The spike-and-slab lasso and scalable algorithm to accommodate multinomial outcomes in variable selection problems.

Spike-and-slab prior distributions are used to impose variable selection in Bayesian regression-style problems with many possible predictors. These priors are a mixture of two zero-centered distributions with differing variances, resulting in different shrinkage levels on parameter estimates based on whether they are relevant to the outcome. The spike-and-slab lasso assigns mixtures of double exponential distributions as priors for the parameters. This framework was initially developed for linear models, later developed for generalized linear models, and shown to perform well in scenarios requiring sparse solutions. Standard formulations of generalized linear models cannot immediately accommodate categorical outcomes with > 2 categories, i.e. multinomial outcomes, and require modifications to model specification and parameter estimation. Such modifications are relatively straightforward in a Classical setting but require additional theoretical and computational considerations in Bayesian settings, which can depend on the choice of prior distributions for the parameters of interest. While previous developments of the spike-and-slab lasso focused on continuous, count, and/or binary outcomes, we generalize the spike-and-slab lasso to accommodate multinomial outcomes, developing both the theoretical basis for the model and an expectation-maximization algorithm to fit the model. To our knowledge, this is the first generalization of the spike-and-slab lasso to allow for multinomial outcomes.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis.

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

Cardiometabolic Disease Staging and Major Adverse Cardiovascular Event Prediction in 2 Prospective Cohorts.

BACKGROUND: Cardiometabolic risk prediction models that incorporate metabolic syndrome traits to predict cardiovascular outcomes may help identify high-risk populations early in the progression of cardiometabolic disease. OBJECTIVES: The purpose of this study was to examine whether a modified cardiometabolic disease staging (CMDS) system, a validated diabetes prediction model, predicts major adverse cardiovascular events (MACE). METHODS: We developed a predictive model using data accessible in clinical practice [fasting glucose, blood pressure, body mass index, cholesterol, triglycerides, smoking status, diabetes status, hypertension medication use] from the REGARDS (REasons for Geographic And Racial Differences in Stroke) study to predict MACE [cardiovascular death, nonfatal myocardial infarction, and/or nonfatal stroke]. Predictive performance was assessed using receiver operating characteristic curves, mean squared errors, misclassification, and area under the curve (AUC) statistics. RESULTS: Among 20,234 REGARDS participants with no history of stroke or myocardial infarction (mean age 64 ± 9.3 years, 58% female, 41% non-Hispanic Black, and 18% diabetes), 2,695 developed incident MACE (13.3%) during a median 10-year follow-up. The CMDS development model in REGARDS for MACE had an AUC of 0.721. Our CMDS model performed similarly to both the ACC/AHA 10-year risk estimate (AUC 0.721 vs 0.716) and the Framingham risk score (AUC 0.673). CONCLUSIONS: The CMDS predicted the onset of MACE with good predictive ability and performed similarly or better than 2 commonly known cardiovascular disease prediction risk tools. These data underscore the importance of insulin resistance as a cardiovascular disease risk factor and that CMDS can be used to identify individuals at high risk for progression to cardiovascular disease.

Bayesian compositional models for ordinal response.

Ordinal response is commonly found in medicine, biology, and other fields. In many situations, the predictors for this ordinal response are compositional, which means that the sum of predictors for each sample is fixed. Examples of compositional data include the relative abundance of species in microbiome data and the relative frequency of nutrition concentrations. Moreover, the predictors that are strongly correlated tend to have similar influence on the response outcome. Conventional cumulative logistic regression models for ordinal responses ignore the fixed-sum constraint on predictors and their associated interrelationships, and thus are not appropriate for analyzing compositional predictors.To solve this problem, we proposed Bayesian Compositional Models for Ordinal Response to analyze the relationship between compositional data and an ordinal response with a structured regularized horseshoe prior for the compositional coefficients and a soft sum-to-zero restriction on coefficients through the prior distribution. The method was implemented with R package rstan using efficient Hamiltonian Monte Carlo algorithm. We performed simulations to compare the proposed approach and existing methods for ordinal responses. Results revealed that our proposed method outperformed the existing methods in terms of parameter estimation and prediction. We also applied the proposed method to a microbiome study HMP2Data, to find microorganisms linked to ordinal inflammatory bowel disease levels. To make this work reproducible, the code and data used in this paper are available at https://github.com/Li-Zhang28/BCO.

