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OBJECTIVES: The aims of this study are to (1) create clusters of joint temporal patterns of diet and physical activity (PA), and (2) determine the association of these clusters with health status indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure and health outcomes including obesity, Type 2 Diabetes (T2DM) and metabolic syndrome (MS) in U.S. adults 20–65 years. METHODS: A random day of PA from accelerometry data and the first day 24 hour dietary recall collected in the National Health and Nutrition Examination Survey 2003–2006 were used to determine absolute PA intensity, absolute energy intake, and the time of these activities. Dietary and PA data from 1,627 U.S. adults were Z-normalized. Dynamic time warping (DTW) coupled with kernel-k means clustering algorithm was used to develop joint temporal dietary and PA patterns that maximally partition individuals with similar temporal behaviors into mutually exclusive clusters derived from the data rather than predefined standards. Multivariate regression models adjusted for ptential confounders, multiple comparisons and survey design factors determined associations between joint temporal patterns and health status indicators along with health outcomes (P

Background Food insecurity is associated with poorer nutrient intakes from food sources and lower dietary supplement use. However, its association with total usual nutrient intakes, inclusive of dietary supplements, and biomarkers of nutritional status among US children remains unknown. Objective The objective was to assess total usual nutrient intakes, Healthy Eating Index-2015 (HEI-2015) scores, and nutritional biomarkers by food security status, sex, and age among US children. Methods Cross-sectional data from 9147 children aged 1-18 y from the 2011-2016 NHANES were analyzed. Usual energy and total nutrient intakes and HEI-2015 scores were estimated using the National Cancer Institute method from 24-h dietary recalls. Results Overall diet quality was poor, and intakes of sodium, added sugars, and saturated fat were higher than recommended limits, regardless of food security status. Food-insecure girls and boys were at higher risk of inadequate intakes for vitamin D and magnesium, and girls also had higher risk for inadequate calcium intakes compared with their food-secure counterparts, when total intakes were examined. Choline intakes of food-insecure children were less likely to meet the adequate intake than those of their food-secure peers. No differences by food security status were noted for folate, vitamin C, iron, zinc, potassium, and sodium intakes. Food-insecure adolescent girls aged 14-18 y were at higher risk of micronutrient inadequacies than any other subgroup, with 92.8% (SE: 3.6%) at risk of inadequate intakes for vitamin D. No differences in biomarkers for vitamin D, folate, iron, and zinc were observed by food security status. The prevalence of iron deficiency was 12.7% in food-secure and 12.0% in food-insecure adolescent girls. Conclusions Food insecurity was associated with compromised intake of some micronutrients, especially among adolescent girls. These results highlight a need for targeted interventions to improve children's overall diet quality, including the reduction of specific nutrient inadequacies, especially among food-insecure children. This study was registered at clinicaltrials.gov as NCT03400436.

Objectives Data-driven methods were recently applied to create temporal dietary patterns (TDPs) incorporating timing and amount of energy intake over 24-hours; their relationships to obesity were determined. However, description of the data-driven TDPs using energy and time cut-offs were not validated against obesity. Aims were to (1) create data-driven TDPs, describe pattern characteristics using energy and time cut-offs, and determine relationships to BMI and waist circumference (WC); (2) assess the concurrent validity of TDPs derived using the cut-offs by determining relationships with BMI and WC. Methods Amount and timing of energy intake from the first day 24-hour dietary recall of 17,916 U.S. adults in NHANES 2007–2016 was used to pattern 4 TDPs. Clusters were created using data-driven methods: dynamic time warping coupled with kernel-k means clustering algorithm. Relationships with BMI and WC were assessed using multivariate regression. Heat maps plotting the cluster proportion by energy amount throughout the day were used to visualize the data and find energy and time cut-offs for mutually exclusive clusters. Next, the cut-off-based descriptions were used to create new clusters and multivariate regression determined their associations with BMI and WC. Strength to predict obesity was evaluated by comparing both inferential model results. Percent of participant overlap between data-driven and cut-off derived clusters was also calculated. Results Both cut-off and data-driven methods showed a cluster, representing a TDP with proportionally equivalent average energy consumed during three eating events throughout a day, was associated with significantly lower BMI (R2 = 0.12 for both methods) and WC (R2 = 0.17 for both methods) compared to the other 3 clusters that had one energy peak throughout a day (all P < 0.0001). Participant membership of ≥ 82% overlapped between the cut-off and data-driven TDP clusters. Conclusions Four cut-off derived clusters highly overlapped with data-driven clusters and showed no differences in strength or pattern relationships with obesity. TDP discovery using a data-driven method was validated through practically interpretable descriptions of energy intake and timing cut-offs. TDPs hold promise for the prediction of obesity and translation to dietary guidance.

