Correlation between Dietary Pattern, Work Environment and ... · amenorrhea, Virilization, male like characteristics like body hair growing on the chest, belly, face, and around the

PSGCAS Search: A Journal of Science and TechnologyVolume: 4 No. : 2 ISSN: 2349 – 5456 1

Correlation between Dietary Pattern, Work Environment and Incidence

of PCOD in Working Women

Bhuvaneswari 1 , Kanjana K.

2, Madhan Shankar S R.

3

1,2 Department of Clinical Nutrition and Dietetics, PSG College of Arts & Science, Coimbatore, Tamil

Nadu, India 3Department of Biotechnology, PSG College of Arts & Science, Coimbatore, Tamil Nadu, India

*Corresponding author: [email protected]

ABSTRACT

Polycystic ovarian disease (PCOD) is the commonest endocrine disease in women of

reproductive age. It affects 5 to 10% of women of reproductive age. PCOD now proves to be a

significant factor in female infertility. Women with PCOD are at increased risk of diabetes,

hypertension, cardiovascular disease, hyper estrogen related cancers. The investigation was pursued to

assess the dietary pattern and study work environment of the working women, and test the incidence

of PCOD in selected women by PCR based methods. Out of the 83 working women (Information

Technology, administration, labour, medical coding and Teaching) from Coimbatore city between the

age group of 20- 35 years, 10 subjects who did not express having irregular menstrual cycle and 10

subjects who complainant of having irregular menstrual cycle, treated as Non-PCOD and PCOD

samples respectively were subjected to blood analysis – Polymerase chain reaction (PCR) techniques.

Consumption of non-vegetarian food against the regular meal pattern, Occupational stress,

Consumption of high fat foods and skipping of regular meals and Age related dietary / life style

changes were found to render the working women to fall into a high risk group with respect to the

incidence of PCOD.

Key words: PCOD, PCR techniques, menstrual cycle, obesity, fat intake

INTRODUCTION

Polycystic Ovarian were described as

early as 19th

century. In1935 Stein &

Leventhal described syndrome of

amenorrhea associated with polycystic

ovaries [1]

. Polycystic ovary syndrome is a

condition in which a woman has an

imbalance of female sex hormones. This

may lead to menstrual cycle changes, cysts

in the ovaries, trouble getting pregnant and

other health changes. PCOD is linked to

changes in the level of certain female

hormones, estrogen and progesterone, help

a woman's ovaries release egg. Androgen, a

male hormone is found in small amounts in

women. The pathophysiology of PCOD

appears to be multifactorial with multiple

genes and environmental factors

contributing to the disease

susceptibility. Multiple genetic pathways

have been implicated in the pathogenesis of

PCOD, including steroid hormone

metabolism, gonadotropin action, obesity

and energy regulation, and insulin action [2]

.

Normally, one or more eggs are released

during a woman's period, called ovulation.

In PCOD, mature eggs are not released

from the ovaries. Instead, they can form

Correlation between Dietary Pattern, Work Environment and Incidence of PCOD in Working Women

PSGCAS Search: A Journal of Science and Technology Volume: 4 No. : 2 ISSN: 2349 – 5456 2

very small cysts in the ovary. These

changes can contribute to infertility.

Symptoms of PCOD include Secondary

amenorrhea, Virilization, male like

characteristics like body hair growing on

the chest, belly, face, and around the

nipples, decreased breast size, enlargement

of the clitoris, thinning of the hair on the

head called male-pattern baldness and

deepen voice.

The women those who had PCOD, may

have the following common health

conditions like Diabetes mellitus, High

cholesterol level, High blood pressure and

Weight gain and obesity. The woman who

has PCOD would more likely to develop

the complications like Infertility, Breast

cancer (slightly increased risk) and

endometrial cancer [3]

.

Approximately 60% of women with

PCOD have weight management issues

which can lead to obesity with only normal

caloric intake. Energy in the form of

glucose (food) is stored right away as fat,

instead of being made available for other

functions within the body. This can lead to

chronic fatigue and undernourishment,

despite the fact that there is adequate food

intake and even an appearance of over-

nourishment.

