The maternal diet index in pregnancy is associated with offspring allergic diseases: the Healthy Start study.

BACKGROUND: A systematic review showed limited associations between pregnancy diet and offspring allergy. We developed a maternal diet index during pregnancy that was associated with offspring allergy outcomes. METHODS: Data came from Healthy Start, a Colorado pre-birth cohort of mother/offspring dyads. Food propensity questionnaires were completed during pregnancy. Offspring allergic rhinitis, atopic dermatitis, asthma, wheeze, and food allergy diagnosis up to age four were verified from electronic medical records. Data were randomized into test and replication sets. The index included the weighted combination of variables that best predicted a combined outcome of any allergy in the test set. Index utility was verified in the replication set. Separate adjusted and unadjusted logistic models estimated associations between the index and each offspring allergy diagnosis in the full sample. RESULTS: The index included weighted measures of intake of vegetables, yogurt, fried potatoes, rice/grains, red meats, pure fruit juice, and cold cereals. Vegetables and yogurt were associated with the prevention of any allergy, while other components were associated with increased disease. In adjusted models, a one-unit increase in the index was significantly associated with reduced odds of offspring allergic rhinitis (odds ratio (CI) 0.82 [0.72-0.94]), atopic dermatitis (0.77 [0.69-0.86]), asthma (0.84 [0.74-0.96]), and wheeze (0.80 [0.71-0.90]), but not food allergy (0.84 [0.66-1.08]). CONCLUSIONS: This is the first study that has shown associations between an index of maternal dietary intake during pregnancy and multiple offspring allergic diseases. The results give hope for prevention of allergic diseases in utero.

Maternal blood glucose level and offspring glucose-insulin homeostasis: what is the role of offspring adiposity?

AIMS/HYPOTHESIS: The aim of this work was to investigate the association of maternal HbA1c during mid-pregnancy with biomarkers of glucose-insulin homeostasis during early childhood (4-7 years of age) and to assess whether and how offspring adiposity at birth and at age 4-7 years mediates this relationship among 345 mother-child pairs in the Healthy Start Study. METHODS: The exposure was maternal HbA1c (mmol/mol) measured at 20-34 gestational weeks and categorised into tertiles. The outcomes were offspring fasting glucose, 1/insulin, HOMA2-IR, and HOMA2-B at age 4-7 years. The mediators were per cent fat mass (%FM) at birth, %FM at age 4-7 years, and the sum of the two as a metric of cumulative adiposity. Mediation analyses were conducted via a counterfactual-based approach. All models accounted for maternal race/ethnicity, offspring age and sex. RESULTS: There was a significant total effect of maternal HbA1c on offspring glucose and 1/insulin. Specifically, we observed a positive trend across tertiles of HbA1c and offspring glucose (p trend <0.001), and an inverse trend across tertiles of HbA1c and offspring 1/insulin (p trend = 0.04). For instance, compared with offspring of women in the lowest tertile of HbA1c, those whose mothers were in the second and third tertiles had 0.04 mmol/l (95% CI -0.05, 0.13) and 0.17 mmol/l (95% CI 0.08, 0.26) higher fasting glucose concentrations at age 4-7 years, respectively. Adjustment for pre-pregnancy BMI did not appreciably change the results. We found no evidence of mediation by offspring adiposity at any life stage. CONCLUSIONS/INTERPRETATION: Offspring of women with higher HbA1c during pregnancy had higher fasting glucose and lower insulin sensitivity by early childhood. These relationships were largely unaffected by the child's own adiposity. Graphical abstract.

Adherence to index-based dietary patterns in childhood and BMI trajectory during the transition to adolescence: the EPOCH study.