Dietary supplement (DS) use is widespread in the U.S. and contributes large amounts of micronutrients to users. Most studies have relied on data from one assessment method to characterize the prevalence of DS use. Combining multiple methods enhances the ability to capture nutrient exposures from DS and examine trends over time.The objective of this study was to characterize DS use and examine trends in any DS as well as micronutrient-containing (MN) DS use among a nationally representative sample of the U.S. population (≥1y) from the 2007-2018 NHANES using a combined approach (i.e., DSMQ and/or ≥1 24HR).NHANES obtains an in-home inventory with a frequency-based DS and Prescription Medicine Questionnaire (DSMQ), and two 24-hr recalls (24HR). The objective of this study was to characterize DS use and examine trends in any and micronutrient-containing (MN) DS use among a nationally representative sample of the U.S. population (≥1y) from the 2007-2018 NHANES, using a combined approach (i.e., DSMQ and/or ≥1 24HR). Trends in the prevalence of use and selected types of products used were estimated for the population and by sex, age, race/Hispanic origin, family income (poverty-to-income ratio (PIR)), and household food security (food-secure vs. food-insecure). Linear trends were tested using orthogonal polynomials (significance set at p0.05).DS use increased from 50% in 2007 to 56% in 2018 (p = 0.001); use of MN products increased from 46% to 49% (p = 0.03), and single-nutrient DS (e.g., magnesium, vitamins B12 and D) use also increased (all p0.001). In contrast, multivitamin-mineral use decreased (70% to 56%; p0.001). Among adults (≥19y), any (54% to 61%) and MN (49% to 54%) DS use increased, especially among men, non-Hispanic Blacks and Hispanics, and low-income adults (PIR≤130%). Among children (1-18y), any DS use remained stable (∼38%), as did MN use, except for food-insecure children, whose use increased from 24% to 31% over the decade (p = 0.03).The prevalence of any and MN DS use increased over time in the U.S. This may be partially attributed to increased use of single-nutrient products. Population subgroups differed in their DS use.

BACKGROUND: The integration of time with dietary patterns throughout a day, or temporal dietary patterns (TDPs), have been linked with dietary quality but relations to health are unknown. OBJECTIVE: The association between TDPs and selected health status indicators and obesity, type 2 diabetes (T2D), and metabolic syndrome (MetS) was determined. METHODS: The first-day 24-h dietary recall from 1627 nonpregnant US adult participants aged 20–65 y from the NHANES 2003–2006 was used to determine timing, amount of energy intake, and sequence of eating occasions (EOs). Modified dynamic time warping (MDTW) and kernel k-means algorithm clustered participants into 4 groups representing distinct TDPs. Multivariate regression models determined associations between TDPs and health status, controlling for potential confounders, and adjusting for the survey design and multiple comparisons (P

Objective:To evaluate total usual intakes and biomarkers of micronutrients, overall dietary quality and related health characteristics of US older adults who were overweight or obese compared with a healthy weight.Design:Cross-sectional study.Setting:Two 24-h dietary recalls, nutritional biomarkers and objective and subjective health characteristic data were analysed from the National Health and Nutrition Examination Survey 2011–2014. We used the National Cancer Institute method to estimate distributions of total usual intakes from foods and dietary supplements for eleven micronutrients of potential concern and the Healthy Eating Index (HEI)-2015 score.Participants:Older adults aged ≥60 years (n 2969) were categorised by sex and body weight status, using standard BMI categories. Underweight individuals (n 47) were excluded due to small sample size.Results:A greater percentage of obese older adults compared with their healthy-weight counterparts was at risk of inadequate Mg (both sexes), Ca, vitamin B6 and vitamin D (women only) intakes. The proportion of those with serum 25-hydroxyvitamin D < 40 nmol/l was higher in obese (12 %) than in healthy-weight older women (6 %). Mean overall HEI-2015 scores were 8·6 (men) and 7·1 (women) points lower in obese than in healthy-weight older adults. In addition, compared with healthy-weight counterparts, obese older adults were more likely to self-report fair/poor health, use ≥ 5 medications and have limitations in activities of daily living and cardio-metabolic risk factors; and obese older women were more likely to be food-insecure and have depression.Conclusions:Our findings suggest that obesity may coexist with micronutrient inadequacy in older adults, especially among women.

