Modified creatinine index as a noninvasive tool for the monitoring of muscle mass in peritoneal dialysis

Abstract

Background

The dialysis process negatively affects skeletal muscle mass and function by disrupting metabolic and nutritional balance. A previous study showed that single time-point measurement of modified creatinine index (MCrI) was a simple and reliable marker of skeletal muscle mass. The present study aims to evaluate whether serial monitoring of MCrI can detect changes in skeletal muscle mass and predict treatment outcomes in patients undergoing peritoneal dialysis (PD).

Methods

We studied 351 new adult PD patients. MCrI values were calculated by a validated formula at baseline and then every 6 months for 2 years. At the same time, lean tissue mass (LTM) was assessed by bioimpedance spectroscopy and fat-free edema-free body mass (FEBM) by traditional creatinine kinetic methods. The patients were followed for 59.2 ± 32.7 months for survival and hospitalization.

Results

When the percentage change in LTM (Δ%LTM) was taken as the reference, modified Bland-and-Altman analysis showed that the bias of percentage change of MCrI (Δ%MCrI) was +4.9%, with the limits of agreement −26.6 to +36.4%. When the percentage change in FEBM (Δ%FEBM) was taken as the reference, the bias of Δ%MCrI was −7.0%, with limits of agreement −35.4 to 21.3%. There was no significant association between Δ%MCrI and patient survival, hospital admission, or peritonitis rate.

Conclusions

The change in MCrI does not reliably represent the change in muscle mass in PD patients. Further research is needed to identify other noninvasive method for evaluating muscle mass in the PD population.

Keywords

malnutrition frailty chronic kidney disease sarcopenia

Key learning points

What was known:

Sarcopenia is a common and serious complication of patients receiving peritoneal dialysis (PD) patients.

A previous study showed that single time-point measurement of modified creatinine index (MCrI) was a simple and reliable marker of skeletal muscle mass for PD patients.

This study adds:

When the percentage change in lean tissue mass measured by bioimpedance spectroscopy or fat-free edema free body mass measure by traditional creatinine kinetics was taken as the gold standard, the percentage change of MCrI had a small bias but a wide limits of agreement.

There was no significant association between Δ%MCrI and patient survival, hospital admission, or peritonitis rate.

Potential impact:

The change in MCrI does not reliably represent the change in muscle mass in PD patients.

Introduction

Sarcopenia is a condition characterized by reduced muscle strength and diminished muscle mass.¹ In both general population and patients with chronic kidney disease (CKD), sarcopenia is associated with an increased risk of adverse outcomes, including falls, functional decline, frailty, and mortality.^2,3 Although sarcopenia predominantly affects the elderly, it can also occur in middle-aged individuals due to various underlying disorders.⁴ In the dialysis population, sarcopenia affects approximately 28.5% patients.⁵ Dialysis treatment per se is known to adversely affect skeletal muscle mass and function, primarily due to the metabolic and nutritional imbalances inherent to the treatment.⁶ Considering the significance of sarcopenia, the development of a convenient and noninvasive method for assessing muscle mass in the dialysis population is imperative.⁷

Current methods for identifying sarcopenia, such as dual-energy X-ray absorptiometry (DXA) scans and bioimpedance spectroscopy (BIS), have their own limitations. DXA scans are costly and require skilled operators, making them unsuitable for bedside examinations.⁸ Conversely, the result of BIS varies considerably across different populations, necessitating the development of population-specific validation equations.⁹ In this regard, the creatinine kinetic method is a reliable approach for assessing skeletal muscle mass.^10,11 The traditional creatinine index refers to the normalized rate of creatinine production, calculated as the sum of creatinine excretion and metabolic degradation.^10,12 Although the traditional creatinine kinetic method is renowned for its accuracy, it involves the cumbersome processes of post-dialysis serum creatinine level measurements and dialysate collection.^10,11 To simplify calculations and facilitate routine clinical use, Canaud et al.¹³ developed a user-friendly formula based on demographic parameters, predialysis serum creatinine concentrations, and single-pool Kt/V for urea. This parameter was originally called the “simplified creatinine index,”¹³ but has more commonly been referred to as the modified creatinine index (MCrI) by subsequent researchers.^12–14 Previous studies reported that MCrI was a predictor of 5-year all-cause mortality in HD patients.^15,16 However, the utility of MCrI in peritoneal dialysis (PD) patients remains controversial. A previous study from our group showed that MCrI computed by the Canaud's formula and total weekly Kt/V was a simple and reliable marker of skeletal muscle mass and may serve as a short-term prognostic indicator of PD patients.¹⁷ This study aims to assess whether serial monitoring of MCrI in PD patients can reliably detect the change in skeletal muscle mass and predict patient outcomes.

