Introduction

Screening for fetal aneuploidy in early pregnancy has become an integral part of antenatal services around the world since the 1990s (1). It is offered in the first trimester, which uses biomarkers like Pregnancy-associated plasma protein A (PAPP- A) and Beta human chorionic gonadotropins (β - hCG). Apart from its main utility for aneuploidy risk stratification, individual markers have been found to have an association with adverse pregnancy events like spontaneous abortion (SA), Gestational diabetes mellitus (GDM), preterm labor (PTL), intrauterine growth restriction (IUGR), pre-eclampsia (PE), and abruption of placenta, etc (2,3).

GDM is defined as abnormal glucose tolerance, detected first-time during the pregnancy and has variable severity. This metabolic and endocrine disorder has various adverse effects on the fetus and mother. The prevalence of GDM in India varies between 10-14% (4,5), and it is rising due to increasing type 2 diabetes and rampant obesity despite widespread awareness(6). Screening for GDM by glucose challenge test (GCT) is offered at 24-28 weeks of gestation in a low-risk pregnant population, universally. In India, we follow Diabetes in pregnancy study group India (DIPSI) guidelines for GDM diagnosis. Which recommends a diagnostic test in each trimester by 75 grams of glucose load irrespective of fasting state. It is found that fasting blood sugar levels and glycosylated HbA1c in the first trimester can diagnose pre-gestational overt diabetes (PGDM) and predict GDM at early gestation age (6).

Since certain metabolic changes may be undersurface for medically predisposed pregnant woman in early pregnancy. Therefore, if the biomarkers used in combined screening tests for aneuploidy could help in detecting such at risk pregnancies, would be of value. Early identification of such pregnancy in prediabetic state would help in taking measures towards prevention or postponement of GDM, and prevention of complications to mother and fetus to a certain extent (7). The current study aims to identify the association of PAPP-A and β-hCG with the subsequent risk of GDM. And if the value is different among GDM and non-GDM groups then, do they have a role in screening for GDM at early gestation? The data about GDM screening by PAPP-A and/or β-hCG in first trimester is conflicting (2,3,7) and scarce from India, hence the study is taken up.

Material and Methods:

The Kasturba Hospital Ethics Committee, Manipal Academy of Higher Education (IEC1 43/2022.) approved the study. This is an observational retrospective cohort study, conducted at Dr. TMA Pai Hospital, Udupi, affiliated with Manipal University, Manipal between November 2020 to November 2023. Dr. TMA Pai Hospital is a secondary care hospital at Udupi, serving a catchment area of almost 2,25,000 residents of a semi-urban area. All the data were retrieved from the electronic database system of the described hospital.

A total of 975 pregnant women who delivered at the hospital and had first-trimester aneuploidy screen results were included. We excluded patients with renal diseases, known diabetics, cardiac disorders, chromosomal abnormalities, spontaneous abortions, age of less than 18 years and more than 40 years, pregestational diabetes and multiple pregnancies. Data collected from the electronic database system were age, weight, height, first trimester events (subchorionic hemorrhage, ultrasound reports), history of aneuploidy, GDM, and macrosomia, family history of diabetes, laboratory test reports at booking visit and follow-up antenatal reports etc. Weight and height measured in early pregnancy at the booking visit were used for BMI calculation.

A study cohort included all the singleton pregnancies having first-trimester aneuploidy screening test (FTAST) between gestation of 11 to 13⁺⁶ days after taking verbal and written informed consent. FTAST uses a combination of ultrasound, biochemical, and demographic data to adjust a woman’s risk for aneuploidy. Ultrasound information obtained were Nuchal Translucency (NT) and CRL, which were as per fetal medicine foundation guidelines (1). About five milliliters of venous blood was collected after ultrasound and serum was sent to the biochemistry department of Kasturba Hospital, Manipal. It was processed on a Cobas 601 analyzer, which provided PAPP-A and B-hCG concentrations in multiple medians (MoM) and the method used was an electro-chemiluminescence assay.

