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Ann. psychophysiol. 
ISSN 2412-3188 (Online)|2410-1354 (Print) 

APP| Published By AEIRC| https://doi.org/10.29052/2412-3188.v9.i2.2022.106-115 

Original Article 

Assessing BDNF correlations with non-
invasive indicators of neurological decline 
in different age groups. 
Syeda Faiza Batool1  & Faizan Mirza1,2

1Psychophysiology Research Lab, M.A.H.Q Biological Research Centre, UoK. 
2Department of Physiology, University of Karachi, Karachi-Pakistan. 

Abstract 
Background: Health is the prime concern of the modern world, and with the 
increasing life span, both the physical and mental health of human being decline, 
eventually affecting the cognitive abilities of a person, which may be due to normal 
aging processes or neuropathological reasons. A cross-sectional study investigated 
the relationship between BDNF level, neurological disturbance, and aging.  
Methodology: Cognitive assessment is done through verbal fluency test (FAS, 
DSST, and 6CIT) and BDNF level in blood found through HPLC utilizing the 
ALIZA kit method.  
Results: Descriptive statistics were applied for continuous variables. Hence, one-
way ANOVA was performed to show the relationship between cognitive 
parameters and aging. 
Conclusion: Our study reports that verbal fluency disturbs as lifetime increases, 
although sex, education, obesity, or lifestyle does not affect cognition.  

Keywords 
Aging, Brain Derive Neurotrophic Factor, Verbal Fluency Test, Cognitive 

Impairment/Cognitive Decline 

Citation: Batool SF, Mirza F. Assessing 
BDNF correlations with non-invasive 
indicators of neurological decline in 
different age groups. APP. 2022; 9(2): 106-
115 

Corresponding Author Email: 
faizan.mirza@uok.edu.pk 

DOI: 10.29052/2412-3188.v9.i2.2022.106-
115 

Received 11/10/2022 

Accepted 20/11/2022 

Published 01/12/2022 

Copyright © The Author(s). 2022. This is 
an open-access article distributed under 
the terms of the Creative Commons 
Attribution 4.0 International License, 
which permits unrestricted use, 
distribution, and reproduction in any 
medium, provided the original author and 
source are credited.  

Funding: The author(s) received no 
specific funding for this work. 

Conflicts of Interests: The authors have 
declared that no competing interests 
exist. 

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Introduction 
A healthy lifestyle increases the life 
expectancy of an individual and hence the 
elderly population of the world. According 
to statistics, in 2050, older adults occupied 
20% of the world population, and most lived 
in developing countries1. Aging is a natural 
phenomenon, despite the world population 
aging rapidly, and mental health problems 
are one of those reasons. Persistent pressure 
or stress disturb the normal functioning of 
the brain and body and eventually causes 
processing psychophysiological 
disturbance, which is one of the cause of 
earlier cognitive impairment observed in the 
different aged group of the world 
population2. 

Brain-derived neurotrophic factor (BDNF), a 
secretory growth factor involved in the 
neuronal plasticity process of learning and 
memory and also provides neuroprotective 
effects by regulating the neuron's survival, 
differentiations, and repair and thus 
provides a shield in different adverse 
conditions such as glutamatergic 
stimulation, cerebral ischemia, 
hypoglycemia, and neurotoxicity3,4. It is 
highly expressed in the hippocampus and 
cortex region while produced and secreted 
by the peripheral tissues as BDNF and pro-
BDNF. Pro-BDNF binds to low-affinity p75 
neurotrophin receptors and induces long-
term depression, promotes neuronal cell 
death, and facilitates the resculpting of 
neuronal circuits. In contrast, mature BDNF 
binds with higher-affinity tropomyosin-
related kinase family (Trk) receptors, 
increasing cell survival and differentiation, 
long-term potentiation (LTP), dendritic 
spine complexity, and synaptic plasticity 5,6. 
Changes in BDNF level are associated with 
healthy and pathological aging; besides this, 
its expression also varies in different 
pathological conditions, i.e., depression, 
eating disorders, schizophrenia, dementia, 

Huntington's disease, and Parkinson's 
disease7. 