Materials and methods

The study received approval from the Joint Chinese University Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (approval numbers CRE-2021.367 and 2024.321). All procedures adhered to the guidelines outlined in the Declaration of Helsinki.

Study design

This is a prospective observational study conducted at a single center in a tertiary academic hospital. The study recruited all adult patients newly started on PD from January 2020 to August 2023. After written informed consent, we performed routine anthropometric measurements, blood biochemistry, multifrequent bioimpedance spectroscopy, dialysis small solute clearance, and other nutritional assessment at around 4 weeks after the patients were stable on PD, and then every 6 months for 2 years. The actual time interval of patient assessment, however, was variable because of pragmatic factors. Three parameters of muscle mass, including MCrI, lean tissue mass (LTM), and fat-free edema-free body mass (FEBM), were determined by blood biochemistry, bioimpedance spectroscopy, and creatinine kinetics study, respectively. Charlson's comorbidity index was computed at the baseline study as previously described.¹⁸

Modified creatinine index (MCrI)

MCrI was calculated by the following formula as previously described¹³:

\begin{aligned} MCrI - PD (mg / kg / day) \\ = 16.21 + (1.12 \times [1 if male; 0 if female]) \\ - (0.06 \times age (years)) \\ - (0.08 \times weekly Kt / V for urea) \\ + (0.009 \times serum creatinine at steady state (μ mol / L)) \end{aligned}

Lean tissue mass and bioimpedance spectroscopy

We used the Body Composition Monitor (Fresenius Medical Care, Bad Homburg, Germany) to assess patients’ body composition. The method of the measurement has been described previously.¹⁹ In addition to the lean tissue mass (LTM), we also measured the adipose tissue mass (ATM), extracellular water (ECW), intracellular water (ICW), the extracellular to intracellular volume ratio (E:I ratio), and the volume of overhydration (OH) in this study.

Fat-free edema free body mass

Fat-free edema-free body mass (FEBM) was calculated using the creatinine kinetic method following the Forbes and Brunining formula²⁰:

\begin{aligned} FEBM (kg) = & 7.38 + [0.029 \times (Creatinine excretion (mg / day) \\ + Creatinine degradation (mg / day))] \end{aligned}

while

\begin{aligned} Creatinine excretion (mg / day) \\ = [urine + dialysate creatinine output (μ mol / day)] \times 0.113 \end{aligned}

\begin{aligned} Creatinine degradation (mg / day) \\ = 0.38 \times (plasma creatinine (μ mol / l) \times 0.0113) \times BW (kg) \end{aligned}

FEBM was adjusted to the percentage of ideal body weight (IBW), which was determined by the body height and sex according to a standard formula validated in Southern Chinese.²¹

Dialysis small solute clearance and other nutritional status

Dialysis small solute clearance was evaluated by the conventional method,¹⁸ which involved the same 24-h collection of urine and dialysate as described above to compute the total Kt/V. The residual glomerular filtration rate (GFR) was determined by the average of 24-h urinary urea and creatinine clearances.²² The normalized protein nitrogen appearance (NPNA) was calculated using the modified Bergstrom's formula²³:

