PREVALANCE OF METABOLIC SYNDROME IN

PATIENTS WITH PCOS

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1 INTRODUCTION

2 REVIEW OF LITERATURE

3 AIMS & OBJECTIVES

4 MATERIALS & METHODS

5 RESULTS

6 DISCUSSION

7 CONSULATION

8 LIMITATIONS

9 BIBLIOGRAPHY

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List of Tables

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Glossary Abbreviations

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INTRODUCTION

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INTRODUCTION

Polycystic Ovary Syndrome (PCOS) is one of the most common endocrine disorder affecting

adolescent girls and women of reproductive age1. It also referred as Stein-Leventhal syndrome.

Rotterdam criteria for diagnosis of PCOS include the presence of any two of the following

criteria: clinical or biochemical hyperandrogenism, polycystic ovaries on ultrasonography,

anovulation or oligomenorrhea. The basic pathology lies in deregulation of enzyme cytochrome

P-450-17-a which is present in ovaries and adrenals. Clinically, hyperandrogenism generally

manifests as acne and hirsutism. Hirsutism can be assessed by Ferryman–Galway (FG) scores of

≥8. Biochemically, hyperandrogenism is manifested by increase in serum testosterone level.

PCOS also contains metabolic component comprising of insulin resistance and hyperinsulinemia

with increased cardiovascular risks which occurs in both lean and obese women. The symptoms

and severity of the syndrome vary greatly by factors such as obesity, genetic, racial

predisposition, geographic diversity and lifestyle factors. It has a strong association with the

metabolic syndrome2-5.

Metabolic syndrome also known as Syndrome X,is another cluster of endocrine disturbances,

including insulinresistance , dyslipidemia, obesity, and hypertension6. It is associated with a two-

fold increased risk of cardiovascular disease and a five-fold increased risk of type 2diabetes7.

This illustrates the importance of early detection of insulin resistance and metabolic syndrome

with subsequent application of preventive measures in women with polycystic ovary syndrome.

The original National Cholesterol Education Programme – Adult Treatment Panel III(NCEP –

ATP ) criteria in 2001 define metabolic syndrome as the co-occurrence of three or more of theⅢ

6

following risk factors(i) central obesity with waist circumference≥88 cm in women, (ii) elevated

systolic and/or diastolic blood pressure of ≥130/85mmHg, (iii) impaired fasting serum glucose

≥110 mg/dL, (iv) elevated fasting serum triglycerides ≥150 mg/dL, and (v) fasting high-density

lipoprotein (HDL) cholesterol<50 mg/dL.8

The main changes in the modified American Heart Association/National Heart Lung and Blood

Institute definition (ATP III 2005) include (i) defining the ethnic specific difference in central

obesity by using theWorld Health Organization recommendation for waist circumference ≥80 cm

in Asian women, and (ii) and reducing the threshold for impaired fasting glucose to 100 mg/dl in

accordance with the American Diabetes Association revised definition.7

The prevalence of metabolic syndrome in polycystic ovary syndrome has been studied in

different populations. Reported prevalence is 43% in U.S., 37.9% in Indian women, 28.4% in

Brazil, 24.9%in Hong Kong Chinese women,19.7% in Iranian women, 19.1% in Chinese

women, 14.5% in Korean women, 8.2% in Italian women and only 1.6% in Czech women9-17.

These variations in data across the globe, increased incidence of metabolic syndrome in the

Indian population and also the scanty data available about the prevalence of metabolic syndrome

in PCOS across the India pointed out the necessity in metabolic syndrome evaluation in different

population. This could help the patients get screened earlier to prevent long term effects. Hence,

the objective of the study was to investigate the prevalence of metabolic syndrome in patients

with PCOS .

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AIMS & OBJECTIVES

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AIMS AND OBJECTIVES:

The aim is to determine the prevalence of Metabolic syndrome (MS) in patients diagnosed with

Polycystic ovarian syndrome (PCOS).

OBJECTIVES OF THE STUDY

To evaluate patients with PCOS for Metabolic syndrome

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REVIEW OF LITERATURE

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REVIEW OF LITERATURE

What is PCOS definition and diagnostic criteria:

Polycystic Ovarian Syndrome (PCOS), also referred to as hyperandrogenic anovulation (HA), or

Stein–Leventhal syndrome18, is one of the most common endocrine system disorders that affect

women in their reproductive age.19

Evidence suggests that PCOS phenotype may vary widely and is most commonly observed in

the post-pubertal period.20 Despite a diversity of phenotypes, women with PCOS are

characterized by polycystic ovaries, chronic anovulation, hyperandrogenism and gonadotropin

abnormalities.21

PCOS is an abnormality in the ovaries is the primary cause of the disorder, but additional

agents, such as obesity and environmental factors, affect the development of individual

symptoms.22

Diagnostic Guidelines:

Diagnosis of PCOS in adults can follow three different guidelines. Even though conditions such

as insulin resistance and obesity are considered intrinsic to PCOS, none of them is included in

the guidelines and should therefore be used for diagnostic purposes.23 Each of the guidelines

requires ruling out any pathological condition that might explain the hyperandrogenism or the

menstrual irregularity.24

NIH/Rotterdam/AE-PCOS Society diagnostic criteria:

In 1990, a group of investigators who attended a National Institutes of Health (NIH) sponsored

conference defined polycystic ovary syndrome (PCOS) as hyperandrogenism and/or

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hyperandrogenemia (HA) with oligoanovulation, excluding other endocrinopathies (on the basis

of a consensus questionnaire) . 25

In 2003, however, the Rotterdam consensus (based on closed session consensus among primarily

European and American investigators)expanded the diagnostic criteria to include at least two of

the following features: 1) clinical or biochemical hyperandrogenism 2) oligo-anovulation; and 3)

polycystic ovaries(PCO), excluding the other endocrinopathies . 26

An Expert Panel from the 2012 NIH Evidence-based Methodology Workshop on PCOS

recommended that clinicians use the more recent Rotterdam criteria for diagnosis

(3).Consequently, the 6–10%prevalence of PCOS(as defined by 1990 NIH criteria) has doubled

under the broader Rotterdam or Androgen Excess-PCOS Society criteria 27,with 1990 NIH-

defined PCOS being the most common phenotype . The increased prevalence of PCOS with the

Rotterdam criteria is due to the expansion of the syndrome to include women without

documented ovulatory dysfunction or hyperandrogenism, but who have PCO.19, 27

Women with 1990 NIH-defined PCOS (with hyperandrogenism and oligo-ovulation) are at

increased risk of developing reproductive and metabolic abnormalities, including infertility and

type 2 diabetes mellitus(T2DM), respectively. Although insulin resistance and obesity are

commonly found in women with PCOS, they are not part of the diagnostic criteria. Ovulatory

women with PCOS have a lower body mass index (BMI) and lesser degrees of hyperinsulinemia

and hyperandrogenism than women with 1990 NIH-defined PCOS. Women with PCO and oligo-

anovulation (without androgen excess) are least affected and do not fulfill the diagnosis of PCOS

by the Androgen Excess-PCOS Society (again based on expert consensus within the society),

which, like the 1990 NIH criteria, emphasizes hyperandrogenism.28, 29

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Exclusion of other endocrinopathies: To properly diagnose PCOS, clinicians need to exclude

other endocrinopathies that mimic PCOS. These disorders include no classic adrenal hyperplasia,

Cushing’s syndrome, androgen-producing tumors, and drug-induced androgen excess. In

addition, clinicians should rule out ovulatory dysfunction from other causes, including thyroid

dysfunction and hyperprolactinemia, as well as pregnancy in reproductive-aged women.25

Burden of PCOS–global:

The prevalence of PCOS is traditionally estimated at 4% to 8% from studies performed in

Greece, Spain and the USA.30, 31 The prevalence of PCOS has increased with the use of different

diagnostic criteria and has recently been shown to be 18% (17.8 ± 2.8%) in the first community-

based prevalence study based on current Rotterdam diagnostic criteria.32 Importantly, 70% of

women in this recent study were undiagnosed.32 While the upper limit of prevalence for this

study was imputed using estimates of polycystic ovaries(PCO) for women who had not had an

ultrasound, non-imputed prevalences were calculated as 11.9 ± 2.4% . PCOS has also been noted

to affect 28% of unselected obese and 5% of lean women .32 33In 2006, based on US data and

traditionally lower prevalence estimates the anticipated economic burden of PCOS in Australia

wasAU$400 million (menstrual dysfunction 31%, infertility12% and PCOS-associated diabetes

40% of total costs), representing a major health and economic burden .33With regards to fertility,

the estimated cost per birth in overweight Australian women with PCOS is high. Promisingly,

lifestyle intervention comprising dietary, exercise and behavioral therapy improves fertility and

reduces costs per birth significantly . 34

Burden of PCOS –India:

Media accounts have suggested that PCOS is on the rise in India and most prevalent

among the urban middle and upper classes because of their lifestyles. The prevalence of obesity,

overweight, and insulin resistance, which are all associated with PCOS pathogenesis, appear to

15

be higher among members of higher socioeconomic strata living in urban areas; medical

researchers have attributed this to more sedentary lifestyles and access to more calorie-dense

foods and labor saving devices in urban and higher socioeconomic populations.35, 36 Although

there have been no rigorous community-based epidemiological studies of PCOS published in

India to date, a preliminary study among women aged 15 to 24 from the lower socioeconomic

strata in Mumbai placed prevalence at near 22.5 percent (around one in four women).37The

authors of the study further observed that given trends in other metabolic disorders and

nutritional and physical activity patterns, this prevalence might be even higher among the higher

socioeconomic strata. This contrasts quite markedly with a prevalence of around 11 percent in

Australia, 8 percent in the UK, and 4 percent in the US, using similar diagnostic criteria.38 A

prominent gynecologist conducting an epidemiological study of PCOS which compared

prevalence across India was interviewed regarding whether the reported rise might be an artifact

of improved diagnosis, increased clinician focus, or shifting health concerns in the population.

Based on preliminary findings from a year of research, the gynecologist rejected these factors as

the primary drivers and confirmed the trend of higher PCOS prevalence among urban middle-to

upper middle-class women. Irregular menstrual cycles and subfertility have long been of

considerable concern in India,39 and a common reason for medical consultation from both

allopathic and ayurvedic practitioners (vaidyas). These practitioners routinely ask patients about

symptoms encompassed by PCOS, with allopaths inquiring about them for decades and vaidyas

treating them as basic to their humoral science. The yet-to-be published research as well as the

observations of several such practitioners interviewed (sample described below) indicated that

PCOS was rapidly increasing among urban middle- and upper-class women and that with

16

increasing prosperity, it was beginning to increase among women from the urban lower classes

and those living in urbanizing semi-rural areas.

