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\begin{document}

\title{Persistent Organic Pollutant exposure as a risk factor of Gestational
Diabetes Mellitus: A systematic review and meta-analysis.}



\author[1]{Malak Kouiti}%
\author[1]{María Ángeles Castillo-Hermoso}%
\author[2]{Ibtissam Youlyouz-Marfak}%
\author[1]{Khalid Khan}%
\author[3]{Shakila Thangaratinam}%
\author[1]{Rocío Olmedo-Requena}%
\author[4]{Javier Zamora}%
\author[1]{José Juan Jiménez-Moleón}%
\affil[1]{Universidad de Granada}%
\affil[2]{Universite Hassan 1er}%
\affil[3]{University of Birmingham}%
\affil[4]{Centro de Investigacion Biomedica en Red de Epidemiologia y Salud Publica}%


\vspace{-1em}



  
  \date{June 08, 2024}


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\selectlanguage{english}
\begin{abstract}
\textbf{Background:} The findings of individual epidemiological studies
that suggest an association between some Persistent Organic Pollutants
(POPs) and Gestational Diabetes Mellitus (GDM) are inconclusive.
\textbf{Objectives:} To estimate the strength of the association between
POPs exposure and GDM in a systematic review with meta-analysis.
\textbf{Search strategy:} MEDLINE, Scopus, and Web of Science were
searched until 2022. \textbf{Selection criteria:} Cohort and
case-control studies analyzing the association between POPs and GDM in
healthy pregnant women. \textbf{Data collection and analysis:} Quality
was assessed using QUIPS scale and standardized mean differences (SMD)
and 95\% confidence intervals (CI) was pooled using random-effect model.
\textbf{Main results:} Fourteen articles including 11,422 participants
were selected. The risk of bias of included studies was high in 4
(28.6\%), moderate in 9 (64.3\%) and low in 1 (7.14\%). Only six POPs
showed a significative SMD between GDM cases and controls:
Perfluorobutanesulfonic acid (PFBS) 0.33 (95\% CI 0.23, 0.43; I2=0\%);
Perfluorodecanoic acid (PFDA) -0.11 (95\% CI -0.20, -0.01, I2 = 0.0\%);
2,2',3,4,4',5,5'-Heptachlorobiphenyl (PCB 180) 0.37 (95\% CI 0.19, 0.56;
I2=25.3\%); 2,2',4,4',5-Decabromodiphenyl ether (BDE 99) 0.36 (95\% CI
0.14, 0.59; I2=0\%); 2,2',4,4',6-Decabromodiphenyl ether (BDE 100) 0.42
(95\% CI 0.19, 0.38; I2=0\%); and, Hexachlorobenzene (HCB) 0.22 (95\% CI
0.01, 0.42, I2=39.6\%). For other POPs, no statistically significant
association was observed. \textbf{Conclusion:} The available evidence is
variable on quality and results were heterogeneous making impossible to
establish a clear association between POPs exposure and risk of GDM.
Improve the methodology of epidemiological studies assessing the
association of POPs and risk of adverse clinical outcomes are needed.%
\end{abstract}%



