Clinical and Translational Research Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Sep 16, 2024; 12(26): 5937-5945
Published online Sep 16, 2024. doi: 10.12998/wjcc.v12.i26.5937
Modifiable factors mediating the effects of educational attainment on gestational diabetes mellitus: A two-step Mendelian randomization study
Ming-Yue Ma, Ya-Song Zhao, Department of Nursing, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, China
ORCID number: Ya-Song Zhao (0009-0007-3866-6413).
Author contributions: Ma MY provide an idea, find data, data analysis, and manuscript checking; Zhao YS write the first draft and data visualization; all authors have read and agreed to publish the manuscript; all authors participated in data interpretation, revisions, and approved the final version submitted for publication.
Conflict-of-interest statement: All authors declare no conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ya-Song Zhao, PhD, Researcher, Department of Nursing, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang 110004, Liaoning Province, China. zys18940252012@163.com
Received: April 24, 2024
Revised: July 1, 2024
Accepted: July 10, 2024
Published online: September 16, 2024
Processing time: 90 Days and 4.2 Hours

Abstract
BACKGROUND

Although there is currently a wealth of evidence to indicate that maternal educational attainment is associated with gestational diabetes mellitus (GDM), the specific modifiable risk factors that mediate the causal relationship between these two variables have yet to be identified.

AIM

To identify the specific modifiable risk factors that mediate the causal relationship between the level of maternal education and GDM.

METHODS

Mendelian randomization (MR) was conducted using data from genome-wide association studies of European populations. We initially performed a two-sample MR analysis using data on genetic variants associated with the duration of education as instruments, and subsequently adopted a two-step MR approach using metabolic and lifestyle factors as mediators to examine the mechanisms underlying the relationship between the level of maternal education and risk of developing GDM. In addition, we calculated the proportions of total causal effects mediated by identified metabolic and lifestyle factors.

RESULTS

A genetically predicted higher educational attainment was found to be associated with a lower risk of developing GDM (OR: 0.71, 95%CI: 0.60-0.84). Among the metabolic factors assessed, four emerged as potential mediators of the education-GDM association, which, ranked by mediated proportions, were as follows: Waist-to-hip-ratio (31.56%, 95%CI: 12.38%-50.70%), body mass index (19.20%, 95%CI: 12.03%-26.42%), high-density lipoprotein cholesterol (12.81%, 95%CI: 8.65%-17.05%), and apolipoprotein A-1 (7.70%, 95%CI: 4.32%-11.05%). These findings proved to be robust to sensitivity analyses.

CONCLUSION

Our findings indicate a causal relationship between lower levels of maternal education and the risk of developing GDM can be partly explained by adverse metabolic profiles.

Key Words: Educational status; Gestational diabetes mellitus; Metabolism; Lifestyle factors; Mendelian randomization analysis

Core Tip: Studies have shown that the level of maternal education is associated with the risk of developing gestational diabetes mellitus (GDM). In this study, we sought to identify the specific modifiable risk factors that mediate the causal relationship between the level of maternal education and the likelihood of developing GDM. We performed Mendelian randomization analyses based on publicly available data obtained in a number of genome-wide association studies of European populations. Our findings indicate that a genetically predicted higher level of maternal education is associated with a lower GDM risk and that four modifiable metabolic factors contribute to mediating this association, namely, waist-to-hip ratio, body mass index, and the contents of high-density lipoprotein cholesterol and apolipoprotein A-1.



INTRODUCTION

Gestational diabetes mellitus (GDM) is defined as a type of glucose intolerance that is initially detected during pregnancy[1]. It is a disorder of substantial public health significance worldwide, with prevalence estimates as high as 14%[2]. Moreover, the prevalence of GDM tends to be characterized by marked inter-population variance due to differences in associated risk factors and approaches to screening and diagnosis. GDM is associated with a higher risk of short- and long-term adverse health outcomes in both mothers and their offspring. Specifically, mothers diagnosed with GDM have been established to be more susceptible to the subsequent development of type 2 diabetes[3], metabolic syndrome[4], and cardiovascular disease[5]. Among the offspring of affected mothers, exposure to GDM in utero tends to increase the predisposition to adverse outcomes, not only in the perinatal period but also in later life. These include, although are not limited to, pre-term birth[6], excessive fetal growth[7], neonatal hypoglycemia[8], autism spectrum disorder[9], obesity[10], and cardiometabolic dysfunction[11].

Educational attainment is a robust predictor of socioeconomic achievement and has extensive implications for lifestyle behaviors and health resource utilization throughout life[12,13]. A robust and compelling body of epidemiological evidence indicates that women with a lower level of educational attainment are disproportionately affected by GDM[14]. An accumulating body of epidemiological research supports the potential advantages of mitigating modifiable risk factors, primarily metabolic factors and lifestyle behaviors, for the prevention and management of GDM[15]. However, whether education independently influences the risk of developing GDM and the degree to which modifiable factors mediate such effects remain unknown. Accordingly, elucidating the mediatory pathways linking educational attainment and GDM could enable the identification of targets for public health policies and interventions aimed at reducing the excess GDM risk arising from socioeconomic disadvantage.

