1. Introduction
Diabetic peripheral neuropathy (DPN) is a prevalent and burdensome complication of chronic diabetes mellitus, characterized by pain, paresthesia, and sensory loss. It affects around 50% of individuals with diabetes, significantly influences their quality of life, and leads to considerable morbidity and mortality [
1]. Diabetes is now reaching epidemic proportions, and the global patient population is anticipated to double by 2030, with over 366 million individuals, according to the International Diabetes Federation [
2]. The most common complication of diabetes, DPN, has a large impact on healthcare costs. The total yearly per-patient cost averages
$6632, with a twofold increase in individuals with peripheral neuropathy and a fourfold rise in those experiencing moderate to severe pain [
3]. This financial strain is further exacerbated by higher healthcare use associated with DPN-related complications, such as foot ulcers and amputations [
4].
DPN typically appears as symmetrical, length-dependent sensorimotor polyneuropathy, starting in the toes and gradually progressing to the upper limbs [
5]. It primarily affects the intrinsic muscles of the foot, including the extensor digitorum brevis and flexor digitorum brevis, the lower leg muscles, such as the tibialis anterior, and the intrinsic muscles of the hand [
6]. Painful symptoms, including burning and tingling sensations, are present in approximately one-third of DPN patients and can severely disrupt their sleep and daily activities [
7]. Despite extensive research, DPN pathogenesis remains multifactorial and complex, involving metabolic, vascular, and inflammatory mechanisms. Treatment is challenging, with the limited efficiency of the available pharmacotherapies and a need for improved management strategies [
4]. There is also a growing recognition of the impact of non-pharmacological interventions, emphasizing the importance of holistic approaches in addressing this debilitating condition. As the prevalence of diabetes continues to rise, there is an urgent need for further research and development of effective therapies to alleviate the considerable DPN burden on individuals and healthcare systems worldwide [
8].
DPN significantly affects the patients’ gait in a range of deficits that compromise mobility and independence. These include reduced walking speed, shortened stride length, and impaired balance, all of which are critical for everyday functioning [
9]. The primary cause of these abnormalities is damaged sensory and motor nerves due to chronic hyperglycemia, which leads to diminished proprioception and muscle strength [
10]. Proprioception is the sense of self-movement and body position and is crucial for coordinating movements and maintaining balance. The loss of proprioceptive feedback in DPN results in an inability to correctly sense foot position when walking, leading to unsteady and abnormal gait patterns and reduced muscle strength [
11]. Motor unit (MU) loss leads to muscle weakness and is closely related to atrophy and fat infiltration [
6]. Abnormal muscle activation patterns, such as delayed muscle activation, increased coactivation, or prolonged muscle activity, further contribute to inefficient and unstable gait, increasing the risk of falls and significantly affecting quality of life [
12,
13].
Physical therapy is a cornerstone intervention in addressing gait abnormalities, involving therapeutic exercises designed to improve muscle strength and coordination to restore normal gait patterns. Balance training exercises help patients develop better stability and prevent falls. Resistance exercises aim to strengthen specific muscle groups, particularly those in the lower limbs associated with DPN [
6]. However, the effectiveness of these therapeutic measures greatly depends on the ability to monitor the patients’ progress and adapt treatment plans as required. An objective assessment method is crucial to accurately gauge the results, track the patients’ improvement over time, and refine the therapeutic approaches to ensure optimal patient outcomes [
6].
Surface electromyography (sEMG) is a valuable low-cost tool for understanding and assessing muscle activation patterns by non-invasively measuring electrical muscle activity [
14] and could reveal how neuropathy affects muscle coordination and its contribution to abnormal activation patterns in DPN patients. By identifying these deficits, clinicians could design individual treatments to correct specific muscle activation issues. Regular sEMG assessment can help monitor changes in muscle activity over time, making it an essential tool for evaluating the effectiveness of the therapy.
