Markers of protein-energy wasting and physical performance in haemodialysis patients: A cross-sectional study.

BACKGROUND: Physical impairments are common in uraemia, as reflected by the high risk of falls of haemodialysis (HD) patients. Furthermore, these patients often suffer from malnutrition.
OBJECTIVE: Up to now, it is unknown which aspects of physical performance are predominantly driven by malnutrition in HD patients. As this answer could steer different interventions, the aim of this study was to evaluate the cross-sectional relationship between nutritional status, muscle strength, exercise capacity and the risk of falls.
METHODS: This study recruited HD patients between December 2016 and March 2018 from two hospital-based and five satellite dialysis units (registration number on clinicaltrial.gov: NCT03910426). The mini-nutritional assessment scale as well as objective measures of protein-energy wasting were obtained (total iron-binding capacity, total protein levels, and CRP). Physical assessment included muscle strength (quadriceps, handgrip force, and sit-to-stand test), exercise capacity (six-minute walking test) and the risk of falls (Tinetti, FICSIT, and dialysis fall index). Their interrelationship was analysed by ridge regression models.
RESULTS: Out of 113 HD patients (mean age 67 years ± 16.1, 57.5% male) 36.3% were malnourished according to the mini-nutritional assessment scale and a majority had impaired quadriceps force (86.7%), six-minute walking test (92%), and an increased risk of falls (73.5%). Total protein and CRP levels were identified as relevant nutritional factors in the association with physical performance. Nutritional parameters explained 9.2% of the variance in the risk of falls and 7.6% of the variance in exercise capacity. No conclusive association was found between nutritional status and muscle strength.
CONCLUSION: Protein-energy wasting is a determinant of the risk of falls and exercise capacity in patients on HD. The association between malnutrition and muscle weakness remains inconclusive.

Introduction

Physical impairments and haemodialysis (HD) therapy itself are major barriers for physical activity in patients with end-stage kidney disease (ESKD) [1, 2]. Physical inactivity leads to a high risk for cardiovascular (CV) disease and results in a more extensive deterioration of physical performance [3]. Although the manifestation of physical impairments (i.e. muscle weakness, exercise intolerance and an increased risk of falls) in ESKD is heterogeneous, the downward spiral of physical inactivity can be positively influenced by a physical rehabilitation programme [4, 5], whether or not supplemented with nutritional interventions [6, 7]. Unravelling the role of the different players is important as a better understanding of the underlying mechanisms might lead to better exercise training outcomes in patients on HD.

According to the International Society of Renal Nutrition and Metabolism (ISRNM), protein-energy wasting (PEW) is defined as a status of nutritional and metabolic impairments characterized by loss of systemic body protein and energy stores, resulting in a decrease in muscle and fat mass [8]. In uraemia, strong dietary restrictions and low protein intake substantially contribute to this phenotype [9-11]. Although a decrease in muscle mass directly results in muscle weakness in healthy subjects, only a fraction of the variance in muscle strength is explained by muscle mass in patients with ESKD [12]. Moreover, muscle strength rather than muscle mass seems to be affected by the presence of PEW [13]. Additionally, PEW along with impairments in physical performance contribute to a remarkably high prevalence of frailty in patients with ESKD [14]. Nevertheless, to date, the relationship between malnutrition and the different domains of physical performance remains poorly understood [15].

The aim of this study is to examine the cross-sectional relationship between markers and scoring systems for nutritional status and different measures of physical performance in prevalent HD patients. Our hypothesis is that PEW is more closely related to measures that reflect broad daily physical performance (e.g. the risk of falls) compared to analytical measures of physical performance (e.g. quadriceps peak torque), albeit based on the broad impact of PEW.

Materials & methods

Consecutive patients on maintenance HD in two dialysis centres (including two hospital-based and five satellite dialysis units) were screened for eligibility between December 2016 and March 2018. Exclusion criteria were age < 18 years, pregnancy, inadequate motor and verbal responses to verbal commands and questions, and recent (< 6 months) surgical musculoskeletal interventions that could bias physical tests. Patients with physical inabilities (e.g. wheelchair bound or amputations) were given the worst possible score for the tests they failed to complete.

This study is part of a larger study examining the determinants of quality of life and mortality in patients with ESKD (registration number on clinicaltrial.gov: NCT03910426). The study complies with the Declaration of Helsinki, was approved by the local ethics committees (project number Ghent EC B670201525559; 15-OCT-2015 and Antwerp EC B300201422642; 07-DEC-2016), and written informed consent was obtained from all participants.

Anthropometric measures and characteristics

Baseline clinical data and anthropometric measures were obtained from the electronic medical records and the Davies comorbidity score was calculated [16]. Blood pressure was evaluated with a single measurement at the opposite upper arm to the side of the vascular access before the dialysis session prior to the physical performance assessment.

Nutritional status

The use of multiple nutritional markers has been recommended in assessing nutritional status [17]. Nutritional status was quantified both subjectively and objectively using the Mini Nutritional Assessment (MNA) Long Form [18, 19] on the one hand and body mass index (BMI), C-reactive protein (CRP) and biochemical indicators such as total iron binding capacity (TIBC) and serum total protein on the other [20-22]. The MNA was obtained by a study nurse and included 2 sections: a screening section (MNA-short form, /14) which was complemented by a more profound assessment section (/16), resulting in a global score on 30 (MNA-long form). The screening section addresses food intake, weight loss, mobility, neuropsychological problems, BMI, and health status over the last three months, whereas the in-depth assessment section comprises questions related to the place of residence, number of prescribed medications, skin ulcers, eating and drinking behaviour, the subjective appreciation of nutritional status, and mid-arm and calf circumference. To increase statistical power, patients were classified as malnourished or at risk for malnutrition based on the median of the MNA-score of patients scoring ≤ 23.5. Accordingly, patients were identified with malnutrition (≤ 19.5), malnutrition risk (20–23.5) and normal nutritional status (≥ 24) [18]. The term PEW is used when an association included the MNA and at least one objective measure associated with loss of body protein and fuel reserves (e.g. CRP, serum total protein and TIBC) [23].

Physical assessments

All individual physical assessments were done within two days. The sequence of assessments was randomized using opaque envelopes. Muscle strength evaluation was performed before the dialysis session, while exercise capacity and the risk of falls were assessed either before dialysis or on non-dialysis days (in patients’ home setting). A minimum 3-minute pause between tests was respected.

