*Corresponding author: Tel. + 387 65 168 330; E-mail: pjelena551@gmail.com
10.2478/sjph-2021-0006 Zdr Varst. 2021;60(1):30-37
30
SERUM BIOMARKERS ASSOCIATED WITH MALNUTRITION AND 
NUTRITIONAL RISK IN ELDERLY PRIMARY CARE PATIENTS: A CROSS-
SECTIONAL STUDY FROM BOSNIA AND HERZEGOVINA
Jelena PAVLOVIĆ
1*
, Maja RAČIĆ
2
, Nedeljka IVKOVIĆ
3
1
University of East Sarajevo, Faculty of Production and Management, Faculty of Medicine, 
Studentska 5, 73300 Foca, Bosnia and Herzegovina
2
Thomas J. Stephens & Associates, Phoenix, USA, 3635 W Altadena Ave, Phoenix, AZ 85029, USA
3
University of East Sarajevo, Faculty of Medicine, Department of Oral Rehabilitation,  
Studentska 5, 73300 Foca, Bosnia and Herzegovina
SERUMSKI BIOLOŠKI OZNAČEVALCI, POVEZANI S PODHRANJENOSTJO 
IN PREHRANSKIM TVEGANJEM PRI STAREJŠIH BOLNIKIH NA 
PRIMARNI RAVNI: ŠTUDIJA IZ BOSNE IN HERCEGOVINE
Pavlović J, Račić M, Ivković N. Serum biomarkers associated with malnutrition and nutritional risk in elderly primary care patients: a cross-sectional 
study from Bosnia and Herzegovina. Zdr Varst. 2021;60(1):30-37. doi: 10.2478/sjph-2021-0006.
Received: Nov 11, 2019
Accepted: Nov 19, 2020 
Original scientific article
ABSTRACT
Keywords: 
elderly patients, 
biomarkers, 
malnutrition, 
nutritional status
IZVLEČEK
Ključne besede: 
starejši bolniki, 
biološki označevalci, 
podhranjenost, stanje 
prehranjenosti
Background: The aim of the study was to determine the ability of ferritin, haemoglobin, albumin and total 
cholesterol to identify nutritional risk and malnutrition among elderly primary care patients.  
Methods: The cross-sectional study included 446 elderly adults over 65 years of age from four areas of Bosnia 
and Herzegovina. In addition to anthropometric, functional, cognitive and biochemical indicators, nutritional 
status was evaluated using 24-hour recall of meals, the Mini Nutritional Assessment (MNA), and Seniors in the 
Community: Risk Evaluation for Eating and Nutrition, Version II (SCREEN II).
Results: Malnourished/at-risk study respondents had lower mean levels of haemoglobin (P=0.001) and total 
cholesterol (P<0.001), compared to those with normal nutritional status. Albumin levels significantly differed 
regarding nutritional status (P=0.004), but not nutritional risk level (P=0.521). Significant differences in serum 
ferritin levels were not found between malnourished and normally nourished study respondents (P=0.779) 
Determinants of albumin level were eating more than three meals a day (P<0.001), fewer than two portions of 
fruit and vegetables a day (P=0.024), drinking one glass of wine (P<0.001) and reporting functional independence 
(P=0.011). The AUC curves for serum ferritin, albumin and total cholesterol levels in men and women, as well as 
for haemoglobin levels in women, were poor to fair (AUC<0.800).
Conclusion: Although ferritin, haemoglobin, albumin and total cholesterol may be useful biomarkers of nutritional 
status, their accuracy in diagnosing malnutrition and nutritional risk among elderly primary health care patients 
is limited. 
Uvod: Cilj študije je bil določiti uporabnost merjenja ravni feritina, hemoglobina, albumina in skupnega 
holesterola za ugotavljanje prehranskega tveganja in podhranjenosti pri starejših bolnikih na primarni ravni.  