Mendelian randomization (MR) has become an important epidemiological method for assessing causal relationships between exposures and outcomes. MR uses genetic variants, typically single-nucleotide polymorphisms (SNPs) identified from genome-wide association studies (GWASs), as instrumental variables (IVs) to better evaluate exposure-outcome associations[16]. Compared with conventional observational studies, the MR approach is potentially less susceptible to residual confounding, as genetic variants are randomly assigned at meiosis and conception[17]. Hence, MR can substantially enhance the validity and reliability of causal inferences. Additionally, MR minimizes the reverse causation bias as germline genotypes cannot be altered by disease onset or progression[18]. The successful application of MR is dependent on three key assumptions. First, the selected instrumental genetic variables should be strongly associated with the exposure of interest; second, these genetic variables should not be associated with any potential confounders of the exposure-outcome relationship; and third, the genetic variables exclusively influence outcomes via exposure[19]. When these assumptions are fulfilled, MR can provide compelling evidence for the causal relationship between exposure and outcome.

In this study, we adopted a two-sample MR approach to assess the causal relationship between maternal educational attainment and the risk of developing GDM. In addition, to guide clinical practice, we conducted MR mediation analyses to examine the extent to which metabolic and lifestyle factors may mediate the effects of educational attainment.

MATERIALS AND METHODS
Study design

We initially performed univariable MR (UVMR) analysis to assess the causal relationship between educational attainment and the risk of developing GDM, in which we conducted a comprehensive screening for potential factors that might mediate this relationship. Subsequently, we employed a two-step MR approach to estimate the mediatory effects of these factors. All data used in this study were derived from publicly available data obtained from studies that had appropriate participant consent and ethical approval. Moreover, the study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for MR studies[20].

Data sources

Education attainment: Genetic variants for educational attainment were extracted from a GWAS of years of schooling encompassing 1131881 individuals of European descent performed by the Social Science Genetic Association Consortium. Summary statistics were accessible for 766345 of these participants upon the exclusion of those from 23andMe (Table 1).

Table 1 Summary of the genome-wide association study data used in the mendelian randomization analyses.
Phenotype
Datatype
Sample size
Population
Consortium/cohort
Exposure
    EducationContinuous766345EuropeanSSGAC
Outcome
    GDMBinary190879EuropeanFinnGen
Metabolic factors
    BMI1Continuous681275EuropeanGIANT
    BF%Continuous65831EuropeanMeta
    Waist circumferenceContinuous231353EuropeanGIANT
    WHR1Continuous212244EuropeanGIANT
    Total GWGContinuous10555EuropeanEGG
    Early GWGContinuous7704EuropeanEGG
    Late GWGContinuous7681EuropeanEGG
    HypertensionBinary342439EuropeanFinnGen
    Systolic blood pressureContinuous757601EuropeanUK Biobank
    Diastolic blood pressureContinuous757601EuropeanUK Biobank
    LDL-CContinuous440546EuropeanUK Biobank
    HDL-CaContinuous403943EuropeanUK Biobank
    TriglycerideContinuous441016EuropeanUK Biobank
    Apolipoprotein A-11Continuous393193EuropeanUK Biobank
Apolipoprotein BContinuous439214EuropeanUK Biobank
    Serum urate Continuous110347EuropeanGUGC
    Serum ironContinuous23986EuropeanGISC
    FerritinContinuous23986EuropeanGISC
    TransferrinContinuous23986EuropeanGISC
    Transferrin saturationContinuous23986EuropeanGISC
Lifestyle factors
    Strenuous sports or other exercisesContinuous350492EuropeanUK Biobank
    Moderate to vigorous physical activity Continuous377234EuropeanUK Biobank
    Sedentary behaviorContinuous91105EuropeanUK Biobank
    Smoking initiationBinary1232091EuropeanGSCAN
    Smoking cessationBinary547219EuropeanGSCAN
    Smoking heavinessContinuous337334EuropeanGSCAN
    Alcohol drinkingContinuous941280EuropeanGSCAN
    Coffee consumption Continuous375833EuropeanUK Biobank
    InsomniaBinary1331010EuropeanUK Biobank
    Sleep durationContinuous446118EuropeanUK Biobank
    Long sleep durationBinary339926EuropeanUK Biobank
    Short sleep durationBinary446118EuropeanUK Biobank

Metabolic and lifestyle factors: Thirty-two modifiable factors (20 metabolic and 12 lifestyle factors) that we hypothesized might mediate the relationship between education and GDM were selected, and genetic variants for these mediators were extracted from the GWAS data (Table 1). Candidate mediators of the effects of education on GDM were selected according to certain prerequisites. First, the causal relationship between education and a mediator must be unidirectional, given that bi-directionality could compromise the validity of the mediation analyses. Second, the causal association between the mediator and GDM should persist irrespective of whether it is adjusted for education. Third, extant evidence dictates that the relationship between education and a mediator, and that between the mediator and GDM, should be in opposite directions. Ultimately, we identified four metabolic mediators that satisfied all stipulations and these were incorporated into the analyses to assess their mediating influence on the causal relationship between the level of maternal education and the risk of developing GDM.

