Insulin Resistance and b-cell Function
Calculated by Homeostasis Model Assessment
in Lean, Overweight, and Obese Women with
Polycystic Ovary Syndrome
Ido Sirota, M.D., M.H.A., Daniel E. Stein, M.D., Mario Vega, M.D., and
Martin D. Keltz, M.D.
86.60±40.91, respectively; p<0.0001). Adequate statisOBJECTIVE: To evaluate the homeostasis model astical power was not present to distinguish a difference
sessment (HOMA) measurement of insulin resistance
between overweight and normal-weight participants.
(IR) and pancreatic β-cell function (%β) and compare
A positive linear correlathose values between groups
tion was found between log
of healthy-weight, over…there is great value in
HOMA-IR and BMI, and
weight, and obese women
between log HOMA-%β and
with polycystic ovary synidentifying women at risk
BMI.
drome (PCOS).
[for insulin resistance]
CONCLUSION:
Obese
STUDY DESIGN: Retro­
PCOS
patients
have
a
higher
spective cohort study of
as early as possible.
risk of elevated insulin resiswomen aged 24–48 with
tance and β-cell function
PCOS, diagnosed according
than do those with BMI <30. (J Reprod Med 2016;61:
to 2004 Rotterdam criteria. Participants were grouped
by BMI. Quantitative variables were compared by one3–10)
way ANOVA and the Tukey method. Analysis for power
to detect a difference between means was conducted.
Keywords: BMI, body mass index, glucose intolerPearson correlation was used to test differences in freance, homeostasis, homeostasis model assessment,
quency distribution.
insulin resistance, obesity, pancreatic β-cell funcRESULTS: By BMI category, 29 participants were
tion, polycystic ovary syndrome, waist-to-hip ratio.
of healthy weight, 11 were overweight, and 11 were
obese. HOMA-IR was significantly higher in obese
The most common endocrinopathy seen in women
women as compared to overweight and healthy-weight
of reproductive age is polycystic ovary syndrome
patients (2.88±2.09, 1.13±0.73, 0.84±0.49, respective(PCOS).1 It is characterized by hyperandrogenism
ly; p<0.0001). Moreover, HOMA-%β was significantly
and chronic anovulation, and its prevalence is esincreased in obese women as compared to overweight and
timated at 5–10% of reproductive-aged women.2-4
healthy-weight patients (186.89±131.62, 106.83±46.77,
Clinical manifestations of PCOS include obesity,
From the Department of Obstetrics and Gynecology, St. Luke’s–Roosevelt Hospital Center, New York, New York.
Address correspondence to: Ido Sirota, M.D., M.H.A., Roosevelt Hospital, 1000 Tenth Avenue, Suite 10C-01, New York, NY 10019
([email protected]).
Financial Disclosure: The authors have no connection to any companies or products mentioned in this article.
0024-7758/16/6101-02–0003/$18.00/0 © Journal of Reproductive Medicine®, Inc.
The Journal of Reproductive Medicine®
3
ORIGINAL ARTICLES
The Journal of Reproductive Medicine®
4
hirsutism, acne, menstrual disorders, insulin resistance (IR), metabolic syndrome, and infertility.5,6
PCOS is a collection of signs and features in which
no single phenomenon is pathognomonic on its
own.7 The diagnosis of PCOS is determined either
by the 2004 revised Rotterdam consensus criteria8,9
or by the National Institutes of Health (NIH) criteria.10 Based on these definitions of PCOS, completely different phenotypes with heterogenic metabolic
risk profiles are possible.7,11
Obesity is highly prevalent in PCOS—reaching
67%12—and exacerbates IR and impaired glucose
tolerance in women with PCOS.13,14 The increased
prevalence of obesity in PCOS is associated with
an increased prevalence of metabolic syndrome
and type 2 diabetes mellitus (T2DM).15 Although
many women with PCOS are also obese, obesity
per se is not one of the diagnostic criteria for PCOS.