BACKGROUND: Accurate and reliable methods to assess prevalence of use of and nutrient intakes from dietary supplements (DSs) are critical for research, clinical practice, and public health monitoring. NHANES has been the primary source of DS usage patterns using an in-home inventory with a frequency-based DS and Prescription Medicine Questionnaire (DSMQ), but little is known regarding DS information obtained from 24-h dietary recalls (24HRs). METHODS: The objectives of this analysis were to compare results from 4 different methods for measuring DS use constructed from two data collection instruments (i.e., DSMQ and 24HR) and to determine the most comprehensive method for measuring the prevalence of use and estimating nutrient intakes from DS for selected nutrients. NHANES 2011–2014 data from US adults (aged ≥19 y; n = 11,451) were used to examine the 4 combinations of methods constructed for measuring the prevalence of use of and amount of selected nutrients from DSs (i.e., riboflavin, vitamin D, folate, magnesium, calcium): 1) DSMQ, 2) 24HR day 1, 3) two 24HRs (i.e., mean), and 4) DSMQ or at least one 24HR. RESULTS: Half of US adults reported DS use on the DSMQ (52%) and on two 24HRs (mean of 49%), as compared with a lower prevalence of DS use when using a single 24HR (43%) and a higher (57%) prevalence when combining the DSMQ with at least one 24HR. Mean nutrient intake estimates were highest using 24HR day 1. Mean supplemental calcium from the DSMQ or at least one 24HR was 372 mg/d, but 464 mg/d on the 24HR only. For vitamin D, the estimated intakes per consumption day were higher on the DSMQ (46 μg) and the DSMQ or at least one 24HR (44 μg) than those on the 24HR day 1 (32 μg) or the mean 24HR (31 μg). Fewer products were also classed as a default or reasonable match on the DSMQ than on the 24HR. CONCLUSIONS: A higher prevalence of use of DSs is obtained using frequency-based methods, whereas higher amounts of nutrients are reported from a 24HR. The home inventory results in greater accuracy for products reported. Collectively, these findings suggest that combining the DSMQ with at least one 24HR (i.e., DSMQ or at least one 24HR) is the most comprehensive method for assessing the prevalence of and estimating usual intake from DSs in US adults. This trial was registered at clinicaltrials.gov as NCT03400436.

BACKGROUND: Most dietary indices reflect foods and beverages and do not include exposures from dietary supplements (DS) that provide substantial amounts of micronutrients. A nutrient-based approach that captures total intake inclusive of DS can strengthen exposure assessment. OBJECTIVE: To examine the construct and criterion validity of the Total Nutrient Index (TNI) among U.S. adults (≥19y; non-pregnant or lactating). METHODS: The TNI includes eight under-consumed micronutrients identified by the Dietary Guidelines for Americans: calcium; magnesium; potassium; choline; and vitamins A, C, D, and E. The TNI is expressed as a percentage of the Recommended Dietary Allowance or Adequate Intake to compute micronutrient component scores; the mean of the component scores yields the TNI score, ranging from 0-100. Data from exemplary menus and the 2003-2006 (≥19y; n = 8,861) and 2011-2014 NHANES (≥19y; n = 9,954) were employed. Exemplary menus were used to determine if the TNI yielded high scores from dietary sources (women 31-50y; men ≥70y). TNI scores were correlated with Healthy Eating Index (HEI)-2015 overall and component scores for dairy, fruits, and vegetables; TNI component scores for vitamins A, C, D, and E were correlated with respective biomarker data. TNI scores were compared between groups with known differences in nutrient intake based on the literature. RESULTS: The TNI yielded high scores on exemplary menus (84.8-93.3/100) and was moderately correlated (r = 0.48) with the HEI-2015. Mean TNI scores were significantly different for DS users (83.5) vs. non-users (67.1), non-smokers (76.8) vs. smokers (70.3), and those living with food security (76.6) vs. food insecurity (69.1). Correlations of TNI vitamin component scores with available biomarkers ranged from r = 0.12 (α-tocopherol) to r = 0.36 (serum 25(OH)D), and were significantly higher than correlations obtained from the diet alone. CONCLUSION: The evaluation of validity supports that the TNI is a useful construct to assess total micronutrient exposures of under-consumed micronutrients among U.S. adults.

Global-local mixtures, including Gaussian scale mixtures, have gained prominence in recent times, both as a sparsity inducing prior in p ≫ n problems as well as default priors for non-linear many-to-one functionals of high-dimensional parameters. Here we propose a unifying framework for global-local scale mixtures using the Cauchy-Schlomilch and Liouville integral transformation identities, and use the framework to build a new Bayesian sparse signal recovery method. This new method is a Bayesian counterpart of the $\sqrt {\text {Lasso}}$ (Belloni et al., Biometrika 98, 4, 791–806, 2011) that adapts to unknown error variance. Our framework also characterizes well-known scale mixture distributions including the Laplace density used in Bayesian Lasso, logit and quantile via a single integral identity. Finally, we derive a few convolutions that commonly arise in Bayesian inference and posit a conjecture concerning bridge and uniform correlation mixtures.

Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics. The l0 penalty is ideal for this task, the caveat being it requires the NP-hard combinatorial evaluation of all models. A recent area of considerable interest is to develop efficient algorithms to fit models with a non-convex lγ penalty for γ ∈ (0,1), which results in sparser models than the convex l1 or lasso penalty, but is harder to fit. We propose an alternative, termed the horseshoe regularization penalty for feature subset selection, and demonstrate its theoretical and computational advantages. The distinguishing feature from existing non-convex optimization approaches is a full probabilistic representation of the penalty as the negative of the logarithm of a suitable prior, which in turn enables efficient expectation-maximization and local linear approximation algorithms for optimization and MCMC for uncertainty quantification. In synthetic and real data, the resulting algorithms provide better statistical performance, and the computation requires a fraction of time of state-of-the-art non-convex solvers.