BACKGROUND: In the United States, one in five adolescents are obese. Index-based dietary patterns are measures of the overall diet that have the potential to serve as valuable obesity risk stratification tools. However, little is known about the association between adherence to index-based dietary patterns in childhood and BMI during the transition from childhood to adolescence. OBJECTIVE: To prospectively examine the relationship between adherence to three index-based dietary patterns in childhood and BMI trajectory during the transition to adolescence. METHODS: The study included 581 children enrolled in a Colorado prospective cohort study conducted between 2006 and 2015. Dietary intake was assessed with the Block Kids Food Frequency Questionnaire at age 10 years. Scores were calculated for the Healthy Eating Index-2010 (HEI-2010), the alternate Mediterranean (aMED) diet, and the Dietary Approaches to Stop Hypertension (DASH) diet. Weight and height were assessed via anthropometry at two research visits (ages 10 and 16 years), with interim clinical measurements extracted from Kaiser Permanente medical records. Separate mixed models were used to assess the association between each diet index score and BMI over a 6-year period. Models were stratified by sex and adjusted for age, race/ethnicity, income, and exposure to gestational diabetes. RESULTS: Median (IQR) number of BMI assessments was 14 (10-18). Among girls, for every ten-unit increase in HEI-2010 score, there was an average 0.64 kg/m2 decrease (p = 0.007) in BMI over time, after adjustment for covariates. Among girls, there was no association between BMI and aMED (ß = -0.19, p = 0.24) or DASH (ß = 0.28, p = 0.38). Among boys, there was no statistically significant association between BMI and HEI-2010 (0.06, p = 0.83), aMED (0.07, p = 0.70), or DASH (0.42, p = 0.06). CONCLUSIONS: Efforts to prevent adolescent obesity could benefit from considering the degree of adherence to federal dietary guidance, as assessed by the HEI, in the period preceding adolescence, especially among girls.

Associations of Nutrient Intake Changes During Childhood with Adolescent Hepatic Fat: The Exploring Perinatal Outcomes Among CHildren Study.

OBJECTIVE: To examine associations of dietary changes from childhood to adolescence with adolescent hepatic fat and whether the PNPLA3 rs738409 risk allele, a strong genetic risk factor for hepatic fat, modifies associations. STUDY DESIGN: Data were from 358 participants in the Exploring Perinatal Outcomes among CHildren (EPOCH) study, a longitudinal cohort in Colorado. Diet was assessed by food frequency questionnaire in childhood (approximately 10 years of age) and adolescence (approximately 16 years of age) and converted to nutrient densities. Hepatic fat was assessed in adolescence by magnetic resonance imaging. Linear regression was used to test associations of dietary changes from childhood to adolescence with adolescent hepatic fat. RESULTS: Increases in fiber, vegetable protein, and polyunsaturated fat intake from childhood to adolescence were associated with lower adolescent hepatic fat, and increases in animal protein were associated with higher hepatic fat (ß per 5-unit increase on log-hepatic fat: -0.12 [95% CI, -0.21 to -0.02] for ▵fiber; -0.26 [95% CI, -0.45 to -0.07] for ▵vegetable protein; -0.18 [95% CI, -0.35 to -0.02] for ▵polyunsaturated fat; 0.13 [95% CI, 0.04-0.22] for ▵animal protein). There was evidence of effect modification by PNPLA3 variant, whereby inverse associations of ▵fiber and ▵vegetable protein and positive associations of ▵saturated fat with adolescent hepatic fat were stronger in risk allele carriers. Most conclusions were similar after adjusting for obesity in adolescence, but associations of ▵saturated fat with hepatic fat were attenuated toward the null. CONCLUSIONS: Our results suggest that nutrient intake changes between childhood and adolescence, particularly decreases in fiber and vegetable protein and increases in saturated fat intake, interact with the PNPLA3 variant to predict higher hepatic fat in adolescence, and may be targets for reducing hepatic fat in high-risk youth.

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 prospective study of associations between in utero exposure to gestational diabetes mellitus and metabolomic profiles during late childhood and adolescence.

AIMS/HYPOTHESIS: This study aimed to: (1) identify metabolite patterns during late childhood that differ with respect to exposure to maternal gestational diabetes mellitus (GDM); (2) examine the persistence of GDM/metabolite associations 5 years later, during adolescence; and (3) investigate the associations of metabolite patterns with adiposity and metabolic biomarkers from childhood through adolescence. METHODS: This study included 592 mother-child pairs with information on GDM exposure (n = 92 exposed), untargeted metabolomics data at age 6-14 years (T1) and at 12-19 years (T2), and information on adiposity and metabolic risk biomarkers at T1 and T2. We first consolidated 767 metabolites at T1 into factors (metabolite patterns) via principal component analysis (PCA) and used multivariable regression to identify factors that differed by GDM exposure, at α = 0.05. We then examined associations of GDM with individual metabolites within factors of interest at T1 and T2, and investigated associations of GDM-related factors at T1 with adiposity and metabolic risk throughout T1 and T2 using mixed-effects linear regression models. RESULTS: Of the six factors retained from PCA, GDM exposure was associated with greater odds of being in quartile (Q)4 (vs Q1-3) of 'Factor 4' at T1 after accounting for age, sex, race/ethnicity, maternal smoking habits during pregnancy, Tanner stage, physical activity and total energy intake, at α = 0.05 (OR 1.78 [95% CI 1.04, 3.04]; p = 0.04). This metabolite pattern comprised phosphatidylcholines, diacylglycerols and phosphatidylethanolamines. GDM was consistently associated with elevations in a subset of individual compounds within this pattern at T1 and T2. While this metabolite pattern was not related to the health outcomes in boys, it corresponded with greater adiposity and a worse metabolic profile among girls throughout the follow-up period. Each 1-unit increment in Factor 4 corresponded with 0.17 (0.08, 0.25) units higher BMI z score, 8.83 (5.07, 12.59) pmol/l higher fasting insulin, 0.28 (0.13, 0.43) units higher HOMA-IR, and 4.73 (2.15, 7.31) nmol/l higher leptin. CONCLUSIONS/INTERPRETATION: Exposure to maternal GDM was nominally associated with a metabolite pattern characterised by elevated serum phospholipids in late childhood and adolescence at α = 0.05. This metabolite pattern was associated with greater adiposity and metabolic risk among female offspring throughout the late childhood-to-adolescence transition. Future studies are warranted to confirm our findings.

