Gestational Diabetes Mellitus among Women of Advanced Maternal Age in Malaysia: A Study of the Prevalence and Sociodemographic Determinants

Gestational Diabetes Mellitus among Women of Advanced Maternal Age in Malaysia: A Study of the Prevalence and Sociodemographic Determinants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gestational Diabetes Mellitus among Women of Advanced Maternal Age in Malaysia: A Study of the Prevalence and Sociodemographic Determinants Chean Tat Chong, Lalitha Palaniveloo, Sulhariza Husni Zain, Muhamad Khairul Nazrin Khalil, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5695754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Gestational diabetes mellitus (GDM) is a growing public health concern, particularly among women of advanced maternal age. Understanding the prevalence and associated sociodemographic factors is crucial for targeted interventions. This study aimed to determine the prevalence of GDM and its association with sociodemographic factors among Malaysian women of advanced maternal age. Method This study utilized data from the National Health and Morbidity Survey 2022: Maternal and Child Health, a nationwide survey employing a two-stage stratified random sampling design. GDM was diagnosed via a modified oral glucose tolerance test. Sociodemographic variables, including ethnicity, locality, education, employment, and household income, were analysed. Multiple logistic regression was performed to identify factors associated with GDM. Results The prevalence of GDM among women of advanced maternal age in Malaysia was 33.7%. Ethnicity was significantly associated with GDM, with Indian women having the highest prevalence (48.8%) and odds ratio (AOR: 7.31, 95% CI: 2.58–20.72; P < 0.001). Working status was another significant factor, with nonworking women having higher odds of GDM than working women (AOR: 1.34, 95% CI: 1.01–1.77; P = 0.003). No significant associations were observed for locality, educational level, or household income. Conclusion The high prevalence of GDM among women of advanced maternal age in Malaysia underscores the urgent need for targeted interventions, particularly among high-risk ethnic groups. Public health strategies should prioritize early screening, culturally tailored programs, and community-based initiatives to address this growing burden. Future research should explore behavioural and genetic determinants to further inform policy and practice. Malaysia Gestational Diabetes Mellitus Prevalence Associated Factors Maternal Age Background Gestational Diabetes Mellitus (GDM) is a growing public health concern especially among women of advanced maternal age ( 1 , 2 ). Characterized by glucose intolerance first identified during pregnancy, GDM 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 ). A globally standardized diagnostic protocol for GDM has yet to be universally accepted, making international comparisons challenging ( 2 , 3 ). 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 hyperglycemia 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 rising rates of obesity, sedentary lifestyles, and changing dietary patterns ( 4 ). The country's ethnically diverse population—comprising Malays, Chinese, Indian, and other ethnic groups—presents unique sociodemographic variables that influence GDM prevalence. Studies suggest that age, body mass index (BMI), and family history of diabetes play critical roles, but the impact of socioeconomic factors such as education level, income, and access to healthcare requires further investigation ( 5 ). Advanced maternal age, typically defined as pregnancy occurring at 35 years or older, is a well-established risk factor for GDM ( 6 – 8 ). Women of advanced maternal age face increased physiological challenges during pregnancy, including reduced insulin sensitivity and a higher likelihood of pre-existing metabolic conditions, which contribute to GDM development. Studies have consistently shown that the prevalence of GDM increases 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. Understanding the prevalence of GDM and its sociodemographic determinants among women of advanced maternal age is essential for guiding 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. Methodology Study Design This study performed secondary data analysis on women of advanced maternal age (≥ 35 years old) from the National Health and Morbidity Survey 2022: Maternal and Child Health (NHMS 2022: MCH) which was conducted between 9th August and 31st October 2022. A two-stage stratified random sampling method was utilized in this national survey to ensure representativeness across Malaysia, encompassing all states and federal territories. The first level of stratification involved grouping by states and federal territories, while the second level distinguished between urban and rural settings. Enumeration Blocks were designated as the primary sampling units, with living quarters within the selected Enumeration Blocks serving as the secondary sampling units. The primary sampling units were randomly selected by the Department of Statistics Malaysia (DOSM) based on the required sample size. The full methodology for the NHMS 2022: MCH is detailed in the report ( 9 ). Questionnaire and survey instruments The survey employed structured and validated questionnaires, which were administered through face-to-face interviews via mobile devices and supplemented by a self-administered questionnaire (SAQ). To address potential technical issues, hardcopy versions of the questionnaires were also prepared as backups. The questionnaires were pretested in both