Abstract
Background
Previous studies have shown that higher levels of physical activity (PA) are generally associated with a lower risk of developing gestational diabetes mellitus (GDM). however, evidence regarding the dose-response relationship remains limited. This study aims to investigate the dose-response relationship between PA and GDM during the second trimester of pregnancy.
Methods
A hospital-based cross-sectional study was conducted at Beijing Changping Hospital of Integrated Chinese and Western Medicine from August 2018 to October 2019. A total of 476 pregnant women, between 14 and 22 weeks of gestation, were enrolled in the study. Participants were categorized into a GDM group (n = 84) and a non-GDM group (n = 392) based on the results of a 75-g oral glucose tolerance test (OGTT) performed at 24–28 weeks of pregnancy. General information, PA, and dietary data were collected through validated questionnaires. PA levels and daily dietary energy intake (DDEI) were calculated using standard methods. Statistical analyses were performed using SAS 9.4 and R 4.2.1 software. The dose-response analysis was conducted, and optimal cut-off values of PA for the prevention of GDM were determined using the restricted cubic spline (RCS) model. Additionally, univariate and multivariate logistic regression analyses were employed to validate the identified cut-off values.
Results
(1) Compared to the non-GDM group, levels of total PA, moderate-to-vigorous intensity physical activity (MVPA), and walking PA were significantly lower (p < 0.05). (2) Non-linear dose-response relationships were identified between total PA, MVPA, and walking PA and the risk of GDM (p < 0.001), with optimal cut-off values established at 1714 MET-min/w, 638 MET-min/w, and 1098 MET-min/w, respectively. (3) Logistic regression analysis indicated that the risk of GDM significantly decreased as PA levels surpassed the established cut-off values (p < 0.001).
Conclusions
A non-linear dose-response relationship exists between PA and GDM during the second trimester of pregnancy. The risk of GDM diminishes as PA levels increase, suggesting that effective prevention of GDM may require achieving adequate levels of PA.
Keywords: Physical activity, Gestational diabetes mellitus, Dose-response relationship, Second trimester of pregnancy
Background
As the most prevalent pregnancy complication, gestational diabetes mellitus (GDM) adversely affects both the short- and long-term health of mothers and their offspring. The global prevalence of GDM varies among different racial and ethnic populations, influenced by the diagnostic criteria used. Notably, an increasing trend has been observed in Asia, particularly in East and South-East Asia, over the past few years [1–4]. China faces significant challenges in this regard; a systematic review and meta-analysis of 25 studies indicate that the prevalence of GDM in Mainland China is approximately 14.8% and has risen markedly in recent decades [5]. GDM is typically asymptomatic in its early stages, however, the risk of adverse pregnancy outcomes escalates once it manifests. Furthermore, several studies have shown that the subsequent risk of developing type 2 diabetes mellitus (T2DM) is particularly elevated in later life [6–8].
Similar to T2DM, various environmental and genetic factors have been linked to the development of GDM. These factors include a family history of diabetes mellitus, advanced maternal age, pre-pregnancy overweight or obesity, and changes in dietary habits and lifestyle patterns [9]. Among these, insufficient physical activity (PA) is recognized as one of the primary risk factors for GDM [10]. Lifestyle modifications are widely regarded as crucial for the management of GDM, with research indicating that approximately 70–85% of pregnant women diagnosed with GDM achieve adequate glycemic control through lifestyle changes alone [11]. As a key component of a healthy lifestyle, PA significantly contributes to the prevention and treatment of GDM. A systematic review has shown that engaging in any form of pre-pregnancy or early-pregnancy PA reduces the risk of developing GDM [12], Furthermore, additional evidence suggests that low- to moderate-intensity aerobic exercise and combined exercise can safely enhance glycemic control in pregnant women diagnosed with GDM [13].
Currently, accumulating evidence suggests that higher levels of PA are generally associated with improved blood glucose regulation. It is widely acknowledged that PA benefits the overall health of pregnant women and reduces the risk of future pregnancy-related adverse outcomes [11, 14]. However, conclusive evidence establishing a definitive dose-response relationship between PA and GDM remains lacking, complicating the formulation of specific exercise recommendations for pregnant women, as the optimal level of exercise has not been clearly defined. Therefore, this study aims to investigate the PA levels of pregnant women in their second trimester, employing the restricted cubic spline (RCS) model to analyze the dose-response relationship between PA and GDM risk. Additionally, it seeks to identify the optimal cut-off value of PA for preventing GDM, thereby providing a scientific basis for future research focused on personalized exercise prescriptions during pregnancy.