In fact, it has been established that women who
are lean can also have PCOS and metabolic characteristics of the syndrome.16
Insulin resistance, defined as reduced sensitivity or responsiveness to the metabolic actions of
insulin, is linked to obesity17 and contributes to
hyperandrogenism.18,19 Despite the fact that the
exact pathophysiology of PCOS is unknown, a
relationship between IR and PCOS was reported
as early as 1980.18 Later studies have demonstrated that IR is a key feature in the pathogenesis
of PCOS,20 with prevalence estimated at 60–79%,
independent of obesity.21-23 In addition, IR has
been found to increase the prevalence of metabolic syndrome 11-fold7,24,25 and has also been
shown to significantly increase the incidence of
endothelial dysfunction23 and T2DM,26 all wellestablished risk factors for cardiovascular disease.24,27,28 As IR—generally asymptomatic in its
early stages—may lead to impaired glucose tol­
erance and T2DM, there is great value in identifying women at risk as early as possible. Currently,
the management of IR in PCOS includes exercise,
weight loss, and insulin sensitizers such as metformin.29
There are various methods used to estimate
IR, and each of these methods has advantages
and limitations.30 The size and type of a study
being conducted often dictates the method used,
and no single method is necessarily appropriate
under all circumstances. The hyperinsulinemiceuglycemic clamp method has been proved to be
the gold standard for measuring insulin sensitivity30,31 but is not in common use in the clinical set-
The Journal of Reproductive Medicine®
ting due to its high cost in dollars, time, and labor32
as compared to homeostatic measurements.30,33
The homeostasis model assessment (HOMA)
method, which was first described in 1985 by
Matthews et al,34 has been shown to correlate well
with the clamp technique and is widely used in
clinical and epidemiological research due to its
simplicity. This model uses fasting plasma insulin
(μU/mL) and fasting plasma glucose (mg/dL) to
calculate IR and pancreatic β-cell function (%β)
values.
The objectives of our study were threefold. In
lean, overweight, and obese groups of women
with PCOS we sought to (1) evaluate HOMA
measurement of IR and pancreatic β-cell function,
(2) compare HOMA-IR and HOMA-%β correlations with body mass index (BMI) and waist-tohip ratio (WHR), and (3) determine the effect of
ethnicity on the predisposition to increased IR and
loss of β-cell function.
Materials and Methods
We retrospectively studied 51 women with PCOS
treated in our center in 2010; the study subjects
were 24–48 years old and did not have concomitant thyroid dysfunction, diabetes, hyperprolactinemia, cardiovascular, renal, or hepatic abnormalities, and had not taken any medication that could
affect glucose or sex hormone metabolism, such as
metformin or hormonal contraceptives, within 3
months of the study.
The study was approved by the Institutional
Review Board of St. Luke’s–Roosevelt Hospital
Center. Demographic and laboratory data were
abstracted from our computerized database. Dur­
ing the clinical examination, height was recorded
to the nearest 0.5 cm, and weight was recorded
with minimum clothing to the nearest 0.1 kg.
According to BMI, women were grouped as
normal weight (BMI 18.5–24.9 kg/m²), overweight
(BMI 25–29.9 kg/m²), or obese (BMI ≥ 30 kg/m²).
These same women were also grouped by WHR
as obese (WHR ≥ 0.80) or healthy weight (WHR
<0.80).
Diagnosis of PCOS
The clinical diagnosis of PCOS was made using
the Rotterdam criteria when women had at least
2 of the 3 criteria: ovulatory dysfunction (oligo­
ovulation or anovulation), excess androgen activity (hirsutism, acne, or elevated serum androgens),
and/or polycystic ovaries viewed by ultrasound.10,11
5
Volume 61, Number 1-2/January-February 2016
Oligomenorrhea was defined as <8 menses per
year or cycles longer than 35 days in length. Hirsutism was evaluated according to the FerrimanGallwey method.35 Polycystic ovarian morphology was diagnosed using transvaginal ultrasound
when at least 1 ovary had ≥12 antral follicles with a
mean diameter <10 mm and/or a classic “string of
pearls” appearance.
glucose level <140 mg/dL (7.8 mmol/L) 2 hours
after the oral glucose load. Abnormal glucose tol­
erance was defined as having either impaired glucose tolerance or T2DM. Impaired glucose tolerance was defined as a plasma glucose level of
140 mg/dL (7.8 mmol/L) to 199 mg/dL (11.05
mmol/L). Diabetes was defined as a plasma glucose level of ≥200 mg/dL (11.1 mmol/L).