Malay and English, and data collectors were provided with a manual that detailed the questionnaire flow and definitions of key terms to ensure consistency and accuracy. Variable definition Gestational diabetes mellitus (GDM) is diagnosed via a modified oral glucose tolerance test (MOGTT), where fasting plasma glucose (FPG) levels are ≥ 5.1 mmol/L and/or the 2-hour postprandial (2-HPP) glucose level is ≥ 7.8 mmol/L ( 10 ). The sociodemographic data were categorized as follows: Locality was classified into two categories: urban and rural. Ethnicity was grouped into Malay, Chinese, Indian, other Bumiputeras, and others. Marital status was categorized into two groups: single, separated, divorced, or widowed; and married or cohabitating. Education levels were categorized into four groups: no formal education, primary education, secondary education, and tertiary education. Employment status was grouped into two categories: employed and unemployed. Household income was categorized into three groups based on the DOSM classification: B40, M40, and T20 ( 11 ). Statistical analysis Statistical analysis was conducted via IBM SPSS Statistics (version 28, Chicago, Illinois, USA). Descriptive statistics were employed to summarize the data. Pearson’s χ² test was used to assess differences between GDM and sociodemographic variables. To identify factors associated with GDM, a multiple logistic regression model was applied, adjusting for potential confounders. Variables with a P-value less than 0.25 were included in the final model, and adjusted odds ratios (AORs) were calculated for each variable. A P-value of less than 0.25 was used to account for residual confounding. Both crude odds ratios (CORs) and AORs are reported with 95% confidence intervals (CIs), and P-values less than 0.05 were considered statistically significant. Results A total of 1754 women of advanced maternal age (≥35 years old) were included in this study. The prevalence of GDM among women of advanced maternal age in Malaysia is presented in Table 1. Overall, the prevalence of gestational diabetes mellitus (GDM) among women of advanced maternal age in Malaysia is 33.7%. 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%), other Bumiputera (28.1%), Chinese (29.3%), and other ethnicities (12.7%). For marital status, married or cohabiting women had a greater prevalence (33.9%) than 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, nonworking women (36.0%) and working women (31.4%) had no significant difference in prevalence (P = 0.090). Household income groups also presented similar prevalence rates: B40 (33.2%), M40 (35.6%), and T20 (32.8%) (P = 0.747). Table 1: Prevalence of GDM among women of advanced maternal age in Malaysia Characteristics Prevalence P value GDM, n (%) Non-GDM, n (%) Overall 611 (33.7) 1143 (66.3) Locality - Urban - Rural 440 (34.4) 171 (31.8) 804 (65.6) 339 (68.2) 0.390 Ethnicity - Malay - Chinese - Indian - Other Bumiputera - Other 510 (35.2) 18 (29.3) 28 (48.8) 44 (28.1) 11 (12.7) 908 (64.8) 55 (70.7) 33 (51.2) 92 (71.9) 55 (87.3) <0.001 Marital Status - Single/Separated/Divorcee/Widow - Married/Cohabiting 6 (16.7) 605 (33.9) 18 (83.3) 1125 (66.1) 0.043 Educational Level - No formal education - Primary - Secondary - Tertiary 7 (22.8) 25 (29.2) 294 (34.5) 274 (34.0) 16 (77.2) 67 (70.8) 528 (65.5) 501 (66.0) 0.568 Working Status - Not working - Working 346 (36.0) 255 (31.4) 569 (64.0) 542 (68.6) 0.090 Household Income - B40 - M40 - T20 433 (33.2) 134 (35.6) 43 (32.8) 809 (66.8) 241 (64.4) 87 (67.2) 0.747 In Table 2, multivariate analysis revealed the factors associated with GDM among women of 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, nonworking 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 (COR: 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 education (AOR: 0.87, 95% CI: 0.51–1.49; P = 0.602), secondary education (AOR: 0.96, 95% CI: 0.72–1.28; P = 0.796), and tertiary education (AOR: 0.96, 95% CI: 0.72–1.28; P = 0.796) 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. Table 2: Factors associated with GDM among women of advanced maternal age in Malaysia Characteristics COR (95% CI) P value AOR (95% CI) P value Locality - Urban - Rural 1.13 (0.86, 1.48) Ref 0.390 Ethnicity - Malay - Chinese - Indian - Other Bumiputera - Other 3.73 (1.81, 7.68) 2.84 (1.07, 7.48) 6.55 (2.56, 16.72) 2.68 (1.24, 5.78) Ref <0.001 0.035 <0.001 0.012 4.42 (1.84, 10.65) 3.56 (1.18, 10.75) 7.31 (2.58, 20.72) 3.01 (1.24, 7.32) Ref <0.001 0.024 <0.001 0.015 Marital Status - Single/Separated/Divorcee/Widow - Married/Cohabiting Ref 2.56 (0.99, 6.55) 0.051 Ref 2.21 (0.88, 5.51) 0.090 Educational Level - No formal education - Primary - Secondary - Tertiary 0.57 (0.22, 1.48) 0.80 (0.48, 1.35) 1.02 (0.80, 1.31) Ref 0.252 0.407 0.850 1.24 (0.45, 3.42) 0.87 (0.51, 1.49) 0.96 (0.72, 1.28) Ref 0.681 0.602 0.796 Working Status - Not working - Working 1.23 (0.97, 1.57) Ref 0.090 1.34 (1.01, 1.77) Ref 0.003 Household Income - B40 - M40 - T20 1.02 (0.65, 1.59) 1.13 (0.69, 1.86) Ref 0.928 0.619 Discussion This study highlights important factors associated with gestational diabetes mellitus (GDM) among women of 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% ( 12 , 13 ), and the global prevalence of 10.9% based on a meta-analysis of 3258 studies ( 14 ). The higher prevalence observed may be attributed to differences in diagnostic criteria, screening methods, and study settings, as well as the increasing prevalence of obesity in the population ( 15 ). These findings align with data from China, where GDM prevalence among women of advanced maternal age was reported to be 37.1%, with an 8% increase in GDM risk for every additional year of maternal age ( 16 ). 