Clinical spectrum of PCOS:

Although PCOS has been traditionally considered a disorder that affects women in their

reproductive years, clinical manifestations may be observed at menarche.In addition, clinical

complications vary according to different phenotypes, age, ethnicity and body weight.40

According to research studies, the classical PCOS phenotype is linked to

hyperandrogenism, anovulation and polycystic ovaries. Symptoms usually worsen with

time.41Among these characteristics, hyperandrogenism is considered a cardinal element for

diagnosing this condition and to define a patient as hyperandrogenic may be of major clinical

significance.42The clinical manifestation of hyperandrogenism in these women varies in different

ethnic groups, with external manifestations like oily skin, acne, hirsutism, central obesity, and

even androgenetic alopecia.43

The cardiovascular system of women with PCOS is affected, regardless of obesity, due

to metabolic disturbance associated with the respective syndrome.44 Factors such as

dyslipidemia, diabetes and obesity are all potent risk factors for cardiovascular disease,

explaining why women with PCOS are more predisposed to hypertension.45

Clinical manifestations of PCO include menstrual irregularities, signs of androgen

excess, obesity, and sometimes hirsutism.

Hirsutism is defined as excessive growth of terminal hair in women with male-like pattern and it

is the most commonly used clinical diagnostic criterion of hyperandrogenism. The most widely

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recognized scoring method for hirsutism is the Ferriman- Gallwey scale. Hirsutism is defined as

a score of 8 or more on the modified Ferriman-Gallway index .46

A score of 1 to 4 is given for nine areas of the body. A total score less than 8 is considered

normal, a score of 8 to 15 indicates mild hirsutism, and a score greater than 15 indicates

moderate or severe hirsutism.

Oligomenorrhea is also one of the clinical manifestations of PCOS. Oligo/amenorrhea cycles are

defined as 8 or less cycles per year and biochemical androgen measurements should be fulfilled

in follicular. phase in patients with preserved menstrual cycles .20 The clinical manifestations of

PCOS are heterogeneous and it looks possible that patients may present some of various

18

symptoms and signs. The heterogeneity seems to be adjusted by several factors, such as genetic

factors, nutritional condition in the uterus, prenatal androgen exposure, insulin resistance,

exaggerated adrenarche, and body weight changes.47

Environmental status and factors, such as obesity, appear to exacerbate the underlying genetic

predisposition. PCOS is characterized by increased levels of circulating androgen, polycystic

ovarian morphology (PCOM), arrested follicle development, and anovulatory infertility. PCOS is

commonly associated with insulin resistance, hyperinsulinemia, components of the Metabolic

Syndrome, and oligo anovulatory cycles.27, 48Although some of the clinical symptoms and

presentations of PCOS is dependent on age, ovarian failure and hyper androgenism (HA) are

common characteristics at any age. 48Although the pathogenesis of PCO syndrome is unknown,

but it is believed that PCO is the result of different interactions between genetic and multiple

environmental factors. This syndrome is a multi-factorial disease, and the different susceptibility

of patients is probably determined by several genetic and environmental.49

Pathophysiology of PCOS:

Previous Hypotheses: Many hypotheses emerged trying to explain the pathophysiology of

PCOS. Initially, excess intrauterine androgen had been thought to be a main culprit in the

development of the disease. Yet recently, human studies showed neither an association between

excessive prenatal androgen exposure and the development of PCO Sinyouth50 nor an elevation

in androgen levels in the cord blood of females born to mothers with PCOS.51 Another

hypothesis, the adipose tissue expandability hypothesis, suggested that infants with intra-uterine

growth restriction (IUGR) and spontaneous catch-up growth might develop decreased tissue

expandability, meaning that they cannot store lipids appropriately in their fat tissues.

Consequently, insulin resistance might ensue contributing to PCOS and hyperandrogenemia.52

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However, this does not apply for patients with PCOS who did not have IUGR or had it but

without spontaneous catch up growth

A Multi-faceted Disease: The best understanding of the pathophysiology of PCOS deals with it

as a multifaceted disease involving uncontrolled ovarian steroidogenesis, aberrant insulin

signaling, excessive oxidative stress, andgenetic/environmental factors. An intrinsic defect in

theca cells can partially explain the hyperandrogenemia in patients with PCOS. Indeed, women

with PCOS have thecacells that, still secrete high levels of androgens due to an intrinsic

activation of steroidogenesis even in the absence of trophic factors.53This intrinsic dysregulation

also affects granulosa cells which produce up to 4 times higher levels of anti-mullerian hormone

in women with PCOS in comparison to healthy controls. 54Studies also show an elevated number

of follicles, primarily pre-antral and small antral follicles, in females with PCOS.55 A defect in

apoptotic processes in some maturing follicles further increases their count in PCOS patients.56

Alternatively, decreased insulin sensitivity attributable to a post receptor binding defect in the

insulin signaling pathways has been identified as an intrinsic component of PCOS, independent

of obesity. It was also reported an alteration in gene expression of some players in insulin

signaling pathways by microarray gene analysis.57 Moreover, PCOS has been associated with

increased glycol oxidative stress secondary to mitochondrial dysfunction. Oxidative stress can

itself induce insulin resistance and hyperandrogenism in patients with PCOS. 58

Familial aggregation of PCOS19 and genomic identification of PCOS-susceptibility loci 59support

the role of genetics in the etiology of this disease. Some studies showed an inherited component

of androgen excess in patients with PCOS.60 Furthermore, a polymorphic marker in fibrillin 3

gene associated with PCOS, D19S884, has been identified in independent sets of families

carrying the disease. 61

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Metabolic syndrome:

MetS is defined by a constellation of an interconnected physiological, biochemical, clinical, and

metabolic factors that directly increases the risk of atherosclerotic cardiovascular disease

(ASCVD), T2DM, and all-cause mortality .62 This collection of unhealthy body measurements

and abnormal laboratory test results include atherogenic dyslipidemia, hypertension, glucose

intolerance, proinflammatory state, and a prothrombotic state. There have been several

definitions of MetS, but the most commonly used criteria for definition at present are from the

World Health Organization (WHO).63

Worldwide prevalence of MetS ranges from <10% to as much as 84%, depending on the region,

urban or rural environment, composition (sex, age, race, and ethnicity) of the population studied,

and the definition of the syndrome used.64 In general, the IDF estimates that one-quarter of the

world’s adult population has the MetS.65 Higher socioeconomic status, sedentary lifestyle, and

high body mass index (BMI) were significantly associated with MetS.

According to the new IDF definition, for a person to be defined as having the metabolic

syndrome they must have 66:

Central obesity -defined as waist circumference ≥ 94cm for men and ≥ 80cm for women (Indian

population).

Plus, any two of the following four factors:

1) Raised TG level: ≥ 150 mg/dl (1.7mmol/l) or specific treatment for this lipid abnormality.

2) Reduced HDL cholesterol: < 40 mg/dl (1.03mmol/l) in males and < 50 mg/dl (1.29mmol/l) in

females, or specific treatment for this lipid abnormality.

3)Raised blood pressure: systolic BP ≥ 130 or diastolic BP ≥ 85 mm Hg,or treatment of

previously diagnosed hypertension

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4)raised fasting plasma glucose (FPG) ≥ 100 mg/dl (5.6mmol/l), or previously diagnosed type 2

diabetes If above 5.6mmol/l or 100 mg/dl.

The metabolic alterations occur simultaneously more frequently than would be expected by

chance and the concurrence of several factors increases cardiovascular risk over and above the

risk associated with the individual factors alone. The risk increases with the number of MetS

components present.67

MetS is a state of chronic low-grade inflammation as a consequence of complex interplay

between genetic and environmental factors. Insulin resistance, visceral adiposity, atherogenic

dyslipidemia, endothelial dysfunction, genetic susceptibility, elevated blood pressure,

hypercoagulable state, and chronic stress are the several factors which constitute the syndrome.

Because PCOS and the metabolic syndrome share insulin resistance as an important element in

their pathophysiology, there is much overlap between the two clinical arenas.

The concept of metabolic syndrome (MetS) as defined by a cluster of risk factors including

dysglycemia, central obesity, hypertension and dyslipidemia, is useful in predicting those at risk

for cardiovascular disease and diabetes .68

Several cohort studies have been conducted in United States, Europe and East Asia to determine

the incidence of MetS and its possible risk factors. 69Data reported by different studies on the

predictive powers of MetS components as well as obesity and baseline insulin for incident MetS

are not consistent .69 However, no report about the long term incidence of MetS has yet been

published from West Asian countries with rapid economic and nutritional transitions leading to

high prevalence of risk factors for MetS including obesity .70 Importantly, more than 30% of The

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Iranian population suffers from MetS, the prevalence of which is significantly higher among

women than in men .71

Recently, some studies have found sex differences in risk predictors of MetS, suggesting that sex

hormone levels and androgen/estrogen balance may play an important role in determining MetS.

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The definitions are based on expert opinion and not on evidence resulting from prospective

epidemiologic studies. Therefore, it remains unclear whether single component features of the

MetS or the thresholds at which each component is defined as present or absent are informative

and optimal for predicting the risk of cardiovascular disease or early death.73 Weiss and

colleagues reported that the prevalence of the MetS increased with the severity of obesity and

reached a proportion of 50% in severely obese youngsters. The prevalence of the MetS increased

significantly with increasing insulin resistance after adjustment for the ethnic group factors and

degree of obesity.74 Type 2 diabetes and the MetS have been regarded as a disease of adults for a

long time,75 but it has clearly become evident that the disease can begin at different ages in all

ethnic groups and can be already present in childhood.76

 Insulin resistance: a conceptual prologue

Insulin is a pleiotropic molecule that has effects on amino acid uptake, protein synthesis,

proteolysis, adipose tissue triglyceride lipolysis, lipoprotein lipase activity, very low-density

lipoprotein (VLDL) triglyceride secretion, muscle and adipose tissue glucose uptake, muscle and

liver glycogen synthesis, and endogenous glucose production. Individuals are generally defined

as insulin sensitive or insulin resistant by their response to an oral or iv glucose or insulin

stimulus. Characteristics of the insulin-sensitive phenotype include a normal body weight

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without abdominal or visceral obesity , being moderately active ,and consuming a diet low in

saturated fats. Alternatively, insulin-resistant individuals demonstrate impaired glucose

metabolism or tolerance by an abnormal response to a glucose challenge, elevated fasting

glucose levels and/or overt hyperglycemia, or reductions in insulin action after iv administration

of insulin (euglycemic clamp technique) with decreased insulin-mediated glucose clearance

and/or reductions in the suppression of endogenous glucose production. In general, the

characteristics of this phenotype are more likely to include being overweight or obese ,being

sedentary ,and consuming a diet high in total or saturated fats.

Insulin sensitivity, however, is not a simple dichotomy of being insulin sensitive or insulin

resistant, but rather exists on a continuum. Moreover, the ability of the pancreas to secrete insulin

in response to a glucose challenge may also reflect insulin resistance at the level of the β-cell. To

define this, Bergman (Reference)proposed the disposition index, a quantitative measure that

describes the relationship between β-cell sensitivity and insulin sensitivity. In metabolically

normal individuals, changes in insulin sensitivity are accompanied by compensatory alterations

in the response of the β-cell to glucose. In practice, disposition index is defined as the product of

the insulin sensitivity index and β-cell function as measured by the acute insulin response to

glucose, a relationship that is typically plotted as an inverse hyperbola. The movement along the

continuum is more complicated than the model implies, and the molecular mechanism(s) by

which insulin sensitivity and β-cell function are coregulated to create a homeostatic environment

are not well understood.

