Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates.

Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.

Mending the gap: Measurement needs to address policy implementation through a health equity lens.

Policies represent a key opportunity to improve the health outcomes of populations, and if implemented well, can reduce disparities affecting marginalized populations. Many policies are only evaluated on whether they elicit their intended health outcome. However, a lack of understanding regarding if and how they are implemented may hinder the intended impact overall and on addressing health disparities. Implementation science offers an array of frameworks and methodological approaches for assessing policy delivery, yet few examples exist that meaningfully include health equity as a core focus. This commentary describes the importance of equity-informed implementation measurement by providing case examples and implications for assessment. In addition, we highlight examples of emerging work in policy implementation grounded in health equity with suggested steps for moving the field forward. The ultimate goal is to move toward open-access measurement approaches that can be adapted to study implementation of a variety of policies at different stages of implementation, driven by input from marginalized populations and implementation practitioners, to move the needle on addressing health disparities.

This article talks about the need to include health equity as a major focus when understanding if and how policies are being implemented. We talk about gaps in the implementation science field and how equity-informed measurement tools can help to bridge this gap. Finally, we give some examples of efforts in place and where others can add to the growing resources to improve policy delivery.

Widespread and increasing near-bottom hypoxia in the coastal ocean off the United States Pacific Northwest.

The 2021 summer upwelling season off the United States Pacific Northwest coast was unusually strong leading to widespread near-bottom, low-oxygen waters. During summer 2021, an unprecedented number of ship- and underwater glider-based measurements of dissolved oxygen were made in this region. Near-bottom hypoxia, that is dissolved oxygen less than 61 µmol kg-1 and harmful to marine animals, was observed over nearly half of the continental shelf inshore of the 200-m isobath, covering 15,500 square kilometers. A mid-shelf ribbon with near-bottom, dissolved oxygen less than 50 µmol kg-1 extended for 450 km off north-central Oregon and Washington. Spatial patterns in near-bottom oxygen are related to the continental shelf width and other features of the region. Maps of near-bottom oxygen since 1950 show a consistent trend toward lower oxygen levels over time. The fraction of near-bottom water inshore of the 200-m isobath that is hypoxic on average during the summer upwelling season increases over time from nearly absent (2%) in 1950-1980, to 24% in 2009-2018, compared with 56% during the anomalously strong upwelling conditions in 2021. Widespread and increasing near-bottom hypoxia is consistent with increased upwelling-favorable wind forcing under climate change.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.

BACKGROUND: Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters. METHODS: We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships. RESULTS: We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community. CONCLUSIONS: Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

An updated end-to-end ecosystem model of the Northern California Current reflecting ecosystem changes due to recent marine heatwaves.

The Northern California Current is a highly productive marine upwelling ecosystem that is economically and ecologically important. It is home to both commercially harvested species and those that are federally listed under the U.S. Endangered Species Act. Recently, there has been a global shift from single-species fisheries management to ecosystem-based fisheries management, which acknowledges that more complex dynamics can reverberate through a food web. Here, we have integrated new research into an end-to-end ecosystem model (i.e., physics to fisheries) using data from long-term ocean surveys, phytoplankton satellite imagery paired with a vertically generalized production model, a recently assembled diet database, fishery catch information, species distribution models, and existing literature. This spatially-explicit model includes 90 living and detrital functional groups ranging from phytoplankton, krill, and forage fish to salmon, seabirds, and marine mammals, and nine fisheries that occur off the coast of Washington, Oregon, and Northern California. This model was updated from previous regional models to account for more recent changes in the Northern California Current (e.g., increases in market squid and some gelatinous zooplankton such as pyrosomes and salps), to expand the previous domain to increase the spatial resolution, to include data from previously unincorporated surveys, and to add improved characterization of endangered species, such as Chinook salmon (Oncorhynchus tshawytscha) and southern resident killer whales (Orcinus orca). Our model is mass-balanced, ecologically plausible, without extinctions, and stable over 150-year simulations. Ammonium and nitrate availability, total primary production rates, and model-derived phytoplankton time series are within realistic ranges. As we move towards holistic ecosystem-based fisheries management, we must continue to openly and collaboratively integrate our disparate datasets and collective knowledge to solve the intricate problems we face. As a tool for future research, we provide the data and code to use our ecosystem model.