Methods
Study design, participants, and selection criteria
We conducted a hospital-based cross-sectional study at Beijing Changping Hospital, which specializes in integrated Chinese and Western medicine, from August 2018 to October 2019. Pregnant women attending regular antenatal check-ups at outpatient obstetrics clinics were recruited based on specific inclusion and exclusion criteria. The inclusion criteria were as follows: (1) aged 20 to 45 years; (2) 14 to 22 weeks of gestation; (3) singleton pregnancy; and (4) willingness to participate in the study. The exclusion criteria included: (1) a prior diagnosis of diabetes before pregnancy; (2) a history of GDM; (3) suffering from kidney or liver failure, cancer, or other serious primary diseases; (4) incomplete survey data or clinical information; and (5) failure to undergo an oral glucose tolerance test (OGTT).
Participants were categorized into a GDM group and a non-GDM group based on the results of a 75-g OGTT conducted between 24 and 28 weeks of gestation. The diagnosis of GDM was determined according to the Chinese guidelines for the diagnosis and treatment of pregnancy with diabetes mellitus (2014 edition) [15], which stipulate that GDM is diagnosed if any of the following criteria are met: (1) fasting blood glucose level ≥ 5.1 mmol/L (92 mg/dL); (2) 1-hour plasma glucose level ≥ 10.0 mmol/L (180 mg/dL); or (3) 2-hour plasma glucose level ≥ 8.5 mmol/L (153 mg/dL). These criteria align with those set forth by the American Diabetes Association (ADA) [16]. All pregnant subjects provided informed consent before participation.
Measures
All participants’ general information, PA, and dietary data were assessed using validated questionnaires
General information encompassed demographic characteristics (age, education level, ethnicity, etc.), pregnancy history, fertility history, and family history of diabetes. Maternal age was calculated using the self-reported date of birth and the date of survey completion. According to commonly used categorization, maternal age was classified into two categories: <35 years and ≥ 35 years (advanced maternal age). Education level was categorized into primary and lower secondary education (low level), higher secondary education (moderate level), and tertiary and higher education (high level). Ethnicity was defined as either Han majority or ethnic minority. Maternal pre-pregnancy height and weight were recorded based on self-reported information at the first prenatal visit. Pre-pregnancy Body Mass Index (BMI) was calculated using the formula: Pre-pregnancy BMI = pre-pregnancy weight (kg) divided by height (m) squared. The continuous BMI values were categorized into four groups: underweight (BMI < 18.5 kg/m²), normal weight (BMI 18.5–23.9 kg/m²), overweight (BMI 24.0–27.9 kg/m²), and obese (BMI ≥ 28.0 kg/m²). Due to the limited number of participants in the obese group, the overweight and obese categories were combined and labeled as the overweight or obese group (BMI ≥ 24.0 kg/m²).
Participants’ PA levels—encompassing walking, moderate-intensity, and vigorous-intensity PA—over the past week were assessed using the short version of the International Physical Activity Questionnaire (IPAQ), which has demonstrated good reliability and validity [17]. Moderate-intensity physical activity (MPA) is defined as any activity that requires a moderate level of physical effort, making breathing somewhat more difficult than normal (e.g., carrying light loads, yoga, bicycling at a moderate pace). In contrast, vigorous-intensity physical activity (VPA) is characterized by activities that demand a high level of physical effort, significantly increasing breathing difficulty (e.g., heavy lifting, climbing stairs, and fast bicycling). PA levels were assessed in metabolic equivalents (MET) using standard calculation methods, with each type of PA assigned a corresponding MET value: 3.3 MET for walking PA, 4.0 MET for MPA, and 8.0 MET for VPA, as outlined in the Compendium of Physical Activities [18, 19]. Given that the study exclusively focused on pregnant women, and noting that only a limited number of participants reported VPA, we merged VPA and MPA into a single category of moderate-to-vigorous-intensity physical activity (MVPA). Total PA was defined as the sum of walking PA, MPA, and VPA, representing the overall volume of all PA performed by participants.
