Human Reproduction Vol.21, No.1 pp. 80–89, 2006
Advance Access publication September 30, 2005.
Combined lifestyle modification and metformin in obese
patients with polycystic ovary syndrome. A randomized,
placebo-controlled, double-blind multicentre study

Thomas Tang1, Julie Glanville1, Catherine J.Hayden1, Davinia White2, Julian H.Barth3
and Adam H.Balen1,4

1Department of Reproductive Medicine, Clarendon Wing, The General Infirmary, Leeds LS2 9NS, UK 2Department of Reproductive Medicine, St Mary’s Hospital, London W2 and 3Department of Clinical Biochemistry, The General Infirmary, Leeds LS1 3EX, UK 4To whom correspondence should be addressed. E-mail: [email protected] BACKGROUND: It has been reported that women with polycystic ovary syndrome (PCOS) benefit from metformin
therapy. METHODS: A randomized, placebo-controlled, double-blind study of obese (body mass index >30 kg/m2),
oligo-/amenorrhoeic women with PCOS. Metformin (850 mg) twice daily was compared with placebo over 6 months.
All received the same advice from a dietitian. The primary outcome measures were: (i) change in menstrual cycle; (ii)
change in arthropometric measurements; and (iii) changes in the endocrine parameters, insulin sensitivity and lipid
profile. RESULTS: A total of 143 subjects was randomized [metformin (MET) = 69; placebo (PL) = 74]. Both groups
showed significant improvements in menstrual frequency [median increase (MET = 1, P
< 0.001; PL = 1, P < 0.001)]
and weight loss [mean (kg) (MET = 2.84; P
< 0.001 and PL = 1.46; P = 0.011)]. However, there were no significant dif-
ferences between the groups. Logistic regression analysis was used to analyse the independent variables (metformin,
percentage of weight loss, initial BMI and age) in order to predict the improvement of menses. Only the percentage
weight loss correlated with an improvement in menses (regression coefficient = 0.199, P
= 0.047, odds ratio = 1.126,
95% CI 1.001, 1.266). There were no significant changes in insulin sensitivity or lipid profiles in either of the groups.
Those who received metformin achieved a significant reduction in waist circumference and free androgen index.
CONCLUSIONS: Metformin does not improve weight loss or menstrual frequency in obese patients with PCOS.
Weight loss alone through lifestyle changes improves menstrual frequency.

Key words: menstrual frequency/metformin/obese/polycystic ovary syndrome/weight loss Introduction
Pasquali et al., 2003), menstrual disorders and hyperinsulinaemia The polycystic ovary syndrome (PCOS) is the commonest endo- (Conway et al., 1990; Lord and Wille, 2002). Obesity corre- crine disturbance in women (Balen and Michelmore, 2002) and lates with an increased rate of menstrual cycle disturbance and the commonest cause of anovulatory infertility. PCOS is a hetero- infertility (Kiddy et al., 1990; Balen et al., 1995). Weight loss geneous disorder with features including hyperandrogenism, improves the endocrine profile, the menstrual cyclicity, the menstrual irregularity and obesity (Balen et al., 1999; Rotterdam likelihood of ovulation and of a healthy pregnancy (Pasquali ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group, et al., 1989; Kiddy et al., 1992; Huber-Buchholz et al., 1999).
2004). The association between insulin resistance, compensatory Studies by Clark et al. (1995, 1998), demonstrated that weight hyperinsulinaemia and hyperandrogenism have provided insight loss achieved by an exercise schedule, combined with a hypo- into the pathogenesis of PCOS (Tsilchorozidou et al., 2004). Insu- caloric diet over a 6 month period, improved insulin sensitiv- lin resistance occurs in both slim and overweight women with ity, endocrine parameters, menstrual regularity, the frequency PCOS, although there is debate on the proportion of women with of spontaneous ovulation and the chance of pregnancy.
