Are Prediction Equations Reliable for Estimating Resting Energy Expenditure in Chronic Kidney Disease Patients?

Nephrol Dial Transplant (2011) 26: 544–550 doi: 10.1093/ndt/gfq452 Advance Access publication 27 July 2010

Are prediction equations reliable for estimating resting energy expenditure in chronic kidney disease patients?

Maria A. Kamimura1,2, Carla M. Avesani3, Ana P. Bazanelli1, Flavia Baria1, Sergio A. Draibe1,2 and Lilian Cuppari1,2 Downloaded from https://academic.oup.com/ndt/article/26/2/544/1893246 by guest on 24 September 2021

1Nutrition Program, Federal University of São Paulo, São Paulo, Brazil, 2Division of Nephrology, Federal University of São Paulo, São Paulo, Brazil and 3Institute of Nutrition, Rio de Janeiro State University, Rio de Janeiro, Brazil Correspondence and offprint requests to: Lilian Cuppari; E-mail: [email protected]

Abstract There is a need to develop population-specific equations in Background. The determination of resting energy expend- order to adequately estimate the energy requirement of these iture (REE) is the primary step for estimating the energy re- patients. quirement of an individual. Although numerous equations Keywords: chronic kidney disease; energy requirement; indirect have been formulated for predicting metabolic rates, there calorimetry; prediction equations; resting energy expenditure is a lack of studies addressing the reliability of those equations in chronic kidney disease (CKD). Thus, the aim of this study was to evaluate whether the main equations developed for estimating REE can be reliably applied for CKD patients. Methods. A total of 281 CKD patients (124 non-dialysis, Introduction 99 haemodialysis and 58 peritoneal dialysis) and 81 healthy control individuals were recruited. Indirect calor- Chronic kidney disease (CKD) is recognized as an import- imetry and blood sample collection were performed after ant public health problem, in which the incidence has in- a 12-h fasting. Two most traditionally used equations for creased markedly in the last few years [1]. A number of estimating REE were chosen for comparison with the metabolic disturbances and catabolic conditions related REE measured by indirect calorimetry: (i) the equation to renal failure and dialysis therapy adversely affect the nu- proposed by Harris and Benedict, and (ii) the equation pro- tritional condition of CKD patients [2]. Thus, the evidence posed by Schofield that is currently recommended by the of high prevalence of protein-energy wasting and, particu- FAO/WHO/UNU. larly, the recognition of protein-energy wasting as a risk Results. Schofield’s equation exhibited higher REE [1492 ± factor for poor clinical outcomes among the CKD popula- 220 kcal/day (mean ± SD)] in relation to Harris and Ben- tion have fostered several strategies for the treatment of edict’s equation (1431 ± 214 kcal/day; P < 0.001), and these patients [2–4]. An important element for promoting both prediction equations showed higher REE in compari- adequate nutritional status relies on the adequate manage- son with the reference indirect calorimetry (1352 ± ment of energy balance. 252 kcal/day; P < 0.001). In patients with diabetes, inflam- The determination of resting energy expenditure (REE) mation or severe hyperparathyroidism, the REE estimated is the primary step for the establishment of the energy re- by the Harris and Benedict equation was equivalent to that quirement of an individual [5]. Few studies on REE in measured by indirect calorimetry. The intraclass correlation CKD patients have brought important contributions in this of the REE measured by indirect calorimetry with the field. Previous reports, based on calorimetric methods, in- Schofield’s equation was r = 0.48 (P < 0.001) and with dicate reduced REE in non-dialysed CKD patients [6,7] the Harris and Benedict’s equation was r =0.58(P< and a normal to increased REE among dialysed patients 0.001). According to the Bland and Altman analysis, there [8–11] when compared with healthy subjects. was a large limit of agreement between both prediction Indirect calorimetry is among the methods that most equations and the reference method. Acceptable prediction accurately measure the REE. However, several factors re- of REE (90–110% adequacy) was found in 47% of the pa- lated to subjects (e.g. need of fasting and resting) and device tients by using the Harris and Benedict’s equation and in (e.g. high cost, test time and trained personnel) make this only 37% by using the Schofield’s equation. method impractical in the clinical routine. Thus, over the Conclusions. The most traditionally used prediction past decades, numerous equations have been developed equations overestimated the REE of CKD patients, and for predicting metabolic rates in the healthy as well as con- the errors were minimized in the presence of comorbidities. dition. The equation proposed by Harris and Benedict, in