NPNA (g / kg / day) = [15.1 + (6.95 \times UNA) + TPr] \div IBW

while urea nitrogen appearance (UNA) and total protein loss (TPr) were in gram per day, while ideal body weight (IBW) was determined by the body height and sex according to a standard formula validated in Southern Chinese²¹:

IBW (kg) (male) = 62 + [(BH (m) - 1.7) \times 60]

IBW (kg) (female) = 52 + [(BH (m) - 1.58) \times 50]

Both the Subjective Global Assessment (SGA) overall score²⁴ and the comprehensive malnutrition-inflammation score (MIS)²⁵ were used for nutritional assessment according to methods previously described.^24,25 Serum albumin levels are measured by the bromocresol purple method.

Clinical outcomes

After the baseline assessment, all patients were monitored until October 2023. During this follow-up period, clinical management was decided by individual physicians and was not influenced by the study parameters. The study outcomes included patient survival, technique survival, peritonitis-free survival, peritonitis rate, number of hospital admission, and duration of hospital stay. For patient survival analysis, recovery of kidney function, loss to follow-up, transfer to another dialysis center, conversion to long-term hemodialysis, and kidney transplantation were censored. In the technique survival analysis, patient death, conversion to long-term hemodialysis, and kidney transplantation were taken as events, while recovery of kidney function, loss to follow-up, and transfers to other centers were censored. The number of hospital admissions and duration of hospital stays were adjusted for the follow-up duration.

Statistical analysis

Statistical analysis was performed using MedCalc Software Version 20.106 (Ostend, Belgium). The Shapiro–Wilk test was employed to assess whether the data followed a normal distribution. Demographic and clinical data were compared among quartiles of Δ%MCrI by Chi square test, one way analysis of variance (ANOVA) or Kruskal–Wallis test as appropriate. Correlations between variables were explored by the Pearson or the Spearman's rank correlation coefficient as appropriate. Survival rates were estimated using a Kaplan–Meier method and further analyzed by univariate Cox regression between Δ%MCrI quartiles. Hospitalization data and peritonitis rates were compared across Δ%MCrI quartiles using the Kruskal–Wallis test. A p-value of less than 0.05 was considered statistically significant; all probabilities were two-tailed.

Results

We screened 501 new PD patients. After exclusion for various specific reasons (Figure S1), there were 351 patients with 1092 MCrI measurements in the final analysis; 234 patients had three or more measurements.

Relation between the changes in MCrI and muscle mass

The median duration between each two consecutive measurement pair was 7.53 months (IQR 3.30 to 12.13 months). The average percentage change of MCrI (Δ%MCrI) was 3.9 ± 10.2%, and the average percentage change of LTM (Δ%LTM) was −1.2 ± 15.2%. When adjusted for the duration between the measurement, the average Δ%MCrI was 3.3 ± 10.5% per 6 months, and the average Δ%LTM was −1.1 ± 17.2% per 6 months. There was no significant correlation between Δ%MCrI and Δ%LTM (Spearman's r = 0.061, p = 0.09). The modified Bland-and-Altman plot, which depicted the relation between Δ%MCrI and Δ%LTM (Figure 1), showed that the bias of Δ%MCrI was +5.0%, with the limits of agreement −29.9 to +39.8%. The difference between Δ%MCrI and Δ%LTM inversely correlated with Δ%LTM (r = −0.831, p < 0.0001).

Figure 1.

Modified Bland-Altman's plot for the difference between the Δ%MCrI and Δ%LTM versus Δ%LTM, which was taken as the reference measurement. Δ%MCrI, percentage change in modified creatinine index; Δ%LTM, percentage change in lean tissue mass. The difference between Δ%MCrI and Δ%LTM inversely correlated with Δ%LTM (r = −0.952, p < 0.0001).

