Determinants of Impaired Fasting Glucose Versus Glucose Intolerance in Polycystic Ovary Syndrome Open Access

A total of 143 women (108 [76%] white, 17 [12%] African American, 8 [6%] Hispanic, 5 [3%] American Indian, and 5 [3%] Asian) with PCOS (diagnosed by National Institutes of Health criteria), aged 18–45 years (means ± SEM 26.1 ± 0.9 years) with BMI 20–50 kg/m² were recruited at the University of California (UC), Davis (Sacramento, CA) (n = 68) and Yale University School of Medicine (New Haven, CT) (n = 75). The study was approved by the institutional review boards of both institutions. The subjects at UC Davis provided written informed consent, and they all were examined by either S.E.K. or A.J.D. Patients using insulin sensitizers or medicines affecting lipids, weight, or insulin sensitivity within 2 months; having diabetes, untreated hypothyroidism, or systemic illnesses (i.e., renal, hepatic, and gastrointestinal); smoking; and drinking >2 servings of alcohol per week were excluded. Pregnant, postpartum, or lactating women were excluded. The studies were carried out at the Clinical and Translational Science Center at UC Davis and at Yale University School of Medicine Reproductive Endocrinology and Fertility Center. The subjects consumed their habitual diets and were weight-stable.

Fasting blood tests and OGTT were done in all subjects. Subjects recruited at UC Davis also underwent a frequently sampled intravenous glucose tolerance test (FSIVGTT).

Weight was measured using a Tanita BWB800-P digital medical scale, and height was measured using an Ayrton model S100 stadiometer.

A standard OGTT was performed using 75 g of glucose (Glucola). Blood samples were obtained every 30 min. The subjects remained supine in bed. Samples were collected in tubes containing sodium fluoride, EDTA, or heparin.

An intravenous catheter was placed in each forearm. The catheters were kept open with normal saline. Heating pads were used to maximize the blood flow. After blood samples were obtained at −20, −10, and 0 min, glucose (0.3 units/kg as 25% dextrose) was injected intravenously at time 0. Intravenous insulin (0.03 units/kg) (Humulin regular; Eli Lilly) was administered at time 20 min. Additional samples were obtained at 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 16, 19, 22, 23, 24, 25, 27, 30, 35, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, and 180 min. The samples were analyzed for glucose and insulin. Acute insulin response (AIR_g), β-cell function, insulin sensitivity index (S_i), and disposition index were calculated using the MINMOD Millennium software (13).

Glucose was measured with a YSI 2300 STAT Plus Glucose & Lactate Analyzer (YSI Life Sciences, Yellow Springs, OH) or a Bayer Dax-48 system analyzer (Bayer Diagnostics, West Haven, CT). Triglyceride, cholesterol, and HDL cholesterol were measured using a Poly-Chem System clinical chemistry analyzer (Polymedco, Cortlandt Manor, NY) or an Olympus AU 600 autoanalyzer (Olympus, Melville, NY). The coefficients of variation (CVs) were 1% for glucose, 3.5% for cholesterol, 4% for triglyceride, and 3.6% for direct HDL. Insulin, leptin, and adiponectin were measured using radioimmunoassay kits (Millipore, St. Charles, MO) with CVs of 8.2, 4.3, and 6.5%, respectively. Insulin was also measured by chemiluminescence (Bayer Diagnostics). High-sensitivity C-reactive protein (hs-CRP) was measured using a highly sensitive latex–enhanced immunonephelometric assay with both interassay and intraassay CVs <5% or immunoturbidimetry with an image analyzer (Hitachi 917; Quest Diagnostics) with an intraassay CV of 8.7%.

Peripheral insulin resistance was assessed by calculating Matsuda's sensitivity index (ISI_Matsuda), from the formula (10,000/square root of [(fasting glucose × fasting insulin) × (mean glucose × mean insulin during OGTT)]), and S_i was calculated by applying the MINMOD program to FSIVGTT data. Hepatic insulin resistance was calculated by homeostasis model assessment (HOMA), [(fasting insulin [microunits per milliliter] × fasting glucose [milligrams per deciliter]/405)], and the quantitative insulin sensitivity check index (QUICKI), 1/[log (fasting insulin) + log (fasting glucose)]). Early insulin secretion, the insulinogenic index, was calculated by dividing the increases in insulin and glucose in the first 30 min of the OGTT (Δ_{Insulin 0–30}/Δ_{Glucose 0–30}) and by calculating AIR_g from FSIVGTT. Pancreatic function was assessed by calculating the area under the curve (AUC) for insulin and calculating β-cell function during FSIVGTT. β-Cell compensation for insulin resistance was assessed by calculating the disposition index from AIR_g and S_i.