However, it is important to note that

40% of women with PCOD are of normal

weight, or even fall under a normal weight

range [4]

.

Weight loss and dietary changes appear

to affect all parameters of hormonal

fluctuation. Follistatin is a single-chain

glycoprotein that is expressed in many

tissues including the ovary, adrenal cortex,

pituitary and pancreas. This polypeptide

specifically binds activin, neutralizing its

biological activity. Activin causes ovarian

follicular development, inhibits theca cell

androgen production, increases pituitary

FSH secretion and pancreatic insulin

secretion. Since follistatin inhibits the

activity of activin, its altered activity due to

over-expression would therefore be

expected to reduce serum follicle

stimulating hormone (FSH), impair ovarian

follicular development, increase ovarian

androgen production and insulin release

which turn out to be characteristic features

of PCOD. Based on its functional

relevance and evidence of strong linkage

with PCOD, the follistatin gene emerged as

the strongest candidate of PCOD. There

have been a limited number of studies that

attempted to identify sequence variants

within this gene and their association with

PCOD. The results of these studies have

been inconsistent [5, 6]

.

Activin promotes ovarian follicular

development, inhibits androgen production

and increases FSH and insulin secretion.

Follistatin, an activin-binding protein,

neutralizes activin bioactivity. Therefore, a

decrease in the ratio of activin/follistatin

Bhuvaneswari , Kanjana K. and Madhan Shankar S R.


might encourage characteristic features of

polycystic ovary syndrome (PCOS) [7]

.

The known association between insulin

resistance and polycystic ovarian disease

(PCOD) has been studied by determination

of the prevalence of a positive family

history of diabetes in a consecutive series of

oligomenorrheic women with polycystic

ovaries and eumenorrheic women with

normal ovaries who served as Non-PCODs.

Paternal and maternal family members

affected were in similar proportions, there

being no evidence of preferential

transmission through the female line in this

study 8.

PCOD is a metabolic disorder affecting

multiple organs. An interdisciplinary

approach of gynaecologists together with

endocrinologists specialized in metabolic

and nutritional disorders is needed to test

whether proposed interventions as weight

loss, treatment of hyper insulinemia,

regulation of menstrual cycle and others can

avoid long-term squeal [9]

. Hence the

present investigation is taken up with the

following objectives:

• To assess the dietary pattern of working

women between age group 20-30years

before joining and after joining for work

• To study the work environment of

subjects

• To test for PCOD in these women by

PCR based methods

• To ascertain a correlation between the

dietary pattern and PCOD levels as

obtained by molecular method

MATERIALS AND METHODS

Eight three working women involved in

Information Technology, administration,

labour, medical coding and Teaching) from

Coimbatore city between the age group of

20- 35 years volunteered to participate in

the study.

Out of 83 subjects, 10 subjects who did

not express having irregular menstrual

cycle and 10 subjects, complainant of

having irregular menstrual cycle, treated as

Non-PCOD (Non-PCOD) and PCOD

(PCOD) subjects respectively were

subjected to blood analysis- PCR

techniques using Case-Non-PCOD study

design.

PCR Technique

The polymerase chain reaction is a

repetitive bidirectional synthesis of DNA

via primer extension of nucleic acid. PCR

technique is used to amplify a region of

DNA between two oligosaccharide primers.

PCR technique was used to find out

whether PCOD gene is present or not for

those who indicated complainant of having

and not having irregular periods from the

past years.

Protocol for DNA amplification:

For the reaction, the mentioned reagents

(Table -1) are added to a PCR tube.



Table 1

Reagents used for DNA amplification

Reagents Quantity

10X Assay Taq Pol Assay

buffer 15 mM MgCl2

5 µl

dNTP Mix 2 µl

Template DNA ( 50 ng/µl) 2 µl

Forward primer (50 ng/µl) 2 µl

Reverse primer (50 ng/µl) 2 µl

Taq DNA Polymerase (3 u/µl) 1 µl

Sterile Water 36 µl

Total Reaction Volume 50 µl

The contents were mixed gently.