During the same period, the average Δ%FEBM was 5.3 ± 4.3% (or 5.5 ± 3.7% per 6 months). There was a significant correlation between Δ%MCrI and Δ%FEBM (r = 0.526, p < 0.0001). The modified Bland-and-Altman plot for Δ%MCrI and Δ%FEBM showed that the bias of Δ%MCrI was −9.9%, with the limits of agreement −51.5 to 31.5% (Figure 2). The difference between Δ%MCrI and Δ%FEBM had a significant correlation with Δ%FEBM (r = −0.898, p < 0.0001).

Figure 2.

Modified Bland-Altman's plot for the difference between the Δ%MCrI and Δ%FEBM versus Δ%FEBM, which was taken as the reference measurement. Δ%MCrI, percentage change in modified creatinine index; Δ%FEBM, percentage change in fat-free edema free body mass. The difference between Δ%MCrI and Δ%FEBM had a significant correlation with Δ%FETM (r = −0.747, p < 0.0001). Patients with and without residual renal function (RRF) are shown in white and red circles, respectively.

The correlation between Δ%MCrI and the concomitant changes of other clinical parameters is summarized in Supplementary Table 1. In essence, there were modest but significant correlations between Δ%MCrI and the percentage changes of total weekly Kt/V, E:I ratio, and overhydration volume.

Prognostic implications of Δ%MCrI

We explored the prognostic implications of Δ%MCrI by analyzing the first Δ%MCrI for the 351 patients. Their baseline demographic, clinical, biochemical, and bioimpedance characteristics are summarized in Tables 1 and 2, respectively. In essence, there were modest but significant differences in baseline body weight, BMI, serum creatinine, MCrI, residual GFR, LTM, ECW, and ICW between the Δ%MCrI quartiles.

Table 1.

Baseline demographic and clinical characteristics according to Δ%MCrI quartiles.

Parameter		Δ% MCrI quartiles				p-value
Parameter	All cases	I	II	III	IV	p-value
No. of patient	351	88	87	88	88
Δ%MCrI (%)	8.22 ± 13.16	−7.06 ± 8.48	3.39 ± 2.01	11.25 ± 2.67	25.24 ± 7.17	0.001^a
Age (year)	59.4 ± 11.2	61.2 ± 10.7	58.4 ± 12.5	60.1 ± 11.0	57.8 ± 10.4	0.17^a
Sex (M:F)	203:148	44:44	51:36	53:35	55:33	0.025^b
Body weight (kg)	63.3 ± 13.8	60.1 ± 14.8	61.9 ± 11.2	63.5 ± 12.4	67.5 ± 15.6	0.001^a
Body mass index (kg/m²)	24.0 ± 4.3	24.0 ± 4.3	24.0 ± 4.3	24.0 ± 4.3	24.0 ± 4.3	0.048^a
Blood pressure (mmHg)
Systolic	136.3 ± 21.1	132.6 ± 21.7	138.2 ± 21.4	137.4 ± 21.3	137.0 ± 20.1	0.29^a
Diastolic	73.5 ± 13.6	70.1 ± 12.5	74.3 ± 13.6	74.8 ± 15.5	74.7 ± 12.4	0.051^a
Primary kidney disease, no. of case (%)						0.65^b
Diabetes mellitus	163 (46.4%)	39 (44.3%)	39 (44.8%)	40 (45.5%)	45 (51.1%)
Hypertension	34 (9.7%)	12 (13.6%)	8 (9.2%)	9 (10.2%)	5 (5.7%)
Glomerulonephritis	82 (23.4%)	20 (22.7%)	22 (25.3%)	17 (19.3%)	23 (26.1%)
Polycystic kidney disease	14 (4.0%)	3 (3.4%)	3 (3.5%)	4 (4.6%)	4 (4.6%)
Urological	15 (4.3%)	3 (3.4%)	3 (3.5%)	4 (4.6%)	5 (5.7%)
Others	16 (4.5%)	4 (4.6%)	4 (4.6%)	6 (6.8%)	2 (2.3%)
Unknown	27 (7.7%)	7 (8.0%)	8 (9.2%)	8 (9.1%)	4 (4.6%)
Comorbidities, no. of case (%)
Diabetes mellitus	197 (56.1%)	45 (51.1%)	47 (54.0%)	48 (54.6%)	57 (64.8%)	0.28^b
Ischemic heart disease	91 (25.9%)	24 (27.3%)	16 (18.4%)	28 (31.8%)	23 (26.1%)	0.24^b
Cerebrovascular accident	81 (23.1%)	29 (33.0%)	23 (26.4%)	14 (15.9%)	15 (17.1%)	0.021^b
Peripheral vascular disease	24 (6.8%)	3 (3.4%)	6 (6.9%)	7 (8.0%)	8 (9.1%)	0.48^b
Charlson's comorbidities score	6.1 ± 2.6	6.3 ± 2.8	5.9 ± 2.4	6.0 ± 2.6	6.1 ± 2.5	0.82^a

