Introduction: The combined clinical impact of muscle mass, muscle function, and adipose mass on hospitalisation events, especially those that have exact causes, such as cardiovascular diseases (CVDs), had been rarely studied in patients on haemodialysis (HD). This study aimed to determine the influence of lean tissue index (LTI), fat tissue index (FTI), and hand grip strength (HGS) on the risk of CVD-related hospitalisation in patients undergoing chronic HD. Methods: This multi-centre observational study enrolled a total of 2,041 clinically stable patients aged >18 years and who had undergone HD for at least 3 months at 17 HD units in 2019. The follow-up period was up to 2 years. LTI and FTI were assessed using a body composition monitoring machine, and HGS was measured by a CAMRY® dynamometer. Cox regression models were fit to estimate the associations of body composition and HGS with CVD-related hospitalisation risk. Results: During a mean follow-up of 22.6 months, CVD-related hospitalisation occurred in 492 patients. Compared with the non-CVD group, patients with CVD-related hospitalisation were older; had lower diastolic blood pressure; were more likely to have a history of diabetes; had worse activity status scores and lower levels of LTI, HGS, serum uric acid, and serum creatinine; and had higher FTI levels, body mass index, and extracellular water/intracellular water ratio. In the Cox regression models, low LTI and high FTI were independently associated with CVD-related hospitalisation in both men and women. In men, low HGS was an independent risk factor for CVD-related hospitalisation. When patients were further stratified into four distinct groups according to the sex-specific median values of LTI and FTI, the combination of low LTI and high FTI was an independent risk factor for CVD-related hospitalization (hazard ratio [HR] = 1.79 in men, 95% confidence interval 1.26–2.55; HR = 2.48 in women, 95% confidence interval 1.66–3.71; reference: high LTI/low FTI group). Conclusions: Among patients on chronic HD, low LTI, and high FTI were associated with CVD-related hospitalisation in men and women, whereas HGS was an independent risk factor for CVD-related hospitalisation in men but not in women. Combining low LTI and high FTI increased the association with hospitalisation risk and was an independent predictor of CVD-related hospitalisation.

The risks of hospitalisation and mortality in patients with end-stage renal disease (ESRD) and undergoing dialysis are higher, compared with those in the general population [1, 2], and are most commonly caused by cardiovascular diseases (CVDs) [3‒6]. In addition to mortality, hospitalisation is a significant measure of clinical outcome by providing insight into the morbidity and cost of treatment among patients with ESRD [7]. Interventions for the risk factors of hospitalisation could improve patient outcomes. However, research on the risk factors of hospitalisation events, especially those that have exact causes, in patients with ESRD and undergoing dialysis remains limited.

In patients with ESRD, accelerated ageing is a prominent feature [8]. Compared with older adults in general, such patients tend to have diminished muscle mass and strength, combined with relatively increased adipose tissue mass (ATM) (i.e., sarcopenic obesity). These changes in muscle and adipose tissues in patients with ESRD are often attributed to multiple factors, including inflammation, oxidative stress, myostatin overexpression, metabolic acidosis, inactivity, protein loss during dialysis, comorbidities, and insulin resistance [9]. Although the individual associations of muscle mass, muscle function, and adipose mass with the survival of patients on haemodialysis (HD) had been analysed often [10‒14], the clinical impact of the combination of these factors on hospitalisation events, especially those that have exact causes, had been rarely studied. Two studies assessed the impact of sarcopenia on hospitalisation outcomes in patients on dialysis [15, 16]. However, both studies had relatively small sample sizes (126 and 170 patients, respectively), included all-cause hospitalisation outcomes, and had inconsistent conclusions.

Therefore, this 2-year multi-centre observational study on patients who were on chronic HD was undertaken to evaluate the importance of lean tissue index (LTI), hand grip strength (HGS), and fat tissue index (FTI) as predictors of CVD-related hospitalisation and to determine the different potential effects of different LTI and FTI combinations on clinical outcomes.

Study Design and Participants

This study included chronic HD patients from 17 dialysis units of tertiary general hospitals in Guizhou province, Southwest China. All participants received HD treatments with conventional dialysers under the standard temperature (35.5–36.5°C). The dialysate composition was usually composed of bicarbonate (32–38 mmol/L), sodium (130–140 mmol/L), chloride (96–110 mmol/L), magnesium (0.6–1.0 mmol/L), potassium (3–4 mmol/L), calcium (1.5–1.75 mmol/L). Eligible patients were screened and enrolled in these units between May and July 2019. Inclusion criteria for participants were as follows: (1) age ≥18 years; (2) the duration of outpatient haemodialysis (thrice weekly, 4 h per session) > 3 months; and (3) patients were willing to participate in this study. The exclusion criteria were as follows: (1) patients with a leg amputation or with a pacemaker, a defibrillator, metallic sutures, or stent implantation; (2) haemodialysis combined with peritoneal dialysis; (3) overt CVDs such as congestive heart failure at baseline enrolment; and (4) the subjects with malignant tumour whose expecting life were less than 1 year. These patients were followed up for 2 years or until August 31, 2021. The purpose of the current study was to analyse the risk factors for the first CVD-related hospitalisation in HD patients.

