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Department of Obstetrics and Gynecology, Università Cattolica del Sacro Cuore (M.C., F.L., F.C., S.M.), 00168 Rome, Italy; Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche Istituto di Analisi dei Sistemi ed Informatica (S.P., A.D.G.), Rome, Italy; and OASI Institute for Research (A.L.), Troina, Italy
Address all correspondence and requests for reprints to: Dr. Antonio Lanzone, Department of Obstetrics and Gynecology, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, 00168 Rome, Italy. E-mail: alanzone{at}rm.unicatt.it.
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Abstract |
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Methods: One hundred PCOS and 110 menopausal subjects were analyzed; all subjects underwent an oral glucose tolerance test (75 g) and euglycemic-hyperinsulinemic clamp study. Seven PCOS patients and 13 menopausal subjects had impaired glucose tolerance or type 2 diabetes mellitus and were excluded from the study. After analysis of correlation coefficients between the evaluated indexes and the clamp studies, the sensitivity and specificity of different cut-off values for each parameter were analyzed by receiver operating characteristic (ROC) curves.
Results: The best correlation coefficients with clamp studies were obtained with the Avignon insulin sensitivity index (SiM) (Rs = 0.7812) in PCOS patients and the Matsuda and De Fronzo index (Rs = 0.6178) in menopausal patients.
The best predictive index of insulin resistance in PCOS was a Avignon insulin sensitivity basal index (SibB) value of 62 or less (78% sensitivity, 95% specificity) and an insulin area under the curve (AUC) of 7,000 µIU/ml or more (
50,225 pmol/liter) x 120 min (83% sensitivity, 90% specificity). In the menopausal population, the best predictive performance was obtained by an insulin AUC of 10,000 µIU/ml or more (71,750 pmol/liter) x 240 min (70% sensitivity, 88% specificity).
Conclusions: The presence of high correlation coefficients does not necessarily mean that the indexes of insulin resistance have an optimal predictive performance; this is probably due to the presence of many borderline values. The simple evaluation of insulin AUC seems to effectively replace the euglycemic-hyperinsulinemic clamp in routine clinical practice, allowing results superimposable to those obtained by minimal model analysis.
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Introduction |
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Insulin resistance, particularly when associated with central obesity, is now recognized as part of a syndrome associated with a number of cardiovascular risk factors, such as dyslipidemia, hypertension, dysfibrinolysis, and glucose intolerance (2). Insulin resistance syndrome, originally described by Reaven (2), is associated with a greater than 2-fold relative risk for myocardial infarction and macrovascular disease (3).
In women’s care clinical practice there are two kinds of patients who should be classified as moderate-high cardiovascular disease (CVD) risk subjects, who are often characterized by impaired glucose tolerance (IGT) or frank diabetes mellitus (type 2): menopausal and polycystic ovary syndrome (PCOS) patients. In both of these groups several alterations of insulin metabolism might play a key role in increasing the risk for CVD and type 2 diabetes mellitus.
Menopause is characterized by a progressive detrimental effect on insulin-mediated glucose disposal, as suggested by the presence of a negative correlation between years of menopause and peripheral or hepatic insulin sensitivity (4, 5).
PCOS is clinically characterized by anovulation and hyperandrogenism; it currently represents one of the most discussed, controversial, and explored areas in reproductive medicine, because this syndrome affects up to 10% of women of reproductive age (6). Although in past years the main requests to gynecologists by PCOS patients concerned an improvement of cause-related hyperandrogenism or a desire for pregnancy, it recently became evident that the metabolic alterations of the syndrome may have important implications for long-term health (6, 7). Because most women with PCOS come to clinical attention when their glucose tolerance is still normal and the cardiovascular damage is presumably at an early stage, screening for insulin resistance would be crucial to identify those subjects at greater risk, allowing appropriate medical intervention; these assumptions are also effective in menopausal population.
