apparent signs of diabetic peripheral neuropathy (DPN), while all individuals in the moderate/high-risk group (MH) exhibited visible signs of DPN.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBiomarker Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1. Correlations between the studied variables and the diabetic Foot Risk for the whole sample (N=65).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"96%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePredictive variable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCorrelation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eR\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eRelaxed arm circumference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.064\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.604\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0040\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.604\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eWaist circumference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.443\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.1960\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNon-Dominant Hand\\u0026nbsp;DYN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0.340\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.004\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0786\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.019*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGlucose\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.532\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.2620\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHbA1c\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.560\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.3400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTriglycerides\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.345\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.004\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0674\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.034*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTYG Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.454\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.1910\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.333\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.005\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0968\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.009**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eVisceral Fat Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.293\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.015*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0756\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.022*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSarcopenia Risk Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.178\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.143\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0159\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.302\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCONUT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.430\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.008\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.2810\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0.332\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.005\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0969\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.009**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBasal Metabolism (Kcal)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.068\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.580\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0045\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.581\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMetabolic Age\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.002\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.1370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.002**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.299\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.012*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0833\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.015*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMuscular Quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0.311\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.009**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0969\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.009**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTot. Body Water (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0.264\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.028*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0697\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.028*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003eAbbreviations: BMI, body mass index; CONUT, controlling nutritional status; DYN, dynamometry; HbA1c, glycated hemoglobin; PA, phase angel; TYG index: triglyceride glucose index.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eP values by Pearson\\u0026apos;s coefficient for normal variables and Spearman\\u0026apos;s for non-normal variables.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eP values Linear Regression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eIn terms of anthropometric measurements, waist circumference showed a moderate positive correlation with diabetic foot risk (r = 0.443, p \\u0026lt; 0.001; R\\u0026sup2; = 0.196, p \\u0026lt; 0.001), similar to BMI (r = 0.333, p = 0.005; R\\u0026sup2; = 0.0968, p = 0.009) and visceral fat index (r = 0.293, p = 0.015; R\\u0026sup2; = 0.0756, p = 0.022) \\u003cstrong\\u003e(\\u003c/strong\\u003e\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e\\u003cstrong\\u003e)\\u003c/strong\\u003e\\u003cstrong\\u003e.\\u003c/strong\\u003e Likewise, both metabolic age (r = 0.370, p = 0.002; R\\u0026sup2; = 0.137, p = 0.002) and chronological age (r = 0.299, p = 0.012; R\\u0026sup2; = 0.0833, p = 0.015) were significantly associated with increased diabetic foot risk.\\u003c/p\\u003e\\n\\u003cp\\u003eRegarding metabolic parameters, glucose (r = 0.532, p \\u0026lt; 0.001; R\\u0026sup2; = 0.262, p \\u0026lt; 0.001) and HbA1c (r = 0.560, p \\u0026lt; 0.001; R\\u0026sup2; = 0.340, p \\u0026lt; 0.001) exhibited strong positive correlations, while triglycerides (r = 0.345, p = 0.004; R\\u0026sup2; = 0.0674, p = 0.034) were also significantly correlated. Likewise, the triglyceride-glucose (TYG) index was positively associated with risk (r = 0.454, p \\u0026lt; 0.001; R\\u0026sup2; = 0.191, p \\u0026lt; 0.001). From a nutritional standpoint, the Controlling Nutritional Status (CONUT) score showed a positive association (r = 0.430, p = 0.008; R\\u0026sup2; = 0.281, p \\u0026lt; 0.001).\\u003c/p\\u003e\\n\\u003cp\\u003eWith respect to muscle function and bioimpedance-derived variables, muscle quality (r = -0.311, p = 0.009; R\\u0026sup2; = 0.0969, p = 0.009), and total body water percentage (r = -0.264, p = 0.028; R\\u0026sup2; = 0.0697, p = 0.028).