Metode: V presečno študijo smo vključili 446 starejših odraslih, starih nad 65 let, s štirih območij Bosne in 
Hercegovine. Poleg upoštevanja antropometričnih, funkcijskih, kognitivnih in biokemijskih kazalnikov smo stanje 
prehranjenosti ocenili z navajanjem zaužitih obrokov v 24 urah, mini prehransko anamnezo (MNA) in vprašalnikom 
Seniors in the Community: Risk Evaluation for Eating and Nutrition, Version II (SCREEN II). [Ocenjevanje 
prehranskega tveganja, različica II (PRESEJANJE II).]
Rezultati: Podhranjeni anketiranci/ anketiranci, ki so bili izpostavljeni tveganju, so imeli nižje povprečne ravni 
hemoglobina (p = 0,001) in skupnega holesterola (p < 0,001) kot normalno prehranjeni anketiranci. Ravni albumina 
so se pomembno razlikovale od ravni stanja prehranjenosti (p = 0,004), ne pa tudi stopnje prehranskega tveganja 
(p = 0,521). Med podhranjenimi in normalno prehranjenimi anketiranci nismo ugotovili pomembnih razlik v 
serumskem feritinu (p = 0,779). Determinante za vrednosti albumina so bile: uživanje več kot treh obrokov na 
dan (p < 0,001), manj kot dve porciji sadja in zelenjave na dan (p = 0,024), uživanje enega kozarca vina na dan (p 
< 0,001) ter poročanje o funkcionalni neodvisnosti (p = 0,011). Površine pod krivuljo (AUC) za ravni serumskega 
feritina, albumina in skupnega holesterola pri moških in ženskah ter ravni hemoglobina pri ženskah so bile majhne 
do zadovoljive (AUC < 0,800).
Zaključek: Čeprav so feritin, hemoglobin, albumin in skupni holesterol lahko uporabni biološki označevalci stanja 
prehranjenosti, je njihova natančnost pri diagnosticiranju podhranjenosti in prehranskega tveganja pri starejših 
bolnikih na primarni ravni omejena. 
© Nacionalni inštitut za javno zdravje, Slovenija. 

1 INTRODUCTION
Malnutrition is a prevalent health problem among the 
elderly population and one that is associated with 
increased morbidity, frailty, a weak immune system 
and functional dependency (1). Although health workers 
frequently use the term malnutrition as a synonym for 
undernutrition, every imbalance between the body’s 
needs and food intake might be included in the definition 
of this health problem (2).
To determine nutritional risk and its contributing factors, 
the assessment of nutritional status and screening for 
malnutrition are recommended for people over 65 years 
of age at all levels of healthcare. Previous studies have 
shown that the intervals of the screening need to be 
adjusted to the living environment. Annual screenings are 
recommended for the population with a low prevalence 
of malnutrition (independent, community-dwellers), while 
frail elderly individuals (inpatients, institutionalised) 
should undergo an assessment at intervals of between one 
and three months (3–5).
The Global Leadership Initiative on Malnutrition 
(GLIM) recently endorsed criteria for the diagnosis of 
malnutrition in clinical practice, requiring the presence 
of at least one phenotypic criterion (weight loss, low body 
mass index and reduced muscle mass) and at least one 
etiological criterion (reduced food intake, disease-related 
inflammation) (6). However, these criteria have not yet 
been disseminated worldwide, and multiple approaches 
are still used to diagnose nutritional risk, including 
analysis of the complete metabolic panel (7).
In many countries, family physicians provide primary 
health care to older adults due of a lack of geriatricians. 
The high number of patients with multimorbidity and 
the burdens placed on the primary healthcare system 
by rising epidemic of non-communicable disease leave 
very little time for physicians to look for problems that 
elderly patients usually do not report if not specifically 
asked about (8), and that require the use of a time-/cost-
effective approach to nutritional assessment (9).
As the results of previous studies analysing the usefulness 
of laboratory markers in assessing nutritional status are 
contradictory, we still do not know whether routine 
biochemical analysis could increase nutritional risk 
detection accuracy in a primary care setting (10–13).
The study’s primary aim was to determine the ability of 
albumin, ferritin, total cholesterol and haemoglobin to 
identify nutritional risk and malnutrition among elderly 
primary care patients. The secondary aim was to identify 
the factors associated with the serum levels of these 
biomarkers.
