# INTRODUCTION chart nearest the dashed line (representing the Body

INTRODUCTION

1.1  BACKGROUND

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obesity has become a major problem all around the world. Ones can classify
whether or not they are obese by calculating his or her Body Mass Index (BMI)
using the formula below that is widely used:

BMI =

This
is the table of classification of weight by BMI:

BMI

Classification

< 18.5 underweight 18.5 – 24.9 normal weight 25.0 – 29.9 overweight 30.0 – 34.9 class I obesity 35.0 – 39.9 class II obesity ? 40.0   class III obesity   Table 1: Classification of BMI There is another method in determining BMI which is by using nomogram. "The Body Mass Index (BMI) Nomogram is a graph that shows a person's Body Mass Index as the point on the chart nearest the dashed line (representing the Body Mass Index) where height (in inches or centimeters) and weight (in pounds or kilograms) intersect." ("Description - Body Mass Index (BMI) Nomogram", 2009) Figure 1: Example of nomogram (BioNinja, n.d.) As BMI increases, the risk for a person of getting diabetes type II disposition increases since an obese person usually stored a lot of fat in his or her body and it leads to, high blood pressure, cholesterol and blood glucose level. This study will include only persons in the age range of 18 and 19, aiming to find the relationship between the factors of diabetes type II, which are family diabetic history and BMI and the percentage risk of getting the disease. As for men, the risk of them getting diabetes type II may decrease as time goes by because they may increase in height soon and proceed their growth, which only stops at average 20 years old. Women at this age have already stopped their growth and maintain in height, but will still experience an increase in weight. In 2006, Margareta Norberg from Umeå University had written a thesis regarding her research on the topic of diabetes type II, specialized to only risks with title 'Identifying risk of type 2 diabetes: Epidemiologic perspectives from biomarkers to lifestyle'. Some of the factors that have been discussed in that thesis are obesity and family history. There are many other factors that may affect the result, whether or not an individual may suffer from diabetes type II in the future apart from those mentioned, including consumption of medicines and smoking status.   1.2  SIGNIFICANCE   This investigation is conducted to identify the possibility of a person to suffer from diabetes type II in the future. By analysing the data apart from BMI, I can make a statistical analysis and find the percentage risk. Since the issue of BMI is in the syllabus of the International Baccalaureate Diploma Programme, I want to explore further how it relates to our own life specifically diabetes type II. I have a normal body mass index (BMI), so do most of my family members, but there are still a few of them who are classified as obese and are suffering from diseases such as high blood pressure and heart disease. It makes me curious if in the future, will I face the same diseases. By carrying out this experiment, I can investigate the reality that is happening to my fellow college students and maybe make them more aware about the importance of taking good care of our health.   1.4 RESEARCH QUESTION:             How do Body Mass Index (BMI) and diabetic family history affect the percentage risk of a person to suffer from diabetes type II among 50 college students aged between 18 to 19 years old?   1.5 HYPOTHESIS: Hereditary and BMI are some factors that affecting diabetic percentage risk. No one would have zero possibility of getting diabetes type II in the future, but his or her lifestyle may help in reducing the probability of suffering from the disease. Higher BMI will lead to higher percentage risk, and having family members who suffer from diabetes type II will also increase the percentage risk. 2.      METHODOLOGY 2.1   SAFETY PRECAUTION: 1. Respondents are asked to sign a consent form as a form of permission in obtaining their personal information. 2. The name of respondents and college is secured to maintain the anonymity.   2.2     VARIABLES: 2.2.1        INDEPENDENT: The factors affecting the risk Data from 50 students regarding the factors that involve in developing diabetes type II is collected, including their Body Mass Index (BMI), status of smoking, consumption of high blood pressure medications and steroids, also the family history of having diabetes. All of these factors are selected because each of them will affect the others, thus may increase or reduce the expected percentage risk. 2.2.2        DEPENDENT: The possibility of a person on suffering diabetes All the data will also be used in order to calculate the risk score for a group of the same characteristics by using the equation, Risk = 100 / (1 + et), where t = 6.322 - Sex – HTNmeds - Steroids - (0.063 * Age) - BMI - FMH – Smoker The value for each characteristic is given in the input table.    Input: (Medscape, 1998)   Score Sex Male 0 Female -0.879 High blood pressure medication consumption On HTN medications 1.222 Not HTN medications 0 Steroids consumption On steroids 2.191 Not on steroids 0 BMI >25

0

25 – 27.49

0.699

27.5 – 29.99

1.97

30

2.518

Family history

No first degree family
members with diabetes

0

Parent OR sibling with diabetes

0.728

Parent AND sibling with diabetes

0.753

Smoker

Respondent is a non
smoker

0

Respondent used to smoke

– 0.218

Respondent is a smoker

0.855

Table 2: Value for respective characteristic

2.2.3   CONTROLLED: i) The community

ii) Age of the students

The
data are collected from 50 cohort students at my college because they live in
the same community. The range of their age must between 18 and 19 years old
only. There will be mix of data for both male and female because of random
selection, thus the number for each gender will not be equal.

