Metabolic approaches to the subclassification of polycystic ovary syndrome*t

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1 FERTILITY AND STERILITY Copyright 1995 American Society for Reproductive Medicine Vol. 63, No, 2, February 1995 Printed on acid-free paper in U. S. A. Metabolic approaches to the subclassification of polycystic ovary syndrome*t Robert J, Norman, M,D,:j: Stacey C, Masters, RN,:j: William Hague, M.D.:j: Clive Beng, M.D. II Peter Pannall, M.D. II Jim X. Wang, Ph.D.:j: Reproductive Medicine Unit, The Queen Elizabeth Hospital, The University of Adelaide, Woodville, South Australia, Australia Objectives: To examine the relationship between various hormonal and metabolic variables in a large group of women with unequivocal evidence of polycystic ovarian syndrome (PCOS) to dissect out the metabolic heterogeneity of this condition. Design: Cross-sectional observational study of PCOS (n = 122) and non-pcas (n = 26) subjects. Setting: Reproductive medicine unit in a tertiary teaching hospital. Patients: Subjects with presumed pcas were recruited from the Reproductive Medicine and Gynaecological Clinics and later confirmed as PCOS with recognized criteria. Several other subjects were identified through recruiting reference subjects. The pcas population consisted of 122 patients. Reference subjects were recruited from partners of male factor infertility patients in the clinics and from the general population (n = 27). Interventions: A 75 g 2-hour oral glucose tolerance test was performed on all subjects in their midluteal phase. Blood was taken at fasting and at 30, 60, 90, and 120 minutes. Main Outcome Measures: Age, body mass index (BMI), waist to hip ratio, levels of integrated glucose and insulin, concentrations of maximum insulin, sex hormone-binding globulin, T, triglyceride, apolipoproteins (Apo AI, B), high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol (LDLC). Results: Five clusters could be identified. They are characterized as a nonobese group, a moderately obese group, and three very obese groups. The non obese group (n = 41, BMI = 24.1) exhibited the lowest level of integrated insulin (236.4 miujl or JLUjmL) and concentration of serum T (5.5 nmol/l). The moderately obese group had the second lowest level of integrated insulin (497.1 miujl) whereas the three very obese groups (n = 15, 13, and 5, respectively) had significantly higher but different levels of integrated insulin (group 3: miujl; group 4: 1,131.5 miujl; and group 5: 1,531.9 miujl), triglyceride (group 3: 1.39 mmol/l; group 4: 1.76 mmol/l; and group 5: 2.78 mmol/l [1 mmol/l = 88mgjmL]), Apo B (group 3: 1.18 gjl; group 4: 1.08 gjl; and group 5: 1.55 gjl) and LDLC (group 3: 3.81 mmol/l; group 4: 3.05 mmol/l; and group 5: 5.06 mmol/l [1 mmol/l = 38.6 mgj100 ml]). Conclusions: The metabolic heterogeneity of the pcas population is reflected at least partly in patients' levels of insulin, lipids, and lipoproteins, dependent and independent of BMI. Fertil Steril 1995;63: Key Words: pcas, subclassification of pcas, glucose metabolism, insulin resistance Received March 21, 1994; revised and accepted September 2, :j: Department of Obstetrics and Gynaecology. * Supported by a National Health and Medical Research, Reprint requests: Robert J. Norman, M.D., Reproductive Australia grant (R.J.N. and W.H.). Medicine Unit, Department of Obstetrics and Gynaecology, The t Presented at the XIth Annual Scientific Meeting of Fertility Society of Australia, Adelaide, South Australia, Australia, ville, SA50n Australia (FAX: ). University of Adelaide, The Queen Elizabeth Hospital, Wood November 1 to 4, II Department of Clinical Chemistry. Vol. 63, No.2, February 1995 Norman et al. Subclassification of peas patients 329

