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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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