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ORIGINAL ARTICLE Evaluation of biochemical markers effectiveness in elderly malnutrition assessment 1 2 3 4 5 Larisa Gavran , Jelena Pavlović , Maja Račić , Nedeljka Ivković , Ksenija Tušek Bunc 1Department of Family Medicine, School of Medicine, University of Zenica, 2Department of Nursing, School of Medicine, University of 3 4 East Sarajevo, Department of Primary Health Care and Public Health, School of Medicine, University of East Sarajevo, Department of Oral Rehabilitation, School of Medicine, University of East Sarajevo; Bosnia and Herzegovina, 5Department for Public Health School of Medicine, University of Maribor, Slovenia ABSTRACT Aim To systematically review the scientific evidence of biomarker validity, reliability, specificity and sensitivity in identifying mal- nutrition in the elderly. Methods Peer-reviewed journals were searched using PUBMED and EBSCO from January 1998 to April 2018. The articles inclu- ded description of the association between malnutrition blood bi- omarkers and validated nutritional status assessment instruments and studies were conducted among community-dwelling elderly or nursing home residents. Corresponding author: Results The research strategy identified a total of 293 studies. This Jelena Pavlović literature review picked out seven articles for follow-up evaluati- Department of Nursing, on. A total of sixteen blood biomarkers were identified. Six studies School of Medicine of Foča, found a significant association between the nutritional assessment score and albumin level. University of East Sarajevo Studentska 5, 73 300 Foča, Conclusion Combining serum concentrations of malnutrition bio- Bosnia and Herzegovina markers with nutritional status assessment tools has a great poten- Phone: +387 58 210 420; tial in identifying the risk of malnutrition in the elderly, while also increasing sensitivity and specificity. Fax: +387 58 210 007; E-mail: pjelena551@gmail.com Keywords: aged, biomarkers, geriatric assessment, humans, mal- ORCID ID: https://ordic.org/0000-0002- nutrition 8591-4316 Original submission: 09 May 2019; Revised submission: 13 May 2019; Accepted: 16 May 2019. doi: 10.17392/1039-19 Med Glas (Zenica) 2019; 16(2): 351-358 351 Medicinski Glasnik, Volume 16, Number 2, August 2019 INTRODUCTION MATERIALS AND METHODS Good nutrition is a fundamental component of he- Study design alth, independence and quality of life of elderly per- sons (1). Malnutrition may cause health problems The systematic literature overview was made such as the increased risk of morbidity (chronic di- according to the Preferred Reporting Items for seases, pathological fractures, impaired wound he- Systematic Reviews and Meta-Analyses (PRI- aling, slow post-operative recovery, development SMA) statement (8). of decubitus ulcers, weakened functionality, lack We considered observational, longitudinal, retros- of appetite), and increased hospitalization rate, pective, and cross-sectional studies that reported number of hospital treatment days and mortality an association between blood biomarkers levels rate (2). Studies have shown that the prevalence of and validated nutrition assessment instruments, malnutrition after the age of 65 has been on the rise such as anthropometric measurements (body reaching a range of between 15-85% (2-4). Accor- mass index – BMI, or skinfold thickness). Additi- ding to Bedogni et al. (5), nutritional status is a re- onally, it followed screening questionnaires: Mini sult of the interaction of three variables: food inge- Nutrition Assessment-Short Form (MNA-SF), stion, absorption, and the use of nutrients. It clearly Malnutrition Universal Screening Tool (MUST), follows from the described definition that an ideal Nutritional Risk Screening 2002 (NRS-2002), nutritional status assessment and malnutrition scre- Geriatric Nutritional Risk Index (GNRI), Nu- ening instrument should include the assessment of tritional Risk Index (NRI), Instant Nutritional dietary, anthropometric, functional indicators, and Assessment (INA), Nutrition Screening Initiati- laboratory biomarkers in the blood (Figure 1) (5,6). ve (NSI), Short Nutritional Assessment Questi- A recent systematic review has shown that multiple onnaire (SNAQ), Subjective Global Assessment biochemical parameters (albumin, prealbumin, he- (SGA), and the Nutritional