The risk of falls

For assessment of the risk of falls, a combination of physical testing, scoring lists and demographic data was used in a slightly adapted version of the Dialysis Fall Risk Index (DFRI, see S1 Table) [24]. The physical examinations included the following: (1) The Frailty and Injuries Cooperative Studies of Intervention Technique (FICSIT) was used to examine static balance (time) based on seven positional challenges; i.e. eyes open and closed with feet closely together, semi-tandem and full tandem stand and standing on the dominant leg with eyes open [25]; (2) The Tinetti test is considered the gold standard for examining gait dysfunctions based on 7 items: the initiation of gait, step length and height, step symmetry, step continuity, distinguished path, trunk and walking stance [26]. Patients scoring < 11 on 12 on the Tinetti test are considered to be at a higher risk of falls [27]; (3) For the five repetition Sit-to-Stand test (STS), patients were instructed to get from a seated to a standing position for 5 times as rapidly as possible with their arms folded across the chest [28]. A cut-off value of 15 seconds is associated with an increased risk of falls [29] and patients unable to perform the test were scored as > 50 seconds. With regard to the original DFRI, the following adaptations were made: (1) a 2.9 mg/dl instead of 1.0 mg/dl cut-off point for CRP; (2) MNA indicator scores were used as an alternative for the Geriatric Nutritional Risk Index [30, 31]; (3) six-minute walking test (6MWT) replaced the ‘4 meter time to walk’ test and (4) the ‘inquiry about fall’ section was replaced by the Tinetti test [32]. The mean arterial pressure was calculated by diastolic blood pressure + 1/3(systolic blood pressure—diastolic blood pressure).

Muscle strength

A handheld dynamometer (Microfet; Biometrics, Almere, the Netherlands) was used to evaluate quadriceps isometric peak torque during 5 seconds in a seated position with knees and hips 90° flexed (intraclass correlation coefficient (ICC) of 0.94) [33, 34]. Manual resistance with fixation of the dynamometer to the anterior tibia of the dominant leg just proximal to the malleoli was applied. Handgrip force was measured using a JAMAR Hydraulic Hand Dynamometer according to the American Society of Hand Therapists guidelines (ICC ≥ 0.93) [35]. Patients were seated with their elbow 90° flexed next to their body, wrist in neutral position and were asked to perform a maximal isometric contraction for 5 seconds [36]. The contralateral arm with regard to the vascular access was used.

Both quadriceps and handgrip force were carried out in triplicate; the best result was expressed as absolute value and as percentage of the predicted value based on age and gender [33, 37]. The lower limit of normal for the quadriceps and handgrip force was set on 80% of the predicted value.

Exercise capacity

The 6MWT was performed following the American Thoracic Society guidelines (ICC = 0.90) [38]. Patients were instructed to walk as fast as possible for 6 minutes, walking aids were allowed and recorded. Results were expressed as absolute value and as percentage to the predicted value, based on Duncan’s equation for adults aged between 50–85 years [39]. The lower limit of normal was set on 80% of the predicted value. Moreover, a 300m cut-off point was used for dichotomisation, as this indicates a worse prognosis and higher mortality in populations comparable to ESKD [40, 41]. Patients unable to perform the tests were scored as 0 meters.

Statistical analysis

IBM Statistical Package for the Social Sciences version 24 (SPSS 24) and R were used for the statistical analyses. Variables are reported as mean ± standard deviation (SD), median and interquartile range [25th; 75th percentage] or as number and percentage when appropriate. Data between groups were compared by using univariate analysis of variance or the Kruskal-Wallis as the nonparametric equivalent. Post hoc comparisons were corrected using the Scheffe’s or pairwise comparison (Mann-Whitney U) test for parametric and nonparametric data respectively. A ridge regression method was used to examine the association between nutritional status and measures of muscle strength, exercise capacity and the risk of falls. By using L2 (ridge) regularization techniques, univariate associations can be examined between a dependent variable (e.g. a measure of physical performance) and a matrix of potentially collinear independent variables (i.e. nutritional status by subjective and objective measures) without overestimating the association. The collinearity penalty allows us to find independent associations that indicate the involvement of different aspects of malnutrition in impaired physical performance. The ridge regression was performed using the lmridge package in R and the general cross-validation method was used to estimate the optimal k [42]. Nutritional measures included in the DFRI (i.e. MNA and CRP) were excluded in the analysis of this index. The spearman rank correlation coefficient was used in correlative analysis. Patients with missing data on primary and secondary outcome measures were excluded from the final analysis.

Results

Demographics

122 patients were enrolled in this study and 9 were excluded based on missing data on questionnaires (n = 6) and on measures of physical performance (n = 3). Accordingly, 113 patients were included in this study. Patient characteristics and outcome measures did not differ between the excluded patients and the study cohort. The study population (age 67 ± 16.1 years, 57.5% male and a range of dialysis vintage between 1 and 191 months) was representative for a cohort of HD patients (Table 1). Impaired quadriceps force was prevalent in the total cohort and the average score was 53.8% of the predicted value. In line, muscle performance of the lower limbs (assessed by STS) was decreased, with 61% of patients scoring above the upper limit of normal and indicating an increased risk of falls. Muscle weakness was less pronounced in the upper limbs. In general, this cohort had an increased risk of falls (by DFRI, 73.5%) and exercise intolerance (by 6MWT, 68.1%). Indeed, the majority of patients were classified as having a bad prognosis based on a physical surrogate measure (6MWT < 300m). Furthermore, these patients were older, more likely to be female, had lower BMI, higher CRP, a higher number of prescribed medications, and scored worse on all other domains of physical performance (Table 1). No differences in measures of physical function were found between patients with and without diabetes (S2 Table).

Table 1

Patient characteristics according to prognosis based on a physical surrogate.