The brain-derived neurotrophic factor is a 
potential marker of cognitive decline. Hence, 
to observe any impairment in a person's 
cognition, a verbal fluency test is one of the 
most common non-invasive 
neuropsychological assessment tools 
utilized in clinical and research settings8. It 
is also utilized to measure verbal ability, 
including lexical knowledge and retrieval 
ability9, and as a test of executive control 
ability10 in a non-clinical group. Since verbal 
fluency requires selective attention, selective 
inhibition, internal response generation, and 
mental shifting to generate words from 
memory, the VF task assesses language 
functions like vocabulary size or naming, 
speed of response, mental organization, 
search strategies, and long-term memory. 

Cognitive factors are necessary for good 
performance on the verbal fluency task. 
These factors include cognitive speed, which 
refers to the rate of verbal retrieval and the 
ability to formulate effective recall 
strategies11; cognitive flexibility, which 
refers to switching strategies12 rapidly; and 
semantic memory13. Phonological fluency is 
helpful in the detection of cognitive deficits 
in pathologies with frontal involvement14. 
Research has shown that a healthy human 
can speak 12 words in a minute, starting with 
a specific letter15. Hence, poor performance 
on VF tasks links to cognitive decline, which 
would be a sign of frontal and temporal lobe 
impairment16,17; even though gender and age 
both influence verbal fluency task 
performance18, i.e., females pronounced 
more words than males, and older people 
have low verbal fluency than youngers19,20,21. 



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Methodology 
Research Design 
To evaluate the correlation of circulating 

BDNF levels with phonemic verbal fluency 

and cognitive impairment in healthy 

subjects, a cross-sectional study was 

conducted from 8th June 2021 to 4th 

December 2022. The study participants are 

healthy males and females aged between 19 

to 69 years with at least twelve years of 

education. Subjects with neurological, 

hematological, or motor disorders were 

excluded from the study. Study parameters 

are divided into an invasive and non-

invasive categories. Consent forms along 

with demographic variables were enlisted 

initially, and with the convenience of 

participants, cognitive assessments were 

performed individually in a quiet, peaceful 

environment. Evaluation is done through 

three non-invasive verbal tests, including 

FAS for phonemic word fluency assessment, 

the Digit symbol substitution test (DSST) to 

evaluate human associate learning, and The 

Cognitive Impairment Test (6CIT) for any 

cognitive disability. For invasive 

parameters, BDNF detection in blood serum 

was done thru RayBio® Human BDNF 

ELISA (Enzyme-Linked Immunosorbent 

Assay) kit, an in-vitro method in which 

immobilized BDNF antibodies, HRP 

conjugated streptavidin, and TMB substrate 

solutions were used while the results 

observed at 450 nm wavelength. 

Statistical Analysis 
The Statistical Package for the Social Sciences 

(SPSS) version 22.0 was used for statistical 

analysis. The variables are demonstrated by 

using descriptive statistics where mean and 

standard deviation were used to present all 

the continuous variables like age, BMI, and 

aging indicators. On the other hand, 

categorical variables such as gender, marital 

status, obesity, socioeconomic status (SES,) 

and education represent frequency and 

percentages. One-way analysis of variance 

(ANOVA) was used to determine the age-

wise alterations in the aging indicators 

invasive like BDNF and non-invasive, 

including DSST Time (sec), FAS Score, and 

6CIT Score. Pearson correlation was used to 

correlate age with the indicators of aging. 

Chi-square (X2) test for the stratification of 

the aging parameters concerning the 

demographic characteristics of the study 

population and the Box and whisker plot for 

graphical presentation. A p-value of less 

than 0.05 was considered statistically 

significant. 

Results 
A total of 412 subjects were enrolled, males 

were 218, and females were 194 with a mean 

age of 32.65±12.62 years and a mean BMI of 

22.69±5.70 kg/m2. Concerning obesity, 

58.5% of subjects were average, 19.9% were 

underweight, and 21.6% were overweight or 

obese. BDNF was 22.88±3.77 ng/ml.