© The Author 2010. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please e-mail: [email protected] Resting energy expenditure equations in CKD 545 1919, has been the most traditionally used for clinical and est 0.1 kg and height to the nearest 0.1 cm with a stadiometer. Body mass research purposes [12]. By using multiple regression ana- index (BMI) was calculated as body weight divided by squared height. lysis, in one of the first applications of this statistical test Body composition was assessed by bioelectrical impedance analysis using a single frequency tetrapolar technique with an electrical current of 800 μA to human physiology, they generated the gender-specific at 50 kHz (BIA 101 Quantum, RJL Systems, USA). With the subject in the equation including easily measurable variables such as supine position, the electrodes were placed in the standard positions (two age, body weight and height. The equation proposed by electrodes placed on the hand and wrist and another two positioned on the Schofield, in 1985, is the one recommended by the FAO/ foot and ankle) on the right side of the body or in the opposite side of the vascular access for haemodiaysis patients, The software Fluids & Nutrition WHO/UNU expert consultation on human energy require- (version 3.0) provided by the manufacturer was used to estimate body com- ment for estimating REE [13]. In view of the evidences that position. Energy intake was assessed by means of 3-day food record and both Harris and Benedict’s equation and Schofield’s equa- protein intake by calculating protein equivalent of nitrogen appearance tion provide a valid estimation of REE as compared with in- (PNA) according to the KDOQI guideline for nutrition [16]. direct calorimetry, those prediction equations have been encouraged by many nutrition societies and guidelines Laboratory data Blood samples were drawn after an overnight fast of 12 h just before the