We develop a new estimator of the inverse covariance matrix for high-dimensional multivariate normal data using the horseshoe prior. The proposed graphical horseshoe estimator has attractive properties compared to other popular estimators, such as the graphical lasso and graphical Smoothly Clipped Absolute Deviation (SCAD). The most prominent benefit is that when the true inverse covariance matrix is sparse, the graphical horseshoe provides estimates with small information divergence from the true sampling distribution. The posterior mean under the graphical horseshoe prior can also be almost unbiased under certain conditions. In addition to these theoretical results, we also provide a full Gibbs sampler for implementing our estimator. MATLAB code is available for download from github at http://github.com/liyf1988/GHS. The graphical horseshoe estimator compares favorably to existing techniques in simulations and in a human gene network data analysis., Comment: 29 pages, 3 figures

OBJECTIVES: Daily temporal patterns of energy intake (temporal dietary patterns, TDPs) and physical activity (temporal PA patterns, TPAPs) have been independently and jointly (joint temporal dietary and PA patterns, TDPAPs) associated with health indicators. The strength of the association between clusters of each pattern and health indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), hemoglobin A1c (A1c), triglyceride (TAG), high-density lipoprotein cholesterol (HDL-C), total cholesterol (Total-C), blood pressure, type 2 diabetes (T2D), metabolic syndrome (MetS), and obesity, were compared. METHODS: The reported energy throughout a day from one reliable 24-hour weekday dietary recall and activity counts from a random weekday of PA accelerometer data of 1,836 U.S. adults from the National Health and Nutrition Examination Survey (2003–2006) were used to create TDP and TPAP respectively, and jointly for TDPAP. Constrained dynamic time warping distances computed over the time series were partitioned to four clusters using kernel-k means clustering algorithm. Measured BMI, WC, FPG, A1c, TAG, HDL-C, Total-C, and classified T2DM, MetS, and obesity were outcomes in multivariate regression models to determine associations with the clusters representing each pattern, controlling for potential confounders and adjusting for multiple comparisons (P

A priori dietary indices provide a standardized, reproducible way to evaluate adherence to dietary recommendations across different populations. Existing nutrient-based indices were developed to reflect food/beverage intake; however, given the high prevalence of dietary supplement (DS) use and its potentially large contribution to nutrient intakes for those that use them, exposure classification without accounting for DS is incomplete. The purpose of this article is to review existing nutrient-based indices and describe the development of the Total Nutrient Index (TNI), an index developed to capture usual intakes from all sources of under-consumed micronutrients among the U.S. population. The TNI assesses U.S. adults' total nutrient intakes relative to recommended nutrient standards for eight under-consumed micronutrients identified by the Dietary Guidelines for Americans: calcium, magnesium, potassium, choline, and vitamins A, C, D, E. The TNI is scored from 0 to 100 (truncated at 100). The mean TNI score of U.S. adults (≥19 y; n = 9,954) based on dietary data from NHANES 2011-2014, was 75.4; the mean score for the index ignoring DS contributions was only 69.0 (t-test; p < 0.001). The TNI extends existing measures of diet quality by including nutrient intakes from all sources and was developed for research, monitoring, and policy purposes.

Background: Few attempts have been made to incorporate multiple aspects of physical activity (PA), including timing and volume, to classify patterns that link to health. Temporal PA patterns integrating time and activity counts were created to determine their association with health. Methods: PA accelerometry data obtained from the cross-sectional National Health and Nutrition Examination Survey 2003-2006 was used to pattern PA counts and time of activity from 1,999 non-pregnant adults with one random valid weekday of activity. Constrained dynamic time warping with Sakoe-Chiba band and kernel k-means clustering grouped participants to 4 clusters representing temporal PA patterns. Multivariate regression models controlling for potential confounders and adjusting for multiple comparisons (pResults: Participants in Cluster 2, represented by a temporal PA pattern with activity counts reaching >1.2e5 counts/ h (cph) and tapering off through the day, had lower mean body mass index (BMI) (pp4 cph from 6:00 to 23:00 (OR: 0.3; 95% CI: 0.2, 0.8). Cluster 3, characterized by a temporal PA pattern with activity counts reaching 9.6e4-1.2e5 cph between 16:00 to 21:00, was associated with lower mean BMI (pp4 cph between 8:00 to 11:00 was associated with lower BMI and WC compared to Cluster 1 (both pConclusions: U.S. adults with temporal PA patterns of higher activity counts ranging between 9.6e4->1.2e5 cph performed early (8:00 to 11:00), late (16:00 to 21:00), or throughout the day had significantly lower mean BMI and WC compared with adults with a temporal PA pattern of the lowest PA counts reaching 4.8e4 cph from 6:00 to 23:00. Temporal PA patterns created by integrating time with PA counts throughout a day meaningfully link to health status.