10.2478/sjph-2021-0006 Zdr Varst. 2021;60(1):30-37
31
2 METHODS 
2.1 Sample and participants
The cross-sectional study included elderly adults over 65 
years of age in four urban and rural areas of Bosnia and 
Herzegovina (Sarajevo, Foca, Pale, Rogatica). 
The sample size was calculated using an EPI Info Statistical 
Package based on previous research, which showed that 
the prevalence of malnutrition among the geriatric 
population was 30% (4). A confidence interval of 95% and α 
of 5% were used. A list of random numbers was generated 
and applied to the primary health care database of older 
people. Elderly individuals were recruited between 
April and September 2018 from family medicine clinics 
and three nursing homes until the sample size of 446 
respondents was achieved. The principal investigator 
informed prospective participants of the study’s purpose 
and allowed them to ask questions before signing their 
informed consent to participate in the study. The 
exclusion criteria were: having a malignant disease, a 
severe mental condition, severe cognitive impairment, 
acute illness, stage 4 or 5 of chronic renal failure and 
stroke, or other conditions that could make collaboration 
with the respondent difficult. The principal investigator 
carried out all physical examinations, anthropometric 
measurements and functional tests, and trained research 
assistants distributed questionnaires. 
2.2 Instruments
The socio-demographic questionnaire included data on 
age, gender, education, marital status, residence, mobility 
and social interactions.  Nutritional status was evaluated by 
means of the 24-hour recall of meals, the Mini Nutritional 
Assessment (MNA) and Seniors in the Community: Risk 
Evaluation for Eating and Nutrition, Version II (SCREEN 
II). MNA measured the probability of malnutrition and 
consisted of two steps: screening and assessment. 
Screening encompassed food intake decline, weight loss, 
mobility, neuropsychological problems and body mass 
index, while assessment scales included independence 
level, protein intake and medical assessment. The scores 
were summarised and grouped into three categories: 
malnourished (<17), at risk of being malnourished (17–23.5) 
and well-nourished (≥24) (14).  
SCREEN II explored nutritional risks with 14 questions 
related to weight change, appetite, eating habits and 
behaviour. The scores are categorised as follows: <50 high 
nutrition risk, 50–53 moderate nutrition risk and ≥54 low 
nutrition risk (15).  The internal consistency of the MNA in 
the Bosnian language was considered good (α=0.726) and 
SCREEN II fair (α=0.610).

10.2478/sjph-2021-0006 Zdr Varst. 2021;60(1):30-37
32
The mean energy, macronutrient and micronutrient 
intakes of the study participants were calculated using 
the Nutrium program and compared with the Dietary 
Reference Intake (DRI) (16–18). 
The Katz Index was used to evaluate the basic activities of 
daily living (ADL) (19). The cut-off point of 2 as scored per 
single item indicated severe impairments; participants 
scoring 5 or 6 were considered independent and fully 
functioning. The Lawton Scale, with scoring ranging 
from 0 (dependence) to 8 (independence), evaluated 
the instrumental activities of daily living (IADL) (20).  
The Functional Reach Test and Timed Up-and-Go Test 
were carried out to analyse physical function, mobility, 
balance and functional motion. For the Functional Reach 
Test, participants were instructed to stand with their 
dominant hand next to the measuring tape and reach as 
far as possible without taking a step. The average of three 
measurements was calculated, and a distance of <15 Cm 
indicated impaired balance (21). For the Timed Up and Go 
Test, the participants rose from the chair, walked three 
metres, turned around, returned to the chair and sat 
down again. The shortest time of three consecutive tests 
represented the test score. The threshold for impaired 
mobility was >20 seconds (22). 
All anthropometric measurements were taken three times, 
with average values being calculated in centimetres (cm). 
Weight and height were recorded using a stadiometer 
and weighbridge.  Calf, waist and mid-arm circumference 
were measured using a non-elastic tape measure. Skinfold 
thickness was assessed using a GIMA code 27320 calliper 
on triceps, biceps, subscapular and suprailiac fold (23). 
A Six-Item Cognitive Impairment Test (6-CIT) was used to 
assess cognitive functions.  The investigator analysed a 
number of mistakes made by the examinee, scoring the 
answers inversely. Scores were categorised as normal 
cognitive functioning (<10), mild cognitive impairment 
(10–19) and significant cognitive impairment (≥20) (24).
