Skip to content
PENUNTUT ILMU
Dr. Abdullaah Jalil
40 Hadith (13)

Academic Advisory, Supervision and Review

Academic Courses

ASWJ

Awards

Books (40)

Chapter in Books

Conferences & Workshops – Participant/ Moderator

Conferences – Presenter/ Author/ Co-Author

Consultation

Courses & Workshops – Trainer/ Member

Educational Background

Fiqh Muamalat

Hak Cipta

Halal

Home

Islamic Banking and Finance

Journals

Kuliah & Kursus Agama – Pengajar/ Pembentang

Laws and Regulations

Mirath & Fara’id

Mutun Ilmiyyah

News Paper / Magazines

PCVE (Deradicalization and Rehabilitation)

Penyelidik IMARAH

Policy / Industry

Q & A

References & Links

Research

Risalah Penuntut Ilmu (RPI)

Selected Fatwa

Services/ Modules Offered

Shariah Compliance and Governance

Takaful

Talaqqi

Waqf

Zakat

Zikrullah and Supplications

About

Blog
Policy / Industry
SHARIAH ADVISORY AND SUPERVISION
(1) A member of Shariah Committee, Bank Rakyat (from April 2012) (
Link
). Including:
A member of Zakat Committee, Bank Rakyat

A member of Shariah Committee, Rakyat Holdings
(2) A member of Shariah Committee, Prudential BSN Takaful (from July 2015) (
Link
)
(3) A member of Shariah Commitee, Koperasi Kakitangan USIM (Universiti Sains Islam Malaysia) (
Link
)
(4) Ahli Jawatankuasa Penasihat Syariah dan Pemasaran Wakaf USIM (Bermula 5 Disember 2016)
(5) Panel Syariah bagi Sistem Pengurusan Berasaskan Syariah MS1900:2014 di USIM.
(6) Ahli Jawatankuasa Panel Shariah USIM.
SHARIAH COMPLIANCE AND FATWA
(3)
Panel Penyelidik Fatwa Negeri Sembilan Darul Khusus
. Jabatan Mufti Kerajaan Negeri, Negeri Sembilan Darul Khusus. Tempoh: September 2016 – September 2018.
(2) Pegawai Pematuhan Shariah bagi Skim Pensijilan Sistem Pengurusan Kualiti MS 1900:2005.Universiti Sain Islam Malaysia (USIM). Mac 2008.
(1) Ahli Panel Shariah INFAD. Institusi Pengurusan dan Penyelidikan Fatwa Sedunia (INFAD), Universiti Sain Islam Malaysia (USIM). September 2006 – 2009.
PUBLICATIONS
(1) MQA Standard –
Programme Standards: Muamalat and Islamic Finance
. [Original
Source
]
Role: A member of the
panel of experts
who were assigned for developing the standard.
(2) JAWHAR. (2014). Manual Pengurusan Wang Khairat. Putrajaya: JAWHAR. [
Original Source
] [Sebagai salah seorang panel rujukan dan penyumbang draf]
(2.1) Abdullaah Jalil.
Draf Pertama bagi Bab 2 Manual Pengurusan Wang Khairat
. Dibentangkan di Bengkel Penyediaan Manual Pengurusan Wang Khairat (MPWK), Jabaran Wakaf, Zakat dan Haji (JAWHAR), Jabatan Perdana Menteri. 27-29 Nov 2013.
(3) JAKIM. (2015).
Dasar Dakwah Negara – Memperkasa Agenda Transformasi Dakwah Negara ke arah Pembentukan Khaira Ummah
.
Peranan: Ahli Jawatankuasa Pembangunan Dasar Dakwah Negara (Salah seorang ahli di dalam kluster Ekonomi). Dasar Dakwah Negara telah dilancarkan pada 16 Jun 2015/ 29 Sya’ban 1436H oleh Menteri di Jabatan Perdana Menteri (JPM) Datuk Seri Jamil Khir Baharom.
Berita IKIM
PROFESSIONAL MEMBERSHIP
(1) A member of
ASAS
(Association of Shariah Advisors).
Associate Member
. Membership No. 1301-02-099.
(2) A member of Asian Academy of Management (
AAOM
) – Starting: December 2005 until: 2007.
Share this:
Twitter

Facebook
Like this:
Blog at WordPress.com.
Follow

Following

PENUNTUT ILMU

Join 37 other followers

												Sign me up

Already have a WordPress.com account?
Log in now.
PENUNTUT ILMU

Customize

Follow

Following

Sign up

Log in

Copy shortlink

Report this content

View post in Reader

Manage subscriptions

Collapse this bar


 *Corresponding author.

Email: hayatimy@umt.edu.my

eISSN: 2550-2166 / © 2020 The Authors. Published by Rynnye Lyan Resources

  Food Research 4 (4) : 1272 - 1286 (August 2020)
  Journal homepage: http://www.myfoodresearch.com

F
U

L
L

 P
A

P
E

R

Ethnic differences on cardiovascular disease risk and quality of life in Selangor

Lee, H.S., Aman, A., Kamaruddin, K.S., Ali, A. and *Yusof, H.M.

Faculty of Fisheries and Food Science, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu,

Malaysia

Article history:

Received: 26 February 2020

Received in revised form: 29

March 2020

Accepted: 1 April 2020

Available Online: 19 April

2020

Keywords:

Cardiovascular disease risk,

Quality of life,

Multiethnic,

Retrospective study

DOI:
https://doi.org/10.26656/fr.2017.4(4).035

Abstract

Malaysia is currently experiencing an epidemiologic and demographic transition. Rapid

urbanization and vast changes in lifestyles among the population has led to widespread

cardiovascular disease (CVD) outcomes. Also, until now, the most prevalent

cardiovascular risk factor has remained unknown among the Malaysian population.  This

study aimed to identify the most significant cardiovascular risk factor among multiethnic

adults in Dengkil, Selangor. A case-control retrospective study comparing past exposure

(retrospective) between the case (total cholesterol (TC) ≥ 5.18 mmol/L) and control

groups (TC < 5.18 mmol/L) on CVD risk via guided questionnaire comprised of

International Physical Activity Questionnaire (IPAQ), Food Frequency Questionnaire

(FFQ), Depression, Anxiety and Stress Scales (DASS)-21 and SF-12 was carried out

among 180 respondents aged 20 to 56 years in Dengkil, Selangor.  In the present study,

purposive and consecutive sampling were both applied to recruit respondents and also

considering ethnic as a factor; thus, quota sampling was also conducted.  The data were

analyzed using Mann-Whitney, Kruskal-Wallis, Chi-Square, Independent Samples t-test

and multiple logistic regression tests. The findings indicated there were no significant

differences (p > 0.05) regarding all the risk factors between the case and control groups

except for personal medical history (p < 0.05).  Chinese possessed the highest number of

significant risk and protective factors.  Lastly, the quality of life of Malay controls was

higher than that of cases in terms of Physical Component Summary (PCS).  Generally, all

the risk factors were independent in terms of total cholesterol status, except for personal

medical history.  The significance of risk factors and protective factors varies according to

ethnicity.

1. Introduction

Cardiovascular disease (CVD) is a series of

malfunctions in the circulatory system, especially in the

heart and blood vessels. This includes coronary heart

disease and ischemic heart disease (IHD),

cerebrovascular disease, and other heart complications.

The most common CVD are IHD and cerebrovascular

disease (Roth et al., 2015).

The severity of CVD among Malaysians is supported

through data collected from the Department of Statistics

Malaysia (2017), indicating the primary cause at 20.1%

of deaths among Malaysians was CVD, ranking it as the

number one ‘killer’ among Malaysians. Obviously, there

is an increasing trend in the prevalence of cardiovascular

risk factors – hypertension, hypercholesterolemia,

diabetes, overweight/obesity and smoking (Kumar,

2014). It has also shown that Malaysians who develop

heart disease are younger from year to year and

becoming more serious in recent years as compared to

neighboring countries such as Singapore and Thailand

(Aniza et al., 2016). Thus, necessary prevention

strategies such as population-based and high-risk

individual-based intervention are crucial to lowering the

prevalence of CVD in Malaysia (Pandey et al., 2013).

Basically, the common causes of CVD can be

divided into two major categories, namely modifiable

and non-modifiable risk factors. Examples of modifiable

risk factors are hypertension, stroke and diabetes

mellitus, whereas examples of non-modifiable risk

factors are age, ethnic, socioeconomic status, family

history and sex (Aniza et al., 2016; André et al., 2017).

In addition, another important predictor for CVD is

health-related quality of life (HRQoL). HRQoL

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

measures the implications of and influences on quality of

life caused by the symptoms and severity of diseases.

Impaired HRQoL is closely associated with an increased

risk of mortality and hospitalization due to CVD

(Thompson et al., 2016).

CVD can occur everywhere, every time, and to

everybody. Unfortunately, until now, the lack of

comprehensive case-control studies to investigate the

most significant CVD risk factors among multiethnic

adults in Malaysia. Previous studies mainly focus on

certain highlighted risk factors, for instance,

hypertension, without taking other risk factors such as

diabetes, stroke, dietary pattern into considerations

(Akter et al., 2010; Thon et al., 2012; Juhan et al., 2018).

The most recent case-control retrospective study was

done in Terengganu among Malay adults only (Erl et al.,

2019). To the best of author’s knowledge, there are

limited findings on the most significant CVD risk among

multiethnic adults. This study is useful to identify the

highest prevalence of CVD risk by investigating various

CVD risk factors according to ethnicity. These findings

are especially important for the implementation of

required interventions which are essential in reducing the

prevalence of CVD now and in the future.

The implementation of this study design, which is a

retrospective case-control study, is due to several

reasons. Firstly, it is quick and inexpensive.

Furthermore, it requires fewer subjects and is best suited

to investigate outcomes with a long latency period (Song

and Chung, 2010). As such, this research was conducted

in Dengkil, a small town located in between the rural and

urban area at Sepang district in Selangor. According to

the Department of Statistics Malaysia (2015), the ethnic

groups of adults in Dengkil are well-distributed, with

Malays (71.3%), Chinese (15.7%) and Indians (13%),

which is approximately same as the ethnic distribution in

Malaysia. Additionally, the proportion of adult in

Dengkil (60.5%) is similar to the adult proportion in

Malaysia (69.7%). Thus, the geography and demography

of Dengkil, Selangor provided motivation for this study

to be conducted. Necessary interventions regarding CVD

can be generated through this study.

The present study aims to determine the most

significant CVD risk factors among multiethnic. Besides,

it aims to compare the past exposure (retrospective)

including the quality of life between case and control

groups on CVD risk, in addition, to identify the ethnic

with highest CVD risk in Selangor. On the other hand,

this study aims to determine the most prevalent CVD

risk and protective factor among multiethnic populations.

2. Materials and methods

2.1 Research design and subjects

A case-control retrospective study was carried out in

Dengkil, Selangor. The respondents were selected via

purposive sampling, from June to August 2018. Kelsey

et al. (1996) were used for sample size calculation for

the confidence level of 95%. The study was done at

hospitals, clinics and restaurants to represent adult the

population in Dengkil. Thus, a total of 180 adults

comprised of 60 Malays, 60 Chinese and 60 Indians were

recruited through consecutive and quota sampling

considering ethnic as a factor as shown in Figure 1.

Consecutive sampling and quota sampling, which are

non-probability samplings, were used to select specific

locations and respondents for the present study.

Consecutive sampling was used in selecting the subjects

who meet the criteria of inclusion from an accessible

population over a specific time interval (Mathieson,

2014). The inclusion criteria for this study were those

who possessed known values for serum lipid profiles as

well as willing to answer the questionnaires honestly.

However, there were also some criteria of exclusion such

as pregnancy and hemophilia. Quota sampling was used

when a quota was then set for each sub-group which

directs the sample to be approached on the basis of set

quota (Alvi, 2016). For this study, the equal numbers of

subjects between cases group and controls group were

obtained by using a quota sampling with 1:1 ratio (90

controls and 90 cases) in the sampling location. The

control and case groups were subdivided into three

smaller groups, namely Malays, Chinese and Indians,

respectively.

2.2 Ethical approval

The present study was approved by National Medical

Research Register (NMRR) of National Institutes of

F
U

L
L

 P
A

P
E

R

Figure 1. Sampling framework

1273

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

Health (NIH) under the Ministry of Health with

registration number NMRR-18-906-41488 (IIR).

Furthermore, consent forms were signed and collected

from all study participants.

2.3 Research instrument

A guided questionnaire was used as the research

instrument in the present study. The questionnaire is

comprised of six sections: socio-demographic

information, medical and health status, lifestyle

behaviors, dietary assessment assessed by Food

Frequency Questionnaire (FFQ), psychosocial status

assessment using the Depression, Anxiety and Stress

Scales-21 (DASS-21). HRQoL assessment using the 12-

item short-form health survey (SF-12).

2.4 Physical Activity (IPAQ)

The scoring guidelines for short form IPAQ were

based on the latest existing guidelines (IPAQ Analysis,

2005).  Classification of the respondents into three

categories was performed once the results of total

physical activity Metabolic Equivalent Task (MET)-

minutes/week were obtained. The low, sedentary or

inactive group of respondents engage in less than 600

MET-minutes/week of exercise, while the minimally

inactive group (moderate physical activity level) engages

in 600-3000 MET-minutes/week of exercise, and the

physically active group (high physical activity level)

engages in higher than 3000 MET-minutes/week of

exercise (IPAQ Analysis, 2005).