GDM: Summary statistics for GDM were extracted from the Release 8 data of the GWAS performed by the FinnGen consortium (Table 1), which encompassed 190879 Finnish women, comprising 11279 cases and 179600 controls. GDM cases were defined by code O24.4 in the International Classification of Diseases-9th and 10th revisions.

To avoid biases from population stratification, in the main analysis, summary statistics for exposures, mediators, and outcomes were extracted from the GWAS data conducted predominantly on subjects of European ancestry.

Selection of IVs: To ensure the validity and accuracy of the inferences from this MR study, stringent quality control procedures were employed to select the optimal IVs for exposure, mediators, and outcomes. To obtain powerful IVs, we initially extracted SNPs from the GWAS dataset at a genome-wide significance threshold of P < 5 × 10-8. However, given the extremely limited number of qualified IVs obtained for total, early, and late gestational weight gain (GWG) at this threshold, we subsequently applied a relatively less stringent threshold (P < 5 × 10-5) to obtain a sufficiently large dataset. For genetic variants of interest, we selected a minor allele frequency threshold of 0.01. A fundamental principle of the MR approach is the absence of linkage disequilibrium (LD) between the included IVs, as substantial LD could yield biased results. We accordingly selected genetic variants that achieved independence at LD (r2 = 0.001) and a distance of 10000 kb from the European 1000 Genome Reference Panel. Furthermore, to avoid accidental bias during harmonization, we removed palindromic SNPs, and to avoid the potential influence of horizontal pleiotropy on the MR estimates, we used PhenoScanner to identify and remove SNPs associated with other potential confounders affecting the outcome[21]. Finally, to evaluate the strength of the IVs, we generated F-statistics for each SNP, and to minimize potential weak instrument bias, SNPs with F values < 10 were deleted[22].

Statistical analysis

UVMR: The UVMR method was used to assess the total impact of the exposure (educational attainment) on the outcomes (GDM and the selected mediators). The primary method of analysis used in this MR study was inverse variance weighted (IVW), which combines Wald ratios via a random-effects meta-analysis[23].

Mediation MR analyses

We conducted a two-stage MR analysis to investigate whether any modifiable factors mediated the causal relationship between educational attainment and the risk of GDM. In the initial stage, we employed IVW as the primary approach to estimate the causal effects of educational attainment on each potential mediator (β1), and in the second stage, we assessed the causal effects of each mediator on GDM risk after adjusting for the genetic influence of the IVs on education (β2) using regression-based multivariable MR (MVMR)[24]. The individual mediatory effect of each mediator was then calculated by multiplying the results from the two stages (β1 × β2)[25]. The proportion of the total effect of educational attainment on the risk of GDM mediated by each mediator was estimated by dividing the indirect effect by the total effect. Standard errors were derived using the delta method, based on the effect estimates obtained from the two-sample MR analysis[26].

Sensitivity analyses: To validate the robustness of the IVW results in UVMR analyses, we employed MR-Egger[27], weighted median[28], and MR-pleiotropic residual sum and outliers (MR-PRESSO) methods[29]. In addition, we used MVMR Egger sensitivity to validate the robustness of the IVW results in the MVMR analyses. Each approach is dependent on different hypothetical models to evaluate the causal effects. The MR-Egger method provides estimates adjusted for pleiotropy[27], whereas the weighted median approach allows for causal effect estimation when 50% of the SNPs are invalid[28], and the MR-PRESSO technique is used to detect and correct outliers, thereby yielding MR estimates that are robust to heterogeneity after removing the identified outliers[29]. We performed MR-Egger regression intercept analysis to assess horizontal pleiotropy, and also assessed heterogeneity using the IVW and MRI-Egger methods based on Cochran's Q statistic.

All MR analyses were conducted using the TwoSampleMR, MRPRESSO, MR, and MVMR packages in R (version 4.1.3). To account for multiple testing in the UVMR analyses, we applied a Bonferroni corrected significance level of P value < 1.52 × 10-3 (= 0.05/33). For MVMR analysis, we set the statistical significance at a P value < 0.05.

RESULTS
Effects of education on GDM

In the UVMR analyses, the results of IVW revealed that a genetically predicted higher level of maternal education attainment (OR: 0.71, 95%CI: 0.60-0.84) was associated with a lower risk of GDM (Table 2). MR-Egger and MR-PRESSO sensitivity analyses confirmed the robustness of the IVW results. There was adequate instrument strength (F-statistics > 10) among the genetic variants assessed for educational attainment (Table 2). The IVs selected based on educational attainment were characterized by sustained heterogeneity and no pleiotropy (Supplementary Tables 1 and 2).