PCOS with reduced insulin sensitivity (Cibula et al., 2004). At Even a modest weight loss of 2–5% of total body weight can least 40% of women with PCOS are obese (Balen et al., 1995) restore ovulation in overweight women with PCOS as well as and they are more insulin resistant than weight-matched individu- achieving a reduction of central fat and an improvement in als with normal ovaries (Dunaif et al., 1995; Morales et al., 1996).
insulin sensitivity (Huber-Buchholz et al., 1999). Rather than Obesity and particularly abdominal obesity as indicated by absolute weight, it is the distribution of fat that is important an increased waist:hip ratio is correlated with reduced fecun- with android (central) obesity being more of a risk factor than dity (Zaadstra et al., 1993; Kirchengast and Huber, 2004; gynaecoid obesity (Despres et al., 2001; Lord and Wille, The Author 2005. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: [email protected] Metformin in obese PCOS patients
2002). Visceral adipose tissue is more metabolically active PCOS was defined as the presence of polycystic ovaries on trans- than subcutaneous fat and the amount of visceral fat correlates vaginal scan, >10 cysts, 2–8 mm in diameter, usually combined with with insulin resistance and hyperinsulinaemia. Weight reduc- increased ovarian volume >10 cm3, and an echo-dense stroma (after tion of 5–10% may result in ∼30% loss of visceral adipose tis- the transabdominal ultrasound criteria of Adams et al., 1985), together sue (Despres et al., 2001) and this may explain why a modest with either oligomenorrhoea or amenorrhoea. Many patients also hadclinical or biochemical hyperandrogenism, although this was not an weight loss can significantly improve metabolic and reproduc- entry criterion for the study. All patients had a baseline androgen pro- tive function. Waist circumference has been shown to correlate file, including measurement of testosterone and androstenedione. If better with visceral fat than waist:hip ratio (WHR) (Lord and either were significantly elevated, additional tests were performed to Wille, 2002), and a waist circumference in women >88 cm is exclude hypercortisolism and congenital adrenal hyperplasia (CAH) indicative of an increased metabolic risk (Despres et al., 2001).
(full steroid profile, 24 h urinary cortisol and adrenocorticotrophin Lifestyle modification is a key component for the improve- hormone stimulation test). When the study was devised the Rotterdam ment of reproductive function for overweight, anovulatory consensus definitions of PCOS (2004) and of the polycystic ovary women with PCOS (Norman et al., 2002, 2004; Pasquali et al., (Balen et al., 2003) had not been defined, although all of our patients 2003). Weight loss should therefore be encouraged prior to would have been classified as having PCOS by those criteria.
ovulation induction treatments, since these are less effective Pretreatment inclusion criteria also included the presence at least when the body mass index (BMI) is >28–30 kg/m2 (Hamilton- one patent Fallopian tube and a normal semen analysis from the malepartner. All participants had normal serum prolactin concentrations, Fairley et al., 1992). Monitoring treatment is also harder in the thyroid, renal and liver function and haematological indices, including obese as visualization of the ovaries is more difficult which raises the risk of multiple ovulation and multiple pregnancy.
Exclusion criteria included concurrent hormone therapy within the Furthermore, pregnancy carries greater risks in the obese, for previous 6 weeks, any chronic disease that could interfere with the example: miscarriage, gestational diabetes, hypertension and absorption, distribution, metabolism or excretion of metformin, and problems with delivery (Gjonnaess et al., 1989; Sebire et al., renal or liver disease. Patients with significant systemic disease or dia- 2001; Cedergren, 2004; Linné, 2004).
betes (Type 1 or 2) were excluded. Patients with irregular menstrual It is logical to assume that therapy that achieves a fall in bleeding were thoroughly assessed to exclude pathology of the genital serum insulin concentrations should improve the symptoms of tract other than PCOS and a negative pregnancy test was a prerequi- PCOS (Norman et al., 2004). The biguanide metformin both inhibits the production of hepatic glucose, thereby decreasing Protocol
insulin secretion, and enhances insulin sensitivity at the cellularlevel (Matthaei et al., 2000). The efficacy of metformin in A multicentre research ethics committee approval (MREC 1999/8/12)and the local research ethics committee approval of each participating PCOS was first described by Velazquez et al. (1994) and a centre were obtained. After obtaining written consent, a full physical number of small, and often short duration, observational studies examination was performed including assessment of BMI, waist and followed which showed variable outcomes. Most of the rand- hip circumference and blood pressure. A baseline transvaginal ultra- omized studies have involved only a small number of partici- sound scan was performed to assess ovarian morphology, uterine size pants. Indeed in a systematic review by Costello and Eden and endometrial thickness. A standardized 75 g oral glucose tolerance (2003), nine out 12 published studies on the effects of met- test (OGTT) was performed with measurement of fasting insulin con- formin alone on the menstrual cycle in women with PCOS had centration and glucose at 0 and 120 min. Baseline serum endocrinol- a sample size of <30 women. Lord et al. (2003) published a sys- ogy included the measurement of FSH, LH, testosterone, sex tematic review in the Cochrane Database which concluded that hormone-binding globulin (SHBG), total cholesterol and triglycerides.
