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Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study
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Journal of the Formosan Medical Association xxx (xxxx) xxx
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Original Article
Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study Yu-Li Lin a, Hung-Hsiang Liou b, Chih-Hsien Wang a, Yu-Hsien Lai a, Chiu-Huang Kuo a, Shu-Yuan Chen c,*, Bang-Gee Hsu a,d,** a Division of Nephrology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97004, Taiwan b Division of Nephrology, Department of Internal Medicine, Hsin-Jen Hospital, New Taipei City 24243, Taiwan c Department of Public Health, Tzu Chi University, Hualien 97004, Taiwan d School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
Received 8 August 2019; received in revised form 23 October 2019; accepted 29 October 2019
KEYWORDS Gait speed; Handgrip strength; Hemodialysis; Muscle quality; Sarcopenia
Background/Purpose: Sarcopenia is prevalent in chronic hemodialysis patients. This prospective cohort study evaluated the impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in 126 chronic hemodialysis patients. Methods: Skeletal muscle mass, handgrip strength (HGS), gait speed, and blood parameters were assessed. Sarcopenia was evaluated using the criteria of the European Working Group on Sarcopenia in Older People and the Taiwanese criteria for Sarcopenia. Muscle quality was defined as HGS divided by mid-arm muscle circumference. Results: Prevalences of uremic sarcopenia were 8.7% and 13.5% according to Taiwanese and European criteria, respectively. Low HGS and gait speed were much more prevalent than low muscle mass. Within 3 years, 79 (62.7%) patients were hospitalized and 26 (20.6%) died. Low HGS and slow gait speed were associated with hospitalization and mortality, while sarcopenia was associated with mortality but not with hospitalization. Notably, in our patients without sarcopenia, close associations between increased hospitalization and mortality risk with low HGS and slow gait speed remained unchanged. In Cox proportional hazard analysis, muscle quality [hazard ratio (HR) Z 0.42, 95% confidence interval (CI) Z 0.19e0.93,
* Corresponding author. ** Corresponding author. Division of Nephrology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, 97004, Taiwan. E-mail addresses: [email protected] (Y.-L. Lin), [email protected] (H.-H. Liou), [email protected] (C.-H. Wang), [email protected] (Y.-H. Lai), [email protected] (C.-H. Kuo), [email protected] (S.-Y. Chen), [email protected] (B.-G. Hsu). https://doi.org/10.1016/j.jfma.2019.10.020 0929-6646/Copyright ª 2019, Formosan Medical Association. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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Y.-L. Lin et al. p Z 0.032] and serum creatinine (HR Z 0.82, 95% CI Z 0.71e0.95, p Z 0.009) were independently associated with composite outcome of hospitalization or death. Conclusion: Muscle functionality and quality can predict hospitalization and overall survival in chronic hemodialysis patients, better than muscle mass. Copyright ª 2019, Formosan Medical Association. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Introduction Sarcopenia, originally described as a geriatric syndrome characterized by age-related progressive decline in both muscle mass and strength, leads to an increased risk of falls and fractures, disabilities, hospitalization, and death in older adults.1e4 In patients with end-stage renal disease (ESRD), accelerated aging is a characteristic feature.5 Compared with older adults in general, patients with ESRD are predisposed to an increased risk of sarcopenia, which is attributed to multifaceted factors, including protein loss during dialysis, inactivity, metabolic acidosis, insulin resistance, inflammation, oxidative stress, myostatin overexpression, reninean giotensin system changes, and comorbidities.6 The European Working Group on Sarcopenia in Older People has established diagnostic criteria for geriatric sarcopenia in 2010,7 along with several follow-up expert consensuses8e10; these criteria encompass three components: low muscle mass, muscle weakness, and poor physical performance. Sarcopenia diagnosis is based on low muscle mass as an essential criterion, accompanied by either muscle weakness or poor physical performance.7,8 There is increasing evidence that these diagnostic criteria strongly predict hospitalization and mortality in the geriatric population.1e3 In chronic hemodialysis (HD) patients, the prevalence of sarcopenia is considerably higher than in the general geriatric population.11 To the best of our knowledge, however, consensus diagnostic criteria for uremic sarcopenia in chronic HD patients has not yet been established. The applicability of diagnostic criteria for geriatric sarcopenia to predict clinical outcomes in chronic HD patients should be determined. Moreover, although both low muscle mass and function are well-known to be associated with poor clinical outcomes in chronic HD patients,12e17 their clinical impacts have rarely been compared together. Accordingly, this 3-year prospective cohort study was undertaken to evaluate the importance of sarcopenia as a predictor of hospitalization and mortality in chronic HD patients; it was also performed to assess potentially different impacts of muscle mass, muscle strength, and gait speed on clinical outcomes, using both Taiwanese and European criteria for sarcopenia.
Institutional Review Board of Tzu Chi Hospital (IRB104-84-B). Patients were recruited in February and March 2016. Patients aged >20 years who had undergone HD with standard 4-h dialysis three times per week for at least 3 months were included. Patients who were bed-ridden, who presented with an infection, amputated limb, pulmonary edema, unsteady gait, history of stroke, or malignancy at the time of enrollment, or who refused to participate were excluded from the study. Informed consent was obtained from each enrolled patient; demographic data and information regarding comorbid diseases were collected. Diabetes mellitus (DM) was defined based on the history of antidiabetic drug use; hypertension was defined based on systolic blood pressure 140 mmHg, diastolic blood pressure 90 mmHg, or history of antihypertensive agent use; hyperlipidemia was defined based on the history of statin or fibrate use. Coronary artery disease (CAD) was confirmed through coronary angiography.
Body composition measurement
Materials and methods
Body weight was measured in light clothing and without shoes to the nearest half-kilogram after HD session, while height was to the nearest half-centimeter. Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Mid-arm circumference was measured at the mid-point from the acromion to olecranon, and triceps skinfold was measured using a caliper (QuickMedical, Issaquah, WA, USA); mid-arm muscle circumference (MAMC) was then calculated by using the following equation: MAMC (cm) Z [mid-arm circumference (cm) e 0.314] triceps skinfold (mm).18 Average values of both arms were used for analysis. Skeletal muscle mass was measured in the standing position, using a bioimpedance analyzer (Tanita BC 706DB, Tanita Corporation, Tokyo, Japan).19,20 The measurement was performed before dialysis. Skeletal muscle index (SMI) was calculated based on skeletal muscle mass (kg) divided by height (m2). Low muscle mass was defined as SMI <10.76 kg/m2 in men and <6.76 kg/m2 in women, using European criteria7; low muscle mass was defined as SMI <8.87 kg/m2 in men and <6.42 kg/m2 in women, using Taiwanese criteria of SMI 2 standard deviations below the sex-specific means of healthy young Taiwanese adults (18e40 years of age).21 Body fat mass (%) was also measured by using the same bioimpedance analyzer.
Patients
Handgrip strength and muscle quality measurement
This 3-year prospective cohort study was conducted at a medical center in Hualien, Taiwan, and was approved by the
Maximum handgrip strength (HGS) was assessed in the hand without an arteriovenous shunt, using a dynamometer
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients (Jamar Plus Digital Hand Dynamometer, SI Instruments Pty Ltd, Hilton, Australia). HGS was measured before the initiation of dialysis to avoid the confounding effect of the dialysis process. The width of the handle was adjusted to the size of the participant’s hand. Patients were allowed to become familiarized with the device, then were instructed to grip the dynamometer with maximum strength in the standing position with the arm at right angles and elbow at the side of the body. Three measurements were performed with 1-min resting intervals between measurements; the average value was used for analysis in this study. The cutoffs for low HGS were <30 kg for men and <20 kg for women, using European criteria,7 whereas they were <26 kg for men and <18 kg for women, using Taiwanese criteria.8 Muscle quality of the upper extremities was defined as HGS (kg) divided by MAMC (cm) of the same arm.22
Gait speed measurement Patients were instructed to walk with their usual speed for 6 m on a flat and straight path. The measurements were performed before the initiation of dialysis. A stopwatch was used and the timing began with a verbal start command (static start). Patients were instructed to maintain their speed without deceleration at the end of the walking course. Gait speed was calculated as dividing the distance travelled (ie, 6 m) by the time to cover that distance. Slow gait speed was defined as gait speed <0.8 m/s, using both European and Taiwanese criteria.7,8 All measurements of body composition, HGS, and gait speed were performed by the same trained operator.
Sarcopenia diagnosis Diagnoses of sarcopenia using Taiwanese or European criteria were based on the presence of low muscle mass as an essential criterion, accompanied by either low HGS or slow gait speed.7,8
Malnutrition inflammatory score (MIS) MIS was used to evaluate nutritional status. Briefly, MIS includes 10 components distributed in four sections: medical and nutritional history, physical examination of fat and muscle stores, assessment of BMI, and biochemical assessment of serum albumin and total iron binding capacity. Each component has four severity levels that range from 0 (normal) to 3 (very severe). A final score of 0e30 was assigned to each patient. A higher total score reflected more severe malnutrition.23
Laboratory determinations Fasting blood samples (w5 mL) were collected before dialysis, and 0.5 mL of the sample was used to calculate blood cell counts (Sysmex SP-1000i, Sysmex American, Mundelein, IL, USA). The remaining blood sample volumes were immediately centrifuged at 3000g for 10 min and analyzed for biochemical content within 1 h of collection. Serum concentrations of creatinine, albumin, phosphorus,
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and C-reactive protein were measured (Siemens Advia 1800, Siemens Healthcare GmbH, Henkestr, Germany). We measured the fractional clearance index for urea (Kt/V) before and immediately after dialysis using a formal, single-compartment dialysis urea kinetic model.
Longitudinal follow-up At the end of the 3-year follow-up period, hospitalization and mortality data were collected from the patients’ medical records. The causes of hospitalization and mortality were also recorded.