\\u003c/p\\u003e\\n\\u003cp\\u003eOther variables, such as relaxed arm circumference (r = 0.064, p = 0.604; R\\u0026sup2; = 0.0040, p = 0.604), the sarcopenia risk index (r = 0.178, p = 0.143; R\\u0026sup2; = 0.0159, p = 0.302), and basal metabolic rate (r = 0.068, p = 0.580; R\\u0026sup2; = 0.0045, p = 0.581), did not display statistically significant associations with diabetic foot risk.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2. Correlations between the studied variables and Non-Dominant Hand Dynamometry (\\u003c/strong\\u003e\\u003cstrong\\u003eDYN)\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;for the whole sample\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e(N=65).\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"604\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePredictive variable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCorrelation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eR\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eRisk Grade of Diabetic Foot\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0,340\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,004\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,078\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,019\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0,344\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;0,004\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,091\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,011\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eArm Circumference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,065\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,594\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,033\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,138\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCalf Circumference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;0,058\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,101\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,008\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4-Meter Walking Speed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0,338\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;0,005\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,093\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,013\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eHbA1c\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0,21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,097\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,031\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0,002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,989\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,024\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,207\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eFat-Free Mass\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,381\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,003\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eLean Mass\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,395\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,293\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMuscle Quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,395\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,003\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eSarcopenia Risk Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,381\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,251\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTotal Body Water\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,471\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eExtracellular Water\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,333\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,005\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,098\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,009\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eIntracellular Water\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,542\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eECW/TBW Ratio\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0,548\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,296\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eECM/BCM Ratio\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e-0,351\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,003\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,083\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,017\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBone Mineral Conten\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,455\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,287\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eVisceral Fat Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,301\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,094\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,011\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBasal Metabolic Rate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,447\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,284\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0,001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003ePA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,331\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,003\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0,109\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,003\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAbbreviations: BMI, body mass index; ECW/TBW ratio, extracellular water/total body water ratio; ECM/BCM ratio, extracellular mass/body cell mass ratio; HbA1c, glycated hemoglobin; PA, phase angel.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eP values by Pearson\\u0026apos;s coefficient for normal variables and Spearman\\u0026apos;s for non-normal variables.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eP values Linear Regression\\u003c/p\\u003e\\n\\n\\u003cp\\u003eThe correlation analysis between potential predictors and non-dominant hand dynamometry revealed several significant relationships. Diabetic foot risk grade showed an inverse association (r = -0.340, p = 0.004; R\\u0026sup2; = 0.078, p = 0.019), indicating lower grip strength in individuals with higher diabetic foot risk. Similarly, older age correlated negatively with DYN (r = -0.344, p = 0.004; R\\u0026sup2; = 0.091, p = 0.011). Functional performance, measured via 4-meter walking speed, was also inversely linked to handgrip strength (r = -0.338, p = 0.005; R\\u0026sup2; = 0.093, p = 0.013) \\u003cstrong\\u003e(Table 2)\\u003c/strong\\u003e\\u003cstrong\\u003e.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAmong anthropometric measures, calf circumference showed a borderline correlation (r = 0.230, p = 0.058), yet reached significance in linear regression (R\\u0026sup2; = 0.101, p = 0.008). In contrast, arm circumference (r = 0.065, p = 0.594; R\\u0026sup2; = 0.033, p = 0.138) and BMI (r = -0.002, p = 0.989; R\\u0026sup2; = 0.024, p = 0.207) did not exhibit statistically significant associations.