Recent advances in sEMG research have revealed how neuropathy alters leg muscle activation. Studies have shown that these patients often have increased co-activation of antagonistic muscles during voluntary contractions in addition to reduced contractile properties in the ankle dorsiflexors [
12]. Other studies have reported a delayed onset of muscle activity in key muscles such as the quadriceps and gastrocnemius, which can impair muscle contraction timing for stable walking [
13]. However, in [
15], the early activation of the lower limb muscles during ascending and descending steps and in [
16], during gait, was used to compensate for the delay in MU recruitment.
Determining the complex neuromuscular control mechanisms in DPN extends beyond individual muscle activity; it also entails quantifying intermuscular interactions to decipher muscle coupling and coordination patterns. This requires sophisticated analytical tools such as information theory methods such as mutual information (MI), cross-correlation (CC), granger causality (GC), and transfer entropy (TE). These methods offer model-free approaches that capture both linear and non-linear relationships, making them effective in quantifying functional neurophysiological coupling in DPN [
17,
18,
19]. Although many studies have used sEMG to detect muscle alterations, most focused on individual muscle assessments [
13,
15,
20,
21]. So far, no studies have used advanced information theory metrics to analyze the possible changes in muscle network connectivity due to DPN. These innovative metrics have been reported in other contexts, including Parkinson’s disease, stroke, exoskeleton-assisted movement, and mapping postural control in healthy individuals [
22,
23,
24,
25]. Applying these methods to DPN research could provide significant insights into muscle coordination deficits and network alterations.
The primary objective of this study was thus to comprehensively evaluate and compare variations in muscle network connectivity and intermuscular coupling among individuals diagnosed with diabetes and DPN by means of information theory methods. The goal was to discern any muscle activation and intermuscular coupling patterns that could be used as reliable and objective biomarkers to assess DPN severity and progress and to evaluate the effectiveness of therapeutic interventions.
3. Results
Following the method outlined above, parameters were computed to capture the interactions between the TA, GM, ED, and FD muscles. To initially assess whether there were any differences between the groups, an ANOVA test was performed. This test provided a general overview of differences without identifying which specific groups differed from each other. For a more detailed examination of specific group differences, pairwise comparisons were conducted using the LSD test. The resulting
p-values are given in
Table 1,
Table 2 and
Table 3.
Table 1 gives the results of the ANOVA test, highlighting general differences for the four parameters between the groups. For the pairwise comparisons using the LSD test,
Table 2 categorizes the parameters without directionality (CC and NMI), while
Table 3 organizes the parameters with directionality (GC-Causality and TE) based on the direction of the interactions. To enhance the clarity of our findings, we have focused on the discussion of the statistically significant results (
p < 0.05).
The ANOVA results in
Table 1 show that the TA-ED muscle pair exhibits statistically significant differences (
p < 0.05) between groups across all the parameters, while NMI shows significant differences between groups for the GM-FD pair. Significant differences can also be seen for the GM-FD pair in the TE parameter for the GM → FD direction, with near-threshold
p-values in the opposite direction and in the GC-Causality parameter for both directions. The TA-GM, TA-FD, and ED-FD muscle pairs do not present statistically significant differences in any parameter in this first evaluation of group differences. To further identify between which groups these differences appear, the pairwise comparison using the LSD test was computed.
The pairwise comparison results from the LSD test (
Table 2) indicate significant differences (
p < 0.05) in the CC parameter for the TA-GM muscle pair between the CT and MH groups. For the TA-ED muscle pair, significant differences can be seen between CT and MH, and between LW and MH. As in the CC, the NMI parameter also presents significant differences for the TA-ED muscle pair between CT and MH and between LW and MH, the GM-FD pair shows a significant difference between CT and LW.
As can be seen in
Table 3, the CG-Causality parameter is significantly different in the TA-ED and GM-FD muscle pairs in both directions for comparisons involving MH (CT-MH and LW-MH). Near-threshold differences also occurred in the TA → GM direction between CT and LW and in GM-ED (both directions) between CT and MH, as well as in GM → FD between LW and MH. For the TE parameter, significant differences are found in the TA-ED pair between CT and MH and between LW and MH in both directions. The LW and MH comparison also presents differences in the TA → GM and GM → FD pairs. Comparing CT and LW, statistically significant differences are found in the GM-FD (both directions), GM → ED and ED ← FD pairs. This group comparison would allow us to identify changes in the LW group and facilitate the early detection of DPN.