In this study, dietary information for each participant was collected through a self-reported survey, supplemented by a simplified and validated Food Frequency Questionnaire (FFQ) to examine the detailed dietary intake of participants over the past month [20]. The intake frequency of nine food groups commonly consumed in China—namely staple foods (e.g., rice, noodles, steamed bread), meat and its products (e.g., pork, lamb, beef), poultry (e.g., chicken, duck, goose), fish and seafood (e.g., fresh fish, marine fish, shrimp), eggs (e.g., chicken eggs, duck eggs, goose eggs), vegetables (e.g., potato, radish, carrot), fruits (e.g., apple, banana, orange), milk and other dairy products (e.g., cow’s milk, goat’s milk, yogurt), and soy products (e.g., bean curd, soybean milk, dried bean curd) —was measured. For each specific food item within these groups, participants were asked to report their consumption frequency using standardized categories: per day, per week, per month, per year, or never. The reported frequencies were then converted into average daily consumption frequencies. In addition, participants were asked to report the portion size typically consumed per occasion, using the commonly used Chinese household unit “liang” (1 liang = 50 g). Based on the reported frequency and portion size, the average daily intake (in grams) for each food item was calculated. Subsequently, the daily dietary energy intake (DDEI) was estimated by multiplying the daily intake of each food item by its corresponding energy value from the Chinese Food Composition Table [21].
Statistical analyses
Microsoft Excel (2019 version) was utilized to establish a database for this study, facilitating data entry and verification. All statistical analyses were conducted using SAS statistical software (version 9.4) and R software (version 4.2.1). The normality of data distribution was assessed using the Kolmogorov-Smirnov normality test. Data that did not conform to a normal distribution were expressed as M (P25, P75) and analyzed using the Mann–Whitney U test. Categorical variables were presented as frequency (percentage), and the Pearson chi-square test was used to compare differences in frequency distribution between groups. The potential nonlinear dose-response relationship between PA in the second trimester of pregnancy and GDM was analyzed using the RCS model. Overall association analysis and nonlinearity tests were performed, and the optimal model was selected based on the Akaike information criterion (AIC). The optimal cut-off values for PA were determined as the GDM risk at baseline when the odds ratio (OR) equals 1. Logistic regression was applied to further validate the rationality of the cut-off value obtained from the RCS model. A p-value of less than 0.05 was considered statistically significant.
Results
General information on the study population
A total of 476 pregnant women were included in the final analysis, with participants divided into a gestational diabetes mellitus (GDM) group (n = 84) and a non-GDM group (n = 392) based on GDM diagnostic criteria. The general characteristics of the participants are summarized in Table 1, stratified by GDM status. The majority of participants were of Chinese Han ethnicity. The proportion of pregnant women classified as having advanced maternal age (≥ 35 years) in the GDM group was nearly 20%, which was statistically significantly higher than that in the non-GDM group (p = 0.009). Additionally, the GDM group exhibited a significantly higher proportion of participants with lower education levels (primary education and lower secondary education) compared to the non-GDM group (p < 0.001). Nearly 40% (39.29%) of pregnant women in the GDM group were classified as pre-pregnancy overweight or obese (BMI ≥ 24) according to Chinese BMI categories, a proportion that was significantly higher than that observed in the non-GDM group (p < 0.001). No statistically significant differences were found between the groups regarding ethnicity distribution, number of pregnancies, reproductive history, Gestational age at data collection/OGTT, or family history of diabetes (all p > 0.05). These findings suggest that advanced maternal age, low educational level, and pre-pregnancy overweight or obesity may be risk factors for GDM.
Table 1.