Persistent effects of in utero overnutrition on offspring adiposity: the Exploring Perinatal Outcomes among Children (EPOCH) study.

AIMS/HYPOTHESIS: We previously showed that intrauterine exposure to gestational diabetes mellitus (GDM) increases selected markers of adiposity in pre-pubertal adolescents. In the present study, we examined these associations in adolescence, and explored whether they are strengthened as the participants transition through puberty. METHODS: Data from 597 individuals (505 unexposed, 92 exposed) participating in the longitudinal Exploring Perinatal Outcomes among Children (EPOCH) study in Colorado were collected at two research visits when the participants were, on average, 10.4 and 16.7 years old. Adiposity measures included BMI, waist/height ratio, and visceral and subcutaneous adipose tissue (as determined by MRI). Separate general linear mixed models were used to assess the longitudinal relationships between exposure to maternal GDM and each adiposity outcome. We tested whether the effect changed over time by including an interaction term between exposure and age in our models, and whether the associations were explained by postnatal behaviours. RESULTS: Compared with unexposed participants, those exposed to maternal GDM had higher BMI (ß = 1.28; 95% CI 0.35, 2.21; p < 0.007), waist/height ratio (ß = 0.03; 95% CI 0.01, 0.04; p = 0.0004), visceral adipose tissue (ß = 4.81; 95% CI 1.08, 8.54; p = 0.01) and subcutaneous adipose tissue (ß = 35.15; 95% CI 12.43, 57.87; p < 0.003). The magnitude of these differences did not change over time and the associations did not appear to be explained by postnatal behaviours. CONCLUSIONS/INTERPRETATION: Our data provide further evidence that intrauterine exposure to maternal GDM is associated with increased offspring adiposity, an effect that appears early in life and tracks throughout adolescence. Efforts to prevent childhood obesity following intrauterine exposure to maternal GDM should target the prenatal or early life periods.

Fetal exposure to maternal active and secondhand smoking with offspring early-life growth in the Healthy Start study.

BACKGROUND: Previous studies have modeled the association between fetal exposure to tobacco smoke and body mass index (BMI) growth trajectories, but not the timing of catch-up growth. Research on fetal exposure to maternal secondhand smoking is limited. OBJECTIVES: To explore the associations between fetal exposure to maternal active and secondhand smoking with body composition at birth and BMI growth trajectories through age 3 years. METHODS: We followed 630 mother-child pairs enrolled in the Healthy Start cohort through age 3 years. Maternal urinary cotinine was measured at ~ 27 weeks gestation. Neonatal body composition was measured using air displacement plethysmography. Child weight and length/height were abstracted from medical records. Linear regression models examined the association between cotinine categories (no exposure, secondhand smoke, active smoking) with weight, fat mass, fat-free mass, and percent fat mass at birth. A mixed-effects regression model estimated the association between cotinine categories and BMI. RESULTS: Compared to unexposed offspring, birth weight was significantly lower among offspring born to active smokers (-343-g; 95% CI: -473, -213), but not among offspring of women exposed to secondhand smoke (-47-g; 95% CI: -130, 36). There was no significant difference in the rate of BMI growth over time between offspring of active and secondhand smokers (p = 0.58). Therefore, our final model included a single growth rate parameter for the combined exposure groups of active and secondhand smokers. The rate of BMI growth for the combined exposed group was significantly more rapid (0.27 kg/m2 per year; 95% CI: 0.05, 0.69; p < 0.01) than the unexposed. CONCLUSIONS: Offspring prenatally exposed to maternal active or secondhand smoking experience rapid and similar BMI growth in the first three years of life. Given the long-term consequences of rapid weight gain in early childhood, it is important to encourage pregnant women to quit smoking and limit their exposure to secondhand smoke.