\\u003c/p\\u003e\\n\\u003cp\\u003eBody composition variables were generally strong predictors of grip strength. Fat-free mass (r = 0.381, p = 0.001; R\\u0026sup2; = 0.126, p = 0.003), lean mass (r = 0.395, p = 0.001; R\\u0026sup2; = 0.293, p \\u0026lt; 0.001), and muscle quality (r = 0.395, p \\u0026lt; 0.001; R\\u0026sup2; = 0.126, p = 0.003) all showed positive correlations. Likewise, the sarcopenia risk index (r = 0.381, p \\u0026lt; 0.001; R\\u0026sup2; = 0.251, p \\u0026lt; 0.001) emerged as a notable predictor, suggesting that higher muscle mass or quality aligns with improved handgrip performance.\\u003c/p\\u003e\\n\\u003cp\\u003eBioimpedance-derived parameters corroborated these findings. Total body water (r = 0.471, p \\u0026lt; 0.001; R\\u0026sup2; = 0.230, p \\u0026lt; 0.001) and intracellular water (r = 0.542, p \\u0026lt; 0.001; R\\u0026sup2; = 0.370, p \\u0026lt; 0.001) were positively associated with handgrip strength, whereas the ECW/TBW ratio (r = -0.548, p \\u0026lt; 0.001; R\\u0026sup2; = 0.296, p \\u0026lt; 0.001) and ECM/BCM ratio (r = -0.351, p = 0.003; R\\u0026sup2; = 0.083, p = 0.017) showed inverse relationships. These results suggest that a higher proportion of intracellular fluid and lower extracellular fluid accumulation support better muscle function.\\u003c/p\\u003e\\n\\u003cp\\u003eBone mineral content (r = 0.455, p \\u0026lt; 0.001; R\\u0026sup2; = 0.287, p \\u0026lt; 0.001) and basal metabolic rate (r = 0.447, p \\u0026lt; 0.001; R\\u0026sup2; = 0.284, p \\u0026lt; 0.001) were also positively correlated with handgrip strength, pointing to the relevance of both skeletal health and metabolic expenditure. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3. Correlations between the studied variables and PA for the whole sample (N=65).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"98%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePredictive variable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCorrelation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP valuea\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eR2\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP valueb\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eRelaxed arm circumference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.336\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.005*\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.113\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.005*\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNon-Dominant Hand\\u0026nbsp;DYN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.331\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.003**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.109\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.003**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHbA1c\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-0.251\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.047*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0682\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.039*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTriglycerides\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.547\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.811\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTYG Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.054\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.666\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.837\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.091\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.457\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.2333\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.211\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eVisceral Fat Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.049\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.688\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.871\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSarcopenia Risk Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.330\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.006**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0926\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.011*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCONUT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-0.457\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.005**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.244\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.002**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBasal Metabolism (Kcal)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.350\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.003**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.102\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.007**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMetabolic Age\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-0.477\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.160\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-0.547\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.258\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMuscular Quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.999\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTot. Body Water (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.391\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.123\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.003**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003eAbbreviations: BMI, body mass index; CONUT, controlling nutritional status; DYN, dynamometry; HbA1c, glycated hemoglobin; TYG index: triglyceride glucose index.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eP values by Pearson\\u0026apos;s coefficient for normal variables and Spearman\\u0026apos;s for non-normal variables.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eP values Linear Regression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eThe correlation analysis between the examined variables and phase angle (PA) revealed several significant associations. Among anthropometric measures, relaxed arm circumference showed a positive correlation with PA (r = 0.336, p = 0.005; R\\u0026sup2; = 0.113, p = 0.005), indicating that greater upper-arm muscle mass may support better cellular integrity. In contrast, age exhibited a strong negative correlation (r = -0.547, p \\u0026lt; 0.001; R\\u0026sup2; = 0.258, p \\u0026lt; 0.001), suggesting a decline in muscle cell function with advancing age. A similar inverse association emerged for metabolic age (r = -0.477, p \\u0026lt; 0.001; R\\u0026sup2; = 0.160, p \\u0026lt; 0.001) were inversely related, indicating a decline in cellular integrity with advancing age (Table 3).\\u003c/p\\u003e\\n\\u003cp\\u003eMetabolic and nutritional variables also played an important role. Hemoglobin A1c (r = -0.251, p = 0.047; R\\u0026sup2; = 0.0682, p = 0.039) was inversely related to PA, implying that suboptimal glycemic control may compromise cellular integrity. In addition, the Controlling Nutritional Status (CONUT) score (r = -0.457, p = 0.005; R\\u0026sup2; = 0.244, p = 0.002) was negatively associated, reinforcing the importance of adequate nutritional status for maintaining muscle cell health.