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Challenges in nutritional evaluation of hospitalized elderly; always with mini-nutritional assessment?
European Geriatric Medicine 4 (2013) 231–236
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Research paper
Challenges in nutritional evaluation of hospitalized elderly; always with mini-nutritional assessment? M.E. Kuyumcu a,*, Y. Yes¸il a, Z.A. Oztu¨rk b, M. Halil a, Z. Ulger a, B.B. Yavuz a, M. Cankurtaran a, E. Gu¨ngo¨r c, G. Erdog˘an c, T. Besler d, S. Arıog˘ul a a
Hacettepe University, Faculty of Medicine, Department of Internal Medicine, Division of Geriatric Medicine, Sihhiye, 06100 Ankara, Turkey Gaziantep University, Faculty of Medicine, Department of Internal Medicine, Division of Geriatric Medicine, Sahinbey, 27100 Gaziantep, Turkey Hacettepe University Hospitals, Dietetic Unit, Sihhiye, 06100 Ankara, Turkey d Hacettepe University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Sihhiye, 06100 Ankara, Turkey b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 8 November 2012 Accepted 21 January 2013 Available online 6 March 2013
Purpose: The mini-nutritional assessment (MNA) has been used most frequently for the determination of malnutrition in older adults; however it is not suitable for some hospitalized patients. The aim of this study was to identify the nutritional status of the elderly hospitalized patients by MNA and by other assessment methods including anthropometric measurements, bioelectrical impedance analysis (BIA) and laboratory parameters and to demonstrate the correlation of them with MNA. Patients: A total of 100 patients were included in the study. Linear regression analysis was performed to compare the effects of parameters on MNA scores. Results: By MNA; 69% of the patients were found to have nutritional risk, 12% were malnourished and 19% were well nourished. Skinfold thicknesses, calf circumference, prealbumin, albumin, C-reactive protein (CRP), glomerular filtration rate (GFR) and some of the BIA parameters including body fat, free fat mass index, extracellular, intracellular and total body water were significantly correlated with MNA score in univariate analysis. On the other hand; only suprailiac skinfold thickness (OR: 0.106, 95%CI: 0.031– 0.181, P: 0.006), prealbumin (OR: 0.256, 95%CI: 0.158–0.354, P < 0.001) and GFR (OR: 0.036, 95%CI: 0.014–0.057, P: 0.01) were significantly correlated with MNA in multivariate analysis. Conclusions: These parameters or their combinations can be used for the evaluation of nutritional status in hospitalized older adults when the MNA cannot be used. ß 2013 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved.
Keywords: Nutritional status Geriatrics Nutritional assessment Malnutrition Skinfold thickness BIA
1. Introduction Malnutrition is highly prevalent in geriatric patients; however it is often regarded as a normal age-associated phenomenon and often neglected by many clinicians. The prevalence of malnutrition risk is approximately 28% in geriatric outpatients, malnutrition rates of older adults are 5.8–13% in the community, 25–38.7% in hospitalized patients, 13.8% in nursing home residents, and 50.5% in patients taking rehabilitation [1–4]. Malnutrition has been associated with increased readmissions of hospital and prolonged hospital stay, increased frequency and severity of infections, poor wound healing, gait disorders, falls, and fractures [5–10]. Therefore, early detection and prevention of malnutrition is very important. Several screening and assessment tools have been proposed, however, there is no gold standard for evaluating nutritional status [11]. The European Society for Clinical
* Corresponding author. Tel.: +90 312 305 15 38; fax: +90 312 309 76 20. E-mail address: [email protected] (M.E. Kuyumcu).
Nutrition and Metabolism (ESPEN) recommends the mini-nutritional assessment (MNA) [12–14] as the basis for nutritional screening in older people, at times supplemented by laboratory values, anthropometric parameters or determination of body composition. However, MNA administration takes time, which may limit application of the test for the nutritional assessment of the elderly patients. It is also important to emphasize the difficulty of assessing the nutritional status of hospitalized older adults since many of them are bedridden, some are unconscious, some have dementia or other problems of communication and thus, cannot be weighed and nutritional history cannot be achieved. In these cases, anthropometric measurements and bioelectric impedance analysis (BIA) may be good alternative indicators of nutritional status [15]. Anthropometric measures including weight, height, circumferences and skinfold thicknesses are non-invasive techniques that provide information or estimation of body composition, fat, and muscle stores [16]. BIA, as a non-invasive and easily performed bedside technique of body composition analysis [17] works mainly through the measurement of the body’s resistance and reactance to an alternative electrical current [18]. Therefore, elucidation of the
1878-7649/$ – see front matter ß 2013 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved. http://dx.doi.org/10.1016/j.eurger.2013.01.010
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correlation of these methods with MNA will be helpful for determining the nutritional status of hospitalized elderly patients when the MNA cannot be used. The aim of this study was to identify the nutritional status of the elderly hospitalized patients by MNA and by other assessment methods including anthropometric measurements, BIA and laboratory parameters and to demonstrate the correlation of them with MNA, as well.
inelastic metric tape measure with 0.1 cm accuracy was used to measure arm and calf circumferences according to the WHO’s standard technique. Skinfold thicknesses were measured with the Holtain skinfold caliper [21] by dieticians. Bioelectric impedance was measured with a Bodystat quadscan 4000 device (Florida, USA) in a multi-frequency, tetrapolar technique on the right side of the body in supine position [22] by a geriatrician (MD). Geriatrician and dieticians were blind to the other results.
2. Patients and methods
2.4. Biochemical analysis
2.1. Study design and subjects
Biochemical measurements relevant to nutritional status such as fasting plasma glucose, blood urea nitrogen, serum creatinine, electrolytes, liver enzymes, total plasma protein, albumin, total plasma levels of total cholesterol (TC), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterols, triglycerides (TG), vitamin B12, C-reactive protein (CRP), erythroid sedimentation rate (ESR), urine analysis, complete blood counts, and prealbumin were performed.
This study was carried out with 100 elderly patients of both genders (56 males and 44 females), staying at the internal medicine service of Hacettepe University Hospital in Turkey between August 2009 and February 2010. The inclusion criteria were: age equal to or greater than 65 years, having undergone a nutritional assessment in the first 72 hours of their stay at the hospital, and information regarding nutritional status, disease and length of hospital stay documented in the medical records of the institution. Patients with severe edema, pacemaker, prothesis, and severe electrolyte imbalance were excluded, because they may result in incorrect measurements of BIA. Co-morbidities (e.g. diabetes mellitus, hypertension, coronary heart disease, cerebrovascular disease, osteoporosis) were defined using patients’ self-report and current medications, after the evaluation of the patient by comprehensive geriatric assessment and laboratory tests. The study protocol was approved by the Hacettepe University Local Research Ethics Committee. Patients were informed about the study and informed consents were taken. 2.2. Comprehensive geriatric assessment tests All patients underwent a complete and a standardized comprehensive geriatric assessment by using the short geriatric depression scale (S-GDS) with 15 questions [19], the mini-mental state examination (MMSE) [19], Barthel index of activities of daily living (ADL) [20] the instrumental activities of daily living (IADL) [19] and the ‘‘Get up and go’’ test (GUGT) [19]. S-GDS scores of 5 and over were considered as suggestive for depression and MMSE scores of 24 and below were considered as impaired, suggesting dementia. ADL and IADL were performed in order to measure the level of dependency. In ADL, 0 point suggest independency, 20 point suggest maximum dependency. In IADL, 24 point suggest independency, 0 point suggest maximum dependency. Patients underwent the GUGT to assess gait and balance problems as well as mobility disorders.