Statistical analysis Continuous variables were assessed for normality using the KolmogoroveSmirnov test. Variables with normal distributions were expressed as mean and standard deviation and compared using Student’s independent t-test; variables that were not normally distributed were expressed as median (interquartile range) and compared using the ManneWhitney U test. Categorical variables were expressed as absolute (n) and relative frequency (%) and compared using the c2 or Fisher’s exact test. In survival analysis, censored data comprised patients who were lost to follow-up, who received renal transplantation, or who did not experience a hospitalization or mortality event during the 3-year follow-up period. KaplaneMeier analyses with log-rank tests were used to compare hospitalizationfree and overall survival between groups; Cox proportional hazards models were used to identify clinical predictors for clinical outcomes. Because standard cut points have not been established in the HD population, we regarded SMI, HGS, gait speed, and muscle quality as continuous variables in this model. Data were analyzed using SPSS for Windows (version 19.0; IBM Corp., Armonk, NY, USA). A p < 0.05 was considered statistically significant.
Results Of the 182 chronic HD patients screened for inclusion in this study, 126 patients were enrolled. The mean age of the patients was 63.2 13.0 years, and median dialysis duration was 55.4 months. The prevalences of DM, HTN, hyperlipidemia, and CAD were 38.9%, 50.8%, 26.2%, and 63.5%, respectively. The prevalences of low SMI, low HGS, slow gait speed, and sarcopenia in 126 chronic HD patients are shown in Fig. 1. The prevalences of low SMI was 10.3% and 18.3%, using Taiwanese or European criteria, respectively; the prevalences of low HGS (51.6% using Taiwanese criteria and 64.3% using European criteria) and slow gait speed (45.2% using either set of criteria) were much higher. Overall, the prevalences of sarcopenia were 8.7% (Taiwanese criteria) and 13.5% (European criteria), which were similar to the prevalences of low SMI. Table 1 shows the demographic and clinical characteristics of 126 chronic HD patients with and without sarcopenia. Patients with sarcopenia, as defined by Taiwanese criteria, were older (p Z 0.005), shorter (p Z 0.007), and weighed less (p < 0.001); they also had lower BMI (p < 0.001), MAMC (p < 0.001), SMI (p < 0.001), HGS
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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Figure 1 Prevalences of low SMI, HGS, slow gait speed, and sarcopenia in 126 hemodialysis patients. GS, gait speed; HGS, handgrip strength; SMI, skeletal muscle index.
(p Z 0.014), and serum creatinine (p Z 0.003), whereas they had higher MIS (p Z 0.018). Using European criteria, comparisons between sarcopenic and non-sarcopenic patients showed similar results. The clinical characteristics of the study population were stratified according to the presence/absence of low SMI, low HGS, or slow gait speed, using Taiwanese criteria (Table 2). Compared with patients with normal SMI, those with low SMI were older, shorter, and weighed less; they also had lower BMI, MAMC, HGS, serum creatinine, and serum phosphorus, whereas they had higher Kt/V. Compared with patients with normal HGS, those with low HGS were older, shorter, and weighed less; they also had lower MAMC, SMI, gait speed, muscle quality, serum creatinine, and serum albumin, whereas they had higher MIS, Kt/V and a higher percentile of DM. Compared with patients with normal gait speed, those with slow gait speed were older, shorter, and weighed less; they also had lower HGS, muscle quality, serum creatinine, and serum albumin, whereas they had higher MIS. During the 3-year follow-up period, 6 patients (4.8%) were lost to follow-up, 79 patients (62.7%) were hospitalized, and 26 patients (20.6%) died. The most common causes of hospitalization were infection (38.0%) and cardiovascular events (18.3%); cardiovascular events (61.5%) comprised the leading cause of mortality. Comparisons of clinical characteristics of 126 patients, stratified according to hospitalization and death, are shown in Table 3. Compared with patients who were not hospitalized, those who were hospitalized were older (p < 0.001); they also had lower muscle quality (p Z 0.001), gait speed
Y.-L. Lin et al. (p Z 0.009), serum creatinine (p < 0.001), serum Hb (p Z 0.013), and serum albumin (p Z 0.002), whereas the rate of DM comorbidity was significantly higher (p Z 0.046). Notably, the MAMC and SMI were comparable between groups (p Z 0.107 and p Z 0.314, respectively). Compared with survivors, the non-survivors were older (p Z 0.002) and had slower gait speed (p Z 0.020) and poorer muscle quality (p Z 0.029); they also had lower serum creatinine (p Z 0.007) and serum albumin (p Z 0.008). Similarly, both MAMC and SMI were not significantly different between survivors and non-survivors (p Z 0.747 and p Z 0.425, respectively). Fig. 2 shows the hospitalization-free survival and overall survival rates of chronic HD patients, stratified according to sarcopenia and its diagnostic criteria, using Taiwanese criteria. Compared with non-sarcopenic patients, those with sarcopenia exhibited a higher risk of mortality (p Z 0.037); however, sarcopenia and low muscle mass did not affect the risk of hospitalization. In addition, patients with low HGS or slow gait speed showed significant associations with hospitalization (p Z 0.010 for HGS; p Z 0.008 for gait speed), as well as with mortality (p Z 0.014 for HGS; p Z 0.020 for gait speed), compared with patients with normal HGS and normal gait speed, respectively. Similar results were achieved using European criteria for sarcopenia (not shown). Furthermore, we analyzed the impact of HGS and gait speed on clinical outcomes among 115 non-sarcopenic patients (Fig. 3); the associations of low handgrip strength and slow gait speed with increased hospitalization (p Z 0.004 for HGS; p Z 0.001 for gait speed) and mortality (p Z 0.052 for HGS; p Z 0.035 for gait speed) remained significant. Cox proportional hazard models for composite outcome of hospitalization or death are shown in Table 4. After full adjustment in model 3, poor muscle quality (hazard ratio [HR] Z 0.42; 95% confidence interval [CI] Z 0.19e0.93; p Z 0.032) was associated with poor clinical outcomes. Moreover, serum creatinine, a surrogate marker of sarcopenia, also independently predict clinical outcomes (HR Z 0.82; 95% CI Z 0.71e0.95; p Z 0.009).
Discussion This study showed respective prevalences of 8.7% and 13.5% for sarcopenia in chronic HD patients, using Taiwanese and European criteria. However, the prevalences of low HGS and slow gait speed were much higher. Overall, low HGS and gait speed were associated with hospitalization-free and overall survival, while sarcopenia and low muscle mass were associated with overall survival but not with hospitalization. Notably, our chronic HD patients without sarcopenia exhibited close associations between increased hospitalization and mortality risk with low HGS and slow gait speed. By using functional domains, both muscle strength and gait speed criteria, they existed better prognostic prediction value than sarcopenia itself. Two previous studies have reported high prevalences of sarcopenia in chronic HD patients (31.5% and 33.7%)11,24; these prevalences were higher than the prevalence observed in our study. Notably, the prevalence in our study may have been underestimated because we excluded
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
Demographic and clinical characteristics of 126 chronic hemodialysis patients, stratified according to the presence of sarcopenia.
Characteristics
European Criteria
Sarcopenia (N Z 11) Non-sarcopenia (N Z 115) p value
Sarcopenia (N Z 17) Non-sarcopenia (N Z 109) p value
73.5 12.7 4 (36.4%) 59.4 (157.0) 153.6 6.5 44.1 3.6 18.8 1.9 27.5 7.0 17.6 2.1 5.9 0.9 14.8 6.4 0.66 (0.37) 0.95 0.25 8.0 (8.0) 1.5 0.1 7.9 1.3 10.4 (0.7) 4.1 (0.3) 4.1 1.8 0.37 (1.28) 2 (18.2%) 6 (54.5%) 2 (18.2%) 7 (63.6%)
68.6 15.7 9 (52.9%) 59.4 (123.7) 156.4 6.6 47.7 6.3 19.5 2.0 24.8 7.8 18.5 2.3 7.0 1.8 15.9 8.5 0.74 (0.31) 0.99 0.30 7.0 (8.0) 1.4 0.1 7.9 1.4 10.3 (1.2) 4.1 (0.3) 4.2 1.6 0.37 (1.36) 5 (29.4%) 8 (47.1%) 4 (23.5%) 10 (58.8%)
62.2 12.7 61 (53.0%) 55.3 (94.8) 160.9 8.5 67.2 14.1 25.9 4.5 29.8 7.8 21.3 2.8 12.2 4.1 22.3 9.9 0.86 (0.45) 1.06 0.42 4.0 (5.0) 1.3 0.2 9.7 2.0 10.4 (1.5) 4.1 (0.5) 4.8 1.2 0.29 (0.74) 47 (40.9%) 58 (50.4%) 31 (27.0%) 73 (63.5%)
0.005* 0.290 0.427 0.007* <0.001* <0.001* 0.345 <0.001* <0.001* 0.014* 0.065 0.435 0.018* 0.001* 0.003* 0.832 0.155 0.267 0.415 0.140 0.794 0.527 0.992
62.3 12.4 56 (51.4%) 55.3 (97.6) 160.8 8.7 67.9 14.1 26.1 4.5 30.4 7.5 21.4 2.9 12.4 4.1 22.6 9.8 0.86 (0.46) 1.06 0.42 4.0 (5.0) 1.3 0.2 9.8 2.0 10.4 (1.5) 4.2 (0.5) 4.8 1.2 0.29 (0.72) 44 (40.4%) 56 (51.4%) 29 (26.6%) 70 (64.2%)
0.062 0.904 0.553 0.048* <0.001* <0.001* 0.006* <0.001* <0.001* 0.009* 0.053 0.572 0.034* 0.007* <0.001* 0.803 0.114 0.082 0.339 0.389 0.741 0.788 0.667
Values for continuous variables are shown as mean standard deviation and were compared using Student’s t-test; variables not normally distributed are shown as median (interquartile range) and were compared using the ManneWhitney U test; values for categorical variables are presented as number (%) and were compared using the chi-squared test or Fisher’s exact test. BMI, body mass index; CAD, coronary artery disease; Hb, hemoglobin; HGS, handgrip strength; Kt/V, fractional clearance index for urea; MAMC, mid-arm muscle circumference; MIS, malnutrition inflammation score; SMI, skeletal muscle index. *p < 0.05 was considered statistically significant. a N Z 119.
