Visual Abstract

Background: Impaired mobility is associated with functional dependence, frailty, and mortality in prevalent patients undergoing dialysis. We investigated risk factors for mobility impairment, (poor gait speed) in patients incident to dialysis, and changes in gait speed over time in a 2-year longitudinal study. Methods: One hundred eighty-three patients enrolled within 6 months of dialysis initiation were followed up 6, 12, and 24 months later. Grip strength, health-related quality of life, and comorbidities were assessed at baseline. Outcomes were (a) baseline gait speed and (b) change in gait speed over time. Gait speed was assessed by 4-meter walk. Multivariate linear regression was used to identify risk factors for low gait speed at baseline. For longitudinal analyses, linear mixed effects modeling with gait speed modeled over time was used as the outcome. Results: Participants were 54.7 ± 12.8 years old, 52.5% men, 73.9% black with mean dialysis vintage of 100.1 ± 46.9 days and median gait speed 0.78 (0.64–0.094) m/s. Lower health utility and grip strength, diabetic nephropathy, and walking aids were associated with lower baseline gait speed. Loss of 0.1 m/s gait speed occurred in 24% of subjects at 1 year. In multivariate mixed effects models, only age, walking aid use, lower health utility, and lower handgrip strength were significantly associated with gait speed loss. Conclusions: In our cohort of incident dialysis patients, overall gait speed is very low and 54.2% of the subjects continue to lose gait speed over 2 years. Older age, lower handgrip strength, and quality of life are risk factors for slowness. Patients at highest risk of poor gait speed can be identified at dialysis initiation to allow targeted implementation of therapeutic options.

CKD is associated with decreased functional independence [1], falls [2, 3], frailty [4], and fractures [5, 6]. Abnormal gait (poor mobility) and poor quality of life are common in patients with CKD and worsen with progression of CKD [7-9]. Initiation of dialysis involves large changes in lifestyle and nutrition that influence mobility, contributing further to these outcomes. In nursing home residents who started dialysis, only 13% of 3,702 patients had preserved functional status in 1 year, independent of age, sex, race, and functional status trajectory prior to starting dialysis [10]. Therefore, establishing the trajectory of mobility impairment and its risk factors at the transition to hemodialysis from pre-dialysis CKD is important in maintaining mobility.

Usual gait or walking speed has been proposed as the “6th vital sign” [11]. Although predominantly established in the geriatric literature as a generic indicator of health status and prognosis [12-14], gait speed summarizes the overall burden of disease and has utility across age and disease states [14-16]. Gait speed less than 0.8 m/s is associated with poor muscle strength and lower life expectancy in community dwelling elderly, regardless of sex. In prevalent dialysis patients, single cross-sectional measures of low gait speed are risk factors for future cardiovascular events [17], hospitalizations, decreases in activity of daily living and SF36 scores [18], and mortality [19]. Considering these adverse associations of low gait speed in those on long-term dialysis, we sought to characterize gait speed earlier in the course of end-stage kidney disease by examining risk factors for low gait speed in incident patients and risk factors for change over time.

Interventional trials have shown that various forms of supervised programs such as resistance exercise [20, 21], combined aerobic exercise and toning, flexibility and cardiovascular training [22, 23], virtual reality training [24], and Tai-Chi [25] lead to improvements of 0.1–0.3 m/s gait speed in small trials in patients undergoing hemodialysis. Yet, these interventions are difficult to implement in everyone since they tend to be time consuming, need dedicated personnel, and are expensive. Therefore, targeting these interventions may be most useful in those patients starting dialysis that are expected to have continued decline in mobility. We hypothesized that routinely collected demographics, comorbidities, and grip strength collected at the bedside would be risk factors for low gait speed and its decline in patients’ incident to dialysis. We tested this hypothesis in a cohort of 183 inner-city dialysis patients recruited within 6 months of starting outpatient dialysis with serial measures of gait speed collected over a follow-up period of 2 years.