Table 2 Univariable mendelian randomization estimating the causal effect of education on candidate mediators and gestational diabetes mellitus.
Phenotype
Method
nSNPs
F-statistics
Beta
95%CI
P value
Outcome
    GDMIVW29449.21-0.34-0.51 to -0.183.80E-05
MR Egger294-0.41-1.04 to 0.220.21
Weighted Median 294-0.38-0.59 to -0.165.47E-04
MR-PRESSO294-0.34-0.51 to -0.184.95E-05
Mediators
    WHRIVW14547.46-0.22-0.29 to -0.162.76E-12
MR Egger145-0.36-0.63 to -0.080.01
Weighted Median 145-0.23-0.32 to -0.151.16E-07
MR-PRESSO145-0.22-0.29 to -0.169.50E-11
    BMIIVW10644.47-0.16-0.21 to -0.111.97E-10
MR Egger106-0.17-0.43 to 0.090.19
Weighted Median 106-0.15-0.19 to -0.107.27E-09
MR-PRESSO106-0.16-0.21 to -0.115.26E-09
    HDL-CIVW24546.180.180.15 to 0.211.75E-34
MR Egger2450.150.03 to 0.270.02
Weighted Median 2450.170.13 to 0.212.81E-20
MR-PRESSO2450.180.15 to 0.213.38E-27
    Apolipoprotein A-1IVW26648.410.130.10 to 0.168.03E-19
MR Egger2660.05-0.06 to 0.170.34
Weighted Median 2660.110.08 to 0.153.07E-10
MR-PRESSO2660.130.10 to 0.161.19E-16
Effects of education on different mediators

We identified four candidate mediators for inclusion in the mediation MR analyses, namely, waist-to-hip ratio (WHR), body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein A-1 (Table 1). In UVMR analyses, each genetically predicted single standard deviation increase in years of schooling was associated with lower levels of WHR (β: -0.22, 95%CI: -0.29 to -0.16) and BMI (β: -0.16, 95%CI: -0.21 to -0.11) and higher levels of HDL-C (β: 0.18, 95%CI: 0.15-0.21) and apolipoprotein A-1 (β: 0.13, 95%CI: 0.10-0.16). At least two or three sensitivity analyses confirmed the IVW estimates (Table 2). Genetic IVs for educational attainment exhibited sustained heterogeneity and no pleiotropy (Supplementary Tables 1 and 2). The F-statistics of the IVs were greater than 10, thereby tending to indicate an absence of any substantial weak instrument bias (Table 2). In the reverse MR analyses, we found that there was a causal association between BMI (β: -0.12, 95%CI: -0.14 to -0.10) and HDL-C (β: 0.02, 95%CI: 0.01-0.03) and educational attainment, which were largely driven by horizontal pleiotropy (Supplementary Table 3).

Effects of different mediators on GDM with adjustment for education

In the MVMR results, a higher WHR (OR: 2.31, 95%CI: 1.65-3.23) and BMI (OR: 1.53, 95%CI: 1.32-1.76) were found to be associated with an increased GDM risk after adjustment for education (Table 3). In contrast, higher levels of HDL-C (OR: 0.76, 95%CI: 0.70-0.82) and apolipoprotein A-1 (OR: 0.81, 95%CI: 0.74-0.89) were shown to be associated with a reduced GDM risk having adjusted for education (Table 3). MVMR sensitivity analyses validated the sustained heterogeneity and absence of pleiotropy across the selected genetic variants (Supplementary Table 4).

Table 3 Multivariable mendelian randomization assessing the causal association between each mediator and gestational diabetes mellitus with adjustment for education from inverse variance weighted results.
Mediators
nSNPs
Beta
SE
OR
95%CI
P value
WHR1540.840.172.311.65 to 3.231.00E-06
BMI4200.420.071.531.32 to 1.765.59E-09
HDL-C381-0.280.040.760.70 to 0.821.59E-10
Apolipoprotein A-1358-0.210.050.810.74 to 0.896.08E-06
Mediating effects of mediators in the association between education and GDM

Figure 1 depicts the proportions of the effects of educational attainment on GDM risk explained by each of the four identified mediators. WHR accounted for 31.56% (95%CI: 12.38%-50.70%) of the total influence of educational attainment on the risk of GDM, whereas BMI explained 19.20% (95%CI: 12.03%-26.42%) of the total effect, an HDL-C and apolipoprotein A-1 mediated 12.81% (95%CI: 8.65%-17.05%) and 7.70% (95%CI: 4.32%-11.05%) of the total effect, respectively.

Figure 1
Figure 1 Mendelian randomization estimates of proportional mediation by candidate mediators in the causal relationship between educational attainment and gestational diabetes mellitus. HDL-C: High-density lipoprotein cholesterol; BMI: Body mass index; WHR: Waist-to-hip ratio.
DISCUSSION

This MR study provides compelling novel evidence of the causal protective effect of educational attainment on GDM susceptibility. To elucidate the factors associated with this effect, we further assessed potential intermediaries in the path from education to GDM and identified four modifiable risk factors as causal mediators, which, ranked in terms of proportional mediation in the association between education and GDM, were WHR (31.56%), BMI (19.20%), HDL-C (12.81%), and apolipoprotein A-1 (7.70%). Our findings accordingly highlight the causal protective role of education and the substantial mediatory influence of several prevalent metabolic factors on the pathogenesis of GDM.