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63.2 13.0 65 (51.6%) 55.4 (66.0) 160.2 8.5 65.2 15.0 25.2 4.8 29.6 7.8 21.0 3.0 11.6 4.3 21.7 9.8 0.83 (0.44) 1.04 0.41 4.5 (6.0) 1.3 0.2 9.6 2.0 10.4 (1.4) 4.1 (0.5) 4.7 1.3 0.33 (0.80) 49 (38.9%) 64 (50.8%) 33 (26.2%) 80 (63.5%)
Taiwanese Criteria
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Age (years) Sex (male) Dialysis duration (months) Height (cm) Body weight (kg) BMI (kg/m2) Body fat mass (%) MAMC (cm)a SMI (kg/m2) HGS (kg) Gait speed (m/s) Muscle qualitya MIS Kt/V Creatinine (mg/dL) Hb (g/dL) Albumin (g/dL) Phosphorus (mg/dL) C-reactive protein (mg/dL) Diabetes mellitus, n (%) Hypertension, n (%) Hyperlipidemia, n (%) CAD, n (%)
All Patients (N Z 126)
Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients 5
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
Table 1
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Characteristics
HGS
Gait Speed
Low (N Z 13)
Normal (N Z 113)
Low (N Z 65)
Normal (N Z 61)
Low (N Z 57)
Normal (N Z 69)
70.8 13.6 5 (38.5%) 89.0 (142.0) 153.5 6.1 44.4 3.8 18.9 1.8 26.6 6.8 17.7 2.1 6.1 1.2 16.8 7.8 0.76 (0.34) 1.04 0.31 8.0 (8.0) 1.5 0.2 7.9 1.2 10.4 (1.0) 4.1 (0.3) 4.0 1.7 0.37 (1.30)
62.3 12.7* 60 (53.1%) 53.4 (91.2) 161.0 8.5* 67.5 13.9* 26.0 4.5* 30.0 7.8 21.4 2.8* 12.3 4.0* 22.3 9.9* 0.86 (0.45) 1.05 0.42 4.0 (5.0) 1.3 0.2* 9.8 2.0* 10.4 (1.5) 4.1 (0.5) 4.8 1.2* 0.29 (0.72)
69.6 12.3 30 (46.2%) 59.4 (100.1) 157.5 7.4 61.2 13.0 24.6 4.7 30.5 7.3 20.4 2.7 10.6 4.0 14.8 6.2 0.63 (0.85) 0.75 0.26 6.0 (7.0) 1.4 0.2 8.9 1.6 10.3 (1.2) 4.1 (0.5) 4.7 1.4 0.32 (0.81)
56.4 10.1* 35 (57.4%) 47.5 (97.0) 163.2 8.8* 69.3 15.9* 25.9 4.8 28.7 8.2 21.6 3.1* 12.7 4.3* 29.0 7.5* 0.99 (0.31)* 1.36 0.29* 3.0 (4.0)* 1.3 0.2* 10.3 2.2* 10.5 (1.6) 4.2 (0.6)* 4.8 1.2 0.34 (0.80)
70.8 9.6 26 (45.6%) 47.9 (77.2) 157.0 7.8 62.0 14.5 25.1 5.0 30.0 8.5 20.5 2.9 10.9 4.3 16.2 7.5 0.54 (0.69) 0.83 0.31 7.0 (7.0) 1.3 0.2 8.6 1.7 10.3 (1.2) 4.1 (0.4) 4.7 1.3 0.37 (1.31)
56.9 12.2* 39 (56.5%) 55.7 (120.4) 162.9 8.2* 67.7 15.0* 25.4 4.6 29.3 7.1 21.3 3.0 12.3 4.1 26.2 9.3* 1.01 (0.27)* 1.23 0.39* 3.0 (4.0)* 1.3 0.2 10.4 2.0* 10.6 (1.5) 4.3 (0.6)* 4.8 1.2 0.29 (0.68)
2 7 2 8
47 57 31 72
31 31 18 43
18 33 15 37
27 27 19 37
22 37 14 43
(15.4%) (53.8%) (15.4%) (61.5%)
(41.6%) (50.4%) (27.4%) (63.7%)
(47.7%) (47.7%) (27.7%) (66.2%)
(29.5%)* (54.1%) (24.6%) (60.7%)
(47.4%) (47.4%) (33.3%) (64.9%)
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Age (years) Sex (male) Dialysis duration (months) Height (cm) Body weight (kg) BMI (kg/m2) Body fat mass (%) MAMC (cm)a SMI (kg/m2) HGS (kg) Gait speed (m/s) Muscle qualitya MIS Kt/V Creatinine (mg/dL) Hb (g/dL) Albumin (g/dL) Phosphorus (mg/dL) C-reactive protein (mg/dL) Diabetes mellitus, n (%) Hypertension, n (%) Hyperlipidemia, n (%) CAD, n (%)
SMI
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(31.9%) (53.6%) (20.3%) (62.3%)
Values for continuous variables are shown as mean standard deviation and were compared using Student’s t-test; variables not normally distributed are shown as median (interquartile range) and were compared using the ManneWhitney U test; values for categorical variables are presented as number (%) and were compared using the chi-squared test or Fisher’s exact test. BMI, body mass index; CAD, coronary artery disease; Hb, hemoglobin; HGS, handgrip strength; Kt/V, fractional clearance index for urea; MAMC, mid-arm muscle circumference; MIS, malnutrition inflammation score; SMI, skeletal muscle index. *p < 0.05 was considered statistically significant. a N Z 119.
Y.-L. Lin et al.
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
Table 2 Demographic and clinical characteristics of 126 chronic hemodialysis patients, stratified according to presence/absence of abnormal SMI, HGS, and gait speed (Taiwanese criteria).
Comparison of demographic and clinical characteristics of 126 chronic hemodialysis patients, stratified according to hospitalization and death.
Characteristics
Death
No (N Z 47)
p value
YES (N Z 26)
No (N Z 100)
p value
66.0 12.6 39 (49.4%) 47.9 (102.6) 159.7 8.8 65.9 15.6 25.7 4.8 30.2 8.0 21.3 3.1 11.9 4.6 20.4 10.0 0.77 (0.53) 0.95 0.41 5.0 (8.0) 1.3 0.2 9.0 1.9 10.2 (1.3) 4.1 (0.4) 4.7 1.2 0.37 (0.89) 36 (45.6%) 38 (48.1%) 19 (24.1%) 48 (60.8%)
58.5 12.4 26 (55.3%) 69.1 (91.8) 161.2 8.1 63.8 14.0 24.5 4.7 28.7 7.3 20.4 2.7 11.1 3.7 23.8 9.4 0.91 (0.34) 1.21 0.35 4.0 (5.0) 1.3 0.2 10.6 1.9 10.7 (1.0) 4.3 (0.5) 4.9 1.4 0.23 (0.52) 13 (27.7%) 26 (55.3%) 14 (29.8%) 32 (68.1%)
0.002* 0.518 0.217 0.339 0.443 0.157 0.285 0.107 0.314 0.063 0.009* 0.001* 0.285 0.717 <0.001* 0.013* 0.002* 0.407 0.123 0.046* 0.433 0.479 0.409
70.0 11.5 15 (57.7%) 51.5 (87.5) 157.4 9.2 62.9 17.9 25.1 5.3 29.2 7.8 21.1 3.0 11.0 4.8 19.5 10.4 0.69 (0.56) 0.89 0.40 6.0 (8.0) 1.4 0.2 8.6 2.1 10.3 (1.1) 4.1 (0.5) 4.6 1.3 0.39 (1.25) 14 (53.8%) 13 (50.0%) 6 (23.1%) 14 (53.8%)
61.4 12.9 50 (50.0%) 55.5 (100.7) 161.0 8.3 65.7 14.2 25.3 4.7 29.8 7.8 20.9 3.0 11.8 4.1 22.3 9.7 0.86 (0.41) 1.09 0.40 4.0 (5.0) 1.3 0.2 9.8 2.0 10.5 (1.5) 4.2 (0.5) 4.8 1.3 0.27 (0.73) 35 (35.0%) 51 (51.0%) 27 (27.0%) 66 (66.0%)
0.002* 0.484 0.838 0.061 0.390 0.832 0.754 0.747 0.425 0.197 0.020* 0.029* 0.501 0.280 0.007* 0.367 0.008* 0.543 0.218 0.079 0.928 0.685 0.251
Values for continuous variables are shown as mean standard deviation and were compared using Student’s t-test; variables not normally distributed are shown as median (interquartile range) and were compared using the ManneWhitney U test; values for categorical variables are presented as number (%) and were compared using the chi-squared test or Fisher’s exact test. BMI, body mass index; CAD, coronary artery disease; Hb, hemoglobin; HGS, handgrip strength; Kt/V, fractional clearance index for urea; MAMC, mid-arm muscle circumference; MIS, malnutrition inflammation score; SMI, skeletal muscle index. *p < 0.05 was considered statistically significant. a N Z 119.
