Abstract
Background
Frailty is highly prevalent in chronic kidney disease patients and associated with mortality. We investigated the relationship between frailty and peripheral neuropathy in hemodialysis patients.
Methods
We enrolled 70 maintenance hemodialysis patients and assessed physical frailty using the five-criteria Fried frailty index. Motor and sensory nerve conduction studies were performed on median, ulnar, peroneal, tibial, and sural nerves. Neuropathy severity was measured using the Total Neuropathy Score (TNS). ANCOVA was performed to control for age and cardiovascular disease as confounders.
Results
Patients were categorized as non-frail (n = 25), pre-frail (n = 26), and frail (n = 19). Frail patients were significantly older and had higher cardiovascular disease prevalence. TNS increased progressively across groups (median values: 3 vs 10.5 vs 13, p < .001). After controlling for age and cardiovascular disease, frailty status remained significantly associated with TNS (F(2,65)=6.415, p = .003, partial η2 = 0.165). Electrophysiological studies showed significantly reduced nerve conduction parameters in frail patients. Frailty score strongly correlated with TNS (rs = .48, p < .001). Subgroup analysis in non-diabetic patients confirmed this relationship independent of diabetes status (p = .024).
Conclusions
Frailty and peripheral neuropathy demonstrate a strong, independent association in hemodialysis patients that persists after controlling for major confounders. This relationship exists regardless of diabetes status, suggesting peripheral nerve dysfunction is an integral component of frailty in this population. Early recognition could enable targeted interventions to improve outcomes in vulnerable hemodialysis patients.
Keywords: Frailty, peripheral neuropathy, hemodialysis, nerve conduction study, total neuropathy score
Graphical Abstract
Introduction
The increasing incidence of hypertension and diabetes mellitus (DM), the aging of the population, and better management of chronic diseases have increased the number and age of patients with end-stage renal disease (ESRD) [1]. Patients with ESRD, particularly those of advanced age, experience higher rates of physical and cognitive functional decline, depression, malnutrition, osteoporosis, and frailty syndrome [2–4]. Frailty is a definition created on geriatric populations and generally refers to physical inactivity and susceptibility to disease-related complications [1,5,6]. In chronic kidney disease (CKD) patients, frailty prevalence increases and is independently associated with mortality regardless of age and comorbidities [5]. A systematic review (n = 36,076) demonstrated that frailty increases as kidney function declines, with rates ranging from 7% in community-dwelling CKD patients to 73% in hemodialysis patients [7].
The pathogenesis of frailty in ESRD involves complex multisystem deterioration without a single diagnostic biomarker [8]. This ‘fragile dialysis phenotype’ [9] is linked to dysregulated inflammation, with frail patients showing higher rates of metabolic and musculoskeletal problems [4,10,11].
A key pathogenic pathway involves skeletal muscle dysfunction. Normally, muscle homeostasis is maintained through balanced protein synthesis, cellular hypertrophy, and protein degradation, orchestrated by neuroendocrine signaling and immune regulation, influenced by nutrition and activity levels [8].
In frailty progression, this homeostasis is disrupted. Chronic inflammation, common in both frailty and CKD, accelerates muscle protein catabolism, causing sarcopenia and cachexia in hemodialysis patients [4,10]. This not only reduces muscle mass but compromises muscle quality and function, suggesting shared pathophysiological pathways between frailty and sarcopenia in CKD patients [8].
Uremic neuropathy affects 60–90% of ESRD patients [12,13], contributing to morbidity through neuropathic symptoms, decreased mobility, impaired sensory feedback, and distal weakness [14]. It typically presents as distal symmetric polyneuropathy, beginning with sensory loss and impaired reflexes in lower extremities before progressing to muscle weakness and atrophy [12]. Preserved peripheral nerve function is crucial for normal posture, gait, and preventing falls. Abnormal gait, falls, and fractures are common in hemodialysis patients, and the presence of neuropathy may be an aggravating factor [15,16].
Some studies have explored frailty’s relationship with neurological findings in ESRD patients. McAdams-DeMarco [17], found frailty independently associated with worse cognitive function in new hemodialysis patients. Chao’s [18] study using quantitative EEG found patients with no/mild frailty had higher delta wave power. However, data on frailty and peripheral neuropathy in hemodialysis patients remains limited. In our study, we investigated the relationship between frailty and peripheral neuropathy in hemodialysis patients.
Materials and methods
Study participants
This cross-sectional observational study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. It was approved by the Scientific Research Ethics Committee of Bursa City Hospital, University of Health Sciences on 13 September 2023, with decision number 2023-15/3. Written informed consent was obtained from all patients.
The study included 70 patients undergoing hemodialysis at three different dialysis centers. Inclusion criteria included patients aged 18–85 years, able to give informed consent, and on hemodialysis for at least three months. Subjects with peripheral vascular disease, malignancy, congestive heart failure, dementia, cerebrovascular disease, depression and psychosis, chronic inflammatory disease such as rheumatoid arthritis and ankylosing spondylitis, carpal tunnel syndrome, or diseases known to cause neuropathy other than DM were excluded. Additionally, patients with vitamin B12 levels below 250 pg/mL were excluded from the study to eliminate the potential confounding effect of vitamin B12 deficiency on peripheral nerve function. All patients underwent laboratory screening for vitamin B12 levels prior to enrollment to ensure this criterion was met.