metformin has a beneficial effect for women with PCOS, by The subjects were randomized to receive either metformin or pla- reducing serum insulin concentrations and thereby lowering cebo. The randomization process was carried out by the clinical trialsoffice in the pharmacy department and blinded to patients and investi- androgen levels and improving reproductive outcomes. Back in gators. A block-of-four randomization technique was performed using 1997 we conceived what we anticipated to be an appropriately random tables from Linder et al. (1970). The code was kept in the trial powered, prospective randomized, double-blind, placebo- office until the last patient completed the study. Placebo tablets for controlled multicentre study to evaluate the combined effects of metformin were identical in appearance (size and colour) to met- lifestyle modification and metformin on obese anovulatory formin and were supplied by Penn Pharmaceuticals Ltd (Tredegar, women (BMI >30 kg/m2) with PCOS. The study has taken a Gwent). One tablet (metformin 850 mg or placebo) was prescribed to considerable time to complete and here we present our findings.
be taken 12 hourly for a period of 6 months.
Patients in each group received standardized dietary advice from a research dietitian. Each subject was assessed by the dietitian and an Materials and methods
individualized diet [high in carbohydrate (50%) and low in fat (10%)] Women were recruited from infertility clinics with anovulatory PCOS was given with the aim of a reduction in daily intake by 500 kcal. Writ- and a BMI of >30 kg/m2, aged between 18 and 39 years inclusive and ten information was given on PCOS and appropriate information on a a desire to conceive. Anovulation was defined as the presence of balanced weight-reducing diet. The patients were also encouraged to amenorrhoea or oligomenorrhoea (cycle length >35 days) (Munster increase daily exercise (such as walking, using stairs) by 15 min, et al., 1993; Berek et al., 1996) and the absence of ovarian follicular although this was not formally assessed. The participants received fur- activity on serial ultrasound scans. The patients had not received ovu- ther encouragement to adhere to the regime at the monthly review visits.
lation induction therapy from the fertility clinic as the usual criterion Each participant was assessed monthly with a re-evaluation of for any form of ovulation induction (clomiphene citrate or gonadotro- anthropometric measurements, endocrine and biochemical parameters pin therapy) was a BMI of <30 kg/m2.
together with an ultrasound scan and record of the patient’s menstrual T.Tang et al.
cycle. Side-effects of the treatment and reason for any withdrawals under steady state conditions; an alternative reference method is the from the study were recorded. The assessment was performed by the intravenous glucose tolerance test (IV-GTT). Both require sophisti- same person in each centre (usually the research nurse). All nursing cated investigation centres, are labour intensive, expensive and cannot and medical personnel were blind to the treatment arm, with the really be performed for large scale studies. In routine clinical practice research pharmacy in Leeds being the only place where this informa- an OGTT or simple ratios of fasting glucose/insulin are fairly sensi- tion was held for the duration of the study. Compliance was assessed tive (Moran and Norman, 2004). More accurate indices of insulin sen- by the return of empty drug containers.
sitivity and secretion derived from fasting plasma insulin and bloodglucose concentrations are reasonable substitutes for the euglycaemic Outcome measures
clamp and IV-GTT, these include the HOMA and QUICKI methods The primary outcome measures were: (i) change in menstrual cycle; (ii) change in arthropometric measurements; and (iii) changes in the The homeostatic model assessment (HOMA) is a computer-gener- endocrine parameters, insulin sensitivity and lipid profile. The main ated model consisting of a series of non-linear empirical equations secondary outcome measure was pregnancy rate.