2.5 Dietary intake (FFQ)

The FFQ used in this study included fifteen food

items (Chee et al., 1996). Lower scores mean lower

intake for a particular food. The ranges were classified

into four categories. Scores of less than 20.0 means

extremely less consumed foods whereas scores of 20.0-

59.9 indicate less consumed foods. On the other hand,

scores of 60.0-79.9 indicate moderately consumed foods

whereas scores if 80.0-100.0 indicates highly consumed

foods.

2.6 Psychosocial assessment (DASS 21)

The scoring of DASS-21 was based on an

established scoring manual by Lovibond and Lovibond

(1995). The final scores were calculated by doubling the

total scores for the relevant items for depression, anxiety

and stress. Higher scores indicate weaker psychosocial

status. Good psychosocial status indicates subjects

without either/ at least one of depression, anxiety and

stress whereas poor psychosocial status indicate subjects

with either/ at least one of depression, anxiety and stress.

2.7 Quality of life (SF-12)

HRQoL was assessed by using the 12-item short-

form health survey (SF-12). Short Form (SF)-12 Health

Survey Standard Scoring (2nd edition) was used to score

the quality of life of respondents based on Physical

Component Summary (PCS) and Mental Component

Summary (MCS) (Ware et al., 1994; Gandek et al.,

2004). The scores range from 0 (lowest health level) to

100 (highest health level).

2.8 Risk and protective factors of CVD

Socioeconomic status was analyzed from the

education level and monthly household income. Low

education level (primary and secondary education) and

low household income (less than RM3,000) considered

as the risk factors of CVD. On the other hand, protective

factors of CVD were identified by high education

(university and college level) and high monthly

household income (more than RM6,500). In addition,

risk factors of CVD were studied by personal and family

medical history (with at least one of the disease such as

hypertension, diabetes and heart problems), low physical

activity, smoking, alcohol drinking and poor

psychosocial status (with at least one among depression,

anxiety and stress). Absence of personal and family

medical history, high physical activity, no smoking,

absence of alcohol drinking and good psychosocial status

were the other aspects used to measure the protective

factors of CVD. Odds Ratio (OR) was then used to

compare the CVD risk and protective factors between

case and control groups according to ethnicity.

2.9 Data analysis

The data was analyzed using SPSS version 20.

Normality tests (Kolmogorov-Smirnov and Shapiro Wilk

tests) were used to examine the distribution of the data of

the variables. Descriptive tests were conducted to

analyses the socio-demographic profiles and quality of

life of respondents.

In this study, Chi-square was used to test the

dependent variables which were categorized as

categorical data. An independent-samples t-test was

applied for the normally distributed data whereas Mann-

Whitney U test was performed for the non-normally

distributed data. OR is defined as a relative measure of

effect which permits the comparison of the group with

specific outcomes relative to the placebo group. In this

study, OR was used to examine the relative risk for CVD

from a specific risk factor between case and control

groups (Schmidt and Kohlmann, 2008). An unmatched

case-control method was used in this study. The

calculation of p-value was followed by the 95%

confidence interval (CI). (Greenland et al., 2016).

F
U

L
L

 P
A

P
E

R

1274

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

3. Results

3.1 Socio-demographic characteristics of respondents

Socio-demographic characteristics of the

respondents involved in this study are shown in Table 1.

The mean age for the control group is 36.2±9.5, with 40

(44.4) males and 50 (55.6) females. On the other hand,

the mean age for the case group is 39.7±9.1 with 45 (50)

males and 45 (50) females. The study found that the

mean body weight for the controls was 61.0 kg with a

mean of BMI of 22.2 kg/m2, which is still classified as

normal BMI. The BMI values for the three ethnic groups

are approximately the same, and still within normal BMI.

Thus, the mean body weight for the case investigated is

61.4 kg, with a median BMI of 22.3 kg/m2.

3.2 Association of CVD risk factors between controls and

cases of different ethnic groups

Table 2 shows the comparison of CVD risk between

control and case multiethnic groups. CVD risk factors

were significantly different according to ethnicity. In

total, all the risk factors showed no significant difference

(p > 0.05) except for personal medical history (p ˂

0.001). Malay and Indian respondents showed significant

differences between groups (p ˂ 0.001) in terms of

personal medical history such as hypertension, diabetes

mellitus, hypercholesterolemia, and stroke. On the other

hand, Chinese respondents showed significant

differences between the case-control groups for personal

medical history (p ˂ 0.001) in addition to smoking (p =

0.002).

For all the ethnicities under investigation, no

significant difference (p > 0.05) between controls and

cases in terms of depression, anxiety and stress were

revealed. However, when the comparison was done

between ethnic groups, there were significant differences

(p < 0.05) among the ethnicities of both groups.

The scores for psychosocial status are the highest in

Malays, followed by Indians and Chinese. On the other

hand, the scores for depression, anxiety and stress of

cases are higher than controls, but the difference is not

significant (p ˃ 0.05).

F
U

L
L

 P
A

P
E

R

Category, n (%)

Control (n=90) Case (n=90)

Malay

(n=30)

Chinese

(n =30)

Indian

(n=30)
Total

Malay

(n=30)

Chinese

(n=30)

Indian

(n=30)
Total

Gender
Male 16 (46.7) 12 (40.0) 12 (40.0) 40 (44.4) 13 (43.3) 18 (60.0) 14 (46.7) 45 (50.0)

Female 14 (46.7) 18 (60.0) 18 (60.0) 50 (55.6) 17 (56.7) 12 (40.0) 16 (53.3) 45 (50.0)

Age (year) 36.9±9.5 33.9±9.2 36.2±9.5 36.2±9.5 39.2±9.2 39.5±9.7 40.5±8.7 39.7±9.1

Weight (kg) 62.7±10.2 59.6±11.0 60.8±9.1 61.0±10.1 61.4±9.9 61.7±10.5 61.1±8.8 61.4±9.7

Height (cm) 167.0±10.9 165.4±8.2 164.4±5.8 165.5 ±8.6 163.2±9.5 166.5±9.4 164.6±8.9 164.5±8.9

BMI (kg/m2) 22.2±2.7 21.8±3.2 22.5±3.0 22.2 ±3.0 23.2±5.0 22.3 (5.0) 21.7 (5.9) 22.3 (5.4)

BMI group
Underweight 3 (10.0) 3 (10.0) 2 (6.7) 8 (8.9) 2 (6.7) 3 (10.0) 3 (10.0) 8 (8.9)

Normal 24 (80.0) 22 (73.3) 23 (76.7) 60 (76.7) 18 (60.0) 21 (70.0) 19 (63.3) 58 (64.4)

Overweight 3 (10.0) 5 (16.7) 5 (16.7) 13 (14.4) 9 (30.0) 5 (16.7) 6 (20.0) 20 (22.2)

Obesity 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (3.3) 1 (3.3) 2 (6.7) 4 (4.4)

Highest education level
Primary 3 (10.0) 1 (3.3) 4 (13.3) 8 (8.9) 6 (20.0) 1 (3.3) 6 (20.0) 13 (14.4)

Secondary 13 (43.3) 10 (33.3) 14 (46.7) 37 (41.1) 9 (30.0) 10 (33.3) 9 (30.0) 28 (31.1)

Tertiary

Monthly income
14 (46.7) 19 (63.3) 12 (40.0) 45 (50.0) 15 (50.0) 19 (63.3) 15 (50.0) 49 (54.4)

< RM3000 15 (50.0) 12 (40.0) 15 (50.0) 42 (46.6) 15 (50.0) 6 (20.0) 14 (46.7) 35 (38.9)

RM3000

– RM6500
11 (36.7) 9 (30.0) 13 (43.3) 33 (36.7) 9 (30.0) 16 (53.3) 13 (43.3) 38 (42.2)

> RM6500 4 (13.3) 9 (30.0) 2 (6.7) 15 (16.7) 6 (20.0) 8 (26.7) 3 (10.0) 17 (18.9)

Medication
None 17 (56.7) 29 (96.7) 14 (46.7) 60 (66.7) 9 (30.0) 16 (53.3) 8 (26.7) 33 (36.7)

Supplement
Yes 3 (10.0) 8 (26.7) 2 (6.7) 13 (14.4) 5 (16.7) 5 (16.7) 3 (10.0) 13 (14.4)

No 27 (90.0) 22 (73.3) 28 (93.3) 77 (85.6) 25 (83.3) 25 (83.3) 27 (90.0) 77 (85.6)

Table 1. Socio-demographic characteristics of respondents

Data are presented as mean ± standard deviation or median (Interquartile range).

1275

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

R
is

k
 f

ac
to

rs

C
o

n
tr

o
ls

 (
n
=

9
0

)

X
2

p
-v

al
u
e

C
as

es
 (

n
=

9
0

)

X
2

p
-v

al
u
e

M
al

a
y

(n
=

3
0

)

n
 (

%
)

C
h
in

e
se

(n
 =

3
0

)

n
 (

%
)

In
d

ia
n

(n
=

3
0

)

n
 (

%
)

M
al

a
y

(n
=

3
0

)

n
 (

%
)

C
h
in

e
se

(n
 =

3
0

)

n
 (

%
)

In
d

ia
n

(n
=

3
0

)

n
 (

%
)

A
L

 M
E

D
IC

A
L

 H
IS

T
O

R
Y

Y

es

1
6

 (
5
3

.3
)

2
 (

6
.7

)
1

8
 (

6
0

.0
)

2
1

.1
1
1

˂
0

.0
0

0
1

*

3
0

 (
1
0

0
.0

)
3

0
 (

1
0

0
.0

)
3

0
 (

1
0

0
.0

)
-

-
N

o

1
4

 (
4
6

.7
)

2
8

 (
9
3

.3
)

1
2

 (
4
0

.0
)

-
-

-

P
H

Y
S

IC
A

L

A

C
T

IV
IT

Y

S
T

A
T

U
S

L

o
w

6

 (
2

0
.0

)
1
1

 (
3
6

.7
)

8
 (

2
6
.7

)

1
4

.1
6
1

0
.0

0
7

*

7
 (

2
3
.3

)
1

2
 (

4
0

.0
)

7
 (

2
3
.3

)

3
.9

1
1

0
.4

1
8

M
o

d
er

at
e

6
 (

2
0
.0

)
1

5
 (

5
0

.0
)

1
0

 (
3
3

.3
)

1
0

 (
3
3

.3
)

1
1

 (
3
6

.7
)

1
2

 (
4
0

.0
)

H
ig

h

1
8

 (
6
0

.0
)

4
 (

1
3
.3

)
1

2
 (

4
0

.0
)

1
3

 (
4
3

.3
)

7
 (

2
3
.3

)
1
1

 (
3
6

.7
)

S
M

O
K

IN
G

Y

es

1
4

 (
4
6

.7
)

2
 (

6
.7

)
1

0
 (

3
3

.3
)

1
2

.1
1

5

0
.0

0
2

*

8
 (

2
6
.7

)
1

2
 (

4
0

.0
)

1
0

 (
3
3

.3
)

1
.2

0
0

0
.5

4
9

N
o

1

6
 (

5
3

.3
)

2
8

 (
9
3

.3
)

2
0

 (
6
6

.7
)

2
2

 (
7
3

.3
)

1
8

 (
6
0

.0
)

2
0

 (
6
6

.7
)

A
L

C
O

H
O

L
 C

O
N

S
U

M
P

T
IO

N

Y
es

0

 (
0

.0
)

1
1

 (
3
6

.7
)

1
1

 (
3
6

.7
)

1
4

.5
5
9

0
.0

0
1

*

0
 (

0
.0

)
1

6
 (

5
3

.3
)

1
5

 (
5
0

.0
)

2
3

.7
1
8

˂
0

.0
0

0
1

*

N
o

3

0
 (

1
0

0
.0

)
1

9
 (

6
3

.3
)

1
9

 (
6
3

.3
)

3
0

 (
1
0

0
.0

)
1

4
 (

4
6

.7
)

1
5

 (
5
0

.0
)

P
S

Y
C

H
O

S
O

C
IA

L
 S

T
A

T
U

S

P
o

si
ti

v
e

2
 (

6
.1

)
2

4
 (

8
0

.0
)

7
 (

2
3
.3

)
3

8
.1

8
2

˂
0

.0
0

0
1

*

1
 (

3
.3

)
2

2
 (

7
3

.3
)

4
 (

1
3
.3

)
4

0
.9

5
2

˂
0

.0
0

0
1

*

N
eg

at
iv

e

2
8

 (
9
3

.3
)

6
 (

2
0
.0

)
2

3
 (

7
6

.7
)

2
9

 (
9
6

.7
)

8
 (

2
6
.7

)

R
is

k
 f

ac
to

rs

M
al

a
y
 (

n
=

6
0

)

X
2

p
-v

al
u
e

C
h
in

e
se

 (
n
=

6
0

)

X
2

p
-v

al
u
e

In
d

ia
n
 (

n
=

6
0

)

X
2

p
-v

al
u
e

C
o

n
tr

o
l

(n
=

3
0

)

C
as

e

(n
=

3
0

)

C
o

n
tr

o
l

(n
=

3
0

)

C
as

e

(n
=

3
0

)

C
o

n
tr

o
l

(n
=

3
0

)

C
as

e

(n
=

3
0

)

P
E

R
S

O
N

A
L

 M
E

D
IC

A
L

 H
IS

T
O

R
Y

Y
es

1

6
 (

5
3

.3
)

3
0

 (
1
0

0
.0

)
1

8
.2

6
1

˂
0

.0
0

0
1

*

2
 (

6
.7

)
3

0
 (

1
0

0
.0

)
5

2
.5

0
0

˂
0

.0
0

0
1

*

1
8

 (
6
0

.0
)

