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\begin{document}

\title{Maternal metabolic risk factors and their association with birthweight
and cord blood insulin}



\author[1]{Jingya Wang}%
\author[2]{Yashu  Kuang}%
\author[3]{Malcolm Price}%
\author[2]{Jinhua  Lu}%
\author[4]{Naveed  Sattar}%
\author[2]{Jianrong  He}%
\author[5]{Marta  Pittavino}%
\author[2]{Songying  Shen}%
\author[2]{Huimin  Xia}%
\author[2]{Xiu Qiu}%
\author[3]{KK Cheng}%
\author[6]{Krishnarajah Nirantharakumar}%
\affil[1]{Affiliation not available}%
\affil[2]{Guangzhou Women and Children's Medical Center}%
\affil[3]{University of Birmingham}%
\affil[4]{University of Glasgow}%
\affil[5]{University of Geneva}%
\affil[6]{University of Birmingham Edgbaston Campus}%


\vspace{-1em}



  
  \date{May 23, 2024}


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\selectlanguage{english}
\begin{abstract}
Objective To quantify the inter-dependency between maternal metabolic
risk factors and their association with birthweight and cord blood
insulin (CBI) level. Design Prospective cohort study. Setting Guangzhou
Women and Children's Medical Centre (GWCMC). Population Pregnant women
with a singleton pregnancy who delivered at GWCMC between Jan 2015 and
Jun 2016 and had umbilical cord blood retained (total 1522). Methods
Multivariable linear regression and Additive Bayesian Network analysis
were used to investigate the association between maternal metabolic risk
factors (pre-pregnancy body mass index {[}BMI{]}, fasting glucose, lipid
profiles, and early gestational weight gain {[}GWG{]}) and their
interdependency in predicting birthweight and CBI concentrations. Main
outcome measures Birthweight and cord blood insulin. Results High
maternal pre-pregnancy BMI was strongly associated with neonatal
birthweight (standardized adjusted regression coefficient {[}\selectlanguage{greek}β\selectlanguage{english}std{]} =
0.27, 95\%CI 0.22-0.32) directly; and with CBI indirectly. Maternal
fasting glucose was positively associated with increased CBI (\selectlanguage{greek}β\selectlanguage{english}std=0.12,
95\%CI 0.07-0.17). Maternal GWG was positively associated with increased
birthweight, but not with CBI. None of the maternal lipids profile was
independently associated with birthweight or CBI. Conclusions Maternal
pre-pregnancy overweight/obesity is the most influential upstream
metabolic risk factor for both maternal and neonatal metabolic health,
therefore weight management should be addressed from the preconception
period. Maternal dyslipidaemia appears to be secondary to maternal
metabolic dysfunction with no clear causality relationship with
metabolic adverse outcomes in neonates.%
\end{abstract}%



\sloppy


\section*{Tweetable abstract}

{\label{322476}}

The most efficient way to optimize maternal and neonatal metabolic
health is to address weight management before pregnancy.

\section*{Introduction}

{\label{introduction}}

Maternal metabolic risk factors, including high pre-pregnancy
body-mass-index(BMI), excessive gestational weight gain(GWG), and
gestational hyperglycaemia, are known to induce adverse pregnancy
outcomes. These include stillbirth, pre-term delivery, low or high
birthweight, pre-eclampsia, and maternal postnatal
diabetes/cardiovascular disease(1, 2). The prevalence of gestational
diabetes mellitus(GDM) varies from 2 to 25 percent worldwide, and has
increased over the last decades in parallel with the increasing obesity
prevalence of women in child-bearing age (3, 4).

A variety of strategies targeting metabolic management during pregnancy
have been developed to prevent endocrine-related adverse pregnancy
outcomes. However, a recent individual patient data meta-analysis
suggested that diet and lifestyle interventions in pregnancy only
achieved modest success in reducing GWG, but have no effects on
composite maternal and foetal outcomes, such as pre-term birth,
macrosomia, and foetal adiposity(5). It might be partly attributed to
the alterations of maternal/placental function and metabolic programming
in preconception and early pregnancy, which occurs prior to when most
interventions are initiated. Other than maternal weight and glucose
level, maternal lipid levels during pregnancy are recognised as ignored
risk factors for adverse pregnancy outcomes recently(6).