solved numerically to predict glucose, insulin and C-peptide concen-trations in the fasting state for the assessment of pancreatic beta cell Power calculation
function and insulin sensitivity (Matthews et al., 1985). The use ofHOMA correlates well with the euglycaemic clamp method and the In the study by Velazquez et al. (1997a), metformin alone improved IV-GTT but cannot be compared between different studies unless the menstrual regularity in 21/40 (53%) of subjects. If we anticipate an insulin assay is standardized (Bonara et al., 2000). The estimation of overall 83% improvement with a combination of diet and metformin the QUICKI provides a robust and reproducible estimate of insulin (i.e. a further 30% improvement compared with metformin alone), the sensitivity that shows excellent linear correlation with the gold stand- standardized difference (d) would be 0.64. The chosen power in the ard clamp estimation with similar variability and discrimination study was 90% with a type I error of 0.05. From the power table power (Katz et al., 2000). The relative advantages of QUICKI over (Machin and Campbell, 1987), when d = 0.64 and the power = 0.90, HOMA include the fact that the data derived from a single blood sam- the projected sample size was 110, with 55 subjects in each arm of the ple performs just as well as an average of multiple sampling, and the study. When the study was designed the literature from which to cal- simplicity of the mathematical model. Furthermore, therapeutic culate power was limited, if we were to consider the more recently changes in insulin sensitivity have been as readily demonstrated with published Cochrane meta-analysis by Lord et al. (2003), which this simple method as with the euglycaemic clamp (Mathur et al., reported an overall improvement in ovulation rates in 71/156 and 2001). A recent review on the determination of insulin sensitivity in 37/154 subjects in the metformin group and the control group respec- PCOS has highlighted the good correlation of QUICKI with the clamp tively with an odds ratio of 3.88 (95% CI 2.25, 6.69) versus placebo technique (Kauffman and Castracane, 2003).
for rate of ovulation in favour of metformin. Based on these recent Data were analysed on the basis of intention to treat. All the sub- values, the standardized difference is 0.57 with the projected sample jects who withdrew within the first 4 months of the study period, size of 130. At the end of the study period, the actual recruitment excluding those who conceived, were classified as non-responders.
This is because we wished to include only those who completed≥4 months, and preferably 6 months of the trial. For parametric data, Biochemical assays
the assumption of normal distribution was assessed by a normal plot All the samples were stored at −20°C and were analysed in the bio- and the Kolmogorov–Smirnov test. The assumption of the two groups chemistry department of the coordinating centre. The analyses were as having the same variances was tested by using the F-test. Paired t-test previously described (Wijeyaratne et al., 2002). Plasma glucose was or two-sample t-test was applied as indicated. When the data did not measured using an enzymatic colorimetric assay (Hitachi, Roche) with meet the above assumptions, a log transformation of the data was intra-assay coefficients of variation (CV) of 1.9% at 20.2 mmol/l and carried out. If the transformed data was still not meeting the assump- 30% at 2.4 mmol/l. A time-resolved fluoroimmunoassay (AutoDEL- tions, non-parametric methods, Wilcoxon signed rank test or Mann– FIA; Perkin Elmer) was used to measure insulin and serum SHBG con- Whitney test were applied. P < 0.05 was considered to be statistically centrations, with plasma insulin intra-assay CV of 1.7% at 180.96 significant. The Z-test was used to analyse the two proportions with pmol/l and 2.4% at 33.9 pmol/l and inter-assay CV of 3.5% at 180.96 pmol/l and 2.3% at 33.9 pmol/l. The serum SHBG intra-assay CV was In the multiple linear regression analysis, the same normality test 6% at 103.36 nmol/l and 7% at 14.88 nmol/l; and the inter-assay CV was used as in the t-test and the test for constant variance was com- was 1% at 103.36 nmol/l and 1% at 14.88 nmol/l. Serum testosterone puted by using the Spearman rank correlation between the absolute was measured after organic extraction using an in-house radioimmu- values of the residuals and the observed value of the dependent varia- noassay with an inter-assay CV of 7.7% at 2.20 nmol/l. Free androgen ble. When the criteria of normality or constant variance were not met, index (FAI) was derived from the ratio of the total testosterone concen- a log transformation of the data was performed. Durbin–Watson sta- tration (nmol/l) to the concentration of SHBG (nmol/l) × 100.
tistic was used to test residuals for their independence of each other.
In the logistic regression analysis, the regression coefficients com- Data analysis and statistics
puted by minimizing the sum of squared residuals in multiple logistic The insulin sensitivity (IS) was calculated from the Quantitative Insu- regression are also the maximum likelihood estimates. P is the lin Sensitivity Check Index (QUICKI), described by Katz et al. P-value calculated for the Wald statistic, which is the regression coef- (2000). QUICKI = 1/[log(I ) + log(G )], with I = fasting insulin con- ficient divided by the SE. All the statistical analyses were performed centrations in mIU/ml (conversion from pmol/l to mIU/ml: multiplied by a factor of 0.144) and G = fasting glucose concentrations in mg/dl (conversion from mmol/l to mg/l: multiplied by a factor of 18.0).
