Correlations among anti‐Müllerian hormone levels, body mass index and lipid profile in reproductive‐aged women: The Korea Nurses' Health Study

Abstract Aim This study aimed to evaluate the correlations among anti‐Müllerian hormone levels, body mass index and lipid profile in female nurses of reproductive age. Design This is a descriptive cross‐sectional study that used data from the Korea Nurses' Health Study, based on Nurses' Health Study 3 in the United States. Methods Participants included 448 female nurses of reproductive age. They provided details about their work experience, shift work, body mass index and history of polycystic ovary syndrome. Serum anti‐Müllerian hormone levels and lipid profile were measured using blood samples. Statistical analysis included quantile regression analysis using STATA 13.0. Data were collected between November 2016 and March 2017 from Module 5 of the Korean Nurses' Health Study. Results Approximately 12% of the participants were underweight, and roughly one‐tenth were either overweight or obese. Although linear regression showed no relationship between anti‐Müllerian hormone levels and body mass index, quantile regression showed that body mass index, total cholesterol levels and low‐density lipoprotein levels were negatively correlated with anti‐Müllerian hormone levels at the lower tails of the dependent variable. Meanwhile, high‐density lipoprotein levels were positively correlated with anti‐Müllerian hormone at the higher percentiles of anti‐Müllerian hormone levels. Conclusion These findings suggest the possibility that changes in the lipid profile may influence anti‐Müllerian hormone levels in women with diminished ovarian function, rather than obesity itself.

According to Statistics Korea, the prevalence of infertility in mar- Affairs, 2018). Therefore, nurses would be a valuable population for a female cohort because of their high interest in health, knowledge about medical conditions and the presence of a registration system (Kim et al., 2017).
Anti-Müllerian hormone (AMH) is recognized as a marker with the most outstanding sensitivity and specificity for ovarian reserve because it undergoes changes during early age and has less fluctuations according to the menstrual cycle than do conventional ovarian reserve markers such as follicle-stimulating hormone (FSH), oestradiol (E2) and inhibin B (Rustamov et al., 2014). AMH levels are influenced by various factors, including environmental factors such as obesity (Bernardi et al., 2017), oral contraceptive use (Dólleman et al., 2013), menstrual irregularity (Dólleman et al., 2013), smoking (White et al., 2016), vitamin D deficiency (Dastorani et al., 2018) and socio-economic status (Barut et al., 2016), as well as some genetic factors (Shahrokhi et al., 2018;Tal & Seifer, 2013).
With the association between obesity and female reproductive health receiving much attention recently, various studies have been conducted to explore the association between AMH and body mass index (BMI). However, such studies have reported conflicting results, with some reporting a negative correlation between AMH and BMI (Bernardi et al., 2017;Moy et al., 2015;Steiner et al., 2010), whereas others reporting no statistically significant associations (Albu & Albu, 2019;Dólleman et al., 2013;Steiner et al., 2017). Moreover, several previous studies regarding AMH had mostly focused on infertility patients and did not include biochemical markers such as lipid profile, making it difficult to comprehensively identify variability in hormone levels due to fat accumulation. Therefore, this study aimed to investigate the factors affecting AMH levels in female nurses of reproductive age and to specifically identify the correlations among AMH levels, BMI and lipid profile. This study's results may suggest future directions for the improvement of nurses' reproductive health and their occupational environment.