Δ%MCrI: percentage change in modified creatinine index.

Data were compared by ^a one way analysis of variance (ANOVA) or ^b Chi-square test.

Table 2.

Baseline biochemical and body composition parameters according to Δ%MCrI quartiles.

Parameter		Δ% MCrI quartiles				p value
Parameter	All cases	I	II	III	IV	p value
No. of patient	351	88	87	88	88
Baseline MCrI
All cases	19.4 ± 2.7	20.4 ± 2.9	19.5 ± 2.8	19.2 ± 2.5	18.7 ± 2.5	<0.001^a
Male	20.2 ± 2.7	21.3 ± 3.1	20.4 ± 2.7	19.9 ± 2.6	19.3 ± 2.3	0.005^a
Female	18.5 ± 2.5	19.7 ± 2.6	18.1 ± 2.3	18.2 ± 2.1	17.5 ± 2.4	0.001^a
Serum creatinine (μmol/L)	661 (531 to 852)	769 (651 to 1013)	665 (522 to 841)	632 (518 to 818)	593 (464 to 708)	<0.001^b
Serum albumin (g/L)	33.9 ± 4.9	33.1 ± 4.6	34.3 ± 4.7	33.2 ± 5.1	34.9 ± 5.2	0.22^a
Weekly Kt/V	2.03 (1.69 to 2.42)	2.01 (1.56 to 2.44)	1.97 (1.71 to 2.41)	2.06 (1.78 to 2.38)	2.14 (1.70 to 2.45)	0.48^b
Residual GFR (ml/min/1.73 m²)	3.04 (1.33 to 5.26)	1.89 (0.52 to 4.11)	2.85 (0.69 to 5.64)	3.49 (1.64 to 4.99)	3.98 (2.33 to 6.38)	<0.001^b
NPNA (g/kg/day)	1.10 (0.98 to 1.27)	1.04 (0.94 to 1.24)	1.10 (0.98 to 1.24)	1.13 (1.00 to 1.25)	1.22 (1.01 to 1.36)	0.047^b
FEBM (kg)	23.8 (20.5 to 29.0)	25.9 (20.8 to 30.4)	23.6 (18.0 to 29.4)	23.7 (21.4 to 29.2)	23.6 (20.8 to 27.1)	0.44^b
Bioimpedance spectroscopy
Lean tissue mass (kg)	37.6 (31.9 to 46.5)	35.2 (28.9 to 41.7)	36.4 (31.3 to 45.9)	38.8 (32.7 to 47.2)	42.0 (32.6 to 49.7)	0.002^b
Adipose tissue mass (kg)	19.3 (13.1 to 27.4)	20.3 (13.1 to 27.1)	20.0 (12.6 to 25.8)	18.3 (13.2 to 28.1)	19.1 (14.0 to 28.0)	0.98^b
Overhydration volume (L)	3.20 (1.70 to 5.30)	2.40 (1.20 to 4.75)	3.00 (1.83 to 5.08)	3.35 (1.95 to 5.40)	3.75 (2.10 to 6.30)	0.049^b
ECW (L)	17.6 (14.2 to 20.3)	15.9 (13.3 to 18.8)	16.9 (14.4 to 19.6)	18.1 (15.2 to 20.2)	19.0 (16.2 to 22.1)	<0.001^b
ICW (L)	17.6 (15.1 to 21.0)	16.5 (14.3 to 19.4)	17.2 (14.9 to 20.7)	18.0 (15.7 to 20.9)	19.5 (16.1 to 22.7)	0.001^b
E:I ratio	0.99 (0.86 to 1.10)	0.97 (0.82 to 1.08)	1.00 (0.85 to 1.09)	0.97 (0.87 to 1.12)	0.99 (0.86 to 1.11)	0.77^b

