Infant Mortality and Life Expectancy: The Inequitable Loss of Potential Life.

Differences in life expectancy between racial and other subgroups of the population indicate inequities in the community. There are both societal and physical factors-including racism, poverty, and access to care-that must be resolved to increase and equalize life expectancy and decrease the infant mortality rate.

Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium.

Pre-pregnancy maternal obesity is associated with adverse offspring outcomes at birth and later in life. Individual studies have shown that epigenetic modifications such as DNA methylation could contribute. Within the Pregnancy and Childhood Epigenetics (PACE) Consortium, we meta-analysed the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs). We attempted to infer causality by comparing the effects of maternal versus paternal BMI and incorporating genetic variation. In four additional cohorts (1,817 mother-child pairs), we meta-analysed the association between maternal BMI at the start of pregnancy and blood methylation in adolescents. In newborns, maternal BMI was associated with small (<0.2% per BMI unit (1 kg/m2), P < 1.06 × 10-7) methylation variation at 9,044 sites throughout the genome. Adjustment for estimated cell proportions greatly attenuated the number of significant CpGs to 104, including 86 sites common to the unadjusted model. At 72/86 sites, the direction of the association was the same in newborns and adolescents, suggesting persistence of signals. However, we found evidence for acausal intrauterine effect of maternal BMI on newborn methylation at just 8/86 sites. In conclusion, this well-powered analysis identified robust associations between maternal adiposity and variations in newborn blood DNA methylation, but these small effects may be better explained by genetic or lifestyle factors than a causal intrauterine mechanism. This highlights the need for large-scale collaborative approaches and the application of causal inference techniques in epigenetic epidemiology.

Overall gestational weight gain mediates the relationship between maternal and child obesity.

BACKGROUND: Approximately 17% of children in the U.S. are obese. Children that are overweight or obese are also more likely to be obese as adults and suffer from various chronic diseases and premature death. Maternal obesity can affect the weight status of her offspring through intrauterine mechanisms like excessive gestational weight gain (GWG). Current literature shows a positive association between maternal weight status and GWG on child obesity, yet the direct and indirect effects have not been decomposed or quantified. The purpose of this study was to estimate the effect of maternal obesity on child obesity, mediated by GWG, which is a modifiable risk factor. METHODS: The study participants were a birth cohort of offspring from women who received prenatal care in the Duke/Durham Regional health care system in Durham, NC between 2005 and 2009. Anthropomorphic data was collected via electronic medical records (EMRs) during each voluntary visit to a health care facility. The exposure of interest was maternal obesity, measured by pre-pregnancy body mass index, the mediator was GWG, dichotomized into excessive and not excessive based on maternal prenatal BMI, and the outcome was child obesity at age 4, measured as BMI z-scores from the last recorded height and weight. A counterfactual theory-based product method analysis estimated the mediated effects of GWG, adjusted for maternal race, socioeconomic status, and smoking status. RESULTS: Of the 766 children, 25% were overweight or obese, and among all mothers, 25 and 31% were overweight and obese, respectively. Maternal BMI was associated with an overall increase of 0.04 in offspring z-score. The proportion of the effect of maternal obesity on child age 4 obesity mediated by GWG was 8.1%. CONCLUSION: GWG, in part, mediated the relationship between maternal BMI and childhood adiposity. Even when the mediator is fixed, children are at an increased risk of a higher BMI if the mother is obese. These findings highlight an important public health education opportunity to stress the impact of a pre-pregnancy weight and excessive GWG on the risk of child obesity for all mothers.

Race-associated biological differences among luminal A and basal-like breast cancers in the Carolina Breast Cancer Study.