3
0

 (
1
0

0
.0

)
1

5
.0

0
0

˂
0

.0
0

0
1

*

N
o

1

4
 (

4
6

.7
)

0
 (

0
.0

)
2

8
 (

9
3

.3
)

0
 (

0
.0

)
1

2
 (

4
0

.0
)

0
 (

0
)

 P
H

Y
S

IC
A

L

A

C
T

IV
IT

Y

S

T
A

T
U

S

L
o

w

6
 (

2
0
.0

)
7

 (
2

3
.3

)
1

.8
8

3

0
.3

9
0

1
1

 (
3
6

.7
)

1
2

 (
4
0

.0
)

1
.4

7
7

0
.4

7
8

8
 (

2
6
.7

)
7

 (
2

3
.3

)
0

.2
9

2

0
.8

6
4

T
ab

le
 2

. A
ss

o
ci

at
io

n
 o

f
C

V
D

 r
is

k
 f

ac
to

rs
 b

et
w

ee
n
 c

a
se

 a
n
d

 c
o

n
tr

o
l

F
U

L
L

 P
A

P
E

R

1276

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

R
is

k
 f

ac
to

rs

M
al

a
y
 (

n
=

6
0

)

X
2

p
-v

al
u
e

C

h
in

e
se

 (
n
=

6
0

)

X
2

p
-v

al
u
e

In

d
ia

n
 (

n
=

6
0

)

X
2

p
-v

al
u
e

C

o
n
tr

o
l

(n
=

3
0

)
C

as
e

(n
=

3
0

)
C

o
n
tr

o
l

(n
=

3
0

)
C

as
e

(n
=

3
0

)
C

o
n
tr

o
l

(n
=

3
0

)
C

as
e

(n
=

3
0

)

M
o

d

6
 (

2
0
.0

)
1

0
 (

3
3

.3
)

1
5

 (
5
0

.0
)

1
1

 (
3
6

.7
)

1
0

 (
3
3

.3
)

1
2

 (
4
0

.0
)

H
ig

h

1
8

 (
6
0

.0
)

1
3

 (
4
3

.3
)

4
 (

1
3
.3

)
7

 (
2

3
.3

)

1

2
 (

4
0

.0
)

1
1

 (
3
6

.7
)

S
M

O
K

IN
G

Y
es

1

4
 (

4
6

.7
)

8
 (

2
6
.7

)
2

.5
8

4

0
.1

0
8

2
 (

6
.7

)
1

2
 (

4
0

.0
)

9
.3

1
7

0
.0

0
2

*

1
0

 (
3
3

.3
)

1
0

 (
3
3

.3
)

0
.0

0
0

1
.0

0
0

N
o

1

6
 (

5
3

.3
)

2
2

 (
7
3

.3
)

2
8

 (
9
3

.3
)

1
8

 (
6
0

.0
)

2
0

 (
6
6

.7
)

2
0

 (
6
6

.7
)

A
L

C
O

H
O

L
 C

O
N

S
U

M
P

T
IO

N

Y
es

-

-
-

-
1
1

 (
3
6

.7
)

1
6

 (
5
3

.3
)

1
.6

8
4

0
.1

9
4

1
1

 (
3
6

.7
)

1
5

 (
5
0

.0
)

1
.0

8
6

0
.2

9
7

N
o

3

0
 (

1
0

0
.0

)
3

0
 (

1
0

0
.0

)
1

9
 (

6
3

.3
)

1
4

 (
4
6

.7
)

1
9

 (
6
3

.3
)

1
5

 (
5
0

.0
)

P
S

Y
C

H
O

S
O

C
IA

L
 S

T
A

T
U

S

P
o

si
ti

v
e

2
 (

6
.7

)
1

 (
3

.3
)

0
.3

5
1

1
.0

0
0

2
4

 (
8
0

.0
)

2
2

 (
7
3

.3
)

0
.3

7
3

0
.5

4
2

7
 (

2
3
.3

)
4

 (
1

3
.3

)
1

.0
0

2

0
.3

1
7

N
eg

at
iv

e

2
8

 (
9
3

.3
)

2
9

 (
9
6

.7
)

6
 (

2
0
.0

)
8

 (
2

6
.7

)
2

3
 (

7
6

.7
)

2
6

 (
8
6

.7
)

R
is

k
 f

ac
to

rs

C
o

n
tr

o
ls

 (
n
=

9
0

)
C

as
es

 (
n
=

9
0

)
X

2

p
-v

al
u
e

P
E

R
S

O
N

A
L

 M
E

D
IC

A
L

 H
IS

T
O

R
Y

Y
es

3

6
 (

4
0

.0
)

9
0

 (
1
0

0
.0

)
7

7
.1

4
3

˂

0
.0

0
0
1

*

N
o

5

4
 (

6
0

.0
)

0
 (

0
.0

)

P
H

Y
S

IC
A

L

A

C
T

IV
IT

Y

S
T

A
T

U
S

L
o

w

2
5

 (
2
7

.8
)

2
6

 (
2
8

.9
)

0
.2

2
1

0
.8

9
6

M
o

d
er

at
e

3
1

 (
3
4

.4
)

3
3

 (
3
6

.7
)

H
ig

h

3
4

 (
3
7

.8
)

3
1

 (
3
4

.4
)

P
S

Y
C

H
O

S
O

C
IA

L
 S

T
A

T
U

S

P
o

si
ti

v
e

3
3

 (
3
6

.7
)

2
7

 (
3
0

.0
)

0
.9

0
0

0
.3

4
3

N
eg

at
iv

e

5
7

 (
6
3

.3
)

6
3

T
ab

le
 2

. A
ss

o
ci

at
io

n
 o

f
C

V
D

 r
is

k
 f

ac
to

rs
 b

et
w

ee
n
 c

a
se

 a
n
d

 c
o

n
tr

o
l

(c
o

n
t.

)

F
U

L
L

 P
A

P
E

R

1277

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

P
sy

c
h
o

so
ci

al
 s

ta
tu

s
C

o
n
tr

o
ls

 (
n
=

3
0

)
 C

as
es

 (
n
=

3
0

)
 p

-v
al

u
e

M
al

a
y

D
ep

re
ss

io
n

1

3
.5

3
±

5
.3

5

1
2

.0
0
 (

7
.0

0
)

0
.8

8
7

A
n

x
ie

ty

1
0

.0
0
 (

5
.0

0
)

1
0

.8
7
±

5
.5

5

0
.8

2
3

S
tr

es
s

1
7

.6
7
±

8
.4

1

2
1

.0
0
±

5
.9

8

0
.0

8
3

C
h
in

e
se

 D
ep

re
ss

io
n

4

.0
0

 (
8

.0
0

)
4

.0
0

 (
2

.0
0

)
0

.5
9

7

A
n

x
ie

ty

4
.0

0
 (

4
.0

0
)

6
.0

0
 (

3
.0

0
)

0
.0

8
1

S
tr

es
s

6
.0

0
 (

8
.0

0
)

8
.3

3
±

5
.6

6

0
.1

0
0

In
d

ia
n

D
ep

re
ss

io
n

1

0
.0

0
 (

8
.0

0
)

1
0

.8
0
±

4
.0

9

0
.7

0
9

A
n

x
ie

ty

8
.0

0
 (

6
.0

0
)

8
.1

3
±

4
.1

7

0
.5

9
0

S
tr

es
s

1
9

.4
0
±

9
.0

2

1
8

.1
3
±

6
.9

5

0
.5

4
5

P
sy

c
h
o

so
ci

al
 s

ta
tu

s
C

o
n
tr

o
ls

 (
n
=

 9
0

)
p

-v
al

u
e

C
as

es
 (

n
=

9
0

)
p

-v
al

u
e

M
al

a
y
 (

n
=

3
0

)
C

h
in

e
se

 (
n
 =

3
0

)
In

d
ia

n
 (

n
=

3
0

)
M

al
a
y
 (

n
=

3
0

)
C

h
in

e
se

 (
n
 =

3
0

)
In

d
ia

n
 (

n
=

3
0

)

D
ep

re
ss

io
n

1

3
.5

3
±

5
.3

5
a

4
.0

0
 (

8
.0

0
)b

1

0
.0

0
 (

8
.0

0
)a

˂
0

.0
0

0
1

*

1
2

.0
0
 (

7
.0

0
)a

4
.0

0
 (

2
.0

0
)b

1

0
.0

0
 (

6
.0

0
)a

˂
0

.0
0

0
1

*

A
n

x
ie

ty

1
0

.0
0
 (

5
.0

0
)a

4
.0

0
 (

4
.0

0
)b

8

.0
0

 (
6

.0
0

)c
˂

0
.0

0
0
1

*

1
0

.8
7
±

5
.5

5
a

6
.0

0
 (

3
.0

0
)b

8

.1
3

 (
4

.1
7

)a
˂

0
.0

0
0
1

*

S
tr

es
s

1
7

.6
7
±

8
.4

1
a

6
.0

0
 (

8
.0

0
)b

1

9
.4

0
±

9
.0

2
a

˂
0

.0
0

0
1

*

2
1

.0
0
±

5
.9

8
a

8
.3

3
±

5
.6

6
b

1
8

.6
1
±

6
.9

5
a

˂
0

.0
0

0
1

*

P
sy

c
h
o

so
ci

al
 s

ta
tu

s
C

o
n
tr

o
ls

 (
n
=

9
0

)
C

as
es

 (
n
=

9
0

)
p

-v
al

u
e

D
ep

re
ss

io
n

8

.0
0

 (
8

.0
0

)
1

0
.0

0
 (

9
.0

0
)

0
.6

3
7

A
n

x
ie

ty

7
.0

0
 (

6
.0

0
)

8
.0

0
 (

6
.0

0
)

0
.2

7
5

S
tr

es
s

1
4

.0
0
 (

1
4

.0
0
)

1
5

.8
2
±

8
.2

2

0
.1

5
0

T
ab

le
 2

. A
ss

o
ci

at
io

n
 o

f
C

V
D

 r
is

k
 f

ac
to

rs
 b

et
w

ee
n
 c

a
se

 a
n
d

 c
o

n
tr

o
l

(c
o

n
t.

)

*
p

 <
 0

.0
5

 i
n
d

ic
at

es
 s

ig
n
if

ic
an

t
d

if
fe

re
n
ce

 b
y
 u

si
n

g
 C

h
i-

sq
u
ar

e
te

st
/

K
ru

sk
al

-
W

al
li

s
te

st
.

V
al

u
es

 w
it

h
 d

if
fe

re
n
t

su
p

er
sc

ri
p

t
le

tt
er

 a
re

 s
ig

n
if

ic
a
n
tl

y
 d

if
fe

re
n
t

at
 p

 ˂
 0

.0
5

 b
y
 M

a
n
n

-
W

h
it

n
e
y
 U

 t
e
st

 o
r

In
d

ep
en

d
en

t
S

a
m

p
le

s
t

te
st

.

p
 ˃

 0
.0

5
 i

n
d

ic
at

es
 n

o
 s

ig
n
if

ic
a
n
t

d
if

fe
re

n
ce

 b
y
 u

si
n
g
 I

n
d

ep
en

d
en

t
t

te
st

 o
r

M
an

n
-W

h
it

n
e
y
 U

 t
es

t.

D
at

a
ar

e
p

re
se

n
te

d
 a

s
m

ea
n
±

st
an

d
ar

d
 d

ev
ia

ti
o

n
/

m
ed

ia
n
 (

in
te

rq
u
ar

ti
le

 r
an

g
e)

.

P
o

si
ti

v
e

p
sy

c
h
o

so
ci

al
 s

ta
tu

s
in

d
ic

at
es

 s
u
b

je
ct

s
w

it
h
o

u
t

ei
th

er
/

at
 l

ea
st

 o
n
e

o
f

d
ep

re
ss

io
n
,

a
n

x
ie

ty
 a

n
d

 s
tr

es
s

w
h

er
ea

s
n
e
g
at

iv
e

p
sy

c
h
o

so
ci

al
 s

ta
tu

s
in

d
ic

at
es

 s
u
b

je
ct

s
w

it
h
 e

it
h
er

/

at
 l

ea
st

 o
n
e

o
f

d
ep

re
ss

io
n
,

a
n
x

ie
ty

 a
n
d

 s
tr

es
s.