Patients demographics (age, sex, and race), comorbidities, time on hemodialysis, etiology of renal disease, and body mass index (BMI) were recorded. Blood samples of the patients were collected after an overnight fast before the dialysis session. The hemogram parameters, C-reactive protein (CRP), serum biochemistry (serum electrolytes, urea, creatinine, uric acid, albumin, total cholesterol, triglyceride, low density lipoprotein, calcium, phosphate, parathyroid hormone and ferritin) were determined using standard biochemical methods. Standardized single-pool Kt/V was recorded.
Frailty assessment
The Fried Index evaluates frailty in HD patients, scoring daily activity, disease symptoms, and dependency from 0 to 5.
The Fried frailty phenotype (FFP), proposed in 2001, is widely used to assess frailty [19,20]. It includes five criteria: weight loss, exhaustion, physical activity, grip strength, and walking speed. Based on these, patients are classified as non-frail (score 0), pre-frail (score 1–2), or frail (score 3–5).
Weight loss is defined as unintentional loss of 4.5 kg or 5% of body weight in the prior year.
For exhaustion, patients respond to two statements: ‘I felt everything was an effort’ and ‘I was unable to take action’, rating how often they felt this way in the past week. Those answering ‘moderate amount’ or ‘most of the time’ meet the exhaustion criterion.
Low physical activity is determined by energy expenditure below gender-specific thresholds.
Grip strength is measured with a dynamometer on the dominant hand, with the highest of three measurements compared against established thresholds.
Walking speed is assessed based on time to walk 4.6 m, with adjustments for sex and height.
Electrophysiological studies
Electrophysiological studies were performed in the electromyography (EMG) laboratory of the Neurology Department of our hospital using the Natus Nicolet Viking Quest EMG device. Median and ulnar motor and sensory nerve conductions were studied from the upper extremity without arteriovenous fistula. Peroneal and tibial motor nerve and sural sensory nerve conductions from at least one lower extremity were studied while the patients were lying in the supine position and under normal room temperature conditions. Stimulation was applied with a bipolar superficial electrode whose anode was located 2.5 cm proximal to the cathode. The cold extremities were heated until their temperature exceeded 28 °C. Motor and sensory nerve conductions were studied antidromically.
For the median motor nerve, the conduction velocities between the wrist and cubital fossa, for the ulnar motor nerve, the conduction velocities of the wrist-below-elbow and below-above-elbow segments, for the peroneal motor nerve, the conduction velocities between the wrist and fibular head, and for the tibial motor nerve, the conduction velocities between the wrist and popliteal fossa were evaluated. In addition, median, ulnar, and sural sensory nerve conduction velocities were calculated.
Neuropathy classification and data recording
Polyneuropathy was classified as axonal, demyelinating, or mixed based on standard electrophysiological criteria. Axonal neuropathy was defined by reduced nerve action potential amplitudes with relatively preserved conduction velocities. Demyelinating neuropathy was characterized by significantly reduced conduction velocities, prolonged distal latencies, and conduction blocks. Mixed neuropathy was diagnosed when both patterns coexisted. When nerve responses could not be elicited despite adequate stimulation, these were recorded as absent responses and assigned a value of zero for statistical analysis, consistent with standard electrophysiological reporting.
Clinical assessment of neuropathy
Detailed neurological examinations were performed on all patients, and both clinical and electrophysiological criteria were used for polyneuropathy diagnosis. Following the recommendations by England et al. [21], we adopted an approach that meets both clinical and nerve conduction study criteria for the diagnosis of definite large-fiber symmetric polyneuropathy.
Patients’ neuropathic symptoms (numbness, burning, tingling sensation, and pain) were thoroughly questioned and recorded. The neurological examination assessed sensory modalities (pain, touch, heat, cold, vibration, and proprioception), motor system (muscle strength and atrophy), tendon reflexes, and autonomic system. This clinical evaluation was used in calculating the Total Neuropathy Score (TNS) [22].
The TNS is a composite score consisting of the following 8 domains: (1) sensory and (2) motor neuropathic symptoms, (3) pinprick sensitivity, (4) vibration detection, (5) strength assessment, (6) deep tendon reflexes, and lower extremity (7) sensory and (8) motor nerve conduction studies. Each domain is assigned a severity score from 0 (normal) to 4 (severely abnormal). The scores from the eight items of the TNS are then added to obtain a total score from 0 to 32, with higher scores indicating more severe neuropathy (see Supplementary Text File).