Poor Diagnostic Reproducibility in the Identification of Nonconventional Dysplasia in Colitis Impacts the Application of Histologic Stratification Tools.

Due to their increased cancer risk, patients with longstanding inflammatory bowel disease are offered endoscopic surveillance with concomitant histopathologic assessments, aimed at identifying dysplasia as a precursor lesion of colitis-associated colorectal cancer. However, this strategy is beset with difficulties and limitations. Recently, a novel classification criterion for colitis-associated low-grade dysplasia has been proposed, and an association between nonconventional dysplasia and progression was reported, suggesting the possibility of histology-based stratification of patients with colitis-associated lesions. Here, a cohort of colitis-associated lesions was assessed by a panel of 6 experienced pathologists to test the applicability of the published classification criteria and try and validate the association between nonconventional dysplasia and progression. While confirming the presence of different morphologic patterns of colitis-associated dysplasia, the study demonstrated difficulties concerning diagnostic reproducibility between pathologists and was unable to validate the association of nonconventional dysplasia with cancer progression. Our study highlights the overall difficulty of using histologic assessment of precursor lesions for cancer risk prediction in inflammatory bowel disease patients and suggests the need for a different diagnostic strategy that can objectively identify high-risk phenotypes.

Computational Advancements in Cancer Combination Therapy Prediction.

Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.

Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.

BACKGROUND: Cancer is a complex disease that is the second leading cause of death in the United States. Despite research efforts, the ability to manage cancer and select optimal therapeutic responses for each patient remains elusive. Chromosomal instability (CIN) is primarily a product of segregation errors wherein one or many chromosomes, in part or whole, vary in number. CIN is an enabling characteristic of cancer, contributes to tumor-cell heterogeneity, and plays a crucial role in the multistep tumorigenesis process, especially in tumor growth and initiation and in response to treatment. AIMS: Multiple studies have reported different metrics for analyzing copy number aberrations as surrogates of CIN from DNA copy number variation data. However, these metrics differ in how they are calculated with respect to the type of variation, the magnitude of change, and the inclusion of breakpoints. Here we compared metrics capturing CIN as either numerical aberrations, structural aberrations, or a combination of the two across 33 cancer data sets from The Cancer Genome Atlas (TCGA). METHODS AND RESULTS: Using CIN inferred by methods in the CINmetrics R package, we evaluated how six copy number CIN surrogates compared across TCGA cohorts by assessing each across tumor types, as well as how they associate with tumor stage, metastasis, and nodal involvement, and with respect to patient sex. CONCLUSIONS: We found that the tumor type impacts how well any two given CIN metrics correlate. While we also identified overlap between metrics regarding their association with clinical characteristics and patient sex, there was not complete agreement between metrics. We identified several cases where only one CIN metric was significantly associated with a clinical characteristic or patient sex for a given tumor type. Therefore, caution should be used when describing CIN based on a given metric or comparing it to other studies.

Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico.

BACKGROUND: Preclinical models like cancer cell lines and patient-derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have demonstrated that preclinical models do not recapitulate all biological aspects of human tissues, particularly with respect to the tissue of origin gene expression signatures. Therefore, it is critical to assess how well preclinical model gene expression profiles correlate with human cancer tissues to inform preclinical model selection and data analysis decisions. AIMS: Here we evaluated how well preclinical models recapitulate human cancer and non-diseased tissue gene expression patterns in silico with respect to the full gene expression profile as well as subsetting by the most variable genes, genes significantly correlated with tumor purity, and tissue-specific genes. METHODS: By using publicly available gene expression profiles across multiple sources, we evaluated cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico. RESULTS: We found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that glioblastoma (GBM) PDX global gene expression correlation to GBM tumor global gene expression outperforms GBM cell line to GBM tumor global gene expression correlations, and demonstrated that preclinical models in our study often failed to reproduce tissue-specific expression. While including additional genes for global gene expression comparison between cell lines and tissues decreases the overall correlation, it improves the relative rank between a cell line and its tissue of origin compared to other tissues. Our findings underscore the importance of using the full gene expression set measured when comparing preclinical models and tissues and confirm that tissue-specific patterns are better preserved in GBM PDX models than in GBM cell lines. CONCLUSION: Future studies can build on these findings to determine the specific pathways and gene sets recapitulated by particular preclinical models to facilitate model selection for a given study design or goal.

