Risk Factors and Diagnostic Performance of Predictors as a Screening Technique for Gestational Diabetes Mellitus: A Retrospective Cross-Sectional Study.
Conditions: Gestational Diabetes
Sponsor: King Abdulaziz Medical City

Risk Factors and Diagnostic Performance of Predictors as 
a Screening Technique for Gestational Diabetes Mellitus: 
A Retrospective Cross-Sectional Study. 
 
 
 
Authors 
Fatimah Mudaia Khobrani 
Abdullah Mohammad alzahrani 
Dina Saleh Binmahfoodh 
Rawan Abdullah Hemedy 
Salwa Ibrahim Abbas 
 
 
 

Introduction 
Gestational diabetes mellitus (GDM) is a prevalent condition that affects women across 
the globe (1). It can lead to a range of complications for both the mother and the baby. The 
mother may be at a higher risk of developing type 2 diabetes and premature cardiovascular 
disease, while the baby may experience macrosomia, obesity, hypoglycemia, diabetes, 
hypertension, and cardiovascular disease in their youth and adulthood (2,3). Early detection of 
gestational diabetes mellitus (GDM) is crucial to avoid its consequences. In 2010, the 
International Association of Diabetes and Pregnancy Study Groups (IADPSG) recommended 
a 2-hour, 75-gram oral glucose tolerance test (OGTT) to diagnose GDM in all pregnant women 
who did not have a history of overt diabetes during the 24th-28th weeks of pregnancy (4). The 
oral glucose tolerance test (OGTT) is considered to be the most reliable method, but it can be 
time-consuming, has poor reproducibility, and is often poorly tolerated during pregnancy (5,6). 
It is important that pregnant women are not required to attend lengthy clinic sessions for 
OGTTs, especially during a pandemic. Therefore, simpler yet accurate alternative screening 
tests should be implemented to reduce the number of OGTTs (7). 
Previous research has suggested that fasting plasma glucose (FPG) can be used as a 
screening technique for gestational diabetes mellitus (GDM) diagnosis. FPG is easier and 
quicker to use, is less expensive, and can lower healthcare costs associated with universal oral 
glucose tolerance test (OGTT) screening. However, to accurately diagnose GDM, it is 
necessary to evaluate the diagnostic performance and determine the ideal FPG cutoff (7). Ping 
et al. found that gestational diabetes mellitus (GDM) can be predicted through both pre-BMI 
and initial fasting plasma glucose (FPG) levels before 24 weeks (8). Similarly, Hao et al. found 
that women who develop GDM tend to have significantly higher FPG levels during the first 
trimester (4.6 ± 0.3 mmol/L) compared to women with normal glucose tolerance (4.4 ± 0.3 
mmol/L; p=0.001), as well as higher BMI during the same period (9). Additionally, Shin Y et 
al. reported that a higher BMI is linked to a greater prevalence of GDM. Despite a higher risk 

of developing type 2 diabetes, lifestyle interventions that aim to reduce BMI have the potential 
to lower the risk of GDM (10). The lack of a standardized agreement on diagnostic criteria and 
cutoff values for screening tests of FBG and pre-BMI has made it difficult to detect women 
with GDM early (8). Thus, our study aims to define optimal levels of FBG for the detection of 
GDM. 
Rationale 
The aim of this study is to examine potential risk factors and complications linked to 
high-risk pregnancies, with a specific focus on gestational diabetes mellitus (GDM). The study 
intends to offer an understanding of the relationship between GDM and factors such as 
impaired fasting glucose or impaired glucose tolerance (IFG/IGT), family history of diabetes 
mellitus (DM), and medical conditions. The study also intends to evaluate the diagnostic 
performance of screening techniques for gestational diabetes mellitus (GDM), including a 
history of GDM and impaired fasting glucose (IFG) or impaired glucose tolerance (IGT). 
 