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YES (N Z 79)
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Age (years) Sex (male) Dialysis duration (months) Height (cm) Body weight (kg) BMI (kg/m2) Body fat mass (%) MAMC (cm)a SMI (kg/m2) HGS (kg) Gait speed (m/s) Muscle qualitya MIS Kt/V Creatinine (mg/dL) Hb (g/dL) Albumin (g/dL) Phosphorus (mg/dL) C-reactive protein (mg/dL) Diabetes mellitus, n (%) Hypertension, n (%) Hyperlipidemia, n (%) CAD, n (%)
Hospitalization
Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients 7
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
Table 3
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Figure 2 Hospitalization-free and overall survival, stratified according to sarcopenia and its diagnostic criteria in 126 hemodialysis patients, using Taiwanese criteria. *p < 0.05 was considered statistically significant.
patients who were bed-ridden as well as those who had a history of stroke or malignancy, and those who showed unsteady gait; all these patients are typically at high risk of sarcopenia. Moreover, our study participants were younger than those enrolled by Bataille S.24 Nevertheless, nearly half of our cohort exhibited either muscle weakness or slow gait speed. Accordingly, in our chronic HD patients, the diagnosis of sarcopenia was mainly determined by low muscle mass, which was consistent with the findings of a previous study.24 Although some large-scale studies have demonstrated an association between low muscle mass and poor clinical outcome,12e14 the potential role of muscle function was not considered in those studies. In 330 incident dialysis patients, Isoyama et al. showed that patients with muscle weakness were at an increased risk of mortality, regardless of low skeletal muscle mass.25 Similarly, Kittiskulnam et al. showed that muscle strength and gait speed were better predictors of mortality than sarcopenia or low muscle mass among 645 prevalent HD patients.26 Consistent with these findings, although our chronic HD patients with sarcopenia exhibited poor overall survival, poor clinical outcomes among non-sarcopenic patients with low muscle strength and slow gait speed remained unchanged. These results indicated that muscle strength and gait speed may be more closely related to clinical outcomes than is muscle mass. However, sarcopenia diagnosis alone in chronic HD patients could not identify patients with low muscle strength or slow gait speed. Accordingly, for better prediction of clinical outcomes in chronic HD patients, dynapenia (i.e., low muscle strength) could be superior to sarcopenia, which is based on low muscle mass. Furthermore, during the muscle wasting process, muscle strength decline occurs at a faster rate than muscle mass
loss.27 Sarcopenia, which constitutes both low muscle mass and low muscle strength, should be regarded as a late stage of muscle wasting. Regarding the importance of early detection of muscle wasting to provide effective therapeutic intervention, assessment of muscle strength and physical performance should be included as routine clinical care for chronic HD patients, in addition to muscle mass measurement. Serum creatinine was lower in patients with low muscle mass, as well as in patients with muscle weakness and slow gait speed. In addition, serum creatinine was an independent predictor for clinical outcomes in the present study. This finding was consistent with the results of several previous studies, which suggests that serum creatinine may serve as a reliable surrogate marker for muscle wasting in chronic HD patients.28e30 There were several limitations in this study. First, muscle mass measurement before dialysis may have led to overestimation due to hydration status. However, we evaluated the difference in muscle mass measurement before and after HD in 74 patients, which showed that this impact was limited (mean SMI difference 0.81 kg/m2, Pearson’s correlation coefficient Z 0.98).19 Second, the hand without an arteriovenous shunt was chosen for HGS measurement, rather than the dominant hand; this may have led to misclassification bias and attenuated the association between HGS and clinical outcomes in the model. Third, we measured baseline muscle mass and function; conversely, dynamic changes in these parameters may provide additional prognostic information. Finally, the prevalence of sarcopenia in our single center study should not be generalized to entire HD population in Taiwan. In conclusion, our results emphasize the high prevalence of muscle dysfunction in chronic HD patients and its critical
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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Figure 3 Hospitalization-free survival (a) and overall survival (b) in 115 non-sarcopenic hemodialysis patients, stratified by handgrip strength (HGS) and gait speed (Taiwanese criteria). *p < 0.05 was considered statistically significant.
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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Table 4 Factors associated with composite outcome of hospitalization or death during a 3-year longitudinal followup. Variables
HR
Skeletal muscle index (kg/m2) Model 1 1.04 Model 2 e Model 3 e Handgrip strength (kg) Model 1 0.98 Model 2 0.99 Model 3 e Gait speed (m/s) Model 1 0.39 Model 2 0.61 Model 3 e Muscle quality Model 1 0.31 Model 2 0.40 Model 3 0.42 Creatinine (mg/dL) Model 1 0.78 Model 2 0.81 Model 3 0.82
95% CI
p value
0.98e1.10 e e
0.175 e e
0.95e1.00 0.97e1.02 e
0.036* 0.647 e
0.22e0.67 0.31e1.20 e
0.001* 0.151 e
0.16e0.58 0.20e0.84 0.19e0.93
<0.001* 0.015* 0.032*
0.70e0.88 0.70e0.93 0.71e0.95
<0.001* 0.003* 0.009*
Model 1: unadjusted. Model 2: adjusted for age, sex, hemoglobin, phosphorus, albumin and Kt/V. Model 3: Model 2 and further adjusted for diabetes mellitus, hypertension and hyperlipidemia. CI, confidence interval; HR, hazard ratio. *p < 0.05 was considered statistically significant.
impact on the risks of hospitalization and mortality, regardless of skeletal muscle mass volume. In addition, sarcopenia diagnosis in chronic HD patients could overlook patients with muscle weakness alone, since they did not exhibit reduced muscle mass. However, in this specific population, the risks of hospitalization and mortality remained high. Further studies are warranted to develop effective therapeutic interventions targeting muscle dysfunction in chronic HD patients.
Funding/support statement This study was supported by a grant from Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taiwan (TCRD105-04 and TCRD106-01).
Declaration of Competing Interest The authors declare no conflict of interest.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jfma.2019.10.020.
References 1. Landi F, Cruz-Jentoft AJ, Liperoti R, Russo A, Giovannini S, Tosato M, et al. Sarcopenia and mortality risk in frail older persons aged 80 years and older: results from ilSIRENTE study. Age Ageing 2013;42(2):203e9. 2. Arango-Lopera VE, Arroyo P, Gutierrez-Robledo LM, PerezZepeda M, Cesari M. Mortality as an adverse outcome of sarcopenia. J Nutr Health Aging 2013;17(3):259e62. 3. Kim JH, Lim S, Choi SH, Kim KM, Yoon JW, Kim KW, et al. Sarcopenia: an independent predictor of mortality in community-dwelling older Korean men. J Gerontol Ser A Biomed Med Sci 2014;69(10):1244e52. 4. Rosenberg IH. Sarcopenia: origins and clinical relevance. J Nutr 1997;127(5):990Se1S. 5. Kooman JP, Broers NJH, Usvyat L, Thijssen S, van der Sande FM, Cornelis T, et al. Out of control: accelerated aging in uremia. Nephrol Dial Transplant 2012;28:48e54. 6. Fahal IH. Uraemic sarcopenia: aetiology and implications. Nephrol Dial Transplant 2014;29(9):1655e65. 7. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European working group on sarcopenia in older people. Age Ageing 2010;39(4):412e23. 8. Chen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, et al. Sarcopenia in Asia: consensus report of the Asian working group for sarcopenia. J Am Med Dir Assoc 2014;15(2):95e101. 9. Fielding RA, Vellas B, Evans WJ, Bhasin S, Morley JE, Newman AB, et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc 2011;12(4):249e56. 10. Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci 2014;69(5):547e58. 11. Kim JK, Choi SR, Choi MJ, Kim SG, Lee YK, Noh JW, et al. Prevalence of and factors associated with sarcopenia in elderly patients with end-stage renal disease. Clin Nutr 2014;33(1): 64e8. 12. Noori N, Kopple JD, Kovesdy CP, Feroze U, Sim JJ, Murali SB, et al. Mid-arm muscle circumference and quality of life and survival in maintenance hemodialysis patients. Clin J Am Soc Nephrol 2010;5(12):2258e68. 13. Huang CX, Tighiouart H, Beddhu S, Cheung AK, Dwyer JT, Eknoyan G, et al. Both low muscle mass and low fat are associated with higher all-cause mortality in hemodialysis patients. Kidney Int 2010;77(7):624e9. 14. Castellano S, Palomares I, Moissl U, Chamney P, Carretero D, Crespo A, et al. Appropriate assessment of body composition to identify haemodialysis patients at risk. Nefrologia 2016;36(3): 268e74. 15. Yoda M, Inaba M, Okuno S, Yoda K, Yamada S, Imanishi Y, et al. Poor muscle quality as a predictor of high mortality independent of diabetes in hemodialysis patients. Biomed Pharmacother 2012;66(4):266e70. 16. Matos CM, Silva LF, Santana LD, Santos LS, Prota ´sio BM, Rocha MT, et al. Handgrip strength at baseline and mortality risk in a cohort of women and men on hemodialysis: a 4-year study. J Ren Nutr 2014;24(3):157e62. 17. Kutner NG, Zhang R, Huang Y, Painter P. Gait speed and mortality, hospitalization, and functional status change among hemodialysis patients: a US renal data system special study. Am J Kidney Dis 2015;66(2):297e304. 18. Weber J, Kelley J. Assessing nutrition. In: Nieginski E, editor. Health assessment in nursing. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2003. p. 165.
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Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients 19. Lin YL, Liou HH, Lai YH, Wang CH, Kuo CH, Chen SY, et al. Decreased serum fatty acid binding protein 4 concentrations are associated with sarcopenia in chronic hemodialysis patients. Clin Chim Acta 2018;485:113e8. 20. Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol 2000;89:465e71. 21. Chien MY, Huang TY, Wu YT. Prevalence of sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc 2008;56(9):1710e5. 22. Landi F, Calvani R, Tosato M, Martone A, Fusco D, Sisto A, et al. Age-related variations of muscle mass, strength, and physical performance in community-dwellers: results from the milan EXPO survey. J Am Med Dir Assoc 2017;18(1). 88.e17-24. 23. Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients. Am J Kidney Dis 2001;38(6):1251e63. 24. Bataille S, Serveaux M, Carreno E, Pedinielli N, Darmon P, Robert A. The diagnosis of sarcopenia is mainly driven by muscle mass in hemodialysis patients. Clin Nutr 2017;36: 1654e60.
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25. Isoyama N, Qureshi AR, Avesani CM, Lindholm B, Ba `ra `ny P, Heimbu ¨rger O, et al. Comparative associations of muscle mass and muscle strength with mortality in dialysis patients. Clin J Am Soc Nephrol 2014;9(10):1720e8. 26. Kittiskulnam P, Chertow GM, Carrero JJ, Delgado C, Kaysen GA, Johansen KL. Sarcopenia and its individual criteria are associated, in part, with mortality among patients on hemodialysis. Kidney Int 2017;92:238e47. 27. Goodpaster BH, Study ftHA, Park SW, Kritchevsky SB, Nevitt M, Schwartz AV, et al. The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J Gerontol: Series A 2006;61(10):1059e64. 28. Patel SS, Molnar MZ, Tayek JA, Ix JH, Noori N, Benner D, et al. Serum creatinine as a marker of muscle mass in chronic kidney disease: results of a cross-sectional study and review of literature. J Cachexia Sarcopenia Muscle 2013;4(1):19e29. 29. Kalantar-Zadeh K, Streja E, Kovesdy CP, Oreopoulos A, Noori N, Jing J, et al. The obesity paradox and mortality associated with surrogates of body size and muscle mass in patients receiving hemodialysis. Mayo Clin Proc 2010;85(11):991e1001. 30. Park J, Mehrotra R, Rhee CM, Molnar MZ, Lukowsky LR, Patel SS, et al. Serum creatinine level, a surrogate of muscle mass, predicts mortality in peritoneal dialysis patients. Nephrol Dial Transplant 2013;28(8):2146e55.