\sloppy


\textbf{Persistent Organic Pollutant exposure as a risk factor of
Gestational Diabetes Mellitus: A systematic review and
meta-analysis.}Malak Kouiti \textsuperscript{1,2}, Mar\selectlanguage{ngerman}ía Ángeles
Castillo-Hermoso\textsuperscript{1}, Ibtissam Youlyouz-Marfak
\textsuperscript{2}, Khalid Saeed Khan\textsuperscript{1,3}, Shakila
Thangaratinam\textsuperscript{4,5}, Rocío
Olmedo-Requena\textsuperscript{1,3,7}, Javier
Zamora\selectlanguage{english}\textsuperscript{3,4,5+}, and Jose Juan
Jimenez-Moleon\selectlanguage{english}\textsuperscript{1,3,7*+}.\textsuperscript{1} Universidad
de Granada, Departamento de Medicina Preventiva y Salud Publica,
Granada, Spain\textsuperscript{2} Laboratory of Health Sciences and
Technologies, Higher Institute of Health Sciences, Hassan First
University of Settat, Settat 26000, Morocco\textsuperscript{3} Consorcio
Centro de Investigacion Biomedica en Red de Epidemiologia y Salud
Publica (CIBERESP), Madrid, Spain\textsuperscript{4} Institute for
Metabolism and Systems Research, University of Birmingham, Birmingham,
United Kingdom\textsuperscript{5} Birmingham Women's and Children's
National Health Service Foundation Trust, Birmingham, United
Kingdom\textsuperscript{6} Clinical Biostatistics Unit, Hospital Ramon y
Cajal (IRYCIS), Madrid, Spain\textsuperscript{7} Instituto de
Investigacion Biosanitaria ibs.GRANADA, Granada,~Spain Correspondence
author: Jose Juan Jimenez Moleon. Universidad de Granada, Departamento
de Medicina Preventiva y Salud Publica, Granada, Spain. Email:
\emph{jjmoleon@ugr.es}.\selectlanguage{english}\textsuperscript{+}These authors contributed
equally to this work.\textbf{Running title:~}Persistent Organic
Pollutant~and~Gestational Diabetes~Mellitus.~\textbf{Word account:
3210ABSTRACTBackground:} The findings of individual epidemiological
studies that suggest an association between some Persistent Organic
Pollutants (POPs) and Gestational Diabetes Mellitus (GDM) are
inconclusive.\textbf{Objectives:} To estimate the strength of the
association between POPs exposure and GDM in a systematic review with
meta-analysis.\textbf{Search strategy:} MEDLINE, Scopus, and Web of
Science were searched until 2022.\textbf{Selection criteria:} Cohort and
case-control studies analyzing the association between POPs and GDM in
healthy pregnant women.\textbf{Data collection and analysis:} Quality
was assessed using QUIPS scale and standardized mean differences (SMD)
and 95\% confidence intervals (CI) was pooled using random-effect
model.\textbf{Main results:} Fourteen articles including 11,422
participants were selected. The risk of bias of included studies was
high in 4 (28.6\%), moderate in 9 (64.3\%) and low in 1 (7.14\%). Only
six POPs showed a significative SMD between GDM cases and controls:
Perfluorobutanesulfonic acid (PFBS) 0.33 (95\% CI 0.23, 0.43; I2=0\%);
Perfluorodecanoic acid (PFDA) -0.11 (95\% CI -0.20, -0.01, I2 = 0.0\%);
2,2',3,4,4',5,5'-Heptachlorobiphenyl (PCB 180) 0.37 (95\% CI 0.19, 0.56;
I2=25.3\%); 2,2',4,4',5-Decabromodiphenyl ether (BDE 99) 0.36 (95\% CI
0.14, 0.59; I2=0\%); 2,2',4,4',6-Decabromodiphenyl ether (BDE 100) 0.42
(95\% CI 0.19, 0.38; I2=0\%); and, Hexachlorobenzene (HCB) 0.22 (95\% CI
0.01, 0.42, I2=39.6\%). For other POPs, no statistically significant
association was observed.\textbf{Conclusion:} The available evidence is
variable on quality and results were heterogeneous making impossible to
establish a clear association between POPs exposure and risk of GDM.
Improve the methodology of epidemiological studies assessing the
association of POPs and risk of adverse clinical outcomes are
needed.\textbf{Funding:}This research did not receive any specific grant
from funding agencies in the public, commercial, or not-for-profit
sectors.\textbf{Keywords:} persistent organic pollutants, exposure, risk
factor, gestational diabetes mellitus, systematic review, meta-analysis.
Introduction Persistent Organic Pollutants (POPs), such as
Organochlorine Pesticides (OCPs) and Polychlorinated Biphenyls (PCBs),
are highly lipophilic compounds and peculiarly persistent and resistant
to biodegradation. Due to their long half-life, POPs have the ability to
bioaccumulate in the environment, food, and organisms. The principal
pathway for human exposure is through dietary intake. However,
occupational exposure and indoor inhalation can also be alternative
pathways. Chronic exposure to POPs can be related to ill health, even in
low doses. In adults, high specimen POPs level was associated with a
high risk of carcinogenic, neurological, endocrine, and metabolic
conditions. Several POPs, such as hexachlorobenzene (HCB),
dichlorodiphenyl-dichloroethylene (p,p'-DDE), and PCBs have been