Biochemical Measurements
Calculation of Insulin Resistance and β-Cell Function
All serum assays were performed by standard procedures after a minimum of an 8-hour fast. Plasma
glucose levels were measured using spectrophotometry (Beckman Coulter AU640, Quest Diagnostics, New York, New York). The calibration range
of the assay was 10–750 mg/dL with an analytical sensitivity of 10 mg/dL. The intraassay critical
values were <40 mg/dL (low) and >500 mg/dL
(high).
Plasma insulin was determined using a solidphase two-site chemiluminescent immunometric
assay (Immulite 2000, Quest Diagnostics). The calibration range of the assay was 5–300 μIU/mL
with an analytical sensitivity of 5 μIU/mL. The
intraassay critical values were 5.5, 4.0, 3.3, 3.9, 3.8,
and 3.7% at the levels of 7.67, 12.5, 17.2, 26.4, 100,
and 291 μIU/mL, respectively. The corresponding
interassay critical values were 7.3, 4.9, 4.1, 5.0, 4.2,
and 5.3%.
Total testosterone (TT) was measured36 with liquid chromatography and tandem mass spectrom­
etry (LC/MS/MS, Quest Diagnostics). The cali­
bration range of the TT assay was 2–2000 ng/dL,
with an analytical sensitivity of 2 ng/dL. The crossreaction with 5α-dihydrotestosterone was 2%.
Dehydroepiandrosterone sulfate (DHEAS) was
measured with chemiluminescent enzyme immunoassays (Immulite 2000). The calibration range
of the DHEAS assay was 0.41–27.14 μmol/L, with
an analytical sensitivity of 0.41 μmol/L. No crossreactivity with other compounds was known.
Insulin resistance and β-cell function were measured using the HOMA-IR and β-cell function
(HOMA-%β) calculations.34 HOMA-IR was calculated using the formula HOMA-IR = (fasting insulin μIU/mL × fasting glucose mmol/L)/22.5, and
IR was diagnosed when HOMA was >2.67.17,18
Insulin levels were considered elevated when they
were above the laboratory reference range based
on the 95th percentile for the U.S. population, and
IR was diagnosed if either fasting insulin was >19
μIU/mL or 2-hour (after 75-g oral glucose) insulin
was >79 μIU/mL. HOMA-%β was calculated using
the formula
Plasma Glucose and Insulin Measurements
Results
Demographic Characteristics
A blood sample was obtained after an 8- to 10-hour
fast to measure glucose and insulin levels. Another blood sample for glucose and insulin was obtained 2 hours after an oral intake of 75 g of glucose (Sun-Dex Glucose Tolerance Test Beverage,
Fisher Health-Care, Houston, Texas). This oral glucose tolerance test (OGTT) was performed according to the World Health Organization criteria.37
Normal glucose tolerance was defined as a plasma
HOMA-%β = (20 × fasting insulin μIU/mL)/(glucose mg/dL – 3.5).
Statistical Analyses
All values are expressed as mean ± SD. Quantitative variables were compared by one-way ANOVA
(data with normal distribution). Post-hoc tests for
homogeneous subsets were performed using the
Tukey method. Post-hoc analysis for power to detect a difference between means was conducted
(Sample Size Calculator, Decision Support Systems, LP, 2013). Pearson correlation was used to
test the differences in frequency distribution. The
level of significance in all analyses was set at 5%
(p<0.05). Unless otherwise noted, statistical analyses were performed using the SPSS 15.0 software
for Windows (SPSS Inc.).
Overall, 51 patients participated in the study. Ac­
cording to BMI criteria 57% (29/51) had a healthy
weight, 21.5% (11/51) were overweight, and 21.5%
(11/51) were obese. No age differences were found
between the 3 groups of patients. When stratifying
the BMI categories by ethnicity, all Asian patients
had normal weight BMI, while the majority of
Hispanic patients were obese (Table I).
6
The Journal of Reproductive Medicine®
Table I Race Distribution by BMI Category
BMI category
Asian African-AmericanCaucasianHispanic
No. (%)*
No. (%)*
No. (%)*
No. (%)*
Normal weight (N=29)
Overweight (N=11)
Obese (N=11)
Total
8 (100)
0 (0)
0 (0)
8
3 (37.5)
2 (25)
3 (37.5)
8
Total
16 (59.3)
2 (25.0)
29
8 (29.6)
1 (12.5)
11
3 (11.1)
5 (62.5)
11
278 51
*% within race.
