Discussion
We have developed a GDM risk model that can be applied during early pregnancy or before pregnancy. AUCs obtained during development were similar to those obtained after development (0.7507 and 0.8256, respectively), supporting the validation of our model. The development of this model is important because early detection of women at high risk of GDM could catalyze timely intervention with the implementation of lifestyle changes prior to week 20 of pregnancy, or preferably before week 16, when interventions have been shown to be effective.7 36 The algorithm used in our model includes 11 SNPs and 4 clinical features.
Our study showed that the presence of the G allele at rs10830963 in MTNR1B, and the T allele at rs1387153 in LOC100128354/MTNR1B are associated with an increased risk of GDM. The association of SNPs in MTNR1B with fasting glucose and insulin secretion is well established.37 Melatonin is the primary hormone secreted by the pineal gland; it regulates sleep, circadian rhythm, and glucose metabolism. MTNR1B is highly expressed in both the placenta and pancreatic islets. Lyssenko et al have shown that genetic variants in this melatonin receptor correlate with impaired glucose-stimulated insulin secretion.38 Furthermore, interactions between variants in MTNR1B, GDM risk, and physical activity and healthy eating interventions in pregnant women have been proposed.22 MTNR1B regulates circadian rhythmicity and influences energy metabolism.37 Furthermore, associations have been found between relative macronutrient intake, higher fasting plasma glucose, short sleep duration (<7 hours), and MTNR1B genetic variants.39 It has been proposed that lower carbohydrate intake and normal sleep duration may ameliorate cardiometabolic abnormalities conferred by common circadian rhythm-related genetic variants.39 In addition, carriers of the CC genotype tend to respond more favorably to a hypocaloric diet enriched with monounsaturated fats.40 Thus, recommendations regarding diet, particularly for carbohydrate and fat consumption, and sleep duration should be emphasized to women who are carriers of MTNR1B gene variants and at high risk of GDM.
Another variant included in our model was rs11715915 in AMT, a gene that encodes aminomethyltransferase, which is a critical component of the glycine cleavage system in mitochondria, where energy production occurs.41 The breakdown of glycine produces a methyl group, which is added to and used by folate. rs11715915 is located either in the 3’ untranslated region or within coding regions of AMT, depending on the transcript, and upstream of RHOA (ras homolog family member A).41 RHOA is a signaling molecule that activates Rho kinase, a regulator of insulin transcription that is differentially regulated in T2D and thought to play a role in glucose homeostasis.42
Our study also identified variants in genes encoding transcription factors (FOXA2, PROX1, RBMS1, and RREB1) that regulate basic processes in the embryonic development of pancreatic beta cells, cell cycle progression in the pancreas, and insulin response in peripheral tissues. FOXA2 encodes the forkhead box protein A2, a member of the forkhead class of DNA-binding proteins. FOXA2 has been previously identified as a master regulator in pancreatic development and is involved in regulating both the glucose-sensing apparatus and insulin release.43 In a study by Yu and Zhong, it was shown that the microRNA miR-141, a post-transcriptional regulator in the pathophysiology of T2D, may lead to impaired glucose-stimulated insulin secretion and beta cell proliferation by targeting FOXA2 at the 3’ untranslated region; a potential role for the antidiabetic drug pioglitazone in regulating the miR-141/FOXA2 axis was also identified.44
Another variant of interest identified in our study is the C allele at rs340874 in PROX1 (Prospero homeobox 1), a transcription factor involved in the embryonic development of the pancreas, liver, and nervous system. Carriers of the CC genotype have been previously shown to have higher non-esterified fatty acid levels after a high-fat meal and lower glucose oxidation after a high-carbohydrate meal in comparison with subjects who have other PROX1 genotypes.45 Subjects with the CC variant also had higher accumulation of visceral fat and, surprisingly, lower daily food consumption.