General information on the study population
| Variables | GDM group | Non-GDM group | t/χ2 | p |
|---|---|---|---|---|
| n = 84 (%) | n = 392 (%) | |||
| Age (years) | ||||
| < 35 | 68 (80.95) | 356 (90.82) | 6.917 | 0.009 |
| ≥ 35 | 16 (19.05) | 36 (9.18) | ||
| Ethnicity | 1.609 | 0.205 | ||
| Han majority | 79 (94.05) | 351 (89.54) | ||
| Ethnic minority | 5 (5.95) | 41 (10.46) | ||
| Education level | 19.215 | < 0.001 | ||
| primary and lower secondary education | 21 (25.00) | 33 (8.42) | ||
| higher secondary education | 24 (28.57) | 151 (38.52) | ||
| Tertiary and higher education | 39 (46.43) | 208 (53.06) | ||
| Pre-pregnancy BMI (kg/m2) | 10.529 | 0.005 | ||
| < 18.5 | 3 (3.57) | 42 (10.71) | ||
| 18.5–23.9 | 48 (57.14) | 256 (65.31) | ||
| ≥ 24.0 | 33 (39.29) | 94 (23.98) | ||
| Gestational age at data collection (weeks) | 18.13 ± 2.92 | 18.08 ± 2.73 | 0.141 | 0.888 |
| Gestational age at OGTT (weeks) | 25.17 ± 6.91 | 25.10 ± 8.17 | 0.701 | 0.483 |
| Number of pregnancies | 1.039 | 0.308 | ||
| first pregnancy | 36 (42.86) | 192 (48.98) | ||
| two or more pregnancies | 48 (57.14) | 200 (51.02) | ||
| Reproductive history | 1.353 | 0.245 | ||
| no | 47 (55.95) | 246 (62.76) | ||
| yes | 37 (44.05) | 146 (37.24) | ||
| Family history of diabetes | 0.712 | 0.399 | ||
| no | 77 (91.67) | 369 (94.13) | ||
| yes | 7 (8.33) | 23 (5.87) |
Values are presented as (mean ± standard deviation) or n (%), as appropriate. Group comparisons were conducted using the independent-samples t test for continuous variables and the chi-square (χ²) test for categorical variables. No missing data
BMI Body Mass Index, GDM Gestational Diabetes Mellitus
PA and DDEI between groups
Participants’ PA and DDEI information are presented as medians and quartiles in Table 2. The median total PA in the GDM group was 1190 MET-min/w, significantly lower than that of the non-GDM group, which was 1789 MET-min/w. The median level of MVPA in the GDM group was 410 MET-min/w, nearly half that of the non-GDM group (700 MET-min/w), with a statistically significant difference (p < 0.001). Walking emerged as the predominant form of PA among pregnant women, as walking PA levels exceeded MVPA levels in both the GDM and non-GDM groups. Further analysis revealed that walking PA levels in the GDM group were significantly lower than those in the non-GDM group (p < 0.001). These findings indicate that low levels of PA, irrespective of intensity, may elevate the risk of GDM in women.
Table 2.
Physical activity and daily dietary energy intake between groups
| PA | GDM group | Non-GDM group | U | p |
|---|---|---|---|---|
| n = 84, M (P25, P75) | n = 387, M (P25, P75) | |||
| Total PA(MET-min/w) | 1190 (901, 1493) | 1789 (1466, 2349) | 6715.50 | < 0.001 |
| MVPA (MET-min/w) | 410 (300, 560) | 700 (480, 960) | 7301.50 | < 0.001 |
| Walking PA (MET-min/w) | 792 (594, 1040) | 1155 (825, 1386) | 8419.50 | < 0.001 |
| DDEI (kcal/d) | 1760 (1407, 2138) | 1572 (1373, 2092) | 17876.00 | 0.217 |
Data are shown as median (P25, P75). The Mann–Whitney U test was used for group comparisons. No missing data
PA Physical Activity, MVPA Moderate-to-Vigorous Physical Activity, DDEI Daily Dietary Energy Intake, MET Metabolic Equivalent of Task
DDEI was calculated based on participants’ dietary intake information. The median DDEI in the GDM group was 1760 kcal/d, slightly higher than that of the non-GDM group (1572 kcal/d), although the difference between the groups was not statistically significant. (p = 0.217).