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Journal of the Formosan Medical Association xxx (xxxx) xxx
Available online at www.sciencedirect.com
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Original Article
Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study Yu-Li Lin a, Hung-Hsiang Liou b, Chih-Hsien Wang a, Yu-Hsien Lai a, Chiu-Huang Kuo a, Shu-Yuan Chen c,*, Bang-Gee Hsu a,d,** a Division of Nephrology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97004, Taiwan b Division of Nephrology, Department of Internal Medicine, Hsin-Jen Hospital, New Taipei City 24243, Taiwan c Department of Public Health, Tzu Chi University, Hualien 97004, Taiwan d School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
Received 8 August 2019; received in revised form 23 October 2019; accepted 29 October 2019
KEYWORDS Gait speed; Handgrip strength; Hemodialysis; Muscle quality; Sarcopenia
Background/Purpose: Sarcopenia is prevalent in chronic hemodialysis patients. This prospective cohort study evaluated the impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in 126 chronic hemodialysis patients. Methods: Skeletal muscle mass, handgrip strength (HGS), gait speed, and blood parameters were assessed. Sarcopenia was evaluated using the criteria of the European Working Group on Sarcopenia in Older People and the Taiwanese criteria for Sarcopenia. Muscle quality was defined as HGS divided by mid-arm muscle circumference. Results: Prevalences of uremic sarcopenia were 8.7% and 13.5% according to Taiwanese and European criteria, respectively. Low HGS and gait speed were much more prevalent than low muscle mass. Within 3 years, 79 (62.7%) patients were hospitalized and 26 (20.6%) died. Low HGS and slow gait speed were associated with hospitalization and mortality, while sarcopenia was associated with mortality but not with hospitalization. Notably, in our patients without sarcopenia, close associations between increased hospitalization and mortality risk with low HGS and slow gait speed remained unchanged. In Cox proportional hazard analysis, muscle quality [hazard ratio (HR) Z 0.42, 95% confidence interval (CI) Z 0.19e0.93,
* Corresponding author. ** Corresponding author. Division of Nephrology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, 97004, Taiwan. E-mail addresses: [email protected] (Y.-L. Lin), [email protected] (H.-H. Liou), [email protected] (C.-H. Wang), [email protected] (Y.-H. Lai), [email protected] (C.-H. Kuo), [email protected] (S.-Y. Chen), [email protected] (B.-G. Hsu). https://doi.org/10.1016/j.jfma.2019.10.020 0929-6646/Copyright ª 2019, Formosan Medical Association. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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Y.-L. Lin et al. p Z 0.032] and serum creatinine (HR Z 0.82, 95% CI Z 0.71e0.95, p Z 0.009) were independently associated with composite outcome of hospitalization or death. Conclusion: Muscle functionality and quality can predict hospitalization and overall survival in chronic hemodialysis patients, better than muscle mass. Copyright ª 2019, Formosan Medical Association. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Introduction Sarcopenia, originally described as a geriatric syndrome characterized by age-related progressive decline in both muscle mass and strength, leads to an increased risk of falls and fractures, disabilities, hospitalization, and death in older adults.1e4 In patients with end-stage renal disease (ESRD), accelerated aging is a characteristic feature.5 Compared with older adults in general, patients with ESRD are predisposed to an increased risk of sarcopenia, which is attributed to multifaceted factors, including protein loss during dialysis, inactivity, metabolic acidosis, insulin resistance, inflammation, oxidative stress, myostatin overexpression, reninean giotensin system changes, and comorbidities.6 The European Working Group on Sarcopenia in Older People has established diagnostic criteria for geriatric sarcopenia in 2010,7 along with several follow-up expert consensuses8e10; these criteria encompass three components: low muscle mass, muscle weakness, and poor physical performance. Sarcopenia diagnosis is based on low muscle mass as an essential criterion, accompanied by either muscle weakness or poor physical performance.7,8 There is increasing evidence that these diagnostic criteria strongly predict hospitalization and mortality in the geriatric population.1e3 In chronic hemodialysis (HD) patients, the prevalence of sarcopenia is considerably higher than in the general geriatric population.11 To the best of our knowledge, however, consensus diagnostic criteria for uremic sarcopenia in chronic HD patients has not yet been established. The applicability of diagnostic criteria for geriatric sarcopenia to predict clinical outcomes in chronic HD patients should be determined. Moreover, although both low muscle mass and function are well-known to be associated with poor clinical outcomes in chronic HD patients,12e17 their clinical impacts have rarely been compared together. Accordingly, this 3-year prospective cohort study was undertaken to evaluate the importance of sarcopenia as a predictor of hospitalization and mortality in chronic HD patients; it was also performed to assess potentially different impacts of muscle mass, muscle strength, and gait speed on clinical outcomes, using both Taiwanese and European criteria for sarcopenia.
Institutional Review Board of Tzu Chi Hospital (IRB104-84-B). Patients were recruited in February and March 2016. Patients aged >20 years who had undergone HD with standard 4-h dialysis three times per week for at least 3 months were included. Patients who were bed-ridden, who presented with an infection, amputated limb, pulmonary edema, unsteady gait, history of stroke, or malignancy at the time of enrollment, or who refused to participate were excluded from the study. Informed consent was obtained from each enrolled patient; demographic data and information regarding comorbid diseases were collected. Diabetes mellitus (DM) was defined based on the history of antidiabetic drug use; hypertension was defined based on systolic blood pressure 140 mmHg, diastolic blood pressure 90 mmHg, or history of antihypertensive agent use; hyperlipidemia was defined based on the history of statin or fibrate use. Coronary artery disease (CAD) was confirmed through coronary angiography.
Body composition measurement
Materials and methods
Body weight was measured in light clothing and without shoes to the nearest half-kilogram after HD session, while height was to the nearest half-centimeter. Body mass index (BMI) was calculated as weight (kg) divided by height (m2). Mid-arm circumference was measured at the mid-point from the acromion to olecranon, and triceps skinfold was measured using a caliper (QuickMedical, Issaquah, WA, USA); mid-arm muscle circumference (MAMC) was then calculated by using the following equation: MAMC (cm) Z [mid-arm circumference (cm) e 0.314] triceps skinfold (mm).18 Average values of both arms were used for analysis. Skeletal muscle mass was measured in the standing position, using a bioimpedance analyzer (Tanita BC 706DB, Tanita Corporation, Tokyo, Japan).19,20 The measurement was performed before dialysis. Skeletal muscle index (SMI) was calculated based on skeletal muscle mass (kg) divided by height (m2). Low muscle mass was defined as SMI <10.76 kg/m2 in men and <6.76 kg/m2 in women, using European criteria7; low muscle mass was defined as SMI <8.87 kg/m2 in men and <6.42 kg/m2 in women, using Taiwanese criteria of SMI 2 standard deviations below the sex-specific means of healthy young Taiwanese adults (18e40 years of age).21 Body fat mass (%) was also measured by using the same bioimpedance analyzer.
Patients
Handgrip strength and muscle quality measurement
This 3-year prospective cohort study was conducted at a medical center in Hualien, Taiwan, and was approved by the
Maximum handgrip strength (HGS) was assessed in the hand without an arteriovenous shunt, using a dynamometer
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients (Jamar Plus Digital Hand Dynamometer, SI Instruments Pty Ltd, Hilton, Australia). HGS was measured before the initiation of dialysis to avoid the confounding effect of the dialysis process. The width of the handle was adjusted to the size of the participant’s hand. Patients were allowed to become familiarized with the device, then were instructed to grip the dynamometer with maximum strength in the standing position with the arm at right angles and elbow at the side of the body. Three measurements were performed with 1-min resting intervals between measurements; the average value was used for analysis in this study. The cutoffs for low HGS were <30 kg for men and <20 kg for women, using European criteria,7 whereas they were <26 kg for men and <18 kg for women, using Taiwanese criteria.8 Muscle quality of the upper extremities was defined as HGS (kg) divided by MAMC (cm) of the same arm.22
Gait speed measurement Patients were instructed to walk with their usual speed for 6 m on a flat and straight path. The measurements were performed before the initiation of dialysis. A stopwatch was used and the timing began with a verbal start command (static start). Patients were instructed to maintain their speed without deceleration at the end of the walking course. Gait speed was calculated as dividing the distance travelled (ie, 6 m) by the time to cover that distance. Slow gait speed was defined as gait speed <0.8 m/s, using both European and Taiwanese criteria.7,8 All measurements of body composition, HGS, and gait speed were performed by the same trained operator.
Sarcopenia diagnosis Diagnoses of sarcopenia using Taiwanese or European criteria were based on the presence of low muscle mass as an essential criterion, accompanied by either low HGS or slow gait speed.7,8
Malnutrition inflammatory score (MIS) MIS was used to evaluate nutritional status. Briefly, MIS includes 10 components distributed in four sections: medical and nutritional history, physical examination of fat and muscle stores, assessment of BMI, and biochemical assessment of serum albumin and total iron binding capacity. Each component has four severity levels that range from 0 (normal) to 3 (very severe). A final score of 0e30 was assigned to each patient. A higher total score reflected more severe malnutrition.23
Laboratory determinations Fasting blood samples (w5 mL) were collected before dialysis, and 0.5 mL of the sample was used to calculate blood cell counts (Sysmex SP-1000i, Sysmex American, Mundelein, IL, USA). The remaining blood sample volumes were immediately centrifuged at 3000g for 10 min and analyzed for biochemical content within 1 h of collection. Serum concentrations of creatinine, albumin, phosphorus,
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and C-reactive protein were measured (Siemens Advia 1800, Siemens Healthcare GmbH, Henkestr, Germany). We measured the fractional clearance index for urea (Kt/V) before and immediately after dialysis using a formal, single-compartment dialysis urea kinetic model.