Laboratory Findings
Fasting insulin was significantly higher in obese
women as compared to overweight and healthyweight patients (14.36±8.68, 5.54±3.26, and 4.34±
2.45, respectively; p<0.0001). In addition, fasting
glucose was significantly higher in obese women
than it was in the other 2 groups (88.00 ±7.19,
81.54±4.94, and 80.41±5.34, respectively; p<0.003).
No differences in fasting insulin and fasting glucose levels were found between healthy-weight
and overweight subjects. The 2-hour OGTT insulin
and glucose values showed no significant differences among the 3 BMI groups. Moreover, neither TT (62.30 ± 29.43, 60.00 ± 28.97, 63.40 ± 29.18;
p=0.96) nor DHEAS (169.66 ±93.65, 235.30 ±110.26,
164.18±87.14; p=0.14) levels varied significantly
between the healthy-weight, overweight, and obese
groups, respectively.
HOMA-IR and HOMA-%b Values Stratified by BMI
BMI (Figure 1) and between log HOMA-%b and
BMI (Figure 2).
HOMA-IR and HOMA-%b Stratified by WHR
According to the WHR parameters 61% (31/51) of
patients had a healthy weight and 39% (20/51) were
obese. Table IV illustrates that log (HOMA-IR) was
significantly higher in obese women as compared
to healthy-weight women (0.53±0.84 and 0.20 ±
0.65, respectively; p<0.002). Table V shows that log
(HOMA-%b) was significantly increased in obese
women as compared to the healthy-weight group
(4.93±0.63 and 4.42±0.51, respectively; p<0.006).
Correlations Between Measures of Obesity and
Measures of Metabolic Function
The correlation between HOMA-IR and BMI (r=
0.62, p<0.0001) was stronger than that with WHR
(0.42, p<0.004) (Figure 2). Similarly, correlation
between HOMA-%b and BMI (r=0.54, p<0.0001)
was stronger than that between HOMA-%b and
WHR (r=0.36, p<0.015).
As shown in Table II, HOMA-IR was significantly
higher in obese women as compared to overweight
and healthy-weight patients (2.88 ±2.09, 1.13±0.73,
0.84±0.49, respectively; p<0.0001). Moreover, as
shown in Table III, HOMA-%b was significantly
increased in obese women as compared to the
other 2 groups (186.89 ±131.62, 106.83 ±46.77, 86.60±
40.91, respectively; p<0.0001). While no differences
in HOMA-IR and HOMA-%b were found between
healthy-weight and overweight women, adequate
statistical power was not present to distinguish a
difference between these groups. A positive linear
correlation was found between log HOMA-IR and
In this study we aimed to evaluate the HOMA
methods of computing IR and pancreatic b-cell
function in obese, overweight, and healthy-weight
women with PCOS. Using the HOMA-IR formula,
we found increased insulin resistance in the obese
PCOS patient group as compared to the overweight
and healthy-weight groups; similarly, our HOMA%b calculations yielded increased b-cell function
in obese PCOS patients as compared to the other
Table II Mean HOMA-IR by BMI Category
Table III Mean HOMA-%β by BMI Category
BMI
BMI
No.Mean SD Significance
18.5–24.9
25–29.9
≥30
29
86.60
40.91
11
106.83
46.77
11 186.89131.62
No.Mean SD Significance
18.5–24.9 29 0.840.49 p < 0.000
25–29.9 11 1.130.73
≥30
11 2.882.09
Discussion
p < 0.001
7
Volume 61, Number 1-2/January-February 2016
Table IV Mean Log HOMA-IR by WHR Category
WHR No.Mean SD Significance
< 0.80 31 0.200.65 p < 0.002
≥0.80 20 0.530.84
Figure 1 Correlation between BMI and log HOMA-IR in all study
patients (N=51). Log transformation was used to normalize the
HOMA-IR values. R²=0.391.
2 groups. Neither metabolic parameter appeared
elevated in the healthy-weight and the overweight
PCOS patient groups; however, the sample size
did not allow detection of a statistical difference
between the means of those groups. BMI was found
to be a better predictor than WHR in detecting insulin resistance and b-cell function. Given the linear
relationship between BMI and both log HOMA-%b
and log HOMA-IR, we expect a larger sample size
to yield a statistically significant difference between
the overweight and healthy-weight groups.