The mechanism of how maternal metabolic dysfunction is linked with
neonatal health remains uncertain, however, most researchers believe it
may be explained by the unfavourable intrauterine nutritional
environment(7, 8). Previous studies mainly focused on one specific
metabolic trait or on the number of metabolic disorders, without
assessing the underlying interacted effect of the natural metabolic
network (9, 10). This is due to the limited analytical ability of
classical statistical methods to analyse multidimensional data.
Understanding how the metabolic network influences neonatal health is
crucial to future interventional studies and potentially to
antenatal/prenatal advice.

In this study, we investigated the independent association of maternal
metabolic traits with birthweight and cord blood insulin in the Born in
Guangzhou Cohort Study(BIGCS). The inter-dependency of maternal
metabolic risk factors and their association with birthweight and cord
blood insulin was then mapped by Additive Bayesian Network(ABN)
analysis, a robust data-driven structure discovery model, which has been
widely used in other disciplines(11, 12).

\section*{Materials and Methods}

{\label{materials-and-methods}}

\subsection*{Participants}

{\label{participants}}

The design and methods of BIGCS have been described previously(13). In
brief, eligible women with Chinese nationality, living in Guangzhou who
are \textless{}20 weeks gestation and who intend to deliver at one of
the two Guangzhou Women and Children's Medical Centre(GWCMC) campuses
were recruited into BIGCS. This study was conducted in a planned
subgroup of BIGCS in whom maternal and cord blood were analysed for
metabolic parameters separately. Pregnant women attending BIGCS with a
singleton pregnancy who delivered at GWCMC between Jan 2015 and Jun 2016
and had umbilical cord blood retained are eligible for this study. Women
were excluded if: 1) maternal blood samples unavailable at 14-27
gestation week; 2) no records of maternal fasting glucose at 20-28
gestation week; 3) lacking maternal demographic information; 4)
diagnosed with health condition prior to pregnancy, including type 1 or
type 2 diabetes, thyroid dysfunction, hypertension, virus hepatitis, and
renal diseases. The study was powered for the association between
maternal triglycerides (the potential weakest risk factors among
maternal metabolic traits) with birthweight according to
literature(Supplementary file S1). The eligible mother-child pairs were
then selected into this study by computer generated randomization.
Ethical permission for the study was granted by the GWCMC Ethics
Committee.

\subsection*{Study Procedures}

{\label{study-procedures}}

Maternal demographic data, including anthropometric measures,
socioeconomic status, family and personal medical history, were
collected through a semi-structured questionnaire(Q1) at recruitment.
Maternal overnight fasting blood samples were collected during second
trimester. At 22-28 weeks gestation, women attending their second
prenatal visit underwent a standard 2h 75g oral glucose tolerance
test(OGTT). Women with OGTT results which met or exceeded at least one
threshold of the International Association of Diabetes and Pregnancy
Study Groups(IADPSG) criteria(FPG[?]5.1 mmol/L, 1h glucose [?]10.0 mmol/l,
and 2h glucose[?]8.5 mmol/L) were diagnosed as having gestational diabetes
mellitus(GDM)(14). For participating children, birth information,
including birth characteristics, delivery mode, and perinatal outcomes
were obtained from routine medical records. Umbilical cord blood samples
were collected by midwives at birth.

\subsection*{Demographic Data}

{\label{demographic-data}}

Maternal demographic information(age, height, pre-pregnancy weight,
parity, date of last menstrual period, monthly income, education levels,
and ethnicity) were collected through Q1 questionnaire. BMI was
calculated by dividing weight in kilograms by height in meters squared.
Based on the recommendations of the China Obesity Task Force of the
Chinese Ministry of Health, maternal pre-pregnancy BMI is classified
into two groups: lean group(\textless{}24 kg/m\textsuperscript{2}) and
overweight group([?]24 kg/m\textsuperscript{2}) (15). Maternal second
trimester weight was measured to the nearest 0.1 kg using an electronic
scale. Maternal early gestational weight gain(GWG) was calculated by
subtracting pre-pregnancy weight from maternal second trimester weight,
with documentation of the gestational age at measurement. Maternal
fasting glucose concentration was obtained from OGTT test zero-time
value in hospital records.