described as potential risk factors for diabetes mellitus type 2. During
pregnancy, POPs exposure increases the risk of several outcomes such as
miscarriage, preterm birth, and low birth weight. However, findings
related to Gestational Diabetes Mellitus (GDM) tend to show more
discrepancies. Zhang et al describe a positive association between PCB
52 and GDM, and no association for PCB 138, 153 and 180. However, Jaack
et al cohort study show a negative association between PCB 138, 153 and
180 with GDM. Regarding PFAS, Yan et al systematic review support that
PFAS increase the risk for GDM, while no association was affirmed by Gao
et al. This disparity may be caused by population characteristics and
selection biases, small sample sizes, lipid adjustment, POPs measurement
procedures, the use of different definitions for GDM, and methodological
issues related to the adjustment for confounding factors. Furthermore,
it would be necessary to ensure that exposure assessment precedes the
outcome's occurrence to reduce possible bias, especially as blood
concentrations of POPs may change throughout pregnancy. Our search for
systematic reviews including prospective studies found only one that was
restricted to DDTs, and no relationship was found between exposure and
GDM. Other POPs were not explored in this review. Therefore, we aimed to
comprehensively explore the association between POPs and GDM using a
systematic review with meta-analysis of cohort and case-control studies.
Methods This systematic review and meta-analysis protocol was previously
registered in PROSPERO (www.crd.york.ac.uk/PROSPERO, CRD42022303450). It
was reported according to the 2020 update of Preferred Reporting Items
for Systematic Reviews and Meta-analysis (PRISMA) statement. Ethical
approval was not required due to the study design. Eligibility criteria
Eligibility criteria was defined a priori according to the PECOS
statement (P: population, E: exposure, C: comparators, O: outcome and S:
study design). More information about these criteria is shown in Table
S1. The selection criteria were: (1) Cohort, case-control studies, and
hybrid studies (nested case-control studies and case-cohort studies);
(2) Based on women of childbearing age; (3) Analyzing the relationship
between the individual contamination levels of POPs and the incidence of
GDM; (4) Published from the inception of the database used for the
search until June 2022. All cross-sectional studies, book chapters, and
conference communications were excluded. Information source and research
strategy A systematic search was conducted in the major biomedical
databases: MEDLINE, Scopus, and Web of Science. MeSH terms and/or free
keywords were combined according to the used source. The following MeSH
terms were used for the research: Organochlorinate, organochlorine,
chlorinated, Persistent organic pollutant, POP, persistent pesticides,
persistent toxic substances, Per-and polyfluoroalkyl substances, PFAs,
Polybrominated diphenyl ethers, PBDEs, Polychlorinated biphenyls, PCBs,
Hexachlorobenzene, HCB, Dichlorodiphenyltrichloroethane, DDT, p,p'DDT,
Dichlorodiphenyldichloroethylene, DDE, p,p'DDE,
Dichlorodiphenyldichloroethane, DDD, p,p'DDD, Gestational diabetes
mellitus, gestational diabetes, GDM Additionally, the reference lists of
selected reviews were hand-searched. Details of search results are
provided for each data resource in (Appendix S2). Two investigators (MK
and MACH) independently conducted the search and identified the eligible
articles. After duplicated articles were removed, a first screening by
title and abstract was done. Articles that met inclusion criteria were
assessed by reading the full text. Disagreement or doubt in the
selection of studies was resolved through discussion with senior
reviewers (JJJM and JZ). Data extraction and quality assessment Selected
articles were reviewed by MK and MACH independently. From each article
the following information was extracted in a standardized form: 1) Basic
data: authors, publication year, study period, country, and research
funding. 2) Study characteristics: type of study design, sample method,
sample size, selection criteria, characteristics of the participants,
and compliance with ethical principles. 3) Exposure data: type of
examined POPs, biomarkers used to assess contamination level,
gestational age for the sample collection, analytic methodology, limit
of detection (LOD) or limit of quantification (LOQ), unit of measurement
for POPs, and lipid adjustment for the final determinations. 4) Outcome
data: The criteria used for the diagnosis of GDM were collected
(National Diabetes Data Group criteria, Carpenter-Coustan criteria,