The dose-response relationship between PA and GDM
Given the statistically significant differences in PA levels between the GDM group and the non-GDM group, we further investigated the dose-response relationship among total PA, MVPA, and walking PA levels concerning GDM. Association analyses and tests for nonlinearity were performed using RCS models, adjusted for potential confounders including maternal age, education level, pre-pregnancy BMI, and family history of diabetes. The analysis revealed a significant association between total PA and GDM (χ²=63.20, p < 0.001), while the nonlinearity test indicated a statistically significant non-linear relationship (χ²=7.15, p = 0.028), characterized by an L-shaped curve. The cut-off value for total PA was determined to be 1714 MET-min/w, suggesting that lower levels of PA correlate with an increased risk of developing GDM (Fig. 1). Furthermore, the overall association analysis demonstrated that MVPA was significantly associated with GDM (χ²=69.74, p < 0.001). The nonlinearity test indicated a statistically significant curvilinear relationship between MVPA and GDM risk (χ²=11.94, p < 0.001). Compared to total PA, the L-shaped curve for MVPA and GDM risk was slightly steeper, with a cut-off value of 638 MET-min/w (Fig. 2). Additionally, the association between walking PA and GDM was statistically significant (χ²=37.49, p < 0.001). The nonlinearity test results further confirmed a nonlinear and statistically significant relationship (χ²=10.65, p = 0.014). As illustrated in Fig. 3, the risk of GDM decreased gradually with increasing walking PA, with a cut-off value of 1098 MET-min/w.
Fig. 1.
Nonlinear relationship between total PA and GDM
Fig. 2.
Nonlinear relationship between MVPA and GDM
Fig. 3.
Nonlinear relationship between Walking PA and GDM
Logistic regression analysis based on the cut-off value
To assess the rationality of the cut-off values established using the RCS model, we transformed total PA, MVPA, and walking PA from continuous to categorical variables based on these cut-off values. A logistic regression model was employed in this analysis, with GDM as the dependent variable. The converted binary variables (total PA, MVPA, and walking PA) served as independent variables. Maternal age, education level, and pre-pregnancy BMI were included as covariates based on statistically significant differences between groups, while family history of diabetes was also included given its established relevance to GDM and its potential role as a confounding factor.
The results of univariate and multivariate logistic regression analysis are shown in Table 3. The cut-off point for total PA was identified as 1714 MET-min/week, which corresponds to approximately 30.60 min of vigorous PA, 61.21 min of moderate PA, or 74.20 min of walking PA per day. The logistic regression results indicate that pregnant women with total PA levels above 1714 MET-min/week have a significantly lower risk of GDM compared to those with lower levels (adjusted OR = 0.11, p < 0.001). Similarly, the analysis reveals that MVPA exceeding 638 MET-min/week (approximately equivalent to 11.39 min of VPA or 22.79 min of MPA per day) is associated with a substantial reduction in GDM risk (adjusted OR = 0.08, p < 0.001). Furthermore, a comparable trend is observed for walking PA, where levels meeting or exceeding 1098 MET-min/week (approximately 47.53 min of walking per day) are also linked to a significant decrease in GDM risk (adjusted OR = 0.15, p < 0.001). These findings collectively support the rationality of the cut-off values determined by the RCS model.
Table 3.
Univariate and multivariate logistic regression analysis
| PA | Univariate logistic regression | Multivariate logistic regression | ||
|---|---|---|---|---|
| OR (95%CI) | p | ORa (95%CI) | p | |
| Total PA | ||||
| ≤ 1714 MET-min/w | 1.00 | 1.00 | ||
| > 1714 MET-min/w | 0.14(0.08–0.26) | < 0.001 | 0.11(0.06–0.21) | < 0.001 |
| MVPA | ||||
| ≤ 638 MET-min/w | 1.00 | 1.00 | ||
| > 638 MET-min/w | 0.13(0.07–0.25) | < 0.001 | 0.08(0.04–0.17) | < 0.001 |
| Walking PA | ||||
| ≤ 1098MET-min/w | 1.00 | 1.00 | ||
| > 1098MET-min/w | 0.17(0.09–0.30) | < 0.001 | 0.15(0.08–0.28) | < 0.001 |
OR Odds ratio, CI Confidence interval, MET Metabolic Equivalent of Task, PA Physical Activity, MVPA Moderate-to-Vigorous Physical Activity, BMI Body Mass Index
aAdjusted OR values from multivariate logistic regression models, controlling for maternal age, education level, pre-pregnancy BMI, and family history of diabetes. No missing data
Discussion
GDM poses serious short- and long-term health risks, including adverse pregnancy outcomes and increased risks of T2DM, metabolic syndrome, and cardiovascular disease in both mothers and their offspring [22–25]. PA is widely recognized as an effective and cost-efficient strategy for GDM prevention [26]. Building on this evidence, the present study examined both total and intensity-specific PA among pregnant women, contributing to a more comprehensive understanding of modifiable behavioral factors associated with GDM risk.