Measurements of Body Composition

Measurements of body composition were performed using a portable whole-body bioimpedance spectroscopy device, body composition monitor (BCM, Fresenius Medical Care, Bad Homburg, Germany). The measurement was carried out approximately 30 min before the HD session by a well-trained renal physician and a dialysis nurse, with four conventional electrodes being placed on the patient lying in a supine position: two on the hand and two on the foot contralateral to the vascular access. BCM data with a measurement quality greater than 90% were recorded, otherwise it was remeasured. Lean tissue mass, ATM, overhydration (OH), and extracellular water/intracellular water (ECW/ICW) were retrieved from BCM software. The lean tissue index (LTI) was calculated as lean tissue mass in kilograms divided by height in metres squared, and the fat tissue index (FTI) was calculated as ATM in kilograms divided by height in metres squared.

Measurements of HGS and Evaluation of Activity Status

Using a CAMRY® dynamometer (Guangdong, China) with a precision of 0.1 kg, HGS was measured in the dominant or non-fistula hand. The patients sitting with their arms bent at an angle of 90° on a horizontal level held the tool with their fingers around it. Three measures were taken with 30 s of rest between each test and the maximum score was adopted for the study. Participants’ activity status was accessed by Eastern Cooperative Oncology Group (ECOG) scores. ECOG score runs from 0 to 5, with 0 denoting perfect health and 5 referring to death.

The Definition of CVD-Related Hospitalisation

In this study, CVD-related hospitalisations were defined as hospitalisations for myocardial infarction, ischaemic heart disease, heart failure, arrhythmias, stroke, and peripheral vascular events. All-cause hospitalisations and CVD-related hospitalisations were ascertained via review of medical records. The outcome evaluators were blinded to participants’ body composition, grip strength, and other test results.

Statistical Analysis

The Kolmogorov-Smirnov test was used to analyse the normality of the data. Continuous data with a skewed distribution were expressed as the medians and interquartile ranges and compared by the Mann-Whitney U test or Kruskal-Wallis tests. Categorical data were expressed as numbers and percentages and compared by χ2 tests. Pairwise comparisons among the four groups were made by the Bonferroni method (for categorical variables) or the Nemenyi method (for continuous variables). Multivariable Cox regression models were used to estimate the hazard ratios (HRs) of the CVD-related hospitalisation associated with HGS, LTI, and FTI and different LTI/FTI category combinations. Variables with p < 0.05 in the univariate Cox regression analysis were considered to be adjusted in the multivariate models. Statistical analyses were performed using SPSS software (version 26.0; SPSS Inc., Chicago, IL, USA). A two-sided p < 0.05 was considered statistically significant.

Selection of Participants and Demographic Characteristics

Between May and July 2019, a total of 2,705 patients were screened for eligibility, 2,546 gave informed consent, and 2,068 patients participated in the study based on inclusion and exclusion criteria. 27 patients were excluded because of missing data on body composition or HGS tests. Table 1 presented the demographic characteristics of the total participants. Of these 2,041 patients, the median age was 55.0 (44.0, 65.0) years, the median dialysis vintage at enrolment was 49.0 (26.0, 78.0) months, 59% were men, 30.9% had diabetes mellitus (DM), the median ECOG score was 1 (0,1), and 82.6% received hemodiafiltration treatment. In men, the median LTI was 15.4 (13.7, 17.4) kg/m2; FTI was 6.7 (4.4, 9.6) kg/m2; HGS was 25.0 (19.0, 31.0) kg; OH was 1.0 (0.5, 2.0) kg, and ECW/ICW was 0.76 (0.70, 0.84). In women, the median LTI was 13.6 (11.9, 15.2) kg/m2; FTI was 8.7 (5.9, 11.4) kg/m2; HGS was 16.0 (12.0, 20.0) kg; OH was 0.8 (0.2, 1.4) kg, and ECW/ICW was 0.78 (0.72, 0.85).

Table 1.

