RESEARCH BRIEF

SGA Scores Have Poor Correlation With SerumAlbumin in Obese Hemodialysis Patients:A Secondary AnalysisEric D. Erb, BS,* Rosa K. Hand, MS, RDN, LD,† and Alison L. Steiber, PhD, RDN†

Abstract: Objective: The objective of this study was to determine the relationship between serum albumin and the Subjective Global

Assessment (SGA) in a sample of obese hemodialysis (HD) patients.

Design andMethod: Study subjects (N5 253) included patients whowere categorized into well-nourished (68%, SGA score 6-7) and

malnourished (score 1-5) groups, and, on the basis of the bodymass index (BMI), into obese (BMI. 30 kg/m2; 35%) and nonobese (BMI

, 30 kg/m2). The mean baseline data (6standard deviation) were as follows: age, 63.5 6 14.3 years; BMI, 29 6 8 kg/m2; and serum

albumin, 3.86 0.4 mg/dL (bromocresol green). The secondary analysis of data from the SGA Validation Project and Nutrition Algorithm

Preliminary Report determined the relationship between the 7-point SGA and serum albumin concentrations in a sample of obese HD

patients. Data were analyzed at Case Western Reserve University from a total of 253 HD patients.

MainOutcomeMeasure: The SGA scores in the BMI groupswere compared with serum albumin as an objective measure of nutrition

and inflammation risk.

Results:By using analysis of variance, the obese and nonobese populations showed statistically significant differences in SGA scores

(obese: P, .3468, nonobese: P, .0080) and serum albumin values (obese, P, .0943; nonobese, P, .0183) between well-nourished

and malnourished groups. A multivariable nominal logistic fit was used to predict SGA group with age and gender as covariates. Serum

albumin values (P , .0057) and BMI values greater than 30 (P , .0090) predicted SGA group.

Conclusion: The secondary analysis showed that SGA does not correlate well with serum albumin; thus, it may not be a valid nutrition

assessment tool among obese HD patients.

� 2014 by the National Kidney Foundation, Inc. All rights reserved.

Introduction

ACCORDINGTO2009 to 2010 data, 35.7%of theU.S.adult population were obese, with a body mass index

(BMI) of 30kg/m2ormore.1 Studieshave shown that obesitystrongly correlateswith progression of chronic kidney disease(CKD) and the resulting complications.2-5 It is estimated thatapproximately 25% of the CKD cases could be prevented byeliminating obesity.6 Althoughweight loss may slow the pro-gression of CKD, by the time the patients progress to end-stage renal disease, the relationship between weight andnutritional status is unclear: On dialysis, the epidemiologicaldata show an inverse relationship between BMI values andmortality rates, suggesting that a higher BMI is protective.7

*Department of Nutrition, Case Western Reserve University, Cleveland, OH.

†Academy of Nutrition and Dietetics, Chicago, IL.

Financial Disclosure: The authors declare that they have no relevant financial in-

terests. At the time of publication, R.K.H. was a Senior Manager at Dietetics Prac-

tice Based Research Network and A.L.S. was a Chief Science Officer, both at the

Academy of Nutrition and Dietetics. A.L.S. and R.K.H. also hold adjunct ap-

pointments in the Department of Nutrition at Case Western Reserve University.

Address correspondence to Rosa K. Hand, MS, RDN, LD, Case Western

Reserve University, Academy of Nutrition and Dietetics, 120 South Riverside

Plaza Suite 2000, Chicago, IL 60606. E-mail: rhand@eatright.org� 2014 by the National Kidney Foundation, Inc. All rights reserved.

1051-2276/$36.00

http://dx.doi.org/10.1053/j.jrn.2014.03.007

Journal of Renal Nutrition, Vol -, No - (-), 2014: pp 1-4

In CKD patients receiving maintenance hemodialysis(HD), avalidatedway to assess thenutritional status is throughthe SubjectiveGlobalAssessment (SGA).The physical exam-ination, widely regarded as the most important part of theSGA, assesses muscle and fat wasting at key locations on thebody.8 Although a large amount of data have shown the val-idityof theSGAinHDpatients,9-14 recent studies question itsvalidity among obese patients because SGA only assesses theundernourished side ofmalnutrition.15 Given the prevalenceof obesity in this population, understanding the validityof theSGA in obesity is critical; however, no data are available onthe subject.16 Serum albumin, although not a pure nutritionmarker, is a strong predictor of mortality in the HD popula-tion17-22; thus, its relationship to the SGA in obese patients isone of interest. Therefore, the purpose of this study was toassess the relationship of the SGA to serum albumin inobese HD patients.