[14,15]. Downloaded from https://academic.oup.com/ndt/article/26/2/544/1893246 by guest on 24 September 2021 To find a simple method able to predict accurately the indirect calorimetry test. Serum creatinine, urea and glucose were determined by a standard autoanalyser. Bicarbonate (normal range: 23– REE of CKD patients would be of relevant importance for 27 mmol/L) was measured by an automated potentiometer, thyroid- the routine care of these patients. Since there is a lack of stimulating hormone (TSH, normal range: 0.3–4.0 mIU/L) by studies addressing the applicability of REE prediction immunofluorometric assays and albumin (normal range: 3.4–4.8 g/dL) equations in CKD, we aimed to evaluate whether the most by bromcresol green technique. Intact parathyroid hormone (PTH) (normal range: 10–65 pg/mL) and high-sensitivity assay for C-reactive protein traditionally used equations of REE are reliable for esti- (CRP) (inflammatory state: >0.5 mg/dL) were determined by immuno- mating REE in a CKD population. chemiluminescence. Glomerular filtration rate was evaluated using standard creatinine clearance (CrCl) corrected for body surface area. Single-pool Kt/V was calculated for haemodialysis and peritoneal dialysis patients Materials and methods according to the KDOQI guidelines for dialysis (2006) [17]. Patients REE A total of 281 CKD patients (124 non-dialysis, 99 haemodialysis and 58 peritoneal dialysis) were included in the present study. We studied patients REE was measured by indirect calorimetry using an open circuit ventilated from the renal outpatient clinic and the dialysis unit of the Federal Univer- computerized metabolic system (Vmax series 29n; SensorMedics Corp; sity of São Paulo (São Paulo, Brazil) who had participated in previous stud- Yorba Linda, CA, USA). The oxygen and carbon dioxide sensors were ca- ies (10, 30–33) by following the same protocol for the assessment of REE. librated before each REE measurement with the use of mixed reference Exclusion criteria were age <18 years, amputation, pregnancy, altered thy- gases of known composition. All subjects were previously instructed to re- roid function, presence of malignancy, and hospitalization in the month frain from any unusual physical activity (24 h period) prior to the test and to prior to the study. Dialysis patients underwent haemodialysis or peritoneal sleep at the same time as usual in the night before the REE measurement. dialysis therapy for at least 3 months. Peritoneal dialysis patients with epi- They were admitted to the clinic at 8:00 a.m. after an overnight fast of 12 h. sodes of peritonitis within 3 months prior to the study were not included. After resting for 30 min in a recumbent position, subjects breathed for Of the entire group, 89% was taking diuretics and/or antihypertensive 30 min through a clear plastic canopy over their heads in a quiet dimly lit medications, and 33% was using β-blockers. The majority of the patients thermoneutral room. They were instructed to avoid hyperventilation, fid- were under use of iron saccharate and renal-specific vitamin supplemen- geting or falling asleep during the test. Oxygen consumption and carbon tation, and none was under use of corticosteroid or immunosuppressive dioxide production were measured at 1-min intervals, and the mean of drugs. Thirteen percent of the patients were taking calcitriol. All haemo- the last 20 min was used to calculate the REE according to the following dialysis patients, 77% of the peritoneal dialysis patients and 3% of the Weir’s equations, without using urinary urea nitrogen [18]: non-dialysis patients were on regular therapy with human recombinant erythropoietin. Haemodialysis patients were dialysed for 4 h thrice a week, Basal metabolic rate ðkcal=minÞ =3:9 ½VO2ðL=minÞ +1:1 ½VO2ðL=minÞ and the predominant vascular access was arteriovenous fistula (92% of the patients). A diet containing 30–35 kcal/kg/day and 0.6–0.8 g of protein/kg/ REE ðkcal=dayÞ = basal metabolic rate × 1440 min day (non-dialysis patients) or 1.2–1.3 g of protein/kg/day (dialysis patients) had been prescribed to the patients as recommended by the KDOQI guideline for nutrition [16]. Eighty-one healthy adult individuals were also where VO2 is the volume of oxygen, and VCO2 is the volume of carbon recruited to form a control group. They were clinic employees or relatives dioxide.The intra-individual variation coefficient for REE by indirect cal- of the patients. All control subjects had normal renal and thyroid function, orimetry obtained in two consecutive occasions was 5%. and none of them were taking any medication. The equations used for the predicting REE are as follows: The study was approved by the University Ethical Advisory Commit- (i) Harris and Benedict’s equations [12]: tee, and informed consent was obtained from each subject.

Study design and protocol Men = 66 + (13.7 × weight) + (5 × height) − (6.8 × age) Women = 655 + (9.6 × weight) + (1.7 × height) − (4.7 × age) In this cross-sectional study, the indirect calorimetry test, nutritional assessment and fasting blood tests were all performed on the same day. In haemodialysis patients, the indirect calorimetry and blood tests were carried out on an interdialytic day, and the nutritional assessment was performed post- (ii) Schofield’s equations reported by the World Health Organization dialysis session of the same week. Peritoneal dialysis patients underwent [13]: nutritional assessment and indirect calorimetry test with emptied peritoneal cavity. Two prediction equations using easily measurable variables were compared with the reference indirect calorimetry for the estimation of REE. Men Women

