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 theinter-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(β
[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(β=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.
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/m2 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.
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.