Brandy M. Ringham

Maternal diet quality in pregnancy and neonatal adiposity: the Healthy Start Study.

Background/Objectives:Poor maternal diet in pregnancy can influence fetal growth and development. We tested the hypothesis that poor maternal diet quality during pregnancy would increase neonatal adiposity (percent fat mass (%FM)) at birth by increasing the fat mass (FM) component of neonatal body composition.Methods:Our analysis was conducted using a prebirth observational cohort of 1079 mother–offspring pairs. Pregnancy diet was assessed via repeated Automated Self-Administered 24-h dietary recalls, from which Healthy Eating Index-2010 (HEI-2010) scores were calculated for each mother. HEI-2010 was dichotomized into scores of ⩽57 and >57, with low scores representing poorer diet quality. Neonatal %FM was assessed within 72 h after birth with air displacement plethysmography. Using univariate and multivariate linear models, we analyzed the relationship between maternal diet quality and neonatal %FM, FM, and fat-free mass (FFM) while adjusting for prepregnancy body mass index (BMI), physical activity, maternal age, smoking, energy intake, preeclampsia, hypertension, infant sex and gestational age.Results:Total HEI-2010 score ranged between 18.2 and 89.5 (mean: 54.2, s.d.: 13.6). An HEI-2010 score of ⩽57 was significantly associated with higher neonatal %FM (β=0.58, 95% confidence interval (CI) 0.07–1.1, P<0.05) and FM (β=20.74; 95% CI 1.49–40.0; P<0.05) but no difference in FFM.Conclusions:Poor diet quality during pregnancy increases neonatal adiposity independent of maternal prepregnancy BMI and total caloric intake. This further implicates maternal diet as a potentially important exposure for fetal adiposity.

Estimates of sensitivity and specificity can be biased when reporting the results of the second test in a screening trial conducted in series

BackgroundCancer screening reduces cancer mortality when early detection allows successful treatment of otherwise fatal disease. There are a variety of trial designs used to find the best screening test. In a series screening trial design, the decision to conduct the second test is based on the results of the first test. Thus, the estimates of diagnostic accuracy for the second test are conditional, and may differ from unconditional estimates. The problem is further complicated when some cases are misclassified as non-cases due to incomplete disease status ascertainment.MethodsFor a series design, we assume that the second screening test is conducted only if the first test had negative results. We derive formulae for the conditional sensitivity and specificity of the second test in the presence of differential verification bias. For comparison, we also derive formulae for the sensitivity and specificity for a single test design, both with and without differential verification bias.ResultsBoth the series design and differential verification bias have strong effects on estimates of sensitivity and specificity. In both the single test and series designs, differential verification bias inflates estimates of sensitivity and specificity. In general, for the series design, the inflation is smaller than that observed for a single test design.The degree of bias depends on disease prevalence, the proportion of misclassified cases, and on the correlation between the test results for cases. As disease prevalence increases, the observed conditional sensitivity is unaffected. However, there is an increasing upward bias in observed conditional specificity. As the proportion of correctly classified cases increases, the upward bias in observed conditional sensitivity and specificity decreases. As the agreement between the two screening tests becomes stronger, the upward bias in observed conditional sensitivity decreases, while the specificity bias increases.ConclusionsIn a series design, estimates of sensitivity and specificity for the second test are conditional estimates. These estimates must always be described in context of the design of the trial, and the study population, to prevent misleading comparisons. In addition, these estimates may be biased by incomplete disease status ascertainment.

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.

Bias in estimating accuracy of a binary screening test with differential disease verification

Sensitivity, specificity, positive and negative predictive value are typically used to quantify the accuracy of a binary screening test. In some studies, it may not be ethical or feasible to obtain definitive disease ascertainment for all subjects using a gold standard test. When a gold standard test cannot be used, an imperfect reference test that is less than 100 per cent sensitive and specific may be used instead. In breast cancer screening, for example, follow-up for cancer diagnosis is used as an imperfect reference test for women where it is not possible to obtain gold standard results. This incomplete ascertainment of true disease, or differential disease verification, can result in biased estimates of accuracy. In this paper, we derive the apparent accuracy values for studies subject to differential verification. We determine how the bias is affected by the accuracy of the imperfect reference test, the percent who receive the imperfect reference standard test not receiving the gold standard, the prevalence of the disease, and the correlation between the results for the screening test and the imperfect reference test. It is shown that designs with differential disease verification can yield biased estimates of accuracy. Estimates of sensitivity in cancer screening trials may be substantially biased. However, careful design decisions, including selection of the imperfect reference test, can help to minimize bias. A hypothetical breast cancer screening study is used to illustrate the problem.