| BACKG ROU N D
Since its first discovery during the early 1990s, AMH has been used as a major indicator for women's reproductive potential as it can account for the number of remaining ovarian follicles. AMH is produced by the ovarian granulosa cells at around 36 weeks of gestational age and reaches peak levels at around 25 years of age. The level of secretion continues to decrease with age and is no longer detectable at menopause (Dólleman et al., 2013;Kelsey et al., 2011).
Clinically, AMH is widely used for predicting oocyte yield during in vitro fertilization (IVF). Particularly in cases with very low levels of AMH (<1 ng/ml), a more aggressive protocol is recommended for ovarian stimulation, while having a high oocyte yield has been reported to increase the likelihood of conception and normal childbirth (Iwase et al., 2015;Santoro, 2017;Yoo et al., 2011). As AMH decreases rapidly over approximately 5 years prior to menopause, it is also used as a predictor of premature ovarian failure and onset of menopause (Santoro, 2017).
Obesity can cause negative reproductive health outcomes including anovulation, infertility, miscarriage and adverse pregnancy (Talmor & Dunphy, 2015). Compared with non-obese women, obese women have a higher incidence of menstrual disorders and anovulation and have a poorer oocyte yield and quality during IVF. Moreover, obese women have a lower success rate for natural conception and conception with assisted reproductive technology and a higher risk of miscarriage and birth defects (Dağ & Dilbaz, 2015;Pandey et al., 2010). It has been reported that obese women exhibit alterations in their ovarian follicular environment, which causes a negative impact on AMH production (Bernardi et al., 2017). However, the underlying physiological mechanism has not been clearly identified, while conflicting study results on the association between AMH levels and BMI have been reported. On the other hand, previous studies on ovarian function and cardiovascular disease have shown that women with diminished ovarian function have higher total cholesterol (TC), low-density lipoprotein (LDL), triglyceride (TG), Creactive protein (CRP), fasting glucose and homeostatic model assessment for insulin resistance (HOMA-IR) and lower high-density lipoprotein (HDL) level than women with normal ovarian function (Tehrani et al., 2014;Verit et al., 2016Verit et al., , 2017, suggesting a possible association between AMH and changes in the endocrine environment, including the lipid profile. Despite the strong association between obesity and lipid profile, however, very few studies have comprehensively identified the associations among AMH levels, BMI and lipid profile. Studies on healthy reproductive-aged women with a low obesity rate are even more rare. Accordingly, this study analysed data from the Korea Nurses' Health Study (KNHS) to explore the correlations among AMH levels, BMI and lipid profile in female nurses of reproductive age and find a basis for enhancing the reproductive health of women of childbearing age. This study aimed to answer the following research questions: (a) What are the factors affecting AMH levels in Korean nurses of reproductive age? (b) Are there correlations among AMH levels, BMI and lipid profile?

| Design
This is a descriptive cross-sectional study that analysed data from the KNHS, which is a prospective cohort study of female nurses of reproductive age.

| Setting and participants
The KNHS is the first large-scale cohort study in Korea, based on the Nurses' Health Study 3 (NHS3) in the United States (Kim et al., 2017). It began as a web-based questionnaire survey in 2013 with 20,613 participants, and follow-up online surveys have been conducted at every 6-8-month intervals. The inclusion criteria for the surveys were female Registered Nurses aged 20-45 years, who were actively working in a hospital for more than one year in South Korea at the time of the baseline survey (module 1). Female Registered Nurses who were not working at a hospital due to being on temporary or sick leave or having resigned from work were also allowed to participate as long as they had experience working in a hospital in the past year. Blood samples were collected from 2,000 participants from November 2016-March 2017 in module 5. Sampling for AMH was based on population parameters, and simple random sampling was employed. To calculate the sample size, G* power (Faul et al., 2007) was used; a needed sample size of 157 participants was determined after applying a medium effect size of 0.15, power of 0.80, significance level of 5% and 20 predictors.

| Measures
The KNHS surveys comprised 112 items in 11 categories, which included demographic characteristics, anthropometric measures, health screening, illness, family history, health behaviour, lifestyle and diet, reproductive health, pregnancy, mood and subjective health perception, employment, and occupational exposure.
Multidisciplinary experts in the research team translated and backtranslated the fifth survey of NHS3. Expert opinions of a multidisciplinary advisory board were used to test the reliability of the edited questionnaires. The advisory board consisted of experts from the fields of epidemiology, statistics, nutrition, public health and nursing. The questionnaire items were mostly consistent with those in the NHS3, while some items were modified to be more suitable for Korea. For example, questions related to shift work were modified because the shift work patterns in Korea differ from those in the United States. In Korea, hospitals do not implement 12-hr shifts or fixed duties. Overall values of Cronbach's alpha of entire module 5 ranged from .74-.92.
Among the data extracted from KNHS, this study analysed demographic characteristics (age, educational level, work experience and rotational shift work), BMI (based on self-reported values), diagnosis of polycystic ovary syndrome (PCOS) and haematological parameters (AMH, TC, HDL, LDL and TG).