– Nutritional evaluation 18 30 years 15.057 × weight + 692.2 14.818 × weight + 486.6 30–60 years 11.472 × weight + 873.1 8.126 × weight + 845.6 Subjects were weighed with light clothes and without shoes on a platform >60 years 11.711 × weight + 587.7 9.082 × weight + 658.5 manual scale balance (Filizola®, Brazil). Weight was measured to the near- 546 M.A. Kamimura et al. Statistical analysis 0.6 in peritoneal dialysis patients. Twenty-six patients Data are expressed as mean ± standard deviation (SD), median and inter- (9%) had diabetes, 25% had inflammation (defined as quartile ranges, or proportions. For comparisons between groups, inde- CRP ≥1.0 mg/dL) and 20% had severe hyperparathyroid- pendent Student’s t-test, Mann–Whitney’s test or the chi-square test were ≥ used as appropriate. The paired Student’s t-test was used to assess individ- ism (defined as PTH 700 pg/mL). ual differences between the REE predicted by the equations and REE mea- Comparisons of REE predicted by the equations with sured by indirect calorimetry. The intraclass correlation was applied to REE measured by indirect calorimetry are presented in evaluate the association between predicted and measured REE. Pearson’s Figure 1. As can be seen, Schofield’s equation exhibited linear correlation coefficients were calculated to evaluate the association of higher REE [1492 ± 220 kcal/day (mean ± SD)] in relation REE and study variables. Skewed variables or non-linearly related vari- ’ ables were log-transformed. Bland and Altman plot analysis allowed us to Harris and Benedict s equation (1431 ± 214 kcal/day; to evaluate the agreement between the prediction equations and the refer- P < 0.001), and both prediction equations showed higher ence for the REE measurements. This approach provides the calculation of REE in comparison with the indirect calorimetry (1352 ± error (mean of the individual differences between two methods) and the 252 kcal/day; P < 0.001) in the CKD group. The same limits of agreement (±1.96 SD from the mean error). Prediction of REE from 90% to 110% measured by indirect calorimetry was considered ac- pattern was found among non-dialysis and haemodialysis ceptable. Predictions below or above the limits were defined as underesti- patients, as well as among controls. In peritoneal dialysis Downloaded from https://academic.oup.com/ndt/article/26/2/544/1893246 by guest on 24 September 2021 mation or overestimation, respectively. Total energy expenditure (TEE) patients, the Harris and Benedict’s equation exhibited was calculated as REE multiplied by 1.55, a mean value of the limits of similar REE in relation to the reference. The intraclass – physical activity level factor (1.40 1.69) for sedentary adults according to correlation of the REE measured by indirect calorimetry the World Health Organization in order to estimate the energy requirement. ’ Kappa statistic test was applied to evaluate the agreement between the pre- with that predicted by Harris and Benedict s equation (r = diction equations and the reference method for the estimation of energy 0.58; P < 0.001) was stronger than with that by Scho- requirement in CKD patients. Analyses were carried out using SPSS for field’sequation(r = 0.48; P < 0.001) among patients. Windows (version 15, 2001, SPSS Inc., Chicago, IL, USA), and statistical In the control group, the correlations between measured significance was considered at the conventional P < 0.05 level (two-tailed). and predicted REE were similar (Harris and Benedict r = 0.65; P < 0.001 and Schofield r = 0.62; P < 0.001). REE by both prediction equations as well as by indirect calor- Results imetry correlated negatively with age and positively with lean body mass, BMI, PNA, and energy intake in CKD Table 1 provides the demographic, clinical and nutrition- patients. C-reactive protein correlated positively only with al characteristics of the studied subjects. CKD patients measured REE (r = 0.22; P < 0.001). The REE error were older, had higher serum concentrations of glucose (predicted minus measured) correlated inversely with and C-reactive protein, and had reduced body fat and serum glucose (Harris and Benedict r = −0.15; P = energy intake in comparison with the healthy control. 0.01 and Schofield r = −0.17; P = 0.004), parathyroid The aetiology of CKD was undetermined in 30% of hormone (Harris and Benedict r = −0.17; P = 0.004 the patients—hypertensive nephrosclerosis accounted and Schofield r = −0.14; P = 0.02) and C-reactive pro- for 26% of the causes, followed by chronic glomerulo- tein (Harris and Benedict r = −0.24; P < 0.001 and Scho- nephritis (15%) polycystic kidney (10%) and others field r = −0.22; P < 0.001). When the analyses were (19%). The duration on therapy was similar between performed according to the presence of comorbidities, haemodialysis patients [24 (2–162)months] and periton- we found that in patients with diabetes, inflammation or se- eal dialysis patients [23 (3–109)months]. Single-pool vere hyperparathyroidism (n = 137), the REE estimated by Kt/V was 1.3 ± 0.2 in haemodialysis patients and 2.2 ± the Harris and Benedict’s equation (1374 ± 263 kcal/day)

Table 1. Demographic, clinical and nutritional characteristics of the patients and controls.