\subsection*{Biochemical Test}

{\label{biochemical-test}}

Sample collection, delivery, pre-treatment, and measurements were
blinded. All blood samples were stored and delivered to pre-treatment
laboratory centre. Blood samples were then separated to serum and plasma
by immediate centrifugation, and were stored in EDTA tube in the
bio-bank at -80 until analysis. Plasma lipids(TC, HDL-C, LDL-C, and TG)
and insulin levels were measured using commercial kits in fully
automated clinical analyser(Roche Diagnostics, Mannheim, Germany).
Intra- and inter-day coefficients of variation(CVs) were consistently
less than 2 percent for all assays.

\subsection*{Neonatal anthropometry}

{\label{neonatal-anthropometry}}

Gestational age was estimated from ultrasound examination during the
first- or second-trimester. Birthweight and other information, including
gestational age at delivery, mode of delivery, neonatal sex, and
pregnancy complications were obtained from hospital records. Birthweight
was measured to the nearest 50g using an electronic scale by midwives
immediately after delivery. Birthweight Z-Score and percentile(adjusted
for gestational age at delivery and neonatal sex) were calculated using
Intergrowth 21\textsuperscript{st} Newborn Size Standard and Tools(16).
Large for gestational age(LGA) was defined as a birthweight larger than
the 90\textsuperscript{th} percentile for gestational age by sex, while
Small for gestational age(SGA) was defined as a birthweight smaller than
the 10\textsuperscript{th} percentile based on the same birthweight
reference.

\subsection*{Statistical Analysis}

{\label{statistical-analysis}}

\subsubsection*{Classic statistical
methods}

{\label{classic-statistical-methods}}

For the baseline table data are summarized as mean \selectlanguage{ngerman}± Standard
Deviation(SD), median(Inter Quartile Range, IQR), or counts with
percentages. Pearson correlation was used to assess the impact of the
long-term -80 °C storage on insulin concentrations in EDTA tube.
Adjustments were then made to account for any degradation by correcting
the initial value using linear regression methods(Supplementary file
S2). Similarly, maternal lipid levels were adjusted for gestational age
using regression model to account for timing of blood
sampling(Supplementary file S3)(17).

Initially, linear and logistic regression were used to estimate the
association between maternal metabolic parameters and neonatal
continuous and binary outcomes, respectively. Further analyses using
linear regression model were performed after all exposures were
transformed to Z-Scores. This was to enable comparison of the effect
size each maternal metabolic parameter had on birthweight Z-Score and
CBI Z-Score. CBI and maternal triglycerides were log-transformed prior
to standardization. Multiple imputation was used to handle missing data.
Subgroup analyses were conducted in boys and girls respectively.
Sensitivity analyses were conducted to compare the estimate differences
between GDM and non-GDM participants, fasting blood samples and
non-fasting samples, primiparous women and non-primiparous women, lean
and overweight group, as well as before and after multiple
imputation(Supplementary file S4). All statistical tests were two-tailed
and a P \textless{}0.05 was considered statistically significant.
Statistical analyses were performed in Stata version 14.0(College
Station, Texas, USA).

\subsubsection*{Additive Bayesian Networks (ABN)
analysis}

{\label{additive-bayesian-networks-abn-analysis}}

To further assess the inter-dependency between maternal metabolic risk
factors and their association with birthweight and CBI, Additive
Bayesian Network(ABN) model - an unsupervised machine learning method -
was conducted. Bayesian network analysis is a form of structure
discovery statistical modelling that derives, from empirical data, a
graphical network describing the dependency structure between variables,
shown as directed acyclic graphs(DAGs)(12). ABNs comprise of DAGs where
each node in the graph comprises a generalized linear model(GLM) or a
generalized linear mixed model(GLMM). ABN model is suitable for
analysing highly complex epidemiological data comprising many
inter-dependent variables(11).

Ten variables were chosen for ABN based on prior knowledge gained from
literature and findings of the classical statistical analyses. These ten
variables were maternal age, maternal pre-pregnancy BMI, maternal
fasting glycaemia in OGTT, early GWG, maternal fasting plasma HDL-C and
triglycerides in the second trimester, birthweight Z-Score, cord blood
insulin, gestational age at delivery, and neonatal sex. GWG was adjusted
for gestational age at weight measurement in mid-pregnancy. Cord blood
insulin was adjusted for sample storage duration. All continuous
variables were standardized to Z-Scores to eliminate the influence of
different measurement units. Mother-child pairs with missing data were
excluded(n=93/1522, 6\%).