These findings build on previous work by providing further evidence to indicate that attainment of a higher level of education is a protective factor against the likelihood of developing GDM. Accumulating evidence from observational and MR studies indicates that a higher level of education is protective against hyperglycemia[14,30]. Educational attainment represents a modifiable and malleable factor with an enduring influence on financial status, access to social capital, and the adoption of healthy lifestyles over the course of an individual’s lifespan[31]. Moreover, although formal education typically concludes in early adulthood, adopting a lifelong learning perspective provides opportunities to continually acquire knowledge and contributes to enhancing cognitive abilities and promoting long-term health throughout adult life[31]. Thus, our findings offer key insights into prioritizing educational policies and reducing educational inequities as effective precautionary measures against GDM and its related disease burden.

A further salient finding of this study was our identification and quantification of the mediatory roles of certain metabolic factors in the relationship between education and GDM. On the basis of the application of stringent criteria, we identified four causal mediators, among which BMI and WHR appeared to be the principal mediators of the effects of education on the risk of developing GDM. These findings are consistent with previous epidemiological and MR evidence indicating that obesity, measured primarily via BMI and WHR, is strongly associated with GDM, thereby indicating that interventions targeting obesity may have the desired effects in low-education scenarios[32,33]. Of the other two identified mediators, HDL-C and apolipoprotein A-1 were shown to be associated with 12.81% and 7.70% of the causal effect of education on the risk of GDM, respectively. Notably, obesity and dyslipidemia are major public health issues that often co-occur and have common biological underpinnings, including immune inflammation and abnormal neuroendocrine regulation and energy metabolism[12,34]. Thus, given the inter-relationships among these four mediators, we suspect that there may be a certain degree of overlap in the proportional mediatory effects of these factors.

Our MR findings of no causal links between genetically predicted GDM and several metabolic and lifestyle factors would tend to indicate that significant relationships detected in observational studies may partly stem from residual confounding or a reverse causation bias, including adiposity traits (body fat percentage, waist circumference[33], total, early, and late GWG[35]), lipids (low-density lipoprotein cholesterol[36], and apolipoprotein B[37]), physical activity and sedentary behaviors[38] (moderate to vigorous physical activity levels and sedentary behavior), sleep-related traits[39,40] (insomnia, sleep duration, long sleep duration, and short sleep duration), and smoking and dietary behaviors[41] (smoking initiation, smoking cessation, smoking heaviness, alcohol consumption[42], and coffee consumption[43]).

To the best of our knowledge, this MR study is the first to establish the causal effects of education on the likelihood of developing GDM, and to identify causal intermediaries in the path between education and GDM. The study has several notable strengths. First, it uses SNPs as genetic instruments to mitigate confounding and reverse causation. Second, the robustness of the IVW estimates was demonstrated by performing multiple MR sensitivity analyses under different assumptions regarding genetic pleiotropy[44]. Third, our application of stringent criteria for mediator selection minimized the reverse causation of mediators on education, thereby ensuring the credibility and rationale of our proposed model explaining the mediating influence. However, despite these strengths, the study does have certain limitations. First, the heterogeneity of the SNPs may have introduced bias and influenced the robustness of our MR findings. Second, the GWAS data used for analyses were obtained from a European population, limiting the generalizability of our results to other ethnicities pending further study. Finally, sample overlap between GWASs may have biased the MR estimates toward observational association estimates[45].

CONCLUSION

In this MR study, we succeeded in elucidating the causal protective influence of educational attainment on the risk of developing GDM and identified four causal mediators underlying the impact of education, namely, WHR, BMI, HDL-C, and apolipoprotein A-1. Our findings in this study provide novel insights into the mechanisms underlying the association between educational attainment and GDM susceptibility.

ACKNOWLEDGEMENTS

We thank the participants of the UK Biobank study and the genome-wide association study consortiums who made their summary statistics publicly available for this study.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Medicine, research and experimental

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade C, Grade C, Grade C

Novelty: Grade B, Grade B, Grade B

Creativity or Innovation: Grade B, Grade B, Grade B

Scientific Significance: Grade B, Grade B, Grade B

P-Reviewer: Arumugam VA; Mogulkoc R S-Editor: Lin C L-Editor: A P-Editor: Zhang XD