It has been suggested that PCOS may be the female-specific manifestation of the metabolic

syndrome. In recognition of this concept, Sam and Dunaif proposed that PCOS be called

“Syndrome XX.”77Using the National Cholesterol Education Program Adult Treatment Panel

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(NCEP ATP III) criteria for the diagnosis of the metabolic syndrome, Glueck and colleagues

found the incidence of the metabolic syndrome in newly diagnosed PCOS patients to be 46%.78

Likewise, in a retrospective chart review of PCOS patients diagnosed over a 3-year period,

Apridonidze, T et al found the prevalence of the metabolic syndrome to be 43%, nearly twofold

higher than the age-adjusted prevalence rate of 24% in women in the general population.79 This

twofold difference in prevalence remained at all ranges of body mass index (BMI), suggesting

that PCOS itself (and not the obesity) causes an increased risk of the metabolic syndrome,

presumably through the insulin resistant pathways special to PCOS. Of the PCOS patients in this

study, 91% had at least one abnormality of the metabolic syndrome and other than elevated BMI,

69% had two or more of the metabolic syndrome abnormalities. The metabolic syndrome

abnormalities found in order of decreasing frequency included low high-density lipoprotein

cholesterol level (68%), elevated BMI (67%), high blood pressure (45%), hypertriglyceridemia

(35%), and high fasting serum glucose level (4%).

Is the insulin resistance found in PCOS separate and distinct from other insulin resistant states,

such as obesity or type 2 diabetes mellitus? Studies of the molecular mechanism of PCOS insulin

resistance suggest that there is a difference. Insulin receptors isolated from cultured PCOS skin

fibroblasts (in 50% of patients tested) exhibit a decrease in insulin receptor tyrosine kinase

activity secondary to increased receptor serine phosphorylation, apparently due to a serine kinase

extrinsic to the receptor. This signaling defect affects metabolic but not mitogenic actions of

insulin.2 Recent follow-up studies in cultured skeletal muscle have shown these defects to be

acquired, rather than intrinsic.80 Adipocytes from women with PCOS also exhibit abnormalities

in regulation of lipolysis by showing an enhanced sensitivity to catecholamine-induced

lipolysis.81 Additionally, a decrease in expression of glucose transport proteins in PCOS

25

adipocytes has been shown. These adipocyte defects are independent of obesity and can

contribute to glucose intolerance by increasing portal free fatty acids, which stimulate hepatic

glucose production.82

Pathophysiology of PCOS:

Fig 1: Pathophysiology of PCOS

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HRT = Hormone Replacement Therapy; PCOS = Poly Cystic Ovary Syndrome 79

Fig 2: Risk factors for Metabolic Syndrome

ASSOCIATION BETWEEN PCOS AND METABOLIC SYNDROME

Most relevant studies global:

Bil, E. et al83 (2016) compared the incidence of metabolic syndrome (MetS) and metabolic risk

profile (MRP) for different phenotypes of PCOS. A total of 100 consecutive newly diagnosed

PCOS women in a tertiary referral hospital were recruited. Patients were classified into four

phenotypes according to the Rotterdam criteria, on the presence of at least two of the three

criteria hyperandrogenism (H), oligo/anovulation (O) and PCO appearance (P): phenotype A, H

+ O + P; phenotype B, H + O; phenotype C, H + P; phenotype D, O + P.Prevalence of MetS and

MRP were compared among the four groups. Based on Natural Cholesterol Education Program

Adult Treatment Panel III diagnostic criteria, MetS prevalence was higher in phenotypes A and

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B (29.6% and 34.5%) compared with the other phenotypes (10.0% and 8.3%; P < 0.001).Based

on the results, he concluded that phenotypes A and B had the highest risk of MetS among the

four phenotypes, and visceral adiposity index (VAI) may be a predictor of metabolic risk in

PCOS women.

Boyle, J. A.et al84 (2015) explored the impact of PCOS on metabolic syndrome in indigenous

Australian women.A cross-sectional reproductive health questionnaire, biochemical and

anthropometric assessments, of 109 Indigenous women (35 with PCOS and 74 without PCOS)

aged 15-44 years.In his study, he found that Women with PCOS had a significantly higher body

mass index (BMI) (P = 0.0001) and MetS was more frequent in women with PCOS (51%) than

those without PCOS (23%) (P = 0.003). The most frequent components of MetS in both groups

were a high density lipoprotein cholesterol </=1.29 mmol/L (80% PCOS, 55% non-PCOS) and a

waist circumference >88 cm (77% PCOS, 41% non-PCOS); these were significantly more

frequent in women with PCOS (P = 0.01). Based on the results, he concluded that MetS was

more common in Indigenous Australian women with PCOS.

Ranasinha, S. et al85(2015) applied rigorous statistical methods to further understand the

interplay between PCOS and metabolic features including insulin resistance, obesity and

androgen status. Retrospective cross-sectional analysis. Women with PCOS demonstrated

clustering of metabolic features that was not observed in the control group. Metabolic syndrome

in the PCOS cohort was strongly represented by obesity (standardized factor loading = 0.95, P <

0.001) and insulin resistance factors (loading = 0.92, P < 0.001) and moderately by blood

pressure (loading = 0.62, P < 0.001) and lipid factors (loading = 0.67, P = 0.002). On further

analysis, the insulin resistance factor strongly correlated with the obesity (r = 0.70, P < 0.001)

and lipid factors (r = 0.68, P < 0.001) and moderately with the blood pressure factor (loading =

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0.43, P = 0.002). The hyperandrogenism factor was moderately correlated with the insulin

resistance factor (r = 0.38, P < 0.003), but did not correlate with any other metabolic factors. The

authors concluded that PCOS women are more likely to display metabolic clustering in

comparison with age- and BMI-matched control women. Obesity and insulin resistance, but not

androgens, are independently and most strongly associated with metabolic syndrome in PCOS.

Romanowski, M. D. et al 86(2015) aimed to determine the prevalence of NAFLD and metabolic

syndrome in patients with PCOS. One hundred and thirty-one patients joined the analysis; 101

were diagnosed with PCOS and 30 formed the control group. We subdivided the PCOS patients

into two subgroups: PCOS+NAFLD and PCOS. All the patients were submitted to hepatic

sonography. For hepatosteatosis screening, hepatic echotexture was compared do spleen's. For

diagnosis of metabolic syndrome, we adopted the National Cholesterol Education Program/Adult

Treatment Panel III (NCEP/ATP III) criteria, as well as the criteria proposed by International

Diabetes Federation. Statistical analysis were performed with t of student and U of Mann-

Whitney test for means and chi square for proportions. At PCOS group, NAFLD was present in

23.8% of the population. At control group, it represented 3.3%, with statistical significance

(P=0.01). Metabolic syndrome, by NCEP/ATP III criteria, was diagnosed in 32.7% of the

women with PCOS and in 26.6% of the women at control group (no statistical difference,

P=0.5). At PCOS+DHGNA subgroup, age, weight, BMI, abdominal circumference and glucose

tolerance test results were higher when compared to PCOS group (P<0.01). Metabolic syndrome

by NCEP/ATPIII criteria was present in 75% and by International Diabetes Federation criteria in

95.8% of women with PCOS+NAFLD with P<0.01. Insulin levels at SOP+DHGNA were higher

than at PCOS group with P<0.01. The authors concluded that almost 25% of the patients with

PCOS were diagnosed for NAFLD. Metabolic syndrome was present between 32.7% and 44.6%

29

of patients with PCOS. At subgroup PCOS+NAFLD, metabolic syndrome is highly prevalent.

These patients are more obese, with higher BMI and higher glucose levels.

Jamil, A. S. et al87 (2015) aimed to compare the metabolic parameters and insulin resistance

among the four PCOS phenotypes defined according to the Rotterdam criteria and to determine

predictors of these complications. A total of 526 reproductive-aged women were included in this

observational case-control study. Of these, 263 were diagnosed as a PCOS based on Rotterdam

criteria and 263 infertile women with no evidence of PCOS were recruited as controls.

Biochemical, metabolic and insulin resistance parameters were compared in the two groups and

the frequency of MetS and IR were compared among the four phenotypes. Data were analyzed

for statistical significance using Student's t-test and one way analysis of variance followed by a

post-hoc test (least significant difference). Chi-square tests were used to compare proportions.

Univariate and multivariate logistic regression analyses were also applied. IR was identified in

112 (42.6%) of the PCOS women and 45 (17.1%) of the control (P <0.001). There were no

significant differences in the frequency of IR and MetS between the four PCOS phenotypes.

Homeostatic model assessment for IR (HOMA-IR) >/=3.8 was the most common IR parameter

in PCOS and control groups. Women with oligo-anovulation (O) and PCO morphology (P) had a

significantly lower level of 2-h postprandial insulin compared to women with O, P and

hyperandrogenism (H) phenotypes. Logistic regression analysis showed that body mass index,

waist circumference, triglyceride/high-density lipoprotein ratio (cardiovascular risk), HOMA-IR

and glucose abnormalities (T2DM) were associated with increased risk of having MetS (P <

0.05).Based on the results, the authors concluded that PCOS women with (O + P) show milder

endocrine and metabolic abnormalities. Although, there were no significant differences in IR,

30

MetS and glucose intolerance between the four PCOS phenotypes, women with PCOS are at

higher risk of impaired glucose tolerance and undiagnosed diabetes.

Daskalopoulos, G. et al 88(2015) aimed to assess the potential differences in the metabolic and

cardiovascular disease (CVD) risk between the distinct phenotypes of the Polycystic Ovary

Syndrome (PCOS) according to the Rotterdam definition regardless of body mass index (BMI).

This study included 300 women; 240 women with PCOS, according to the Rotterdam criteria

and 60 controls without PCOS. All women were further subdivided, according to their BMI, into

normal-weight and overweight/obese and PCOS women were furthermore subdivided to the 4

phenotypes of the syndrome. A complete hormonal and metabolic profile as well as the levels of

high sensitivity C reactive protein (hsCRP) and lipoprotein-associated phospholipase A2 (Lp-

PLA2) were measured. Levels of surrogate markers of subclinical atherosclerosis (hsCRP and

Lp-PLA2), levels of evaluated CVD risk score using risk engines, and several correlations of

CVD risk factors. hsCRP levels were higher but not significantly so in PCOS women compared

with controls. In lean PCOS patients, Lp-PLA2 levels were significantly higher, compared with

lean controls, mainly in the 2 classic phenotypes. Overweight/obese patients in all 4 phenotypes

had significantly higher Lp-PLA2 levels compared with overweight/obese controls. Evaluated

CVD risk according to 4 risk engines was not different among phenotypes and between PCOS

patients and controls. There were several correlations of risk factors with metabolic syndrome

and non-alcoholic fatty liver disease requiring appropriate treatment. The authors concluded that

only 2 of 4 Rotterdam phenotypes, identical with those of the classic PCOS definition, have

excess cardiometabolic risk. These need to be treated to prevent CVD events.