Recruitment progress
The hyperinsulinaemic–euglycaemic glucose clamp technique is During a 4 year period, between 1999 and 2003, a total of eight cen- the ‘gold standard’ for quantifying insulin sensitivity in vivo because tres took part in the recruitment process. A total of 183 women were it directly measures the effects of insulin to promote glucose utilization screened for inclusion in the study. Of these, 40 women were Metformin in obese PCOS patients
excluded due to previously undiagnosed tubal disease or co-existing pregnancies). Eight women withdrew from the placebo arm (six due male factor infertility. As a result, a total of 143 subjects were rand- to ‘side-effects’ and two due to spontaneous pregnancies, within the omized to receive metformin (n = 69) or to receive placebo (n = 74) first 2 months of the study). The difference in the drop-out rates, (Figure 1). In the metformin arm, 13 subjects withdrew within the first excluding because of pregnancy (metformin, 15.9% versus placebo, 4 months of the trial (11 due to side-effects and two due to spontaneous 8.0%) was not significant (P = 0.229, 95% CI –2.69 to 18.5). At theend of the study, the numbers of patients who completed the trial inthe metformin and placebo arms were 56 and 66 respectively. Com-pliance was high and the drop-out rate relatively low as these werepatients motivated by a desire to conceive and the knowledge that they needed to attain a BMI of <30 kg/m2 to qualify for ovulation The total number of patients per centre, with those who withdrew in parentheses, were: Leeds 65 (6), St Mary’s Hospital, London 41 (1), MRC Reproductive Medicine Centre, Edinburgh 3 (1), Royal Shrews- bury Hospital, Shrewsbury 12 (4), Royal Free Hospital, London 6 (0),St Bartholomew’s Hospital, London 4 (3), Hope Hospital, Salford 4 (0)and The Jessop Hospital for Women, Sheffield 8 (2).
Demographic data
There were no significant differences in the baseline character- istics of the subjects between the two groups (Table I). In themetformin and placebo groups respectively, the mean BMI (37.6 versus 38.9 kg/m2), the median number of menstrual cycles in the preceding 6 months (2 versus 2), the mean waistcircumference (111.9 versus 108.8 cm) and waist:hip ratio(WHR) (0.907 versus 0.900) were similar. The anthropometric measurements of the subjects who withdrew prematurely werealso not significantly different from those who completed the Figure 1. The progress of the subjects through the study.
Table I. The baseline characteristics of the subjects in metformin and placebo groups
Proportion of subjects who withdrew, excluding pregnancy (%) aMann–Whitney rank sum test was used to analyse the difference, and the median instead of the mean is reported.
bLog transformation was carried out on the data before the analysis with two sample t-test.
cQuantitative Insulin Sensitivity Check Index (QUICKI) method = 1/[log(I ) + log(G )].
I = fasting insulin levels in mIU/ml (conversion from pmol/l to mIU/ml: multiplied by a factor of 0.144).
G = fasting glucose levels in mg/dl (conversion from mmol/l to mg/l: multiplied by a factor of 18.0).
T.Tang et al.
As expected, there was a positive correlation between insu- cycle pattern and fertility. Thus the observed improvement in lin sensitivity and serum SHBG concentrations (log insulin menstrual frequency can be viewed as an indication of improve- sensitivity = –0.593 + 0.093 × (log SHBG), adjusted R2 = 0.11, ment of ovulation rate and potential fecundity.
P = 0.001). Additionally, there was a negative correlation On the basis of intention to treat (ITT), 36 women (52.2%) between insulin sensitivity and serum triglyceride concentra- in the metformin group and 43 women (58.1%) in the placebo tions and BMI (log insulin sensitivity = –0.454 – 0.061 × (log group experienced improvement in menses. However, the dif- triglycerides), adjusted R2 = 0.060, P = 0.011), even after ference between the two groups was not significant (P = 0.589, adjustment for age, waist circumference and serum testoster- one concentration. Surprisingly, no association between waistcircumference and serum insulin concentration and insulin sen- Anthropometric measurements
Significant reductions in body weight and BMI were observed The mean duration of infertility was similar in each group in both groups (Tables II and III). However, the changes in [MET 4.5 (SD 2.9) years versus PLA 4.9 (3.0) years, P = the means between the groups were not significant (–1.02 versus 0.624]. There was no difference in the percentage of primary –0.46, 95% CI –1.15, 0.03, P = 0.063). The study was not infertility (MET 69% versus PLA 73%, P = 0.851) or subjects powered to determine a difference in weight even though the who had previously been prescribed clomiphene citrate, usu- metformin group lost twice as much weight as the placebo ally by their primary care physician and not in the context of group. There was a significant reduction of waist circumfer- the fertility clinic, where body mass would have precluded ence in the metformin group (before 113.5 cm, after 111.1 cm, treatment (MET 43% versus PLA 49%, P = 0.718).