2.3 Key variables
Blood analyses were carried out in accordance with the 
protocols of Biochemical Medical Laboratory (Luzani, 
Sarajevo). Blood samples (fasting) were drawn from each 
subject using vacutainers and eprouvettes with separation 
gels (5 ml) and EDTA (3 ml). 
On the same day, the samples for an analysis of total 
cholesterol, albumin and ferritin were separated by 
standard centrifugation at 2,000 rpm/min for seven 
minutes. After centrifugation, the samples were stored 
in the refrigerator at a temperature of 4–8°C for three 
days. Haemoglobin was measured on the Sysmex XP 300 
(EDTA full blood sample). Serum concentrations for total 
cholesterol (using the GOD PAP method) and albumin 
(photometric test using Bromocresol) were determined in 
the Roche Modular autoanalyser (Roche, Stockholm).  The 
analysis of ferritin levels was carried out on the Modular 
E170 analyser (CLIA analytics). 
Based on previous research, the cut-off values for 
malnutrition were as follows: haemoglobin <120 g/L for 
men and <110 g/L for women (reference range 140–175g/l 
men, 123–153 g/l women), albumin <35 g/L (normal range 
35–52 g/L), ferritin 30–400 ng/ml men, 13–150 ng/ml 
women, total cholesterol <3.88 mmol/l (11, 25).
2.4 Statistical analyses
All statistical analyses were performed using Statistical 
Package for Social Sciences for Windows (SPSS, version 
25.0). The normality of distribution of variables was 
tested using the Kolmogorov-Smirnov test. Analysis of 
variance (ANOVA) and Student’s t-test were applied to 
compare numerical variables. Owing to the small number 
of participants, individuals with malnutrition and those at 
risk of malnutrition as assessed by MNA were grouped, as 
were individuals with low and moderate nutritional risk 
per SCREEN II.
Associations between serum biomarkers and other 
malnutrition indicators were explored using univariate 
linear regression analysis, and independent variables 
with P<0.05 were included in multivariate regression 
models. To derive the sensitivity and specificity of the 
previously proposed cut-off values of serum biomarkers for 
differentiating between malnourished and well-nourished 
individuals, the receiver operating characteristic (ROC) 
curve and area under the curve (AUC) were used. An MNA 
score of <23.5 was used as a criterion. Criteria for the 
accuracy of serum biomarkers in diagnosing nutritional 
risk were as follows: excellent (AUC 0.90–1), good (AUC 
0.80–0.90), fair (AUC 0.70–0.80), poor (AUC 0.60–0.70) 
and fail (AUC 0.50–0.60). A p-value of less than 0.05 was 
considered to be significant.
3 RESULTS
The study incorporated a total of 446 respondents. 
Of those, 300 were living in the community and 146 
were residents of nursing homes. The majority of the 
respondents were retired women (251, 56.3%), widowers/
single/unmarried (53%), with an average age of 76±7 years, 
and holding a secondary-school diploma or lower (85%).
Fifty-eight per cent of the participants (women 59%, men 
57%) were at risk of malnutrition or had malnutrition per 
MNA, and 88% (90% of men, 87% of women) had a high 
nutritional risk per SCREEN II. The clinical characteristics 
of respondents by MNA and SCREEN II scores are listed in 
Table 1.

The mean values of all anthropometric parameters, 
functional indicators, cognitive assessment scoring, 
haemoglobin and total cholesterol were lower in a group 
of malnourished/at-risk respondents as assessed per MNA 
scoring (P<0.001). Albumin levels significantly differed 
in nutritional status (P=0.004), but not nutritional risks 
(P=0.521).
The mean values of albumin, total cholesterol, ferritin 
and haemoglobin of study participants were stratified by 
gender and age. In men and women, the levels of total 
cholesterol (P=0.004; P=0.031) and haemoglobin (P<0.001) 
were the lowest among octogenarians. A statistically 
10.2478/sjph-2021-0006 Zdr Varst. 2021;60(1):30-37
33
Table 1.
Table 2.