Study Population

The Indiana University Longitudinal Study of Incident Dialysis patients consist of 195 participants, who were recruited from outpatient dialysis units affiliated with Indiana University Health Nephrology and located in inner-city areas. Eligible participants were those who were above the age of 18 years and started dialysis within the past 6 months. Subjects unable to walk 4 m (with or without assistive devices) were excluded. Study visits were conducted at baseline, month 6, year 1, and then at year 2. Follow-up visits were completed ±1 month of the scheduled visit date. Variables were collected from the chart when available with addition of self-report as needed. Patients on peritoneal dialysis have been shown to have higher gait speed than those undergoing maintenance hemodialysis [26]; therefore, for this analysis, the 12 peritoneal dialysis patients were excluded. There were 183 participants included in this analysis after all these exclusions. The study was approved by the Institutional Review Board at Indiana University, and all participants signed consent before participating.

Measurements

Exposure Variables

Clinical and Demographic

Age, BMI (kg/m2), self-reported sex, race, and smoking status (past or current) were recorded. The cause of ESRD was obtained by self-report and chart review and later grouped into “diabetic nephropathy” versus “other.” Diabetes, hypertension, and peripheral vascular disease were determined by a combination of self-report and/or chart review. Participants with a history of coronary artery disease, congestive heart failure, or arrhythmia were classified as having cardiovascular disease. Those who had a stroke or a cerebral hemorrhage were categorized as having cerebrovascular disease.

Muscle-Related Measures and Health Utility Assessment

Grip Strength is a simple, clinically implementable measure that provides an indication of overall strength [27]. We used the Jamar hand dynamometers (Lafayette Instrument Company, Lafayette, IN, USA) in accordance with the Southampton Grip-Strength Measurement Protocol [28]. The Jamar hand dynamometer has excellent concurrent validity with known weights (r > 0.96), has the most extensive normative database, and is accepted as the “gold standard” by which other dynamometers are evaluated. Subjects were seated upright with elbow unsupported with 90° elbow flexion and grip position 2. Patients performed 3 trials with each hand, alternating hands between trials, with the maximum score from all 6 trials being recorded.

Health Utilities Index-3 (HUI-3) is a numerical descriptor of health-related quality of life. The HUI-3 provides a score for 9 dimensions of health status: vision, hearing, speech, ambulation/mobility, pain, dexterity, self-care, emotion, and cognition. Each dimension has 3–6 levels with a unique score that is scaled based on the measurement samples of the general population. The composite score is along a continuum from deceased (0.00) to perfect health (1.00) [29]. The HUI-3 is a measure that is reported to capture health-related quality of life accurately in health conditions associated with severe disability, such as in CKD [30].

Outcome Variables

Our 2 outcomes of interest were (1) baseline gait speed in m/s and (2) gait speed modeled over time in m/s/year. The distance walked (4 m)/time, was performed twice at each study visit. The maximum value of the 2 tests and use of an assistive device was recorded. We used usual gait speed over 4 m, as this assessment of gait speed is highly reproducible (ICC = 0.97) [31]. Differences in gait speed of 0.05 and 0.10 m/s represent small and substantial meaningful changes in older adults, respectively, according to Perera et al. [32]. Timing of gait speed measures were performed before dialysis for those in the afternoon and evening shift and after completion for those dialyzing in the first (early morning) shift. For follow-up visits, the timing of the gait speed assessment around dialysis was kept constant.

