Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts

Abstract

Background

Central obesity is common among patients undergoing peritoneal dialysis (PD) and may have important metabolic consequences. The body roundness index (BRI) is a convenient anthropometric measurement that represents central adiposity and visceral fat. However, its prognostic value in PD patients has not yet been explored.

Method

We conducted a retrospective study involving two cohorts of PD patients: an exploratory cohort of 249 prevalent PD patients and a validation cohort of 162 incident ones. In addition to BRI, we performed bioimpedance spectroscopy and other routine clinical and biochemical tests. Outcome measures included patient survival, rate of alive and remained on PD, peritonitis-free survival, and hospitalization rates.

Results

In the exploratory cohort, the baseline BRI was an independent risk factor for all-cause mortality, with an adjusted hazard ratio of 1.346 (95% CI: 1.094 to 1.655, p = 0.005), and for rate of alive and remained on PD, with an adjusted hazard ratio of 1.279 (95% CI: 1.079 to 1.517, p = 0.004). A high BRI was also associated with a higher frequency of hospitalizations (p = 0.001) and longer hospital stays (p = 0.002). In the validation cohort, baseline BRI was also associated with patient survival by univariable Cox regression analysis, but the result became insignificant after adjusting for confounding clinical factors by multi-variable Cox regression.

Conclusion

A high BRI is associated with increased rates of all-cause mortality and hospitalization. However, the results from the exploratory and validation cohorts were somewhat inconsistent, indicating the necessity for further research to validate our findings.

Index Words: kidney failure; obesity; cardiovascular disease

Keywords

chronic kidney disease malnutrition obesity cardiovascular disease

Introduction

Peritoneal dialysis (PD) is a life-saving treatment for patients with kidney failure. PD presents several advantages over hemodialysis (HD), including increased flexibility and enhanced quality of life.¹ However, PD may also be associated with heightened risks of progressive weight gain and metabolic disturbances, which may confer risks for adverse clinical outcomes.^2–4 Although conventional measurements like body mass index (BMI), along with advanced techniques such as bioimpedance spectrometry, are commonly employed to evaluate patients’ nutritional status, these methods do not adequately capture body fat distribution and visceral adiposity, which may have important implications for the patient's prognosis.⁵

The body roundness index (BRI) has recently emerged as an innovative anthropometric measurement that integrates both body height and waist circumference.⁶ As compared to BMI, BRI provides a more accurate estimation of visceral fat levels and reflects the severity of central obesity. In the general population, BRI serves as a robust predictor of metabolic syndromes, cardiovascular diseases, and even all-cause mortality.^7–9 Given that metabolic syndromes and cardiovascular diseases are common complications in PD patients,^10,11 BRI holds promise as a non-invasive tool for the evaluation and risk stratification of PD patients.

Despite its potential relevance, BRI has not been thoroughly studied in patients undergoing PD. In addition, the presence of peritoneal dialysate could lead to an overestimation of waist circumference, thereby affecting the accuracy of BRI measurements. It is therefore imperative to evaluate the prognostic role of BRI in PD patients. In the present study, we investigated the prognostic implications of BRI across two cohorts of PD patients.

Patients and methods

The study received approval from the Joint Chinese University Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (approval numbers CRE-2007.366 and CRE-2021.367). All procedures adhered to the guidelines outlined in the Declaration of Helsinki.

Patient selection

This study is a retrospective analysis of two prospective observational cohorts of adult PD patients at a single center. The exploratory cohort consists of 249 prevalent PD patients who were recruited between April 2008 and Aug 2009. A separate validation cohort consists of 162 new PD patients enrolled between January 2021 and June 2022. Following written informed consent, baseline assessments, including anthropometric measurements for the calculation of the BRI, routine biochemistry, and small solute clearance test were performed. Patients who were planned for living donor kidney transplants or transfers to other medical centers within 6 months were excluded from this study.

BRI calculation

BRI was calculated by the formula as previously described¹²

364.2 - 365.5 \times (1 – [waist \times 2 π^{2}] / {[0.5 \times height]}^{2})

Waist circumference and height measurements were obtained as part of routine clinical care, with full abdominal dwell of PD solution. Patients were classified into four groups based on their baseline BRI following the initiation of PD: ≤ 4, 4 to < 5, 5 to < 6, and ≥ 6.