Bayesian compositional generalized linear models for analyzing microbiome data.

The crucial impact of the microbiome on human health and disease has gained significant scientific attention. Researchers seek to connect microbiome features with health conditions, aiming to predict diseases and develop personalized medicine strategies. However, the practicality of conventional models is restricted due to important aspects of microbiome data. Specifically, the data observed is compositional, as the counts within each sample are bound by a fixed-sum constraint. Moreover, microbiome data often exhibits high dimensionality, wherein the number of variables surpasses the available samples. In addition, microbiome features exhibiting phenotypical similarity usually have similar influence on the response variable. To address the challenges posed by these aspects of the data structure, we proposed Bayesian compositional generalized linear models for analyzing microbiome data (BCGLM) with a structured regularized horseshoe prior for the compositional coefficients and a soft sum-to-zero restriction on coefficients through the prior distribution. We fitted the proposed models using Markov Chain Monte Carlo (MCMC) algorithms with R package rstan. The performance of the proposed method was assessed by extensive simulation studies. The simulation results show that our approach outperforms existing methods with higher accuracy of coefficient estimates and lower prediction error. We also applied the proposed method to microbiome study to find microorganisms linked to inflammatory bowel disease (IBD). To make this work reproducible, the code and data used in this article are available at https://github.com/Li-Zhang28/BCGLM.

Gut microbiota impacts bone via Bacteroides vulgatus-valeric acid-related pathways.

Although the gut microbiota has been reported to influence osteoporosis risk, the individual species involved, and underlying mechanisms, remain largely unknown. We performed integrative analyses in a Chinese cohort of peri-/post-menopausal women with metagenomics/targeted metabolomics/whole-genome sequencing to identify novel microbiome-related biomarkers for bone health. Bacteroides vulgatus was found to be negatively associated with bone mineral density (BMD), which was validated in US white people. Serum valeric acid (VA), a microbiota derived metabolite, was positively associated with BMD and causally downregulated by B. vulgatus. Ovariectomized mice fed B. vulgatus demonstrated increased bone resorption and poorer bone micro-structure, while those fed VA demonstrated reduced bone resorption and better bone micro-structure. VA suppressed RELA protein production (pro-inflammatory), and enhanced IL10 mRNA expression (anti-inflammatory), leading to suppressed maturation of osteoclast-like cells and enhanced maturation of osteoblasts in vitro. The findings suggest that B. vulgatus and VA may represent promising targets for osteoporosis prevention/treatment.

Predicting body fat percentage at 36 weeks of postmenstrual age in infants born preterm: A diagnostic accuracy study.

BACKGROUND: Current standards for assessing body composition can be costly and technically challenging. There is a need for a predictive equation that combines multiple clinical and anthropometric factors to predictbody composition outcomes at 36 weeks of postmenstrual age (PMA) or discharge. METHODS: To develop a widely applicable equation that predicts body fat percentage in preterm infants, we analyzed anthropometric data collected prospectively from a cohort of infants born very preterm between 2017 and 2018. We integrated clinical variables significantly associated with adiposity into a predictive equation using Bayesian linear regression models and leave-one-out cross-validation. RESULTS: We analyzed data from 86 infants born at 32 weeks of gestation or less (median gestational age, 30 weeks; mean birthweight, 1471 ± 270 g). Weight gain and increase in length per week from birth to 36 weeks of PMA, midarm circumference at 36 weeks of PMA, male sex, and higher enteral fluid intake (>180 ml/kg/day) were the strongest predictors of body fat percentage in the model with the highest predictive value (R2 = 0.65). The correlation between actual and predicted body fat percentage using this Bayesian model was high (r = 0.82). CONCLUSIONS: Weight gain and increase in length per week from birth to 36 weeks of PMA, midarm circumference at 36 weeks of PMA, male sex, and enteral fluid intake are significant predictors of body fat percentage at 36 weeks of PMA in very preterm infants.

PAI-1 Regulation of p53 Expression and Senescence in Type II Alveolar Epithelial Cells.

