MD Lindsay M Silva
email: l.silva@erasmusmc.nl.

Pauline W Jansen

Eric Ap Steegers

Vincent Wv Jaddoe

Lidia R Arends

Henning Tiemeier

Frank C Verhulst

Eap Steegers

Vwv Jaddoe

H A Moll

A Hofman

J P Mackenbach

H Raat

Mother's educational level and foetal growth; the genesis of health inequalities

Keywords: socioeconomic status, education, pregnancy, foetal, intrauterine, growth socioeconomic status, education, pregnancy, foetal, intrauterine, growth

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Foetal growth is an important determinant of future health 1-5. An impaired foetal growth increases the risk of perinatal and neonatal death 1, and of various medical and developmental problems in childhood 3,4,6. Furthermore, there is accumulating evidence that poor foetal growth is associated with chronic diseases in adult life, particularly cardiovascular diseases 2,5.

Foetal growth is determined by a complex interplay of genetic and environmental factors 7. One important environmental factor is socioeconomic status, as indicated by educational level, income level or occupation. Compared with women of high socioeconomic status, those of low socioeconomic status give birth to babies with a lower birth weight 8,9. These socioeconomic inequalities in birth weight suggest that factors related to a low socioeconomic status of the mother impair foetal growth 9. Until now, only one study actually related socioeconomic status to direct measures of foetal growth rather than size at birth 10. However, the authors used an area-based index of socioeconomic status rather than an individual-based measure, and studied foetalgrowth characteristics measured only in mid pregnancy, which limited the possibility to assess foetal-growth patterns. Because prospective population-based studies on the effect of maternal socioeconomic status on foetal growth trajectories are lacking, it is not known whether 1) socioeconomic differences in foetal growth are constant over time, 2) from which moment onwards differences in foetal size become apparent, and 3) whether low socioeconomic status equally affects different parts of the foetal body.

Therefore, among pregnant women participating in a population-based cohort study, we studied level of maternal education as an indicator of socioeconomic status and its association with foetal weight, head circumference, abdominal circumference, and femur length, measured in different periods of pregnancy. We also studied the extent to which the association of maternal education with these foetal-growth characteristics is explained by known mediating factors. Educational level was selected as main indicator of socioeconomic status because it has been shown to be the best socioeconomic predictor of pregnancy outcomes 9,11. Assuming that a low maternal education is associated with a slower foetal growth, we expected that educational differences in foetal size can be observed from late pregnancy onwards, since in that period inter-individual variability in foetal size is highest 12. Because available data suggest that socioeconomic status does not affect proportionality at birth 13, we hypothesized head circumference, abdominal circumference, and femur length to be equally affected by low maternal education.

METHODS

The Generation R Study

The present study was embedded within the Generation R Study, a populationbased prospective cohort study from foetal life until young adulthood. The Generation R Study has previously been described in detail 14. Briefly, all mothers with an expected delivery date between April 2002 and January 2006 and living in Rotterdam, the Netherlands, were eligible for participation in the study. While enrolment ideally took place in early pregnancy, it was possible until after the birth of the child. In total, 9778 mothers of various ethnicities and their children were included and followed-up (participation rate 61%) 14.

Assessments during pregnancy took place in early pregnancy (gestational age <18 weeks), mid pregnancy (gestational age 18-25 weeks) and late pregnancy (gestational age ≥25 weeks). The study was conducted in accordance with the

Study population

Of the 9778 women, 91% (n=8880) were enrolled during pregnancy 14. Because prevalences of risk factors for adverse pregnancy outcomes, as well as the effects of these risk factors on pregnancy outcomes may vary by ethnicity 15,16,17, and because preliminary results showed that educational inequalities in foetal weight varied by ethnicity, we restricted the present analyses to women with a Dutch ethnicity (n=4057). A woman was classified as Dutch if she reported that both her parents had been born in the Netherlands 18. For several reasons, 512 women were excluded from analysis (figure 1), leaving a study population of 3545 women.

Educational level

At enrolment, we used a questionnaire to establish the highest education achieved by each mother. This was categorized into four levels: 1.) high (university degree), 2.) mid-high (higher vocational training), 3.

Foetal ultrasound measurements and birth weight

Trained sonographers carried out foetal ultrasound measurements in early, mid and late pregnancy, which were used to establish gestational age and to measure foetalgrowth characteristics 20. For the analyses presented below, we used the

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measurements in mid and late pregnancy of head circumference, abdominal circumference and femur length, as measurements in early pregnancy were intended primarily for pregnancy dating. All growth characteristics were measured to the nearest millimetre using standardized procedures 21. The estimated foetal weight was calculated on the basis of head circumference, abdominal circumference and femur length 22. For the models for estimated foetal weight, we also used information on birth weight and gestational age at birth, which was obtained from midwife and hospital registries.

Longitudinal growth curves and gestational-age adjusted standard-deviation (SD) scores were constructed for all growth measurements 20.

Covariates

Any effect of educational level on foetal growth is probably an indirect one, acting through other more proximal determinants of foetal growth, so-called mediators 23. The factors listed below were included in this study as potential mediators, because these factors have been shown to contribute to explaining socioeconomic inequalities in size at birth 8.

Maternal anthropometrics

Maternal height was measured in the research centres.

Pre-pregnancy weight was established at enrolment through questionnaire. On the basis of height and pre-pregnancy weight (weight/height 2 ) we calculated pre-pregnancy body mass index (BMI).

Smoking

Through questionnaires in early, mid and late pregnancy, we obtained information on smoking during pregnancy (no, until pregnancy was known, continued in pregnancy).

F

Statistical analyses

We started by evaluating the effect of educational level on overall foetal growth, after which we separately analysed the associations of educational level and of the potential mediators with head circumference, abdominal circumference and femur length. These associations were examined using longitudinal multilevel analysis, as this type of analysis takes account of the correlation between repeated measures on the same subject and allows for incomplete outcome data

Educational level and estimated foetal weight

Relative to foetuses of women in the highest educational subgroup, those of women with mid-high, mid-low and low education had a slower foetal growth (figures 2a and 2b). Foetal growth rate was lowest in foetuses of women with a low educational level, and the difference in foetal growth rate increased as pregnancy progressed.

According to the basic models, women with a low educational level had smaller foetuses from 30 weeks onwards (difference at 30 weeks: -0.16 SD; 95% CI: -0.25,-0.08; p=0.0001; table 2). This difference became larger towards term (difference at 40 weeks:

-0.35 SD; 95% CI: -0.46,-0.24; p<0.0001). After adjustment for the potential mediators, the educational differences in estimated foetal weight attenuated, but at 40 weeks the difference did not disappear (-0.18 SD; 95% CI: -0.29,-0.07; p=0.002).

Educational level and head circumference, abdominal circumference and femur length

Table 3 shows the association of educational level with head, abdominal and femur growth, and supplemental table 2 shows the associations of each potential mediator with head, abdominal and femur growth. Relative to foetuses of women with a high educational level, in foetuses of women with a low educational level growth of the head was on average 0.16 mm/week slower (95% CI: -0.25,-0.07; p=0.0004), growth of the abdomen 0.10 mm/week slower (95% CI: -0.21, 0.01; p=0.08) and that of the femur 0.03 mm/week slower (95% CI: -0.05,-0.006; p=0.01) after adjusment for the confounders. Adjustment for the potential mediators attenuated the difference in head growth and that in femur growth, but not the difference in abdominal growth. Even after full adjustment, foetuses of women with a low educational level still had a slower head growth compared with those of women with a high level (table 3).

Table 3 here.

Table 4 presents the educational differences in size of the foetal head, abdomen and femur at 20 and 30 weeks gestation, expressed in SD-scores. Compared with foetuses of women with a high educational level, those of women with a low educational level had a smaller head circumference from 20 weeks onwards; femur length was smaller from 30 weeks onwards (basic models). Although abdominal circumference was also smaller in these foetuses, the difference did not reach statistical significance.

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Remarkably, at 20 weeks gestation a mid-low educational level was associated with a greater femur length compared with a high educational level.

The effect of low education was larger for head circumference than for femur length or abdominal circumference. Even after adjustment for the potential mediators, head circumference at 30 weeks gestation was smaller in fetuses of women with low educational level than those of women with a high level.

The largest attenuations were due to the adjustment for smoking, followed by maternal height (see supplemental table 3). We additionally tested for interaction between educational level, smoking and gestational age; this 3-way interaction was not statistically significant in any of the models (p values > 0.30, data not shown).

Table 4 here.

DISCUSSION

The present study is the first to present a longitudinal assessment of the effect of an individual-level indicator of socioeconomic status on foetal growth. We demonstrated that a low maternal educational level is associated with a progressively slower foetal growth, causing differences in foetal weight that are observable from late pregnancy onwards. This study also suggests that low maternal educational level predominantly affects growth of the foetal head, followed by growth of the foetal femur and abdomen.

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12

Methodological considerations

The main strength of this study lies in its population-based prospective design, with enrolment of a large number of women early in pregnancy, and extensive measurements during pregnancy 14.

Although there are other measures of socioeconomic status, including income level and occupational class 28, we selected maternal educational level as a main indicator of socioeconomic status for two reasons: first educational level not only partly reflects material resources because it structures occupation and income, it also reflects non-economic social characteristics, such as general and health-related knowledge, literacy, problem-solving skills and prestige 28,29; second, educational level has been shown to be the best socioeconomic predictor of pregnancy outcomes 9,11.

Furthermore, when we repeated the analyses using household income level as determinant, we found comparable results, although the effect estimates were smaller and income-related differences in foetal head circumference were observable from 30 weeks gestation onwards.

For When interpreting the results of this study, one should take account of a number of limitations.

First, our study was conducted in a Dutch, urban population, which limits generalizability of our results to non-Dutch or rural populations. Furthermore, although the participation rate was relatively high (68% among the Dutch women) 14, there was some selection towards a study population that was relatively highly educated and more healthy 33.

Second, while foetal ultrasound examinations are a more reliable basis than the last menstrual period for establishing gestational age 34, it also has a disadvantage: the growth variation before the first measurement of the foetal characteristics that were used for pregnancy dating, i.e. crown-rump length and biparietal diameter, was set to zero 20.

Since these characteristics are correlated throughout pregnancy with head circumference, abdominal circumference and femur length, our study may have underestimated the variation in the latter three growth characteristics, resulting in an underestimation of our effect estimates. An additional limitation to the foetal ultrasound equipment is that it will be equally precise to detect absolute differences in foetal size throughout pregnancy, and therefore less likely to detect differences in foetal size early in pregnancy than later in pregnancy. This might explain the lack of significant educational differences in many of the studied growth characteristics at 20 weeks gestation.

Third, our study may have been vulnerable to misclassification, because many covariates were measured using questionnaires. In particular, smoking behaviour and

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14 pre-pregnancy weight may have been underreported. The effect on our results of this misclassification is difficult to predict, since we cannot be certain whether this misclassification was random or not.

Finally, since we did not include a variable indicating the number of cigarettes smoked during pregnancy, we could not account for the dose-related effect of smoking on foetal growth 35, nor the contribution of this dose-related effect to the explanation of socioeconomic inequalities in foetal growth.

Maternal educational level and foetal growth

In contrast with our expectations the effect of low maternal education was not equal for the various body segments of the foetus. Relative to growth of the foetal femur and abdomen, the adverse effect of a low educational level seemed greatest for growth of the foetal head.

Clear educational differences in foetal head circumference were detectable already at 20 weeks gestation. By 30 weeks, educational differences in femur length could also be detected, but not in abdominal circumference, although there was a clear trend towards a smaller abdominal circumference in foetuses of lower educated women.