Statistical analyses
A post-hoc power analysis was conducted using the current study findings and effect size value. The urea value was calculated as 9.08 ± 2.06 for the non-frail group, 7.83 ± 2.46 for the pre-frail group, and 6.73 ± 2.80 for the frail group. Considering the relevant quantities, the effect size value was calculated as f = 0.38, and with this effect size value, an 80% power level was achieved for a total of n = 70 patients at α = 0.05.
The normality of continuous variables was examined using the Shapiro-Wilk test. Variables with normal distribution were expressed as mean ± standard deviation, while non-normally distributed variables were expressed as median (minimum: maximum). Categorical variables were presented as n (%).
For comparisons between multiple groups, ANOVA was used for normally distributed variables, followed by Bonferroni test for significant findings. For non-normally distributed variables, the Kruskal-Wallis test was applied, followed by Dunn-Bonferroni test when significant. Categorical variables were compared using Pearson Chi-Square and Fisher Freeman Halton tests.
Multivariable analysis
To control for potential confounding effects of age and cardiovascular disease (variables that differed significantly between frailty groups), Analysis of Covariance (ANCOVA) was performed with TNS as the dependent variable, frailty group as the factor, and age and cardiovascular disease presence as covariates. Pairwise comparisons between frailty groups were conducted using Bonferroni correction. Effect sizes were reported using partial eta squared (η2), with values of 0.01, 0.06, and 0.14 representing small, medium, and large effect sizes, respectively.
Correlation analyses
Spearman’s rank correlation coefficient was used to examine the relationship between frailty score and TNS, which was our predefined primary outcome correlation.
Additional correlations between TNS, frailty scores, and biochemical parameters were performed as exploratory analyses. These included correlations with albumin, creatinine, CRP, PTH, and other laboratory values. Results from these exploratory analyses are interpreted with appropriate caution and require further validation.
A subgroup analysis was performed in non-diabetic patients (n = 20) to evaluate whether the relationship between frailty and peripheral neuropathy was independent of diabetes status. Kruskal-Wallis test with Dunn-Bonferroni post-hoc comparisons was used for this analysis.
Multiple comparisons adjustment
For our primary hypothesis testing (comparison of TNS across frailty groups), appropriate post-hoc corrections were applied (Bonferroni for parametric, Dunn-Bonferroni for non-parametric comparisons). Exploratory correlation analyses were clearly distinguished from hypothesis-driven testing and interpreted accordingly.
Statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY), with p < .05 considered statistically significant.
Results
Baseline characteristics and neuropathy prevalence
Among the 70 patients evaluated, 40 (57.1%) reported at least one neuropathic symptom while 30 (42.9%) were asymptomatic. Nerve conduction studies revealed abnormal electrophysiological findings in 56 (80%) patients, with normal EMG findings observed in only 14 (20%) patients. Notably, all 40 symptomatic patients demonstrated abnormal EMG findings (100%), confirming the presence of polyneuropathy in this subgroup. Furthermore, among the 30 asymptomatic patients, 16 (53.3%) exhibited abnormal electrophysiological parameters consistent with polyneuropathy despite the absence of subjective complaints.
Frailty distribution and group characteristics
A total of 70 hemodialysis patients were categorized as non-frail (n = 25), pre-frail (n = 26), and frail (n = 19) using Fried frailty criteria. Frail patients were significantly older (median 68 years vs. 57 years in non-frail; p = .049) and had higher cardiovascular disease prevalence (57.9% vs. 24% in non-frail and 15.4% in pre-frail; p = .006).
Laboratory analysis showed significantly lower serum creatinine in frail patients (6.73 ± 2.80 mg/dL) compared to non-frail patients (9.08 ± 2.06 mg/dL; p = .007). Total Neuropathy Score (TNS) differed significantly between groups (p < .001): non-frail patients had median TNS of 3, pre-frail 10.5, and frail 13. Post-hoc analysis revealed that the non-frail group had significantly lower TNS compared to both pre-frail (p = .008) and frail groups (p < .001) (Table 1).
Table 1.
Characteristics of hemodialysis patients by frailty status.