MCrI: modified creatinine index; Δ%MCrI: percentage change in modified creatinine index; GFR: glomerular filtration rate; NPNA: normalized protein nitrogen appearance; FEBM: fat-free edema free body mass; ECW: extracellular water; ICW: intracellular water; E:I ratio: extracellular to intracellular volume ratio.

Data are presented as mean ± SD or median (interquartile range), and compared by ^a one-way analysis of variance (ANOVA) or ^b Kruskal–Wallis test.

The patients were followed for an average of 59.2 ± 32.7 months after the second MCrI assessment. During the follow-up period, 247 patients died. The causes of death were ischemic heart diseases (59 patients), cerebrovascular disease (25 patients), sudden cardiac arrest (17 patients), peritonitis (27 patients), nonperitonitis infection (91 patients), malignancy (six patients), termination of dialysis (10 patients), and other specific causes (12 patients). The 2-year patient survival rates for quartiles I–IV of Δ%MCrI were 71.2%, 81.2%, 79.8%, and 83.4%, respectively (log-rank test, p = 0.84) (Figure 3(a)). The quartile groups also did not have significant contribution to the predictive power of the model of patient survival (likelihood ratio test, p = 0.26).

Figure 3.

Kaplan–Meier plots of (a) patient survival; (b) technique survival; and (c) peritonitis-free survival. Patients was grouped by the quartile of percentage change in modified creatinine index (Δ%MCrI), with group IV having the highest values. Groups were compared by the log-rank test.

During the study period, 47 patients were switched to long term hemodialysis, 27 had kidney transplantation, and 10 were transferred to other centers. The 2-year technique survival rates for Δ%MCrI quartiles I to IV were 69.4%, 76.9%, 74.5%, and 78.4%, respectively (p = 0.81) (Figure 3(b)). The quartile groups also did not have significant contribution to the predictive power of the model of technique survival (likelihood ratio test, p = 0.12). There remained no significant relation between Δ%MCrI and patient or technique survival when the effect of Δ%MCrI was analyzed as a continuous variable or as a time-varying covariate by univariate Cox models (Supplementary Table 2).

During the follow-up period, there were 3352 hospital admissions for a total of 29,986 days. The median rate of hospital admission was 2.41 episodes per year (IQR 1.21 to 4.31), with a median during of hospital stay for 18.85 days per year (IQR 7.21 to 39.81). The relation between the Δ%MCrI quartiles and hospitalization are summarized in Supplementary Table 3. In essence, Δ%MCrI quartile did not have any significant association with the number of hospital admissions or duration of hospitalization.

During the study period, there were 536 peritonitis episodes; the median rate of peritonitis was 0.22 episode per patient-year (IQR 0.00 to 0.68). The 2-year peritonitis-free survival rates for Δ%MCrI quartiles I to IV were 52.5%, 68.2%, 47.8%, and 66.0%, respectively (p = 0.053) (Figure 3(c)). There remained no significant effect on peritonitis-free survival when the effect of Δ%MCrI was analyzed as a continuous variable by univariate Cox model (Supplementary Table 2), and there was no significant association between Δ%MCrI quartile and peritonitis rate (Supplementary Table 3).