2 The polycystic ovary syndrome (PCOS) often is presented as a single condition that can be defined by a set of standard clinical and biochemical criteria. Although severe cases of PCOS usually are associated with clear characteristics such as obesity, menstrual abnormalities, hirsutism, increased androgens, and increased LH to FSH ratio, many women with PCOS do not fit easily into these categories. Consequently, there are considerable practical difficulties in defining patients as having this condition for both clinical and research purposes because of disagreements between researchers regarding the relevance of ultrasonographic evidence of polycystic ovaries and the relative importance of several altered biochemical and endocrine parameters (1). For instance, although many groups consider ultrasonographic signs sufficient to make the diagnosis of PC OS (2,3), other researchers demand associated clinical and biochemical criteria (4, 5). The aim of the current study was to examine the relationship between various hormonal and metabolic variables in a large group of women with unequivocal evidence of PCOS, as assessed ultrasonographically and hormonally, in an attempt to dissect out the metabolic heterogeneity of this condition. The method of cluster analysis has been used to group subjects, not defined a priori, on the basis of the similarities in the parameters from the data obtained prospectively. Clustering is synonymous to classifying and refers to the grouping of objects into sets on the basis of their similarities and the differentiating between sets based on their differences. We have chosen to use hierarchical methods of clustering because we assume that there is no specific model of patient classification. As there are so many potential parameters involved and the interrelationships existing between them are complex, cluster analysis using many variables simultaneously is an ideal tool. In this study it was used to investigate the heterogeneity of PCOS in terms of metabolic and hormonal profiles and to identify subgroups with clearly distinguishable characteristics that could be used for clinical practice and research. As PCOS is a condition thought to have some genetic propensity (5-7), subgroupings would be of benefit in establishing the pattern of genetic inheritance. The results also would be important for long-term follow-up of the clinical significance of various PCOS patients. MATERIALS AND METHODS Subjects with presumed PCOS were recruited from the Reproductive Medicine and Gynaecologi- 330 Norman et a1. Subclassification of peds patients cal Clinics. Most presented with infertility, menstrual abnormalities, or hirsutism. Several other subjects were identified through recruiting reference subjects who were shown subsequently to have PCOS. Altogether 161 subjects were evaluated for suspected PCOS. After excluding PCO by scan only (n = 25) and others with incomplete data (n = 14), the final PCOS population consisted of 122 patients. Female reference subjects were recruited from partners of male factor infertility patients in the Reproductive Medicine and Gynecological Clinics and from the general population (n = 27). The study was carried out between January 1990 and December 1992 and the subjects underwent testing of their hormones, lipids, and glucose tolerance. Body mass index (BMI) was defined as weight (kg) divided by height (m) squared. Waist and hip were measured at the narrowest waist diameter and the maximal hip measurement, respectively. Medical, personal, and family history characteristics in relation to fertility, menstrual cycles, and hirsutism were reconfirmed at the time of glucose testing. All but a few of the subjects were of Caucasian extraction. Consent for this study was obtained from subjects as determined by the Human Ethics Committees of the University of Adelaide and the Queen Elizabeth Hospital. Definition of PCOS For the purposes of this study, patients were included as PCOS if they showed an increased concentration of serum T or A together with a low concentration of sex hormone-binding globulin (SHBG), in addition to characteristic ovarian morphology on ultrasound (US) (defined as the presence of eight or more peripheral cysts < 10 mm in diameter with increased stroma in one or both ovaries) (3). It should be recognized that use of this particular definition, although used commonly, may exclude subjects who have PCOS by other criteria. Glucose Tolerance Testing A 2-hour 75 g oral glucose tolerance test (GTT) was carried out on all subjects after an overnight fast, a 3-day standardized diet, and abstinence from smoking. The GTT was performed in the early follicular phase ofthe cycle where women were having regular periods or at a time of minimal follicular activity in those whose periods were irregular. An indwelling catheter was used throughout the procedure and blood was obtained for glucose and insulin Fertility and Sterility