Cellular senescence contributes importantly to aging and aging-related diseases, including idiopathic pulmonary fibrosis (IPF). Alveolar epithelial type II (ATII) cells are progenitors of alveolar epithelium, and ATII cell senescence is evident in IPF. Previous studies from this lab have shown that increased expression of plasminogen activator inhibitor 1 (PAI-1), a serine protease inhibitor, promotes ATII cell senescence through inducing p53, a master cell cycle repressor, and activating p53-p21-pRb cell cycle repression pathway. In this study, we further show that PAI-1 binds to proteasome components and inhibits proteasome activity and p53 degradation in human lung epithelial A549 cells and primary mouse ATII cells. This is associated with a senescence phenotype of these cells, manifested as increased p53 and p21 expression, decreased phosphorylated retinoblastoma protein (pRb), and increased senescence-associated beta-galactose (SA-ß-gal) activity. Moreover, we find that, although overexpression of wild-type PAI-1 (wtPAI-1) or a secretion-deficient, mature form of PAI-1 (sdPAI-1) alone induces ATII cell senescence (increases SA-ß-gal activity), only wtPAI-1 induces p53, suggesting that the premature form of PAI-1 is required for the interaction with the proteasome. In summary, our data indicate that PAI-1 can bind to proteasome components and thus inhibit proteasome activity and p53 degradation in ATII cells. As p53 is a master cell cycle repressor and PAI-1 expression is increased in many senescent cells, the results from this study will have a significant impact not only on ATII cell senescence/lung fibrosis but also on the senescence of other types of cells in different diseases.

Factors Affecting the Gut Microbiome in Pediatric Intestinal Failure.

BACKGROUND: There is little data on gut microbiome and various factors that lead to dysbiosis in pediatric intestinal failure (PIF). This study aimed to characterize gut microbiome in PIF and determine factors that may affect microbial composition in these patients. METHODS: This is a single-center, prospective cohort study of children with PIF followed at our intestinal rehabilitation program. Stool samples were collected longitudinally at regular intervals over a 1-year period. Medical records were reviewed, and demographic and clinical data were collected. Medication history including the use of acid blockers, scheduled prophylactic antibiotics, and bile acid sequestrants was obtained. Gut microbial diversity among patients was assessed and compared according to various host characteristics of interest. RESULTS: The final analysis included 74 specimens from 12 subjects. Scheduled prophylactic antibiotics, presence of central line associated bloodstream infection (CLABSI) at the time of specimen collection, use of acid blockers, and ≥50% calories delivered via parenteral nutrition (PN) was associated with reduced alpha diversity, whereas increasing age was associated with improved alpha diversity at various microbial levels ( P value <0.05). Beta diversity differed with age, presence of CLABSI, use of scheduled antibiotics, acid blockers, percent calories via PN, and presence of oral feeds at various microbial levels ( P value <0.05). Single taxon analysis identified several taxa at several microbial levels, which were significantly associated with various host characteristics. CONCLUSION: Gut microbial diversity in PIF subjects is influenced by various factors involved in the rehabilitation process including medications, percent calories received parenterally, CLABSI events, the degree of oral feeding, and age. Additional investigation performed across multiple centers is needed to further understand the impact of these findings on important clinical outcomes in PIF.

Birth weight moderates the association between obesity and mortality rate.

PURPOSE: The strength of the association between obesity and mortality rate (MR) varies by body mass index (BMI) and sociodemographic groups. We test the hypothesis that the association between obesity and MR varies, in part, due to the moderating effect of parental BMI and birth weight. METHODS: Data come from the 1958 National Child Development Study, an ongoing longitudinal dataset initiated in 1958 with baseline measures of birth weight from 18,059 infants born in Great Britain over 1 week. We tested whether the association between BMI and MR was moderated by parental BMI and birth weight using generalized additive proportional hazards models. RESULTS: The association between adult BMI and MR was moderated by birth weight and maternal BMI, such that the association between BMI and MR was weaker among individuals with a higher birth weight (P = .0148) and stronger among individuals born to mothers with a higher BMI (P = .032). At any given level of BMI approximately greater than 25, individuals with low birth weight or born to mothers with a higher BMI, had a higher MR. Paternal BMI did not significantly modify the relationship between BMI and MR (P = .5168). CONCLUSIONS: Results suggest that the relationship between obesity and MR is modified by birth weight and maternal BMI.