Statistical Analyses

Descriptive statistics on baseline characteristics, including means, standard deviations, medians, interquartile range, count, and percentage were calculated. t tests and Fisher’s exact tests were used to compare baseline characteristics between groups of subjects split at the median value of the baseline maximum gait speed. Wilcoxon rank sum test was used to compare the median baseline maximum gait speed between dialysis shifts. Univariate linear regression models were used to identify the association of baseline characteristics on baseline maximum gait speed. Variables univariately significant at the alpha = 0.15 level were included in a series of multiple linear regression models using backward model fitting to identify characteristics significant at the alpha = 0.05 level after including age at baseline, sex, and race. Similarly, in subjects with a baseline walk test, mixed effects models were used to identify the association of baseline characteristics on maximum gait speed over time after adjusting for visit. As subjects were seen up to 4 times, a random intercept model was fitted to account for subject-to-subject variability, and visit was included as a covariate in the model. Variables significant at the alpha = 0.15 level were included in a series of mixed effects models using backward model fitting to identify characteristics significant at the alpha = 0.05 level after including visit, age at baseline, sex, and race. Since baseline gait speed is lower in females, we used the final mixed effects model to further investigate sex effects over time. A model was fitted including an interaction between sex, baseline gait speed split at sex-based medians, and visit. If the interaction was not significant, it was dropped from the model. From this model, slope differences between the sex/baseline gait speed groups and sex differences were calculated and tested without adjusting for multiple comparisons.

Four sets of sensitivity analysis were performed. t tests and Fisher’s exact tests were used to compare baseline characteristics between subjects who completed a baseline walking test compared to those who did not. The second sensitivity analysis repeated the final mixed effects model on gait speed over time after dropping subjects who changed walking aid over time. The third sensitivity analysis was performed to compare baseline gait speeds (Wilcoxon rank sum tests) between those who were followed up or not at each of the longitudinal time points and between those who died with survivors. Finally, sensitivity analyses were done by adding a random effect for dialysis shift to the baseline gait speed final model and a random effect for subject nested within shift to the final model of gait speed modeled over time.

Baseline characteristics for the final cohort (N = 183) are shown in Table 1. The mean (SD) age was 54.7 (12.8) years; 52.5% were men, 73.9% were black, 51% had diabetes, and mean dialysis vintage was 100.1 (46.9) days. Median gait speed was 0.78 (IQR: 0.64–0.94) m/s. The median gait speed for subjects that dialyzed in the first shift was 0.82 m/s compared to 0.76 m/s in those who dialyzed in the afternoon or evening shifts (p = 0.1362). Of the 183 hemodialysis patients consented, 17 (9.3%) refused the mobility testing, leaving 166 subjects for inclusion in this analyses. Comparison of these 17 subjects with the studied cohort (n = 166) revealed that the excluded group was significantly older, more likely to have diabetes and cerebrovascular disease with lower overall health utility and handgrip strength (online suppl. Table 1; for all online suppl. material, see www.karger.com/doi/10.1159/000508225).

Table 1.

Demographics, comorbidities, and baseline clinical characteristics (N = 183)

Demographics, comorbidities, and baseline clinical characteristics (N = 183)
Demographics, comorbidities, and baseline clinical characteristics (N = 183)

Predictors of Baseline Gait Speed

Older subjects, being female, those with diabetic nephropathy, use of a walking aid, having lower overall health utility or lower health utility for ambulation and lower handgrip strength were all significantly associated with gait speed less than median of 0.78 m/s at baseline (Table 2). There was no difference in the gait speed in patients with versus without different manifestations of diabetes (neuropathy, retinopathy, and peripheral vascular disease), and thus, we utilized the general diagnosis of diabetes in subsequent models. Lower gait speed was not associated with time since starting dialysis or with self-report of residual urine output. Results from univariate linear regression models on baseline maximum gait speed by baseline characteristics are shown in the left columns of Table 3. Factors with p < 0.15 by univariate analyses were included in multiple linear regression models. The results of the backward selection models are shown in right columns in Table 3. Diabetic nephropathy, as compared to other etiologies for ESRD, was associated with an average gait speed 0.075 m/s lower than other etiologies (p = 0.023). Use of a walking aid was also related to a 0.23 m/s lower baseline gait speed (p < 0.0001). Increases in overall health utility and grip strength were associated with significantly higher baseline gait speed (p = 0.001 and p < 0.0001, respectively).

Table 2.

Demographic and baseline characteristics* by baseline gait speed divided at median

Demographic and baseline characteristics* by baseline gait speed divided at median
Demographic and baseline characteristics* by baseline gait speed divided at median
Table 3.