F
U

L
L

 P
A

P
E

R

1278

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

T
y
p

es
 o

f
fo

o
d

M

al
a
y
 (

n
=

6
0

)
p

-v
al

u
e

C
h
in

e
se

 (
n
=

6
0

)
p

-v
al

u
e

p
-v

al
u
e

T
o

ta
l
A

v
er

ag
e

In
d

ia
n
 (

n
=

6
0

)

F
ru

it

6
1

.3
7

6

1
.4

3

0
.8

3
4

4
9

.9
6

5

5
.7

6

0
.4

7
1

6
3

.8
1

5

9
.5

1

0
.0

5
4

5
8

.5
9

V
eg

et
ab

le

7
0

.9
3

7

4
.7

6

0
.2

7
8

7
9

.1
0

7

5
.7

0

0
.1

9
1

6
9

.5
1

6

8
.1

0

0
.6

3
7

6
1

.0
6

P
o

u
lt

ry

8
2

.8
7

6

8
.4

6

0
.0

0
2

*

7
3

.3
6

7

2
.4

0

0
.2

9
5

6
9

.4
9

7

3
.3

3

0
.4

4
9

7
3

.6
3

M
ea

t
7

8
.5

6

6
7

.1
4

0
.0

1
9

*

3
9

.4
7

5

5
.7

1

0
.0

3
6

*

6
5

.2
3

7

0
.0

1

0
.5

1
6

6
2

.7
3

F
is

h

7
7

.6
4

6

4
.7

7

˂
0

.0
0

0
1

*

5
4

.7
3

5

5
.2

3

0
.9

1
6

6
0

.9
1

6

5
.6

4

0
.2

0
2

6
3

.1
3

B
ea

n
s

3
5

.2
4

5

3
.8

1

0
.0

0
2

*

4
6

.6
6

4

7
.6

0

0
.9

3
4

5
1

.0
0

4

7
.1

6

0
.4

5
4

4
7

.0
3

D
ai

ry
 p

ro
d

u
ct

s
4

8
.1

3

4
7

.6
3

0
.9

2
8

4
9

.9
7

3

9
.5

1

0
.1

1
1

4
1

.9
1

4

6
.6

7

0
.6

1
2

4
6

.8
7

E
g
g

s
8

7
.0

6

8
6

.1
1

0
.7

3
6

6
7

.6
3

7

0
.0

0

0
.2

9
9

7
7

.0
9

7

5
.7

1

0
.4

2
8

7
7

.2
0

W
h
o

le
 g

ra
in

s
3

4
.8

3

5
2

.9
0

˂
0

.0
0

0
1

*

4
1

.4
7

6

2
.3

1

0
.0

0
1

*

6
7

.6
1

7

0
.4

9

0
.5

0
2

5
5

.8
1

F
at

s
6

2
.8

6

4
6

.1
9

˂
0

.0
0

0
1

*

6
2

.7
6

5

7
.6

1

0
.2

5
0

5
2

.3
4

6

5
.2

3

˂
0

.0
0

0
1

*

5
7

.9
0

P
as

tr
ie

s
6

9
.9

3

5
1

.8
9

˂
0

.0
0

0
1

*

3
7

.0
9

4

5
.2

7

0
.0

4
2

*

5
1

.8
9

6

0
.9

4

0
.0

1
4

*

5
2

.8
6

F
as

t
fo

o
d

s
6

6
.6

7

4
4

.3
9

˂
0

.0
0

0
1

*

4
3

.8
3

4

5
.2

4

0
.4

6
9

4
7

.1
0

5

4
.2

9

0
.0

2
4

*

5
0

.2
0

C
ar

b
o

n
at

ed
 s

o
ft

 d
ri

n
k
s

6
6

.1
4

4

3
.8

7

˂
0

.0
0

0
1

*

4
0

.9
4

4

2
.4

3

0
.4

9
7

4
6

.1
4

5

2
.8

6

0
.0

5
3

4
8

.6
9

C
af

fe
in

a
te

d
 d

ri
n
k
s

8
0

.9
3

5

8
.6

1

˂
0

.0
0

0
1

*

5
8

.1
6

5

5
.6

6

0
.6

5
2

5
9

.9
3

7

0
.0

3

0
.1

0
8

6
3

.9
1

T
ab

le
 3

.
F

o
o
d

 f
re

q
u
en

c
y
 c

o
n
su

m
p

ti
o

n
 s

co
re

s
o

f
ca

se
 c

o
n
tr

o
l

g
ro

u
p

s

*
p

 ˂
 0

.0
5

 i
n
d

ic
at

es
 s

ig
n
if

ic
an

t
d

if
fe

re
n
ce

 b
et

w
ee

n
 c

o
n
tr

o
l

an
d

 c
as

e
g
ro

u
p

s
b

y
 u

si
n
g
 I

n
d

ep
e
n
d

en
t

S
a
m

p
le

s
t

te
st

.

S
co

re
 2

0
.0

 –
 5

9
.9

 c
at

eg
o

ri
ze

d
 a

s
le

ss
 c

o
n

su
m

ed
 f

o
o

d
s.

S
co

re
 6

0
.0

 –
 7

9
.9

 c
at

eg
o

ri
ze

d
 a

s
m

o
d

er
at

el
y
 c

o
n

su
m

ed
 f

o
o

d
s.

F
U

L
L

 P
A

P
E

R

1279

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

R
is

k
 f

ac
to

rs

M
al

a
y

(n
=

 6
0

)

C
h
in

e
se

(n
=

 6
0

)

In
d

ia
n

(n
=

 6
0

)
O

R
 (

9
5

%
 C

I)

p
-v

al
u
e

O
R

 (
9

5
%

 C
I)

p

-v
al

u
e

O
R

 (
9

5
%

 C
I)

p

-v
al

u
e

S
o

ci
o

ec
o

n
o

m
ic

 s
ta

tu
s

L
o

w
 m

o
n
th

ly
 h

o
u

se
h
o

ld
 i

n
co

m
e

(<
 R

M
3

0
0

0
)

1
.0

0
0
 (

0
.3

6
3
 –

 2
.7

5
1

)
1

.0
0

0

4
.0

0
0
 (

1
.2

7
2
 –

 1
2

.5
7

8
)

0
.0

1
8

*

1
.1

4
3
 (

0
.4

1
5
 –

 3
.1

4
8

)
0

.7
9

6

P
er

so
n
al

 m
ed

ic
al

 h
is

to
ry

H
y
p

er
te

n
si

o
n

0

.5
7

1
 9

0
.2

0
1
 –

 1
.6

2
4

)
0

.2
9

4

0
.1

2
3
 (

0
.0

2
5
 –

 0
.6

2
1

)
0

.0
1
1

*

1
.1

4
7
 (

0
.4

1
1
 –

 3
.2

0
4

)
0

.7
9

3

O
v
er

w
ei

g
h
t/

 o
b

es
it

y

0
.2

2
2
 (

0
.0

5
4
 –

 0
.9

1
4

)
0

.0
3

7
*

0
.8

0
0
 (

0
.2

1
5
 –

 2
.9

7
2

)
0

.7
3

9

0
.5

5
0
 (

0
.1

5
7
 –

 1
.9

3
1

)
0

.3
5

1

F
a
m

il
y
 h

is
to

ry

H
y
p

er
te

n
si

o
n

0

.7
1

0
 (

0
.2

2
5
 –

 2
.2

4
6

)
0

.5
6

0

3
.1

4
3
 (

1
.0

6
6
 –

 9
.2

6
7

)
0

.0
3

8
*

1
.0

0
0
 (

0
.3

5
6
 –

 2
.8

0
9

)
1

.0
0

0

H
ig

h
 b

lo
o

d
 c

h
o

le
st

er
o

l
0

.4
5

7
 (

0
.1

4
9
 –

 1
.3

9
6

)
0

.1
6

9

2
.7

5
0
 (

0
.9

3
4
 –

 8
.1

0
0

)
0

.0
6

6

0
.2

5
5
 (

0
.0

8
7
 –

 0
.7

4
9

)
0

.0
1

3
*

O
v
er

w
ei

g
h
t/

 o
b

es
it

y

1
.1

4
4
 (

0
.4

1
4
 –

 3
.1

6
6

)
0

.7
9

5

0
.8

2
1
 (

0
.2

4
0
 –

 2
.8

1
4

)
0

.7
5

4

0
.3

0
4
 (

0
.1

0
0
 –

 0
.9

2
2

)
0

.0
3

5
*

C
u
rr

en
tl

y
 s

m
o

k
in

g

 1
.9

0
2

 (
0
.6

1
7

 –
 5

.8
6
3

)
0

.2
6

3

 0
.0

8
0

 (
0
.0

0
9

 –
 0

.6
8
5

)
 0

.0
2
1

*

 0
.6

0
9

 (
0
.1

9
5

 –
 1

.8
9
7

)
0

.3
9

2

S
tr

es
s

 0
.1

6
7

 (
0
.0

4
1

 –
 0

.6
7
5

)
 0

.0
1
2

*

 0
.6

4
3

 (
0
.1

0
0

 –
 4

.1
5
3

)
0

.6
4

3

 0
.8

4
8

 (
0
.2

7
6

 –
 2

.6
1
2

)
0

.7
7

5

P
ro

te
ct

iv
e

fa
ct

o
rs

M
al

a
y

(n
=

 6
0

)

C
h
in

e
se

(n
=

 6
0

)

In
d

ia
n

(n
=

 6
0

)

O
R

 (
9

5
%

 C
I)

p

-v
al

u
e

O
R

 (
9

5
%

 C
I)

p

-v
al

u
e

O
R

 (
9

5
%

 C
I)

p

-v
al

u
e

A
b

se
n
ce

 o
f

p
er

so
n
al

 m
ed

ic
al

 h
is

to
ry

0

.0
0

0
 (

0
.0

0
0
 –

 0
.0

0
0

)
0

.9
9

9

0
.0

0
0
 (

0
.0

0
0
 –

 0
.0

0
0

)
0

.9
9

7

0
.0

0
0
 (

0
.0

0
0
 –

 0
.0

0
0

)
0

.9
9

8

N
o

 h
y
p

er
te

n
si

o
n

1

.7
5

0
 (

0
.6

1
6
 –

 4
.9

7
3

)
0

.2
9

4

8
.1

0
5
(1

.6
1

2
–
 4

0
.7

6
6

)
0

.0
1
1

*

0
.8

7
2
 (

0
.3

1
2
 –

 2
.4

3
5

)
0

.7
9

3

A
b

se
n
ce

 o
f

fa
m

il
y
 h

is
to

ry

1
.0

0
0
 (

0
.2

2
6
 –

 4
.4

3
1

)
1

.0
0

0

1
.1

7
9
 (

0
.3

8
3
 –

 3
.6

2
9

)
0

.7
7

5

1
.0

0
0
 (

0
.2

5
7
 –

 3
.8

8
8

)
1

.0
0

0

N
o

 h
ig

h
 b

lo
o

d
 c

h
o

le
st

er
o

l
2

.1
9

0
 (

0
.7

1
6
 –

 6
.6

9
8

)
0

.1
6

9

0
.3

6
4
 (

0
.1

2
3
 –

 1
.0

7
1

)
0

.0
6

6

5
.2

3
1
 (

1
.6

5
7
 –

 1
6

.5
1

5
)

0
.0

0
5

*

N
o

 o
v
er

w
ei

g
h

t/
 o

b
es

it
y

0

.8
7

4
 (

0
.3

1
6
 –

 2
.4

1
8

)
0

.7
9

5

1
.2

1
7
 (

0
.3

5
5
 –

 4
.1

7
0

)
0

.7
5

4

3
.2

8
6
 (

1
.0

8
5
 –

 9
.9

5
2

)
0

.0
3

5
*

N
o

 c
u
rr

en
tl

y
 s

m
o

k
in

g

0
.5

2
6
 (

0
.1

7
1
 –

 1
.6

2
0

)
0

.2
6

3

1
2

.4
2
9

(1
.4

6
1

-1
0

5
.7

3
7

)
0

.0
2

1
*

1
.6

4
3
 (

0
.5

2
7
 –

 5
.1

2
0

)
0

.3
9

2

A
b

se
n
ce

 o
f

st
re

ss

6
.0

0
0
(1

.4
8

2
–
 2

4
.2

9
9

)
0

.0
1

2
*

1
.5

5
6
 (

0
.2

4
1
 –

 1
0

.0
4

9
)

0

.6
4

3

1
.1

7
9
 (

0
.3

8
3
 –

 3
.6

2
9

)
0

.7
7

5

T
ab

le
 4

.
C

o
m

p
ar

is
o

n
 o

f
C

V
D

 r
is

k
 a

n
d

 p
ro

te
ct

iv
e

fa
ct

o
rs

 b
et

w
ee

n
 c

as
e

a
n
d

 c
o

n
tr

o
l

g
ro

u
p

s
o

f
d

if
fe

re
n

t
et

h
n
ic

 g
ro

u
p

s
b

y
 o

d
d

s
ra

ti
o

.

*
p

 <
 0

.0
5

 i
n
d

ic
at

es
 s

ig
n
if

ic
an

t
d

if
fe

re
n
ce

 a
cr

o
ss

 t
h
re

e
et

h
n
ic

 g
ro

u
p

s.

F
U

L
L

 P
A

P
E

R

1280

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

3.3 Food frequency consumption of respondents

Table 3 shows the food frequency consumption

scores combining all the cases and controls according to

ethnicity. All the controls and cases showed no

significant differences in terms of the consumption of the

15 food items selected. By averaging the scores for both

controls and cases groups, there was no food categorized

under both extremely less and highly consumed foods,

whereas fruit, nuts, beans, dairy products, whole grains,

fats, pastries, fast foods and carbonated soft drinks were

categorized as less consumed foods. While vegetable,

poultry, meat, fish, eggs and caffeinated drinks were

classified as moderately consumed foods. On the other

hand, cases and controls exhibited great significant

differences (p ˂ 0.05) in terms of dietary pattern, and

especially in terms of fats, protein, fast food, sugar and

caffeinated drinks, according to ethnicity. For all ethnic

groups, the consumption of pastries was statistically

significant (p ˂ 0.05).