Table 1: Mean and Standard Deviation of Neuropsychological Assessment of Population. 

Parameters Mean± Standard deviation 

6CIT score 6.24±4.831 

DSST Time 131.05±78.634 



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FAS Score 26.03±17.071 

It was found that the mean serum BDNF level declined with age, i.e., 23.84±3.53 ng/ml (20 to 36 

years) to 19.62±1.68 ng/ml among subjects aged 54 to 70 years (p<0.05). 

Table 2: Brain-derived Neurotrophic Factor analysis in different age groups. 

Invasive Indicator Age Groups 
p-value

20 to 36 years 37 to 53 years 54 to 70 years 

BDNF (ng/ml) 23.84±3.53 21.25±3.98 19.62±1.68 .000 

Figure 1: Whisker Plot showing the age-wise decline in the serum brain-derived 

neurotrophic factor. 

Figure 1 shows a Box and whisker plot comparing the serum BDNF level in three different tertiles 

of age, i.e., 20 to 36 years, 37 to 53 years, and 54 to 70 years. The serum BDNF concentration was 

significantly high (median; 25th–75th percentiles) among the age group 20 to 36 years (24.6; 24.60-

26.60 ng/dl) as compared to those with 54 to 70 years (19.6; 18.30-21.4 ng/dl) of age.  

We have also estimated the association of study characteristics with BDNF level. A significant 

relationship was observed between characteristics including gender, education, marital status, 

obesity (BMI), age group, longevity, 6CIT score (cognitive abilities), and BDNF level (p<0.05). The 

results cannot be generalized as most of the study participants had normal BDNF levels. 



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Table 3: Representation of cognitive impairment in study participants. 

Variable 
BDNF 

Low (N=10) Normal (N=402) 

6CIT 

Normal - 251(62.4) 

.000 Mild Cognitive Impairment - 36(9.0) 

Significant Cognitive Impairment 10(100) 115(28.6) 
*P value 0.000

Table 4: Representation of Non-Invasive Indicators in study participants. 

Non-Invasive Indicators 
Age Groups p-

value 20 to 36 years 37 to 53 years 54 to 70 years 

DSST Time (sec) 118.47±53.20 125.60±109.28 208.85±104.25 .000 

FAS Score 28.38±17.50 23.71±17.03 15.85±8.61 .000 

6CIT Score 5.96±4.83 7.19±4.94 6.54±4.59 .148 

Figure 2: Whisker Plot showing the FAS score (Verbal Fluency) stratified by age. 



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The FAS score was significantly high (median; 25th–75th percentiles) among the age group 20 to 

36 years (26.0; 14.0-40.0) as compared to those with 54 to 70 years (16.0; 7.0-22.0) of age as shown 

in Figure 02. 

Figure 3: Whisker Plot showing the Digit Symbol Substitution Test (DSST) time 

stratified by age. 

Figure 3 shows how DSST time gradually increased with age, i.e., 116; 89-146 seconds (20 to 36 

years) compared to 205; 170-236 seconds (54 to 70 years). 

Figure 4: Whisker Plot showing the 6CIT scores stratified by age. 



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There were no significant changes in the 6CIT score in the studied age groups, i.e., the median 

was six among 20 to 36 years, and the same was for those in the 37 to 53 years and 54 to 70 years 

age groups, as shown in figure 4. 

Table 5 illustrates the correlation between age, BDNF, and non-invasive evaluative 

constraints. 

Correlation of Parameters Age BDNF 

Age 1 

BDNF -0.438** 1 

FAS Score -0.318** 0.027 

DSST Time 0.341** -0.095

6CIT Score 0.021 -0.013

   ** Correlation is significant at the 0.01 level (2-tailed). 

*Correlation is significant at the 0.05 level (2-tailed).