Univariate and multivariate analysis for predictors for baseline gait speed

Univariate and multivariate analysis for predictors for baseline gait speed
Univariate and multivariate analysis for predictors for baseline gait speed

Predictors of Gait Speed Modeled over Time

Of the 166 subjects with baseline gait speed measured, follow-up gait speeds were available for N = 131 (78.9%) at 6 months, N = 109 (65.7%) at 1 year, and N = 68 (41.0%) at year 2. During the follow-up period, 17 subjects (10.2%) died. Online suppl. Fig. 1 shows the flow of subjects through each study time point. As early as 6 months, 38% of subjects had a decrease of 0.05 m/s and by 1 year, 24% of subjects had a decrease in gait speed of 0.1 m/s or more.

Over the entire study period, the overall decrease in gait speed with time was 0.012 m/s/year, and 54.2% of the initial study population had some decrease in gait speed during the follow-up period. Table 4 shows the univariate analyses and final backward selection model results of factors associated with gait speed modeled over time. The co-efficient for time was overall not significant. In the final adjusted model, every 10-year increase in age was associated with a lower gait speed of 0.037 m/s (p = 0.003); use of a walking aid was associated with a 0.14 m/s lower gait speed (p < 0.0001). Each 0.1 unit increase in overall health utility was associated with a 0.029 m/s higher gait speed (p < 0.0001) and each 1 kg/m2 increment in handgrip strength was associated with a 0.0068 m/s higher gait speed (p = 0.0006).

Table 4.

Univariate and multivariate analysis for gait speed modeled over time (linear mixed effects model)

Univariate and multivariate analysis for gait speed modeled over time (linear mixed effects model)
Univariate and multivariate analysis for gait speed modeled over time (linear mixed effects model)

There was no statistically significant difference (p = 0.10) in slopes of change in gait speed between sexes when split at their median baseline gait speed so the interaction was dropped from the model. After removing the nonsignificant interaction, males were faster than females although not significantly with their gait speed averaged over time (estimate = 0.04 m/s, SE = 0.03, p = 0.139). Figure 1 shows gait speed trajectories over time divided into males and females stratified according to their baseline gait speed (above or below median). At baseline, 30 subjects (18%) used a walking aid. Baseline walking aid use was associated significantly with higher prevalence of diabetes, peripheral vascular disease, and cerebrovascular disease (all p < 0.05). A second sensitivity analyses (online suppl. Table 2) shows the results of the multivariate linear fixed effects model of gait speed (Table 4) after dropping 23 subjects who changed their walking aid over time. The results were similar, although age at baseline lost significance (p = 0.152). As a third sensitivity analysis, we compared the baseline gait speeds of those who were available for analysis at each time point (6, 12, and 24 months) for the longitudinal analysis. Baseline gait speeds of the subjects available for follow-up at each time point when compared to those not available at each time point were not significantly different (all p > 0.15). Baseline gait speeds were significantly slower for those who died during follow-up than those who did not. The addition of the random effects for dialysis shift to both the baseline gait speed model (online suppl. Table 3) and the gait speed over time model (online suppl. Table 4) did not alter any of the results.

Fig. 1.

Comparisons of means of gait speed averaged over time by sex and sex-based median splits of baseline gait speed. Individual patient trajectories split by sex are shown in the background.

Fig. 1.

Comparisons of means of gait speed averaged over time by sex and sex-based median splits of baseline gait speed. Individual patient trajectories split by sex are shown in the background.