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. Similarly, Malay, Chinese, and ther Bumiputera women also presented significantly greater odds of GDM as 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 ( 17 – 22 ). 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, with nonworking women having 34% higher odds of developing GDM than working women. This association may reflect differences in physical activity levels, socioeconomic stressors, and access to healthcare services ( 23 ). Addressing these disparities through community-based health promotion programs and improving healthcare accessibility for nonworking women could help mitigate this risk. The significantly higher prevalence of GDM among women of advanced maternal age underscores the importance of enhanced preconception and antenatal counselling ( 23 – 25 ). 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 increasing burden of GDM, particularly among high-risk groups. Furthermore, community-based nonpharmacological interventions for managing GDM offer effective and accessible alternatives to pharmacological treatments ( 26 ). The 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 with routine care, while combined diet and exercise interventions further reduce maternal weight gain more effectively than diet alone. Strengths and limitations This study has several notable strengths. First, it utilizes nationally representative data from the NHMS 2022, ensuring that the findings are generalizable across Malaysia because of the survey's robust sampling design. Second, the focus on women of advanced maternal age—a high-risk group for GDM—fills a critical gap in the 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, although the use of validated questionnaires helps mitigate this concern. Finally, the study did 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 multidimensional research. Conclusion This study underscores the high prevalence of gestational diabetes mellitus (GDM) among women of advanced maternal age in Malaysia, highlighting significant associations with sociodemographic factors such as ethnicity and working status. Indian women presented 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 aimed at reducing GDM prevalence and its associated complications in Malaysia. Future research should explore longitudinal data, behavioural determinants, and genetic predispositions to enhance the understanding and management of GDM. Abbreviations DOSM Department of Statistics Malaysia GDM Gestational Diabetes Mellitus MCH Maternal and Child Health NHMS National Health and Morbidity Survey Declarations Ethical Approval The Medical Research and Ethics Committee of the Ministry of Health Malaysia approved the methodology, protocol, and procedures for the NHMS 2022: MCH. The survey was registered with the National Medical Research Registry under NMRR-20-959-53329. Written informed consent was obtained from all participants prior to their interviews during the NHMS 2022: MCH data collection. Availability of Data and Materials The datasets generated and/or analysed during the current study are available in the National Institutes of Health – Data Repository System (NIH-DaRS) [https://nihdars.nih.gov.my/] Competing Interests The authors declare that they have no competing interests. Authors’ Contributions CCT, LP, SHZ, MKNK and KKYR conceived and performed experiments, analysed the data and wrote the manuscript. All authors read and approved the final manuscript. Funding The author(s) received no financial support for this article’s research, authorship, and/or publication. Acknowledgement The authors would like to thank the Director General of Health Malaysia for his permission to publish this article. We would also like to thank all research team members and data collectors for their contributions and commitment to this study. We appreciate the funding and support from the Ministry of Health Malaysia. We are also grateful for the kind cooperation of all participants. References McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nature reviews Disease primers. 2019;5(1):47. Modzelewski R, Stefanowicz-Rutkowska MM, Matuszewski W, Bandurska-Stankiewicz EM. Gestational diabetes mellitus—recent literature review. Journal of Clinical Medicine. 2022;11(19):5736. Sweeting A, Wong J, Murphy HR, Ross GP. A clinical update on gestational diabetes mellitus. 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Journal of Women's Health. 2021;30(2):160-7. Glick I, Kadish E, Rottenstreich M. Management of pregnancy in women of advanced maternal age: improving outcomes for mother and baby. International journal of women's health. 2021:751-9. Igwesi-Chidobe CN, Okechi PC, Emmanuel GN, Ozumba BC. Community-based non-pharmacological interventions for pregnant women with gestational diabetes mellitus: a systematic review. BMC Women's Health. 2022;22(1):482. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5695754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394421773,"identity":"aac7d4ca-98e8-4433-9580-9163a806c7fb","order_by":0,"name":"Chean Tat Chong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACCRBRwcBgQKKWMyRrYWwjRQv/7OaDnwvnbZMzZ2C/+JiHYVtiA0FL7hxLlp657baxZQNPsTEPw23CWgwkcgykebfdTtxwgCdNcgZxWvI//+adQ5qWHDZp3gaQFvZjEh+I0SJxI83MmufYbWODwzzMBh8MbhsT1MI/I/nxbZ6a23IGx9sfPkiouC1LUAsCMPMYgGLHkQQtDOwPQKQ9CTpGwSgYBaNghAAAcUY+P2zm78YAAAAASUVORK5CYII=","orcid":"","institution":"National Institutes of Health, Ministry of Health","correspondingAuthor":true,"prefix":"","firstName":"Chean","middleName":"Tat","lastName":"Chong","suffix":""},{"id":394421774,"identity":"b45b0f39-6018-4bef-82ec-8ba61612420b","order_by":1,"name":"Lalitha Palaniveloo","email":"","orcid":"","institution":"National Institutes of Health, Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Lalitha","middleName":"","lastName":"Palaniveloo","suffix":""},{"id":394421775,"identity":"56d5d7f5-b1e1-4c94-952a-0de988d9989b","order_by":2,"name":"Sulhariza Husni Zain","email":"","orcid":"","institution":"National Institutes of Health, Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Sulhariza","middleName":"Husni","lastName":"Zain","suffix":""},{"id":394421776,"identity":"ebe9be34-d4e3-4c5d-8d8f-edde9fa60eb9","order_by":3,"name":"Muhamad Khairul Nazrin Khalil","email":"","orcid":"","institution":"National Institutes of Health, Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Muhamad","middleName":"Khairul Nazrin","lastName":"Khalil","suffix":""},{"id":394421777,"identity":"0e222e26-31ff-4312-8366-2e97be1f2d3a","order_by":4,"name":"Kishwen Kanna Yoga Ratnam","email":"","orcid":"","institution":"National Institutes of Health, Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Kishwen","middleName":"Kanna Yoga","lastName":"Ratnam","suffix":""}],"badges":[],"createdAt":"2024-12-23 01:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5695754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5695754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72692594,"identity":"31d23b55-3397-4b8e-b2fd-818fc14a8c6a","added_by":"auto","created_at":"2024-12-31 09:55:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":534293,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5695754/v1/ea853a33-54ac-46fd-a1f2-b22e7a850788.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gestational Diabetes Mellitus among Women of Advanced Maternal Age in Malaysia: A Study of the Prevalence and Sociodemographic Determinants","fulltext":[{"header":"Background","content":"\u003cp\u003eGestational Diabetes Mellitus (GDM) is a growing public health concern especially among women of advanced maternal age (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Characterized by glucose intolerance first identified during pregnancy, GDM 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 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA globally standardized diagnostic protocol for GDM has yet to be universally accepted, making international comparisons challenging (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). 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 hyperglycemia during pregnancy (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eGDM is driven by multiple factors, including rising rates of obesity, sedentary lifestyles, and changing dietary patterns (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The country's ethnically diverse population\u0026mdash;comprising Malays, Chinese, Indian, and other ethnic groups\u0026mdash;presents unique sociodemographic variables that influence GDM prevalence. Studies suggest that age, body mass index (BMI), and family history of diabetes play critical roles, but the impact of socioeconomic factors such as education level, income, and access to healthcare requires further investigation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdvanced maternal age, typically defined as pregnancy occurring at 35 years or older, is a well-established risk factor for GDM (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Women of advanced maternal age face increased physiological challenges during pregnancy, including reduced insulin sensitivity and a higher likelihood of pre-existing metabolic conditions, which contribute to GDM development. Studies have consistently shown that the prevalence of GDM increases 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.\u003c/p\u003e \u003cp\u003eUnderstanding the prevalence of GDM and its sociodemographic determinants among women of advanced maternal age is essential for guiding 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.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eStudy Design\u003c/p\u003e \u003cp\u003eThis study performed secondary data analysis on women of advanced maternal age (\u0026ge;\u0026thinsp;35 years old) from the National Health and Morbidity Survey 2022: Maternal and Child Health (NHMS 2022: MCH) which was conducted between 9th August and 31st October 2022. A two-stage stratified random sampling method was utilized in this national survey to ensure representativeness across Malaysia, encompassing all states and federal territories. The first level of stratification involved grouping by states and federal territories, while the second level distinguished between urban and rural settings. Enumeration Blocks were designated as the primary sampling units, with living quarters within the selected Enumeration Blocks serving as the secondary sampling units. The primary sampling units were randomly selected by the Department of Statistics Malaysia (DOSM) based on the required sample size. The full methodology for the NHMS 2022: MCH is detailed in the report (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQuestionnaire and survey instruments\u003c/p\u003e \u003cp\u003eThe survey employed structured and validated questionnaires, which were administered through face-to-face interviews via mobile devices and supplemented by a self-administered questionnaire (SAQ). To address potential technical issues, hardcopy versions of the questionnaires were also prepared as backups. The questionnaires were pretested in both Malay and English, and data collectors were provided with a manual that detailed the questionnaire flow and definitions of key terms to ensure consistency and accuracy.