3 concentrations every 0.5 hour for 2 hours. Before commencement ofthe GTT, blood was obtained for fasting insulin and glucose, sex steroids and SHBG, gonadotropins, lipids, and apolipoproteins. Ovarian morphology and dimensions had been obtained by transvaginal US before commencing the GTT. Assay Methods Glucose was assayed by a glucose oxidase method on the Synchron AS8 System (Beckman Instruments, Fullerton, CA). Cholesterol was measured by a cholesterol esterase-cholesterol oxidase method on the RA-I000 (Technicon Instruments Corp., Tarrytown, NY) and calibrated against the Australian Lipid Standard of the Commonwealth Serum Laboratories. High-density lipoprotein cholesterol (HDLC) was assayed after phosphotungstate-magnesium chloride precipitation and the low-density lipoprotein cholesterol (LDLC) was calculated. Triglycerides were measured by an enzymatic method (glycerokinase). Apolipoproteins (Apo) Al and B were assayed nephelometrically on the Behring Nephelometer System (Behring, Marburg, Germany) using Behring Apo standard. Testosterone, A, SHBG, 17a-hydroxyprogesterone (17-0HP), DHEAS, FSH, E2, and P were measured by previously described immunoassays (8, 9). Luteinizing hormone was measured by RIA (Farmos Diagnostica, Turku, Finland) and immunoradiometric assay (Bioclone, Melbourne, Victoria, Australia) and no difference was found in absolute terms or correlation analysis between the methods. The RIA results were subsequently used for calculations. Insulin was measured using an RIA kit from Pharmacia (Haymarket, New South Wales, Australia) with a cross-reaction with proinsulin of approximately 20% (10,11). All immunoassays had an interassay and intra-assay coefficient of variation of <12%. Specimens of glucose and insulin from a single GTT were measured in the same assay. The integrated insulin and glucose measurements were calculated by the formula (b + c + d + e - 4a), where a is the value at 0 minutes and b, c, d, and e are the values at 30, 60, 90, and 120 minutes. Statistical Analysis Statistical analysis was performed using hierarchical cluster analysis methods with the procedure Cluster in the Statistical Analysis System (SAS; SAS Institute Inc., Cary, NC) using an IBM compatible computer. The optimal number of clusters ~ 300.g ~ ~ ~ '" -c' 400~~ ~~---, :t: r.~~\laq. F....1 *"Pseudo T2 I No. Clusters C' -3 Figure 1 Cluster number and Ca, Pseudo T2, and Pseudo F. The optimal number of clusters in a cluster analysis is usually determined by the change of certain statistical estimates, e.g., Ca, Pseudo F and Pseudo T2 against the number of actual clusters selected. The relationships between the number of possible clusters (X), listing from 1 to 15, and the three statistical estimates (Ca, Pseudo F, Pseudo T2) obtainable if this particular number of clusters is selected. The optimal number of clusters, five, has been suggested clearly by the pattern of the relations. The first peak of Ca and pseudo F and trough of T2 occurred at two clusters, indicating the existence of two major groups, corresponding with obese and nonobese PCGS. The second peak of Ca and pseudo F and trough oft2 occurred at five clusters, indicating the existence of five groups in the study population. Although another peak of Ca and pseudo F and trough of T2 did occur at nine clusters, the size of the subsequent clusters is too small to be studied meaningfully and was not pursued. has been determined by the method of plotting numbers of potential clusters against three relevant statistical estimates, Ca, Pseudo T2, and Pseudo F. It was determined by the peaking of Ca and Pseudo F and troughing of Pseudo T2, as was explained and shown in