P = 0.002) (Table II) but not in the placebo group (before 108.5cm, after 109.1 cm, P = 0.764) (Table III). The difference in Menstrual frequency
the changes of the mean values between the two groups was At the end of the study period, the menstrual cycles over the not statistically significant (–2.34 versus +0.58, 95% CI –7.14, time-course of the study increased significantly with a median 1.30, P = 0.173). Similarly, the changes in the mean of both of improvement of one menstrual cycle per 6 months in both systolic and diastolic blood pressure were not significantly dif- groups (Tables II and III). However, there was no difference between the groups (P = 0.580). Patients who menstruated<4 weeks from starting treatment were not considered to have Endocrine parameters and lipid profiles
ovulated in response to the study. A number of studies have used Both the fasting insulin and glucose data were skewed and menstrual frequency as an assessment of reproductive function therefore logarithmic transformations were performed on the women with PCOS and an improvement in menstrual regularity data before analysis. The geometric means of the fasting insu- is considered to be a good surrogate for ovarian function and lin concentrations in the metformin group did not change sig- ovulatory frequency in women with PCOS (Morin-Papunen nificantly over the course of the study (baseline 72.8 pmol/l, et al., 1998; Fleming et al., 2002; Haas et al., 2003). Furthermore, final 80.7 pmol/l, ratio of means 1.11, P = 0.524, Table II).
Kolstad et al. (1999) studied the relationship between menstrual Similarly, no significant changes in the geometric means of the Table II. The outcomes in the metformin group (n = 56)
aWilcoxon signed rank test was used to analyse the difference and the median instead of the mean is reported.
bLog transformation was carried out on the data before the analysis. Geometric means, mean ratio (a/b) and the corresponding 95% CI were reported after the results were back-transformed.
cQuantitative insulin sensitivity check index (QUICKI) method = 1/[log(I ) + log(G )].
CI = confidence interval; NS = not significant; SHBG = sex hormone-binding globulin.
Metformin in obese PCOS patients
Table III. The outcomes in the placebo group (n = 66)
aWilcoxon signed rank test was used to analyse the difference, and the median instead of the mean is reported.
bLog transformation was carried out on the data before the analysis. Geometric means, mean ratio (a/b) and the corresponding 95% CI were reported after the results were back-transformed.
cQuantitative insulin sensitivity check index (QUICKI) method = 1/[log(I ) + log(G )].
CI = confidence interval; NS = not significant; SHBG = sex hormone-binding globulin.
fasting insulin concentrations in the placebo group occurred Table IV. Multiple linear regression analysis of the change of free androgen
(baseline 74.1 pmol/l, final 81.8 pmol/l, ratio of means 1.10, index (log end of study levels – log baseline levels) on the percentage of P = 0.438, Table III). The difference between the changes weight change, the use of metformin, the change of insulin sensitivity and the between the two treatment arms was also not significantly dif- ferent (1.11 versus 1.10, 95% CI 0.672–1.49, P = 0.985). Simi- larly, there were no significant changes in fasting glucoseconcentrations within and between groups (Tables II and III).
Improvements in insulin sensitivity were not observed in either the metformin group or the placebo group (Tables II and III).
The changes of means in insulin sensitivity were also not dif- ferent between the two groups (data not shown).
aPercentage of weight change = 100%×(baseline-end of study weight)/base- There were no significant changes in the geometric mean SHBG concentrations in either the metformin or placebo arms Adjusted R2 = 0.0442.
The analysis of variance for the regression: F = 1.855, P = 0.128, residual (Tables II and III), neither was there a difference between the groups (data not shown). There was, however, a significant reduction in the FAI in the metformin arm of the study, with amean ratio (final:baseline) of 0.84 (95% CI 0.73, 0.96, P =0.013) and this was because of a significant fall in total testo- difference is 0.26 and the required sample size to assess a dif- sterone of –0.3 nmol/l (95% CI –0.08, –0.47, P = 0.008) ference in pregnancy rates would be 600 subjects.