Additionally, rs6742799, mapping to RBMS1 (RNA binding motif, single-stranded interacting protein 1) was found to have a significant association with GDM. RBMS1 is expressed in the placenta and has a possible anti-inflammatory role. Alvine et al proposed that increased expression of placental RBMS1 in obese women may serve as an adaptive response to reduce oxidative stress in a maternal obesogenic environment.46 Oxidative stress is now recognized as playing an essential role in certain pregnancy-related disorders such as GDM, pre-eclampsia, and intrauterine growth retardation.47 The maternal obesity associated with metabolic alterations seems to lead to the appearance of an elevated placental oxidative stress, compromising both placental metabolism and antioxidant status.48
The A allele at rs9379084 in RREB1 (Ras-responsive element binding protein 1) was found to have a protective effect in our study. RREB1 is a member of zinc finger transcription factors and functions both as a transcriptional activator and repressor, and its role in target gene regulation may depend on its binding partner and the status of epigenetic modifications.49 The cell cycle regulator CDKN2A increases susceptibility to T2D and is regulated by RREB1. Furthermore, RREB1 also directly promotes the expression of insulin genes.49
Our GDM risk algorithm also included genetic variants in genes with a signaling function and association with insulin resistance (IRS1, RSPO3, CILP2). IRS1 is a signaling intermediate downstream of activated cell-surface insulin receptors.48 RSPO3 encodes R-Spondin-3, which regulates Wnt and beta-catenin signaling pathways; RSPO3 gene knockdown results in abnormal adipogenesis, lipid metabolism, and insulin signaling.49 In addition, CILP2 encodes cartilage intermediate layer protein 2, a glycoprotein initially identified in collagen. CILP2 is located in the NCAN-CILP2-PBX4 region, an intergenic region spanning 300 kb associated with serum cholesterol, low-density lipoprotein and triglyceride concentrations, cardiovascular disease, and non-alcoholic fatty liver disease.50
The 11 SNPs identified in this analysis are located in genetic loci that have been reported to participate in molecular processes related to fasting glucose (MTNR1B, GCK, AMT, PROX1, and FOXA2), insulin resistance (CILP2, IRS1, and RBMS1), insulin secretion (MTNR1B), and fasting insulin (IRS1). Four of these SNPs have previously been associated with T2D (LOC100128354/MTNR1B, PROX1, CILP2, and RBMS1), while two other SNPs have previously been reported in GDM (LOC100128354/MTNR1B and RREB1). Overall, this initial annotation of potential genetic loci characteristics, as reported in the literature, is just an initial investigation into how genetic variants may contribute to GDM susceptibility.
The GDM risk algorithm described in this study also included four phenotypic variables: maternal age, pre-gestational BMI, family history of T2D, and previous pregnancies. Each of these is a well-known risk factor for GDM. The four phenotypic variables alone yielded an AUC of 0.65 and 0.68 in the development and validation sets, respectively. The 11 SNPs alone yielded respective AUCs of 0.71 and 0.77. The additive contributions of phenotype and genotype increased the overall AUCs to a respective 0.75 and 0.83. To our knowledge, this is the highest performance for a genotype-informed GDM prediction algorithm reported in the literature to date. Although the current rise in GDM prevalence is driven mainly by changes in lifestyle, complex genetic determinants contribute to the inherent susceptibility of this disease. Inclusion of genotype-based susceptibility information will support the use of precision medicine, the identification of women at high risk of GDM during the early stages of pregnancy, and the application of personalized preventive interventions. Translation of new findings from genetic studies to the clinic is the most attractive aspect of genome research. One potential clinical application is the development of genetically informed personalized susceptibility profiles and lifestyle recommendations. However, at present, precision medicine has not yet fulfilled such expectations,51 as it requires a much-needed process of internal and external validation and calibrations to target specific populations. It is therefore necessary to apply sufficient funding and infrastructure to promote the transfer of knowledge, such as the findings presented herein, to society as a whole.
The strengths of this study include a robust modeling strategy for significant attributes, as well as the analysis of a carefully selected list of 114 SNPs according to their reported predictive value. It is worth noting that we did not simply focus on the correlation of each SNP with GDM, but rather on the combined effect of the significant SNPs. Our analysis yielded both a combination and predictive weight of variables that were predictive of the population studied. Our study had some limitations. The analysis was based on data from two cohorts of women and, as such, the results may not be applicable to the entire Mexican population. Ancestry markers were also not genotyped because our aim was to identify markers with a predictive power in the global Mexican population; we were not evaluating variants specific to any particular subethnicity. This, however, could be considered a limitation of our study and should be evaluated in future analyses. Data regarding patient lifestyle, such as diet and sleep duration, which are associated with MTNR1B genetic variants, were not collected in this study. Additionally, the small sample size may have affected the accuracy and reliability of the model to an extent. Large-scale multicenter studies need to be performed to further verify this prediction model for GDM.