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Background: Although insulin resistance is closely related to hypertension, the debate continues as to whether insulin resistance is a cause or a consequence of hypertension. This study investigated the associations of cord blood insulin concentration with blood pressure (BP) and hypertension in childhood and adolescence. Methods: This study included 951 children enrolled from 1998 to 2012 and followed from birth onwards at the Boston Medical Center, Boston, MA. Cord blood insulin concentration was measured using a sandwich immunoassay. Hypertension in childhood and adolescence was defined based on the 2017 American Academy of Pediatrics Clinical Practice Guidelines. Results: The median (interquartile range) for cord blood insulin concentration was 12.1 (7.2–19.0) µIU/mL. The age range of BP measurements was 3 to 18 years (median, 10.6 years). Cord blood insulin concentration was positively associated with systolic and diastolic BP as well as the risk of hypertension at age 3 to 18 years. Compared with the lowest tertile of cord blood insulin concentration, the top tertile insulin concentration was associated with a 5.18 (95% CI, 1.97–8.39) percentile increase in systolic BP, 4.29 (95% CI, 1.74–6.84) percentile increase in diastolic BP, and 1.62-fold (95% CI, 1.27–2.08) higher risk of hypertension. The association between insulin and hypertension was stronger among children born preterm (P for interaction=0.048). Furthermore, preterm birth and childhood overweight or obesity enhanced the associations. Conclusions: Our results suggest that elevated insulin concentration at birth plays a critical role in the early life origins of hypertension and support the hypothesis implicating insulin resistance in the etiology of hypertension. [ABSTRACT FROM AUTHOR]

Background: No studies have examined whether the cord blood metabolome-a reflection of in utero metabolism-influences blood pressure (BP) in children.Objectives: To examine prospective associations of cord blood metabolites with systolic BP (SBP), diastolic BP (DBP), and risk of elevated BP in childhood and adolescence.Methods: In the Boston Birth Cohort, we measured metabolites in cord blood plasma, and SBP and DBP at clinic visits between 3 and 18 years. We examined associations of cord metabolites with SBP and DBP percentiles using linear mixed models and with elevated BP using mixed-effects Poisson regression.Results: Our study included 902 mother-child dyads (60% Black, 23% Hispanic, 45% female). Children were followed for a median of 9.2 (interquartile range, 6.7-11.7) years, and the median number of BP observations per child was 7 (interquartile range, 4-11). After false discovery rate correction, 3 metabolites were associated with SBP, 96 with DBP, and 24 with elevated BP; 2 metabolites (1-methylnicotinamide, dimethylguanidino valeric acid) were associated with all 3 outcomes, and 21 metabolites were associated with both DBP and elevated BP. After multivariable adjustment, 48 metabolites remained significantly associated with DBP. Metabolites that showed the strongest associations with SBP, DBP, and elevated BP included nucleotides (eg, xanthosine, hypoxanthine, xanthine) and acylcarnitines (eg, C6 and C7 carnitines), which represent fatty acid oxidation and purine metabolism pathways.Conclusions: In our urban and predominantly racial/ethnic minority cohort, we provide evidence that metabolomic alterations in utero, in particular, acylcarnitine- and purine-metabolism metabolites, may be involved in the early life origins of hypertension. [ABSTRACT FROM AUTHOR]

Epidemiologic studies often quantify exposure using biomarkers, which commonly have statistically skewed distributions. Although normality assumption is not required if the biomarker is used as an independent variable in linear regression, it has become common practice to log-transform the biomarker concentrations. This transformation can be motivated by concerns for nonlinear dose-response relationship or outliers; however, such transformation may not always reduce bias. In this study, we evaluated the validity of motivations underlying the decision to log-transform an independent variable using simulations, considering eight scenarios that can give rise to skewed X and normal Y. Our simulation study demonstrates that (1) if the skewness of exposure did not arise from a biasing factor (e.g., measurement error), the analytic approach with the best overall model fit best reflected the underlying outcome generating methods and was least biased, regardless of the skewness of X and (2) all estimates were biased if the skewness of exposure was a consequence of a biasing factor. We additionally illustrate a process to determine whether the transformation of an independent variable is needed using NHANES. Our study and suggestion to divorce the shape of the exposure distribution from the decision to log-transform it may aid researchers in planning for analysis using biomarkers or other skewed independent variables. [ABSTRACT FROM AUTHOR]