Discussion

PD patients are known to have an elevated risk of sarcopenia, which subsequently increases the likelihood of adverse events.^2,26 A quick and simple assessment of muscle mass is important because it allows for appropriate dietary treatment of patients.^27,28 Currently, most clinicians use lean tissue mass (LTM) determined by bioimpedance spectroscopy or fat-free, edema-free body mass (FEBM) by creatinine kinetics to monitor muscle mass in PD patients.^22–24 However, there is a pressing need to develop a simple and cost-effective method to assess muscle mass in PD patients, and MCrI emerged as a promising tool.

In our previous study,¹⁷ we evaluated the relationship between single MCrI measurements and LTM as well as FEBM. A significant correlation was found between MCrI and both conventional indices. This study also noted that patients who were older, had lower body weight, a greater burden of comorbidities, and lower serum albumin levels exhibited lower MCrI values, while MCrI tends to be higher in patients with diminished residual kidney function and lower total Kt/V.

In the present study, we evaluated whether serial monitoring of the MCrI in PD patients could reliably detect changes in skeletal muscle mass. Although the percentage change in MCrI (Δ%MCrI) significantly correlated with the concomitant percentage change of FEBM (Δ%FEBM), Δ%MCrI had no significant correlation with the percentage change of lean tissue mass (Δ%LTM). With modified Bland-Altman plots that used Δ%FEBM and Δ%LTM as the reference measures, the bias of Δ%MCrI was small (4.9% and −7.0%, respectively, but the limits of agreement was wide (over ±20% in all directions), suggesting that Δ%MCrI is not a suitable parameter for the monitoring of skeletal muscle mass. Furthermore, the modified Bland-Altman plots had strong negative correlations between the difference of Δ%MCrI with either Δ%LTM or Δ%FEBM and the corresponding gold standard measurement, with the regression line passing through the origin, indicating that Δ%MCrI tends to underestimate both the increase and decrease in muscle mass. Although an overall trend of increasing Δ%MCrI with time suggests an increasing muscle mass, we noted that Δ%MCrI correlated with the percentage changes in total weekly Kt/V, the E:I ratio, and the overhydration volume, indicating that in addition to the skeletal muscle mass, both small solute clearance and body fluid status have substantial effects on Δ%MCrI.

It is important to note that the assessment of muscle mass in our study relied on bioimpedance spectroscopy, which is intrinsically sensitive to the hydration status of the patients. Since bioimpedance spectroscopy cannot fully discriminate between increased muscle mass and expanded extracellular fluid, fluctuations in volume status may have confounded measures of LTM and affects its correlation with MCrI. Notably, the average overhydration in our cohort exceeded 2 kg, indicating a predisposition to volume overload. Such overhydration could result in an overestimation of lean tissue compartments, thereby inflating muscle mass as measured by bioimpedance spectroscopy and potentially obscuring true physiological changes. Future studies should incorporate more rigorous control or adjustment for hydration status to enhance the accuracy and interpretability of BCM-derived muscle mass indices.

To the best of our knowledge, this study is also the first to assess the relationship between serial monitoring of MCrI and the clinical outcome of PD patients. Previous studies has demonstrated a correlation between muscle mass and survival in PD patients.^2,22,29,30 The relation between the change in muscle mass and the clinical outcome of PD patients, however, is less well studied. In hemodialysis patients, progressive loss of LTM has been shown to be related to worse quality of life and increased overall mortality.³¹ In another study, changes in lean tissue index (another surrogate marker of muscle ass) after 2 years of PD had a strong association with all-cause mortality than single LTI measurement.³² In our present study, however, there was no significant association between Δ%MCrI and patient survival, technique survival, or peritonitis-free survival.

Our result may appear different from the extensive literature on hemodialysis patients, which consistently showed a correlation between low MCrI values with an elevated risk of mortality.^33–36 In our study, patients in the lowest quartile of Δ%MCrI had an trend of worse patient, technique, and peritonitis-free survival, but the difference between quartiles did not reach statistical significance. Previous studies also suggested that HD patients in the highest quartile of MCrI were also at an increased risk of mortality, but this trend was not observed in our patients.