| Data collection procedure
Among the 11,620 nurses who participated in module 5 of the KNHS between November 2016-March 2017, this study randomly selected and analysed a total of 448 nurses who did not have any missing data for major research variables and blood test results, including AMH levels. Module 5 used in this study did not include any questions regarding the diagnosis of PCOS; thus, the diagnosis status was verified through ID matching based on module 3, which included such questions.

| Assays
Blood samples were collected on a random day of the menstrual cycle. AMH was measured in serum obtained after centrifugation and aliquoted, frozen at −20°C on the same day of collection and stored for batch analysis. All samples were analysed under the same conditions on the same day.
A commercial enzyme-linked immunosorbent assay kit (AMH Gen II ELISA Kit; Beckman-Coulter) was used to perform an enzymatically amplified two-site immunoassay for detecting AMH, according to the manufacturer's instructions. For this assay, the inter-assay imprecision with coefficient of variation was reported as <11.9% at levels >2.2 ng/ml. The inter-and intra-assay imprecision were reported as 11.4% and 17.3%, respectively (Wheeler, 2013), and the limit of detection was 0.08 ng/ml.
For the Elecsys ® AMH automated assay, the intra-and interassay coefficients of variation were reported as 0.5%-1.4% and 0.7%-1.9%, respectively, and the limit of detection was 0.08 ng/ml.

| Analysis
Data were analysed using STATA 13.0 in the following manner. Due to the characteristic of AMH levels being skewed to the right instead of showing a normal distribution, haematological parameters, including AMH levels, were analysed by medians and quartiles. Differences in AMH levels according to general characteristics, including BMI category (underweight, normal, overweight or obese), were analysed by non-parametric methods (Wilcoxon rank-sum test and Kruskal-Wallis rank test), while correlations between AMH levels, BMI (kg/ m 2 ) and lipid profile were analysed by quantile regression analysis.
Although a multiple linear regression is useful for examining the average influence of independent variables on changes in the dependent variables, it has its statistical limitations for a dependent variable with non-normal distribution (Wyka et al., 2017), such as AMH in this study. Accordingly, quantile regression was used to overcome such limitations by analysing correlations with independent variables at all points on the dependent variable distribution. In other words, quantile regression demonstrates various possibilities with outliers, in regions that deviate from the mean, allowing for understanding relationships between variables outside of the mean. Lê Cook and Manning (2013) emphasized the unique advantages of quantile regression in relation to assessing medical services. They also stated that the group with the highest medical resource utilization and medical expenditure is not the average group and that identifying information about such group is difficult with linear regression, and even if results are derived, there is a high likelihood of such relationships being over-or underestimated.
Accordingly, this study considered the non-normal distribution of AMH levels and used quantile regression to analyse the correlations between AMH levels, BMI and lipid profile at different percentiles of AMH levels.

| Ethics
The KNHS was approved by the Institutional Review Board (IRB) of Ewha Womans University . Informed consent was obtained via online forms from all participants who voluntarily agreed to participate prior to initiation of the online or mobile survey, and their anonymity and confidentiality were guaranteed in compliance with the Declaration of Helsinki.

| Differences in AMH levels according to general characteristics
The differences in AMH levels according to the general characteristics of participants are shown in Table 1. Among the 448 participants, the age group of 25-29 years had the greatest proportion of participants (N = 180, 40.2%), and as suggested in previous studies, AMH levels tended to decrease with age (p ≤ .001). Moreover, AMH levels showed significant correlations with educational level (p = .021) and work experience (p ≤ .001). Rotational shift work accounted for 80.4% of all work types, and AMH levels were higher among shift workers than among non-shift workers (p ≤ .001). Moreover, among rotational shift workers, 90.3% responded that they worked night shifts, and those who worked night shifts showed higher AMH levels than those who did not (p = .003). Such results may be due to the fact that the percentage of non-shift workers and those who work during weekdays is higher among relatively older age groups in Korea (Kim & Jo, 2013;Son & Ham, 2018). BMI was classified as underweight (<18.5 kg/m 2 ), normal (18.5-22.9 kg/m 2 ), overweight (23-24.9 kg/m 2 ) and obese (≥25 kg/m 2 ), according to the 2000 World Health Organization (WHO) Asia-Pacific classification.