© 2019 American Society for Nutrition. All rights reserved. The use of dietary supplements (DS) is pervasive and can provide substantial amounts of micronutrients to those who use them. Therefore when characterizing dietary intakes, describing the prevalence of inadequacy or excess, or assessing relations between nutrients and health outcomes, it is critical to incorporate DS intakes to improve exposure estimates. Unfortunately, little is known about the best methods to assess DS, and the structure of measurement error in DS reporting. Several characteristics of nutrients from DS are salient to understand when comparing to those in foods. First, DS can be consumed daily or episodically, in bolus form and can deliver discrete and often very high doses of nutrients that are not limited by energy intakes. These characteristics contribute to bimodal distributions and distributions severely skewed to the right. Labels on DS often provide nutrient forms that differ from those found in conventional foods, and underestimate analytically derived values. Finally, the bioavailability of many nutrient-containing DS is not known and it may not be the same as the nutrients in a food matrix. Current methods to estimate usual intakes are not designed specifically to handle DS. Two temporal procedures are described to refer to the order that nutrient intakes are combined relative to usual intake procedures, referred to as a shrinking the distribution to remove random error. The shrink then add approach is preferable to the add then shrink approach when users and nonusers are combined for most research questions. Stratifying by DS before usual intake methods is another defensible option. This review describes how to incorporate nutrient intakes from DS to usual intakes from foods, and describes the available methods and fit-for-purpose of different analytical strategies to address research questions where total usual intakes are of interest at the group level for use in nutrition research and to inform policy decisions. Clinical Trial Registry: NCT03400436. J Nutr 2019;149:181-197.

Type 2 diabetes is a major public health concern. Management of this condition has focused on behavior modification through diet and exercise interventions. A growing body of evidence has focused on temporality of dietary intake and exercise and potential effects on health. This review summarizes current literature that investigates the question "how does the timing of exercise relative to eating throughout the day effect postprandial response in adults?" Databases PubMed, Scopus, Cochrane Library, CINAHL, and SPORTDiscus were searched between March-May 2019. Experimental studies conducted in healthy adults (≥18 y) and those with type 2 diabetes were included. Full texts were examined by at least two independent reviewers. Twenty studies with a total of 352 participants met the inclusion criteria. The primary finding supports that exercise performed post-meal regardless of time of day had a beneficial impact on postprandial glycemia. There was insufficient evidence regarding whether timing of exercise performed pre- vs. post-meal or vice versa in a day is related to improved postprandial glycemic response due to inherent differences between studies. Future studies focusing on the investigation of timing and occurrence of meal intake and exercise throughout the day are needed to inform whether there is, and what is, an optimal time for these behaviors regarding long-term health outcomes.

Background and purpose Lower grade gliomas (LGGs), lesions of WHO grades II and III, comprise 10-15% of primary brain tumors. In this first-of-a-kind study, we aim to carry out a radioproteomic characterization of LGGs using proteomics data from the TCGA and imaging data from the TCIA cohorts, to obtain an association between tumor MRI characteristics and protein measurements. The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features. Materials and methods Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions. Results The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways. Conclusion Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.

Summary Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach.

Type 2 diabetes is a major public health concern. Management of this condition has focused on behavior modification through diet and exercise interventions. A growing body of evidence has focused on temporality of dietary intake and exercise and potential effects on health. This review summarizes current literature that investigates the question “how does the timing of exercise relative to eating throughout the day effect postprandial response in adults?” Databases PubMed, Scopus, Cochrane Library, CINAHL, and SPORTDiscus were searched between March−May 2019. Experimental studies conducted in healthy adults (≥18 y) and those with type 2 diabetes were included. Full texts were examined by at least two independent reviewers. Twenty studies with a total of 352 participants met the inclusion criteria. The primary finding supports that exercise performed post-meal regardless of time of day had a beneficial impact on postprandial glycemia. There was insufficient evidence regarding whether timing of exercise performed pre- vs. post-meal or vice versa in a day is related to improved postprandial glycemic response due to inherent differences between studies. Future studies focusing on the investigation of timing and occurrence of meal intake and exercise throughout the day are needed to inform whether there is, and what is, an optimal time for these behaviors regarding long-term health outcomes.

This study examined total usual micronutrient intakes from foods, beverages, and dietary supplements (DS) compared to the Dietary Reference Intakes among U.S. adults (&ge, 19 years) by sex and food security status using NHANES 2011&ndash, 2014 data (n = 9954). DS data were collected via an in-home interview, the NCI method was used to estimate distributions of total usual intakes from two 24 h recalls for food and beverages, after which DS were added. Food security status was categorized using the USDA Household Food Security Survey Module. Adults living in food insecure households had a higher prevalence of risk of inadequacy among both men and women for magnesium, potassium, vitamins A, B6, B12, C, D, E, and K, similar findings were apparent for phosphorous, selenium, and zinc in men alone. Meanwhile, no differences in the prevalence of risk for inadequacy were observed for calcium, iron (examined in men only), choline, or folate by food security status. Some DS users, especially food secure adults, had total usual intakes that exceeded the Tolerable Upper Intake Level (UL) for folic acid, vitamin D, calcium, and iron. In conclusion, while DS can be helpful in meeting nutrient requirements for adults for some micronutrients, potential excess may also be of concern for certain micronutrients among supplement users. In general, food insecure adults have higher risk for micronutrient inadequacy than food secure adults.