Risk Screening Tool moglobin, total cholesterol, and total protein) may (NRST). Inclusion criteria were studies conduc- be used in diagnosing malnutrition in the elderly ted among community-dwelling elderly and/or (7). However, it remains unknown which are the nursing home residents. Country and English-lan- reference cut-off values of these biomarker blood guage restrictions were not applied. Outcomes of parameters, and which biomarker is usable, precise interest were the sensitivity and specificity of blo- and reproducible, acceptable to the patient, easy for od biomarkers, as well as their ability to identify clinical interpretation, and cost-effective, while ha- malnutrition risk among the elderly (Table 1). ving the high sensitivity and specificity necessary for the expected outcome. Such a biomarker would have a promising potential for the malnutrition di- Table 1. Study inclusion and exclusion criteria agnosing system. Variable Inclusion criteria Exclusion criteria People over the age of 60, People under the age of well-oriented in time and 60, persons with dementia, Population space, without malign di- persons with malign disea- seases, dementia, chronic ses and with chronic renal renal insufficiency insufficiency People living in commu- People in hospital envi- Environment nity or in gerontology ronment institution Observational, longi- Study type tudinal, retrospective, Non-empirical studies transversal Identification of bio- Non-identification of Outcome chemical malnutrition biochemical malnutrition markers markers Figure 1. Definition of nutritional status indicators Development Described Not described and validation The aim of this systematic review was to study, Abstract availability, Abstract and full text investigate, analyse, and synthetize the scientific Other year of publication unavailability, year of evidence of biomarker validity, reliability, speci- from1998, publication before 1998 full text available ficity and sensitivity in identifying malnutrition in elderly patients. 352 Gavran et al. Elderly malnutrition assessment Methods form in order to facilitate comparison. Each study Malnutrition was defined as deficiency or imba- included the name of author(s), publication year, lances in an intake of energy and macro/micro nu- sample size, study design, methodology, identified trients (5). The studies were downloaded via the biochemical markers, and results (Table 2). electronic databases PUBMED and EBSCO, and RESULTS by manual search of relevant studies from a list The research strategy identified a total of 293 stu- of reference key articles. The electronic databa- dies. Following data deduplication and selection ses were searched from the period January 1998 of papers based on titles and abstracts, a total of to April 30 2018 by defining key words adapted 277 papers were excluded because they were not for each database (malnutrition, nutrition, blood focused on malnutrition, the population was un- markers, serum, elderly), and words from MESH der the age of 60, the authors did not use labora- (Medical Subject Headings) and Boolean ope- tory analysis to identify malnutrition, or the stu- rators, AND/OR words establishing a logical dies did not undergo a validation process. Nine of connection with the paper search concepts at the remaining studies were included for a full text Medline. There was an advanced search moda- review, of which 7 were selected for extrapolati- lity. The manual search of original papers, loo- on and final analysis (Figure 2). king for additional acceptable studies, was con- ducted through the Electronic Journals Library. Papers were searched through various journals (Nutrition, The American Journal of Clinical Nu- trition, Nutrients, Nutrition Reviews, Journal of Nutrition, and European Journal of Clinical Nu- trition). Titles and abstracts were reviewed and, if an abstract met the inclusion criteria, the full text was downloaded. In accordance with the search criteria, the full texts of papers selected were in- dependently assessed by two investigators and, in case of any doubt before the final decision, the in- vestigators sought a third investigator’s opinion. During this step, the application of the final criteria for inclusion of papers into the analysis resulted Figure 2. Flow diagram of the research and selection process in the selection of biomarker