| Non-frail (n, %) | Pre-frail (n, %) | Frail (n, %) | p | |
|---|---|---|---|---|
| Demographics | ||||
| Age (year) | 57 (29–74) 55.52 ± 12.98 |
60.5 (33–82) 61.19 ± 13.61 |
68 (24–80) 64 ± 15.37 |
.049a |
| Sex | ||||
| • Male | 17 (68%) | 15 (57.7%) | 11 (57.9%) | .702b |
| • Female | 8 (32%) | 11 (42.3%) | 8 (42.1%) | |
| Time on dialysis (months) | 42 (6–192) | 45.5 (10–226) | 36 (6–142) | .808a |
| Comorbidities | ||||
| Diabetes | 4 (16%) | 8 (30.8%) | 8 (42.1%) | .157b |
| Hypertension | 20 (80%) | 18 (69.2%) | 17 (89.5%) | .257b |
| Cardiovascular disease | 6 (24%) | 4 (15.4%) | 11 (57.9%) | .006b |
| BMI, kg/m2 | 25.37 ± 3.44 | 23.85 ± 3.18 | 23.85 ± 4.25 | .244c |
| Laboratory | ||||
| Creatinine, mg/dL | 9.08 ± 2.06 | 7.83 ± 2.46 | 6.73 ± 2.80 | .008c |
| Potassium, mmol/L | 5 ± 0.72 | 5.05 ± 0.77 | 5.01 ± 0.62 | .972c |
| Albumin, g/dL | 39.32 ± 3.21 | 37.91 ± 5.77 | 35.91 ± 4.22 | .057c |
| Ferritin, µg/L | 714 (23–2,974) | 733.5 (57–1,769) | 763 (77.5–1,901) | .955a |
| Hemoglobin, g/dL | 11.1 (7.3–13.7) | 10.35 (7.1–12.9) | 11.2 (8.4–12.6) | .755a |
| High sensitivity-CRP, mg/L | 5.2 (0.4–55) | 7.65 (0.8–57.6) | 14.6 (1.3–130) | .123a |
| Vitamin B12 | 433 (276–611) | 421 (254–1,900) | 414 (207–1,620) | .841a |
| HbA1c | 5.5 (4.5–7.6) | 5.4 (4.7–8.9) | 5.6 (4.5–9) | .532a |
| Dialysis related parameters | ||||
| KT/V | 1.52 ± 0.28 | 1.53 ± 0.26 | 1.59 ± 0.25 | .600c |
| Total neuropathy score (TNS) | 3 (0–23) | 10.5 (0–24) | 13 (2–26) | <.001a |
Note: Data are given as median (minimum – maximum) and mean ± standard deviation. Bold values are statistically significant at p < .05.
Abbreviations: BMI: body mass index; CRP: C-reactive protein; LD: low-density lipoprotein; PTH: parathyroid hormone.
aKruskal–Wallis test.
bChi square test.
cOne-way ANOVA test.
Electrophysiological findings
Electrophysiological studies showed significant differences in nerve conduction parameters. Median nerve motor conduction velocity decreased progressively from non-frail to frail groups (p = .038). Frail patients had higher median nerve sensory latency (p = .031) and lower sensory conduction velocity (p = .002) compared to non-frail patients.
Ulnar nerve parameters (sensory amplitude and conduction velocity), tibial nerve motor amplitude, peroneal nerve motor conduction velocity, and sural nerve sensory parameters were all significantly worse in frail patients compared to non-frail patients. Post-hoc analyses confirmed specific pairwise differences between groups for these electrophysiological parameters. Some patients had non-recordable nerve responses (recorded as zero values), particularly affecting sensory nerves, which is consistent with the typical pattern of uremic neuropathy progression (Table 2).
Table 2.
Results of nerve conduction study in the frailty groups.
| Non-frail | Pre-frail | Frail | p | |
|---|---|---|---|---|
| Median nerve (motor) | ||||
| Latency, ms | 2.71 (1.92–3.58) | 2.79 (0–3.65) | 2.97 (0–6.02) | .548a |
| Amplitude, mV | 18.1 (7.9–42.7) | 15.1 (0–57.9) | 13.7 (0–39.9) | .102a |
| Velocity, m/s | 48 (37–68) | 44 (0–59) | 43 (0–58) | .038a |
| Ulnar nerve (motor) | ||||
| Latency, ms | 2.39 (1.61–2.97) | 2.47 (0–3.75) | 2.39 (0–3.85) | .709a |
| Amplitude, mV | 21.5 (8.7–42.3) | 15.9 (0–47.4) | 10.75 (0–39.6) | .102a |
| Velocity, m/s | 49 (37–69) | 44 (0–56) | 42.5 (0–64) | .092a |
| f-Latency, ms | 27.75 (24.9–33.4) | 29.85 (25.2–38.5) | 29 (24.9–43.5) | .164a |
| Median nerve (sensorial) | ||||
| Latency, ms | 3.25 (2.4–7.17) | 3.33 (0–5.26) | 3.8 (2.75–4.74) | .031a |
| Amplitude, µV | 8.18 ± 2.14 | 7.11 ± 2.93 | 6.43 ± 2.88 | .094b |
| Velocity, m/s | 53 (40–68) | 49.5 (0–64) | 47 (35–61) | .002a |
| Ulnar nerve (sensorial) | ||||
| Latency, ms | 2.64 (1.93–6.35) | 2.63 (2.13–3.59) | 2.76 (1.93–5.05) | .248a |
| Amplitude, µV | 7.75 (5.4–13.8) | 6.4 (3.9–10.4) | 5.2 (0.2–13.4) | .003a |
| Velocity, m/s | 55.95 ± 8.49 | 53.17 ± 6.71 | 48.53 ± 10.82 | .034b |
| Tibial nerve (motor) | ||||
| Latency, ms | 4.36 (2.86–7.55) | 5.31 (0–9.80) | 5.56 (0–10.21) | .563a |
| Amplitude, mV | 4.8 (0.5–10.8) | 3 (0–10.5) | 2.3 (0–11.7) | .023a |
| velocity, m/s | 40 (24–52) | 37 (0–50) | 34 (0–45) | .095a |
| f-Latency, ms | 45.15 (26.3–54.1) | 34.4 (0–66.6) | 44.8 (26.1–64.7) | .530a |
| Peroneal nerve (motor) | ||||
| Latency, ms | 3.7 (0–6.25) | 4.22 (0–5.83) | 3.7 (0–6.72) | .306a |
| Amplitude, mV | 2.2 (0–5.7) | 1.7 (0–5.16) | 0.9 (0–4.8) | .054a |
| Velocity, m/s | 42 (0–54) | 38 (0–58) | 35 (0–51) | .027a |
| Sural nerve (sensorial) | ||||
| Latency, ms | 2.55 (0–4.21) | 2.16 (0–3.91) | 2.08 (0–4.11) | .283a |
| Amplitude, µV | 13.7 (0–26) | 4.25 (0–37.8) | 4.3 (0–44.7) | .049a |
| Velocity, m/s | 51 (0–73) | 35 (0–61) | 35 (0–67) | .015a |
Notes: Data are given as median (minimum–maximum) and mean ± standard deviation. Zero values represent non-recordable responses indicating absent nerve conduction. Bold values are statistically significant at p < .05.