Differences in anthropometric measurements, biomedical analyses and functional parameters in regard to categories of 
nutritional status per MNA
(a)
 and SCREEN II
(b)
.
Mean ferritin, haemoglobin albumin and total cholesterol concentrations in the Bosnian elderly population by age and gender.
*ANOVA
Data is presented as mean and SD
(a) 
MNA-SF – Mini Nutritional Assessment-Short Form, (b) SCREEN II – Seniors in the Community: Risk Evaluation for Eating and Nutrition, 
version II among octogenarians, (c) 6-CIT-Six Item Cognitive Impairment Test, *Students’ t-test for independent samples
Albumin, g/l
Ferritin, ng/ml
Total cholesterol, mmol/l
Haemoglobin, g/l
Waist circumference (cm)
Mid-upper arm circumference (cm)
Skinfold measurement (mm)
Calf circumference (cm)
BMI, kg/m
2
6-CIT
(c)
Timed Go and Up Test (secs)
Functional Reach Test (cm)
Katz Index
Lawton Scale
Men
(n=195)
Women
(n=251)
0.521
0.507
0.035
0.002
0.718
0.953
0.001
0.017
0.269
<0.001
<0.001
<0.001
<0.001
<0.001
65–74
75–84
>85
65–74
75–84
>85
96 (49)
66 (33)
33 (16)
111 (44)
110 (43)
30 (12)
0.080
<0.001
<0.001
<0.001
0.005
0.062
0.004
0.031
227.7 (170.6)
170.8 (131.3)
194.3 (176.2)
101.6 (6.3)
119.6 (9.4)
168.9 (22.7)
146.3 (15.1)
138.5 (17.9)
135.4 (15.7)
133.6 (11.9)
124.2 (13.9)
117.4 (15.6)
46.2 (3.2)
44.6 (3.1)
44.6 (3.7)
46.2 (8.4)
44.3 (3.8)
44.4 (4.6)
5.3 (1.1)
5.0 (1.1)
4.6(1)
5.5 (1)
5.4 (1.3)
4.9 (1.2)
45.2 (2.6)
144.8 (118.7)
5.6 (1)
140.3 (11.0)
95.3 (8.5)
26.9 (2.8)
19.6 (6.07)
33.8 (2.9)
27.8 (2.9)
5.3 (2.9)
9.7 (4.8)
38.2 (10.4)
6.0 (0)
8.0 (0)
45.2 (5.6)
156.3 (135.8)
5.2 (1.2)
132.8 (17.5)
96.0 (13.7)
26.8 (3.7)
15.9 (6.2)
32.2 (4.6)
27.0 (5.2)
11.8 (7.2)
19.1 (10.9)
24.2 (11.2)
5.2 (1.6)
6.5 (2.7)
0.004
0.779
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
45.8 (5.9)
153.6 (138.5)
5.4 (1.7)
136.7 (14.5)
99.0 (12.4)
27.6 (3.3)
17.8 (7.3)
34.4 (3.5)
28.6 (4.5)
7.2 (5.8)
13.4 (8.9)
31.5 (11.1)
5.8 (0.8)
7.6 (1.3)
44.5 (4.1)
157.1 (127.1)
5.0 (1.2)
130 (18.9)
91.9 (13.1)
25.9 (3.7)
14.4 (3.5)
29.9 (4.3)
25.3 (5.2)
15.7 (5.9)
23.8 (9.9)
18.5 (8.5)
4.5 (1.9)
5.4 (3.1)
Variable
Variable
P-value*
Age/years N (%) P* P* P* P* Mean (SD) Mean (SD) Mean (SD) Mean (SD)
Moderate/low 
risk
(N=52)                   
SCREEN II
(b)
High risk
(N=394)                   
P-value* Normal 
nutrition              
(N=187)
MNA
(a)
                                  
Haemoglobin, g/l Albumin, g/l Total cholesterol, mmol/l    Ferritin, ng/ml
Malnutrition/
at-risk                    
(N=259)
significant change with age in mean ferritin levels was 
found in women (P<0.001), but not in men (P=0.080). The 
mean values of albumin decreased with age only in men 
(P=0.005) (Table 2).