References
1.  Ye W, Luo C, Huang J, Li C, Liu Z, Liu F. Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis. BMJ. 2022;377:e067946.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 28]  [Cited by in F6Publishing: 188]  [Article Influence: 94.0]  [Reference Citation Analysis (0)]
2.  Sweeting A, Hannah W, Backman H, Catalano P, Feghali M, Herman WH, Hivert MF, Immanuel J, Meek C, Oppermann ML, Nolan CJ, Ram U, Schmidt MI, Simmons D, Chivese T, Benhalima K. Epidemiology and management of gestational diabetes. Lancet. 2024;.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 1]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
3.  Bellamy L, Casas JP, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009;373:1773-1779.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2141]  [Cited by in F6Publishing: 2246]  [Article Influence: 149.7]  [Reference Citation Analysis (1)]
4.  Lauenborg J, Mathiesen E, Hansen T, Glümer C, Jørgensen T, Borch-Johnsen K, Hornnes P, Pedersen O, Damm P. The prevalence of the metabolic syndrome in a danish population of women with previous gestational diabetes mellitus is three-fold higher than in the general population. J Clin Endocrinol Metab. 2005;90:4004-4010.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 204]  [Cited by in F6Publishing: 201]  [Article Influence: 10.6]  [Reference Citation Analysis (0)]
5.  Kramer CK, Campbell S, Retnakaran R. Gestational diabetes and the risk of cardiovascular disease in women: a systematic review and meta-analysis. Diabetologia. 2019;62:905-914.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 402]  [Cited by in F6Publishing: 500]  [Article Influence: 100.0]  [Reference Citation Analysis (0)]
6.  Hedderson MM, Ferrara A, Sacks DA. Gestational diabetes mellitus and lesser degrees of pregnancy hyperglycemia: association with increased risk of spontaneous preterm birth. Obstet Gynecol. 2003;102:850-856.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 58]  [Cited by in F6Publishing: 102]  [Article Influence: 4.9]  [Reference Citation Analysis (0)]
7.  Kim SY, Sharma AJ, Sappenfield W, Wilson HG, Salihu HM. Association of maternal body mass index, excessive weight gain, and gestational diabetes mellitus with large-for-gestational-age births. Obstet Gynecol. 2014;123:737-744.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 151]  [Cited by in F6Publishing: 166]  [Article Influence: 16.6]  [Reference Citation Analysis (0)]
8.  Voormolen DN, de Wit L, van Rijn BB, DeVries JH, Heringa MP, Franx A, Groenendaal F, Lamain-de Ruiter M. Neonatal Hypoglycemia Following Diet-Controlled and Insulin-Treated Gestational Diabetes Mellitus. Diabetes Care. 2018;41:1385-1390.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 36]  [Cited by in F6Publishing: 34]  [Article Influence: 5.7]  [Reference Citation Analysis (0)]
9.  Xiang AH, Wang X, Martinez MP, Walthall JC, Curry ES, Page K, Buchanan TA, Coleman KJ, Getahun D. Association of maternal diabetes with autism in offspring. JAMA. 2015;313:1425-1434.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 220]  [Cited by in F6Publishing: 230]  [Article Influence: 25.6]  [Reference Citation Analysis (0)]
10.  Furse S, Koulman A, Ozanne SE, Poston L, White SL, Meek CL. Altered Lipid Metabolism in Obese Women With Gestational Diabetes and Associations With Offspring Adiposity. J Clin Endocrinol Metab. 2022;107:e2825-e2832.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 11]  [Cited by in F6Publishing: 17]  [Article Influence: 8.5]  [Reference Citation Analysis (0)]
11.  Tam WH, Ma RC, Yang X, Li AM, Ko GT, Kong AP, Lao TT, Chan MH, Lam CW, Chan JC. Glucose intolerance and cardiometabolic risk in adolescents exposed to maternal gestational diabetes: a 15-year follow-up study. Diabetes Care. 2010;33:1382-1384.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 83]  [Cited by in F6Publishing: 88]  [Article Influence: 6.3]  [Reference Citation Analysis (0)]
12.  Wang Y, Ye C, Kong L, Zheng J, Xu M, Xu Y, Li M, Zhao Z, Lu J, Chen Y, Wang W, Ning G, Bi Y, Wang T. Independent Associations of Education, Intelligence, and Cognition With Hypertension and the Mediating Effects of Cardiometabolic Risk Factors: A Mendelian Randomization Study. Hypertension. 2023;80:192-203.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 31]  [Reference Citation Analysis (0)]
13.  Lövdén M, Fratiglioni L, Glymour MM, Lindenberger U, Tucker-Drob EM. Education and Cognitive Functioning Across the Life Span. Psychol Sci Public Interest. 2020;21:6-41.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 398]  [Cited by in F6Publishing: 452]  [Article Influence: 113.0]  [Reference Citation Analysis (0)]
14.  Swaminathan G, Swaminathan A, Corsi DJ. Prevalence of Gestational Diabetes in India by Individual Socioeconomic, Demographic, and Clinical Factors. JAMA Netw Open. 2020;3:e2025074.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 20]  [Cited by in F6Publishing: 33]  [Article Influence: 8.3]  [Reference Citation Analysis (0)]