Baseline characteristics of the patients in CVD and non-CVD Groups

CharacteristicsTotal (n = 2,041)Non-CVD (n = 1,549)CVD (n = 492)p value
Age, years 55.0 (44.0, 65.0) 52.0 (42.0, 63.0) 62.0 (52.0, 71.0) <0.001 
Male sex, n (%) 1,205 (59.0) 928 (59.9) 277 (56.3) 0.156 
Diabetes, n (%) 631 (30.9) 420 (27.1) 211 (42.9) <0.001 
HDF treatment, n (%) 1,685 (82.6) 1,274 (82.2) 411 (83.5) 0.511 
Urine volume >200 mL/day, n (%) 612 (30) 470 (30.3) 142 (28.9) 0.532 
Dialysis vintage, months 49.0 (26.0, 78.0) 43.0 (26.0, 78.0) 46 (25.0, 79.0) 0.564 
BMI, kg/m2 22.8 (20.7, 25.2) 22.6 (20.6, 25.1) 23.2 (21.1, 25.7) 0.002 
SBP, mm Hg 138.0 (124.0, 151.0) 137.0 (124.0, 151.0) 140.0 (126.0, 152.0) 0.244 
DBP, mm Hg 78.0 (70.0, 88.0) 79.0 (71.0, 88.0) 75.0 (67.0, 86.0) <0.001 
ECOG score, n (%) 1 (0.1) 0 (0.1) 1 (0.2) <0.001 
Haemoglobin, g/L 111.0 (98.0, 123.0) 112.0 (98.0, 124.0) 110.0 (97.0, 123.0) 0.109 
Albumin, g/L 40.4 (38.1, 42.9) 40.5 (38.2, 43.0) 40.1 (37.9, 42.5) 0.014 
Creatinine, μmol/L 945 (752, 1139) 984 (773, 1163) 846 (696, 1031) <0.001 
Uric acid, μmol/L 440 (374, 515) 445 (381, 519) 422 (359, 496) <0.001 
Total cholesterol, mmol/L 3.8 (3.2, 4.5) 3.9 (3.2, 4.5) 3.8 (3.1, 4.5) 0.266 
Triglyceride, mmol/L 1.6 (1.1, 2.3) 1.6 (1.1, 2.4) 1.6 (1.0, 2.3) 0.412 
Serum bicarbonate, mmol/L 21.7 (19.0, 24.3) 21.7 (19.0, 24.3) 21.8 (19.0, 24.6) 0.281 
CRP, mg/L 2.5 (1.3, 6.3) 2.4 (1.3, 6.4) 2.8 (1.4, 6.2) 0.667 
Body composition in men 
 LTI, kg/m2 15.4 (13.7, 17.4) 15.6 (13.9, 17.7) 14.2 (12.6, 16.4) <0.001 
 FTI, kg/m2 6.7 (4.4, 9.4) 6.2 (4.1, 9.0) 8.2 (5.6, 10.7) <0.001 
 ECW/ICW 0.76 (0.70, 0.84) 0.75 (0.70, 0.82) 0.80 (0.74, 0.87) <0.001 
 OH, L 1.0 (0.5, 2.0) 1.0 (0.4, 1.9) 1.1 (0.6, 2.2) 0.218 
 HGS, kg 25.0 (19.0, 31.0) 26.0 (20.0, 32.0) 22.0 (16.0, 27.0) <0.001 
Body composition in women 
 LTI, kg/m2 13.6 (11.9, 15.2) 14.0 (12.5, 15.4) 12.1 (10.7, 14.0) <0.001 
 FTI, kg/m2 8.7 (5.9, 11.4) 8.4 (15.6, 10.7) 10.4 (7.6, 13.5) <0.001 
 ECW/ICW 0.78 (0.72, 0.85) 0.77 (0.71, 0.84) 0.81 (0.73, 0.89) <0.001 
 OH, L 0.8 (0.2, 1.4) 0.8 (0.2, 1.3) 0.9 (0.3, 1.5) 0.179 
 HGS, kg 16.0 (12.0, 20.0) 16.0 (12.0, 21.0) 14.0 (11.0, 18.0) <0.001 
CharacteristicsTotal (n = 2,041)Non-CVD (n = 1,549)CVD (n = 492)p value
Age, years 55.0 (44.0, 65.0) 52.0 (42.0, 63.0) 62.0 (52.0, 71.0) <0.001 
Male sex, n (%) 1,205 (59.0) 928 (59.9) 277 (56.3) 0.156 
Diabetes, n (%) 631 (30.9) 420 (27.1) 211 (42.9) <0.001 
HDF treatment, n (%) 1,685 (82.6) 1,274 (82.2) 411 (83.5) 0.511 
Urine volume >200 mL/day, n (%) 612 (30) 470 (30.3) 142 (28.9) 0.532 
Dialysis vintage, months 49.0 (26.0, 78.0) 43.0 (26.0, 78.0) 46 (25.0, 79.0) 0.564 
BMI, kg/m2 22.8 (20.7, 25.2) 22.6 (20.6, 25.1) 23.2 (21.1, 25.7) 0.002 
SBP, mm Hg 138.0 (124.0, 151.0) 137.0 (124.0, 151.0) 140.0 (126.0, 152.0) 0.244 
DBP, mm Hg 78.0 (70.0, 88.0) 79.0 (71.0, 88.0) 75.0 (67.0, 86.0) <0.001 
ECOG score, n (%) 1 (0.1) 0 (0.1) 1 (0.2) <0.001 
Haemoglobin, g/L 111.0 (98.0, 123.0) 112.0 (98.0, 124.0) 110.0 (97.0, 123.0) 0.109 
Albumin, g/L 40.4 (38.1, 42.9) 40.5 (38.2, 43.0) 40.1 (37.9, 42.5) 0.014 
Creatinine, μmol/L 945 (752, 1139) 984 (773, 1163) 846 (696, 1031) <0.001 
Uric acid, μmol/L 440 (374, 515) 445 (381, 519) 422 (359, 496) <0.001 
Total cholesterol, mmol/L 3.8 (3.2, 4.5) 3.9 (3.2, 4.5) 3.8 (3.1, 4.5) 0.266 
Triglyceride, mmol/L 1.6 (1.1, 2.3) 1.6 (1.1, 2.4) 1.6 (1.0, 2.3) 0.412 
Serum bicarbonate, mmol/L 21.7 (19.0, 24.3) 21.7 (19.0, 24.3) 21.8 (19.0, 24.6) 0.281 
CRP, mg/L 2.5 (1.3, 6.3) 2.4 (1.3, 6.4) 2.8 (1.4, 6.2) 0.667 
Body composition in men 
 LTI, kg/m2 15.4 (13.7, 17.4) 15.6 (13.9, 17.7) 14.2 (12.6, 16.4) <0.001 
 FTI, kg/m2 6.7 (4.4, 9.4) 6.2 (4.1, 9.0) 8.2 (5.6, 10.7) <0.001 
 ECW/ICW 0.76 (0.70, 0.84) 0.75 (0.70, 0.82) 0.80 (0.74, 0.87) <0.001 
 OH, L 1.0 (0.5, 2.0) 1.0 (0.4, 1.9) 1.1 (0.6, 2.2) 0.218 
 HGS, kg 25.0 (19.0, 31.0) 26.0 (20.0, 32.0) 22.0 (16.0, 27.0) <0.001 
Body composition in women 
 LTI, kg/m2 13.6 (11.9, 15.2) 14.0 (12.5, 15.4) 12.1 (10.7, 14.0) <0.001 
 FTI, kg/m2 8.7 (5.9, 11.4) 8.4 (15.6, 10.7) 10.4 (7.6, 13.5) <0.001 
 ECW/ICW 0.78 (0.72, 0.85) 0.77 (0.71, 0.84) 0.81 (0.73, 0.89) <0.001 
 OH, L 0.8 (0.2, 1.4) 0.8 (0.2, 1.3) 0.9 (0.3, 1.5) 0.179 
 HGS, kg 16.0 (12.0, 20.0) 16.0 (12.0, 21.0) 14.0 (11.0, 18.0) <0.001 