Variable	Total (n = 113)	Good prognosis (6MWT > 300m, n = 46)	Bad prognosis (6MWT < 300m, n = 67)	p value
Demographics
Age (years)	67.5 ± 16.1	58.2 ± 18.0	73.8 ± 10.9	<0.001
Sex (male)	65 (57.5)	32 (69.6)	33 (49.3)	0.003
BMI (kg/m2)	26.1 ± 5.4	25.2 ± 4.7	26.7 ± 5.8	0.135
BMI <18	3 (2.7)	1 (2.2)	2 (3.0)
BMI 18–24	51 (45.1)	25 (54.3)	26 (38.8)
BMI 25–29	35 (31.0)	14 (30.4)	21 (31.3)
BMI ≥30	23 (20.5)	6 (13.1)	18 (26.9)
DBP (mmHg)	65.4 ± 16.0	71.2 ± 18.5	62.8 ± 14.1	0.023
SBP (mmHg)	138.2 ± 21.6	142.1 ± 21.1	136.5 ± 21.8	0.208
ΔMAP (mmHg)	0.17 ± 15.5	-0.12 ± 15.5	0.36 ± 15.6	0.873
MAP pre-dialysis (mmHg)	89.7 ± 15.3	92.0 ± 16.7	88.2 ± 14.2	0.223
MAP post-dialysis (mmHg)	89.3 ± 17.8	90.8 ± 17.0	88.3 ± 18.3	0.474
Dialysis vintage (months)	22.5 [10.3; 49.8]	20.5 [9.8; 35.3]	25.0 [10.8; 64.3]	0.169
Dialysis vintage <24	57 (50.5)	26 (56.5)	32 (47.8)
Dialysis vintage 24–47	26 (23.0)	12 (26.1)	13 (19.4)
Dialysis vintage ≥48	30 (26.5)	8 (17.4)	22 (32.8)
Number of prescribed medications (n)	13.6 ± 3.7	12.6 ± 3.9	14.3 ± 3.5	0.016
Aetiology of CKD (n)				0.062
Diabetic nephropathy	30 (26.5)	6 (13.0)	24 (35.8)
Hypertension or angiosclerosis	40 (35.4)	16 (34.8)	24 (35.8)
ADPKD	6 (5.4)	4 (8.7)	2 (3.0)
Other	37 (32.7)	20 (43.5)	17 (25.4)
Davies comorbidity score (0–7)	2 [1; 3]	2 [2; 3]	1 [0; 2]	<0.001
Ethnicity				0.456
Black	3 (2.7)	3 (6.5)	0 (0.0)
White	110 (97.3)	43 (93.5)	67 (100.0)
Other	0 (0.0)	0 (0.0)	0 (0.0)
Nutritional and physical assessments
Quadriceps force (N)	180 ± 75	222.6 ± 78.8	136.7 ± 65.1	<0.001
Relative value (% to predicted)	53.8 ± 17.8	56.4 ± 18.0	48.4 ± 22.0	0.035
Patients with pathological value (n)	98 (86.7)	40 (87.0)	58 (86.6)	0.895
Handgrip force (kg)	28.8 ± 11.1	36.1 ± 10.0	23.7 ± 8.8	<0.001
Relative value (% to predicted)	91.7 ± 30.7	94.7 ± 20.6	90.6 ± 36.1	0.439
Patients with pathological value (n)	39 (34.5)	9 (19.6)	30 (44.8)	0.060
DFRI (/12)	5.9 ± 3.0	3.1 ± 2.2	7.8 ± 1.8	<0.001
Patients at increased risk of falls (n)	83 (73.5)	17 (37.0)	66 (98.5)	<0.001
Tinetti (/12)	11.0 [5.5; 12.0]	12.0 [12.0; 12.0]	7.0 [0.0; 10.0]	<0.001
Patients at increased risk of falls (n)	55 (48.7)	2 (4.3)	53 (79.1)	<0.001
Sit-to-Stand (s)	23.0 [12.0; 50.0]	12.0 [10.0; 15.3]	50.0 [23.0; 50.0]	<0.001
Patients at increased risk of falls (n)	78 (69.0)	14 (30.4)	64 (95.5)	<0.001
FICSIT	15.0 [8.0; 21.0]	22.0 [16.0; 26.0]	10.0 [2.0; 15.0]	<0.001
6MWT (meters)	236 [66.5; 396.5]	455 [400.0; 514.8]	130 [0.0; 239.0]	<0.001
Relative value (% to predicted)	44.1 [12.7; 60.3]	67.8 [59.2; 79.7]	24.4 [0.0; 46.2]	<0.001
				<0.001
Patients with pathological value (n)	104 (92.0)	37 (80.4)	67 (100.0)
Patients scoring <300m (n)	77 (68.1)	0 (0.0)	67 (100.0)
Mini-nutritional assessment	20.7 ± 2.9	21.1 ± 3.1	20.4 ± 2.7	0.262
Normal nutritional status	18 (15.9)	9 (19.6)	9 (13.4)
At risk of malnutrition	54 (47.8)	21 (45.7)	33 (49.3)
Malnourished	41 (36.3)	16 (34.8)	25 (37.3)
C-reactive protein (mg/L)	4.3 [2.7; 10.0]	2.9 [1.4; 6.7]	5.7 [2.9; 10.8]	0.003
Total iron-binding capacity (μg/dL)	240.4 ± 76.6	238.8 ± 77.6	241.5 ± 76.6	0.859
Serum total protein (g/L)	65.2 ± 6.1	64.7 ± 5.4	65.5 ± 6.6	0.508

Data are reported as mean ± standard deviation, median [25%; 75%] or as number (percentage) as appropriate; patients were allocated to a good or poor functional prognosis groups based on 6MWT; p-values from ANOVA were reported for normal distributed parameters, otherwise they were reported from the Kruskal-Wallis test. Abbreviations: ADPKD, autosomal dominant polycystic kidney disease; BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; DFRI, dialysis fall risk index; SBP, systolic blood pressure; Δ, difference pre- to post-dialytic blood pressure.

Only a minority of participants was rated as well-nourished, whereas 47.8% and 36.3% were identified as being at risk for malnutrition and malnourished respectively. Of note, MNA scores did not significantly differ between patients with a good and a bad prognosis based on the 6MWT.

Determinants of physical performance to malnutrition

Table 2 shows the characteristics of the study population according to nutritional status based on MNA. No differences were found for muscle strength between the three groups of nutritional status. Patients identified with normal nutritional well-being noted a lower risk of falls compared to those without. Also, malnourished participants had lower exercise capacity than expected for their age and gender compared to the participants at risk for malnutrition, as depicted in Fig 1.

Table 2

Patient characteristics according to the nutritional status.

Variable	Normal nutritional status (n = 18)	Impaired nutritional status (n = 95)	p	Impaired nutritional status		p (3 groups)
				At risk of malnutrition (n = 54)	malnutrition (n = 41)
Age (years)	70.2 ± 10.3	67.0 ± 17.0	0.93	69.9 ± 13.7	63.1 ± 20.0	0.351
Male sex (%)	11 (61.1)	54 (57.0)	0.74	32 (59.3)	22 (53.7)	0.814
BMI (kg/m2)	30.1 ± 4.6	25.3 ± 5.2	<0.001	27.2 ± 4.9	22.9 ± 4.6	<0.001 a
CRP (mg/L)	3.0 [2.9; 8.8]	4.7 [2.6; 10.5]	0.39	5.4 [2.4; 11.0]	2.7 [2.9; 9.2]	0.569
TIBC (μg/dL)	215.6 ± 65.0	245.2 ± 78.1	0.10	251.8 ± 79.8	236.4 ± 75.9	0.209
Total protein (g/L)	65.0 ± 5.9	65.3 ± 6.2	0.86	67.1 ± 5.3	62.7 ± 6.5	0.002 b
Dialysis vintage (months)	31.5 [10.5; 71.0]	21.0 [10.0; 49.0]	0.26	20.0 [10.5; 49;5]	23.0 [9.0; 42.5]	0.488
Number of prescribed medications (n)	12.1 ± 4.0	13.9 ± 3.6	0.09	13.5 ± 3.9	14.3 ± 3.1	0.118
Davies comorbidity score (0–7)	2 [1; 3]	2 [1; 3]	0.89	2 [1; 3]	2 [0.5; 3]	0.529
Quadriceps force (N)	183.9 ± 81.5	178.7 ± 74.4	0.88	193.6 ± 69.3	157.4 ± 77.1	0.060
Quadriceps force (%)	50.2 ± 20.7	54.5 ± 17.2	0.35	57.8 ± 15.1	49.6 ± 19.0	0.063
Handgrip force (kg)	32.3 ± 9.6	28.1 ± 11.3	0.08	29.7 ± 10.8	26.0 ± 11.6	0.076
Handgrip force (%)	97.4 ± 25.7	90.6 ± 31.6	0.19	93.8 ± 28.4	86.4 ± 35.2	0.163
DFRI (/12)	4.6 ± 3.0	6.1 ± 3.0	0.042	6.1 ± 2.7	6.2 ± 3.4	0.099
Tinetti (/12)	11.0 [8.5; 12.0]	10.0 [5.0; 12.0]	0.20	10.0 [5.8; 12.0]	10.0 [2.0; 12.0]	0.455
FICSIT (/28)	17 [14; 23]	15 [6; 21]	0.11	15 [8; 20]	14 [3; 22]	0.258
Sit-to-Stand (s)	17 [11; 29]	30 [12; 50]	0.08	23 [12; 50]	50 [12; 50]	0.116
6MWT (m)	290 [201; 367]	220 [0; 400]	0.23	239 [98; 405]	144 [0; 389]	0.249
6MWT (%)	49.3 [40.8; 70.7]	41.8 [0.0; 59.3]	0.05	44.6 [19.1; 66.3]	29.0 [0.0; 55.7]	0.021 b