3.4 Comparison of risk and protective factors for

cardiovascular disease between case and control groups

by odds ratio

Table 4 shows the comparison of CVD risk and

protective factors between case and control groups

according to ethnicity by odds ratio (OR). For Malays,

risk factors such as overweight/ obesity (OR: 0.222, CI:

0.054 – 0.914) and stress (OR: 0.167, CI: 0.041 - 0.675)

were statistically significant (p ˂ 0.05). Absence of stress

with OR: 6.000 (CI: 1.482, 24.299) is the only factor

which is statistically significant but not protective, with

p= 0.012. For Chinese, risk factors such as low monthly

household income (OR: 4.000, CI: 1.272-12.578),

personal medical history of hypertension (OR: 0.123, CI:

0.025- 0.621), family history of hypertension (OR:

3.143, CI: 1.066- 9.267) and currently smoking (OR:

0.080, CI: 0.009- 0.685) were statistically significant (p

˂ 0.05). The inverse relationship between the absence of

a family history of hypertension with the occurrence of

CVD with OR less than 1 (OR: 0.318, CI: 0.108-0.938, p

= 0.038), indicates it is statistically significant protective

against CVD. Among Chinese, the factors with OR

which were statistically significant but not protective

against CVD due to large OR obtained included the

absence of a personal medical history of hypertension

(OR: 8.105, CI: 1.612-40.766) and not currently smoking

(OR: 12.429, CI: 1.461-105.737). For Indians, only

family history of high blood cholesterol (OR: 0.255, CI:

0.087-0.749) and overweight/ obesity (OR: 0.304, CI:

0.100-0.922) were statistically significant with (p ˂

0.05). Factors which were statistically significant but not

protective included absence of a family history of high

blood cholesterol (OR: 5.231, CI: 1.657-16.515) and no

family history of overweight/ obesity (OR: 3.286, CI:

1.085-9.952) with p = 0.005 and p = 0.035 respectively.

3.5 Quality of life of respondents

Table 5 shows the Physical Component Summaries

(PCS) and Mental Component Summaries (MCS) of the

respondents according to ethnicity. The scores of both

PCS and MCS among Malay and Indian respondents,

together with the PCS of Chinese respondents, are higher

in controls than that of cases, in which only the PCS of

Malay respondents showed significant difference (p <

0.05) between groups. For MCS of Chinese, the scores

are higher in cases than that in controls.

All controls and cases showed no significant

differences (p > 0.05) across ethnicities. In the control

group, the higher scores belong to Malays, followed

Indian and Chinese for PCS while the highest scores for

MCS belongs to Indian, Malay and lastly, Chinese.

However, the difference in scores obtained for different

ethnicity was not significant (p > 0.05). On the other

hand, in the case group, the highest scores in PCS and

MCS belongs to Indian, then followed by Chinese and

Malay. All of these differences were not significant (p >

0.05). In addition, HRQoL scores of total controls were

higher than that those of both PCS and MCS. However,

only the difference in the scores for PCS showed

significant difference (p < 0.05). The PCS scores were

Quality of Life Control (n=90) p-value

Physical component summary (PCS) 52.89 (6.17) 50.82±12.73 51.64 (10.03) 0.268

Mental component summary (MCS) 52.48 (7.58) 50.83 (7.83) 53.86 (6.31) 0.796

Quality of Life
Case (n=90)

p-value
Malay (n=30) Chinese (n=30) Indian (n=30)

Physical component summary (PCS) 47.99(24.96) 48.79(14.57) 50.06(21.05) 0.371

Mental component summary (MCS) 51.00 (10.08) 53.56(4.31) 53.58(4.47) 0.080

Quality of Life Control (n=90) Case (n=90) p-value

Physical component summary (PCS) 51.87 (11.31) 49.51 (19.36) ˂0.0001*

Mental component summary (MCS) 53.24 (8.59) 53.10 (7.36) 0.911

Table 5. Physical and mental component summary scores of multi ethnic respondents

*p < 0.05 indicates significant difference by using Mann Whitney U test.

p ˃ 0.05 indicates no significant difference between ethnic groups by using Kruskal- Wallis test.

F
U

L
L

 P
A

P
E

R

1281

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

relatively lower compared to MCS.

4. Discussion

CVD risk factors were significantly different

according to ethnicity. Even in the same residential area,

different ethnic groups possessed different eating

patterns, SES and psychosocial status, contributing to

different levels of significance for the CVD risk factors

investigated.

Based on Table 2, controls without personal medical

history contributed to lower CVD risk compared to

cases. This was in line with the studies which showed

that lower prevalence for personal medical history such

as hypertension contributed to lower risk of in-hospital

CVD mortality among respondents (Lu and Nordin,

2013). In terms of physical activity, the finding obtained

were in line with a study which revealed that when the

physical activity involvement of population was

investigated, the Chinese engaged significantly less

physical activity (mean = 11.51, SD = 5.86) as compared

to Malays (mean = 12.80, SD = 5.77) and other groups

(mean = 15.14, SD = 5.22) (Lian et al., 2016).

Differences in levels of physical activity may be due to

the complex interaction between socio-economic,

cultural, and physical environmental factors (Teh et al.,

2011). Lu and Nordin (2013) also found that Malays had

the highest prevalence of smoking in Malaysia as

compared to other groups.

For monthly household income, Tan et al. (2011)

discovered that Malays and other-Bumis tended to have

lower household income than Indians, who tended to

have lower household incomes than Chinese. For

example, monthly household income for other-Bumis

was roughly RM1200 per month, compared to RM1545

for Malays, RM2135 for Indians, and RM2535 for

Chinese. This discovery was in line with the findings

obtained. On the other hand, the Islamic background of

Malay respondents which prohibits the consumption of

alcohol contributes to a significant difference between

ethnic groups. Malays occupied the highest prevalence of

negative psychosocial status, as evidenced by a previous

study which indicated that perceived stress was

dominated by Malays (52.2%), compared to all the other

races (Jia and Loo, 2018). For both control and case

groups, alcohol consumption and psychosocial status

showed significant differences between ethnic groups.

These risk factors were remarkable and should be further

investigated in detail.

Overall, personal medical history showed significant

differences between the controls and cases (p ˂ 0.001),

as greatly supported by a study which concluded that the

prevalence of heart failure is higher in patients with

personal diseases such as diabetes (16% - 31%) than

normal subjects (4% - 6%) (Leon and Maddox, 2015).

Malay and Indian respondents showed significant

differences between groups (p ˂ 0.001) in terms of the

personal medical history of hypertension, diabetes

mellitus, hypercholesterolemia, and stroke. On the other

hand, Chinese respondents showed significant

differences between case-control groups for personal

medical history (p ˂ 0.001), in addition to smoking (p =

0.002).  This was evidenced via a prospective cohort

study which showed the hazard ratios (HR) for all-cause

CVD were 1.7 for current smokers, 1.07 for former

smokers, and 1 for never smokers (McEvoy et al., 2015).

The scores for psychosocial status were the highest

in Malays, followed by Indians and Chinese. The highest

score for all the three components of psychosocial status

is stress, followed by depression and lastly anxiety which

was in line with a study conducted by Saadat et al.

(2015). This finding is inconsistent with a study which

reported a significant relationship (p < 0.05) between the

case and control for the psychosocial status, in which

cases with higher scores for psychosocial status was then

identified as at increased risk for incident CHD and

health behaviors (Cohen et al., 2018).

Almost all food groups were dependent on ethnicity,

but not lipid profile status. Poultry and meat intake has

been positively associated with the increase risk of CVD

through several studies (Micha et al., 2010; Pan et al.,

2012). For specific CVD mortality, red meat intake was

positively associated with ischemic heart disease when

women and men were combined (HR = 1.41, 95% CI =

1.05–1.89, p-value = 0.04) (Takata et al., 2013).

Additionally, Mozaffarian and Rimm (2006) reported

that modest consumption of fish (1 to 2 servings per

week), especially those higher in the omega-3 fatty acids,

reduced the risk of coronary death by 36% (95% CI:

20% to 50%, p < 0.001) and total mortality by 17% (95%

CI: 0% to 32%, p = 0.046). A study showed Malays

possessed highest fish intake as compared to other

ethnics. This finding was in line with the study that

Malaysians consumed 168 g/day fish, with Malays’

consumption of fish (175±143 g/day) significantly (p <

0.001) higher compared with the other two ethnic groups

(Chinese = 152±133 g/day, Indians = 136±141 g/day)

(Ahmad et al., 2016).

Next, the consumption of nuts, beans and whole-

grain were dependent on ethnicity, and not dependent on

groups. Fats, especially ‘bad fats’ such as trans fats and

saturated fats in various foods, are considered the main

contributor to CVD from foods (Briggs et al., 2017). The

2013 American Heart Association and American College

of Cardiology (AHA/ACC) Guideline on Lifestyle

F
U

L
L

 P
A

P
E

R

1282

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

Management to Reduce Cardiovascular Risk reports

strong evidence (level A) for reducing SFA intake (5% to

6% of calories) to lower LDL cholesterol (Eckel et al.,

2014). High intake of Western-style food with high

saturated and trans fats, sodium and sugar contribute to

various health impacts. Western-style food intake varies

according to ethnicity, particularly due to different

occupations, education levels and family background

(Osei-Kwasi et al., 2016). The present study showed that

Malays consumed more fats, fast food and sugary drinks,

followed by Indian and Chinese. This finding was in line

with a previous study that Malay had significantly higher

scores for the Western-based food pattern as compared to

Chinese (Abdullah et al., 2016).

The previous study demonstrates that for the general

population of healthy adults, moderate caffeine

consumption of 400 mg/d equivalent to 4 cups of brewed

coffee is not associated with toxicity, cardiovascular

effects, effects on bone status and calcium balance (with

consumption of adequate calcium) (Turnbull et al.,

2017). Basically, there was no significant difference in

terms of caffeine consumption except for Malay

respondents in which its controls had higher intake than

that of cases. Thus, it can be concluded that caffeine

consumption was not associated with CVD risk.

There are contradictory findings, as the consumption

of poultry, meat, fats, pastries, fast foods, carbonated soft

drinks, and caffeinated were higher in the control group

as compared to case groups. In addition to the higher

consumption of fish, nuts, beans and whole grains in the

case group, this can be best explained by increased

educational level and awareness of CVD among cases.

This allows them to be aware and more concern about

the foods which they will consume in order to reduce the

risk for CVD. This leads to the development and

implementation of action plans against CVD. Thus,

taking corrective action should include consuming the

right foods to fight against health complications.

Based on Table 4, factors such as overweight/obesity

and stress for Malays in addition to the family history of

high blood cholesterol and overweight/obesity for

Indians were negatively associated (OR < 1) with CVD.

This finding can be best explained by the continuously

increasing in the health and nutrition awareness among

overweight/ obese people, especially towards CVD risk.

They tend to develop and implement an efficient action

plan targeting CVD prevention in order to avoid further

weight gain. In addition, suitable normal stress allows

people to fit their motivational orientation and, thus, lead

to a regulatory fit. In the working world, this pursuit

could lead to attuning the regulatory focus of the workers

to the working environment in order to achieve better

performance.

On the other hand, low-income level and family

history of hypertension were positively associated (OR >

1) with CVD among Chinese. This can be best explained

by low-income families not being able to afford more

expensive healthy food, and thus spending their limited

resources on ‘cheaper’ food sources such as fast food and

sweetened beverages. The contradictory findings for

Chinese obtained from this study were personal medical

history of hypertension (OR: 0.123, CI: 0.025-0.621) and

currently smoking (OR: 0.080, CI: 0.009-0.685). This

can be explained by the reason for higher education and

increase awareness towards the prevention of CVD

among the hypertensive population and smokers, thus

protecting them from CVD risk. Among all ethnic

groups, Chinese had the highest number of significant

risk factors for CVD.

The significant protective factor for Chinese is the

absence of the family history of hypertension with the

occurrence of CVD with OR less than 1. A total of 2,302

male and female Framingham offspring study

participants with parental history of premature CVD

(father less than 55 years and mother less than 65 years)

were diagnosed for CVD risk. CVD increased 75% with

paternal and about 60% with maternal history of

premature CVD after 8 years of follow-up (Kolber and

Scrimshaw, 2014).

Absence of personal medical history of hypertension

and not currently smoking were statistically significant

but not protective against CVD due to the large OR

obtained among Chinese population. For Malay, absence

of stress is the only factor which is statistically

significant with p = 0.012. However, it is not protective

factor as the OR was much greater than 1. For Indian,

factors which were statistically significant but not

protective were the absence of the family history of high

blood cholesterol and no family history of overweight/

obesity. These findings can be best explained for several

reasons. Although respondents were not under

hypertension condition, there are still many other

confounding factors such as unhealthy diet, physical

inactivity and alcohol consumption might influence the

risk for CVD. Besides, this may be due to the limitation

that all respondents involved were all located in Dengkil,

a small town in Selangor, which may not have been

comprehensive enough.

Besides, the finding obtained which showed there

was no significant difference (p < 0.05) in HRQoL

scores according to ethnicity also in line with the

findings obtained via a cross-sectional study involving

652 of multiethnic adolescents (aged 13 years) which

showed the slight difference in the HRQoL scores were

F
U

L
L

 P
A

P
E

R

1283

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

not significant (p > 0.05) (Loh et al., 2015). According to

Loh et al. (2015), Chinese occupied the highest scores

(mean: 71.15, CI: 65.28, 77.02), followed by Indians

(mean: 69.91, CI: 64.23, 75.60) and lastly, Malays

(mean: 69.53, CI: 68.12, 70.93). However, the

prevalence of HRQoL scores in the present study was not

consistent among the ethnic groups most probably due to

sociodemographic resources, clinical and environmental

factors (Leow et al., 2013).