Multi-frequency bioimpedance spectroscopy

Multi-frequency bioimpedance spectroscopy (BIS) was used to evaluate the lean tissue mass (LTM) and adipose tissue mass (ATM) as previously described¹³ in the validation cohort. Briefly, electrodes were positioned on the patient's right hand and foot while in a supine position. Measurements were taken using the body composition monitor (BCM, Fresenius Medical Care, Germany). In addition to LTM and ATM, we also measured the overhydration volume (OH), total body water (TBW), extracellular water (ECW), and intracellular water (ICW). The extracellular-to-intracellular (E/I) volume ratio was then calculated. All body composition measurements were performed during PD fluid dwell, as our previous study indicated that peritoneal dialysate had a negligible effect on the measurements.¹⁴

Small solute clearance and other laboratory indices

Assessment of dialysis small solute clearance was performed using a 24-h collection of dialysate and urine, as previously described, with the total weekly Kt/V calculated utilizing the standard formula.¹⁵ The residual glomerular filtration rate (GFR) was determined by calculating the average of the 24-h urinary urea and creatinine clearances.¹⁶ Serum albumin levels were measured by the bromocresol purple method.¹⁷ Other laboratory tests, including plasma biochemistry and hemoglobin levels, were performed as part of routine clinical care.

Follow-up and outcome parameters

The exploratory and validation cohorts were followed till December 2024. Throughout the follow-up period, the clinical management decisions were made by the individual clinician responsible for patient care and were not affected by the study. The primary outcome measures included patient survival and rate of alive and remained on PD. Secondary outcome measures consisted of peritonitis-free survival, peritonitis rate, number of hospital admissions, and total duration of hospitalizations. In this study, the events of alive and remained on PD included transfer to hemodialysis, kidney transplantation, or death, while transferal to other dialysis centers or recovery of kidney function were considered as censoring events. Kidney transplantation was considered an event because there were local policies that PD patients with impending treatment failure had a priority of receiving kidney transplantation. The peritonitis rate was calculated as the number of peritonitis episodes per patient-year.

Statistical analysis

Statistical analyses were conducted utilizing SPSS version 28.0 (IBM Corporation, Armonk, NY) and GraphPad Prism (version 10.1.1, GraphPad Software, San Diego, CA). Data are presented as mean ± standard deviation or median (interquartile range [IQR]) unless otherwise specified. Spearman's or Pearson's correlation coefficient was applied to examine the association between baseline BRI and other body composition metrics or biochemical parameters as appropriate. The linearity of the effect of BRI on survival was checked by the Martingale residual plot, which showed a roughly straight fitting line (Supplementary figure 1). The Kaplan–Meier method was employed to display data on patient survival, rate of alive and remained on PD, and peritonitis-free survival rates. As described above, patients were stratified into four groups based on their baseline BRI value, and their survival rates were compared using the univariable Cox regression analysis. Multivariable Cox regression models were further constructed to adjust for confounding clinical factors. We screened all clinical parameters that we recorded by univariable Cox analysis, and then performed multivariable Cox analysis for variables that are potentially associated with the survival outcome (p < 0.1 by univariable Cox). The complete multi-variable Cox analysis was performed without stepwise elimination. The peritonitis rate, the number of hospital admissions, and hospitalization duration among different patient groups were compared using Kendall's tau test. A p-value of less than 0.05 was considered statistically significant. All probabilities were two-tailed.

Results

We studied 249 prevalent PD patients in the exploratory cohort. Their median duration of dialysis was 36 months (IQR 18 to 64 months). Their baseline demographic and clinical information are summarized in Tables 1 and Table S1. The distribution of baseline BRI in the exploratory cohort is summarized in Figure 1. In essence, the baseline BRI was 4.7 ± 1.4, with 93 patients (37.3%) having a baseline BRI ≥5.

Figure 1.

Distribution histogram of baseline body roundness index (BRI) for the exploratory cohort

Table 1.