Our results that demonstrate increased IR in
obese PCOS patients are in agreement with those of
Stovall et al.16 They also found that obese women
with PCOS have a greater degree of IR as com­
pared to lean women with PCOS, and that BMI is
highly predictive of both insulin and glucose levels
in women with PCOS. Further support of our findings comes from Cupisti et al,38 who found that in
PCOS patients, those with BMI ≥25 kg/m² demonstrated more significant changes in endocrine and
metabolic parameters, including IR, than did leaner
PCOS women. We managed to further stratify the
weight distribution among our study population
and showed that IR is associated with obesity
(BMI ≥30 kg/m²) but not with overweight status
(BMI 25–29.9 kg/m²). When using the HOMA-%b
calculation, we found that the pancreatic b-cells
produced excess insulin only in PCOS patients with
BMI of ≥30.0 kg/m².
In our study population, among different races
we found Asian patients with PCOS to be lean,
while Hispanic patients with PCOS were more
likely to be obese. BMI and WHR were used as
measurements to evaluate obesity. Elevated BMI
has been described as a risk factor for adverse
health consequences, especially among patients
with metabolic syndrome or IR.39 Moreover, BMI
has been found to be highly predictive of both insulin and glucose levels in women with PCOS.16
With regard to the correlation between BMI and
IR in PCOS patients, our findings were different
from those of Hurd et al,40 who concluded that
obesity was not an accurate marker for IR in PCOS
women. In contrast to our findings of absent IR in
Table V Mean Log HOMA-%β by WHR Category
Figure 2 Correlation between BMI and log HOMA-%β in all
study patients (N=51). Log transformation was used to normalize
the HOMA-%β values. R² = 0.296.
WHR No.Mean SD Significance
< 0.80 31 4.420.51 p < 0.006
≥0.80 20 4.930.63
8
lean PCOS women, a recent study in Japan using
the HOMA method found that lean PCOS women
had IR.41 One possible explanation for the differences between our findings and those of other
studies may be differences in ethnicity. Our study
population was ethnically diverse and urban, while
other studies might have had a more homogeneous population. A review of the literature demonstrates a relationship between race and the prevalence of insulin resistance.41-45 Gue et al found
that associations specific for ethnic background
were found between BMI, HOMA-IR, and PCOS
when comparing Chinese and Dutch Caucasian pa­
tients.42 A BMI of ≥30.0 kg/m² was associated with
a greater incidence of PCOS than was a lower BMI
in indigenous women in Australia.43 Studies conducted in the USA demonstrated greater IR among
Mexican-Americans with PCOS as compared to
age- and weight-matched non-Hispanic whites.46,47
In Japan, 30–40% of young PCOS patients are obese
(as compared to the 18% incidence of obesity in their
general population), but the incidence of obe­sity is
lower in Japanese PCOS patients than in American
and European PCOS patients.41 Previous studies
support our findings that the prevalence of glucose
intolerance in Asian PCOS patients appears to be
lower than that in non-Asian PCOS patients.26,48,49
In summary, the extent to which racial background
affects the risk of IR in PCOS patients has not been
fully determined, but it has been shown that ethnic
and racial variations strongly affect the clinical presentation of PCOS in women.48,50
Although the pathophysiology of IR in PCOS is
not completely understood, it has been shown that
compensatory hyperinsulinism plays an essential
role by stimulating androgen synthesis in the thecal
cells and decreasing hepatic sex-hormone-binding
globulin synthesis in the liver, thereby increasing
free androgen availability.51,52
In our study the sample size may have been
too small to differentiate androgen levels between
healthy-weight, overweight, and obese patients.
However, our data may indicate that hyperandrogenism and obesity relate to insulin resistance in a
manner that is not completely interdependent.
An alternative mechanism for the pathophysiology of IR in PCOS is a possible post-receptor
defect in insulin receptor–mediated cells.20,53 Studies on adipocytes, fibroblasts, and skeletal muscles
obtained from women with PCOS demonstrated
no changes in insulin binding or receptor affinity
but did demonstrate decreased tyrosine phosphor-
The Journal of Reproductive Medicine®
ylation and increased serine phosphorylation of
the insulin receptor.51 Nestler et al52 found that
insulin-receptor serine phosphorylation, and thereby decreased tyrosine kinase activity, might induce
a decrease in glucose transporters.