Data are reported as mean ± standard deviation; p-values from ANOVA were reported for normal distributed parameters, otherwise they were reported from the Kruskal-Wallis test. Abbreviations: 6MWT, six-minute walking test; BMI, body mass index; DFRI, dialysis fall risk index; CRP, C-reactive protein; TIBC, total iron binding capacity.

a p<0.05 patients without or at risk for malnutrition vs. malnourished patients.

b p<0.05 at risk for malnutrition vs. malnourished patients.

Fig 1

Exercise capacity between groups of nutritional status.

Boxplots of the relative 6MWT for patients identified with normal nutritional status (MNA ≥ 24), at risk for malnutrition (MNA 20–23.5) and with malnutrition (MNA ≤ 19.5).

A ridge regression analysis of the measures of nutritional status to the different domains of physical performance is demonstrated in Table 3. Nutritional status explained 9.2% of the variance in the risk of falls, as assessed by Tinetti. No associations were found with muscle strength and exercise capacity. However, a tendency towards an association was observed between nutritional status and exercise capacity, as assessed by 6MWT (R2 = 0.05). A more detailed analysis showed the involvement of total protein levels and CRP in the association with the risk of falls and exercise capacity respectively (see S3–S5 Tables). Note that the association with the risk of falls was independent of age, gender and level of comorbidity.

Table 3

Association between measures of nutritional status and physical performance.

Outcome	Model 1		Model 2 (Model 1 + Age + Gender)		Model 3 (Model 2 + Comorbidity index)
	R2	p value	R2	p value	R2	p value
Quadriceps strength (N)	0.047	0.145	0.374	< 0.001a	0.394	< 0.001a
Handgrip strength (kg)	0.018	0.311	0.526	< 0.001a	0.544	< 0.001a
Tinetti (/12)	0.092	0.011b	0.190	< 0.001b	0.196	< 0.001b
FICSIT (/28)	0.033	0.169	0.241	< 0.001a	0.245	< 0.001a
Sit-to-Stand (s)	0.061	0.046a	0.254	< 0.001a	0.279	< 0.001a
DFRI (/12)	0.010	0.301	0.034	< 0.118	0.231	< 0.001
6MWT (m)	0.050	0.079b	0.325	< 0.001a	0.354	< 0.001a

The following variables were introduced in ridge regression model 1: mini-nutritional assessment scale, total protein levels, total iron-binding capacity, C-reactive protein and body mass index; age and sex were added to model 1, resulting in Model 2

a ≥ 1 measure of nutritional status contributed to the overall effect size

b ≥ 1 objective measure of protein-energy wasting contributed to the overall effect size, albeit in addition to the mini-nutritional assessment scale (p < 0.05). Abbreviations: 6MWT, six-minute walking test; DFRI: dialysis fall risk index.

Muscle strength (R2 = 0.07) and exercise capacity (R2 = 0.08) as expected for the patients’ age and gender were associated with nutritional status (Table 4). Remarkably, apart from MNA, BMI and CRP contributed to these associations respectively (see S6 and S7 Tables).

Table 4

Association between measures of nutritional status and physical performance as expected for the patients’ age, gender and height.

Outcome	Model 1		Model 2
	R2	p value	R2	p value
Quadriceps strength (%)	0.066	0.034b	0.068	0.044b
Handgrip strength (%)	0.009	0.487	0.009	0.540
6MWT (%)	0.076	0.024b	0.130	0.003a

The following variables were introduced in the ridge regression model 1: mini-nutritional assessment scale, total protein levels, total iron-binding capacity, C-reactive protein and body mass index; The davies comorbidity score was added to model 1 in model 2

a ≥ 1 measure of nutritional status contributed to the overall effect size

b ≥ 1 objective measure of protein-energy wasting contributed to the overall effect size, albeit in addition to the mini-nutritional assessment scale (p < 0.05). Abbreviations: 6MWT, six-minute walking test.

Discussion

The present study explored the impact of nutritional status on physical performance in prevalent HD patients (67.5 years ± 16.1). As expected, the prevalence of impaired nutritional status and physical impairments in the studied population was strikingly high. PEW, represented by total protein levels and MNA, explained 9.2% of the variance in gait quality as assessed by Tinetti. Measures of inflammatory as well as nutritional status were associated with functional exercise capacity and prognosis based on a surrogate measure. Finally, although a relationship between impaired nutritional status and muscle weakness could not be confirmed, BMI was identified as an important contributor to quadriceps strength. Despite comparable muscle strength, patients with impaired nutritional status had lower exercise capacity than well-nourished patients.

Whereas various definitions of PEW are available in scientific literature, they all disclose the extraction from energy stored in proteins, such as in the muscles, as compensation for an insufficient energy intake [43]. Consecutively, this whole body protein imbalance results in a reduced turnover of contractile proteins, muscle mass and force generating capacity, leading to physical impairments such as muscle weakness [44, 45]. In this study, the MNA long form is used which is a reliable tool in the screening, differentiation and diagnosis of PEW in patients with ESKD [46, 47]; moreover, in line with recent recommendations, objective measures of nutritional status are quantified as well [48]. Different measures that derive from different physiological systems enable us to differentiate between the pathways of malnutrition and PEW to physical impairments; such as markers of protein balance and iron homeostasis that are associated with frailty and anaemia, respectively, and markers of inflammation which are involved in the malnutrition-inflammation-atherosclerosis (MIA) syndrome [48-50].

Serum protein levels are prognostic factors related to frailty in patients with ESKD [50]. Moreover, a negative protein balance as well as malnutrition contribute to a frailty prevalence of > 60% in patients with ESKD [51, 52]. Frailty is defined as a decline in physical resilience to stressors, resulting in a substantially decreased ability to cope with illness and in general health deterioration. Consequently, frailty is associated with an increased risk of adverse outcomes such as unintentional weight loss, functional degradation, delirium, slow walking speed, and an increased risk of falls [53-55]. In line with this definition, the present study reports an association of PEW (by MNA and total protein levels) with the risk of falls in ESKD patients. Hence, PEW and frailty may promote an already increased risk of falls, which will contribute to a high risk for hospitalization, disability and death in patients on HD [56, 57]. Interestingly, an increased risk of falls as well as malnutrition are well-established treatable aspects of frailty in patients with chronic kidney disease [52]. Furthermore, the multidisciplinary management of frailty, including exercise and nutritional interventions is effective in frail elderly adults and is recommended by the European Renal Best Practice working group [58, 59]. However, to our knowledge, it has not yet been examined whether fall prevention can be improved by improving nutritional status in patients with ESKD.