Baseline demographic and clinical characteristics

	Exploratory cohort	Validation cohort
No. of patients	249	162
Sex (male: female)	127:122	94:68
Age (years)	60.2 ± 11.5	61.5 ± 12.8
Height (cm)	160.0 ± 7.7	162.3 ± 9.0
Body weight (kg)	62.2 ± 12.2	63.8 ± 14.5
Body mass index (kg/m²)	24.4 ± 4.0	24.1 ± 4.5
Blood pressure (mmHg)
systolic	144.2 ± 22.2	142.4 ± 20.1
diastolic	76.0 ± 12.4	75.9 ± 13.1
Renal diagnosis, no. of case (%)
diabetic nephropathy	76 (30.5%)	90 (55.6%)
glomerulonephritis	78 (31.3%)	33 (20.4%)
hypertension	25 (10.0%)	15 (9.3%)
urological problem	11 (4.4%)	6 (3.7%)
polycystic kidney disease	11 (4.4%)	3 (1.9%)
others or unknown	48 (19.3%)	15 (9.3%)
Major comorbidities, no. of case (%)
Diabetes mellitus	107 (43.0%)	98 (60.5%)
Coronary artery disease	62 (24.9%)	28 (17.3%)
Cerebrovascular disease	68 (27.3%)	13 (8.0%)
Peripheral vascular disease	30 (12.0%)	7 (4.3%)
Charlson's comorbidity score	5.2 ± 2.2	5.9 ± 2.3
Type of PD, no. of case (%)
Machine-assisted automated PD	9 (3.6%)	29 (17.9%)
Low GDP solution	30 (12.0%)	38 (23.5%)

PD, peritoneal dialysis; GDP, glucose degradation products.

The correlation between baseline BRI and other clinical parameters is summarized in Table S2. In brief, BRI significantly correlated with age, BMI, Charlson's comorbidity scores, systolic blood pressure, triglyceride level, and HbA1c, and it is inversely correlated with small solute clearance (weekly total Kt/V) and HDL level.

Patient survival and rate of alive and remained on PD

The median follow-up of the exploratory cohort was 30.5 (IQR 17.0 to 64.5) months. During this period, 172 patients died. The causes of death were non-peritonitis infections (38 cases), peritonitis (32 cases), ischemic heart disease (36 cases), cerebrovascular accident (14 cases), sudden cardiac death (25 cases), termination of dialysis (18 cases), and other specific causes (9 cases). With univariable Cox regression analysis, baseline BRI was significantly associated with patient survival (unadjusted hazard ratio (HR) 1.281, 95% confidence interval (CI) 1.120 to 1.465, p = 0.0003) (Table 2A). The 2-year patient survival rates for patients with baseline BRI <4, 4 to <5, 5 to <6, and ≥6 were 81.3%, 75.5%, 73.2%, and 58.3%, respectively (Figure 2A). After adjusting for confounding clinical factors by multivariable Cox regression analysis, baseline BRI was still an independent predictor of patient survival (AHR [adjusted hazard ratio] = 1.346, 95% CI 1.094 to 1.655, p = 0.005).

Figure 2.

Kaplan–Meier plots of (A) patient survival; and (B) alive and remained on PD for the exploratory cohort. Patients were categorized into four groups according to their baseline body roundness index (BRI): <4, 4 to <5, 5 to <6, and ≥6). Data were analyzed by Spearman's rank correlation coefficient for the BRI category

Table 2.

Cox regression analysis of the exploratory cohort

A. Patient survival
	Univariable		Multivariable
	unadjusted HR	P values	adjusted HR	95%CI	P values
Body roundness Index	1.281	0.0003	1.346	1.094 to 1.655	0.005
Sex (male to female)	0.986	0.938
Age (years)	1.042	<0.0001	0.994	0.962 to 1.026	0.705
Body Mass Index	1.009	0.709
Systolic blood pressure (mmHg)	1.004	0.337
Diastolic blood pressure (mmHg)	0.980	0.011	0.999	0.975 to 1.023	0.905
Charlson's comorbidity score	1.319	<0.0001	1.196	1.045 to 1.369	0.009
Waist circumference (cm)	1.024	0.012
Systemic global assessment score	0.703	0.009	0.937	0.593 to 1.479	0.779
Malnutrition inflammation score	1.136	<0.0001	1.234	0.899 to 1.199	0.052
Hemoglobin (g/dL)	0.931	0.244
Albumin	0.889	<0.0001	0.916	0.852 to 0.986	0.019
Fasting Glucose Level (mmol/L)	1.067	0.009	1.007	0.945 to 1.072	0.839
HbA1C (%)	1.191	0.005	1.093	0.958 to 1.246	0.185
Total Cholesterol (mmol/L)	0.875	0.076
LDL Cholesterol (mmol/L)	0.788	0.008	0.780	0.623 to 0.977	0.03
HDL Cholesterol (mmol/L)	0.890	0.542
Triglyceride (mmol/L)	0.993	0.904
Dialysate-to-plasma creatinine at 4 h	2.446	0.207
MTAC creatinine (ml/min/1.73m²)	1.033	0.120
Total weekly Kt/V	0.502	0.004	1.455	0.623 to 3.397	0.386
Residual GFR	0.774	0.002	0.829	0.680 to 1.011	0.065
NPNA (g/kg/day)	0.192	0.0002	1.216	0.267 to 5.538	0.800
FEBM (kg)	0.999	0.941