Figure 1. A stepwise strategy was used to determine which parameters were more important contributors to the variation between the potential clusters. The final clusters were determined by 12 of 24 parameters. The result in clusters was analyzed further by methods of discriminant analysis using the procedure Discrim in SAS. By plotting the first two index values (canonical values 1 and 2), the separation of the clusters was shown as in Figure 2. The differences among the clusters were then studied by analysis of variance (ANOV A) using GLM method (SAS). A post hoc test (Student Newman-Keuls method) was applied to detect the differences between the clusters. Student's t-test or x2 analysis were applied where appropriate. The means and the standard deviations of all the parameters of each cluster are presented. The significance level was set at P < Vol. 63, No.2, February 1995 Norman et al. Subclassification of peds patients 331

4 Canonical Value 2 2r , -3 _4L ~ ~ Canonical Value 1 Figure 2 Plot of first two canonical values of the clusters. To show visually the overall difference between the five clusters, a discriminant analysis was carried out. The first two canonical values (a linear combination of all the variables included in the initial cluster analysis that can be called index values) from a discriminant analysis, on the basis of the five clusters, are plotted, demonstrating the similarity within clusters and dissimilarity between the clusters. RESULTS The PCOS population studied predominantly was composed of young women aged between 20 and 40 years with mean age of 28.7 years. A high proportion of them have the conditions of hirsutism, acne, or seborrhea (84%, 53%, and68%, respectively). Approximately two thirds were infertile, reflecting their presentation to a reproductive medicine-infertility unit. As expected, the proportions of hirsutism, acne, and seborrhea in the PCOS group were high whereas an increased BMI (>25) was present in 63% of the PCOS group and 53% have a waist to hip ratio> Menstrual abnormalities also were frequent in the PCOS population; nearly two thirds are oligoamenorrhea. The optimal number of clusters has been suggested clearly by the pattern of the relationship between the number of possible clusters, listing from 1 to 15, and the three statistical estimates obtainable if this particular number of clusters is selected (see Figure 1). Table 1 shows the mean and SD of the various parameters measured in the PCOS clusters and the obese (BMI > 25) and nonobese reference subjects. There were significant differences in BMI, waist to hip ratio, integrated glucose, integrated insulin, maximum insulin, SHBG, T, and lipids between the five clusters. Subjects in cluster 1 tended to be of normal weight and had a waist to hip ratio of They tended to have lower glucose and insulin values with higher SHBG and lower T than the other clusters. Their lipid values were more likely to be in the reference range. The remaining clusters exhibited high BMI. Cluster 2 had a normal waist to hip ratio (0.80), with relatively lower glucose, insulin, and lipid values than clusters 3, 4, and 5. The remaining three clusters were significantly overweight with increased waist to hip ratio, integrated glucose, integrated insulin, and maximum insulin levels and abnormal lipids. Because there were significant differences in BMI and some difference in age between the clusters, an attempt was made to exclude the possible confounding effects of age and BMI. Five subjects from each cluster were chosen, matched for age and BMI, and compared for levels of integrated glucose, integrated insulin, and maximum insulin (Table 2). They were compared using a block design ANOV A. The results indicate that after adjusting for age and BMI, significant differences remain between the clusters with respect to glucose and insulin levels. To demonstrate the similarity within clusters and dissimilarity between the clusters, the first two canonical values from a discriminant analysis, on the basis of the five clusters, are plotted. The separation of the five clusters is illustrated in