The gold standard methods to assess insulin sensitivity (euglycemic hyperinsulinemic clamps and minimal model analysis) are time-consuming and difficult to apply in large- scale clinical or epidemiological studies, where easier methods are required. This has raised interest in obtaining estimates from glucose and insulin measured in the fasting state or during an oral glucose tolerance test (OGTT). Several indexes have been described, and most of them have been validated with reference methods (8).
The aim of the present study was to verify the validity of different indexes of insulin sensitivity in PCOS and menopausal patients by comparing data obtained by these indexes with data from euglycemic-hyperinsulinemic clamp studies. Furthermore, we tried to elaborate cut-off values of insulin resistance for the different indexes, then we analyzed their predictive performance.
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Subjects and Methods |
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PCOS was diagnosed on the basis of clinical findings (the presence of amenorrhea or oligomenorrhea and hirsutism), plasma androgen levels at the upper limit of or above the normal range [androstenedione, 0.5–2.5 ng/ml (1.745–8.725 nmol/liter); testosterone, 0.2–0.6 ng/ml (0.6934–2.080 nmol/liter)], and the presence of bilaterally normal or enlarged ovaries containing at least 7–10 microcysts (<5 mm in diameter) on ultrasonography. All of the subjects showed an ovarian stroma/total area ratio greater than 0.34 (9). In approximately 40% of the cases, the diagnosis was also confirmed by laparoscopy. A normal LH/FSH ratio was not considered an exclusion criterion (9). The presence of a late-onset adrenal enzyme defect was excluded by an ACTH test (250 µg, iv; Synacthen, Ciba-Geigy, Basel, Switzerland), according to the criteria reported by New et al. (10).
We also recruited 110 healthy Caucasian menopausal women aged 45–60 yr, who were referred to our department for the relief of menopausal symptoms. Women were between 1 and 5 yr postmenopausal; none had undergone hysterectomy or bilateral oophorectomy. Before a woman’s enrollment in the study, assessment of plasma FSH [>50 mIU/ml (50 IU/liter)] and 17ß-estradiol [
10 pg/ml (36.71 pmol/liter)] concentrations, mammography, transvaginal ultrasound examination, cervical cytology, and hysteroscopic endometrial biopsy were performed and found to be normal or compatible with menopausal status. No patient was currently taking drugs known to affect lipid or glucose metabolism, nor had they taken any steroid within the previous 6 months. None smoked more than 10 cigarettes per day or drank more than 300 g alcohol/wk. Breast cancer, altered liver or kidney parameters, history of major thromboembolism, thyroid disease, and uncontrolled or ß-blocker-treated hypertension (systolic blood pressure >160 mm Hg or diastolic pressure >90 mm Hg) were considered exclusion criteria. Each patient gave written informed consent before entering the study; the study protocol had been previously approved by our institutional review board.
Studies were conducted on random days; in no case had recent ovulation occurred in the women with PCOS, as evidenced by retrospective measurement of serum progesterone levels on the days of the study.
Obesity was defined as a body mass index (BMI) greater than 27 kg/m2 (11, 12, 13) (normal range, 19–25 kg/m2); a BMI greater than 25 kg/m2 defined overweight women.
Waist circumference was obtained as the minimum value between the iliac crest and the lateral costal margin, whereas hip circumference was determined as the maximum value over the buttocks.
The patients were hospitalized before and after a standard carbohydrate diet (300 g/d) for 3 d. After overnight fasting for 10–12 h, blood samples were collected for basal hormone and lipoprotein assays. Then patients underwent an OGTT. Two days later, after another overnight fast, a euglycemic-hyperinsulinemic clamp was performed. Plasma levels of testosterone, dehydroepiandrosterone sulfate, androstenedione, 17-hydroxyprogesterone, FSH, LH, SHBG, triglycerides, high-density lipoprotein cholesterol, very low density lipoprotein cholesterol, low-density lipoprotein cholesterol, cholesterol, and free fatty acids were determined in basal conditions. The OGTTs (75 g) and euglycemic-hyperinsulinemic clamps were performed as previously described (14, 15). Insulin sensitivity was evaluated as total body glucose utilization (M) expressed as milligrams per kilogram body weight per minute. We preferred this index as the measure of insulin sensitivity, because the M/I ratio fails to narrow the range of individual sensitivity values (15).