B. Alive and remained on PD
	Univariable		Multivariable
	unadjusted HR	P values	adjusted HR	95%CI	P values
Body roundness Index	1.187	0.003	1.279	1.079 to 1.517	0.004
Sex (male to female)	1.082	0.606
Age (years)	1.008	0.223
Body Mass Index	1.001	0.894
Systolic blood pressure (mmHg)	1.005	0.194
Diastolic blood pressure (mmHg)	0.999	0.847
Charlson's comorbidity score	1.159	<0.0001	1.100	0.993 to 1.218	0.067
Waist circumference (cm)	1.023	0.005
Systemic global assessment score	0.827	0.032	1.034	0.730 to 1.465	0.851
Malnutrition inflammation score	1.074	0.007	1.105	0.98 to 1.235	0.076
Hemoglobin (g/dL)	0.944	0.256
Albumin	0.946	0.002	0.983	0.926 to 1.043	0.565
Fasting Glucose Level (mmol/L)	1.051	0.033	1.022	0.963 to 1.083	0.476
HbA1C (%)	1.112	0.011	1.057	0.954 to 1.171	0.292
Total Cholesterol (mmol/L)	0.921	0.191
LDL	0.905	0.173
HDL	0.953	0.750
Triglyceride (mmol/L)	1.004	0.931
Dialysate-to-plasma creatinine at 4 h	2.196	0.182
MTAC creatinine (ml/min/1.73m²)	1.023	0.215
Total weekly Kt/V	0.522	0.001	0.677	0.330 to 1.388	0.286
Residual GFR	0.874	0.003	0.995	0.861 to 1.149	0.942
NPNA (g/kg/day)	0.622	0.180
FEBM (kg)	1.014	0.090	1.013	0.984 to 1.042	0.374

MTAC, mass transfer area coefficient; GFR, glomerular filtration rate; NPNA, normalized protein nitrogen appearance; FEBM, fat-free edema-free mass; PWV, pulse-wave velocity; E/I, extra to intra cellular volume ratio; HbA1C, glycated hemoglobin level; LDL, low-density lipoprotein; HDL, high-density lipoprotein; FPG, fasting plasma glucose.

During the follow-up period, 44 patients were converted to hemodialysis, 23 patients received kidney transplants, 7 patients were transferred to other centers, and 1 patient recovered. By univariable Cox regression analysis, there was a statistically significant association between baseline BRI and rate of alive and remained on PD (HR = 1.187, p = 0.003) (Table 2B). The 2-year rate of alive and remained on PD rates for patients with baseline BRI <4, 4 to <5, 5 to <6, and ≥6 were 70.0%, 67.6%, 68.1%, and 50.0%, respectively (Figure 2B). After adjusting for confounding clinical factors by multivariable Cox regression analysis, baseline BRI remained an independent predictor of rate of alive and remained on PD (AHR = 1.279, 95% CI 1.079 to 1.517, p = 0.004).

Hospitalization

In the exploratory cohort, there were 807 hospital admissions for a total of 6024 days. The median numbers of hospital admissions for patients with baseline BRI <4, 4 to <5, 5 to <6, and ≥6 were 0.25, 0.56, 0.71, and 0.77 per year, respectively (Kendall's tau test, p = 0.001) (Figure 3A). Similarly, their durations of hospital stay were 0.91, 3.36, 4.29, and 4.62 days per year, respectively (p = 0.002) (Figure 3B). Post hoc analysis showed that patients in the BRI <4 group had a significantly lower peritonitis rate than the others.

Figure 3.

Relation between baseline body roundness index (BRI) for the exploratory cohort and (A) hospital admission rate; and (B) duration of hospital stay, both adjusted for the duration of follow-up. Patients were categorized into four groups according to their baseline BRI: <4, 4 to <5, 5 to <6, and ≥6. Data were analyzed by Kendall's tau test for the BRI category. Error bars represent the standard error of the mean

During the study period, there were 381 peritonitis episodes in 154 patients; 95 patients (38.2%) were peritonitis-free. The overall peritonitis rate was 0.45 episodes per patient-year follow-up, and the baseline BRI did not have a significant association with the peritonitis-free survival rate. There was a modest but statistically significant association between the peritonitis rate and the baseline BRI (Spearman's r = 0.137, p = 0.030). However, baseline BRI did not have a significant association with peritonitis-free survival rate; the 2-year peritonitis-free survival rates for patients with baseline BRI <4, 4 to <5, 5 to <6, and ≥6 were 72.1%, 63.8%, 51.1%, and 65.2%, respectively (p = 0.783).