Methods

The secondary analysis combined data from 2 observa-tional studies of CKD patients on maintenance HD toform a sample of 253 patients. Both studies were completedatCaseWesternReserveUniversity and used networks of di-etitians to recruit patients. The first study, the SubjectiveGlobal Assessment Validation Project (SGAVP), comprised

1

ERB ET AL2

153 patients. The SGAVP tested the validity of the SGA inthe HD population.8 The second study, the Nutrition Algo-rithmPreliminaryReport (NAPR), examined the function-ality of a web-based nutrition algorithm in an internationalsample of 100 patients.23 Specific information about the in-clusion and exclusion criteria and study procedures can befound in the original papers. The secondary study analyzedthe baseline data from both studies. In the SGAVP and theNAPR, the SGA was performed using the 7-point scaleadopted from the Canada-USA Peritoneal Dialysis study.24

On this scale, a rating of 1 indicates severe nutritional losswhereas a rating of 7 indicates no nutritional loss.

BMI and SGA values were each categorized into 2groups. On the basis of BMI values, the subjects were clas-sified into obese and nonobese groups, with the obesegroup consisting of individuals with BMI values greaterthan 30 kg/m2. SGA scores were categorized into 5 orless and greater than 5. Following the standard conventionthat is based on the Detsky A/B/C SGA,25 subjects withSGA scores from 6 to 7 were classified as well-nourishedand subjects with SGA scores of 5 or less were placed intothe nutritional loss category.

All statistical analysis was performed using the JMPversion 9.0.2 (SAS Institute Inc., Cary, NC). Statistical sig-nificance was established as P , .05. Bivariate associationswere determined using Spearman’s correlations. One-wayanalysis of variance (ANOVA) was used to determine thedifferences between SGA score groups and continuous var-iables, such as BMI and serum albumin, and between BMIgroups and serum albumin. Finally, a multivariate logisticanalysis assessed the relationship of several variables withthe SGA score.

Results

Patient demographics were compared among theSGAVP, the NAPR, and the total sample of patients(Table 1). Thirty-two percent of the patients had SGAscores of 5 or less. Furthermore, 65% of the total sample

Table 1. Patient Demographics

Patient Characteristic SGAVP NAPR

Total 153 100

Male (%) 54 45Age* (y) 64 6 14 61 6 15

BMI* 28 6 7 30 6 8

Serum albumin* 3.7 6 0.4 3.9 6 0.3

SGA (%)#5 29 37

.5 71 63

BMI (%)

,30 kg/m2 71 56.30 kg/m2 29 44

BMI, bodymass index; NAPR, Nutrition AlgorithmPreliminary Report; SG

ment Validation Project.

*Data are presented as mean (6standard deviation) or %.

had a BMI less than 30 kg/m2, whereas 35% were catego-rized as obese with a BMI greater than 30 kg/m2.Bivariate analysis showed significant differences in BMI

values and SGA scores between the obese and nonobesegroups (Fig. 1). The mean BMI values were statisticallydifferent between SGA groups in the nonobese sample (AN-OVA, P,.0080). However, in the obese sample, they werenot statistically different (ANOVA, P , .3468; Fig. 2).The mean serum albumin concentrations were

compared across SGA groups using ANOVA. In the obesepatients, the mean albumin concentration was not differentbetween the SGA groups (P , .0943). However, for non-obese patients, the data were statistically significant(P , .0183), with a lower mean serum albumin in theSGA less than 5 group. A multivariable nominal logisticfit was used to predict SGA group with age and gender ascovariates. Serum albumin (P , .0057) and BMI valuesless than 30 (P , .0090) predicted SGA group.