Correction: Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: A nationwide cohort and modeling study.

[This corrects the article DOI: 10.1371/journal.pmed.1003232.].

Rationale and study protocol for a randomized controlled feeding study to determine the structural- and functional-level effects of diet-specific interventions on the gut microbiota of non-Hispanic black and white adults.

BACKGROUND: Colorectal cancer (CRC), the third leading cause of cancer-related deaths in the US, has been associated with an overrepresentation or paucity of several microbial taxa in the gut microbiota, but causality has not been established. Black men and women have among the highest CRC incidence and mortality rates of any racial/ethnic group. This study will examine the impact of the Dietary Approaches to Stop Hypertension (DASH) diet on gut microbiota and fecal metabolites associated with CRC risk. METHODS: A generally healthy sample of non-Hispanic Black and white adults (n = 112) is being recruited to participate in a parallel-arm randomized controlled feeding study. Participants are randomized to receive the DASH diet or a standard American diet for a 28-day period. Fecal samples are collected weekly throughout the study to analyze changes in the gut microbiota using 16 s rRNA and selected metagenomics. Differences in bacterial alpha and beta diversity and taxa that have been associated with CRC (Bacteroides, Fusobacterium, Clostridium, Lactobacillus, Bifidobacterium, Ruminococcus, Porphyromonas, Succinivibrio) are being evaluated. Covariate measures include body mass index, comorbidities, medication history, physical activity, stress, and demographic characteristics. CONCLUSION: Our findings will provide preliminary evidence for the DASH diet as an approach for cultivating a healthier gut microbiota across non-Hispanic Black and non-Hispanic White adults. These results can impact clinical, translational, and population-level approaches for modification of the gut microbiota to reduce risk of chronic diseases including CRC. TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov, identifier NCT04538482, on September 4, 2020 (https://clinicaltrials.gov/ct2/show/NCT04538482).

Mother-child cardiometabolic health 4-10 years after pregnancy complicated by obesity with and without gestational diabetes.

Objective: Obesity in pregnancy and gestational diabetes (GDM) increase cardiometabolic disease risk but are difficult to disentangle. This study aimed to test the hypothesis that 4-10 years after a pregnancy complicated by overweight/obesity and GDM (OB-GDM), women and children would have greater adiposity and poorer cardiometabolic health than those with overweight/obesity (OB) or normal weight (NW) and no GDM during the index pregnancy. Methods: In this cross-sectional study, mother-child dyads were stratified into three groups based on maternal health status during pregnancy (OB-GDM = 67; OB = 76; NW = 76). Weight, height, waist and hip circumferences, and blood pressure were measured, along with fasting glucose, insulin, HbA1c, lipids, adipokines, and cytokines. Results: Women in the OB and OB-GDM groups had greater current adiposity and poorer cardiometabolic health outcomes than those in the NW group (p < 0.05). After adjusting for current adiposity, women in the OB-GDM group had higher HbA1c, glucose, HOMA-IR and triglycerides than NW and OB groups (p < 0.05). Among children, adiposity was greater in the OB-GDM versus NW group (p < 0.05), but other indices of cardiometabolic health did not differ. Conclusions: Poor cardiometabolic health in women with prior GDM is independent of current adiposity. Although greater adiposity among children exposed to GDM is evident at 4-10 years, differences in cardiometabolic health may not emerge until later.

Maternal soybean diet on prevention of obesity-related breast cancer through early-life gut microbiome and epigenetic regulation.

Overnutrition-induced obesity and metabolic dysregulation are considered major risk factors contributing to breast cancer. The origin of both obesity and breast cancer can retrospect to early development in human lifespan. Genistein (GE), a natural isoflavone enriched in soybean products, has been proposed to associate with a lower risk of breast cancer and various metabolic disorders. Our study aimed to determine the effects of maternal exposure to soybean dietary GE on prevention of overnutrition-induced breast cancer later in life and explore potential mechanisms in different mouse models. Our results showed that maternal dietary GE treatment improved offspring metabolic functions by significantly attenuating high-fat diet-induced body fat accumulation, lipid panel abnormalities and glucose intolerance in mice offspring. Importantly, maternal dietary GE exposure effectively delayed high-fat diet-simulated mammary tumor development in female offspring. Mechanistically, we found that maternal dietary GE may exert its chemopreventive effects through affecting essential regulatory gene expression in control of metabolism, inflammation and tumor development via, at least in part, regulation of offspring gut microbiome, bacterial metabolites and epigenetic profiles. Altogether, our findings indicate that maternal GE consumption is an effective intervention approach leading to early-life prevention of obesity-related metabolic disorders and breast cancer later in life through dynamically influencing the interplay between early-life gut microbiota, key microbial metabolite profiles and offspring epigenome.