2.5. Statistical analysis Statistical Package for Social Sciences (SPSS) for Windows 15.0 programme was used for statistical analysis. All data were entered into a database and were verified by a second independent person. The variables were investigated using visual (histograms, probability plots) and analytical methods to determine whether or not they are normally distributed. Data are presented as mean and S.D. for normally distributed variables and as median, (minimum–maximum) for skew distributed continuous variables [ADL, S-GDS, CRP and body fat mass index (BFMI)]. Categorical variables are shown as frequencies. In order to make comparison between MNA groups, One-Way ANOVA was used for normally distributed variables and Kruskal-Wallis variance analysis was used for ADL, S-GDS, CRP and BFMI. Correlation analyses were performed with Pearson test for normally distributed variables and Spearman test for not normally distributed parameters. Linear regression analysis was performed to compare the effects of parameters to MNA scores with 95% confidence intervals (CI). MNA score was selected as the dependent variable, and biceps, triceps, subscapular, suprailiac skinfold thicknesses, body fat (% and kg), total body water (%), extracellular and intracellular body waters (%), Free Fat Mass Index (FFMI), prealbumin, albumin, CRP and GFR were selected as independent variables. The stepwise variable selection method was used to avoid multicolinerearity problem in final model. Colinerearity among the variables was checked using the generalized varianceinflation factor method. The colinearity threshold was defined at the value of 5; lower values indicate no colinearity. Two-sided values of P < 0.05 were considered as statistically significant. 3. Results
2.3. Nutritional evaluation and anthropometric measurements 3.1. Subjects The nutritional status of the patients was assessed within the first 3 days of their hospital stay by MNA, anthropometric measurements and BIA. The MNA contains 18 items, which are divided into four categories: anthropometric measurements, general assessment, dietary assessment and subjective assessment. Final score classifies nutritional state as ‘well nourished’ (MNA scores 24), ‘at risk for malnutrition’ (MNA scores from 17 to 23.5) and ‘malnourished’ (MNA scores < 17) [12–14]. The following anthropometric indicators were measured: current weight, height, mid-upper arm circumference, body mass index (BMI), biceps, triceps, subscapular and suprailiac skinfold thicknesses, waist, hip, and calf circumferences. A flexible and
A total of 100 patients (56 male, 44 female) were included in this study. The mean age was 72.74 6.36. The number of patients aged 65–74 ages was 64 (64%), 75–84 was 32 (32%) and 85 was four (4%). The most frequent co-morbidity diseases were hypertension (80%), diabetes mellitus (49%) and coronary heart disease (38%). Most important reasons of hospital admission of patients were dyspnea (73%), regulation of diabetes mellitus (32%) and infections (27%). Additionally, prevalence of dementia was 9%, depression was 23% and urinary incontinence was 32%. Overall, within the study group, 69% were found to have nutritional risk, 12% of the subjects were malnourished and only 19% of the subjects were well nourished by MNA (Fig. 1).
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Table 2 Correlation analyses results between skinfold thicknesses, calf circumference, BIA, and laboratory parameters with MNA score. MNA
Fig. 1. The number of patients according to the MNA groups.
3.2. Bioelectrical impedance analysis (BIA), laboratory and anthropometric measurement parameters of patients according to mini-nutritional assessment (MNA) Characteristics of the patients according to the nutritional status determined with the MNA are depicted in Table 1. The results of comprehensive geriatric assessment tests and the skinfold thickness of the patients were worst in the malnourished group (Table 1). The length of stay in the malnourished group, the nutritionally at-risk group and well nourished group were 19.08 16.61, 17.60 11.17 and 12.72 7.28 days, respectively (P: 0.258). Prealbumin levels and GFR of the patients with malnutrition were significantly lower and CRP was significantly higher than the other groups (Table 1). There were no significant differences in other laboratory tests. There were no significant differences in BIA parameters between MNA groups. Biceps (P: 0.002, r: 0.303), triceps (P < 0.001, r: 0.384),
Biceps (mm) Triceps (mm) Subscapular (mm) Suprailiac (mm) Calf (cm) Body fat (%) Body fat (kg) TBW (%) ECW (%) ICW (%) FFMI (kg/m2) Prealbumin (mg/dL) Albumin (g/dL) CRP (mg/dL) GFR (mL/min)
r
P value
0.303 0.384 0.402 0.642 0.510 0.201 0.300 0.304 0.337 0.240 0.235 0.485 0.239 0.259 0.298
0.002a < 0.001a < 0.001a < 0.001a < 0.001a 0.046a 0.003a 0.002a 0.001a 0.017a 0.019a < 0.001a 0.017a 0.009a 0.003a
TBW: total body water; ECW: extracellular body water; ICW: intracellular body water; FFMI: free fat mass index; MNA: mini-nutritional assessment; BIA: bioelectrical impedance analysis; GFR: glomerular filtration rate; CRP: C-reactive protein. a Statistically significant differences (P < 0.05).
subscapular (P < 0.001, r: 0.402), and suprailiac (P < 0.001, r: 0.642) skinfold thicknesses, calf circumference (P < 0.001, r: 0.510), prealbumin (P < 0.001, r: 0.485), albumin (P: 0.017, r: 0.239), GFR (P: 0.003, r: 0.298), and some of the BIA parameters including percent of body fat (%) (P: 0.046, r: 0.201), body fat presented with kg (P: 0.003, r: 0.300), and FFMI (P: 0.019, r: 0.235) were significantly and positively correlated with MNA score. Correlations between suprailiac, subscapular skinfold thicknesses, calf circumference and prealbumin with MNA scores are presented on Fig. 2. Negative and significant correlations were found between CRP (P: 0.009, r: 0.259), total body water (P: 0.002, r: 0.304), extracellular body water (P: 0.001, r: 0.337) and MNA scores (Table 2).
Table 1 Characteristics of the patients according to the nutritional status determined with the MNA.
Age (years) Gender (male/female) Length of hospital stay (days) Number of comorbid disease Number of drugs ADL, median, (min-max) IADL MMSE S-GDS, median, (min-max) GUGT Height (cm) Weight (kg) Waist (cm) Hip (cm) BMI (kg/m2) Mid-upper Arm (cm) Calf (cm) Biceps (mm) Triceps (mm) Suprailiac (mm) Subscapuler (mm) GFR (mL/min) Albumin (g/dL) LDL (mg/dL) Hemoglobin (g/dL) CRP, mg/dL, median, (min-max) Prealbumin, mg/dL
< 17 (n = 12)
17–23.5 (n = 69)
24 (n = 19)
P value
75.00 2.30 9/3 19.08 16.61 5.33 2.01 7.16 4.06 0.00, (0.00–20.00) 15.83 1.00 23.75 8.72 5.00, (0.00–10.00) 3.83 3.35 166.01 11.74 62.81 16.54 85.45 13.10 96.12 13.64 22.87 6.75 27.47 4.78 29.77 3.44 5.52 2.58 10.71 5.17 12.00 6.67 14.22 6.19 39.46 22.28 3.49 0.75 100.21 26.22 11.55 2.72 4.35, (0.29–515) 12.29 4.05
73.28 0.76 36/33 17.60 11.17 4.86 1.77 6.72 3.23 0.00, (0.00–18.00) 19.13 7.50 27.01 4.39 0.00, (0.00–10.00) 6.40 1.88 161.43 10.24 71.62 11.89 96.98 11.94 104.81 11.66 27.55 5.17 31.40 3.34 33.96 3.78 9.82 6.15 18.61 8.43 20.45 8.07 21.89 7.97 59.34 29.20 3.66 0.58 101.47 38.82 11.84 2.11 1.52, (0.01–43) 17.86 6.01
69.37 0.89 11/8 12.72 7.28 4.68 1.33 6.52 1.95 0.00, (0.00–10.00) 22.89 3.68 29.00 1.33 0.00, (0.00–8.00) 6.78 0.91 160.57 11.87 77.84 15.91 101.73 14.87 107.86 14.78 30.50 7.67 32.94 3.63 37.12 4.14 8.98 4.21 19.64 9.26 21.10 9.60 23.10 8.57 68.59 31.10 3.99 0.55 108.73 36.76 12.55 2.20 0.79, (0.15–18.70) 23.05 6.46
0.029a 0.337 0.258 0.570 0.963 0.073 0.012a 0.020a 0.084 0.001a 0.341 0.011a 0.003a 0.038a 0.003a < 0.001a < 0.001a 0.011a 0.003a 0.003a 0.004a 0.030a 0.078 0.850 0.376 0.028a < 0.001a
ADL: the activities of daily living; IADL: the instrumental activities of daily living; MMSE: the mini-mental state examination; GUGT: ‘‘get-up and go’’ test; BMI: body mass index; GFR: glomerular filtration rate; LDL: low-density lipoprotein cholesterol; CRP: C-reactive protein; MNA: mini-nutritional assessment. a Statistically significant differences (P < 0.05).