Appendix 2. To compare with the PCOS subgroups, the reference population was separated arbitrarily into two groups, obese (BMI > 25) and nonobese. For both reference groups, the mean BMI was similar to that of the comparable PCOS subgroups (clusters 3 and 1, respectively), but their mean age was significantly greater than the PCOS groups. Cluster 1 (nonobese PCOS) did not differ significantly from nonobese reference subjects in glucose, insulin levels, and SHBG value, had a significant increase in the concentrations of LH, T, A, DHEAS, 17-0HP, triglycerides, and Apo B, and had a significant reduction in FSH and HDLC concentrations. This suggests a relationship between androgens and lipids independent of insulin in the nonobese PCOS group. Obese PCOSwomen (cluster 3 was used in the comparison because of the similarity of its BMI to the obese reference subjects), however, had significantly higher values of integrated glucose, integrated insulin, and maximum insulin than their obese reference counterparts. In addition, obese PCOS women had significantly increased concentrations of T, A, 17-0HP, and triglyceride and significantly decreased concentrations of HDLC and FSH. Obese PCOS women had significantly higher waist to hip ratios than reference subjects with similar BMI whereas the waist-to-hip ratios of nono- 332 Norman et al. Subclassification of peas patients Fertility and Sterility

5 Table 1 Age, BM!, Waist-to-Hip Ratio, and Metabolic, Hormonal, Lipid, and Lipoprotein Profiles of the Five PCDS Clusters and Two Control Groups*t Cluster Comparison between Control Control PCOS (1) pcos (3) pcos nonobese obese nonobese obese 4 clusters (P) (BMI < 25) (BMI", 25) control (P) control (P) Age (yr) 29.1 ± ± ± ± ± 1.9 NS 34.2 ± ± BMI 24.1 ± 5.9" 29.6 ± 7.2b 34.8 ± 6.9< 37.1 ± 4.8< 35.8 ± 3.6" ± ± 7.8 NS NS Waist to hip ratio 0.76 ± ± 0.06 ob 0.83 ± 0.07"" 0.88 ± 0.06<d 0.91 ± 0.06d ± ± 0.06 NS 0.03 Diastolic blood pressure (mmhg) 70.5 ± ± ± ± ± 12.0 NS 70.2 ± ± 3.9 NS NS Systolic blood pressure (mmhg) ± ± ± ± ± ± ± 9.0 NS NS Fasting glucose (mmol/l) 4.2 ± ± ± ± ± ± ± 0.6 NS NS Integrated glucose (mmol/l) 12.6 ± ± 8.7' 21.0 ± 12.7-b 26.1 ± 7.9b 35.1 ± 4.2< ± ± 10.5 NS 0.04 Fasting insulin (miu/l) 9.5 ± ± ± ± ± ± ± 9.7 NS NS Integrated insulin (miu/l) ± ± 100.1b ± 50.7< ± 97.8 d ± 119.7< ± ± NS Maximum insulin during GTT (miu/l) 59.1 ± ± 38.6b ± 32.9< ± 33.3d ± 15.6< ± ± 63.4 NS Ovary volume (cm 3 ) 23.6 ± ± ± ± ± 3.1 NS 12.7 ± ± 7.0 NS LH (lull) 11.0 ± ± ± ± ± 3.6 NS 6.9 ± ± NS FSH (lull) 5.5 ± ± ± ± ± 1.3 NS 6.6 ± ± PRL (miu/l) ± ± ± ± ± 99.5 NS ± ± 65.1 NS NS SHBG (nmol/l) 37.5 ± 23.6' 23.9 ± 14.28b 22.5 ± 20.8"' 17.9 ± 9.4b 12.6 ± 9.0b ± ± 25.9 NS NS T (nmol/l) 5.5 ± ± ± 4.6ab 5.9 ± ± 4.5b ± ± A (nmol/l) 8.9 ± ± ± ± ± 3.3 NS 3.9 ± ± E, (nmol/l) 0.35 ± ± ± ± ± 0.19 NS 0.36 ± ± 0.41 NS NS P (nmol/l) 2.84 ± ± ± ± ± 2.91 NS 3.72 ± ± 0.53 NS NS DEAS (umol/l) 7.1 ± ± ± ± ± 2.2 NS 3.3 ± ± NS 17 OHP (nmol/l) 3.8 ± ± ± ± ± 1.4 NS 2.4 ± ± Cholesterol (mmol/l) 5.4 ± 1.0'" 5.6 ± l.3 ab 5.6 ± O.79ab 4.8 ± LOb 6.3 ± ± ± 1.5 NS NS Triglycerides (mmol/l) 0.86 ± ± 0.85"' 1.39 ± 0.57'" 1.76 ± 1.02b 2.78 ± 1.68< ± ± Apo Al (gil) 1.44 ± ± 0.17-b 1.31 ± 0.25"' 1.19 ± 0.12b 1.18 ± 0.06b ± ± 0.32 NS NS Apo B (gil) 1.01 ± ± ± ± ± 0.52b ± ± NS HDLC (mmol/l) 1.38 ± ± 0.26'" 1.11 ± 0.24"" 0.96 ± 0.15< 0.92 ± 0.13< ± ± LDLC (mmol/l) 3.64 ± ± ± ± 0.99' 5.06 ± 2.77b ± ± 1.45 NS NS No. in cluster * Values are mean ± SD. Conversion factors from SI units: glucose. 1 mmol/l ~ 18 mg/100 ml; insulin, 1 mlu/l ~ 1!,u/ml; LH. 1 lull ~ 1 mlu/ml; FSH, 1 lull ~ 1 mlu/ml; PRL, 1 ng/ml ~ 32.5 miu/l; T 1 ng/ml ~ 3.47 nmol/l; A, 1 ng/ml ~ 3.49 nmol/l; E" 1 pg/ml ~ nmol/l; p. 1 ng/ml ~ 3.18 nmol/l; DEAS, 1!,g/mL ~ 2.8!,mol/L; 17-0HP, 1 ng/ml ~ 3.03 nmol/l; triglycerides, 1 mmol/l ~ 88.5 mg/100 ml; HDLC and LDLC, 1 mmol/l ~ 38.6 mg/100 ml. t Differences between the clusters was tested by Student-Newman-Keuls multiple range test and clusters with different letters were significantly different at P < 0.05 level. bese PCOS and reference subjects were not different. DISCUSSION The ongoing controversy about the significance