The study also found that HRQoL scores of total

controls were higher than that of cases for both the PCS

and MCS. This shows that the control group had higher

quality of life compared to the case group in terms of

physical and mental health. However, only the difference

in the scores for PCS showed significant difference (p <

0.05). The PCS score was relatively lower compared to

MCS. The mean scores of PCS and MCS among 596

Malaysian adults with mean age of 35.7 years were 48.9

and 49.1, respectively (Shafie et al., 2011). Therefore,

PCS and MCS scores of current respondents were

considered normal as they were slightly higher than the

average scores of the Malaysian population.

5. Conclusion

It could be concluded that generally, all the risk

factors were independent on the total cholesterol status

except for personal medical history. Chinese is the ethnic

group with the highest CVD risk as due to the highest

number of significant CVD risk factors. With the

determination of each factor in each ethnicity, strategies

of nutrition management of CVD can be identified

easily. Further study might better consider research on

randomly selected samples with the intention to increase

the validity of the research. Besides, FFQ should be

improved by including the type of food and drinks

consumed, food preparation methods, and portion size.

Anthropometric measurements and blood withdrawals

should be implemented to obtain the actual serum lipid

profile and accurate body weight and height.

Acknowledgement

The authors would like to express their gratitude to

all the adult respondents for their participation, full

cooperation, and patience with the study. Also, highest

appreciations go to those who assisted in completing this

project, including Mr Liew Rock Keey.

References

Abdullah, N-.F., Teo, P.S. and Foo, L.H. (2016). Ethnic

differences in the food intake patterns and its

associated factors of adolescents in Kelantan,

Malaysia. Nutrients, 8(9), 551. https://

doi.org/10.3390/nu8090551

Ahmad, N.I., Wan Mahiyuddin, W.R., Tengku

Mohamad, T.R., Ling, C.Y., Daud, S.F., Hussein,

N.C., Abdullah, N.A., Shaharudin, R. and Sulaiman,

L.H. (2016). Fish consumption pattern among adults

of different ethnics in Peninsular Malaysia. Food

and Nutrition Research, 60(1), 1-15. https://

doi.org/10.3402/fnr.v60.32697

Akter, S.F.U., Fauzi, A., Nordin, M., Satwi, S.,

Mohamed, A., Aznan, M. and Samsul, D. (2010).

Prevalence of cardiovascular risk factors in a

selected community at Kuantan, Pahang, Malaysia.

International Journal of Medicine and Medical

Sciences, 2, 322-328.

Alvi, M. (2016). A manual for selecting sampling

techniques in research. Munich Personal RePEc

Archive, p. 1-57. Pakistan: University of Karachi.

André, N., Klopp-Dutote, N., Biet-Hornstein, A.,

Strunski, V. and Page, C. (2018). Cardiovascular risk

and severity factors in patients admitted to hospital

for spontaneous epistaxis. Eur. Annals of

Otorhinolaryngology, Head and Neck Diseases, 135

(2), 119-122. https://doi.org/10.1016/

j.anorl.2017.11.004

Aniza, I., Nurmawati, A., Hanizah, Y. and Ahmad

Taufik, J. (2016). Modifiable risk factors of

cardiovascular disease among adults in rural

community of Malaysia: A cross sectional study.

Malaysian Journal of Public Health Medicine, 16,

53-61.

Briggs, M.A., Petersen, K.S. and Kris-Etherton, P.M.

(2017). Saturated fatty acids and cardiovascular

disease: Replacements for saturated fat to reduce

cardiovascular risk. Healthcare. Multidisciplinary

Digital Publishing Institute, 5(2), 29. https://

doi.org/10.3390/healthcare5020029

Department of Statistics. (2015). Dengkil. Retrieved on

March 26, 2018 from Department of Statistics

website:  https://www.citypopulation.de/php/

malaysia-mukim-admin.php?adm2id=100701

Department of Statistics. (2017). Statistics on causes of

death, Malaysia. Retrieved on March 26, 2018 from

Department of Statistics website:  https://

www.dosm.gov.my/v1/index.php?r=column/

pdfPrev&id=Y3psYUI2VjU0ZzRhZU1kcVFMMTh

GUT09

Eckel, R.H., Jakicic, J.M., Ard, J.D., De Jesus, J.M.,

Miller, N.H., Hubbard, V.S., Lee, I.-M.,

Lichtenstein, A.H., Loria, C.M. and Millen, B.E.

(2014). 2013 AHA/ACC guideline on lifestyle

management to reduce cardiovascular risk: A report

of the American College of Cardiology/American

F
U

L
L

 P
A

P
E

R

1284

https://doi.org/10.3390/nu8090551

https://doi.org/10.3390/nu8090551

https://doi.org/10.3402/fnr.v60.32697

https://doi.org/10.1016/j.anorl.2017.11.004

https://doi.org/10.1016/j.anorl.2017.11.004

https://doi.org/10.3390/healthcare5020029

https://doi.org/10.3390/healthcare5020029

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

Heart Association task force on practice guidelines.

Journal of American College of Cardiology, 63(25,

Part B), 2960-2984. https://doi.org/10.1016/

j.jacc.2013.11.003

Gandek, B., Sinclair, S.J., Kosinski, M. and Ware Jr, J.E.

(2004). Psychometric evaluation of the sf-36® health

survey in medicare managed care. Health Care

Financing Review, 25(4), 5-25.

Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.B.,

Poole, C., Goodman, S.N. and Altman, D.G. (2016).

Statistical tests, p-values, confidence intervals, and

power: A guide to misinterpretations. European

Journal of Epidemiology, 31, 337-350. https://

doi.org/10.1007/s10654-016-0149-3

IPAQ Analysis. (2005). Guidelines for Data Processing

and Analysis of the International Physical Activity

Questionnaire (IPAQ). Retrieved on March 26, 2018

from IPAQ Analysis website: https://

www.researchgate.net/file.PostFileLoader.html?

id=5641f4c36143250eac8b45b7&assetKey=AS%

3A294237418606593%401447163075131

Jia, Y. and Loo, Y. (2018). Prevalence and determinants

of perceived stress among undergraduate students in

a Malaysian university. Journal of Health and

Translational Medicine, 21(1), 1-4.

Juhan, N., Zubairi, Y.Z., Zuhdi, A., Khalid, Z.M. and

Ahmad, W.A.W. (2018). Gender differences in

mortality amongst elevation myocardial infarction

patients in Malaysia from 2006 to 2013. Annals of

Saudi Medicine, 38(1), 1-7. https://

doi.org/10.5144/0256-4947.2018.1

Kelsey, J.L., Whittemore, A.S., Evans, A.S. and

Thompson, W.D. (1996). Methods in Observational

Epidemiology, Monographs in Epidemiology and

Biostatistics, p. 1 – 444. New York: Oxford

University Press Inc.

Kolber, M.R. and Scrimshaw, C. (2014). Family history

of cardiovascular disease. Canadian Family

Physician, 60, 1016.

Kumar, A. (2014). Changing trends of cardiovascular

risk factors among Indians: A review of emerging

risks. Asian Pacific Journal of Tropical Biomedicine,

4(12), 1001-1008. https://doi.org/10.12980/

APJTB.4.201414B401

Leon, B.M. and Maddox, T.M. (2015). Diabetes and

cardiovascular disease: Epidemiology, biological

mechanisms, treatment recommendations and future

research. World Journal of Diabetes, 6(13), 1246-

1258. https://doi.org/10.4239/wjd.v6.i13.1246

Leow, M.K.-S., Griva, K., Choo, R., Wee, H.-L.,

Thumboo, J., Tai, E.S. and Newman, S. (2013).

Determinants of health-related quality of life

(HRQoL) in the multiethnic Singapore population–a

national cohort study. PloS One, 8, 67-138. https://

doi.org/10.1371/journal.pone.0067138

Lian, T.C., Bonn, G., Han, Y.S., Choo, Y.C. and Piau,

W.C. (2016). Physical activity and its correlates

among adults in Malaysia: A cross-sectional

descriptive study. PloS One, 11, 73-157. https://

doi.org/10.1371/journal.pone.0157730

Lim, K.S., Ng, C.C., Chan, C.K., Foo, W.S., Low, J.S.Y.

and Tan, C.T. (2017). Ethnic variation of genetic

(idiopathic) generalized epilepsy in Malaysia.

Seizure, 45, 24-27. https://doi.org/10.1016/

j.seizure.2016.11.011

Loh, D., Moy, F., Zaharan, N. and Mohamed, Z. (2015).

Disparities in health‐related quality of life among

healthy adolescents in a developing country–the

impact of gender, ethnicity, socio‐economic status

and weight status. Child Care Health and

Development, 41(6), 1216-1226. https://

doi.org/10.1111/cch.12252

Lovibond, P.F. and Lovibond, S.H. (1995). The structure

of negative emotional states: Comparison of the

Depression Anxiety Stress Scales (DASS) with the

beck depression and anxiety inventories. Behaviour

Research and Therapy, 33(3), 335-343. https://

doi.org/10.1016/0005-7967(94)00075-U

Lu, H.T. and Nordin, R.B. (2013). Ethnic differences in

the occurrence of acute coronary syndrome: Results

of the Malaysian National Cardiovascular Disease

(NCVD) database registry (March 2006-February

2010). BMC Cardiovascular Disorders, 13, 97.

https://doi.org/10.1186/1471-2261-13-97

Mathieson, K. (2014). Making sense of biostatistics:

Types of probability sampling. Journal of Clinical

Research Best Practices, 10(10), 192-245.

McEvoy, J.W., Blaha, M.J., Defilippis, A.P., Lima, J.A.,

Bluemke, D.A., Hundley, W.G., Min, J.K., Shaw,

L.J., Lloyd-Jones, D.M. and Barr, R.G. (2015).

Cigarette smoking and cardiovascular events: Role

of inflammation and subclinical atherosclerosis: The

multiethnic study of atherosclerosis.

Arteriosclerosis, Thrombosis, and Vascular Biology,

35, 700-709. https://doi.org/10.1161/

ATVBAHA.114.304562

Micha, R., Michas, G. and Mozaffarian, D. (2012).

Unprocessed red and processed meats and risk of

coronary artery disease and type 2 diabetes–an

updated review of the evidence. Current

Atherosclerosis Reports, 14, 515-524. https://

doi.org/10.1007/s11883-012-0282-8

Mozaffarian, D. (2016). Dietary and policy priorities for

cardiovascular disease, diabetes, and obesity: A

F
U

L
L

 P
A

P
E

R

1285

https://doi.org/10.1016/j.jacc.2013.11.003

https://doi.org/10.1016/j.jacc.2013.11.003

https://doi.org/10.1007/s10654-016-0149-3

https://doi.org/10.5144/0256-4947.2018.1

https://doi.org/10.12980/APJTB.4.201414B401

https://doi.org/10.12980/APJTB.4.201414B401

https://doi.org/10.4239/wjd.v6.i13.1246

https://doi.org/10.1371/journal.pone.0067138

https://doi.org/10.1371/journal.pone.0067138

https://doi.org/10.1371/journal.pone.0157730

https://doi.org/10.1016/j.seizure.2016.11.011

https://doi.org/10.1016/j.seizure.2016.11.011

https://doi.org/10.1111/cch.12252

https://doi.org/10.1016/0005-7967(94)00075-U

https://doi.org/10.1186/1471-2261-13-97

https://doi.org/10.1161/ATVBAHA.114.304562

https://doi.org/10.1161/ATVBAHA.114.304562

https://doi.org/10.1007/s11883-012-0282-8

Lee et al. / Food Research 4 (4) (2020) 1272 - 1286

eISSN: 2550-2166 © 2020 The Authors. Published by Rynnye Lyan Resources

comprehensive review. Circulation, 133, 187-225.

https://doi.org/10.1161/

CIRCULATIONAHA.115.018585

Osei-Kwasi, H.A., Nicolaou, M., Powell, K., Terragni,

L., Maes, L., Stronks, K., Lien, N. and Holdsworth,

M. (2016). Systematic mapping review of the factors

influencing dietary behaviour in ethnic minority

groups living in Europe: A dedipac study.

Internationa Journal of Behavioural Nutrition and

Physical Activity, 13, 85. https://doi.org/10.1186/

s12966-016-0412-8

Pan, A., Sun, Q., Bernstein, A.M., Schulze, M.B.,

Manson, J.E., Stampfer, M.J., Willett, W.C. and Hu,

F.B. (2012). Red meat consumption and mortality:

Results from 2 prospective cohort studies. Archives

of Internal Medicine, 172, 555-563. https://

doi.org/10.1001/archinternmed.2011.2287

Pandey, A.K., Pandey, S., Blaha, M.J., Agatston, A.,

Feldman, T., Ozner, M., Santos, R.D., Budoff, M.J.,

Blumenthal, R.S. and Nasir, K. (2013). Family

history of coronary heart disease and markers of

subclinical cardiovascular disease: Where do we

stand? Atherosclerosis, 228(2), 285-294. https://

doi.org/10.1016/j.atherosclerosis.2013.02.016

Roth, G.A., Huffman, M.D., Moran, A.E., Feigin, V.,

Mensah, G.A., Naghavi, M. and Murray, C.J. (2015).

Global and regional patterns in cardiovascular

mortality from 1990 to 2013. Circulation, 132, 1667-

1678. https://doi.org/10.1161/

CIRCULATIONAHA.114.008720

Saadat, M., Behboodi, Z.M. and Saadat, E. (2015).