(Table II). This was confirmed by multiple linear regressionanalysis after adjustment for baseline BMI, change in insulin Subgroup analysis of those who lost weight
sensitivity and the percentage of weight change (P = 0.046, On the basis of intention to treat (ITT), 36 women (52.2%) in the metformin group and 43 women (58.1%) in the placebo At the end of the study period, both the total cholesterol and group experienced improvement in menses. However, the dif- triglyceride concentrations remained unchanged (Tables II and ference between the two groups was not significant (P = 0.589, III) with no between-group differences (data not shown).
95% CI –10.4, 22.2). If these data are analysed by completionof protocol, the difference is still not significant (P = 0.94, 95% Pregnancy rates
There were two pregnancies in each arm of the study within Forty-two subjects (60.8%) in the metformin group and 35 sub- 2 months of commencing and a further four pregnancies in the jects (47.3%) in the placebo group managed to lose weight. The metformin arm in the 5th and 6th months of the study. The difference between the two groups was not significant (P = 0.147, total numbers of conceptions in the metformin (8.7%) and the 95% CI –28.5, 29.9). When we calculated the actual percentage placebo (2.7%) groups were not significantly different (P = 0.233, weight change (PWC) [100% × (baseline weight – end of study 95% CI –1.5, 13.5). Based on our findings, the standardized weight)/baseline weight] among only those women who managed T.Tang et al.
database (Lord et al., 2003). We were unable to demonstrate Table V. Multiple logistic regression analysis of the improvement in menses
that metformin had an additional benefit on the improvement on the percentage of weight change and initial body mass index (BMI) of menstrual frequency over weight loss through lifestyle mod- ification and, furthermore, in the study population metformin did not induce weight loss. After adjustment for baseline BMI and age, only weight loss, but not the use of metformin, was associated with a significant improvement in menstrual cyclic- ity. In addition, the higher the BMI, the more likely women with PCOS were to benefit from weight loss with respect to aPercentage of weight change = 100%×(baseline – end of study weight)/base- The entry criteria required BMI to be >30 kg/m2, yet the Pearson χ2 statistic: 116.7 (P = 0.411).
Likelihood ratio test statistic: 14.0 (P < 0.001).
mean BMI was ∼38 kg/m2 and comprised typical central obes- Hosmer–Lemeshow statistic: 10.1 (P = 0.261).
ity. These were patients who would not be suitable for ovula- tion induction for anovulatory infertility because of theirobesity and so had not yet been enrolled in the ovulation induc-tion programme, although some had previously received clo- to lose weight, we showed that the mean percentage of weight miphene citrate from their primary care physician before loss in the metformin and placebo groups was 3.98 and 4.41% referral to the fertility clinic. The rate of withdrawal in the met- respectively. The difference was not significant (P = 0.554, 95% formin group was not significantly different from the placebo group and was lower than that reported by Fleming et al. By using multiple logistic regression analyses of the improve- (2002) in their large trial in which 42% dropped out of the met- ment in menses on the PWC, the use of metformin, the baseline formin arm compared with 17% of the placebo arm. This may BMI and age, we were able to demonstrate that weight loss (a be explained by the fact that all of our patients had a wish to positive value of PWC) had a significantly positive effect on conceive and may therefore have had a greater incentive to improvement in menses (P = 0.047, regression coefficient = 0.199, odds ratio 1.126, 95% CI 1.00, 1.27). The use of met- A surprise finding was the lack of change in insulin sensitiv- formin had no influence on menstrual frequency in our study ity in either the metformin or placebo groups. This is probably explained by the extreme obesity of our patients and the rela- The best model to predict the improvement in menses is tively small amount of weight lost. It has been demonstrated 0.127 × (PWC) + 0.098 × (initial BMI) – 3.185 (see Table V).
that insulin sensitivity and androgen concentrations are This implies that the greater the BMI the more likely it was that unlikely to improve in patients who lose <5% of their initial improvement in menses would have been experienced through weight (Kiddy et al., 1992). Furthermore, the effect of met- formin in women with PCOS is reduced by increasing obesity(Crave et al., 1995; Fleming et al., 2002; Maciel et al., 2004).
