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\begin{document}

\title{Insights into risk factors of medical abortion: A Mendelian
randomization study}



\author[1]{Fengping shao}%
\affil[1]{Sun Yat-sen University First Affiliated Hospital Department of Obstetrics and Gynecology}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
\textbf{Objective}: To explore the potential factors that contribute to
the occurrence of medical abortion(MA) through a Mendelian
randomization(MR) study. \textbf{Design:} Univariate MR(UVMR) and
multivariate MR(MVMR) analyses. \textbf{Setting:} Genetic variants from
European populations. \textbf{Population or Sample:} Instrumental
variants for MA were obtained from FinnGen with 36,232 cases and 149,622
controls. \textbf{Methods:} The inverse variance weighting method was
adopted as the primary analysis. \textbf{Main outcome measures:} The
associations of MA with household income(HI), education attainment(EA),
cognitive performance(CP), risky behaviors: smoking behavior(SB),
alcohol consumption(AC), and reproductive traits: age at first sexual
intercourse(AFS), lifetime number of sexual partners(LNSP), age at first
birth(AFB), age at last birth(ALB). \textbf{Results:} In the UVMR,
increasing HI, EA, AFS and AFB appeared to reduce MA risk(HI, OR=0.569,
P = 7.93E-08; EA, OR=0.875, P = 6.02E-21; AFS, OR=0.439, P = 5.17E-25;
AFB, OR=0.815, P = 5.46E-12), whereas SB and LNSP appeared to add to MA
risk((SB, OR=1.424, P =8.32E-11; LNSP, OR=2.777, P =2.14E-11). In the
MVMR, EA, SB, LNSP and AFS seems to be the predominant risk factor for
MA risk with the independent effect, while HI had no effect after
controlling EA(HI in model 1, OR=0.890, P = 5.78E-01). AFB functioned as
mediators in the causal chain of MA risk reduction by EA, with the
mediated proportion of AFS and AFB being 57.8\%. \textbf{Conclusions:}
Our MR study demonstrated the causal potential of the associations of
HI, EA, SB, LNSP, AFS and AFB with medical abortion.%
\end{abstract}%



\sloppy


\textbf{Insights into risk factors of medical abortion: A Mendelian
randomization study}

\textbf{Fengping shao}

Department of Obstetrics and Gynecology, The First Affiliated Hospital,
Sun Yat-sen University, Zhongshan Second Road 58, Guangzhou 510080,
Guangdong, China

\textbf{Objective} : To explore the potential factors that contribute to
the occurrence of medical abortion(MA) through a Mendelian
randomization(MR) study.

\textbf{Design:} Univariate MR(UVMR) and multivariate MR(MVMR) analyses.

\textbf{Setting:} Genetic variants from European populations.

\textbf{Population or Sample:} Instrumental variants for MA were
obtained from FinnGen with 36,232 cases and 149,622 controls.

\textbf{Methods:} The inverse variance weighting method was adopted as
the primary analysis.

\textbf{Main outcome measures:} The associations of MA with household
income(HI), education attainment(EA), cognitive performance(CP), risky
behaviors: smoking behavior(SB), alcohol consumption(AC), and
reproductive traits: age at first sexual intercourse(AFS), lifetime
number of sexual partners(LNSP), age at first birth(AFB), age at last
birth(ALB).

\textbf{Results:} In the UVMR, increasing HI, EA, AFS and AFB appeared
to reduce MA risk(HI, OR=0.569, P = 7.93E-08; EA, OR=0.875, P =
6.02E-21; AFS, OR=0.439, P = 5.17E-25; AFB, OR=0.815, P = 5.46E-12),
whereas SB and LNSP appeared to add to MA risk((SB, OR=1.424, P
=8.32E-11; LNSP, OR=2.777, P =2.14E-11). In the MVMR, EA, SB, LNSP and
AFS seems to be the predominant risk factor for MA risk with the
independent effect, while HI had no effect after controlling EA(HI in
model 1, OR=0.890, P = 5.78E-01). AFB functioned as mediators in the
causal chain of MA risk reduction by EA, with the mediated proportion of
AFS and AFB being 57.8\%.

\textbf{Conclusions:} Our MR study demonstrated the causal potential of
the associations of HI, EA, SB, LNSP, AFS and AFB with medical abortion.

\textbf{Keywords:} medical abortion, risk, causality, Mendelian
randomization.