Comparison of depression, anxiety, stress, and

related factors among women and men with human

immunodeficiency virus infection. Journal of

Human Reproductive Sciences, 8(1), 48-51. https://

doi.org/10.4103/0974-1208.153128

Schmidt, C.O. and Kohlmann, T. (2008). When to use

the odds ratio or the relative risk? International

Journal of Public Health, 53, 165-167. https://

doi.org/10.1007/s00038-008-7068-3

Shafie, A.A., Hassali, M.A. and Liau, S.Y. (2011). A

cross-sectional validation study of EQ-5D among the

Malaysian adult population. Quality of Life

Research, 20(4), 593-600. https://doi.org/10.1007/

s11136-010-9774-6

Song, J.W. and Chung, K.C. (2010). Observational

studies: Cohort and case-control studies. Plastic and

Reconstructive Surgery, 126(6), 2234-2242. https://

doi.org/10.1097/PRS.0b013e3181f44abc

Takata, Y., Shu, X.-O., Gao, Y.-T., Li, H., Zhang, X.,

Gao, J., Cai, H., Yang, G., Xiang, Y.-B. and Zheng,

W. (2013). Red meat and poultry intakes and risk of

total and cause-specific mortality: Results from

cohort studies of Chinese adults in Shanghai. PloS

One, 8, 59-63. https://doi.org/10.1371/

journal.pone.0056963

Teh, P.L., Yong, C.C., Chong, C.W. and Yew, S.Y.

(2011). Do the Big Five Personality Factors affect

knowledge sharing behaviour? A study of Malaysian

universities. Malaysian Journal of Library and

Information Science, 16(1), 47-62

Thompson, D.R., Ski, C.F., Garside, J. and Astin, F.

(2016). A review of health-related quality of life

patient-reported outcome measures in cardiovascular

nursing. European Journal of Cardiovascular

Nursing, 15(2), 114-125. https://

doi.org/10.1177/1474515116637980

Thon, C.C., Yein, L.P. and Lian, C.W. (2012). The

prevalence of cardiovascular risk factors in the

young and middle-aged rural population in Sarawak,

Malaysia. Malaysian Journal of Medical Science,19

(2), 27-34.

Turnbull, D., Rodricks, J.V., Mariano, G.F. and

Chowdhury, F. (2017). Caffeine and cardiovascular

health. Regulatory Toxicology and Pharmacology,

89, 165-185. https://doi.org/10.1016/

j.yrtph.2017.07.025

Ware, J.E., Keller, S.D. and Kosinski, M. (1994). Sf-36

physical and mental health summary scales. Health

Assessment Lab, p. 30-56. Boston, USA: New

England Medical Center.

F
U

L
L

 P
A

P
E

R

1286

https://doi.org/10.1161/CIRCULATIONAHA.115.018585

https://doi.org/10.1161/CIRCULATIONAHA.115.018585

https://doi.org/10.1186/s12966-016-0412-8

https://doi.org/10.1186/s12966-016-0412-8

https://doi.org/10.1001/archinternmed.2011.2287

https://doi.org/10.1016/j.atherosclerosis.2013.02.016

https://doi.org/10.1161/CIRCULATIONAHA.114.008720

https://doi.org/10.1161/CIRCULATIONAHA.114.008720

https://doi.org/10.4103/0974-1208.153128

https://doi.org/10.1007/s00038-008-7068-3

https://doi.org/10.1007/s11136-010-9774-6

https://doi.org/10.1007/s11136-010-9774-6

https://doi.org/10.1097/PRS.0b013e3181f44abc

https://doi.org/10.1371/journal.pone.0056963

https://doi.org/10.1371/journal.pone.0056963

https://doi.org/10.1177/1474515116637980

https://doi.org/10.1016/j.yrtph.2017.07.025

https://doi.org/10.1016/j.yrtph.2017.07.025


429 Too Many Requests

nginx


 *Corresponding author.

Email: sarafadeenshittu@gmail.com; skshittu.age@buk.edu.ng

eISSN: 2550-2166

© 2018 The Authors. Published by Rynnye Lyan Resources

  Food Research 3 (2) : 182 - 187 (April 2019)
  Journal homepage: http://www.myfoodresearch.com

F
U

L
L

 P
A

P
E

R

Statistical model for solvent oil extraction from soybean (Glycine max (L.))

*Shittu S.K., Mari H.H. and Dangora N.D.

Department of Agricultural and Environmental Engineering, Faculty of Engineering, Bayero University

P.M.B 3011 Kano, Nigeria.

Article history:

Received: 21 May 2018

Received in revised form: 26

September 2018

Accepted: 26 November 2018

Available Online: 4

December 2018

Keywords:

Soybean,

Flake moisture,

Flake thickness,

Solvent temperature oil yield,

Model.

DOI:
https://doi.org/10.26656/fr.2017.3(2).111

Abstract

Oil extraction process using hexane is common among large-scale oil industries in

Nigeria. Various factors play vital roles in the quantity and quality of oil extracted from oil

-bearing crops using this method. This study therefore aimed at determining the effect of

flake moisture content, flake thickness and solvent temperature on oil extraction of

soybean using hexane solvent in soxhlet apparatus. A 3 x 3 x 3 factorial experiment in a

complete randomized design involving flake moisture content, flake thickness and solvent

temperature each at three levels in three replications was adopted. The experiments were

done at flake moisture contents of 4.5, 7 and 9% (wb), flake thicknesses of 0.4, 0.7 and 0.9

mm, and solvent temperatures of 60, 65 and 70°C. ANOVA using SAS software procedure

was employed to determine the effect of variables on the oil extraction and a regression

model for predicting oil yield on the basis of the three factors was developed using

XLSTAT Microsoft Excel. The results showed that combination of 4.5% flake moisture

content, 0.4 mm flake thickness and 70°C solvent temperature gave the highest oil yield

(19.37%), whereas 9% flake moisture content, 0.9 mm flake thickness and 60oC solvent

temperature gave the lowest oil yield (14.57%). All evaluated factors have a significant

effect on oil yield at 1% level. The interaction between the factors did not have a

significant effect on oil yield. Model verification results gave mean square error, root

mean square error, bias and accuracy of 0.084, 0.290, 0.173 and 0.030 respectively. High

value of the coefficient of determination R2 (0.966) obtained indicated that there is a good

agreement between the observed and the predicted values.

1. Introduction

Oil-bearing agricultural products are numerous and

are of great economic importance to mankind.  The oil

contents in these products can be categorized as the

edible and non-edible oils. Most oils are embedded in

their parent materials and there is a need to extract them

out before usage. Oil extraction is the process of

recovering oil from oil-bearing agricultural products

through manual, mechanical, chemical or mechano-

chemical extraction. Sorin-Stefan et al. (2007) stated that

oil products industry produces edible and non-edible oils

with about two-thirds of total oil products being the

edible oils used directly in foods or in the manufacture of

margarine, mayonnaise, bakery and pastry products, and

as cooking fats. The remaining one-third of the total

volume of produced oil is the technical oils, used in the

production of various non-edible products, such as

detergents, paint, glycerin, fatty acids, varnish,

pharmaceuticals or cosmetics. Some common oil-seed

crops include cotton, castor, groundnut, sunflower,

sesame and soybean seeds.

 In the past, soybean is grown primarily for its

protein and secondary for oil but nowadays it is one of

the most commonly used raw materials by large-scale

vegetable oil industries in Nigeria. A survey on the types

of seeds commonly used for oil extraction by industries

in Kano Nigeria, revealed that: soybean seed 47%,

groundnut seed 29%, sesame seed 14% and cottonseed

14% (Mari, 2017). Oil extraction from soybean can be

done by mechanical or solvent method or combination of

both. Lawson et al. (2010) revealed that soybean seed

with low oil content are better extracted using the solvent

method because the method is characterized by higher oil

yield, larger processing capacity and lower refining

losses. The solvent oil extraction process is more popular

and it is preferred over mechanical expulsion because of

its high-quality oil with minimal refining (Ramanujan,

2006). Generally, the demand for vegetable oil is on the

increasing trend (Parcell et al., 2018). One of the ways

adequate oil extraction could be achieved is by

183 Shittu et al. / Food Research 3 (2) (2019) 182 - 187

eISSN: 2550-2166 © 2018 The Authors. Published by Rynnye Lyan Resources

specifying the best factors combination for optimum oil

extraction. Factors affecting oil extraction have been

studied by some researchers.

Mwithiga and Moriasi (2007) study the yield

characteristics during mechanical oil extraction of

preheated and ground soybeans. The study showed that

the initial heating of material before the mechanical

expression is very important. It was found that the

duration of the application and the temperature of the

soybean has a linear relationship with the oil yield for

temperature falling below 75°C. Sayyar et al. (2009)

carried out extraction of oil from Jatropha seeds using

organic solvent. The study revealed the effects of five

operating parameters on the oil extraction. The factors

considered include type of solvents, temperature, solvent

to solid ratio, processing time and particle size of the

meal. Optimum conditions were found using hexane as

solvent at 8 h reaction time, temperature of 68°C, coarse

particle size (0.5-0.75 mm) and solvent to solid ratio of

6:1. Abdulkarim and Saady (2010) studied the factors

affecting the extraction process of oil-bearing flakes of

sunflower, cotton and soybean seeds. It was concluded

that the best condition for extraction of sunflower cotton

and soybean seeds were moisture content of 9-11%,

flake thickness of 2-3 mm, solvent ratio of 1:1 by weight

temperature of the extraction of 60-65°C for pure hexane

and the optimum extraction time of 5 hrs. A study

conducted by Lawson et al. (2010) on the parameters

affecting the solvent extraction of soybean oil. It

revealed that at a particle size of 2 mm, flake thickness

of 0.25 mm, moisture content of 12-13%, temperature of

69°C and the extraction time between 3.5 and 4.5 hrs

gave the maximum oil yield. Yusuf et al. (2014) study

the effects of heating temperature and seed condition on

the yield and quality of mechanically expressed

groundnut oil. The study revealed that the oil yield

increases with increase in heating temperature but tends

to decrease as the temperature increase from 90-100°C.

Generally, factors that affect solvents oil extraction

are solvent temperature, flake moisture content, flake

thickness, solvent to flake ratio and extraction time.

These factors could be used to generate models equations

which describe the physical/chemical process of oil

extraction. Simulation and Modelling are of great

importance in engineering because they provide

information fast and safe cost; finding important input

parameters with changing simulation inputs; can be used

with new design and policies before implementation and

can be used to investigate a wide verity of what if

questions about real-world system (Banks et al., 2010).

An informal study carried out in Kano cluster areas

of oil extraction indicated that most of the companies

carry out their oil extraction at different flake moisture

contents, flake thicknesses and solvent temperatures

using their own discretion. This led to low oil yield in

some oil companies. Hence, determination of the best

combination of the above factors through modeling is

therefore imperative. The objectives of this study are

therefore to: (i) determine the effect of flake moisture

content, flake thickness and solvent temperature on

solvent oil extraction of soybean using hexane in a

Soxhlet apparatus. (ii) use the obtained data from

experiments to develop a statistical model using multiple

linear regression analysis and (iii) validate the developed

model using the coefficient of determination.

2. Materials and methods

The materials used for the experiment were: soybean

seeds, hexane and distilled water. The equipment used

includes Soxhlet apparatus, flaking machine, desiccators,

digital thermometer, glass beakers, sensitive weighing

balance (model 88 ADAM), Ohaus moisture analyzer

(model ATS60) and Mitutoyo digital micrometer screw

gauge (model PS65).

2.1 Samples preparation and experimental procedure

Samples were conditioned to desired moisture

contents of 4.5%, 7% and 9% (wb) using the method

described by Lawson et al. (2010). The method involves

adding calculated amount of distilled water and applying

Equation (1).

Where, w1 = initial mass of the sample (g); mi = initial

moisture content of the sample % (wb); md = final

(desired) moisture content of sample % (wb); and Q =

mass of water to be added (g)

Conditioned soybean seeds samples were fed into a

flaking machine to achieve flakes thicknesses of 0.4, 0.7

and 0.9 mm. After flaking, samples thicknesses were

checked using the digital micrometer screw gauge.

Flakes of various thicknesses and moisture contents were

weighed 10 g and wrapped in filter paper and placed in

the thimble of the extractor of the Soxhlet apparatus

F
U

L
L

 P
A

P
E

R

(1)

Figure 1. Soxhlet apparatus

  Shittu et al. / Food Research 3 (2) (2019) 182 - 187 184

eISSN: 2550-2166 © 2018 The Authors. Published by Rynnye Lyan Resources

(Figure 1). Extraction flask of the apparatus was filled

with hexane up to 150 mL and heated to temperature of

60°C. The experiment was allowed to run for 3.5 hrs

Lawson et al. (2010). Extracted oil was removed and

emptied into a beaker to remove traces of solvent and

moisture. The oil was placed in desiccators to cool. After

cooling, the contents of the beaker was weighed and the

percentage of the oil yield was calculated using Equation

(2) (Lawson et al., 2010). The experiment was repeated

at hexane temperatures of 65 and 70°C.

Where, Y0 = oil yield (%); W0 = weight of extracted oil

(g); and Ws = weight of sample (g)

2.2 Experimental design and procedure

Factorial experiment in a completely randomized

design was adopted. The factors considered were three

levels of flake moisture content, three levels of flake

thickness and three levels of solvent temperature. All the

treatments were replicated three (3) times totaling eighty

-one experiments. Analysis of variance (ANOVA) using

SAS software procedure was employed to determine the

effect of the variables on the oil yield.

  2.3 Model development

 A multiple regression model for predicting oil yield

on the basis of the three factors (flake moisture content,

flake thickness and solvent temperature) was developed

using XLSTAT Microsoft Excel.