CKD Control CKD Non-dialysis Haemodialysis Peritoneal dialysis n =81 n = 281 n = 124 n =99 n =58

Male 34 (42%) 174 (62%) 77 (62%) 64 (65%) 33 (57%) Age (years) 42.7 ± 12.6 50.1 ± 15.9** 54.2 ± 16.0** 44.2 ± 13.8 51.4 ± 16.2** Serum creatinine (mg/dL) 0.87 ± 0.21 7.1 ± 4.6** 3.0 ± 1.6** 11.2 ± 3.3** 9.0 ± 3.3** CrCl (mL/min/1.73 m2) –– 30.2 ± 14.3 –– Proteinuria (g/day) –– 0.91 ± 2.1 –– Serum glucose (mg/dL) 84.6 ± 9.7 96.1 ± 60.3* 90.0 ± 9.1** 89.5 ± 60.2 120.3 ± 103.3* Parathyroid hormone (pg/mL) – 345 ± 413 220 ± 248 345 ± 404 603 ± 565 Serum albumin (g/dL) – 4.0 ± 0.4 4.0 ± 0.4 4.1 ± 0.4 3.8 ± 0.5 C-reactive protein (mg/dL) 0.31 (0.01–2.48) 1.11 (0.01–21.5)** 0.68 (0.01–8.5)** 1.42 (0.01–21.5)** 1.51 (0.05–16.1)** Body mass index (kg/m2) 25.2 ± 3.8 24.9 ± 4.3 26.1 ± 4.5 23.5 ± 3.7** 24.9 ± 3.8 Body fat (%) 28.6 ± 7.9 26.0 ± 10.1* 27.4 ± 10.7 24.4 ± 9.3** 25.6 ± 9.8* Lean body mass (kg) 49.0 ± 10.4 48.9 ± 10.4 50.0 ± 10.7 48.2 ± 10.4 47.7 ± 10.0 nPNA (g/kg/day) – 0.92 ± 0.23 0.94 ± 0.19 1.10 ± 0.22 0.71 ± 0.15 Energy intake (kcal/kg/day) 34.2 ± 7.1 28.1 ± 8.4** 25.8 ± 7.4** 28.8 ± 11.0 30.8 ± 5.9

CKD, chronic kidney disease; CrCl, creatinine clearance; nPNA, normalized protein equivalent of nitrogen appearance. *P < 0.05, **P < 0.01 patients vs control, independent Student’s t-test or Mann–Whitney’s test. Resting energy expenditure equations in CKD 547 2500

** * * ** * ** ** 2000

1500 REE (kcal/day) 1000 Indirect calorimetry Harris & Benedict

Schofield Downloaded from https://academic.oup.com/ndt/article/26/2/544/1893246 by guest on 24 September 2021 500 Non-dialysis Haemodialysis Peritoneal dialysis Control