15.  Brown J, Alwan NA, West J, Brown S, McKinlay CJ, Farrar D, Crowther CA. Lifestyle interventions for the treatment of women with gestational diabetes. Cochrane Database Syst Rev. 2017;5:CD011970.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 71]  [Cited by in F6Publishing: 97]  [Article Influence: 13.9]  [Reference Citation Analysis (0)]
16.  Lin J, Zhou J, Xu Y. Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain. 2023;146:3364-3372.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 69]  [Reference Citation Analysis (0)]
17.  Smith GD. Mendelian Randomization for Strengthening Causal Inference in Observational Studies: Application to Gene × Environment Interactions. Perspect Psychol Sci. 2010;5:527-545.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 92]  [Cited by in F6Publishing: 93]  [Article Influence: 6.6]  [Reference Citation Analysis (0)]
18.  Pingault JB, O'Reilly PF, Schoeler T, Ploubidis GB, Rijsdijk F, Dudbridge F. Using genetic data to strengthen causal inference in observational research. Nat Rev Genet. 2018;19:566-580.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 207]  [Cited by in F6Publishing: 261]  [Article Influence: 52.2]  [Reference Citation Analysis (0)]
19.  Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:1133-1163.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1660]  [Cited by in F6Publishing: 2618]  [Article Influence: 163.6]  [Reference Citation Analysis (0)]
20.  Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M, Richards JB. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326:1614-1621.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 828]  [Cited by in F6Publishing: 1334]  [Article Influence: 444.7]  [Reference Citation Analysis (0)]
21.  Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, Butterworth AS, Staley JR. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 2019;35:4851-4853.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 381]  [Cited by in F6Publishing: 1195]  [Article Influence: 298.8]  [Reference Citation Analysis (0)]
22.  Burgess S, Thompson SG. Bias in causal estimates from Mendelian randomization studies with weak instruments. Stat Med. 2011;30:1312-1323.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 115]  [Cited by in F6Publishing: 221]  [Article Influence: 17.0]  [Reference Citation Analysis (0)]
23.  Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658-665.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1330]  [Cited by in F6Publishing: 2975]  [Article Influence: 270.5]  [Reference Citation Analysis (1)]
24.  Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181:251-260.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 532]  [Cited by in F6Publishing: 900]  [Article Influence: 100.0]  [Reference Citation Analysis (0)]
25.  VanderWeele TJ. Mediation Analysis: A Practitioner's Guide. Annu Rev Public Health. 2016;37:17-32.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 611]  [Cited by in F6Publishing: 952]  [Article Influence: 105.8]  [Reference Citation Analysis (0)]
26.  MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593-614.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3974]  [Cited by in F6Publishing: 3104]  [Article Influence: 182.6]  [Reference Citation Analysis (0)]
27.  Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512-525.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2275]  [Cited by in F6Publishing: 5031]  [Article Influence: 559.0]  [Reference Citation Analysis (0)]
28.  Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304-314.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4015]  [Cited by in F6Publishing: 4582]  [Article Influence: 572.8]  [Reference Citation Analysis (0)]
29.  Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693-698.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2495]  [Cited by in F6Publishing: 4186]  [Article Influence: 697.7]  [Reference Citation Analysis (0)]
30.  Zhang J, Chen Z, Pärna K, van Zon SKR, Snieder H, Thio CHL. Mediators of the association between educational attainment and type 2 diabetes mellitus: a two-step multivariable Mendelian randomisation study. Diabetologia. 2022;65:1364-1374.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 11]  [Cited by in F6Publishing: 33]  [Article Influence: 16.5]  [Reference Citation Analysis (0)]
31.  Lawrence EM. Why Do College Graduates Behave More Healthfully than Those Who Are Less Educated? J Health Soc Behav. 2017;58:291-306.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 100]  [Cited by in F6Publishing: 77]  [Article Influence: 11.0]  [Reference Citation Analysis (0)]
32.  Wang Y, Wu P, Huang Y, Ye Y, Yang X, Sun F, Ye YX, Lai Y, Ouyang J, Wu L, Li Y, Li Y, Zhao B, Wang Y, Liu G, Pan XF, Chen D, Pan A. BMI and lipidomic biomarkers with risk of gestational diabetes in pregnant women. Obesity (Silver Spring). 2022;30:2044-2054.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 17]  [Article Influence: 8.5]  [Reference Citation Analysis (0)]