The timing of the emergence of educational differences in head, femur and abdomen might be explained by the inability of the ultrasound instrument to detect small differences, or by the different growth patterns of the various foetal-growth components.

Peak growth velocity for head circumference is steeper and occurs earlier (around 18 weeks) than that for femur length (around 20 weeks) and abdomen (around 22 weeks) One possible explanation for low maternal education being more strongly associated with foetal head circumference is that the factors that mediate the effect of maternal education affect foetal head growth more than growth of the foetal femur and abdomen. However, smoking in pregnancy, the strongest mediator in this study, has been found to cause a more general, symmetrical growth restriction 37,38. Some studies even suggest that smoking affects the lower extremities more than the head or abdomen 39,40. On the other hand, the contribution of maternal height to the education-related inequalities, is more in line with the above explanation; shorter mothers tend to give birth to babies that are shorter and have smaller heads for their weight 13. This corresponds with the type of growth impairment associated with low maternal education.

The potential mediators included in this study explained only about half the educational differences in foetal head circumference at 30 weeks gestation. This may be explained by measurement error in the mediating variables. The remaining effect may also be due to other factors, such as nutritional factors or genetic factors

  the World Medical Association Declaration of Helsinki, and has been approved by the Medical Ethical Committee at the Erasmus University Medical Centre Rotterdam. Written consent was obtained from all participating parents.

  ) mid-low (>3 years general secondary school, intermediate vocational training), and 4.) low (no education, primary school, lower vocational training, intermediate general school, or ≤3 years at general secondary school) 19.

  Psychosocial and material factors Using questionnaires during pregnancy we established marital status (married/cohabiting, single motherhood), whether the pregnancy was planned (yes, no), and the presence of financial difficulties (yes, no).All models were adjusted for maternal age and parity. We treated maternal age and parity as potential confounders, since they cannot be considered indisputable mediators 23. Maternal age was established at enrolment in the study. Parity, which in this study was defined as the number of previous live births (0, ≥1), was obtained through a

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questionnaire at enrolment. Foetal gender, which was obtained from midwife and hospital registries, was not included as confounder, since it had no association with maternal education (see table

1

).

Table 2

 2

here.

  One should also take account of the fact that, in our study, only 2.5% of these measurements took place after the 32 nd week of gestation. For physiological pregnancies, it has been shown that the difference in abdominal circumference between smaller and larger babies increases with increasing gestational age 36. Therefore, the observed educational differences in abdominal circumference might have been larger if we had had availability to more growth measurements near term. It is thus important that our results are confirmed in future studies with more comprehensive foetal-growth data and with information on proportionality at birth.

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	12,36.
	15

Table 1 . General characteristics in the total study population and by maternal educational level (n=3545)*.

 1 16circumference is a strong predictor of neonatal head circumference 43, there may be genetic factors linking head circumference of the mother, her educational achievement and head growth of her offspring. However, given the results from Genome Wide Association Studies, suggesting that individual genes explain little of the variances in diseases and traits 44, the contribution of genes to the remaining association between educational level and foetal head circumference may be small. Furthermore, we could not explain why, only at 20 weeks gestation, the second lowest educational subgroup had a larger femur length compared with the highest educational subgroup. It is possible that other unmeasured factors that specifically determine femur growth explain this unexpected finding. This merits further investigation.

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	O n The socioeconomic inequalities in foetal growth as demonstrated here may
	l y represent the genesis of socioeconomic health inequalities in infancy, childhood and
	adulthood. In particular, since foetal head growth is associated with future cognitive
	functioning and academic achievement 3,42, the observed socioeconomic inequalities in
	foetal head growth might have consequences for later cognitive ability, educational
	attainment and job performance for the offspring of low-educated mothers. Taking
	measures to narrow inequalities in foetal growth should be an important public health
	issue. Smoking during pregnancy being the most important modifiable factor explaining
	these inequalities, such measures should primarily be aimed at reducing smoking rates

7,41. Since head circumference is associated with academic achievements 3,42 and maternal head In conclusion, this unique study demonstrates that, relative to a high maternal educational level, a low educational level is associated with a slower foetal growth, in particular growth of the head. Important mediators such as smoking explained only part of this association. Future research should aim at additional determinants of foetal growth, such as nutritional, psychosocial or genetic factors to further disentangle the pathway between SES and foetal growth. 38. Vik T, Jacobsen G, Vatten L, Bakketeig LS. Pre-and post-natal growth in children of women who smoked in pregnancy. Early Hum Dev 1996;45(3):245-55. 39. Jaddoe VW, Verburg BO, de Ridder MA, Hofman A, Mackenbach JP, Moll HA, et al. Maternal smoking and fetal growth characteristics in different periods of pregnancy: the generation R study. Am J Epidemiol 2007;165(10):1207-15. 40. Lindsay CA, Thomas AJ, Catalano PM. The effect of smoking tobacco on neonatal body composition. Am J Obstet Gynecol 1997;177(5):1124-8. 41. Godfrey K, Robinson S, Barker DJ, Osmond C, Cox V. Maternal nutrition in early and late pregnancy in relation to placental and fetal growth. Bmj 1996;312(7028):410-

Table 1 . (continued)

 1

	Maternal education

Table 2 . Associations between maternal educational level and standard deviation scores for estimated foetal weight at 20, 30 and 40 weeks gestation (n=3545). Difference in standard deviation score for estimated foetal weight at 20 weeks gestation (with 95% CI and P values)

 2

		Crude model	Basic model*	Fully adjusted †
	Maternal education
	High	Reference	Reference	Reference
	Mid-high	0.02 (-0.07,0.11)	0.02 (-0.07,0.11)	0.02 (-0.07,0.12)
		p=0.73)	p=0.61	p=0.59)
	Mid-low	0.06 (-0.03,0.14)	0.08 (-0.01,0.17	0.07 (-0.02,0.17)
		p=0.21	p=0.10	p=0.14)
	Low	-0.01 (-0.11,0.089)	0.02 (-0.09,0.13)	0.05 (-0.07,0.17)
		p=0.82	p=0.70)	p=0.44)

Difference in standard deviation score for estimated foetal weight at 30 weeks gestation (with 95% CI and P values)

		F o
		r
	Maternal education High	Crude model Reference R e v i Basic model* Reference e w	Fully adjusted † Reference
	Mid-high Mid-low	-0.02 (-0.09,0.05) p=0.61 -0.04 (-0.11,0.03) p=0.29	-0.009 (-0.08,0.06) p=0.80 -0.03 (-0.10,0.05) p=0.48 O n l y 0.002 (-0.07,0.07) p=0.96 -0.01 (-0.09,0.06) p=0.72
	Low	-0.16 (-0.24,-0.08)	-0.16 (-0.25,-0.08)	-0.07 (-0.16,0.02)
		p=0.0001	p=0.0001	p=0.15
				4

Table 2 . (Continued) Difference in standard deviation score for estimated birth weight at 40 weeks gestation (with 95% CI and P values)

 2 Values are based on multilevel models. CI: confidence interval. * Basic model: adjusted for maternal age and parity. † Fully adjusted: adjusted for maternal age and parity, maternal height, pre-pregnancy BMI, smoking during pregnancy, single motherhood, whether the pregnancy was planned and financial difficulties. The following covariate*gestational age interactions were also included: age*gestational age, parity*gestational age, height*gestational age, BMI* gestational age, smoking*gestational age, financial difficulties*gestational age.

		Crude model	Basic model*	Fully adjusted †
	Maternal education
	High	Reference	Reference	Reference
	Mid-high	-0.05 (-0.14,0.04)	-0.04 (-0.13,0.05)	-0.02 (-0.11,0.06)
		p=0.25	p=0.35	p=0.62)
	Mid-low	-0.13 (-0.22,-0.04)	-0.13 (-0.22,-0.04)	-0.10 (-0.19,-0.008)
		p=0.003	p=0.005	p=0.03
	Low	-0.30 (-0.40,-0.20);	-0.35 (-0.46,-0.24)	-0.18 (-0.29,-0.07)
		p<0.0001	p<0.0001	p=0.002

Table 3 . Associations between maternal educational level and growth of the foetal head, abdomen and femur (n=3545). Differences in foetal head circumference growth (mm/week) (with 95% CI and P values)

 3

		Crude model	Basic model*	Fully adjusted †
	Educational level
	High	Reference	Reference	Reference
	Mid-high	-0.03 (-0.22,0.05)	-0.03 (-0.11,0.05)	-0.02 (-0.09,0.06)
		p=0.42	p=0.46	p=0.63
	Mid-low	-0.10 (-0.17,-0.02)	-0.09 (-0.17,-0.02)	-0.08 (-0.15,-0.003)
		p=0.02	p=0.02	p=0.04
	Low	-0.15 (-0.24,-0.06)	-0.16 (-0.25,-0.07)	-0.10 (-0.18,-0.01)
		p=0.0007	p=0.0004	p=0.04
		Differences in foetal abdominal circumference growth (mm/week) (with 95% CI
			and P values)
		Crude model	Basic model*	Fully adjusted †
	Maternal education
	High	Reference	Reference	Reference
	Mid-high	0.01 (-0.09,0.11)	0.02 (-0.09,0.12)	0.02 (-0.08,0.12)
		p=0.78	p=0.73	p=0.67
	Mid-low	-0.02 (-0.12,0.08)	-0.01 (-0.11,0.09)	-0.03 (-0.14,0.07)
		p=0.75	p=0.80	p=0.50
	Low	-0.09 (-0.20,0.03)	-0.10 (-0.21,0.01)	-0.10 (-0.22,0.02)
		p=0.13	p=0.08	p=0.11

Table 3 . (continued) Differences in foetal femur length growth (mm/week) (with 95% CI and P values)

 3

		Crude model	Basic model*	Fully adjusted †
	Maternal education
	High	Reference	Reference	Reference
	Mid-high	-0.004 (-0.02,0.01)	-0.003 (-0.02,0.02)	0.001 (-0.02,0.02)
		p=0.66	p=0.72	p=0.92
	Mid-low	-0.01 (-0.03,0.004)	-0.01 (-0.03,0.004)	-0.003 (-0.02,0.01)
		p=0.66	p=0.14	p=0.71
	Low	-0.025 (-0.046,-0.004)	-0.03 (-0.05,-0.006)	0.0004 (-0.02,0.02)
		p=0.02	p=0.01	p=0.96

Values are based on multilevel models. CI: confidence interval. * Basic model: adjusted maternal age and parity. † Fully adjusted: adjusted for maternal age and parity, maternal height, pre-pregnancy BMI, smoking during pregnancy, single motherhood, whether the pregnancy was planned and financial difficulties. The following covariate*gestational age interactions were also included: for head-circumference model: parity*gestational age, height*gestational age, BMI* gestational age, smoking*gestational age; for abdominal-circumference model: parity*gestational age, BMI* gestational age, smoking*gestational age; for femur-length model: parity*gestational age, height*gestational age, smoking*gestational age.

Table 4 . Associations between maternal educational level and foetal head circumference, abdominal circumference and femur length (in standard deviation scores) at 20 and 30 weeks gestation (n=3545).