In this study, consistent with previous research [27–29], we found that the proportion of pregnant women of advanced age in the GDM group was statistically significantly higher than in the non-GDM group. It is widely acknowledged that advanced age is positively correlated with insulin resistance, and the age-related decline in mitochondrial function may serve as an underlying mechanism [30, 31]. Additionally, during pregnancy, concentrations of maternal insulin-antagonistic substances increase, ultimately leading to decreased insulin sensitivity and the development of GDM [32]. Regarding educational level, our research indicated that the proportion of participants in the GDM group with lower education levels was significantly higher than that in the non-GDM group. A plausible explanation for this finding is that individuals with higher education levels tend to possess a better understanding of the disease, which may encourage them to prioritize personal health management and collaborate closely with healthcare professionals to prevent complications during pregnancy. Furthermore, we investigated the relationship between pre-pregnancy BMI and GDM. Our findings revealed that the proportion of participants classified as overweight or obese (BMI ≥ 24 kg/m2) was significantly higher in the GDM group compared to the non-GDM group. Maternal pre-pregnancy overweight or obesity is a well-established risk factor for adverse pregnancy outcomes. Recent studies conducted in China have shown that pre-pregnancy BMI not only independently contributes to the risk of GDM but also interacts with advanced maternal age [33, 34]. The underlying mechanism suggests that maternal overweight or obesity leads to fat accumulation in various organs, including the pancreas and liver. Ectopic fat storage in these organs is associated with impaired β-cell function and insulin resistance, which ultimately results in GDM [35–38]. Consequently, it is reasonable to anticipate that managing BMI during the preconception period may help reduce the risk of GDM.
This study examined the relationship between PA and the risk of GDM. There is substantial scientific evidence supporting the benefits of PA at all stages of life. Pregnancy is a unique period characterized by significant biochemical and physiological changes in maternal metabolism, increased nutritional demands, and safety concerns regarding exercise, all of which can heighten the risk of hyperglycemia during pregnancy. Our research indicates that PA levels, irrespective of intensity, were significantly lower in the GDM group compared to the non-GDM group. A recent meta-review study corroborates our findings, highlighting that PA during pregnancy is associated with an elevated risk of GDM [26]. Currently, PA (and exercise) is recognized as an effective behavioral strategy for the prevention and treatment of disease, and it has been explicitly recommended in national guidelines for the prevention and management of GDM [39–41]. In 2022, the Chinese Society of Obstetrics and Gynecology published guidelines for the diagnosis and treatment of hyperglycemia in pregnancy, stating that regular exercise both pre-pregnancy and during pregnancy significantly reduces the risk of GDM, particularly in normal-weight pregnant women, as well as in those who are overweight or obese [42]. However, Previous guidelines for GDM have primarily concentrated on exercise therapy while overlooking the preventive role of PA. Furthermore, these guidelines lack precise recommendations regarding the optimal amount of PA. The American Diabetes Association recommends engaging in exercise for 20–50 min per day, 2–7 days per week, at a moderate intensity [40], In contrast, the Chinese Society of Obstetrics and Gynecology suggests a minimum of 30 min of moderate intensity exercise per day, 5 days per week [42]. Consequently, our research aimed to investigate the dose-response relationship between PA and GDM risk. Results from the RCS model indicated a significantly non-linear relationship between total PA, MVPA, walking PA levels, and GDM. A systematic review and dose-response meta-analysis revealed a significant inverse association between PA before and during early pregnancy and GDM risk [43]. Additionally, our previous research identified a dose-response relationship between PA levels during the first and second trimesters and GDM risk [44]. In this study, we recognized L-shaped curve relationships between various PA levels and GDM risk. Furthermore, we expanded upon previous findings regarding the cut-off values for total PA, MVPA, and walking PA levels in GDM prevention. For instance, a cut-off value of 1714 MET-min/week for total PA suggests that engaging in walking, the most accessible form of exercise for most pregnant women, for more than 74.20 min per day can significantly reduce the risk of developing GDM. The rationality of these results was further corroborated by subsequent logistic regression analysis. These findings imply that the risk of GDM diminishes as PA levels increase, with effective prevention achievable only upon reaching a specific threshold of PA.