Close modal

In this longitudinal study of a cohort of predominantly black, inner-city patients new to dialysis, we show that median gait speed is low at 0.78 m/s. Operational definitions of sarcopenia by international working groups [33, 34] use a single gait speed cutoff of ≤0.8 m/s for assessment of the functional component of sarcopenia. Using this definition, more than 50% of patients starting dialysis in our population have functional impairments consistent with sarcopenia. To our knowledge, this is the first study that reports serial measures of gait speed in patients who have been initiated on dialysis within the past 3.3 ± 1.5 months, in a predominantly African American population. We identify that higher age, female sex, and diabetic nephropathy as the causes for ESRD, and use of a walking aid, overall lower health utility, lower handgrip strength, having diabetes, cardiovascular disease, or cerebrovascular disease are all univariately significantly associated with lower gait speed at dialysis initiation. However, after adjustment, only lower overall health utility, diabetic nephropathy as the cause for ESRD, walking with an aid, and lower handgrip strength were significantly associated with lower baseline gait speed after adjustment. In these same patients just starting outpatient dialysis, we show a decline of gait speed with time of 0.012 m/s/year, though the overall change of gait speed over time was not significant. A significant loss of 0.1 m/s gait speed occurred in 24% of subjects within the first year of starting of dialysis. In addition to the significant predictors of baseline gait speed (overall health utility, handgrip strength, and the use of a walking aid), higher age also was significantly associated with gait speed decreases over time in our analysis. These results demonstrate high prevalence of mobility impairment when starting dialysis.

It is noteworthy that our patients were slow, the median gait speed, at 0.78 m/s falls within the slower range of a recently published systematic review of 28 studies of hemodialysis patients (1,673 subjects), reporting a mean gait speed of 1.12 m/s (range: 0.71–1.71) [35]. Higher gait speeds in these other studies of prevalent dialysis patients may reflect survival bias since the sickest patients at dialysis initiation likely had the slowest gait speeds and were more likely to have died before the first 1–2 years on dialysis, and thus not included in those studies. Mortality was highest during the first year of dialysis initiation [36], and yet many studies of the 28 studies in this systematic review excluded patients in the first 3 months, when patients are not yet Medicare eligible. In the USRDS “ACTIVE-ADIPOSE” study population of 752 prevalent dialysis, 62% were African American and 42% with a gait speed <0.8 m/s [18], comparable to our population. However, their rate of gait speed decline, reported in a follow-up report, was notable at 0.05–0.08 m/s/year [37]. The pronounced gait speed decline in those prevalent dialysis patients may be either a result of or a surrogate marker of their accumulated comorbidity burden and disability over longer dialysis vintage [7]. Since we enrolled only patients nearly incident to dialysis and followed them for 2 years, our patients may not have had enough time to develop equivalent levels of disability.

Our study population is unique in that it assesses predominantly (∼74%) African American patients recruited from inner-city outpatient dialysis units. African Americans with CKD obtain lower routine medical care [38] and tend to be of lower socioeconomic status, predisposing them to overall increased risk of adverse outcomes. However, in our study as well as in the above USRDS study [37], race was not a determinant of gait trajectories over time. In a cohort of 122 Japanese prevalent dialysis patients, with higher median baseline gait speeds of 1.4 ± 0.2 m/s, although the population was younger, muscle strength, cardiac disease, and standing balance were cross-sectional determinants of low gait speed [39]. Similar to this, in our analyses, grip strength and use of a walking device (a marker of poor balance) remained significant risk factors for both slow baseline gait speed as well as its trajectory over time. Walking requires preserved muscle strength as well as intact balance, and the effect of these factors likely outweighs other demographic determinants such as race and sex.

We show that the average rate of gait speed decline in the first year of dialysis initiation was 0.012 m/s/year in our study population with a mean age of just 54.7 ± 12.8 years. Though this rate was not significant, it is quantitatively comparable to the approximate decline in a gait speed of 0.022 m/s/year in a study of much older community dwelling adults (mean age of 76.4 ± 3.6 years) [40] as well as a decline of 0.025 m/s per year in the Health Aging and Body Composition Study of well-functioning adults aged 70–79 years at baseline [41]. More importantly, we show that those with mobility impairment (lower gait speeds) at start of dialysis do not improve, or worsen significantly, despite initiation of dialysis. Therefore, the time to attempt interventions that have shown to be helpful in preserving gait may be early after initiation of dialysis or potentially in pre-dialysis CKD or during the period of transition to dialysis dependence. The success of such interventions will need to be evaluated in future trials.