The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. Lasso is a gold standard for predictor selection while horseshoe is a state-of-the-art Bayesian estimator for sparse signals. Lasso is fast and scalable and uses convex optimization whilst the horseshoe is non-convex. Our novel perspective focuses on three aspects: (i) theoretical optimality in high dimensional inference for the Gaussian sparse model and beyond, (ii) efficiency and scalability of computation and (iii) methodological development and performance., Comment: 32 pages, 4 figures

OBJECTIVES: To estimate total mean usual micronutrient intakes (inclusive of dietary supplements (DS)), Dietary Reference Intakes (DRI) adherence, and the % contribution of DS to total usual micronutrient intakes for U.S. adults (≥19y) using data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES, n = 9474), by sex, age, and race and Hispanic origin groupings. METHODS: Dietary data were collected using two 24-hour recalls; DS data was collected via an in-home 30-day questionnaire that ascertained participants’ usage in the past 30 days. The National Cancer Institute Method was used to estimate mean total usual micronutrient intakes and the proportion of the population complying with DRI by population subgroups: sex, age, and race and Hispanic origin. RESULTS: DS contributed substantially to meeting the DRI recommendations for several key nutrients, including calcium, magnesium, folate, zinc, and vitamins C, D, B6, and K in both men and women. However, approximately half of U.S. adults still failed to meet the estimated average requirement (EAR) for magnesium (45%), and vitamins C (35%), K (45%), and D (63%), even with the inclusion of DS. The proportion of total usual intakes

We treat the problem of maximum likelihood estimation in measurement error models. Direct maximization of the analytically intractable likelihood in such models is difficult. We derive an efficient expectation–maximization scheme for truncated polynomial spline models of degree one. Simulation results confirm the effectiveness of the proposed method.

OBJECTIVES: Little is known whether the prevalence of dietary supplement (DS) use among children varies by assessment method as it does among adults. We assessed the prevalence of use of different DS product types among U.S. children using four different combinations of assessment methods constructed from two instruments (i.e., frequency-based questionnaire and 24-hour dietary recall (24HR)). METHODS: DS use information is collected in the National Health and Nutrition Examination Survey (NHANES) via a 30-day frequency questionnaire in conjunction with an in-home inventory (Dietary Supplement and Prescription Medicine, DSMQ) and during up to two 24HR. NHANES 2013–2016 data were used to estimate the prevalence of use of any DS and selected types of DS products among 8369 children (≤18y) using four methods constructed from DSMQ and 24HR: 1) DSMQ; 2) 24HR day 1; 3) 24HR day 1 and day 2; and 4) DSMQ and ≥1 24HR. RESULTS: About one-third (34.4%) of children took at least one DS in the previous 30 days according to the DSMQ. This decreased to 21.0% when data from only the 24HR day 1 were used, increased to 26.2% when data from both the 24HR day 1 and day 2 were used, and increased further to 37.3% when data from the DSMQ and ≥1 24HR were combined. Similar patterns were found when stratified by age (

OBJECTIVES: Integration of time to physical activity (PA) patterns is novel and may be linked to health. Temporal PA patterns integrating time and PA were created to determine their association with health indicators: body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure, and chronic diseases obesity, diabetes, and metabolic syndrome in US adults 20–65 years. METHODS: Objectively measured PA accelerometry data collected from the National Health and Nutrition Examination Survey 2003–2006 was used to pattern absolute PA intensity and time of activity from 1627 non-pregnant adults with one random valid PA day. Modified dynamic time warping and kernel k-means clustering grouped individuals representing temporal PA patterns. Multivariate regression models controlling for potential confounders and adjusting for multiple comparisons (P

OBJECTIVES: The integration of time with dietary patterns throughout a day, or temporal dietary patterns (TDP), have been linked with dietary quality but links to health outcomes are unknown. TDP were created with the objective to examine their association with health status indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure, and chronic diseases obesity, diabetes, and metabolic syndrome in US adults 20–65 years. METHODS: The first-day 24-hour dietary recall from 1627 non-pregnant US adult participants of the cross-sectional National Health and Nutrition Examination Survey 2003–2006 was used to determine energy intake (kcal), time of intake (min), and sequence of intake occasions throughout the 24-hour day. Modified dynamic time warping coupled with kernel k-means algorithm, clustered participants into four groups representing distinct TDP. Multivariate regression models determined associations between TDP clusters and all outcomes, controlling for potential confounders, energy misreporting, and adjusting for multiple comparisons and the complex survey design (P