CVD, cardiovascular disease; HDF, hemodiafiltration; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; ECOG, Eastern Cooperative Oncology Group; CRP, C-reactive protein; LTI, lean tissue index; FTI, fat tissue index; ECW, extracellular water; ICW, intracellular water; OH, overhydration; HGS, hand grip strength.

Difference between the CVD-Related Hospitalisation and Non-CVD Groups

During a mean follow-up of 22.6 months, 492 patients suffered from their first CVD-related hospitalisation, and the incidence rate of CVD-related hospitalisation was 24.1%. Compared with the non-CVD group, patients with CVD-related hospitalisation had older age and lower diastolic blood pressure (DBP); were more likely to have a history of diabetes; had worse ECOG score; lower levels of LTI, HGS, serum uric acid, and serum creatinine; higher levels of FTI, body mass index, and ECW/ICW ratio (all p < 0.05) (Table 1).

Differences in LTI, FTI, and HGS among Patients with Non-Hospitalisation, Hospitalisation for Non-CVD, and CVD Hospitalisation

In this study, 966 patients experienced all-cause hospitalisation events during the follow-up, including 492 CVD-related hospitalisations, accounting for 50.9% of all hospitalisation events. There were 474 hospitalized patients for other reasons, accounting for 49.1%. As shown in Figure 1, both in men and women, patients with CVD-related hospitalisation had much lower HGS and LTI levels, but higher FTI levels compared with the other two groups (all p < 0.001).

Fig. 1.

Comparison of LTI, FTI, and HGS among different HD patient groups. LTI, lean tissue index; FTI, fat tissue index; HGS, hand grip strength; HD, haemodialysis.

Fig. 1.

Comparison of LTI, FTI, and HGS among different HD patient groups. LTI, lean tissue index; FTI, fat tissue index; HGS, hand grip strength; HD, haemodialysis.

Close modal

Association of HGS, LTI, and FTI with the CVD-Related Hospitalisation

We explored whether body composition indices and HGS were associated with CVD-related hospitalisation risk among patients receiving chronic HD. To address this question, we performed analyses in male and female patients, respectively, in which the LTI, FTI, and HGS were treated as continuous variables. In men, after adjusting for potential confounders including age, DM, DBP, ECOG score, ECW/ICW ratio, albumin, and so on, the results showed HGS (HR 0.98, 95% CI: 0.97–0.99), LTI (HR 0.93, 95% CI: 0.88–0.98), and FTI (HR 1.05, 95% CI: 1.01–1.09) were independently associated with CVD-related hospitalisation risk. In women, after adjusting for the above confounding factors, LTI (HR 0.84, 95% CI: 0.78–0.90) and FTI (HR 1.07, 95% CI: 1.03–1.11) were independently associated with CVD-related hospitalisation, while HGS was not an independent risk factor for CVD-related hospitalisation (Table 2).

Table 2.

Multivariate Cox regression model of LTI, FTI, and HGS for the occurrence risk of CVD-related hospitalization in men and women

VariableUnadjustedModel 1Model 2Model 3
HR (95% CI)p valueHR (95% CI)p valueHR (95% CI)p valueHR (95% CI)p value
Men 
 HGS 0.95 (0.94–0.97) <0.001 0.97 (0.95–0.98) 0.001 0.98 (0.96–0.99) 0.021 0.98 (0.97–0.99) 0.036 
 LTI 0.86 (0.82–0.90) <0.001 0.90 (0.86–0.94) <0.001 0.91 (0.86–0.95) <0.001 0.93 (0.88–0.98) 0.007 
 FTI 1.10 (1.07–1.13) <0.001 1.07 (1.03–1.10) <0.001 1.06 (1.03–1.10) 0.001 1.05 (1.01–1.09) 0.026 
Women 
 HGS 0.95 (0.93–0.97) <0.001 0.98 (0.95–1.00) 0.094  
 LTI 0.80 (0.75–0.85) <0.001 0.83 (078–0.88) <0.001 0.81 (0.76–0.87) <0.001 0.84 (0.78–0.90) <0.001 
 FTI 1.11 (1.08–1.15) <0.001 1.09 (1.06–1.13) <0.001 1.09 (1.06–1.13) <0.001 1.07 (1.03–1.11) 0.001 
VariableUnadjustedModel 1Model 2Model 3
HR (95% CI)p valueHR (95% CI)p valueHR (95% CI)p valueHR (95% CI)p value
Men 
 HGS 0.95 (0.94–0.97) <0.001 0.97 (0.95–0.98) 0.001 0.98 (0.96–0.99) 0.021 0.98 (0.97–0.99) 0.036 
 LTI 0.86 (0.82–0.90) <0.001 0.90 (0.86–0.94) <0.001 0.91 (0.86–0.95) <0.001 0.93 (0.88–0.98) 0.007 
 FTI 1.10 (1.07–1.13) <0.001 1.07 (1.03–1.10) <0.001 1.06 (1.03–1.10) 0.001 1.05 (1.01–1.09) 0.026 
Women 
 HGS 0.95 (0.93–0.97) <0.001 0.98 (0.95–1.00) 0.094  
 LTI 0.80 (0.75–0.85) <0.001 0.83 (078–0.88) <0.001 0.81 (0.76–0.87) <0.001 0.84 (0.78–0.90) <0.001 
 FTI 1.11 (1.08–1.15) <0.001 1.09 (1.06–1.13) <0.001 1.09 (1.06–1.13) <0.001 1.07 (1.03–1.11) 0.001 