\textbf{Introduction}

Abortion is a common healthcare intervention for unintended pregnancy.
Changes in abortion knowledge and attitudes over time that are perceived
as safer rather than more dangerous, easier as opposed to more difficult
to access, acceptable as opposed to wrong, or self-identifying as
pro-choice as opposed to pro-life{[}1{]}. It was estimated that there is
a global average of 73.3 million abortions per year, which corresponded
to 39 abortions for every 1,000 women aged 15-49 years{[}2{]}. However,
abortion may be associated with a series of immediate complications(e.g.
failed attempted abortion, hemorrhage, uterine perforation, cervical
trauma, repeat aspiration, disseminated intravascular coagulation), late
complications(e.g. pelvic inflammatory diseases, retained products of
conception, continuing pregnancy) and poor reproductive outcomes (e.g.
asherman syndrome, subfertility, ectopic pregnancy, miscarriage, preterm
birth and low birth weight){[}3{]}.

It is particularly important to prevent abortions among minors and
adults to the maximum extent possible, which requires extensive
education, improvement of lifestyles and psychological conditions, and a
focus on medical, legal and economic, key direction in health protection
strategy not only of adolescents and the youth, but also of population
as a whole{[}4{]}. Thus, reducing unintended pregnancy and abortion is
an urgently need. Consequently, the risk factors for abortion need to be
explored. Bearak J et al. estimated the relationship between income,
region or legal status of abortion and rate of abortion or unintended
pregnancy by a new statistical model{[}2{]}.

Household income(HI), education attainment(EA), cognition
performance(CP), risky behaviors: smoking behavior(SB), alcohol
consumption(AC), and reproductive traits: age at first sexual
intercourse(AFS), lifetime number of sexual partners(LNSP), age at first
birth(AFB), age at last birth(ALB) may contribute to the occurrence of
unintended pregnancies and may be responsible for forcing women to
choose medical abortion(MA).

Mendelian randomization(MR), using genetic variants as instrumental
variables (IV) from genome-wide association studies(GWAS), provides a
more robust method for assessing a causal effect between a risk factor
and an outcome than does much conventional observational
epidemiology{[}5{]}. Furthermore, a two-sample MR design utilizing
existing genetic data or DNA samples from large-scale genetic
association studies increase the feasibility and cost-efficiency of MR
studies{[}6{]}. MR studies are less susceptible to confounding factors
and reverse causality based on the principle of genetic variants being
randomly distributed at meiosis and fixed at conception.

Multivariable MR (MVMR) is a recent extension to MR, retaining the
benefits of using genetic instruments for causal inference, such as
avoiding bias due to confounding. This approach can be used to estimate
the effect of multiple exposures on an outcome and/or the genetic
variants associated with multiple, potentially related exposures on a
single outcome{[}7{]}.

MR studies have been successfully applied to various causal relationship
analyses between individual behaviors, cognition, education, household
income with various diseases{[}8-11{]}. Hence, we performed univariate
MR(UVMR) and MVMR to explore the causal associations between income,
education, cognition, risky behaviors, reproductive traits and MA by
using newly published summary genetic association statistics from
large-scale genome-wide association studies.

\textbf{Materials and methods}

\textbf{2.1 Study design}

We performed UVMR and MVMR to comprehensively explore the effects of HI,
EA, CP, and risky behaviors: SB, AC, and reproductive traits: AFS, LNSP,
AFB, ALB on MA.

\textbf{2.2 Data Sources}

\textbf{2.2.1 EA and CP}

The variable genetic information for EA represented by ``years of
education'' were extracted from the BOLT-LMM (a mixed-model association
method) summary association statistics released by Loh, P. R. et
al.{[}12{]}, which were available on the GWAS catalogue website
(\emph{https://www.ebi.ac.uk/gwas/}). The GWAS summary dataset for CP
was extracted from Social Science Genetic Association Consortium (SSAGC)
Data Portal (http://thessgac.com). We extracted single-nucleotide
polymorphisms(SNPs) for CP from a meta-analysis combined a published
study of general cognitive ability (n=35,298) conducted by the Cognitive
Genomics Consortium with new GWAS of cognitive performance in the UKB (n
=222,543){[}13{]}.

\textbf{2.2.2 HI, LNSP, ALB}

The variable genetic information for HI, LNSP, ALB in this study was
extracted from the Integrative Epidemiology Unit (IEU) GWAS database
(https://gwas.mrcieu.ac.uk/), which is a publicly available GWAS summary
database{[}14{]}. HI status was represented by ``average total household
income before tax'' of the GWAS summary dataset with 397,751 samples
output from GWAS pipeline using Phesant derived variables from
UKBiobank. The HI was collected using a 5-point scale corresponding to
the average total household income before tax, 1 being less than 18,000
pounds, 2 being18,000--30,999 pounds, 3 being 31,000--51,999 pounds, 4
being 52,000--100,000 pounds, and 5 being greater than 100,000 pounds.
Genetic instruments for lifetime number of sexual partners and age at
last live birth were extracted from the GWAS summary datasets of 378,882
and 170,248 samples, respectively, which were output from GWAS pipeline
using Phesant derived variables from UKBiobank.