Discussion

The purpose of the secondary analysis was to assess therelationship in obese HD patients between serum albumin,a strong predictor of mortality, and SGA. SGA has been anassessment tool used as a reliable way to determine thenutritional status among CKD patients.9-14 However, asSacks and colleagues15 explain, a drawback of the SGA isthat it only attempts to identify malnutrition whereas obesepatients are classified as well-nourished by the SGA. In anobese individual, the extra fat can be easily mistaken forhealthy tissue, which masks the actual state of undernutri-tion and muscle loss. With obesity, in more than 25% oftheCKDpopulation,6 the importance of an accurate assess-ment of nutritional status in these patients is vital.One of the challenges in this analysis was to define

obesity. When we classified the data into quartiles, the cut-points were as follows: less than 23, 23 to 26.9, 27 to 32, andgreater than 32. However, given the limited evidence forany cutpoint in this population, we felt that it was more

Combined Sample P Value, SGAVP vs. NAPR

253

4963 6 14 .12

29 6 8 .10

3.8 6 0.4 .01

.0532

68

6535

A, SubjectiveGlobal Assessment; SGAVP, SubjectiveGlobal Assess-

Figure 1. Distribution of patients (N 5 253) by SubjectiveGlobal Assessment scores.

SGA SCORES IN OBESE HD PATIENTS 3

appropriate to define obesity as greater than 30 kg/m2,following the precedent for studies examining the obesityparadox in HD patients.7

We examined several factors to determine the relation-ship of SGA and serum albumin in obese patients. Serumalbumin concentrations are often used to evaluate the nutri-

Figure 2. (Top) In the total number of patients, the mean BMI valuand those with SGA scores #5 (ANOVA, P , .0069). (Bottom left)tween the two SGA groups (ANOVA, P , .3468). (Bottom right) BMtients (ANOVA, P , .0080). ANOVA, analysis of variance; BMI, bo

tional status and the overall health risk in HD patients,17-22

and for that reason, we compared serum albuminconcentration between the 2 SGA groups. Serumalbumin concentrations were statistically different amongSGA groups in the underweight/normal weight patients,but not among the obese patients. Although Gama-Axelsson and colleagues17 found that the reliability of serumalbumin as a marker of nutritional status in end-stage renaldisease patients is limited, numerous studies reported thatserum albumin levels play a crucial role in the predictionof severe disease-related malnutrition and mortality.18-22

Furthermore, data relating SGA scores and BMI valuesindicated that SGA scores are a good indicator of BMIvalues in nonobese populations. In obese patients, theSGA score may not be an accurate assessment ofmalnutrition.The training and experience of clinicians likely influence

the SGA scores; therefore, they are covariates in the rela-tionship between SGA and serum albumin in obese HD

es are statistically different between well-nourished patientsIn obese patients, mean BMI does not vary significantly be-I varies significantly between SGA groups in nonobese pa-

dy mass index; SGA, Subjective Global Assessment.

ERB ET AL4

patients. Thus, to enhance the sensitivity for detectingmalnutrition in patients who are obese, clinicians mayneed to be trained to assess for muscle and wasting in pa-tients of all sizes and integrate SGA into their clinical prac-tice for all patients to gain experience and confidence inusing the assessment on patients with varying body types.Other tools, such as bedside body composition analysis orobjective measures of functional capacity such as handgripstrength, may be useful to assess muscle loss in obesepatients.

However, only the overall SGA scores were used in thisanalysis, and subscores for each component were notconsidered. This has potential for interesting future anal-ysis, and further work should look at the SGA subscoresin obese and nonobese patients. Patients should also betracked over time to see whether the well-established rela-tionship between decreasing SGA score and increasingmortality26 is different in obese patients.

Practical Application

Assessing the nutritional status, and, particularly, malnu-trition or risk of malnutrition is extremely important indialysis patients given the link between poor nutrition sta-tus and mortality and morbidity. SGA has been used as avalid and reliable nutrition assessment in this population;however, given its reliance on the physical examination,its accuracy in obese patients is questionable. This studyshows that serum albumin is significantly different acrossSGA groups in nonobese HD patients, but not in obeseHD patients, suggesting that this useful and clinically rele-vant tool may need reexamination and increased trainingfor clinicians working with obese patients.

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