Samples for hormone assay were promptly centrifuged, and plasma samples were stored at –20 C until assay, whereas samples for biochemical assay were immediately processed. All hormones and lipoproteins were assayed as previously described (9, 14, 16). A normal glycemic response to OGTT was defined according to criteria of the American Diabetes Association (17).
The areas under the curve (AUC) at 240 min were evaluated according to the formula: [([V30 + V60 + V90 + V120 + (mean V120-V180) + V180 + (mean V180-V240)] x 2) + V0 + V240] x 15, where V is the glucose, insulin, or C-peptide concentration at the indicated time. The AUC at 120 min was evaluated according to the formula: ([(V30 + V60 + V90) x 2] + V0 + V120) x 15. The insulin assay cross-reactivity with proinsulin at the middle curve is approximately 40%.
To estimate insulin sensitivity, several indexes from either fasting- or OGTT-derived measurements were used, as shown in Table 1
. The results obtained by replacing insulin with C-peptide values were also evaluated for each single index. For the stimulated indexes, formulas were calculated both for 120- and 240-min curves.
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All results are expressed as the mean and range. All variables for evaluation of insulin sensitivity were examined for normality of distribution with the Kolmogorov-Smirnov goodness of fit test. Because of the nonparametric distribution of the analyzed variables, Spearman’s rank correlation coefficient (Rs) was used to study the strength of association between measurements of insulin sensitivity.
The performance of each index for insulin sensitivity evaluation was described and compared as follows. Receiver operating characteristic (ROC) curves were built and analyzed by using the SPSS release 9.0.0 packet (SPSS, Inc., Chicago, IL). A statistical comparison of the ROC AUCs was made according to methods previously described by Hanley and McNeil (29). For each studied group, comparisons were made between the best predictive indexes (the insulin AUC x 240 min for the menopausal subjects; the Avignon insulin sensitivity index calculated in basal condition, replacing insulin value with C-peptide value (Table 1
and Ref. 26) for PCOS) and all other parameters considered. Under the hypothesis of normal distribution of the statistical quantity z (29), a one-sided z-test was conducted.
Starting from arbitrarily set up cut-off values, ROCs were computed assuming a nonparametric distribution. SE, asymptotic significance, and asymptotic 95% confidence intervals were also reported. The positive predictive value (PPV) and the negative predictive value (NPV) were calculated as well. P < 0.05 was considered statistically significant.
Because determination of the indexes did not significantly differ in most cases, as resulted from the ROC analysis curve, and because different indexes misclassified different patients, we tried to build a linear combination of the considered predictors to obtain a suitable criterion presumably able to predict insulin resistance. Because the hypothesis of normality for the analyzed variables was not confirmed, the discriminant analysis could not be performed, and a logistic regression was applied. Because the predictors strongly correlated, a forward stepwise procedure, based on the likelihood ratio, was used in selecting variables. Following the standard classification, patients were classified as insulin resistant when logit was greater than zero, resulting in a probability greater than 0.5 of being an insulin-resistant case. The two tests were then combined, in series and in parallel, and the new sensitivity and specificity were computed as well as the test efficacy.
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Results |
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Table 2 shows clinical, endocrine, and biochemical features of the studied populations; both groups included a wide range of BMI. Fifty-eight of 93 (62.4%) PCOS patients were obese; this was also true for 38 of 97 (39.1%) menopausal women.