33.  Song X, Wang C, Wang T, Zhang S, Qin J. Obesity and risk of gestational diabetes mellitus: A two-sample Mendelian randomization study. Diabetes Res Clin Pract. 2023;197:110561.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 5]  [Reference Citation Analysis (0)]
34.  Vekic J, Zeljkovic A, Stefanovic A, Jelic-Ivanovic Z, Spasojevic-Kalimanovska V. Obesity and dyslipidemia. Metabolism. 2019;92:71-81.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 220]  [Cited by in F6Publishing: 326]  [Article Influence: 65.2]  [Reference Citation Analysis (0)]
35.  LifeCycle Project-Maternal Obesity and Childhood Outcomes Study Group; Voerman E, Santos S, Inskip H, Amiano P, Barros H, Charles MA, Chatzi L, Chrousos GP, Corpeleijn E, Crozier S, Doyon M, Eggesbø M, Fantini MP, Farchi S, Forastiere F, Georgiu V, Gori D, Hanke W, Hertz-Picciotto I, Heude B, Hivert MF, Hryhorczuk D, Iñiguez C, Karvonen AM, Küpers LK, Lagström H, Lawlor DA, Lehmann I, Magnus P, Majewska R, Mäkelä J, Manios Y, Mommers M, Morgen CS, Moschonis G, Nohr EA, Nybo Andersen AM, Oken E, Pac A, Papadopoulou E, Pekkanen J, Pizzi C, Polanska K, Porta D, Richiardi L, Rifas-Shiman SL, Roeleveld N, Ronfani L, Santos AC, Standl M, Stigum H, Stoltenberg C, Thiering E, Thijs C, Torrent M, Trnovec T, van Gelder MMHJ, van Rossem L, von Berg A, Vrijheid M, Wijga A, Zvinchuk O, Sørensen TIA, Godfrey K, Jaddoe VWV, Gaillard R. Association of Gestational Weight Gain With Adverse Maternal and Infant Outcomes. JAMA. 2019;321:1702-1715.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 354]  [Cited by in F6Publishing: 333]  [Article Influence: 66.6]  [Reference Citation Analysis (0)]
36.  Paradisi G, Ianniello F, Tomei C, Bracaglia M, Carducci B, Gualano MR, La Torre G, Banci M, Caruso A. Longitudinal changes of adiponectin, carbohydrate and lipid metabolism in pregnant women at high risk for gestational diabetes. Gynecol Endocrinol. 2010;26:539-545.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 38]  [Cited by in F6Publishing: 41]  [Article Influence: 2.9]  [Reference Citation Analysis (0)]
37.  Zheng S, Han T, Xu H, Zhou H, Ren X, Wu P, Zheng J, Wang L, Zhang M, Jiang Y, Chen Y, Qiu H, Liu W, Hu Y. Associations of apolipoprotein B/apolipoprotein A-I ratio with pre-diabetes and diabetes risks: a cross-sectional study in Chinese adults. BMJ Open. 2017;7:e014038.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 15]  [Article Influence: 2.1]  [Reference Citation Analysis (0)]
38.  Mijatovic-Vukas J, Capling L, Cheng S, Stamatakis E, Louie J, Cheung NW, Markovic T, Ross G, Senior A, Brand-Miller JC, Flood VM. Associations of Diet and Physical Activity with Risk for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Nutrients. 2018;10.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 148]  [Cited by in F6Publishing: 177]  [Article Influence: 29.5]  [Reference Citation Analysis (0)]
39.  Xu YH, Shi L, Bao YP, Chen SJ, Shi J, Zhang RL, Lu L. Association between sleep duration during pregnancy and gestational diabetes mellitus: a meta-analysis. Sleep Med. 2018;52:67-74.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 37]  [Cited by in F6Publishing: 39]  [Article Influence: 6.5]  [Reference Citation Analysis (0)]
40.  Reutrakul S, Anothaisintawee T, Herring SJ, Balserak BI, Marc I, Thakkinstian A. Short sleep duration and hyperglycemia in pregnancy: Aggregate and individual patient data meta-analysis. Sleep Med Rev. 2018;40:31-42.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 37]  [Cited by in F6Publishing: 41]  [Article Influence: 6.8]  [Reference Citation Analysis (0)]
41.  Bar-Zeev Y, Haile ZT, Chertok IA. Association Between Prenatal Smoking and Gestational Diabetes Mellitus. Obstet Gynecol. 2020;135:91-99.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 15]  [Article Influence: 3.0]  [Reference Citation Analysis (0)]
42.  Hayes L, McParlin C, Azevedo LB, Jones D, Newham J, Olajide J, McCleman L, Heslehurst N. The Effectiveness of Smoking Cessation, Alcohol Reduction, Diet and Physical Activity Interventions in Improving Maternal and Infant Health Outcomes: A Systematic Review of Meta-Analyses. Nutrients. 2021;13.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 13]  [Cited by in F6Publishing: 27]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]
43.  Hinkle SN, Gleason JL, Yisahak SF, Zhao SK, Mumford SL, Sundaram R, Grewal J, Grantz KL, Zhang C. Assessment of Caffeine Consumption and Maternal Cardiometabolic Pregnancy Complications. JAMA Netw Open. 2021;4:e2133401.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3]  [Cited by in F6Publishing: 10]  [Article Influence: 3.3]  [Reference Citation Analysis (0)]
44.  Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377-389.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1644]  [Cited by in F6Publishing: 2155]  [Article Influence: 307.9]  [Reference Citation Analysis (0)]
45.  Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40:597-608.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 525]  [Cited by in F6Publishing: 941]  [Article Influence: 117.6]  [Reference Citation Analysis (0)]