\textbf{2.2.3 SB and AC}

The GWAS summary dataset for SB and AC was extracted from GWAS and the
Sequencing Consortium of Alcohol and Nicotine use (GSCAN)
(\emph{https://genome.psych.umn.edu/index.php/GSCAN).}SB represented by
smoking initiation (n =1,232,091) is a binary phenotype, indicating
whether an individual had ever smoked regularly; AC is a continuous
phenotype measured with drinks per week (n=941,280){[}15{]}.

\textbf{2.2.4 AFS and AFB}

The variable genetic information for AFS and AFB were extracted from a
GWAS study on the GWAS catalogue website
(\emph{https://www.ebi.ac.uk/gwas/}), conducted by Mills MC{[}16{]}, in
which 16,426,473 and 10,766,720 SNPs were identified, respectively.

\textbf{2.2.5 GWAS summary statistics of MA from FinnGen}

We used the medical abortion GWAS summary statistics from FinnGen
(https://r9.finngen.fi/). This GWAS consisted of 36,232 cases and
149,622 controls, with about 20,159,347 single nucleotide polymorphisms
(SNPs) were analyzed. In UKB, the GWAS was performed in 4,281
participants by Neale Lab (\emph{http://www.nealelab.is/uk-biobank}).

\textbf{2.3 Mendelian randomization design and instrumental variable
(IV) selection}

Figure 1 showed a brief description of the overall design of the study
for UVMR and MVMR. The potential causal relationships between factors
and abortion were deduced from UVMR and the main risk factors for MA
were identified through different models of MVMR. In MR, genetic
variants are used as IVs to assess the causal effect of an exposure on
an outcome. Genetic variants used as IV need to meet the following three
pivotal assumptions: First, the SNPs are closely associated with
exposure. Second, the SNPs are independent from any confounders of
exposure-outcome causality. Third, the SNPs do not directly affect
outcome, but only have an impact on outcomes via the exposure. We
selected the significant genetic variants that match with the
genome-wide statistical significance threshold
(P \textless{} 5 \selectlanguage{ngerman}×10\textsuperscript{-}8). Using PLINK clustering, the
linkage disequilibrium between these SNPs for each exposure was
estimated on the basis of the 1000 Genomes reference panel confined to
the European population, and SNPs were thresholded at linkage
disequilibrium r\textsuperscript{2} \textless{} 0.001 at a 0-10,000 kb
window to guarantee the independence of the selected genetic variants.
Removing the SNPs for being palindromic with intermediate allele
frequencies. For UVMR, we calculated the F-statistic for each exposure
to assess the strength of each tool in the abortion GWAS, and SNPs with
F statistics \textless{}10 were identified as weak
instruments(Supplementary table 3-12). In addition, we calculated the
conditional F-statistic for assessing instrument strength in two sample
summary MVMR

\textbf{Statistical analyses}

\textbf{2.4.1 UVMR and MVMR analyses}

For UVMR, we used inverse variance weighted MR (IVW MR) along with the
complementary methods: simple mode, MR--Egger, weighted mode and
weighted median to assess the evidence of the causal effects of each
factor on MA, in order to detect the sensitivity of the results to
different patterns of violations of IV assumptions, as consistency of
results across methods strengthens an inference of causality{[}17{]}.

For our MVMR analyses, combining EA along with HI, and risky behaviors
along with HI or EA, and reproductive factors (AFS, LNSP) along with HI
or EA, we used the multivariable inverse-variance weighted (IVW) method
as the primary method to estimate the causal associations since it
possesses the highest statistical power. The multivariable MR-Egger
method was adopted as a complementary method to strengthen the main
results.

\textbf{2.4.2 Heterogeneity and pleiotropy analyses}

To evaluate heterogeneity, we used Cochran heterogeneity test. If there
is heterogeneity, random-effects IVW models are applied; otherwise, the
fixed-effect IVW model is applied. To evaluate pleiotropy, we used
MR-Egger regression (assessing whether the MR-Egger intercept was
different from zero). If causality was estimated in the same direction
among the various MR methods, the conclusions will be considered as
relatively robust causal inferences. Analyses were carried out using
MendelianRandomization version 0.9.0, TwoSampleMR version 0.5.7 and MVMR
version 0.4 in the R environment version 3.6.3 (The R Foundation for
Statistical Computing, Vienna, Austria).

