Academic Editor: Jane A. Leopold
Background: Higher age-specific circulating anti-Müllerian hormone
(AMH) levels have been linked to a lower risk of cardiometabolic outcomes.
However, whether AMH has a casual role in the etiology of these diseases is
unknown. The objective of this study was therefore to explore if circulating AMH
levels have a causal effect on risk of coronary artery disease (CAD), ischemic
stroke and type 2 diabetes (T2D) in women, using a two-sample Mendelian
randomization (MR) approach. Methods: We used four single nucleotide
polymorphisms (SNPs) from the most recent AMH GWAS meta-analysis as instrumental
variables. Summary-level data for CAD (n = 149,752; 11,802 cases), ischemic
stroke (n = 17,541; 4678 cases) and T2D (n = 464,389; 30,052 cases) were
extracted from the UK Biobank, the Stroke Genetics Network, and DIAMANTE
consortia, respectively. To assess the presence of potential pleiotropy we tested
the association of the four AMH SNPs, both individually and combined in a
weighted genetic risk score, with a range of cardiovascular risk factors and
intermediate traits using UK Biobank data. Results: MR estimates, i.e.,
inverse variance-weighted odds ratios (OR
In women, anti-Müllerian hormone (AMH) is expressed by early antral stage ovarian follicles [1]. AMH levels decline with age, and circulating levels become undetectable after menopause, when the ovarian reserve is depleted. Consequently, AMH can be used as a marker for reproductive aging [2]. Accelerated female reproductive aging, often quantified as an earlier age at menopause, has been linked to a higher risk of cardiometabolic diseases [3, 4, 5], but the causal mechanisms underlying these associations remain to be established. Based on recent observational studies that provided evidence for an association between higher circulating AMH levels and lower risk of cardiovascular disease [6], and diabetes [7], in women, it has been postulated that AMH may have a causal role in the etiology of these diseases. However, a potential causal effect of AMH on risk of cardiometabolic disease is difficult to establish in observational studies. In Mendelian randomization (MR) studies, genetic variants are used as instrumental variables for the risk factor of interest, to estimate causal effects on outcomes that are not influenced by confounding, and are not altered by disease occurrence (reverse causation) [8]. In two-sample MR, summary-level data from independent genome-wide association studies (GWASs) for the exposure and outcome(s) are used instead of individual-level data from one study population. Consequently, two-sample MR studies generally include data on a larger number of participants, which increases statistical power to detect a causal association [9].
For AMH, we have recently identified four genetic variants in ~7000 premenopausal women [10]. Using these genome-wide significant genetic variants for AMH levels, we aimed to explore if circulating AMH levels could have a causal effect on risk of cardiometabolic disease in women. Specifically, we estimated causal effects of AMH on coronary artery disease (CAD), ischemic stroke and type 2 diabetes (T2D).
Recently, we have identified four single nucleotide polymorphisms (SNPs) in an
AMH GWAS meta-analysis that included data of 7049 premenopausal women of European
ancestry [10]. One of these variants is a missense variant located in the
AMH gene (rs10417628). However, for this SNP the possibility that it is
associated with AMH levels through impaired detection by specific AMH assays,
instead of reduced AMH bioactivity, could not be excluded [10, 11]. Therefore,
and because inclusion of multiple genetic instruments increases statistical power
to detect a causal association [12], we included all four SNPs associated with
circulating AMH levels in premenopausal women at genome-wide significance
(p
We included summary-level data for genetic associations of the four AMH variants with CAD, ischemic stroke and T2D in women of European descent from the UK Biobank [13], the Stroke Genetics Network (SiGN) [14, 15] and DIAMANTE [16] consortia, respectively.
The UK Biobank is a large, population-based cohort established to study the interrelationships between environment, lifestyle, and genes. The UK Biobank (https://www.ukbiobank.ac.uk) recruited over 500,000 men and women between 2006 and 2010 [13], aged 37 to 73 years at baseline. The UK Biobank was approved by the North West Multi-Centre Research Ethics Committee, and all participants provided written informed consent to participate in the study. Prevalence of CAD was determined using self-reported data as per prior analysis [17]. Additionally, the Hospital Episode Statistics “Spell and Episode” category with hospital in-patient stay diagnoses was used. CAD was defined using the International classification of disease (ICD) version 9 codes 410, 412 and 414, ICD version 10 codes I21-I25, Z951 and Z955, and the Office of Population Censuses and Surveys Classification of Interventions and Procedures, version 4 (OPCS-4) codes K40-K46, K49, K50 and K75. Controls were excluded if their father, mother, or sibling was reported to suffer from any heart disease in order to reduce biological misclassification. CAD GWAS analyses were performed using linear mixed models implemented in BOLT-LMM software [18] (v2.3.1), and adjusted for age at inclusion, genotyping array (UK Biobank Axiom or UK BiLEVE Axiom), and the first 30 principal components provided by the UK Biobank. BOLT-LMM effect estimates and standard errors were transformed to log odds ratios and corresponding standard errors as previously described [19].