Statistical analyses were performed using SAS statistical software (version 9.1; SAS Institute, Cary, NC). Data were first examined to identify any significant variations between the populations from two sites, UC Davis versus Yale School of Medicine. The age distribution of the subjects from Yale was shifted to younger ages. In addition, age appeared to be confounded with BMI. Therefore, the pooled data were analyzed by including age and BMI as covariates in all of the subsequent statistical analyses. After correction for age and BMI, the association between study site and any outcome was attenuated toward the null.

Descriptive statistics were calculated. A Spearman correlation coefficient and its P value for significance of correlation were calculated to assess the magnitude and direction of an association between two given outcomes based on their ordered ranks. The data were log-transformed to improve the normality of residuals and homoscedasticity of errors where appropriate before statistical analysis. Group comparisons for mean in the cross-sectional outcome were performed by ANCOVA, adjusted for the baseline values and covariates (age and BMI). When the overall difference among the group means was significant in ANCOVA, post hoc pairwise group comparisons were conducted using Bonferroni multiple comparisons to identify the groups with different means. The longitudinal trajectories of 120-min changes in glucose and insulin level were estimated by a repeated-measures ANOVA. Individual trajectories of change in glucose and insulin level over five time points, observed every 30 min over 2 h, were estimated from linear random-effects models. Each observed level was entered as the dependent variable. Group (i.e., type of glucose intolerance), time (in 30 min), and a group × time interaction term were entered as independent variables. The coefficients for the interaction term were used to estimate the additional changes in glucose and insulin level over time associated with type of glucose intolerance. To account for between-subject heterogeneity in the change of glucose or insulin level, intercept and time were modeled as random effects. Multiple comparisons were controlled by the Bonferroni method where appropriate. Two-sided P < 0.05 was considered significant.

Forty-six of 143 women with PCOS (32%) had glucose tolerance abnormalities. Sixteen (11%) had IFG, 10 (7%) had IGT, and 20 (14%) had CGI. The remaining 97 women (68%) had normal glucose tolerance (NGT). Different ethnic groups were similarly distributed among NGT, IFG, IGT, and CGI. None of the minorities were overrepresented in any of the groups.

The IGT group was older and the IFG group was more obese than the NGT group (Table 1). By definition, the IFG, IGT, and CGI groups had higher glucose levels than the NGT group. All glucose-intolerant groups had higher fasting insulin than the NGT group (IFG 131 ± 16 pmol/l, IGT 112 ± 22 pmol/l, and CGI 145 ± 12 pmol/l vs. NGT 88 ± 5 pmol/l, P = 0.0002). The difference between the fasting insulin values of the CGI and IGT groups was also significant (P = 0.0047).

Both IFG and CGI groups had lower ISI_Matsuda values than the NGT groups (2.12 ± 0.29 and 1.90 ± 0.28 vs. 4.61 ± 0.34, P = 0.0004 and P < 0.0001, respectively) (Table 1, Fig. 2). In 68 women who underwent FSIVGTT, differences in S_i did not reach significance (NGT 3.47 ± 0.60, IFG 1.58 ± 0.34, IGT 2.46 ± 0.29, and CGI 2.27 ± 0.44, P = 0.1468). On the other hand, the disposition index was significantly reduced in the CGI group compared with the NGT group (987 ± 296 vs. 1,615 ± 236, P = 0.0234). The CGI group also had lower serum adiponectin than the NGT group (6.24 ± 0.8 vs. 11.1 ± 1.1 ng/ml, P = 0.0096). Compared with the NGT group, both IFG and CGI groups had higher HOMA (6.1 ± 0.9 and 6.4 ± 0.5 vs. 3.3 ± 0.2, P = 0.0006 and P < 0.0001) and lower QUICKI (0.30 ± 0.006 and 0.30 ± 0.004 vs. 0.33 ± 0.003, P = 0.0009 and P < 0.0001).