The associations between relative and absolute body mass index with mortality rate based on predictions from stigma theory.

Background: The social consequences of obesity may influence health and mortality rate (MR), given obesity's status as a highly stigmatized condition. Hence, a high absolute body mass index (BMI) in conjunction with the stigmatization of a high BMI may each independently increase the rate of MR. Objectives: We tested whether relative BMI, defined as ordinal rank within a social reference group jointly defined by age, sex, and race/ethnicity, is associated with MR independent of absolute BMI. Methods: Data were from three nationally representative datasets: the Health and Retirement Study (n = 31,115), the National Health Interview Survey (NHIS, n = 529,362), and the National Health and Nutrition Examination Survey (n = 31,115). Relative BMI kg/m2 deciles were calculated within twenty-four subgroups jointly defined by age (6 levels), sex (2 levels), and race/ethnicity (4 levels). The association between ordinal rank BMI and MR was assessed using Cox survival generalized additive models in each dataset with adjustments for age, race, sex, smoking, educational attainment, and absolute BMI. Results: Absolute BMI had a significant non-monotonic association with MR, such that BMI was positively associated with mortality at BMI levels above approximately 25 kg/m2. Contrary to expectations, results from NHIS indicated that individuals in the first decile of relative BMI had the highest MR whereas relative BMI was not associated with MR in the NHANES and HRS. Conclusion: We hypothesized that the stigmatization of obesity might lead to an increased MR after controlling for absolute BMI. Contrary to expectations, a higher relative BMI was not associated with an increased MR independent of absolute BMI.

Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics.

Existing studies suggest that m6A methylation is closely related to the prognosis of cancer. We developed three prognostic models based on m6A-related transcriptomics in lung adenocarcinoma patients and performed external validations. The TCGA-LUAD cohort served as the derivation cohort and six GEO data sets as external validation cohorts. The first model (mRNA model) was developed based on m6A-related mRNA. LASSO and stepwise regression were used to screen genes and the prognostic model was developed from multivariate Cox regression model. The second model (lncRNA model) was constructed based on m6A related lncRNAs. The four steps of random survival forest, LASSO, best subset selection and stepwise regression were used to screen genes and develop a Cox regression prognostic model. The third model combined the risk scores of the first two models with clinical variable. Variables were screened by stepwise regression. The mRNA model included 11 predictors. The internal validation C index was 0.736. The lncRNA model has 15 predictors. The internal validation C index was 0.707. The third model combined the risk scores of the first two models with tumor stage. The internal validation C index was 0.794. In validation sets, all C-indexes of models were about 0.6, and three models had good calibration accuracy. Freely online calculator on the web at https://lhj0520.shinyapps.io/LUAD_prediction_model/.

Spike-and-slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high-dimensional data analysis.

There are proposals that extend the classical generalized additive models (GAMs) to accommodate high-dimensional data ( p ≫ n $$ p\gg n $$ ) using group sparse regularization. However, the sparse regularization may induce excess shrinkage when estimating smooth functions, damaging predictive performance. Moreover, most of these GAMs consider an "all-in-all-out" approach for functional selection, rendering them difficult to answer if nonlinear effects are necessary. While some Bayesian models can address these shortcomings, using Markov chain Monte Carlo algorithms for model fitting creates a new challenge, scalability. Hence, we propose Bayesian hierarchical generalized additive models as a solution: we consider the smoothing penalty for proper shrinkage of curve interpolation via reparameterization. A novel two-part spike-and-slab LASSO prior for smooth functions is developed to address the sparsity of signals while providing extra flexibility to select the linear or nonlinear components of smooth functions. A scalable and deterministic algorithm, EM-Coordinate Descent, is implemented in an open-source R package BHAM. Simulation studies and metabolomics data analyses demonstrate improved predictive and computational performance against state-of-the-art models. Functional selection performance suggests trade-offs exist regarding the effect hierarchy assumption.