31

Kim, M. J. et al16 (2014) investigated differences in metabolic parameters and the prevalence of

MetS in two major phenotypic subgroups of PCOS in Korea. Furthermore, they investigated the

relationship between HA-associated parameters and MetS. The cross-sectional observational

study with a total of 837 females with PCOS, aged 15-40, were recruited from Departments of

Obstetrics and Gynecology at 13 hospitals. Of those, 700 subjects with either polycystic ovaries

(PCO)+HA+oligomenorrhea/amenorrhea (O) or PCO+O were eligible for this study. In this

study, MetS was more prevalent in the PCO+HA+O group (19.7%) than in the PCO+O (11.9%)

group. There were statistically significant trends for an increased risk of MetS in the

PCO+HA+O group compared to the PCO+O group. After adjustment for age, the odds ratio of

MetS was 2.192 in non-obese subjects with PCO+HA+O compared to those with PCO+O,

whereas the risk of MetS was not different in obese patients. Multivariate logistic regression

analysis showed that high free androgen index and low sex hormone-binding globulin were

significantly associated with MetS in non-obese women with PCOS, with odds ratios of 4.234

(95% CI, 1.893-9.474) and 4.612 (95% CI, 1.978-10.750), respectively. However, no

associations were detected between MetS and SHBG and FAI in obese PCOS subjects. The

authors concluded that HA and its associated parameters (FAI and SHBG) are significantly

associated with MetS in non-obese PCOS subjects, whereas this association was not observed in

obese subjects.

Li, R. et al14 (2014) aimed to determine the prevalence and predictors of the metabolic

abnormalities in Chinese women with and without PCOS. A cross sectional study with 833

reproductive aged PCOS women was done. Clinical history, ultra-sonographic exam (ovarian

follicle), hormonal and metabolic parameters were the main outcome measures. The prevalence

of metabolic syndrome (MetS) as compared in PCOS and non-PCOS women from community

32

were 18.2% vs 14.7%, and IR (insulin resistance) were 14.2% vs 9.3% (p < 0.001) respectively.

After adjusting for age, the indicators (central obesity, hypertension, fasting insulin, SHBG,

dyslipidaemia) for metabolic disturbances were significantly higher in PCOS than in non-PCOS

groups. Using multivariate logistic regression, central obesity and FAI were risk factors, while

SHBG was a protective factor on the occurrence of MetS and IR in PCOS women (OR: 1.132,

1.105 and 0.995). The authors concluded that the risk factors of the metabolic syndrome and

insulin resistance were BMI and FAI for PCOS women, respectively. The decrease of SHBG

level was also a risk factor for insulin resistance in both PCOS and metabolic disturbance.

Panidis, D. et al89 (2013)compared the prevalence of MetS between a large cohort of patients

with PCOS and body mass index -matched controls. A cross-sectional study with 1223 PCOS

women and 277 healthy women was done. Diagnosis of PCOS was based on the revised

Rotterdam criteria. Women with PCOS were divided into those who fulfilled both the Rotterdam

criteria and the diagnostic criteria of the 1990 National Institutes of Health definition of PCOS

(group 1, n = 905) and into those with the additional phenotypes introduced by the Rotterdam

criteria (group 2, n = 318). Diagnosis of MetS was based on four different definitions.

Anthropometric, metabolic, hormonal and ultrasonographic features were main outcomes

measures.The prevalence of metabolic syndrome (MetS) was higher in women with PCOS than

in controls when the National Cholesterol Education Program Adult Treatment Panel III

definition of MetS was applied (15.8% and 10.1%, respectively; P = 0.021) but not with the three

more recent MetS definitions. The prevalence of MetS was higher in group 1 than in controls

regardless of the applied MetS definition. In contrast, the prevalence of MetS was similar in

group 2 and in controls regardless of the applied MetS definition. In logistic regression analysis,

33

PCOS did not predict the presence of MetS. The authors concluded that PCOS per se does not

appear to increase the risk of MetS independent of abdominal obesity.

Panidis, D. et al90 (2013) compared the prevalence of metabolic syndrome (MetS) between

women with polycystic ovary syndrome (PCOS) and controls across different age (</=20, 21-30

and 31-39 years old) and body mass index (BMI) (normal weight, overweight and obese) groups.

They studied 1223 women with PCOS and 277 BMI-matched controls. The prevalence of MetS

in women with PCOS and controls was estimated according to four different MetSdefinitions.In

subjects </=20 and 21-30 years old, the prevalence of MetS did not differ between women with

PCOS and controls regardless of the MetS definition, even though women with PCOS were more

obese than controls in the </=20 years old group. In subjects 31-39 years old, the prevalence of

MetS was higher in women with PCOS than in controls but the former were more obese than

controls. The prevalence of MetS did not differ significantly between women with PCOS and

controls in any of the BMI groups (normal weight, overweight or obese) regardless of the MetS

definition. The authors concluded that the prevalence of Mets appears to be primarily determined

by obesity and age whereas PCOS per se appears to be a less important contributing factor.

Rahmanpour, H. et al91 (2012)investigated the association between PCOS, overweight, and

metabolic syndrome in adolescents. 30 PCOS adolescents were randomly selected from a PCOS

population with NIH 1990 criteria and 71 adolescents from the normal adolescents.

Anthropometric, hormonal and metabolic parameters were evaluated in four sub-groups

including obese and non-obese PCOS and obese and non-obese normal adolescents. The

prevalence of overweight and metabolic syndrome in adolescents with PCOS was 52% and

33.3% respectively vs 22.4% (P = 0.005) and 11.26% in control (normal) adolescents (P =

0.0001). Among all subjects, including obese and non-obese adolescents with or without PCOS,

34

the prevalence of insulin resistance, hypercholesterolemia, central obesity, and metabolic

syndrome was higher in obese PCOS with 61.5%, 46.2%, 53.8%, and 69.2%, respectively. The

authors concluded that Obesity and IR are important risk factors for metabolic syndrome in

PCOS. Considering the long-term health risks, it is necessary to identify metabolic disorders in

adolescents with PCOS as early as possible.

Pantasri, T. et al92(2010) determined the prevalence of metabolic syndrome and insulin resistance

in Thai women with polycystic ovary syndrome (PCOS).Oral glucose tolerance tests were

performed in 70 PCOS women. Blood was taken for the measurement of fasting insulin, 2-hr

postprandial insulin, lipid profiles, testosterone and sex hormone binding globulin.The

prevalence of metabolic syndrome and insulin resistance was 24.3% and 30.7%, respectively.

Diabetes mellitus and impaired glucose tolerance was detected in 11.4% and 31.4%, respectively

Homeostatic model assessment (HOMA) was the most sensitive screening test for insulin

resistance. PCOS women with metabolic syndrome had significantly higher body mass index,

free testosterone index and insulin levels than those without the syndrome. The authors

concluded thatthere was a high prevalence of metabolic syndrome, insulin resistance, diabetes

mellitus and impaired glucose tolerance in Thai PCOS women. A combination of fasting

glucose, a 2-hr glucose tolerance test and fasting insulin level effectively identified PCOS

women who were at high risk of metabolic syndrome.

Most relevant studies India:

Tripathy, P. et al 93 (2018) compared the clinical, biochemical and metabolic parameters among

the different PCOS phenotypes and controls. A total of 394 newly diagnosed PCOS women and

108 controls were enrolled consecutively. PCOS women were divided into four phenotypes

35

based on the presence of two of the following Rotterdam criteria: oligo/anovulation (O),

hyperandrogenism (H), and polycystic ovaries (P): A (O+H+P), B (O+H),C (H+P), D (O+P).

Phenotype A (55.8%) was the most common phenotype in the PCOS cohort. Prevalence of

metabolic syndrome was highest in phenotype A and B compared to other two phenotypes and

controls. The clinical, biochemical and metabolic characteristics, of phenotypes A and B, were

similar, but phenotype A had higher hirsutism score and androgen level. Phenotype C had

intermediate metabolic characteristics between A and controls whereas phenotype D had the

mildest metabolic abnormalities among the four phenotypes. Significant predictors for metabolic

syndrome within the PCOS cohort are waist circumference >80cm, hypertension, fasting glucose

>100mg/dL, HDL-cholesterol <50mg/dL and triglyceride >150mg/dL (p<0.001).The authors

concluded that Indian PCOS women with Phenotype A and B lie at increased metabolic risk

compared to other phenotypes. Phenotypic classification of PCOS women may facilitate more

effective application of screening and treatment strategies for high-risk metabolic phenotypes.

Kiranmayee, D. et al 94 (2017)examined the correlations between anthropometric parameters and

lipid profile in women with PCOS.A observational cross-sectional study examined

anthropometry and lipid profile in 86 married women with PCOS in the age group of 18-35 years

and correlated them by using Pearson's correlation coefficient. More than 80% of the women

with PCOS demonstrated abnormal anthropometric parameters, and in more than 70% women,

lipid abnormalities such as low levels of high-density lipoprotein (HDL) cholesterol and high

levels of triglycerides and low-density lipoprotein cholesterol were observed. Significant positive

correlations were seen between body mass index (BMI) and triglycerides (P </= 0.001) and waist

circumference (WC) and triglycerides (P </= 0.029). Negative correlations were observed

between BMI and HDL cholesterol (P </= 0.013). The authors concluded that BMI and WC are

36

the most important anthropometric parameters correlated to dyslipidemia in the south Indian

women with PCOS.

Pillai, et al 95(2015) The prevalence of MetS differs based on the defining criteria used. Neck

circumference (NC) has been proposed as a surrogate marker of MetS which is simple and easy

to perform in the outpatient setting. The authors aimed to estimate the prevalence of metabolic

syndrome in women with PCOS and to study the use of NC in defining metabolic syndrome.

This was a prospective observational cross-sectional study involving 121 PCOS patients over a

period of 2 years. The prevalence of metabolic syndrome was estimated using the modified

Adult Treatment Panel (ATP) III criteria as well as the International Diabetes Federation (IDF)

criteria. The Pearson correlation coefficient was used to find the degree of correlation between

NC and waist circumference (WC). The Receiver operating characteristic (ROC) curves of NC

were used to predict the metabolic syndrome. The independent sample t test and the Mann-

Whitney U test were used for comparing the average NC and WC between the groups of patients

with and without MetS. The prevalence of MetS was found to be 30.6 % using the modified ATP

III criteria and 52 % using the IDF criteria. There is a statistically significant positive correlation

between NC and WC (r = 0.758, p < 0.001). The mean NC is higher in patients who have MetS

by both criteria (p < 0.001). Based on ROC curve analysis, the NC cutoff of 33.5 cm detected

MetS (by IDF criteria) with a sensitivity of 60.3 % and a specificity of 70.7 % (area under ROC

curve = 0.70, p < 0.001) and the NC cutoff of 33.87 cm detected MetS (by ATP III criteria) with

a sensitivity of 73 % and a specificity of 69 % (area under ROC curve = 0.722 p < 0.001). The

IDF criteria identified a higher number of PCOS subjects with MetS compared to the ATP III

criteria. NC correlated very well with MetS as well as WC, and this could replace the waist

circumference to define MetS in the future.