Validation cohort

We analyzed a separate validation cohort of 162 new PD patients. Their baseline demographic and clinical information are summarized and compared with the exploratory cohort in Tables 1 and Table S1. For the validation cohort, the average BRI was 5.1 ± 1.6. The correlations between the baseline BRI and other clinical parameters were similar to but slightly different from the exploratory cohort (Table S2).

The median follow-up of the validation cohort was 30.3 (IQR 22.0 to 35.5) months. The association between baseline BRI and survival rates for the validation cohort was summarized in Table S3. With univariable Cox regression analysis, baseline BRI was associated with patient survival, and only marginally with survival and remained on PD. However, after adjusting for other clinical confounders, baseline BRI was not an independent predictor of patient survival or survival and remained on PD in the validation cohort. However, the baseline BRI of the validation cohort was associated with peritonitis-free survival even after adjusting for clinical confounders (AHR = 1.243, 95% CI 1.023 to 1.509, p = 0.028) (Table S2). There was also a marginal but insignificant correlation between baseline BRI and peritonitis rate (p = 0.079) (Table S4).

Discussion

In this study, we explored the clinical significance of the BRI in two cohorts of PD patients. In the exploratory cohort of prevalent PD patients, we found that high BRI level was an independent predictor of lower patient and rate of alive and remained on PD, and was associated with higher hospitalization rates and longer hospital stays. However, in the validation cohort of incident PD patients, the association between BRI and patient and rate of alive and remained on PD was not significant after adjusting for confounding clinical factors, although BRI had a significant association with peritonitis-free survival in this cohort.

Several previous studies also showed that visceral fat was associated with mortality in hemodialysis patients,^18,19 which is consistent with our observation in the exploratory cohort but not the validation one. A possible explanation is that the latter had a smaller sample size (169 patients, as compared to 249 in the exploratory cohort). Although several studies have suggested a U-shaped relationship between BRI and all-cause mortality in the general population,^9,20 neither our cohorts nor previous studies in hemodialysis patients made such an observation. The difference between the general population and dialysis patients may simply be attributed to the smaller sample sizes in studies that involved dialysis patients, but it may also suggest that the association between BRI and survival differs between dialysis patients and the general population.

We found that BRI, but not BMI, was associated with the clinical outcome of PD patients, suggesting that BRI provides extra prognostic information that conventional BMI. Since BRI is derived from simple anthropometric measurements and does not require complicated computation, it is particularly suitable for clinical use and repeated measurement. However, our findings may appear to be different from many previous studies that have investigated the association between BMI and CKD, even though there is a strong association between BRI and BMI (see Table S1). For example, a prior systematic review by Ekart et al. indicated that PD and HD patients with high BMI or obesity generally had better survival outcomes, although these effects might be specific to certain races.²¹ Another meta-analysis also highlighted a negative linear correlation between BMI and mortality, noting that every 1 kg/m² increase in BMI led to a 3% reduction in all-cause mortality among HD patients.²² However, the duration of follow-up might influence the relationship between BMI and mortality rates, as low body weight was linked to higher mortality only in the first year but not in the subsequent 2-, 3-, or 5-year periods.²³ In contrast, other studies have reported inconsistent findings. Research by Liu et al. and Obi et al. suggested a U-shaped relationship between BMI and all-cause mortality, indicating that both obese and underweight individuals had higher mortality risks,^24,25 which aligns with previous studies on BRI.⁹ However, we believe our observation does not necessarily contradict with these previous publications. In essence, BRI is a marker of central obesity or visceral fat, while BMI assesses the overall body built and includes both muscle and adipose tissue mass. Multiple previous studies showed that the amount of visceral fat is independently associated with metabolic disturbances, insulin resistance, and the risk of prolonged hospital stay.^10,26,27

An important point to note is that the findings from the exploratory cohort should be interpreted with caution, as the analysis did not demonstrate the expected association between increased age and mortality. This absence of correlation raises concerns regarding the robustness and generalizability of the results, since age is a well-established prognostic factor in mortality models. Potential explanations may include limited sample size, other hidden confounding variables, or model mis-specification. This unexpted result highlights the need for further validation in larger, well-characterized cohorts.

