Is the Association Between Fruits and Vegetables and Preeclampsia Due to Higher Dietary Vitamin C and Carotenoid Intakes?

Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning

BACKGROUND

The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator.

METHODS

We used data from 10,038 pregnant women and a 10% subsample (N = 1004) to examine the extent to which the risk difference for the relation between fruit and vegetable consumption and preeclampsia risk changes under different seed values. We fit an augmented inverse probability weighted estimator with two Super Learner algorithms: a simple algorithm including random forests and single-layer neural networks and a more complex algorithm with a mix of tree-based, regression-based, penalized, and simple algorithms. We evaluated the distributions of risk differences, standard errors, and P values that result from 5000 different seed value selections.

RESULTS

Our findings suggest important variability in the risk difference estimates, as well as an important effect of the stacking algorithm used. The interquartile range width of the risk differences in the full sample with the simple algorithm was 13 per 1000. However, all other interquartile ranges were roughly an order of magnitude lower. The medians of the distributions of risk differences differed according to the sample size and the algorithm used.

CONCLUSIONS

Our findings add another dimension of concern regarding the potential for "p-hacking," and further warrant the need to move away from simplistic evidentiary thresholds in empirical research. When empirical results depend on pseudo-random number generator seed values, caution is warranted in interpreting these results.

Genetically predicted circulating concentrations of micronutrients and risk of hypertensive disorders of pregnancy: a Mendelian randomization study

PURPOSE

Epidemiological studies examining the association between circulating micronutrients and the risk of hypertensive disorders during pregnancy (HDP) have produced inconsistent results. Therefore, we conducted a Mendelian randomization (MR) analysis to evaluate the potential causal relationship between micronutrients and HDP.

METHODS

Nine micronutrients (beta-carotene, vitamin B6, vitamin B12, calcium, zinc, selenium, copper, folate, and phosphorus) were selected as the exposure factors. Summary data for gestational hypertension (14,727 cases and 196,143 controls) and preeclampsia/eclampsia (7212 cases and 174,266 controls) were extracted from the FinnGen consortium. The MR analysis employed the inverse variance weighted method and conducted a range of sensitivity analyses.

RESULTS

The inverse variance weighted method indicated no significant causal relationship between nine genetically predicted micronutrient concentrations and gestational hypertension, as well as preeclampsia/eclampsia. Sensitivity analyses suggested the absence of pleiotropy.

CONCLUSION

There is no strong evidence to support the causation between circulating micronutrients and hypertensive disorder during pregnancy.

Periconceptional Dietary Patterns and Adverse Pregnancy and Birth Outcomes

BACKGROUND

The periconceptional period is a critical window for the origins of adverse pregnancy and birth outcomes, yet little is known about the dietary patterns that promote perinatal health.

OBJECTIVE

We used machine learning methods to determine the effect of periconceptional dietary patterns on risk of preeclampsia, gestational diabetes, preterm birth, small-for-gestational-age (SGA) birth, and a composite of these outcomes.

METHODS

We used data from 8259 participants in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (8 US medical centers, 2010‒2013). Usual daily periconceptional intake of 82 food groups was estimated from a food frequency questionnaire. We used k-means clustering with a Euclidean distance metric to identify dietary patterns. We estimated the effect of dietary patterns on each perinatal outcome using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders including health behaviors and psychological, neighborhood, and sociodemographic factors.

RESULTS

The 4 dietary patterns that emerged from our data were identified as "Sandwiches and snacks" (34% of the sample); "High fat, sugar, and sodium" (29%); "Beverages, refined grains, and mixed dishes" (21%); and "High fruits, vegetables, whole grains, and plant proteins" (16%). One-quarter of pregnancies had preeclampsia (8% incidence), gestational diabetes (5%), preterm birth (8%), or SGA birth (8%). Compared with the "High fat, sugar, and sodium" pattern, there were 3.3 to 4.3 fewer cases of the composite adverse outcome per 100 pregnancies among participants following the "Beverages, refined grains and mixed dishes" pattern (risk difference -0.043; 95% confidence interval -0.078, -0.009), "High fruits, vegetables, whole grains and plant proteins" pattern (-0.041; 95% confidence interval -0.078, -0.004), and "Sandwiches and snacks" pattern (-0.033; 95% confidence interval -0.065, -0.002).

CONCLUSIONS

Our results highlight that there are a variety of periconceptional dietary patterns that are associated with perinatal health and reinforce the negative health implications of diets high in fat, sugars, and sodium.