Dietary correlates of urinary phthalate metabolite concentrations in 6-19 Year old children and adolescents.

BACKGROUND: Children are vulnerable to adverse health effects associated with phthalates, and food is one source of exposure. A comprehensive analysis investigating urinary phthalate metabolite concentrations in relation to food type and source has yet to be undertaken. OBJECTIVES: We use reduced rank regression, a dimension reduction method, to identify dietary patterns associated with urinary phthalate metabolites in children in a large US study. METHODS: We used data from 2369 participants 6-19 years old from the 2011-2016 National Health and Nutrition Examination Survey who recalled their diet over the 24 h prior to urine collection. We used dietary data to estimate intake and source (i.e., prepared at a restaurant vs. purchased from a grocery store) of 136 food groups. We used reduced rank regression to identify dietary patterns explaining variation in overall urinary concentrations of ∑di-2-ethylhexyl phthalate and seven phthalate metabolites. We also examined pairwise associations between food groups and urinary phthalate metabolites. RESULTS: We identified eight dietary patterns that cumulatively explained 12.1% of variation in urinary phthalate metabolites, including a dietary pattern characterized by certain starchy vegetables (e.g., plantains and lima beans), quick breads, and citrus juice prepared at a restaurant. A one SD increase in this food pattern score was associated with a 37.2% higher monocarboxyoctyl phthalate (MCOP) concentration (95% CI: 30.3, 44.4). We also observed weak associations between certain food groups and urinary phthalate metabolites (e.g., a one SD increase in intake of certain starchy vegetables prepared at a restaurant was associated with a 1.8% [95% CI: 0.7, 2.8] higher MCOP). CONCLUSIONS: Children whose diets were characterized by higher consumption of certain starchy vegetables, quick breads, and citrus juices prepared at a restaurant had higher urinary phthalate metabolites. More detailed information on the specific methods of food processing and details on packaging materials is needed.

Dietary intake of calcium and magnesium and the metabolic syndrome in the National Health and Nutrition Examination (NHANES) 2001-2010 data.

Higher dietary intakes of Mg and Ca, individually, have been associated with a decreased risk for the metabolic syndrome (MetSyn). Experimental studies suggest that a higher intra-cellular ratio of Ca:Mg, which may be induced by a diet high in Ca and low in Mg, may lead to hypertension and insulin resistance. However, no previous epidemiological studies have examined the effects of the combined intake of Mg and Ca on MetSyn. Thus, we evaluated the association between dietary intakes of Ca and Mg (using 24-h recalls), independently and in combination, and MetSyn in the National Health and Nutrition Examination Study 2001-2010 data, which included 9148 adults (4549 men and 4599 women), with complete information on relevant nutrient, demographic, anthropometric and biomarker variables. We found an inverse association between the highest (>355 mg/d) v. the lowest (<197 mg/d) quartile of Mg and MetSyn (OR 0.70; 95% CI 0.57, 0.86). Women who met the RDA for both Mg (310-320 mg/d) and Ca (1000-1200 mg/d) had the greatest reduced odds of MetSyn (OR 0.59; 95% CI 0.45, 0.76). In men, meeting the RDA for Mg (400-420 mg/d) and Ca (1000-1200 mg/d), individually or in combination, was not associated with MetSyn; however, men with intakes in the highest quartile for Mg (≥ 386 mg/d) and Ca (≥ 1224 mg/d) had a lower odds of MetSyn (OR 0.74; 95% CI 0.59, 0.93). Our results suggest that women who meet the RDA for Mg and Ca have a reduced odds of MetSyn but men may require Ca levels higher than the RDA to be protected against MetSyn.

Quantitative methods for descriptive intersectional analysis with binary health outcomes.

Intersectionality recognizes that in the context of sociohistorically shaped structural power relations, an individual's multiple social positions or identities (e.g., gender, ethnicity) can interact to affect health-related outcomes. Despite limited methodological guidance, intersectionality frameworks have increasingly been incorporated into epidemiological studies, both to describe health disparities and to examine their causes. This study aimed to advance methods in intersectional estimation of binary outcomes in descriptive health disparities research through evaluation of 7 potentially intersectional data analysis methods: cross-classification, regression with interactions, multilevel analysis of individual heterogeneity (MAIHDA), and decision trees (CART, CTree, CHAID, random forest). Accuracy of estimated intersection-specific outcome prevalence was evaluated across 192 intersections using simulated data scenarios. For comparison we included a non-intersectional main effects regression. We additionally assessed variable selection performance amongst decision trees. Example analyses using National Health and Nutrition Examination Study data illustrated differences in results between methods. At larger sample sizes, all methods except for CART performed better than non-intersectional main effects regression. In smaller samples, MAIHDA was the most accurate method but showed no advantage over main effects regression, while random forest, cross-classification, and saturated regression were the least accurate, and CTree and CHAID performed moderately well. CART performed poorly for estimation and variable selection. Sensitivity analyses examining the bias-variance tradeoff suggest MAIHDA as the preferred unbiased method for accurate estimation of high-dimensional intersections at smaller sample sizes. Larger sample sizes are more imperative for other methods. Results support the adoption of an intersectional approach to descriptive epidemiology.