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.