\textbf{Results}

\textbf{3.1 UVMR analyses of risk factors on medical abortion}

We selected 45, 207, 136, 34, 85, 154, 60, 55 and 6 SNPs as genetic
instruments meting genome-wide statistical significance for HI, EA, CP,
AC, SB, AFS, LNSP, AFB and ALB after LD clumping, respectively. The mean
F-statistics for risk factors ranged from 38.28 to 77.30 (see
Supplementary table 1).

Primary IVW method showed that genetic variants associated with
increased HI, EA, AFS, and AFB, were negatively causally related to the
risk of medical abortion(HI, OR=0.569, 95\% CI, 0.463--0.699, P
=7.93E-08; EA, OR=0.875, 95\% CI, 0.851--0.900, P=6.02E-21; AFS,
OR=0.439, 95\% CI, 0.376--0.513, P=5.17E-25; AFB, OR=0.815, 95\% CI,
0.769-0.864, P=5.46E-12). Adversely, SB and LNSP were risk factors for
medical abortion (SB, OR=1.424, 95\% CI, 1.280-1.584, P =8.32E-11; LNSP,
OR=2.777, 95\% CI, 2.059-3.745, P=2.14E-11). We found no evidence for
the causal effect of AC and ALB on the risk of MA(AC, OR=1.297, 95\% CI,
0.980-1.716, P=6.92E-02; ALB, OR=0.659, 95\% CI, 0.408-1.063,
P=8.75E-02). The causal connection between CP and MA was in doubt, as
the causal estimates were directionally inconsistent(see Figure 2).

\textbf{3.2 MVMR analyses of risk factors on medical abortion}

\textbf{Household income}

Using MVMR to assess each factor associated with MA by various model
after controlling/adjusting EA(model 1), or EA plus LNSP(model 2), or
EA, AC, LNSP(model 3), EA, SB, LNSP(model 4), we found increased HI not
associated with risk of MA(HI in model 1, OR=0.896, 95\%CI, 0.610-1.317,
P=5.78E-01; HI in model 2, OR=0.815, 95\%CI, 0.559-1.190, P= 2.90E-01;
HI in model 3, OR=0.775, 95\%CI, 0.531-1.132, P= 1.88E-01; HI in model
4, OR=0.830, 95\%CI, 0.568-1.212, P= 3.34E-01) (see Figure 3).

\textbf{Educational attainment}

Impressively, EA not only presented a significant association with a
decreased risk of MA in UVMR analysis, but was also causally associated
with a decreased risk of abortion in MVMR analysis after adjusting for
different factors(EA in model 1, OR=0.886, 95\%CI, 0.840-0.934,
P=8.44E-06; EA in model 2, OR=0.900, 95\%CI, 0.854-0.948, P= 8.88E-05;
EA in model 3, OR=0.905, 95\%CI, 0.858-0.955, P=2.66E-04; EA in model 4,
OR=0.917, 95\%CI, 0.869-0.967, P=1.53E-03) (see Figure 3).

\textbf{Risky behaviors(SB and AC)}

Consistent with UVMR analysis, in the MVMR analysis, SB contributes to
increased risk of MA, when controlling/adjusting EA, LNSP, HI (SB in
model 4, OR=1.220, 95\% CI, 1.020-1.458, P=1.53E-03). We found that AC
was not associated with the risk of MA after adjusting EA, LNSP, HI (SB
in model 3, OR=1.284, 95\% CI, 0.941-1.790, P=1.41E-01) (see Figure 3).

\textbf{Reproductive factors (AFS, LNSP and AFB)}

In the MVMR analyses, we arrived at a result of causality between later
age at first sexual intercourse and MA: reverse causality, by adjusting
LNSP (AFS in model 5, OR=0.831, 95\%CI, 0.785-0.880, P=1.64E-10). An
increase in LNSP indicated greater likelihood of MA, after
controlling/adjusting AFS(model 5), or EA, HI(model 2), or EA, AC,
HI(model 3), or EA, SB, HI(model 4) (see Figure 3). Increasing years of
schooling are accompanied by increases AFB (effect of EA on AFB, \selectlanguage{greek}β\selectlanguage{english}=
0.377, 95\% CI, 0.344 to 0.409, P = 1.56E-114), so EA may indirectly
have an impact on the risk of MA through AFB (effect of AFB on MA, \selectlanguage{greek}β\selectlanguage{english}=
-0.205, 95\% CI, -0.263 to -0.147, P = 5.46E-12) (Supplementary Table
1).By multiplying the estimate of the effect of EA on AFS, AFB with the
estimate of the effect of AFB on MA, the mediation effect of AFB in the
causal pathway from EA to MA was -0.08, accounting for 57.8\% of the
total effect (Supplementary Table 2).