BACKGROUND: We examined racial differences in the expression of eight genes and their associations with risk of recurrence among 478 white and 495 black women who participated in the Carolina Breast Cancer Study Phase 3. METHODS: Breast tumor samples were analyzed for PAM50 subtype and for eight genes previously found to be differentially expressed by race and associated with breast cancer survival: ACOX2, MUC1, FAM177A1, GSTT2, PSPH, PSPHL, SQLE, and TYMS. The expression of these genes according to race was assessed using linear regression and each gene was evaluated in association with recurrence using Cox regression. RESULTS: Compared to white women, black women had lower expression of MUC1, a suspected good prognosis gene, and higher expression of GSTT2, PSPHL, SQLE, and TYMS, suspected poor prognosis genes, after adjustment for age and PAM50 subtype. High expression (greater than median versus less than or equal to median) of FAM177A1 and PSPH was associated with a 63% increase (hazard ratio (HR) = 1.63, 95% confidence interval (CI) = 1.09-2.46) and 76% increase (HR = 1.76, 95% CI = 1.15-2.68), respectively, in risk of recurrence after adjustment for age, race, PAM50 subtype, and ROR-PT score. Log2-transformed SQLE expression was associated with a 20% increase (HR = 1.20, 95% CI = 1.03-1.41) in recurrence risk after adjustment. A continuous multi-gene score comprised of eight genes was also associated with increased risk of recurrence among all women (HR = 1.11, 95% CI = 1.04-1.19) and among white (HR = 1.14, 95% CI = 1.03-1.27) and black (HR = 1.11, 95% CI = 1.02-1.20) women. CONCLUSIONS: Racial differences in gene expression may contribute to the survival disparity observed between black and white women diagnosed with breast cancer.

DSSTox chemical-index files for exposure-related experiments in ArrayExpress and Gene Expression Omnibus: enabling toxico-chemogenomics data linkages.

SUMMARY: The Distributed Structure-Searchable Toxicity (DSSTox) ARYEXP and GEOGSE files are newly published, structure-annotated files of the chemical-associated and chemical exposure-related summary experimental content contained in the ArrayExpress Repository and Gene Expression Omnibus (GEO) Series (based on data extracted on September 20, 2008). ARYEXP and GEOGSE contain 887 and 1064 unique chemical substances mapped to 1835 and 2381 chemical exposure-related experiment accession IDs, respectively. The standardized files allow one to assess, compare and search the chemical content in each resource, in the context of the larger DSSTox toxicology data network, as well as across large public cheminformatics resources such as PubChem (http://pubchem.ncbi.nlm.nih.gov). AVAILABILITY: Data files and documentation may be accessed online at http://epa.gov/ncct/dsstox/.

Question Use in Adults With Right-Hemisphere Brain Damage.

Purpose Right-hemisphere brain damage (RHD) can affect pragmatic aspects of communication that may contribute to an impaired ability to gather information. Questions are an explicit means of gathering information. Question types vary in terms of the demands they place on cognitive resources. The purpose of this exploratory descriptive study is to test the hypothesis that adults with RHD differ from neurologically healthy adults in the types of questions asked during a structured task. Method Adults who sustained a single right-hemisphere stroke and neurologically healthy controls from the RHDBank Database completed the Unfamiliar Object Task of the RHDBank Discourse Protocol (Minga et al., 2016). Each task was video-recorded. Questions were transcribed using the Codes for the Human Analysis of Transcripts format. Coding and analysis of each response were conducted using Computerized Language Analysis (MacWhinney, 2000) programs. Results The types of questions used differed significantly across groups, with the RHD group using significantly more content questions and significantly fewer polar questions than the neurologically healthy control group. In their content question use, adults with RHD used significantly more "what" questions than other question subtypes. Conclusion Question-asking is an important aspect of pragmatic communication. Differences in the relative usage of question types, such as the reduced use of polar questions or increased use of content questions, may reflect cognitive limitations arising from RHD. Further investigations examining question use in this population are encouraged to replicate the current findings and to expand on the study tasks and measures. Supplemental Material https://doi.org/10.23641/asha.11936295.