Persistent low-grade inflammation is a well-established component in the ESKD phenotype for PEW and CV disease [60]. In particular, an increase in pro-inflammatory and catabolic agents due to the upregulation of the immune system results in a high energy consumption and negative protein balance [61, 62]. Additionally, these inflammatory markers indicate endothelial dysfunction and vascular remodelling, resulting in a high risk for CV disease [63]. As a consequence to the convergent input of inflammation in the pathophysiology of PEW and CV disease, the term malnutrition-inflammation-atherosclerosis syndrome was established in patients with ESKD [64]. Remarkably, a prevalence of MIA of 53.9% is reported in patients on HD [65]. Consistent with CV impairments being the cornerstone of MIA, the finding that PEW (by MNA and CRP) associates with impairments in cardiorespiratory function, assessed via the 6MWT, is not unexpected. Furthermore, a correlation between inflammation and exercise capacity but not muscle strength, suggests that inflammation will affect physical performance by other mechanisms, for example endothelial dysfunction, rather than muscle wasting [63, 66]. Notwithstanding MIA, inflammation is associated with risk factors for falls in HD patients as well, such as frailty, autonomic and peripheral neuropathy, and hypotensive episodes [24, 67].

Interestingly, despite differences in age, BMI and CRP levels between patients with and without poor prognosis based on a physical surrogate, the present study shows that especially relative measures of exercise capacity are associated with malnutrition and inflammation. The Duncan’s equation for expected 6MWT distance enables us to analyse exercise capacity in a model controlled for age, gender and BMI. Accordingly, our results indicate that the impact of inflammation and, by extension, MIA, on exercise capacity is different for HD patients of different age, gender and BMI. Based on the equation, we hypothesise that MIA has a greater absolute impact on exercise capacity in younger male subjects with a low BMI compared to female elderly with a high BMI, for a similar relative impairment in exercise capacity.

Although the concept of muscle wasting is included in the definition of PEW, the present study does not strongly confirm this association as similar muscle strength values are noted between groups differing in nutritional status. This finding is in agreement with a study on 330 HD patients reporting that merely 23% of the variance in muscle weakness can be explained by muscle mass [13]. Hence, we posit that the physical screening and rehabilitation of HD patients should focus on functional measures of physical performance rather than only on improving muscle strength.

Ridge regression methods can be used to examine the true global effect of collinear and synergistic determinants (e.g. different measures of nutritional status) on a dependent variable. Borne in mind the high prevalence of frailty in patients with ESKD [51, 52], these true associations between measures of nutritional status and measures of physical performance were lower than expected, albeit nutritional status explained merely 10% of physical performance.

Limitations

This study has some limitations. First, the cross-sectional nature of this study is a limitation on itself due to the inability to discern temporal relationships and directionality of associations. Second, the original MNA classification was disregarded and an arbitrary cut-off point between the categories of an increased risk for malnutrition and malnutrition was used. Nevertheless, we countered this limitation by focussing the results on the MNA global assessment score. Third, some recommended objective measures of nutritional status could not be obtained due to practical reasons, such as serum albumin and high-sensitive CRP. Hence, conclusions may be biased or missed in the present study. Fourth, the analysis of nutritional status could have benefited from body composition assessments, such as DEXA. Fifth, although various risk of falls assessment tools were included, no history of actual falls was obtained and a few adjustments to the original DFRI were required [24]. Final, as the atherosclerosis component of MIA was not assessed, the involvement of MIA in the association between PEW and physical performance is merely a valid hypothesis.

Nevertheless, this study has several strengths as well. First, a comprehensive examination of physical performance was performed, including a recently developed risk of falls assessment tool tailored to HD patients [24]. Second, objective measures of malnutrition were assessed, which enables us to perform analysis between domains of physical performance and nutritional status. Third, a model that penalizes for multicollinearity and multiple comparisons was used. Fourth and Final, a low threshold for eligibility improved the generalisability of our results to the majority of patients on HD.

Conclusions and guidelines for further research

In conclusion, this study shows an association between protein-energy wasting and exercise intolerance and gait quality, to which especially measures of the malnutrition-inflammation-atherosclerosis syndrome and frailty contribute to, respectively. In contrast, malnutrition is not associated with muscle strength in patients on haemodialysis. Future research should aim (1) to further elucidate the relationship between malnutrition and the risk of falls, focussing especially on gait quality, and (2) to assess the effects of interdisciplinary care on nutritional status and physical performance.

The main clinical implication is that nutritionists and physical therapists should collaborate in the rehabilitation of patients on haemodialysis based on the frequently occurring co-existence of exercise intolerance and malnutrition.

Dialysis Fall Risk Index.

(DOCX)

Click here for additional data file.

Patient characteristics according to diabetes.

(DOCX)

Click here for additional data file.

Detailed association between relevant associations of nutritional measures and domains of physical performance.

(DOCX)

Click here for additional data file.

Detailed association of relevant associations controlled for age and gender.

(DOCX)

Click here for additional data file.

Detailed association of relevant associations controlled for age, gender and the Davies comorbidities score.

(DOCX)

Click here for additional data file.

Detailed association between measures of nutritional status and physical performance as expected for the patients’ age and gender.

(DOCX)

Click here for additional data file.

Detailed association controlled for the Davies comorbidities score.

(DOCX)

Click here for additional data file.

Anonymized dataset.

(XLSX)

Click here for additional data file.

16 Apr 2020

PONE-D-20-01868

Nutritional status and physical performance in haemodialysis patients: a cross-sectional study

PLOS ONE

Dear Dr Van Craenenbroeck,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please considering if the recommendations from the manuscript peer reviewers would strengthen your manuscript.

We would appreciate receiving your revised manuscript by May 31 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Melissa M Markofski

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

3. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Wyngaert et al have submitted a well-written manuscript correlating physical performance with nutritional status in the hemodialysis population.

Line 65: Should the word “relation” actually be “relationship”

Line 76: Unclear what the difference is between high vs low care dialysis units?

Line 103/201/208/229: The use of “drug use” is unclear. Are these illicit drugs? Or medications?

Seems like most of the diabetic patients are in the “bad prognosis, 6MWT < 30m” group. Would this influence your results?

Reviewer #2: The authors performed a cross-sectional analysis comparing markers of physical capacity and nutrition to identify relationships in 113 chronic hemodialysis patients. My concerns about the study are the following:

1. So many cross-sectional analyses are performed that the results are confusing. This is compounded by the fact that the few statistically significant relationships were not adjusted for multiple comparisons.