\\u003c/p\\u003e\\n\\u003cp\\u003eRegarding muscle function and composition, non-dominant hand dynamometry (r = 0.331, p = 0.003; R\\u0026sup2; = 0.109, p = 0.003) correlated positively with PA, as did the sarcopenia risk index (r = 0.330, p = 0.006; R\\u0026sup2; = 0.0926, p = 0.011), suggesting that stronger muscle performance and lower sarcopenia risk correspond to enhanced cellular function. Furthermore, muscle quality stood out with a near-perfect association (r = 1.000, p \\u0026lt; 0.001; R\\u0026sup2; = 0.999, p \\u0026lt; 0.001), underscoring the essential role of muscle integrity in PA. Basal metabolic rate (r = 0.350, p = 0.003; R\\u0026sup2; = 0.102, p = 0.007) showed a positive correlation, indicating that higher metabolic activity aligns with better cellular health, and total body water percentage (r = 0.391, p \\u0026lt; 0.001; R\\u0026sup2; = 0.123, p = 0.003) also emerged as a key factor, highlighting the relevance of appropriate fluid distribution.\\u003c/p\\u003e\\n\\u003cp\\u003eConversely, several variables did not show significant correlations with PA, including triglycerides (r = 0.075, p = 0.547), triglyceride-glucose index (r = 0.054, p = 0.666), BMI (r = 0.091, p = 0.457), and visceral fat index (r = 0.049, p = 0.688).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 4. Correlations between the studied variables and the claw toes or prominent metatarsal heads for the whole sample (N=65).\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"95%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePredictive variable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCorrelation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eR\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eRelaxed arm circumference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,336\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,005\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,113\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,005\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNon-Dominant Hand DYN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0,34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,004\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,0786\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,019\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGlucose\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,573\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,295\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHbA1c\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTriglycerides\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,434\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,137\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,002\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTYG Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,565\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,314\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,239\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,048\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,084\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,016\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eVisceral Fat Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,304\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,011\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,019\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,254\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSarcopenia Risk Index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,187\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,124\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,065\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,035\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCONUT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,345\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,037\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,007\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0,285\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,018\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,023\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBasal Metabolism (Kcal)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,003\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,102\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,007\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMetabolic Age\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0,477\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0,547\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,258\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMuscular Quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e-0,311\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,009\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,0969\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,009\\u003c/strong\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTot. Body Water (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,391\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0,123\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0,003\\u003c/strong\\u003e\\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003eAbbreviations: BMI, body mass index; CONUT, controlling nutritional status; DYN, dynamometry; HbA1c, glycated hemoglobin; PA, phase angel; TYG index: triglyceride glucose index.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e P values by Pearson\\u0026apos;s coefficient for normal variables and Spearman\\u0026apos;s for non-normal variables.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e P values Linear Regression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eThe correlation analysis between the predictive variables and the presence of claw toes or prominent metatarsal heads (N = 65) revealed several significant associations (Table 4). Among anthropometric measures, relaxed arm circumference showed a moderate positive correlation (r = 0.336, p = 0.005; R\\u0026sup2; = 0.113, p = 0.005), whereas non-dominant hand dynamometry was inversely associated with these foot deformities (r = -0.340, p = 0.004; R\\u0026sup2; = 0.0786, p = 0.019).\\u003c/p\\u003e\\n\\u003cp\\u003eRegarding metabolic parameters, both glucose (r = 0.573, p \\u0026lt; 0.001; R\\u0026sup2; = 0.295, p \\u0026lt; 0.001) and HbA1c (r = 0.560, p \\u0026lt; 0.001; R\\u0026sup2; = 0.340, p \\u0026lt; 0.001) exhibited strong positive correlations, underscoring the role of poor glycemic control in the development of structural foot abnormalities. Similarly, dyslipidemia and insulin resistance appear to contribute to these complications, as evidenced by the significant associations observed for triglycerides (r = 0.434, p \\u0026lt; 0.001; R\\u0026sup2; = 0.137, p = 0.002) and the triglyceride-glucose (TYG) index (r = 0.565, p \\u0026lt; 0.001; R\\u0026sup2; = 0.314, p \\u0026lt; 0.001).\\u003c/p\\u003e\\n\\u003cp\\u003eBody composition indices also played a role; BMI was positively correlated with foot deformities (r = 0.239, p = 0.048; R\\u0026sup2; = 0.084, p = 0.016), and visceral fat index showed a significant positive correlation by Pearson\\u0026rsquo;s analysis (r = 0.304, p = 0.011), although its linear regression did not reach statistical significance (R\\u0026sup2; = 0.019, p = 0.254). The sarcopenia risk index, while showing a modest positive correlation (r = 0.187, p = 0.124), was significantly associated with foot deformities in the regression model (R\\u0026sup2; = 0.065, p = 0.035).