2.4 Methods of model verification

Verification of the model was carried out using

Equations 3-6 as given by Wilmott (1981):

Where MSE = Mean square error; RMSE = root mean

square error; Vi = Pi – Oi at data locations where i = 1, 2,

3…; Pi = predicted value at n = i; Oi = observed values at

n = i; and n = number of values in the check data set.

2.5 Method of model validation

To validate the model, two sets of experiments were

used. The first experiment was used to create the model

and the second experiment was used for validation. The

predicted values were obtained from the model equation

while the observed values were the second experimental

values. In order to validate the model, the predicted

values were plotted against the observed values and the

coefficient of determination, R2 was determined to

ascertain the agreement between predicted and observed

values.

3. Results and discussion

The mean values of the oil yield at different moisture

content, flake thickness and temperature are presented in

Table 1. The table revealed that the maximum oil yield

of 19.37% was obtained at the lowest flake moisture

content of 4.5%, lowest flake thickness of 0.4 mm and

the highest solvent temperature of 70°C. Also, the

minimum oil yield of 14.57% was obtained at the highest

flake moisture content of 9%, highest flake thickness of

0.9 mm and the lowest solvent temperature of 60°C. The

results show that the higher the moisture content, the

lower the oil yields. This agrees with the findings of

Lawson et al. (2010)  and Jacq et al. (2006). The reason

for these is that hexane is highly insoluble in water, with

high moisture content the extraction efficiency of the

solvent will be drastically reduced leading to poor oil

yield results. The results revealed that the thinner the

flake thickness, the higher the oil yields. The reason for

this observation is due to the less resistance offered by

the thin flakes to the flow of the solvent and there are

more surface areas of flakes at lower thickness resulting

in more solvent actions on the samples. This observation

agrees with the findings of Lawson et al. (2010) and

Abdulkarim and Saady (2010). The results show that the

higher the temperature, the higher the oil yields. But the

temperature should not exceed the boiling point of the

solvent. The reason for this observation is that at higher

temperature of the solvent, there is a breakdown of oil

cells leading to decrease in oil viscosity and increasing

the number of oil siphons which allow oil to flow more

readily through. This trend corroborates with the findings

of Ramanujan (2006), Ebewele et al. (2010), Momoh et

al. (2015) and Abdulkarim and Saady (2010).

3.1 Effect of flake moisture content, flake thickness and

solvent temperature on oil yield

Results of analysis of variance carried out on the oil

yield of soybean during solvent oil extraction are

presented in Table 2. The results show that flake

moisture content, flake thickness and solvent

temperature have a significant effect on oil yield at 1%

level. All the interactions were not statistically

significant on the oil yield.

The results for further analysis using Duncan

multiple range analysis is presented in Table 3. The

results revealed that the mean oil yield was found to be

highest (17.92%) at the lowest flake moisture content for

the given experimental conditions. The results showed

F
U

L
L

 P
A

P
E

R

(2)

(3)

(4)

(5)

(6)

185 Shittu et al. / Food Research 3 (2) (2019) 182 - 187

eISSN: 2550-2166 © 2018 The Authors. Published by Rynnye Lyan Resources

Moisture content (%) Flake thickness (mm) Temperature (°C) Oil Yield (%)

4.5 0.4 60 18.76

4.5 0.7 60 17.20

4.5 0.9 60 16.40

4.5 0.4 65 19.07

4.5 0.7 65 17.83

4.5 0.9 65 16.87

4.5 0.4 70 19.37

4.5 0.7 70 18.47

4.5 0.9 70 17.33

7 0.4 60 17.97

7 0.7 60 16.30

7 0.9 60 15.40

7 0.4 65 18.65

7 0.7 65 16.95

7 0.9 65 16.08

7 0.4 70 19.33

7 0.7 70 17.60

7 0.9 70 16.77

9 0.4 60 18.10

9 0.7 60 16.00

9 0.9 60 14.57

9 0.4 65 18.43

9 0.7 65 16.20

9 0.9 65 16.08

9 0.4 70 18.77

9 0.7 70 16.40

9 0.9 70 16.57

Table 1. Mean values of oil yield at different flake moisture content, flake thickness and solvent temperature

Sources of variation   Degree of freedom   Sum of squares   Mean square
Tabular F

Calculated F
1% 5%

Replication 2 11.84 5.92 1.52 5.02 3.17

Flake moisture content, M 2 21.19 10.59 23.74** 5.02 3.17

Flake thickness, S 2 86.09 43.04 96.48** 5.02 3.17

Solvent Temperature, T 2 16.41 8.20 18.39** 5.02 3.17

Interactions: M × S  4 2.45  0.61  1.37NS  3.69  2.54

S×T 4 0.89 0.22 0.50NS 3.69 2.54

M×T 4 0.57 0.14 0.32NS 3.69 2.54

M×T×S 8 1.99 0.25 0.56NS 2.86 2.12

Error 54 24.09 0.45

Total 80 152.81

Table 2. Analysis of variance results for effect of flake moisture content, flake thickness and solvent temperature on oil yield of

soybean

NS= Not significant **= Significant at 1% probability level (Highly significant)

Treatment Mean value Duncan Grouping

M1 (4.5%) 17.922 A

M2 (7%) 17.222 B

M3 (9%) 16.733 C

S1 (0.4mm) 18.7077 A

S2 (0.7mm) 16.9944 B

S3 (0.9mm) 16.2714 C

T3 (70oC) 17.8444 A

T2 (65oC) 17.2944 B

T1 (60oC) 16.744 C

Table 3.  Duncan multiple range analysis

F
U

L
L

 P
A

P
E

R

  Shittu et al. / Food Research 3 (2) (2019) 182 - 187 186

eISSN: 2550-2166 © 2018 The Authors. Published by Rynnye Lyan Resources

that oil yield decrease with increase in the flakes

moisture content and the values of oil yields were

statistically different at 1% level for all of the flake

moisture content levels. This show that controlling the

flake moisture content is important during solvent oil

extraction. The results from Table 3 using Duncan

multiple range analysis show that the mean values of oil

yield were found to be highest (18.70 %) at the flake

thickness of 0.4 mm. The table showed that oil yield

decrease with increase in the flakes thickness and the

values of oil yields were statistically different at 1%

level for all of the flake thicknesses. It implies that

regulating the flake thickness is essential in solvent oil

extraction. Duncan multiple range analysis (Table 3)

revealed that the mean values of oil yield were highest

(17.84 %) at the highest temperature of 70°C.  From the

table, it is evident that oil yield increased with increase

in the solvent temperature and the values of oil yields

were statistically different at 1% level for all the

temperature levels. It can, therefore, be concluded that

correct solvent temperature is necessary for solvent oil

extraction.

3.2 Model equation

Regression equation model for predicting soybean

oil yield with respect to flake moisture contents, flake

thickness and solvent temperature developed using

XLSTAT of Microsoft Excel can be expressed as:

Where Y = oil yield (%); M = flake moisture content (%

wb); S = flake thickness (mm); and T = solvent

temperature (°C)

The model shows that flake thickness (S) have the

highest coefficient (absolute value).  This implies that

the flake thickness has a strong influence on oil yield.

From the model equation, it can be deduced that flake

moisture content (M) and flake thickness (S) have a

negative correlation with the oil yield percentage (Y),

while solvent temperature (T) is positively correlated

with the oil yield. This implies that oil yield increases

with solvent temperature but decreases with flake

moisture content and flake thickness. This is in

agreement with the results in Table 3.

3.3 Model verification

Mean square error and root mean square error

determined were found to be 0.084 and 0.290

respectively. These indicate the magnitude of error in the

prediction. Bias and accuracy obtained were 0.173 and

0.030 respectively. The value obtained for bias is

positive, indicating over-prediction by the developed

model (Fowler et al., 2012). A good estimator should be

unbiased with even distribution of under and over-

prediction which gives an overall bias of zero (Walther

and Moore, 2005).

3.4 Model validation

Substituting different values of flake moisture

content, flake thickness and solvent temperature into the

model equation values of the oil yield were predicted.

Figure 2 presents the plot of predicted against observed

values (the value for the second experiment). The plot

(Figure 2) has a coefficient of determination R2 of 0.966.

The results indicated that there is a strong agreement

between the observed and predicted values.

4. Conclusion

Using the data collected from solvent oil extraction

from soybean with Soxhlet apparatus, effect of flake

moisture content, flake thickness and solvent

temperature on oil yield was determined and model

equation was developed to predict oil yield at a given

combination of the process parameters. All the factors

under consideration significantly affect oil yield. The

interaction between factors did not have a significant

effect on oil yield.  Highest oil yield was obtained at the

highest solvent temperature, lowest flake thickness and

lowest flake moisture content. The flake thickness has a

strong influence on oil yield. The validation results of the

developed model showed that there is good agreement

between the predicted and the observed values. It is

therefore recommended that the developed model be

optimized to establish the optimum values of the

evaluated factors.

Acknowledgements

The authors respectfully appreciate Bayero

University, Kano, Nigeria and Fortune Oil Mills Nigeria

Limited, Kano and Tertiary education trust fund

(Tetfund) for their support in this research work and

sponsorship to CAFEi 2018.

(7)

Figure 2. Graph of predicted against observed values

F
U

L
L

 P
A

P
E

R

187 Shittu et al. / Food Research 3 (2) (2019) 182 - 187

eISSN: 2550-2166 © 2018 The Authors. Published by Rynnye Lyan Resources

References

Abdulkarim, A. and Saady, A.M. (2010). Factors

Affecting the Extraction Process of Oil-bearing

flakes of sunflower, cotton and soybean seeds.

Journal of Kerbala University, 8(3), 32–47.

Banks, J., Carson J., Nelson B.L. and Nicol D. (2010).

Discrete-Event System Simulation, 5th ed. USA:

Pearson

Ebewele, R.O., Iyayi, A.F. and Hymore, F.K. (2010).

Considerations of the extraction process and

potential technical applications of Nigerian rubber

seed oil. International Journal of the Physical

Sciences, 5(6), 826–831.

Fowler, T.L., Jensen, T.L. and Brown, B.G. (2012).

Introduction to Forecast Verification. National

Center for Atmospheric Research Boulder Colorado

USA. Retrieved from website: https://dtcenter.org/

met/users/docs/presentations/WRF_Users_2012.pdf

Jacq, E., Akinoso, R. and Igbeka, J.C. (2006). Modeling

of Oil Expression from Palm Kernel (Elaeis

guineensis Jacq.). Agricultural Engineering

International: The CIGR Ejournal, 8, 1–8.

Lawson, O., Oyewumi, A., Ologunagba, F. and Ojomo,

A.O. (2010). Evaluation of the Parameters Affecting

the Solvent, ARPN Journal of Engineering and

Applied Sciences, 5(10), 51–55.

Mari, H.H. (2017). Modelling of Soybean Oil Extraction

Using Hexane. Nigeria: Bayero University Kano, M.

Eng Thesis.

Momoh, O.J., Okafor, V.N. and Obada, D.O. (2015).

Mathematical Modeling of the Solvent Extraction of

Palm Kernel Oil from Palm Kernel. Palestine

Technical University Research Journal, 3(1), 23–29.

Mwithiga, G. and Moriasi, L. (2007). A Study of Yield

Characteristics During Mechanical Oil Extraction of

Preheated and Ground Soybeans. Journal of Applied

Science Research, 3(10), 1146–1151.

Parcell. J, Kojima. Y, Roach. A. and Cain. W. (2018).

Global Edible Vegetable Oil Market Trends. Biomed

Journal of Scientific and Technical Research, 2(1), 1

-10

Ramanujan, P.A. (2006). Process Simulation, Modeling

and Design For Soybean Oil Extraction Using Liquid

Propane. India: Mumbai University, BSc. Thesis.

Sayyar, S., Abidin, Z.Z., Yunus, R. and Muhammad, A.

(2009). Extraction of oil from Jatropha Seeds-

Optimization and Kinetics. American Journal of

Applied Science, 6(7), 1–7. https://doi.org/10.3844/

ajassp.2009.1390.1395

Sorin-Stefan, B., Lonescu, M., Voicu, G., Ungureanu, N.

and Vladut, V. (2007). Food industry. Calculus

Elements for Mechanical Presses in Oil Industry. In

Tech Open science. Retrieved on 2nd September

2018 from In Tech Open Science Website:  http://

dx.doi.org/10.5772/53167.

Walther B.A. and Moore J.L. (2005). The concepts of

bias, precision and accuracy, and their use in testing

the performance of species richness estimators, with

a literature review of estimator performance.

Ecography, 28(6), 815-829. https://doi.org/10.1111/

j.2005.0906-7590.04112.x

Wilmott, C. (1981). On the validation of models.

Physical Geography, 2(2), 184–194. https://

doi.org/10.1080/02723646.1981.10642213

Yusuf, K.A., Olaniyan, A.M., Atanda, E.O. and

Sulieman, I.A. (2014). Effects of Heating

Temperature and Seed Condition on the Yield and

Quality of Mechanically Expressed Groundnut Oil.

International Journal of Technology Enhancements

and Emerging Engineering Research, 2(7), 73–78.

F
U

L
L

 P
A

P
E

R

https://doi.org/10.3844/ajassp.2009.1390.1395

https://doi.org/10.3844/ajassp.2009.1390.1395

https://doi.org/10.1111/j.2005.0906-7590.04112.x

https://doi.org/10.1111/j.2005.0906-7590.04112.x

https://doi.org/10.1080/02723646.1981.10642213

https://doi.org/10.1080/02723646.1981.10642213