Longitudinal follow-up At the end of the 3-year follow-up period, hospitalization and mortality data were collected from the patients’ medical records. The causes of hospitalization and mortality were also recorded.
Statistical analysis Continuous variables were assessed for normality using the KolmogoroveSmirnov test. Variables with normal distributions were expressed as mean and standard deviation and compared using Student’s independent t-test; variables that were not normally distributed were expressed as median (interquartile range) and compared using the ManneWhitney U test. Categorical variables were expressed as absolute (n) and relative frequency (%) and compared using the c2 or Fisher’s exact test. In survival analysis, censored data comprised patients who were lost to follow-up, who received renal transplantation, or who did not experience a hospitalization or mortality event during the 3-year follow-up period. KaplaneMeier analyses with log-rank tests were used to compare hospitalizationfree and overall survival between groups; Cox proportional hazards models were used to identify clinical predictors for clinical outcomes. Because standard cut points have not been established in the HD population, we regarded SMI, HGS, gait speed, and muscle quality as continuous variables in this model. Data were analyzed using SPSS for Windows (version 19.0; IBM Corp., Armonk, NY, USA). A p < 0.05 was considered statistically significant.
Results Of the 182 chronic HD patients screened for inclusion in this study, 126 patients were enrolled. The mean age of the patients was 63.2 13.0 years, and median dialysis duration was 55.4 months. The prevalences of DM, HTN, hyperlipidemia, and CAD were 38.9%, 50.8%, 26.2%, and 63.5%, respectively. The prevalences of low SMI, low HGS, slow gait speed, and sarcopenia in 126 chronic HD patients are shown in Fig. 1. The prevalences of low SMI was 10.3% and 18.3%, using Taiwanese or European criteria, respectively; the prevalences of low HGS (51.6% using Taiwanese criteria and 64.3% using European criteria) and slow gait speed (45.2% using either set of criteria) were much higher. Overall, the prevalences of sarcopenia were 8.7% (Taiwanese criteria) and 13.5% (European criteria), which were similar to the prevalences of low SMI. Table 1 shows the demographic and clinical characteristics of 126 chronic HD patients with and without sarcopenia. Patients with sarcopenia, as defined by Taiwanese criteria, were older (p Z 0.005), shorter (p Z 0.007), and weighed less (p < 0.001); they also had lower BMI (p < 0.001), MAMC (p < 0.001), SMI (p < 0.001), HGS
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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Figure 1 Prevalences of low SMI, HGS, slow gait speed, and sarcopenia in 126 hemodialysis patients. GS, gait speed; HGS, handgrip strength; SMI, skeletal muscle index.
(p Z 0.014), and serum creatinine (p Z 0.003), whereas they had higher MIS (p Z 0.018). Using European criteria, comparisons between sarcopenic and non-sarcopenic patients showed similar results. The clinical characteristics of the study population were stratified according to the presence/absence of low SMI, low HGS, or slow gait speed, using Taiwanese criteria (Table 2). Compared with patients with normal SMI, those with low SMI were older, shorter, and weighed less; they also had lower BMI, MAMC, HGS, serum creatinine, and serum phosphorus, whereas they had higher Kt/V. Compared with patients with normal HGS, those with low HGS were older, shorter, and weighed less; they also had lower MAMC, SMI, gait speed, muscle quality, serum creatinine, and serum albumin, whereas they had higher MIS, Kt/V and a higher percentile of DM. Compared with patients with normal gait speed, those with slow gait speed were older, shorter, and weighed less; they also had lower HGS, muscle quality, serum creatinine, and serum albumin, whereas they had higher MIS. During the 3-year follow-up period, 6 patients (4.8%) were lost to follow-up, 79 patients (62.7%) were hospitalized, and 26 patients (20.6%) died. The most common causes of hospitalization were infection (38.0%) and cardiovascular events (18.3%); cardiovascular events (61.5%) comprised the leading cause of mortality. Comparisons of clinical characteristics of 126 patients, stratified according to hospitalization and death, are shown in Table 3. Compared with patients who were not hospitalized, those who were hospitalized were older (p < 0.001); they also had lower muscle quality (p Z 0.001), gait speed
Y.-L. Lin et al. (p Z 0.009), serum creatinine (p < 0.001), serum Hb (p Z 0.013), and serum albumin (p Z 0.002), whereas the rate of DM comorbidity was significantly higher (p Z 0.046). Notably, the MAMC and SMI were comparable between groups (p Z 0.107 and p Z 0.314, respectively). Compared with survivors, the non-survivors were older (p Z 0.002) and had slower gait speed (p Z 0.020) and poorer muscle quality (p Z 0.029); they also had lower serum creatinine (p Z 0.007) and serum albumin (p Z 0.008). Similarly, both MAMC and SMI were not significantly different between survivors and non-survivors (p Z 0.747 and p Z 0.425, respectively). Fig. 2 shows the hospitalization-free survival and overall survival rates of chronic HD patients, stratified according to sarcopenia and its diagnostic criteria, using Taiwanese criteria. Compared with non-sarcopenic patients, those with sarcopenia exhibited a higher risk of mortality (p Z 0.037); however, sarcopenia and low muscle mass did not affect the risk of hospitalization. In addition, patients with low HGS or slow gait speed showed significant associations with hospitalization (p Z 0.010 for HGS; p Z 0.008 for gait speed), as well as with mortality (p Z 0.014 for HGS; p Z 0.020 for gait speed), compared with patients with normal HGS and normal gait speed, respectively. Similar results were achieved using European criteria for sarcopenia (not shown). Furthermore, we analyzed the impact of HGS and gait speed on clinical outcomes among 115 non-sarcopenic patients (Fig. 3); the associations of low handgrip strength and slow gait speed with increased hospitalization (p Z 0.004 for HGS; p Z 0.001 for gait speed) and mortality (p Z 0.052 for HGS; p Z 0.035 for gait speed) remained significant. Cox proportional hazard models for composite outcome of hospitalization or death are shown in Table 4. After full adjustment in model 3, poor muscle quality (hazard ratio [HR] Z 0.42; 95% confidence interval [CI] Z 0.19e0.93; p Z 0.032) was associated with poor clinical outcomes. Moreover, serum creatinine, a surrogate marker of sarcopenia, also independently predict clinical outcomes (HR Z 0.82; 95% CI Z 0.71e0.95; p Z 0.009).
Discussion This study showed respective prevalences of 8.7% and 13.5% for sarcopenia in chronic HD patients, using Taiwanese and European criteria. However, the prevalences of low HGS and slow gait speed were much higher. Overall, low HGS and gait speed were associated with hospitalization-free and overall survival, while sarcopenia and low muscle mass were associated with overall survival but not with hospitalization. Notably, our chronic HD patients without sarcopenia exhibited close associations between increased hospitalization and mortality risk with low HGS and slow gait speed. By using functional domains, both muscle strength and gait speed criteria, they existed better prognostic prediction value than sarcopenia itself. Two previous studies have reported high prevalences of sarcopenia in chronic HD patients (31.5% and 33.7%)11,24; these prevalences were higher than the prevalence observed in our study. Notably, the prevalence in our study may have been underestimated because we excluded
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
Demographic and clinical characteristics of 126 chronic hemodialysis patients, stratified according to the presence of sarcopenia.
Characteristics
European Criteria
Sarcopenia (N Z 11) Non-sarcopenia (N Z 115) p value
Sarcopenia (N Z 17) Non-sarcopenia (N Z 109) p value
73.5 12.7 4 (36.4%) 59.4 (157.0) 153.6 6.5 44.1 3.6 18.8 1.9 27.5 7.0 17.6 2.1 5.9 0.9 14.8 6.4 0.66 (0.37) 0.95 0.25 8.0 (8.0) 1.5 0.1 7.9 1.3 10.4 (0.7) 4.1 (0.3) 4.1 1.8 0.37 (1.28) 2 (18.2%) 6 (54.5%) 2 (18.2%) 7 (63.6%)
68.6 15.7 9 (52.9%) 59.4 (123.7) 156.4 6.6 47.7 6.3 19.5 2.0 24.8 7.8 18.5 2.3 7.0 1.8 15.9 8.5 0.74 (0.31) 0.99 0.30 7.0 (8.0) 1.4 0.1 7.9 1.4 10.3 (1.2) 4.1 (0.3) 4.2 1.6 0.37 (1.36) 5 (29.4%) 8 (47.1%) 4 (23.5%) 10 (58.8%)
62.2 12.7 61 (53.0%) 55.3 (94.8) 160.9 8.5 67.2 14.1 25.9 4.5 29.8 7.8 21.3 2.8 12.2 4.1 22.3 9.9 0.86 (0.45) 1.06 0.42 4.0 (5.0) 1.3 0.2 9.7 2.0 10.4 (1.5) 4.1 (0.5) 4.8 1.2 0.29 (0.74) 47 (40.9%) 58 (50.4%) 31 (27.0%) 73 (63.5%)
0.005* 0.290 0.427 0.007* <0.001* <0.001* 0.345 <0.001* <0.001* 0.014* 0.065 0.435 0.018* 0.001* 0.003* 0.832 0.155 0.267 0.415 0.140 0.794 0.527 0.992
62.3 12.4 56 (51.4%) 55.3 (97.6) 160.8 8.7 67.9 14.1 26.1 4.5 30.4 7.5 21.4 2.9 12.4 4.1 22.6 9.8 0.86 (0.46) 1.06 0.42 4.0 (5.0) 1.3 0.2 9.8 2.0 10.4 (1.5) 4.2 (0.5) 4.8 1.2 0.29 (0.72) 44 (40.4%) 56 (51.4%) 29 (26.6%) 70 (64.2%)
0.062 0.904 0.553 0.048* <0.001* <0.001* 0.006* <0.001* <0.001* 0.009* 0.053 0.572 0.034* 0.007* <0.001* 0.803 0.114 0.082 0.339 0.389 0.741 0.788 0.667
Values for continuous variables are shown as mean standard deviation and were compared using Student’s t-test; variables not normally distributed are shown as median (interquartile range) and were compared using the ManneWhitney U test; values for categorical variables are presented as number (%) and were compared using the chi-squared test or Fisher’s exact test. BMI, body mass index; CAD, coronary artery disease; Hb, hemoglobin; HGS, handgrip strength; Kt/V, fractional clearance index for urea; MAMC, mid-arm muscle circumference; MIS, malnutrition inflammation score; SMI, skeletal muscle index. *p < 0.05 was considered statistically significant. a N Z 119.