\\u003c/p\\u003e\\n\\u003cp\\u003eNutritional status also appears to be an important determinant, as reflected by the moderate positive correlation observed for the CONUT score (r = 0.345, p = 0.037; R\\u0026sup2; = 0.190, p = 0.007). Conversely, phase angle (PA), a marker of cellular integrity, was negatively associated with the presence of claw toes or prominent metatarsal heads (r = -0.285, p = 0.018; R\\u0026sup2; = 0.075, p = 0.023), indicating that diminished cellular health may contribute to foot structural alterations.\\u003c/p\\u003e\\n\\u003cp\\u003eAdditional metabolic and musculoskeletal parameters further elucidated these relationships. Basal metabolic rate (r = 0.350, p = 0.003; R\\u0026sup2; = 0.102, p = 0.007) was positively correlated with foot deformities, suggesting that metabolic inefficiencies may be implicated. Moreover, both age (r = -0.547, p \\u0026lt; 0.001; R\\u0026sup2; = 0.258, p \\u0026lt; 0.001) and metabolic age (r = -0.477, p \\u0026lt; 0.001; R\\u0026sup2; = 0.160, p \\u0026lt; 0.001) were strongly and negatively correlated with these structural abnormalities, highlighting the impact of advanced biological aging. Finally, muscular quality was inversely associated (r = -0.311, p = 0.009; R\\u0026sup2; = 0.0969, p = 0.009), while total body water percentage was positively correlated (r = 0.391, p \\u0026lt; 0.001; R\\u0026sup2; = 0.123, p = 0.003) with the presence of claw toes or prominent metatarsal heads.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 5. One-Way ANOVA (Fisher\\u0026rsquo;s) and Tukey Post-Hoc Test.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"89%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eanova\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePost Hoc\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ev\\u003c/strong\\u003e\\u003cstrong\\u003eariables\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eF\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eComparisons\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eP value\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003ePA\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.002 **\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.999\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.002 **\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0 vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.029 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eFat Mass (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.039 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.985\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.036 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0 vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.210\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eFat-Free Mass (%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.045 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.996\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.044 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0 vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.205\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMuscle Quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.002 **\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.999\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.002 **\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0 vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.029 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTotal Body Water (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.028 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.996\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.027 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0 vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.156\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMetabolic Age\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.006 **\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.956\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eC vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.008 **\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0 vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.037 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNon-Dominant Hand DYN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4,04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.022*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eC vs 0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.758\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eC vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.0089 **\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0 vs 1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.031 *\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eAbbreviations: DYN, dynamometry; PA, phase angel.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e\\u0026nbsp; a\\u0026nbsp;\\u003c/sup\\u003eP value ANOVA Test\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e\\u0026nbsp; b\\u0026nbsp;\\u003c/sup\\u003eP value Tukey post-hoc test\\u003c/p\\u003e\\n\\u003cp\\u003eThe one-way ANOVA (Fisher\\u0026rsquo;s test) and subsequent Tukey post-hoc analysis revealed significant differences among control participants (C) and individuals classified into diabetic foot risk groups (0 and 1\\u0026ndash;2) according to the IWGDF criteria\\u0026nbsp;(Table 5). Phase angle (PA) differed significantly between groups (F = 6.99, p = 0.002), with controls exhibiting significantly higher PA values than the risk grade 1\\u0026ndash;2 group (p = 0.002), and a notable difference also observed between risk grade 0 and risk grade 1\\u0026ndash;2 (p = 0.029), indicating a progressive decline in cellular integrity with increasing diabetic foot risk.\\u003c/p\\u003e\\n\\u003cp\\u003eBody composition parameters also varied significantly across groups. Fat mass (%) showed significant overall differences (F = 3.42, p = 0.039), with controls having lower fat mass compared to the risk grade 1\\u0026ndash;2 group (p = 0.036). Similarly, fat-free mass (%) differed significantly (F = 3.24, p = 0.045), with controls displaying higher values than those in risk grade 1\\u0026ndash;2 (p = 0.044).\\u003c/p\\u003e\\n\\u003cp\\u003eMuscle quality, a critical indicator of neuromuscular function, also showed significant group differences (F = 6.99, p = 0.002); controls exhibited markedly better muscle quality than risk grade 1\\u0026ndash;2 (p = 0.002), and a significant decline was evident when comparing risk grade 0 with risk grade 1\\u0026ndash;2 (p = 0.029). Total body water percentage varied significantly as well (F = 3.79, p = 0.028), with the control group demonstrating higher hydration status than the risk grade 1\\u0026ndash;2 group (p = 0.027).\\u003c/p\\u003e\\n\\u003cp\\u003eMetabolic age was significantly different across groups (F = 5.53, p = 0.006). Controls had a significantly lower metabolic age compared to individuals in risk grade 1\\u0026ndash;2 (p = 0.008), and a significant increase in metabolic age was observed between risk grade 0 and risk grade 1\\u0026ndash;2 (p = 0.037), suggesting accelerated metabolic deterioration with increased diabetic foot risk.