At booking visits in the first trimester, for low-risk pregnancies for GDM, glycosylated hemoglobin (glyco HbA1c) ± fasting blood sugar was offered along with routine tests. Screening cum diagnostic test for GDM was done as per FOGSI (The Federation of Obstetric and Gynecological Societies of India) guidelines by DIPSI (Diabetes in Pregnancy Study Group India) method at 24-28 weeks of gestation and then at 34-34 weeks to identify GDM cases. Here, 75 grams of glucose was given with 300 ml of water irrespective of fasting state and a value of more than 140mg/dl was considered as a GDM case. A pregnant woman with age >35 years, previous macrosomia, recurrent urinary and/or reproductive infection, history of intrauterine death (IUD), glycosylated HbA1c>5.9%, history of GDM, 1^st-degree relative with DM (diabetes mellitus), obesity and overweight (BMI >25 and 30mg/m² respectively), etc. were considered as an additional high-risk factor for GDM. As all Indian women are considered high risk due to ethnic, racial, cultural and genetic basis, and were asked to get either the above-mentioned DIPSI GCT test or the WHO 75-gram glucose tolerance test (GTT) in each trimester until detection of GDM. Fasting plasma glucose of ≥92-125mg/dl, 1-hour plasma glucose ≥180mg/dl, and 2-hour value of ≥153-199 were considered as GDM (any one value of GTT). Detected GDM cases were monitored and treated according to guidelines.

The pregnancies diagnosed with GDM in any of the trimesters were taken as group 1 and all other pregnancies without GDM were taken as group 2(non-GDM group). Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) software version 20. The prevalence of GDM was obtained by frequency analysis. Study groups were compared for demographic characteristics by chi-square test for categorical data. p values <.05 was considered statistically significant. Mean values of PAPP-A and β-hCG were compared in GDM and non-GDM groups by student t-test. Receiver operating characteristic curve (ROC) analysis was performed for individual markers to get their efficacy in predicting GDM. Spearman correlation coefficient was applied to detect the relation between GDM and PAPP-A as well as β-hCG.

Results

A total of 975 pregnancies were enrolled in the analysis. GDM was diagnosed in 171 patients, therefore the incidence of diabetes was 17.5% in the present cohort.

Table 1 mentions the demographic and biochemical characteristics of GDM and non-GDM groups. Table 2 mentions first-trimester aneuploidy screen test results in terms of ultrasound and biochemical markers at 11 to 13⁺⁶ days. The mean PAPP-A level, age, maternal weight, BMI, and history were significantly different in GDM and non-GDM groups. β-hCG MoM and raw values, Glycosylated Hb, gravidae, parity index, NT, CRL, and Period of gestation (POG) were not significantly different among groups. PAPP-A value was inversely proportionate to GDM (-0.08 and so was with β-hCG (-0.08)), which was significant. We found a difference in the value of PAPP-A when GDM was diagnosed in the different trimesters. The values were highest in women with GDM diagnosed in the third trimester (1.3 MOM), and lowest in the first/early second trimester (1.1 MoM).

ROC curve was constructed (Figure 1) for PAPP-A marker to know sensitivity, specificity, and its potential as a diagnostic marker. The area under the curve (AUC) for the exclusion of GDM was 0.57 with SE of 0.024 and 95% CI of 0.52-0.62. The cut-off PAPP-A value of less than 0.95 MoM can identify GDM with a sensitivity of 45% and a specificity of 70%.

Figure 1: Receiver operating characteristic (ROC) analyses of PAPP-A MoM

Discussion

The prevalence of GDM is 17.4% in the study which is higher than described in the Indian literature which is between 10-14.3% (4,5). Difference in prevalence may be due to change in ethnicity, environment, and culture. Similarly, the urban, rural, and partly rural area also has a difference in prevalence (8). The coated prevalence of GDM is 13.8% in semi-urban area (8), and Udupi as semi-urban/partly rural area found to have a bit higher prevalence. This suggests a rising trend of GDM over the past few decades. Our observations suggest that increasing age of pregnancy, being overweight and obese, family history of DM, and history of GDM have a significant impact on the development of GDM, which is supported by various studies (4,5).