aKruskal Wallis test.
bANOVA test.
Neuropathy pattern analysis
While polyneuropathy types didn’t differ significantly among groups (Table 3), the distribution showed sensorimotor neuropathy as predominant across all groups (non-frail: 64%, pre-frail: 76.9%, frail: 78.9%, p = .457). Normal EMG findings decreased from non-frail (32%) to frail (10.5%) groups (p = .160), suggesting that frailty affects neuropathy severity rather than type.
Table 3.
Types of polyneuropathy in the frailty groups.
| Non-frail | Pre-frail | Frail | p | |
|---|---|---|---|---|
| Pure axonal | 11 (44%) | 13 (50%) | 8 (42.1%) | .851a |
| Pure demyelinating | 1 (4%) | 1 (3.8%) | 1 (5.3%) | 1.000b |
| Mix (demyelinating–axonal) | 5 (20%) | 8 (30.8%) | 8 (42.1%) | .283a |
| Motor | 0 | 1 (3.8%) | 1 (5.3%) | .731b |
| Sensorial | 1 (4%) | 1 (3.8%) | 1 (5.3%) | 1.000b |
| Sensorimotor | 16 (64%) | 20 (76.9%) | 15 (78.9%) | .457a |
| Normal | 8 (32%) | 4 (15.4%) | 2 (10.5%) | .160a |
Note: Data are given as n%.
aChi-Square test.
bFisher Freeman Halton test.
Multivariable analysis controlling for confounders
To address potential confounding effects of age and cardiovascular disease – both of which differed significantly between frailty groups – we performed ANCOVA analysis. After adjusting for age and cardiovascular disease, frailty status maintained a statistically significant effect on TNS (F(2,65) = 6.415, p = .003, partial η2 = 0.165). Pairwise comparisons with Bonferroni correction showed that non-frail patients had significantly lower TNS scores compared to both pre-frail (mean difference = −5.452, p = .014) and frail groups (mean difference = −6.492, p = .008), and no significant difference was observed between pre-frail and frail groups (mean difference = −1.040, p = 1.000)
The moderate to large effect size (partial η2 = 0.165) indicates that frailty status explains approximately 16.5% of the variance in TNS scores, independent of age and cardiovascular disease.
Correlation analyses
Primary outcome correlations
Frailty score demonstrated a strong positive correlation with TNS (rs = .48, p < .001), indicating that as frailty increases, neuropathy severity also increases correspondingly.
Exploratory biochemical correlations
Exploratory correlation analyses revealed several significant associations with TNS. A strong negative correlation was observed between TNS and albumin levels (rs = −.43, p < .001), suggesting that malnutrition, as reflected by decreased albumin, is associated with increased neuropathy severity. TNS also demonstrated a moderate negative correlation with creatinine levels (rs = −.34, p = .003), indicating that lower creatinine levels, potentially reflecting muscle wasting, correlate with higher neuropathy scores. Additionally, TNS showed a moderate positive correlation with CRP (rs = .31, p = .009), suggesting that systemic inflammation contributes to neuropathy severity. A weak negative correlation was observed between TNS and PTH levels (rs = −.24, p = .043), showing that lower PTH levels are associated with higher TNS (Table 4).
Table 4.
Correlation analysis between TNS and frailty and biochemical parameters.
| TNS |
||
|---|---|---|
| Events | r s | p |
| Frailty score | .48 | <.001 |
| Creatinine, mg/dL | −.34 | .003 |
| Potassium, mmol/L | 0 | .999 |
| Intact PTH, ng/L | −.24 | .043 |
| Time on dialysis (months) | .024 | .846 |
| Albumin, g/dL | −.43 | <.001 |
| High sensitivity-CRP, mg/L | .31 | .009 |
| KT/V | −.19 | .109 |
Note: Bold values are statistically significant at p < .05. rs: Spearman correlation coefficient; TNS: total neuropathy score; CRP: C-reactive protein; PTH: parathyroid hormone.
