Introduction

Gestational Diabetes Mellitus (GDM) is a growing public health concern especially among women with advanced maternal age (AMA) [1,2], typically defined as pregnancy occurring at 35 years or older, is a well-established risk factor for GDM [3–5]. GDM, characterized by glucose intolerance first identified during pregnancy, poses significant risks to both maternal and neonatal health. Women diagnosed with GDM are at increased risk of developing type 2 diabetes later in life, and their children may face long-term health challenges, including obesity and metabolic disorders [2,6,7].

Women of AMA face increased physiological challenges during pregnancy, including reduced insulin sensitivity and a higher likelihood of pre-existing metabolic conditions, which contribute to GDM development [3–5]. Studies have consistently shown that the prevalence of GDM rises with maternal age, likely due to the cumulative effect of aging on pancreatic beta-cell function and the body’s ability to regulate glucose levels. Additionally, pregnancies at advanced ages are often associated with other complications, such as hypertension and obesity, further compounding the risk.

A globally standardized diagnostic protocol for GDM has yet to be universally accepted, making international comparisons challenging [2,8]. In 2010, the International Association of Diabetes in Pregnancy Study Groups (IADPSG) introduced criteria, which the World Health Organization (WHO) endorsed in 2013, to differentiate between two categories of women diagnosed with hyperglycaemia during pregnancy [2]. One category includes women who meet the diagnostic criteria for diabetes outside of pregnancy, referred to as having “overt diabetes” by the IADPSG and “diabetes in pregnancy” by the WHO.

GDM is driven by multiple factors, including the rising rates of obesity, sedentary lifestyles, and changing dietary patterns [9]. The country’s ethnically diverse population—comprising Malays, Chinese, Indians, and other ethnicity groups—presents unique sociodemographic variables that influence GDM prevalence. Studies suggest that age, body mass index (BMI), and family history of diabetes play a critical role, but the impact of socioeconomic factors such as education level, income, and access to healthcare requires further investigation [10].

In Malaysia, the prevalence of GDM increased from 12.5% in 2016 to 27.1% in 2022 [11,12]. According to the Clinical Practice Guidelines for the Management of Diabetes in Pregnancy in Malaysia, GDM is diagnosed using a 75-gram modified oral glucose tolerance test (MOGTT) [13]. This test is administered at the initial booking visit, and if the result is negative, it is repeated between 24 and 28 weeks of gestation. Understanding the prevalence of GDM and its sociodemographic determinants among women with advanced maternal age is essential to guide healthcare policies and interventions aimed at reducing maternal and neonatal complications. This study aims to explore the prevalence of GDM among women in Malaysia with advanced maternal age and examine the sociodemographic factors associated with this condition, providing valuable insights into the management and prevention strategies for GDM in Malaysia.

Materials and methods

Results

A total of 1754 respondents were included in this study. The prevalence of GDM among women with advanced maternal age in Malaysia is presented in Table 1. Overall, the prevalence of gestational diabetes mellitus (GDM) among women with advanced maternal age in Malaysia was 33.7% (95% CI: 30.9%−36.5%). Analysis of demographic and socioeconomic factors revealed notable variations. There was no significant difference in GDM prevalence between urban (34.4%) and rural areas (31.8%) (P = 0.390). However, ethnicity was significantly associated with GDM prevalence (P < 0.001). Indian women had the highest prevalence (48.8%), followed by Malay (35.2%), Chinese (29.3%), Other Bumiputera (28.1%), and other ethnicities (12.7%). For marital status, married or cohabiting women having a higher prevalence (33.9%) compared to single, separated, divorced, or widowed women (16.7%) (P = 0.043). In contrast, educational level, working status, and household income were not significantly associated with GDM prevalence. The prevalence was comparable across education levels: no formal education (22.8%), primary (29.2%), secondary (34.5%), and tertiary (34.0%) (P = 0.568). Similarly, non-working women (36.0%) and working women (31.4%) had no significant difference in prevalence (P = 0.090). Household income groups also showed similar prevalence rates: B40 (33.2%), M40 (35.6%), and T20 (32.8%) (P = 0.747).