The current study suggests that pregnancies with GDM have decreased mean PAPP-A levels (1.2 ± 0.69 vs 1.39 ±0.80) and β-hCG levels (1.01 ± 0.75 vs 1.09 ± 1.02) in MoM than in a non-GDM group. The difference was significant (p-value <0.001) for PAPP-A MoM. The raw value of PAPP-A and β-hCG was also decreased in the GDM group but it was not significant. PAPP-A is a matrix metalloprotease, secreted by trophoblast and detected as early as the 28th day of conception in maternal blood. It is maintained high in circulation throughout the pregnancy. It cleaves insulin-like growth factor binding protein (IGF-BP) and releases IGF (1,2). Now, Free IGF regulates the uptake of glucose; and hence, has a role in carbohydrate metabolism and in the genesis of diabetes (7,8). IGF is important for fetal growth, placental formation and protein synthesis, angiogenesis, and adipocyte hypertrophy (9). Now, if the bioavailability of IGF is decreased, (secondary to insufficient levels of PAPP-A), it would result in inadequate angiogenesis, and lipotoxicity, resulting in insulin resistance and GDM (7,10). PAPP-A modulates local bioavailability of IGF1 and 2 through its autocrine and paracrine action.

First-trimester aneuploidy screening has become usual nowadays. No more, it is only for the elderly or high-risk group for aneuploidy. Hence, in almost all pregnancies, these biomarkers can be traced, and values can be achieved with no extra cost. And this biomarker values have a potential to provide a clue about the future development of pregnancy complications. For example, a low value of PAPP-A (<5%) needs a careful watch for pre-eclampsia, growth restriction, GDM, etc (2,3), few of these complications can be prevented by life style modification and preventive pharmacotherapy. If not able to prevent the condition, then severity can be reduced to a certain extent.

Table 3 mentions different studies stating the association between PAPP-A and GDM (10-17). The results of these studies are conflicting and such a heterogenous results may be due to different ethnicity, women's risk profiles, the severity of GDM, and the screening method selected in the different studies (7). Our findings are consistent with a meta-analysis having predictive value of PAPP-A for GDM as 55% and 90% (sensitivity and specificity respectively) (7). We demonstrated that low levels of PAPP-A are associated with GDM but as a predictor, it has a low accuracy (45% sensitivity and 70% specificity at a cutoff of 0.95). Hence, a glucose screening test by DIPSI method, which has the advantage of both screening and diagnostic test is recommended in each trimester. It has the added advantage of being simple, cost-effective, and patient-friendly.

We studied both raw and adjusted values of β-hCG. A study by Yildiz A showed a significant decrease in the adjusted value of β-hCG concentrations in GDM cases compared to non-GDM cases, similar findings were noted by Spenser K (12,17). We observed a reduced level of β-hCG, but was not statistically significant, which was aligning with a couple of previous studies (14,15). Contradictory to our observations, the literature reports higher β-hCG values in the GDM group, however, it was not statistically significant (13,16). A study by P. Sirikunalai has reported that first-trimester low β-hCG had a significantly increased risk of IUGR, LBW (low birth weight), PTL, and low APGAR score but high levels may decrease the risk of PT birth and GDM (18). The author explained that it could be due to insufficient placental damage to result in a significant change in serum-free β hCG levels. Caliskan R studied the raw value of these two biomarkers in 278 pregnant women and suggested that low PAPP-A and high β-hCG were detected in GDM cases. However, only the PAPP-A value was significantly decreased, and our results are identical to that for PAPP-A results (19).

The above findings confirm that first-trimester aneuploidy markers have a known association with placental pathology. A low PAPP-A value results in low IGF, which is associated with abnormal glucose metabolism and placentation abnormality. Hence, pregnancy complicated by abnormal trophoblastic invasion (for ex. IUGR, PE) is found more with pre-gestational and gestational diabetes (20). It is proven that alteration in placental function is possible by cytokines, growth factors and insulin etc. in selected women. Hence, it may help to control metabolic disturbances such as hyperglycemia and hyperinsulinemia. This in turn would help to reduce oxidative stress, and inflammation and hence placental pathologies (21). That’s the reason, we infer that the low normal value of PAPP-A can be considered as high-risk factor for development of GDM and needs early glucose testing. This would help due interventions in halting the progress of GDM. Hence, the harmful effects on the mother and fetus like spontaneous abortions, pre-eclampsia, infections, macrosomia, etc can be prevented to a certain extent.

Conclusion

A low β-hCG value was found in the GDM group, however, it was not significant, hence, it is not a useful marker for the prediction of GDM. A low normal value of PAPP-A could provide a clue to early development of GDM. Due to low sensitivity and specificity as a predictor, utility as a standalone marker for GDM prediction is not recommended. The low and low normal value of PAPP-A warrants early testing for GDM, preferably the DIPSI group method in India. It would help in the timely detection of gestational diabetes and intervention to improve GDM outcome.

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