Compared with the NGT group, the IFG group had an overall increase in insulin response during an OGTT, and the differences were significant at every time point (Table 1, Fig. 1), whereas the IGT and CGI groups exhibited a delayed response. During the first half of the OGTT, the IFG group had higher insulin levels than the CGI group (30 min 820 ± 42 vs. 508 ± 54 pmol/l and 60 min 1,032 ± 54 vs. 738 ± 72 pmol/l, P < 0.001 for both). After 60 min, the insulin response pattern changed; at 120 min the CGI group had higher insulin than the IFG group (996 ± 34 vs. 583 ± 45 pmol/l, P < 0.001). In 68 individuals who underwent FSIVGTTs, there were no significant differences in AIR_g (P = 0.4842) or β-cell function (P = 0.0901).

The IFG and CGI groups had similar insulin resistance based on HOMA (6.1 ± 0.9 vs. 6.4 ± 0.5), QUCKI (0.30 ± 0.006 vs. 0.30 ± 0.004), and ISI_Matsuda (2.12 ± 0.29 vs. 1.90 ± 0.28) but different insulin response patterns (Table 2) (Fig. 1). Insulin response in insulin-resistant subjects with NTG was also investigated. When divided into tertiles based on ISI_Matsuda, the insulin-resistant (NGT-IR) tertile was similar to subjects with IFG and CGI, based on their HOMA (5.6 ± 0.3), QUICKI (0.30 ± 0.002), and ISI_Matsuda (1.90 ± 0.09). Their BMI (39.9 ± 1.2 kg/m²) was similar to those of the subjects with IFG (38.7 ± 1.5 kg/m²) and CGI (38.1 ± 1.8 kg/m²). The NGT-IR group had higher a insulinogenic index than the IFG group (421 ± 130 vs. 268 ± 68, P < 0.05).

The tertiles were referred to as insulin-sensitive (NGT-IS), intermediate (NGT-IN), and NGT-IR (Table 2) (Fig. 2). In these tertiles, fasting glucose and insulin increased stepwise (from 4.8 ± 0.02 to 5.0 ± 0.02 and to 5.1 ± 0.02 mmol/l, P < 0.0001 and from 41 ± 2 to 79 ± 2 and to 146 ± 2 pmol/l, respectively, P < 0.0001). The NGT-IN and NGT-IR tertiles had higher BMI, fasting glucose, HOMA, ΔInsulin_0–30, hs-CRP, and free androgen index and lower QUICKI and HDL cholesterol than the NGT-IS tertile. In addition, the NGT-IR tertile had higher insulinogenic index, AUC_{Glucose 0–120}, AUC_{Insulin 0–120}, and triglyceride and lower sex hormone–binding globulin than the NGT-IS tertile. Even after correction for BMI, differences in fasting glucose, fasting insulin, ΔInsulin_0–30, AUC_{Glucose 0–120}, AUC_{Insulin 0–120}, and HDL cholesterol remained significant.

Next, partial correlations among insulin resistance parameters, cardiovascular risk factors, and androgens were calculated after adjustment for the differences in BMI. Plasma triglyceride and hs-CRP correlated directly with HOMA (r = 0.316 and 0.253, P = 0.009 and P = 0.037, respectively) and inversely with ISI_Matsuda (r = −0.282 and −0.306, P = 0.020 and 0.011, respectively).

This study demonstrated that, first, patients with PCOS with IFG exhibited severe peripheral insulin resistance and developed IFG despite having an increased early insulin response. Second, having normal glucose levels during an OGTT did not indicate normal insulin sensitivity or a low risk for cardiovascular disease. Third, in the NGT group, the BMI, sex hormone–binding globulin, HDL cholesterol, and hs-CRP levels appeared to have value in assessing insulin resistance. Because we did not have a control group of age- and weight-matched women with normal reproductive function, we cannot conclusively state that these findings are specific to patients with PCOS. However, our observations differ significantly from those obtained from middle-aged men and women (14).

Diabetes risk increases with increasing age and obesity (15). We found age and weight to be important in development of glucose intolerance and insulin resistance, respectively (Tables 1 and 2).