\textbf{Sensitivity analysis}

In UVMR analyses, in the causality of MA in relation to EA, AFS, and
AFB, heterogeneity was found in the results of the Cochran Q-test.
However, the results analysed by IVW MR along with the complementary
methods: simple mode, weighted mode and weighted median, apart from
MR--Egger, showed that EA, AFS, AFB all had statistically significant
causal relationship with MA, which suggested that these associations
were robust and plausible. Uncertainty existed about the causal
relationship between CP and MA, as the results of this analysis were not
confirmed by all complementary methods. The MR Egger intercepts indicate
directional pleiotropy. In the MVMR analyses, using the primary method
IVW and the complementary method Egger, the consistency of the results
of both can confirm the inference of causality. In addition, there was
no significant evidence for a nonzero intercept of multivariable MR
Egger regression, which also supports the reliability of the results for
multivariable MR analyses.

\textbf{4. Discussion}

\textbf{4.1 Main Findings}

Our findings highlighted an important possible public health benefits
for women that may accrue by moderating the positive and negative
aspects of medical abortion: reducing the incidence of it. Firstly, both
univariate and multivariate results confirmed a robust reverse causality
between MA and EA, AFS, as well as a forward causality between MA and
smoking, LNSP. Secondly, the inverse relationship between HI and MA was
no longer observed in the MVMR analyses after EA or AFS and LNSP after
accounting for EA, or AFS and LNSP, which may be interpreted as a
stronger effect of EA or AFS and LNSP on MA than that of HI on MA.
Thirdly, AFS and AFB mediated EA to reduce the risk of abortion.

\textbf{4.2 Strengths and limitations}

Our work has several important strengths. The MR approach can largely
reduce the risk of reverse causality bias and the effect of confounders
by using genetic variation as instrumental variation to analyse the
potential risks of medical abortion. In addition, complementary methods
and MVMR methods were used for pleiotropy and sensitivity analysis,
resulting in reliable conclusions that EA, SB, LNSP, AFB, AFS were
associated with MA. The causal chains of EA-AFB-MA causality were
elaborated through mediated MR. EA was elaborated to impact MA more than
HI by MVMR. Finally, we limited the bias due to population
stratification by using only genetic variants from European populations
in all datasets.

There are several limitations to our study. Firstly, the strength of the
test for multivariate MR was modest, leading to the possibility of false
positives, which could be explained by the insufficient sample size.
Secondly, there were significant heterogeneity in the analyses
addressing the exposure factors: EA, AFS, and AFB, but the conclusions
were plausible because of the consistency of the results from MVMR and
mediated MR. Thirdly, there is a lack of published GWAS articles on
outcome-medical abortion. A bidirectional study is essential if complete
GWAS statistics for medical abortion become available in the future.
Finally, abortion is a worldwide issue, but we only used summary data
from European populations, resulting in data deficits, and this finding
should be validated in other populations in further study.

\textbf{4.3 Interpretation}

\textbf{4.3.1 Household income and medical abortion}

As the costs of childbearing and parenting increase, households with low
incomes struggle to support women to continue their pregnancies. By
exploring mechanisms influencing the socioeconomic conditions leading to
abortion, it was found that in Russia an increase of \$1,000 in per
capita gross regional product (GRP) led to a reduction in the abortion
rate of 0.075{[}18{]}. Socioeconomic status is a closely associated with
employment status, which is an important factor leading medical abortion
demonstrated by Klutsey EE, et al{[}19{]}.

Based on the data that economic conditions were procyclically associated
with abortion rates from approximately 2004 to 2010, with abortion rates
declining by approximately 5 per cent for every percentage point
increase in the unemployment rate during that period, it can be argued
that economic conditions may be an important factor influencing women's
reproductive choices in the United States{[}20{]}. On the contrary, in
Ghana, the risk of abortion is considerably higher among wealthy
women{[}21, 22{]}.

It should be noted, however, that the associations observed in these
conventional epidemiological studies suffered from the limitations of
modest sample sizes, reverse causality, and the inability to fully
alleviate confounding effects. Using MR method, our study reliably
concluded that HI was negatively associated with the risk of medical
abortion.

\textbf{4.3.2 Educational attainment, cognitive performance and medical
abortion}

Previous observational studies yielded inconsistent results on the
association between educational attainment and medical abortion{[}19,
23, 24{]}. Women with primary and secondary education had higher odds of
terminating their pregnancies compared to women with no education, but
the odds of terminating their pregnancies were not higher among women
with tertiary education{[}24{]}, suggesting that there may not be a
linear causal relationship between education and termination of
pregnancies{[}19, 24{]}.