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Fig. 2. Correlations between suprailiac, subscapular skinfold thicknesses, calf circumference and prealbumin with MNA scores.
Results of linear regression analysis are summarized in Table 3. MNA score significantly correlated with prealbumin (OR: 0.256, 95%CI: 0.158–0.354, P < 0.001), suprailiac skinfold thickness (OR: 0.106, 95%CI: 0.031–0.181, P: 0.006) and GFR (OR: 0.036, 95%CI: 0.014–0.057, P: 0.01) in multivariate analysis. 4. Discussion This study examined the nutritional status of the hospitalized elderly patients on admission by MNA, anthropometric measurements and BIA. The nutritional assessment of the study population by MNA revealed that 69% was at nutritional risk and 12% was malnourished. The relationship between nutritional status and functional capacity was also investigated. The importance of this study was to investigate the nutritional status by various nutritional indicators and to explore the correlation of them with MNA. Skinfold thicknesses (biceps, triceps, subscapular and
Table 3 Results of linear regression analysis of the possible correlates with MNA score. Odds ratio (b)
Prealbumin (mg/dL) Suprailiac (mm) GFR (mL/min) CRP (mg/dL)
0.256 0.106 0.036 0.013
95%CI
P value
Lower
Upper
0.158 0.031 0.014 0.025
0.354 0.181 0.057 0.0001
< 0.001a 0.006a 0.010a 0.044a
GFR: glomerular filtration rate; CRP: C-reactive protein; MNA: mini-nutritional assessment; CI: confidence interval. a Statistically significant differences (P < 0.05).
suprailiac), calf circumference, prealbumin, albumin, GFR, and some of the BIA parameters including percent of body fat (%), body fat presented with kg, and FFMI were the nutritional indicators that were found to be correlated with MNA in this study. Therefore, this valuable information would lead these nutritional indicators, to be used to determine nutritional status when the MNA cannot be used. The prevalence of malnutrition is 25–38.7% in hospitalized elderly [2,3]. So, it is a common problem and has significant effects on health and the economy. Therefore, an emphasis should be placed on an effective nutritional policy in hospitals. A systematic early identification and treatment of malnutrition is important. Screening for malnutrition is an essential measure in the prevention of malnutrition. A variety of different methods are available for the assessment of malnutrition in older adults, but there is no gold standard [16,23]. MNA is recommended by the ESPEN for detecting the presence of malnutrition and the risk of developing malnutrition among the elderly [11,12]. By using the MNA test, 78% of the patients can be classified correctly [24]. However, MNA is time consuming and as reported by a supportive data [25,26], it cannot be completed in a substantial part of patients because of cognitive impairment and communication problems. Anthropometric measurements including weight, height, circumference, and skinfold thickness have also been used as nutritional indicators. The measurement of mid-upper arm circumference and skinfold thicknesses is thought to provide a crude assessment of fat stores and muscle mass [16]. Skinfold thicknesses are also found to be highly correlated with body fat as estimated by densitometry [27]. However, skinfold thickness
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measurement may be less accurate in older adults because of the age-associated physical changes including redistribution of fat from subcutaneous to deep adipose tissues, decreased elasticity of skin, alterations in skin thickness and atrophy of subcutaneous adipocytes contributing to increased tissue compression. Another significant problem is difficulty in accurately locating anatomic landmarks for measurements, even when attempted by experienced clinicians [28]. Notwithstanding, very low or high skinfold thicknesses have been displayed as an indicator of poor nutritional status [29]. In a recent study mid-upper arm circumference, BMI, triceps skinfold thickness, and mid-upper arm muscle circumference were reported to be capable of predicting MNA classification [15]. We also found a significant correlation between skinfold thicknesses (biceps, triceps, subscapular and suprailiac) and MNA in this present study supporting this hypothesis. The calf circumference represents an anthropometric parameter of muscle mass; it reflects disability and self-reported physical function [30]. Furthermore, a significant correlation between MNA and calf circumference was found in this study suggesting that the calf circumference represents a valid parameter of malnutrition [31]. According to the results of the multivariate analysis in our study, especially suprailiac skinfold thickness is a useful and practical indicator for nutritional status. BIA allows the determination of the fat-free mass and total body water in subjects. It is very suitable for older adults because of the low physical demand [16]. However, BIA measurements may not be reliable in patients who often suffer from disturbances of fluid distribution and content. The association of BIA and nutrition in geriatric patients has already been examined in other settings. The measurement of FFM and FM of older persons by BIA may be affected by nutritional status, so that in undernourished subjects the correlation of BIA measurements with those obtained by dualenergy x-ray absorptiometry is less strong than in eutrophic subjects. Moreover, there is a tendency of BIA to overestimate FFM and underestimate FM in undernourished subjects. So they said that BIA is not a reliable method for the individual assessment of body composition [32]. In our study, patients with severe edema, pacemaker, prothesis, and severe electrolyte imbalance were excluded, because they may result in incorrect measurements of BIA. After all, body fat (% and kg), TBW (%), IBW (%), EBW (%), and FFMI were significantly correlated with MNA in univariate analysis but significant correlations were not found in multivariate analysis. Laboratory tests may also reflect malnutrition. Although a number of serum proteins have been described to be related to malnutrition [33–35], many of them are influenced by factors other than nutrition, especially inflammation. Therefore, these serum proteins are not specific for malnutrition [36]. Biochemical nutritional assessment mainly aims to identify individuals who will benefit from nutritional therapies, to detect and treat micronutrient deficiencies and to measure the effectiveness of nutritional intervention. Laboratory tests can be more sensitive than other methods in showing recent changes in nutritional status, but, there is no clear criterion for the interpretation in the geriatric age group [23]. Prealbumin is popularized by its short half-life and superior sensitivity in evaluating acute nutritional change. Severely low levels of prealbumin were reported to be related with increased in-hospital stay [23]. CRP reflects a catabolic state, however the close relation between nitrogen balance and catabolic state suggests that CRP can be used as an indirect marker of undernutrition [37]. In our study GFR and prealbumin levels were lowest and CRP levels were highest in malnourished group (P: 0.030, < 0.001, 0.028, respectively). Multivariate analysis indicated prealbumin and GFR as significantly important correlates of MNA in our study. Previously, studies demonstrated that GFR was significantly associated with malnutrition independent of relevant
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demographic, social and medical conditions. Although mechanisms underlying this association are not clear, some explanations can be put forward. It was determined that in patients with lower GFR, levels of some inflammatory markers are increased. This inflammatory state may play a role in malnutrition in these patients. The other explanation is that a decreased nutrient intake was found to be evident to a GFR less than 60 mL/min 1.73 m2, so anorexia frequently accompanies renal insufficiency [38]. Although CRP appears to be negatively and significantly correlated with MNA in univariate analysis, a significant correlation was not found between CRP and MNA in multivariate analysis. So, merely CRP seems not to be suitable for nutritional assessment. Combination of prealbumin and CRP may be more reliable for the evaluation of malnutrition. This study also suggested the relationship between nutritional status, functional capacity and mobility of hospitalized older adults. Malnourished individuals were more dependent and more limited. We found that 12% of patients were malnourished and 69% were at the risk of malnutrition. Considering the evaluation of the nutritional status in the first 72 hours of their hospital stay, we may suggest that these patients were already malnourished or at risk of malnourishment at admission. Therefore, screening for malnutrition in every geriatric patient (both out-patient and inpatient) is an essential measure in the prevention of malnutrition. The limitations of the study should be noted. The relatively small study sample and relatively short follow-up for determining the effect of parameters on length of hospital stay can be defined as the limitations.