\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, non-dominant hand dynamometry exhibited significant differences (F = 4.04, p = 0.022), with risk grade 1\\u0026ndash;2 participants demonstrating significantly lower grip strength than controls (p = 0.0089), and subjects in risk grade 0 showing greater strength compared to those in risk grade 1\\u0026ndash;2 (p = 0.031).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;This discussion builds on the findings previously published by Junquera-Godoy et al. (2024)(6), who analyzed sEMG activity in 60 of the 65 participants included in this sample. In that earlier study, they reported a reduction in sEMG amplitude in the tibialis anterior and extensor digitorum brevis muscles, accompanied by an increase in the mean frequency of the signal, which was interpreted as indicative of a progressive denervation process and loss of motor units. Furthermore, significant alterations in intermuscular muscular network connectivity mechanisms due DPN were observed with the TE parameter showing the best performance in discriminating DPN patients, even at early stages: TE from medial gastrocnemius-flexor digitorum brevis and medial gastrocnemius-extensor digitorum brevis muscle pairs differentiated could be potential biomarkers for early DPN detection. The data revealed a significant increase in information transfer and muscle connectivity in the LW group with respect to the CT group, while the MH group obtained significantly lower values for these metrics than the other two groups. These findings could uncover essential neuromuscular mechanisms for clinical practice, aid in developing suitable rehabilitation strategies, and act as biomarkers for tracking muscle synergy evolution (7).\\u003c/p\\u003e\\n\\u003cp\\u003eBuilding on these observations, the present study broadens the scope by evaluating metabolic, anthropometric, and systemic neuromuscular functionality markers, focusing especially on claw toe deformity and its relationship to motor dysfunction.The results reinforce the premise that claw toe deformity cannot be explained solely by unrecognized fractures or microtrauma associated with loss of sensation. Rather, its primary origin lies in neuromuscular deterioration\\u0026nbsp;(2,25). Although the combination of muscle weakness and biomechanical instability could predispose to microfractures that may hasten or exacerbate the deformity (26), evidence points to the loss of motor units, and the alteration of muscle synergy as the predominant pathophysiological mechanism (27). In particular, the imbalance between extensor and flexor muscles in the foot, documented through sEMG (7), translates into abnormal activation patterns, compensatory overactivation, and ultimately global desynchronization in advanced neuropathy (28). This progressive motor disorganization is reflected in the digitiform posture characteristic of claw toe.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Intrinsic muscle atrophy of the foot emerges as a key factor in the development of claw toe deformity. Evidence shows a progressive reduction in the cross-sectional area of the interosseous and lumbrical muscles (29-31), leading to partial collapse of the plantar arch and impairing normal toe alignment (32, 33).\\u003c/p\\u003e\\n\\u003cp\\u003eOur findings reinforce this association, demonstrating that claw toe deformity coexists with markers of poor metabolic control, including HbA1c (r = 0.560, R\\u0026sup2; = 0.340, p \\u0026lt; 0.001), blood glucose (r = 0.573, R\\u0026sup2; = 0.295, p \\u0026lt; 0.001), and the triglyceride-glucose index (TYG) (r = 0.565, R\\u0026sup2; = 0.314, p \\u0026lt; 0.001). These findings are consistent with previous studies linking chronic hyperglycemia to an acceleration of the denervation process\\u0026nbsp;(28, 30). Consequently, dysregulated glycemia further impairs foot biomechanics, increasing the likelihood of both digital deformities and ulcerations.\\u003c/p\\u003e\\n\\u003cp\\u003eFrom a systemic perspective, hand dynamometry in the non-dominant hand was inversely correlated with diabetic foot risk (r = -0.340, R\\u0026sup2; = 0.0786, p = 0.019), suggesting a global pattern of neuromuscular dysfunction. This aligns with observations in the distal musculature of the lower extremities, where lower electromyographic amplitude, reduced motor synchronization, and decreased strength have been documented (7, 25). Moreover, the reduction in phase angle (PA) (r = -0.332, R\\u0026sup2; = 0.0969, p = 0.009) and poorer muscle quality (r = -0.311, R\\u0026sup2; = 0.0969, p = 0.009) further corroborate that cellular deterioration and fatty infiltration extend beyond the lower limbs, affecting other body segments.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;The comparative group analysis (ANOVA) revealed significant differences in handgrip strength (F = 4.04, p = 0.022), particularly between participants at high diabetic peripheral neuropathy (DPN) risk and controls (p = 0.0089). Additionally, subjects in risk grade 0 exhibited greater strength than those in risk grade 1\\u0026ndash;2 (p = 0.031), supporting the notion of progressive\\u0026mdash;rather than merely localized\\u0026mdash;muscle impairment. These results highlight the need for early detection and targeted interventions to mitigate neuromuscular decline and preserve functional capacity in individuals at risk for DPN.\\u003c/p\\u003e\\n\\u003cp\\u003eThis scenario calls for a multifaceted therapeutic approach: optimal glycemic control, targeted muscle strengthening, and neuromotor training. Proposed methods include functional electrical stimulation, vibration therapy, and specific exercise programs (33), which could slow atrophy and preserve motor connectivity\\u0026nbsp;(35, 36). Recent clinical trials have also explored novel therapeutic options for diabetic peripheral neuropathy, such as PDA-002, highlighting the ongoing search for effective treatments (36). This approach is consistent with studies demonstrating improvements in muscular connectivity through the reinforcement of distal musculature\\u0026nbsp;(28, 32). Nonetheless, the variability in treatment protocols and the lack of consensus on frequency and duration limit the generalization of results (29). Future longitudinal investigations should incorporate high-resolution electromyographic measurements and indicators of strength and body composition to evaluate the temporal progression of claw toe deformity and validate the efficacy of various interventions (6).\\u003c/p\\u003e\\n\\u003cp\\u003eIn conclusion, this study\\u0026mdash;complementary to the previous sEMG-based analysis of the same cohort (7)\\u0026mdash;supports the view that claw toe deformity arises from a multifactorial process led by neuromuscular disorganization and worsened by poor metabolic control. Although microfractures (25) may secondarily contribute, the determining factor is the loss of synchrony and atrophy of the intrinsic foot musculature, ultimately constraining foot biomechanics and confirming recent findings regarding the differential impact of diabetic neuropathy on distal muscle weakness (38). Reduced intermuscular connectivity in dorsiflexor and plantar flexor muscle pairs and metabolic imbalance highlight the need for comprehensive intervention programs. Optimizing neural connectivity and preserving muscle mass could delay or mitigate contractures and severe complications, underscoring the imperative to design rehabilitation protocols coupled with rigorous clinical follow-up. In this way, integrating accessible tools (dynamometry, bioimpedance) with advanced methods (electromyography, IMC analysis) offers a holistic and promising perspective for improving quality of life in patients with DPN (39).