Improving the diagnostic accuracy of a stratified screening strategy by identifying the optimal risk cutoff.

BACKGROUND: The American Cancer Society (ACS) suggests using a stratified strategy for breast cancer screening. The strategy includes assessing risk of breast cancer, screening women at high risk with both MRI and mammography, and screening women at low risk with mammography alone. The ACS chose their cutoff for high risk using expert consensus. METHODS: We propose instead an analytic approach that maximizes the diagnostic accuracy (AUC/ROC) of a risk-based stratified screening strategy in a population. The inputs are the joint distribution of screening test scores, and the odds of disease, for the given risk score. Using the approach for breast cancer screening, we estimated the optimal risk cutoff for two different risk models: the Breast Cancer Screening Consortium (BCSC) model and a hypothetical model with much better discriminatory accuracy. Data on mammography and MRI test score distributions were drawn from the Magnetic Resonance Imaging Screening Study Group. RESULTS: A risk model with an excellent discriminatory accuracy (c-statistic [Formula: see text]) yielded a reasonable cutoff where only about 20% of women had dual screening. However, the BCSC risk model (c-statistic [Formula: see text]) lacked the discriminatory accuracy to differentiate between women who needed dual screening, and women who needed only mammography. CONCLUSION: Our research provides a general approach to optimize the diagnostic accuracy of a stratified screening strategy in a population, and to assess whether risk models are sufficiently accurate to guide stratified screening. For breast cancer, most risk models lack enough discriminatory accuracy to make stratified screening a reasonable recommendation.

Childhood Metabolic Biomarkers Are Associated with Performance on Cognitive Tasks in Young Children.

OBJECTIVE: To evaluate the hypothesis that metabolic measures (fasting glucose, insulin, and Homeostatic Model of Assessment for Insulin Resistance [HOMA-IR] levels) are inversely associated with performance on cognitive tasks using data from young (4- to 6-year-old), typically developing, healthy children. STUDY DESIGN: Data were obtained from children participating in the Healthy Start study, a pre-birth cohort in Colorado. HOMA-IR, glucose, and insulin values were centered and scaled using the study sample means and SD. Thus, they are reported in number of SD units from the mean. Fully corrected T scores for inhibitory control (Flanker task), cognitive flexibility (Dimensional Change Card Sort test), and receptive language (Picture Vocabulary test) were obtained via the National Institutes of Health Toolbox cognition battery. RESULTS: Children included in this analysis (n = 137) were 4.6 years old, on average. Per 1-SD unit, fasting glucose (B = -2.0, 95% CI -3.5, -0.5), insulin (B = -1.7, 95% CI -3.0, -0.4), and HOMA-IR values (B = -1.8, 95% CI -3.1, -0.5) were each significantly and inversely associated with inhibitory control (P < .05 for all, respectively). Fasting glucose levels were also inversely associated with cognitive flexibility (B = -2.0, 95% CI -3.7, -0.2, P = .03). CONCLUSIONS: Our data suggest that metabolic health may impact fluid cognitive function in healthy, young children.

Fetal Overnutrition and Adolescent Hepatic Fat Fraction: the Exploring Perinatal Outcomes in Children Study.

OBJECTIVE: To determine if fetal overnutrition resulting from maternal obesity or gestational diabetes mellitus (GDM) is associated with increased liver fat during adolescence, adjusting for past and current metabolic risk factors. STUDY DESIGN: Data come from a historical prospective cohort study (Exploring Perinatal Outcomes in Children) of 254 mother-child pairs in Colorado who participated in 2 research visits at T1 (mean age 10.4, SD = 1.5 years) and at T2 (mean age 16.4, SD = 1.5 years), and had complete exposure and outcome data. Multiple linear regression was used to evaluate the effects of pre-pregnancy body mass index (BMI) and GDM on hepatic fat fraction (HFF) by magnetic resonance imaging at T2. RESULTS: Maternal pre-pregnancy obesity (BMI 30+) was significantly associated (ß = 1.59, CI = 0.66, 2.52) with increased HFF relative to mothers with normal pre-pregnancy weight (BMI <25) independent of maternal GDM and sociodemographic factors. Moreover, this association was independent of T2 and T1 metabolic risk factors (acanthosis nigricans, BMI, fasting glucose) (ß = 1.03, CI = 0.10, 1.97). Prenatal GDM exposure was not associated with HFF in either unadjusted or adjusted models. CONCLUSIONS: Maternal pre-pregnancy obesity was associated with increased HFF in offspring independent of childhood and adolescent adiposity. Intervention studies are needed to test the hypothesis that maternal obesity is a modifiable risk factor for childhood fatty liver disease.