However, education was observed to reduce the risk of medical abortion
in present study in UVMR analysis using the IVW method, complementary
methods and in MVMR analysis. The causal relationship between education
and induced abortion may be influenced by the economic level of the
region, as the frequency of abortion was high among less educated women
in the developed region of Norway, however, the frequency of abortion
was conversely high among university educated women in the less
developed region of Pakistan{[}25{]}. We found that EA was still
associated with decreased risk of MA, after moderating for HI.
Educational attainment is a proxy for intelligence, and cognitive
ability is often measured through intelligence as well as educational
attainment. Research published by Woodley Menie MA confirmed that there
was no significant linear or quadratic relationship between the number
of abortions and maternal cognitive ability (as measured by educational
attainment or general intelligence){[}26{]}.

\textbf{4.3.3 Risky behaviors and medical abortion}

Women with planned pregnancies are reluctantly choosing abortion because
of concerns that smoking and alcohol consumption may have a negative
impact on the outcome of the pregnancy. Compared with never-smokers,
active smokers in their reproductive years had a corresponding increase
in the risk of spontaneous abortion, stillbirth and ectopic pregnancy by
16\%, 44\% and 43\%, respectively{[}27{]}. A systematic review concluded
that smoking interventions significantly increased birth weight{[}28{]}.
Previous MR studies have found a positive correlation between smoking
initiation and increased risk of pregnancy loss, but did not support any
association between moderate alcohol and pregnancy loss{[}29, 30{]}. The
present study found that smoking behavior motivated women to choose
medical abortion.

Alcohol is known to have a significant negative impact on human health.
Alcohol exposure is associated with an increased risk of congenital
problems, including Fetal Alcohol Spectrum Disorder (FASD) and its most
severe form, Fetal Alcohol Syndrome (FAS), which are long-lasting and
lifelong with no treatment or cure available{[}31{]}. Many pregnant
women choose abortion due to accidental exposure to alcohol before and
during pregnancy, but our MR analyses did not provide valid evidence for
this causal link.

\textbf{4.3.4 Reproductive traits and medical abortion}

Previous studies have provided robust genetic evidence of risk
associations between sexual intercourse at an early age{[}32{]}, having
more sexual partners, and a higher risk of major depressive
disorder{[}33{]}. Intriguingly, studies have found that increased
genetic susceptibility to schizophrenia affects increased numbers of
sexual partners, suggesting that schizophrenia susceptibility increases
mating success in the wider population and may reflect potential
reproductive success{[}34, 35{]}. Hence, reproductive traits factors may
play a non-negligible role in induced abortion. We found that AFS, AFB,
LNSP were causally associated with MA, and that LNSP were associated
with MA independently of EA and HI, furthermore AFB mediated in
education to reduce MA.

\textbf{Conclusions}

Our findings provided strong genetic evidence for a reverse causation of
HI, EA, AFS, and AFB with MA, as well as a positive causation of SB and
LNSP with MA. AFB played a mediating role in the causal link between EA
and MA. These results may provide a strong guiding value for
strengthening public health services and safeguarding women's
reproductive health.

\textbf{Author contribution}

Fengping Shao was responsible for all elements of the study, including
concepts, methods, software, data interpretation, graphical
manipulation, writing-original draft preparation, etc.

\textbf{Acknowledgments}

This research was facilitated by the prior research of the Social
Science Genetic Association Consortium (SSGAC), the Sequencing
Consortium of Alcohol and Nicotine use (GSCAN), the NHGRI-EBI Catalog of
human genome-wide association studies and the Medical Research Council
Integrative Epidemiology Unit (MRCIEU, University of Bristol, UK). We
acknowledge their contributing studies and the participants in those
studies. We express our gratitude for all GWAS shared summary data used
in this study. We also thanks to Ma Yuanlin, M.D. and Xu Zhang of Youhe
AI and Medicine.

\textbf{Funding statement}

Publishing fees are funded by The First Affiliated Hospital of Sun
Yat-sen University.

\textbf{Ethics statement}

The GWAS summary statistics data used in our study are publicly
available, and thus ethical committee approval is not required.

\textbf{Conflict of interest}

The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.

\textbf{Data availability statement}

All original data used in this study are publicly available GWAS summary
data. The sources of these data and the analysed data are presented in
the Materials and Methods and in the Supplementary Tables. Further
inquiries can be directed to the corresponding author.