 4

	20 weeks gestation		High education	Mid-high education	Mid-low education	Low education
	HC (SD score)	Crude model	Reference	-0.09 (-0.19,0.002;	-0.11 (-0.21,-0.02);	-0.17 (-0.28,-0.06)
				p=0.06	p=0.02	p=0.002
		Basic Model*	Reference	-0.09 (-0.19,0.005)	-0.10 (-0.20,0.00)	-0.14 (-0.25,-0.03)
				p=0.06	p=0.05	p=0.01
		Fully adjusted †	Reference	-0.09 (-0.19,0.007)	-0.09 (-0.19,0.01)	-0.10 (-0.22,0.03)
				p=0.07	p=0.07	p=0.13
	AC (SD score)	Crude model	Reference	-0.01 (-0.10,0.08);	-0.05 (-0.14,0.04)	-0.09 (-0.19,0.01)
				p=0.81	p=0.28	p=0.09
		Basic Model*	Reference	0.005 (-0.09,0.10)	-0.006 (-0.10,0.09)	-0.03 (-0.14,0.08)
				p=0.92	p=0.90	p=0.61
		Fully adjusted †	Reference	0.005 (-0.09,0.10)	-0.001 (-0.10,0.10)	0.01 (-0.11,0.13)
				p=0.88	p=0.98	p=0.87
	FL (SD score)	Crude model	Reference	0.02 (-0.07,0.11)	0.13 (0.04,0.22)	0.05 (-0.05,0.16)
				p=0.68	p=0.005	p=0.30
		Basic Model*	Reference	0.01 (-0.08,0.11)	0.12 (0.02,0.21)	0.02 (-0.08,0.13)
				p=0.77	p=0.02	p=0.67
		Fully adjusted †	Reference	0.02 (-0.08,0.11)	0.10 (0.008,0.20)	0.04 (-0.08,0.16)
				p=0.73	p=0.03	p=0.48

Table 4 . (continued)

 4 Values are based on multilevel models and represent differences in head circumference, abdominal circumference and femur length (expressed in standarddeviation scores, with associated 95% CI and P values) relative to foetuses of women with high educational level. HC: head circumference; AC: abdominal circumference; FL: femur length; SD-score: standard deviation score. * Basic model: adjusted for maternal age and parity. † Fully adjusted: adjusted for maternal age and parity, maternal height, pre-pregnancy BMI, smoking during pregnancy, single motherhood, whether the pregnancy was planned and financial difficulties. The following covariate*gestational age interactions were also included: for head-circumference model: parity*gestational age, height*gestational age, BMI* gestational age, smoking*gestational age; for abdominal-circumference model: parity*gestational age, BMI* gestational age, smoking*gestational age; for femurlength model: parity*gestational age, height*gestational age, smoking*gestational age.

	30 weeks gestation		High education	Mid-high education	Mid-low education	Low education
	HC (SD score)	Crude model	Reference	-0.08 (-0.17,0.007)	-0.17 (-0.25,-0.08)	-0.28 (-0.37,-0.18)
				p=0.07	p=0.0002	p<0.0001
		Basic Model*	Reference	-0.07 (-0.16,0.01)	-0.15 (-0.24,-0.06)	-0.26 (-0.36,-0.15)
				p=0.10	p=0.001	p<0.0001
		Fully adjusted †	Reference	-0.06 (-0.15,0.03)	-0.12 (-0.21,-0.03)	-0.14 (-0.25,-0.03)
				p=0.17	p=0.01	p=0.01
	AC (SD score)	Crude model	Reference	0.0014 (-0.09,0.0);	-0.04 (-0.13,0.04)	-0.14 (-0.24,-0.04)
				p=0.97	p=0.30	p=0.005
		Basic Model*	Reference	0.02 (-0.07,0.11)	0.001 (-0.09,0.09)	-0.08 (-0.19,0.02)
				p=0.66	p=0.99	p=0.10
		Fully adjusted †	Reference	0.03 (-0.06,0.11)	-0.008 (-0.10,0.08)	-0.04 (-0.15,0.07)
				p=0.56	p=0.86	p=0.44
	FL (SD score)	Crude model	Reference	-0.01 (-0.10,0.07)	0.03 (-0.05,0.11)	-0.09 (-0.18,0.006)
				p=0.73	p=0.49	p=0.07
		Basic Model*	Reference	-0.02 (-0.10,0.07)	0.02 (-0.07,0.10)	-0.13 (-0.22,-0.02)
				p=0.69	p=0.70	p=0.01
		Fully adjusted †	Reference	-0.001 (-0.08,0.08)	0.05 (-0.04,0.13)	-0.006 (-0.11,0.10)
				p=0.99	p=0.31	p=0.92

model for analyses with standard-deviation (SD) scores for estimated foetal weight, head circumference, abdominal circumference, and femur length

  Figure 2a. Estimated differences in foetal growth rate (grams/week) for foetuses of women with low, mid-low and mid-high education relative to foetuses of women with high Values are based on multilevel models and represent the differences in foetal growth rate for foetuses of women with low, mid-low and mid-high education relative to those of women with high education. All values are adjusted for maternal age and parity. The following covariate*gestational age interactions were also included: age*gestational age, parity*gestational age. Figure 2b. Estimated differences in foetal growth rate (grams/week) for foetuses of women with low, mid-low and mid-high education relative to foetuses of women with high education, fully adjusted (n=3545)*.

	Figure 1. Flow chart participants
	N= 9778
	Generation R cohort education (n=3545)*.
	N=8880 4 Participants enrolled during pregnancy 4
	2
	F o Participants with a Dutch ethnicity N= 4057 F o 0 2 0 F o 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
	SD score = ß 0 + ß 1 *educational level + ß 2 *gestational age + ß 3 *educational level*gestational age. r R e v i e w Excluded: data on 2 nd (n=332) or 3 rd (n=5) pregnancy of the same participant, twin pregnancies (n=54), induced abortions (n=14), foetal death (n=20), lost to follow up (n=3) N= 3629 Participants eligible for present study r R e v i e w -8 -6 -4 -2 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 low education mid-low education mid-high education R e v i e w -10 -8 -6 -4 Difference in foetal growth (grams/week) r -2
	-12 -10 -14 -12	O n l y -educational level (n=20) -foetal gender (n=7) l y y l Excluded due missing information on: O n n O
	-14	-parity (n=7)
	Gestational age (weeks)	-marital status (n=32)
		-all ultrasound measurements (n=18
	N=3545
	Population for present analysis
	Created by LM Silva
	Created by LM Silva
		1 1 2

*

Gestational age (weeks) Difference in foetal growth (grams/week)

  Values are based on multilevel models and represent the differences in foetal growth rate for foetuses of women with low, mid-low and mid-high education relative to those of women with high education. All values are adjusted for maternal age and parity, maternal height, pre-pregnancy BMI, smoking during pregnancy, single motherhood, whether the pregnancy was planned and financial difficulties. The following covariate*gestational age interactions were also included: age*gestational age, parity*gestational age, height*gestational age, BMI* gestational age, smoking*gestational age, financial difficulties*gestational age.

	Supplementary
	F o r	low education
	R	mid-low education mid-high education
	e v
	i e
	w
	O n l
	y
	Created by LM Silva

*

table 1 . Estimated foetal weight, head circumference, abdominal circumference and femur length at median gestational age in mid and late pregnancy in the total study population. Mid pregnancy (median 20.5 weeks) Late pregnancy (median: 30.4 weeks)

 1

	Estimated foetal weight (grams)	371.9 (43.7)	1622.0 (188.7)
	Head circumference (mm)	178.1 (6.3)	285.4 (9.3)
	Abdominal circumference (mm)	155.9 (8.2)	264.6 (13.2)
	Femur length (mm)	33.1 (1.8)	57.4 (2.2)
	Values are means (with standard deviations)

table 2 .

 2 Crude associations between potential mediators and growth of the foetal head, abdomen and femur.Values are based on multilevel models. The effect estimates represent the difference in growth rate in mm/week per unit increase in case of continuous variables, or relative to the reference category in case of categorical variables. CI: confidence interval.

		Head circumference	Abdominal	Femur length growth
		growth (mm/week,	circumference	(mm/week, with 95%
		with 95% and p-value	growth (mm/week,	and p-value)
			with 95% and p-
			value)
	Maternal height (per cm increase)	0.015 (0.01,0.02)	0.0028 (-0.003,0.009)	0.0039 (0.0028,0.005)
		p<0.0001	p=0.3622	p<0.0001
	Maternal pre-pregnancy BMI (per	0.011 (0.003,0.019)	0.04 (0.03,0.05)	0.00098 (-
	kg/m 2 increase)	p=0.0050	p<0.0001	0.00089,0.0029)
				p=0.3054
	Smoking
	No	Reference	Reference	Reference
	Until pregnancy known	0.007 (-0.10,0.12)	0.05 (-0.09,0.19)	-0.015 (-0.04,0.01)
		p=0.9049	p=0.4964	p=0.2509)
	Continued during pregnancy	-0.22 (-0.30,-0.14)	-0.25 (-0.35,-0.15)	-0.05 (-0.07,-0.03)
		p<0.0001	p<0.001	p<0.0001
	Pregnancy was planned
	Yes	Reference	Reference	Reference
	No	-0.07 (-0.15,0.006)	-0.09 (-0.19,0.005)	-0.02 (-0.04,-0.002)
		p=0.07	p=0.0624	p=0.0259
	Marital status
	Married/cohabiting	Reference	Reference	Reference
	Single	-0.13 (-0.24,-0.02)	-0.21 (-0.35,-0.07)	-0.03 (-0.06,-0.006)
		p=0.0197	p=0.0027	p=0.0148
	Financial difficulties
	No	Reference	Reference	Reference
	Yes	-0.13 (-0.22,-0.03)	-0.13 (-0.25,-0.009)	-0.028 (-0.051,-0.003)
		p=0.009	p=0.035	p=0.0239

table 3 .

 3 Associations between maternal educational level and standarddeviation scores for foetal head circumference, abdominal circumference and femur length at 30 weeks gestation, after individual adjustment for the potential mediators.

HC (SD-

score) at 30 weeks gestation (with 95% CI)

	Educational level	High	Mid-high	Mid-low	Low
	Basic model*	Reference	-0.07 (-0.16,0.01)	-0.15 (-0.24,-0.06)	-0.26 (-0.36,-0.15)
	Basic model +	Reference	-0.07 (-0.16,0.02)	-0.13 (-0.22,-0.04)	-0.20 (-0.30, -0.10)
	maternal height
	Basic model +	Reference	-0.08 (-0.16,0.01)	-0.18 (-0.27, -0.09)	-0.30 (-0.40,-0.190)
	Pre-pregnancy BMI
	Basic model +	Reference	-0.06 (-0.15, 0.03)	-0.11 (-0.20, -0.02)	-0.17 (-0.28, -0.06)
	Smoking during
	pregnancy
	Basic model +	Reference	-0.07 (-0.16,0.01)	-0.14 (-0.23,-0.05)	-0.23 (-0.33,-0.13)
	Single motherhood
	Basic model +	Reference	-0.07 (-0.16,0.02)	-0.14 (-0.23,-0.06)	-0.25 (-0.35,-0.14)
	Whether pregnancy
	planned
	Basic model +	Reference	-0.07 (-0.16,0.02)	-0.14 (-0.23,-0.05)	-0.24 (-0.34,-0.13)
	Financial difficulties

AC (SD-score) 30 weeks gestation (with 95% CI) Educational level High Mid-high Mid-low Low

  Values are based on multilevel models and represent differences in head circumference, abdominal circumference and femur length (expressed in standard-deviation scores) relative to foetuses of women with high educational level. HC: head circumference; AC: abdominal circumference; FL: femur length; SD-score: standard deviation score. * Basic model: adjusted for maternal age and parity.