Figure 3,
Figure 4,
Figure 5,
Figure 6 and
Figure 7 show the distribution of the values for the CC, NMI, CG-Causality, and TE parameters from the muscle pairs with statistically significant differences between the groups.
Figure 3 gives the distribution of the CC parameter values for the TA-GM and TA-ED muscle interactions, which were significantly different between the groups. For the TA-GM pair, there is a reduction of the CC in the diabetic cohort; the difference between CT and MH is statistically significant. In TA-ED, the LWs showed higher values than those of CT and MH, with statistical significance for this second group, while the values for MH were significantly lower than CT.
Figure 4 gives the distribution of the NMI values for the two muscle pairs with significant differences between the groups. The TA-ED pair presents very similar results to those obtained from the CC parameter, with a reduction of the NMI for the MH group and statistically significant differences from the other two groups. However, while the CC for the LW group presents an increase over the controls, the CT and LW NMI distributions are very similar. For the GM-FD pair, there is an increase in NMI in the LW group, with a significant difference to the CT group. This increase can also be seen when compared with the MH group, although the difference is not significant.
The CG-Causality parameter distribution for the two muscle pairs with significant differences between the groups is shown in
Figure 5. As this parameter also presents directionality, one of the directionalities (e.g., TA → ED) is shown in black whiskers, while the opposite (e.g., TA ← ED) is shown in grey. The CG-Conditional values for the MH group show a clear reduction of the muscle interaction between TA-ED and GM-FD in both directions, in line with previous results. A slight increase can be seen in the LW group when compared to CT, although in this parameter it is not as pronounced as in the NMI. All the population groups presented a statistically significant foot-to-leg directionality (red background) of information transfer, except for the TA-ED interaction in the LW group.
Figure 6 represents the distribution of the TE muscle pairs with significant differences between groups. All the muscle pairs present results consistent with those of the previous metrics: increased muscle interaction for the LW group and reduced muscle interaction in the MH group when comparing both to the CT group. The decrease of the MH group can be especially seen in the TA → GM muscle pair direction and is even greater for the TA-ED muscle pair, with statistically significant differences between MH and the other two groups (CT and MH) in both directions. Increased muscle interaction for the LW group appears in the GM-FD pair in both directions as compared to the CT group, as well as in the ED → FD interaction. In the GM → ED direction, the increase in the LW group stands out from the other two population groups. In the directionality results, all the groups present significant TA → GM information transfer. TA-ED shows results consistent with the CG-Conditional, while the CT and MH groups present significant foot-to-leg directionality. The GM-FD and GM-ED pairs only show statistically significant directionality for the CT group, unlike the results obtained in the CG-Conditional, where the three population groups presented significant FD → GM directionality. The ED-FD pair did not indicate any predominant muscle directionality in any group.
To summarize the results obtained in this study,
Figure 7 gives a visual representation of the differences in the muscle network observed from the CC, NMI, TE, and CG-Causality parameters between the three population groups (CT-LW, CT-MH, and LW-MH). Each line connects two muscles, indicating the muscle interaction between that pair. Only the muscle interactions with statistically significant differences between groups are displayed. The color represents the value of the parameter for that muscle interaction. A line that is more intensely red indicates stronger connectivity between that muscle pair. The arrow in the parameters with directionality characterizes the net direction of that muscle pair. The bidirectional arrows represent muscle interactions without a statistically significant directionality.
The TA-GM interaction shows differences between groups in the CC and TE parameters. Both present reduced muscle connectivity in the MH group compared to CT in the CC parameter and to LW in the TE parameter. The directionality from the TE parameter of that muscle couple indicates a transfer from TA to GM. The results from the TA-ED pair are the most consistent of them all, with differences between groups across all the parameters and with identical tendencies. The LW group presents an increase in this muscle interaction compared to CT, while the MH group displays the opposite behavior with reduced connectivity of that muscle couple compared to CT and LW groups. The directionality in this pair is from ED to TA in the CT and MH groups, while the LW group does not present such a well-defined directionality.