The potential mechanisms underlying the relationship between central obesity (i.e., higher BRI) and adverse hospitalization outcomes warrant further exploration. It is plausible that increased central adiposity may exacerbate inflammatory processes or entail other metabolic derangements that compromise the patient's health status, resulting in more frequent or prolonged hospital admissions.^28–30 However, we did not observe any association between BRI and the malnutrition inflammation score in the two cohorts that we analyzed. Unfortunately, because of the limitations in the original study design, we do not have data on the serum C-reactive protein levels in our patients.

The relationship between the BRI and the risk of peritonitis is intriguing but not consistent in the two cohorts that we analyzed. In the exploratory cohort, there was a positive and significant correlation between BRI and the peritonitis rate, but no association was found between BRI and peritonitis-free survival rates. In contrast, the validation cohort showed that BRI was independently associated with peritonitis-free survival, but not with the peritonitis rate. This discrepancy might result from the long vintage of PD before BRI was measured in the exploratory cohort, which may somehow allow previous peritonitis episodes to influence subsequent peritonitis risk. Further studies are necessary to answer this question.

This study has several important limitations. First, the single-center design restricts generalisability, as findings may not be applicable to other settings or populations. Moreover, the observational nature of the study introduces a high risk of bias, notably the prevalence-incidence bias,³¹ which may have influenced the results. Specifically, the baseline assessment of the prevalent (i.e., exploratory) cohort was conducted years after the patients were started on dialysis, potentially introducing survival bias that obscures the true relationship between BRI and clinical outcomes.³² We analyzed two independent cohorts of PD patients, but the certainty of our findings is lowered by the inconsistency observed between the exploratory and validation cohorts. The sample sizes are relatively small, which may affect the validity of our findings and increases the risk of imprecision in the estimated effect sizes. Although we adjusted for a number of relevant variables, there remains a risk of residual confounding due to differences in baseline and clinical characteristics between cohorts and potential unmeasured confounders. Moreover, assessment of central obesity by BRI alone may not be entirely reliable, and we do not have other measurements of central obesity or visceral fat for comparison.

In conclusion, our study has underscored the prognostic value of BRI in PD patients, especially in predicting survival and hospitalization. We found that a high baseline BRI is linked to increased rates of all-cause mortality and the rate of hospitalization. However, the results from the exploratory and validation cohorts were somewhat inconsistent, indicating the necessity for further research to validate our findings in larger PD populations.

Supplemental Material

sj-docx-1-ptd-10.1177_08968608261446827 - Supplemental material for Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts

Supplemental material, sj-docx-1-ptd-10.1177_08968608261446827 for Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts by Lixing Xu, Jack Kit-Chung Ng, Gordon Chun-Kau Chan, Winston Wing-Shing Fung, Kai-Ming Chow and Cheuk-Chun Szeto in Peritoneal Dialysis International

Supplemental Material

sj-docx-2-ptd-10.1177_08968608261446827 - Supplemental material for Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts

Supplemental material, sj-docx-2-ptd-10.1177_08968608261446827 for Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts by Lixing Xu, Jack Kit-Chung Ng, Gordon Chun-Kau Chan, Winston Wing-Shing Fung, Kai-Ming Chow and Cheuk-Chun Szeto in Peritoneal Dialysis International

Supplemental Material

sj-pdf-3-ptd-10.1177_08968608261446827 - Supplemental material for Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts

Supplemental material, sj-pdf-3-ptd-10.1177_08968608261446827 for Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts by Lixing Xu, Jack Kit-Chung Ng, Gordon Chun-Kau Chan, Winston Wing-Shing Fung, Kai-Ming Chow and Cheuk-Chun Szeto in Peritoneal Dialysis International

Supplemental Material

sj-pdf-4-ptd-10.1177_08968608261446827 - Supplemental material for Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts

Supplemental material, sj-pdf-4-ptd-10.1177_08968608261446827 for Body roundness Index on the clinical outcome of peritoneal dialysis patients: Analysis of two cohorts by Lixing Xu, Jack Kit-Chung Ng, Gordon Chun-Kau Chan, Winston Wing-Shing Fung, Kai-Ming Chow and Cheuk-Chun Szeto in Peritoneal Dialysis International

Footnotes

ORCID iDs

Winston Wing-Shing Fung

Kai-Ming Chow

Cheuk-Chun Szeto

Human ethics declaration

The study was approved by the Joint Chinese University Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (approval number CRE-2007.366 and CRE-2021.367). All procedures in this study followed the guidelines outlined in the Declaration of Helsinki.