	Basic model +	Reference		0.02 (-0.06,0.11)	0.007 (-0.08,0.10)	-0.07 (-0.17,0.03)
	Whether pregnancy
	planned
	Basic model +	Reference		0.02 (-0.07,0.11)	0.006 (-0.08,0.09)	-0.07 (-0.17,0.03)
	Financial difficulties
				FL (SD-score) 30 weeks (with 95% CI)
	Educational level	High		Mid-high	Mid-low	Low
	Basic model*	Reference		-0.02 (-0.10,0.07)	0.02 (-0.07,0.10)	-0.13 (-0.22,-0.02)
	Basic model +	Reference		-0.01 (-0.10,0.07)	0.04 (-0.05, 0.12)	-0.06 (-0.15,0.05)
	maternal height Pre-pregnancy BMI Basic model +	F r o Reference	-0.02 (-0.11,0.06)	-0.05 (-0.11,0.07)	-0.19 (-0.29,-0.09)
	Smoking during Basic model +	R Reference 0.001 (-0.08,0.09)	0.07 (-0.02,0.15)	0.002(-0.10,0.11)
	pregnancy Basic model +	Reference	e v -0.02 (-0.10,0.07)	0.02 (-0.07,0.11)	-0.11 (-0.21,-0.01)
	Single motherhood Whether pregnancy Basic model +	Reference		i e w -0.02 (-0.10,0.07)	0.01 (-0.07,0.11)	-0.12 (-0.22,-0.01)
	Basic model* Financial difficulties planned Basic model +	Reference Reference		0.02 (-0.07,0.11) O 0.001 (-0.09,-0.09) l n -0.02 (-0.10,0.07) 0.02 (-0.07,0.11)	-0.09 (-0.19,-0.02) -0.12 (-0.22,-0.01)
					y
	Basic model +	Reference		0.02 (-0.07,0.11)	0.01 (-0.08,0.10)	-0.06 (-0.16,0.05)
	maternal height
	Basic model +	Reference		0.01 (-0.07,0.10)	-0.05 (-0.14,0.04)	-0.16 (-0.26,-0.06)
	Pre-pregnancy BMI
	Basic model +	Reference		0.03 (-0.06,0.12)	0.031 (-0.06,0.12)	-0.002 (-0.11,0.11)
	Smoking during
	pregnancy
	Basic model +	Reference		0.02 (-0.07,0.11)	0.007 (-0.08,0.10)	-0.06 (-0.17,0.04)
	Single motherhood

ACKNOWLEDGEMENTS

The Generation R Study is conducted by the Erasmus MC, University Medical Centre Rotterdam in close collaboration with the Erasmus University Rotterdam, School of Law and Faculty of Social Sciences, the Municipal Health Service Rotterdam area, the Rotterdam Homecare Foundation, and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR), Rotterdam. We gratefully acknowledge the contribution of participating mothers, general practitioners, hospitals, midwives and pharmacies in Rotterdam.

Duality of interest:

The authors declare that there is no duality of interest for this study.

FUNDING

This work was supported by Erasmus MC, University Medical Centre Rotterdam, Erasmus University Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw), and the Netherlands Organization for Scientific Research (NWO) (017.002.107 to L.M.S.)


Patricia M Crofton

Nancy Evans

Louise Bath

Pamela Warner

Tessa Whitehead

Christopher Hilary O D Critchley

J H Kelnar

William Hamish Wallace

Crofton Pm

Evans

Bath Le

) Warner

) Whitehead

) Critchley

Kelnar Cjh

Dr W H B Wallace

Physiological versus standard sex steroid replacement in young women with premature ovarian failure: effects on bone

Keywords: Ovarian failure, sex hormone replacement, bone mineral density, bone turnover markers Ovarian failure, sex hormone replacement, bone mineral density, bone turnover markers. 80 85

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Osteoporosis, reduced bone mineral density (BMD), and increased risk of fracture have been reported in Turner syndrome [START_REF] Gravholt | Clinical practice in Turner Syndrome[END_REF] and in women with premature ovarian 90 failure (POF) due to other causes, including after successful treatment of childhood cancer. For the survivor of childhood cancer the risk of developing POF depends on the treatment received by the patient [START_REF] Wallace | Fertility preservation for young patients with cancer. Who is at risk and what can be offered?[END_REF]. The highest risk is to those girls who received radiotherapy to a field that includes the ovary [START_REF] Sanders | Growth and development following marrow transplantation for leukaemia[END_REF][START_REF] Sarafoglou | Gonadal function after bone marrow transplantation for acute leukaemia during childhood[END_REF][START_REF] Wallace | Ovarian failure following abdominal irradiation in childhood: the radiosensitivity of the human oocyte[END_REF][START_REF] Wallace | Predicting age of ovarian failure after radiation to a field that includes the ovaries[END_REF]. There is some evidence that oestrogen replacement and treatment of short stature with growth 95 hormone will improve bone mass in women with Turner syndrome [START_REF] Gravholt | Effect of growth hormone and 17β-oestradiol treatment on metabolism and body composition in girls with Turner syndrome[END_REF].

The menopause has an adverse affect on bone health that is cumulative over time. A very early menopause has a potentially devastating effect on long-term bone health.

There is a large body of research that has evaluated hormone replacement regimens 100 for older women who are menopausal but young women with POF, who will require sex steroid replacement (SSR) therapy for many years, are under researched [START_REF] Panay | Management of premature ovarian failure[END_REF].

There is no agreed consensus on the optimum oestrogen and progesterone replacement regimens for young women with POF. Currently young women with POF 105 are offered combined hormone replacement in the convenient form of the oral contraceptive pill, or hormone replacement therapy designed for older women after the menopause [START_REF] Bath | Choice of hormone therapy in young women with ovarian failure[END_REF]. These preparations are not designed to achieve physiological replacement of oestrogen or progesterone either in dosage or in biochemical structure. With these young women looking to a future of 30 or more years of replacement 110 therapy, the optimum mode of SSR to sustain and improve bone health is not known [START_REF] Panay | Management of premature ovarian failure[END_REF].

Oestrogen is a potent stimulator of bone mineral accretion through puberty [START_REF] Slemanda | Influences on skeletal mineralization in children and adolescents: evidence for varying effects of sexual maturation and physical activity[END_REF]. Following the development of the menopause bone loss proceeds at an increased rate. 115

Achieving an inadequate peak bone mass increases the risk of osteoporosis and bone fractures in later life. In Turner syndrome there is evidence that women who do not achieve optimal peak bone mass have a higher rate of bone fractures [START_REF] Davies | Osteoporosis in Turner's syndrome and other forms of primary amenhorroea[END_REF]. There is also evidence that prolonged oral contraceptive use in skeletally immature female young adult monkeys leads to a lower peak bone mass [START_REF] Register | Oral contraceptive treatment inhibits the normal acquisition of bone mineral in skeletally immature young adult female monkeys[END_REF]. 120 Physiological SSR (pSSR) achieves sex steroid serum concentrations similar to concentrations in women with normal ovarian function [START_REF] Critchley | Experience with a 'physiological' steroid replacement regimen for the establishment of a receptive endometrium in women with premature ovarian failure[END_REF] and improves parameters of uterine function [START_REF] Critchley | Abdominal irradiation in childhood; the potential for pregnancy[END_REF]. Recently we have shown that pSSR in women with POF results in lower blood pressure, better renal function and less activation of the renin-125 angiotensin system than a standard sex steroid replacement regimen [START_REF] Langrish | Cardiovascular effects of physiological and standard sex steroid replacement regimens in premature ovarian failure[END_REF]. By further study of the same group of young women with POF due to different causes, the aim of this study was to establish whether we could improve skeletal health with a physiological regimen of SSR.

Data analysis

BMD was expressed as g/cm 2 and as z-scores (SD scores), calculated by subtracting the mean BMD of the adult age-matched female population from the subject's BMD and dividing by the population standard deviation. Changes in BMD (in g/cm 2 and as 210 z-scores) in response to each treatment arm (pSSR or sHRT) were also calculated by subtracting the BMD at the start of each treatment period from that measured at the end of each treatment period. Changes in bone markers in response to each treatment period were expressed both as percentage change in relation to post-washout baseline and as absolute values. For each subject, we calculated their mean hormone 215 concentration during pSSR and sHRT treatments as the within-individual average of measurements at 3, 6 and 12 months of each respective treatment period. Data were summarised as mean (reported with 95% confidence interval (CI) of the mean).

Comparisons between groups were performed using unpaired t-tests. Paired t-test were used for within-subject comparisons of BMD and bone marker responses to 220 pSSR and sHRT. One-way within subject analysis of variance was used for repeated measures of bone markers through time. We used Pearson correlation to explore relations between variables. Multiple linear regression was used to identify independent predictors of BMD response to treatment. To check for period effects and treatment period interactions, we used unpaired t tests to compare the differences and 225 sums respectively of each BMD or mean bone marker response to each treatment for the group who had pSSR first and the group who had it second. Statistical analyses were performed using Analyse-it software (v2.03). Statistical significance was accepted at P <0.05 (two-tailed). 230

Results

Of the 42 subjects identified as eligible for the study, 34 women proceeded to randomisation and had baseline investigations and 18 women completed the full study protocol (Figure 1, previously published in ref 15). In general, more women withdrew 235 during the first treatment phase, suggesting lack of tolerance of the intensive nature of the research protocol rather than the intervention. Both treatments were generally well tolerated, although some women reported adverse reactions to the patch adhesive during pSSR. 240

Table 1 shows the characteristics of the subjects who completed the study protocol compared with those who withdrew. Among those who completed, there were more Turner syndrome patients, more women with pre-pubertal onset of POF and more women randomised to sHRT as first treatment. Those who completed had similar age, height, weight and BMI to those who withdrew. Lumbar spine BMD was lower in the 245 women who completed than in those who withdrew (P =0.03) but bone marker levels at baseline were similar in the two groups.

18 women completed the study (age range 19 -39 years) of whom 7 had Turner syndrome and 11 had other causes of POF: treatment for cancer (n = 4), bilateral 250 ovariectomy (n = 1) or idiopathic (n = 6). At baseline, women with Turner syndrome were younger and shorter than those with other causes of POF, but did not differ in terms of BMD at any site, bone markers or hormonal status on their pre-study hormonal replacement regimen (Table 2). Similarly, women with pre-pubertal onset of POF were younger than those with post-pubertal onset, but did not differ in terms 255 of BMD, bone markers or hormonal status (Table 2). Regardless of aetiology or age of onset of POF, the lumbar spine was more affected than the femoral neck or total hip.

After first wash-out, LH and FSH were high in all subjects (minimum LH 21.3 U/l, 260 minimum FSH 45.3 U/l), confirming ovarian failure (Table 3). Thereafter, LH and FSH decreased to a similar extent during both pSSR and sHRT, suggesting equivalent sex steroid replacement.

Table 4 shows the changes in BMD in response to pSSR and sHRT. Lumbar spine 265 BMD z-score increased by +0.17 (CI +0.07 to +0.27) from baseline in response to pSSR (P =0.003) whereas there was no significant change in response to sHRT (P =0.2). However, when the two treatments were directly compared in the same subjects, the mean difference in lumbar spine BMD z-score response was +0.09 (95% CI -0.06 to +0.25) which did not reach statistical significance (P = 0.2). There were no 270 significant changes in femoral neck or total hip BMD in response to either treatment. There were no detectable treatment order interactions for any BMD changes in response to the two treatments.