37

Sharma, S. et al96 (2015)studied the prevalence of metabolic syndrome (MetS) in Indian women

and its correlation to body mass index (BMI) and polycystic ovarian syndrome (PCOS) in this

population. Prospective cross-sectional observational study.Two hundred women, 120 with

PCOs and 80 age-matched controls were enrolled. The prevalence of MetS was studied in the

women with and without and was co related to BMI by further subgrouping as team (BMI <23

kg/m3) and obese (BMI >23 kg/m2). The prevalence of MBS was significantly higher in the

women with PCOS, as compared to age-matched controls. Similarly, when BMI was considered,

MBS was more prevalent in overweight subjects than in lean subjects with or without PCOS. In

subgroup analysis, the presence of PCOS had a lesser impact on the prevalence of MetS as

compared to non-PCOS controls with higher BMI. The relative risk of MetS increased as

follows: lean controls-1, lean PCOS-2.66, obese controls-5.33, and obese PCOS-6.5. The most

appropriate cut-off level of BMI for predicting the risk of MetS in Indian women without PCOS

seems to be 23 kg/m(2), whereas, with PCOS, it was 22.5 kg/m(2). The authors concluded that

MetS is more prevalent in women with PCOS. However, obesity is an independent and stronger

risk factor for developing MetS. To reduce the risk of MetS and its related long-term health

consequences, lifestyle modification is advisable above BMI of 23 kg/m(2) in the normal

population and 22.5 kg/m(2) in women with PCOS.

Shabir, I. et al 97(2014) studied the prevalence of metabolic syndrome in the family members of

women with polycystic ovary syndrome. The present study assessed the clinical, biochemical

and hormonal parameters including prevalence of metabolic syndrome by two different criteria

in the first- degree relatives of patients with PCOS. The average age of 37 index patients was 23

+/- 3.6 years, with the mean age of menarche as 13.3 +/- 1.2 years. The mean age and age of

menarche in mothers (n = 22) was 48.8 +/- 5.1 and 13 +/- 1.3 years, respectively, whereas as it

38

was 23.5 +/- 4.7 and 13.3 +/- 1.2 years in sisters (n = 22), respectively. Metabolic syndrome

(MetS) defined by International Diabetes Federation (IDF) criteria was present in 10 index

patients, 1 brother, 4 sisters, 17 mothers and 15 fathers while as by Adult Treatment Panel III

(ATP III) it was in 8 index patients, 5 sisters, 16 mothers and 11 fathers.The authors concluded

that the presence of MetS or related metabolic derangements is high in the family members of

women with PCOS.

Kar, S.98(2013) studied the distribution of various Rotterdam classified phenotypes of polycystic

ovarian syndrome (PCOS) women, in our population, compared the four phenotypes with respect

to anthropometric, clinical, and metabolic parameters and reported the prevalence of insulin

resistance (IR) and metabolic syndrome in these women. Prospective cross-sectional

comparative study. Four hundred and ten women with a clinical diagnosis of PCOS based on

Rotterdam criteria were included in the study. All women were also evaluated for metabolic

syndrome (American Heart Association/National Heart, Lung, and Blood Institute

(AHA/NHLBI), modified Adult Treatment Panel (ATP) III 2005 guidelines) and IR (homeostatic

model assessment-IR (HOMA-IR)). Largest group was PCOS complete (65.6%) followed by P +

O (22.2%); H + O (11.2%); and P + H (0.9%). Overall prevalence of metabolic syndrome was

35.07%. Hyperandrogenic phenotyptes; H + O (50%) and P + H + O (37.04%), had significantly

higher prevalence of metabolic syndrome than normoandrogenic P + O phenotype (10%) (P </=

0.001). Body mass index (BMI) >/= 25 (P = 0.0004; odds ratio (OR) = 3.07 (1.6574-5.7108,

95% CI)), waist circumference (WC) >/= 80 cm (P = 0.001; OR = 3.68 (1.6807-8.0737, 95%

CI)) and family history of diabetes (P = 0.019; OR 1.82 (1.1008-3.0194, 95% CI)), were strongly

associated with the presence of metabolic syndrome. The overall prevalence of IR in PCOS

women was 30.44% (HOMA-IR cutoff >/= 3.8) and 34.94% (HOMA-IR cutoff >/= 3.5). The

39

authors concluded that the prevalence of metabolic syndrome and IR was 35.07 and 30.44%,

respectively. The hyperandrogenic phenotypes have significantly higher metabolic morbidity

compared to norm androgenic phenotype. BMI > 25, WC >/= 80 cm, and family history of

diabetes carry the highest risk for developing metabolic syndrome.

Karoli, R. et al99(2013) aimed to determine the presence of NAFLD and associated factors of

hepatic steatosis in women with PCOS. A cross-sectional hospital-based study of 54 women

with PCOS and 55 healthy controls who were age and weight matched were included.

Anthropometric parameters, biochemical and hormonal investigations were done in all the

patients. Insulin resistance was calculated by Homeostasis model assessment (HOMA).

Abdominal ultrasonography and biochemical tests were used to determine the presence of

hepatic steatosis after excluding other causes liver disease. Women with PCOS had a higher

prevalence of hepatic steatosis (67% vs 25%, P = 0.001) MS (35% vs. 7%, P < 0.01) and

elevated transaminases (31% vs. 7%, P = 0.03) than controls. All patients with PCOS and

controls with MS had presence of hepatic steatosis. Age, BMI, waist-hip ratio, HOMA-IR, HDL

and PCOS diagnosis were the factors associated with presence of hepatic steatosis. The authors

concluded that NAFLD is commonly present in women with PCOS in combination with other

metabolic derangements. Evaluation for liver disease should be considered at an earlier age in

women with PCOS, particularly those who have an evidence of MS.

Karoli, R. et al100 (2012) assessed atherosclerotic risk factors in women with PCOS. A cross-

sectional study, 50 women with PCOS and 50 age and weight-matched healthy controls were

enrolled. Endothelial dysfunction by flow-mediated dilatation (FMD) of brachial artery, highly

sensitive C-reactive protein (hs CRP), and carotid intima media thickness (CIMT) were

measured in both cases and control groups. The mean age of women with PCOS was 26.82 +/-

40

3.26 years and Body-mass index (BMI) of 26.2 +/- 4.8 kg/ m (2). Thirty-six (72%) patients were

overweight or obese,54% had central obesity and 12% had impaired glucose tolerance. Among

the markers of atherosclerosis, hsCRP levels were non significantly higher in patients with PCOS

than in controls. The FMD was 12.18 +/- 2.3% vs 8.3 +/- 2.23% in patients with PCOS and

controls respectively (P=0.01). CIMT was significantly different in two study groups (0.68 +/-

0.11 in PCOS vs 0.52 +/- 0.02 in normal subjects, (P=0.01). FMD had significant negative

correlation with homeostasis model assessment (HOMA) index (r = -0.32, P=0.02) and hs CRP

(r = -0.37, P=0.04) while hs CRP was correlated with BMI (r = 0.54, P=0.005), HOMA (r = 0.38,

P=0.02) and FMD (r = -0.33, P=0.01). CIMT was significantly different in women with PCOS

and control subjects, and it had significant correlation with age (r = 0.42, P=0.03), BMI (r = 0.36,

P=0.01), waist circumference (r = 0.52, P=0.001) and HOMA (r = 0.31, P=0.04). The authors

concluded that women with PCOS definitely have increased risk for future cardiovascular

events. Clinicians should consider early cardiovascular screening and interventions to control all

modifiable cardiovascular risk factors.

Mandrelle, K.1(2012) evaluated the prevalence of metabolic syndrome in women with polycystic

ovary syndrome (PCOS). A prospective cross-sectional study. The women with PCOS

underwent screening for metabolic syndrome as defined by the modified American Heart

Association/National Heart Lung Blood Institute (AHA/NHLBI) modified ATP 111 (2005)

definition. A multivariate logistic regression analysis was applied and significant predictors

identified for the prediction of metabolic syndrome. The overall prevalence of metabolic

syndrome according to the modified AHA/NHLBI ATP III (2005) criteria was 37.5%. A total of

5.8 % cases were detected to have diabetes mellitus, 8.3% had impaired fasting glucose, and 11.7

% had an impaired glucose test. Dyslipidemia was present in 93.3% cases of PCOS. Among all

41

the risk factors, age and waist hip ratio >/=0.85 were strongly associated with the presence of

metabolic syndrome. The authors concluded that infertile women with PCOS, particularly those

with age >/=25 years or with central obesity (a waist hip ratio of >/=0.85), are at a higher risk of

developing metabolic syndrome and should be offered screening tests.

Bhattacharya, S. M. et al 101 (2011)studied the prevalence and risk of metabolic syndrome (MS)

among adolescent Indian girls with polycystic ovary syndrome (PCOS), compared to those

without, as per the recent 'joint interim statement' criteria.Cross-sectional data of 96 adolescent

girls were retrospectively analyzed applying the 2009 'joint interim criteria' for MS. Fifty-one of

them were diagnosed with PCOS as per the Androgen excess society criteria 2006. The

remaining 45 adolescent girls (no androgen excess manifestations and regular cycles) formed the

comparison group. The prevalence of MetS among adolescents with PCOS (60.78%; 95% CI =

50.78%, 70.78%) was significantly more compared to those without (P = 0.002). The odds ratio

of MetS among PCOS adolescents was 4.26 (95% CI = 1.79, 10.15). Only the mean waist

circumference differed significantly between the PCOS and non-PCOS groups (P = 0.046).

Interestingly, the contrast between the MetS and non-MetS subgroups of the PCOS adolescent

sample produced significant differences in body mass index, waist circumference, blood pressure

and biochemical parameters such as fasting plasma glucose, high-density lipoprotein-cholesterol

and triglyceride levels. The authors concluded that adolescent Indian girls with PCOS were

reported to have 4.26 times more chances of developing MetS compared to those without. Waist

circumference was found to be the cheapest and simplest significant marker of MS. The study

underlines the need for routine screening of MetS among adolescent girls suffering from PCOS

to reduce future co-morbidities.

42

Bhattacharya, S. M.17 (2010) compared the prevalence rate of metabolic syndrome (MS) in

women with polycystic ovary syndrome (PCOS) using the Adult Treatment Panel III (ATP III)

criteria, with that using the International Diabetes Federation (IDF) criteria and also assessed the

metabolic risk factors for this syndrome.A cross-sectional study, 198 women with PCOS were

studied. MetS was diagnosed as per the ATP III and IDF criteria, separately. MetS was found in

37.9% cases (ATP III criteria) and 47.5% cases of PCOS (IDF criteria) (p = 0.02). In

adolescents, prevalence of MetS was more with the IDF criteria (p = 0.009) but in adults, the

prevalence rates were similar between the two criteria (p = 0.08). Women with MS had

significantly higher body mass index, irrespective of age and the definition used. Dyslipidemia

was found more common than elevated fasting glucose abnormality, using either of the criteria.

The authors concluded that prevalence rate of MetS in PCOS depends on the definition used.

With IDF criteria, in the whole group, the prevalence was significantly higher. A universally

accepted definition of MetS, suitable for adolescents and adults, is urgently needed.

43

MATERIALS & METHODS

44

Study site: This study was conducted in the Department of Obstetrics and Gynecology

department at G. Kuppuswamy Naidu Memorial Hospital

Study population: Women in the reproductive age (18-45yrs) group with polycystic ovaries

(confirmed by ultrasound) attended in the Obstetrics and Gynecology department of at G.

Kuppuswamy Naidu Memorial Hospital were considered as study population.