Consent to participate declaration

All patients provided written inform consent for the study.

Consent to publish declaration

Consent to Publish declaration: not applicable

Author contribution

Research idea and study design: LXX, JKCN, CCS; data acquisition: JKCN, WWSF, GCSC; data analysis/interpretation: LXX, JKCN, CCS; statistical analysis: LXX, JKCN, CCS; supervision or mentorship: KMC, CCS; manuscript preparation: LXX, CCS. 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

This study was supported by the Richard Yu Chinese University of Hong Kong (CUHK) PD Research Fund, and CUHK research accounts 6905134, 6906662, and 8601286. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The original data supporting the findings of this study are openly available in Github repository at:

Clinical trial number

Clinical trial number: not applicable.

Supplemental material

Supplemental material for this article is available online.

References

Jung

Jeon

Park

, et al. Better quality of life of peritoneal dialysis compared to hemodialysis over a two-year period after dialysis initiation. Sci Rep 2019; 9: 10266. Epub 20190716. PubMed PMID: 31312004; PubMed Central PMCID: PMC6635359.

Wang

Yin

Liao

, et al. GLIM Criteria for definition of malnutrition in peritoneal dialysis: a new aspect of nutritional assessment. Ren Fail 2024; 46: 2337290. Epub 20240404. PubMed PMID: 38575339; PubMed Central PMCID: PMC10997366.

Lai

Leung

. Inflammation in peritoneal dialysis. Nephron Clin Pract 2010; 116: c11–c18. Epub 20100512. PubMed PMID: 20460934.

Stenvinkel

Chung

Heimburger

, et al. Malnutrition, inflammation, and atherosclerosis in peritoneal dialysis patients. Perit Dial Int 2001; 21: S157–S162. PubMed PMID: 11887812.

L-c

Yen

C-J

Chao

C-T

, et al. Visceral fat area is associated with HbA1c but not dialysate-related glucose load in nondiabetic PD patients. Sci Rep 2015; 5: 12811.

Thomas

Bredlau

Bosy-Westphal

, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring) 2013; 21: 2264–2271. Epub 20130611. PubMed PMID: 23519954; PubMed Central PMCID: PMC3692604.

Zhang

Han

Zheng

, et al. Associations of trajectories in body roundness index with incident cardiovascular disease: a prospective cohort study in rural China. Front Nutr 2024; 11: 1291093. Epub 20240221. PubMed PMID: 38450226; PubMed Central PMCID: PMC10914955.

Rico-Martin

Calderon-Garcia

Sanchez-Rey

, et al. Effectiveness of body roundness index in predicting metabolic syndrome: a systematic review and meta-analysis. Obes Rev 2020; 21: e13023. Epub 20200408. PubMed PMID: 32267621.

Zhang

Lin

, et al. Body roundness Index and all-cause mortality among US adults. JAMA Netw Open 2024; 7: e2415051.. Epub 20240603. PubMed PMID: 38837158; PubMed Central PMCID: PMC11154161.

10.

Szeto

Kwan

Chow

, et al. Metabolic syndrome in peritoneal dialysis patients: choice of diagnostic criteria and prognostic implications. Clin J Am Soc Nephrol 2014; 9: 779–787. Epub 20140123. PubMed PMID: 24458080; PubMed Central PMCID: PMC3974356.

11.

Nitta

. Nontraditional risk factors for cardiovascular disease in patients on peritoneal dialysis. Renal Replacement Therapy 2024; 10: 25.

12.

Zhang

Zeng

, et al. Association between cardiometabolic multimorbidity, body roundness index, and frailty index in Chinese middle-aged and older adults. J Nutr Health Aging 2025; 29: 100445. Epub 20241210. PubMed PMID: 39662154.

13.

Moissl

Wabel

Chamney

, et al. Body fluid volume determination via body composition spectroscopy in health and disease. Physiol Meas 2006; 27: 921–933. Epub 20060725. PubMed PMID: 16868355.

14.

Parmentier

Schirutschke

Schmitt

, et al. Influence of peritoneal dialysis solution on measurements of fluid status by bioimpedance spectroscopy. Int Urol Nephrol 2013; 45: 229–232. Epub 20120619. PubMed PMID: 22710970.

15.