MODEL
63.2 13.0 65 (51.6%) 55.4 (66.0) 160.2 8.5 65.2 15.0 25.2 4.8 29.6 7.8 21.0 3.0 11.6 4.3 21.7 9.8 0.83 (0.44) 1.04 0.41 4.5 (6.0) 1.3 0.2 9.6 2.0 10.4 (1.4) 4.1 (0.5) 4.7 1.3 0.33 (0.80) 49 (38.9%) 64 (50.8%) 33 (26.2%) 80 (63.5%)
Taiwanese Criteria
+
Age (years) Sex (male) Dialysis duration (months) Height (cm) Body weight (kg) BMI (kg/m2) Body fat mass (%) MAMC (cm)a SMI (kg/m2) HGS (kg) Gait speed (m/s) Muscle qualitya MIS Kt/V Creatinine (mg/dL) Hb (g/dL) Albumin (g/dL) Phosphorus (mg/dL) C-reactive protein (mg/dL) Diabetes mellitus, n (%) Hypertension, n (%) Hyperlipidemia, n (%) CAD, n (%)
All Patients (N Z 126)
Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients 5
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
Table 1
6
Characteristics
HGS
Gait Speed
Low (N Z 13)
Normal (N Z 113)
Low (N Z 65)
Normal (N Z 61)
Low (N Z 57)
Normal (N Z 69)
70.8 13.6 5 (38.5%) 89.0 (142.0) 153.5 6.1 44.4 3.8 18.9 1.8 26.6 6.8 17.7 2.1 6.1 1.2 16.8 7.8 0.76 (0.34) 1.04 0.31 8.0 (8.0) 1.5 0.2 7.9 1.2 10.4 (1.0) 4.1 (0.3) 4.0 1.7 0.37 (1.30)
62.3 12.7* 60 (53.1%) 53.4 (91.2) 161.0 8.5* 67.5 13.9* 26.0 4.5* 30.0 7.8 21.4 2.8* 12.3 4.0* 22.3 9.9* 0.86 (0.45) 1.05 0.42 4.0 (5.0) 1.3 0.2* 9.8 2.0* 10.4 (1.5) 4.1 (0.5) 4.8 1.2* 0.29 (0.72)
69.6 12.3 30 (46.2%) 59.4 (100.1) 157.5 7.4 61.2 13.0 24.6 4.7 30.5 7.3 20.4 2.7 10.6 4.0 14.8 6.2 0.63 (0.85) 0.75 0.26 6.0 (7.0) 1.4 0.2 8.9 1.6 10.3 (1.2) 4.1 (0.5) 4.7 1.4 0.32 (0.81)
56.4 10.1* 35 (57.4%) 47.5 (97.0) 163.2 8.8* 69.3 15.9* 25.9 4.8 28.7 8.2 21.6 3.1* 12.7 4.3* 29.0 7.5* 0.99 (0.31)* 1.36 0.29* 3.0 (4.0)* 1.3 0.2* 10.3 2.2* 10.5 (1.6) 4.2 (0.6)* 4.8 1.2 0.34 (0.80)
70.8 9.6 26 (45.6%) 47.9 (77.2) 157.0 7.8 62.0 14.5 25.1 5.0 30.0 8.5 20.5 2.9 10.9 4.3 16.2 7.5 0.54 (0.69) 0.83 0.31 7.0 (7.0) 1.3 0.2 8.6 1.7 10.3 (1.2) 4.1 (0.4) 4.7 1.3 0.37 (1.31)
56.9 12.2* 39 (56.5%) 55.7 (120.4) 162.9 8.2* 67.7 15.0* 25.4 4.6 29.3 7.1 21.3 3.0 12.3 4.1 26.2 9.3* 1.01 (0.27)* 1.23 0.39* 3.0 (4.0)* 1.3 0.2 10.4 2.0* 10.6 (1.5) 4.3 (0.6)* 4.8 1.2 0.29 (0.68)
2 7 2 8
47 57 31 72
31 31 18 43
18 33 15 37
27 27 19 37
22 37 14 43
(15.4%) (53.8%) (15.4%) (61.5%)
(41.6%) (50.4%) (27.4%) (63.7%)
(47.7%) (47.7%) (27.7%) (66.2%)
(29.5%)* (54.1%) (24.6%) (60.7%)
(47.4%) (47.4%) (33.3%) (64.9%)
MODEL
Age (years) Sex (male) Dialysis duration (months) Height (cm) Body weight (kg) BMI (kg/m2) Body fat mass (%) MAMC (cm)a SMI (kg/m2) HGS (kg) Gait speed (m/s) Muscle qualitya MIS Kt/V Creatinine (mg/dL) Hb (g/dL) Albumin (g/dL) Phosphorus (mg/dL) C-reactive protein (mg/dL) Diabetes mellitus, n (%) Hypertension, n (%) Hyperlipidemia, n (%) CAD, n (%)
SMI
+
(31.9%) (53.6%) (20.3%) (62.3%)
Values for continuous variables are shown as mean standard deviation and were compared using Student’s t-test; variables not normally distributed are shown as median (interquartile range) and were compared using the ManneWhitney U test; values for categorical variables are presented as number (%) and were compared using the chi-squared test or Fisher’s exact test. BMI, body mass index; CAD, coronary artery disease; Hb, hemoglobin; HGS, handgrip strength; Kt/V, fractional clearance index for urea; MAMC, mid-arm muscle circumference; MIS, malnutrition inflammation score; SMI, skeletal muscle index. *p < 0.05 was considered statistically significant. a N Z 119.
Y.-L. Lin et al.
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
Table 2 Demographic and clinical characteristics of 126 chronic hemodialysis patients, stratified according to presence/absence of abnormal SMI, HGS, and gait speed (Taiwanese criteria).
Comparison of demographic and clinical characteristics of 126 chronic hemodialysis patients, stratified according to hospitalization and death.
Characteristics
Death
No (N Z 47)
p value
YES (N Z 26)
No (N Z 100)
p value
66.0 12.6 39 (49.4%) 47.9 (102.6) 159.7 8.8 65.9 15.6 25.7 4.8 30.2 8.0 21.3 3.1 11.9 4.6 20.4 10.0 0.77 (0.53) 0.95 0.41 5.0 (8.0) 1.3 0.2 9.0 1.9 10.2 (1.3) 4.1 (0.4) 4.7 1.2 0.37 (0.89) 36 (45.6%) 38 (48.1%) 19 (24.1%) 48 (60.8%)
58.5 12.4 26 (55.3%) 69.1 (91.8) 161.2 8.1 63.8 14.0 24.5 4.7 28.7 7.3 20.4 2.7 11.1 3.7 23.8 9.4 0.91 (0.34) 1.21 0.35 4.0 (5.0) 1.3 0.2 10.6 1.9 10.7 (1.0) 4.3 (0.5) 4.9 1.4 0.23 (0.52) 13 (27.7%) 26 (55.3%) 14 (29.8%) 32 (68.1%)
0.002* 0.518 0.217 0.339 0.443 0.157 0.285 0.107 0.314 0.063 0.009* 0.001* 0.285 0.717 <0.001* 0.013* 0.002* 0.407 0.123 0.046* 0.433 0.479 0.409
70.0 11.5 15 (57.7%) 51.5 (87.5) 157.4 9.2 62.9 17.9 25.1 5.3 29.2 7.8 21.1 3.0 11.0 4.8 19.5 10.4 0.69 (0.56) 0.89 0.40 6.0 (8.0) 1.4 0.2 8.6 2.1 10.3 (1.1) 4.1 (0.5) 4.6 1.3 0.39 (1.25) 14 (53.8%) 13 (50.0%) 6 (23.1%) 14 (53.8%)
61.4 12.9 50 (50.0%) 55.5 (100.7) 161.0 8.3 65.7 14.2 25.3 4.7 29.8 7.8 20.9 3.0 11.8 4.1 22.3 9.7 0.86 (0.41) 1.09 0.40 4.0 (5.0) 1.3 0.2 9.8 2.0 10.5 (1.5) 4.2 (0.5) 4.8 1.3 0.27 (0.73) 35 (35.0%) 51 (51.0%) 27 (27.0%) 66 (66.0%)
0.002* 0.484 0.838 0.061 0.390 0.832 0.754 0.747 0.425 0.197 0.020* 0.029* 0.501 0.280 0.007* 0.367 0.008* 0.543 0.218 0.079 0.928 0.685 0.251
Values for continuous variables are shown as mean standard deviation and were compared using Student’s t-test; variables not normally distributed are shown as median (interquartile range) and were compared using the ManneWhitney U test; values for categorical variables are presented as number (%) and were compared using the chi-squared test or Fisher’s exact test. BMI, body mass index; CAD, coronary artery disease; Hb, hemoglobin; HGS, handgrip strength; Kt/V, fractional clearance index for urea; MAMC, mid-arm muscle circumference; MIS, malnutrition inflammation score; SMI, skeletal muscle index. *p < 0.05 was considered statistically significant. a N Z 119.
MODEL
YES (N Z 79)
+
Age (years) Sex (male) Dialysis duration (months) Height (cm) Body weight (kg) BMI (kg/m2) Body fat mass (%) MAMC (cm)a SMI (kg/m2) HGS (kg) Gait speed (m/s) Muscle qualitya MIS Kt/V Creatinine (mg/dL) Hb (g/dL) Albumin (g/dL) Phosphorus (mg/dL) C-reactive protein (mg/dL) Diabetes mellitus, n (%) Hypertension, n (%) Hyperlipidemia, n (%) CAD, n (%)
Hospitalization
Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients 7
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
Table 3
+
MODEL
8
Y.-L. Lin et al.