research studies with Sixteen biomarkers were identified in the litera- validated instruments in identifying malnutrition ture review. Most commonly analysed were albu- in persons over 60 years of age. The data from min and total cholesterol. Other biomarkers found each paper using a data extrapolation form based were lymphocyte count, leucocytes, haemoglobin, on the Best Evidence Medical Education (BEME) prealbumin, triglycerides, zinc, copper, transthyre- coding sheet (9) were pulled out. After investiga- tin, leptin, orosomucoid, insulin-like growth fac- tors checked the extrapolated data, they focused tor-1 (IGF-1), IGF binding protein-1 (IGFBP-1), on biochemical markers, study methodology, and and C-reactive protein (CRP). results. No exact meta-analysis could be done due to discrepancy between the methods used, the The biomarkers values were evaluated against GNRI different statistical analyses of the studies included (10) , NRST (11), MNA (12,13) , SGA in the final analysis, the difference in measurement (13,14), MNA-SF/NRS2010 (15), and antro- outcomes, different biomarker validity values in pometric measurements (BMI and skinfold relation to the instruments used, as well as the lack thickness) (16). of reliable borderline values of biomarkers for el- Biochemical concentrations were measured derly persons. The synthesis showed the ability of using well-accepted methods, with variations de- blood biomarkers in identifying an elderly indivi- pending on the setting. Three studies detected a dual with malnutrition or at high risk of malnutriti- significant, positive correlation between nutritio- on. The extrapolated data are presented in a tabular nal assessment and albumin level (10-12) and, in 353 Medicinski Glasnik, Volume 16, Number 2, August 2019 e) ymphocytes r=0.01; p=0.935 10; p=0.212; and biomarkerse and biomarketsoteins levelsoteins levels esult scor The r 17.12 ±22.45; p=0.154; f point <17) for hypoalbuminemia (<3.5 g /dL)- 0.810f point <17) for hypoalbuminemia (<3.5 g /dL)-0.860f point <17) for hypocholesterolemia (<150 mg/dL) 0.786f point <17) for hypocholesterolemia (<150 mg/dL) 0.822 Zinc: 93.76 ± 17.59; p=0.882; otal protein: r=0.161; p=0.557; ymphocytes: r=0.093; p=0.157; Albumin: r=0.463; p<0.001; riglycerides: r=0.004; p=0.965; Zinc: r=0.004; p=0.965;Albumin r=0.60; p<0.001; L cutof cutof cutof cutof class and serum pr class and serum pr ymphocytes: 1.55±0.67; p=0.637; Copper : 1TLHaemoglobin: r=0.052; p=0557; Prealbumin: r= -0.1otal cholesterol: r=0.128; p=0.171; T WBCs (103/cm): 8.56 ±6.21; p=0.448; Lotal protein (g/dL)- 6.39 ± 0.78; p=0.243; Albumin (g/dL): 2.66 ±0.46; p<0.001; Prealbumin (mg/dL):-6 (3-10) ; p=0.902; Telations between NRSTotal cholesterol (mg/dL): r=0.057; p=0.012;Serum albumin (g/dL): r=0.094; p<0.001elation between MNASGAMNA Haemoglobin (g/dL): 10.95 ± 3.01; p=0.026; Triglycerides (mg/dL): 79 (57-107) ; p=0.882; elations between GNRI and biomarkers valuesT otal cholesterol (mg/dL): 142.1±.74.2, p=0.213; TCorr T Corr Corr verage values of biomarkers in malnutrition (GNRI scor A otal cholesterol r=0.36; p<0.001; ransthyretin (g/L): 0.24±0.06 (well-nourished), 0.23±0.06 (risk), 0.19±0.06 (malnourished)Albumin (g/ L): 35.8±4.5(well-nourished), 34.5±5.0(risk), 30.2±5.6 (malnourished) TSensitivity of malnutrition (MNASpecificity of malnutrition (MNASensitivity of malnutrition (MNASpecificity of malnutrition (MNAransthyretin (g/l): 0.24±0.06 (well-nourished), 0.22±0.06 (moderate), 0.19±0.06 (severe malnutrition)Albumin (g/ L): 35.0±5.0 (well-nourished), 4.0±5.0 (moderate), 31.0±6.0 (severe malnutrition)T T Markerotal proteinymphocytesAlbuminotal proteinPrealbuminriglyceridesZincCopperCholesterolAlbuminAlbuminCholesterolymphocytesAlbuminransthyretin T LHaemoglobinTotal cholesterolT L T T - - - - InstrumentmentsGNRI mentsNRSTGNRIMNA-SFmentsMNAMNA-SFmentsSGAMNA Anthropometric measure Anthropometric measureAnthropometric measureAnthropometric measure Design studystudy study Prospective cohort Prospective cohort Cross section studyCross section study 1, 2015 , year able 2. Identified biochemical markers for the evaluation of nutritional status in the elderly T Author(sample size, n)Abd-El-Gawad, et al.10, 2004 (n = 131)Htun NC, et al.1(n = 1921)Kuzuya M, et al.12, 2005 (n = 226)Christensson L, et al.13, 2002 (n = 261) 354
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