The reaction mixture was layered with 50

µl of mineral oil and the amplification was

carried out using the reaction condition

for 30 cycles and the details of which are

given in Table 2. After the reaction got

over, the reaction mix was taken out and

10 ml of the aqueous layer was run in 1%

agarose gel for 1 to 2 hours at 100 volts.

The sample was run along with marker and

the amplified product was located by

comparing with the 0.8 kb fragment of the

marker. Once the amplification was

completed, the sample was loaded on the

agarose gel and subjected to

electrophoresis.

Table 2 Reaction conditions for

DNA amplification

Reaction Tempr

Time

Initial denaturation 94oc 1min

Denaturation 94 o

c 30 secs

Annealing 48 o

c 30 secs

Extension 72 o

c 1 min.

Final extension 72 o

c 2 mins.

Preparation of 1.5 % Agarose Gel and

Set Up of Electrophoresis

• Prepared 1X TAE by diluting

appropriate amount of 50X TAE buffer

with distilled water.

• Took 50 ml of 1X TAE in a 250 ml

conical flask and added 0.75 g of

agarose. Boiled to dissolve agarose (till a

clear solution results).

• Placed the combs of the electrophoresis

set such that the comb was about 2 cm

away from the cathode (black electrode).

• When the agarose gel temperature was

around 60oC, added 2ml of 10mg/ml

ethidium bromide and swirled to mix.

• Poured the warm agarose solution slowly

into the central platform of the gel tank.

• Made sure that the gel was 0.5 to 0.7 cm

thick, without air bubbles. Kept the set

undisturbed till the agarose solidified.

• Once the gel had solidified, poured 1X

TAE buffer slowly into the gel tank till the

buffer level stood 0.5-0.8 cm above the gel

surface.

• Gently lifted the combs. Ensured that the

wells were intact. Connected the power

cords, the red cord to the red electrode and

the black cord to the black electrode.

• Loaded the samples into the well. Set the

voltage to 50 V. Run till the second dye

(purple dye) from the well had reached

3/4th of the gel.



• Viewed under UV trans illuminator and

recorded the observation

Assessment of Nutritional profile

Socio demographic information of the

working women participated in the study

were collected through questionnaire

covering the details regarding working

hours, leisure time, working environment,

dietary pattern before and after joining to

work, and the kind of health problems they

are diagnosed.

The nutritional profile of the selected

subjects were assessed through

anthropometry (weight, height, waist and

hip circumferences), Percent body fat

using Omron digital body fat analyser (bio

elective impedance method), arterial blood

pressure and pulse rate using Digital

Blood pressure monitor and nutrients

intake through 24 hours Recall method).

Body mass index (BMI), a simple health

indicator of body fatness was calculated

from the height and weight of the selected

subjects using the formula9:

BMI= Weight (in kg) / Height (in meters

square)

Waist-Hip Ratio (WHR) 10

of the

subjects was assessed using waist

circumference measured at the umbilicus

and hip circumference measured at the most

extended part of the buttocks. WHR =

Waist circumference/Hip circumference

Dietary intake of the selected subjects

was assessed through 24 hour recall method

and the nutrients intake was calculated

using the food guide of ICMR (2010) 11

.

RESULTS AND DISCUSSION

Demographic profile of selected subjects

Majority of the subjects were from the

age group of 20-25 years (72.28%),

whereas more than 14% of the subjects

were from 26-30 years (16.86%) and 31- 35

years (10.84%) age groups. Highest

percentage (48.19%) of the subjects

responded was from information

technology. There were 66.26% of married

and 33.73% unmarried working women.

Dietary pattern

Most of the subjects were Non-

vegetarians (85.54%) and negligible

numbers of subjects were vegetarians

(7.22%), ova- vegetarians (6.02%) and

lacto-ova vegetarians (0.01%). 55.42% of

the subjects were having their meals 3 times

per day; 40.96 % of the subjects followed

two meals a day; and 3.61% of the subjects

were having only single meal per day.

