Introduction
Gestational diabetes mellitus (GDM) is a metabolism-related pregnancy complication, defined as any degree of glucose intolerance with onset or first recognition during pregnancy [
1]. The International Diabetes Federation estimated that 16.7% (21.1 million) of live births to women in 2021 had hyperglycemia during pregnancy [
2]. Of these, more than 80% were due to GDM [
2]. In recent years, with the improvement of China’s living standards and the implementation of the “universal two-child” policy, there are more and more overweight or elderly pregnant women, resulting in a sharp increase in the incidence of GDM in China [
3]. Numerous studies have shown that women with GDM have a significantly increased risk of having a baby with macrosomia [
4]. The GDM related macrosomia not only significantly increases short-term maternal and infant complications such as shoulder dystocia, postpartum hemorrhage and neonatal asphyxia, but also greatly increases the risk of long-term obesity, diabetes and metabolic syndrome [
4]. And the DOHaD theory holds that the nutritional and nurturing environment in the first 1000 days of life is crucial to an individual’s health throughout life [
5]. Therefore, early screening of GDM related macrosomia is of great significance not only for clinical control of macrosomia, but also for reducing maternal and infant complications and delaying the development of obesity or diabetes in the offspring of women with GDM.
The mechanism of the association between GDM and macrosomia is not fully understood. Risk factors including older age, overweight and obesity, excessive weight gain during pregnancy, hyperglycemia and hyperlipidemia in women developing GDM, do only partly explain the association between GDM and macrosomia risk [
4], indicating that GDM may promote or induce additional maternal metabolic changes. Women with GDM have been shown to present significant metabolic changes, and studies have measured levels of a single or a few metabolic biomarkers in GDM [
6‐
9], some of which were related to newborn weight status [
7,
10]. For example, early pregnancy leptin and TNFα were reported as determinants of birth weight in women with normal weight [
10], and plasma-glycated CD59 was indicated as a simple and accurate biomarker for detection of GDM in early pregnancy and risk assessment of delivering a large for gestational age (LGA) infant [
7]. Differences in circulating metabolic biomarkers between GDM women with and without macrosomia could reflect macrosomia specific metabolic changes, reflecting different macrosomia risks. This biomarker phenotyping could be used to target women with GDM at the highest risk of macrosomia, to tailor GDM follow-up and preventive measures.
Overall, as women with GDM have a higher risk of having a baby with macrosomia, there should be systematic screening for macrosomia to increase early detection. The Proximity Extension Assay (PEA) technology combines the advantages of antibody-based and DNA-based methods to provide a unique tool for protein biomarker discovery and development [
11]. This is a scalable, multiplex, and highly specific method for quantifying the concentrations of hundreds of protein biomarkers simultaneously [
11]. In this study, using the PEA technology, we assessed whether a multiplex panel of 92 circulating metabolic biomarkers differed between GDM women with and without macrosomia. Furthermore, based on the different circulating metabolic markers and clinical characteristics, we analysed and identified risk factors affecting risk of GDM related macrosomia, and developed a scientific risk prediction model for obstetricians to conduct early screening of GDM related macrosomia.
Discussion
The pathogenesis of macrosomia induced by GDM is particularly complex, involving many factors such as heredity, nutrition, and metabolic disturbance [
4,
16,
17]. Recent studies have shown that the high glucose environment in the mother of GDM can stimulate the expression of various molecules (such as PTH-rP, PTH-R1, VEGF, CD31, etc.) from the placenta to the fetus, and combine with the high glucose in the uterus, resulting in fetal overgrowth [
18,
19]. In our previous study, we reported that the expressions of GDF3, PROM1, AC006064.4, lnc-HPS6–1:1 and circ_0014635 were significantly increased, and the expression of lnc-ZFHX3–7:1 was significantly decreased in cord blood exosomes of the women with GDM-M [
20]. Even so, current studies cannot fully elucidate the molecular mechanism of GDM-induced macrosomia [
21], suggesting that there may be other mechanisms involved in the maternal and fetal communication stimulated by maternal high glucose.