and long-term effects of PCOS (12-14), together with the uncertainty surrounding its etiology and genetic background (5, 7, 15), has its roots in the heterogeneity of this condition. Few publications have attempted to subdivide PCOS into different groups in the way that other endocrine conditions (e.g., Cushing's syndrome, hypoparathyroidism) now are classified. Initially, it was recognized that BMI could be an important determinant (16), but most investigators have arbitrarily divided normal weight from obese at a single cutoff point. Clearly the effects of BMI have a gradient of influence rather than an all-or-nothing effect. In a similar fashion, other investigators have divided subjects into insulin-resistant or nonresistant groups based on comparable non -PCOS reference subjects of similar BMI (4, 17, 18, 19). These studies suffer from the problems of defining an upper limit on the normal insulin response in a reference population, many of which have not been investigated thoroughly for occult presentations of PCOS. Another shortcoming of these studies is that they consider only one or two factors as the determinant of PCOS subclassification, which may well not be the case. The present study included women with features that would be recognized to be PCOS by most investigators, i.e., biochemical, ultrasonographic, and clinical criteria. It excluded those women who have polycystic ovaries on scan only and the importance of this PCO group needs further evaluation. The study used the statistical method of cluster analysis, which maximizes both the similarities within clusters and the dissimilarities between clusters, similar to the approach used previously by Dewailly et al. (20). With this statistical analysis method, the most important parameters in subgrouping the pa- Vol. 63, No.2, February 1995 Norman et al. Subclassification of pcas patients 333

6 Table 2 Metabolic Profiles of Five Clusters With Age, BMI-8elected Matched 8ubjects*t Cluster Comparison between clusters (P) Age (yr) 27.3 ± ± ± ± ± 1.9 N8 BMI 35.6 ± ± ± ± ± 3.6 N8 Waist-to-hip ratio ± ± ± ± 0.06 N8 Integrated glucose (mmol/l) 10.8 ± 7.8 a 21.6 ± 16.6 b 21.1 ± 4.5 ab 23.5 ± 5.1 b 35.1 ± 4.2c 0.02 Integrated insulin (miu/l) ± 53.7 a ± 10.6 b ± 46.0c ± 117.5d ± 119.7" Maximum insulin (miu/l):i: 74.6 ± 35.4 a ± 53.5 ab ± 46.7 bc ± 48.1cd ± 15.6 d * Values are means ± 8D. Conversion factors from 81 units: glucose, 1 mmol/l = 18 mg/ioo ml; insulin, 1 miu /L = 1/LU/mL. t Where significant differences exist between the clusters, 8tudent-Newman-Keuls multiple range test was performed and groups with different letters differ significantly from each other at P < 0.05 level. :I: Maximum insulin during the GTT. tients into five major clusters also are identified. Previously described differences in BMI and insulin concentrations after glucose challenge were highlighted with three large groups based on BMI (termed normal, overweight, and obese) and five major groups based on integrated insulin values and other parameters. Each group also exhibited differences in waist to hip ratios, SHBG, free androgens' and lipids, which have been described previously in the literature but not been placed in the context of subgroups such as have been identified in this study. The nonobese cluster (cluster 1) showed no differences with regard to glucose and insulin levels from the reference population but their higher concentrations of LH, triglyceride, androgens, and lower concentrations of HDLC suggest that PCOS has a significant effect on these parameters in the absence of hyperinsulinemia. It is possible, therefore, that this group may have a different etiology and long-term prognosis from the other clusters, and analysis of PCOS data should recognize the possible unique nature of this group. The overweight and obese groups exhibited a gradient of increasing hyperinsulinemia, high plasma glucose, hyperlipidemia, and hyperandrogenism. The work of Dunaif and other investigators (21-23) supports these observations. The general differences between normal weight, overweight, and obese groups support the common view of a