Model 1 is adjusted for age, DM.

Model 2 is adjusted for age, DM, DBP, ECOG score, and ECW/ICW ratio.

Model 3 is adjusted for age, DM, DBP, ECOG score, ECW/ICW ratio, albumin, uric acid, FTI (when LTI being considered as an independent variable), LTI (when FTI being considered as an independent variable).

CVD, cardiovascular disease; HGS, hand grip strength; LTI, lean tissue index; FTI, fat tissue index; HR, hazard ratio; CI, confidence interval; DBP, diastolic blood pressure; ECOG, Eastern Cooperative Oncology Group; ECW, extracellular water; ICW, intracellular water.

To further explore the worst combination of LTI and FTI to predict CVD-related hospitalisation, we further stratified patients into four distinct groups according to the sex-specific median values of LTI and FTI: high LTI/high FTI, high LTI/low FTI, low LTI/high FTI, and low LTI/low FTI (Table 3). Most of the variables such as CVD-related hospitalisation, all-cause hospitalisation, diabetes, ECOG score, C-reactive protein (CRP), and DBP differed significantly among the four groups. As shown in Table 4, male patients in the low LTI/high FTI group were 1.79-fold (95% CI: 1.26–2.55) as likely to experience CVD-related hospitalisation compared with the high LTI/low FTI group after adjusting for age, DM, DBP, ECOG score, ECW/ICW ratio, albumin, and uric acid. In the fully adjusted model, female patients in the low LTI/high FTI group were 2.48-fold (95% CI: 1.66–3.71) as likely to experience CVD-related hospitalisation compared with the high LTI/low FTI group.

Table 3.

Characteristics of patient groups stratified as high LTI/high FTI and high LTI/low FTI and as low LTI/high FTI and low LTI/low FTI according to sex-specific median values of both LTI and FTI