Analyzing the overall effects of the microbiome abundance data with a Bayesian predictive value approach.

The microbiome abundance data is known to be over-dispersed and sparse count data. Among various zero-inflated models, zero-inflated negative binomial (ZINB) model and zero-inflated beta binomial (ZIBB) model are the methods to analyze the microbiome abundance data. ZINB and ZIBB have two sets of parameters, which are for modeling the zero-inflation part and the count part separately. Most previous methods have focused on making inferences in terms of separate case-control effect for the zero-inflation part and the count part. However, in a case-control study, the primary interest is normally focused on the inference and a single interpretation of the overall unconditional mean (also known as the overall effect) of the microbiome abundance in microbiome studies. Here, we propose a Bayesian predictive value (BPV) approach to estimate the overall effect of the microbiome abundance. This approach is implemented based on R package brms. Hence, the parameters in the models will be estimated with two Markov chain Monte Carlo (MCMC) algorithms used in Stan. We performed simulations and real data applications to compare the proposed approach and R package glmmTMB with simulation method in the estimation and inference in terms of the ratio function between the overall effects from two groups in a case-control study. The results show that the performance of the BPV approach is better than R package glmmTMB with the simulation method in terms of lower absolute biases and relative absolute biases, and coverage probability being closer to the nominal level especially when the sample size is small and zero-inflation rate is high.

Race Versus Social Determinants of Health in COVID-19 Hospitalization Prediction.

INTRODUCTION: Including race as a biological construct in risk prediction models may guide clinical decisions in ways that cause harm and widen racial disparities. This study reports on using race versus social determinants of health (SDoH) in predicting the associations between cardiometabolic disease severity (assessed using cardiometabolic disease staging) and COVID-19 hospitalization. METHODS: Electronic medical record data on patients with a positive COVID-19 polymerase chain reaction test in 2020 and a previous encounter in the electronic medical record where cardiometabolic disease staging clinical data (BMI, blood glucose, blood pressure, high-density lipoprotein cholesterol, and triglycerides) were available from 2017 to 2020, were analyzed in 2021. Associations between cardiometabolic disease staging and COVID-19 hospitalization adding race and SDoH (individual and neighborhood level [e.g., Social Vulnerability Index]) in different models were examined. Area under the curve was used to assess predictive performance. RESULTS: A total of 2,745 patients were included (mean age of 58 years, 59% female, 47% Black). In the cardiometabolic disease staging model, area under the curve was 0.767 vs 0.777 when race was included. Adding SDoH to the cardiometabolic model improved the area under the curve to 0.809 (p<0.001), whereas the addition of SDoH and race increased the area under the curve to 0.811. In race-stratified models, the area under the curve for non-Hispanic Blacks was 0.781, whereas the model for non-Hispanic Whites performed better with an area under the curve of 0.821. CONCLUSIONS: Cardiometabolic disease staging was predictive of hospitalization after a positive COVID-19 test. Adding race did not markedly increase the predictive ability; however, adding SDoH to the model improved the area under the curve to ≥0.80. Future research should include SDoH with biological variables in prediction modeling to capture social experience of race.

Diet Quality and the Gut Microbiota in Women Living in Alabama.

INTRODUCTION: The gut microbiota is associated with obesity and modulated by individual dietary components. However, the relationships between diet quality and the gut microbiota and their potential interactions with weight status in diverse populations are not well understood. This study examined the associations between overall diet quality, weight status, and the gut microbiota in a racially balanced sample of adult females. METHODS: Female participants (N=71) residing in Birmingham, Alabama provided demographics, anthropometrics, biospecimens, and dietary data in this observational study from March 2014 to August 2014, and data analysis was conducted from August 2017 to March 2019. Weight status was defined as a BMI (weight [kg]/height [m2]) <30 kg/m2 for non-obese participants and ≥30 kg/m2 for participants who were obese. Dietary data collected included an Automated Self-Administered 24-Hour recall and Healthy Eating Index-2010 (HEI-2010) score. Diet quality was defined as having a high HEI score (≥median) or a low HEI score (