5. Conclusion In our cohort, 69% of the study population was at malnutrition risk and 12% was malnourished according to MNA. However, MNA cannot be performed in some situations. Some anthropometric measurements, BIA and laboratory parameters or combinations of them may be used for the evaluation of nutritional status in hospitalized older adults when the MNA cannot be used.
Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. References ¨ lger Z, Halil M, Kalan I, et al. Comprehensive assessment of malnutrition risk [1] U and related factors in a large group of community-dwelling older adults. Clin Nutr 2010;29:507–11. [2] Kaiser MJ, Bauer JM, Ra¨msch C, et al. Frequency of malnutrition in older adults: a multinational perspective using the mini nutritional assessment. J Am Geriatr Soc 2010;58:1734–8. [3] Saka B, Kaya O, Ozturk GB, et al. Malnutrition in the elderly and its relationship with other geriatric syndromes. Clin Nutr 2010;29:745–8. [4] Norman K, Pichard C, Lochs H, Pirlich M. Prognostic impact of disease-related malnutrition. Clin Nutr 2008;27:5–15. [5] Herrmann FR, Safran C, Levkoff SE, Minaker KL. Serum albumin level on admission as a predictor of death, length of stay, and readmission. Arch Intern Med 1992;152:125–30. [6] Sullivan DH, Sun S, Walls RC. Protein-energy undernutrition among elderly hospitalized patients: a prospective study. JAMA 1999;281:2013–9. [7] Potter J, Klipstein K, Reilly JJ, Roberts M. The nutritional status and clinical course of acute admissions to a geriatric unit. Age Ageing 1995;24:131–6. [8] Paillaud E, Herbaud S, Caillet P, et al. Relations between undernutrition and nosocomial infections in elderly patients. Age Ageing 2005;34:619–25. [9] Schneider SM, Veyres P, Pivot X, et al. Malnutrition is an independent factor associated with nosocomial infections. Br J Nutr 2004;92:105–11. [10] de Luis DA, Izaola O, Cuellar L, et al. Nutritional assessment: predictive variables at hospital admission related with length of stay. Ann Nutr Metab 2006;50:394–8. [11] Jeejeebhoy KN. Nutritional assessment. Nutrition 2000;16:585–90.
236
M.E. Kuyumcu et al. / European Geriatric Medicine 4 (2013) 231–236
[12] Guigoz Y. The mini nutritional assessment (MNA) review of the literature – What does it tell us? J Nutr Health Aging 2006;10:466–85 [Discussion 485–467]. [13] Gazzotti C, Albert A, Pepinster A, Petermans J. Clinical usefulness of the mini nutritional assessment (MNA) scale in geriatric medicine. J Nutr Health Aging 2000;4:176–81. [14] Van Nes MC, Herrmann FR, Gold G, et al. Does the mini nutritional assessment predict hospitalization outcomes in older people? Age Ageing 2001;30:221–6. [15] Leandro-Merhi VA, De Aquino JL. Anthropometric parameters of nutritional assessment as predictive factors of the mini nutritional assessment (MNA) of hospitalized elderly patients. J Nutr Health Aging 2011;15:181–6. [16] Omran ML, Morley JE. Assessment of protein energy malnutrition in older persons, part I: history, examination, body composition, and screening tools. Nutrition 2000;16:50–63. [17] Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis – part I: review of principles and methods. Clin Nutr 2004;23:1226–43. [18] Erselcan T, Candan F, Saruhan S, Ayca T. Comparison of body composition analysis methods in clinical routine. Ann Nutr Metab 2000;44:243–8. [19] Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 1982;17: 37–49. [20] Mahoney FI, Barthel DW. Functional evaluation: the Barthel index. Md State Med J 1965;14:61–5. [21] Zillikens MC, Conway JM. Anthropometry in blacks: applicability of generalized skinfold equations and differences in fat patterning between blacks and whites. Am J Clin Nutr 1990;52:45–51. [22] Verdich C, Barbe P, Petersen M, et al. Changes in body composition during weight loss in obese subjects in the NUGENOB study: comparison of bioelectrical impedance vs. dual-energy X-ray absorptiometry. Diabetes Metab 2011;37: 222–9. [23] Omran ML, Morley JE. Assessment of protein energy malnutrition in older persons, Part II: laboratory evaluation. Nutrition 2000;16:131–40. [24] Guigoz Y, Vellas B. The Mini Nutritional Assessment (MNA) for grading the nutritional state of elderly patients: presentation of the MNA, history and validation. Nestle Nutr Workshop Ser Clin Perform Programme 1999;1:3–11 [Discussion 11–12]. [25] Bauer JM, Vogl T, Wicklein S, et al. Comparison of the mini nutritional assessment, subjective global assessment, and nutritional risk screening
[26]
[27]
[28]
[29] [30]
[31]
[32]
[33]
[34] [35] [36]
[37] [38]
(NRS 2002) for nutritional screening and assessment in geriatric hospital patients. Z Gerontol Geriatr 2005;38:322–7. Cuervo M, Ansorena D, Garcı´a A, et al. Food consumption analysis in spanish elderly based upon the mini nutritional assessment test. Ann Nutr Metab 2008;52:299–307. Heymsfield SB, McManus C, Smith J, et al. Anthropometric measurement of muscle mass: revised equations for calculating bone-free arm muscle area. Am J Clin Nutr 1982;36:680–90. Sullivan DH, Patch GA, Baden AL, Lipschitz DA. An approach to assessing the reliability of anthropometrics in elderly patients. J Am Geriatr Soc 1989;37: 607–13. Frisancho AR. New norms of upper limb fat and muscle areas for assessment of nutritional status. Am J Clin Nutr 1981;34:2540–5. Rolland Y, Lauwers-Cances V, Cournot M, et al. Sarcopenia, calf circumference, and physical function of elderly women: a cross-sectional study. J Am Geriatr Soc 2003;51:1120–4. Drescher T, Singler K, Ulrich A, et al. Comparison of two malnutrition risk screening methods (MNA and NRS 2002) and their association with markers of protein malnutrition in geriatric hospitalized patients. Eur J Clin Nutr 2010;64: 887–93. Vilaca KH, Paula FJ, Ferriolli E, et al. Body composition assessment of undernourished older subjects by dual-energy x-ray absorptiometry and bioelectric impedance analysis. J Nutr Health Aging 2011;15:439–43. Corti MC, Guralnik JM, Salive ME, Sorkin JD. Serum albumin level and physical disability as predictors of mortality in older persons. JAMA 1994;272: 1036–42. Mu¨hlethaler R, Stuck AE, Minder CE, Frey BM. The prognostic significance of protein-energy malnutrition in geriatric patients. Age Ageing 1995;24:193–7. Sergi G, Coin A, Enzi G, et al. Role of visceral proteins in detecting malnutrition in the elderly. Eur J Clin Nutr 2006;60:203–9. Devoto G, Gallo F, Marchello C, et al. Prealbumin serum concentrations as a useful tool in the assessment of malnutrition in hospitalized patients. Clin Chem 2006;52:2281–5. Kaysen GA, Rathore V, Shearer GC, Depner TA. Mechanisms of hypoalbuminemia in hemodialysis patients. Kidney Int 1995;48:510–6. Garg AX, Blake PG, Clark WF, et al. Association between renal insufficiency and malnutrition in older adults: results from the NHANES III. Kidney Int 2001;60: 1867–74.