Creating a Metabolic Syndrome Research Resource using the National Health and Nutrition Examination Survey.

Metabolic syndrome (MetS) is multifaceted. Risk factors include visceral adiposity, dyslipidemia, hyperglycemia, hypertension and environmental stimuli. MetS leads to an increased risk of cardiovascular disease, type 2 diabetes and stroke. Comparative studies, however, have identified heterogeneity in the pathology of MetS across groups though the etiology of these differences has yet to be elucidated. The Metabolic Syndrome Research Resource (MetSRR) described in this report is a curated database that provides access to MetS-associated biological and ancillary data and pools current and potential biomarkers of MetS extracted from relevant National Health and Nutrition Examination Survey (NHANES) data from 1999-2016. Each potential biomarker was selected following the review of over 100 peer-reviewed articles. MetSRR includes 28 demographics, survey and known MetS-related variables, including 9 curated categorical variables and 42 potentially novel biomarkers. All measures are captured from over 90 000 individuals. This biocuration effort provides increased access to curated MetS-related data and will serve as a hypothesis-generating tool to aid in novel biomarker discovery. In addition, MetSRR provides the ability to generate and export ethnic group-/race-, sex- and age-specific curated datasets, thus broadening participation in research efforts to identify clinically evaluative MetS biomarkers for disparate populations. Although there are other databases, such as BioM2MetDisease, designed to explore metabolic diseases through analysis of miRNAs and disease phenotypes, MetSRR is the only MetS-specific database designed to explore etiology of MetS across groups, through the biocuration of demographic, biological samples and biometric data. Database URL:  http://www.healthdisparityinformatics.com/MetSRR.

Serum miR-17 levels are downregulated in obese, African American women with elevated HbA1c.

PURPOSE: Type 2 diabetes is heterogeneous disease characterized by several conditions including hyperglycemia. It is estimated that over 350 million people worldwide are suffering from type 2 diabetes and this number is expected to rise. According to the CDC, African Americans were observed to have a 40% higher incidence of diabetes compared to European Americans. Epigenetic modulating mechanisms such as microRNAs (miRNAs), have recently been established as a massive regulatory machine in metabolic syndrome, obesity and type 2 diabetes. In the present study, we aimed to investigate the serum levels of circulating miRNA 17 (miR-17) of obese, African American women with elevated HbA1c. METHODS: We investigated miR-17 serum levels using qPCR. Then we used Pairwise Pearson Correlation Test to determine the relationship between clinical metabolic parameters and miR-17 serum levels. RESULTS: The results indicated that participants with elevated HbA1c exhibited a down regulation of serum miR-17 levels compared to participants with normal HbA1c. MiR-17 was also correlated with serum calcium in participants with normal HbA1c. CONCLUSIONS: The results suggest that serum miR-17 is involved in the regulation of glucose and calcium homeostasis, which may contribute to the development of type 2 diabetes.

The diversity and disparity in biomedical informatics (DDBI) workshop.

The Diversity and Disparity in Biomedical Informatics (DDBI) workshop will be focused on complementary and critical issues concerned with enhancing diversity in the informatics workforce as well as diversity in patient cohorts. According to the National Institute of Minority Health and Health Disparities (NIMHD) at the NIH, diversity refers to the inclusion of the following traditionally underrepresented groups: African Americans/Blacks, Asians (>30 countries), American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Latino or Hispanic (20 countries). Gender, culture, and socioeconomic status are also important dimensions of diversity, which may define some underrepresented groups. The under-representation of specific groups in both the biomedical informatics workforce as well as in the patient-derived data that is being used for research purposes has contributed to an ongoing disparity; these groups have not experienced equity in contributing to or benefiting from advancements in informatics research. This workshop will highlight innovative efforts to increase the pool of minority informaticians and discuss examples of informatics research that addresses the health concerns that impact minority populations. This workshop topics will provide insight into overcoming pipeline issues in the development of minority informaticians while emphasizing the importance of minority participation in health related research. The DDBI workshop will occur in two parts. Part I will discuss specific minority health & health disparities research topics and Part II will cover discussions related to overcoming pipeline issues in the training of minority informaticians.