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shows several correlation coefficients (Rs) between M values obtained by euglycemic-hyperinsulinemic clamp studies and the indexes obtained by fasting or stimulated insulin, C-peptide, and glucose values. The Rs values are presented in decreasing order within PCOS patients; the corresponding correlation coefficients for menopausal subjects are also reported in Table 3 (right side). Only the most significant data are reported in the table, although almost all of the evaluated indexes showed a statistically significant correlation with the M value. In particular, the original formula of homeostasis model assessment (HOMA) showed an Rs of –0.5754 in PCOS and of –0.4616 in menopausal subjects. The Rs values for fasting insulin were –0.5591 and –0.4178, respectively. The glucose to insulin ratio of the AUC (G/I area) and in the fasting state (G/I basal) showed Rs of –0.6293 and –0.5030, respectively, in PCOS women and values of –0.5223 and –0.3737 in menopausal subjects. The data for BMI showed correlation coefficients of –0.6666 and –0.3129, respectively, in PCOS and menopausal women.
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According to the cut-off of 4.45 mg/kg·min (M value) for the diagnosis of insulin resistance evaluated by euglycemic-hyperinsulinemic clamp studies, 66.1% of PCOS women and 44.3% of menopausal women were classified as insulin resistant. As described above, using arbitrary initial cut-off values for insulin resistance, we examined the sensitivity and specificity of various cut-off values for these indexes. Figure 1 shows the ROC graphs, where sensitivity is plotted against 1 – specificity (or the false positive rate).
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The predictive performance of the best cut-off values obtained for the investigated indexes of insulin resistance are shown in Tables 4 and 5; sensitivity, specificity, PPV, and NPV are reported. Furthermore, all statistical analysis concerning the ROC curves for each index have been reported; tables specify the area value, SD, confidence interval, and P value between several indexes. The best predictive performance in diagnosing insulin resistance was obtained in PCOS subjects using a SibB (Sib calculated by glucose and C-peptide values) value of 62 or less (AUC ROC = 87.66%), an insulin AUC of 7,000 µIU/ml or more (50,225 pmol/liter)·120 min (AUC ROC = 87.26%), and an Si120 of 2 or less (AUC ROC = 84.57%). In menopausal subjects the best predictive indexes of insulin resistance were an insulin AUC of 10,000 µIU/ml or more (71,750 pmol/liter)·240 min (AUC ROC = 79.37%) and a Belfiore area calculated at 120 min of 0.85 or less (AUC ROC = 77.01%).
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In PCOS women, the only significant factors were SiM and insulin AUC at 240 min. Results of the logistic regression are reported in Table 6. According to this classification, seven cases were misclassified (two insulin-resistant patients were coded as insulin sensitive, and five insulin-sensitive patients were coded as resistant, giving rise to a specificity of 73.68% and a sensitivity of 94.59%) compared with nine misclassified cases (eight insulin-resistant patients were coded as insulin sensitive, and one insulin-sensitive patient was coded as resistant) when the performance of the SibB, the best test in the ROC analysis, was taken into account.
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provides detailed results.
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). According to the logistic regression test, 14 cases were misclassified (nine insulin-resistant patients were coded as insulin sensitive, and five insulin-sensitive patients were coded as resistant, giving rise to a specificity of 87.5% and a sensitivity of 71.87%), whereas 15 cases were misclassified when the classification was made according to insulin AUC at 240 min, the variable that produced the highest ROC AUC (10 insulin-resistant patients were coded as insulin sensitive, and five insulin-sensitive patients were coded as resistant). The highest efficacy was obtained with the tests combined in series (81.94%; Table 9); however, very slight differences among the tests were found. Our results lead to a preference for the combined tests, because the increase in performance is so slight that it does not warrant the more cumbersome procedure.
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Discussion |
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A well-accepted alternative to the clamp technique was the iv glucose tolerance test interpreted with the classic minimal model of glucose disappearance (32, 33). This method has gained increasing popularity over the years (34); however, although this approach is less labor intensive than the glucose clamp, the minimal model is still not ideal for large studies because approximately 30 blood samples over 3 h are required. Furthermore, there is not general agreement about the levels of correlation obtained between direct measures of insulin sensitivity (i.e. glucose clamp) and indirect measures, such as minimal model analysis, with correlation coefficients varying from 0.44–0.90 (25, 35, 36).