\u003c/p\u003e \u003cp\u003eVariable definition\u003c/p\u003e \u003cp\u003eGestational diabetes mellitus (GDM) is diagnosed via a modified oral glucose tolerance test (MOGTT), where fasting plasma glucose (FPG) levels are \u0026ge;\u0026thinsp;5.1 mmol/L and/or the 2-hour postprandial (2-HPP) glucose level is \u0026ge;\u0026thinsp;7.8 mmol/L (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sociodemographic data were categorized as follows: Locality was classified into two categories: urban and rural. Ethnicity was grouped into Malay, Chinese, Indian, other Bumiputeras, and others. Marital status was categorized into two groups: single, separated, divorced, or widowed; and married or cohabitating. Education levels were categorized into four groups: no formal education, primary education, secondary education, and tertiary education. Employment status was grouped into two categories: employed and unemployed. Household income was categorized into three groups based on the DOSM classification: B40, M40, and T20 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted via IBM SPSS Statistics (version 28, Chicago, Illinois, USA). Descriptive statistics were employed to summarize the data. Pearson\u0026rsquo;s χ\u0026sup2; test was used to assess differences between GDM and sociodemographic variables. To identify factors associated with GDM, a multiple logistic regression model was applied, adjusting for potential confounders. Variables with a P-value less than 0.25 were included in the final model, and adjusted odds ratios (AORs) were calculated for each variable. A P-value of less than 0.25 was used to account for residual confounding. Both crude odds ratios (CORs) and AORs are reported with 95% confidence intervals (CIs), and P-values less than 0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1754 women of advanced maternal age (\u0026ge;35 years old) were included in this study. The prevalence of GDM among women of advanced maternal age in Malaysia is presented in Table 1. Overall, the prevalence of gestational diabetes mellitus (GDM) among women of advanced maternal age in Malaysia is 33.7%. 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 \u0026lt; 0.001). Indian women had the highest prevalence (48.8%), followed by Malay (35.2%), other Bumiputera (28.1%), Chinese (29.3%), and other ethnicities (12.7%). For marital status, married or cohabiting women had a greater prevalence (33.9%) than 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, nonworking women (36.0%) and working women (31.4%) had no significant difference in prevalence (P = 0.090). Household income groups also presented similar prevalence rates: B40 (33.2%), M40 (35.6%), and T20 (32.8%) (P = 0.747).\u003c/p\u003e\n\u003cp\u003eTable 1: Prevalence of GDM among women of advanced maternal age in Malaysia\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGDM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eNon-GDM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e611 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1143 (66.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocality\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eUrban\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eRural\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e440 (34.4)\u003c/p\u003e\n \u003cp\u003e171 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e804 (65.6)\u003c/p\u003e\n \u003cp\u003e339 (68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eMalay\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eChinese\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eIndian\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eOther Bumiputera\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e510 (35.2)\u003c/p\u003e\n \u003cp\u003e18 (29.3)\u003c/p\u003e\n \u003cp\u003e28 (48.8)\u003c/p\u003e\n \u003cp\u003e44 (28.1)\u003c/p\u003e\n \u003cp\u003e11 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e908 (64.8)\u003c/p\u003e\n \u003cp\u003e55 (70.7)\u003c/p\u003e\n \u003cp\u003e33 (51.2)\u003c/p\u003e\n \u003cp\u003e92 (71.9)\u003c/p\u003e\n \u003cp\u003e55 (87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eSingle/Separated/Divorcee/Widow\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eMarried/Cohabiting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (16.7)\u003c/p\u003e\n \u003cp\u003e605 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (83.3)\u003c/p\u003e\n \u003cp\u003e1125 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eNo formal education\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003ePrimary\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eSecondary\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (22.8)\u003c/p\u003e\n \u003cp\u003e25 (29.2)\u003c/p\u003e\n \u003cp\u003e294 (34.5)\u003c/p\u003e\n \u003cp\u003e274 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (77.2)\u003c/p\u003e\n \u003cp\u003e67 (70.8)\u003c/p\u003e\n \u003cp\u003e528 (65.5)\u003c/p\u003e\n \u003cp\u003e501 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking Status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eNot working\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eWorking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e346 (36.0)\u003c/p\u003e\n \u003cp\u003e255 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e569 (64.0)\u003c/p\u003e\n \u003cp\u003e542 (68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Income\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eB40\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eM40\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eT20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e433 (33.2)\u003c/p\u003e\n \u003cp\u003e134 (35.6)\u003c/p\u003e\n \u003cp\u003e43 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e809 (66.8)\u003c/p\u003e\n \u003cp\u003e241 (64.4)\u003c/p\u003e\n \u003cp\u003e87 (67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn Table 2, multivariate analysis revealed the factors associated with GDM among women of 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\u0026ndash;20.72; P \u0026lt; 0.001), followed by Malay (AOR: 4.42, 95% CI: 1.84\u0026ndash;10.65; P \u0026lt; 0.001), Chinese (AOR: 3.56, 95% CI: 1.18\u0026ndash;10.75; P = 0.024), and Other Bumiputera (AOR: 3.01, 95% CI: 1.24\u0026ndash;7.32; P = 0.015), compared to the other ethnicities. Additionally, nonworking women had significantly higher odds of developing GDM compared to working women (AOR: 1.34, 95% CI: 1.01\u0026ndash;1.77; P = 0.003). In contrast, other factors were not significantly associated with GDM. Locality (COR: 1.13, 95% CI: 0.86\u0026ndash;1.48; P = 0.390) and marital status (COR: 2.21, 95% CI: 0.88\u0026ndash;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\u0026ndash;3.42; P = 0.681), primary education (AOR: 0.87, 95% CI: 0.51\u0026ndash;1.49; P = 0.602), secondary education (AOR: 0.96, 95% CI: 0.72\u0026ndash;1.28; P = 0.796), and tertiary education (AOR: 0.96, 95% CI: 0.72\u0026ndash;1.28; P = 0.796) showing similar odds. Similarly, household income showed no significant association across income categories, with B40 (COR: 1.02, 95% CI: 0.65\u0026ndash;1.59; P = 0.928) and M40 (COR: 1.13, 95% CI: 0.69\u0026ndash;1.86; P = 0.619) being comparable to the T20 group.\u003c/p\u003e\n\u003cp\u003eTable 2: Factors associated with GDM among women of advanced maternal age in Malaysia\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1498%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8011%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.9504%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocality\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eUrban\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eRural\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1498%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.13 (0.86, 1.48)\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8011%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2872%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.9504%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eMalay\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eChinese\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eIndian\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eOther Bumiputera\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1498%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.73 (1.81, 7.68)\u003c/p\u003e\n \u003cp\u003e2.84 (1.07, 7.48)\u003c/p\u003e\n \u003cp\u003e6.55 (2.56, 16.72)\u003c/p\u003e\n \u003cp\u003e2.68 (1.24, 5.78)\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8011%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2872%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.42 (1.84, 10.65)\u003c/p\u003e\n \u003cp\u003e3.56 (1.18, 10.75)\u003c/p\u003e\n \u003cp\u003e7.31 (2.58, 20.72)\u003c/p\u003e\n \u003cp\u003e3.01 (1.24, 7.32)\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.9504%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eSingle/Separated/Divorcee/Widow\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eMarried/Cohabiting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1498%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.56 (0.99, 6.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8011%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2872%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e2.21 (0.88, 5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.9504%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eNo formal education\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003ePrimary\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eSecondary\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eTertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1498%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.57 (0.22, 1.48)\u003c/p\u003e\n \u003cp\u003e0.80 (0.48, 1.35)\u003c/p\u003e\n \u003cp\u003e1.02 (0.80, 1.31)\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8011%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2872%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.24 (0.45, 3.42)\u003c/p\u003e\n \u003cp\u003e0.87 (0.51, 1.49)\u003c/p\u003e\n \u003cp\u003e0.96 (0.72, 1.28)\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.9504%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking Status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eNot working\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eWorking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1498%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.23 (0.97, 1.57)\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8011%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2872%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.34 (1.01, 1.77)\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.9504%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Income\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eB40\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eM40\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e- \u003cstrong\u003eT20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1498%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02 (0.65, 1.59)\u003c/p\u003e\n \u003cp\u003e1.13 (0.69, 1.86)\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8011%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2872%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.9504%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights important factors associated with gestational diabetes mellitus (GDM) among women of 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% (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and the global prevalence of 10.9% based on a meta-analysis of 3258 studies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The higher prevalence observed may be attributed to differences in diagnostic criteria, screening methods, and study settings, as well as the increasing prevalence of obesity in the population (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These findings align with data from China, where GDM prevalence among women of advanced maternal age was reported to be 37.1%, with an 8% increase in GDM risk for every additional year of maternal age (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This underscores the critical need for heightened attention to GDM risk in older maternal populations.\u003c/p\u003e \u003cp\u003eEthnicity emerged as a significant determinant of GDM, with Indian women demonstrating the highest prevalence and significantly greater odds of developing GDM. Similarly, Malay, Chinese, and ther Bumiputera women also presented significantly greater odds of GDM as 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 (\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These results underscore the need for targeted interventions tailored to high-risk ethnic groups, including culturally sensitive dietary counselling and screening programs.\u003c/p\u003e \u003cp\u003eWorking status was another significant factor, with nonworking women having 34% higher odds of developing GDM than working women. This association may reflect differences in physical activity levels, socioeconomic stressors, and access to healthcare services (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Addressing these disparities through community-based health promotion programs and improving healthcare accessibility for nonworking women could help mitigate this risk.\u003c/p\u003e \u003cp\u003eThe significantly higher prevalence of GDM among women of advanced maternal age underscores the importance of enhanced preconception and antenatal counselling (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). 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 increasing burden of GDM, particularly among high-risk groups.\u003c/p\u003e \u003cp\u003eFurthermore, community-based nonpharmacological interventions for managing GDM offer effective and accessible alternatives to pharmacological treatments (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The 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 with routine care, while combined diet and exercise interventions further reduce maternal weight gain more effectively than diet alone.\u003c/p\u003e\n\u003ch3\u003eStrengths and limitations\u003c/h3\u003e\n\u003cp\u003eThis study has several notable strengths. First, it utilizes nationally representative data from the NHMS 2022, ensuring that the findings are generalizable across Malaysia because of the survey's robust sampling design. Second, the focus on women of advanced maternal age\u0026mdash;a high-risk group for GDM\u0026mdash;fills a critical gap in the 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.\u003c/p\u003e \u003cp\u003eHowever, 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, although the use of validated questionnaires helps mitigate this concern. Finally, the study did 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 multidimensional research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the high prevalence of gestational diabetes mellitus (GDM) among women of advanced maternal age in Malaysia, highlighting significant associations with sociodemographic factors such as ethnicity and working status. Indian women presented 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 aimed at reducing GDM prevalence and its associated complications in Malaysia. Future research should explore longitudinal data, behavioural determinants, and genetic predispositions to enhance the understanding and management of GDM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDOSM\u0026nbsp; \u0026nbsp;Department of Statistics Malaysia\u003c/p\u003e\n\u003cp\u003eGDM\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gestational Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eMCH\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Maternal and Child Health\u003c/p\u003e\n\u003cp\u003eNHMS \u0026nbsp; National Health and Morbidity Survey\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Medical Research and Ethics Committee of the Ministry of Health Malaysia approved the methodology, protocol, and procedures for the NHMS 2022: MCH. The survey was registered with the National Medical Research Registry under NMRR-20-959-53329. Written informed consent was obtained from all participants prior to their interviews during the NHMS 2022: MCH data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the National Institutes of Health – Data Repository System (NIH-DaRS) [https://nihdars.nih.gov.my/]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCCT, LP, SHZ, MKNK and KKYR conceived and performed experiments, analysed the data and wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for this article’s research, authorship, and/or publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Director General of Health Malaysia for his permission to publish this article. We would also like to thank all research team members and data collectors for their contributions and commitment to this study. We appreciate the funding and support from the Ministry of Health Malaysia. We are also grateful for the kind cooperation of all participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nature reviews Disease primers. 