| Lipid profile
The study population was divided into those diagnosed with PCOS (N = 29, 6.5%) and those who were not diagnosed with PCOS (N = 419). The sample included 419 women who were not diagnosed with PCOS but reported various health histories (1 cervical cancer, 9 endometriosis, 4 oophorectomy, 9 oral contraceptive use and 5 other contraceptive hormone therapies).
The results of analysing the lipid profiles of both groups are shown in Table 2. No statistically significant differences in the lipid profile were observed according to the diagnosis of PCOS. Table 3 shows the results of analysing the correlations among AMH

| D ISCUSS I ON
This study on healthy reproductive-aged nurses showed that AMH levels had negative correlations with BMI, TC and LDL levels at the 10-20th percentiles of AMH levels and positive correlations with HDL levels at the 50th, 90th and 97th percentiles of AMH levels.
Such findings partially support the negative correlation between AMH levels and BMI in women with diminished ovarian function and suggest the possibility that changes in the lipid profile influenced the AMH levels, rather than obesity itself. A previous study of infertile women also showed similar results on the correlation between AMH levels and BMI (Buyuk et al., 2011). In that study, 290 infertile women were divided into groups with normal ovarian function (N = 138) and diminished ovarian function (N = 152) based on serum FSH levels. In the group with diminished ovarian function, AMH levels were lower by 33% in women who were overweight or obese (BMI ≥25 kg/m 2 ) than in normal-weight women. In the group with normal ovarian function, however, no statistically significant association was found between AMH levels and BMI. Among the study participants, 109 women actually underwent a controlled ovarian hyperstimulation cycle during IVF, of which 58 women with diminished ovarian function showed a negative correlation between BMI and oocyte yield, whereas women with normal ovarian function showed no correlation between both variables (Buyuk et al., 2011).
Our findings provide more detail than some previous studies which reported a negative correlation between AMH levels and BMI. A cohort study of 1,654 community-dwelling African American women aged 23-35 years showed that AMH levels were lower by 23.7% in obese women (BMI ≥30 kg/m 2 ) than in normal-weight and underweight women (BMI <25 kg/m 2 ) and that not only current BMI but also BMI and maximum weight at age of 18 years were also negatively correlated with AMH levels (Bernardi et al., 2017). Similarly, a study by Steiner et al. (2010) examined the effects of oral contraceptives on AMH levels in women aged 18-35 years who had regular menstruation and found that AMH levels were lower by 34% in obese women than in non-obese women. Contrarily, a cohort study conducted in the Netherlands examined 2,320 premenopausal women and found no significant differences in AMH levels according to BMI or waist circumference (Dólleman et al., 2013). Moreover, a study of 2,204 Romanian infertile women showed no statistically significant correlation between BMI and AMH levels, and the age-based subgroup analysis in that study showed that AMH levels increased with higher BMI in women aged ≤35 years (Albu & Albu, 2019).
Regarding the general characteristics of the study popula-  Bernardi et al. (2017) also found a negative correlation between current BMI and AMH levels, but when multiple regression analysis was performed according to the BMI categories, a statistically significant correlation was found only in the extremely obese group (BMI ≥45 kg/m 2 ). Based on these findings, it appears that lipid disturbance above a certain level has an influence on AMH level, but because 89.5% of the study population in this study had a relatively healthy BMI (<25 kg/m 2 ), the correlation between obesity and AMH levels requires cautious interpretation.
To determine the association between obesity and AMH levels, it is necessary to understand the mechanism by which obesity influences the ovarian follicular microenvironment (Bernardi et al., 2017), but despite significant effort, such mechanism has yet to be clearly identified. One possible mechanism presented to date is increased aromatase activity and oestrogen-to-androgen ratio in obese women interfering with the production of AMH (Steiner et al., 2010). In relation to this, adiponectin has been reported to inhibit ovarian aromatase activity (Ledoux et al., 2006), and obese women are known to secrete relatively less adiponectin than normal-weight women (Parida et al., 2019). Other studies also claimed that insulin resistance in obese women influences ovarian granulosa cells to induce changes in AMH levels (Park et al., 2010). Furthermore, it is suspected that AMH is metabolized, stored and eliminated differently between obese and normal-weight women (Tal & Seifer, 2013). In other words, obesity may cause changes in the ovarian follicular environment; thus, follow-up studies are needed to confirm whether such effect is due to the number or physiology of follicles or dysfunction of granulosa cells (Bernardi et al., 2017).
This study also showed that AMH levels were negatively cor-  supply. Another mechanism suggested is that changes in the endocrine environment cause diminished ovarian function, which in turn causes an increased risk of cardiovascular disease. It is also thought that somatic ageing occurs relatively faster in women with diminished ovarian function, which causes an increased risk of cardiovascular disease, including changes in the lipid profile (Verit et al., 2016).
In particular, HDL levels and age influenced the AMH levels in this study. HDL is known to prevent cardiovascular disease by reducing oxidation, vascular inflammation and thrombosis and improving endothelial function (Barter, 2011). Therefore, lipid disturbance in reproductive-aged women may affect the ovarian vessels, resulting in diminished ovarian function. Future studies are warranted to identify the effects of changes in lipid metabolism on the synthesis, storage and secretion of AMH.
Recently, there has been growing interest in racial and ethnic disparity in AMH levels. A study by Bleil et al. (2014) (Bhide et al., 2015). These conflicting results could be attributed to imbalance in the sample size for each ethnic group (Bhide et al., 2015;Moy et al., 2015) or differences in the general characteristics of subjects, including BMI and smoking status. This study provided data on relatively healthy, non-smoking Korean women of reproductive age, adding to the gap in the literature. Follow-up studies that explore differences in AMH levels among different ethnic groups will be beneficial, perhaps by pooling existing data from prior studies.
In this study, 6.5% were diagnosed with PCOS, which falls within its reported prevalence of 6%-10% (Bozdag et al., 2016). PCOS women showed significantly higher AMH levels than their healthy counterparts, related to androgens facilitating AMH production in the ovarian granulosa cells (Anderson & Lossl, 2008). A study by Lin et al. (2011) divided 290 women into three groups based on serum AMH levels and prospectively estimated the incidence of PCOS. The results showed that PCOS incidence increased by 21%, 37% and 80% in groups with low AMH (<4 ng/ml), moderate AMH (4-11 ng/ ml) and high AMH levels (>11 ng/ml), respectively. Some research- .000 (.98) .001 (.85) .022 (.19) .  (Fernandez et al., 2016), this study evaluated AMH as the primary marker for fertility in reproductive-aged nurses. Although correlations were relatively weak, the findings may provide evidence to help evaluate the reproductive health status and improve occupational environment for women of childbearing age.
As such, our findings provide information on non-Caucasian women and can be compared with those of prior studies on women with infertility or an underlying disease such as PCOS. As this cohort is ongoing, examination of future changes in BMI and lipid profile, with reference to AMH as a parameter, may also be possible.