International Association of Diabetes and Pregnancy Study Groups
criteria, and World Health Organization criteria). 5) Descriptive
measurements of POPs by comparison groups and analytic results: mean and
standard deviation (SD), median and interquartile range (IQR) or
geometric mean (GSD) to describe the levels of POPs; and relative risk
(RR), odds ratio (OR) and their 95\% confidence interval as association
measures. Confusion factors used for adjustment analyses were also
collected. The authors have been contacted by e-mail in case of missing
information. Risk of bias and methodological quality of each included
study in the systematic review were evaluated independently by two
researchers (MK and MACH) using the Quality in Prognosis Studies scale
(QUIPS). The following describe the six domains with their respective
issues to consider for judging the risk of bias in QUIPS: 1) Study
participation: factors such as the source of target population, method/s
used to identify the population, recruitment period, inclusion and
exclusion criteria, adequate study participation, and baseline
characteristics were evaluated. 2) Study attribution related to
strategies to avoid losses. For example, the number of attempts to
collect information on participants who dropped out, the reasons for the
losses, and the potential impact of subjects lost to follow-up on the
results based on outcome and prognostic factor/s information on those
lost to follow-up. 3) Information about prognostic factors was
collected. 4) Outcome measurement collection: definition of the outcome
(gestational diabetes or not), valid and reliable measurement of
outcome, method and setting of outcome measurement. 5) Collection of
confounding factors and their characteristics, such as definition used
for the confounding factor, methods and setting of confounding
measurement, validity and reliability of the measurements, methods used
for working with missing data, and appropriate strategies to avoid or
eliminate the effect of confounding factors. 6) Finally, information
about statistical analysis and reporting, including the presentation of
the analytical strategy, models of development strategy, and reporting
of results. In addition to the guidelines provided by QUIPS scale, to
judge the risk of bias in each item, supplementary comments were
developed to facilitate the consensus. Studies were classified as
follow: a) Low risk of bias. Requires at least five domains judged as
low risk of bias and none classified as high risk of bias; b) Moderate
risk of bias for those cases with: i) five items classified as low risk
of bias and one item judged as high risk of bias, or ii) two items
evaluated as moderate risk of bias; c) High risk of bias for those cases
with \selectlanguage{english}[?] 2 items judged as high risk of bias or \selectlanguage{english}[?] 3 items evaluated as
moderate risk of bias. Weighted kappa coefficient~(Kw) for the six
domains was measured to assess inter-rater reliability. Disagreement and
doubt were solved through discussion with senior reviewers (JJJM and
JZ). Data synthesis and meta-analysis To determine the method to combine
individual studies data in the meta-analysis, the characteristics and
the results of each included study was assessed. To combine the
information from every study, the exposed levels of POPs expressed as
continuous data in groups of GDM and non GDM pregnant women was used.
studies that only showed association measurements (i.g. OR, logOR, ln-OR
per-unit increment, RR per unit of increase of SD, terciles, quartiles
and quintiles) were excluded from meta-analyses. mean values and
standard deviations (SD) was used when provided. If not provided the
median as a mean approximation was used, and standard deviation (SD) was
estimated using the Interquartile Range (IQR) according to the formula:
(SD = IQR/1.35). A random-effects meta-analysis was conducted separately
for each exposure according to POP types. Heterogeneity was assessed
using the I\textsuperscript{2}test. Publication bias was evaluated using
a funnel plot and Egger's lineal regression asymmetry tests.
Significance was considered at\emph{p value \textless{} 0.05.} Analyses
were conducted using STATA software version 14.0. Results Literature
search and study characteristic From 161 identified studies, 78
duplicated records were removed, and 83 screened by title and abstract.
Accordingly, 19 studies were selected for full-text screening, and 13
records met the selection criteria (Figure 1). One additional article
was identified by hand searching references. Excluded records are
reported in Table S2. Of the 14 articles finally included in our