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStudy Limitations and Future Research\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough the results of this study provide valuable insight into the relationship between neuromuscular dysfunction and the onset of digital deformities in diabetic peripheral neuropathy (DPN), several limitations must be taken into account when interpreting the findings. First, this is a case-control design conducted at a single hospital center with a moderately small sample size. Such a setup limits the generalizability of the data to other populations and clinical settings, and reduces the statistical power to detect more nuanced or subtle differences.\\u003c/p\\u003e\\n\\u003cp\\u003eSecond, although the diabetic foot risk classification outlined by the International Working Group on the Diabetic Foot (IWGDF) was applied, patients with risk grade 3 were not included. This exclusion was based on concerns about skin integrity during the application and removal of sEMG electrodes, given the lesions and dermal fragility characteristic of advanced disease stages. As a result, the range of severity examined does not encompass the most severe form of diabetic foot, which may lead to an underestimation of the clinical complexity associated with more advanced DPN.\\u003c/p\\u003e\\n\\u003cp\\u003eLikewise, the neuromuscular assessment was performed through sEMG and included measuring non-dominant hand strength. Although manual dynamometry has been proposed as a highly useful complementary biomarker, this approach may not capture all the dimensions of motor dysfunction in the lower extremities, the main focus of diabetic foot pathology. Moreover, factors such as potential musculoskeletal issues in the wrist or elbow and interindividual variability can affect grip strength values, making it difficult to directly extrapolate these findings to the muscular status of the foot and leg.\\u003c/p\\u003e\\n\\u003cp\\u003eRegarding body composition, although bioimpedance analysis is validated, it remains subject to fluctuations related to hydration status, fat distribution, and the patient\\u0026rsquo;s nutritional condition. These variables can result in changes to phase angle, the ratio of intracellular to extracellular water, and other markers of cellular health, thus limiting the precision of the estimates.\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, the cross-sectional nature of the study prevents establishing causality between neuromuscular changes, metabolic control, and the appearance or progression of digital deformities. Future longitudinal studies with larger sample sizes and extended follow-up periods will be needed to confirm the progression of DPN and to assess the efficacy of therapeutic interventions aimed at preventing severe complications in the diabetic foot.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study demonstrates that diabetic peripheral neuropathy (DPN) is a systemic condition involving neuromuscular deterioration and metabolic dysfunction, with consequences that extend beyond the local foot structure. First, the observed correlation between digital deformity (claw toe), glycemic imbalance (HbA1c, fasting glucose, and the triglyceride-glucose index), and intrinsic muscle atrophy underscores the critical role of chronic hyperglycemia in the progressive loss of motor function. The disruption of muscle synergy\\u0026mdash;driven by the reduction in motor units, decreased transfer entropy in the advanced stages, but increased in early stages, and altered recruitment patterns\\u0026mdash;directly affects foot biomechanics, facilitating contractures and deformities. Moreover, these findings highlight the influence of factors such as chronological and \\u0026ldquo;metabolic\\u0026rdquo; age, a reduced phase angle, and diminished muscle quality, which collectively heighten the vulnerability of the diabetic foot to injury and ulceration.\\u003c/p\\u003e\\n\\u003cp\\u003eAdditionally, our evidence underscores the value of non-dominant hand dynamometry as a cost-effective and complementary biomarker for the early detection of neuromuscular dysfunction associated with DPN. The inverse relationship between grip strength and diabetic foot risk\\u0026mdash;along with the higher prevalence of digital deformities in individuals presenting elevated HbA1c or triglyceride-glucose index\\u0026mdash;emphasizes the need for a holistic approach that integrates both muscular/nutritional evaluation and stringent glycemic control. In this regard, bioimpedance analysis emerges as a relevant tool for assessing cellular integrity and fluid distribution, given that reduced intracellular water and expanded extracellular compartments appear to indicate greater tissue fragility and lower functional capacity.\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, the comparative analysis across risk groups supports a progressive and multifactorial pattern of deterioration. Integrating personalized therapeutic strategies\\u0026mdash;ranging from metabolic interventions (to optimize glycemic control) to rehabilitation protocols focused on restoring muscle strength and neuromuscular connectivity\\u0026mdash;may help contain or even delay the onset of digital deformities and severe diabetic foot complications. This underlines the importance of early intervention programs and close monitoring, drawing on accessible tools (dynamometry, bioimpedance measurements) and advanced methods (electromyography, muscular connectivity analysis by transfer entropy computed on surface electromyographic recordings) to enhance clinical surveillance. Future longitudinal studies with larger samples will be essential for validating these recommendations and refining prevention and treatment protocols aimed at minimizing the functional impact of DPN.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was conducted in accordance with the Declaration of Helsinki. Ethical approvals were obtained from the Research Ethics Committee. Participants were informed of the study\\u0026apos;s objective and provided signed written informed consent.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026ldquo;Not applicable\\u0026rdquo;\\u0026nbsp;\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors report no conflict of interest.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by grants from the Ag\\u0026egrave;ncia Valenciana de la Innovaci\\u0026oacute; (INNEST/2021/365) and the promoter POLISABIO (POLISABIO22_AP05).\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eCrediT statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eE. Soler-Climent: Writing \\u0026ndash; review \\u0026amp; editing, Writing \\u0026ndash; original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.\\u003c/p\\u003e\\n\\u003cp\\u003eE. Melendez Oliva: review \\u0026amp; editing, Validation, \\u0026nbsp; Formal analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eJ. Roman-Marroqui:\\u0026nbsp;review \\u0026amp; editing, Validation, Methodology, Formal analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eC. Martinez-Corbalan: Writing \\u0026ndash; review \\u0026amp; editing, Writing \\u0026ndash; original draft, Supervision, Methodology, Formal analysis.