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Research paper
Challenges in nutritional evaluation of hospitalized elderly; always with mini-nutritional assessment? M.E. Kuyumcu a,*, Y. Yes¸il a, Z.A. Oztu¨rk b, M. Halil a, Z. Ulger a, B.B. Yavuz a, M. Cankurtaran a, E. Gu¨ngo¨r c, G. Erdog˘an c, T. Besler d, S. Arıog˘ul a a
Hacettepe University, Faculty of Medicine, Department of Internal Medicine, Division of Geriatric Medicine, Sihhiye, 06100 Ankara, Turkey Gaziantep University, Faculty of Medicine, Department of Internal Medicine, Division of Geriatric Medicine, Sahinbey, 27100 Gaziantep, Turkey Hacettepe University Hospitals, Dietetic Unit, Sihhiye, 06100 Ankara, Turkey d Hacettepe University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Sihhiye, 06100 Ankara, Turkey b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 8 November 2012 Accepted 21 January 2013 Available online 6 March 2013
Purpose: The mini-nutritional assessment (MNA) has been used most frequently for the determination of malnutrition in older adults; however it is not suitable for some hospitalized patients. The aim of this study was to identify the nutritional status of the elderly hospitalized patients by MNA and by other assessment methods including anthropometric measurements, bioelectrical impedance analysis (BIA) and laboratory parameters and to demonstrate the correlation of them with MNA. Patients: A total of 100 patients were included in the study. Linear regression analysis was performed to compare the effects of parameters on MNA scores. Results: By MNA; 69% of the patients were found to have nutritional risk, 12% were malnourished and 19% were well nourished. Skinfold thicknesses, calf circumference, prealbumin, albumin, C-reactive protein (CRP), glomerular filtration rate (GFR) and some of the BIA parameters including body fat, free fat mass index, extracellular, intracellular and total body water were significantly correlated with MNA score in univariate analysis. On the other hand; only suprailiac skinfold thickness (OR: 0.106, 95%CI: 0.031– 0.181, P: 0.006), prealbumin (OR: 0.256, 95%CI: 0.158–0.354, P < 0.001) and GFR (OR: 0.036, 95%CI: 0.014–0.057, P: 0.01) were significantly correlated with MNA in multivariate analysis. Conclusions: These parameters or their combinations can be used for the evaluation of nutritional status in hospitalized older adults when the MNA cannot be used. ß 2013 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved.
Keywords: Nutritional status Geriatrics Nutritional assessment Malnutrition Skinfold thickness BIA
1. Introduction Malnutrition is highly prevalent in geriatric patients; however it is often regarded as a normal age-associated phenomenon and often neglected by many clinicians. The prevalence of malnutrition risk is approximately 28% in geriatric outpatients, malnutrition rates of older adults are 5.8–13% in the community, 25–38.7% in hospitalized patients, 13.8% in nursing home residents, and 50.5% in patients taking rehabilitation [1–4]. Malnutrition has been associated with increased readmissions of hospital and prolonged hospital stay, increased frequency and severity of infections, poor wound healing, gait disorders, falls, and fractures [5–10]. Therefore, early detection and prevention of malnutrition is very important. Several screening and assessment tools have been proposed, however, there is no gold standard for evaluating nutritional status [11]. The European Society for Clinical
* Corresponding author. Tel.: +90 312 305 15 38; fax: +90 312 309 76 20. E-mail address: [email protected] (M.E. Kuyumcu).
Nutrition and Metabolism (ESPEN) recommends the mini-nutritional assessment (MNA) [12–14] as the basis for nutritional screening in older people, at times supplemented by laboratory values, anthropometric parameters or determination of body composition. However, MNA administration takes time, which may limit application of the test for the nutritional assessment of the elderly patients. It is also important to emphasize the difficulty of assessing the nutritional status of hospitalized older adults since many of them are bedridden, some are unconscious, some have dementia or other problems of communication and thus, cannot be weighed and nutritional history cannot be achieved. In these cases, anthropometric measurements and bioelectric impedance analysis (BIA) may be good alternative indicators of nutritional status [15]. Anthropometric measures including weight, height, circumferences and skinfold thicknesses are non-invasive techniques that provide information or estimation of body composition, fat, and muscle stores [16]. BIA, as a non-invasive and easily performed bedside technique of body composition analysis [17] works mainly through the measurement of the body’s resistance and reactance to an alternative electrical current [18]. Therefore, elucidation of the
1878-7649/$ – see front matter ß 2013 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved. http://dx.doi.org/10.1016/j.eurger.2013.01.010
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correlation of these methods with MNA will be helpful for determining the nutritional status of hospitalized elderly patients when the MNA cannot be used. The aim of this study was to identify the nutritional status of the elderly hospitalized patients by MNA and by other assessment methods including anthropometric measurements, BIA and laboratory parameters and to demonstrate the correlation of them with MNA, as well.
inelastic metric tape measure with 0.1 cm accuracy was used to measure arm and calf circumferences according to the WHO’s standard technique. Skinfold thicknesses were measured with the Holtain skinfold caliper [21] by dieticians. Bioelectric impedance was measured with a Bodystat quadscan 4000 device (Florida, USA) in a multi-frequency, tetrapolar technique on the right side of the body in supine position [22] by a geriatrician (MD). Geriatrician and dieticians were blind to the other results.
2. Patients and methods
2.4. Biochemical analysis
2.1. Study design and subjects
Biochemical measurements relevant to nutritional status such as fasting plasma glucose, blood urea nitrogen, serum creatinine, electrolytes, liver enzymes, total plasma protein, albumin, total plasma levels of total cholesterol (TC), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterols, triglycerides (TG), vitamin B12, C-reactive protein (CRP), erythroid sedimentation rate (ESR), urine analysis, complete blood counts, and prealbumin were performed.
This study was carried out with 100 elderly patients of both genders (56 males and 44 females), staying at the internal medicine service of Hacettepe University Hospital in Turkey between August 2009 and February 2010. The inclusion criteria were: age equal to or greater than 65 years, having undergone a nutritional assessment in the first 72 hours of their stay at the hospital, and information regarding nutritional status, disease and length of hospital stay documented in the medical records of the institution. Patients with severe edema, pacemaker, prothesis, and severe electrolyte imbalance were excluded, because they may result in incorrect measurements of BIA. Co-morbidities (e.g. diabetes mellitus, hypertension, coronary heart disease, cerebrovascular disease, osteoporosis) were defined using patients’ self-report and current medications, after the evaluation of the patient by comprehensive geriatric assessment and laboratory tests. The study protocol was approved by the Hacettepe University Local Research Ethics Committee. Patients were informed about the study and informed consents were taken. 2.2. Comprehensive geriatric assessment tests All patients underwent a complete and a standardized comprehensive geriatric assessment by using the short geriatric depression scale (S-GDS) with 15 questions [19], the mini-mental state examination (MMSE) [19], Barthel index of activities of daily living (ADL) [20] the instrumental activities of daily living (IADL) [19] and the ‘‘Get up and go’’ test (GUGT) [19]. S-GDS scores of 5 and over were considered as suggestive for depression and MMSE scores of 24 and below were considered as impaired, suggesting dementia. ADL and IADL were performed in order to measure the level of dependency. In ADL, 0 point suggest independency, 20 point suggest maximum dependency. In IADL, 24 point suggest independency, 0 point suggest maximum dependency. Patients underwent the GUGT to assess gait and balance problems as well as mobility disorders.