We also evaluated the association between Δ%MCrI and other clinical outcome measures, including the number of hospitalizations, the duration of hospitalization, and the incidence of peritonitis. Overall, no significant relationship was observed between Δ%MCrI quartiles and these clinical outcomes, although patients with the lowest Δ%MCrI values had marginally (but not statistically significant) longer durations of hospitalization and higher incidence of peritonitis. It is possible that with a larger sample size, a statistically significant association between Δ%MCrI and clinical outcome may be demonstrated, but we believe the magnitude of the effect would be small.

Our study has several other limitations that should be acknowledged. First, it is a single-center study conducted with Chinese PD patients, which may limit the generalizability of our findings. Second, given the different correlation results between patients with and without residual kidney function, it may be necessary to specific subgroup analysis with a larger sample size. Furthermore, we did not assess the effect of Δ%MCrI on several important outcome measures. For example, Δ%MCrI was reported to be associated with an increased risk of bone fractures in the hemodialysis population.¹⁴

In conclusion, although single time-point MCrI measurement has a good correlation with the skeletal muscle mass, serial monitoring MCrI is not a reliable marker of the change in muscle mass in PD patients. The change in MCrI does not have significant association with the clinical outcome of PD patients. Further research should be continued to identify other convenient noninvasive method for evaluating muscle mass in the dialysis population.

Supplemental Material

sj-docx-1-ptd-10.1177_08968608261456380 - Supplemental material for Modified creatinine index as a noninvasive tool for the monitoring of muscle mass in peritoneal dialysis

Supplemental material, sj-docx-1-ptd-10.1177_08968608261456380 for Modified creatinine index as a noninvasive tool for the monitoring of muscle mass in peritoneal dialysis by Miłosz Miedziaszczyk, Jack Kit-Chung Ng, Winston Wing-Shing Fung, Gordon Chun-Kau Chan, Kai-Ming Chow and Cheuk-Chun Szeto in Peritoneal Dialysis International

Supplemental Material

sj-pdf-2-ptd-10.1177_08968608261456380 - Supplemental material for Modified creatinine index as a noninvasive tool for the monitoring of muscle mass in peritoneal dialysis

Supplemental material, sj-pdf-2-ptd-10.1177_08968608261456380 for Modified creatinine index as a noninvasive tool for the monitoring of muscle mass in peritoneal dialysis by Miłosz Miedziaszczyk, Jack Kit-Chung Ng, Winston Wing-Shing Fung, Gordon Chun-Kau Chan, Kai-Ming Chow and Cheuk-Chun Szeto in Peritoneal Dialysis International

Footnotes

ORCID iDs

Winston Wing-Shing Fung

Kai-Ming Chow

Cheuk-Chun Szeto

Ethical considerations

The study was approved by the Joint Chinese University of Hong Kong—New Territories East Cluster Clinical Research Ethics Committee (approval numbers CRE-2024.321). All study procedures were in accordance with the Declaration of Helsinki. All patients provided written informed consent.

Author contributions

JKCN and CCS: research idea and study design. JKCN, WWSF, and GCSC: data acquisition. MM, JKCN, and CCS: data analysis/interpretation. KMC and CCS: supervision or mentorship. MM and CCS: manuscript preparation. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported in part by the Health and Medical Research Fund (HMRF) under the Health Bureau, Hong Kong Special Administrative Region government (project code 11220376; account code 6907239), Richard Yu Chinese University of Hong Kong (CUHK) PD Research Fund, and CUHK research accounts 6905134, 6906662, and 8601286. Miłosz Miedziaszczyk was supported by NAWA – Polish National Agency for Academic Exchange in cooperation with Medical Research Agency under the Walczak Program (Grant Number BPN/WAL/2023/1/00055). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Declaration of conflicting interests

‌CC Szeto serves as an Associate Editor for PDI. He was not involved in the editorial handling, peer review, or decision-making process for this manuscript. The authors declared no other potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data underlying this article will be shared upon reasonable request to the corresponding author.

Supplemental material

Supplemental material for this article is available online.

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