As discussed previously, IR is known to be a key
feature associated with the risk of the metabolic
syndrome and the development of T2DM. The
urgent need for a simple way of measuring insulin
resistance has led to a search for a noninvasive
measurement of insulin sensitivity. Several models have been evaluated that are based on fasting
insulin and fasting glucose levels and use mathematical calculations to assess insulin sensitivity
and pancreatic b-cell function. Matsuda et al54
concluded that HOMA is a simple and accurate
method for the assessment of insulin sensitivity;
it is widely used in the majority of clinical studies.
In 2006 Mohlig et al55 reported that HOMA calculations were a better predictor of IR than fasting
insulin by itself, allowing the formula to be utilized
in stratified metabolic screening of PCOS patients
undergoing OGTT.
In conclusion, we believe that HOMA is a simple and reliable method for identifying IR and the
compensatory pancreatic b-cell function in woman
with PCOS when stratifying for BMI. Given the
evidence of increased IR and b-cell function with
obesity, clinicians should counsel overweight and
obese women with PCOS to lose weight to prevent
or minimize IR. Special consideration should be
given to race in relation to IR. Further studies are
needed to assess the extent to which racial background affects the pathogenesis of IR in women
with PCOS.
Acknowledgment
The authors wish to acknowledge the writing assistance of Carolyn Waldron, M.S., M.A., departmental medical editor.
References
1. Otta CF, Wior M, Iraci GS, et al: Clinical, metabolic, and endocrine
parameters in response to metformin and lifestyle intervention in
women with polycystic ovary syndrome: A randomized, doubleblind, and placebo control trial. Gynecol Endocrinol 2010;26:173-178
2. Azziz R, Woods KS, Reyna R, et al: The prevalence and features of
the polycystic ovary syndrome in unselected population. J Clin Endocrinol Metab 2004;89:2745-2749
3. Asuncion M, Calvo RM, San Millan JL, et al: A prospective study of
the prevalence of the polycystic ovary syndrome in unselected Caucasian women from Spain. J Clin Endocrinol Metab 2000;85:2434-2438
4. Diamanti-Kandarakis E, Kouli CR, Bergiele AT, et al: A survey of the
polycystic ovary syndrome in the Greek island of Lesbos: Hormonal
and metabolic profile. J Clin Endocrinol Metab 1999;84:4006-4011
Volume 61, Number 1-2/January-February 2016
9
5. Ehrmann D: Polycystic ovary syndrome. N Engl J Med 2005;352:12231236
and atherosclerotic cardiovascular disease. Diabetes Care 1991;14:173194
6. Ehrmann DA, Barnes RB, Rosenfield RL, et al: Prevalence of impaired
glucose tolerance and diabetes in women with polycystic ovary syndrome. Diabetes Care 1999;22:141-146
26. Legro RS, Kunselman AR, Dodson WC, et al: Prevalence and predictors of risk for type 2 diabetes mellitus and impaired glucose tolerance
in polycystic ovary syndrome: A prospective, controlled study in 254
affected women. J Clin Endocrinol Metab 1999;84:165-169
7. Azziz R, Carmina E, Dewailly D, et al: Androgen Excess Society
position statement: Criteria for defining polycystic ovary syndrome
as a predominantly hyperandrogenic syndrome: An Androgen Excess
Society guideline. J Clin Endocrinol Metab 2006;91:4237-4245
8. Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop
Group: Revised 2003 consensus on diagnostic criteria and long-term
health risks related to polycystic ovary syndrome. Fertil Steril 2004;81:
19-25
9. The Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group: Revised 2003 consensus on diagnostic criteria and longterm health risks related to polycystic ovary syndrome (PCOS). Hum
Reprod 2004;19:41-47
10. Zawadzki JK, Dunaif A: Diagnostic criteria for polycystic ovary syndrome towards a rational approach. In Polycystic Ovary Syndrome.
Edited by A Dunaif, JR Givens, FF Haseline, GR Merriam. Boston,
Blackwell Scientific, 1992, pp 377-384
11. Azziz R: Controversy in clinical endocrinology: Diagnosis of poly­
cystic ovarian syndrome: The Rotterdam criteria are premature. J Clin
Endocrinol Metab 2006;91:781-785
12. Lo JC, Feigenbaum SL, Yang J, et al: Epidemiology and adverse
cardiovascular risk profile of diagnosed polycystic ovary syndrome.