2019;5(1):47.\u003c/li\u003e\n\u003cli\u003eModzelewski R, Stefanowicz-Rutkowska MM, Matuszewski W, Bandurska-Stankiewicz EM. Gestational diabetes mellitus\u0026mdash;recent literature review. Journal of Clinical Medicine. 2022;11(19):5736.\u003c/li\u003e\n\u003cli\u003eSweeting A, Wong J, Murphy HR, Ross GP. A clinical update on gestational diabetes mellitus. Endocrine reviews. 2022;43(5):763-93.\u003c/li\u003e\n\u003cli\u003eAltemani AH, Alzaheb RA. The prevention of gestational diabetes mellitus (The role of lifestyle): a meta-analysis. Diabetology \u0026amp; Metabolic Syndrome. 2022;14(1):83.\u003c/li\u003e\n\u003cli\u003eGajera D, Trivedi V, Thaker P, Rathod M, Dharamsi A. Detailed review on gestational diabetes mellitus with emphasis on pathophysiology, epidemiology, related risk factors, and its subsequent conversion to type 2 diabetes mellitus. Hormone and Metabolic Research. 2023;55(05):295-303.\u003c/li\u003e\n\u003cli\u003eFrick AP. Advanced maternal age and adverse pregnancy outcomes. Best practice \u0026amp; research Clinical obstetrics \u0026amp; gynaecology. 2021;70:92-100.\u003c/li\u003e\n\u003cli\u003eDeng L, Ning B, Yang H. Association between gestational diabetes mellitus and adverse obstetric outcomes among women with advanced maternal age: a retrospective cohort study. Medicine. 2022;101(40):e30588.\u003c/li\u003e\n\u003cli\u003eChakraborty A, Yadav S. Prevalence and determinants of gestational diabetes mellitus among pregnant women in India: an analysis of National Family Health Survey Data. BMC Women\u0026apos;s Health. 2024;24(1):147.\u003c/li\u003e\n\u003cli\u003eIPH. Technical Report National Health \u0026amp; Morbidity Survey: Maternal and Child Health. Malaysia; 2023.\u003c/li\u003e\n\u003cli\u003eMOH. Clinical Practice Guidelines: Management of Diabetes in Pregnancy. Malaysia; 2017.\u003c/li\u003e\n\u003cli\u003eDOSM. 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BMC Women\u0026apos;s Health. 2022;22(1):482.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Malaysia, Gestational Diabetes Mellitus, Prevalence, Associated Factors, Maternal Age","lastPublishedDoi":"10.21203/rs.3.rs-5695754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5695754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGestational diabetes mellitus (GDM) is a growing public health concern, particularly among women of advanced maternal age. Understanding the prevalence and associated sociodemographic factors is crucial for targeted interventions. This study aimed to determine the prevalence of GDM and its association with sociodemographic factors among Malaysian women of advanced maternal age.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study utilized data from the National Health and Morbidity Survey 2022: Maternal and Child Health, a nationwide survey employing a two-stage stratified random sampling design. GDM was diagnosed via a modified oral glucose tolerance test. Sociodemographic variables, including ethnicity, locality, education, employment, and household income, were analysed. Multiple logistic regression was performed to identify factors associated with GDM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of GDM among women of advanced maternal age in Malaysia was 33.7%. Ethnicity was significantly associated with GDM, with Indian women having the highest prevalence (48.8%) and odds ratio (AOR: 7.31, 95% CI: 2.58\u0026ndash;20.72; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Working status was another significant factor, with nonworking women having higher odds of GDM than working women (AOR: 1.34, 95% CI: 1.01\u0026ndash;1.77; P\u0026thinsp;=\u0026thinsp;0.003). No significant associations were observed for locality, educational level, or household income.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe high prevalence of GDM among women of advanced maternal age in Malaysia underscores the urgent need for targeted interventions, particularly among high-risk ethnic groups. Public health strategies should prioritize early screening, culturally tailored programs, and community-based initiatives to address this growing burden. Future research should explore behavioural and genetic determinants to further inform policy and practice.\u003c/p\u003e","manuscriptTitle":"Gestational Diabetes Mellitus among Women of Advanced Maternal Age in Malaysia: A Study of the Prevalence and Sociodemographic Determinants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-27 09:14:06","doi":"10.21203/rs.3.rs-5695754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcc9f050-09be-47ec-907b-81c3f48798cb","owner":[],"postedDate":"December 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-31T09:54:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-27 09:14:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5695754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5695754","identity":"rs-5695754","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}