2.2.4 APPARATUS AND MATERIALS

APPARATUS

MATERIALS

Weighing
scale ± 0.5 kg

50
students

± 0.5 cm

2.2.5 PROCEDURE: (Norberg,
2006)

1.      A
student at range 18 and 19 years old was picked randomly and given a survey
form.

2.      His
or her weight was measured using a weighing scale followed by measuring the
height of the student using stadiometer.

3.      The
body mass index of the respondent then was calculated using the formula,

and the class of BMI will be identified.

4.      All
the measurement value is written on the survey form given.

5.      The
respondent then will be asked to fill in his or her personal information, about
the intake of HTN medications or steroids, status of smoking and family background
which includes family history whether there are his or her family members
suffered from diabetes.

6.      Steps
1-5 are repeated with different students until the total respondent is 50
students.

7.      The
respondents then divided into groups of same variables members and the number
of members in each group is then being taken into account.

8.      The
percentage of risk for each group will be calculated using the formula,

Risk
Percentage: 100 / (1 + et) where t = 6.322 – Sex – HTNmeds
– Steroids – (0.063 * Age) – BMI – FMH – Smoker

3.
RESULT
AND ANALYSIS

3.1  QUALITATIVE:

From what I observed, the appearances of the
respondents are different from each other. Among the tall respondents, most of
them having an oval face, with a slim body, but there are still some who are
tall and have big body that is quite chubby. As for the one with short to
medium height, they seem to have chubby face, but in fact their bodies are
quite slim, yet not so fatty. There are minorities, especially the one who is classified
as obese, short in height, but their body is bigger than they should.

3.2  QUANTITATIVE:

No.

Age

Gender

Class of BMI

Prescription of

Status of smoking

Family history of diabetes

Number of
students

High blood
pressure medication (HTN meds)

Steroids
medicines

1

18

Male

>25

2

2

1

1

3

2

18

Male

>25

2

2

1

2

2

3

19

Male

>25

2

2

1

1

6

4

19

Male

>25

2

2

1

2

1

5

19

Male

>25

2

2

2

1

1

6

18

Female

>25

2

2

1

1

9

7

18

Female

>25

2

2

1

2

1

8

19

Female

>25

2

2

1

1

14

9

19

Female

>25

2

2

1

2

4

10

18

Male

Overweight

2

2

1

1

1

11

19

Male

Overweight

2

2

1

2

1

12

19

Male

Overweight

2

2

2

1

1

13

19

Male

Overweight

2

2

1

1

1

14

19

Female

Overweight

2

2

1

1

1

15

19

Male

Class
II obesity

2

2

1

1

1

18

19

Male

Class
II obesity

2

2

1

2

1

16

18

Female

Class
II obesity

2

2

1

2

1

17

19

Female

Class
II obesity

2

2

1

1

1

Legend:

Prescription
of                      Status of
smoking:              Family history
of diabetes:
HTN
meds &
Steroids:                                                1.
Non smoker                      1.
None
1.
Yes                                     2.
Used to smoke                 2.
Parent OR sibling
2.
No                                       3.
Smoking                            3.
Parent AND sibling

3.3  PROCESSING AND ANALYSIS

2 out of 50 data
collected will not be used in the analysis because the factor of smoking occurs
only on them, which is the minority. So I dropped the factors from being
discussed. As for the age, all respondents only differ by a year, therefore
making only an acute effect on the results, so the factors will also be
excluded from discussion. The factors of medicines prescribed will not be
included as well since none of the respondents consume any medicines, so it
gives no effect on the result.