Analysis of those with the metabolic syndrome
Our findings were similar to the study of Ehrmann et al. (1997) The metabolic syndrome is defined as requiring three out of the in which the average BMI was 39 kg/m2. Furthermore, the dose following five criteria: waist circumference >88 cm, elevated of metformin (850 mg twice daily) may be insufficient in this triglycerides ≥1.7 mmol/l, lowered high-density lipoprotein cho- group of patients and we are currently performing a dose-finding lesterol <1.3 mmol/l, elevated blood pressure (≥130/85 mmHg) study, using different doses at different body weights.
and impaired glucose tolerance test. Twenty-six of those in the Metformin, however, did improve the FAI, secondary to a metformin arm and 23 in the placebo arm had the metabolic syn- significant fall in total testosterone without a change in the drome. There was no difference in outcome between the met- insulin sensitivity or SHBG. This observation suggests that formin group and placebo group respectively in the median metformin may have a direct effect on ovarian steroidogenesis change of menstrual frequency (1 versus 1, P = 0.916), percent- without effecting a change in circulating insulin concentrations age weight loss (3.14 versus 2.65% P = 0.79), change in waist (Pirwany et al., 1999; la Marca et al., 2002; Mansfield et al., circumference (–1.5 versus –0.93 cm, P = 0.692), change in 2003). There is a consensus that metformin has an additive serum testosterone concentration (0.889 versus 0.968 nmol/l, effect in achieving ovulation and pregnancy when combined P = 0.408), change in FAI (0.891 versus 0.995, P = 0.435), with drugs to induce ovulation (mainly clomiphene citrate) change in insulin sensitivity (–0.003 versus 0.000, P = 0.914) or (Costello and Eden, 2003; Lord et al., 2003). The effect may be either cholesterol or triglyceride concentrations.
quick and this too supports the possibility of a direct effect onthe ovary rather than a systemic effect on metabolism.
The use of metformin and other insulin-lowering or -sensi- Discussion
tizing agents has excited much interest in the management of We report a large randomized controlled trial (RCT) to investi- PCOS. The literature is replete with studies of varying design, gate the effects of metformin on very obese patients with ano- using varying regimens and assessing different outcomes. A vulatory PCOS. The duration of the study period (6 months) relatively small number of these studies (a total of 13) have and the dose of metformin used (850 mg, twice daily) were the been of appropriate design to be included in the Cochrane sys- longest and the highest of the RCT reported in the Cochrane tematic review (Lord et al., 2003). This included seven studies Metformin in obese PCOS patients
comparing metformin with placebo in a total of 310 patients, androgen-lowering medication (Gambineri et al., 2004). We which showed that metformin was beneficial for ovulation have found, however, that in very obese women with anovula- (odds ratio 3.88, 95% CI 2.25, 6.69, P < 0.0001). The largest tory PCOS, metformin, at a dose of 850 mg twice daily, had no study to be included in this series was of 92 patients (Fleming effect on menstrual frequency, body weight or insulin sensitiv- et al., 2002). The meta-analysis also demonstrated that met- ity, despite a fall in total testosterone and waist circumference.
formin was effective in reducing fasting insulin and total testo- Furthermore a modest reduction in weight through lifestyle sterone concentrations but had no effect on BMI or waist modification was the most significant predictor for an circumference (Lord et al., 2003).
Costello and Eden (2003) in their systematic review reached similar conclusions and again a wide range of entry criteriawere reported. In particular the average ‘mean BMI’ of those Acknowledgements
studies that compared metformin with placebo was 31.3 kg/m2 We are grateful to colleagues in participating centres: Dr R.Anderson(MRC Reproductive Medicine Centre, Edinburgh), Dr B.Bentick (range 21.4–39.8 kg/m2). There were variable effects reported, (Consultant Gynaecologist, Royal Shrewsbury Hospital, Shrewsbury), with not all studies demonstrating an improvement in insulin Dr P.Hardiman (Consultant Gynaecologist, Royal Free Hospital, sensitivity or fall in testosterone levels (Costello and Eden, London), Dr N.Panay (formerly Gynaecologist, Fertility Centre, 2003). As with our study, neither of the two RCT that reported St Bartholomew’s Hospital, London), Dr H.Buckler (Consultant an improvement in menstrual cyclicity showed a fall in BMI; Physician, Hope Hospital, Salford) and Professor W.Ledger (Depart-ment of Obstetrics and Gynaecology, The Jessop Hospital for women, both reported a fall in testosterone concentrations and only one Sheffield). We also thank Ms M.O’Kane (Clinical Specialist Dietitian, an improvement in fasting insulin (Moghetti et al., 2000; Department of Nutrition and Dietetics, The General Infirmary at Leeds, Leeds) for her advice and efforts to encourage our subjects to Pasquali et al. (2000) studied 20 obese women with PCOS lose weight. Grant: The Special Trustees of the Leeds Teaching Hos- with a control group of 20 obese women without PCOS who were comparable for age and pattern of body fat distribution.
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