GRAPHICAL ABSTRACT

Highlights

• Pregravid weight gain is dose-dependently associated with an increased risk of GDM.

• Minimizing pregravid weight gain could reduce the risk of developing GDM.

INTRODUCTION

Gestational diabetes mellitus (GDM) stands as the most prevalent medical complication during pregnancy, entailing diverse perinatal challenges and heightening the risk of metabolic diseases and type 2 diabetes mellitus in both mothers and their offspring [1,2]. Advanced maternal age, maternal obesity, and excessive weight gain during pregnancy are acknowledged as prominent risk factors for GDM [3-8].

In South Korea, there has been a notable increase in obesity and abdominal obesity prevalence in recent decades, especially among women in their 20s and 30s. For instance, from 2009 to 2019, the prevalence of obesity among women in their 20s surged from 8.6 % to 16.8%, while among women in their 30s, it escalated from 14.0% to 20.4% [9]. Additionally, the average maternal age at childbirth in Korea stands at 33.5 years, with mothers aged 35 years and older constituting 35.7% of the total in 2022 [10]. Consequently, there are mounting concerns regarding heightened pregnancy complications, including GDM, owing to the concurrent rise in maternal age and obesity rates among young women of reproductive age.

Similar to trends observed in other Asian countries, recent studies have documented an increasing prevalence of GDM in Korea [11,12]. Moreover, research has emphasized a significant correlation between pregravid weight gain and the subsequent development of GDM during pregnancy across diverse populations [13-16]. However, to date, no specific investigations have targeted Korean women on this subject. Addressing this gap, the present study endeavors to explore the association between pregravid weight gain and the risk of developing GDM in subsequent pregnancies, utilizing a comprehensive nationwide population-based cohort.

METHODS

Data source and study population

This study utilized the Korean National Health Insurance Service (KNHIS) database (NHID) as the primary data source. Almost all Koreans (97.1%) are mandatory subscribers to the KNHIS, a single government-run medical insurer of the Republic of Korea that provides comprehensive medical care to all subscribers. The NHID contains baseline demographic data (age, sex, income level, etc.), diagnosis codes of various diseases, records of inpatient and outpatient medical service usage, pharmacy dispensing claims, and mortality data. The KNHIS offers a regular biannual health screening program for all subscribers. Therefore, the NHID is a comprehensive repository of medical records of the entire population of South Korea, as citizens remain in the national health insurance system continuously unless there are reasons such as long-term overseas stay. Access to the NHID is granted to medical researchers once their study protocols are approved by the official review committee (https://nhiss.nhis.or.kr/). Prior epidemiological studies have used this database, and its details have been described elsewhere [17,18].

For this study, a subset of the NHID consisting of 1,098,353 Korean women who gave birth between 2015 and 2017 was retrieved. The conception date was calculated as 280 days before the delivery date. Among this group, 270,374 women had taken the national health examination (referred to as the index checkup) within a year preceding the suspected date of conception. Among them, 159,798 women who had also undergone the national health examination (referred to as the baseline checkup) within 2 years before the index checkup were selected for the study. Women were excluded if they had a previous diagnosis of diabetes (those with a registered disease code of E10–14 and insulin or oral antidiabetic medications at least once during 2 years before the suspected date of conception, n=1,414), had fasting blood glucose (FBG) levels ≥126 mg/dL at any of the examinations (n=1,009), or if their data were incomplete or missing (n=11,012). The diagnostic codes used in the selection process of study population are presented in Supplementary Table 1. Finally, a total of 146,363 women were included in the analysis, and a flowchart illustrating the enrollment process of the study population is presented in Fig. 1, Supplementary Fig. 1.

Smoking, alcohol consumption, and physical exercise data were collected through participants’ self-reported responses on the health survey questionnaire during the health checkup. Smoking status was categorized as follows: current smokers (those who had smoked at least 100 cigarettes in their lifetime and were still smoking within 1 month of the index examination), ex-smokers (those who had smoked at least 100 cigarettes in their lifetime but had not smoked within 1 month of the index examination), and never-smokers (those who had smoked less than 100 cigarettes in their lifetime). Patients were classified based on their daily alcohol consumption as non-drinkers (0 g of alcohol per day), mild-to-moderate drinkers (less than 30 g of alcohol per day), or heavy drinkers (30 g or more of alcohol per day). Regular physical activity was defined as moderate physical activity for more than 30 minutes daily and for more than 5 days per week, or vigorous exercise on more than 3 days per week.