Firstly, an optimal DAG with the best goodness of fit(highest log
marginal likelihood) was identified. Next, parametric
bootstrapping(12800 samples) was performed to address the potential
overfitting. Full technical details are provided in the supplementary
file(S5). ABN analysis was conducted in R 3.4.4(The R Foundation for
Statistical Computing) using `abn' package(11).

\section*{Results}

{\label{results}}

\textbf{Figure 1} shows the flow diagram of mother-child pairs'
selection procedure(power calculation provided in supplementary file
S1). The baseline characteristics of participants are shown
in\textbf{Table 1} . The majority(91.20\%) of maternal blood samples
were collected after overnight fasting. Maternal mid-pregnancy weight,
fasting glucose, and lipids profile were measured at a mean of
20.0(SD=4.0), 24.6(SD=1.4), and 20.5(SD=3.5) gestation weeks,
respectively. Cord blood samples were stored for a median of 488(IQR 394
to 707) days before analysis.

\subsection*{Association of maternal metabolic parameters with
birthweight and
CBI}

{\label{association-of-maternal-metabolic-parameters-with-birthweight-and-cbi}}

\textbf{Table 2} presents the associations between the association of
maternal pre-pregnancy BMI, GWG, and maternal mid-pregnancy fasting
glucose/HDL-C/TG levels with neonatal outcomes. Pre-pregnancy
BMI(adjusted\selectlanguage{greek}β\selectlanguage{english}=29.25, 95\%CI: 22.77 to 35.73g per
Kg/m\textsuperscript{2}), GWG(adjusted\selectlanguage{greek}β \selectlanguage{english}=18.75, 95\%CI: 13.06 to 24.43g
per Kg), fasting glucose(adjusted\selectlanguage{greek}β\selectlanguage{english}=84.32, 95\%CI: 42.65 to 125.98g per
mmol/L), and triglycerides(adjusted\selectlanguage{greek}β\selectlanguage{english}=67.97, 95\%CI: 42.38 to 93.55g per
mmol/L) were positively associated with birthweight, while HDL-C was
negatively associated with birthweight(adjusted\selectlanguage{greek}β\selectlanguage{english}=45.78, 95\%CI: 5.59 to
85.97g per mmol/L). There was no evidence of an association between
maternal TC and LDL-C levels and birthweight(Supplementary file S6).

Elevated maternal pre-pregnancy BMI, early GWG, fasting blood glucose
and triglycerides level were significantly associated with an increased
risk of LGA. The risk was particularly high for fasting glucose(OR=2.06,
95\%CI 1.31 to 3.24). Higher pre-pregnancy BMI and triglycerides were
significantly associated with lower odds of SGA. There was no evidence
of an association between maternal HDL-C level and risk of LGA/SGA.

Maternal fasting glucose(adjusted\selectlanguage{greek}β\selectlanguage{english}=2.23, 95\%CI: 0.89 to 3.57 \selectlanguage{greek}μ\selectlanguage{english}U/ml per
mmol/L) and triglycerides levels(adjusted\selectlanguage{greek}β\selectlanguage{english}=0.88, 95\%CI: 0.05 to 1.71
\selectlanguage{greek}μ\selectlanguage{english}U/ml per mmol/L) were significantly associated with higher CBI.
Pre-pregnancy BMI, GWG, TC, and LDL-C levels did not show an association
with CBI(Table 2 and Supplementary file S6).

\subsection*{Association of maternal metabolic parameter Z-scores with
birthweight and CBMI
Z-scores}

{\label{association-of-maternal-metabolic-parameter-z-scores-with-birthweight-and-cbmi-z-scores}}

\textbf{Table 3} shows the estimates of the association of maternal
metabolic parameter Z-Scores with birthweight Z-Score and CBI Z-Score,
and subgroup estimates for boys and girls. Maternal pre-pregnancy
BMI(adjusted\selectlanguage{greek}β\selectlanguage{english}=0.20, 95\%CI 0.15 to 0.24), early GWG(adjusted\selectlanguage{greek}β\selectlanguage{english}=0.17,
95\%CI 0.12 to 0.22), triglycerides(adjusted\selectlanguage{greek}β\selectlanguage{english}=0.12, 95\%CI 0.08 to
0.16), and glucose(adjusted\selectlanguage{greek}β\selectlanguage{english}=0.08, 95\%CI 0.04 to 0.12) Z-scores had
positive associations with birthweight Z-Score. Maternal HDL-C Z-Score
showed a negative association with birthweight Z-Score in boys(adjusted
\selectlanguage{greek}β\selectlanguage{english}=-0.06, 95\%CI -0.12 to -0.01) only. The association of pre-pregnancy
BMI, GWG, glucose, and triglycerides Z-Scores with birthweight Z-Score
remained statistically significant after adjusting for other four
maternal metabolic risk factors.

