Abstract

Two population-based case-control studies have reported an increased risk of neural tube defect (NTD)-affected pregnancies among women with low carbohydrate diet in the periconceptional period. Given that only two studies have investigated this association, it is unclear to what degree the findings could be impacted by residual confounding. Here, we further interrogated both studies that observed this association with the objective to identify factors from a much larger number of factors that might explain the association. By employing a machine learning algorithm (random forest), we investigated a baseline set of over 200 variables. These analyses produced the top 10 variables in each data set for cases and controls that predicted periconceptional low carbohydrate intake. Examining those prediction variables with logistic regression modeling, we did not observe any particular variable that substantially contributed to the NTD-low carbohydrate association in either data set. If there are underlying factors that explain the association, our findings suggest that none of the 200+ variables we examined were sufficiently correlated with what that true explanatory exposure may be. Alternatively, our findings may suggest that there are other unidentified factor(s) at play, or the association observed in two independent data sets is directly related to low carbohydrate intake.

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