Study design: The current study was a hospital-based time bound cross-sectional study.

Sample size: Sample size was calculated assuming the prevalence of Metabolic syndrome in

PCOS patients as 30.6% as per the study by Pillai, B. P., et al.95 The other parameters considered

for sample size calculation were 95% confidence level and 5% absolute precision. The following

formula was used for sample size calculation.

n=Z2 P (1−P )

d2

Where n = Sample size

Z = Z statistic for a level of confidence= 1.96

P = Expected prevalence of proportion

(If the expected prevalence is 30.6%, then P= 0.306), and

d = Precision (If the precision is 5%, then d=0.05)

45

The number of subjects required to be included in the study, as per the above-mentioned formula

were 327 subjects. To account for a non-participation rate of about 5%, another 17 subjects were

added making final required sample size to be 344.

For precision of 6% = 227

For precision of 7% = 167

Sampling method: All the eligible subjects were recruited into the study consecutively by

convenient sampling till the sample size is reached.

Study duration: The data collection for the study was done between August2016 to April 2018

for a period of2 years.

Inclusion Criteria:

Patients with PCOS after excluding other causes of polycystic ovaries

Patients in the reproductive Age between 18-45 years, irrespective of fertility and

menstrual pattern.

Exclusion criteria:

Patients with PCOS on medications such as oral contraceptive pills.

Patients who were underweight with BMI less than 18.5kg/m2.

Lactating and pregnant women.

Women with hypothyroidism, hyperprolactinemia, congenital adrenal hyperplasia,

androgen producing tumor and cushing’s syndrome.

Ethical considerations:Study was approved by institutional human ethics committee. Informed

written consent was obtained from all the study participants and only those participants willing to

46

sign the informed consent were included in the study. The risks and benefits involved in the

study and voluntary nature of participation were explained to the participants before obtaining

consent. Confidentiality of the study participants was maintained.

Data collection tools: All the relevant parameters were documented in a structured study proforma.

Methodology:

After obtaining patient particulars such as name, age etc., a written informed consent was taken

from patients. The following data was compiled in all patients.

1.Clinical history and Menstrual history (Oligomenorrhea)

2. Family history (diabetes, hypertension, cardiac diseases)

3.BMI (wt in kg/ht in m2).

Under weight: <18.5 kg/m2

Normal: 18.5 -24.9 kg/m2

Over weight: ≥25kg/m2

Pre obese: 25-29.9 kg/m2

Obese:≥30kg/m2

4.Transabdominal ultrasound.

5.Patients fulfilling the diagnostic criteria of PCOS were screened for Metabolic syndrome by

collecting the following details

6.Three readings of Blood pressure recorded 10 minutes apart after taking rest for 5 minutes in

both arms sitting/supine position.

8.Waist circumference (measured in a horizontal plane midway between the inferior margin of

the ribs and superior border of the iliac crest)

47

9.On day 3 to day 5 of menstrual cycle Blood sample of 10ml was drawn after 12 hours

overnight fasting for the measurement of lipid profile, fasting plasma glucose, luteinizing

hormone, follicle stimulating hormone, TSH, prolactin, testosterone levels, and DHEAS.

Statistical Methods:

Metabolic syndrome was considered as outcome variable:

Age, BMI, WC, WH, blood pressure, fasting blood sugar, triglycerides, HDL

level ,comorbidities areexplanatory variables.

Descriptive analysis: Descriptive analysis was carried out by mean and standard deviation for

quantitative variables, frequency and proportion for categorical variables. Data was also

represented using appropriate diagrams like bar diagram, pie diagram and box plots.

Categorical outcome:

The association between explanatory variables and categorical outcomes was assessed by cross

tabulation and comparison of percentages. Chi square test was used to test statistical

significance.

P value < 0.05 was considered statistically significant. IBM SPSS version 22 was used for

statistical analysis. 88

48

OBSERVATIONS AND RESULTS

49

RESULTS:A total people 170 were included in the analysis

Table1: Age distribution of study population (N=170)

Age group Frequency PercentageLess than 20 years 30 17.60%20 to 24 years 56 32.90%25 to 29 years 70 41.20%30 and above 14 8.20%

Among the study population 30(17.60%) people were aged less than 20 years, 56(32.90%) were

aged between 20 to 24 years, 70(41.20%) were aged between 25 to 29 years and remaining

14(8.20%) were aged 30 years and above. (Table 1& figure1)

Figure1: Bar graph for age distribution of study population (N=170)

Less than 20 years 20 to 24 years 25 to 29 years 30 and above0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

17.60%

32.90%

41.20%

8.20%

Age group

Perc

enta

ge

Table2: Descriptive analysis of BMI distribution in study population (N=170)

50

BMI distribution Frequency PercentagesNormal(18.5 to 24.9 ) 73 42.90%Over weight (25 to 29.9 ) 37 21.80%Obese(>30) 60 35.30%

Among the study population 73(42.90%) people had Normal (18.5 to 24.9) BMI, 37(21.80%)

were Overweight (25 to 29.9) and remaining 60(35.30%) people were obese (>30).

(Table2&figure2)

Figure2: Bar graph for BMI distribution in study population (N=170)

Normal Over weight Obese0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

42.90%

21.80%

35.30%

BMI

Perc

enta

ge

51

Table3: Descriptive analysis of waist circumference in study population (N=170)

Waist circumference Frequency Percentagesup to 79.9 50 29.40%80 and above 120 70.60%Among the study population 50(29.40%) people had waist circumference up to 79.9,

120(70.60%) people had 80 and above. (Table3)

Table4: Descriptive analysis of blood pressure in study population (N=170)

Frequency PercentagesSystolic blood pressureUp to 129.9 108 63.50%130 and above 62 36.50%Diastolic blood pressureUp to 84.9 136 80.00%85 and above 34 20.00%FBS categoryUp to 99.9 156 91.80%100 and above 14 8.20%TriglyceridesUp to 149.9 136 80.00%150 and above 34 20.00%HDLUp to 49.9 128 75.30%50 and above 42 24.70%

Among the study population 108(63.50%) people had systolic blood pressure Up to 129.99,

62(36.50%) people had 130 and above. Among the study population 136(80%) people had

diastolic blood pressure Up to 84.9, 34(20.00%) people had 85 and above.Among the study

52

population 156(91.80%) people had FBS category Up to 99.9 mg/dl, 14(8.20%) people had 100

mg/dl and above. Among the study population 136(80.00%) people had Triglycerides Up to

149.9 mg/dl, 34 (20.00%) people had 150 mg/dl and above. Among the study population

128(75.30%) people had HDL Up to 49.9 mg/dl, 42 (24.70%) people had 50 mg/dl and above.

(Table4)

Table5: Descriptive analysis of Presenting complaints of PCOS in study population (N=170)

Parameter Frequency PercentOligomenrrhea 166 97.60%Infertility 35 20.60%Hirsutism 48 28.23%

Among the study population 166(97.60%) people had Oligomenrrhea, 35(20.60%) had

infertility, 48(28.23%) had Hirsutism. (Table5&figure3)

,

Figure3: Bar diagram for Presenting complaints of Pco’s in study population (N=170)

Oligomenrrhea Infertility Hirsutism0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

97.60%

20.60%28.23%

Presenting complaints in Pco's

Perc

enta

ge

53

Table6: Descriptive analysis of comorbidities in study population (N=170)

Parameter Frequency PercentDiabetes mellitus 3 1.80%Hypertension 10 5.90%

Among the study population 3(1.80%) had Diabetes mellitus and 10(5.90%) had hypertension.

(Table6&figure4)

Figure4: Bar diagram for comorbidities in study population (N=170)

Diabetes mellitus Hypertension0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

1.80%

5.90%

Comorbidities

Perc

enta

ge

Table7: Descriptive analysis of family history in study population (N=170)

Parameter Frequency PercentFamily history of diabetes mellitus 31 18.20%Family history of hypertension 39 22.90%Family history of Cardiac disease 31 18.20%

54

Among the study population 31(18.20%) had family history of diabetes, 39(22.90%) had

hypertension and 31(18.20%) had family history of cardiac diseases. (Table7)

Table8: Prevalence of metabolic syndrome components in study population (N=170)

Parameter Frequency Percentage

WC(80 and above) 120 70.60%Systolic blood pressure 130/K/C/O hypertension 62 36.50%Diastolic blood pressure ( 85 / K/C/O hypertension 34 20%Fasting blood sugar (100mg/dl and above)/ /Diabetes mellitus 14 8.20%Triglycerides (≥150mg/dl)

34 20%HDL (Up to 49.9mg/dl 128 91.80%

Among the study population 120(70.60%) people had WC(80 and above), 62(36.50%) had

Systolic blood pressure 130/K/C/O hypertension, 34(20%) had Diastolic blood pressure ( 85

/ K/C/O hypertension, 14(8.20%) had Fasting blood sugar (100mg/dl and above)/ /Diabetes

mellitus, 34(20%) had Triglycerides (≥150mg/dl) and 128(91.80%) had HDL(Up to 49.9mg/dl).

(Table8&figure5)

55

Figure5: Bar graph for metabolic syndrome components in study population (N=170)

WC(80 and above)

Systolic blood pressure ³ 130/K/C/O hypertension

Diastolic blood pressure (³ 85 / K/C/O hypertension

Fasting blood sugar (100mg/dl and above)/ /Diabetes mellitus

Triglycerides (

150mg/dl)

HDL (Up to 49.9mg/dl

0.00%20.00%

40.00%60.00%

80.00%

100.00%

0.706

0.365

0.8

0.082

0.2

0.918

Percentage

Met

abol

ic sy

ndro

me

com

pone

nts

Table9: Descriptive analysis for metabolic syndrome in study population (N=170)

Parameter Frequency PercentageMetabolic syndrome 51 30.00%No Metabolic syndrome 119 70.00%

Among the study population 51(30%) people had metabolic syndrome. (Table9&fig6)

Figure6: Bar graph for metabolic syndrome distribution in study population (N=170)

56

Metabolic syndrome No Metabolic syndrome0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

30.00%

70.00%

Metabolic syndrome

Perc

enta

ge

INFERENTIAL ANALYSIS

Table10: Comparison of metabolic syndrome with age group (N=170)

Age groupMetabolic syndrome Chi square P-value

Metabolic syndrome

No metabolic syndrome

1.060 0.787

Less than 20 years(N=30) 8 (26.7%) 22 (73.3%)

20 to 24 years(N=56) 19 (33.9%) 37 (66.1%)

25 to 29 years(N=70) 21 (30%) 49 (70%)

30 and above(N=14) 3 (21.4%) 11 (78.6%)

Among the people less than 20 years 8 (26.7%) people had metabolic syndrome, among the

people between 20 to 24 years 19 (33.9%) had metabolic syndrome, among the people between

25 to 29 years 21 (30%) had metabolic syndrome, among the people 30 and above years old 3

(21.4%) had metabolic syndrome. The difference between age group and metabolic syndrome

was statistically not significant. (P value-0.787). (Table10& fig7)

57

Figure7: Cluster bar graph for Comparison of metabolic syndrome with age group (N=170)

Less than 20 years 20 to 24 years 25 to 29 years 30 and above0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