Low income status was determined based on two criteria: having an income in the lower 25% according to the amount of health insurance premiums paid (Korean premiums are determined by income level), or receiving medical aid with an income lower than the lowest 3% income of the entire population.

Measurements and definitions

All women underwent overnight fasting prior to the laboratory tests conducted using standard methods. A diagnosis of metabolic syndrome (MetS) was established if three or more of the following criteria were met [19]: abdominal obesity (waist circumference [WC] ≥85 cm in women [20]), elevated blood pressure (≥130/85 mm Hg) or the use of antihypertensive medication, FBG ≥100 mg/dL or the use of glucose-lowering medication, triglyceride (TG) levels ≥150 mg/dL or the use of lipid-lowering medication, and low levels of high-density lipoprotein cholesterol (HDL-C; <50 mg/dL for women) or the use of lipid-lowering treatment. The estimated glomerular filtration rate was calculated using the equation derived from the Modification of Diet in Renal Disease study [21]. On the day of the health examination, body weight, height, and WC were measured. WC was measured using a tape measure at the midpoint between the lower costal margin and the iliac crest. Obesity was defined as a body mass index (BMI) of 25.0 kg/m² or higher according to the World Health Organization guideline for the Asia-Pacific region [22].

Definition of GDM, insulin-requiring GDM

The Korea Society of Obstetrics and Gynecology recommends universal GDM screening for all pregnant women between 24 and 28 weeks, regardless of their risk factors. The screening process, as recommended by the American College of Obstetricians and Gynecologists, involves a 50 g glucose challenge test followed by a diagnostic 100 g, 3-hour oral glucose tolerance test if the initial test results are abnormal (two-step approach). A diagnosis of GDM is established if two or more glucose values meet or exceed the thresholds proposed by either the Carpenter and Coustan (CC) criteria or the National Diabetes Data Group (NDDG) criteria [1,23,24].

Women who developed GDM were identified based on having more than three claims of GDM (International Classification of Diseases, 10th Revision [ICD-10] code O24.4 and O24.9), regardless of whether they received prescriptions for insulin or oral diabetic medication. GDM cases requiring insulin therapy were defined as having more than three claims of GDM (ICD-10 code O24.4 and O24.9) and receiving insulin prescriptions at least once during the pregnancy period. The control group for insulin-requiring GDM was comprised of both non-GDM and GDM that did not require insulin. The disease codes used for the classification of the study participants are summarized in Supplementary Table 1.

Statistical analysis

Numerical variables are expressed as mean±standard deviation or geometric mean (95% confidence interval [CI]), while categorical variables are presented as numbers and percentages. The t-test was used for comparing continuous variables, and the chi-squared test was employed for comparing categorical variables. Logistic regression analyses were conducted to calculate the odds ratio (OR) and 95% CI for each independent variable.

In model 1, no adjustments were made, while in multiple regression model 2, adjustments were made for age, smoking, regular physical activity, income, primigravidity, hypertension, dyslipidemia, FBG, and pregravid weight (weight at index checkup). Statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R Software version 3.6.4 (The R Foundation for Statistical Computing, Vienna, Austria; http://www.R-project.org). Two-tailed significance tests were conducted, with a P value of ≤0.05 considered statistically significant.

Ethics statement

All procedures involving human participants in this study followed the ethical standards set forth in the Helsinki Declaration. The study’s retrospective design, along with the use of an anonymized database and medical records that are openly accessible to the public, resulted in the exemption of review and approval (IRB No. 2306-008-1436) by the Institutional Review Board of Seoul National University Hospital.

Previous publication

The abstract of this study was presented as a poster at the 2023 Endo Meeting, in Chicago, USA [25].