Figure 2 Hospitalization-free and overall survival, stratified according to sarcopenia and its diagnostic criteria in 126 hemodialysis patients, using Taiwanese criteria. *p < 0.05 was considered statistically significant.
patients who were bed-ridden as well as those who had a history of stroke or malignancy, and those who showed unsteady gait; all these patients are typically at high risk of sarcopenia. Moreover, our study participants were younger than those enrolled by Bataille S.24 Nevertheless, nearly half of our cohort exhibited either muscle weakness or slow gait speed. Accordingly, in our chronic HD patients, the diagnosis of sarcopenia was mainly determined by low muscle mass, which was consistent with the findings of a previous study.24 Although some large-scale studies have demonstrated an association between low muscle mass and poor clinical outcome,12e14 the potential role of muscle function was not considered in those studies. In 330 incident dialysis patients, Isoyama et al. showed that patients with muscle weakness were at an increased risk of mortality, regardless of low skeletal muscle mass.25 Similarly, Kittiskulnam et al. showed that muscle strength and gait speed were better predictors of mortality than sarcopenia or low muscle mass among 645 prevalent HD patients.26 Consistent with these findings, although our chronic HD patients with sarcopenia exhibited poor overall survival, poor clinical outcomes among non-sarcopenic patients with low muscle strength and slow gait speed remained unchanged. These results indicated that muscle strength and gait speed may be more closely related to clinical outcomes than is muscle mass. However, sarcopenia diagnosis alone in chronic HD patients could not identify patients with low muscle strength or slow gait speed. Accordingly, for better prediction of clinical outcomes in chronic HD patients, dynapenia (i.e., low muscle strength) could be superior to sarcopenia, which is based on low muscle mass. Furthermore, during the muscle wasting process, muscle strength decline occurs at a faster rate than muscle mass
loss.27 Sarcopenia, which constitutes both low muscle mass and low muscle strength, should be regarded as a late stage of muscle wasting. Regarding the importance of early detection of muscle wasting to provide effective therapeutic intervention, assessment of muscle strength and physical performance should be included as routine clinical care for chronic HD patients, in addition to muscle mass measurement. Serum creatinine was lower in patients with low muscle mass, as well as in patients with muscle weakness and slow gait speed. In addition, serum creatinine was an independent predictor for clinical outcomes in the present study. This finding was consistent with the results of several previous studies, which suggests that serum creatinine may serve as a reliable surrogate marker for muscle wasting in chronic HD patients.28e30 There were several limitations in this study. First, muscle mass measurement before dialysis may have led to overestimation due to hydration status. However, we evaluated the difference in muscle mass measurement before and after HD in 74 patients, which showed that this impact was limited (mean SMI difference 0.81 kg/m2, Pearson’s correlation coefficient Z 0.98).19 Second, the hand without an arteriovenous shunt was chosen for HGS measurement, rather than the dominant hand; this may have led to misclassification bias and attenuated the association between HGS and clinical outcomes in the model. Third, we measured baseline muscle mass and function; conversely, dynamic changes in these parameters may provide additional prognostic information. Finally, the prevalence of sarcopenia in our single center study should not be generalized to entire HD population in Taiwan. In conclusion, our results emphasize the high prevalence of muscle dysfunction in chronic HD patients and its critical
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
+
MODEL
Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients
9
Figure 3 Hospitalization-free survival (a) and overall survival (b) in 115 non-sarcopenic hemodialysis patients, stratified by handgrip strength (HGS) and gait speed (Taiwanese criteria). *p < 0.05 was considered statistically significant.
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
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MODEL
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Y.-L. Lin et al.
Table 4 Factors associated with composite outcome of hospitalization or death during a 3-year longitudinal followup. Variables
HR
Skeletal muscle index (kg/m2) Model 1 1.04 Model 2 e Model 3 e Handgrip strength (kg) Model 1 0.98 Model 2 0.99 Model 3 e Gait speed (m/s) Model 1 0.39 Model 2 0.61 Model 3 e Muscle quality Model 1 0.31 Model 2 0.40 Model 3 0.42 Creatinine (mg/dL) Model 1 0.78 Model 2 0.81 Model 3 0.82
95% CI
p value
0.98e1.10 e e
0.175 e e
0.95e1.00 0.97e1.02 e
0.036* 0.647 e
0.22e0.67 0.31e1.20 e
0.001* 0.151 e
0.16e0.58 0.20e0.84 0.19e0.93
<0.001* 0.015* 0.032*
0.70e0.88 0.70e0.93 0.71e0.95
<0.001* 0.003* 0.009*
Model 1: unadjusted. Model 2: adjusted for age, sex, hemoglobin, phosphorus, albumin and Kt/V. Model 3: Model 2 and further adjusted for diabetes mellitus, hypertension and hyperlipidemia. CI, confidence interval; HR, hazard ratio. *p < 0.05 was considered statistically significant.
impact on the risks of hospitalization and mortality, regardless of skeletal muscle mass volume. In addition, sarcopenia diagnosis in chronic HD patients could overlook patients with muscle weakness alone, since they did not exhibit reduced muscle mass. However, in this specific population, the risks of hospitalization and mortality remained high. Further studies are warranted to develop effective therapeutic interventions targeting muscle dysfunction in chronic HD patients.
Funding/support statement This study was supported by a grant from Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taiwan (TCRD105-04 and TCRD106-01).
Declaration of Competing Interest The authors declare no conflict of interest.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jfma.2019.10.020.
References 1. Landi F, Cruz-Jentoft AJ, Liperoti R, Russo A, Giovannini S, Tosato M, et al. Sarcopenia and mortality risk in frail older persons aged 80 years and older: results from ilSIRENTE study. Age Ageing 2013;42(2):203e9. 2. Arango-Lopera VE, Arroyo P, Gutierrez-Robledo LM, PerezZepeda M, Cesari M. Mortality as an adverse outcome of sarcopenia. J Nutr Health Aging 2013;17(3):259e62. 3. Kim JH, Lim S, Choi SH, Kim KM, Yoon JW, Kim KW, et al. Sarcopenia: an independent predictor of mortality in community-dwelling older Korean men. J Gerontol Ser A Biomed Med Sci 2014;69(10):1244e52. 4. Rosenberg IH. Sarcopenia: origins and clinical relevance. J Nutr 1997;127(5):990Se1S. 5. Kooman JP, Broers NJH, Usvyat L, Thijssen S, van der Sande FM, Cornelis T, et al. Out of control: accelerated aging in uremia. Nephrol Dial Transplant 2012;28:48e54. 6. Fahal IH. Uraemic sarcopenia: aetiology and implications. Nephrol Dial Transplant 2014;29(9):1655e65. 7. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European working group on sarcopenia in older people. Age Ageing 2010;39(4):412e23. 8. Chen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, et al. Sarcopenia in Asia: consensus report of the Asian working group for sarcopenia. J Am Med Dir Assoc 2014;15(2):95e101. 9. Fielding RA, Vellas B, Evans WJ, Bhasin S, Morley JE, Newman AB, et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc 2011;12(4):249e56. 10. Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci 2014;69(5):547e58. 11. Kim JK, Choi SR, Choi MJ, Kim SG, Lee YK, Noh JW, et al. Prevalence of and factors associated with sarcopenia in elderly patients with end-stage renal disease. Clin Nutr 2014;33(1): 64e8. 12. Noori N, Kopple JD, Kovesdy CP, Feroze U, Sim JJ, Murali SB, et al. Mid-arm muscle circumference and quality of life and survival in maintenance hemodialysis patients. Clin J Am Soc Nephrol 2010;5(12):2258e68. 13. Huang CX, Tighiouart H, Beddhu S, Cheung AK, Dwyer JT, Eknoyan G, et al. Both low muscle mass and low fat are associated with higher all-cause mortality in hemodialysis patients. Kidney Int 2010;77(7):624e9. 14. Castellano S, Palomares I, Moissl U, Chamney P, Carretero D, Crespo A, et al. Appropriate assessment of body composition to identify haemodialysis patients at risk. Nefrologia 2016;36(3): 268e74. 15. Yoda M, Inaba M, Okuno S, Yoda K, Yamada S, Imanishi Y, et al. Poor muscle quality as a predictor of high mortality independent of diabetes in hemodialysis patients. Biomed Pharmacother 2012;66(4):266e70. 16. Matos CM, Silva LF, Santana LD, Santos LS, Prota ´sio BM, Rocha MT, et al. Handgrip strength at baseline and mortality risk in a cohort of women and men on hemodialysis: a 4-year study. J Ren Nutr 2014;24(3):157e62. 17. Kutner NG, Zhang R, Huang Y, Painter P. Gait speed and mortality, hospitalization, and functional status change among hemodialysis patients: a US renal data system special study. Am J Kidney Dis 2015;66(2):297e304. 18. Weber J, Kelley J. Assessing nutrition. In: Nieginski E, editor. Health assessment in nursing. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2003. p. 165.
Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020
+
MODEL
Sarcopenia and Clinical Outcomes in Chronic Hemodialysis Patients 19. Lin YL, Liou HH, Lai YH, Wang CH, Kuo CH, Chen SY, et al. Decreased serum fatty acid binding protein 4 concentrations are associated with sarcopenia in chronic hemodialysis patients. Clin Chim Acta 2018;485:113e8. 20. Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol 2000;89:465e71. 21. Chien MY, Huang TY, Wu YT. Prevalence of sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc 2008;56(9):1710e5. 22. Landi F, Calvani R, Tosato M, Martone A, Fusco D, Sisto A, et al. Age-related variations of muscle mass, strength, and physical performance in community-dwellers: results from the milan EXPO survey. J Am Med Dir Assoc 2017;18(1). 88.e17-24. 23. Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients. Am J Kidney Dis 2001;38(6):1251e63. 24. Bataille S, Serveaux M, Carreno E, Pedinielli N, Darmon P, Robert A. The diagnosis of sarcopenia is mainly driven by muscle mass in hemodialysis patients. Clin Nutr 2017;36: 1654e60.
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Please cite this article as: Lin Y-L et al., Impact of sarcopenia and its diagnostic criteria on hospitalization and mortality in chronic hemodialysis patients: A 3-year longitudinal study, Journal of the Formosan Medical Association, https://doi.org/10.1016/ j.jfma.2019.10.020