Higher percentage of the subjects was

found to be skipping their meals (53.01%).

Most of the working women preferred

meals which are prepared at home (48.19%)

and secondly, they preferred fried foods

(21.68%). However 8.43% preferred to

have foods outside their home.

Work environment and Meal

Majority of the selected women were

involved in working for 8-10 hours

(57.83%) and 28.91% of them were



working below 8 hours (28.91%). 55.42%

of the selected women were having leisure

time and 44.57% of them were not having

leisure time. Out of the subjects who had

leisure time, maximum (36.95%) number

had duration of 15mins whereas 23.91% of

the subjects had only 20 minutes of leisure

duration.

Before joining to work, majority of the

selected subjects took breakfast (96.38%),

lunch (96.38%) and dinner (96.38%) at

home. Even after joining to work, most of

the selected subjects had their breakfast

(56.62%), lunch (40.96%) and dinner

(57.83%) at home but exhibiting 30-40%

reduction; however 37.34% of the selected

subjects had their breakfast from hostel

mess, more than 30% had their lunch at

office canteen (31.32%) and dinner

(32.53%) in the hostel.

Past Medical History

Majority of the selected women were

found to have stress (62.65%), followed by

back pain (60.24%) and spinal pain

(34.93%). The next 39.75% and 30.12% of

the selected subjects were obese and

hypertensive respectively. 24.09% of them

were found to be suffering from irregular

periods and surprisingly none of the

selected women had Diabetes mellitus.

Nutritional Profile of selected subjects

based on age category

The selected subjects from different age

groups were found to be significantly

differing on Weight (F = 3.637**), waist

circumference (F = 4.974**) and diastolic

pressure (F=5.259**). There is a significant

difference between the intake of fried

snacks (F=7.867**

) and fried salty snacks

(F= 6.074**

) when analysed based on age

category. There is no significant difference

between the nutrient intakes based on age

category.

PCR Technique - Banding Pattern based

on PCR amplification

The PCOD subjects who volunteered

(N=10) to undergo DNA profile PCOD

(N=10) were studied for the demographic

profile, occupation category, Dietary

pattern, BMI category, Skipping meals, Fat

category, Irregular menstrual cycle and Past

medical History along with DNA profile

(Interleukin 1β polymorphisms) and

compared with that of Non-PCOD group

(Table 4). Banding pattern obtained in Non-

PCOD and PCOD groups based on PCR

amplification is presented in Figure 1 and 2.

Table 4 Degree of polymorphism for Non-PCOD and PCOD samples

Selected samples Degree of polymorphism band

Non-PCOD 64%

PCOD 66.65%

Bhuvaneswari

PSGCAS Search: A Journal of Science and

Figure1

Figure 2

62%

64%

66%

68%

Non

Pe

rce

nta

ge

(%)

Figure3


PSGCAS Search: A Journal of Science and Technology Volume: 4 No. : 2 ISSN: 2349 – 5456

Result of PCR for Non-PCOD samples

Figure 2 Result of PCR for PCOD samples

Non-PCOD PCOD

64%

66.65%

Selected samples

Degree of polymorphism

band

Non-PCOD

PCOD

5456 7

PCOD



Interleukin-1 beta polymorphism is a

way to locate many disease incidences.

The higher the degree of polymorphism

indicated the greater the chance of

progression through the disease. In the

present study, Non-PCOD sample (N=10)

showed a 64% degree of polymorphic

bands when compared to PCOD (N=10)

which showed a slightly higher degree of

polymorphism (i.e.) 66.5%. This pattern

can be correlated to the dietary pattern

amongst the individuals.

Degree of polymorphism as per age group

The majority of the selected Non-

PCOD (70%) and PCOD (80%) samples

belonged to the age group of 20-25 years

which revealed that the individuals in this

age group inhibited greater degree of

polymorphisms which indicate that this

age group is more vulnerable for more

erratic food habits which could culminate

in incidence of being obese.