Proinflammatory Diets during Pregnancy and Neonatal Adiposity in the Healthy Start Study.

OBJECTIVE: To evaluate the association between dietary inflammatory index (DII) scores during pregnancy and neonatal adiposity. STUDY DESIGN: The analysis included 1078 mother-neonate pairs in Healthy Start, a prospective prebirth cohort. Diet was assessed using repeated 24-hour dietary recalls. DII scores were obtained by summing nutrient intakes, which were standardized to global means and multiplied by inflammatory effect scores. Air displacement plethysmography measured fat mass and fat-free mass within 72 hours of birth. Linear and logistic models evaluated the associations of DII scores with birth weight, fat mass, fat-free mass, and percent fat mass, and with categorical outcomes of small- and large-for-gestational age. We tested for interactions with prepregnancy BMI and gestational weight gain. RESULTS: The interaction between prepregnancy BMI and DII was statistically significant for birth weight, neonatal fat mass, and neonatal percent fat mass. Among neonates born to obese women, each 1-unit increase in DII was associated with increased birth weight (53 g; 95% CI, 20, 87), fat mass (20 g; 95% CI, 7-33), and percent fat mass (0.5%; 95% CI, 0.2-0.8). No interaction was detected for small- and large-for-gestational age. Each 1-unit increase in DII score was associated a 40% increase in odds of a large-for-gestational age neonate (1.4; 95% CI, 1.0-2.0; P = .04), but not a small-for-gestational age neonate (1.0; 95% CI, 0.8-1.2; P = .80). There was no evidence of an interaction with gestational weight gain. CONCLUSIONS: Our findings support the hypothesis that an increased inflammatory milieu during pregnancy may be a risk factor for neonatal adiposity. TRIAL REGISTRATION: Clinicaltrials.gov: NCT02273297.

Predictors of Infant Body Composition at 5 Months of Age: The Healthy Start Study.

OBJECTIVE: To examine associations of demographic, perinatal, and infant feeding characteristics with offspring body composition at approximately 5 months of age. STUDY DESIGN: We collected data on 640 mother/offspring pairs from early pregnancy through approximately 5 months of age. We assessed offspring body composition with air displacement plethysmography at birth and approximately 5 months of age. Linear regression analyses examined associations between predictors and fat-free mass, fat mass, and percent fat mass (adiposity) at approximately 5 months. Secondary models further adjusted for body composition at birth and rapid infant growth. RESULTS: Greater prepregnant body mass index and gestational weight gain were associated with greater fat-free mass at approximately 5 months of age, but not after adjustment for fat-free mass at birth. Greater gestational weight gain was also associated with greater fat mass at approximately 5 months of age, independent of fat mass at birth and rapid infant growth, although this did not translate into increased adiposity. Greater percent time of exclusive breastfeeding was associated with lower fat-free mass (-311 g; P < .001), greater fat mass (+224 g; P < .001), and greater adiposity (+3.51%; P < .001). Compared with offspring of non-Hispanic white mothers, offspring of Hispanic mothers had greater adiposity (+2.72%; P < .001) and offspring of non-Hispanic black mothers had lower adiposity (-1.93%; P < .001). Greater adiposity at birth predicted greater adiposity at approximately 5 months of age, independent of infant feeding and rapid infant growth. CONCLUSIONS: There are clear differences in infant body composition by demographic, perinatal, and infant feeding characteristics, although our data also show that increased adiposity at birth persists through approximately 5 months of age. Our findings warrant further research into implications of differences in infant body composition.

Exposure to maternal diabetes in utero and offspring eating behavior: The EPOCH study.