During pSSR, the increment in lumbar spine BMD z-score was positively related to 275 mean within-subject oestradiol (r = +0.49, P =0.04) and inversely related to mean within-subject FSH (r = -0.65, P =0.004) but not LH (r = -0.24, P =0.3) over the same period. Multiple linear regression analysis indicated that both FSH and oestradiol were independent predictors of lumbar spine BMD z-score increments during pSSR (P = 0.005 and 0.05 respectively), explaining 55% of the variance. 280 Table 5 shows bone marker data at baseline and after first and second wash-out. Prewashout markers were similar to levels reported in normal pre-menopausal women, reflecting their pre-study hormone replacement. CrossLaps increased markedly by +86% (CI +38 to +135%) and +73% (+23 to +123%) during first and second wash-285 outs, whereas bone ALP (+22%, CI +8 to +35%, and +18% , +4 to +31%) and PINP (+24%, -3% to +51% and +17%, -3 to +37%) showed smaller, less consistent changes. There were no significant differences between the markers after second washout compared with after first washout (P =0.5 for CrossLaps, P =0.2 for bone ALP and P =0.5 for PINP), indicating that bone turnover was similar at the start of 290 each treatment period.

All bone markers showed significant changes through time in response to pSSR (ANOVA P = 0.003, <0.001 and <0.001 for bone ALP, PINP and CrossLaps respectively) and sHRT (ANOVA P <0.001, <0.001 and <0.001). The bone marker 295 responses to pSSR and sHRT, expressed as percentage changes from post wash-out baseline, are compared in Figure 2. Bone ALP and PINP increased in response to pSSR but decreased in response to sHRT (Figure 1A This is the first prospective exploratory study comparing hormone replacement regimens on skeletal health parameters in young women with POF. In this study, pSSR therapy over a 12-month period resulted in a significant improvement in lumbar spine BMD z-scores, whereas sHRT did not. Both regimens suppressed the bone resorption marker, CrossLaps, but only pSSR was associated with an increase in bone 315 formation markers, bone ALP and P1NP. Although our study used different hormone regimens from those in the large randomised HRT trials, we have demonstrated that there was a significant improvement in bone health for women with POF when they received physiological sex steroid replacement therapy, but not when they received standard HRT. These results have important implications for the long-term use of 320 hormone replacement to maximise bone health in women with POF.

Young women with untreated POF are at increased risk of developing osteoporosis, along with cardiovascular disease and cognitive impairment.. There is no agreed consensus on the optimum oestrogen and progesterone replacement regimens for 325 young women with premature ovarian failure. Factors that influence choice include convenience of preparation and suitability of preparation to achieve optimum skeletal and cardiovascular health. We audited the prescribing habits of all United Kingdom European Society of Paediatric Endocrinology members. A questionnaire was sent to members to enquire as to their choice of hormone replacement in young women with 330 premature ovarian failure. There was a good response to the questionnaire -42 were sent and 30 (71%) returned. 28 of the 30 respondents had a first preference. The most commonly used is the oral contraceptive pill (64%), Loestrin being the most frequently prescribed OCP (67%). 18% prescribed an "HRT" preparation used for post menopausal women, Prempak C being the only prescribed "HRT". As a result of 335 this small study we chose the oral contraceptive pill as standard HRT for this group of young women [START_REF] Bath | Choice of hormone therapy in young women with ovarian failure[END_REF]. Some women may require hormone replacement for over three decades [START_REF] Conway | Oestrogen replacement in young women with Turner's syndrome[END_REF]. Despite the widespread use of these hormonal preparations, the longterm continuous use of these conventional doses by women with POF may not provide hormone concentrations that optimise bone health. There is a higher incidence 340 of osteoporotic fractures amongst women with Turner syndrome despite long-term hormone replacement [START_REF] Gravholt | Increased fracture rates in Turner syndrome: a nationwide questionnaire survey[END_REF]. Thus physiological levels of oestradiol may be required to achieve optimum bone mass and rates of bone formation that are not attained through standard, conventional hormone replacement. 345

It is well documented that bone health significantly deteriorates after biological menopause in young or older women due to reduction of circulating oestrogen [START_REF] Purdie | Hormone replacement therapy and osteoporosis[END_REF][START_REF] Compston | Sex steroids and bone[END_REF]. Large, randomized trials have shown that HRT in postmenopausal women can dramatically improve BMD and reduce fracture risk [START_REF] Cauley | Women's Health Initiative investigators. Effect of estrogen plus progestin on risk of fracture and bone mineral density: the Women's Health Initiative randomized trial[END_REF][START_REF] Bush | Writing group for the PEPI. 1996. Effects of hormone therapy on bone mineral density-results from the Postmenopausal Estrogen/Progestin Interventions (PEPI) trial[END_REF][START_REF] Wells | Meta-analyses of therapies for postmenopausal osteoporosis. V. Meta-analysis of the efficacy of hormone replacement therapy in treating and preventing osteoporosis in postmenopausal women[END_REF]. However in these studies, patients were older, had a later onset and shorter duration of menopause and 350 did not have Turner syndrome, nor had they undergone anti-cancer treatment or ovariectomy like some of the patients in our study. Results from these studies cannot therefore be extrapolated to women with POF. Few studies have investigated the effect of HRT in young women with POF, but in Turner syndrome there is evidence to indicate the importance of oestrogen exposure on bone mass acquisition in 355 adolescence [START_REF] Högler | Importance of estrogen on bone health in Turner syndrome: a cross-sectional and longitudinal study using dual-energy x-ray absorptiometry[END_REF]. Our results demonstrate that treatment with pSSR produced a significant increase in lumbar spine BMD z-score whereas sHRT did not. There was no significant change in femoral neck or total hip BMD z-scores in response to either regimen. However, when 360 the two treatments were directly compared in the same subjects, the mean difference in lumbar spine BMD z-score response did not reach statistical significance. We accept that one year is a short time to see BMD changes in response to any intervention in adults. In clinical practice in postmenopausal women with osteoporosis on treatment, DEXA scans are usually done at not less than 2 year 365 intervals for this reason. It is well recognised that different parts of the skeleton respond in different ways to oestrogen. The lumbar spine is predominantly trabecular bone, compared to femoral neck which is predominantly cortical, thus oestrogen will affect the BMD of these sites to differing extents. It has been suggested that it is preferable to monitor bone at an area containing a significant proportion of trabecular 370 bone [START_REF] Hosie | Differential effect of longterm oestrogen therapy on trabecular and cortical bone[END_REF]. Trabecular bone is more metabolically active, and a greater proportion is undergoing remodelling at any one time compared with cortical bone. Agents such as oestrogen that affect bone formation and resorption influence it to a much greater degree and more rapidly. [START_REF] Miller | Hormone replacement therapy in the elderly[END_REF] In our study, during pSSR, the increase in lumbar spine BMD was positively related to oestradiol levels and inversely related to FSH, 375 consistent with a direct beneficial effect of oestrogen on net bone mineral accrual at this site.

Results from this study show that whilst both pSSR and sHRT regimens suppressed the bone resorption marker CrossLaps, only pSSR had a positive effect on the bone 380 formation markers bone ALP and PINP, whereas sHRT, in contrast, had a negative effect on bone formation. This indicates that standard doses of HRT may be adequate to suppress osteoclast activity and reduce bone resorption but are insufficient to stimulate bone formation through an increase of osteoblast action. pSSR parenteral administration allows a more consistent, stable physiological level of oestrogen to be 385 achieved since the regimen avoids the hepatic first-pass effect of drug metabolism, which occurs in sHRT therapy. Animal studies of oestrogen receptor knockout mice also suggest that different levels of oestrogen activate different oestrogen receptors and thus exert different actions on bone resorption and formation. At low oestrogen levels, ER β is predominantly activated which is enough to suppress bone resorption. 390 At increased oestrogen levels, both ER α and ER β are activated, stimulating bone formation [START_REF] Sims | Deletion of estrogen receptors reveals a regulatory role for estrogen receptor-β in bone remodelling in females but not in males[END_REF][START_REF] Hall | The estrogen receptor beta-isoform (ER beta) of the human estrogen receptor modules ER alpha transcriptional activity and is a key regulator of the cellular responses to estrogens and antiestrogens[END_REF]. In our study, there was an early rise in bone formation markers at 3 months before a plateau or reduction during subsequent months, suggesting a period of increasing osteoblast proliferation and activity after initial administration, a finding common to another study [START_REF] Hannon | Response of biochemical markers of bone turnover to hormone replacement therapy: impact of biological variability[END_REF]. 395

An important weakness of our study is the heterogeneous aetiology of the women with POF. Women with Turner syndrome have often been treated with growth hormone during childhood, whereas other women with POF have not, which may affect BMD and body composition [START_REF] Gravholt | Effect of growth hormone and 17β-oestradiol treatment on metabolism and body composition in girls with Turner syndrome[END_REF]. BMD measurements by DEXA are also 400 influenced by bone size and may give misleadingly low z-scores in women below the height of 150cm. However, this was not a major factor in our study as only one woman with Turner syndrome who completed the protocol was below 150cm in height (height 149cm). Although Turner syndrome women were generally younger and shorter than non-Turner women, they did not differ in terms of baseline BMD at 405 any site nor in baseline biochemistry. Furthermore, we did not observe any difference in treatment effect between Turner and non-Turner subjects, nor between those with pre-pubertal or post-pubertal onset of POF (data not shown). However due to such small sample groups, no firm conclusions can be drawn. It is impossible to generalise treatment benefits for all patients with POF disregarding its aetiology and timing. 410

However, to minimise these potential confounding effects, we chose to use a crossover design in our study to ensure that all subjects with different aetiologies and timing of onset of POF received both treatment regimens to enable each patient to act as their own control. 415

There was a high dropout rate in this study with only 18 out of 34 subjects (52%) completing the whole protocol, which is a further significant weakness of this study.

As more women withdrew during the first phase of treatment, we can attribute these dropouts to difficulty in adhering to study protocol rather than the intervention treatment itself. 420

Although during the relatively short period of this study, pSSR did not dramatically affect all aspects of skeletal health, it significantly improved BMD z-score at lumbar spine (which was low at baseline) and increased bone formation whereas sHRT did not. These observed differences in treatment effect support the hypothesis that the 425 type and profile of hormone replacement are critical and can have considerable effects on the bone health of women with POF. Long-term randomised studies with larger sample sizes from diagnostically homogeneous populations are needed in order to confirm these results and for an evidence base to be constructed. If evidence continues to grow in support of these skeletal health findings, together with evidence of an 430 improvement in blood pressure and cardiovascular risk previously reported in this group of women [START_REF] Langrish | Cardiovascular effects of physiological and standard sex steroid replacement regimens in premature ovarian failure[END_REF], the quality of hormone replacement for women with POF could be significantly improved.