These data suggest that investigators should be cautious in applying minimal model analysis of insulin sensitivity to population studies. This is highlighted by recent studies demonstrating particular inadequacies of the minimal model approach that result in overestimation of glucose effectiveness and underestimation of insulin sensitivity (34, 37).
Alternative methods applicable to large studies have been proposed for measurement of insulin sensitivity (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28), such as fasting insulin and glucose as well as stimulated (OGTT) plasma levels. Indeed, although it is well known that OGTT provides information about insulin secretion and action, but does not directly yield a measure of insulin sensitivity, several formulas have been proposed and successfully tested against the clamp (20, 21, 24, 26, 27, 28).
The significance of a valid index of insulin sensitivity as a screening method in the primary prevention of diabetes or CVD in the gynecological populations analyzed in the present study is suggested by two observations. First, these patients are characterized by increased type 2 diabetes mellitus or CVD risk (6, 7, 38, 39). Furthermore, our data indicate a higher prevalence of insulin resistance in PCOS and menopausal women (66.1% and 44.3%, respectively) compared with the 20–25% insulin-resistant subjects found in the nondiabetic general population (21) (a limitation could be identified in the selection of menopausal women, because most of them were referred to our clinical service for the relief of menopausal symptoms).
In the present work we analyzed almost all the known formulas for insulin sensitivity measurement; for each index we also evaluated the results obtained by replacing C-peptide to insulin values, because C-peptide assays are less influenced by possible assay interactions, such as that between insulin and proinsulin. The subjects already diagnosed as IGT or type 2 diabetes mellitus patients were excluded from our analysis because we wanted to validate a screening method for primary prevention.
Our results showed that the indexes of insulin sensitivity best correlated to the clamp studies were, for the PCOS population, those processed by Avignon et al. (26); in particular, SiM reached an Rs of 0.7812, whereas evaluation in the fasting state using C-peptide instead of insulin concentrations reached an Rs of 0.7545. In contrast, in the menopausal population the best correlation coefficients were obtained for the formulas reported by Belfiore et al. (20) and Matsuda and De Fronzo (21) (Rs = 0.5913 and Rs = 0.5805 for Belfiore index evaluated by 240- or 120-min curves; Rs = 0.6156 and Rs = 0.6178 for a similar evaluation by the formula proposed by Matsuda and De Fronzo). We generally found quite superimposable correlation coefficients by using curves at 120 or 240 min; these data suggest the usefulness of the 120-min curves.
Concerning the HOMA index, our results showed a weaker association with the clamp-measured total glucose disposal (Rs = –0.57 in PCOS, Rs = –0.46 in menopausal women) compared with other correlation coefficients reported previously (0.64–0.88) (16, 17, 19, 24). However, our data are in line with those reported by McAuley et al. (23) (Rs = –0.53) in 178 normoglycemic individuals and by Anderson et al. (Rs = –0.40) (22). These discrepant data cannot be easily explained; it is possible that our results are influenced by the selection of two different populations with a high prevalence of insulin resistance. In any case, our data do not seem to validate the use of a simple HOMA for screening insulin resistance in PCOS and menopausal subjects.
Although many indexes have been tested for the evaluation of insulin sensitivity, with different correlation coefficients compared with clamp or minimal model analysis (18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28), a flaw in the reported studies concerns the lack of a predictive performance of cut-off values for the analyzed formulas. The introduction of indexes allowing an easy determination of insulin resistance is aimed at replacing the euglycemic-hyperinsulinemic clamp and the minimal model analysis, so there is a need for cut-off values characterized by a good predictive performance to identify insulin-resistant subjects.