| CON CLUS ION
Using KNHS data, this study revealed that AMH levels were negatively correlated with BMI and TC and LDL levels at the lower percentiles of AMH levels and positively correlated with HDL levels at the higher percentiles of AMH levels. Such findings suggest the possibility that changes in the lipid profile may influence the AMH levels in women with diminished ovarian function, rather than obesity itself. Although this study has not identified any direct correlations among AMH, BMI and lipid profile, findings suggest that detailed analyses of association between AMH and different components of lipid profile may be beneficial. In-depth further studies will give better information regarding the mechanisms of change in AMH levels along with lipid disturbance.

ACK N OWLED G EM ENTS
We thank all the nurses who participated in the study.

CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interests.

AUTH O R CO NTR I B UTI O N S
S.K. and H.L. contributed to the study design, data interpretation and writing of the manuscript. S.L. contributed to statistical analysis, data interpretation and writing of the manuscript. B.K. and H.J. contributed to data collection and discussion of the results. H.J., O.K. and K.-P.K. contributed to organize the survey and discussion of the results. All authors contributed to manuscript preparations and discussion and approved the final manuscript.

DATA AVA I L A B I L I T Y S TAT E M E N T
The participants of the KNHS were selected from among those who were living in Korea and were between 20-45 years of age. Individual Hyangkyu Lee https://orcid.org/0000-0002-0821-6020