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Table 1. Prevalence of GDM among women with AMA by sociodemographic characteristics in Malaysia, n = 1,754.

https://doi.org/10.1371/journal.pone.0333005.t001

In Table 2, multivariate analysis revealed the factors associated with GDM among women with advanced maternal age in Malaysia. The factors significantly associated included ethnicity and working status. Indian women had the highest odds of developing GDM (AOR: 7.31, 95% CI: 2.58–20.72; P < 0.001), followed by Malay (AOR: 4.42, 95% CI: 1.84–10.65; P < 0.001), Chinese (AOR: 3.56, 95% CI: 1.18–10.75; P = 0.024), and Other Bumiputera (AOR: 3.01, 95% CI: 1.24–7.32; P = 0.015), compared to the other ethnicities. Additionally, non-working women had significantly higher odds of developing GDM compared to working women (AOR: 1.34, 95% CI: 1.01–1.77; P = 0.003). In contrast, other factors were not significantly associated with GDM. Locality (COR: 1.13, 95% CI: 0.86–1.48; P = 0.390) and marital status (AOR: 2.21, 95% CI: 0.88–5.51; P = 0.090) showed no significant differences. Educational level also did not have a significant association, with no formal education (AOR: 1.24, 95% CI: 0.45–3.42; P = 0.681), primary (AOR: 0.87, 95% CI: 0.51–1.49; P = 0.602), and secondary (AOR: 0.96, 95% CI: 0.72–1.28; P = 0.796) levels showing similar odds. Similarly, household income showed no significant association across income categories, with B40 (COR: 1.02, 95% CI: 0.65–1.59; P = 0.928) and M40 (COR: 1.13, 95% CI: 0.69–1.86; P = 0.619) being comparable to the T20 group.

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Table 2. Factors associated with GDM among women with advanced maternal age in Malaysia.

https://doi.org/10.1371/journal.pone.0333005.t002

Discussion

This study highlights important factors associated with gestational diabetes mellitus (GDM) among women with advanced maternal age in Malaysia, providing valuable insights into demographic and socioeconomic disparities. The prevalence of GDM in this study was 33.7%, which is substantially higher than the pooled prevalence of GDM in Asia, reported at 11.5% [18,19], and the global prevalence of 10.9% based on a meta-analysis of 3258 studies [20]. The higher prevalence observed may be attributed to differences in diagnostic criteria, screening methods, and study settings, as well as the rising prevalence of obesity in the population [21]. These findings align with data from China, where GDM prevalence among women of advanced maternal age was recorded at 37.1%, with an 8% increase in GDM risk for every additional year of maternal age [22]. This underscores the critical need for heightened attention to GDM risk in older maternal populations.

Ethnicity emerged as a significant determinant of GDM, with Indian women demonstrating the highest prevalence and significantly greater odds of developing GDM. Similar patterns were observed for Malay, Chinese, and Other Bumiputera women, compared to other ethnicities. This finding aligns with previous studies suggesting ethnic disparities in metabolic and gestational outcomes, possibly influenced by genetic predispositions, dietary patterns, and lifestyle factors [23–27]. A study in Malaysia found that the ‘Cereals-confectionaries’ dietary pattern was significantly associated with abnormal glucose tolerance and this pattern was characterized by high-glycaemic index foods such as white rice, white bread, brown rice, flavoured rice, and traditional confectionaries, may contribute to insulin resistance and impaired glucose metabolism [28]. Furthermore, the higher prevalence of obesity among individuals of Indian ethnicity may further contribute to their elevated risk of GDM [29,30]. These results underscore the need for targeted interventions tailored to high-risk ethnic groups, including culturally sensitive dietary counselling and screening programs.