Figure 1 :Figure 2 :F

 12 Figure legends

Table 1

 1 Baseline characteristics of subjects who completed the study protocol 455 compared with subjects who subsequently withdrew from the study

		Characteristic	Completed	Withdrew
			n = 18	n = 16
		Aetiology of ovarian failure:
		Turner Syndrome / Other	7 / 11	2 / 14
		Onset of POF: pre / post puberty	12 / 6	4 / 12
		Treatment order:
		pSSR-sHRT / sHRT-pSSR	6 / 12	10 / 6
		Age, years	27	31
			(23 -30)	(28 -34)
		Height, cm	160	162
			(156 -164)	(156 -168)
		Weight, kg	71.4	71.2
			(62.3 -80.4)	(61.2 -81.2)
		BMI, kg/m 2	27.7	26.7
			(24.8 -30.7)	(23.9 -29.7)
		Lumbar spine BMD, g/cm 2	0.933	1.041 a
			(0.892 -0.974)	(0.964 -1.118)
		Lumbar spine BMD z-score	-0.89	-0.04 a
			(-1.26 to -0.51)	(-0.77 to +0.70)
		Femoral neck BMD, g/cm 2	0.800	0.833
			(0.744 -0.856)	(0.744 -0.921)
		Femoral neck BMD z-score	-0.38	-0.10
			(-0.88 to +0.12)	(-0.89 to +0.69)
		Total hip BMD, g/cm 2	0.892	0.947
			(0.827 -0.957)	(0.859 -1.034)
		Total hip BMD z-score	-0.28	+0.04
			(-0.74 to +0.18)	(-0.67 to +0.74)
		CrossLaps, ng/L	312	266
			(237 -386)	(193 -338)
		Bone ALP, U/L	14.9	15.3
			(12.1 -17.8)	(12.9 -17.6)
		PINP, ug/L	53.1	54.9
			(42.9 -63.3)	(37.3 -72.5)
		LH b , U/L	12.3	25.4 a
			(5.6 -19.0)	(15.0 -35.9)
		FSH b , U/L	21.9	37.4
			(10.7 -33.2)	(22.2 -52.6)
		Oestradiol b , pmol/L	125	315 a
			(61 -190)	(109 -521)
		Data are expressed as mean (95% CI mean).
		a P <0.05 compared with those who completed the study.
	460	b Pre-washout, reflecting previous HRT prior to commencing study.

Table 2 :

 2 Baseline characteristics of patients who completed the study protocol according to aetiology and age of onset of premature ovarian failure (POF) 465 The assay used for oestradiol does not cross-react with ethinyloestradiol used during 480 sHRT.

			Aetiology	Onset of POF
			Turner	Other	Pre-puberty	Post-puberty
		N	7	11	12	6
		Age at study, years	22.9 a	29.1	24.1	31.8 c
			(20.9 -24.8)	(24.1 -34.1)	(21.0 -27.1)	(24.3 -39.3)
		Height, cm	154 b	164	158	163
			(150 -159)	(159 -168)	(154 -163)	(155 -171)
		Weight, kg	69.4	72.6	72.5	69.1
			(56.1 -82.7)	(58.6 -86.6)	(61.1 -84.0)	(48.0 -90.1)
		BMI, kg/m 2	29.0	26.9	28.7	25.8
			(24.4 -33.7)	(22.5 -31.4)	(25.0 -32.5)	(19.3 -32.3)
		Lumbar spine BMD,	0.932	0.934	0.921	0.957
		g/cm 2	(0.834 -1.031)	(0.888 -0.979)	(0.867 -0.975)	(0.877 -1.037)
		Lumbar spine BMD,	-0.91	-0.87	-1.01	-0.65
		z-score	(-1.84 to +0.01)	(-1.28 to -0.46)	(-1.51 to -0.51)	(-1.37 to +0.07)
		Femoral neck BMD,	0.809	0.794	0.775	0.850
		g/cm 2	(0.713 -0.905)	(0.713 -0.876)	(0.717 -0.834)	(0.703 -0.996)
		Femoral neck BMD,	-0.36	-0.39	-0.64	+0.15
		z-score	(-1.20 to +0.49)	(-1.14 to +0.36)	(-1.16 to -0.13)	(-1.15 to +1.45)
		Total hip BMD,	0.894	0.891	0.868	0.939
		g/cm 2	(0.754 -1.033)	(0.807 -0.974)	(0.789 -0.947)	(0.790 -1.087)
		Total hip BMD,	-0.16	-0.36	-0.45	+0.05
		z-score	(-0.91 to +0.60)	(-1.04 to +0.32)	(-0.96 to +0.06)	(-1.14 to +1.24)
		CrossLaps, ng/L	393	260	336	263
			(280 -507)	(161 -359)	(232 -440)	(142 -384)
		Bone ALP, U/L	15.0	14.9	16.0	12.8
			(12.6 -17.5)	(10.1 -19.7)	(12.4 -19.6)	(6.9 -18.8)
		PINP, ug/L	60.3	48.6	58.8	41.8
			(52.0 -68.6)	(32.0 -65.1)	(45.4 -72.1)	(25.8 -57.9)
		LH d , U/L	5.8	16.5	11.0	14.9
			(<0.6 -11.6)	(6.2 -26.8)	(3.6 -18.4)	(<0.6 -33.3)
		FSH d , U/L	13.7	27.2	20.3	25.3
			(2.4 -25.0)	(9.4 -45.0)	(7.9 -32.6)	(<1.0 -56.4)
		Oestradiol d , pmol/L	100	142	87	203
			(<36 -173)	(37 -246)	(47 -127)	(<36 -406)
		Data are expressed as mean (95% CI mean).
		a P <0.05 compared with non-Turner (other).
		b P <0.01 compared with non-Turner (other).
		c P <0.05 compared with pre-pubertal onset of ovarian failure
	470	d Pre-washout, reflecting previous HRT prior to commencing study.

a

Table 4

 4 Changes in BMD in response to pSSR and sHRT 485

	BMD measurement	pSSR	sHRT
	Lumbar spine BMD, g/cm 2	+0.019 a	+0.01
	(+0.008 to +0.029)	(-0.002 to +0.022)
	Lumbar spine BMD, z-score	+0.17 a	+0.07
		(+0.07 to +0.27)	(-0.03 to +0.18)
	Femoral neck BMD, g/cm 2	+0.012	+0.011
		(-0.007 to +0.030)	(-0.005 to +0.027)
	Femoral neck BMD, z-score	+0.12	+0.11
		(-0.05 to +0.29)	(-0.04 to +0.25)
	Total Hip BMD, g/cm 2	-0.009	+0.005
		(-0.051 to +0.034)	(-0.007 to +0.017)
	Total Hip BMD, z-score	-0.04	+0.03
		(-0.16 to +0.08)	(-0.08 to +0.13)
	Data are expressed as mean (95% CI mean)
	a P <0.01 versus baseline BMD
	490

Table 5

 5 Bone markers at baseline, and after first and second wash-out Data are expressed as mean (95% CI mean) in study subjects a Reference data provided by the kit manufacturer for pre-menopausal women, expressed as median (bone ALP) or mean (PINP and CrossLaps) and 2.5 th to 97.5 th percentile reference ranges. b P <0.001 versus baseline 500 c P <0.05 versus baseline

	495
	Bone marker	Baseline	After first	After second	Reference range a
			wash-out	wash-out
	CrossLaps, ng/L	312	487 b	519	287
		(237 -386)	(405 -569)	(438 -601)	(112 -738)
	Bone ALP, U/L	14.9	17.7 c	19.0	18.3
		(12.1 -17.8)	(13.4 -21.9)	(14.8 -23.3)	(11.6 -29.6)
	PINP, µg/L	53.1	61.8	58.1	46
		(42.9 -63.3)	(50.2 -73.4)	(49.6 -66.5)	(19 -102)

Acknowledgements

This research was supported by a project grant from CLIC/Sargent (R35464). 440

We would like to thank Jim Hannan, Medical Physics, Western General Hospital for DEXA scans, Caroline Valentine and Morag Chambers, our research nurses, and Angela Smith for her administrative support for the study.


MD Dorte Glintborg
email: d.glintborg@gmail.com

MD Hanne Mumm

MD David Hougaard

MD Pernille Ravn

MD Marianne Andersen

Ethnic differences in Rotterdam criteria and metabolic risk factors in a multiethnic group

Keywords: < Ovary, Polycystic ovary syndrome < Conditions: < Ovary, Insulin resistance < oral glucose tolerance test, insulin resistance, diabetes, ACTH test, ethnicity, Caucasian

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Hirsutism affects 10% females in reproductive age and is caused by increased androgenicity in the pilosebacceus gland [START_REF] Azziz | Idiopathic hirsutism[END_REF]. Hirsutism is the result of high androgen concentrations often in combination with increased androgen sensitivity (2). Hirsutism is most often caused by polycystic ovary syndrome (PCOS) or idiopathic hirsutism (IH), whereas less than 5% hirsute patients are diagnosed with rare endocrine disorders [START_REF] Glintborg | Prevalence of endocrine diseases and abnormal glucose tolerance tests in 340 Caucasian premenopausal women with hirsutism as the referral diagnosis[END_REF]. PCOS can be defined according to the Rotterdam criteria: 1. Irregular/no ovulations, 2.

Clinical/biochemical hyperandrogenaemia, and 3. Polycystic ovaries [START_REF]Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome[END_REF]. Two out of the three criteria need to be fulfilled and other causes of hyperandrogenaemia should be excluded. In contrast, the previously used PCOS criteria as suggested by the National Institute of Health (NIH) include only the first two criteria: Irregular/no ovulations and clinical/biochemical hyperandrogenaemia. The manifestations of PCOS may differ among different ethnic populations [START_REF] Huddleston | Racial and ethnic disparities in reproductive endocrinology and infertility[END_REF]. It is recognized that Asian women are rarely hirsute [START_REF] Huddleston | Racial and ethnic disparities in reproductive endocrinology and infertility[END_REF]. The enzyme 5α-reductase is responsible for the conversion of testosterone to the more potent androgen dihydrotesterone (DHT) [START_REF] Azziz | Idiopathic hirsutism[END_REF]. Inherited variations in 5α-reductase activity may explain differences in the prevalence of hirsutism among diverse ethnic populations. Individual variations in dermal 5α-reductase activity may furthermore explain why hirsute patients often have near-normal testosterone levels and that testosterone levels are poorly associated with clinical hirsute manifestations [START_REF] Azziz | Idiopathic hirsutism[END_REF]. We are not aware of previous studies reporting differences of the individual diagnostic criteria between different ethnic populations with PCOS.

PCOS and IH is characterized by hyper-inflammation and is associated with insulin resistance and increased risk for cardiovascular disease and diabetes (6;7). It is therefore recommended that PCOS patients are screened for the metabolic syndrome and cardiovascular risk factors (6;7). The metabolic disturbances of PCOS seem to be more pronounced in hyperandrogen patients compared to patients with no hyperandrogenaemia [START_REF] Azziz | The Androgen Excess and PCOS Society criteria for the polycystic ovary syndrome: the complete task force report[END_REF]. The prevalence of metabolic and cardiovascular risk factors may furthermore vary in different ethnic populations with PCOS. Several studies documented that immigrants in western countries may be at increased risk for life style diseases such as obesity and diabetes [START_REF] Daryani | Risk factors for coronary heart disease among immigrant women from Iran and Turkey, compared to women of Swedish ethnicity[END_REF]. Increased risk for diseases 4 may be attributable to genetic predisposition or to environmental influences such as different exercise or eating habits. It remains to be documented whether some ethnic PCOS populations are at increased risk for diabetes and therefore need closer follow up. In addition, some ethnic subgroups may be relatively protected against the long term side effects of PCOS.

Prolactin levels are routinely measured in hirsute patients to exclude prolactinomas as a cause of menstrual irregularity. Increased prolactin levels could be associated with irregular menses, but we are not aware of previous studies evaluating associations between prolactin and manifestations of PCOS in population studies.

The aim of the present study was to evaluate differences in clinical manifestations, metabolic risk factors, and diabetes risk in a multiethnic group of women with PCOS studied in Denmark Exclusion criteria: Women younger than 15 years were referred to the department of Pediatrics at Odense University Hospital and patients with a primary complaint of infertility were referred to the local Fertility Clinic. Patients not attending for the measurement of hormonal or metabolic parameters were excluded.