2.5. Statistical analysis Statistical Package for Social Sciences (SPSS) for Windows 15.0 programme was used for statistical analysis. All data were entered into a database and were verified by a second independent person. The variables were investigated using visual (histograms, probability plots) and analytical methods to determine whether or not they are normally distributed. Data are presented as mean and S.D. for normally distributed variables and as median, (minimum–maximum) for skew distributed continuous variables [ADL, S-GDS, CRP and body fat mass index (BFMI)]. Categorical variables are shown as frequencies. In order to make comparison between MNA groups, One-Way ANOVA was used for normally distributed variables and Kruskal-Wallis variance analysis was used for ADL, S-GDS, CRP and BFMI. Correlation analyses were performed with Pearson test for normally distributed variables and Spearman test for not normally distributed parameters. Linear regression analysis was performed to compare the effects of parameters to MNA scores with 95% confidence intervals (CI). MNA score was selected as the dependent variable, and biceps, triceps, subscapular, suprailiac skinfold thicknesses, body fat (% and kg), total body water (%), extracellular and intracellular body waters (%), Free Fat Mass Index (FFMI), prealbumin, albumin, CRP and GFR were selected as independent variables. The stepwise variable selection method was used to avoid multicolinerearity problem in final model. Colinerearity among the variables was checked using the generalized varianceinflation factor method. The colinearity threshold was defined at the value of 5; lower values indicate no colinearity. Two-sided values of P < 0.05 were considered as statistically significant. 3. Results
2.3. Nutritional evaluation and anthropometric measurements 3.1. Subjects The nutritional status of the patients was assessed within the first 3 days of their hospital stay by MNA, anthropometric measurements and BIA. The MNA contains 18 items, which are divided into four categories: anthropometric measurements, general assessment, dietary assessment and subjective assessment. Final score classifies nutritional state as ‘well nourished’ (MNA scores 24), ‘at risk for malnutrition’ (MNA scores from 17 to 23.5) and ‘malnourished’ (MNA scores < 17) [12–14]. The following anthropometric indicators were measured: current weight, height, mid-upper arm circumference, body mass index (BMI), biceps, triceps, subscapular and suprailiac skinfold thicknesses, waist, hip, and calf circumferences. A flexible and
A total of 100 patients (56 male, 44 female) were included in this study. The mean age was 72.74 6.36. The number of patients aged 65–74 ages was 64 (64%), 75–84 was 32 (32%) and 85 was four (4%). The most frequent co-morbidity diseases were hypertension (80%), diabetes mellitus (49%) and coronary heart disease (38%). Most important reasons of hospital admission of patients were dyspnea (73%), regulation of diabetes mellitus (32%) and infections (27%). Additionally, prevalence of dementia was 9%, depression was 23% and urinary incontinence was 32%. Overall, within the study group, 69% were found to have nutritional risk, 12% of the subjects were malnourished and only 19% of the subjects were well nourished by MNA (Fig. 1).
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Table 2 Correlation analyses results between skinfold thicknesses, calf circumference, BIA, and laboratory parameters with MNA score. MNA
Fig. 1. The number of patients according to the MNA groups.
3.2. Bioelectrical impedance analysis (BIA), laboratory and anthropometric measurement parameters of patients according to mini-nutritional assessment (MNA) Characteristics of the patients according to the nutritional status determined with the MNA are depicted in Table 1. The results of comprehensive geriatric assessment tests and the skinfold thickness of the patients were worst in the malnourished group (Table 1). The length of stay in the malnourished group, the nutritionally at-risk group and well nourished group were 19.08 16.61, 17.60 11.17 and 12.72 7.28 days, respectively (P: 0.258). Prealbumin levels and GFR of the patients with malnutrition were significantly lower and CRP was significantly higher than the other groups (Table 1). There were no significant differences in other laboratory tests. There were no significant differences in BIA parameters between MNA groups. Biceps (P: 0.002, r: 0.303), triceps (P < 0.001, r: 0.384),
Biceps (mm) Triceps (mm) Subscapular (mm) Suprailiac (mm) Calf (cm) Body fat (%) Body fat (kg) TBW (%) ECW (%) ICW (%) FFMI (kg/m2) Prealbumin (mg/dL) Albumin (g/dL) CRP (mg/dL) GFR (mL/min)
r
P value
0.303 0.384 0.402 0.642 0.510 0.201 0.300 0.304 0.337 0.240 0.235 0.485 0.239 0.259 0.298
0.002a < 0.001a < 0.001a < 0.001a < 0.001a 0.046a 0.003a 0.002a 0.001a 0.017a 0.019a < 0.001a 0.017a 0.009a 0.003a
TBW: total body water; ECW: extracellular body water; ICW: intracellular body water; FFMI: free fat mass index; MNA: mini-nutritional assessment; BIA: bioelectrical impedance analysis; GFR: glomerular filtration rate; CRP: C-reactive protein. a Statistically significant differences (P < 0.05).
subscapular (P < 0.001, r: 0.402), and suprailiac (P < 0.001, r: 0.642) skinfold thicknesses, calf circumference (P < 0.001, r: 0.510), prealbumin (P < 0.001, r: 0.485), albumin (P: 0.017, r: 0.239), GFR (P: 0.003, r: 0.298), and some of the BIA parameters including percent of body fat (%) (P: 0.046, r: 0.201), body fat presented with kg (P: 0.003, r: 0.300), and FFMI (P: 0.019, r: 0.235) were significantly and positively correlated with MNA score. Correlations between suprailiac, subscapular skinfold thicknesses, calf circumference and prealbumin with MNA scores are presented on Fig. 2. Negative and significant correlations were found between CRP (P: 0.009, r: 0.259), total body water (P: 0.002, r: 0.304), extracellular body water (P: 0.001, r: 0.337) and MNA scores (Table 2).
Table 1 Characteristics of the patients according to the nutritional status determined with the MNA.
Age (years) Gender (male/female) Length of hospital stay (days) Number of comorbid disease Number of drugs ADL, median, (min-max) IADL MMSE S-GDS, median, (min-max) GUGT Height (cm) Weight (kg) Waist (cm) Hip (cm) BMI (kg/m2) Mid-upper Arm (cm) Calf (cm) Biceps (mm) Triceps (mm) Suprailiac (mm) Subscapuler (mm) GFR (mL/min) Albumin (g/dL) LDL (mg/dL) Hemoglobin (g/dL) CRP, mg/dL, median, (min-max) Prealbumin, mg/dL
< 17 (n = 12)
17–23.5 (n = 69)
24 (n = 19)
P value
75.00 2.30 9/3 19.08 16.61 5.33 2.01 7.16 4.06 0.00, (0.00–20.00) 15.83 1.00 23.75 8.72 5.00, (0.00–10.00) 3.83 3.35 166.01 11.74 62.81 16.54 85.45 13.10 96.12 13.64 22.87 6.75 27.47 4.78 29.77 3.44 5.52 2.58 10.71 5.17 12.00 6.67 14.22 6.19 39.46 22.28 3.49 0.75 100.21 26.22 11.55 2.72 4.35, (0.29–515) 12.29 4.05
73.28 0.76 36/33 17.60 11.17 4.86 1.77 6.72 3.23 0.00, (0.00–18.00) 19.13 7.50 27.01 4.39 0.00, (0.00–10.00) 6.40 1.88 161.43 10.24 71.62 11.89 96.98 11.94 104.81 11.66 27.55 5.17 31.40 3.34 33.96 3.78 9.82 6.15 18.61 8.43 20.45 8.07 21.89 7.97 59.34 29.20 3.66 0.58 101.47 38.82 11.84 2.11 1.52, (0.01–43) 17.86 6.01
69.37 0.89 11/8 12.72 7.28 4.68 1.33 6.52 1.95 0.00, (0.00–10.00) 22.89 3.68 29.00 1.33 0.00, (0.00–8.00) 6.78 0.91 160.57 11.87 77.84 15.91 101.73 14.87 107.86 14.78 30.50 7.67 32.94 3.63 37.12 4.14 8.98 4.21 19.64 9.26 21.10 9.60 23.10 8.57 68.59 31.10 3.99 0.55 108.73 36.76 12.55 2.20 0.79, (0.15–18.70) 23.05 6.46
0.029a 0.337 0.258 0.570 0.963 0.073 0.012a 0.020a 0.084 0.001a 0.341 0.011a 0.003a 0.038a 0.003a < 0.001a < 0.001a 0.011a 0.003a 0.003a 0.004a 0.030a 0.078 0.850 0.376 0.028a < 0.001a
ADL: the activities of daily living; IADL: the instrumental activities of daily living; MMSE: the mini-mental state examination; GUGT: ‘‘get-up and go’’ test; BMI: body mass index; GFR: glomerular filtration rate; LDL: low-density lipoprotein cholesterol; CRP: C-reactive protein; MNA: mini-nutritional assessment. a Statistically significant differences (P < 0.05).