The SiGN consortium is a previously compiled dataset consisting of 14,549 ischemic stroke cases of several cohorts and publicly available controls [15]. The SiGN study population has been described previously, together with details on genetic quality control and genotype imputation methodology [14]. Different procedures were used to establish ischemic stroke diagnosis, which have been described into detail elsewhere [14]. Female sex was defined as the presence of XX chromosomes. GWAS analyses for ischemic stroke were performed using BOLT-LMM [18] (v2.3.1), and adjusted for population stratification, by inclusion of a genetic relation matrix, and age. BOLT-LMM estimates for ischemic stroke were also transformed to log odds ratios and corresponding standard errors using a previously published approximation [19].
The DIAMANTE consortium included 74,124 T2D cases and 824,006 controls from 32 GWASs and has been described into detail elsewhere [16]. Studies included in DIAMANTE based T2D diagnosis on different criteria, including but not limited to, fasting glucose and HbA1c levels, hospital discharge diagnosis, use of diabetes medication, and self-report. For the current study, we requested results from sex-specific GWAS analyses, which were adjusted for population stratification and study-specific covariates [16].
There was no overlap in participants between the UK Biobank and the AMH GWAS meta-analysis. However, there may be some overlap in participants between SiGN and DIAMANTE and the AMH GWAS meta-analysis, since all three studies included participants from the Nurses’ Health Study (maximum overlap n = 642). An additional 127 participants of EPIC-Interact [20] may overlap between the AMH GWAS meta-analysis and DIAMANTE (total maximum overlap n = 769). Due to the nature of both data from the Nurses’ Health Study and EPIC-Interact study included in SiGN and DIAMANTE meta-analyses, i.e., GWAS summary-level data, we were not able to identify potential overlapping individuals. As a result, overlapping participants were not excluded.
All individual studies that were included in the GWAS meta-analyses for AMH, stroke and diabetes, and the UKBiobank cohort, received ethical approval from qualified institutional boards and all included study participants provided informed consent.
We calculated MR estimates for the individual SNPs in relation to each disease
outcome using the Wald ratio method. Individual Wald ratio estimates were
meta-analyzed using a random-effects inverse-variance weighted (IVW) method. To
assess the strength of included genetic variants for AMH we calculated
F-statistics corresponding to the IVW analyses, using the proportion of variance
in AMH explained by the genetic variants, the sample size of the outcome GWASs,
and the number of variants included [21]. We compared overall MR estimates (i.e.,
IVW estimates) to SNP-specific MR estimates (i.e., Wald ratio estimates) since
inconsistent estimates are indicative of horizontal pleiotropy. In addition, we
tested for heterogeneity in causal effects amongst the individual SNPs using
Cochrane’s Q statistics, and performed leave-one-out sensitivity analyses to
assess the influence of outlying variants. For stroke, we examined whether causal
associations were affected by exclusion of early onset cases (age
To assess potential pleiotropy (i.e., whether genetic variants are associated
with multiple traits) we tested if the four AMH SNPs, either individually or
combined as a genetic risk score, were associated with a range of traits in the
UK Biobank. For this analysis, we selected 44 traits that were either likely to
be confounders or that could affect cardiometabolic health through pathways not
involving AMH (i.e., horizontal pleiotropy; e.g., active smoking and body mass
index), or traits that could be intermediates in the causal pathway between AMH
and cardiometabolic disease (i.e., vertical pleiotropy; e.g., markers for
subclinical atherosclerosis and glycemic traits). An overview of the 44
investigated traits has been included in Supplementary Table 2.
Depending on the type of trait linear or logistic regression models were fitted.
We created a heatmap of z-scores aligned with higher genetically predicted AMH
levels to visually represent potential pleiotropy. To correct for multiple
testing, we considered false discovery rate (FDR) values
The included number of cases and controls for each outcome are presented in Table 1 (Ref. [13, 14, 15, 16]).
Outcome | Study [Ref] | Number of cases | Number of controls | Age | Ancestry |
Coronary artery disease | UK Biobank [13] | 11,802 | 137,950 | Cases: 62.0 (6.3)* | 93.2% White British |
Controls: 55.9 (8.3)* | |||||
Ischemic stroke | SiGN [14, 15] | 4678 | 12,863 | Cases |
European |
Age at onset |
4247 | 12,863 | Cases | ||
Type 2 diabetes | DIAMANTE [16] | 30,053*** | 434,336*** | Unavailable | European |
Abbreviations: SiGN, Stroke Genetics Network.