RESULTS

Clinical characteristics according to the presence of GDM and insulin-requiring GDM

Among all participants, 7.52% (n=11,012) had GDM, and 0.76% (n=1,114) had insulin-requiring GDM. Women with GDM or insulin-requiring GDM were older (33.5±3.7 years vs. 32.4±3.6 years, P<0.001 for GDM; 34.4±4.0 years vs. 32.5±3.6 years, P<0.001 for insulin-requiring GDM), more likely to be pregnant with multiple fetuses (3.1% vs. 1.8%, P<0.001 for GDM; 3.7% vs. 1.9%, P<0.001 for insulin-requiring GDM), former or current smokers (6.9% vs. 5.5%, P<0.001 for GDM; 9.9% vs. 5.5%, P<0.001 for insulin-requiring GDM), and had higher rates of obesity (18.0% vs. 9.3%, P<0.001 for GDM; 33.0% vs. 9.8%, P<0.001 for insulin-requiring GDM), hypertension (2.4% vs. 1.3%, P<0.001 for GDM; 4.3% vs. 1.3%, P<0.001 for insulin-requiring GDM), dyslipidemia (5.4% vs. 3.3%, P<0.001 for GDM; 8.6% vs. 3.4%, P<0.001 for insulin-requiring GDM), and MetS (6.4% vs. 1.9%, P<0.001 for GDM; 17.2% vs. 2.1%, P<0.001 for insulin-requiring GDM). They also had a higher prevalence of a family history of DM (15.9% vs. 10.1%, P<0.001 for GDM; 23.7% vs. 10.4%, P<0.001 for insulin-requiring GDM) and exhibited elevated levels of FBG (91.3±10.3 mg/dL vs. 88.2±8.8 mg/dL, P<0.001 for GDM; 96.3±11.4 mg/dL vs. 88.4±8.9 mg/dL, P<0.001 for insulin-requiring GDM), total cholesterol (185.2±31.1 mg/dL vs. 178.9±29.5 mg/dL, P<0.001 for GDM; 191.2±32.2 mg/dL vs. 179.3±29.6 mg/dL, P<0.001 for insulin-requiring GDM), TG (77.2 mg/dL [95% CI, 76.5 to 76.5] vs. 67.8 mg/dL [95% CI, 67.7 to 67.7], P<0.001 for GDM; 95.9 mg/dL [95% CI, 92.9 to 98.9] vs. 68.3 mg/dL [95% CI, 68.2 to 68.5], P<0.001 for insulin-requiring GDM), and low-density lipoprotein cholesterol (105.3±28.5 mg/dL vs. 99.6±28.1 mg/dL, P<0.001 for GDM; 111.0±33.8 mg/dL vs. 100.0±28.1 mg/dL, P<0.001 for insulin-requiring GDM), while their HDL-C levels were lower (62.3±14.3 mg/dL vs. 64.4±15.4 mg/dL, P<0.001 for GDM; 58.8±13.9 mg/dL vs. 64.3±15.3 mg/dL, P<0.001 for insulin-requiring GDM). The percentage of being primigravid (72.6% vs. 74.5%, P<0.001), mild-to-moderate, or heavy drinkers was significantly lower (49.0% vs. 50.5%, P=0.0072), and doing regular physical exercise was significantly higher (13.0% vs. 12.2%, P=0.0115), in the GDM group than in the non-GDM group. There were no statistically significant differences between the insulin-requiring GDM and control group in these parameters (Table 1).

Associations with GDM according to the weight changes before conception

The incidence of GDM was 7.05% (n=350), 6.59% (n=861), 7.14% (n=6,358), 8.15% (n=2,108), and 9.97% (n=1,335) for <–10%, –10% to –5%, –5% to 5%, 5% to 10%, and ≥10% weight change groups, respectively. In multiple regression analyses with the stable weight group (–5% to 5% weight change) as the reference, those who gained ≥10% had a 34% increased risk of GDM (OR, 1.34; 95% CI, 1.26 to 1.43), and those who gained 5% to 10% had a 12% increased risk (OR, 1.12; 95% CI, 1.06 to 1.17). On the other hand, those who lost more than 10% showed a 13% decreased risk of developing GDM (OR, 0.87; 95% CI, 0.78 to 0.98), while those with a weight loss of 5% to 10% had a 10% decreased risk (OR, 0.90; 95% CI, 0.83 to 0.97) (Table 2).

Associations with insulin-requiring GDM according to the weight changes before conception

Among the 1,114 (0.76%) women who developed insulin-requiring GDM, 0.67% (n=33), 0.51% (n=67), 0.66% (n=586), 0.99% (n=257), and 1.28% (n=171) were classified in the five weight change groups, respectively. In binary logistic analysis, those who gained weight 10% or more had a 61% increased risk (OR, 1.61; 95% CI, 1.35 to 1.92), and those who gained weight 5% to 10% had a 41% increased risk (OR, 1.41; 95% CI, 1.22 to 1.64) of developing insulin-requiring GDM, while those who lost weight 5% to 10% showed a significantly decreased risk (–10% to –5%: OR, 0.73; 95% CI, 0.56 to 0.94). In those who lost more than 10%, there was no significant association observed (<–10%: OR, 0.74; 95% CI, 0.52 to 1.07) (Table 2).