Methods and materials 
Study design and area 
This study was a hospital-based retrospective cross-sectional study. The study was 
conducted at the clinic of King Abdul-Aziz Medical City, a tertiary care hospital in Jeddah, 
Saudi Arabia. Data were collected from the target population through the use of medical 
records. 
Study Population 
For this study, we looked at all pregnant patients who visited high-risk pregnancy 
outpatient clinics at King Abdul-Aziz Medical City in Jeddah from 2021 to 2022. Our focus 
was on singleton pregnant women who received prenatal care services in Obstetrics clinics at 
King Abdul-Aziz Medical City. However, to ensure accurate and valid results, we excluded 
pregnant women with diabetes mellitus or autoimmune diseases. By examining this specific 

population of pregnant women, we aimed to investigate potential risk factors and complications 
associated with high-risk pregnancies. 
 
Sampling technique and size 
The sampling technique for this study is non-probability consecutive sampling, which 
will include all pregnant patients who visited high-risk pregnancy outpatient clinics at King 
Abdul-Aziz Medical City in Jeddah from 2021 to 2022. The study aims to collect data from all 
members of the target population instead of sampling from a larger population. 
Data Collection Technique and Tool 
The data collection tool was developed and face-validated by two consultants in the 
field. The tool was designed to collect data from the medical records of the participants, 
including their fasting blood glucose (FBG) levels and oral glucose tolerance test (OGTT) 
results. The tool was also used to collect information about the participants' obstetric history, 
demographic characteristics, and medical conditions. 
Data Entry and Statistical Analysis 
The researchers used IBM SPSS Statistics (version 29.0) for data analysis. Categorical 
variables were presented as proportions, while numerical variables were presented as medians 
and interquartile ranges due to non-normal distribution. Inferential analyses of categorical 
variables were conducted using statistical tests such as chi-square and Fisher-Freeman-Halton 
Exact Tests. Additionally, multivariate analyses were conducted using binary logistic 
regression, with significance determined by p-values less than 0.05 and inferences made with 
a 95% confidence level. 
References 
1. Babaniamansour S, Aliniagerdroudbari E, Afrakhteh M, Hosseinpanah F, Farzaneh F, 
Niroomand M. Can fasting plasma glucose replace oral glucose-tolerance test for 
diagnosis of gestational diabetes mellitus? Diabetol Int. 2021 Jul 1;12(3):277–85.  
2. Reyes-Muñoz E, Sandoval-Osuna NL, Reyes-Mayoral C, Ortega-González C, Martínez-
Cruz N, Ramírez-Torres MA, et al. Sensitivity of fasting glucose for gestational diabetes 
mellitus screening in Mexican adolescents based on International Association of Diabetes 

and Pregnancy Study Groups criteria: a diagnostic accuracy study based on retrospective 
data analysis. BMJ Open. 2018 Apr 1;8(4):e021617.  
3. Rabizadeh S, Firouzabadi FD, Noshad S, Esteghamati S, Afarideh M, Ghajar A, et al. 
Beneficial Effects of Pentoxifylline Plus Losartan Dual Therapy in Type 2 Diabetes with 
Nephropathy. Am J Med Sci. 2018 May 1;355(5):442–8.  
4. Trujillo J, Vigo A, Reichelt A, Duncan BB, Schmidt MI. Fasting plasma glucose to avoid 
a full OGTT in the diagnosis of gestational diabetes. Diabetes Res Clin Pract. 2014 Sep 
1;105(3):322–6.  
5. Feng H, Zhu WW, Yang HX, Wei YM, Wang C, Su RN, et al. Relationship between Oral 
Glucose Tolerance Test Characteristics and Adverse Pregnancy Outcomes among 
Women with Gestational Diabetes Mellitus. Chin Med J (Engl). 2017 May 
5;130(9):1012.  
6. Agarwal MM. Gestational diabetes mellitus: Screening with fasting plasma glucose. 
World J Diabetes. 2016 Jul 25;7(14):279–89.  
7. Beunen K, Neys A, Van Crombrugge P, Moyson C, Verhaeghe J, Vandeginste S, et al. 
Fasting plasma glucose level to guide the need for an OGTT to screen for gestational 
diabetes mellitus. Acta Diabetol. 2022 Mar 1;59(3):381–94.  
8. Li P, Yin Y, Lin S, Cui J, Zhou S, Li L, et al. Utility of Pregestational Body Mass Index 
and Initial Fasting Plasma Glucose in Predicting Gestational Diabetes Mellitus. Am J 
Med Sci. 2016 Apr 1;351(4):420–5.  
9. Hao M, Lin L. Fasting plasma glucose and body mass index during the first trimester of 
pregnancy as predictors of gestational diabetes mellitus in a Chinese population. Endocr 
J. 2017;64(5):561–9.  
10. Mnatzaganian G, Woodward M, McIntyre HD, Ma L, Yuen N, He F, et al. Trends in 
percentages of gestational diabetes mellitus attributable to overweight, obesity, and 
morbid obesity in regional Victoria: an eight-year population-based panel study. BMC 
Pregnancy Childbirth. 2022 Feb 1;22(1):95.  
11. Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, et al. IDF Diabetes Atlas: 
Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by 
International Association of Diabetes in Pregnancy Study Group's Criteria. Diabetes Res 
Clin Pract [Internet]. 2022 Jan 1 [cited 2023 Sep 1];183. Available from: 
https://www.diabetesresearchclinicalpractice.com/article/S0168-8227(21)00409-
5/fulltext 
12. Choudhury AA, Devi Rajeswari V. Gestational diabetes mellitus - A metabolic and 
reproductive disorder. Biomed Pharmacother. 2021 Nov 1;143:112183.  
13. Omazić J, Viljetić B, Ivić V, Kadivnik M, Zibar L, Müller A, et al. Early markers of 
gestational diabetes mellitus: what we know and which way forward? Biochem Medica. 
2021 Oct 15;31(3):0–0.  