*Mean (sd). **Median. ***These numbers were extracted from the DIAMANTE GWAS meta-analysis manuscript [16]; the actual number of female cases and controls for whom data on the four AMH SNPs was available was not provided. |
We did not find evidence for a causal association between circulating AMH levels
and CAD risk (OR
Outcome | Method | F-statistic | Odds Ratio | 95% CI | p-value |
Coronary artery disease | IVW | 558.5 | 1.13 | 0.95–1.35 | 0.18 |
Wald ratio estimate for rs10417628 (AMH) | 1.06 | 0.82–1.37 | 0.65 | ||
Wald ratio estimate for rs13009019 (TEX41) | 1.43 | 1.07–1.91 | 0.02 | ||
Wald ratio estimate for rs16991615 (MCM8) | 1.15 | 0.85–1.57 | 0.37 | ||
Wald ratio estimate for rs11683493 (CDCA7) | 0.92 | 0.67–1.26 | 0.60 | ||
Ischemic stroke | IVW | 65.4 | 1.11 | (0.83–1.49) | 0.48 |
Wald ratio estimate for rs10417628 (AMH) | 1.31 | (0.78–2.20) | 0.30 | ||
Wald ratio estimate for rs13009019 (TEX41) | 0.97 | (0.55–1.70) | 0.90 | ||
Wald ratio estimate for rs16991615 (MCM8) | 0.85 | (0.46–1.59) | 0.62 | ||
Wald ratio estimate for rs11683493 (CDCA7) | 1.35 | (0.71–2.56) | 0.35 | ||
Type 2 diabetes | IVW | 1732.1 | 0.98 | (0.87–1.10) | 0.74 |
Wald ratio estimate for rs10417628 (AMH) | 1.01 | (0.83–1.23) | 0.93 | ||
Wald ratio estimate for rs13009019 (TEX41) | 0.91 | (0.72–1.15) | 0.43 | ||
Wald ratio estimate for rs16991615 (MCM8) | 0.99 | (0.77–1.26) | 0.93 | ||
Wald ratio estimate for rs11683493 (CDCA7) | 1.01 | (0.79–1.30) | 0.93 | ||
Odds ratio and 95% CI are per 1 unit increase in inverse normally transformed
AMH.
AMH, anti-Müllerian hormone; IVW, inverse variance weighted. |
The IVW estimate did not provide clear evidence for a causal association between
higher genetically predicted AMH levels and risk of ischemic stroke (OR
Exclusion of women younger than 50 years of age at stroke diagnosis attenuated
IVW estimates (OR
IVW MR estimates did not support an association between genetically predicted
AMH and T2D (OR
Associations between the individual AMH SNPs, and the weighted genetic risk score including all four variants, and possible pleiotropic traits are presented in Fig. 1. After correction for multiple testing, we observed a positive significant association between the SNP in the MCM8 locus (rs16991615) and age at menopause and age at menarche. The weighted genetic risk score was only associated with age at menopause. We did not find associations with intermediate traits on the causal pathway between AMH and cardiometabolic health, such as subclinical atherosclerosis or HbA1c and glucose levels.
Heatmap of associations between the individual genetic variants
for AMH and the weighted genetic risk score (AMH GRS) and 44 traits of the UK
Biobank. The heatmap presents z-scores for 44 UK Biobank traits that correspond
to higher genetically predicted AMH levels. Only associations between rs16991615
(MCM8 locus) and age at menopause and age at menarche, and the
association between the AMH GRS and age at menopause were statistically
significant at false discovery rate
Our MR analyses did not provide evidence for causal effects of circulating AMH levels on the development of CAD, ischemic stroke and T2D in women. However, due to the limited number of genetic instruments, these findings should be interpreted with due caution.
Genetic instruments used for MR analyses have to meet the following assumptions
to yield valid MR estimates: (1) genetic variants have to be strongly associated
with the exposure; (2) genetic variants cannot be associated with confounders of
the studied associations; and (3) genetic variants cannot affect the studied
outcomes through mechanisms that do not involve the exposure [25]. To meet the
first criterion we only included SNPs associated with circulating AMH levels at
genome-wide significance as genetic instruments. We also quantified the strength
of the combination of these four SNPs through calculation of F-statistics for
each outcome (558.5 for CAD, 65.4 for ischemic stroke, and 1732.1 for T2D).
