Bodnar, Lisa M.; Kaufman, Jay S.
Magee-Women's Research Institute Department of Obstetrics, Gynecology, and Reproductive Sciences University of Pittsburgh School of Medicine 204 Craft Avenue Pittsburgh, PA 15213 email@example.com (Bodnar)
Department of Epidemiology University of North Carolina School of Public Health Chapel Hill, North Carolina (Kaufman)
To the Editor:
In a study recently published in Epidemiology, O'Brien and colleagues1 presented results of a metaanalysis examining the relation between prepregnancy body mass index (BMI) and risk of preeclampsia. Quantifying this relation is of major public health importance given the increasing prevalence of obesity, as well as the high risk of maternal and neonatal morbidity and mortality associated with preeclampsia. However, results from this overview should be interpreted with caution because the component studies did not estimate a common causal parameter.
The authors included 13 studies in their metaanalysis. For 4 of these studies,2–5 only unadjusted odds ratios for the BMI–preeclampsia relation were presented. It is likely that unmeasured confounding by sociodemographic variables and health-related behaviors biased these effect measures.
Multivariate analyses were conducted for the remaining 9 studies.6–14 Of these, 26,7 presented results from a causal model (ie, measured confounders were used to adjust the BMI–preeclampsia odds ratio). Three others8–10 had BMI as the primary exposure, but adjusted for variables such as chronic hypertension and gestational diabetes, which are likely on the causal pathway from BMI to preeclampsia.15 The adjusted effect estimates therefore do not represent the total causal effect of BMI, but rather its direct effect, the portion not relayed through these intermediates.16
The final 4 studies using multivariate methods11–14 presented adjusted odds ratios derived from predictive models. The objective of predictive modeling is not to determine the causal effect of an exposure on the outcome, but to best predict the outcome by including all variables associated with it.17 Unlike causal modeling, confounding is not an issue in predictive modeling because there is no “primary exposure.” Including variables that are potentially intermediates on the pathway between a predictor and the outcome is not a problem. Therefore, adjusted odds ratios derived from predictive modeling do not necessarily have causal interpretations and may bias the results of metaanalyses.18
Additionally, O'Brien and colleagues1 included papers that examined either preeclampsia (gestational hypertension and proteinuria) or gestational hypertension alone,1 yet these outcomes are recognized as separate entities19 with different risk factors and clinical findings.20 To assess the causal effect of prepregnancy BMI and hypertensive disorders of pregnancy accurately, subclassification may be preferred.21
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