2. The definition of nutrition (using the mini nutritional status and objective markers) used is flawed. Admittedly, differentiating pure malnutrition from underlying inflammatory processes is difficult, especially in dialysis patients. But it’s not clear to me that C-reactive protein or TIBC or serum total protein are sensitive markers of (under)nutrition. As for BMI, the majority of the study subjects had a BMI in the overweight or obese range. Does this qualify as malnutrition (though it’s actually overnutrition)? Overall the study fails to differentiate inflammation from nutrition.

3. It is unclear how the findings advance in any way our understanding of how malnutrition influences physical health in dialysis patients.

Reviewer #3: Well thought of study. Many limitations especially that one can not have a spacial association but acceptable strengths. It may be interesting to know if there were any difference in diabetics as compared to non diabetics

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Professor Aasim Ahmad

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

6 Jun 2020

REVIEWER #1

Vanden Wyngaert et al., have submitted a well-written manuscript correlating physical performance with nutritional status in the hemodialysis population.

We thank the reviewer for his/her constructive criticism and interest in our work.

1) Line 65: Should the word “relation” actually be “relationship”

Indeed, the use of the terms relation and relationship have been used interchangeably in the manuscript. To be consistent throughout the manuscript, we changed all relation into relationship.

The following sentences were adapted in the revised version of the manuscript:

Introduction, page 4, lines 66-67: “Nevertheless, to date, the relationship between malnutrition and the different domains of physical performance remains poorly understood [15].”

Conclusions and guidelines for further research, page 19, lines 385-387: “Future research should aim (1) to further elucidate the relationship between malnutrition and the risk of falls, focussing especially on gait quality, and (2) to assess the effects of interdisciplinary care on nutritional status and physical performance.”

2) Line 76: Unclear what the difference is between high vs low care dialysis units?

The indications “low care dialysis” and “high care dialysis” are typically used in Belgium to indicate “satellite unit” and “hospital-based dialysis unit”. We have re-coded this paragraph accordingly. The main intention was to indicate we have included in our cohort a representative mix of patients for the Belgian dialysis landscape.

The following sentences were adapted in the revised version of the manuscript:

Abstract, page 1, lines 24-26: “This study recruited HD patients between December 2016 and March 2018 from two hospital-based and five satellite dialysis units (registration number on clinicaltrial.gov: NCT03910426).”

Materials and methods, page 5, lines 76-78: “Consecutive patients on maintenance HD in two dialysis centres (including two hospital-based and five satellite dialysis units) were screened for eligibility between December 2016 and March 2018.”

3) Line 103/201/208/229: The use of “drug use” is unclear. Are these illicit drugs? Or medications?

We agree with the reviewer that the use of ‘drug use’ has different meanings in different contexts. Again, to improve the transparency of our manuscript, we adjusted ‘drug use’ by ‘number of prescribed medications’.

The following information was changed in the revised version of the manuscript:

Materials and methods, page 6, lines 104-108: “The screening section addresses food intake, weight loss, mobility, neuropsychological problems, BMI, and health status over the last three months, whereas the in-depth assessment section comprises questions related to the place of residence, number of prescribed medications, skin ulcers, eating and drinking behaviour, the subjective appreciation of nutritional status, and mid-arm and calf circumference.”

Results, page 10, lines 204-206: “Furthermore, these patients were older, more likely to be female, had lower BMI, higher CRP, a higher number of prescribed medications, and scored worse on all other domains of physical performance (Table 1).”

Results, page 10, line 214: “Table 1. Patient characteristics according to prognosis based on a physical surrogate.”

Variable Total

(n=113) Good prognosis (6MWT > 300m,

n=46) Bad prognosis

(6MWT < 300m,

n=67) p value

Number of prescribed medications (n) 13.6 ± 3.7 12.6 ± 3.9 14.3 ± 3.5 0.016

Results, page 12, line 235: “Table 2. Patient characteristics according to nutritional status.”

Variable Normal nutritional status

(n=18) Impaired nutritional status (n=95) p Impaired nutritional status p

(3 groups)

At risk of malnutrition (n=54) malnutrition (n=41)

Number of prescribed medications (n) 12.1 ± 4.0 13.9 ± 3.6 0.09 13.5 ± 3.9 14.3 ± 3.1 0.118

4) Seems like most of the diabetic patients are in the “bad prognosis, 6MWT < 300m” group. Would this influence your results?

Indeed, the majority of the patients diagnosed with diabetic nephropathy (i.e. 24 out of 30 patients, 80%) were included in the bad prognosis group. Whether diabetes alone influences the prognosis based on a physical measure is unclear but most likely. Disease burden in type 2 diabetes is high as it is associated with an unhealthy lifestyle, physical inactivity and several comorbidities such as peripheral vascular disease, diabetic neuropathy and musculoskeletal complications such as diabetic foot (Fritschi C., Early declines in physical function among aging adults with type 2 diabetes. Journal of Diabetes and its Complications. 2017). Physical inactivity and comorbid diseases have been associated with impaired physical function in patients with diabetes (Hamasaki H. Daily physical activity and type 2 diabetes: A review. World journal of diabetes. (2016)). Hence, it does not surprise that the proportion of patients with diabetes is higher in the bad prognosis group compared to the good prognosis group based on the 6MWT.

Based on the reviewers’ suggestion, we performed a between-groups analysis of patients with and without diabetes. The analysis shows a higher age, BMI, CRP concentration and morbidity index in patients with diabetes compared to those without, but no significant differences in measures of physical function were found.

The following information was added to the revised version of the manuscript:

Results, page 10, lines 206-208: “No differences in measures of physical function were found between patients with and without diabetes (Table S2).”

Supplementary files, S2 Table: “S2 Table. Patient characteristics according to diabetes.”

Variable Patients with diabetes (n=52) Patients without diabetes (n=61) p

Age (years) 71 ± 14 63 ± 17 <0.01

BMI (kg/m2) 28 ± 4 24 ± 5 <0.01

CRP (mg/L) 8 ± 7 5 ± 6 0.01

TIBC (µg/dL) 236 ± 77 243 ± 76 0.65

Total protein (g/L) 65 ± 6 64 ± 6 0.48

Dialysis vintage (months) 35 ± 33 36 ± 40 0.86

Number of prescribed medications (n) 14 ± 3 13 ± 4 0.53

Davies comorbidity score (0-7) 2.5 ± 1.1 1.5 ± 1.4 <0.01

Quadriceps force (N) 159 ± 62 182 ± 95 0.13

Quadriceps force (%) 51 ± 19 52 ± 22 0.66

Handgrip force (kg) 27 ± 10 30 ± 12 0.60

Handgrip force (%) 90 ± 28 95 ± 33 0.38

DFRI (/12) 6 ± 3 5 ± 3 0.08

Tinetti (/12) 8 ± 4.2 8 ± 4.7 0.66

FICSIT (/28) 13 ± 7 15 ± 10 0.38

Sit-to-Stand (s) 32 ± 17 27 ± 18 0.12

6MWT (m) 205 ± 164 272 ± 209 0.07

6MWT (%) 36 ± 28 43 ± 31 0.20

REVIEWER #2

The authors performed a cross-sectional analysis comparing markers of physical capacity and nutrition to identify relationships in 113 chronic hemodialysis patients. My concerns about the study are the following:

We thank the reviewer for his/her well-taken observations and appreciate the suggestion to substantiate the methodology of this study.