Similarly, frailty score showed significant correlations with several biochemical parameters. A positive correlation was observed between frailty score and age (rs = .34, p = .004), confirming that frailty increases with advancing age. Frailty score demonstrated a negative correlation with creatinine levels (rs = −.39, p = .001), suggesting that lower creatinine levels, indicative of muscle wasting, are associated with increased frailty. Additionally, a negative correlation was found between frailty score and albumin levels (rs = −.37, p = .002), indicating that malnutrition contributes to frailty development. Finally, frailty score showed a positive correlation with CRP (rs = .24, p = .042), demonstrating the role of systemic inflammation in frailty development (Table 5).
Table 5.
Correlation analysis of frailty score and biochemical parameters.
| Frailty score |
||
|---|---|---|
| r s | p | |
| Age (year) | .34 | .004 |
| Creatinine, mg/dL | −.39 | .001 |
| Albumin, g/dL | −.37 | .002 |
| High sensitivity-CRP, mg/L | .24 | .042 |
| Hemoglobin, g/dL | .01 | .920 |
| Time on dialysis (months) | −.04 | .748 |
| TNS | .48 | <.001 |
| KT/V | .06 | .618 |
Note: Bold values are statistically significant at p < .05. rs: Spearman correlation coefficient; TNS: total neuropathy score; CRP: C-reactive protein.
Subgroup analysis in non-diabetic patients
To investigate whether the relationship between frailty and peripheral neuropathy was independent of diabetes status, we conducted a subgroup analysis of non-diabetic patients (n = 20). Even with diabetic patients excluded, we observed significant differences in Total Neuropathy Score across frailty categories (non-frail: 3 (0–23), pre-frail: 6 (0–23), frail: 10 (2–25), p = .024). Post-hoc analyses revealed that non-frail patients had significantly lower TNS values compared to both pre-frail and frail groups (p = .001 for both comparisons), while no significant difference was observed between pre-frail and frail groups (p > .05). This finding demonstrates that the frailty-neuropathy relationship exists independent of diabetes mellitus and is likely driven by uremic neuropathy mechanisms.
Power analysis
Post-hoc power analysis confirmed adequate study power, achieving 80% power with the observed effect size (f = 0.38) and sample size (n = 70) at α = 0.05. The ANCOVA results further validate the robustness of our findings with a substantial effect size (partial η2 = 0.165), indicating that our study was sufficiently powered to detect clinically meaningful differences between groups.
Discussion
In this study, we examined the relationship between frailty and polyneuropathy in hemodialysis patients through comprehensive clinical and electrophysiological evaluations. To the best of our knowledge, this is the first investigation to demonstrate the relationship between peripheral neuropathy and frailty index among patients undergoing hemodialysis.
Our primary finding demonstrates a robust, independent association between frailty and peripheral neuropathy in hemodialysis patients. Even after controlling for age and cardiovascular disease – two variables that differed significantly between frailty groups and could independently influence both conditions – the relationship remained highly significant (F(2,65) = 6.415, p = .003, partial η2 = 0.165). This moderate to large effect size indicates that frailty status explains approximately 16.5% of the variance in neuropathy severity, independent of these important confounders. The persistence of this association after multivariable adjustment strengthens our conclusion that the frailty-neuropathy relationship represents a genuine pathophysiological connection rather than merely reflecting shared risk factors.
Frail patients were older, had more cardiovascular disease, lower creatinine levels, and had higher TNS scores (Table 1). These findings align with contemporary studies showing strong associations between frailty and advanced age, comorbidities, and poor outcomes in CKD patients [1,5,7]. While age and cardiovascular disease are established risk factors for both frailty and neuropathy, our ANCOVA analysis demonstrates that these factors do not fully explain the observed association. This suggests that the relationship between frailty and neuropathy involves additional mechanisms beyond simple demographic and comorbidity effects.
Prevalence and pattern of neuropathy
Our study found polyneuropathy in 80% of patients via nerve conduction studies, consistent with the 60–90% prevalence reported in dialysis patients [12]. Arnold et al. [23] demonstrated neuropathy in 77% of CKD stage 3–4 patients, including those without diabetes. This aligns with Moorthi et al.’s [24] findings showing significant neuropathy prevalence in CKD independent of diabetes status.
Importantly, our data highlights that neuropathy often progresses subclinically. Among the 70 patients evaluated, 30 (42.9%) were asymptomatic, yet 16 of these asymptomatic patients (53.3%) exhibited abnormal electrophysiological findings consistent with polyneuropathy. Conversely, all 40 symptomatic patients demonstrated abnormal EMG findings, suggesting that the presence of neuropathic symptoms highly correlates with electrophysiological abnormalities in this population. These findings are consistent with previous studies – Tilki et al. [25] found clinical signs in only 59% of hemodialysis patients despite electrophysiological abnormalities in 97.6%, and Ezzeldin et al. [26] detected neuropathy in 92.5% of asymptomatic patients. This substantial proportion of subclinical polyneuropathy in hemodialysis patients underscores the value of early electrophysiological screening for timely detection and management of neuropathic changes before clinical manifestation.