Subgroup analysis

The relationship was evaluated in clinical subgroups stratified by age (<35 and ≥35 years), obesity parameters (BMI and WC), MetS, FBG, and obstetrical parameters (multifetal gestation and first pregnancy). In all subgroups, pregravid weight gain was associated with an increased risk of GDM, although statistical significance was not reached in some subgroups (such as those with a BMI of 25 kg/m² or higher or individuals with MetS). Among obstetrical parameters, primigravid women exhibited a dose-dependent significant association between pregravid weight gain and increased GDM risk (≥10% [OR, 1.44; 95% CI, 1.34 to 1.55], 5% to 10% [OR, 1.17; 95% CI, 1.10 to 1.24]), while the association was not statistically significant for women with a second or subsequent pregnancy (≥10% [OR, 1.12; 95% CI, 0.995 to 1.27], 5% to 10% [OR, 0.97; 95% CI, 0.88 to 1.08]) (P for interaction=0.0009). The reduced risk associated with weight loss of 5% to 10% was statistically significant only among individuals with WC <85 cm (OR, 0.92; 95% CI, 0.86 to 0.998) or those without MetS (OR, 0.92; 95% CI, 0.86 to 0.99) within the obesity subgroups (Fig. 2).

DISCUSSION

This study shows a higher prevalence of obesity, abdominal obesity, and MetS in the GDM group. Additionally, it reveals a dose-dependent relationship between pregravid weight gain and the risk of developing GDM during subsequent pregnancy. Notably, there was a 12% rise in risk for those gaining 5% to 10% of weight and a 34% increase for those gaining 10% or more. This association was further pronounced for insulin-requiring GDM, as the severe form of GDM, with a 41% increase for 5% to 10% weight gain and a 61% rise for 10% or more. Subgroup analyses, stratified by various factors including age, obesity parameters, MetS, and obstetrical parameters, consistently revealed an elevated risk of GDM with pregravid weight gain across subgroups, except for those with a BMI ≥25 kg/m², MetS, or non-primigravid women. Additionally, we observed an association between pregravid weight loss of 5% to 10% and a reduced risk of GDM among women with normal WC and without MetS.

Previous studies have reported an association between pregravid weight gain and increased GDM risk, emphasizing the importance of weight management before pregnancy. However, these studies were mostly small-scale and examined the relationship between weight changes and GDM by comparing the weight remembered by individuals in early adulthood (18 to 20 years) with their weight before pregnancy or during early pregnancy [13-16,26,27]. Our study, unique in its analysis of pregravid weight changes over several years and subsequent GDM development in Korean women, utilizing a nationwide health screening cohort, reinforces the significance of this association. The loss of statistical significance in certain subgroups, such as those with obesity or MetS, suggests a potential attenuation of the weight gain effect due to the inherent influences of obesity or MetS on GDM development [28-30]. Additionally, the lack of significance in multiparous women could potentially be attributed to their generally higher likelihood of obesity compared to primigravid women [31]. Furthermore, the association was more pronounced in non-obese individuals, highlighting the importance of weight management before pregnancy in this demographic.

Regarding the association between pregravid weight loss and the incidence of GDM, a 27% decreased risk was observed with weight loss of 5% to 10%, while statistical significance was observed primarily in groups without abdominal obesity or MetS in subgroup analyses. Additionally, no dose-dependent negative association was observed with a 10% or greater decrease compared to a 5% to 10% decrease. This suggests that only cases with moderate weight loss of about 5% to 10%, leading to the normalization of WC and MetS, may experience a reduced risk of GDM.

In South Korea, the rapidly declining birth rate has emerged as a significant social concern. Concurrently, the age of mothers at their first childbirth has risen sharply from 26 years in 1993 to 33 years in 2022 [10]. Given that advanced maternal age is a well-established risk factor for GDM [7,32,33], it’s advisable to consider avoiding pregravid weight gain before attempting pregnancy, especially when there’s already a risk factor of advanced maternal age. Notably, the observed association is more pronounced in non-obese women, as also observed in an Australian study [16]. This highlights the importance of maintaining pregravid weight gain to less than 5%, even for individuals not classified as obese, thus contributing to minimizing the risk of GDM. This conveys an important public health message in preventive medicine. Moreover, women diagnosed with GDM are at a higher risk of developing type 2 diabetes mellitus at an earlier age compared to those without GDM [34,35]. Therefore, efforts to prevent GDM during pregnancy hold substantial implications for long-term postpartum health maintenance.