Szeto

Wong

Chow

, et al. Oral sodium bicarbonate for the treatment of metabolic acidosis in peritoneal dialysis patients: a randomized placebo-control trial. J Am Soc Nephrol 2003; 14: 2119–2126. PubMed PMID: 12874466.

16.

van Olden

Krediet

Struijk

, et al. Measurement of residual renal function in patients treated with continuous ambulatory peritoneal dialysis. J Am Soc Nephrol 1996; 7: 745–750. PubMed PMID: 8738810.

17.

Delanghe

Biesen

Velde

, et al. Binding of bromocresol green and bromocresol purple to albumin in hemodialysis patients. Clin Chem Lab Med 2018; 56: 436–440. PubMed PMID: 28985181.

18.

El Said

Mohamed

El Said

, et al. Central obesity and risks of cardiovascular events and mortality in prevalent hemodialysis patients. Int Urol Nephrol 2017; 49: 1251–1260. Epub 20170317. PubMed PMID: 28315007.

19.

Iida

Morimoto

Okuda

, et al. Impact of abdominal fat distribution on mortality and its changes over time in patients undergoing hemodialysis: a prospective cohort study. J Ren Nutr 2023; 33: 575–583. Epub 20230323. PubMed PMID: 36963738.

20.

Zhou

Liu

Huang

, et al. A nonlinear association between body roundness index and all-cause mortality and cardiovascular mortality in general population. Public Health Nutr 2022; 25: 3008–3015. Epub 2022/08/19.

21.

Ekart

Hojs

. Obese and diabetic patients with end-stage renal disease: Peritoneal dialysis or hemodialysis? Eur J Intern Med 2016; 32: –6.. Epub 20160407. PubMed PMID: 27067614.

22.

Ladhani

Craig

Irving

, et al. Obesity and the risk of cardiovascular and all-cause mortality in chronic kidney disease: a systematic review and meta-analysis. Nephrol Dial Transplant 2017; 32: 439–449. PubMed PMID: 27190330.

23.

Ahmadi

Zahmatkesh

Streja

, et al. Association of body mass Index with mortality in peritoneal dialysis patients: a systematic review and meta-analysis. Perit Dial Int 2016; 36: 315–325. Epub 20151016. PubMed PMID: 26475847; PubMed Central PMCID: PMC4881795.

24.

Liu

Zeng

Hong

, et al. The association between body mass index and mortality among Asian peritoneal dialysis patients: a meta-analysis. PLoS One 2017; 12: e0172369.. Epub 20170216. PubMed PMID: 28207885; PubMed Central PMCID: PMC5313204.

25.

Obi

Streja

Mehrotra

, et al. Impact of obesity on modality longevity, residual kidney function, peritonitis, and survival among incident peritoneal dialysis patients. Am J Kidney Dis 2018; 71: 802–813. Epub 20171207. PubMed PMID: 29223620; PubMed Central PMCID: PMC5970950.

26.

Fox

Massaro

Hoffmann

, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the framingham heart study. Circulation 2007; 116: 39–48. Epub 20070618. PubMed PMID: 17576866.

27.

McLaughlin

Lamendola

Liu

, et al. Preferential fat deposition in subcutaneous versus visceral depots is associated with insulin sensitivity. J Clin Endocrinol Metab 2011; 96: E1756–E1760. Epub 20110824. PubMed PMID: 21865361; PubMed Central PMCID: PMC3205890.

28.

Zhou

Zhan

Yuan

, et al. Comparison of visceral, general and central obesity indices in the prediction of metabolic syndrome in maintenance hemodialysis patients. Eat Weight Disord 2020; 25: 727–734. Epub 20190409. PubMed PMID: 30968371.

29.

Tchernof

Despres

. Pathophysiology of human visceral obesity: an update. Physiol Rev 2013; 93: 359–404. PubMed PMID: 23303913.

30.

Stoffel

El-Mallah

Herter-Aeberli

, et al. The effect of central obesity on inflammation, hepcidin, and iron metabolism in young women. Int J Obes (Lond) 2020; 44: 1291–1300. Epub 20200123. PubMed PMID: 31974407.

31.

Tripepi

Jager

Dekker

, et al. Bias in clinical research. Kidney Int 2008 Jan; 73: 148–153. Epub 2007 Oct 31. PMID: 17978812.

32.

Rosansky

Eggers

Jackson

, et al. Survivor bias in early- vs late-start hemodialysis studies—reply. Arch Intern Med 2011; 171: 477–478.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.07 MB

0.08 MB

0.03 MB

0.06 MB

0.00 MB