Electrophysiological patterns and clinical implications
Uremic neuropathy typically presents with axonal degeneration patterns, beginning with reduced sensory nerve action potentials, followed by diminished muscle action potentials, while conduction velocities remain relatively preserved [15]. Our study showed distinctive peripheral nerve dysfunction across frailty groups. Frail patients exhibited significantly reduced conduction velocities in median motor/sensory nerves (p = .038/p = .002) and ulnar sensory nerves (p = .034). More pronounced were amplitude reductions in frail patients, particularly in ulnar sensory (p = .003), tibial motor (p = .023), and sural sensory nerves (p = .049). These amplitude changes were more significant than conduction velocity alterations, consistent with typical uremic neuropathy progression described in the literature [15,16,25,26].
Polyneuropathy pattern analysis showed no significant differences between frailty groups. Sensorimotor neuropathy was predominant across all groups (non-frail:64%, pre-frail:76.9%, frail:78.9%, p = .457). Normal EMG findings decreased from non-frail (32%) to frail (10.5%) groups (p = .160). While electrophysiological findings suggest axonal involvement, classification showed mixed involvement in frail patients. This suggests frailty progression leads to a more complex neuropathy pattern rather than pure axonal damage, and frailty may affect neuropathy severity rather than type.
We deliberately included patients with diabetes in our study cohort, rather than excluding them, because we aimed to comprehensively evaluate the relationship between peripheral neuropathy and frailty across all etiologies in hemodialysis patients. While diabetes is a well-established cause of neuropathy, our findings suggest that neuropathy from any cause – including uremic neuropathy – may contribute to frailty in this population. This comprehensive approach provides a more clinically relevant assessment of how neuropathy impacts frailty in the real-world hemodialysis population, where multiple comorbidities often coexist.
To further investigate whether the relationship between frailty and peripheral neuropathy was independent of diabetes status, we conducted a subgroup analysis of non-diabetic patients. In this analysis, we similarly observed significant differences in TNS scores between frailty groups (non-frail: 3 (0–23), pre-frail: 6 (0–23), frail: 10 (2–25), p = .024). This finding demonstrates that uremic neuropathy developing in the context of CKD has a strong relationship with frailty, independent of diabetes. In a study published by Issar et al. [27], it was shown that both diabetic and uremic neuropathy contribute to nerve dysfunction in diabetic nephropathy patients, but the primary pathophysiological mechanism more closely resembles uremic neuropathy. Our study observed a strong relationship between frailty and neuropathy even in non-diabetic hemodialysis patients. These results indicate that the relationship between frailty and neuropathy in hemodialysis patients is significant regardless of the presence of diabetes and should be considered in clinical assessments.
Biochemical insights and pathophysiological implications
The role of uremic toxins in neuropathy has been investigated without establishing causality. Some studies show hyperkalemia impairs nerve functions, restorable by removing excess serum potassium [28]. Our study found no significant association between TNS and potassium levels, though conventional nerve conduction studies may lack sensitivity for detecting acute electrolyte effects.
Our exploratory correlation analyses revealed several intriguing associations that, while requiring cautious interpretation, provide insights into potential pathophysiological mechanisms. The strong negative correlation between TNS and albumin levels (rs = −.43, p < .001) suggests that malnutrition adversely affects peripheral nerve function, possibly through impaired myelin synthesis and reduced protein binding of uremic toxins. The positive correlation between TNS and CRP (rs = .31, p = .009) highlights inflammation’s potential role in neuropathy pathogenesis, consistent with the known inflammatory milieu in both frailty and CKD.
The negative correlation between TNS and creatinine (rs = −.34, p = .003) is particularly interesting, as it suggests that lower creatinine levels – potentially reflecting muscle wasting – correlate with worse neuropathy. This finding aligns with the concept that frailty, muscle wasting, and neuropathy may share common pathophysiological pathways in the uremic milieu. Similarly, the negative correlation between frailty score and creatinine (rs = −.39, p = .001) supports the hypothesis that muscle wasting is central to frailty development in this population.
The weak negative correlation between TNS and PTH (rs = −.24, p = .043) contrasts with some literature showing elevated PTH associated with deteriorated nerve function [29]. This discrepancy may be attributed to the limited representation of nerve conduction parameters within TNS, as only 2 of the 8 TNS components assess nerve conduction studies, or may reflect the complex interplay of mineral and bone disorders in advanced CKD.
Clinical implications and bidirectional relationship
Our findings support the concept of a bidirectional relationship between peripheral neuropathy and frailty. The strong correlation between frailty score and TNS (rs = .48, p < .001), maintained even after controlling for important confounders, suggests that these conditions may mutually reinforce each other. Neuropathy can decrease mobility and physical activity – key components of frailty – through impaired proprioception, muscle weakness, and increased fall risk. Conversely, the systemic inflammation, oxidative stress, and metabolic derangements associated with frailty may exacerbate nerve damage. This bidirectional relationship is illustrated in Figure 1, which shows the complex interactions between frailty and peripheral neuropathy.