Fig. 1. REE predicted by the equations against the reference indirect calorimetry. *P < 0.001 Harris and Benedict > indirect calorimetry; **P < 0.001 Schofield > Harris and Benedict and indirect calorimetry. was equivalent to the REE measured by indirect calorim- Benedict and by Schofield overestimated REE measure- etry (1402 ± 222 kcal/day; P = 0.13). ments in CKD patients. As far as we are concerned, this The agreement of the REE prediction equations with the is the first study that investigated the reliability of predic- indirect calorimetry is shown in Figure 2. There was a tion equations for estimating REE in a large cohort of large limit of agreement between both prediction equations CKD population by comparing with indirect calorimetry. and the reference indirect calorimetry among non-dialysis The knowledge of REE is essential for determining en- patients (Figure 2A) and, particularly, among dialysis pa- ergy requirement since REE is the predominant component tients (Figure 2B and C). The individual variability of of the TEE [19]. Due to the limitations of sophisticated the prediction equations against the reference method calorimetric methods for evaluating REE in the clinical was also evident in the control group (Figure 2D). Accept- settings, simple prediction equations of REE are valuable able REE prediction, from 90% to 110%, was found in to guide a correct dietary energy supply. Actually, a num- 47% of the patients by using the Harris and Benedict’s ber of equations have been developed for such a purpose. equation and in only 37% by using the Schofield’s equa- Recently, when Weijs et al. performed a systematic search tion (Figure 3). As can be seen, the overestimation of REE for publications of REE prediction equations for use in (adequacy >110%) by the predictive equations was highly adults, they found a total of 18 equations developed based prevalent in both CKD and control groups. on gender, age, body weight and/or height. The authors ob- In order to estimate the energy requirement of the CKD served that the equation proposed by Schofield had the patients, we calculated the total energy expenditure (TEE) smallest prediction error against indirect calorimetry, fol- as the REE multiplied by 1.55 (the mean physical activity lowed by the Harris and Benedict’s equation in outpatients factor for sedentary adults according to the World Health as well as inpatients. However, in agreement with the Organization). The results from the Harris and Benedict’s present study, the errors of both equations were relatively equation [36.9 (34.9–39.5) kcal/kg/day; median (interquar- large. In addition, there appeared to be an overestimation tile ranges)] as well as from the Schofield’s equation [38.4 for the low REE values and an underestimation of high (36.0–41.6) kcal/kg/day] differed significantly from that REE values [20]. based on indirect calorimetry [34.3 (31.4–38.2) kcal/kg/ Some reasons for the inaccuracy of the REE prediction day; P < 0.001]. In addition, the analyses by quartiles of equations can be discussed. Firstly, the error of such equa- TEE exhibited kappa statistic of 0.15 between Harris and tions might be attributed to the fact that they were devel- Benedict’s equation and indirect calorimetry, and 0.12 be- oped for estimating basal metabolic rate and not REE. tween Schofield’s and indirect calorimetry, indicating the Then, an underestimation of REE by the equations would lack of agreement between the predicted and measured be expected since basal metabolic rate is ~10–20% lower methods for estimating energy requirement of these pa- than the REE [19]. However, both equations overestimated tients. By using Harris and Benedict’s equation, 64% of REE against the reference indirect calorimetry in the the patients and, by using Schofield’s equation, 66% of present study (mean error by Harris and Benedict was the patients did not meet the same quartile of TEE esti- 5.8% and by Schofield was 10.4%). Although this is not mated by using indirect calorimetry. a homogenous finding in the literature, a number of previous studies in the general population as well as in different clinical settings have also evidenced overestimation of Discussion these equations for predicting REE [21–24]. In accord- ance, overestimation of REE by the equations was notice- The present study demonstrated that the most frequently able not only among non-dialysed and dialysed CKD used REE prediction equations proposed by Harris and patients but also in our healthy control group (7.9% by 548 M.A. Kamimura et al. A) Non-dialysis 800 800

600 600 +1.96 SD +501 400 +1.96 SD +428 400

200 200 149 ±179 95 ±170 0 0

-200 -1.96 SD -238 -200 -1.96 SD -202 -400 -400

-600 -600 Difference REE (HB-IC) kcal/day -800 -800 800 1200 1600 2000 2400 Difference REE (Schofield-IC) kcal/day 800 1200 1600 2000 2400 Mean REE [(HB+IC)/2] kcal/day Mean REE [(Schofield+IC)/2] kcal/day

B) Haemodialysis Downloaded from https://academic.oup.com/ndt/article/26/2/544/1893246 by guest on 24 September 2021 800 800

600 600 +1.96 SD +595 +1.96 SD +520 400 400

200 200 150 ±227 88 ±220 0 0

-200 -200 -1.96 SD -295 -1.96 SD -343 -400 -400

800 800

600 600 +1.96 SD +555 +1.96 SD +477 400 400

200 200 ± ± 103 231 0 27 228 0

-200 -200 -1.96 SD -350 -400 -1.96 SD -424 -400 -600 -600 Difference REE (HB-IC) kcal/day -800 -800 800 1200 1600 2000 2400 Difference REE (Schofield-IC) kcal/day 800 1200 1600 2000 2400 Mean REE [(HB+IC)/2] kcal/day Mean REE [(Schofield+IC)/2] kcal/day D) Control 800 800

600 600 +1.96 SD +470 +1.96 SD +489 400 400

200 200 109 ±184 129 ±184 0 0

-200 -1.96 SD -252 -200 -1.96 SD -232 -400 -400

Fig. 2. Bland and Altman comparative analysis for REE predicted by the Harris and Benedict’s (HB) and Schofield’s equations against indirect calorimetry (IC) in non-dialysis, haemodialysis, peritoneal dialysis and controls [men (filled diamonds) and women (unfilled diamonds)].