14. Alsaedi SA, Altalhi AA, Nabrawi MF, Aldainy AA, Wali RM. Prevalence and risk 
factors of gestational diabetes mellitus among pregnant patients visiting National Guard 
primary health care centers in Saudi Arabia. Saudi Med J. 2020 Feb 1;41(2):144–50.  
15. Alfadhli EM, Osman EN, Basri TH, Mansuri NS, Youssef MH, Assaaedi SA, et al. 
Gestational diabetes among Saudi women: prevalence, risk factors and pregnancy 
outcomes. Ann Saudi Med. 2015 May;35(3):222–30.  
16. Miller C, Lim E. The risk of diabetes after giving birth to a macrosomic infant: data from 
the NHANES cohort. Matern Health Neonatol Perinatol. 2021 May 12;7(1):12.  
17. Kc K, Shakya S, Zhang H. Gestational Diabetes Mellitus and Macrosomia: A Literature 
Review. Ann Nutr Metab. 2015;66:14–20.  
18. TABATABAEE HR, ZAHEDI A, ETEMAD K, VALADBEIGI T, MAHDAVI S, 
ENAYATRAD M, et al. Risk of Stillbirth in Women with Gestational Diabetes and High 
Blood Pressure. Iran J Public Health. 2020 Apr;49(4):773–81.  
19. Rahnemaei FA, Abdi F, Kazemian E, Shaterian N, Shaterian N, Behesht Aeen F. 
Association between body mass index in the first half of pregnancy and gestational 
diabetes: A systematic review. SAGE Open Med. 2022 Jan 1;10:20503121221109910.  
20. Li P, Lin S, Li L, Cui J, Zhou S, Fan J. First-trimester fasting plasma glucose as a 
predictor of gestational diabetes mellitus and the association with adverse pregnancy 
outcomes: Pak J Med Sci [Internet]. 2019 Jan 22 [cited 2023 Sep 1];35(1). Available 
from: https://www.pjms.org.pk/index.php/pjms/article/view/216 
21. Wang C, Wei Y, Yang Y, Su R, Song G, Kong L, et al. Evaluation of the value of fasting 
plasma glucose in the first trimester for the prediction of adverse pregnancy outcomes. 
Diabetes Res Clin Pract [Internet]. 2021 Apr 1 [cited 2023 Sep 1];174. Available from: 
https://www.diabetesresearchclinicalpractice.com/article/S0168-8227(21)00089-
9/fulltext 
22. Peng X, Liu M, Gang J, Wang Y, Ma X. Use of oral glucose tolerance testing and HbA1c 
at 6–14 gestational weeks to predict gestational diabetes mellitus in high-risk women. 
Arch Gynecol Obstet. 2023 May 1;307(5):1451–7.  
23. Jager KJ, Tripepi G, Chesnaye NC, Dekker FW, Zoccali C, Stel VS. Where to look for 
the most frequent biases? Nephrology. 2020;25(6):435–41.