positive relationship between BMI and insulin resistance. On the other hand, the differences between the three obese clusters were obviously independent of BMI, although their waist to hip ratio was significantly different and appeared to be associated positively with their insulin levels. This may suggest a limitation of BMI in reflecting the metabolism of PCOS women and the potential usefulness of the waist to hip ratio. Unfortunately, waist to hip ratio data was missed in a small number of subjects and prevented use of this parameter in the cluster analysis. With subjects matched for BMI and age, the close association between waist to hip ratio and integrated insulin values of the five clusters is obvious. The significant differences in age and BMI between clusters raises the possibility that the changes in glucose and insulin actually were caused by these parameters. Comparison of age- and BMI-matched subjects (clusters 1 to 5), however, showed that large differences remained between the clusters in glucose and insulin independent of BMI and age. The dynamic relationship between the five clusters also would be of interest and several questions arise. For instance, what would happen if subjects in clusters 3, 4, or 5, respectively, lose weight? Could subjects in cluster 1 be separated further by their potential change if they put on weight? These questions can be answered only by the follow-up of these subjects. Also of note are the parameters that do not differ between clusters, some of which have been proposed as important discriminators in classification of PCOS, e.g., concentrations of LH, PRL, and DHEAS and ovarian volume. In the present study, although these parameters discriminated PC OS from non -PCOS subjects, they were not important factors in subclassification of the condition. Simi- 1arly' E 2, 17-0HP, LDLC, and blood pressure were nondiscriminating. The metabolic heterogeneity also was not reflected clearly in clinical differences 334 Norman et al. Subclassification of peas patients Fertility and Sterility

7 between the groups despite previously documented differences in concentrations of insulin, androstenedione, and 17 -OHP between ovulatory and anovulatory peas (24, 25). The only statistically significant trend was the increasing frequency of oligomenorrhea and amenorrhea from the nonobese (cluster 1) to obese (cluster 4). It should be noted, however, that the selective nature of our peas subjects' recruitment, i.e., mainly from a large infertility and endocrine clinic, means that other subgroups exist. The present study illustrates that a metabolic approach to classification of peas is a reasonable way to attempt to classify this condition for studies of clinical etiology and sequelae. Previously characterized parameters such as insulin, BMI, waist to hip ratio, SHBG, T, and lipids provide for a subclassification that will permit a more accurate description of this condition. With this approach, new patients potentially could be grouped based on the values obtained for such parameters and a prospective study is underway. REFERENCES 1. Zawadzki JK, Dunaif A. Diagnostic criteria for polycystic ovary syndrome: towards a rational approach. In: Dunaif A, Givens JR, Haseltine FP, Merriam GR, editors. Polycystic ovary syndrome. Boston: Blackwell, 1992: Polson DW, Adams J, Wadsworth J, Franks S. Polycystic ovaries-a common finding in normal women. Lancet 1988;1: Adams J, Polson DW, Franks S. Prevalence of polycystic ovaries in women with anovulation and idiopathic hirsutism. Br Med J 1986;293: Barbieri RL, Smith S, Ryan KJ. The role ofhyperinsulinemia in the pathogenesis of ovarian hyperandrogenism. Fertil Steril1988;50: Givens JR, Wild RA. Historical overview of the polycystic ovary. In: Dunaif A, Givens JR, Haseltine FP, Merriam GR, editors. Polycystic ovary syndrome. Boston: Blackwell, 1992: Cooper HE, Spellacy WN, Prem KA, Cohen WD. 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Intraovarian regulators and polycystic ovarian syndrome. Ann NY Acad Sci 1993;687: Vol. 63, No.2, February 1995 Norman et ai. Subclassification of peas patients 335