CharacteristicsLow LTI/low FTI (n = 357)Low LTI/high FTI (n = 663)High LTI/low FTI (n = 663)High LTI/high FTI (n = 358)p value
Age, years 54.0 (41.5, 65.5) 60.0 (51.0, 70.0) 48.0 (39.0, 58.0) 55.0 (46.0, 63.0) <0.001 
Male sex, n (%) 202 (56.6) 410 (61.8) 401 (60.5) 192 (53.6) 0.047 
Diabetes, n (%) 85 (23.8) 283 (42.7) 142 (21.4) 121 (33.8) <0.001 
HDF treatment, n (%) 300 (84.0) 539 (81.3) 550 (83.0) 296 (82.7) 0.718 
ECOG score, n (%) 1 (0.1) 1 (0.2) 0 (0.1) 1 (0.1) <0.001 
Urine volume>200 mL/day, n (%) 97 (27.2) 214 (32.3) 186 (28.1) 115 (32.1) 0.175 
All-cause hospitalization, n (%) 173 (48.5) 376 (56.7) 268 (40.3) 147 (41.1) <0.001 
CVD-related hospitalization, n (%) 77 (21.6) 246 (37.1) 94 (14.2) 75 (20.9) <0.001 
Dialysis vintage, months 54.0 (25.0, 89.0) 49.0 (27.0, 79.0) 49.0 (25.0, 77.0) 42.0 (25.0, 72.0) 0.007 
BMI, kg/m2 19.5 (18.3, 20.7) 23.7 (22.1, 25.6) 22.1 (20.5, 23.7) 26.6 (25.0, 28.7) <0.001 
HGS, kg 19.0 (14.0, 25.0) 19.0 (14.0, 25.0) 22.0 (17.0, 30.0) 21.0 (15.0, 29.0) <0.001 
ECW/ICW 0.78 (0.72, 0.86) 0.83 (0.76, 0.90) 0.72 (0.67, 0.77) 0.76 (0.72, 0.81) <0.001 
OH, L 1.1 (0.5, 2.2) 1.0 (0.3, 1.6) 1.0 (0.4, 1.6) 0.8 (0.3, 1.4) <0.001 
SBP, mm Hg 137 (123, 151) 137 (124, 149) 139 (126, 152) 138 (124, 151) 0.171 
DBP, mm Hg 79 (70, 90) 76 (67, 86) 80 (72, 89) 77 (71, 86) <0.001 
Haemoglobin, g/L 112 (99, 123) 113 (98, 126) 111 (97, 123) 110 (99, 119) 0.154 
Albumin, g/L 40.2 (38.0, 43.1) 39.8 (37.3, 42.3) 40.9 (38.8, 43.2) 41.0 (38.6, 42.9) <0.001 
Creatinine, μmol/L 838 (682, 1015) 876 (715, 1,060) 1,065 (840, 1,245) 998 (774, 1,185) <0.001 
Uric acid, μmol/L 416 (362, 4184) 437 (373, 514) 448 (380, 523) 454 (386, 531) <0.001 
Total cholesterol, mmol/L 3.8 (3.1, 4.5) 3.9 (3.2, 4.5) 3.8 (3.2, 4.5) 3.9 (3.3, 4.5) 0.219 
Triglyceride, mmol/L 1.2 (0.9, 1.8) 1.7 (1.2, 2.6) 1.4 (1.0, 2.0) 2.0 (1.4, 3.2) <0.001 
Serum bicarbonate, mmol/L 22.0 (19.6, 24.6) 21.6 (19.3, 24.3) 21.6 (18.6, 24.4) 21.7 (19.1, 24.2) 0.279 
CRP, mg/L 2.0 (1.0, 5.0) 3.1 (1.6, 7.7) 2.0 (1.1, 4.5) 3.4 (1.8, 7.9) <0.001 
CharacteristicsLow LTI/low FTI (n = 357)Low LTI/high FTI (n = 663)High LTI/low FTI (n = 663)High LTI/high FTI (n = 358)p value
Age, years 54.0 (41.5, 65.5) 60.0 (51.0, 70.0) 48.0 (39.0, 58.0) 55.0 (46.0, 63.0) <0.001 
Male sex, n (%) 202 (56.6) 410 (61.8) 401 (60.5) 192 (53.6) 0.047 
Diabetes, n (%) 85 (23.8) 283 (42.7) 142 (21.4) 121 (33.8) <0.001 
HDF treatment, n (%) 300 (84.0) 539 (81.3) 550 (83.0) 296 (82.7) 0.718 
ECOG score, n (%) 1 (0.1) 1 (0.2) 0 (0.1) 1 (0.1) <0.001 
Urine volume>200 mL/day, n (%) 97 (27.2) 214 (32.3) 186 (28.1) 115 (32.1) 0.175 
All-cause hospitalization, n (%) 173 (48.5) 376 (56.7) 268 (40.3) 147 (41.1) <0.001 
CVD-related hospitalization, n (%) 77 (21.6) 246 (37.1) 94 (14.2) 75 (20.9) <0.001 
Dialysis vintage, months 54.0 (25.0, 89.0) 49.0 (27.0, 79.0) 49.0 (25.0, 77.0) 42.0 (25.0, 72.0) 0.007 
BMI, kg/m2 19.5 (18.3, 20.7) 23.7 (22.1, 25.6) 22.1 (20.5, 23.7) 26.6 (25.0, 28.7) <0.001 
HGS, kg 19.0 (14.0, 25.0) 19.0 (14.0, 25.0) 22.0 (17.0, 30.0) 21.0 (15.0, 29.0) <0.001 
ECW/ICW 0.78 (0.72, 0.86) 0.83 (0.76, 0.90) 0.72 (0.67, 0.77) 0.76 (0.72, 0.81) <0.001 
OH, L 1.1 (0.5, 2.2) 1.0 (0.3, 1.6) 1.0 (0.4, 1.6) 0.8 (0.3, 1.4) <0.001 
SBP, mm Hg 137 (123, 151) 137 (124, 149) 139 (126, 152) 138 (124, 151) 0.171 
DBP, mm Hg 79 (70, 90) 76 (67, 86) 80 (72, 89) 77 (71, 86) <0.001 
Haemoglobin, g/L 112 (99, 123) 113 (98, 126) 111 (97, 123) 110 (99, 119) 0.154 
Albumin, g/L 40.2 (38.0, 43.1) 39.8 (37.3, 42.3) 40.9 (38.8, 43.2) 41.0 (38.6, 42.9) <0.001 
Creatinine, μmol/L 838 (682, 1015) 876 (715, 1,060) 1,065 (840, 1,245) 998 (774, 1,185) <0.001 
Uric acid, μmol/L 416 (362, 4184) 437 (373, 514) 448 (380, 523) 454 (386, 531) <0.001 
Total cholesterol, mmol/L 3.8 (3.1, 4.5) 3.9 (3.2, 4.5) 3.8 (3.2, 4.5) 3.9 (3.3, 4.5) 0.219 
Triglyceride, mmol/L 1.2 (0.9, 1.8) 1.7 (1.2, 2.6) 1.4 (1.0, 2.0) 2.0 (1.4, 3.2) <0.001 
Serum bicarbonate, mmol/L 22.0 (19.6, 24.6) 21.6 (19.3, 24.3) 21.6 (18.6, 24.4) 21.7 (19.1, 24.2) 0.279 
CRP, mg/L 2.0 (1.0, 5.0) 3.1 (1.6, 7.7) 2.0 (1.1, 4.5) 3.4 (1.8, 7.9) <0.001 

The p value was calculated by Kruskal-Wallis test (for continuous data) or χ2 test (for categorical data).

LTI, lean tissue index; FTI, fat tissue index; HDF, hemodiafiltration; ECOG, Eastern Cooperative Oncology Group; CVD, cardiovascular disease; BMI, body mass index; HGS, hand grip strength; ECW, extracellular water; ICW, intracellular water; OH, overhydration; SBP, systolic blood pressure; DBP, diastolic blood pressure; CRP, C-reactive protein.

Table 4.