systematic review, 64.3\% (n=9) were cohort studies, and 35.7\% (n=5)
were nested case-control studies. Six studies were conducted in China,
five in the United States, and one for each of the following countries:
Spain, Greece, and Canada. Four studies were derived from the Xicheng
hospital cohort and three from the Life cohort. Total sample size ranged
from 154 to 2747 pregnant women, cases and controls sample size median
(IQR) were 68 (53-77) and 230 (154-871), respectively. Most of the
studies included in the systematic review collected women aged \selectlanguage{english}[?] 18
years, except two studies included women aged \selectlanguage{english}[?] 16 years. Serum was used
as a biological sample in most studies 64.29\% (n=9), plasma in 28.57\%
(n=4), and only one study also combined two types of biological sample
(urine and plasma). GDM was screened using International Association of
Diabetes and Pregnancy Study Groups criteria in five studies. Each of
the following criteria; American College of Obstetrics and
Gynecologists, National Diabetes Data Group, Carpenter and Coustan was
used only one time. One study screened GDM using two criteria; Canadian
Diabetes Association and Society of Obstetricians and Gynecologists of
Canada. Whereas, GDM diagnostic was self-reported in the three studies
from Life cohort (Table 3). In all studies, regression analysis was used
to adjust for maternal age and Body Mass Index (BMI), at least, except
for Xicheng hospital cohort studies where age was used for a paired
matched design. Exposure contrast was provided in different scales, and
some studies supply two different measures. Three studies
log-transformed the exposure level to estimate odds ratios, Two studies
presented log\textsubscript{10}-unit change OR, one study provided
ln-unit change OR and one study provided risk ratio per each unit of
increase of SD. Exposure levels were categorized as quartiles in four
studies, terciles in two. Study quality assessment Risk of bias related
to study participation, study attrition, exposure measurement, outcomes
measurement, study confounding, and statistical analysis reporting was
assessed using QUIPS scale. Most studies 64.3\% (n=9) had a moderate
risk of bias, 28.6\% (n=4) a high risk of bias, and only one study a low
risk of bias. Weaknesses were related to limited reporting of study
attrition details in 78.6\% (n=11), exposure factor measurement in
35.7\% (n=5), outcome measurement in 28.6\% (n=4), and study confounding
in 21.4\% (n=3) (Figure S1). A weighted Kappa was calculated of the six
domains and agreement was substantial between raters (Weighted
Kappa=0.75). Data synthesis Results were summarized for ten PFAS,
sixteen PCBs, seven PBDE, and three OCPs (Table 3-6).\emph{PFAS exposure
and GDM risk}Findings regarding 10 PFAS were reported in eight studies.
Results are summarized in Table S4. The approaches to measure the
exposure to PFAS were very variable and were reported as per unit of
increase of SD, per unit of increase according to a log scale, or
categorized from the original data. The concentration of PFAS seems to
be higher in women with gestational diabetes as compared to control
group. Total Toxic Equivalents (TEQ) were 0.025 vs. 0.015 ng/ml in cases
and controls respectively (P = 0.020). However, for most PFAS, such as
PFBS, PFDoA, and PFHpA, the association was isolated and reported in a
specific studies with moderate risk of bias (Table S4). The exposure
analysed more frequently was PFHpA. However, the results were very
heterogeneous to estimate a pooled effect. Our meta-analysis based on
continuous data show an association between PFBS and GDM with
standardized mean difference between cases and controls of 0.33 (95\% CI
0.23 to 0.43, I\textsuperscript{2} = 0.0\%). On the other hand, a
possible inverse association was observed for PFDA with GDM,
standardized mean difference between cases and controls was -0.11 (95\%
CI -0.20 to -0.01, I\textsuperscript{2} = 0.0\%).\emph{PCBs exposure and
GDM risk} Five studies analyzed the association between 16 PCBs and risk
of GDM (Table S5). Only two studies with low and moderate risk of bias
reported a positive association between some PCBs, such as PCB18 and
PCB101, and GDM (Table S5). Additionally, Total Toxic Equivalents TEQ of
PCB101 were 1.40 vs. 0.99 pg/g in cases and controls respectively
P=0.005. Although Jaack et al. results stressed an inverse association
between PCB (\#138-153, 156, 167, 170, 180, 194) and GDM (Table S5),
the risk of bias was classified as high. The pooled standardized mean
difference for three PCBs (PCB138, PCB153 and PCB180) was estimated. Our
results showed a significant association between PCB180 and GDM with a