J Clin Endocrinol Metab 2006;91:1357-1363
13. Dos Reis RM, Foss MC, de Moura MD, et al: Insulin secretion in obese
and non-obese women with polycystic ovary syndrome and its relationship with hyperandrogenism. Gynecol Endocrinol 1995;9:45-50
14. Premoli AC, Santana LF, Ferriani RA, et al: Growth hormone secretion
and insulin-like growth factor-1 are related to hyperandrogenism in
nonobese patients with polycystic ovary syndrome. Fertil Steril 2005;
83:1852-1855
15. Dokras A, Bochner M, Hollinrake E, et al: Screening women with
polycystic ovary syndrome for metabolic syndrome. Obstet Gynecol
2005;106:131-137
16. Stovall DW, Bailey AP, Pastore LM: Assessment of insulin resistance
and impaired glucose tolerance in lean women with polycystic ovary
syndrome. J Womens Health 2011;20:37-43
17. Reaven GM: Insulin resistance: The link between obesity and cardiovascular disease. Med Clin North Am 2011;95:875-892
18. Burghen GA, Givens JR, Kitabchi AE: Correlation of hyperandrogenism with hyperinsulinism in polycystic ovarian disease. J Clin
Endocrinol Metab 1980;50:113-116
19. Nestler JE, Strauss JF III: Insulin as an effector of human ovarian
and adrenal steroid metabolism. Endocrinol Metab Clin North Am
1991;20:807-823
20. Dunaif A: Insulin resistance and the polycystic ovary syndrome: Mechanism and implications for pathogenesis. Endocr Rev 1997;18:774-800
21. DeUgarte CM, Bartolucci AA, Azziz R: Prevalence of insulin resistance in the polycystic ovary syndrome using the homeostasis model
assessment. Fertil Steril 2005;83:1454-1460
22. Carmina E, Lobo RA: Use of fasting blood to assess the prevalence of
insulin resistance in women with polycystic ovary syndrome. Fertil
Steril 2004;82:661-655
23. Paradisi G, Steinberg HO, Hempfling A, et al: Polycystic ovary syndrome is associated with endothelial dysfunction. Circulation 2001;
103:1410-1415
27. Isomaa B, Almgren P, Tuomi T, et al: Cardiovascular morbidity and
mortality associated with the metabolic syndrome. Diabetes Care
2001;24:683-689
28. Hsueh WA, Lyon CJ, Quinones MJ: Insulin resistance and the endothelium. Am J Med 2004;117:109-117
29. Legro RS, Finegood D, Dunaif A: A fasting glucose to insulin ratio is a
useful measure of insulin sensitivity in women with polycystic ovary
syndrome. J Clin Endocrinol Metab 1998;83:2694-2698
30. Legro RS, Castracane VD, Kauffman RP: Detecting insulin resistance
in polycystic ovary syndrome: purposes and pitfalls. Obstet Gynecol
Surv 2004;59:141-154
31. DeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: A meth­
od for quantifying insulin secretion and resistance. Am J Physiol 1979;
237:E214–E223
32. Wallace TM, Levy JC, Matthews DR: Use and abuse of HOMA modeling. Diabetes Care 2004;27:1487-1495
33. Muniyappa R, Lee S, Chen H, et al: Current approaches for assessing
insulin sensitivity and resistance in vivo: advantages, limitations, and
appropriate usage. Am J Physiol Endocrinol Metab 2008;294:e15-26
34. Matthews DR, Hosker JP, Rudenski AS, et al: Homeostasis model
assessment: Insulin resistance and beta-cell function from fasting
plasma glucose and insulin concentrations in man. Diabetologia 1985;
28:412-419
35. Ferriman D, Gallwey JD: Clinical assessment of body hair growth in
women. J Clin Endocrinol Metab 1961;21:1440-1447
36. Rosner W, Auchus RJ, Azziz R, et al: Position statement: Utility, limitations, and pitfalls in measuring testosterone: An Endocrine Society
Position Statement. J Clin Endocrinol Metab 2007;92:405-413
37. World Health Organization: Diabetes Mellitus: Report of a WHO
Study Group. World Health Organ Tech Rep Ser 1985;727:1-113