Multivariate Cox regression model for the occurrence risk of CVD-related hospitalization calculated for patients stratified as high LTI/high FTI and high LTI/low FTI and as low LTI/high FTI and low LTI/low FTI according to sex-specific median values of both LTI and FTI

VariableUnadjustedModel 1Model 2
HR (95% CI)p valueHR (95% CI)p valueHR (95% CI)p value
Men 
 Low LTI/high FTI (LH) 2.68 (1.93–3.67) <0.001 1.87 (1.35–2.61) <0.001 1.79 (1.26–2.55) 0.001 
 Low FTI/low LTI (LL) 1.55 (1.03–2.33) 0.037 1.32(0.87–1.99) 0.190 1.15 (0.75–1.77) 0.528 
 High LTI/high FTI (HH) 1.68 (1.13–2.51) 0.011 1.39 (0.93–2.08) 0.112 1.43 (0.94–2.16) 0.092 
 High LTI/low FTI (HL) 
Women 
 Low LTI/high FTI (LH) 2.91 (2.03–4.19) <0.001 2.30 (1.58–3.34) <0.001 2.48 (1.66–3.71) <0.001 
 Low FTI/low LTI (LL) 1.58 (1.01–2.47) 0.046 1.47 (0.94–2.32) 0.092 1.49 (0.94–2.39) 0.093 
 High LTI/high FTI (HH) 1.24 (0.78–1.97) 0.374 1.12 (0.70–1.79) 0.627 1.15 (0.71–1.84) 0.579 
 High LTI/low FTI (HL)    
VariableUnadjustedModel 1Model 2
HR (95% CI)p valueHR (95% CI)p valueHR (95% CI)p value
Men 
 Low LTI/high FTI (LH) 2.68 (1.93–3.67) <0.001 1.87 (1.35–2.61) <0.001 1.79 (1.26–2.55) 0.001 
 Low FTI/low LTI (LL) 1.55 (1.03–2.33) 0.037 1.32(0.87–1.99) 0.190 1.15 (0.75–1.77) 0.528 
 High LTI/high FTI (HH) 1.68 (1.13–2.51) 0.011 1.39 (0.93–2.08) 0.112 1.43 (0.94–2.16) 0.092 
 High LTI/low FTI (HL) 
Women 
 Low LTI/high FTI (LH) 2.91 (2.03–4.19) <0.001 2.30 (1.58–3.34) <0.001 2.48 (1.66–3.71) <0.001 
 Low FTI/low LTI (LL) 1.58 (1.01–2.47) 0.046 1.47 (0.94–2.32) 0.092 1.49 (0.94–2.39) 0.093 
 High LTI/high FTI (HH) 1.24 (0.78–1.97) 0.374 1.12 (0.70–1.79) 0.627 1.15 (0.71–1.84) 0.579 
 High LTI/low FTI (HL)    

Model 1 is adjusted for age, DM.

Model 2 is adjusted for age, DM, DBP, ECOG score, ECW/ICW ratio, albumin, uric acid.

CVD, cardiovascular disease; HGS, hand grip strength; LTI, lean tissue index; FTI, fat tissue index; HR, hazard ratio; CI, confidence interval; DBP, diastolic blood pressure; ECOG, Eastern Cooperative Oncology Group; ECW, extracellular water; ICW, intracellular water.

This study mainly investigated the associations of LTI, FTI, and HGS with the occurrence of CVD-related hospitalisation in patients on chronic HD. When considered as continuous variables, low HGS, low LTI, and high FTI were independently associated with increased risk of CVD-related hospitalisation in men. In women, only LTI and FTI were independently associated with CVD-related hospitalisation. ECOG score, which reflected the activity status of the patients, was significantly worse in the CVD-related hospitalisation group than the non-CVD group, but it did not affect the association between body composition and CVD-related hospitalisation. Moreover, other indicators of the BCM machine including OH and ECW/ICW did not change the correlations of LTI, FTI, and HGS with CVD-related hospitalisation. The significantly lower serum creatinine level in the CVD-related hospitalisation group than in the non-CVD group indicated that serum creatinine can reflect the skeletal muscle mass and confirm the relationship between muscle mass and CVD-related hospitalisation risk in clinically stable patients on chronic HD [17].

Some studies have reported the association of muscle strength with all-cause hospitalisation events in both the general population and patients on chronic HD [15, 16, 18, 19]. Moreover, dynapenia was found to be associated with CVD hospitalisations or CVD risks in patients with ESRD [13, 20, 21]. Among patients with HD, Kim et al. [22] reported that the risk of CVD events was three times higher in those who had low muscle mass than in the control group. Conversely, another study on patients with HD found that appendicular skeletal muscle mass, which was measured by bioimpedance analysis, was not a significant risk factor for major adverse cardiovascular events [23]. Furthermore, although other reports showed more consistent associations between loss of muscle strength and mortality [13, 24‒27], the importance of low muscle mass as an independent predictor of mortality in dialysis populations remains controversial [22, 23]. In this study on patients on HD, both muscle mass and strength were independently associated with CVD-related hospitalisation in men; however, only low muscle mass was a risk factor for this clinical outcome in women. The reason for this difference may be the less willingness of women than of men to compete physically; therefore, the results of the HGS tests in women could not accurately reflect muscle quality.

For FTI, we observed a significantly higher value in the CVD-related hospitalisation group than the non-CVD groups both among men and women, and high FTI was a predictor of CVD-related hospitalisation. Similarly, Cawthon et al. [19] reported that muscle fat infiltration or myosteatosis was associated with an increased risk of hospitalisation among healthy non-disabled older adults. Conversely, Lin et al. [15] did not find any significant difference in fat tissue mass between hospitalised and non-hospitalised patients on HD. The other attempts to find the potential correlation between adipose tissue and patient survival have yielded mixed results [28‒32].