NO-BOUNDARY THINKING IN BIOINFORMATICS.

The following sections are included:Bioinformatics is a Mature DisciplineThe Golden Era of Bioinformatics Has BegunNo-Boundary Thinking in BioinformaticsReferences.

Gender differences in preclinical markers of kidney injury in a rural north Carolina african-american cohort.

INTRODUCTION: The incidence rate of end-stage renal disease (ESRD) is highest among African-American (AA) males. The reason for this disparity in ESRD for AA males remains unclear, but it is well established that diabetes is the leading risk factor. Prediabetes may also be a risk for kidney disease since prediabetics have increased risk for cardiovascular disease and often do not receive drug interventions unless their hemoglobin A1c (A1c) level is above 6%. Perhaps, AA males are at greater risk because they often are untreated prediabetics and this predisposes them to renal injury. Therefore, we hypothesize that prediabetic AA males have higher albumin:creatinine ratio (ACr), a biomarker of renal injury, than their female counterparts. METHODS: Male and female AAs were recruited (53 females and 47 males; 45 ± 2 years old) from a rural northeastern region of NC. Blood and urine samples were collected for A1c and albumin measurements, respectively. Participants were stratified based on their A1c levels: non-diabetic: <5.7%, prediabetic: ≥5.7% but <6.5%, and diabetic: ≥6.5%. RESULTS: The proportion of males that are normal, prediabetic, and diabetic differed from that of females (p = 0.002). Interestingly, prediabetic men tended to be younger (41 ± 4 vs. 51 ± 3, respectively; p = 0.027) than prediabetic females (p = 0.027). A1c and ACr were not associated with blood pressure in males or females. AA males had a relative risk of 0.9, 2.5, and 1.4 for microalbuminuria for non-diabetic, prediabetic, and diabetic, respectively, compared to AA females. CONCLUSION: These results support our hypothesis that AA males may be predisposed to prediabetes kidney injury compared to their female counterpart. Thus, young AA males should be screened for biomarkers of kidney injury even if they have normal glucose and blood pressure levels.

Data-driven asthma endotypes defined from blood biomarker and gene expression data.

The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes) driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.

Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes.

BACKGROUND: Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. RESULTS: A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student's t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. CONCLUSIONS: The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.

Toward a public toxicogenomics capability for supporting predictive toxicology: survey of current resources and chemical indexing of experiments in GEO and ArrayExpress.

A publicly available toxicogenomics capability for supporting predictive toxicology and meta-analysis depends on availability of gene expression data for chemical treatment scenarios, the ability to locate and aggregate such information by chemical, and broad data coverage within chemical, genomics, and toxicological information domains. This capability also depends on common genomics standards, protocol description, and functional linkages of diverse public Internet data resources. We present a survey of public genomics resources from these vantage points and conclude that, despite progress in many areas, the current state of the majority of public microarray databases is inadequate for supporting these objectives, particularly with regard to chemical indexing. To begin to address these inadequacies, we focus chemical annotation efforts on experimental content contained in the two primary public genomic resources: ArrayExpress and Gene Expression Omnibus. Automated scripts and extensive manual review were employed to transform free-text experiment descriptions into a standardized, chemically indexed inventory of experiments in both resources. These files, which include top-level summary annotations, allow for identification of current chemical-associated experimental content, as well as chemical-exposure-related (or "Treatment") content of greatest potential value to toxicogenomics investigation. With these chemical-index files, it is possible for the first time to assess the breadth and overlap of chemical study space represented in these databases, and to begin to assess the sufficiency of data with shared protocols for chemical similarity inferences. Chemical indexing of public genomics databases is a first important step toward integrating chemical, toxicological and genomics data into predictive toxicology.