Snack frequency, size, and energy density are associated with diet quality among US adolescents.

OBJECTIVE: To evaluate snacking and diet quality among US adolescents. DESIGN: Cross-sectional analysis examined snack frequency (snacks/day), size (kcal/snack) and energy density (kcal/g/snack) as predictors of diet quality using the mean of two 24-h dietary recalls. Diet quality was assessed using the Healthy Eating Index (HEI-2015, 0-100), a mean adequacy ratio (MAR, 0-100) for under-consumed nutrients (potassium, fibre, Ca, vitamin D) and mean percentage of recommended limits for over-consumed nutrients (added sugar, saturated fat, Na). Linear regression models examined total snacks, food only snacks and beverage only snacks, as predictors of diet quality adjusting for demographic characteristics and estimated energy reporting accuracy. SETTING: 2007-2018 National Health and Nutrition Examination Survey. PARTICIPANTS: Adolescents 12-19 years (n 4985). RESULTS: Snack frequency was associated with higher HEI-2015 (ß = 0·7 (0·3), P < 0·05) but also with higher intake of over-consumed nutrients (ß = 3·0 (0·8), P ≤ 0·001). Snack size was associated with lower HEI (ß = -0·005 (0·001), P ≤ 0·001) and MAR (ß = -0·005 (0·002), P < 0·05) and higher intake of over-consumed nutrients (ß = 0·03 (0·005), P ≤ 0·001). Associations differed for food only and beverage only snacks. Food only snack frequency was associated with higher HEI-2015 (ß = 1·7 (0·03), P ≤ 0·001), while food only snack size (ß = -0·006 (0·0009), P ≤ 0·001) and food only snack energy density (ß = -1·1 (0·2), P ≤ 0·001) were associated with lower HEI-2015. Conversely, beverage only snack frequency (ß = 4·4 (2·1) P < 0·05) and beverage only snack size (ß = 0·03 (0·01), P ≤ 0·001) were associated with higher intake of over-consumed nutrients. CONCLUSIONS: Smaller, frequent, less energy-dense food only snacks are associated with higher diet quality in adolescents; beverages consumed as snacks are associated with greater intake of over-consumed nutrients.

Age-related differences in eating location, food source location, and timing of snack intake among U.S. children 1-19 years.

BACKGROUND: Snacking is nearly universal and contributes significant energy to U.S. children's diets. Little is known, however, about where and when snacking intake occurs and if such patterns change with age. This research evaluated age-related differences in eating location, food source location, and timing of snacking among U.S. children aged 1-19 years (y). METHODS: A cross-sectional analysis of snacking among 14,666 children in the 2007-2018 U.S. National Health and Nutrition Examination Survey was conducted using a single 24-hour dietary recall. Snacking was participant-defined and included all eating occasions outside of meals. Linear regression and analysis of covariance were used to examine the effects of age (toddler 1-2 y, preschooler 3-5 y, school-age 6-11 y, adolescent 12-19 y) on the percentage of daily snack energy consumed by eating location (at home vs. away from home), food source location (grocery store, convenience store, school/childcare, restaurant, from someone else (i.e. "socially"), and other), and time of day (morning, 6am-12pm; early afternoon, 12pm-3pm; late afternoon/afterschool 3pm-6pm; evening 6pm-9pm, late-night 9pm-12am, and overnight 12am-6am). RESULTS: On average, U.S. children consumed most of their daily snacking energy at home (71%), from foods and beverages obtained from grocery stores (75%), and in the late afternoon/afterschool (31%). Toddlers and preschoolers consumed a greater percentage of their daily snack energy during the morning hours compared to school-age children and adolescents (both p < 0.001); school-age children consumed the most in the evening (27%, p < 0.001), and adolescents consumed the most in the late-night period (22%, p < 0.001). Age-related increases were seen in the percentage of daily snacking energy eaten outside the home (p < 0.001), and obtained socially (p < 0.001), from restaurants (p < 0.001), and convenience stores (p < 0.001). CONCLUSION: Findings reveal age-related differences in eating location, food source location, and timing of snack intake among U.S. children aged 1-19 y. Younger children consume a greater percentage of snacking calories in the morning and at home relative to older children. School-age children and adolescents show greater snacking in the evening and at night and from foods obtained and eaten outside the home. Efforts to promote healthy snacking behaviors among children should consider developmental differences in snacking patterns.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.