The risk of becoming overweight among offspring exposed to gestational diabetes (GDM) in utero is two-fold higher than in the general population. The responsible mechanisms are likely multifactorial, with some evidence that GDM exposure alters brain satiety signaling, which may impact eating behavior. To better understand these effects, we investigated the relationship between GDM exposure, eating behavior, and total energy intake in 268 adolescents from the Exploring Perinatal Outcomes among Children cohort, who were exposed (n = 50) or not exposed (n = 217) to GDM in utero. Eating behavior was measured by the Eating in the Absence of Hunger in Children and Adolescents (EAH-C) questionnaire, which included subscale scores for Negative Affect, External Stimuli, and Fatigue/Boredom. Total energy intake (kcal/day) was derived from the Block Kid's Food Questionnaire. The associations between GDM exposure and the outcomes of total score and each EAH-C subscale were evaluated in separate multivariable models. In addition to the main predictor, GDM, the models included a GDM-by-sex interaction term and were adjusted for important covariates. The associations between EAH-C total and subscale scores and the outcome of total energy intake were also tested in separate multivariable models. Female offspring exposed to GDM in utero (vs unexposed males and females) were more likely to continue eating beyond satiation due to feelings of boredom and fatigue (ß = 0.47, 95% CI: 0.11, 0.83), and in general (EAH-C total score; ß = 4.20, 95% CI: 0.56, 7.86) compared to unexposed males. All EAH-C subscale and total scores were significantly, positively associated with higher energy intake (p < 0.05 for all, respectively). Our findings highlight the need for further investigation into the possible early life programming of eating behaviors by GDM exposure in utero.

Infant Adiposity is Independently Associated with a Maternal High Fat Diet but not Related to Niacin Intake: The Healthy Start Study.

Objectives Over-nutrition during pregnancy resulting from maternal obesity or an unhealthy diet can lead to excess infant adiposity at birth. Specific dietary macro- and micronutrients have been shown to increase fat cell development in both in-vitro and in-vivo models and may therefore link maternal diet to increased infant adiposity. We hypothesized that high maternal dietary niacin intake during pregnancy, especially in combination with a high-fat diet (HFD) would increase infant adiposity. Methods We included 1040 participants from a pre-birth cohort of mother-infant pairs. Maternal diet was assessed using multiple 24-hour dietary recalls. HFD was defined as ≥30% of calories from fat and ≥12% of fat calories from saturated fat. Neonatal body composition (% fat mass [%FM], fat mass [FM], fat-free mass [FFM]) was measured by PEAPOD. We used multivariate regression to assess the joint effect of maternal dietary niacin and maternal HFD on neonatal body composition. Results Dietary niacin was not associated with neonatal body composition, and maternal HFD did not modify this finding. However, maternal HFD was independently associated with %FM (ß = 0.8 [0.1, 1.4]%, p < 0.01] and FM (ß = 32.4 [6.7, 58.0] g, p < 0.01). Conclusions for Practice Our results suggest that a HFD during pregnancy may increase infant adiposity, therefore supporting the need for improved diet counseling of pregnant women at both the clinical and community levels.

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.

GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate.

GLIMMPSE is a free, web-based software tool that calculates power and sample size for the general linear multivariate model with Gaussian errors (http://glimmpse.SampleSizeShop.org/). GLIMMPSE provides a user-friendly interface for the computation of power and sample size. We consider models with fixed predictors, and models with fixed predictors and a single Gaussian covariate. Validation experiments demonstrate that GLIMMPSE matches the accuracy of previously published results, and performs well against simulations. We provide several online tutorials based on research in head and neck cancer. The tutorials demonstrate the use of GLIMMPSE to calculate power and sample size.

Power for balanced linear mixed models with complex missing data processes.

When designing repeated measures studies, both the amount and the pattern of missing outcome data can affect power. The chance that an observation is missing may vary across measurements, and missingness may be correlated across measurements. For example, in a physiotherapy study of patients with Parkinson's disease, increasing intermittent dropout over time yielded missing measurements of physical function. In this example, we assume data are missing completely at random, since the chance that a data point was missing appears to be unrelated to either outcomes or covariates. For data missing completely at random, we propose noncentral F power approximations for the Wald test for balanced linear mixed models with Gaussian responses. The power approximations are based on moments of missing data summary statistics. The moments were derived assuming a conditional linear missingness process. The approach provides approximate power for both complete-case analyses, which include independent sampling units where all measurements are present, and observed-case analyses, which include all independent sampling units with at least one measurement. Monte Carlo simulations demonstrate the accuracy of the method in small samples. We illustrate the utility of the method by computing power for proposed replications of the Parkinson's study.