Working status was another significant factor in the multivariate analysis, with non-working women having 34% higher odds of developing GDM than working women. This contrasts with the univariate analysis, where working status was not significantly associated with GDM (p = 0.090). This discrepancy is explained by confounding due to ethnicity. Further analysis revealed that while both the Chi-square test (p = 0.090) and a single-variable logistic regression (p = 0.090) showed no significant association between working status and GDM, the association became significant (p = 0.029) when ethnicity was included in the logistic regression model. These results suggest that ethnicity functions as a suppressor variable, masking the relationship between working status and GDM in the absence of adjustment. This highlights the importance of multivariate analysis in revealing independent effects that may be obscured in univariate analyses. Consequently, this association may reflect differences in physical activity levels, socioeconomic stressors, and access to healthcare services [31]. Addressing these disparities through community-based health promotion programs and improving healthcare accessibility for non-working women could help mitigate this risk.

The significantly higher prevalence of GDM among women with advanced maternal age underscores the importance of enhanced preconception and antenatal counselling [31–33]. Healthcare providers should use this information to guide women in making informed decisions about the timing of childbearing. This is especially relevant given the increasing trend of delayed parenthood and its associated risks. Early screening, lifestyle interventions, and tailored education programs should be prioritized to address the rising burden of GDM, particularly among high-risk groups.

Furthermore, community-based non-pharmacological interventions for managing GDM offer effective and accessible alternatives to pharmacological treatments [34]. Evidence suggests that self-management programs significantly improve self-efficacy, lifestyle behaviours, and postprandial blood glucose levels, making them highly beneficial for GDM patients. Medical nutrition or diet therapy has also shown notable efficacy in lowering postprandial blood glucose compared to routine care, while combined diet and exercise interventions further reduce maternal weight gain more effectively than diet alone. Interestingly, digital health interventions, such as smartphone apps and telehealth, have shown to improve glycaemic control in pregnant women with GDM, reducing the need for caesarean delivery and the risk of foetal macrosomia [35]. There is currently a smartphone app being developed as part of the Explore-MYGODDESS study to evaluate the acceptability and feasibility of a culturally tailored digital diabetes prevention intervention for women with gestational diabetes mellitus in Malaysia, aiming to support dietary and lifestyle modifications, as well as addressing barriers to effective diabetes prevention [36,37]. However, more robust evidence is needed before these interventions can be recommended as a replacement for regular clinic follow-ups.

This study has several notable strengths. First, it utilizes nationally representative data from the NHMS 2022, ensuring the findings are generalizable across Malaysia due to the survey’s robust sampling design. Second, the focus on women with advanced maternal age—a high-risk group for GDM—fills a critical gap in Malaysian literature, offering specific insights into this vulnerable population. Third, the identification of key sociodemographic factors, such as ethnicity and working status, provides actionable insights for targeted public health interventions. However, there are also limitations to consider. The cross-sectional design restricts the ability to establish causal relationships between GDM and sociodemographic factors. Additionally, reliance on self-reported data for variables such as employment status and socioeconomic factors introduces the possibility of recall bias or misreporting, though the use of validated questionnaires helps mitigate this concern. Lastly, the study does not explore behavioural factors (e.g., physical activity, diet) or genetic predispositions, which could provide deeper insights into GDM risk. Despite these limitations, the findings serve as a valuable foundation for future longitudinal and multi-dimensional research.

Conclusion

This study underscores the high prevalence of gestational diabetes mellitus (GDM) among women with advanced maternal age in Malaysia, highlighting significant associations with sociodemographic factors such as ethnicity and working status. Indian women exhibited the highest risk, emphasizing the need for culturally tailored interventions targeting high-risk ethnic groups. The findings provide critical insights into the importance of early screening, lifestyle interventions, and community-based programs to mitigate the burden of GDM. Despite limitations such as the cross-sectional design and lack of behavioural or genetic data, this study contributes valuable evidence to guide public health policies and strategies such as implementing mandatory GDM screening for high-risk ethnic groups and promoting workplace health programs for non-working women to reduce GDM prevalence and its associated complications in Malaysia. Future research should assess whether reducing sedentary time in non-working women lowers GDM incidence and should also investigate behavioural determinants and genetic predispositions through longitudinal studies to enhance the understanding and management of GDM.

Acknowledgments

The authors would like to thank the Director General of Health Malaysia for his permission to publish this paper. We would also like to thank all research team members and data collectors for their contributions and commitment to this study. We are also grateful for the kind cooperation of all participants.