Maternal glucose Z-Score(adjusted\selectlanguage{greek}β\selectlanguage{english}=0.13, 95\%CI 0.08 to 0.18) appears to
be the most important contributor to CBI Z-Score in both boys and girls.
Both maternal pre-pregnancy BMI(adjusted\selectlanguage{greek}β\selectlanguage{english}=0.10, 95\%CI 0.05 to 0.15) and
triglycerides(adjusted\selectlanguage{greek}β\selectlanguage{english}=0.06, 95\%CI 0.01 to 0.11) Z-Scores showed
positive associations with CBI Z-Score. No statistically significant
association was observed between maternal early GWG and HDL-C Z-Scores
with CBI Z-Score.

\subsection*{ABN analysis results for interdependent maternal metabolic
parameters}

{\label{abn-analysis-results-for-interdependent-maternal-metabolic-parameters}}

\textbf{Figure 2} shows the optimal summary DAGs inferred by ABN
analysis(Supplementary file S5). The adjusted regression coefficients(\selectlanguage{greek}β\selectlanguage{english})
in the graph represent how much the dependent variable changes per unit
increase in the independent variable. Maternal pre-pregnancy BMI
appeared to be the most influential upstream factor for both maternal
metabolic parameters in pregnancy(glycaemia: \selectlanguage{greek}β\selectlanguage{english}=0.14, 95\%CI 0.09 to
0.19; early GWG: \selectlanguage{greek}β\selectlanguage{english}=-0.12, 95\%CI -0.17 to -0.06; triglycerides: \selectlanguage{greek}β\selectlanguage{english}=0.23,
95\%CI 0.18 to 0.28; HDL-C: \selectlanguage{greek}β\selectlanguage{english}=-0.12, 95\%CI -0.17 to -0.07) and
birthweight(\selectlanguage{greek}β\selectlanguage{english}=0.27, 95\%CI 0.22 to 0.32). An indirect effect on neonatal
insulin secretion was also observed. Our DAGs results showed that
maternal glycaemia was associated with CBI(\selectlanguage{greek}β\selectlanguage{english}=0.12, 95\%CI 0.07 to 0.17).
Birthweight was also associated with CBI(\selectlanguage{greek}β\selectlanguage{english}=0.24, 95\%CI 0.19 to 0.29).
Neither triglycerides nor HDL-C were linked to birthweight or CBI.

\section*{Discussion}

{\label{discussion}}

To our knowledge, this is perhaps the first large prospective birth
cohort study to map the metabolic network, and assess the
inter-dependent associations between maternal modifiable metabolic risk
factors, birthweight, and insulin secretion in neonates. We showed that
high maternal pre-pregnancy BMI appears the most influential upstream
metabolic risk factor for both maternal and neonatal health. Maternal
early GWG is directly associated with birthweight, but not neonatal
insulin secretion. Maternal fasting glucose is significantly associated
with increased neonatal insulin secretion. Although maternal HDL-C and
triglycerides concentrations are significantly associated with
birthweight, our results demonstrated that these lipid pathways may not
be meaningfully involved in the metabolic network pathway between
mothers and neonates, and instead be a proxy measure for maternal
metabolic health. These findings suggest: 1) the primary focus of weight
management in clinical practice to prevent adverse pregnancy outcomes
should start from preconception; 2) the observed association between
maternal glucose and birthweight is likely to be partly mediated through
elevated neonatal insulin secretion; 3) The pathogenic relationships of
maternal glucose/triglycerides with birthweight/CBI need to be inferred
with caution and evaluated in further studies.