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eG. Prats-Boluda: Writing \\u0026ndash; review \\u0026amp; editing, Validation, Supervision, Funding acquisition, Project administration.\\u003c/p\\u003e\\n\\u003cp\\u003eI. Junquera-Godoy: Writing \\u0026ndash; review \\u0026amp; editing, Validation, Supervision, Funding acquisition, Project administration\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eG. Gonzalez-Lorente: Writing \\u0026ndash; review \\u0026amp; editing, Validation, Formal analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eJ. L. Martinez-de-Juan: Writing \\u0026ndash; review \\u0026amp; editing, Validation, Methodology, Formal analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eR.M. Cuadrado-Zaplana: Writing \\u0026ndash; review \\u0026amp; editing, Writing \\u0026ndash; original draft, Validation, Supervision, Investigation.\\u0026nbsp;\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u0026ldquo;Not applicable\\u0026rdquo;\\u0026nbsp;\\u003c/p\\u003e\\n\\n\\n\\n\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eKimura, T., Thorhauer, E. D., Kindig, M. W., Shofer, J. B., Sangeorzan, B. 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(2020). \\u003cem\\u003eMulti-System Factors Associated with Metatarsophalangeal Joint Deformity in Individuals with Type 2 Diabetes\\u003c/em\\u003e. Journal of Clinical Medicine, 9. https://doi.org/10.3390/jcm9041012\\u003c/li\\u003e\\n\\u003cli\\u003eZhu, J., et al. (2024). \\u003cem\\u003eDiabetic peripheral neuropathy: Pathogenetic mechanisms and treatment\\u003c/em\\u003e. Frontiers in Endocrinology, 14. https://doi.org/10.3389/fendo.2023.1265372\\u003c/li\\u003e\\n\\u003cli\\u003eHolmes, C. J., \\u0026amp; Hastings, M. K. (2021). The Application of Exercise Training for Diabetic Peripheral Neuropathy. \\u003cem\\u003eJournal of clinical medicine\\u003c/em\\u003e, \\u003cem\\u003e10\\u003c/em\\u003e(21), 5042. https://doi.org/10.3390/jcm10215042\\u003c/li\\u003e\\n\\u003cli\\u003eReena, R. A. (2024). \\u003cem\\u003eEffect of lower extremity training in diabetic peripheral neuropathy\\u003c/em\\u003e. Journal of Novel Physiotherapy and Rehabilitation. https://doi.org/10.29328/journal.jnpr.1001056\\u003c/li\\u003e\\n\\u003cli\\u003eGibbons, C. H., Zhu, J., Zhang, X., Habboubi, N., Hariri, R., \\u0026amp; Veves, A. (2021). Phase 2a randomized controlled study investigating the safety and efficacy of PDA-002 in diabetic peripheral neuropathy. \\u003cem\\u003eJournal of the peripheral nervous system : JPNS\\u003c/em\\u003e, \\u003cem\\u003e26\\u003c/em\\u003e(3), 276\\u0026ndash;289. https://doi.org/10.1111/jns.12457\\u003c/li\\u003e\\n\\u003cli\\u003eVan Eetvelde BLM, Lapauw B, Proot P, Vanden Wyngaert K, Helleputte S, Stautemas J, Cambier DC, Calders P. The impact of diabetic neuropathy on the distal versus proximal comparison of weakness in lower and upper limb muscles of patients with type 2 diabetes mellitus: a cross-sectional study. J Musculoskelet Neuronal Interact. 2021 Dec 1;21(4):464-474. PMID: 34854385; PMCID: PMC8672402.\\u003c/li\\u003e\\n\\u003cli\\u003eSalmen, T., Pietrosel, V. A., Hernest, G., Chiper, G. V., Florea, D. E., Popa, L. M., ... \\u0026amp; Radulian, G. (2020). Early Diagnosis of Peripheral Diabetic Neuropathy\\u0026ndash;Something Old that Should Always Be Considered Something New. \\u003cem\\u003eRomanian Journal of Diabetes Nutrition and Metabolic Diseases\\u003c/em\\u003e, \\u003cem\\u003e27\\u003c/em\\u003e(2), 99-103.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Diabetic Peripheral Neuropathy, Digital Deformities, Hand Dynamometry, Surface Electromyography, Bioimpedance, Transfer Entropy\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6812313/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6812313/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eDiabetic peripheral neuropathy (DPN), a common diabetes complication, arises from neuromuscular deterioration and metabolic dysregulation. These changes heighten the risk of hammer- and claw-toe deformities, disrupt foot biomechanics, and predispose patients to ulcers and amputations. Because DPN is multifactorial, integrating metabolic and neuromuscular indicators is critical.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eObjective\\u003c/b\\u003e: Identify predictors of digital deformities and diabetic-foot risk by combining surface electromyography (sEMG), hand dynamometry, bioimpedance, and intermuscular connectivity metrics\\u0026mdash;transfer entropy (TE) and normalised mutual information (NMI).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eMethods\\u003c/b\\u003e: In this case-control study, 65 adults (28 with type 2 diabetes, 37 controls) were assessed at a single centre. Outcomes included IWGDF foot-risk grade, bioimpedance-derived body composition, metabolic markers (HbA1c, triglyceride\\u0026ndash;glucose index), and neuromuscular tests (handgrip, sEMG, IMC/PDC). Correlations, ANOVA with post-hoc contrasts, and multiple imputation handled statistical analysis and missing data.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eResults\\u003c/b\\u003e: Greater waist circumference, higher BMI, and poorer metabolic profiles (glucose, HbA1c, triglycerides) were linked to elevated foot risk. Claw or hammer toes co-occurred with weaker handgrip, lower muscle quality, and reduced phase angle. Hand dynamometry proved a simple yet sensitive biomarker of neuromuscular decline. Findings suggest that interventions combining strict glycaemic control with strategies to enhance neuromuscular connectivity\\u0026mdash;such as functional electrical stimulation and targeted muscle strengthening\\u0026mdash;may attenuate deformity progression.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eConclusions\\u003c/b\\u003e: DPN reflects an interplay of metabolic, biomechanical, and neuromuscular factors extending beyond the foot itself. An integrated clinical assessment that merges anthropometric, metabolic, and neuromuscular data can flag high-risk patients earlier. Holistic management\\u0026mdash;tight glycaemic control plus focused rehabilitation\\u0026mdash;offers potential to prevent digital deformities and downstream complications. Larger longitudinal studies are warranted to validate these approaches and optimise outcomes.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\",\"manuscriptTitle\":\"Integrated Analysis of Neuromuscular Dysfunction and Metabolic Dysregulation in Diabetic Peripheral Neuropathy: Associations with Digital Deformities and Clinical Risk Stratification in a Case-Control Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-27 06:00:42\",\"doi\":\"10.21203/rs.3.rs-6812313/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"ed9dba8b-0353-4c94-ba22-59482be3e564\",\"owner\":[],\"postedDate\":\"August 27th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-09-12T10:23:32+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-08-27 06:00:42\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6812313\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6812313\",\"identity\":\"rs-6812313\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}