Background: Guidance is lacking for how to combine urinary biomarker data across studies that use different measures of urinary dilution, that is, creatinine or specific gravity.Methods: Among 741 pregnant participants from four sites of The Infant Development and Environment Study (TIDES) cohort, we assessed the relation of maternal urinary di-2-ethylhexyl phthalate (DEHP) concentrations with preterm birth. We compared scenarios in which all sites measured either urinary creatinine or specific gravity, or where measure of dilution differed by site. In addition to a scenario with no dilution adjustment, we applied and compared three dilution-adjustment approaches: a standard regression-based approach for creatinine, a standard approach for specific gravity (Boeniger method), and a more recently developed approach that has been applied to both (covariate-adjusted standardization method). For each scenario and dilution-adjustment method, we estimated the association between a doubling in the molar sum of DEHP (∑DEHP) and odds of preterm birth using logistic regression.Results: All dilution-adjustment approaches yielded comparable associations (odds ratio [OR]) that were larger in magnitude than when we did not perform dilution adjustment. A doubling of ∑DEHP was associated with 9% greater odds of preterm birth (OR = 1.09, 95% confidence interval [CI] = 0.91, 1.30) when applying no dilution-adjustment method, whereas dilution-adjusted point estimates were higher, and similar across all scenarios and methods: 1.13-1.20 (regression-based), 1.15-1.18 (Boeniger), and 1.14-1.21 (covariate-adjusted standardization).Conclusions: In our applied example, we demonstrate that it is possible and straightforward to combine urinary biomarker data across studies when measures of dilution differ. [ABSTRACT FROM AUTHOR]

Background: Collaborative research often combines findings across multiple, independent studies via meta-analysis. Ideally, all study estimates that contribute to the meta-analysis will be equally unbiased. Many meta-analyses require all studies to measure the same covariates. We explored whether differing minimally sufficient sets of confounders identified by a directed acyclic graph (DAG) ensures comparability of individual study estimates. Our analysis applied four statistical estimators to multiple minimally sufficient adjustment sets identified in a single DAG.Methods: We compared estimates obtained via linear, log-binomial, and logistic regression and inverse probability weighting, and data were simulated based on a previously published DAG.Results: Our results show that linear, log-binomial, and inverse probability weighting estimators generally provide the same estimate of effect for different estimands that are equally sufficient to adjust confounding bias, with modest differences in random error. In contrast, logistic regression often performed poorly, with notable differences in effect estimates obtained from unique minimally sufficient adjustment sets, and larger standard errors than other estimators.Conclusions: Our findings do not support the reliance of collaborative research on logistic regression results for meta-analyses. Use of DAGs to identify potentially differing minimally sufficient adjustment sets can allow meta-analyses without requiring the exact same covariates. [ABSTRACT FROM AUTHOR]

Background: An inverse association between maternal smoking and preeclampsia has been frequently observed in epidemiologic studies for several decades. In the May 2015 issue of this journal, Lisonkova and Joseph described a simulation study suggesting that bias from left truncation might explain the inverse association. The simulations were based on strong assumptions regarding the underlying mechanisms through which bias might occur.Methods: To examine the sensitivity of the previous authors' conclusions to these assumptions, we constructed a new Monte Carlo simulation using published estimates to frame our data-generating parameters. We estimated the association between smoking and preeclampsia across a range of scenarios that incorporated abnormal placentation and early pregnancy loss.Results: Our results confirmed that the previous authors' findings are highly dependent on assumptions regarding the strength of association between abnormal placentation and preeclampsia. Thus, the bias they described may be less pronounced than was suggested.Conclusions: Under empirically derived constraints of these critical assumptions, left truncation does not appear to fully explain the inverse association between smoking and preeclampsia. Furthermore, when considering processes in which left truncation may result from the exposure, it is important to precisely describe the target population and parameter of interest before assessing potential bias. We comment on the specification of a meaningful target population when assessing maternal smoking and preeclampsia as a public health issue. We describe considerations for defining a target population in studies of perinatal exposures when those exposures cause competing events (e.g., early pregnancy loss) for primary outcomes of interest. [ABSTRACT FROM AUTHOR]