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Fig. 2. Correlations between suprailiac, subscapular skinfold thicknesses, calf circumference and prealbumin with MNA scores.
Results of linear regression analysis are summarized in Table 3. MNA score significantly correlated with prealbumin (OR: 0.256, 95%CI: 0.158–0.354, P < 0.001), suprailiac skinfold thickness (OR: 0.106, 95%CI: 0.031–0.181, P: 0.006) and GFR (OR: 0.036, 95%CI: 0.014–0.057, P: 0.01) in multivariate analysis. 4. Discussion This study examined the nutritional status of the hospitalized elderly patients on admission by MNA, anthropometric measurements and BIA. The nutritional assessment of the study population by MNA revealed that 69% was at nutritional risk and 12% was malnourished. The relationship between nutritional status and functional capacity was also investigated. The importance of this study was to investigate the nutritional status by various nutritional indicators and to explore the correlation of them with MNA. Skinfold thicknesses (biceps, triceps, subscapular and
Table 3 Results of linear regression analysis of the possible correlates with MNA score. Odds ratio (b)
Prealbumin (mg/dL) Suprailiac (mm) GFR (mL/min) CRP (mg/dL)
0.256 0.106 0.036 0.013
95%CI
P value
Lower
Upper
0.158 0.031 0.014 0.025
0.354 0.181 0.057 0.0001
< 0.001a 0.006a 0.010a 0.044a
GFR: glomerular filtration rate; CRP: C-reactive protein; MNA: mini-nutritional assessment; CI: confidence interval. a Statistically significant differences (P < 0.05).
suprailiac), calf circumference, prealbumin, albumin, GFR, and some of the BIA parameters including percent of body fat (%), body fat presented with kg, and FFMI were the nutritional indicators that were found to be correlated with MNA in this study. Therefore, this valuable information would lead these nutritional indicators, to be used to determine nutritional status when the MNA cannot be used. The prevalence of malnutrition is 25–38.7% in hospitalized elderly [2,3]. So, it is a common problem and has significant effects on health and the economy. Therefore, an emphasis should be placed on an effective nutritional policy in hospitals. A systematic early identification and treatment of malnutrition is important. Screening for malnutrition is an essential measure in the prevention of malnutrition. A variety of different methods are available for the assessment of malnutrition in older adults, but there is no gold standard [16,23]. MNA is recommended by the ESPEN for detecting the presence of malnutrition and the risk of developing malnutrition among the elderly [11,12]. By using the MNA test, 78% of the patients can be classified correctly [24]. However, MNA is time consuming and as reported by a supportive data [25,26], it cannot be completed in a substantial part of patients because of cognitive impairment and communication problems. Anthropometric measurements including weight, height, circumference, and skinfold thickness have also been used as nutritional indicators. The measurement of mid-upper arm circumference and skinfold thicknesses is thought to provide a crude assessment of fat stores and muscle mass [16]. Skinfold thicknesses are also found to be highly correlated with body fat as estimated by densitometry [27]. However, skinfold thickness
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measurement may be less accurate in older adults because of the age-associated physical changes including redistribution of fat from subcutaneous to deep adipose tissues, decreased elasticity of skin, alterations in skin thickness and atrophy of subcutaneous adipocytes contributing to increased tissue compression. Another significant problem is difficulty in accurately locating anatomic landmarks for measurements, even when attempted by experienced clinicians [28]. Notwithstanding, very low or high skinfold thicknesses have been displayed as an indicator of poor nutritional status [29]. In a recent study mid-upper arm circumference, BMI, triceps skinfold thickness, and mid-upper arm muscle circumference were reported to be capable of predicting MNA classification [15]. We also found a significant correlation between skinfold thicknesses (biceps, triceps, subscapular and suprailiac) and MNA in this present study supporting this hypothesis. The calf circumference represents an anthropometric parameter of muscle mass; it reflects disability and self-reported physical function [30]. Furthermore, a significant correlation between MNA and calf circumference was found in this study suggesting that the calf circumference represents a valid parameter of malnutrition [31]. According to the results of the multivariate analysis in our study, especially suprailiac skinfold thickness is a useful and practical indicator for nutritional status. BIA allows the determination of the fat-free mass and total body water in subjects. It is very suitable for older adults because of the low physical demand [16]. However, BIA measurements may not be reliable in patients who often suffer from disturbances of fluid distribution and content. The association of BIA and nutrition in geriatric patients has already been examined in other settings. The measurement of FFM and FM of older persons by BIA may be affected by nutritional status, so that in undernourished subjects the correlation of BIA measurements with those obtained by dualenergy x-ray absorptiometry is less strong than in eutrophic subjects. Moreover, there is a tendency of BIA to overestimate FFM and underestimate FM in undernourished subjects. So they said that BIA is not a reliable method for the individual assessment of body composition [32]. In our study, patients with severe edema, pacemaker, prothesis, and severe electrolyte imbalance were excluded, because they may result in incorrect measurements of BIA. After all, body fat (% and kg), TBW (%), IBW (%), EBW (%), and FFMI were significantly correlated with MNA in univariate analysis but significant correlations were not found in multivariate analysis. Laboratory tests may also reflect malnutrition. Although a number of serum proteins have been described to be related to malnutrition [33–35], many of them are influenced by factors other than nutrition, especially inflammation. Therefore, these serum proteins are not specific for malnutrition [36]. Biochemical nutritional assessment mainly aims to identify individuals who will benefit from nutritional therapies, to detect and treat micronutrient deficiencies and to measure the effectiveness of nutritional intervention. Laboratory tests can be more sensitive than other methods in showing recent changes in nutritional status, but, there is no clear criterion for the interpretation in the geriatric age group [23]. Prealbumin is popularized by its short half-life and superior sensitivity in evaluating acute nutritional change. Severely low levels of prealbumin were reported to be related with increased in-hospital stay [23]. CRP reflects a catabolic state, however the close relation between nitrogen balance and catabolic state suggests that CRP can be used as an indirect marker of undernutrition [37]. In our study GFR and prealbumin levels were lowest and CRP levels were highest in malnourished group (P: 0.030, < 0.001, 0.028, respectively). Multivariate analysis indicated prealbumin and GFR as significantly important correlates of MNA in our study. Previously, studies demonstrated that GFR was significantly associated with malnutrition independent of relevant
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demographic, social and medical conditions. Although mechanisms underlying this association are not clear, some explanations can be put forward. It was determined that in patients with lower GFR, levels of some inflammatory markers are increased. This inflammatory state may play a role in malnutrition in these patients. The other explanation is that a decreased nutrient intake was found to be evident to a GFR less than 60 mL/min 1.73 m2, so anorexia frequently accompanies renal insufficiency [38]. Although CRP appears to be negatively and significantly correlated with MNA in univariate analysis, a significant correlation was not found between CRP and MNA in multivariate analysis. So, merely CRP seems not to be suitable for nutritional assessment. Combination of prealbumin and CRP may be more reliable for the evaluation of malnutrition. This study also suggested the relationship between nutritional status, functional capacity and mobility of hospitalized older adults. Malnourished individuals were more dependent and more limited. We found that 12% of patients were malnourished and 69% were at the risk of malnutrition. Considering the evaluation of the nutritional status in the first 72 hours of their hospital stay, we may suggest that these patients were already malnourished or at risk of malnourishment at admission. Therefore, screening for malnutrition in every geriatric patient (both out-patient and inpatient) is an essential measure in the prevention of malnutrition. The limitations of the study should be noted. The relatively small study sample and relatively short follow-up for determining the effect of parameters on length of hospital stay can be defined as the limitations.