Figure 1.
Proposed bidirectional relationship between frailty and peripheral neuropathy in hemodialysis patients. The diagram illustrates key mediators including systemic inflammation, muscle weakness, sensory deficits, mobility impairment, and uremic toxins that contribute to the complex interactions between these conditions. CRP: C-reactive protein.
Recent studies support these observations. Ward et al. [30] showed poor nerve function predicts lower quadriceps strength and faster decline over time, suggesting neuropathy may contribute to frailty through effects on physical performance. In a systematic review, Ward et al. [31] demonstrated reduced lower-extremity peripheral nerve function associates with poor mobility in older adults, both with and without diabetes. Arnold et al. [23] showed strong correlations between neuropathy severity and reduced walking speed in CKD patients, independent of diabetes status. This functional impact appeared even in stages 3–4 CKD, aligning with our finding that frail individuals have worse neuropathy parameters.
Comparison with contemporary literature
Our findings align with recent large-scale studies on frailty in CKD populations. Mei et al. [32] conducted a meta-analysis of 18 cohort studies including 22,788 participants, reporting frailty prevalence of 41.8% and demonstrating significant associations with increased mortality, hospitalization, and falls. Similarly, Karnabi et al. [33] performed a comprehensive systematic review encompassing 140 studies, emphasizing the prognostic importance of frailty and functional status in CKD patients for mortality and hospitalization outcomes. Zhang et al. [34] found a 34.5% frailty prevalence in an even larger meta-analysis and demonstrated a 94.1% increased mortality risk in frail CKD patients. Our study extends these findings by demonstrating that peripheral neuropathy represents an important, measurable component of the frailty syndrome that persists even after controlling for major confounding factors.
Arias-Guillén et al. [35] found 68% frail or pre-frail patients in their hemodialysis cohort, mirroring our distribution (67.9% combined frail and pre-frail), confirming frailty’s high prevalence in this population. Their findings that frail patients following exercise recommendations improved or stabilized suggest that interventions targeting the frailty-neuropathy relationship might be beneficial.
Study strengths and limitations
This study demonstrated several important methodological strengths. The comprehensive assessment strategy, combining clinical evaluation and electrophysiological testing, provided thorough assessment of peripheral nerve function. The multivariable analysis controlling for age and cardiovascular disease strengthens causal inference. The clear distinction between primary hypothesis testing and exploratory analyses enhances interpretive validity. The evaluation of multiple nerve conduction parameters enabled comprehensive detection of both sensory and motor nerve involvement, and the multi-center nature enhances generalizability.
However, the study also had notable limitations. The cross-sectional design prevented establishing temporal relationships and limited our ability to observe how neuropathy progresses over time. Though the sample size was adequate for primary analyses, it potentially limited statistical power for detailed subgroup analyses. While major confounding conditions were controlled, other potentially influential factors such as detailed nutritional status, and medication effects could not be fully accounted for. The exploratory nature of biochemical correlations requires cautious interpretation and further validation in larger cohorts.
Conclusions
Our study demonstrates a significant, independent association between frailty and peripheral neuropathy in hemodialysis patients that persists even after controlling for age and cardiovascular disease. The progressive increase in TNS from non-frail to frail groups and its strong correlation with frailty score suggest that peripheral nerve dysfunction is an integral component of the frailty syndrome in this population. The relationship exists independent of diabetes status, highlighting the importance of uremic neuropathy in frailty development. The observed electrophysiological pattern suggests an axonal-predominant process, though the tendency toward mixed-type neuropathy in frail patients indicates complex pathophysiological mechanisms.
Early recognition of this relationship could have important implications for clinical management of hemodialysis patients. Future longitudinal studies are needed to establish temporal relationships and develop targeted interventions that may help prevent or slow the progression of both frailty and neuropathy in this vulnerable patient population. The integration of neuropathy assessment into frailty screening protocols, and vice versa, represents a promising avenue for improving outcomes in hemodialysis patients.
Supplementary Material
Acknowledgments
We thank the personnel of the Neurology and Nephrology clinics and acknowledge Halil Işık for his technical expertise with electroneuromyography measurements. Ayşe Şeker contributed to conception, design, data acquisition, analysis, interpretation, drafting, writing, critical revision, and accountability for all aspects of the work; Mehmet Usta, Sinan Gönüllü, Nur Özer Şensoy, Can Hüzmeli, and Hatice Ortaç contributed to data acquisition, analysis, and interpretation; Abdulcemal Özcan provided final approval of the publishable version; and Özden Kamışlı contributed to drafting and critical revision of the manuscript, with all authors having read and approved the final version.
Funding Statement
No funding was received.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author, Ayşe Şeker, upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, Ayşe Şeker, upon reasonable request.