Background: Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters. Methods: We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships. Results: We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community. Conclusions: Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.

Background: Cancer is a complex disease that is the second leading cause of death in the United States. Despite research efforts, the ability to manage cancer and select optimal therapeutic responses for each patient remains elusive. Chromosomal instability (CIN) is primarily a product of segregation errors wherein one or many chromosomes, in part or whole, vary in number. CIN is an enabling characteristic of cancer, contributes to tumor-cell heterogeneity, and plays a crucial role in the multistep tumorigenesis process, especially in tumor growth and initiation and in response to treatment. Aims: Multiple studies have reported different metrics for analyzing copy number aberrations as surrogates of CIN from DNA copy number variation data. However, these metrics differ in how they are calculated with respect to the type of variation, the magnitude of change, and the inclusion of breakpoints. Here we compared metrics capturing CIN as either numerical aberrations, structural aberrations, or a combination of the two across 33 cancer data sets from The Cancer Genome Atlas (TCGA). Methods and results: Using CIN inferred by methods in the CINmetrics R package, we evaluated how six copy number CIN surrogates compared across TCGA cohorts by assessing each across tumor types, as well as how they associate with tumor stage, metastasis, and nodal involvement, and with respect to patient sex. Conclusions: We found that the tumor type impacts how well any two given CIN metrics correlate. While we also identified overlap between metrics regarding their association with clinical characteristics and patient sex, there was not complete agreement between metrics. We identified several cases where only one CIN metric was significantly associated with a clinical characteristic or patient sex for a given tumor type. Therefore, caution should be used when describing CIN based on a given metric or comparing it to other studies.

CINmetrics: an R package for analyzing copy number aberrations as a measure of chromosomal instability.

Genomic instability is an important hallmark of cancer and more recently has been identified in others like neurodegenrative diseases. Chromosomal instability, as a measure of genomic instability, has been used to characterize clinical and biological phenotypes associated with these diseases by measuring structural and numerical chromosomal alterations. There have been multiple chromosomal instability scores developed across many studies in the literature; however, these scores have not been compared because of the lack of a single tool available to calculate and facilitate these various metrics. Here, we provide an R package CINmetrics, that calculates six different chromosomal instability scores and allows direct comparison between them. We also demonstrate how these scores differ by applying CINmetrics to breast cancer data from The Cancer Genome Atlas (TCGA). The package is available on CRAN at https://cran.rproject.org/package=CINmetrics and on GitHub at https://github.com/lasseignelab/CINmetrics.

Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse Pkd2 model gene expression reversion.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is one of the most prevalent monogenic human diseases. It is mostly caused by pathogenic variants in PKD1 or PKD2 genes that encode interacting transmembrane proteins polycystin-1 (PC1) and polycystin-2 (PC2). Among many pathogenic processes described in ADPKD, those associated with cAMP signaling, inflammation, and metabolic reprogramming appear to regulate the disease manifestations. Tolvaptan, a vasopressin receptor-2 antagonist that regulates cAMP pathway, is the only FDA-approved ADPKD therapeutic. Tolvaptan reduces renal cyst growth and kidney function loss, but it is not tolerated by many patients and is associated with idiosyncratic liver toxicity. Therefore, additional therapeutic options for ADPKD treatment are needed. METHODS: As drug repurposing of FDA-approved drug candidates can significantly decrease the time and cost associated with traditional drug discovery, we used the computational approach signature reversion to detect inversely related drug response gene expression signatures from the Library of Integrated Network-Based Cellular Signatures (LINCS) database and identified compounds predicted to reverse disease-associated transcriptomic signatures in three publicly available Pkd2 kidney transcriptomic data sets of mouse ADPKD models. We focused on a pre-cystic model for signature reversion, as it was less impacted by confounding secondary disease mechanisms in ADPKD, and then compared the resulting candidates' target differential expression in the two cystic mouse models. We further prioritized these drug candidates based on their known mechanism of action, FDA status, targets, and by functional enrichment analysis. RESULTS: With this in-silico approach, we prioritized 29 unique drug targets differentially expressed in Pkd2 ADPKD cystic models and 16 prioritized drug repurposing candidates that target them, including bromocriptine and mirtazapine, which can be further tested in-vitro and in-vivo. CONCLUSION: Collectively, these results indicate drug targets and repurposing candidates that may effectively treat pre-cystic as well as cystic ADPKD.