Harris and Benedict and 9.3% by Schofield). In fact, in a 15% [22] and the Schofield’s equation by 8–12% series of published articles, the Harris and Benedict’s equa- [23,24]. In CKD patients, REE might be reduced due to tion has been demonstrated to overestimate REE by 10– the diminished lean body mass as a result of a cluster of Resting energy expenditure equations in CKD 549 100% 90% 80% 70% > 110% (overestimation) 60% 90 – 110% (acceptable) 50% < 90% (underestimation) 40% % subjects 30% 20% 10% 0% Harris-Benedict Schofield Harris-Benedict Schofield CKD CKD Control Control

Fig. 3. Percentage of subjects according to the adequacy of REE [(REE predicted by the equations × 100) / REE measured by indirect calorimetry]. Downloaded from https://academic.oup.com/ndt/article/26/2/544/1893246 by guest on 24 September 2021 catabolic conditions to which these patients are commonly In conclusion, this study showed that the main available exposed. And, as known, lean body mass is the main de- prediction equations overestimated the REE of CKD pa- terminant of the energy expenditure accounting for 73% of tients. Overestimation was greatest with the equation pro- the variations in REE and 80% of the variations in TEE posed by Schofield followed by the one by Harris and [25]. Thus, the use of total body weight by the prediction Benedict. The errors are minimized in the presence of equations could be a potential source of overestimation of comorbidities such as diabetes, inflammation and severe REE in these particular patients. This rationale is consist- hyperparathyroidism. Dietitians should be aware of the lim- ent with the findings that overestimation by such equations itations of the REE equations when prescribing energy in- is systematically greater among obese individuals [26,27]. take for CKD patients. Since the establishment of energy Another reason for the overestimation of the REE equa- recommendation in CKD patients is still a matter of debate, tions in CKD patients could be related to the lack of en- to pursue a more thorough analysis of existing information, ergy expended by the kidneys, since in healthy individuals, there is the need to promote further studies with geographic the kidneys are thought to account for ~7% of the resting and ethnic representative sample in order to confirm our energy expenditure [28]. However, the overestimation by findings and also to develop CKD population-specific the equations was noticeable also among healthy controls. equations for estimating REE. Finally, a potential explanation for an inaccurate estimation of REE by the prediction equations in the present study can Acknowledgements. This study was supported by the Fundação de Am- be the differences between the study population and the paro à Pesquisa do Estado de São Paulo (FAPESP) and Oswaldo Ramos Foundation. population from which the equations were originally derived. For instance, the equation developed by Harris and Conflict of interest statement. None declared. Benedict was based on a sample of young and healthy non-obese men and women and the equation by Schofield References was derived mainly from Italian men with relatively high REE values. Thus, population differences are probably 1. Schoolwerth AC, Engelgau MM, Hostetter TH et al. Chronic kidney one of the most important reasons for the considerable vari- disease: a public health problem that needs a public health action ability of the prediction equations in a variety of clinical plan. 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Nephrol Dial Transplant (2011) 26: 550–556 doi: 10.1093/ndt/gfq443 Advance Access publication 25 July 2010

Estimated glomerular filtration rate in the nephrotic syndrome

Julia M. Hofstra1, Johannes L. Willems2 and Jack F.M. Wetzels1

1Department of Nephrology and 2Department of Laboratory Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands Correspondence and offprint requests to: Julia M. Hofstra; E-mail: [email protected]

Abstract We have evaluated the abbreviated MDRD formula in a Background. Plasma creatinine concentration and creatin- large cohort of patients with proteinuria. ine-based equations are most commonly used as markers of Methods. Data on a cohort of patients with glomerular glomerular filtration rate (GFR). The abbreviated MDRD diseases were available from a large database. We have formula is considered the best available formula. Altered studied the relationship between estimated GFR (MDRD renal handling of creatinine, which may occur in the neph- formula), and plasma cystatin C (CysC) and plasma beta- rotic syndrome, will invalidate creatinine-based formulas. 2-microglobulin (β2m) as markers of GFR.