Cutoff estimation and construction of their confidence intervals for continuous biomarkers under ternary umbrella and tree stochastic ordering settings.

Tuberculosis (TB) studies often involve four different states under consideration, namely, "healthy," "latent infection," "pulmonary active disease," and "extra-pulmonary active disease." While highly accurate clinical diagnosis tests do exist, they are expensive and generally not accessible in regions where they are most needed; thus, there is an interest in assessing the accuracy of new and easily obtainable biomarkers. For some such biomarkers, the typical stochastic ordering assumption might not be justified for all disease classes under study, and usual ROC methodologies that involve ROC surfaces and hypersurfaces are inadequate. Different types of orderings may be appropriate depending on the setting, and these may involve a number of ambiguously ordered groups that stochastically exhibit larger (or lower) marker scores than the remaining groups. Recently, there has been scientific interest on ROC methods that can accommodate these so-called "tree" or "umbrella" orderings. However, there is limited work discussing the estimation of cutoffs in such settings. In this article, we discuss the estimation and inference around optimized cutoffs when accounting for such configurations. We explore different cutoff alternatives and provide parametric, flexible parametric, and non-parametric kernel-based approaches for estimation and inference. We evaluate our approaches using simulations and illustrate them through a real data set that involves TB patients.

Device-estimated sleep metrics do not mediate the relation between race and blood pressure dipping in young black and white women.

Short, disturbed, and irregular sleep may contribute to blunted nocturnal blood pressure (BP) dipping, a predictor of cardiovascular disease. Black women (BLW) demonstrate less BP dipping and poorer sleep health than White women (WHW). However, it remains unclear whether device-estimated sleep health metrics mediate the relation between race and BP dipping in young women. We hypothesized that the relation between race and BP dipping would be partly mediated by sleep health metrics of sleep duration, sleep efficiency, and sleep regularity. Participants (20 BLW, 17 WHW) were 18-29 years old, normotensive, nonobese, and without evidence of sleep disorders. Systolic and diastolic BP dipping were derived from 24-h ambulatory BP monitoring. Habitual sleep duration and sleep efficiency were estimated via 14 days of wrist actigraphy. Sleep duration regularity was calculated as the standard deviation (SD) of nightly sleep duration (SDSD). Sleep timing regularity metrics were calculated as the SD of sleep onset and sleep midpoint (SMSD). Mediation analysis tested the mediating effect of each sleep metric on the relation between race and BP dipping. BLW experienced less systolic (P = .02) and diastolic (P = .01) BP dipping. Sleep duration (P = .14) was not different between groups. BLW had lower sleep efficiency (P < .01) and higher SDSD (P = .02), sleep onset SD (P < .01) and SMSD (P = .01). No sleep metrics mediated the relation between race and BP dipping (all indirect effects P > .38). In conclusion, mediation pathways of sleep health metrics do not explain racial differences in nocturnal BP dipping between young BLW and WHW.

Aspects of Dietary Diversity Changes across Adulthood in Racially Diverse Adults.

Knowledge of various aspects of dietary diversity (DD)-an essential healthful dietary component-across adulthood is limited. This study examined three DD aspects over time in racially diverse adults. Participants were from the National Institute on Aging, Healthy Aging in Neighborhoods of Diversity across the Life Span study. DD measures were calculated at baseline (N = 2177), and first and second examination follow-ups (N = 2140 and N = 2066, respectively) using two 24-h recalls. The count was based on the consumption of ≥50% of an equivalent from 21 food groups. Evenness was derived using the Berry-Index adjusted by the food's health value; dissimilarity, by Mahalanobis Distance. Mixed-effects linear regression models were conducted to test changes in DD across adulthood, adjusting for sex, race, poverty status and education as fixed effects, and adjusting for smoking, age and energy as time-dependent variables. Only dissimilarity showed significant interactions of time × race (p = 0.0005), and time × poverty status (p = 0.0325), indicating a slower rate of increase over time in dissimilarity scores among Whites compared with African-Americans and those with income >125% poverty versus <125% poverty. A significant interaction between time×energy (p < 0.0001) was noted for both evenness and dissimilarity scores. To our knowledge, this is the first study to document the differential change in dissimilarity scores by race and income over time.

Correction: Fanelli Kuczmarski, M.; et al. Aspects of Dietary Diversity Differ in Their Association with Atherosclerotic Cardiovascular Risk in a Racially Diverse US Adult Population. Nutrients 2019, 11, 1034.

The authors wish to make a correction to Table 2 in the published version of their paper [...].

Aspects of Dietary Diversity Differ in Their Association with Atherosclerotic Cardiovascular Risk in a Racially Diverse US Adult Population.

The study objectives were to measure dietary diversity (DD) of an urban US population and to determine if associations of 10 year atherosclerotic cardiovascular (ASCVD) risk with DD were independent of dietary quality. Participants were drawn from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study, wave 4 (n = 2066, 1259 African Americans (AA), 807 Whites (W). Three DD measures were derived from 2 days of 24 h recall data collected with the USDA automated-multiple-pass-method. Count was based on consumption of at least half an equivalent of food from 21 subgroups. Evenness was calculated using Berry Index (BI) and BI-adjusted by food health values. Dissimilarity was calculated by Mahalanobis Distance. Diet quality was assessed by Mean Nutrient Adequacy (MAR) and DASH scores. Associations of DD and quality with ASCVD risk, calculated using 2013 American College of Cardiology and American Heart Association guidelines, were assessed with multivariable regression. Covariates included income, education, food security, and energy/kg weight. Count and MAR were positively associated whereas dissimilarity was negatively associated with ASCVD risk. There was no evidence that evenness contributed to cardiovascular health. The findings suggest more diversity in food attributes and diets rich in micronutrients rather than increased count support cardiovascular health.