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.

An observational cohort study of weight- and length-derived anthropometric indicators with body composition at birth and 5 mo: the Healthy Start study

Background: Despite widespread use of weight- and length-based anthropometric indexes as proxies for adiposity, little is known regarding the extent to which they correspond with fat mass (FM) or fat-free mass (FFM) during infancy.Objective: This study aimed to examine associations of 3 derived indicators-weight-for-age z score (WFAZ), weight-for-length score (WFLZ), and body mass index z score (BMIZ)-with FM, percentage of FM, and FFM measured by air-displacement plethysmography during the first 5 mo of life.Design: Applying prospectively collected data from 1027 infants in a Colorado prebirth cohort, we used multivariate regression to evaluate associations between the derived indicators and body composition at birth and at 5 mo, and with change (Δ) during follow-up.Results: At birth, all 3 derived indicators were more strongly associated with FFM than with FM. Each unit of WFAZ corresponded with 0.342 kg FFM (95% CI: 0.331, 0.351 kg FFM), compared with 0.121 kg FM (95% CI: 0.114, 0.128 kg FM) (P < 0.0001); similar trends were observed for WFLZ and BMIZ. By 5 mo, WFLZ and BMIZ were more strongly associated with FM than with FFM, whereas WFAZ correlated similarly with the 2 components of body composition. ΔWFLZ and ΔBMIZ were both more strongly related to ΔFM than to ΔFFM; however, a direct comparison of the 2 indexes with respect to change in the percentage of FM indicated that ΔBMIZ was the optimal proxy of adiposity gain (P < 0.0001, pairwise difference).Conclusions: Weight- and length-based indexes are poor surrogates for newborn adiposity. However, at 5 mo, WFLZ and BMIZ are suitable proxies of FM. When assessing adiposity gain, ΔBMIZ is the best indicator of fat accrual during the first 5 postnatal months. This trial was registered at clinicaltrials.gov as NCT02273297.

Bias in trials comparing paired continuous tests can cause researchers to choose the wrong screening modality

BackgroundTo compare the diagnostic accuracy of two continuous screening tests, a common approach is to test the difference between the areas under the receiver operating characteristic (ROC) curves. After study participants are screened with both screening tests, the disease status is determined as accurately as possible, either by an invasive, sensitive and specific secondary test, or by a less invasive, but less sensitive approach. For most participants, disease status is approximated through the less sensitive approach. The invasive test must be limited to the fraction of the participants whose results on either or both screening tests exceed a threshold of suspicion, or who develop signs and symptoms of the disease after the initial screening tests.The limitations of this study design lead to a bias in the ROC curves we call paired screening trial bias. This bias reflects the synergistic effects of inappropriate reference standard bias, differential verification bias, and partial verification bias. The absence of a gold reference standard leads to inappropriate reference standard bias. When different reference standards are used to ascertain disease status, it creates differential verification bias. When only suspicious screening test scores trigger a sensitive and specific secondary test, the result is a form of partial verification bias.MethodsFor paired screening tests with bivariate normally distributed scores, we give formulae and programs to quantify the effect of paired screening trial bias on a paired comparison of area under the curves. We fix the prevalence of disease, and the chance a diseased subject manifests signs and symptoms. We derive the formulas for true sensitivity and specificity, and those for the sensitivity and specificity observed by the study investigator.ResultsThe observed area under the ROC curves is quite different from the true area under the ROC curves. The typical direction of the bias is a strong inflation in sensitivity, paired with a concomitant slight deflation of specificity.ConclusionIn paired trials of screening tests, when area under the ROC curve is used as the metric, bias may lead researchers to make the wrong decision as to which screening test is better.

Exploring the association between maternal prenatal multivitamin use and early infant growth: The Healthy Start Study

Prenatal multivitamin supplementation is recommended to improve offspring outcomes, but effects on early infant growth are unknown.

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 (&bgr; = 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) (&bgr; = 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.

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