Strengths and limitations
The primary strengths of this study lie in its application of the RCS model to investigate the dose-response relationship between PA and GDM during the second trimester of pregnancy. The PA levels of participants were quantified in MET based on the Compendium of Physical Activities. Comprehensive analyses, including overall association assessments, nonlinearity tests, and dose-response curve fitting, were conducted using the optimally selected RCS model. Furthermore, the optimal cut-off value of PA for GDM prevention was determined and subsequently validated through logistic regression analysis. The scientific design and rigorous logical framework of this research contribute to the robustness of the findings, ultimately offering more quantified and precise recommendations for the prevention of GDM.
This study has several limitations. First, the cross-sectional design precludes causal inference; however, the observed associations between intensity-specific PA and GDM provide valuable insights that may inform future hypothesis-driven research. Second, PA was assessed only during the second trimester, without accounting for changes across the full course of pregnancy. Third, as a single-center study with a limited sample size, the generalizability of the findings may be limited. Future large-scale longitudinal studies are warranted to validate and extend these results.
Conclusions
Previous research has identified that increased PA during pregnancy is beneficial for both maternal and fetal health. In the guidelines for GDM in many countries, PA is recommended as an effective behavioral approach for the control and management of GDM. However, current research lacks precise recommendations regarding the optimal levels of PA.
In this study, we identified a nonlinear dose-response relationship between PA and GDM risk during the second trimester of pregnancy, indicating that the risk of GDM gradually decreases with increasing PA levels and that effective prevention of GDM can only be achieved by reaching a sufficient level of PA.
Acknowledgements
The authors sincerely acknowledge all study participants for their time and patience.
Abbreviations
- ADA
American Diabetes Association
- AIC
Akaike information criterion
- BMI
Body Mass Index
- DDEI
Daily dietary energy intake
- FFQ
Food Frequency Questionnaire
- GDM
Gestational diabetes mellitus
- IPAQ
International Physical Activity Questionnaire
- MET
Metabolic equivalents
- MPA
Moderate physical activity
- MVPA
Moderate-to-vigorous-intensity physical activity
- OGTT
Oral glucose tolerance test
- OR
Odds ratio
- PA
Physical activity
- RCS
Restricted cubic spline
- T2DM
Type 2 diabetes mellitus
- VPA
Vigorous physical activity
Authors’ contributions
All authors contributed to the article. Liuwei Zhang, Lichao Sun and Xiaoyan Zhang: Conceptualized and designed the study. Liping Zuo and Shengjun Sun: Investigated and collected data. Yijia Ren and Yi Gao: Searched and collated the literature for the study. Liuwei Zhang: Validated and analyzed the data, and prepared the manuscript. All authors reviewed the manuscript.
Funding
This work was funded by the National Natural Science Foundation of China [Grant number 81803324], the Chinese Universities Scientific Fund [Grant number 2024TZJK007], the Elite Medical Professionals project of China-Japan Friendship Hospital [Grant number ZRJY2024-BJ05], and the National High Level Hospital Clinical Research Funding [Grant number 2022-NHLHCRF-LX-01-0305].
Data availability
All data used for this study are not publicly available due to regulations on sensitive personal data but are available from the corresponding author (2466@bsu.edu.cn) upon reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol received approval from the Ethics Committee of Sport Science Experiment at Beijing Sport University (2018006 A). Informed consent to participate was obtained from all of the participants. The study was conducted in accordance with the Declaration of Helsinki and its later amendments.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Liuwei Zhang, Email: 2466@bsu.edu.cn.
Lichao Sun, Email: xccxyy@126.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data used for this study are not publicly available due to regulations on sensitive personal data but are available from the corresponding author (2466@bsu.edu.cn) upon reasonable request.