standardized mean difference between cases and controls of 0.37 (95\% CI
0.19 to 0.56, I\textsuperscript{2}=25.3\%). PCB 138 and PCB 153
meta-analysis show a high heterogeneity (Figure 3).\emph{PBDE exposure
and GDM risk}Results related to seven PBDE were summarized from three
studies (Table S6). The association between PBDE and GDM was positive or
negative, depending on the type of PBDE. Two studies, with moderate
quality, describe a higher risk of GDM for BDE47, 54 and 183. On the
other hand, our meta-analysis showed a possible association between
BDE99 and BDE100 and GDM (Figure 3).\emph{OCPs exposure and GDM
risk}Findings related to three OCPs were reported in four studies (Table
S7). Meta-analysis results between HCB and GDM show a standardized mean
difference of 0.22 (95\% CI 0.01 to 0.42, I\textsuperscript{2}=39.6\%)
between cases and controls. p,p'DDE show a standardized mean difference
of 0.10 (95\% CI -0.11 to 0.31, I\textsuperscript{2} = 0.0\%) between
GDM cases and controls (Figure 4). Unfortunately, we could not assess
publication bias because the low number of studies included in each
meta-analysis. Discussion\emph{Main findings}This systematic review
summarizes findings about the exposure to 36 POPs and risk of GDM. We
included 16 POPs in the meta-analysis, however only 6 show an
association with GDM (PFDA, PFBS, PCB180, BDE99, BDE100 and HCB). The
direction of the association varied, PFDA compound seems decreasing this
risk of GDM, while PFBS, PCB 180, BDE99, BDE100, HCB increasing the risk
of GDM. Generally, the associations founded were sporadic and concerned
individual studies or specific pollutants. Unfortunately, available
information about POPs and the way in which are analyzed do not allow
for more insightful analyses.\emph{Strengths and Limitations}Our
systematic review and meta-analysis have several strengths. First, to
our knowledge this is the first systematic review with meta-analysis
including exclusively prospective studies assessing the association of
several POPs and risk of GDM. Second, a strengths algorithm for research
that included the different possible nominations of included POPs was
used. Moreover, only exposures measured in biospecimens were included.
Third, to reduce possible bias due to the design of studies and estimate
a possible causal effect association between the exposure and the
outcome, only prospective cohort and case-controls studies where the
exposure was measured at the beginning of pregnancy was included.
However, we cannot be sure that no cases of gestational diabetes
appeared at the beginning of pregnancy, even if diagnosed later. And
finally, this systematic review was conducted according to the protocol
previously registered in PROSPERO and was reported according to PRISMA
recommendations. Our finding can be limited by the quality relatively
low of included studies and therefore should be interpreted with
caution. ~Furthermore, owing to the limited data combinable for each
exposure, we were unable to conduct a dose-response analysis, subgroups
analysis, or publication of bias assessment. However, the risk of bias
of each study was assessed using an adapted and strong instrument
(QUIPS) by two authors independently. Another limitation can be related
to residual confounders. Information related to diet and physical
activity, factors closely associated with GDM, and the possible effect
of not measured contaminants, such as metals and non-organic pollutants,
was missed in most studies.\emph{Interpretation}The systematic review by
Wang et al. suggested a significant association between PFOA and GDM,
while no association was observed for the rest of PFAS. A recent
systematic review found a significant association with GDM estimated for
the sum of subgroups POPs; \selectlanguage{english}[?]PCBs congeners, \selectlanguage{english}[?]PBDEs compounds, \selectlanguage{english}[?]PFASs
chemicals, and also when most of these exposures were analyzed
separately. Meanwhile, high heterogeneity was observed in all
meta-analyses, including the sum for each POPs categories, and in most
meta-analyses analyzing POPs separately. Discrepancies between
systematic reviews can be explained by the differences in the way the
individual studies were combined. These systematic reviews combined
different scales of measurement of association in the same
meta-analysis. Another factor influencing the results could be the
selection criteria established in each systematic review. Although
several studies consider that the sum of POPs may increase the risk of
GDM, interpreting these results is challenging as the correlation