1) So many cross-sectional analyses are performed that the results are confusing. This is compounded by the fact that the few statistically significant relationships were not adjusted for multiple comparisons.

We agree with the reviewer that many cross-sectional studies have been performed and only few have corrected for multiple comparisons. It is indeed important to solely report statistical models that are reliable and correct. Overshooting and overfitting a statistical analysis will result in false positive associations and, consequently, may have a major (adverse) impact on future studies and the standard care of patients. For this reason, we are thrilled to have the opportunity to highlight and explain the statistical background and methods used in this study.

First, the authors chose to use a ridge regression method based on its ability to penalize multicollinearity. Ridge regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression models penalize variables with high multicollinearity. A shrinkage estimator (k) is used and produces new estimators that are shrunk closer to the “true” population parameters. For this reason, a ridge regression model is especially good at improving the least-squared estimate when multicollinearity is present.

Second, ridge regression models can be used to create a parsimonious statistical model when the number of predictor variables exceeds the rule of thumb for the number of observations (15-20 observations per included variable). For each variable added, the estimates will be shrunken more heavily, eventually to the mean, so the estimates will be conservative (Kumamaru H., Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data. Emerg Themes Epidemiol. 2016 and Pavlou M., How to develop a more accurate risk prediction model when there are few events, BMJ. 2016). In other words, a penalized estimation can be used for small sample sizes and models suffering from multiple comparisons as well as multicollinearity (Muhammad Imdad Ullah., Lecture notes on RR. The R Journal. 2018). The downside of such a model is that, due to the shrinking, the absence of a statistically significant effect does not mean there is no significant effect. It is indeed plausible that associations were missed in our analysis that would have been found in a larger study sample.

The following sentence was added to the strengths of the present study:

Discussions, pages 18-19, lines 376-377: “Third, a model that penalizes for multicollinearity and multiple comparisons was used.”

2) The definition of nutrition (using the mini nutritional status and objective markers) used is flawed. Admittedly, differentiating pure malnutrition from underlying inflammatory processes is difficult, especially in dialysis patients. But it’s not clear to me that C-reactive protein or TIBC or serum total protein are sensitive markers of (under)nutrition. As for BMI, the majority of the study subjects had a BMI in the overweight or obese range. Does this qualify as malnutrition (though it’s actually overnutrition)? Overall the study fails to differentiate inflammation from nutrition.

We agree with the reviewer that differentiating malnutrition from comorbid conditions is difficult. Patients on dialysis are characterized by various catabolic conditions such as metabolic acidosis, low-grade chronic inflammation, secondary hyperparathyroidism amongst many others. Although these conditions do not necessarily result in pure malnutrition (i.e. poor balance of protein input/output), they will contribute to the clinical presentation of malnutrition substantially. Consequently, the terms Protein-Energy Wasting (PEW) and Malnutrition-Inflammation-Atherosclerosis (MIA-) syndrome were established to describe the multifactorial catabolic environment with the clinical presentation of malnutrition in patients on haemodialysis (Obi Y. Latest consensus and update on protein-energy wasting in chronic kidney disease. Curr Opin Clin Nutr Metab Care. 2015). Also, our research group examined the use of the Mini-Nutritional Assessment scale as a prognostic marker in patients on dialysis and provided data that this screening tool is a relevant measure of nutritional status in this population (Holvoet E., The screening score of Mini Nutritional Assessment is a useful routine screening tool for malnutrition risk in patients on maintenance dialysis. PLOS ONE. 2020). This citation was added to the revised version of the manuscript.

First, we are of the opinion that it is not appropriate – nor clinically relevant - to control for all these different conditions and markers as they coincide in the majority of the malnourished/frail patients with end-stage kidney disease (Kirushnan., Impact of Malnutrition, Inflammation, and Atherosclerosis on the Outcome in Hemodialysis Patients. Journal of nephrology. 2017). Accordingly, the aim of our study was to examine the association between poor protein balance (incl. the related comorbid and contributing factors) and physical function in patients on haemodialysis. Based on the recommendations of Obi et al., we included markers of the following three physiological systems that could influence the association between PEW and physical function: (i) inflammation, measured by CRP and is a marker for MIA-syndrome, (ii) depletion of iron reserves, measured by TIBC and is associated with nutrition-related anaemia, and (iii) protein balance, measured by serum total protein and is associated with the ratio of protein intake/output (Naeeni AE., Assessment of Severity of Malnutrition in Peritoneal Dialysis Patients via Malnutrition: Inflammatory Score. Adv Biomed Res. 2017 and Obi Y., Latest consensus and update on protein-energy wasting in chronic kidney disease. Curr Opin Clin Nutr Metab Care. 2015). By including these measures into a ridge regression, a reliable association between PEW and physical function can be examined. Furthermore, the ridge regression method will penalize the multicollinearity between these markers, ensuring that our model would not overestimate the true association.

Second, we agree with the reviewer that BMI is a poor indicator of nutritional status. Although BMI in the overweight-obese ranges is associated with increased cardiovascular risk and decreased survival in the general population, the opposite can been observed in patients with end-stage kidney disease (Calabia J., Does the obesity survival paradox of dialysis patients differ with age?. Blood Purif. 2015). Higher BMI has been paradoxically associated with better survival in patients with end-stage kidney disease. Recent data indicate that both higher skeletal muscle mass as well as increased total body fat are protective, albeit the data on increased visceral (intra-abdominal) fat is inconclusive (Ghorbani A., The prevalence of malnutrition in hemodialysis patients. J Renal Inj Prev. 2020 and Obi Y., Latest consensus and update on protein-energy wasting in chronic kidney disease. Curr Opin Clin Nutr Metab Care. 2015). Possible causes of this paradox include e PEW, MIA, inflammation, hemodynamic stability, sequestration of uremic toxins in adipose tissue amongst many others. The obesity paradox dovetails with the aim of this study to focus on PEW rather than only on pure nutritional status (Naderi N., Obesity Paradox in Advanced Kidney Disease: From Bedside to the Bench. Prog Cardiovasc Dis. 2018 and Park J., Obesity paradox in end-stage kidney disease patients. Prog Cardiovasc Dis. 2014). Last, we believe the reviewer is indeed correct to state that at least part of patients with high BMI might be malnourished, especially from the protein/anabolic perspective, as in patients on dialysis, there is a tendency for excess fat mass and decreased lean tissue mass. In this regard, some patients with high BMI can be considered “malnourished” (Van Biesen W., A multicentric, international matched pair analysis of body composition in peritoneal dialysis versus haemodialysis patients. Nephrol Dial Transplant. 2013).

3) It is unclear how the findings advance in any way our understanding of how malnutrition influences physical health in dialysis patients.