Our results are generally consistent with previous relevant evidence,
but importantly we also provide new insights that differ from the
conclusions of previous studies. The Hyperglycaemia and Adverse
Pregnancy Outcome Study research group(HAPO) published a series of
network analyses reporting that maternal metabolites(acylcarnitines,
fatty acids, carbohydrates, and amino acids) during pregnancy are
associated with BMI, fasting glucose, and insulin resistance in
mothers(18) and birth size, growth, adiposity, and cord blood C-peptide
in neonates(8, 19). Consistent with our findings, another study that
looked at all metabolic parameters but not in the context of network
analysis and restricted to women with GDM only(n=357) reported that the
number of altered maternal metabolic characteristics(pre-pregnancy BMI,
fasting glycaemia, HbA1c, triglycerides, and HDL-C) are associated with
incidence of LGA(9). However, none of those studies explored
the\emph{inter-dependent relationships} between maternal metabolic risk
factors and compared the strength of their associations with neonatal
conditions.

Maternal high pre-pregnancy BMI has been linked with increased
birthweight(20). Beyond that, our analyses demonstrated that maternal
pre-pregnancy BMI is the most important contributor to increased
birthweight, which is independent of maternal early GWG, glucose, and
triglycerides levels during pregnancy. It is also worth noting that high
maternal pre-pregnancy BMI is closely related to gestational metabolic
disorders, namely, increased fasting glucose and triglycerides levels,
therefore, further contributing to elevated birthweight and insulin
secretion in offspring. Similar to our results, a small study containing
66 mother-offspring pairs found that obese mothers might induce
increased insulin secretion in offspring. Their results also showed a
clear sexual dimorphism(boys have higher insulin secretion than
girls)(21). In this study, the association between maternal
pre-pregnancy BMI with CBI in boys seemed stronger than in girls(\selectlanguage{greek}β\selectlanguage{english}
{[}95\%CI{]}, boys 0.13{[}0.07, 0.20{]} vs. girls 0.08{[}0.00, 0.15{]}),
but the difference was not statistically significant. On the other hand,
we found that maternal early GWG is only statistically associated with
birthweight, but not CBI, which suggests that the weight accumulation in
the early pregnancy may indirectly affect neonatal metabolism through
increased birthweight.

Maternal glucose has been closely associated with increased birthweight
and cord blood C-peptide levels(1, 22). Our results of multivariable
regression model are in line with previous findings. The ABN results
suggest that maternal fasting glucose is perhaps not directly linked
with birthweight, while the concentration of cord blood insulin is
largely determined by birthweight and maternal glucose jointly. When we
entered CBI Z-Score in the regression model, the association between
maternal glucose Z-Score and birthweight Z-Score decreased dramatically
but remained statistically significant(\selectlanguage{greek}β\selectlanguage{english}=0.05, 95\%CI 0.01 to 0.09).
This suggests that maternal hyperglycaemia drives neonatal insulin
secretion, and the de novo anabolic effect of CBI plays a critical role
in adipose accumulation in neonates(23, 24). The enlarged adipocyte will
gradually become resistant to insulin to avoid further expansion,
therefore contributes to the increased insulin secretion in
neonates(25).

We recently published a systematic review which found that increased
maternal triglycerides and decreased HDL-C are positively associated
with high birthweight(26). Similar results were observed using
multivariate regression analysis in this study. However, our ABN
analysis now take us a step further by suggesting that both maternal
HDL-C and triglycerides are likely to be measures of gestational
metabolic disorder, and not themselves involved in the metabolic pathway
that increases birthweight and CBI. Similar to our results, a Mendelian
randomization study analysing data from 30,487 women in 18 studies
concluded that genetically higher maternal fasting HDL-C/triglycerides
was not potentially causally associated with higher birthweight(27).
Thus, both detailed pathways analyses in this paper and genetic findings
go against lipid pathways being directly relevant to birthweight.

\subsection*{Clinical Implication}

{\label{clinical-implication}}

Most current clinical guidelines on preconception and antenatal care
only focus on weight management during pregnancy. Our results provide
further important evidence on the clinical importance of maternal
pre-pregnancy high BMI for both maternal and neonatal health outcomes.
Interventions to reduce weight in overweight/obese women before
conception to reduce adverse effects of high maternal pre-pregnancy BMI
may need further investigation in randomized trials. Recommendations on
pre-pregnancy weight management is limited and ambiguous(28-30).None of
guidelines on weight management in adults provides advice to women in
child-bearing age to prevent adverse pregnancy outcomes. Only one public
health guideline in UK mentions the potential course of actions that
could be taken by health professionals to improve outcomes in women with
a BMI equal or in excess of 30 kg/m\textsuperscript{2} prior to
pregnancy(28). Our results, if applied to wider communities, provide
further evidence for public health measures at improving weight levels
in women in general and particularly those of child-bearing age.