Currently, there are no objective measures of mobility performed routinely in dialysis units in the USA. Physicians state lack of time in clinic visits as well as logistic issues such as lack of access to complex instruments required to assess sarcopenia [42]. In dialysis units, yearly assessments of perceived physical function using the SF36 or the Kidney Disease Specific Quality of Life Physical Function Survey are performed. A study of trends in the physical functioning score of the SF36 between 1998 and 2006 showed no improvement in subjective function over time in 11,079 dialysis patients, but the authors demonstrate that there was also a significant role of responder bias: subjects who did not complete the SF36 when compared to those who did had a >2-fold increased annualized mortality rate [43]. Our study demonstrates that obtaining a handgrip strength combined with a gait speed test, which together can be performed in minutes before the dialysis session at a single time point close to dialysis initiation can be useful in objectively quantifying declines in gait speed within individuals. In dialysis patients, there are no studies that have been designed to test the effect of improvement of gait speed on hard clinical outcomes [7]. For such studies to be performed, it will be essential to risk stratify potential subjects to those with the greatest risk of decline in physical function with time. Our study identifies higher age, use of a walking aid, low handgrip strength, and overall health utility as risk factors for that progressive decline in gait speed at dialysis incidence using objective measures. This is the initial step to such intervention trials.

A strength of our study is that it is the first study to focus on incident subjects, recruited between 6 weeks to within 6 months of dialysis initiation (mean 3.3 months), to ensure that they had “equilibrated” physiologically and emotionally after this transition. The first year on dialysis is a time of overwhelming lifestyle changes, as patients adjust to a new daily routine coupled with alterations in their diets, increased sedentary time, as well as potentially less motivation to be physically active. In addition, there is fluctuation in volume status until a dry weight is established. Other strengths are the size of the cohort and the longitudinal nature of the collection. Our study used objective, reliable, and reproducible measures of gait speed and grip strength measurements. We consistently timed our gait speed and grip strength measures pre- or post-dialysis within individual subjects. This is important since there are changes in gait speed with a single dialysis session, dependent on dialysis run hemodynamics [44]. A limitation of the study is that gait speed was measured post dialysis for the 1st shift subjects (36%) but prior to dialysis session in the rest. However, in sensitivity analyses, this did not affect our results. However, the lack of specific details about the dialysis treatments, such as episodes of hypotension, rapid fluid shifts during dialysis run inter-dialytic weight gain, nutritional status, and biochemical measures, remains a limitation. Limitations of the present study include loss to follow-up with time due to dialysis patients changing units, transitioning to home dialysis, moving out of the area, withdrawing voluntarily from the study, transplantation, death, and study ending. Due to this, gait speed assessment was available on 78.9, 65.7, and 41.0% at 6 months, 1 year, and, 2 years, respectively. This loss to follow-up would not change the results of modeling the baseline gait speed but may have affected the modeled gait speed over time.

In summary, higher age, use of a walking aid, low handgrip strength, and overall health utility, all collected at a single time point during dialysis initiation can serve to assess the risk of progressive mobility impairment in patients new to dialysis. These measures are all easily obtained at a dialysis unit on acceptance of new patients, with minimal needs for equipment or time. The availability of these measures allows the care team to identify those at risk, and patients who are more likely to benefit from pragmatic, rehabilitation, and exercise-based interventions. The ultimate long-term goal is to improve mobility in an effort to preserve functional status and quality of life for patients starting dialysis.

The study was approved by the Institutional Review Board at Indiana University and all participants signed an informed consent document before participating.

R.I.T. is a consultant to Fresenius Medical Care North America, and has received consulting support from Pfizer, Merck, Alnylam, Bayer, Agios, Roche, Aggamin, Kaneka, Genzyme, Amgen, and Thermo Fisher.

This study was supported by a peer-reviewed grant to R.I.T. from the Canadian Institutes for Health Research industry partnership program (grant MOP-84258; industry partner Abbott Laboratories). This work was supported by NIH NIDDK K23 DK102824 (R.N.M.). S.M.M. is supported by NIH P30 AR072581 and R01DK11087103 and VA BX001471. K.G.A. is supported by NIH NIDDK K08-DK-110429.

All co-authors have contributed to this manuscript and approved this submission.

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