Impairments in physical health and physical function are common in patients on dialysis and these impairments have a multifactorial aetiology. Decreased physical functioning is closely related to objective and subjective health-related quality of life. Exercise training programmes have shown to improve physical function in patients on dialysis, but levels remain considerably below the recommended/expected levels of physical function and physical activity. That is why unravelling of the role of the different players is important as a better understanding of the underlying mechanisms might lead to better exercise training outcomes in patients on haemodialysis. This study advances our understanding of malnutrition in two ways: 1/ by differentiating between aspects of physical function and identifying those aspects that are or are not associated with PEW, and 2/ by examining the true association between PEW (instead of pure malnutrition) and physical function.

1/ Poor nutritional status has been associated with muscle strength and the risk of falls in patients on haemodialysis, albeit the literature on the strength of these associations is inconclusive. To our knowledge, this study is the first to differentiate between different aspects of physical function (i.e. functional vs. absolute measures of physical function and measures of endurance vs. strength vs. balance). The results of our study indicate that PEW affect endurance capacity and gait quality in patients on haemodialysis but not muscle strength.

2/ Various cross-sectional studies have examined the relationship between malnutrition and physical function (mainly muscle strength). However, to our knowledge, a cross-sectional relationship between PEW (defined as malnutrition together with a marker indicating a catabolic environment and analysed by a ridge regression model to correct for multicollinearity) and different aspects of physical function has not yet been examined. The term PEW is a composite of malnutrition and pro-malnutrition markers as proposed by Rodrigues et al., (Juliana Rodrigues., Nutritional assessment of elderly patients on dialysis: pitfalls and potentials for practice, Nephrology Dialysis Transplantation, 2017). Using a ridge regression to incorporate the weight of the different markers, the final marker gives a representation of PEW in general. This methodological approach allows to avoid the problem of multi-collinearity and multiple testing when different markers are each introduced individually in the model and is thus a more-sound approach from the statistical/methodological approach. Furthermore, our results suggest that the MIA-syndrome is an important contributor to exercise intolerance in patients on haemodialysis.

REVIEWER #3

Well thought of study. Many limitations especially that one can not have a spacial association but acceptable strengths. It may be interesting to know if there were any difference in diabetics as compared to non diabetics

We thank the reviewer for this pertinent remark, which is in line with the last comment of reviewer 1. Indeed, diabetes is a syndrome which has an impact on both nutritional as well as physical status. Whether diabetes alone influences the prognosis based on a physical measure is unclear but most likely. Disease burden in type 2 diabetes is high as it is associated with an unhealthy lifestyle, physical inactivity and several comorbidities such as peripheral vascular disease, diabetic neuropathy and musculoskeletal complications such as diabetic foot (Fritschi C., Early declines in physical function among aging adults with type 2 diabetes. Journal of Diabetes and its Complications. 2017). Physical inactivity and comorbid diseases have been associated with impaired physical function in patients with diabetes (Hamasaki H. Daily physical activity and type 2 diabetes: A review. World journal of diabetes. (2016)). Hence, it does not surprise that the proportion of patients with diabetes is higher in the bad prognosis group compared to the good prognosis group based on the 6MWT.

Based on the reviewers’ suggestion, we performed a between-groups analysis of patients with and without diabetes. The analysis shows a higher age, BMI, CRP concentration and morbidity index in patients with diabetes compared to those without, but no significant differences in measures of physical function were found.

The following information was added to the revised version of the manuscript:

Results, page 10, lines 206-208: “No differences in measures of physical function were found between patients with and without diabetes (Table S2).”

Supplementary files, S2 Table: “S2 Table. Patient characteristics according to diabetes.”

Variable Patients with diabetes (n=52) Patients without diabetes (n=61) p

Age (years) 71 ± 14 63 ± 17 <0.01

BMI (kg/m2) 28 ± 4 24 ± 5 <0.01

CRP (mg/L) 8 ± 7 5 ± 6 0.01

TIBC (µg/dL) 236 ± 77 243 ± 76 0.65

Total protein (g/L) 65 ± 6 64 ± 6 0.48

Dialysis vintage (months) 35 ± 33 36 ± 40 0.86

Number of prescribed medications (n) 14 ± 3 13 ± 4 0.53

Davies comorbidity score (0-7) 2.5 ± 1.1 1.5 ± 1.4 <0.01

Quadriceps force (N) 159 ± 62 182 ± 95 0.13

Quadriceps force (%) 51 ± 19 52 ± 22 0.66

Handgrip force (kg) 27 ± 10 30 ± 12 0.60

Handgrip force (%) 90 ± 28 95 ± 33 0.38

DFRI (/12) 6 ± 3 5 ± 3 0.08

Tinetti (/12) 8 ± 4.2 8 ± 4.7 0.66

FICSIT (/28) 13 ± 7 15 ± 10 0.38

Sit-to-Stand (s) 32 ± 17 27 ± 18 0.12

6MWT (m) 205 ± 164 272 ± 209 0.07

6MWT (%) 36 ± 28 43 ± 31 0.20

Submitted filename: Response To Reviewers.docx

Click here for additional data file.

17 Jun 2020

PONE-D-20-01868R1

Nutritional status and physical performance in haemodialysis patients: a cross-sectional study

PLOS ONE

Dear Dr. Van Craenenbroeck,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

There are two comments from reviewers for consideration:

1) consider changing the title (not required, but the suggested title may be more accurate);

2) carefully read through the manuscript and check that it is in final form. PLOS ONE does not have a copy editor review the manuscript prior to publication, so the authors must ensure that it meets point five of the PLOS ONE publication criteria.

Please submit your revised manuscript by Aug 01 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Melissa M Markofski

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: I would suggest that you change the title of the manuscript to: Markers of Protein-Energy Wasting and Physical Performance in Haemodialysis Patients: A Cross-Sectional Study

This is more accurate than using "Nutritional Status" in the title since strictly speaking you are not measuring nutritional status

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Aasim Ahmad

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

23 Jun 2020

REVIEWER #1 and #3

We thank the reviewers for their feedback.

REVIEWER #2

1) I would suggest that you change the title of the manuscript to: Markers of Protein-Energy Wasting and Physical Performance in Haemodialysis Patients: A Cross-Sectional Study. This is more accurate than using "Nutritional Status" in the title since strictly speaking you are not measuring nutritional status.

We agree with the reviewer. Changing “nutritional status” to “markers of protein-energy wasting” is more in line with the true message of our manuscript.

The following adjustments were made in the title of the revised version of the manuscript:

Title page, page 1, lines 1-2: “Markers of protein-energy wasting and physical performance in haemodialysis patients: a cross-sectional study”

Submitted filename: Response To Reviewers.docx

Click here for additional data file.

15 Jul 2020

Markers of protein-energy wasting and physical performance in haemodialysis patients: a cross-sectional study

PONE-D-20-01868R2

Dear Dr. Van Craenenbroeck,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Melissa M Markofski

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

17 Jul 2020

PONE-D-20-01868R2

Markers of protein-energy wasting and physical performance in haemodialysis patients: a cross-sectional study

Dear Dr. Van Craenenbroeck:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Melissa M Markofski

Academic Editor

PLOS ONE