The GM-ED muscle pair only presents differences in the TE parameter, with a significant increase in muscle connectivity for the LW group over the other two population groups. The NMI and TE parameters give identical results for the GM-FD interaction, an increase in muscle connectivity in the LW group compared to the CT group. While the CG-Causality parameter also presents this increase for the LW group to a lesser extent, this parameter also gives a decrease in the connectivity for the MH group in comparison with the other two.
Differences between the groups for the ED-FD pair were only found in the TE parameter, indicating an increase in the connectivity between this pair for the LW group compared to the CT group. In short, the results show consistency between the different information theory metrics, with the LW group showing an overall increase in connectivity between muscle pairs compared to the CT group and a decrease in muscle interactions for the MH group compared to the CT and LW groups. TE is the parameter with the largest number of differences between groups in multiple muscle pairs and can identify changes in the LW group in comparison to CT, which could be used as a valuable tool for early DPN detection.
4. Discussion
This study assessed intermuscular coupling in healthy subjects, diabetics with a low risk of DPN, and diabetics with a moderate/high risk of contracting this disease to evaluate network connectivity disorders associated with diabetes and neuropathy. Previous research has reported alterations in muscle activation patterns of leg and foot muscles in DPN patients compared to healthy volunteers [
13,
15,
16,
20]. The traditional sEMG parameters such as root mean square (RMS), median frequency (MDF), discrete wavelet transform (DWT) and sample entropy (SAMPEN) have also been studied in the context of DPN [
21,
32,
33]. While traditional parameters focus on individual muscle characteristics, in contrast, our study presents a novel approach by evaluating changes in the muscle network rather than focusing solely on individual muscles. This approach helps to uncover how alterations in muscle coupling and coordination contribute to the overall muscle network changes in DPN. Intermuscular coupling has also been analyzed in other contexts, including post-stroke patients during gait, muscle coordination with exoskeletons, muscle fatigue conditions, and functional interactions in healthy subjects [
22,
23,
25,
34]. To the best of our knowledge, the impact of diabetes and DPN on muscle network connectivity has not been documented in the literature.
The four information theory metrics analyzed in the study (CC, NMI, CG-Causality, and TE) indicated that TE presented the highest number of statistical differences between groups in all muscle pairs (except TA-FD) providing directionality information, unlike CC and NMI. This corroborates the presence of differences in non-linear relationships in muscle coupling between groups that are indetectable by GC [
18]. This finding was also reported in a previous study in which multivariate sample entropy, a non-linear theory metric similar to TE, was more sensitive to changes in muscle coupling than CG-Causality [
25].
Of the six muscle pairs analyzed, only the TA-FD pair did not indicate differences between any group or parameter. The TA-ED muscle pair yielded the most consistent results across all the parameters analyzed, showing statistically significant differences between the moderate/high risk (MH) group and the other two groups (control (CT) and low risk (LW)), with a decrease in muscle coupling and information transfer in the presence of DPN. However, this muscle pair did not differentiate LW from the CT group, which would enable early detection of the pathology. Previous studies have found alterations in TA muscle activation patterns in diabetic patients, particularly in those with DPN, who exhibit reduced MU recruitment and slower contractile properties [
12]. Differences in TA muscle activation between diabetics with and without DPN have been observed in gait, though not consistently between controls and DPN patients [
13,
15,
16]. This suggests that the muscles responsible for dorsiflexion exhibit lower connectivity, potentially related to DPN severity and characterized by axonal degeneration and muscle fiber loss [
6]. TE and CG-Causality showed consistent directionality results for this muscle pair, with significant information transfer from ED to TA in the CT and MH groups but not in the LW group, indicating possible compensatory neuromuscular regulation in the early stages of DPN [
13]. This reduced information transfer between the TA, GM, gastrocnemius lateralis, and soleus ankle muscles has previously been reported in post-stroke patients during the double support phase of gait [
23].
The GM-FD muscle pair also showed consistent results across the parameters. The general trend indicated higher muscle coupling in the LW group than in the CT group and reduced coupling in the MH group. CG-Causality was particularly sensitive to the lower muscle coupling in the MH group, while NMI and TE were more selective in detecting increased intermuscular coupling in the LW group, which could be a promising biomarker for early DPN detection. The directionality results for this muscle pair showed a significant information transfer from FD to GM in the CT group, but this did not appear in the diabetic cohort (LW and MH).