Patients with serious endocrine diseases were excluded as previously described (3).

The study was designed as a retrospective observational audit and we did therefore not search for approval from the local Ethics Committee.

Methods

Routine evaluation included medical history, clinical examination, transvaginal ultrasound (US), and fasting blood samples.

Medical history:

Cycle history was recorded in all patients using a standard scheme prepared for hirsute patients. Ancestry was recorded by using an evaluation scheme where the doctor marked the patient as originating from Caucasia, Middle East, Africa or Asia and the country of origin was specified. In few cases the originating country was not specified in the patient reports (Table 1). Only patients where both parents originated from the same continent were included in the study population. All participating patients were first or second generation immigrants. Marital status was not routinely recorded.

Clinical evaluation: Hirsutism was assessed by the Ferriman Gallwey score [START_REF] Ferriman | Clinical assessment of body hair growth in women[END_REF]. Waist circumference was measured to the nearest cm in a standing position midway between the lower costal margin and the iliac crest. The BMI was calculated as the weight in kilograms divided by the square height in meters. Transvaginal US were performed at the Department of Gynecology, Odense University Hospital and polycystic ovaries were defined according to Adams [START_REF] Adams | Prevalence of polycystic ovaries in women with anovulation and idiopathic hirsutism[END_REF]. Ovarian volume was not routinely measured in the present study. All patients were offered transvaginal US but a small group of patients did not attend for the examination. Transvaginal US was performed in 840/974 patients.

Blood samples: Fasting blood samples was drawn during the morning in follicular phase (cycle days 2-8) in patients with a cycle length shorter than three months, patients with cycle length > 3 months had the blood samples drawn on a random cycle day. Blood tests included androgens (total testosterone, free-testosterone, sex hormone binding globulin (SHBG), dehydroepiandrosterone sulfate (DHEAS), 17-hydroxyprogesterone (17OHP)), LH, FSH, prolactin, and lipid profile.

During 1997-2002 patients routinely underwent OGTT and ACTH tests. These data on Caucasian patients have previously been published (3;12). During 2002-2009 OGTT were only performed in high risk patients as recommended by international guidelines [START_REF] Salley | POSITION STATEMENT: Glucose Intolerance in Polycystic Ovary Syndrome A Position Statement of the Androgen Excess Society[END_REF]. Similarly, ACTH tests were only performed in patients with increased baseline 17OHP levels as previously discussed [START_REF] Glintborg | Significantly higher adrenocorticotropin-stimulated cortisol and 17-hydroxyprogesterone levels in 337 consecutive, premenopausal, caucasian, hirsute patients compared with healthy controls[END_REF]. The 0.25 mg ACTH test (Synachten 0.25 mg/ml, Novartis Healthcare, Copenhagen, Denmark) was performed between 08.00 h and 10.00 h during follicular phase. Cortisol and 17OHP were measured at baseline and at 30 and 60 min. ACTH tests were performed in 434 individuals (1997-2002, n=416).

OGTT was performed at 08.00 on a random day of the cycle. Insulin, C-peptide, and capillary blood glucose (CBG) were measured at baseline and at 30, 60, and 120 minutes after oral ingestion of 75 g glucose dissolved in water. OGTT was performed in 499 patients (1997-2003, n=390) excluding 33 patients with an established diabetes diagnosis.

Assays

LH, FSH, cortisol, insulin, and C-peptide were analyzed by time-resolved flouroimmunoassay using commercial kits (AutoDELFIA, Wallac Oy, Turku, Finland). Intra-assay variations were: FSH 1.0 -1.4%, LH 1.8 -9.4%, cortisol 2.7 -3.6%, insulin 2.1 -3.7%, and C-peptide 3.1 -5.0%. Inter-assay variations were: FSH 2.1 -3.7%, LH 2.0 -3.9%, cortisol 0.8 -1.9%, insulin 3.4 -4.0%, and C-peptide 1.9 -3.0%. Corporation, Los Angeles). Intra-and inter-assay variations were 3.5 -7.1% and 5.0 -11%, respectively. Serum total testosterone and sex hormone binding globulin (SHBG) were analyzed using a specific radioimmunoassay (RIA) after extraction as previously described [START_REF] Lykkesfeldt | Abnormal androgen and oestrogen metabolism in men with steroid sulphatase deficiency and recessive X-linked ichthyosis[END_REF]. In this method, testosterone, dihydrotestosterone and androstendione are extracted before applying RIA and overestimation of testosterone levels is avoided. The method shows a close correlation with the determination of testosterone levels by using mass spectrometry. The intra-assay CV for total testosterone was 8.2% and for SHBG it was 5.2%. The inter-assay CV for total testosterone was 13.8% and for SHBG it was 7.5%. Free testosterone levels were calculated from measurements of total testosterone and SHBG. DHEAS was measured by RIA on diluted serum using specific antibodies. The inter-assay variation for DHEAS was 10.7%. Plasma total cholesterol, high-density lipoprotein (HDL), cholesterol, and triglyceride (TG) were analyzed by enzymatic colorimetric reactions (Modular P, Roche), and LDL cholesterol was calculated using the Friedewald equation. Blood glucose was measured on capillary ear blood using Hemo Cue. We calculated HOMA-r = fasting insulin * fasting CBG / 22.5 [START_REF] Radziuk | Insulin sensitivity and its measurement: structural commonalities among the methods[END_REF], reference ≤ 15 pmol mmol l-2 . Area under the curve (AUC) for insulin during OGTT was calculated using the trapezium rule.

Statistic methods

All presented data represent pre-treatment values. Pakistan is most often referred to as part of the South Asian continent but may according to recent definitions also be included as part of the Middle East. Due to the limited number of subjects we chose to omit Asian patients. In order not to exclude the 14 patients from Pakistan we included these patients as part of the Middle Eastern patient group. Omitting the 14

Pakistani women from the calculations did not change the significant differences between ME and CA patients. Blood pressures measured in patients using antihypertensive treatment were not included in the dataset. Most parameters were not normally distributed and were therefore described using medians and quartiles. The Mann Whitney test was used to compare differences between two groups. The Chi squared test was used to test for differences in frequencies between groups. We performed multiple regression analyses to take into account that the two study populations differed significantly in age and BMI. These analyses were performed on log data. In regression analyses metabolic and clinical characteristics were entered as the dependent variable and age, BMI, and ethnicity (Caucasian =1, Middle East =2) were entered as the explanatory variables. In tables, p-values for the relevant explanatory variable are presented. We used SPSS version 13 (SPSS, Chicago, IL) in our calculations. Pvalues < 0.05 were considered significant.

Results

Clinical and biochemical characteristics

1206 patients were referred for evaluation. 1002 patients fulfilled the inclusion criteria and 204 patients were excluded. Table 1 shows the geographic distribution of included patients. The patients were divided according to ethnicity: Caucasian (CA, n=784), Middle East (ME, n=190), Asian (n=14), various (n=14).

Table 2 presents clinical and biochemical characteristics in CA and ME patients. CA patients were significantly older and had higher BMI compared to ME patients. After correcting for differences in age and BMI between the two study populations, clinical hyperandrogenicity was lower in CA women and SHBG and androgen levels were higher. Prolactin levels were lower in CA women. CA women had increased blood pressure and smoking prevalence, but no significant differences were found in lipid profiles (Table 2, Figure 1). The risk of diabetes and IGT was comparable in the two study populations, but CA women had decreased AUC insulin and 2 hour glucose during OGTT and decreased frequency of high fasting insulin (Table 3). Basal 17OHP was increased in CA women but no significant differences were detected in adrenal activity during ACTH tests. NIH criteria: The NIH criteria were fulfilled in 439 patients, 44.5% (349/784) CA vs. 47.3% (90/190) ME

PCOS criteria

women. Differences in metabolic and hormonal parameters between CA and ME women were not significantly affected when NIH criteria were applied instead of the Rotterdam criteria (data not shown).

Idiopathic hirsutism defined as regular menses, testosterone levels within reference interval and US with normal ovaries was diagnosed in 232 patients (CA n=183, ME n=49).

Multiple regression analysis

Ethnicity as an independent predictor of testosterone levels and metabolic risk factors is shown in Table 5.

ME women had increased AUC insulin and 2h glucose and decreased androgen and SHBG levels after correcting for BMI and age.

Multiple regression analyses were performed as described above with prolactin entered as the dependent variable. HDL (B= 0.21, p= 0.03), LDL (B= -0.17, p= 0.02), 17OHP 0 (B= 0.12, p= 0.004), and smoking status (B= -0.05, p= 0.007) were independent predictors of prolactin levels after correcting for ethnicity, age, and BMI. Prolactin levels were 7(5-10) vs. 6(5-9) µg/l in non-smoking vs. smoking patients (p<0.001). No significant associations were found between prolactin and sex hormones, cycle history, or PCO status.

Discussion

Limited data are available on ethnic differences in the manifestations of PCOS in North European study populations. In the present study we included data from a large well characterized Danish population of premenopausal PCOS women. CA women were significantly older than ME women and had significantly higher BMI. After correcting for differences in age and BMI, ME women were characterized by increased androgenicity, which could be explained by decreased SHBG, and increased diabetes risk whereas a more unfavourable cardiovascular risk profile was found in CA women.

It has been reported that African-American and Hispanic women with PCOS are more insulin resistant and have increased diabetes risk compared to Caucasian women (5;16). Our findings of increased metabolic risk factors in ME women compared to CA women are in agreement with studies in populations without PCOS (9;17;18), whereas few studies have been performed in PCOS populations. Wijeyaratne et al included 47 south Asian women with PCOS (including 40 women from Pakistan) and 40 Caucasian women with PCOS [START_REF] Wijeyaratne | Clinical manifestations and insulin resistance (IR) in polycystic ovary syndrome (PCOS) among South Asians and Caucasians: is there a difference?[END_REF]. In agreement with our study, south Asian patients were younger with decreased BMI and increased FGscore compared to Caucasian patients [START_REF] Wijeyaratne | Clinical manifestations and insulin resistance (IR) in polycystic ovary syndrome (PCOS) among South Asians and Caucasians: is there a difference?[END_REF]. Asian women had significantly decreased insulin sensitivity compared to CA women, whereas no significances were found in fasting glucose levels [START_REF] Wijeyaratne | Clinical manifestations and insulin resistance (IR) in polycystic ovary syndrome (PCOS) among South Asians and Caucasians: is there a difference?[END_REF]. We and Wijeyaratne et al [START_REF] Wijeyaratne | Clinical manifestations and insulin resistance (IR) in polycystic ovary syndrome (PCOS) among South Asians and Caucasians: is there a difference?[END_REF] reported significantly increased SHBG levels in Caucasian women, which may support higher insulin sensitivity (19;20). Sixty-four percent Asian women vs. 30% Caucasian women with PCOS had a family history of diabetes, which may have affected results [START_REF] Wijeyaratne | Clinical manifestations and insulin resistance (IR) in polycystic ovary syndrome (PCOS) among South Asians and Caucasians: is there a difference?[END_REF]. The effects of race and family history on diabetes risk were studied by Ehrmann et al in a mixed ethnic study population of 408 PCOS patients of primarily black and white race [START_REF] Ehrmann | Effects of race and family history of type 2 diabetes on metabolic status of women with polycystic ovary syndrome[END_REF]. Ethnicity was found to influence parameters of insulin resistance and body composition independent of family history [START_REF] Ehrmann | Effects of race and family history of type 2 diabetes on metabolic status of women with polycystic ovary syndrome[END_REF]. Intra-family marriages are more prevalent in ME women than in CA women [START_REF] Haq | Infertility and polycystic ovarian syndrome: a study of association between body mass index and intrafamily marriages[END_REF]. In the present study we did not have data on family marriages. In a Pakistan population of PCOS women, consanguineous marriage was associated with insulin resistance and glucose intolerance and a family history of diabetes and cardiovascular disease was observed more often in the group of women marrying within family [START_REF] Haq | Infertility and polycystic ovarian syndrome: a study of association between body mass index and intrafamily marriages[END_REF].