between the different compounds is unclear, and different congeners can
have opposite effects. For example, when association between a PFAS
exposure and GDM was controlled by the others PFAS, it appears the PFOS,
PFNA and PFHpA are the main contributors to this association. This is
the reason why results of the overall effect for \selectlanguage{english}[?]PFAS, \selectlanguage{english}[?]PCBs, \selectlanguage{english}[?]PBDE,
\selectlanguage{english}[?]OCPs were not provided in our meta-analysis. When exposure was measured
after the occurrence of the outcome, the association was less clear. A
cohort analyzing placental samples of 86 participants showed an negative
association between PCBs and PBDE and GDM. A case-control study of 140
participants, whose serum samples were collected in the third semester
of pregnancy, showed a positive association between Ln PCB 187, 118
and Ln PBDE99, 28 and GDM; the association with Ln PCB28 was negative.
Findings from another retrospective study showed a significative
association between DDE and GDM, while the association with PCBs
congeners and PFAS was not significant. Several factors closely
associated with GDM, such as gestational weight gain, diabetes mellitus,
and GDM history, may be responsible for these differences. Our results
suggest a possible association between some types of POPs and GDM. Data
with better quality and homogeneity are required to carry out stronger
reviews and more consistent and concise conclusions. In this systematic
review, we join other authors in stressing the need a requires a
standardized method to create a consortium with individual data to be
able for adjustment and establish a standardized approach to studying
and analyzing POPs. Conclusion This systematic review and meta-analysis
of prospective studies provides a synthesis of the possible effect of
POPs exposure in increasing the risk of GDM. Therefore, there are
insufficient data to analyze each exposure with more consistency and
conduct a dose-response analysis. Ensure that the effect is due to a
specific pollutant or the entire sub-category, confirm our results and
draw stronger conclusions requires a standardized method of studying
POPs to make combining results more consistent. Author contributions
This work was conceptualized and supervised by J.J.J.M., J.Z., R.O.R.
and M.K. The methodology was developed by J.J.J.M., J.Z., M.K.,
M.A.C.H., I.Y.M., and R.O.R., All analyses and data curation were
performed by M.K. and M.A.C.H. and supervised by J.Z., J.J.J.M., R.O.R.,
and I.Y.M. The interpretation of data was realized by all authors. The
original manuscript draft was written by M.K. and J.J.J.M. Critical
review and editing of the manuscript was provided by J.J.J.M., M.K.,
J.Z., I.Y.M., S.T., K.S.K., M.A.C.H., and R.O.R. All other authors
provided final approval of this manuscript. K.S.K. is a distinguished
investigator at the University of Granada funded by the Beatriz Galindo
(senior modality) program of the Spanish Ministry of Education.
Acknowledgments The results of this study are part of the doctoral
thesis of Malak Kouiti.\textbf{Funding information}This research did not
receive any specific grant from funding agencies in the public,
commercial, or not-for-profit sectors. Conflict of interest The authors
declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work
reported in this paper. Ethics approval This study involved only
literature review of previously published studies and the contained
data. It involved no primary research on human or animal subjects, or
medical records. As such, this work was considered exempt from ethical
review.



\textbf{References}

Table/Figure Caption List Figure 1. Flow chart diagram: Study selection
process. Figure 2. Pooled estimate of SMD with 95\% CI of PFAS and
gestational diabetes mellitus cases versus controls Figure 3.~Pooled
estimate of SMD with 95\% CI of PCBs and PBDE with gestational diabetes
mellitus cases versus controls. Figure 4.~Pooled estimate of SMD with
95\% CI of OCPs and gestational diabetes mellitus cases versus controls.\selectlanguage{english}
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\textbf{Hosted file}

\verb`kouiti et al_Tables S1_S7.docx`  available at \url{https://authorea.com/users/599714/articles/631581-persistent-organic-pollutant-exposure-as-a-risk-factor-of-gestational-diabetes-mellitus-a-systematic-review-and-meta-analysis}

\textbf{Hosted file}

\verb`Kouiti et al_Appendix.docx`  available at \url{https://authorea.com/users/599714/articles/631581-persistent-organic-pollutant-exposure-as-a-risk-factor-of-gestational-diabetes-mellitus-a-systematic-review-and-meta-analysis}

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