Evaluating cancer cell line and patient-derived xenograft recapitulation of tumor and non-diseased tissue gene expression profiles in silico.

Preclinical models like cancer cell lines and patient-derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have demonstrated that preclinical models do not recapitulate all biological aspects of human tissues, particularly with respect to the tissue of origin gene expression signatures. Therefore, it is critical to assess how well preclinical model gene expression profiles correlate with human cancer tissues to inform preclinical model selection and data analysis decisions. Here we evaluated how well preclinical models recapitulate human cancer and non-diseased tissue gene expression patterns in silico with respect to the full gene expression profile as well as subsetting by the most variable genes, genes significantly correlated with tumor purity, and tissue-specific genes by using publicly available gene expression profiles across multiple sources. We found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that GBM PDX global gene expression correlation to GBM tumor global gene expression outperforms GBM cell line to GBM tumor global gene expression correlations, and demonstrated that preclinical models in our study often failed to reproduce tissue-specific expression. While including additional genes for global gene expression comparison between cell lines and tissues decreases the overall correlation, it improves the relative rank between a cell line and its tissue of origin compared to other tissues. Our findings underscore the importance of using the full gene expression set measured when comparing preclinical models and tissues and confirm that tissue-specific patterns are better preserved in GBM PDX models than in GBM cell lines. Future studies can build on these findings to determine the specific pathways and gene sets recapitulated by particular preclinical models to facilitate model selection for a given study design or goal.

Caregiver feeding decisions and sociodemographic characteristics are associated with snack food intake during infancy and toddlerhood.

Snacking starts early in childhood, yet little is known about child versus family influences on snacking during infancy and toddlerhood. This secondary analysis of baseline data examined associations of child characteristics (e.g., appetitive traits, temperament), caregiver feeding decisions, and sociodemographic characteristics with the mean frequency of (times/day) and mean energy from (kcal/day) child snack food intake. Caregivers and their children (ages 9-15 months) were recruited in Buffalo, NY from 2017 to 2019. Caregivers reported on sociodemographics, child appetitive traits (Baby Eating Behaviour Questionnaire), and child temperament (Infant Behavior Questionnaire-Revised). Three 24-h dietary recalls were collected, and USDA food categories were used to categorize snack foods (e.g., cookies, chips, and puffs). Hierarchical multiple linear regression models examined associations of child characteristics (Step 1: age, sex, baseline weight-for-length z-score, appetitive traits, and temperament), caregiver feeding decisions (Step 2: breastfeeding duration and age of solid food introduction), and caregiver sociodemographic characteristics (Step 3: caregiver age, prepregnancy BMI, education, and household size) with mean child snack food intake. Caregivers (n = 141) were on average 32.6 years of age, predominantly white (89.1%), and college-educated (84.2%). Age of solid food introduction (B = -0.21, p = 0.03), prepregnancy BMI (B = 0.03, p = 0.04), and household size (B = 0.23, p = 0.02) were significantly associated with the mean frequency of (times/day) snack food intake, over and above other variables of interest. Child age (B = 15.96, p = 0.002) was significantly associated with mean energy from (kcal/day) snack food intake. Household size (B = 28.51, p = 0.006) was significantly associated with mean energy from (kcal/day) snack food intake, over and above other variables of interest. There were no significant associations of other child characteristics with snack food intake. Findings show that child snack food intake is more closely associated with caregiver feeding decisions and sociodemographic characteristics than child characteristics. TRIAL REGISTRATION: National Institute on Child Health and Human Development, Grant/Award Number R01HD087082-01.

Child Weight Status: The Role of Feeding Styles and Highly Motivated Eating in Children.