Discussion 
The mean FAS scores of the participating 

individuals support the evidence that with 

increasing age, the verbal fluency of an 

individual decrease (p<0.05) (Figure 02). A 

longitudinal study on the German 

population reported a similar result, which 

states that verbal fluency defect is the 

prominent indicator of cognitive 

impairment22. However, our study found no 

significant correlation between verbal 

fluency and sex, education, obesity, or 

lifestyle. Studies have also suggested that the 

brain area, mainly the hippocampus is 

sensitive to stress and can cause verbal 

declarative memory23. Gaillard conducted a 

study of verbal fluency tests and observed 

that younger individuals and children tend 

to have an active cortex compared to adults 

and suggested that the pattern of verbal 

fluency mainly developed in early 

childhood. Moreover, the results of his study 

propose that the verbal fluency test can be 

used in determining the brain regions with 

language dominance24. 

With another non-invasive constant, it was 

observed that the DSST time increased as the 

age increased (p<0.05) (Figure 03). It is the 

only neuropsychology test with a low impact 

on the language, culture, and education of an 

individual's task performance25. It was 

considered that during World War 2, the 

DSST test was the only clinical measure used 

to distinguish the patients according to brain 

damage compared to the controls26. Previous 

Studies suggest that DSST is more useful 

clinically since it is sensitive to individuals' 

cognitive defects and is associated with brain 

disorders. Better results on DSST can be 

achieved with intact motor speed,  

visuoperceptual functions, and individual 

ability to draw/write. DSST results are 



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affected by the individual's performance 

which is associated with their learning 

ability. That is why it is also known as the 

"measure of complex attention" in an 

individual25 

Our results showed that the median 6CIT 

score in all studied age groups was six, and 

the effects of cognitive decline were not 

prominent (Figure 26). It measured that the 

mean score of 6CIT was less in younger 

individuals (5.96±4.83) as compared to elder 

individuals (6.54±4.59) in the study (p>0.05) 

(Table 04). Studies suggest that any injury to 

neurons of the hippocampus CA3 region due 

to any stress, decreases in BDNF, increased 

glutamate level, or elevation of 

inflammatory cytokines can induce 

inhibition of neurogenesis28, which causes 

defects in new learning that are also 

associated with increased levels of 

glucocorticoids due to stress29,30. Wide-

ranging cognitive domains with 

compromised functions include verbal 

learning and memory, attention working 

memory, executive functions, and 

information processing speed31. 

Similarly, evidence suggests that increased 

inflammation causes detrimental effects on 

the brain and cognitive levels, called 

neuroinflammation, which has a significant 

role in peripheral pro-inflammatory 

cytokines that play its role through several 

pathways. Among the cytokines playing an 

essential role in neuroinflammation is IL-632. 

As suggested by the evidence from the 

studies, the ability of BDNF to induce 

functional synaptic plasticity and changes in 

synaptic morphology has been considered 

an attractive candidate as a molecular 

mediator of learning and memory33. Studies 

have shown that individuals with cognitive 

decline-related conditions and diseases, 

including any traumatic event or mild 

cognitive impairment, tend to have 

significantly low BDNF levels34. Moreover, 

recent studies suggested that BDNF is the 

circulating biomarker for cognitive and 

memory functions in healthy individuals35,36.     

Conclusion 
It is concluded that increased inflammation 
causes detrimental effects on the brain and 
cognitive levels. With increased age, serum 
BDNF levels decline, along with the verbal 
fluency of an individual, and our results 
show an increase in the DSST time with 
aging. Moreover, it is suggested that 
individuals with cognitive decline-related 
conditions and diseases, including any 
traumatic event or mild cognitive 
impairment, tend to have significantly low 
BDNF levels, with decreased verbal fluency 
and DSST time. Based on the data collected, 
BDNF was in the normal range & the non-
existence of a relationship between 
psychological parameters and BDNF cannot 
be ruled out. 

Acknowledgment 
We are thankful to our mentor Dr. Sadaf 
Ahmed for guiding us in the study 
throughout and our labmate Ms. Ujala Sajid 
for her continuous support.  

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