\subsection*{Strengths and limitations}

{\label{strengths-and-limitations}}

The major strengths of this study are the prospective design based on
relatively large sample size, standardization of strength of association
for the comparison among maternal metabolic risk factors, and the use of
powerful analytical tools for interpretation of multi-dimensional data.
Given the practical constraints, maternal fasting glucose and
triglycerides levels were measured only once during pregnancy.
Therefore, we could not investigate the dynamic long-term influences of
maternal metabolic risk factors in detail, although such levels
generally track well over gestation. The average pre-pregnancy BMI of
included women and incidence of LGA/SGA babies in this study were
significantly lower than for people living in the northern part of
China. The relative healthiness of our cohort suggests that our results
might underestimate the true impact of maternal metabolic disorders on
neonatal health outcomes if extrapolated to this wider population. The
pre-pregnancy weight was self-reported, which might potentially
underestimate the true value. However, evidence suggests that
utilization of self-reported or measured pre-pregnancy weight for
pre-pregnancy BMI classification results in identical categorization for
most women(31). In addition, due to lacking of dynamic data, the ABN
analysis might have limited ability on exploring feedback loop.
Therefore, the results of ABN, as with any observational analyses, need
to be interpreted with a degree of caution.

\section*{Conclusion}

{\label{conclusion}}

In conclusion, in this cohort study, high maternal pre-pregnancy BMI
appeared to be the most influential upstream risk factor for gestational
hyperglycaemia/hypertriglyceridemia in mothers and increased
birthweight/insulin secretion in neonates. Our findings based on robust
novel statistical methods highlights the impact of maternal
pre-pregnancy BMI on maternal and neonatal metabolic outcomes. We also
found that maternal hyperglycaemia was positively associated with
elevated neonatal insulin secretion, suggesting this may be a key reason
for foetal fat deposition. By contrast, maternal lipid levels do not
appear to have meaningful independent associations with neonatal
birthweight or CBI.

\textbf{Grants and/or financial support:}

This work was supported by National Natural Science Foundation of China
{[}grant numbers 81673181, 81703244, and 81803251{]}. JW was supported
by the LiSiguang scholarship provided by the University of Birmingham
and the China Scholarship Council jointly. The funder had no role in
study design, data collection, data analysis, data interpretation, or
writing of the report. The corresponding author had full access to all
the data in the study and had final responsibility for the decision to
submit for publication.

\textbf{Disclosure Statement} : The authors report no conflict of
interest.

\textbf{Contribution statement:}

KN, QX, KKC, and JW conceived the research question. JW defined the
question, designed the study, and conducted data analysis. KN and MJP
advised on study design and contributed to data analysis. KKC, HX, and
QX provided input for study design. YK, JH, SS, JL, and JW contributed
to the sample collection, storage, and biochemical tests, and data
pre-processing. MP checked the ABN analysis externally. All authors
contributed to the interpretation of the results. JW led the writing of
the manuscript with critical input from all other authors. JW, XQ, and
KN had full access to all data (including statistical reports and
tables) in the study and can take responsibility for the integrity of
the data and the accuracy of the data analysis. JW is the guarantor.

\textbf{Data sharing:} No additional data available.

\textbf{Transparency:} The lead author (JW) affirms that this manuscript
is an honest, accurate, and transparent account of the study being
reported; that no important aspects of the study have been omitted; and
that any discrepancies from the study as planned (and, if relevant,
registered) have been explained.

\textbf{Ethical approval:} Consent has been obtained from each patient
or subject after full explanation of the purpose and nature of all
procedures used.

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\textbf{Hosted file}

\verb`Figure 1 Flow chart.docx`  available at \url{https://authorea.com/users/297717/articles/426838-maternal-metabolic-risk-factors-and-their-association-with-birthweight-and-cord-blood-insulin}

\textbf{Hosted file}

\verb`Figure 2 Additive Bayesian Network Graph.docx`  available at \url{https://authorea.com/users/297717/articles/426838-maternal-metabolic-risk-factors-and-their-association-with-birthweight-and-cord-blood-insulin}

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