OBJECTIVES: To estimate distributions of total usual vitamin D intakes from foods and supplements among U.S. adults, using four different methods of assessment. Usual intake is defined as long-term average daily intake. METHODS: Distributions of total usual vitamin D intakes were estimated from data provided by 9954 adults (≥19 y) from the 2011–2014 NHANES. Dietary and dietary supplement (DS) data were collected using two 24-hour recalls (24HR), and DS data were also collected via an in-home inventory that ascertained participants’ DS use in the past 30 days. The four methods used included: combined “add then shrink” (Method 1); combined “shrink then add” (Method 2); stratified “shrink then add” (Method 3); three-part (Method 4). Briefly, Method 1 analyzes DS users and nonusers together and includes DS use as a covariate in the model. Nutrient intake from foods and DS are “added” prior to applying usual intake procedures to “shrink” the total intake distribution. Method 2 combines DS users and nonusers identically, but “shrinks” the nutrient intake distribution from foods prior to incorporating nutrient intake from DS to produce total usual intakes. Method 3 also follows the “shrink then add” framework but stratifies DS users and nonusers separately in the model. Method 4 also stratifies DS users and nonusers but computes total usual intakes in three parts: 1) usual intake from foods for DS nonusers; 2) usual intake from foods for DS users; and 3) usual intake from DS for DS users. RESULTS: All methods following the “shrink then add” framework (i.e., methods 2- 4) resulted in similar estimated distributions of total usual vitamin D intakes; but Method 1, the “add then shrink” method, resulted in distributions that were wider as a result of inflated within-person variation. Among the “shrink then add” methods, the main differences existed between methods 2 and 3 vs. method 4, partly due to differences in modeling of DS intake information reported on 24HRs vs. the in-home inventory. CONCLUSIONS: Currently, no consensus exists on the best method for incorporating DS nutrients into usual intake models designed to estimate intake from foods and beverages. These findings support the notion that the “shrink then add” methods are more amenable to modeling when compared to “add then shrink.” FUNDING SOURCES: This work was supported by a grant from the National Institutes of Health.

Objective Few attempts to determine dietary patterns have incorporated concepts of time, specifically time and proportion of energy intake consumed throughout a day. A type of modified dynamic time warping (MDTW) was previously developed using an appropriate distance metric for patterning these aspects to determine temporal dietary patterns (TDP). This study further explores dynamic time warping (DTW) distance metrics including unconstrained DTW (UDTW), constrained DTW (CDTW), and MDTW with modern spectral clustering methods to optimize TDP related to dietary quality. MDTW was expected to create TDP with the strongest relationships to dietary quality and distinct visualization among U.S. adults 20-65y of the National Health and Nutrition Examination Survey 1999–2004. Methods Proportional energy intake by time of day metrics were optimized to create TDP from complete day-one 24-h dietary recalls using MDTW, UDTW with only a standard local constraint, and CDTW with standard local and global banding constraints, then clustered using spectral clustering. The association between each TDP distance metric clustering and mean dietary quality, as indicated by the 2005 Healthy Eating Index (HEI-2005), were determined using multiple linear regression controlled for potential confounders. Strength of association for each model was compared using adjusted R-squared. The results were also visualized to make qualitative comparisons. Results Four clusters representing distinct TDP for each distance metric by spectral clustering were generated among participants. MDTW exhibited TDP clusters with strongest associations to HEI compared with the TDP clusters generated from unconstrained and constrained DTW, and visualization of the TDP clusters from MDTW supported the association. Implication MDTW paired with spectral clustering is a useful tool for dimension reduction and uncovering temporal patterns with dietary data.

This analysis characterizes use of dietary supplements (DS) and motivations for DS use among U.S. children (&le, 18 years) by family income level, food security status, and federal nutrition assistance program participation using the 2011&ndash, 2014 National Health and Nutrition Examination Survey data. About one-third (32%) of children used DS, mostly multivitamin-minerals (MVM, 24%). DS and MVM use were associated with higher family income and higher household food security level. DS use was lowest among children in households participating in the Supplemental Nutrition Assistance Program (SNAP, 20%) and those participating in the Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC, 26%) compared to both income-eligible and income-ineligible nonparticipants. Most children who used DS took only one (83%) or two (12%) products, although children in low-income families took fewer products than those in higher income families. The most common motivations for DS and MVM use were to &ldquo, improve (42% or 46%)&rdquo, or &ldquo, maintain (34 or 38%)&rdquo, health, followed by &ldquo, to supplement the diet (23 or 24%)&rdquo, for DS or MVM, respectively. High-income children were more likely to use DS and MVM &ldquo, to supplement the diet&rdquo, than middle- or low-income children. Only 18% of child DS users took DS based on a health practitioner&rsquo, s recommendation. In conclusion, DS use was lower among children who were in low-income or food-insecure families, or families participating in nutrition assistance programs.