38. Cupisti S, Kajaia N, Dittrich R, et al: Body mass index and ovarian
function are associated with endocrine and metabolic abnormalities
in women with hyperandrogenic syndrome. Eur J Endocrinol 2008;
158:711-719
39. Meigs JB, Wilson PW, Fox CS, et al: Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. J Clin
Endocrinol Metab 2006;9:2906-2912
40. Hurd WW, Abdel-Rahman MY, Ismail SA, et al: Comparison of diabetes mellitus and insulin resistance screening methods for women with
polycystic ovary syndrome. Fertil Steril 2011;96:1043-1047
41. Takeuchi T, Tsutsumi O, Taketani Y: Abnormal response of insulin
to glucose loading and assessment of insulin resistance in non-obese
patients with polycystic ovary syndrome Gynecol Endocrinol 2008;
24:385-391
42. Guo M, Chen ZJ, Eijkemans MJ, et al: Comparison of the phenotype
of Chinese versus Dutch Caucasian women presenting with polycystic
ovary syndrome and oligo/amenorrhoea. Hum Reprod 2012;27:14811488
43. Boyle JA, Cunningham J, O’Dea K, et al: Prevalence of polycystic
ovary syndrome in a sample of Indigenous women in Darwin, Australia. Med J Aust 2012;196:62-66
24. Eckel RH, Grundy SM, Zimmet PZ: The metabolic syndrome. Lancet
2005;365:1415-1428
44. Haffner SM, D’Agostino R, Saad MF, et al: Increased insulin resistance
and insulin secretion in nondiabetic African-Americans and Hispanics
compared with non-Hispanic whites. The Insulin Resistance Atherosclerosis Study. Diabetes 1996;45:742-748
25. DeFronzo RA, Ferrannini E: Insulin resistance: A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia,
45. Bahceci M, Tuzcu A, Bahceci S, et al: Is hyperprolactinemia associated
with insulin resistance in non-obese patients with polycystic ovary
10
syndrome? J Endocrinol Invest 2003;26:655-659
46. Kauffman RP, Baker TE, Graves-Evenson K, et al: Lipoprotein profiles
in Mexican American and non-Hispanic white women with polycystic
ovary syndrome. Fertil Steril 2011;96:1503-1507
47. Kauffman RP, Baker VM, Dimarino P, et al: Polycystic ovarian syndrome and insulin resistance in white and Mexican American women:
A comparison of two distinct populations. Am J Obstet Gynecol 2002;
187:1362-1369
48. Ehrmann DA, Liljenquist DR, Kasza K, et al; PCOS/Troglitazone
Study Group: Prevalence and predictors of the metabolic syndrome
in women with polycystic ovary syndrome. J Clin Endocrinol Metab
2005;91:48-53
49. Vrbikova J, Dvorakova K, Grimmichova T, et al: Prevalence of insulin
resistance and prediction of glucose intolerance and type 2 diabetes
mellitus in women with polycystic ovary syndrome. Clin Chem Lab
Med 2007;45:639-644
50. Solomon CG: The epidemiology of polycystic ovary syndrome. Prev-
The Journal of Reproductive Medicine®
alence and associated disease risks. Endocrinol Metab Clin North Am
1999;28:247-263
51. Barbieri R, Makris R, Randall R, et al: Insulin stimulates androgen
accumulation in incubations of ovarian stroma obtained from women
with hyperandrogenism. J Clin Endocrinol Metab 1986;62:904-910
52. Nestler JE, Powers LP, Matt DW, et al: A direct effect of hyperinsulinemia on serum sex hormone-binding globulin levels in obese
women with polycystic ovary syndrome. J Clin Endocrinol Metab
1991;72:83-89
53. Ciaraldi TP, El-Roeiy A, Madar Z, et al: Cellular mechanisms of insulin resistance in polycystic ovary syndrome. J Clin Endocrinol Metab
1992;75:577-583
54. Matsuda M, De Fronzo RA: Insulin sensitivity indices obtained from
oral glucose tolerance testing. Diabetes Care 1999;22:1462-1470
55. Mohlig, M, Spranger J, Ristow M, et al: Predictors of abnormal glucose
metabolism in women with polycystic ovary syndrome. Eur J Endocrinol 2006;154:295-301