26.70%33.90%

30.00%21.40%

73.30%66.10%

70.00%78.60%

Metabolic syndrome No metabolic syndrome

Age group

Perc

enta

ge

Table11: Comparison of metabolic syndrome with BMI distribution (N=170)

BMI distribution

Metabolic syndrome Chi square P-value

Metabolic syndrome

No metabolic syndrome

22.80 <0.001Normal(18.5 to 24.9 )(N=73) 10 (13.7%) 63 (86.3%)

Over weight (25 to 29.9 ) (N=37) 10 (27%) 27 (73%)

Obese(>30)(N=60) 31 (51.7%) 29 (48.3%)

Among the people with Normal (18.5 to 24.9) BMI 10 (13.7%) had metabolic syndrome, among

the people with Over weight (25 to 29.9 ) BMI 10 (27%) had metabolic syndrome, among the

people with Obese(>30)BMI 31 (51.7%) had metabolic syndrome. The difference between BMI

58

distribution and metabolic syndrome was statistically significant. (P value-<0.001). (Table11&

fig8)

Figure8: Cluster bar graph Comparison of metabolic syndrome with BMI distribution (N=170)

Normal(18.5 to 24.9 ) Over weight (25 to 29.9 ) Obese(>30)0.00%

10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%

100.00%

14%

27%

52%

86%

73%

48%

Metabolic syndrome No metabolic syndrome

BMI distribution

Perc

enta

ge

Table12: Comparison of metabolic syndrome with HIRSUTISM (N=170)

HIRSUTISM

Metabolic syndrome Chi square P-value

Metabolic syndrome

No metabolic syndrome

4.335 0.037Yes(N=48) 20 (41.7%) 28 (58.3%)No(N=122) 31 (25.4%) 91 (74.6%)

Among the people with Hirsutism only 20 (41.7%) had metabolic syndrome. The difference

between Hirsutism and metabolic syndrome was significant. (P value-0.037). (Table12& fig9)

59

Figure9: Cluster bar graph for Comparison of metabolic syndrome with HIRSUTISM (N=170)

Yes No0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

41.70%

25.40%

58.30%

74.60%

Metabolic syndrome No metabolic syndrome

Hirsutism

Perc

enta

ge

Table13: Comparison of metabolic syndrome with oligomenrrhea (N=170)

Oligomenrrhea

Metabolic syndrome

Metabolic syndrome No metabolic syndrome

Yes(N=166) 47 (28.3%) 119 (71.7%)No(N=4) 4 (100%) 0 (0%)

Among the people with oligomenrrhea only 47 (28.3%) had metabolic syndrome. (Table13&

fig10)

Figure10:Cluster bar graph for Comparison of metabolic syndrome with oligomenrrhea (N=170)

60

Yes No0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

28%

100%

72%

0%

Metabolic syndrome No metabolic syndrome

Oligomenrrhea

Perc

enta

ge

Table14: Comparison of metabolic syndrome with infertility (N=170)

Infertility

Metabolic syndrome Chi square P-value

Metabolic syndrome

No metabolic syndrome

2.099 0.147Yes(N=35) 14 (40%) 21 (60%)No(N=135) 37 (27.4%) 98 (72.6%)

Among the people with Infertility only 14 (40%) had metabolic syndrome. The difference

between Infertility and metabolic syndrome was not significant. (P value-0.147). (Table14&

fig11)

Figure11:Cluster bar graph for Comparison of metabolic syndrome with infertility (N=170)

61

Yes No0%

10%

20%

30%

40%

50%

60%

70%

80%

40.00%

27.40%

60.00%

72.60%

Metabolic syndrome No metabolic syndrome

Infertility

Perc

enta

ge

Table15: Comparison of metabolic syndrome with family history of diabetes mellitus (N=170)

Family history of diabetes mellitus

Metabolic syndrome Chi square P-value

Metabolic syndrome

No metabolic syndrome

25.71 <0.001Yes(N=31) 21 (67.7%) 10 (32.3%)No(N=139) 30 (21.6%) 109 (78.4%)

Among the people with Family history of diabetes mellitus only 21 (67.7%) had metabolic

syndrome. The difference between Family history of diabetes mellitus and metabolic syndrome

was significant. (P value-<0.001). (Table15& fig12)

Figure12:Cluster bar graph for Comparison of metabolic syndrome with family history of diabetes mellitus (N=170)

62

Yes No0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

67.70%

21.60%

32.30%

78.40%

Metabolic syndrome No metabolic syndrome

Family history of diabetus

Perc

enta

ge

Table16: Comparison of metabolic syndrome with family history of hypertension (N=170)

Family history of

hypertension

Metabolic syndrome Chi square P-value

Metabolic syndrome

No metabolic syndrome

20.23 <0.001Yes(N=39) 23 (59%) 16 (41%)No(N=131) 28 (21.4%) 103 (78.6%)

Among the people with Family history of hypertension only 23 (59%) had metabolic syndrome.

The difference between Family history of hypertension and metabolic syndrome was significant.

(P value-<0.001). (Table17& fig13)

63

Figure13: Cluster bar graph for Comparison of metabolic syndrome with family history of hypertension (N=170)

Yes No0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

59.00%

21.40%

41.00%

78.60%

Metabolic syndrome No metabolic syndrome

Family history of hypertension

Perc

enta

ge

Table17: Comparison of metabolic syndrome with family history of cardiac disease (N=170)

Family history of cardiac disease

Metabolic syndrome Chi square P-value

Metabolic syndrome

No metabolic syndrome

21.50 <0.001Yes(N=31) 20 (64.5%) 11 (35.5%)No(N=139) 31 (22.3%) 108 (77.7%)

Among the people with Family history of cardiac disease only 20 (64.5%)had metabolic

syndrome. The difference between Family history of cardiac disease and metabolic syndrome

was significant. (P value-<0.001). (Table18& fig14)

64

Figure14: Cluster bar graph for Comparison of metabolic syndrome with family history of cardiac disease (N=170)

Yes No0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

64.50%

22.30%

35.50%

77.70%

Metabolic syndrome No metabolic syndrome

Family history of cardiac disease

Perc

enta

ge

65

DISCUSSION

66

DISCUSSION:

Polycystic ovary syndrome (PCOS) is a gynecological endocrinopathy and affects 4–12% of

women of reproductive age.102 Since its description in 1935 by Stein and Leventhal , it has been a

subject of extensive controversy and research. Metabolic syndrome (MetS) is a complex cluster

of cardiometabolic risk factors with adipose tissue dysfunction and insulin resistance as the

underlying pathology.103 By virtue of insulin resistance being the common etiology for PCOS

and MetS, many features of these two syndromes are shared.

Women with PCOS have abnormalities in insulin action, metabolism, and the control

of androgen production. These patients have several cardiovascular risk factors, similar to MetS,

such as insulin resistance, hyperinsulinemia, obesity, hyperandrogenemia, hyperlipidemia,

hypertension and sleep disorders. The most worrisome concerns of these patients may change

with age, from cosmetic troubles like hirsutism and acne, as teenagers, oligomenorrhea and

infertility as adults, to cardiometabolic disorders later in life. 104

Though many studies are available in this context both globally84, 105, 106 and Indian scenario,17, 101,

107 the increased risk of MetS in women with PCOS has raised further interest in identifying the

predictors for MetS in these women.

In the Current study 170 Patients with PCOS after excluding other causes of

polycystic ovaries were analyzed to predict the prevalence of Mets and its predictors. All the

study participants were aged between 18 to 45 years.

PCOS- Symptoms:

In our study 97.60% of the people had Oligomenorrhea, only 20.60% had infertility and the

proportion of study participants with Hirsutism was 28.23%. Zahiri, Z., et al108 (2016) have

67

shown that 87.6% of the study participants had oligomenorrhea. In a study of Mandrelle, K., et

al1 (2012) 84.2% having oligomenrrhea,81.7% were presented with infertility and similar to our

study 28.3% were had Hirsutism. Contrary to our study Bhattacharya, S. M.17 (2010) have

reported that 68.68% of the PCOS patients had Hirsutism and similarly study by Soares, E. M.,

et al11 (2008) shows that the proportion of oligomenorrhea was 56.9% which is lower than our

study. The proportions of infertility and hirsutism were 41.2% and 64.7%, which were higher

than current study findings.

Comparative table for symptoms of PCOS

Study Oligomenorrhea (%)

Infertility (%)

Hirsutism(%)

Current study 97.60% 20.60% 28.23%

Zahiri, Z., et al108 (2016) 87.6 % - -

Mandrelle, K et al1 (2012) 84.2% 81.7% 28.3%

Bhattacharya, S. M.17 (2010)- -

68.68%

Soares, E. M., et al11 (2008) 56.9% 41.2% 64.7%

Comorbidities and family history of comorbidities:

In the present study the proportion of cases with Diabetes mellitus was 1.80% and hypertension

was 5.90%. Zahiri, Z., et al108 (2016) showed that the proportion of DM was 6.0% and

Hypertension was 9.3% which are higher than current study findings. In Mandrelle, K et al1

(2012) study a total of 5.8% of cases were detected with DM which is higher than current study

findings. Similarly Jamil, A. S., et al87 (2015) reported the prevalence of DM-2 in 4.6% of cases

out of 263 PCOS cases .

68

In our study family history of diabetes was there for 18.2% of cases, family history of

hypertension and cardiac diseases were seen in 22.90% and 18.20%. contrary to our study

greater proportion of family history of diabetes was reported by Jamil, A. S., et al 87 (2015) , have

shown family history of diabetes in 49.8% of cases. Similarly study by Zahiri, Z., et al108 (2016)

have shown that 39.3% of PCOS cases have family history of DM. Slightly higher proportions

of family history of diabetes mellitus (34.2%) and hypertensive disorders (30.8%) were found in

Mandrelle, K et al1 (2012) than the current study but the proportion of history of ischemic heart

diseases in the family was 11.7% in Mandrelle, K et al1 (2012) study which is slightly lower

than current study findings.

Prevalence of metabolic syndrome and its individual components:

In the present study Prevalence of Metabolic syndrome was reported as 30%. Contradictory to

our study various Indian studies have reported higher prevalence values than current study.1, 17, 106

This was lower than the prevalence of 53.3% by NCEP ATP III criteria in the study of Varghese,

J., et al 106(2017). Study by Bhattacharya, S. M17 (2010) have reported 47.5% as per IDF criteria

which is also higher than current study findings. Mandrelle, K et al1 (2012) also reported higher

values of prevalence of Mets compared to current study where the prevalence was 37.5% by

modified NCEP ATP III criteria. Whereas study by Zahiri, Z., et al. (2016) have shown nearly

similar findings as our study where the prevalence of metabolic syndrome was 28.8%.

Ali Shaman, A., et al109 (2017). Have reported the prevalence of Mets in PCOS women

was 57.9% (by IDF criteria) which is greater than current study findings. The prevalence of

MetS in the cohort by Soares, E. M., et al11 (2008) of 102 women with PCOS was 28.4% (n = 29

patients)as per NCEP ATP III criteria.

