Disparities in Risks of Inadequate and Excessive Intake of Micronutrients during Pregnancy.

BACKGROUND: Inadequate or excessive intake of micronutrients in pregnancy has potential to negatively impact maternal/offspring health outcomes. OBJECTIVE: The aim was to compare risks of inadequate or excessive micronutrient intake in diverse females with singleton pregnancies by strata of maternal age, race/ethnicity, education, and prepregnancy BMI. METHODS: Fifteen observational cohorts in the US Environmental influences on Child Health Outcomes (ECHO) Consortium assessed participant dietary intake with 24-h dietary recalls (n = 1910) or food-frequency questionnaires (n = 7891) from 1999-2019. We compared the distributions of usual intake of 19 micronutrients from food alone (15 cohorts; n = 9801) and food plus dietary supplements (10 cohorts with supplement data; n = 7082) to estimate the proportion with usual daily intakes below their age-specific daily Estimated Average Requirement (EAR), above their Adequate Intake (AI), and above their Tolerable Upper Intake Level (UL), overall and within sociodemographic and anthropometric subgroups. RESULTS: Risk of inadequate intake from food alone ranged from 0% to 87%, depending on the micronutrient and assessment methodology. When dietary supplements were included, some women were below the EAR for vitamin D (20-38%), vitamin E (17-22%), and magnesium (39-41%); some women were above the AI for vitamin K (63-75%), choline (7%), and potassium (37-53%); and some were above the UL for folic acid (32-51%), iron (39-40%), and zinc (19-20%). Highest risks for inadequate intakes were observed among participants with age 14-18 y (6 nutrients), non-White race or Hispanic ethnicity (10 nutrients), less than a high school education (9 nutrients), or obesity (9 nutrients). CONCLUSIONS: Improved diet quality is needed for most pregnant females. Even with dietary supplement use, >20% of participants were at risk of inadequate intake of ≥1 micronutrients, especially in some population subgroups. Pregnancy may be a window of opportunity to address disparities in micronutrient intake that could contribute to intergenerational health inequalities.

A power approximation for the Kenward and Roger Wald test in the linear mixed model.

We derive a noncentral [Formula: see text] power approximation for the Kenward and Roger test. We use a method of moments approach to form an approximate distribution for the Kenward and Roger scaled Wald statistic, under the alternative. The result depends on the approximate moments of the unscaled Wald statistic. Via Monte Carlo simulation, we demonstrate that the new power approximation is accurate for cluster randomized trials and longitudinal study designs. The method retains accuracy for small sample sizes, even in the presence of missing data. We illustrate the method with a power calculation for an unbalanced group-randomized trial in oral cancer prevention.

Combined environmental and social exposures during pregnancy and associations with neonatal size and body composition: the Healthy Start study.

BACKGROUND: Prenatal environmental and social exposures have been associated with decreased birth weight. However, the effects of combined exposures in these domains are not fully understood. Here we assessed multi-domain exposures for participants in the Healthy Start study (Denver, CO) and tested associations with neonatal size and body composition. METHODS: In separate linear regression models, we tested associations between neonatal outcomes and three indices for exposures. Two indices were developed to describe exposures to environmental hazards (ENV) and social determinants of health (SOC). A third index combined exposures in both domains (CE = ENV/10 × SOC/10). Index scores were assigned to mothers based on address at enrollment. Birth weight and length were measured at delivery, and weight-for-length z-scores were calculated using a reference distribution. Percent fat mass was obtained by air displacement plethysmography. RESULTS: Complete data were available for 897 (64%) participants. Median (range) ENV, SOC, and CE values were 31.9 (7.1-63.2), 36.0 (2.8-75.0), and 10.9 (0.4-45.7), respectively. After adjusting for potential confounders, 10-point increases in SOC and CE were associated with 27.7 g (95%CI: 12.4 - 42.9 g) and 56.3 g (19.4 - 93.2 g) decreases in birth weight, respectively. SOC and CE were also associated with decreases in % fat mass. CONCLUSIONS: Combined exposures during pregnancy were associated with lower birth weight and % fat mass. Evidence of a potential synergistic effect between ENV and SOC suggests a need to more fully consider neighborhood exposures when assessing neonatal outcomes.

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd.

Reducing decision errors in the paired comparison of the diagnostic accuracy of screening tests with Gaussian outcomes.

BACKGROUND: Scientists often use a paired comparison of the areas under the receiver operating characteristic curves to decide which continuous cancer screening test has the best diagnostic accuracy. In the paired design, all participants are screened with both tests. Participants with suspicious results or signs and symptoms of disease receive the reference standard test. The remaining participants are classified as non-cases, even though some may have occult disease. The standard analysis includes all study participants, which can create bias in the estimates of diagnostic accuracy since not all participants receive disease status verification. We propose a weighted maximum likelihood bias correction method to reduce decision errors. METHODS: Using Monte Carlo simulations, we assessed the method's ability to reduce decision errors across a range of disease prevalences, correlations between screening test scores, rates of interval cases and proportions of participants who received the reference standard test. RESULTS: The performance of the method depends on characteristics of the screening tests and the disease and on the percentage of participants who receive the reference standard test. In studies with a large amount of bias in the difference in the full areas under the curves, the bias correction method reduces the Type I error rate and improves power for the correct decision. We demonstrate the method with an application to a hypothetical oral cancer screening study. CONCLUSION: The bias correction method reduces decision errors for some paired screening trials. In order to determine if bias correction is needed for a specific screening trial, we recommend the investigator conduct a simulation study using our software.

Dual-energy X-ray absorptiometry modeling to explain the increased resting energy expenditure associated with the HIV lipoatrophy syndrome.

BACKGROUND: The HIV lipoatrophy syndrome is characterized by loss of subcutaneous fat and is associated with increased resting energy expenditure (REE). Recently, dual-energy X-ray absorptiometry (DXA) modeling of organ-tissue mass combined with specific organ-tissue metabolic rates has been used to gain further insight into the relation of the lean body mass to REE and to better understand differences in REE between groups. OBJECTIVE: This study examined the organ-tissue basis of the increased REE shown in HIV lipoatrophy. DESIGN: REE was measured in 29 HIV-infected patients with lipoatrophy and in 29 HIV-infected and 19 healthy control subjects. Five organ-tissue mass components (brain, bone, skeletal muscle, adipose tissue, and residual mass) were calculated with the use of DXA modeling and body weight. RESULTS: DXA modeling showed no significant differences in predicted REE between the 3 groups. However, measured REE was significantly greater in subjects with lipoatrophy than in control subjects. Measured REE remained significantly greater in lipoatrophy subjects after routine adjustment for lean body mass and after adjustment for each organ-tissue mass component. Finally, DXA and regression modeling of REE suggests that increased energy expenditure in skeletal muscle may account for the resting hypermetabolism of patients with HIV lipoatrophy. CONCLUSIONS: Increased REE in subjects with HIV lipoatrophy cannot be explained by differences in organ-tissue mass as modeled by DXA. Instead, DXA and regression modeling of REE suggests that skeletal muscle is hypermetabolic in patients with HIV lipoatrophy. This may be a form of adaptive thermogenesis in response to an inability to store triglyceride fuel in a normal manner.