5. Conclusion In our cohort, 69% of the study population was at malnutrition risk and 12% was malnourished according to MNA. However, MNA cannot be performed in some situations. Some anthropometric measurements, BIA and laboratory parameters or combinations of them may be used for the evaluation of nutritional status in hospitalized older adults when the MNA cannot be used.
Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. References ¨ lger Z, Halil M, Kalan I, et al. Comprehensive assessment of malnutrition risk [1] U and related factors in a large group of community-dwelling older adults. Clin Nutr 2010;29:507–11. [2] Kaiser MJ, Bauer JM, Ra¨msch C, et al. Frequency of malnutrition in older adults: a multinational perspective using the mini nutritional assessment. J Am Geriatr Soc 2010;58:1734–8. [3] Saka B, Kaya O, Ozturk GB, et al. Malnutrition in the elderly and its relationship with other geriatric syndromes. Clin Nutr 2010;29:745–8. [4] Norman K, Pichard C, Lochs H, Pirlich M. Prognostic impact of disease-related malnutrition. Clin Nutr 2008;27:5–15. [5] Herrmann FR, Safran C, Levkoff SE, Minaker KL. Serum albumin level on admission as a predictor of death, length of stay, and readmission. Arch Intern Med 1992;152:125–30. [6] Sullivan DH, Sun S, Walls RC. Protein-energy undernutrition among elderly hospitalized patients: a prospective study. JAMA 1999;281:2013–9. [7] Potter J, Klipstein K, Reilly JJ, Roberts M. The nutritional status and clinical course of acute admissions to a geriatric unit. Age Ageing 1995;24:131–6. [8] Paillaud E, Herbaud S, Caillet P, et al. Relations between undernutrition and nosocomial infections in elderly patients. Age Ageing 2005;34:619–25. [9] Schneider SM, Veyres P, Pivot X, et al. Malnutrition is an independent factor associated with nosocomial infections. Br J Nutr 2004;92:105–11. [10] de Luis DA, Izaola O, Cuellar L, et al. Nutritional assessment: predictive variables at hospital admission related with length of stay. Ann Nutr Metab 2006;50:394–8. [11] Jeejeebhoy KN. Nutritional assessment. Nutrition 2000;16:585–90.
236
M.E. Kuyumcu et al. / European Geriatric Medicine 4 (2013) 231–236
[12] Guigoz Y. The mini nutritional assessment (MNA) review of the literature – What does it tell us? J Nutr Health Aging 2006;10:466–85 [Discussion 485–467]. [13] Gazzotti C, Albert A, Pepinster A, Petermans J. Clinical usefulness of the mini nutritional assessment (MNA) scale in geriatric medicine. J Nutr Health Aging 2000;4:176–81. [14] Van Nes MC, Herrmann FR, Gold G, et al. Does the mini nutritional assessment predict hospitalization outcomes in older people? Age Ageing 2001;30:221–6. [15] Leandro-Merhi VA, De Aquino JL. Anthropometric parameters of nutritional assessment as predictive factors of the mini nutritional assessment (MNA) of hospitalized elderly patients. J Nutr Health Aging 2011;15:181–6. [16] Omran ML, Morley JE. Assessment of protein energy malnutrition in older persons, part I: history, examination, body composition, and screening tools. Nutrition 2000;16:50–63. [17] Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis – part I: review of principles and methods. Clin Nutr 2004;23:1226–43. [18] Erselcan T, Candan F, Saruhan S, Ayca T. Comparison of body composition analysis methods in clinical routine. Ann Nutr Metab 2000;44:243–8. [19] Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 1982;17: 37–49. [20] Mahoney FI, Barthel DW. Functional evaluation: the Barthel index. Md State Med J 1965;14:61–5. [21] Zillikens MC, Conway JM. Anthropometry in blacks: applicability of generalized skinfold equations and differences in fat patterning between blacks and whites. Am J Clin Nutr 1990;52:45–51. [22] Verdich C, Barbe P, Petersen M, et al. Changes in body composition during weight loss in obese subjects in the NUGENOB study: comparison of bioelectrical impedance vs. dual-energy X-ray absorptiometry. Diabetes Metab 2011;37: 222–9. [23] Omran ML, Morley JE. Assessment of protein energy malnutrition in older persons, Part II: laboratory evaluation. Nutrition 2000;16:131–40. [24] Guigoz Y, Vellas B. The Mini Nutritional Assessment (MNA) for grading the nutritional state of elderly patients: presentation of the MNA, history and validation. Nestle Nutr Workshop Ser Clin Perform Programme 1999;1:3–11 [Discussion 11–12]. [25] Bauer JM, Vogl T, Wicklein S, et al. Comparison of the mini nutritional assessment, subjective global assessment, and nutritional risk screening
[26]
[27]
[28]
[29] [30]
[31]
[32]
[33]
[34] [35] [36]
[37] [38]
(NRS 2002) for nutritional screening and assessment in geriatric hospital patients. Z Gerontol Geriatr 2005;38:322–7. Cuervo M, Ansorena D, Garcı´a A, et al. Food consumption analysis in spanish elderly based upon the mini nutritional assessment test. Ann Nutr Metab 2008;52:299–307. Heymsfield SB, McManus C, Smith J, et al. Anthropometric measurement of muscle mass: revised equations for calculating bone-free arm muscle area. Am J Clin Nutr 1982;36:680–90. Sullivan DH, Patch GA, Baden AL, Lipschitz DA. An approach to assessing the reliability of anthropometrics in elderly patients. J Am Geriatr Soc 1989;37: 607–13. Frisancho AR. New norms of upper limb fat and muscle areas for assessment of nutritional status. Am J Clin Nutr 1981;34:2540–5. Rolland Y, Lauwers-Cances V, Cournot M, et al. Sarcopenia, calf circumference, and physical function of elderly women: a cross-sectional study. J Am Geriatr Soc 2003;51:1120–4. Drescher T, Singler K, Ulrich A, et al. Comparison of two malnutrition risk screening methods (MNA and NRS 2002) and their association with markers of protein malnutrition in geriatric hospitalized patients. Eur J Clin Nutr 2010;64: 887–93. Vilaca KH, Paula FJ, Ferriolli E, et al. Body composition assessment of undernourished older subjects by dual-energy x-ray absorptiometry and bioelectric impedance analysis. J Nutr Health Aging 2011;15:439–43. Corti MC, Guralnik JM, Salive ME, Sorkin JD. Serum albumin level and physical disability as predictors of mortality in older persons. JAMA 1994;272: 1036–42. Mu¨hlethaler R, Stuck AE, Minder CE, Frey BM. The prognostic significance of protein-energy malnutrition in geriatric patients. Age Ageing 1995;24:193–7. Sergi G, Coin A, Enzi G, et al. Role of visceral proteins in detecting malnutrition in the elderly. Eur J Clin Nutr 2006;60:203–9. Devoto G, Gallo F, Marchello C, et al. Prealbumin serum concentrations as a useful tool in the assessment of malnutrition in hospitalized patients. Clin Chem 2006;52:2281–5. Kaysen GA, Rathore V, Shearer GC, Depner TA. Mechanisms of hypoalbuminemia in hemodialysis patients. Kidney Int 1995;48:510–6. Garg AX, Blake PG, Clark WF, et al. Association between renal insufficiency and malnutrition in older adults: results from the NHANES III. Kidney Int 2001;60: 1867–74.