\textbf{Reference}

1. Kim T and Steinberg JR. Individual changes in abortion knowledge and
attitudes. Soc Sci Med. 2023; 320:115722.

2. Bearak J, Popinchalk A, Ganatra B, Moller AB, Tun\selectlanguage{ngerman}çalp Ö, Beavin C,
Kwok L and Alkema L. Unintended pregnancy and abortion by income,
region, and the legal status of abortion: estimates from a comprehensive
model for 1990-2019. Lancet Glob Health. 2020; 8(9):e1152-e1161.

3. Costescu D and Guilbert É. No. 360-Induced Abortion: Surgical
Abortion and Second Trimester Medical Methods. J Obstet Gynaecol Can.
2018; 40(6):750-783.

4. Vartanova ML. The abortions in minors as a medical social problem.
Probl Sotsialnoi Gig Zdravookhranenniiai Istor Med. 2022; 30(4):580-586.

5. Smith GD and Ebrahim S. 'Mendelian randomization': can genetic
epidemiology contribute to understanding environmental determinants of
disease? Int J Epidemiol. 2003; 32(1):1-22.

6. Pierce BL and Burgess S. Efficient design for Mendelian randomization
studies: subsample and 2-sample instrumental variable estimators. Am J
Epidemiol. 2013; 178(7):1177-1184.

7. Sanderson E. Multivariable Mendelian Randomization and Mediation.
Cold Spring Harb Perspect Med. 2021; 11(2).

8. Davies NM, Dickson M, Davey Smith G, Windmeijer F and van den Berg
GJ. The causal effects of education on adult health, mortality and
income: evidence from Mendelian randomization and the raising of the
school leaving age. Int J Epidemiol. 2023.

9. Zheng X, Yang Y, Chen J and Lu B. Dissecting the causal relationship
between household income status and genetic susceptibility to
cardiovascular-related diseases: Insights from bidirectional mendelian
randomization study. BMC Public Health. 2023; 23(1):749.

10. Cai J, Wei Z, Chen M, He L, Wang H, Li M and Peng Y. Socioeconomic
status, individual behaviors and risk for mental disorders: A Mendelian
randomization study. Eur Psychiatry. 2022; 65(1):e28.

11. Xu S, Liu Y, Wang Q, Liu F, Xu F and Liu Y. Mendelian randomization
study reveals a causal relationship between coronary artery disease and
cognitive impairment. Front Cardiovasc Med. 2023; 10:1150432.

12. Loh PR, Kichaev G, Gazal S, Schoech AP and Price AL. Mixed-model
association for biobank-scale datasets. Nat Genet. 2018; 50(7):906-908.

13. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, Nguyen-Viet
TA, Bowers P, Sidorenko J, Karlsson Linnér R, Fontana MA, Kundu T, Lee
C, et al. Gene discovery and polygenic prediction from a genome-wide
association study of educational attainment in 1.1 million individuals.
Nat Genet. 2018; 50(8):1112-1121.

14. Lyon M, Andrews SJ, Elsworth B, Gaunt TR, Hemani G and Marcora E.
The variant call format provides efficient and robust storage of GWAS
summary statistics. bioRxiv. 2020:2020.2005.2029.115824.

15. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, Datta G,
Davila-Velderrain J, McGuire D, Tian C, Zhan X, Choquet H, Docherty AR,
et al. Association studies of up to 1.2 million individuals yield new
insights into the genetic etiology of tobacco and alcohol use. Nat
Genet. 2019; 51(2):237-244.

16. Mills MC, Tropf FC, Brazel DM, van Zuydam N, Vaez A, Pers TH,
Snieder H, Perry JRB, Ong KK, den Hoed M, Barban N and Day FR.
Identification of 371 genetic variants for age at first sex and birth
linked to externalising behaviour. Nat Hum Behav. 2021; 5(12):1717-1730.

17. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin
C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, et
al. The MR-Base platform supports systematic causal inference across the
human phenome. Elife. 2018; 7.

18. Wang L, Yang LS, Li HR, Cai HY, Huang JX and Malkhazova SM. Abortion
and infant mortality change driven by socioeconomic conditions in
Russia. Geospatial Health. 2021; 16(1):46-53.

19. Klutsey EE and Ankomah A. Factors associated with induced abortion
at selected hospitals in the Volta Region, Ghana. Int J Womens Health.
2014; 6:809-816.

20. Gonzalez F and Quast T. The relationship between abortion rates and
economic fluctuations. Econ Hum Biol. 2022; 46:101120.

21. Nyarko SH and Potter L. Effect of socioeconomic inequalities and
contextual factors on induced abortion in Ghana: A Bayesian multilevel
analysis. PLoS One. 2020; 15(7):e0235917.

22. Owoo NS, Lambon-Quayefio MP and Onuoha N. Abortion experience and
self-efficacy: exploring socioeconomic profiles of GHANAIAN women.
Reprod Health. 2019; 16(1):117.

