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Aims: Gestational diabetes (GDM) is associated with the development of postpartum (PP) glucose intolerance. Plasma glycated CD59 (pGCD59) is an emerging biomarker for the detection of hyperglycaemia. The aim of this study was to assess the ability of PP pGCD59 to predict the development of PP GI as defined by the 2 h 75 g OGTT using the ADA criteria, in a cohort of women diagnosed with prior GDM in the index pregnancy using the 2 h 75 g OGTT at 24–28 weeks of gestation according to the World Health Organization (WHO) 2013 criteria. Methods: Of the 2017 pregnant women recruited prospectively 140 women with gestational diabetes had samples for pGCD59 taken PP at the time of the OGTT. The ability of pGCD59 to predict the results of the PP OGTT was assessed using nonparametric receiver operating characteristic (ROC) curves. Results: Women with PP glucose intolerance had significantly higher PP pGCD59 levels compared to women with normal glucose tolerance PP (3.8 vs. 2.7 SPU). PP pGCD59 identified women who developed glucose intolerance PP with an AUC of 0.80 (95% CI: 0.70–0.91). A PP pGCD59 cut‐off value of 1.9 SPU generated a sensitivity of 100% (95% CI: 83.9–100), specificity of 16.9% (95% CI: 9.8–26.3), positive predictive value of 22.1% (95% CI: 21.0–22.6), and negative predictive value of 100% (95% CI: 87.4–100). PP fasting plasma glucose generated an AUC of 0.96 (95% CI: 0.89–0.99) for the identification of PP glucose intolerance. Conclusion: Our study found that PP pGCD9 may be a promising biomarker to identify women not requiring PP glucose intolerance screening using the traditional OGTT. While the diagnostic accuracy of pGCD59 is good, fasting plasma glucose remains a better test for the identification of PP glucose intolerance. [ABSTRACT FROM AUTHOR]

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators. [ABSTRACT FROM AUTHOR]

When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G-formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches, which are biased under misspecification of a parametric outcome model. In contrast propensity score methods require the correct specification of an exposure model. Double-robust methods only require correct specification of either the outcome or the exposure model. Targeted maximum likelihood estimation is a semiparametric double-robust method that improves the chances of correct model specification by allowing for flexible estimation using (nonparametric) machine-learning methods. It therefore requires weaker assumptions than its competitors. We provide a step-by-step guided implementation of TMLE and illustrate it in a realistic scenario based on cancer epidemiology where assumptions about correct model specification and positivity (ie, when a study participant had 0 probability of receiving the treatment) are nearly violated. This article provides a concise and reproducible educational introduction to TMLE for a binary outcome and exposure. The reader should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. Extensive R-code is provided in easy-to-read boxes throughout the article for replicability. Stata users will find a testing implementation of TMLE and additional material in the Appendix S1 and at the following GitHub repository: https://github.com/migariane/SIM-TMLE-tutorial. [ABSTRACT FROM AUTHOR]

Background Migraine is associated with a number of cardiometabolic risk factors including abnormalities in lipid metabolism. However, little is known about these associations among pregnant migraineurs. We conducted the present study to evaluate the extent to which altered lipid profiles are associated with history of migraine among pregnant women. Methods A cohort of 1062 Peruvian women were interviewed at 24-28 weeks of gestation. Migraine status was classified based on the International Classification of Headache Disorders- II diagnostic criteria. Serum lipid concentrations were measured enzymatically using standardized assays. Multivariable logistic regression was used to estimate adjusted odds ratios ( AORs) and 95% confidence intervals ( CIs) as measures of associations of migraine status with varying concentrations of lipids and lipoproteins during pregnancy. Results Approximately 18.5% of the study participants were identified as migraineurs (196 of 1062). Maternal serum total cholesterol, high-density lipoprotein ( HDL), low-density lipoprotein ( LDL), triglycerides, and total cholesterol : HDL ratio were all statistically significantly elevated among pregnant migraineurs compared with pregnant non-migraineurs. In multivariate adjusted models, pregnant women with migraine had higher odds of elevated total cholesterol, LDL, and total cholesterol : HDL ratio as compared with pregnant women without migraine. For instance, the AOR and 95% CI for successive quartiles of the total cholesterol associated with history of migraine were Q2 (219-247 mg/d L): 1.05 (0.64-1.70), Q3 (248-281 mg/d L): 1.16 (0.72-1.86), and Q4 (≥282 mg/d L): 1.87 (1.20-2.91) with the lowest quartile (<219 mg/d L) as the referent group ( P value for trend = .003). Obese women with elevated total cholesterol (≥282 mg/d L) were more likely to be migraineurs ( OR = 3.71; 95% CI 1.58-8.71) as compared with non-obese women with lower total cholesterol (<219 mg/d L). Similar elevated odds of migraine were observed for obese women with elevated LDL cholesterol, elevated triglycerides and high total cholesterol : HDL ratio. Conclusion Pregnant migraineurs had elevated odds of dyslipidemia, particularly hypercholesterolemia, elevated LDL, and total cholesterol : HDL ratio as compared with pregnant non-migraineurs. The observed associations were more pronounced among obese migraineurs. Our findings add to the accumulating evidence of adverse cardiometabolic risk profiles among migraineurs and extend these associations to pregnant women. [ABSTRACT FROM AUTHOR]

Background: Vitamin D deficiency during pregnancy has been associated with increased risk of complications and adverse perinatal outcomes. We evaluated seasonal variation of 25-hydroxyvitamin D [25(OH)D] among pregnant women, focusing on patterns and determinants of variation.Methods: Data came from three cohort studies in the US that included 2583 non-Hispanic Black and White women having prenatal 25(OH)D concentrations determined. Fourier time series and generalised linear models were used to estimate the magnitude of 25(OH)D seasonality. We modelled seasonal variability using a stationary cosinor model to estimate the phase shift, peak-trough difference, and annual mean of 25(OH)D.Results: We observed a peak for 25(OH)D in summer, a nadir in winter, and a phase of 8 months, which resulted from fluctuations in 25(OH)D3 rather than 25(OH)D2. After adjustment for covariates, the annual mean concentrations and estimated peak-trough difference of 25(OH)D among Black women were 19.8 ng/mL [95% confidence interval (CI) 18.9, 20.5] and 5.8 ng/mL [95% CI 4.7, 6.7], and for non-Hispanic White women were 33.0 ng/mL [95% CI 32.6, 33.4] and 7.4 ng/mL [95% CI 6.0, 8.9].Conclusions: Non-Hispanic Black women had lower average 25(OH)D concentrations throughout the year and smaller seasonal variation levels than non-Hispanic White women. This study's confirmation of 25(OH)D seasonality over a calendar year has the potential to enhance public health interventions targeted to improve maternal and perinatal outcomes. [ABSTRACT FROM AUTHOR]