The objective of this study was to estimate the prevalence of use and types of dietary supplements (DS) used by U.S. adults (&ge, 19 years) by sociodemographic characteristics: family income-to-poverty ratio (PIR), food security status, and Supplemental Nutrition Assistance Program (SNAP) participation using NHANES 2011&ndash, 2014 data (n = 11,024). DS use was ascertained via a home inventory and a retrospective 30-day questionnaire. Demographic and socioeconomic differences related to DS use were evaluated using a univariate t statistic. Half of U.S. adults (52%) took at least one DS during a 30-day period, multivitamin-mineral (MVM) products were the most commonly used (31%). DS and MVM use was significantly higher among those with a household income of &ge, 350% of the poverty level, those who were food secure, and SNAP income-ineligible nonparticipants across all sex, age, and race/ethnic groups. Among women, prevalence of use significantly differed between SNAP participants (39%) and SNAP income-eligible nonparticipants (54%). Older adults (71+ years) remained the highest consumers of DS, specifically among the highest income group (82%), while younger adults (19&ndash, 30 years), predominantly in the lowest income group (28%), were the lowest consumers. Among U.S. adults, DS use and the types of products consumed varied with income, food security, and SNAP participation.

We propose a new prior for ultra-sparse signal detection that we term the “horseshoe+ prior.” The horseshoe+ prior is a natural extension of the horseshoe prior that has achieved success in the estimation and detection of sparse signals and has been shown to possess a number of desirable theoretical properties while enjoying computational feasibility in high dimensions. The horseshoe+ prior builds upon these advantages. Our work proves that the horseshoe+ posterior concentrates at a rate faster than that of the horseshoe in the Kullback–Leibler (K-L) sense. We also establish theoretically that the proposed estimator has lower posterior mean squared error in estimating signals compared to the horseshoe and achieves the optimal Bayes risk in testing up to a constant. For one-group global–local scale mixture priors, we develop a new technique for analyzing the marginal sparse prior densities using the class of Meijer-G functions. In simulations, the horseshoe+ estimator demonstrates superior performance in a standard design setting against competing methods, including the horseshoe and Dirichlet–Laplace estimators. We conclude with an illustration on a prostate cancer data set and by pointing out some directions for future research.

Background Imaging features derived from MRI scans can be used for not only breast cancer detection and measuring disease extent, but can also determine gene expression and patient outcomes. The relationships between imaging features, gene/protein expression, and response to therapy hold potential to guide personalized medicine. We aim to characterize the relationship between radiologist-annotated tumor phenotypic features (based on MRI) and the underlying biological processes (based on proteomic profiling) in the tumor. Methods Multiple-response regression of the image-derived, radiologist-scored features with reverse-phase protein array expression levels generated association coefficients for each combination of image-feature and protein in the RPPA dataset. Significantly-associated proteins for features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis determined which features were most strongly correlated with pathway activity and cellular functions. Results Each of the twenty-nine imaging features was found to have a set of significantly correlated molecules, associated biological functions, and pathways. Conclusions We interrogated the pathway alterations represented by the protein expression associated with each imaging feature. Our study demonstrates the relationships between biological processes (via proteomic measurements) and MRI features within breast tumors.

OBJECTIVES: The purpose was to evaluate nutritional status, food security, and related health factors of older adults who were overweight or obese compared to those with a healthy weight. METHODS: Data from 2969 adults aged ≥60 years from NHANES 2011–2014 were analyzed. Participants were categorized by sex and body weight status as healthy weight (body mass index (BMI, kg/m(2)) 18.5–24.9), overweight (BMI 25–29.9), or obese (BMI ≥ 30); underweight individuals were excluded. Healthy Eating Index (HEI)-2015 scores and total usual micronutrient intakes from foods and dietary supplements were estimated using two 24-hour dietary recalls and the National Cancer Institute method. Nutritional biomarkers, including serum vitamin D, vitamin B-12, and methylmalonic acid, and cardiometabolic risk factors were also assessed. RESULTS: A substantial proportion of older adults (>30%) had intakes below Estimated Average Requirements (EAR) for calcium, magnesium, and vitamins C and D even with dietary supplements. Men and women with obesity had a higher prevalence of usual magnesium intakes

We consider the problem of feature selection in a high-dimensional multiple predictors, multiple responses regression setting. Assuming that regression errors are i.i.d. when they are in fact dependent leads to inconsistent and inefficient feature estimates. We relax the i.i.d. assumption by allowing the errors to exhibit a tree-structured dependence. This allows a Bayesian problem formulation with the error dependence structure treated as an auxiliary variable that can be integrated out analytically with the help of the matrix-tree theorem. Mixing over trees results in a flexible technique for modelling the graphical structure for the regression errors. Furthermore, the analytic integration results in a collapsed Gibbs sampler for feature selection that is computationally efficient. Our approach offers significant performance gains over the competing methods in simulations, especially when the features themselves are correlated. In addition to comprehensive simulation studies, we apply our method to a high-dimensional breast cancer data set to identify markers significantly associated with the disease. Copyright © 2014 John Wiley & Sons, Ltd.