Although a F-statistic higher than 10 is considered to indicate a strong genetic
instrument, the estimated F-statistics may be overestimated due to the use of the
R
We did assess potential pleiotropy of the genetic instruments for AMH with 44 traits in the UK Biobank. These analyses did not provide evidence for associations of the genetic variants, either individually or combined into a genetic risk score, with intermediate traits on the causal pathway between AMH and cardiometabolic health, such as subclinical atherosclerosis or HbA1c and glucose levels. We also did not observe associations between genetically predicted AMH and potential confounders like body mass index and active smoking. Heterogeneity tests and leave-one-out analyses did not support bias due to horizontal pleiotropy, although their results should also be interpreted with caution due to the limited number of SNPs. Our results suggested that higher genetically predicted AMH levels are associated with age and menarche and age at menopause. Indeed, previous GWASs identified rs16991615 at the MCM8 locus as genetic variant for age at menopause [27, 28]. Whether these associations reflect horizontal or vertical pleiotropy remains difficult to disentangle since AMH, age at menarche and age at menopause are all linked to the functional ovarian reserve [27, 29, 30].
Potential overlap in study participants between the exposure and outcome GWASs from which summary-level data were used, could bias MR estimates towards observational associations [31]. For both SiGN and DIAMANTE, numbers of overlapping participants were small compared to the total numbers in the study (642 vs 17,541 and 769 vs 464,389, respectively). We assessed the magnitude of potential bias due to sample overlap in the current study using a web application developed by Burgess et al. [31] (https://sb452.shinyapps.io/overlap) , and observed that, if anything, this bias would have been minimal for both ischemic stroke and T2D. Moreover, MR estimates for each outcome indicated null effects, whereas previous observational studies showed that higher AMH levels were associated with risk of cardiometabolic disease [6, 7]. Therefore, the effect of this type of bias on the MR estimates seems negligible.
We are aware of one previous MR study on AMH, looking at the association with ischemic heart disease in men and women [32], using genetic variants that were significant in male adolescents only [33]. In contrast with our results, this MR provided some evidence for an association of higher genetically predicted AMH levels with a lower risk of ischemic heart disease in women and men combined, yet the validity of this finding is questionable since the used genetic instruments violated the first MR assumption of being strongly related to AMH levels in females. In addition, no details about possible heterogeneous effects across the individual SNPs were described.
Our findings are not in agreement with observational studies that found that women with higher age-specific AMH levels had a lower risk of these cardiometabolic diseases [6, 7]. On the other hand, previous MR studies investigating the causal effect of age at menopause, another indicator for reproductive aging, on CAD also did not find evidence for a causal association [34, 35]. To date, no MR studies investigated whether age at menopause may be causally associated with stroke or diabetes.
An explanation for the discrepancy between the observational and MR findings for the relation between AMH, but also other indicators of reproductive aging, and cardiometabolic disease may be residual confounding by (biological) aging. Given its role in ovarian follicle development and the expression of AMH in these follicles, lower AMH levels are strongly correlated with higher age in women. Also, decelerated reproductive aging, corresponding to higher age-specific AMH levels, has been linked to longevity [36, 37]. Future studies in which both circulating AMH levels and markers for biological aging (e.g., DNA methylation) are available could explore this hypothesis.
Another explanation for the discrepancy with observational findings may be that
signaling factors that are either upstream or downstream of AMH in the same
pathway, instead of AMH itself, are causally associated with risk of
cardiovascular disease. Among the suggested upstream regulators of AMH is BMP4
[38], and reported downstream targets of AMH include NF-
In conclusion, our results do not support a causal effect of circulating AMH levels on CAD, ischemic stroke and T2D in women. These results should be interpreted carefully, since bias towards the null due to weak instrument bias in our analyses cannot be excluded.
RMGV, CHvG, YTvdS and NCOM were involved in the initial study design. JvB, MAS and PvdH provided advice on (part of) the analyses. RMGV, JvB, MAS and AM carried out analyses. RMGV drafted the article. All authors critically reviewed and approved the final version of the manuscript.
This manuscript only includes summary-level data from previously published GWAS (meta-analyses). All individual studies that were included in the GWAS meta-analyses for AMH, stroke and diabetes, and the UKBiobank cohort, received ethical approval from qualified institutional boards and all included study participants provided informed consent.
This research has been conducted using the UK Biobank Resource under Application Numbers 29916, 12006, and 15031.
RMGV was funded by the Honours Track of MSc Epidemiology, University Medical Center Utrecht with a grant from the Netherlands Organization for Scientific Research (NWO) (Grant number: 022.005.021). JvB is supported by R01NS100178 from the U.S. National Institutes of Health.
As of January 2020, AM is an employee of Genentech, and a holder of Roche stock. The other authors declare no conflict of interest.
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