3.3.1
Example
of calculation

i)
Terms =
6.322 – Sex – HTNmeds – Steroids – (0.063 * Age) – BMI – FMH – Smoker

Terms
(No. 1) = 6.322 – (-0.879) – 0 – (0.063 * 18) – 0 – 0 – 0 = 6.067

ii)
Percentage
risk =
100 / (1 + e(Terms)) = 100 / (1 + e6.067) = 0.231%

iii)
Uncertainty:
(Male without diabetic
family)

Uncertainty of weighing scale = 1 × 5 = 5 kg

Uncertainty of stadiometer = 0.1 × 5 = 0.5 cm

iv)
Pearson’s Correlation coefficient, r (Male without diabetic family)

BMI, x

% of risk, y

xy

(x ?

)2

(y ?

)2

18.12

0.591

10.70892

29.3764

1.82898576

20.97

0.591

12.39327

6.6049

1.82898576

21.05

0.555

11.68275

6.2001

1.92765456

26.46

1.111

29.39706

8.5264

0.69288976

31.1

6.869

213.6259

57.1536

24.26153536

?x = 117.70

?y = 9.717

?xy =
277.808

?(x

)2 = 107.861

?(y

)2 = 30.5401

23.54

1.943

45.748

2 = 554.13

r
=

where Sxy
=

?

, Sx =

and Sy
=

Sxy =

–
45.748 = 9.814

Sx =

= 4.645

Sy =

= 2.471

r =

= 0.855

r2 = 0.731

Therefore, there is a strong correlation between the variables.

3.3.2
Graph

Graph
1: BMI and family history against percentage risk

3.4  DISCUSSION

As
what can be seen from Graph 1, all of the line graphs have the same pattern
which greater BMI will lead to a greater possibility of getting diabetes type
II, whether the respondent has diabetic family members or not. The percentage
risk for the group of female with diabetic family seems to be almost the same
with the group of male without diabetic family, but only for class of BMI
underweight, ideal or overweight, which refers to BMI less than 27.5 kgm-2.
Those who are categorized as an obese, have a higher percentage to suffer from
the disease. Its pattern grows sharply for all graphs, but even greater for the
group of respondents who has diabetic family members. “Studies suggest that abdominal fat causes fat cells to
release ‘pro-inflammatory’ chemicals, which can make the body less sensitive to
the insulin it produces by disrupting the function of insulin responsive cells
and their ability to respond to insulin.” (“Diabetes and Obesity”,
n.d.)

Males have a higher tendency in developing diabetes
than women because they tend to store fat in the abdomen, contradict with women
who typically store their fat under the skin, in certain areas such as the hips
or thighs. In other words, women need more fat accumulated before they begin to
deposit harmful deposits in the abdominal area. That is why the graphs for male
are higher than females’, whether or not they have diabetic family members.

From the Graph 1, it is also can be seen that those
who have diabetic family members have a higher risk percentage, for both males
and females. Genes are the major factor causing this to happen. The tendency
for respondents who have diabetic family is much higher caused by the diabetes
gene that is already inside their bodies.

The risk percentage will be even higher if the
person is classified as an obese. An increment number of factors will also
increase the risk percentage. It is shown by the graphs of male and female who
has diabetic family members, where the line graphs become steeper proving its
significant changes when there are more factors.

4.      EVALUATION

4.1 LIMITATIONS AND WAYS TO
OVERCOME

Although
this research has been carried out carefully, there are still some limitations
that are hard to remove therefore affecting the precision of the result. One of
them is the way on how people stand on the weighing balance and stadiometer.
The sensitivity of the tools may reduce if one does not stand upright
straightly or if the individual unintentionally brings along items while on the
weighing scale. This limitation can be reduced the weighing scale is placed on
a flat surface, while the stadiometer is lay on a wall. By then, students may
stand best and the results will also be more accurate.

There are also problems regarding
the BMI, which is limited and not reliable for everyone such as athletes or
others who have a muscular build. BMI may be will overestimate their body fat deposition.

The other limitations of this
research are the respondents are not specifying to certain body shape only,
besides each individuals are having a different bone density from each other.

CONCLUSION

Diabetes is surely a big issue that
happens all around the globe, and we should take this matter into our concern.
It is prone to happen to those who have diabetic family members, also who is
classified as obese. Those factors are one of the major factors that may affect
the tendency of suffering from diabetes type II. This research makes us be more
particular regarding our diet and the lifestyle we practice. Lower BMI will
result lower percentage risk, so we need to maintain the ideal BMI, in order to
minimize the probability, hence, the factor of family history can be controlled
as well. My research seems to be reliable and parallel to the other researches
before. Therefore, it can be said that my hypothesis is supported.

In further studies, improvement in
the methodology can be done by considering more external factors that may
affect the result such as the environmental factors. By then, more precise
result can be produced.