In this retrospective study of women with GDM, we found a dysregulation of maternal circulating metabolic biomarkers in pregnancy with GDM-M by using Olink multiplex proteomics. Among the 92 metabolism linked biomarkers, we identified 4 markers different for women with GDM-M compared with women with GDM-N after adjusting for multiple testing, namely CLUL1, VCAN, FCRL1 and RNASE3. We are unaware of previous studies of pregnancy cohorts using this Olink metabolism linked multiplex biomarkers analysis. Several previous studies of pregnancy cohorts using Olink cardiovascular I/II or inflammation panel, have found maternal circulating Olink biomarkers differed by preeclampsia subtypes [
22], associated with cord blood leukocyte telomere length [
23], and in relation to abdominal fat distribution [
24].
The dysregulated metabolic biomarkers identified in the GDM-M group in this study (CLUL1, VCAN, FCRL1 and RNASE3) may reflect heterogeneous physiological and pathological processes linked to the fetal development and metabolism. Although we found no previous studies investigating prospective associations of the dysregulated markers with GDM related macrosomia, all have been implicated in metabolic bioprocess in other settings. CLUL1 was firstly reported as a novel retinal specific clusterin (CLU)-like protein [
25]. A BLAST search showed the CLUL1 and CLU proteins have a 23 to 25% sequence identity [
25]. CLU is a highly conserved secreted glycoprotein widely expressed in many tissues [
26], which has been suggested to function as a molecular chaperone [
27]. Recently, it is reported that plasma CLU alteration could induce HDL dysfunction and contribute to peripheral artery disease that is aggravated by type 2 diabetes (T2D) [
28]. VCAN, a large chondroitin sulfate proteoglycan in the extracellular matrix, could facilitate chondrocyte differentiation and regulate joint morphogenesis, having an important role in the musculoskeletal health [
29]. In a large meta-analysis of genome-wide association studies for lean mass,
VCAN loci (rs2287926) was identified and successfully replicated for whole body lean mass, and was important for sarcopenia diagnosis [
30]. Moreover, a significant correlation between the
VCAN loci and body fat mass in non-elderly individuals with T2D was also successfully replicated in another cross-sectional study [
31]. FCRL1, a newly identified co-receptor of B cell receptors (BCR), was passively recruited into B cell immunological synapses upon BCR engagement in the absence of FCRL1 cross-linking, thus regulating B cell activation and function [
32]. In a genome-wide association analysis of autoantibody positivity in type 1 diabetes (T1D) cases, the authors associated rs4971154 in exon 5 of
FCRL1 with islet antigen-2 (IA-2A), one of T1D-associated anti-islet autoantibodies [
33]. RNASE3, a member of the RNaseA superfamily involved in host immunity, is expressed by leukocytes and shows broad-spectrum antimicrobial activity [
34]. Together with a direct antimicrobial action, RNASE3 exhibits immunomodulatory properties [
34]. The roles of FCRL1 and RNASE3 in inflammatory pathways point to an important role of the immune system in metabolic pathology in GDM related macrosomia. Whether these biomarkers might serve as treatment targets remains to be assessed in future studies.
By combining the above Olink biomarkers and different clinical characteristics, a total of 9 predictors, namely pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an AUC of 0.970, and was well calibrated. In the present study, we used a novel statistical method (LASSO regression) to identify the risk factors for GDM-M. The LASSO regression analysis minimizes prediction error for a quantitative response variable by imposing a constraint on the model parameters that cause the regression coefficients for some variables to shrink toward zero [
13], thus providing more accurate results. A graphical nomogram was produced for obstetricians to easily use the constructed model to quantitatively predict the risk probability of macrosomia in women with GDM. Moreover, ROC, calibration and clinical impact curves were constructed to verify the accuracy and stability of the model.
However, our study had some limitations. First, this is a retrospective study. Second, this study was limited by funding, and we were unable to conduct Olink biomarker analysis for more women with GDM. Therefore, we could not divide the limited sample into training set and validation set. And the population we studied was limited to ethnic Han Chinese in Nanjing city, so caution should be taken when generalizing our findings to other populations. Third, the present study does lack a review of other factors contributing to macrosomia in women with GDM, including diet, lifestyle factors and family/personal history of GDM-M. Future studies will include a more detailed investigation by involving a larger sample of pregnant women with GDM and a more comprehensive list of factors.
Conclusions
The 9 indicators verified by nomogram in this analysis, including pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, are very meaningful in terms of identifying macrosomia risk in GDM. Also, these indicators are helpful for early screening of macrosomia and the timely prevention of related complications. As a result, introducing these 9 indicators in the risk nomogram is useful for the prediction of macrosomia risk in women with GDM.
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