Given the well-documented positive correlation between excessive pregravid obesity and various pregnancy complications, including GDM, and the guidelines advocating for restricting excessive weight gain during pregnancy in obese women [36], it is advisable for obese women to consider limiting weight gain or, preferably, aiming for weight reduction before conception.

Meanwhile, understanding the lack of association with weight loss exceeding 10% poses a challenge, warranting further investigation into its underlying causes. This may include cases of natural weight reduction following a previous pregnancy and childbirth. In this study population, a total of 21,630 cases (14.8%) were identified within 4 years before the index health examinations. Moreover, caution is advised in interpreting the results of our study, especially for non-obese women, due to potential associations with adverse pregnancy outcomes such as small for gestational age infants [37,38].

Our study has several notable strengths. Firstly, we utilized a large, racially homogeneous, and nationally representative cohort of young Korean women of reproductive age. The substantial sample size enhances the reliability and generalizability of our findings. Additionally, we adopted a rigorous approach by utilizing measured body weight data collected twice over the 2 to 3 years preceding the index pregnancy, thereby minimizing potential recall bias. By analyzing weight changes during the 2 to 3 years prior to pregnancy and their association with the risk of developing GDM during subsequent pregnancies, our study provides valuable insights into the importance of weight management during the preconception period.

There were several limitations to our study. Firstly, we couldn’t conduct a specific analysis of the causes of weight change over the period. Situations where weight fluctuation is inevitable, such as cases involving pregnancy and childbirth, could have been included. Secondly, since our study was limited to Korean women, the association between short-term weight change and GDM might differ in other populations. Thirdly, the important clinical outcomes of GDM include cesarean delivery, preterm labor, large for gestational age, and macrosomia, rather than the use of insulin during pregnancy itself. However, due to the limitations of our data, we defined the group requiring insulin treatment as severe GDM for the purpose of this analysis. Fourthly, while our study demonstrated a significant association between pregravid weight change and GDM in a large cohort, we couldn’t provide a definitive pathophysiological explanation for this relationship, particularly concerning the more significant association during the first pregnancy and in non-obese women. Lastly, the retrospective study design may also limit the interpretation of causal inferences. Therefore, future prospective research with a more detailed study design is needed to analyze the risk of GDM in relation to weight changes before pregnancy.

In conclusion, pregravid weight gain is dose-dependently associated with a higher risk of GDM and insulin-requiring GDM. Minimizing pregravid weight gain appears to be a strategy that could reduce the risk of developing GDM, even in non-obese women.

SUPPLEMENTARY MATERIALS

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: K.H., S.Y.C., S.Y.Y.

Acquisition, analysis, or interpretation of data: S.K., K.H., M.J.K., S.Y.Y., S.H.C., J.Y.Y., J.J.K., M.J.K.

Drafting the work or revising: S.K., K.H., S.Y.C.

Final approval of the manuscript: all authors.

FUNDING

None

ACKNOWLEDGMENTS

None

Fig. 1.

Flowchart of study population. DM, diabetes mellitus; GDM, gestational diabetes mellitus.

Fig. 2.

Effect of pregravid weight change on gestational diabetes mellitus risk according to age, underlying medical and obstetrical status. Odds ratios (ORs) are adjusted for age, smoking, regular exercise yes/no, income, primigravid, hypertension, dyslipidemia, fasting blood glucose (FBG), and pregravid weight. CI, confidence interval; BMI, body mass index; WC, waist circumference.

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About this article

Kim S, Han K, Choi SY, Kim MJ, Yang SY, Choi SH, Yim JY, Kim JJ, Kim MJ. Pregravid Weight Gain Is Associated with an Increased Risk of Gestational Diabetes. Diabetes Metab J. 2025 Mar 26. doi: 10.4093/dmj.2024.0491. Epub ahead of print.

Received: Aug 19, 2024; Accepted: Nov 15, 2024

DOI: https://doi.org/10.4093/dmj.2024.0491.

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