Only McAuley et al. (23), in analyzing a weighted combination of fasting insulin and triglycerides, reported a sensitivity of 62% and a specificity of 84% in identifying insulin resistance. Legro et al. (40) tried to validate a screening test for insulin resistance in women with PCOS; the researchers found that setting a value of the fasting G/I ratio of less than 4.5 as abnormal, the sensitivity of this parameter was 95%, the specificity was 84%, the PPV was 87%, and the NPV was 94%; these data suggest very interesting results in terms of predictive performance. However, some personal comments arose from the data presented. Only obese (n = 40) PCOS patients were studied; thus, the results cannot be validated in a general PCOS population. The researchers themselves commented that the fasting G/I ratio would not be predicted to be a reliable measure of insulin resistance in nonobese PCOS women because they have neither fasting hyperinsulinemia nor increased basal hepatic glucose production. Another important flaw found in the report by Legro consists of the definition of insulin resistance; in fact, the researchers defined abnormal insulin sensitivity values as the 10th percentile for insulin sensitivity index found in 15 obese controls, because they wanted to assess insulin resistance that was beyond that due to obesity per se. In our opinion this could not be defined as insulin resistance, as also suggested by the presence of normal insulin sensitivity in 47% of obese PCOS women.
In our series, the best predictive indexes of insulin resistance in PCOS were the insulin AUC and those proposed by Avignon et al. (26). Within menopausal subjects, the best predictivity was obtained by the insulin AUC calculated by 240-min curves and the Belfiore area (20) index calculated by 120-min curves.
Several important considerations might be drawn from our results. Firstly, even if there are higher correlation coefficients for some indexes, there is no really significant difference in terms of predictive performance between the simple insulin AUC and the other reported indexes. Furthermore, the cut-off values for each variable are not completely superimposable between PCOS and menopausal women, suggesting the need to establish different cut-off values for different populations.
We urge caution in the application of the insulin AUC or other indexes in monitoring the incidental effects of diet or insulin-lowering drug administration on insulin resistance. Indeed, we previously showed (41) that in PCOS subjects the administration of an oral opioid antagonist (naltrexone) for about 1 month was able to reduce the exaggerated insulin secretion after a glucose load by about 30% without affecting the glucose level. An improvement of insulin sensitivity in these patients could be expected; by contrast, data from euglycemic-hyperinsulinemic clamp studies did not show any modification in the insulin-mediated glucose disposal, yielding the conclusion that naltrexone might act at the hepatic level (insulin extraction).
In conclusion, our data show that several indirect indexes of insulin resistance obtained from fasting or stimulated glucose, insulin, or C-peptide concentrations are superimposable, in terms of strength of association with the clamp data, to those reported by other researchers for the minimal model (25, 35, 36), suggesting that they can effectively replace the minimal model or clamp evaluations in large population studies, in particular those involving PCOS or menopausal women. The presence of a high correlation coefficient does not mean that these indexes have the best predictive performance in diagnosing insulin resistance, because of the presence of many borderline values. Our data suggest that different indexes of insulin resistance could be indifferently used to establish insulin resistance, because there is no statistically significant difference in the ROC curve analysis. However, we believe that it is better to use the one with the highest ROC curve area, because it can be more representative of all populations, even considering its PPV. Thus, the analysis of predictivity showed that the simple evaluation of insulin AUC seems to be the best predictive index of insulin resistance in PCOS and menopausal patients.
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Footnotes |
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Abbreviations: AUC, Area under the curve; BMI, body mass index; CVD, cardiovascular disease; HOMA, homeostasis model assessment; IGT, impaired glucose tolerance; NPV, negative predictive value; OGTT, oral glucose tolerance test; PCOS, polycystic ovary syndrome; PPV, positive predictive value; ROC, receiver operating characteristic. Sib, Avignon insulin sensitivity basal index; SiM, Avignon insulin sensitivity index.
Received March 1, 2004.
Accepted December 2, 2004.
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