Per definition, glucose levels were higher in patients with IFG, IGT, and CGI compared with patients with NGT. Insulin responses of patients with IFG, IGT, or CGI were not decreased. In fact, AUC_Insulin-120 was higher in both patients with IFG and CGI, indicating that patients with PCOS can develop IFG and CGI in the presence of hyperinsulinemia. This was consistent with the findings of Kulshreshtha et al. (12), who reported increased insulin responses in patients with PCOS with IGT/CGI or type 2 diabetes. The time course of the insulin response was the most significant difference between patients with IFG versus those with IGT and CGI. Patients with IFG had a brisk early insulin response that declined during the second half of OGTT. In contrast, patients with CGI and IGT exhibited a decreased early insulin response followed by delayed hyperinsulinemia. Previous reports showed similar response patterns in those with IFG versus those with IGT and CGI among subjects without PCOS (12,14,16,17).

The brisk, early insulin response of patients with IFG appeared to be specific to PCOS because several studies in different populations, ethnic groups, sex, and age distributions have reported decreased cumulative and early insulin response in patients with IFG (14,17,,–20), although a recent report in healthy nondiabetic men and women showed an increased insulin response (16). The only available study in PCOS demonstrated an increased insulin response similar to ours (12).

In patients with PCOS who have IGT, the early and cumulative insulin responses (AUC_Insulin-30 and AUC_Insulin-120) did not differ significantly from the responses of those with NGT, whereas Kulshreshtha et al. (12) reported increased insulin response in glucose-intolerant patients with PCOS. However, their study did not distinguish between IGT and CGI, and the subjects were less obese and of different ethnicities.

Studies using intravenous glucose tolerance tests in subjects without PCOS reported that AIR_g was decreased by ∼30% in those with IFG and by 8–18% in those with IGT (20,–22). As seen in Table 1, AIR_g did not decrease in our patients with PCOS with IFG. Taken altogether, these findings indicate that patients with PCOS can develop fasting hyperglycemia and glucose intolerance even with increased early and cumulative insulin responses.

The literature indicates that primary sites of insulin resistance differ in IFG, IGT, and CGI (9,10,14): hepatic in IFG; peripheral in IGT; and both hepatic and peripheral in CGI. These distinctions cannot be made without using a hyperinsulinemic clamp. The surrogate measures of hepatic insulin resistance include HOMA and QUICKI. Consistent with the literature, we found increased HOMA and decreased QUICKI only when fasting glucose was impaired (IFG/CGI) but not in IGT (Table 1). In contrast with the literature, the surrogates for peripheral insulin resistance, ISI_Matsuda and S_i, of our IFG group were similar to those of the IGT and CGI groups, indicating that patients with PCOS with isolated IFG also have peripheral insulin resistance.

The cause of IFG was an enigma because this group had significant early and late hyperinsulinemia. Thus, we compared the IFG group to subjects with equal insulin resistance with NTG (NGT-IR). Although the NGT-IR group and those with IFG had similar HOMA, QUICKI, and ISI_Matsuda values, the NGT-IR group had a higher insulinogenic index. As shown in Fig. 1, patients with IFG also had decreased overall insulin secretion relative to the degree of insulin resistance.

The studies of tertiles demonstrated that patients with PCOS who have NGT can still be severely insulin-resistant. In addition, cardiovascular risk factors and hyperandogenemia worsen before overt hyperglycemia. Consistent with these findings, a recent report indicated that low HDL cholesterol levels correlate with hyperinsulinemia in PCOS (23). Previous studies had found increased cardiovascular risk factors only in subjects with IGT and CGI without PCOS (11,21).

We propose the natural course of glucose intolerance in PCOS as follows. Insulin resistance increases with weight gain, as suggested by the stepwise increase in BMI in the NGT tertiles (Fig. 2). As long as insulin response can compensate, plasma glucose remains within the “normal” range. A relatively small decrease in overall insulin response results in isolated IFG. A decrease in the early insulin response results in IGT/CGI, even with late hyperinsulinemia. Factors leading to impairment of the early versus overall response are not known, although genetic factors may be important (4,24).

This study was supported by the National Center for Complementary and Alternative Medicine (Grants AT-002280 and AT-003401 to S.E.K.), the ALSAM Foundation (Los Angeles, California), the Eunice Kennedy Shriver National Institute of Child Health and Human Development, and the National Institutes of Health (Grant R01-HD-050656 to A.J.D.). The clinical studies were partially supported by the University of California, Davis (Clinical and Translational Science Center Grant RR-024146).

No potential conflicts of interest relevant to this article were reported.

We thank Elizabeth Garamendi, Dumindra Gurushinghe, Luisa M. Vargas, and Rogelio Almario for their diligent work.