The mean levels of fat intake by study participants 
met the target amounts in the DRI recommendations, 
while carbohydrate intake exceeded the guidance and 
protein intake was slightly lower than recommended. 
The intake of vitamins C, D, E and folate, as well as 
the intake of magnesium, calcium, zinc, and potassium, 
were substantially lower in comparison with the DRI 
recommendations (Table 3).

Multivariate linear regression analyses showed that 
respondents that ate more than three meals a day 
(P<0.001) and fewer than two portions of fruit and 
vegetables (P=0.024), drank one glass of wine a day 
(P<0.001) and performed complex activities without 
assistance (P=0.011) had higher mean albumin values. 
Being female (P<0.001), having edentulism (P=0.011) and 
suffering from multimorbidity (P=0.005) had a negative 
10.2478/sjph-2021-0006 Zdr Varst. 2021;60(1):30-37
34
Table 3.
Table 4.
Average daily dietary intakes of study participants in comparison with DRI recommendations.
Variables associated with biochemical biomarkers (multivariate linear regression analyses).
DRI – Recommended Dietary Allowance
*Data presented as mean and SD
Energy (Cal)
Carbohydrate, g 
Fat, g
Protein, g
Vitamin B6, mg
Vitamin B12, µg
Vitamin C, mg
Vitamin E, mg
Vitamin D, µg
Magnesium, mg
Zinc, mg
Calcium, mg
Iron, mg
Potassium, g 
Sodium, g
Folate, µg
Albumin
b
Meals, 3/day
Fruit/vegetable servings
Wine, one glass a day
IADL
Ferritin
c
Female
Living in nursing home
Edentulism
Chronic diseases, more than 3
Fruit/vegetable servings
BMI
Haemoglobin
d
Female
Age
Number of medications, more than 3
Wine, one glass a day
Mobility
1330 (329)
 224 (79)
 32 (13)
 42 (14)
0.84 (0.1)
1 (0.6)
33 (15)
2 (2)
2 (0.2)
127 (45)
4 (1)
486 (116)
13 (4)
1 (0.6)
0.60 (455)
115 (32)
1.55
-0.59
4.21
0.27
-0.48
-0.61
-0.03
-0.01
0.01
0.01
-12.95
-3.91
-0.83
6.53
-0.31
0.73
-1.02
2.35
0.07
-0.72 
-0.84 
-0.70
-0.28
-0.02
0.00
-15.45- 
-7.14
-1.39
1.62
-0.54 
0.19
-0.10
0.23
0.13
-0.21
-0.25
-0.08
-0.01
-0.01
0.07
-0.37
-0.10
-0.01
0.01
-0.20
2.22
0.13
5.87
0.46
0.23
-0.34
0.00
0.02
0.06
0.02
-9.85
-0.68
-0.26
11.44
-0.11
<0.001
0.024
<0.001
0.011
<0.001
<0.001
0.011
0.005
0.028
0.015
<0.001
0.011
0.004
0.037
0.003
1900–2300
  130
20–35
46–56
1.5–1.7
2.4 
75–90
15                                 
15–20
320–420
9–11
1,200
8–18
4.7
1.2
400 
56–70%
>100%
>91%
75–91%
49–56%
  74%
36–44 %
33%
12–16
30–40%
37–46%
40%
>50%
28%
50%
29%
Variable*
Variable
DRI
Recommendation
% DRI Study participants’ intake 
(n=446)
B
min
β
max
P-value
a
95% CI for B
association, while eating fruit/vegetables (P=0.028) and 
BMI (P=0.015) had a positive association with ferritin 
concentration. Women (P<0.001), older than 85 years 
of age (P=0.011) who used more than three medications 
(P=0.004) and had poor mobility (P=0.003) had lower 
haemoglobin values. Determinants of total cholesterol 
levels were being female (P<0.001), age (P=0.016), drinking 
sweetened drinks (P=0.024), balance (P<0.001) and whole 
milk consumption (P=0.001) (Table 4).