23. Seidu AA, Ahinkorah BO, Ameyaw EK, Hubert A, Agbemavi W, Armah-Ansah
EK, Budu E, Sambah F and Tackie V. What has women's reproductive health
decision-making capacity and other factors got to do with pregnancy
termination in sub-Saharan Africa? evidence from 27 cross-sectional
surveys. PLoS One. 2020; 15(7):e0235329.

24. Ba DM, Zhang Y, Pasha-Razzak O, Khunsriraksakul C, Maiga M,
Chinchilli VM and Ssentongo P. Factors associated with pregnancy
termination in women of childbearing age in 36 low-and middle-income
countries. PLOS Glob Public Health. 2023; 3(2):e0001509.

25. Eskild A, Nesheim BI, Busund B, Vatten L and Vangen S. Childbearing
or induced abortion: the impact of education and ethnic background.
Population study of Norwegian and Pakistani women in Oslo, Norway. Acta
Obstet Gynecol Scand. 2007; 86(3):298-303.

26. Woodley Menie MA, Sanger J and Meisenberg G. No relationship between
abortion numbers and maternal cognitive ability. Pers Individ Differ.
2017; 104:489-492.

27. Hyland A, Piazza KM, Hovey KM, Ockene JK, Andrews CA, Rivard C and
Wactawski-Wende J. Associations of lifetime active and passive smoking
with spontaneous abortion, stillbirth and tubal ectopic pregnancy: a
cross-sectional analysis of historical data from the Women's Health
Initiative. Tob Control. 2015; 24(4):328-335.

28. Hayes L, McParlin C, Azevedo LB, Jones D, Newham J, Olajide J,
McCleman L and Heslehurst N. The Effectiveness of Smoking Cessation,
Alcohol Reduction, Diet and Physical Activity Interventions in Improving
Maternal and Infant Health Outcomes: A Systematic Review of
Meta-Analyses. Nutrients. 2021; 13(3).

29. Yuan S, Liu J and Larsson SC. Smoking, alcohol and coffee
consumption and pregnancy loss: a Mendelian randomization investigation.
Fertil Steril. 2021; 116(4):1061-1067.

30. Wang Q, Liu F, Tuo Y, Ma L and Feng X. Associations between obesity,
smoking behaviors, reproductive traits and spontaneous abortion: a
univariable and multivariable Mendelian randomization study. Front
Endocrinol (Lausanne). 2023; 14:1193995.

31. Oei JL. Alcohol use in pregnancy and its impact on the mother and
child. Addiction. 2020; 115(11):2148-2163.

32. Wang Z, Lu J, Weng W, Fu J and Zhang J. Women's reproductive traits
and major depressive disorder: A two-sample Mendelian randomization
study. J Affect Disord. 2023; 326:139-146.

33. Lu Z, Sun Y, Liao Y, Kang Z, Feng X, Zhao G, Sun J, Zhang Y and Yue
W. Identifying causal associations between early sexual intercourse or
number of sexual partners and major depressive disorders: A
bidirectional two-sample Mendelian randomization analysis. J Affect
Disord. 2023; 333:121-129.

34. Lawn RB, Sallis HM, Taylor AE, Wootton RE, Smith GD, Davies NM,
Hemani G, Fraser A, Penton-Voak IS and Munafò MR. Schizophrenia risk and
reproductive success: a Mendelian randomization study. R Soc Open Sci.
2019; 6(3):181049.

35. Nettle D and Clegg H. Schizotypy, creativity and mating success in
humans. Proc Biol Sci. 2006; 273(1586):611-615.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Schematic diagram of MR study design -- potential
risk factors for MA. Abbreviations: MA, medical abortion; HI, household
income; EA, educational attainment; SB, smoking behaviors; AC, alcohol
consumption; AFS, age at first sexual intercourse; LNSP, lifetime number
of sexual partners; AFB, age at first birth; ALB, age at last birth.

\textbf{Figure 2.} UVMR results for associations between potential
factors and MA. Abbreviations: MA, medical abortion; HI, household
income; EA, educational attainment; SB, smoking behaviors; AC, alcohol
consumption; AFS, age at first sexual intercourse; LNSP, lifetime number
of sexual partners; AFB, age at first birth; ALB, age at last birth.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3.} Results of various MVMR models with education as the
primary risk factor, controlling for other factors. Abbreviations: MA,
medical abortion; HI, household income; EA, educational attainment; SB,
smoking behaviors; AC, alcohol consumption; AFS, age at first sexual
intercourse; LNSP, lifetime number of sexual partners; AFB, age at first
birth; ALB, age at last birth.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

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\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

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