Degree of polymorphism as per marital

status

Majority of the selected Non-PCOD

(90%) and PCOD (60%) individuals were

unmarried which showed that they had a

higher degree of polymorphism. 40% of

the PCOD group and 10% of the Non-

PCOD group individuals were married.

Though they were married, they are also

had an equal degree of the same which can

lead to ovarian disorders. Despite the food

habit of married / unmarried, which

showed almost equal probability of

indicating a higher degree of

polymorphism bands which refers the

impact of eating habits does have

substantial influence on the chance of

processing symptoms related to PCOD or

similar ovarian disorders even before

marriage.

Degree of polymorphism as per

Occupation

Most of the Non-PCOD (40%) and

PCOD (70%) group of IT professionals

were showed a higher degree of

polymorphisms; whereas equal percentage

of both Non-PCOD and PCOD samples of

administrative workers showed higher

degree of the same; 20% of the selected

subjects who were working as lecturers

also had the higher degree of

polymorphism. This outcome showed that

IT professionals had a higher chance of

incidence of PCOD. Added to the changes

in food patterns among individuals;

occupational stress could also be a

cumulative factor which strictly dictate the

incidence of PCOD. The results shown are

chemical representations of these

phenomena.

Degree of polymorphism as per dietary

pattern

Most of the Non-PCOD (90%) and

PCOD (70%) subjects were non

vegetarians indicating the possibility of

polymorphism irrespective of subjects



complaining of having or not having

irregular menstrual disorders.

Degree of polymorphism as per BMI

20% of selected Non-PCOD and 60%

of PCOD samples belonging to overweight

group showed that they have higher degree

of polymorphism. Hence, it can be

understood that these selected samples

were at the risk of incidence of PCOD.

Degree of polymorphism as per skipping

meals

50% and 70% of the Non-PCOD and

PCOD samples were found to be skipping

their meals respectively whereas 50% and

30% of the Non-PCOD and PCOD groups

did not skip their meals. The risk of

incidence of PCOD was exhibited among

the subjects who skipped meals frequently.

Degree of polymorphism based on fat

intake

Majority of the Non-PCOD (10%)

and PCOD (30%) subjects were found to

have consumed 45-55g of fat per day

which showed that consumption of high

fat foods could eventually lead to

incidence of PCOD. In the present study, it

is deciphered that an average of 45-55g of

fat consumption per day would increase

the chance of incidence of being PCOD

positive.

Degree of polymorphism based on

irregular menstrual cycle

All the selected PCOD individuals had

irregular periods which correlate with the

pattern of polymorphism obtained

herewith.

Degree of polymorphism as per Past

medical history

Obese subjects (60%) expressed

irregular menstrual disorders and were

likely to show higher degree of

polymorphism; whereas equal percentage

of both Non-PCOD and PCOD subjects

having the past medical history of

hypertension and spinal pain also showed

equal degree of the same; and anxiety

present in 100% of the Non-PCOD

subjects represented higher degree of

polymorphism.

Factors contributing Polymorphism

Figure 4 reveals that age (70%),

anxiety (100%), increased fat intake (10%)

and dietary pattern (Non vegetarian) (90%)

are the factors that contribute towards a

greater degree of polymorphism among the

individuals in Non-PCOD groups.

Figure 5 indicates that age (80%),

increased stress (30%), irregular menstrual

cycle (100%), dietary pattern (Non

vegetarian) (70%) and Obesity (60%) are

the factors contributing towards a higher

degree of polymorphism among the

individuals in PCOD group.

Correlation between Nutritional Profile

and Degree of Polymorphism

In general positive relationship was

observed between “% body fat (r = 0.506*)

and hip circumference(r = 0.458*)” and

Correlation between Dietary Pattern, Work Environment and

PSGCAS Search: A Journal of Science and Technology

Fig. 4 Factors contributing to higher degree of polymorphism among

Fig. 5 Factors contributing to higher degree of polymorphism among

“degree of polymorphism”; whereas

negative relationship was found between

“WHR (r = -0.012) and degree of

polymorphism”.

