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Title: The Effects of Age on White Blood Cell Count
Author: Karan Kohli
Date: 3/29/2015
Abstract: This study looks at how declining age can be a factor for white blood cell
count. We are using IBM SPSS Statistics to analyze people in twelve different
counties in the U.S., which are visited fifteen times a year. The results do support
that with declining age the white blood cell count does decrease. White blood cells
are important part of the immune system that helps our body fight infections. The
review will be looking at the study we conducted and also peer-reviewed articles
that will be important to understanding the mechanisms of white blood cells. This
lab report was written for BBH 411W class of 2015.
Disclaimer: The purpose of the writing is to fulfill course requirements for BBH
411W and to stand as a personal writing sample, but the findings should not be
treated as generalizable research.
The Effects of Age on White Blood Cell Count
By. Karan Kohli
Introduction:
The outcome variable in this analysis is the white blood cell count. White
blood cells (WBC) are a part of the immune system and their main responsibility is
to protect the body against foreign microbes that cause infectious diseases
(Aminzadeh, 238). WBC’s are important because they make up only one percent of
our blood and are very important in maintaining good health and protecting our
bodies against illnesses (Satimone, 1181). Low WBC count can contribute to a
variety of problems including weakened immune system, higher chances of
infections, blood cancer, and abnormal cell reproduction (Wang, 2006). The bone
marrow is the producer of the WBCs, and we can see a less responsive and
weakened bone marrow with increasing age (Satimone, 1182). As we get older our
white blood count goes down resulting in higher rates of infection and disease,
which can influence the quality of life.
The predictor variable for this analysis is the age of the participant. Age can
be huge predictors of white blood cell count in the participants described in Dr.
Aminzadeh. Dr. Aminzadeh did a huge study, which tested the relationship of age
and the white blood cell count. This study had 700 participants and concluded that
as we get older, the lower our white blood cell count will get (Aminzadeh, 240).
According to the conclusion of the study, white blood cells are major fighters of
foreign infectious microbes, and as our body ages the immune system grows
unresponsive to these microbes, resulting in lower WBC count (Aminzadeh, 241).
After doing research the concepts that most pertain to age and WBC is a weaker
immune system, unresponsive immune system, weaker bone marrow, and slower
blood production (Wang, 2006).
The covariate I am going to include in this analysis is ethnicity. We can see
different diets and lifestyles in ethnicities. Diet can be a huge behavioral predictor
of white blood cell count, where we can see how green vegetables and multigrain
foods can help an immune system across a lifespan (Wang, 2006). Green vegetables
have been found to increase intra- epithelial lymphocytes (IELs), which help
support the immune system in all mammals (Wang, 2006). Researchers believe that
Asians consume more green vegetables than any other ethnicities and that the age
won’t be a deciding factor for their white blood cell count. I believe that as we age,
our white blood cell count gets lower, but won’t effect the Asian population as much
as others.
Methods:
The sample data comes from people in twelve different counties, which are
visited fifteen times a year. This includes millions of people in all different age
groups and ethnicities, which are spread out thru the United States. The study is
specifically going to focus on comparing 18-25 to 65 and above. The data was
collected from 1999-2014, and the participants were recruited from pre-existing
medical conditions like anemia, diabetes, obesity, oral health, and nutritional
problems. The collection of data for our analysis was done in lab visit because we
needed to measure the white blood cell count in individuals and record their age
and ethnicity. The organization to conduct this survey was National Health and
Nutrition Examination survey (NHANES).
The outcome variable needed the white blood count measure that was
conducted by doing a complete blood cell count in a lab setting. It was measured in
G/MCL, which is the concentration of white blood cells per microliter. The predictor
variable was age and is recorded by asking the participant in the study “What is
your biological age” and the participant would answer with a numerical response
representing the age in biological years. We just needed to group 18-25 in one
group and 65 and above in another to see the difference from two completely
different age groups. The covariate variable was ethnicity and was recorded by
asking the participants “what is your ethnicity” and was answered with a verbal
response of what your ethnicity is. We just need to code Asians in one group and
everyone else in another group to see how it effects just the Asian population. The
code for Asians was 1 and code for all others was 2.
I used both descriptive and frequencies in SPSS to analyze my data because I
had two data sets that were numerical quantities and the covariate, which was a
categorical response. I used the linear regression for my analyses because the
dependent variable is continuous. I started the analysis with a simple regression of
age and white blood cell count then added a multiple regression with the single
covariate of Asian ethnicity. Throughout the whole analysis we consider values less
than 0.05 to be accepted as significant values.