CA women in the present study had increased blood pressure, smoked at a higher frequency, and tended to have a more adverse lipid profile than ME women. These findings suggest that CA women are at increased (9;17). A meta-analysis found a tendency to improved lipid profile in ME women compared to CA women whereas this could not be reproduced in other studies [START_REF] Daryani | Risk factors for coronary heart disease among immigrant women from Iran and Turkey, compared to women of Swedish ethnicity[END_REF]. In an American population, the blood pressure in Asian PCOS patients was comparable to CA women [START_REF] Lo | Epidemiology and adverse cardiovascular risk profile of diagnosed polycystic ovary syndrome[END_REF]. The present data are in agreement with the findings of Wijeyaratne et al reporting similar lipid profile in Asian vs. CA women with PCOS but significantly decreased blood pressure in Asian women (systolic 114 vs. 120 mmHg and diastolic 72 vs. 76 mmHg) [START_REF] Wijeyaratne | Clinical manifestations and insulin resistance (IR) in polycystic ovary syndrome (PCOS) among South Asians and Caucasians: is there a difference?[END_REF].

ACTH stimulated 17OHP levels did not differ between ME and CA women in the present study suggesting comparable adrenal androgen production. In agreement with this finding, the age and BMI corrected DHEAS and cortisol levels were similar in the two study populations. In contrast to these findings, two studies found decreased DHEAS levels in Black and Mexican American vs. white women with PCOS (23;24). Using race specific cut offs, black women with PCOS had increased prevalence of high DHEAS levels [START_REF] Kumar | Prevalence of adrenal androgen excess in patients with the polycystic ovary syndrome (PCOS)[END_REF] suggesting that adrenal activity may differ between some ethnic groups.

An unexpected finding of the present study was the higher prevalence of polycystic ovaries in CA women compared to ME women. We are not aware of previous studies reporting similar differences in the prevalence of PCO between different ethnic populations with PCOS. In the present study PCO was defined according to the criteria suggested by Adams and ovarian volume was not systematically recorded. The Rotterdam criteria suggest that increased ovarian volume >10 ml alone can be used to define PCO (4). It is therefore possible that the prevalence of PCO may be higher than estimated in the present study. In the study by Wijeyaratne, there was a trend to increased ovarian volume in Asian PCOS patients, but this did not reach statistical significance [START_REF] Wijeyaratne | Clinical manifestations and insulin resistance (IR) in polycystic ovary syndrome (PCOS) among South Asians and Caucasians: is there a difference?[END_REF]. Welt et al reported similar prevalence of PCO between Caucasian PCOS patients from Boston and Iceland but Icelandic patients had smaller ovaries with decreased follicle number supporting that ovarian morphology may vary in different study populations [START_REF] Welt | Defining constant versus variable phenotypic features of women with polycystic ovary syndrome using different ethnic groups and populations[END_REF].

The median age upon referral was relatively high in our study population and CA patients were seven years older than ME women. Patients with a primary wish for conception were referred directly to the fertility clinic and therefore the primary complaints of our population were irregular menses and hirsutism. The median age for first child birth in Denmark is 29-30 years and more than 25% women in reproductive age use oral contraceptives (OC) [START_REF] Spinelli | Patterns of contraceptive use in 5 European countries. European Study Group on Infertility and Subfecundity[END_REF]. OC pills efficiently alleviate the symptoms of PCOS and this may explain the relatively high age upon referral in our population. It is possible that a lesser percentage of ME women use OC as birth control and therefore the symptoms of PCOS present in an earlier age. Impaired quality of life due to for example overweight and hirsutism is well described in PCOS and may be the primary reason for referral of the patient [START_REF] Jones | Health-related quality of life measurement in women with polycystic ovary syndrome: a systematic review[END_REF]. In a previous Austrian study, quality of life regarding infertility, overweight,

hirsutism, and menstrual disturbances were significantly impaired in infertile Moslem immigrant women with PCOS compared to Austrian women with comparable clinical or biochemical symptoms of PCOS (28).

Prolactin levels were routinely measured to exclude prolactinomas as a cause of menstrual irregularity and none of the included patients had prolactin levels above reference interval. To our knowledge we are the first to report increased prolactin levels in ME patients compared to CA patients. Multiple regression analyses suggested that the difference could in part be explained by decreased smoking prevalence in ME women.

Increased prolactin levels were furthermore associated with increased HDL, decreased TG and increased 17OHP levels after correcting for BMI, race, and age. Our findings are in agreement with previous studies on the effect of nicotine on prolactin (29;30). We are not aware of previous studies documenting associations between prolactin secretion and lipid metabolism. Further studies are needed to evaluate prolactin levels among different ethnic populations with PCOS.

The present study included patients referred to our specialist center from general practitioners and local gynecologists and dermatologists. The evaluated patient population therefore probably represents a selected patient population. Furthermore, the ME patient group included patients from a large geographic area. The patients underwent a standardized evaluation program including check schemes to minimize the inter observer variation but clearly more prospective studies are needed to confirm our study results in well characterized PCOS populations of mixed ethnicity.

F

  All premenopausal women referred to the outpatient clinic at the Department of Endocrinology during 1997 -2008 with the referral diagnoses hirsutism and/or PCOS were included. The patients should fulfill the Rotterdam criteria for PCOS or have clinical/biochemical hyperandrogenemia. As part of the clinical practice protocol of the department patients suspended oral contraceptives for at least three months prior to evaluation and metformin treatment was paused for at least one month prior to evaluation. Seventeen patients in the study population were on antihypertensive treatment. All included patients had normal thyroid function. All presented data are pretreatment values.

7 17OHP

 7 was analyzed by radioimmunoassay using a commercial kit (Coat-A-Count, Diagnostic Products

  BMI. Overweight patients were significantly more insulin resistant and had a more adverse cardiovascular risk profile than normal weight patients within both ethnic subgroups of patients. The different prevalence of hyperandrogenism and PCO remained significant between CA and ME women fulfilling the Rotterdam criteria (data not shown).

  ME women. In populations without PCOS, the data on cardiovascular risk profile in ME vs. CA populations are conflicting

Table 4

 4 Rotterdam criteria: A total of 557 patients fulfilled the Rotterdam criteria for PCOS: 56.2% (448/784) CA vs. 56.8% (109/190) ME women. The prevalence of the individual criteria differed significantly between the two study populations, Figure 1. 47.0% (314/668) CA women vs. 29.0% (50/172) ME women had PCO and 89.1% (699/684) vs. 93.2% (179/190) had clinical and/or biochemical hyperandrogenism (both p<0.05). presents clinical and biochemical characteristics of the 557 patients fulfilling the Rotterdam criteria

Conclusion

  ME women with PCOS are referred at a younger age and are characterized by decreased BMI and increased androgenicity compared to CA women with PCOS. Insulin resistance is increased in ME women whereas the cardiovascular risk profile is more beneficial regarding blood pressure and smoking status. The present study support that ethnic groups differ in clinical and biochemical presentation of PCOS and future studies should focus on prospective data in different ethnic populations with PCOS. Kirchengast S, Vytiska-Binstorfer E, et al. (2004) Infertility caused by PCOS-health-related quality of life among Austrian and Moslem immigrant women in Austria.

	28. Schmid J, Hum Reprod, 10, 2251-7.
	29. Weisberg E. (1985) Smoking and reproductive health. Clin Reprod Fertil, 3, 175-86.
	30. Fuxe K, Andersson K, Eneroth P, et al. (1989) Neuroendocrine actions of nicotine and of
	exposure to cigarette smoke: medical implications. Psychoneuroendocrinology, 1-2, 19-41.
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	13 16

Table 1 :

 1 Geographic distribution of included patients (n=1002)

	Caucasian (n=784)	Middle East (n=190)	Asian (n=14)	Various (n=14)
	Albania (n=2)	Afghanistan (n=4)	India (n=1)	African countries (n=6)
	Armenia (n=1)	Arabia (n=29)	Korea (n=1)	South America (n=3)
	Bosnia (n=4)	Iraq (n=36)	Sri Lanka (n=11)	Not noted (n=6)
	Croatia (n=1)	Iran (n=19)	Vietnam (n=1)
	Denmark (n=765)	Jordan (n=2)
	Poland (n=3)	Lebanon (n=12)
	Rumania (n=1)	Pakistan (n=15)
	Russia (n=1)	Palestine (n=3)
	Yugoslavia (n=6)	Syria (n=2)
		Turkey (n=57)
		Middle east* (n=11)
	* Exact country not specified in patient reports.

Table 2 :

 2 Clinical and paraclinical data from patients divided according to ethnicity (n=974)

	Caucasian	Middle east
	(n=784)	(n=190)

Table 3 :

 3 Outcome of glucose tolerance tests

		Caucasian	Middle east
	Normal glucose tolerance	344/414 = 83.1 %	69/83 = 83.1 %
	IGT	57/414 = 13.8 %	10/83 = 12.0 %
	Diabetes	13/414 = 3.1 %	4/83 = 4.8 %
	Increased fasting insulin	227/447 = 50.6% §	64/122 = 52.4%
	§<0.05 vs. ME patients after correcting for age and BMI
	Glucose tolerance defined according to WHO criteria
	Increased fasting insulin defined as >55pmol/l

Table 4 :

 4 Clinical and paraclinical data from patients with PCOS according to Rotterdam criteria (n=557).

	Caucasian	Middle East
	BMI≤25 kg/m 2	BMI>25 kg/m 2	BMI≤25 kg/m 2	BMI>25 kg/m 2
	(n=141)	(n=262)	(n=38)	(n=66)

Table 5 .

 5 Regression analyses on the effect of ethnicity on hormonal and metabolic outcomesMultiple regression analyses were performed with metabolic outcomes as the dependent variables and ethnicity (cau=1, ME=2), BMI, and age as explanatory variables.Metabolic outcomes, BMI, and age were log-transformed. Figure 1. Metabolic risk profile and prevalence of individual Rotterdam criteria in CA vs. ME patients (n=974) *<0.001 vs. ME patients §<0.05 vs. ME patients after correcting for age and BMI § §<0.001 vs. ME patients after correcting for age and BMI

	(n=974)
	Log Dependent			Ethnicity
	variable	Log BMI	Log age	(Cau vs. ME)	R 2 model
	T-testosterone	0.21 *	-0.32**	-0.06*	0.04**
	SHBG	-1.09**	0.22**	-0.05*	0.17**
	Free-testosterone	0.82**	-0.50**	-0.06*	0.11**
	Fasting insulin	2.07**	-0.46**	0.002	0.47**
	AUC insulin	1.73**	-0.31**	0.084*	0.27**
	HOMA	2.18**	-0.39**	0.16	0.44**
	F. glucose	0.09*	0.085**	0.003	0.05**
	2 h. glucose	0.37**	0.10*	0.035*	0.09**
	Data presented as B-value
	* p<0.05
	** p<0.001

*

Clinical Endocrinology