Background: Phthalates are hypothesized to cause obesity, but few studies have assessed whether prenatal phthalate exposures are related to childhood body mass index (BMI).Methods: We included 707 children from three prospective cohort studies enrolled in the US between 1998 and 2006 who had maternal urinary phthalate metabolite concentrations measured during pregnancy, and measures of weight and height at ages 4 to 7 years. We calculated age- and sex-standardized BMI z scores and classified children with BMI percentiles ≥85 as overweight/obese. We used mixed effects regression models to estimate associations between a 1 standard deviation increase in natural log phthalate metabolite concentrations and BMI z scores and overweight/obesity. We estimated associations in multiple metabolite models adjusted for confounders, and evaluated heterogeneity of associations by child's sex, race/ethnicity, and cohort.Results: Mono-3-carboxypropyl phthalate concentrations were positively associated with overweight/obese status in children (odds ratio [95% credible interval] = 2.1 [1.2, 4.0]) but not with BMI z scores (β = -0.02 [-0.15, 0.11]). We did not observe evidence of obesogenic effects for other metabolites. However, monoethyl phthalate and summed di-(2-ethylhexyl) phthalate metabolites (∑DEHP) concentrations were inversely associated with BMI z scores among girls (monoethyl phthalate beta = -0.14 [-0.28, 0.00]; ∑DEHP beta = -0.12 [-0.27, 0.02]).Conclusions: Maternal urinary mono-3-carboxypropyl phthalate, a nonspecific metabolite of several phthalates, was positively associated with childhood overweight/obesity. Metabolites of diethyl phthalate and DEHP were associated with lower BMI in girls but not in boys, suggesting that prenatal exposures may have sexually dimorphic effects on physical development. [ABSTRACT FROM AUTHOR]

Healthy worker survivor bias may occur in occupational studies due to the tendency for unhealthy individuals to leave work earlier, and consequently accrue less exposure, compared with their healthier counterparts. If occupational data are not analyzed using appropriate methods, this bias can result in attenuation or even reversal of the estimated effects of exposures on health outcomes. Recent advances in computing power, coupled with state-of-the-art statistical methods, have greatly increased the ability of analysts to control healthy worker survivor bias. However, these methods have not been widely adopted by occupational epidemiologists. We update the seminal review by Arrighi and Hertz-Picciotto (Epidemiology.1994; 5: 186-196) of the sources and methods to control healthy worker survivor bias. In our update, we discuss methodologic advances since the publication of that review, notably with a consideration of how directed acyclic graphs can inform the choice of appropriate analytic methods. We summarize and discuss methods for addressing this bias, including recent work applying g-methods to account for employment status as a time-varying covariate affected by prior exposure. In the presence of healthy worker survivor bias, g-methods have advantages for estimating less biased parameters that have direct policy implications and are clearly communicated to decision-makers. [ABSTRACT FROM AUTHOR]

Background: Traditional regression analysis techniques used to estimate associations between occupational radon exposure and lung cancer focus on estimating the effect of cumulative radon exposure on lung cancer. In contrast, public health interventions are typically based on regulating radon concentration rather than workers' cumulative exposure. Estimating the effect of cumulative occupational exposure on lung cancer may be difficult in situations vulnerable to the healthy worker survivor bias., Methods: Workers in the Colorado Plateau Uranium Miners cohort (n = 4,134) entered the study between 1950 and 1964 and were followed for lung cancer mortality through 2005. We use the parametric g-formula to compare the observed lung cancer mortality to the potential lung cancer mortality had each of 3 policies to limit monthly radon exposure been in place throughout follow-up., Results: There were 617 lung cancer deaths over 135,275 person-years of follow-up. With no intervention on radon exposure, estimated lung cancer mortality by age 90 was 16%. Lung cancer mortality was reduced for all interventions considered, and larger reductions in lung cancer mortality were seen for interventions with lower monthly radon exposure limits. The most stringent guideline, the Mine Safety and Health Administration standard of 0.33 working-level months, reduced lung cancer mortality from 16% to 10% (risk ratio = 0.67 [95% confidence interval = 0.61 to 0.73])., Conclusions: This work illustrates the utility of the parametric g-formula for estimating the effects of policies regarding occupational exposures, particularly in situations vulnerable to the healthy worker survivor bias.