Dietary Pattern

(Non veg)

90%

Dietary Pattern

(Non veg)

70%

Obesity

60%


PSGCAS Search: A Journal of Science and Technology Volume: 4 No. : 2 ISSN: 2349 – 5456

Factors contributing to higher degree of polymorphism among Non-PCOD

Factors contributing to higher degree of polymorphism among PCOD

“degree of polymorphism”; whereas

negative relationship was found between

0.012) and degree of

To summarize, Consumption of

vegetarian food against the regular meal

pattern, Occupational stress, Consumption

of high fat foods and skipping of regular

meals and Age related diet


Age (20-25 yrs)

70%

Anxiety

100%

Increased fat intake

(10%)


Age (20-25 yrs)

80%

Increased stress

30%

Irregular menstrual

cycle

100%

Dietary Pattern

(Non veg)

70%

Incidence of PCOD in Working Women

5456 10

PCOD group

PCOD group

To summarize, Consumption of non-

vegetarian food against the regular meal

pattern, Occupational stress, Consumption

of high fat foods and skipping of regular

meals and Age related dietary/life style

Anxiety



changes could contribute to the incidence

of PCOD in working women and it can be

concluded that all the above mentioned

factors would render working women to

fall into a high risk group with respect to

the incidence of PCOD; which can have a

post-marital impact on reproductive health

of the women.

REFERENCES

1. Jones MR., Wilson SG., Mullin BH.,

Mead R., Watts GF. and Stuckey BG.,

2007. Polymorphism of the follistatin

gene in polycystic ovary syndrome. Mol

Hum Reprod. 13:237-41.

2. Homburg R., 2002. What is polycystic

ovarian syndrome? A proposal for a

consensus on the definition and

diagnosis of polycystic ovarian

syndrome. Hum Reprod. 17:2495-9

3. Ferriman D, Gallwey JD, 1961. Clinical

assessment of body hair growth in

women, J Clin Endocrinol Metab. 21:

1440-7.

4. Crosignani PG., Colombo M., Vegetti

W., Somigliana E., Gessati A. and Ragni

G., 2003. Overweight and obese

anovulatory patients with polycystic

ovaries: parallel improvements in

anthropometric indices, ovarian

physiology and fertility rate induced by

diet. Hum Reprod. 18(9): 1928-32.

5. Urbanek M., Wu X., Vickery KR., Kao

LC., Christenson LK., Schneyer A. et

al., 2000. Allelic variants of the

follistatin gene in polycystic ovary

syndrome, J Clin Endocrinol Metab. 85:

4455-61.

6. Eldar-Geva T., Spitz I.M., Groome

N.P., Margalioth E.J. and Homburg, R.,

2001. Follistatin and Activin A serum

concentrations in obese and non-obese

patients with polycystic ovary syndrome. Dec;16(12): 2552-6

7. Fox R., 2002. Prevalence of a positive

family history of type 2 diabetes in

women with polycystic ovarian disease,

Gynecol Endocrinol. 13 (6):390-3.

PubMed PMID: 10685332

8. Heim SC., De Geyter C., Siegrist W., Bilz S.

and Keller U., 2003. Polycystic ovary

syndromeonly relevant in reproductive

medicine? Ther Umsch. 56(5): 271-5.

PubMed PMID: 10409902

9. WHO, 2000. Obesity: Preventing and

managing the global Endemic of WHO

Technical Report Series in 2000. The

International obesity task force of WHO.

10. WHO, 2008. Waist circumference and

waist–hip ratio: Report of a WHO expert

consultation, Geneva, 8–11. December

2008.

11. Gopalan C., Rama Sastri B.V and

Balasubramanian S. C., 1989. Revised and

updated by Narasingha Rao., Deosthale YG

and Pant KC., 1989. Nutritive Value of

Indian Food. NIN, ICMR, India.

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Correlation between Dietary Pattern, Work Environment and ... · amenorrhea, Virilization, male like characteristics like body hair growing on the chest, belly, face, and around the