The fact that the TA-ED and GM-FD muscle pairs showed the best discrimination results could be due to anatomical reasons. The muscles in each pair are innervated by the same nerves, which further support their coordination. The TA and ED muscles are both innervated by the deep fibular (peroneal) nerve [
35], which ensures that signals for muscle activation are transmitted simultaneously, facilitating synchronous movement patterns. The GM and FD muscles are innervated by the medial plantar nerve branch of the posterior tibial nerve [
35], another shared neural pathway that promotes coordinated activation. The foot-to-leg directionality could be due to the intrinsic biomechanics of the dynamic exercise. The muscle recruitment for the movement originates in the toes, followed by the rest of the foot, which then engages in dorsiflexion or plantar flexion at the ankle.
CC, NMI, CG-Causality, and TE revealed differences in the neuromuscular coupling mechanisms in diabetic DPN patients. The LW group showed increased muscle network connectivity over CT, while MH was lower than CT. Apart from what was found in the TA-ED and GM-FD pairs, there are other muscle pairs that corroborate this trend. The reduction in the MH group is also present in the TA-GM muscle pairs in the CC and TE parameters, while the increase in muscle connectivity of the LW group is also present in other muscle pairs, such as the GM-ED and ED-FD in the TE parameter. The decrease in the MH group may be due to multiple mechanisms such as joint stiffness, lack of coordination from loss of proprioception, and primarily the intrinsic loss of MU, leading to reduced information transfer in the muscle network than in people without DPN [
12,
36]. Conversely, the increase in the LW group could have been due to the compensatory mechanisms for sensorimotor deficits. Although this group does suffer a certain loss of MU, it has a smaller effect, and the reinnervation following denervation may contribute to greater information transfer within the muscle network than in healthy individuals [
6,
36]. Identifying these changes in the LW group is fundamental for obtaining biomarkers for early detection of DPN and targeting preventive interventions.
The CT group showed significant foot-to-leg information transfer, although not as clearly as in the LW group. This lack of a predominant directionality could result from compensatory neuromuscular regulation strategies, as the LW group may be in the early stages of DPN, experiencing common ankle stiffness and MU loss in diabetic individuals. The TA muscle could thus compensate by increasing and balancing information transfer in both directions. In contrast, MH patients are unable to develop compensatory strategies due to their sensorimotor deficits, leading to weaker muscle coupling [
13].
These findings suggest the potential use of these metrics to provide objective feedback on the therapy effectiveness of motor control and muscle recruitment strategies to reveal DPN-related neuromuscular mechanisms. It should be noted that peripheral arterial disease (PAD), a common complication of diabetes, could significantly impact muscle connectivity [
37,
38]. As we did not include any subjects with advanced PAD, future studies should focus on the effects of PAD on these parameters in the more advanced stages. As the present study’s main limitation was the small sample size, future research with larger samples and an analysis of more muscles bilaterally could gain further insights into motor strategies.
5. Conclusions
The presence of diabetes and DPN deeply affects the intermuscular coupling mechanisms, manifesting differently depending on the degree of severity of the disease. An increase in the information transfer and muscle connectivity in the LW group in comparison with CT was observed, while the MH group presented a significant decrease in comparison with CT.
Of the four parameters analyzed, TE outperformed CC, NMI, and CG-Causality in detecting coupling alterations in DPN patients. This parameter provided statistically significant differences in both the quantity and directionality of information transfer between the groups of most of the muscle pairs.
The TA-ED pair showed the most consistent performance across all four parameters. As the GM-FD and GM-ED couples differentiated the LW group, they could be potential biomarkers for early DPN detection.
These findings have the potential to reveal the crucial neuromuscular mechanisms for clinical practice, help to develop appropriate rehabilitation strategies, and serve as biomarkers for monitoring muscle synergy evolution. Using TE to assess muscle network connectivity can provide deeper insights into the complexities of neuromuscular coupling in DPN patients and facilitate more targeted and effective therapeutic interventions.