69

Study Metabolic syndrome (%)

Current study 30.00% (IDF criteria)

Varghese, J., et al 106(2017) 53.3% (NCEP ATP III)

Ali Shaman, A., et al109 (2017). 57.9% (IDF criteria)

Zahiri, Z., et al108 (2016) 28.8% (ATP III)

Mandrelle, K et al1 (2012) 37.5% (NCEP ATP III)

Bhattacharya, S. M.17 (2010) 47.5% (IDF criteria)

Soares, E. M., et al11 (2008) 28.4% (NCEP ATP III)

Few studies in literature have reported lower prevalence values that the current study.

Mehrabian et al 110(2011)showed that the prevalence of MetS were 24.9% among Iranian women

diagnosed with different phenotypic subgroups of PCOS, based on the Rotterdam criteria. Moini

et al.111(2012) who conducted a study in Tehran, Iran, reported that prevalence of MetS in PCOS

women was 22.7%.

Differences in the prevalence of MBS in different parts of the world could be due to variations in

the population studied and because of different criteria used for diagnosing MBS.

The present study had reported the prevalence of the individual components of the metabolic

syndrome among PCOS cases were low HDL (91.80%), high WC (70.60%) and Systolic blood

pressure 130/K/C/O hypertension (36.50%). The prevalence of (8.20%) had Fasting blood

sugar was 8.2%, 20% had Triglycerides (≥150mg/dl). Similar findings was reported by

Varghese, J., et al 106(2017),where the prevalence of waist circumference ≥80 cm in 36 (86.6%),

High Density Lipoprotein (HDL-C) less than 50 mg/dl in 42 (93.3%), triglycerides ≥150 mg/dl in 17

70

(37.8%), blood pressure ≥130/85 mmHg in 7 (15.6%), and fasting plasma glucose ≥100 mg/dl in 13

(28.8%). Soares, E. M., et al11 (2008) The most prevalent isolated MetS abnormality in women

with PCOS was high-density lipoprotein cholesterol levels below 50 mg/dL, present in 69.6%

(71 out of 102). Other abnormalities were increased waist circumference in 57.9% (59/102),

increased serum triglycerides in 31.4% (32 out of 102), hypertension in 18.6% (19 out of 102),

and high fasting glucose concentrations in 2.9% (3 out of 102). Ehrmann, D. A., et al 112

(2006)have reported that the waist circumference exceeded 88 cm in 80%, HDL cholesterol was

less than 50 mg/dl in 66%, triglycerides were 150 mg/dl or greater in 32%, whereas blood

pressure was 130/85 mm Hg or greater in 21% and fasting glucose concentrations were 110

mg/dl or greater in 5%.

Association of Mets and its components:

In our study majority of the cases (33.9%) had metabolic syndrome aged between 20 to 24

years,30% of the participants had Mets between 25 to 29 years followed by 26.7% among people

less than 20 years. 21.4% of people aged 30 and above were had MetS. However, there was no

statistically significant association was found between age distributions of PCOS cases with

development of metabolic syndrome (P = 0.787). Similar findings were reported by Varghese, J.,

et al 106(2017) their findings have shown that there was no significant association between age

distributions of PCOS cases with development of metabolic syndrome (P=0.12). In their study

MBS was found in greater proportion of PCOS cases aged 31-35 years (83.3%) followed by 21-

25 years (57.9%). Study by Kumar, S. V., et al 113(2013). have shown that there was majority of

the PCOS cases in 51-60 years were found to be more affected with metabolic syndrome

(34.8%), followed by 41 to 50 years age group with the prevalence of 23.6%. Association

between age groups and Mets prevalence was statistically significant. (P<0.005). Study of

71

Soares, E. M., et al11 (2008) have reported similar findings where the prevalence of MetS in

women with PCOS aged 20 to 24 years was 12.1%, with a progressive increase to 31.7% in the

25- to 29-year group and 42.9% in the 30- to 34-year group. A previous case-control study by

ural, B., et al114 (2005) involving 86 women with PCOS aged between 18 and 22 years found a

MetS prevalence of 11%, supporting the idea that the prevalence of MetS in women with PCOS

is high in all age groups.

In the current study there was an increased trend in the prevalence of metabolic syndrome with

increasing BMI. Among the people with Normal (18.5 to 24.9) BMI, 13.7% had metabolic

syndrome. Where as in Overweight (25 to 29.9) people it was 27%. Prevalence of MetS was high

(51.7%) among obese (>30) BMI participants. Statistically significant association was there

between BMI distribution and metabolic syndrome (P value-<0.001). Study by Mandrelle, K et

al1 (2012) have shown similar study findings where the prevalence of Mets was increased with

increasing BMI. MetS was seen in 15.7% of people with ≤ 24.9 Kg /m2 and it was raised to 60%

among the people with ≥ 30 Kg /m2 BMI. Findings of Varghese, J., et al 106(2017) were in

agreement with current study findings i.e, raise in the prevalence of MetS with increase in BMI.

The prevalence of MetS was 36.4% among normal BMI people and it was raised to 69.2%

among Obese people with significant association (P=0.039). Study by Zahiri, Z., et al108 (2016)

study findings were in agreement with our study findings i.e 67.7% of people had MetS who are

Obese (BMI≥30) with statistically significant association(P=0.0001). Like current study the

prevalence of MetS was also statistically significantly associated with BMI in Soares, E. M., et

al11 (2008) the proportion of MetS in normal BMI people was only3.2% and it was raised to

52.3% in Obese people (BMI≥30) .

72

In our study the prevalence of metabolic syndrome in people with Hirsutism was 41.7%, with

statistically significant difference (P value-0.037). Study by Mandrelle, K et al1 (2012) have

shown similar study findings where the prevalence of Mets was 42.2% in People with Hirsutism

with statistically significant association.(P=0.009). Soares, E. M., et al11 (2008) have shown that

people with Hirsutism had the prevalence of MetS was 48.3% and statistically non significant

association (P=0.62)

In the present study among the people with oligomenorrhea only 28.3% had metabolic

syndrome. But the few existing studies have shown high prevalence values of MetS among the

people with oligomenorrhea. Zahiri, Z., et al108 (2016) have reported that the prevalence of MetS

among the people with oligomenorrhea was 89.7%. Soares, E. M., et al11 (2008) have shown that

the prevalence of Mets among the people with Amenorrhea was 72.4% which is higher than

current study finding with statistically non-significant association.

In our study the people with Infertility only 40% had metabolic syndrome with `statistically non-

significant difference between Infertility and metabolic syndrome. Similar findings was reported

by Soares, E. M., et al11 (2008), shows that the people with infertility had 62.1% of MetS and the

association was statistically nonsignificant.

In the current study the prevalence of metabolic syndrome 67.7% in people with Family history

of diabetes mellitus and it was 59% and 64.5% among the people with family history of

hypertension and cardiac diseases respectively. The difference between Family history of

diabetes mellitus, hypertension, cardiac diseases and metabolic syndrome was statistically

significant. (P value-<0.001). Study by Soares, E. M., et al11 (2008) Among the People with

Family History of Diabetes, Hypertension and cardiac diseases the prevalence of MetS was

73

72.4%,51.7% and 41.4% respectively and the association was statistically non-significant. Zahiri,

Z., et al108 (2016) have reported that the prevalence of MetS among the people with family

history of diabetes was 50.8% with statistically significant association. Our study findings were

in agreement with the findings of Ehrmann, D. A et al112 (2006) where women with a family

history of diabetes exhibited a significantly greater number of individual components of the

metabolic syndrome (2.37_0.11 vs. 1.92_0.08; P_0.01)

Conclusions:

Among the study population 30(17.60%) people were aged less than 20 years,

56(32.90%) were aged between 20 to 24 years, 70(41.20%) were aged between 25 to 29

years and remaining 14(8.20%) were aged 30 years and above.

Among the study population 73(42.90%) people had Normal (18.5 to 24.9) BMI,

37(21.80%) were Overweight (25 to 29.9) and remaining 60(35.30%) people were obese

(>30)

Among the study population 50(29.40%) people had waist circumference up to 79.9,

120(70.60%) people had 80 and above.

Among the study population 108(63.50%) people had systolic blood pressure Up to

129.99, 62(36.50%) people had 130 and above. Among the study population 136(80%)

people had diastolic blood pressure Up to 84.9, 34(20.00%) people had 85 and

above.Among the study population 156(91.80%) people had FBS category Up to 99.9

mg/dl, 14(8.20%) people had 100 mg/dl and above. Among the study population

136(80.00%) people had Triglycerides Up to 149.9 mg/dl, 34 (20.00%) people had 150

74

mg/dl and above. Among the study population 128(75.30%) people had HDL Up to 49.9

mg/dl, 42 (24.70%) people had 50 mg/dl and above.

Among the study population 166(97.60%) people had Oligomenrrhea, 35(20.60%) had

infertility, 48(28.23%) had Hirsutism.

Among the study population 3(1.80%) had Diabetes mellitus and 10(5.90%) had

hypertension.

Among the study population 31(18.20%) had family history of diabetes, 39(22.90%) had

hypertension and 31(18.20%) had family history of cardiac diseases.

Among the study population 120(70.60%) people had WC(80 and above), 62(36.50%)

had Systolic blood pressure 130/K/C/O hypertension, 34(20%) had Diastolic blood

pressure ( 85 / K/C/O hypertension, 14(8.20%) had Fasting blood sugar (100mg/dl and

above)/ /Diabetes mellitus, 34(20%) had Triglycerides (≥150mg/dl) and 128(91.80%) had

HDL(Up to 49.9mg/dl).

Among the study population 51(30%) people had metabolic syndrome.

Among the people less than 20 years 8 (26.7%) people had metabolic syndrome, among

the people between 20 to 24 years 19 (33.9%) had metabolic syndrome, among the

people between 25 to 29 years 21 (30%) had metabolic syndrome, among the people 30

and above years old 3 (21.4%) had metabolic syndrome. The difference between age

group and metabolic syndrome was statistically not significant. (P value-0.787).

Among the people with Normal (18.5 to 24.9) BMI 10 (13.7%) had metabolic syndrome,

among the people with Over weight (25 to 29.9 ) BMI 10 (27%) had metabolic

syndrome, among the people with Obese(>30)BMI 31 (51.7%) had metabolic syndrome.

75

The difference between BMI distribution and metabolic syndrome was statistically

significant. (P value-<0.001).

Among the people with Hirsutism only 20 (41.7%) had metabolic syndrome. The

difference between Hirsutism and metabolic syndrome was significant. (P value-0.037).

Among the people with oligomenrrhea only 47 (28.3%) had metabolic syndrome

Among the people with Infertility only 14 (40%) had metabolic syndrome. The difference

between Infertility and metabolic syndrome was not significant. (P value-0.147).

Among the people with Family history of diabetes mellitus only 21 (67.7%) had

metabolic syndrome. The difference between Family history of diabetes mellitus and

metabolic syndrome was significant. (P value-<0.001).

Among the people with Family history of hypertension only 23 (59%) had metabolic

syndrome. The difference between Family history of hypertension and metabolic

syndrome was significant. (P value-<0.001).

Among the people with Family history of cardiac disease only 20 (64.5%)had metabolic

syndrome. The difference between Family history of cardiac disease and metabolic

syndrome was significant. (P value-<0.001).

76

77

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ANNEXURES

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