10.2478/sjph-2021-0006 Zdr Varst. 2021;60(1):30-37
35
The AUC curves for serum ferritin concentrations 
(AUC=0.447, 95 CI% of 0.191–0.703, P=0.717 in men; 
AUC=0.608, 95 CI% of 0.462–0.755, P=0.173 in women), 
haemoglobin concentrations in women (AUC=0.656, 95% of 
0.482–0.830, P=0.050), albumin concentrations (AUC=0.539 , 
95% CI of 0.114–0.964, P=0.792 in men; AUC=0.538, 95% CI 
of 0.367–0.710, P=0.620 in women) and total cholesterol 
levels (AUC=0.679, 95%CI of 0.560–0.790, P=0.220 in men; 
AUC=0.633, 95%CI of 0.468–0.799, P=0.094 in women) were 
poor to fair, showing that these biomarkers have limited 
diagnostic accuracy in elderly primary care patients. The 
AUC of haemoglobin concentration in men was good and 
statistically significant (AUC=0.863, 95% CI of 0.751–0.974, 
P=0.013). The sensitivity of the proposed cut-off value of 
120 g/l was 50% and the specificity was 92%.
4 DISCUSSION 
The current study focused on the ability of the serum 
biomarkers to detect malnutrition and nutritional risk. 
The malnutrition indicators associated with the serum 
biomarkers concentrations were also analysed. The results 
showed that the ability of ferritin, haemoglobin, albumin and 
total cholesterol to identify nutritional risk and malnutrition 
among elderly primary health care was limited. 
Although the study produced by Cabrerizo et al. (26) 
described the relationship between hypoalbuminemia 
and malnutrition in clinically stable people, the research 
conducted by Kuzuya et al. (27) demonstrated that a 
cut-off value of 35 g/l cannot be used as a marker of 
malnutrition among older people, which is in line with the 
current study. Mean albumin concentrations were in the 
normal range for older adults, regardless of nutritional 
risk. Whether the relationship between serum albumin and 
disability in ADL is mediated through nutritional risk or the 
effects of physical inactivity on muscle-wasting is not well 
understood. Nevertheless, inadequate nutrition combined 
with impaired mobility could trigger inflammation, and 
therefore down-regulate protein synthesis, regardless of 
protein consumption (27), which was lower in the current 
study than recommended. Despite the recommendation 
to use albumin as a biomarker in geriatric evaluation in 
all healthcare settings (28), its value in distinguishing 
malnourished from well-nourished elderly people in 
primary healthcare settings appears to be very low. 
Mean ferritin concentrations were not statistically higher 
among malnourished or “at risk” respondents compared 
to those who had normal nutritional status, and were 
within the optimal reference range for general adult 
populations. Our findings corroborate the study produced 
by Schreiber et al. (29) that showed no relationship 
between nutritional status and ferritin levels. A previous 
study has identified alterations in serum ferritin caused 
by chronic disease, alcohol consumption, inflammation, 
edentulism, multimorbidity, infection, decreasing sex 
hormone activity or other problems related to ageing (30), 
all of which may potentially influence elderly people’s 
nutritional status.  While ferritin could be an indicator 
of overall nutrition and health, its contribution to the 
BMI
Fruit/vegetable servings
Total cholesterol
e
Female
Age
Drinking sweetened drinks, >2
Whole milk consumption
Balance
0.17
1.56
0.40
-0.16
0.07
0.28
0.03
0.07
0.50
0.19
-0.41
0.00
0.12
0.20
0.15
0.01
0.17
-0.06
0.08
0.16
0.32
0.26
1.93
0.60
0.08
0.14
0.44
0.40
0.003
0.031
<0.001
0.016
0.024
<0.001
<0.001
Variable B
min
β
max
P-value
a
95% CI for B
IADL – Instrumental Activities of Daily Living, BMI – body mass index
a 
Only statistically significant results are presented.
b 
Variables included in the model were age, number of meals, daily intake of fruit/vegetables, daily intake of wine, physical activity, 
IAD as assessed using the Lawton Scale.
c 
Variables included in the model were gender, living environment, edentulism, health perception, number of chronic diseases, daily 
intake of fruit/vegetables, daily intake of soft drinks, BMI.
d 
Variables included in the model were age, gender, having a hobby, number of visits to a family physician per year, number of 
medications, daily intake of wine, daily intake of dairy products, daily intake of fruit/vegetables, weekly intake of fish, 6-CIT score, 
BMI, mobility as assessed with the Timed Get-up-and-Go Test.
e 
Variables included in the model were age, gender, number of meals, number of meals eating outside of home per week, daily intake 
of soft drinks, daily intake of dairy products, daily intake of wine, balance as assessed by the Functional Reach Test, skinfold thickness 
and calf circumference.