Although parental feeding plays an important role in child eating and weight status, high food motivation among children may also be a factor shaping how feeding impacts child weight. This study explored whether individual differences in preschool children's food motivation interacted with mothers' feeding styles in predicting subsequent child weight status. Participants included 129 Hispanic Head Start mother/child dyads. Data were collected at ages 4-5 years (Time 1) and 7-9 (Time 3). Staff measured heights/weights and observed children in an eating in the absence of hunger task. Mothers reported on feeding styles/practices and children's eating behaviors. A principal components analysis derived a measure of highly motivated eating in children. Multiple regressions predicted Time 3 child BMI z-scores. Time 3 BMI z-scores were positively predicted by authoritative and indulgent feeding styles and negatively predicted by monitoring. Since feeding style interacted with highly motivated eating, separate regressions were run for high and low food motivation in children. Unexpectedly, results showed that authoritative feeding positively predicted Time 3 child BMI z-scores only for children showing low levels of food motivation. Characterizing differential parental feeding and child eating phenotypes may assist in tailoring childhood obesity prevention programs for the target populations.

Association of Snacking Frequency, Size, and Energy Density with Weight Status among Preschool-Aged Children in the United States.

BACKGROUND: Snacking (ie, eating between meals) is common among US preschool-aged children, but associations with weight status are unclear. OBJECTIVE: This research evaluated associations of snack frequency, size, and energy density as well as the percent of daily energy from snacking with weight status and sociodemographic characteristics among US children aged 2 to 5 years. DESIGN: Cross-sectional analysis of 2007-2018 National Health and Nutrition Examination Survey data using two, caregiver proxy, 24-hour dietary recalls. PARTICIPANTS/SETTING: US children aged 2 to 5 years (n = 3,313) with at least one snack occasion over 2 days of intake. MAIN OUTCOME MEASURES: Snacking parameters included frequency (number of occasions per day), size (kilocalories per occasion), and energy density (kilocalories per gram per occasion) as well as percent of daily energy from snacking. STATISTICAL ANALYSES: Generalized linear regression models evaluated associations of snacking with child weight status (ie, normal weight and overweight/obesity), adjusting for survey weights, energy misreporting, mean meal size, and sociodemographic covariates. RESULTS: Children with overweight/obesity consumed more frequent snacks (2.8 [0.06] vs 2.5 [0.03] snacks/day, respectively; P < 0.001), larger snacks (188 [4] vs 162 [23] kcal/occasion, respectively; P < 0.001), and a greater percent of daily energy from snacking (29.80% [1.00%] vs 26.09% [0.40%], respectively; P < 0.001) than children with normal weight. Mean snack frequency and size as well as percentage of daily energy from snacking varied with child age, gender, and head of household education. Associations of snacking with child race and ethnicity were less consistent. CONCLUSIONS: These nationally representative findings provide evidence that the consumption of larger, more frequent snacks is associated with overweight/obesity among US children aged 2 to 5 years and snacking varies by sociodemographic characteristics.

Association of weight status with the types of foods consumed at snacking occasions among US adolescents.

OBJECTIVE: This study aimed to evaluate snack food-group composition by weight status among United States adolescents. METHODS: Cross-sectional analysis of adolescent food-group-component intake from snacking occasions using two 24-hour dietary recalls from the 2007 through 2018 National Health and Nutrition Examination Survey (NHANES; n = 5264; 12-19 years) was conducted. ANCOVA models evaluated food intakes by BMI percentile (BMI%; normal weight [NW]: <85th BMI%; overweight [OW]: 85th-95th BMI%; and obesity [OB]: ≥95th BMI%), adjusting for energy misreporting and key covariates. RESULTS: Adolescents with OB consumed greater total daily energy from snacks (mean [SE]: NW = 424 [10] kcal; OW = 527 [16] kcal; OB = 603 [22] kcal; p < 0.001) than adolescents with OW and NW. Adolescents with OW or OB consumed higher amounts of refined grains, dairy, protein, oil, solid fat, and added sugar from snacks than adolescents with NW (p < 0.05-0.001). CONCLUSIONS: Adolescents with OW or OB consume more calories and higher levels of overconsumed dietary components, i.e., added sugar, solid fats, and refined grains, from snacks than adolescents with NW. Age-specific snacking recommendations to inform dietary guidance are needed to prevent excess intake of overconsumed nutrients and calories.