Results:
There were a total of 251,097,002 participants that answered the age
question and had a white blood cell count. There was a reporting error in 10.8% of
the total population. We only analyzed the 18-25 and 65 and over age group
because they provided us with results of two widely separated groups in which we
can see a difference if there was one. The total population for 18-25 was about 30
million people and 65+ were 45 million people. The average white blood cell count
for the age group 18-25 was 76,834 g/mcl, while the average blood count for the 65
and over group was 15,462 g/mcl (stdev= 2287.932). The average age for the study
was 34.41 years old and the average for the WBC count was 6345.54 g/mcl. The
covariate was the Asian ethnicity, which was 6.8% of the total population who
answered the survey.
The simple regression outcome showed us how there was significance of the
association between the predictor and outcome (B=2.56, Cl=. 98, and p= .0231), see
figure 1. After controlling the Asian ethnicity, we found no change in the outcome
variable, indicating that the Asian ethnicity didn’t covariate the analysis (OR=1.08,
Cl=2.06, p= .343). This showed us that people with advanced age have good chance
of having a low white blood cell count.
Figure 1. - You can see there is a correlation with age and white blood cell count.
As age increases, white blood cell count looks like it is decreasing.
Discussion:
I successfully analyzed the relationship of age and white blood cell count in
the large population represented by 12 counties in the United States. The
hypothesis I tested was as individual age increases, the white blood cell count would
decrease. The results did support the hypothesis and yielded results that I did
expect. The covariate of the Asian ethnicity didn’t have significance in the data and
was expected to have huge impacts in the hypothesis. To figure out if my results
matched other studies, I compared one study that had a lot of evidence and validity
in the scientific community. Dr. Parsa did a case-control study, which had 130
admitted hospital patients aged 65 and older. She did a blood test for white blood
cell count and got an average of 13,567 g/mcl from all the 130 patients (Parsa, 242).
The results support the analysis because it was close to my average value (15,462
g/mcl) and this experiment had a limited about of test subjects compared to mine
(Parsa, 242).
The limitations for these study-included populations from 12 counties in the
United States with most being white followed by African American. I felt like their
could have been a more wide spread of ethnic populations. There were also too
many people surveyed which made it hard analyzing specific topics. Another way I
would do this study while still analyzing the same topics of interest is by doing a
cohort study. I would pick four specific ethnic groups like Asian, white-European,
African, and Hispanic that are living in the United States. I would pick 1,000 people
from each ethnic group stated above and do total white blood count test while
recording their age. This study makes sense because we are equally getting 4 major
ethnic groups and we can see the correlation of age and ethnicity equally (Sharma,
90). There will be no biases in having more of a specific population and we can see
the ethnicity effects on white blood cell count (Sharma, 90). In this study I still
expect to see WBC count decreasing with age and I also expect to see that Asians
having no difference in WBC count as they age. The study found a significant
relationship between age and white blood cell count that supported our hypothesis
because the group with older participants had lower white blood cell count.
Citations
1. Aminzadeh, Z., & Parsa, E. (2011). Relationship between Age and Peripheral
White Blood Cell Count in Patients with Sepsis. International Journal of
Preventive Medicine, 2(4), 238–242.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3237266/
2. Santimone, I., Di Castelnuovo, A., De Curtis, A., Spinelli, M., Cugino, D.,
Gianfagna, F., … Iacoviello, L. (2011). White blood cell count, sex and age are
major determinants of heterogeneity of platelet indices in an adult general
population: results from the MOLI-SANI project. Haematologica, 96(8), 1180–
1188. doi:10.3324/haematol.2011.043042
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3148912/
3. Wang Shitong; Wang Min "A new detection algorithm (NDA) based on fuzzy
cellular neural networks for white blood cell detection", Information
Technology in Biomedicine, IEEE Transactions on, On page(s): 5 - 10 Volume:
10, Issue: 1, Jan. 2006
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4. Ajeet D. Sharma, Gautam Sreeram, Thomas Erb, Hilary P. Grocott, Thomas F.
Slaughter, Leukocyte-Reduced Blood Transfusions: Perioperative Indications,
Adverse Effects, and Cost Analysis, Anesthesia & Analgesia, 2000, 90, 6, 1315