precise diagnosis of malnutrition and nutritional risk has 
been shown to be weak. 
Previous research showed a decrease in haemoglobin in 
malnutrition (11, 28), which is in line with the current study. 
Although the AUC for men was good, the AUC for women 
was poor. Haemoglobin is a useful and cost-effective 
biomarker of malnutrition in hospitalised elderly patients 
(11, 29), but its use in primary care does not appear to add 
very much to the assessment of nutritional risk.
The study produced by Kuzuya et al. proposed a cut-off 
value of 3.88 mmol/l for cholesterol (sensitivity 73%, 
specificity 50%) as a reflection of malnutrition (27). In the 
current study, despite the statistical difference in values 
between three categories of nutritional status, mean 
concentrations of total cholesterol were detected to be 
within the optimal to borderline reference range. The 
use of cholesterol in clinical practice as a single indicator 
of nutritional status would miss the identification of 
malnourished respondents and elderly people at risk of 
malnutrition (11).
Although in terms of energy and macronutrients, the study 
participants met or exceeded the recommendations, they 
consumed low amounts of micronutrients, such as vitamins 
C, D, and E, as well as minerals, which might have affected 
the results. Low micronutrient intake makes it imperative 
that primary care providers focus more on advising older 
individuals to select foods rich with micronutrients to 
ensure the proper balance of nutrients (31, 32).
This is the first study to analyse concentrations of serum 
biomarkers in relation to malnutrition among elderly 
primary care patients in Bosnia and Herzegovina. Elderly 
persons living in the community and nursing home 
residents over 65 years of age were enrolled in the study, 
making the sample heterogeneous and representative 
of older Bosnian adults. Standardised, validated and 
comprehensive instruments were used to assess nutritional 
status.  There are several limitations to be discussed. The 
study was cross-sectional and did not evaluate nutritional 
status over a period of time. Owing to limited financial 
resources, four biomarkers were included and were only 
measured at one point in time. The high prevalence of 
high nutritional risk might have influenced the results. 
Longitudinal studies are required to analyse the response 
of biomarkers to nutritional interventions. 
5 CONCLUSION
Although ferritin, haemoglobin, albumin and total 
cholesterol may be useful biomarkers of nutritional 
status, their accuracy in diagnosing malnutrition and 
nutritional risk among elderly primary healthcare patients 
is poor. An overview of our findings (high prevalence of 
high nutritional risk) also suggests the need to routinely 
assess nutritional status and screen for malnutrition 
among elderly primary care patients. Assessment needs 
to be comprehensive and to include anthropometric, 
functional, dietary and medical indicators. As family 
physicians are the gatekeepers of the healthcare system, 
and are overburdened by an ageing population and 
multimorbidity, it is vital for the assessment/screening 
instruments to be cost- and time-effective. 
ACKNOWLEDGEMENTS
Special thanks to Dr Heather Keller, the author of SCREEN 
II, for permission to translate SCREEN II and use it in our 
research. We would like to thank Elizabeth Klaassen for 
the English language editing.
CONFLICTS OF INTEREST 
The authors declare that no conflicts of interest exist in 
relation to this research.
FUNDING
The current study was supported by a grant awarded by 
the Ministry for Scientific and Technological Development, 
Higher Education and Information Society of the Republic 
of Srpska, Bosnia and Herzegovina, contract no. 19/6-020/ 
961-113/1.
ETHICAL APPROVAL
All procedures performed in studies involving human 
participants were carried out in accordance with the ethical 
standards of the institutional and/or national research 
committee Ethics Committee of Faculty of Medicine Foca 
(No: 01-2-1) and the 1964 Helsinki Declaration and its later 
amendments, or with comparable ethical standards. 
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