Because of the lack of definition for abnormal body composition in patients on HD, the sex-specific median values of LTI and FTI were applied as the cutoffs for grouping in our study. We divided the patients into four groups, based on the combination of LTI and FTI. Compared with patients who had high LTI and low FTI, those who had low LTI and high FTI (i.e., sarcopenic obesity) had higher relative risk of CVD-related hospitalisation (2.68 times in men and 2.91 times in women) in unadjusted models. This result may be partially explained by the older age, unfavourable metabolic profile, poor nutritional status, relatively excessive ECW, and more obvious status of microinflammation in the group with low LTI and high FTI at baseline, compared with those in the other groups. However, after adjusting for potential confounders including age, DM, DBP, ECOG score, ECW/ICW ratio, albumin, and uric acid, these associations still existed. The risk of CVD-related hospitalisation in the groups of low LTI combined with low FTI and high LTI combined with high FTI was not increased, compared with that in the reference group, suggesting that a high LTI might offset the adverse effects of a high FTI and that a low FTI might offset the adverse effects of a low LTI. Therefore, stratifying the risks of CVD-related hospitalisation by the presence or absence of both low muscle mass and high fat tissue mass may help in the daily management of patients on HD.

Several underlying mechanisms may be involved in the association between sarcopenic obesity with CVD-related hospitalisation. Sarcopenia leads to a reduction in muscle contraction-induced factors, such as myokines. Myokines have effects against inflammation, which induces vascular calcification and endothelial dysfunction [33]. In addition, sarcopenia is known to cause physical inactivity, which could result in an increased risk of CVDs secondary to abnormalities in blood pressure and glucose metabolism [34, 35]. In our study, physical activity assessment, such as the sitting-rising or gait speed test, was not performed; therefore, the impact of physical activity on CVD-related hospitalisation could not be further analysed. However, we did not observe a definitive influence of the ECOG score on the association between body composition and CVD-related hospitalization. In a dialysis population, high fat mass or obesity is well-known to cause deleterious effects, such as inflammation, dyslipidemia, insulin resistance, atherosclerosis, and coronary calcification [36]. Similarly, our previous study showed that high fat mass was an independent risk factor of frequent intradialytic hypotension [37]. Appropriate interventions for modifiable factors, such as body composition and muscle strength, may help in delaying disability onset and be effective in controlling the dramatic economic burden associated with CVD-related hospitalisation. It has to be mentioned that the observation period of this study included the first year of the COVID-19 pandemic. However, because of strict prevention measures, the number of COVID-19 cases in our province by the end of the study period was <100, and most of them were imported cases from other provinces or countries. The patients included in this study period continued to receive regular dialysis every week, and none of them were infected with COVID-19. Therefore, COVID-19 was unlikely to have affected data collection or hospitalisation events.

The strengths of the current study were the relatively large sample size, multi-centre representation, separate and joint analyses of body composition, detailed ascertainment of potential confounders, and follow-up observations, all of which increased the reliability of the results. However, several limitations should also be considered. First, muscle mass measurement before HD might have led to overestimation because of the hydration status. However, Lin et al. [38] found negligible difference in muscle mass measurements before and after HD in 74 patients. Second, we excluded patients who were on dialysis treatment for <3 months and those with unstable medical conditions. Compared with the patients included in this study, the clinically unstable patients were likely to have more abnormal body composition and muscle strength. Therefore, this selection bias may have caused an underestimation of the observed associations in this study. Third, although the 24-month evaluation of the impact of body compositions and HGS on CVD-related hospitalisation brought certain scientific contributions, the observational nature of the study limited the assessment of the cause-effect relationship between the variables and outcomes. Last but not least, the presence of unmeasured or unknown residual factors that might have confounded the relationship between body composition and hospitalisation could not be fully ruled out.

This study on patients on chronic HD revealed that CVD-related hospitalisation risk was associated with LTI, FTI, and HGS in men and with LTI and FTI in women. The combination of low LTI and high FTI was an independent risk factor of CVD-related hospitalisation in both men and women. Therefore, our results supported the emphasis on objective measurements of body composition and muscle strength in the assessment of CVD-related hospitalisation risk. Intervention studies are needed to enable maintenance of adequate muscle strength and suitable body composition in patients on dialysis, especially those with high CVD risk.

This study protocol was approved by the Ethics Commission of Guizhou Provincial People’s Hospital, approval number 2019-029. It was performed in compliance with the Declaration of Helsinki. Written informed consent was obtained from participants to participate in the study.

The authors have no conflicts of interest to declare.

This research was funded by a governmental grant from the Science and Technology Foundation of Guizhou Provincial Health Commission, Grant No. gzwkj2023-321, Guizhou Science and Technology Plan Project (QKH [2020] 2201), and Guizhou High-level innovative Talent Project (QKH [2018] 5636-2).

Maolu Tian: methodology, formal analysis, writing – original draft, visualization, funding acquisition. Qin Lan: investigation, methodology, formal analysis. Jing Yuan: conceptualization, methodology, formal analysis, review and editing, project administration. Pinghong He: methodology, writing – original draft, investigation. Fangfang Yu: methodology, investigation, supervision. Changzhu Long: methodology, investigation. Yan Zha: conceptualization, methodology, resources, review and editing, supervision, project administration.

The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from the corresponding author (Yan Zha) upon reasonable request.

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